Sys.setenv(LANG = "en") # make R environment in english
require(haven)
require(srvyr)
require(tidyverse)
require(MASS)
require(MVN)
require(RVAideMemoire)
require(lavaan) # get SEM, CFA, MGCFA programs
require(leaps) # FactoMineR() doesn't seem to work without leaps installed or loaded
require(FactoMineR) # get PCA() function
require(nFactors) # get nScree() and parallel() functions
require(GPArotation) # get quartimax rotation
require(psych) # get fa.parallel() and VSS() functions
require(semPlot)
require(psy)
require(dplyr)
require(mice)
require(rms) # for rcs() but it may require install.packages("rstudioapi")
require(NlsyLinks)
require(floor) # to create bins
## Loading required package: floor
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'floor'
Mc<-function(object, digits=3){ # McDonald's NC Index, from Beaujean
fit<-inspect(object, "fit")
chisq=unlist(fit["chisq"])
df<-unlist(fit["df"])
n<-object@SampleStats@ntotal
ncp<-max(chisq-df,0)
d<-ncp/(n-1)
Mc=exp((d)*-.5)
Mc
}
# RMSEAd functions taken from Savalei et al. (2023) : https://osf.io/ne5ar for RMSEA CI and https://osf.io/cfubt for RMSEAd
# make sure you DO NOT FORGET TO ADJUST the sample size in the N function or it will produce wrong numbers
RMSEA.CI<-function(T,df,N,G){
#functions taken from lavaan (lav_fit_measures.R)
lower.lambda <- function(lambda) {
(pchisq(T, df=df, ncp=lambda) - 0.95)
}
upper.lambda <- function(lambda) {
(pchisq(T, df=df, ncp=lambda) - 0.05)
}
#RMSEA CI
lambda.l <- try(uniroot(f=lower.lambda, lower=0, upper=T)$root,silent=TRUE)
if(inherits(lambda.l, "try-error")) { lambda.l <- NA; RMSEA.CI.l<-NA
} else { if(lambda.l<0){
RMSEA.CI.l=0
} else {
RMSEA.CI.l<-sqrt(lambda.l*G/((N-1)*df))
}
}
N.RMSEA <- max(N, T*4)
lambda.u <- try(uniroot(f=upper.lambda, lower=0,upper=N.RMSEA)$root,silent=TRUE)
if(inherits(lambda.u, "try-error")) { lambda.u <- NA; RMSEA.CI.u<-NA
} else { if(lambda.u<0){
RMSEA.CI.u=0
} else {
RMSEA.CI.u<-sqrt(lambda.u*G/((N-1)*df))
}
}
RMSEA.CI<-c(RMSEA.CI.l,RMSEA.CI.u)
return(RMSEA.CI)
}
#p-values associated with the critical values of the RMSEA for most common cutoffs
pvals<-function(T,df,N,G){
RMSEA0<-c(0,.01,.05,.06,.08,.1)
eps0<-df*RMSEA0^2/G
nonc<-eps0*(N-G)
pvals<-pchisq(T,df=df,ncp=nonc)
names(pvals)<-c("RMSEA>0","RMSEA>.01","RMSEA>.05","RMSEA>.06","RMSEA>.08","RMSEA>.10")
return(pvals)
}
#based on Yuan & Chan (2016)
min.tol<-function(T,df,N,G){
lower.lambda.mintol <- function(lambda) {
(pchisq(T, df=df, ncp=lambda) - 0.05)
}
l.min.tol<-try(uniroot(f=lower.lambda.mintol, lower=0, upper=T)$root)
l.min.tol
RMSEA.mintol<-sqrt(l.min.tol*G/((N-1)*df))
RMSEA.mintol
out<-c(l.min.tol,RMSEA.mintol)
names(out)<-c("ncp(T)","RMSEA.pop")
return(out)
}
d<-read_csv(file = "C:\\Users\\mh198\\OneDrive\\Documents\\Data\\NLSY\\NLSY97 IQ wage.csv")
## Rows: 8984 Columns: 76
## ── Column specification ────────────────────────────────────────────────
## Delimiter: ","
## dbl (76): R0000100, R0536300, R0536401, R0536402, R0538700, R0554500...
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.
#Users should note that the final abilitly estimates may have positive or negative values because the scores are on the scale of the original calibration study, which set the mean of the latent ability distribution to 0 and the standard deviation to 1 in the calibration population of respondents.
#Because negative codes are reserved for missing data in the NLSY97 data set, one variable cannot contain both positive and negative scores. Therefore, the final ability estimates are reported in two separate variables, one for positive scores and one for negative scores.
#Each respondent will have a valid value for only one of the two variables. Researchers must combine the variables to examine the scores for the full set of respondents.
d$R9705200[d$R9705200<0] <- NA
d$R9705300[d$R9705300<0] <- NA
d$R9705400[d$R9705400<0] <- NA
d$R9705500[d$R9705500<0] <- NA
d$R9705600[d$R9705600<0] <- NA
d$R9705700[d$R9705700<0] <- NA
d$R9705800[d$R9705800<0] <- NA
d$R9705900[d$R9705900<0] <- NA
d$R9706000[d$R9706000<0] <- NA
d$R9706100[d$R9706100<0] <- NA
d$R9706200[d$R9706200<0] <- NA
d$R9706300[d$R9706300<0] <- NA
d$R9706400[d$R9706400<0] <- NA
d$R9706500[d$R9706500<0] <- NA
d$R9706600[d$R9706600<0] <- NA
d$R9706700[d$R9706700<0] <- NA
d$R9706800[d$R9706800<0] <- NA
d$R9706900[d$R9706900<0] <- NA
d$R9707000[d$R9707000<0] <- NA
d$R9707100[d$R9707100<0] <- NA
d$R9707200[d$R9707200<0] <- NA
d$R9707300[d$R9707300<0] <- NA
d$R9707400[d$R9707400<0] <- NA
d$R9707500[d$R9707500<0] <- NA
d$R9706400<-d$R9706400*-1
d$R9706500<-d$R9706500*-1
d$R9706600<-d$R9706600*-1
d$R9706700<-d$R9706700*-1
d$R9706800<-d$R9706800*-1
d$R9706900<-d$R9706900*-1
d$R9707000<-d$R9707000*-1
d$R9707100<-d$R9707100*-1
d$R9707200<-d$R9707200*-1
d$R9707300<-d$R9707300*-1
d$R9707400<-d$R9707400*-1
d$R9707500<-d$R9707500*-1
d$ssgs<- rowSums(cbind(d$R9705200, d$R9706400), na.rm = TRUE)
d$ssar<- rowSums(cbind(d$R9705300, d$R9706500), na.rm = TRUE)
d$sswk<- rowSums(cbind(d$R9705400, d$R9706600), na.rm = TRUE)
d$sspc<- rowSums(cbind(d$R9705500, d$R9706700), na.rm = TRUE)
d$ssno<- rowSums(cbind(d$R9705600, d$R9706800), na.rm = TRUE)
d$sscs<- rowSums(cbind(d$R9705700, d$R9706900), na.rm = TRUE)
d$ssai<- rowSums(cbind(d$R9705800, d$R9707000), na.rm = TRUE)
d$sssi<- rowSums(cbind(d$R9705900, d$R9707100), na.rm = TRUE)
d$ssmk<- rowSums(cbind(d$R9706000, d$R9707200), na.rm = TRUE)
d$ssmc<- rowSums(cbind(d$R9706100, d$R9707300), na.rm = TRUE)
d$ssei<- rowSums(cbind(d$R9706200, d$R9707400), na.rm = TRUE)
d$ssao<- rowSums(cbind(d$R9706300, d$R9707500), na.rm = TRUE)
d$bw<- rep(NA)
d$bw[d$R0538700==2] <- 1
d$bw[d$R0538700==1 & d$R1482600==4] <- 3
d$bhw<- rep(NA)
d$bhw[d$R0538700==2] <- 1
d$bhw[d$R1482600==2] <- 2
d$bhw[d$R0538700==1 & d$R1482600==4] <- 3
d$sweight<- d$R3923701
d$id<-d$R0000100
d$hhid<-d$R1193000
d$dadeduc<-ifelse(d$R1302400 < 0, NA, d$R1302400)
d$momeduc<-ifelse(d$R1302500 < 0, NA, d$R1302500)
d$pareduc<- rowMeans(d[, c("momeduc", "dadeduc")], na.rm = TRUE)
d$educ2011<- ifelse(d$T6656500 < 0, NA, d$T6656500)
d$birth<-d$R0536401/100*8.333
d$age<-1997-(d$birth+d$R0536402)
d$agebin<- floor(d$age)
d$agec<-d$age-14.50002
d$agec2<- d$agec^2
d$sex<-d$R0536300-1 # hence 0=male 1=female
d$sexage<-d$agec*d$sex
d$R9829600 <- ifelse(d$R9829600 >= 0, d$R9829600, NA) # ASVAB original variable
d %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw) %>% summarise(MEAN = survey_mean(R9829600, na.rm = TRUE), SD = survey_sd(R9829600, na.rm = TRUE))
## # A tibble: 4 Ă— 4
## bhw MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 1 30206. 666. 24531.
## 2 2 38799. 838. 26668.
## 3 3 57207. 470. 27438.
## 4 NA 55223. 1762. 27876.
d$asvab1<-(d$R9829600-56544)/27608 # uses the White non-weighted mean and SD
d$asvabz<- scale(d$R9829600, center = TRUE, scale = TRUE)
d$asvab<- d$asvabz*15+100
cor(d$asvabz, d$asvab, use="pairwise.complete.obs", method="pearson")
## [,1]
## [1,] 1
d %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(asvab, na.rm = TRUE), SD = survey_sd(asvab, na.rm = TRUE))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 102. 0.280 15.2
## 2 1 103. 0.269 14.4
d %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex, bhw) %>% summarise(MEAN = survey_mean(asvab, na.rm = TRUE), SD = survey_sd(asvab, na.rm = TRUE))
## # A tibble: 8 Ă— 5
## # Groups: sex [2]
## sex bhw MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 1 91.0 0.476 12.1
## 2 0 2 96.3 0.620 14.0
## 3 0 3 106. 0.348 14.6
## 4 0 NA 105. 1.34 15.0
## 5 1 1 93.5 0.489 13.0
## 6 1 2 97.0 0.592 13.4
## 7 1 3 107. 0.334 13.6
## 8 1 NA 105. 1.22 13.7
d <- filter(d, as.vector(!is.na(R0536401)) & as.vector(!is.na(R0536402)) & as.vector(!is.na(asvab)))
nrow(d) # n=7093
## [1] 7093
d <- d %>% mutate(across(starts_with("ss"), ~scale(.x, center = TRUE, scale = TRUE)))
datagroup <- dplyr::select(d, starts_with("ss"))
nrow(datagroup) # N=7093
## [1] 7093
efa_fit = fa(datagroup[,1:10], nfactors=1, rotate="none", scores="Bartlett") # Bartlett necessary if the factor is used as dependent variable in a regression but here produces same numbers as the default "regression", also could use weight=datagroup$sweight but it didn't change the results
d$efa = efa_fit$scores[, 1]
d$efa<- d$efa*15+100
describeBy(d$efa)
## Warning in describeBy(d$efa): no grouping variable requested
## vars n mean sd median trimmed mad min max range skew
## X1 1 7093 100 15.39 100.73 100.25 16.55 58.73 146.25 87.52 -0.12
## kurtosis se
## X1 -0.54 0.18
describeBy(d$asvab)
## Warning in describeBy(d$asvab): no grouping variable requested
## vars n mean sd median trimmed mad min max range skew
## X1 1 7093 100 15 98.75 99.56 19.14 76.7 128.12 51.42 0.2
## kurtosis se
## X1 -1.19 0.18
cor(d$efa, d$asvab, use="pairwise.complete.obs", method="pearson")
## [,1]
## [1,] 0.904695
datagroup<- na.omit(datagroup)
describeBy(d$efa, d$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew
## X1 1 3590 100.12 16.2 100.81 100.33 17.63 61.4 146.25 84.85 -0.08
## kurtosis se
## X1 -0.62 0.27
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 3503 99.87 14.52 100.59 100.2 15.45 58.73 142.07 83.34 -0.17
## kurtosis se
## X1 -0.49 0.25
d %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(efa, na.rm = TRUE), SD = survey_sd(efa, na.rm = TRUE))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 103. 0.289 15.9
## 2 1 103. 0.260 14.1
d %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex, bhw) %>% summarise(MEAN = survey_mean(efa, na.rm = TRUE), SD = survey_sd(efa, na.rm = TRUE))
## # A tibble: 8 Ă— 5
## # Groups: sex [2]
## sex bhw MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 1 90.7 0.553 14.2
## 2 0 2 97.2 0.635 14.5
## 3 0 3 107. 0.352 14.7
## 4 0 NA 105. 1.42 15.9
## 5 1 1 92.4 0.491 13.4
## 6 1 2 96.5 0.584 13.3
## 7 1 3 106. 0.319 12.9
## 8 1 NA 104. 1.19 13.3
d<- d %>% mutate(sibling = case_when(
R1309100>=13 & R1309100<=14 ~ 0, # brother and sister
R1309100>=15 & R1309100<=28 ~ 1, # brother/sister (half, step, adoptive, foster)
R1309100>=82 & R1309100<=83 ~ 1, # cousin
R1309100==85 ~ 1)) # other non-relative
#d <- d %>% filter(!(R4521500 %in% c(1))) ## remove twins and triplets
#d$ssno<- scale(d$ssno, center = TRUE, scale = TRUE)
#d$sscs<- scale(d$sscs, center = TRUE, scale = TRUE)
dk<-d
d %>%
as_survey_design(ids = id, weights = sweight) %>%
group_by(sex) %>%
summarise(across(starts_with("ss"), list(MEAN = ~ survey_mean(.), SD = ~ survey_sd(.)),
.names = "{.col}_{.fn}")) %>%
pivot_longer(cols = starts_with("ss"), names_to = c(".value", "variable"), names_sep = "_")
## Warning: Expected 2 pieces. Additional pieces discarded in 12 rows [2, 5, 8, 11,
## 14, 17, 20, 23, 26, 29, 32, 35].
## # A tibble: 6 Ă— 14
## sex variable ssgs ssar sswk sspc ssno sscs ssai
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 MEAN 0.276 0.194 0.179 0.0415 -0.00164 -0.0803 0.382
## 2 0 MEAN 0.0190 0.0187 0.0184 0.0188 0.0198 0.0189 0.0210
## 3 0 SD 1.04 1.02 1.01 1.02 1.06 1.02 1.12
## 4 1 MEAN 0.120 0.148 0.181 0.284 0.173 0.271 -0.0974
## 5 1 MEAN 0.0169 0.0169 0.0175 0.0172 0.0181 0.0178 0.0147
## 6 1 SD 0.913 0.911 0.944 0.928 0.946 0.932 0.789
## # â„ą 5 more variables: sssi <dbl>, ssmk <dbl>, ssmc <dbl>, ssei <dbl>,
## # ssao <dbl>
# NORMALITY TEST
dblack<- subset(dk, bhw==1)
dwhite<- subset(dk, bhw==3)
datablack<- dplyr::select(dblack, starts_with("ss"))
datawhite<- dplyr::select(dwhite, starts_with("ss"))
mqqnorm(datablack, main = "Multi-normal Q-Q Plot")
## [1] 1335 694
mqqnorm(datawhite, main = "Multi-normal Q-Q Plot")
## [1] 665 3086
mqqnorm(datagroup, main = "Multi-normal Q-Q Plot")
## [1] 6253 6124
mvn(data = datablack, mvnTest = "mardia")
## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 1622.23319173653 6.75193866317596e-158 NO
## 2 Mardia Kurtosis 22.1629188613151 0 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 2.5309 <0.001 NO
## 2 Anderson-Darling ssar 11.3667 <0.001 NO
## 3 Anderson-Darling sswk 1.2237 0.0035 NO
## 4 Anderson-Darling sspc 9.1587 <0.001 NO
## 5 Anderson-Darling ssno 0.9503 0.0163 NO
## 6 Anderson-Darling sscs 6.4711 <0.001 NO
## 7 Anderson-Darling ssai 1.1573 0.005 NO
## 8 Anderson-Darling sssi 1.0132 0.0114 NO
## 9 Anderson-Darling ssmk 3.3560 <0.001 NO
## 10 Anderson-Darling ssmc 6.9602 <0.001 NO
## 11 Anderson-Darling ssei 4.5376 <0.001 NO
## 12 Anderson-Darling ssao 20.4252 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th
## ssgs 1808 -0.5602749 0.8576911 -0.6075809 -2.628479 2.406451 -1.1766371
## ssar 1808 -0.5569764 0.9427041 -0.4368357 -2.692580 2.042919 -1.2042228
## sswk 1808 -0.5135819 0.9374915 -0.5076726 -2.739342 2.335238 -1.1348322
## sspc 1808 -0.4689775 0.9372254 -0.5168520 -2.173948 2.284316 -1.2382764
## ssno 1808 -0.1874061 1.0012540 -0.1971845 -3.869159 3.504552 -0.8454621
## sscs 1808 -0.2540052 1.0188066 -0.3296052 -4.658058 3.461256 -0.8210782
## ssai 1808 -0.4578721 0.8821322 -0.4922414 -2.476341 3.838862 -1.0696654
## sssi 1808 -0.5825558 0.7825301 -0.6081880 -2.832183 3.125798 -1.1075435
## ssmk 1808 -0.4479258 0.9393551 -0.5110345 -2.528175 2.411681 -1.1290023
## ssmc 1808 -0.6302241 0.8953393 -0.6398287 -2.548375 2.611383 -1.3344587
## ssei 1808 -0.4674670 0.8509563 -0.4737383 -2.399650 3.403434 -1.1407629
## ssao 1808 -0.5233415 0.8768762 -0.6426025 -1.986244 2.380850 -1.2641016
## 75th Skew Kurtosis
## ssgs 0.05069296 0.25764209 -0.27775177
## ssar 0.12243361 -0.22962800 -0.54907858
## sswk 0.12797639 -0.01276827 -0.37488761
## sspc 0.23248302 0.23730319 -0.78094218
## ssno 0.53957292 -0.15109539 -0.06602827
## sscs 0.43696961 -0.24589209 1.04824126
## ssai 0.10807604 0.33615417 0.36643910
## sssi -0.03728319 0.20999732 0.16548201
## ssmk 0.22644591 0.17786234 -0.57563052
## ssmc 0.08055108 0.06346339 -0.77652385
## ssei 0.15319995 0.20552507 -0.20354453
## ssao 0.03674057 0.56662284 -0.44582337
mvn(data = datawhite, mvnTest = "mardia")
## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 3016.82103024417 0 NO
## 2 Mardia Kurtosis 31.884830888758 0 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 3.2255 <0.001 NO
## 2 Anderson-Darling ssar 21.6763 <0.001 NO
## 3 Anderson-Darling sswk 10.6275 <0.001 NO
## 4 Anderson-Darling sspc 19.6277 <0.001 NO
## 5 Anderson-Darling ssno 2.0834 <0.001 NO
## 6 Anderson-Darling sscs 4.4825 <0.001 NO
## 7 Anderson-Darling ssai 9.8908 <0.001 NO
## 8 Anderson-Darling sssi 6.1793 <0.001 NO
## 9 Anderson-Darling ssmk 3.4318 <0.001 NO
## 10 Anderson-Darling ssmc 33.3151 <0.001 NO
## 11 Anderson-Darling ssei 8.7225 <0.001 NO
## 12 Anderson-Darling ssao 10.8813 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th
## ssgs 3659 0.4060491 0.9185653 0.4589225 -2.529870 3.536933 -0.21666660
## ssar 3659 0.3407109 0.9042484 0.4052216 -2.707716 2.847137 -0.11746946
## sswk 3659 0.3655980 0.9119170 0.4611881 -2.672487 3.369893 -0.26360039
## sspc 3659 0.3052834 0.9459662 0.3970231 -2.146304 2.310898 -0.33024927
## ssno 3659 0.1592336 1.0044293 0.1907158 -3.646253 4.197971 -0.49027670
## sscs 3659 0.1626818 0.9752993 0.1585684 -4.658058 3.424302 -0.50352341
## ssai 3659 0.3261749 0.9800041 0.2793531 -2.422075 4.869916 -0.30570209
## sssi 3659 0.4135998 0.9469105 0.3607105 -2.496795 4.552315 -0.22062861
## ssmk 3659 0.2879194 0.9365134 0.3197241 -2.552466 2.766332 -0.34245367
## ssmc 3659 0.3858658 0.9020433 0.5146182 -2.583934 4.145332 -0.08620917
## ssei 3659 0.3456966 0.9727177 0.3226826 -2.253453 4.508166 -0.25503205
## ssao 3659 0.2630017 0.9678218 0.3078034 -1.953042 2.525071 -0.42212081
## 75th Skew Kurtosis
## ssgs 0.9642937 -0.1068977 0.05359687
## ssar 0.8855322 -0.5128211 0.56131939
## sswk 0.9949635 -0.3120768 0.18241960
## sspc 1.0333864 -0.3678016 -0.49818087
## ssno 0.8233837 -0.1557867 0.18661806
## sscs 0.8389906 -0.2095849 0.36781600
## ssai 0.8889975 0.4682245 0.91818357
## sssi 0.9800603 0.2763817 0.33420160
## ssmk 0.9386635 -0.2242105 -0.22864838
## ssmc 0.9437807 -0.4375366 0.66375991
## ssei 0.9086502 0.3345275 0.72133889
## ssao 1.0123073 -0.1449285 -0.71302655
mvn(data = datagroup, mvnTest = "mardia")
## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 4942.51741584424 0 NO
## 2 Mardia Kurtosis 42.2408822487633 0 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 5.3718 <0.001 NO
## 2 Anderson-Darling ssar 37.6577 <0.001 NO
## 3 Anderson-Darling sswk 9.3056 <0.001 NO
## 4 Anderson-Darling sspc 27.4902 <0.001 NO
## 5 Anderson-Darling ssno 1.5272 6e-04 NO
## 6 Anderson-Darling sscs 14.1867 <0.001 NO
## 7 Anderson-Darling ssai 9.2228 <0.001 NO
## 8 Anderson-Darling sssi 7.8235 <0.001 NO
## 9 Anderson-Darling ssmk 6.8879 <0.001 NO
## 10 Anderson-Darling ssmc 40.3322 <0.001 NO
## 11 Anderson-Darling ssei 7.6968 <0.001 NO
## 12 Anderson-Darling ssao 30.1132 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max
## ssgs 7093 1.982538e-17 1 0.019877641 -2.628479 3.536933
## ssar 7093 2.190755e-17 1 0.098468532 -2.778350 2.847137
## sswk 7093 3.844688e-17 1 0.017613285 -2.739342 3.369893
## sspc 7093 1.931346e-17 1 0.056778698 -2.185644 2.310898
## ssno 7093 -1.105101e-16 1 0.013525797 -3.869159 4.197971
## sscs 7093 -1.016029e-17 1 -0.069314348 -4.658058 3.709082
## ssai 7093 -1.128737e-16 1 -0.036067982 -2.552653 4.869916
## sssi 7093 6.646381e-17 1 -0.017905221 -2.832183 4.552315
## ssmk 7093 -2.356424e-18 1 0.044747838 -2.584530 2.766332
## ssmc 7093 1.589501e-17 1 0.146764716 -2.620720 4.145332
## ssei 7093 -9.580385e-18 1 0.003171008 -2.399650 4.508166
## ssao 7093 -5.972471e-17 1 -0.039779561 -2.013221 2.525071
## 25th 75th Skew Kurtosis
## ssgs -0.7220378 0.6831407 0.07343529 -0.35365437
## ssar -0.5594360 0.6352863 -0.36902714 -0.06902024
## sswk -0.6679114 0.7583195 -0.15732864 -0.22306138
## sspc -0.7555547 0.7957469 -0.11591752 -0.80536063
## ssno -0.6582620 0.6756487 -0.08978372 0.09562429
## sscs -0.6385813 0.6958673 -0.19457332 0.65434610
## ssai -0.6753891 0.6117322 0.41686112 0.67724495
## sssi -0.7110403 0.6170960 0.31719836 0.16371979
## ssmk -0.7160521 0.7093547 -0.06424753 -0.52193664
## ssmc -0.6968460 0.7206162 -0.21736702 -0.29067932
## ssei -0.6912655 0.6362991 0.33506407 0.35832505
## ssao -0.7795606 0.7861190 0.09525610 -0.85873214
mvn(data = datablack, mvnTest = "hz", multivariatePlot = "qq", multivariateOutlierMethod = "quan")
## $multivariateNormality
## Test HZ p value MVN
## 1 Henze-Zirkler 1.063951 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 2.5309 <0.001 NO
## 2 Anderson-Darling ssar 11.3667 <0.001 NO
## 3 Anderson-Darling sswk 1.2237 0.0035 NO
## 4 Anderson-Darling sspc 9.1587 <0.001 NO
## 5 Anderson-Darling ssno 0.9503 0.0163 NO
## 6 Anderson-Darling sscs 6.4711 <0.001 NO
## 7 Anderson-Darling ssai 1.1573 0.005 NO
## 8 Anderson-Darling sssi 1.0132 0.0114 NO
## 9 Anderson-Darling ssmk 3.3560 <0.001 NO
## 10 Anderson-Darling ssmc 6.9602 <0.001 NO
## 11 Anderson-Darling ssei 4.5376 <0.001 NO
## 12 Anderson-Darling ssao 20.4252 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th
## ssgs 1808 -0.5602749 0.8576911 -0.6075809 -2.628479 2.406451 -1.1766371
## ssar 1808 -0.5569764 0.9427041 -0.4368357 -2.692580 2.042919 -1.2042228
## sswk 1808 -0.5135819 0.9374915 -0.5076726 -2.739342 2.335238 -1.1348322
## sspc 1808 -0.4689775 0.9372254 -0.5168520 -2.173948 2.284316 -1.2382764
## ssno 1808 -0.1874061 1.0012540 -0.1971845 -3.869159 3.504552 -0.8454621
## sscs 1808 -0.2540052 1.0188066 -0.3296052 -4.658058 3.461256 -0.8210782
## ssai 1808 -0.4578721 0.8821322 -0.4922414 -2.476341 3.838862 -1.0696654
## sssi 1808 -0.5825558 0.7825301 -0.6081880 -2.832183 3.125798 -1.1075435
## ssmk 1808 -0.4479258 0.9393551 -0.5110345 -2.528175 2.411681 -1.1290023
## ssmc 1808 -0.6302241 0.8953393 -0.6398287 -2.548375 2.611383 -1.3344587
## ssei 1808 -0.4674670 0.8509563 -0.4737383 -2.399650 3.403434 -1.1407629
## ssao 1808 -0.5233415 0.8768762 -0.6426025 -1.986244 2.380850 -1.2641016
## 75th Skew Kurtosis
## ssgs 0.05069296 0.25764209 -0.27775177
## ssar 0.12243361 -0.22962800 -0.54907858
## sswk 0.12797639 -0.01276827 -0.37488761
## sspc 0.23248302 0.23730319 -0.78094218
## ssno 0.53957292 -0.15109539 -0.06602827
## sscs 0.43696961 -0.24589209 1.04824126
## ssai 0.10807604 0.33615417 0.36643910
## sssi -0.03728319 0.20999732 0.16548201
## ssmk 0.22644591 0.17786234 -0.57563052
## ssmc 0.08055108 0.06346339 -0.77652385
## ssei 0.15319995 0.20552507 -0.20354453
## ssao 0.03674057 0.56662284 -0.44582337
mvn(data = datawhite, mvnTest = "hz", multivariatePlot = "qq", multivariateOutlierMethod = "quan")
## $multivariateNormality
## Test HZ p value MVN
## 1 Henze-Zirkler 1.132594 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 3.2255 <0.001 NO
## 2 Anderson-Darling ssar 21.6763 <0.001 NO
## 3 Anderson-Darling sswk 10.6275 <0.001 NO
## 4 Anderson-Darling sspc 19.6277 <0.001 NO
## 5 Anderson-Darling ssno 2.0834 <0.001 NO
## 6 Anderson-Darling sscs 4.4825 <0.001 NO
## 7 Anderson-Darling ssai 9.8908 <0.001 NO
## 8 Anderson-Darling sssi 6.1793 <0.001 NO
## 9 Anderson-Darling ssmk 3.4318 <0.001 NO
## 10 Anderson-Darling ssmc 33.3151 <0.001 NO
## 11 Anderson-Darling ssei 8.7225 <0.001 NO
## 12 Anderson-Darling ssao 10.8813 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th
## ssgs 3659 0.4060491 0.9185653 0.4589225 -2.529870 3.536933 -0.21666660
## ssar 3659 0.3407109 0.9042484 0.4052216 -2.707716 2.847137 -0.11746946
## sswk 3659 0.3655980 0.9119170 0.4611881 -2.672487 3.369893 -0.26360039
## sspc 3659 0.3052834 0.9459662 0.3970231 -2.146304 2.310898 -0.33024927
## ssno 3659 0.1592336 1.0044293 0.1907158 -3.646253 4.197971 -0.49027670
## sscs 3659 0.1626818 0.9752993 0.1585684 -4.658058 3.424302 -0.50352341
## ssai 3659 0.3261749 0.9800041 0.2793531 -2.422075 4.869916 -0.30570209
## sssi 3659 0.4135998 0.9469105 0.3607105 -2.496795 4.552315 -0.22062861
## ssmk 3659 0.2879194 0.9365134 0.3197241 -2.552466 2.766332 -0.34245367
## ssmc 3659 0.3858658 0.9020433 0.5146182 -2.583934 4.145332 -0.08620917
## ssei 3659 0.3456966 0.9727177 0.3226826 -2.253453 4.508166 -0.25503205
## ssao 3659 0.2630017 0.9678218 0.3078034 -1.953042 2.525071 -0.42212081
## 75th Skew Kurtosis
## ssgs 0.9642937 -0.1068977 0.05359687
## ssar 0.8855322 -0.5128211 0.56131939
## sswk 0.9949635 -0.3120768 0.18241960
## sspc 1.0333864 -0.3678016 -0.49818087
## ssno 0.8233837 -0.1557867 0.18661806
## sscs 0.8389906 -0.2095849 0.36781600
## ssai 0.8889975 0.4682245 0.91818357
## sssi 0.9800603 0.2763817 0.33420160
## ssmk 0.9386635 -0.2242105 -0.22864838
## ssmc 0.9437807 -0.4375366 0.66375991
## ssei 0.9086502 0.3345275 0.72133889
## ssao 1.0123073 -0.1449285 -0.71302655
mvn(data = datagroup, mvnTest = "hz", multivariatePlot = "qq", multivariateOutlierMethod = "quan")
## $multivariateNormality
## Test HZ p value MVN
## 1 Henze-Zirkler 1.157528 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 5.3718 <0.001 NO
## 2 Anderson-Darling ssar 37.6577 <0.001 NO
## 3 Anderson-Darling sswk 9.3056 <0.001 NO
## 4 Anderson-Darling sspc 27.4902 <0.001 NO
## 5 Anderson-Darling ssno 1.5272 6e-04 NO
## 6 Anderson-Darling sscs 14.1867 <0.001 NO
## 7 Anderson-Darling ssai 9.2228 <0.001 NO
## 8 Anderson-Darling sssi 7.8235 <0.001 NO
## 9 Anderson-Darling ssmk 6.8879 <0.001 NO
## 10 Anderson-Darling ssmc 40.3322 <0.001 NO
## 11 Anderson-Darling ssei 7.6968 <0.001 NO
## 12 Anderson-Darling ssao 30.1132 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max
## ssgs 7093 1.982538e-17 1 0.019877641 -2.628479 3.536933
## ssar 7093 2.190755e-17 1 0.098468532 -2.778350 2.847137
## sswk 7093 3.844688e-17 1 0.017613285 -2.739342 3.369893
## sspc 7093 1.931346e-17 1 0.056778698 -2.185644 2.310898
## ssno 7093 -1.105101e-16 1 0.013525797 -3.869159 4.197971
## sscs 7093 -1.016029e-17 1 -0.069314348 -4.658058 3.709082
## ssai 7093 -1.128737e-16 1 -0.036067982 -2.552653 4.869916
## sssi 7093 6.646381e-17 1 -0.017905221 -2.832183 4.552315
## ssmk 7093 -2.356424e-18 1 0.044747838 -2.584530 2.766332
## ssmc 7093 1.589501e-17 1 0.146764716 -2.620720 4.145332
## ssei 7093 -9.580385e-18 1 0.003171008 -2.399650 4.508166
## ssao 7093 -5.972471e-17 1 -0.039779561 -2.013221 2.525071
## 25th 75th Skew Kurtosis
## ssgs -0.7220378 0.6831407 0.07343529 -0.35365437
## ssar -0.5594360 0.6352863 -0.36902714 -0.06902024
## sswk -0.6679114 0.7583195 -0.15732864 -0.22306138
## sspc -0.7555547 0.7957469 -0.11591752 -0.80536063
## ssno -0.6582620 0.6756487 -0.08978372 0.09562429
## sscs -0.6385813 0.6958673 -0.19457332 0.65434610
## ssai -0.6753891 0.6117322 0.41686112 0.67724495
## sssi -0.7110403 0.6170960 0.31719836 0.16371979
## ssmk -0.7160521 0.7093547 -0.06424753 -0.52193664
## ssmc -0.6968460 0.7206162 -0.21736702 -0.29067932
## ssei -0.6912655 0.6362991 0.33506407 0.35832505
## ssao -0.7795606 0.7861190 0.09525610 -0.85873214
dwhitem<- subset(dk, sex==0 & bhw==3)
dwhitef<- subset(dk, sex==1 & bhw==3)
datawhitem<- dplyr::select(dwhitem, starts_with("ss"))
datawhitef<- dplyr::select(dwhitef, starts_with("ss"))
mqqnorm(datawhitem, main = "Multi-normal Q-Q Plot")
## [1] 333 945
mqqnorm(datawhitef, main = "Multi-normal Q-Q Plot")
## [1] 920 74
mvn(data = datawhitem, mvnTest = "mardia")
## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 1805.09743096755 3.19643493183969e-189 NO
## 2 Mardia Kurtosis 21.8295923002114 0 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 1.6659 3e-04 NO
## 2 Anderson-Darling ssar 12.8480 <0.001 NO
## 3 Anderson-Darling sswk 7.8069 <0.001 NO
## 4 Anderson-Darling sspc 12.0398 <0.001 NO
## 5 Anderson-Darling ssno 0.3827 0.3975 YES
## 6 Anderson-Darling sscs 2.6129 <0.001 NO
## 7 Anderson-Darling ssai 3.0204 <0.001 NO
## 8 Anderson-Darling sssi 1.4020 0.0013 NO
## 9 Anderson-Darling ssmk 1.4068 0.0012 NO
## 10 Anderson-Darling ssmc 18.0639 <0.001 NO
## 11 Anderson-Darling ssei 2.1029 <0.001 NO
## 12 Anderson-Darling ssao 6.5919 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max
## ssgs 1889 0.49647856 0.9724641 0.55518372 -2.529870 3.536933
## ssar 1889 0.36621078 0.9652107 0.45769247 -2.707716 2.847137
## sswk 1889 0.36715955 0.9381782 0.48984002 -2.672487 3.161901
## sspc 1889 0.18610173 0.9822521 0.28963344 -2.146304 2.310898
## ssno 1889 0.08080787 1.0542697 0.08839048 -3.646253 4.197971
## sscs 1889 -0.01351932 1.0012591 -0.04174552 -4.658058 3.424302
## ssai 1889 0.59419415 1.0955753 0.59307827 -2.406813 4.869916
## sssi 1889 0.75265341 0.9838194 0.72143891 -2.392452 4.552315
## ssmk 1889 0.21505104 0.9555505 0.24393557 -2.552466 2.766332
## ssmc 1889 0.54080482 0.9540743 0.66053345 -2.583934 4.145332
## ssei 1889 0.55309432 1.0916696 0.57263255 -2.190965 4.508166
## ssao 1889 0.19512613 1.0122913 0.18329607 -1.944742 2.525071
## 25th 75th Skew Kurtosis
## ssgs -0.16207947 1.1210116 -0.14372453 -0.0228568
## ssar -0.12049662 0.9874469 -0.55013252 0.4544226
## sswk -0.27845696 1.0193707 -0.39957046 0.1538084
## sspc -0.51951020 0.9616161 -0.28367765 -0.7086257
## ssno -0.59781797 0.7669284 -0.01831289 0.1415563
## sscs -0.63946120 0.6512879 -0.16357940 0.5229429
## ssai -0.13272927 1.2154413 0.28161160 0.3704136
## sssi 0.14755284 1.3966867 0.00861978 0.2486403
## ssmk -0.45565060 0.8628750 -0.12897625 -0.3001803
## ssmc 0.04989663 1.1154456 -0.43496068 0.6678097
## ssei -0.16306932 1.1833594 0.18610911 0.2543486
## ssao -0.56478546 1.0123073 -0.01864938 -0.8438983
mvn(data = datawhitef, mvnTest = "mardia")
## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 1316.30617111177 7.30458519912656e-108 NO
## 2 Mardia Kurtosis 18.282671398268 0 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 2.4013 <0.001 NO
## 2 Anderson-Darling ssar 9.6307 <0.001 NO
## 3 Anderson-Darling sswk 3.4747 <0.001 NO
## 4 Anderson-Darling sspc 6.8677 <0.001 NO
## 5 Anderson-Darling ssno 2.0614 <0.001 NO
## 6 Anderson-Darling sscs 2.7013 <0.001 NO
## 7 Anderson-Darling ssai 0.9661 0.0149 NO
## 8 Anderson-Darling sssi 1.6453 3e-04 NO
## 9 Anderson-Darling ssmk 2.5222 <0.001 NO
## 10 Anderson-Darling ssmc 22.6328 <0.001 NO
## 11 Anderson-Darling ssei 3.3324 <0.001 NO
## 12 Anderson-Darling ssao 6.0167 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th
## ssgs 1770 0.30953996 0.8469990 0.36853094 -2.486435 2.742191 -0.2630363
## ssar 1770 0.31349666 0.8337019 0.36132762 -2.663318 2.586801 -0.1131810
## sswk 1770 0.36393148 0.8832912 0.42563841 -2.548329 3.369893 -0.2484785
## sspc 1770 0.43247780 0.8884416 0.51451370 -2.080381 2.303455 -0.1471021
## ssno 1770 0.24293210 0.9414683 0.30822878 -2.808934 3.431528 -0.3523984
## sscs 1770 0.35072920 0.9101917 0.37178692 -3.642411 2.855328 -0.3302650
## ssai 1770 0.04013625 0.7391656 0.04448309 -2.422075 2.687406 -0.4294964
## sssi 1770 0.05175105 0.7532114 0.03352093 -2.496795 3.401561 -0.4322957
## ssmk 1770 0.36568684 0.9096095 0.40182828 -2.507770 2.708034 -0.2229410
## ssmc 1770 0.22050999 0.8112195 0.38035169 -2.489519 2.823512 -0.2256870
## ssei 1770 0.12435519 0.7679073 0.14642065 -2.253453 2.824540 -0.3328467
## ssao 1770 0.33544062 0.9127215 0.40481535 -1.953042 2.481494 -0.2802343
## 75th Skew Kurtosis
## ssgs 0.8492499 -0.15826093 0.06355653
## ssar 0.7846266 -0.47487682 0.60011112
## sswk 0.9564955 -0.19966651 0.19234139
## sspc 1.1048908 -0.41355811 -0.25374777
## ssno 0.8832908 -0.29600410 0.25518568
## sscs 1.0118824 -0.17154111 0.08470877
## ssai 0.5218542 -0.10449743 0.29927042
## sssi 0.5172249 0.11111652 0.60828008
## ssmk 1.0146949 -0.31979405 -0.10257049
## ssmc 0.7632259 -0.72160807 0.50307178
## ssei 0.6091818 -0.04459034 0.49823261
## ssao 1.0146419 -0.27534831 -0.50685222
mvn(data = datawhitem, mvnTest = "hz", multivariatePlot = "qq", multivariateOutlierMethod = "quan")
## $multivariateNormality
## Test HZ p value MVN
## 1 Henze-Zirkler 1.09975 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 1.6659 3e-04 NO
## 2 Anderson-Darling ssar 12.8480 <0.001 NO
## 3 Anderson-Darling sswk 7.8069 <0.001 NO
## 4 Anderson-Darling sspc 12.0398 <0.001 NO
## 5 Anderson-Darling ssno 0.3827 0.3975 YES
## 6 Anderson-Darling sscs 2.6129 <0.001 NO
## 7 Anderson-Darling ssai 3.0204 <0.001 NO
## 8 Anderson-Darling sssi 1.4020 0.0013 NO
## 9 Anderson-Darling ssmk 1.4068 0.0012 NO
## 10 Anderson-Darling ssmc 18.0639 <0.001 NO
## 11 Anderson-Darling ssei 2.1029 <0.001 NO
## 12 Anderson-Darling ssao 6.5919 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max
## ssgs 1889 0.49647856 0.9724641 0.55518372 -2.529870 3.536933
## ssar 1889 0.36621078 0.9652107 0.45769247 -2.707716 2.847137
## sswk 1889 0.36715955 0.9381782 0.48984002 -2.672487 3.161901
## sspc 1889 0.18610173 0.9822521 0.28963344 -2.146304 2.310898
## ssno 1889 0.08080787 1.0542697 0.08839048 -3.646253 4.197971
## sscs 1889 -0.01351932 1.0012591 -0.04174552 -4.658058 3.424302
## ssai 1889 0.59419415 1.0955753 0.59307827 -2.406813 4.869916
## sssi 1889 0.75265341 0.9838194 0.72143891 -2.392452 4.552315
## ssmk 1889 0.21505104 0.9555505 0.24393557 -2.552466 2.766332
## ssmc 1889 0.54080482 0.9540743 0.66053345 -2.583934 4.145332
## ssei 1889 0.55309432 1.0916696 0.57263255 -2.190965 4.508166
## ssao 1889 0.19512613 1.0122913 0.18329607 -1.944742 2.525071
## 25th 75th Skew Kurtosis
## ssgs -0.16207947 1.1210116 -0.14372453 -0.0228568
## ssar -0.12049662 0.9874469 -0.55013252 0.4544226
## sswk -0.27845696 1.0193707 -0.39957046 0.1538084
## sspc -0.51951020 0.9616161 -0.28367765 -0.7086257
## ssno -0.59781797 0.7669284 -0.01831289 0.1415563
## sscs -0.63946120 0.6512879 -0.16357940 0.5229429
## ssai -0.13272927 1.2154413 0.28161160 0.3704136
## sssi 0.14755284 1.3966867 0.00861978 0.2486403
## ssmk -0.45565060 0.8628750 -0.12897625 -0.3001803
## ssmc 0.04989663 1.1154456 -0.43496068 0.6678097
## ssei -0.16306932 1.1833594 0.18610911 0.2543486
## ssao -0.56478546 1.0123073 -0.01864938 -0.8438983
mvn(data = datawhitef, mvnTest = "hz", multivariatePlot = "qq", multivariateOutlierMethod = "quan")
## $multivariateNormality
## Test HZ p value MVN
## 1 Henze-Zirkler 1.041362 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 2.4013 <0.001 NO
## 2 Anderson-Darling ssar 9.6307 <0.001 NO
## 3 Anderson-Darling sswk 3.4747 <0.001 NO
## 4 Anderson-Darling sspc 6.8677 <0.001 NO
## 5 Anderson-Darling ssno 2.0614 <0.001 NO
## 6 Anderson-Darling sscs 2.7013 <0.001 NO
## 7 Anderson-Darling ssai 0.9661 0.0149 NO
## 8 Anderson-Darling sssi 1.6453 3e-04 NO
## 9 Anderson-Darling ssmk 2.5222 <0.001 NO
## 10 Anderson-Darling ssmc 22.6328 <0.001 NO
## 11 Anderson-Darling ssei 3.3324 <0.001 NO
## 12 Anderson-Darling ssao 6.0167 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th
## ssgs 1770 0.30953996 0.8469990 0.36853094 -2.486435 2.742191 -0.2630363
## ssar 1770 0.31349666 0.8337019 0.36132762 -2.663318 2.586801 -0.1131810
## sswk 1770 0.36393148 0.8832912 0.42563841 -2.548329 3.369893 -0.2484785
## sspc 1770 0.43247780 0.8884416 0.51451370 -2.080381 2.303455 -0.1471021
## ssno 1770 0.24293210 0.9414683 0.30822878 -2.808934 3.431528 -0.3523984
## sscs 1770 0.35072920 0.9101917 0.37178692 -3.642411 2.855328 -0.3302650
## ssai 1770 0.04013625 0.7391656 0.04448309 -2.422075 2.687406 -0.4294964
## sssi 1770 0.05175105 0.7532114 0.03352093 -2.496795 3.401561 -0.4322957
## ssmk 1770 0.36568684 0.9096095 0.40182828 -2.507770 2.708034 -0.2229410
## ssmc 1770 0.22050999 0.8112195 0.38035169 -2.489519 2.823512 -0.2256870
## ssei 1770 0.12435519 0.7679073 0.14642065 -2.253453 2.824540 -0.3328467
## ssao 1770 0.33544062 0.9127215 0.40481535 -1.953042 2.481494 -0.2802343
## 75th Skew Kurtosis
## ssgs 0.8492499 -0.15826093 0.06355653
## ssar 0.7846266 -0.47487682 0.60011112
## sswk 0.9564955 -0.19966651 0.19234139
## sspc 1.1048908 -0.41355811 -0.25374777
## ssno 0.8832908 -0.29600410 0.25518568
## sscs 1.0118824 -0.17154111 0.08470877
## ssai 0.5218542 -0.10449743 0.29927042
## sssi 0.5172249 0.11111652 0.60828008
## ssmk 1.0146949 -0.31979405 -0.10257049
## ssmc 0.7632259 -0.72160807 0.50307178
## ssei 0.6091818 -0.04459034 0.49823261
## ssao 1.0146419 -0.27534831 -0.50685222
dfullm<- subset(dk, sex==0)
dfullf<- subset(dk, sex==1)
datafullm<- dplyr::select(dfullm, starts_with("ss"))
datafullf<- dplyr::select(dfullf, starts_with("ss"))
mqqnorm(datafullm, main = "Multi-normal Q-Q Plot")
## [1] 3184 2396
mqqnorm(datafullf, main = "Multi-normal Q-Q Plot")
## [1] 3005 130
mvn(data = datafullm, mvnTest = "mardia")
## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 2793.80067787689 0 NO
## 2 Mardia Kurtosis 29.0379761670123 0 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 2.7040 <0.001 NO
## 2 Anderson-Darling ssar 18.4978 <0.001 NO
## 3 Anderson-Darling sswk 7.3432 <0.001 NO
## 4 Anderson-Darling sspc 18.4602 <0.001 NO
## 5 Anderson-Darling ssno 0.6630 0.0834 YES
## 6 Anderson-Darling sscs 8.7314 <0.001 NO
## 7 Anderson-Darling ssai 3.6458 <0.001 NO
## 8 Anderson-Darling sssi 1.7177 2e-04 NO
## 9 Anderson-Darling ssmk 3.1254 <0.001 NO
## 10 Anderson-Darling ssmc 26.9472 <0.001 NO
## 11 Anderson-Darling ssei 3.5225 <0.001 NO
## 12 Anderson-Darling ssao 19.8002 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max
## ssgs 3590 0.072654196 1.052161 0.09853007 -2.628479 3.536933
## ssar 3590 0.015191054 1.051973 0.13176738 -2.778350 2.847137
## sswk 3590 -0.005213396 1.031023 0.01549092 -2.690527 3.161901
## sspc 3590 -0.119498863 1.025404 -0.10217922 -2.185644 2.310898
## ssno 3590 -0.099733402 1.045060 -0.11641352 -3.869159 4.197971
## sscs 3590 -0.181391941 1.021915 -0.26156294 -4.658058 3.424302
## ssai 3590 0.214504538 1.116535 0.18947501 -2.552653 4.869916
## sssi 3590 0.273620101 1.079264 0.29214232 -2.832183 4.552315
## ssmk 3590 -0.072718114 1.013231 -0.04172879 -2.552466 2.766332
## ssmc 3590 0.122757578 1.074592 0.31107262 -2.620720 4.145332
## ssei 3590 0.145231459 1.125225 0.13993610 -2.399650 4.508166
## ssao 3590 -0.052083186 1.022771 -0.12278445 -2.013221 2.525071
## 25th 75th Skew Kurtosis
## ssgs -0.6935703 0.7840975 0.0609376986 -0.391510574
## ssar -0.5985369 0.7056680 -0.3448930451 -0.212859909
## sswk -0.6878086 0.7869715 -0.2084181729 -0.308855826
## sspc -0.9421575 0.7109517 -0.0058749424 -0.920176965
## ssno -0.7767722 0.6147061 0.0005396258 0.122389795
## sscs -0.7685068 0.5063316 -0.2511461820 0.851363553
## ssai -0.5549865 0.9063795 0.3437899586 0.265047056
## sssi -0.4975100 0.9685080 0.1322130065 -0.134801224
## ssmk -0.7906261 0.6342949 0.0023743017 -0.507529778
## ssmc -0.6438138 0.8649987 -0.2465863805 -0.324689532
## ssei -0.6464632 0.8859543 0.2758184354 -0.001697834
## ssao -0.8812416 0.7409851 0.1945848633 -0.868331637
mvn(data = datafullf, mvnTest = "mardia")
## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 1729.20286503675 4.10441555806517e-176 NO
## 2 Mardia Kurtosis 21.7315526252497 0 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 3.5640 <0.001 NO
## 2 Anderson-Darling ssar 20.5756 <0.001 NO
## 3 Anderson-Darling sswk 2.7142 <0.001 NO
## 4 Anderson-Darling sspc 10.0051 <0.001 NO
## 5 Anderson-Darling ssno 1.6095 4e-04 NO
## 6 Anderson-Darling sscs 7.3979 <0.001 NO
## 7 Anderson-Darling ssai 1.4092 0.0012 NO
## 8 Anderson-Darling sssi 0.5749 0.1354 YES
## 9 Anderson-Darling ssmk 4.3820 <0.001 NO
## 10 Anderson-Darling ssmc 22.8413 <0.001 NO
## 11 Anderson-Darling ssei 5.3910 <0.001 NO
## 12 Anderson-Darling ssao 12.6820 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max
## ssgs 3503 -0.074458625 0.9378840 -0.064644372 -2.579174 2.742191
## ssar 3503 -0.015568337 0.9436683 0.080305523 -2.743033 2.586801
## sswk 3503 0.005342876 0.9672932 0.019735653 -2.739342 3.369893
## sspc 3503 0.122466719 0.9580778 0.181180544 -2.173948 2.303455
## ssno 3503 0.102210366 0.9408498 0.137328663 -2.954215 3.449631
## sscs 3503 0.185896965 0.9415825 0.144197438 -4.217250 3.709082
## ssai 3503 -0.219831942 0.8074651 -0.198865935 -2.547566 2.687406
## sssi 3503 -0.280415690 0.8221771 -0.281743753 -2.616044 3.401561
## ssmk 3503 0.074524131 0.9808211 0.118593045 -2.584530 2.708034
## ssmc 3503 -0.125806368 0.9002292 -0.004055222 -2.548375 2.823512
## ssei 3503 -0.148838407 0.8268980 -0.100581818 -2.253453 3.012003
## ssao 3503 0.053376716 0.9733703 0.069164350 -1.973793 2.481494
## 25th 75th Skew Kurtosis
## ssgs -0.7461031 0.5980317 0.030528040 -0.41086027
## ssar -0.5332005 0.5828154 -0.411530231 0.07343937
## sswk -0.6323617 0.7301981 -0.091074791 -0.14101114
## sspc -0.5508765 0.8574162 -0.203835091 -0.63018652
## ssno -0.5308540 0.7368598 -0.142929524 0.03445009
## sscs -0.4869528 0.8408970 -0.037929716 0.20837040
## ssai -0.7491569 0.3370107 -0.071978903 -0.07074088
## sssi -0.8414690 0.2593488 0.111324492 0.14727545
## ssmk -0.6412353 0.7919447 -0.125265270 -0.51919734
## ssmc -0.7440539 0.5697963 -0.351415475 -0.45644974
## ssei -0.7195617 0.4205403 0.028168182 -0.01901502
## ssao -0.6882552 0.8188022 -0.005091041 -0.81986617
mvn(data = datafullm, mvnTest = "hz", multivariatePlot = "qq", multivariateOutlierMethod = "quan")
## $multivariateNormality
## Test HZ p value MVN
## 1 Henze-Zirkler 1.126711 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 2.7040 <0.001 NO
## 2 Anderson-Darling ssar 18.4978 <0.001 NO
## 3 Anderson-Darling sswk 7.3432 <0.001 NO
## 4 Anderson-Darling sspc 18.4602 <0.001 NO
## 5 Anderson-Darling ssno 0.6630 0.0834 YES
## 6 Anderson-Darling sscs 8.7314 <0.001 NO
## 7 Anderson-Darling ssai 3.6458 <0.001 NO
## 8 Anderson-Darling sssi 1.7177 2e-04 NO
## 9 Anderson-Darling ssmk 3.1254 <0.001 NO
## 10 Anderson-Darling ssmc 26.9472 <0.001 NO
## 11 Anderson-Darling ssei 3.5225 <0.001 NO
## 12 Anderson-Darling ssao 19.8002 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max
## ssgs 3590 0.072654196 1.052161 0.09853007 -2.628479 3.536933
## ssar 3590 0.015191054 1.051973 0.13176738 -2.778350 2.847137
## sswk 3590 -0.005213396 1.031023 0.01549092 -2.690527 3.161901
## sspc 3590 -0.119498863 1.025404 -0.10217922 -2.185644 2.310898
## ssno 3590 -0.099733402 1.045060 -0.11641352 -3.869159 4.197971
## sscs 3590 -0.181391941 1.021915 -0.26156294 -4.658058 3.424302
## ssai 3590 0.214504538 1.116535 0.18947501 -2.552653 4.869916
## sssi 3590 0.273620101 1.079264 0.29214232 -2.832183 4.552315
## ssmk 3590 -0.072718114 1.013231 -0.04172879 -2.552466 2.766332
## ssmc 3590 0.122757578 1.074592 0.31107262 -2.620720 4.145332
## ssei 3590 0.145231459 1.125225 0.13993610 -2.399650 4.508166
## ssao 3590 -0.052083186 1.022771 -0.12278445 -2.013221 2.525071
## 25th 75th Skew Kurtosis
## ssgs -0.6935703 0.7840975 0.0609376986 -0.391510574
## ssar -0.5985369 0.7056680 -0.3448930451 -0.212859909
## sswk -0.6878086 0.7869715 -0.2084181729 -0.308855826
## sspc -0.9421575 0.7109517 -0.0058749424 -0.920176965
## ssno -0.7767722 0.6147061 0.0005396258 0.122389795
## sscs -0.7685068 0.5063316 -0.2511461820 0.851363553
## ssai -0.5549865 0.9063795 0.3437899586 0.265047056
## sssi -0.4975100 0.9685080 0.1322130065 -0.134801224
## ssmk -0.7906261 0.6342949 0.0023743017 -0.507529778
## ssmc -0.6438138 0.8649987 -0.2465863805 -0.324689532
## ssei -0.6464632 0.8859543 0.2758184354 -0.001697834
## ssao -0.8812416 0.7409851 0.1945848633 -0.868331637
mvn(data = datafullf, mvnTest = "hz", multivariatePlot = "qq", multivariateOutlierMethod = "quan")
## $multivariateNormality
## Test HZ p value MVN
## 1 Henze-Zirkler 1.059078 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling ssgs 3.5640 <0.001 NO
## 2 Anderson-Darling ssar 20.5756 <0.001 NO
## 3 Anderson-Darling sswk 2.7142 <0.001 NO
## 4 Anderson-Darling sspc 10.0051 <0.001 NO
## 5 Anderson-Darling ssno 1.6095 4e-04 NO
## 6 Anderson-Darling sscs 7.3979 <0.001 NO
## 7 Anderson-Darling ssai 1.4092 0.0012 NO
## 8 Anderson-Darling sssi 0.5749 0.1354 YES
## 9 Anderson-Darling ssmk 4.3820 <0.001 NO
## 10 Anderson-Darling ssmc 22.8413 <0.001 NO
## 11 Anderson-Darling ssei 5.3910 <0.001 NO
## 12 Anderson-Darling ssao 12.6820 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max
## ssgs 3503 -0.074458625 0.9378840 -0.064644372 -2.579174 2.742191
## ssar 3503 -0.015568337 0.9436683 0.080305523 -2.743033 2.586801
## sswk 3503 0.005342876 0.9672932 0.019735653 -2.739342 3.369893
## sspc 3503 0.122466719 0.9580778 0.181180544 -2.173948 2.303455
## ssno 3503 0.102210366 0.9408498 0.137328663 -2.954215 3.449631
## sscs 3503 0.185896965 0.9415825 0.144197438 -4.217250 3.709082
## ssai 3503 -0.219831942 0.8074651 -0.198865935 -2.547566 2.687406
## sssi 3503 -0.280415690 0.8221771 -0.281743753 -2.616044 3.401561
## ssmk 3503 0.074524131 0.9808211 0.118593045 -2.584530 2.708034
## ssmc 3503 -0.125806368 0.9002292 -0.004055222 -2.548375 2.823512
## ssei 3503 -0.148838407 0.8268980 -0.100581818 -2.253453 3.012003
## ssao 3503 0.053376716 0.9733703 0.069164350 -1.973793 2.481494
## 25th 75th Skew Kurtosis
## ssgs -0.7461031 0.5980317 0.030528040 -0.41086027
## ssar -0.5332005 0.5828154 -0.411530231 0.07343937
## sswk -0.6323617 0.7301981 -0.091074791 -0.14101114
## sspc -0.5508765 0.8574162 -0.203835091 -0.63018652
## ssno -0.5308540 0.7368598 -0.142929524 0.03445009
## sscs -0.4869528 0.8408970 -0.037929716 0.20837040
## ssai -0.7491569 0.3370107 -0.071978903 -0.07074088
## sssi -0.8414690 0.2593488 0.111324492 0.14727545
## ssmk -0.6412353 0.7919447 -0.125265270 -0.51919734
## ssmc -0.7440539 0.5697963 -0.351415475 -0.45644974
## ssei -0.7195617 0.4205403 0.028168182 -0.01901502
## ssao -0.6882552 0.8188022 -0.005091041 -0.81986617
# NLSYLINKS
d<- dplyr::select(d, id, hhid, starts_with("ss"), asvab, efa, dadeduc, momeduc, pareduc, educ2011, T6665000, age, agebin, agec, agec2, sex, sexage, bhw, bw, sweight, sibling)
links97<- subset(Links97PairExpanded)
links97$hhid<-links97$ExtendedID
links97<- dplyr::select(links97, hhid, RelationshipPath, R, RFull)
matching_indices<- match(d$hhid, links97$hhid)
d$R<- links97$R[matching_indices]
d<- subset(d, R==0.5)
nrow(d) # N=2809
## [1] 2809
result <- d %>% group_by(hhid) %>% summarise(identical_count = n()) %>% group_by(identical_count) %>% summarise(case_count = n())
print(result)
## # A tibble: 5 Ă— 2
## identical_count case_count
## <int> <int>
## 1 1 72
## 2 2 1133
## 3 3 136
## 4 4 12
## 5 5 3
d<- d %>% group_by(hhid) %>% filter(n() > 1) %>% ungroup() # remove cases with one count in hhid
d<- d %>% group_by(hhid) %>% filter(any(sex == 0) & any(sex == 1)) %>% ungroup() # retain hhid with 2 distinct sexes
nrow(d) # N=1443
## [1] 1443
result <- d %>% group_by(hhid) %>% summarise(identical_count = n()) %>% group_by(identical_count) %>% summarise(case_count = n())
print(result)
## # A tibble: 4 Ă— 2
## identical_count case_count
## <int> <int>
## 1 2 555
## 2 3 93
## 3 4 11
## 4 5 2
result <- d %>% group_by(hhid) %>% summarise(sex_distinct = n_distinct(sex)) %>% ungroup() # calculates the number of distinct values of sex within each group
result$all_same_sex <- result$sex_distinct == 1
result$all_same_sex # check if number of distinct sex = 1
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [111] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [144] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [177] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [188] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [199] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [210] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [221] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [254] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [298] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [331] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [364] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [375] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [386] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [408] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [419] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [430] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [441] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [452] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [463] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [485] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [518] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [540] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [551] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [584] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [606] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [628] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [639] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [650] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [661] FALSE
# SELECTING HOUSEHOLDS WITH EQUAL NUMBER OF OPPOSITE SEX SIBLINGS
two<- d %>% group_by(hhid) %>% filter(n() == 2) %>% ungroup() # keep hhid with 2 members
# among cases sharing the same hhid value, if there are 2 individuals with sex=0 or sex=1, removes the youngest individual in the respective category.
three<- d %>% group_by(hhid) %>% filter(n() == 3) %>% ungroup() # keep hhid with 3 members
three <- three %>%
group_by(hhid, sex) %>%
arrange(desc(age)) %>%
filter(row_number() <= ifelse(n() == 2, 1, n())) %>%
arrange(hhid) %>%
ungroup()
four<- d %>% group_by(hhid) %>% filter(n() == 4) %>% ungroup() # keep hhid with 4 members
four <- four %>%
group_by(hhid, sex) %>%
arrange(desc(age)) %>%
filter(row_number() <= ifelse(n() == 3, 1, n())) %>%
arrange(hhid) %>%
ungroup()
# among cases sharing the same hhid value, if there are 4 same sex individuals then remove 3 of youngest individuals in the respective categories, if there are 3 same sex individuals then remove the 1 youngest individual in the respective categories.
five<- d %>% group_by(hhid) %>% filter(n() == 5) %>% ungroup() # keep hhid with 5 members
five <- five %>%
group_by(hhid, sex) %>%
arrange(desc(age)) %>%
filter(row_number() <= ifelse(n() == 3, 2, ifelse(n() == 4, 1, n()))) %>%
arrange(hhid) %>%
ungroup()
d<- bind_rows(two, three, four, five) %>% arrange(id)
nrow(d) # N=1336
## [1] 1336
result <- d %>% group_by(hhid) %>% summarise(identical_count = n()) %>% group_by(identical_count) %>% summarise(case_count = n())
print(result)
## # A tibble: 2 Ă— 2
## identical_count case_count
## <int> <int>
## 1 2 654
## 2 4 7
result <- d %>% group_by(hhid) %>% summarise(sex_distinct = n_distinct(sex)) %>% ungroup() # calculates the number of distinct values of sex within each group
result$all_same_sex <- result$sex_distinct == 1
result$all_same_sex # check if number of distinct sex = 1
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [199] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [221] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [232] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [243] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [254] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [276] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [287] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [298] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [320] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [331] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [342] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [353] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [364] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [375] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [386] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [408] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [419] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [430] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [441] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [452] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [463] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [474] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [485] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [496] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [507] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [518] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [540] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [551] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [562] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [573] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [584] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [595] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [606] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [628] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [639] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [650] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [661] FALSE
hist(d$age)
find_mismatches <- function(data) {
mismatches <- data %>%
group_by(hhid) %>%
filter(n_distinct(bhw) > 1) %>%
summarise(ids = list(id), .groups = 'drop')
return(mismatches$ids)
}
mismatch_ids <- find_mismatches(d)
mismatch <- d[d$id %in% unlist(mismatch_ids), ]
View(mismatch) # found a total of 12 pairs of biological siblings with different self reported race: 8 pairs of H/W, 2 pairs of W/NA, 1 pair of B/W, 1 pair of B/H.
ds <- d %>% filter(!(id %in% unlist(mismatch_ids)))
nrow(ds) # N=1312
## [1] 1312
find_mismatches_dk <- function(data) {
mismatches <- data %>%
group_by(hhid) %>%
filter(n_distinct(bhw) > 1) %>%
summarise(hhids = first(hhid), .groups = 'drop')
return(mismatches$hhids)
}
dw<- subset(ds, bhw==3)
nrow(dw) # N=670
## [1] 670
mismatch_hhids_dk <- find_mismatches_dk(dk)
mismatch_dk <- dk %>% filter(hhid %in% mismatch_hhids_dk)
View(mismatch_dk) # 92 cases with same hhid but different bhw values
dkw <- dk %>% filter(!(hhid %in% mismatch_hhids_dk))
nrow(dkw) # N=7001, same as dk but minus potential white misclassification
## [1] 7001
dw<- subset(dk, bhw==3)
nrow(dw) # N=3659, dw is the total white
## [1] 3659
dw<- subset(dkw, bhw==3)
nrow(dw) # N=3621, total white minus misclassification, which is a loss of 38 whites who could potentially be misclassified
## [1] 3621
# DESCRIPTIVE STATS
describeBy(d$age, d$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew
## X1 1 668 14.51 1.41 14.5 14.51 1.85 12 16.92 4.92 -0.02
## kurtosis se
## X1 -1.17 0.05
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 668 14.52 1.46 14.54 14.53 1.92 12 16.92 4.92 -0.07
## kurtosis se
## X1 -1.23 0.06
describeBy(dk$age, dk$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew
## X1 1 3590 14.39 1.41 14.33 14.38 1.73 12 16.92 4.92 0.05
## kurtosis se
## X1 -1.14 0.02
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 3503 14.47 1.43 14.5 14.47 1.85 12 16.92 4.92 -0.03
## kurtosis se
## X1 -1.17 0.02
describeBy(d$pareduc, d$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 638 12.29 2.88 12 12.44 2.22 2 20 18 -0.53 1.02
## se
## X1 0.11
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 637 12.33 2.89 12 12.48 2.22 2 20 18 -0.54 1.03
## se
## X1 0.11
describeBy(dk$pareduc, dk$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 3424 12.68 3.41 12 12.67 2.22 1 95 94 6.34 126.88
## se
## X1 0.06
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 3336 12.59 3.17 12 12.66 2.22 1 95 94 5.61 145.37
## se
## X1 0.05
describeBy(d$momeduc, d$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 628 12.26 3.04 12 12.48 1.48 1 20 19 -0.66 1.41
## se
## X1 0.12
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 626 12.27 3.04 12 12.49 1.48 1 20 19 -0.66 1.41
## se
## X1 0.12
describeBy(dk$momeduc, dk$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 3336 12.74 3.71 12 12.75 1.48 1 95 94 9.61 214.66
## se
## X1 0.06
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 3267 12.61 3.23 12 12.73 2.97 1 95 94 4.7 130.3
## se
## X1 0.06
describeBy(d$dadeduc, d$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 547 12.51 3.32 12 12.62 2.97 2 20 18 -0.35 0.79
## se
## X1 0.14
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 543 12.55 3.32 12 12.67 2.97 2 20 18 -0.36 0.83
## se
## X1 0.14
describeBy(dk$dadeduc, dk$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 2927 12.81 4.11 12 12.78 2.97 1 95 94 8.07 162.8
## se
## X1 0.08
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 2815 12.78 3.44 12 12.8 2.97 2 95 93 4.69 115.89
## se
## X1 0.06
t.test(age ~ sex, data = d)
##
## Welch Two Sample t-test
##
## data: age by sex
## t = -0.17615, df = 1332.3, p-value = 0.8602
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.1680758 0.1403793
## sample estimates:
## mean in group 0 mean in group 1
## 14.50713 14.52098
t.test(age ~ sex, data = dk)
##
## Welch Two Sample t-test
##
## data: age by sex
## t = -2.2688, df = 7080.3, p-value = 0.02331
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.14238243 -0.01038558
## sample estimates:
## mean in group 0 mean in group 1
## 14.39167 14.46805
t.test(pareduc ~ sex, data = d)
##
## Welch Two Sample t-test
##
## data: pareduc by sex
## t = -0.20692, df = 1273, p-value = 0.8361
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.3503448 0.2834929
## sample estimates:
## mean in group 0 mean in group 1
## 12.29232 12.32575
t.test(pareduc ~ sex, data = dk)
##
## Welch Two Sample t-test
##
## data: pareduc by sex
## t = 1.1335, df = 6742.8, p-value = 0.257
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.06611981 0.24742150
## sample estimates:
## mean in group 0 mean in group 1
## 12.68283 12.59218
t.test(momeduc ~ sex, data = d)
##
## Welch Two Sample t-test
##
## data: momeduc by sex
## t = -0.079246, df = 1252, p-value = 0.9368
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.3505153 0.3232977
## sample estimates:
## mean in group 0 mean in group 1
## 12.25955 12.27316
t.test(momeduc ~ sex, data = dk)
##
## Welch Two Sample t-test
##
## data: momeduc by sex
## t = 1.5338, df = 6507.9, p-value = 0.1251
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.03649209 0.29893778
## sample estimates:
## mean in group 0 mean in group 1
## 12.74341 12.61218
t.test(dadeduc ~ sex, data = d)
##
## Welch Two Sample t-test
##
## data: dadeduc by sex
## t = -0.18358, df = 1088, p-value = 0.8544
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.4315256 0.3576857
## sample estimates:
## mean in group 0 mean in group 1
## 12.51188 12.54880
t.test(dadeduc ~ sex, data = dk)
##
## Welch Two Sample t-test
##
## data: dadeduc by sex
## t = 0.29766, df = 5633.1, p-value = 0.766
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.1659320 0.2253419
## sample estimates:
## mean in group 0 mean in group 1
## 12.80697 12.77726
describeBy(d$educ2011, d$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 556 13.29 2.82 13 13.28 2.97 6 20 14 0.12 -0.52
## se
## X1 0.12
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 580 14.01 2.86 14 14.02 2.97 6 20 14 0.02 -0.61
## se
## X1 0.12
describeBy(dk$educ2011, dk$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 2960 13.42 3.54 13 13.32 2.97 6 95 89 8.36 189.11
## se
## X1 0.07
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 2950 14.13 3.89 14 14.08 2.97 6 95 89 9.07 187.2
## se
## X1 0.07
t.test(educ2011 ~ sex, data = dk)
##
## Welch Two Sample t-test
##
## data: educ2011 by sex
## t = -7.3044, df = 5850.9, p-value = 3.156e-13
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.8965643 -0.5171508
## sample estimates:
## mean in group 0 mean in group 1
## 13.42297 14.12983
t.test(educ2011 ~ sex, data = d)
##
## Welch Two Sample t-test
##
## data: educ2011 by sex
## t = -4.2771, df = 1132.9, p-value = 2.053e-05
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -1.0514217 -0.3901313
## sample estimates:
## mean in group 0 mean in group 1
## 13.28957 14.01034
# FACTOR ANALYSIS (fa uses "minres" by default while factanal uses "ml")
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(age), sd=sd(age))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 14.6 1.32
## 2 1 1 14.7 1.48
## 3 2 0 14.7 1.44
## 4 2 1 14.4 1.40
## 5 3 0 14.4 1.41
## 6 3 1 14.5 1.49
## 7 NA 0 14.7 1.56
## 8 NA 1 14.3 1.20
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(ssgs), sd=sd(ssgs))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.673 0.914
## 2 1 1 -0.623 0.859
## 3 2 0 -0.343 0.926
## 4 2 1 -0.575 0.794
## 5 3 0 0.510 1.01
## 6 3 1 0.343 0.937
## 7 NA 0 0.670 0.947
## 8 NA 1 0.173 0.921
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(ssar), sd=sd(ssar))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.709 0.991
## 2 1 1 -0.594 0.879
## 3 2 0 -0.227 0.983
## 4 2 1 -0.317 0.841
## 5 3 0 0.356 0.977
## 6 3 1 0.370 0.875
## 7 NA 0 0.708 0.914
## 8 NA 1 0.416 1.01
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(sswk), sd=sd(sswk))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.724 0.931
## 2 1 1 -0.615 0.994
## 3 2 0 -0.393 0.884
## 4 2 1 -0.480 0.811
## 5 3 0 0.344 1.01
## 6 3 1 0.345 0.952
## 7 NA 0 0.330 1.16
## 8 NA 1 0.0240 1.00
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(sspc), sd=sd(sspc))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.738 0.870
## 2 1 1 -0.433 0.915
## 3 2 0 -0.366 0.997
## 4 2 1 -0.271 0.865
## 5 3 0 0.124 0.967
## 6 3 1 0.404 0.937
## 7 NA 0 0.162 0.831
## 8 NA 1 0.339 0.906
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(ssno), sd=sd(ssno))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.434 1.07
## 2 1 1 -0.0941 0.984
## 3 2 0 -0.307 0.919
## 4 2 1 -0.190 0.814
## 5 3 0 0.0982 1.07
## 6 3 1 0.265 0.986
## 7 NA 0 0.585 0.803
## 8 NA 1 0.0978 0.795
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(sscs), sd=sd(sscs))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.588 0.959
## 2 1 1 -0.110 1.04
## 3 2 0 -0.360 1.11
## 4 2 1 -0.0724 0.789
## 5 3 0 -0.0423 1.02
## 6 3 1 0.326 0.950
## 7 NA 0 0.503 0.873
## 8 NA 1 0.243 1.24
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(ssai), sd=sd(ssai))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.460 0.950
## 2 1 1 -0.632 0.801
## 3 2 0 -0.0228 1.00
## 4 2 1 -0.520 0.753
## 5 3 0 0.661 1.14
## 6 3 1 0.0422 0.756
## 7 NA 0 0.426 0.934
## 8 NA 1 -0.332 0.729
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(sssi), sd=sd(sssi))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.503 0.815
## 2 1 1 -0.846 0.669
## 3 2 0 -0.169 0.948
## 4 2 1 -0.627 0.734
## 5 3 0 0.817 1.01
## 6 3 1 0.129 0.797
## 7 NA 0 0.130 1.06
## 8 NA 1 -0.274 0.831
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(ssmk), sd=sd(ssmk))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.716 0.939
## 2 1 1 -0.499 0.976
## 3 2 0 -0.241 1.00
## 4 2 1 -0.314 0.888
## 5 3 0 0.229 0.950
## 6 3 1 0.410 0.972
## 7 NA 0 0.686 0.998
## 8 NA 1 0.337 1.24
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(ssmc), sd=sd(ssmc))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.705 0.950
## 2 1 1 -0.771 0.846
## 3 2 0 -0.158 0.957
## 4 2 1 -0.486 0.822
## 5 3 0 0.559 0.996
## 6 3 1 0.239 0.871
## 7 NA 0 0.419 1.08
## 8 NA 1 0.0700 0.939
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(ssei), sd=sd(ssei))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.697 0.869
## 2 1 1 -0.590 0.815
## 3 2 0 -0.214 1.01
## 4 2 1 -0.592 0.758
## 5 3 0 0.565 1.13
## 6 3 1 0.156 0.862
## 7 NA 0 0.363 1.34
## 8 NA 1 0.00944 1.11
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(ssao), sd=sd(ssao))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.644 0.857
## 2 1 1 -0.513 0.908
## 3 2 0 -0.0935 0.967
## 4 2 1 -0.200 0.935
## 5 3 0 0.229 1.00
## 6 3 1 0.322 0.923
## 7 NA 0 0.477 1.18
## 8 NA 1 0.377 1.18
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(asvab), sd=sd(asvab))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 87.9 11.1
## 2 1 1 90.4 12.2
## 3 2 0 93.5 12.9
## 4 2 1 92.9 11.3
## 5 3 0 105. 14.4
## 6 3 1 106. 13.9
## 7 NA 0 107. 14.2
## 8 NA 1 105. 15.8
ds %>% group_by(bhw, sex) %>% summarise(mean=mean(efa), sd=sd(efa))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 87.6 14.0
## 2 1 1 89.8 13.9
## 3 2 0 94.8 14.6
## 4 2 1 92.7 12.1
## 5 3 0 106. 15.2
## 6 3 1 106. 14.1
## 7 NA 0 109. 15.9
## 8 NA 1 103. 15.8
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(age), SD = survey_sd(age))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 14.5 0.122 1.35
## 2 1 1 14.7 0.125 1.46
## 3 2 0 14.8 0.126 1.42
## 4 2 1 14.4 0.132 1.45
## 5 3 0 14.4 0.0824 1.43
## 6 3 1 14.6 0.0863 1.52
## 7 NA 0 14.7 0.359 1.55
## 8 NA 1 14.3 0.279 1.22
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssgs), SD = survey_sd(ssgs))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.619 0.0829 0.909
## 2 1 1 -0.573 0.0784 0.867
## 3 2 0 -0.236 0.0881 0.954
## 4 2 1 -0.500 0.0741 0.813
## 5 3 0 0.542 0.0565 0.995
## 6 3 1 0.378 0.0509 0.912
## 7 NA 0 0.730 0.220 0.961
## 8 NA 1 0.163 0.214 0.938
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssar), SD = survey_sd(ssar))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.687 0.0895 0.978
## 2 1 1 -0.567 0.0810 0.896
## 3 2 0 -0.164 0.0962 1.02
## 4 2 1 -0.257 0.0710 0.823
## 5 3 0 0.392 0.0549 0.970
## 6 3 1 0.384 0.0493 0.874
## 7 NA 0 0.763 0.202 0.906
## 8 NA 1 0.416 0.227 1.01
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(sswk), SD = survey_sd(sswk))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.672 0.0886 0.958
## 2 1 1 -0.537 0.0867 0.992
## 3 2 0 -0.313 0.0864 0.922
## 4 2 1 -0.388 0.0738 0.829
## 5 3 0 0.371 0.0572 1.01
## 6 3 1 0.382 0.0525 0.940
## 7 NA 0 0.380 0.273 1.19
## 8 NA 1 0.0125 0.229 1.01
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(sspc), SD = survey_sd(sspc))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.704 0.0779 0.870
## 2 1 1 -0.396 0.0810 0.918
## 3 2 0 -0.231 0.0944 1.01
## 4 2 1 -0.207 0.0748 0.850
## 5 3 0 0.143 0.0557 0.979
## 6 3 1 0.445 0.0512 0.916
## 7 NA 0 0.206 0.189 0.835
## 8 NA 1 0.329 0.201 0.902
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssno), SD = survey_sd(ssno))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.376 0.0932 1.04
## 2 1 1 -0.0732 0.0869 0.994
## 3 2 0 -0.260 0.0908 0.969
## 4 2 1 -0.170 0.0738 0.822
## 5 3 0 0.122 0.0611 1.08
## 6 3 1 0.285 0.0557 0.989
## 7 NA 0 0.638 0.184 0.809
## 8 NA 1 0.0923 0.175 0.789
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(sscs), SD = survey_sd(sscs))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.528 0.0808 0.925
## 2 1 1 -0.125 0.103 1.10
## 3 2 0 -0.337 0.112 1.17
## 4 2 1 -0.00486 0.0690 0.781
## 5 3 0 -0.0261 0.0583 1.03
## 6 3 1 0.358 0.0530 0.939
## 7 NA 0 0.501 0.211 0.905
## 8 NA 1 0.208 0.274 1.23
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssai), SD = survey_sd(ssai))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.455 0.0841 0.946
## 2 1 1 -0.666 0.0693 0.800
## 3 2 0 -0.000914 0.0933 1.01
## 4 2 1 -0.475 0.0681 0.750
## 5 3 0 0.684 0.0668 1.16
## 6 3 1 0.0692 0.0426 0.758
## 7 NA 0 0.454 0.220 0.959
## 8 NA 1 -0.333 0.161 0.725
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(sssi), SD = survey_sd(sssi))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.505 0.0755 0.823
## 2 1 1 -0.823 0.0571 0.663
## 3 2 0 -0.111 0.0978 1.02
## 4 2 1 -0.543 0.0712 0.750
## 5 3 0 0.827 0.0586 1.03
## 6 3 1 0.163 0.0437 0.781
## 7 NA 0 0.143 0.248 1.09
## 8 NA 1 -0.277 0.184 0.825
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssmk), SD = survey_sd(ssmk))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.671 0.0863 0.939
## 2 1 1 -0.452 0.0890 0.999
## 3 2 0 -0.162 0.0913 1.01
## 4 2 1 -0.264 0.0758 0.871
## 5 3 0 0.259 0.0544 0.957
## 6 3 1 0.448 0.0542 0.964
## 7 NA 0 0.708 0.231 1.01
## 8 NA 1 0.331 0.274 1.23
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssmc), SD = survey_sd(ssmc))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.658 0.0848 0.943
## 2 1 1 -0.740 0.0670 0.804
## 3 2 0 -0.0567 0.0889 0.972
## 4 2 1 -0.450 0.0722 0.822
## 5 3 0 0.578 0.0565 0.997
## 6 3 1 0.263 0.0483 0.859
## 7 NA 0 0.382 0.238 1.06
## 8 NA 1 0.0577 0.210 0.940
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssei), SD = survey_sd(ssei))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.663 0.0827 0.897
## 2 1 1 -0.543 0.0734 0.822
## 3 2 0 -0.129 0.103 1.07
## 4 2 1 -0.513 0.0696 0.761
## 5 3 0 0.595 0.0631 1.11
## 6 3 1 0.188 0.0482 0.859
## 7 NA 0 0.371 0.308 1.36
## 8 NA 1 0.00575 0.252 1.12
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssao), SD = survey_sd(ssao))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.631 0.0731 0.834
## 2 1 1 -0.523 0.0759 0.893
## 3 2 0 -0.0343 0.0896 0.986
## 4 2 1 -0.169 0.0788 0.912
## 5 3 0 0.225 0.0576 1.02
## 6 3 1 0.343 0.0520 0.923
## 7 NA 0 0.440 0.266 1.18
## 8 NA 1 0.368 0.263 1.18
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(asvab), SD = survey_sd(asvab))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 88.4 1.02 11.2
## 2 1 1 91.1 1.09 12.4
## 3 2 0 94.8 1.25 13.3
## 4 2 1 94.2 1.03 11.5
## 5 3 0 106. 0.817 14.4
## 6 3 1 106. 0.774 13.8
## 7 NA 0 108. 3.12 13.8
## 8 NA 1 105. 3.57 15.8
ds %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(efa), SD = survey_sd(efa))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 88.4 1.30 14.1
## 2 1 1 90.5 1.22 13.9
## 3 2 0 96.4 1.43 15.3
## 4 2 1 93.9 1.05 12.0
## 5 3 0 107. 0.862 15.2
## 6 3 1 107. 0.774 13.8
## 7 NA 0 110. 3.66 16.1
## 8 NA 1 103. 3.55 15.8
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(age), sd=sd(age))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 14.4 1.36
## 2 1 1 14.5 1.47
## 3 2 0 14.4 1.43
## 4 2 1 14.4 1.39
## 5 3 0 14.4 1.42
## 6 3 1 14.5 1.42
## 7 NA 0 14.3 1.45
## 8 NA 1 14.4 1.38
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(ssgs), sd=sd(ssgs))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.571 0.886
## 2 1 1 -0.550 0.830
## 3 2 0 -0.294 0.913
## 4 2 1 -0.462 0.858
## 5 3 0 0.496 0.972
## 6 3 1 0.310 0.847
## 7 NA 0 0.219 0.975
## 8 NA 1 0.0800 0.884
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(ssar), sd=sd(ssar))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.603 0.975
## 2 1 1 -0.514 0.910
## 3 2 0 -0.222 0.943
## 4 2 1 -0.251 0.902
## 5 3 0 0.366 0.965
## 6 3 1 0.313 0.834
## 7 NA 0 0.357 1.05
## 8 NA 1 0.261 0.895
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(sswk), sd=sd(sswk))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.577 0.952
## 2 1 1 -0.454 0.920
## 3 2 0 -0.330 0.918
## 4 2 1 -0.333 0.866
## 5 3 0 0.367 0.938
## 6 3 1 0.364 0.883
## 7 NA 0 0.174 1.08
## 8 NA 1 0.139 0.893
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(sspc), sd=sd(sspc))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.643 0.916
## 2 1 1 -0.304 0.928
## 3 2 0 -0.333 0.954
## 4 2 1 -0.146 0.892
## 5 3 0 0.186 0.982
## 6 3 1 0.432 0.888
## 7 NA 0 0.118 1.01
## 8 NA 1 0.318 0.845
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(ssno), sd=sd(ssno))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.361 1.03
## 2 1 1 -0.0231 0.950
## 3 2 0 -0.314 0.921
## 4 2 1 -0.137 0.835
## 5 3 0 0.0808 1.05
## 6 3 1 0.243 0.941
## 7 NA 0 0.185 1.07
## 8 NA 1 0.302 1.01
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(sscs), sd=sd(sscs))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.502 1.01
## 2 1 1 -0.0193 0.973
## 3 2 0 -0.302 0.976
## 4 2 1 -0.00361 0.861
## 5 3 0 -0.0135 1.00
## 6 3 1 0.351 0.910
## 7 NA 0 0.188 1.02
## 8 NA 1 0.373 1.06
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(ssai), sd=sd(ssai))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.361 0.949
## 2 1 1 -0.550 0.803
## 3 2 0 -0.0891 0.964
## 4 2 1 -0.442 0.775
## 5 3 0 0.594 1.10
## 6 3 1 0.0401 0.739
## 7 NA 0 0.204 1.07
## 8 NA 1 -0.261 0.689
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(sssi), sd=sd(sssi))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.437 0.840
## 2 1 1 -0.721 0.697
## 3 2 0 -0.117 0.963
## 4 2 1 -0.557 0.789
## 5 3 0 0.753 0.984
## 6 3 1 0.0518 0.753
## 7 NA 0 0.196 0.925
## 8 NA 1 -0.238 0.722
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(ssmk), sd=sd(ssmk))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.583 0.920
## 2 1 1 -0.320 0.940
## 3 2 0 -0.284 0.956
## 4 2 1 -0.213 0.933
## 5 3 0 0.215 0.956
## 6 3 1 0.366 0.910
## 7 NA 0 0.323 1.08
## 8 NA 1 0.392 1.02
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(ssmc), sd=sd(ssmc))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.603 0.968
## 2 1 1 -0.656 0.821
## 3 2 0 -0.134 0.969
## 4 2 1 -0.355 0.811
## 5 3 0 0.541 0.954
## 6 3 1 0.221 0.811
## 7 NA 0 0.321 0.925
## 8 NA 1 0.113 0.833
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(ssei), sd=sd(ssei))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.479 0.926
## 2 1 1 -0.457 0.773
## 3 2 0 -0.187 0.980
## 4 2 1 -0.474 0.770
## 5 3 0 0.553 1.09
## 6 3 1 0.124 0.768
## 7 NA 0 0.204 1.06
## 8 NA 1 -0.00528 0.891
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(ssao), sd=sd(ssao))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.600 0.852
## 2 1 1 -0.451 0.894
## 3 2 0 -0.110 0.950
## 4 2 1 -0.0521 0.933
## 5 3 0 0.195 1.01
## 6 3 1 0.335 0.913
## 7 NA 0 0.363 1.09
## 8 NA 1 0.348 0.965
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(asvab), sd=sd(asvab))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 90.1 11.7
## 2 1 1 92.7 12.6
## 3 2 0 94.6 13.4
## 4 2 1 95.3 13.1
## 5 3 0 105. 14.6
## 6 3 1 106. 13.7
## 7 NA 0 105. 14.9
## 8 NA 1 105. 13.9
dk %>% group_by(bhw, sex) %>% summarise(mean=mean(efa), sd=sd(efa))
## `summarise()` has grouped output by 'bhw'. You can override using the
## `.groups` argument.
## # A tibble: 8 Ă— 4
## # Groups: bhw [4]
## bhw sex mean sd
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 89.6 13.9
## 2 1 1 92.0 13.4
## 3 2 0 95.2 14.1
## 4 2 1 94.6 13.1
## 5 3 0 107. 14.7
## 6 3 1 106. 12.9
## 7 NA 0 104. 15.7
## 8 NA 1 104. 13.3
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(age), SD = survey_sd(age))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 14.4 0.0554 1.41
## 2 1 1 14.5 0.0504 1.43
## 3 2 0 14.5 0.0619 1.43
## 4 2 1 14.4 0.0638 1.43
## 5 3 0 14.4 0.0351 1.44
## 6 3 1 14.5 0.0365 1.45
## 7 NA 0 14.3 0.134 1.47
## 8 NA 1 14.3 0.128 1.40
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssgs), SD = survey_sd(ssgs))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.505 0.0335 0.887
## 2 1 1 -0.513 0.0314 0.843
## 3 2 0 -0.174 0.0411 0.940
## 4 2 1 -0.333 0.0386 0.878
## 5 3 0 0.523 0.0234 0.974
## 6 3 1 0.331 0.0207 0.843
## 7 NA 0 0.243 0.0871 0.975
## 8 NA 1 0.126 0.0784 0.875
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssar), SD = survey_sd(ssar))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.551 0.0382 0.988
## 2 1 1 -0.502 0.0329 0.910
## 3 2 0 -0.128 0.0410 0.952
## 4 2 1 -0.146 0.0405 0.908
## 5 3 0 0.395 0.0228 0.953
## 6 3 1 0.327 0.0209 0.835
## 7 NA 0 0.382 0.0945 1.06
## 8 NA 1 0.292 0.0780 0.883
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(sswk), SD = survey_sd(sswk))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.517 0.0369 0.965
## 2 1 1 -0.409 0.0341 0.929
## 3 2 0 -0.203 0.0405 0.937
## 4 2 1 -0.233 0.0386 0.882
## 5 3 0 0.392 0.0225 0.937
## 6 3 1 0.379 0.0217 0.882
## 7 NA 0 0.202 0.0960 1.08
## 8 NA 1 0.160 0.0805 0.901
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(sspc), SD = survey_sd(sspc))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.590 0.0357 0.928
## 2 1 1 -0.266 0.0338 0.929
## 3 2 0 -0.203 0.0430 0.979
## 4 2 1 -0.0428 0.0381 0.887
## 5 3 0 0.211 0.0236 0.985
## 6 3 1 0.453 0.0216 0.880
## 7 NA 0 0.140 0.0908 1.02
## 8 NA 1 0.345 0.0741 0.835
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssno), SD = survey_sd(ssno))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.316 0.0388 1.02
## 2 1 1 -0.0252 0.0339 0.943
## 3 2 0 -0.263 0.0401 0.929
## 4 2 1 -0.0720 0.0353 0.826
## 5 3 0 0.0964 0.0256 1.07
## 6 3 1 0.244 0.0234 0.947
## 7 NA 0 0.205 0.0986 1.10
## 8 NA 1 0.334 0.0912 1.02
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(sscs), SD = survey_sd(sscs))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.458 0.0381 1.00
## 2 1 1 -0.0178 0.0355 0.971
## 3 2 0 -0.234 0.0444 1.00
## 4 2 1 0.0621 0.0381 0.869
## 5 3 0 0.00735 0.0240 1.00
## 6 3 1 0.358 0.0227 0.906
## 7 NA 0 0.188 0.0925 1.03
## 8 NA 1 0.405 0.0936 1.04
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssai), SD = survey_sd(ssai))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.336 0.0360 0.944
## 2 1 1 -0.546 0.0299 0.814
## 3 2 0 -0.0191 0.0424 0.976
## 4 2 1 -0.363 0.0348 0.783
## 5 3 0 0.614 0.0265 1.10
## 6 3 1 0.0551 0.0182 0.738
## 7 NA 0 0.251 0.0975 1.09
## 8 NA 1 -0.246 0.0607 0.689
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(sssi), SD = survey_sd(sssi))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.396 0.0329 0.855
## 2 1 1 -0.713 0.0256 0.699
## 3 2 0 0.00643 0.0443 0.996
## 4 2 1 -0.471 0.0348 0.797
## 5 3 0 0.769 0.0238 0.990
## 6 3 1 0.0594 0.0186 0.751
## 7 NA 0 0.235 0.0823 0.928
## 8 NA 1 -0.237 0.0641 0.722
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssmk), SD = survey_sd(ssmk))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.517 0.0375 0.948
## 2 1 1 -0.315 0.0341 0.940
## 3 2 0 -0.173 0.0426 0.973
## 4 2 1 -0.105 0.0405 0.931
## 5 3 0 0.242 0.0230 0.959
## 6 3 1 0.382 0.0225 0.911
## 7 NA 0 0.343 0.0975 1.09
## 8 NA 1 0.414 0.0929 1.04
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssmc), SD = survey_sd(ssmc))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.544 0.0375 0.979
## 2 1 1 -0.650 0.0293 0.813
## 3 2 0 -0.00804 0.0428 0.986
## 4 2 1 -0.277 0.0358 0.821
## 5 3 0 0.563 0.0228 0.953
## 6 3 1 0.235 0.0200 0.812
## 7 NA 0 0.338 0.0818 0.923
## 8 NA 1 0.118 0.0744 0.828
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssei), SD = survey_sd(ssei))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.428 0.0360 0.946
## 2 1 1 -0.436 0.0286 0.777
## 3 2 0 -0.0728 0.0456 1.02
## 4 2 1 -0.384 0.0344 0.786
## 5 3 0 0.582 0.0264 1.10
## 6 3 1 0.139 0.0190 0.766
## 7 NA 0 0.226 0.0941 1.06
## 8 NA 1 0.0509 0.0793 0.883
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(ssao), SD = survey_sd(ssao))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 -0.561 0.0332 0.856
## 2 1 1 -0.437 0.0327 0.895
## 3 2 0 -0.0242 0.0421 0.971
## 4 2 1 0.0212 0.0405 0.930
## 5 3 0 0.214 0.0243 1.02
## 6 3 1 0.356 0.0222 0.907
## 7 NA 0 0.386 0.0973 1.09
## 8 NA 1 0.328 0.0865 0.972
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(asvab), SD = survey_sd(asvab))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 91.0 0.476 12.1
## 2 1 1 93.5 0.489 13.0
## 3 2 0 96.3 0.620 14.0
## 4 2 1 97.0 0.592 13.4
## 5 3 0 106. 0.348 14.6
## 6 3 1 107. 0.334 13.6
## 7 NA 0 105. 1.34 15.0
## 8 NA 1 105. 1.22 13.7
dk %>% as_survey_design(ids = id, weights = sweight) %>% group_by(bhw, sex) %>% summarise(MEAN = survey_mean(efa), SD = survey_sd(efa))
## # A tibble: 8 Ă— 5
## # Groups: bhw [4]
## bhw sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 90.7 0.553 14.2
## 2 1 1 92.4 0.491 13.4
## 3 2 0 97.2 0.635 14.5
## 4 2 1 96.5 0.584 13.3
## 5 3 0 107. 0.352 14.7
## 6 3 1 106. 0.319 12.9
## 7 NA 0 105. 1.42 15.9
## 8 NA 1 104. 1.19 13.3
# white sibling sample
dw<- subset(ds, bhw==3)
m<- subset(dw, sex==0)
f<- subset(dw, sex==1)
dm<- dplyr::select(m, starts_with("ss"), sweight)
df<- dplyr::select(f, starts_with("ss"), sweight)
ev <- eigen(cor(dm)) # get eigenvalues
ev$values
## [1] 7.2136264 1.4095444 1.0143387 0.6508700 0.5509553 0.4241368
## [7] 0.3575865 0.3195064 0.2866785 0.2381929 0.2060529 0.1776118
## [13] 0.1508995
ev <- eigen(cor(df)) # get eigenvalues
ev$values
## [1] 7.2067447 1.0929905 0.9671518 0.6564131 0.6294458 0.4779097
## [7] 0.4250565 0.3820870 0.2952830 0.2674813 0.2516615 0.1757482
## [13] 0.1720268
fa3<-fa(dm[,1:12], nfactors=3, rotate="promax", fm="minres", weight=dm$sweight)
fa3
## Factor Analysis using method = minres
## Call: fa(r = dm[, 1:12], nfactors = 3, rotate = "promax", fm = "minres",
## weight = dm$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## ssgs 0.84 0.14 -0.10 0.78 0.22 1.1
## ssar 0.71 -0.03 0.23 0.75 0.25 1.2
## sswk 0.71 0.17 0.02 0.71 0.29 1.1
## sspc 0.99 -0.16 0.00 0.78 0.22 1.1
## ssno -0.11 0.04 0.91 0.73 0.27 1.0
## sscs 0.23 0.00 0.56 0.54 0.46 1.3
## ssai -0.11 0.87 0.07 0.68 0.32 1.0
## sssi -0.04 0.87 -0.01 0.71 0.29 1.0
## ssmk 0.61 0.00 0.36 0.82 0.18 1.6
## ssmc 0.61 0.34 -0.04 0.73 0.27 1.6
## ssei 0.45 0.49 -0.02 0.72 0.28 2.0
## ssao 0.66 -0.02 0.07 0.48 0.52 1.0
##
## MR1 MR2 MR3
## SS loadings 4.63 2.19 1.61
## Proportion Var 0.39 0.18 0.13
## Cumulative Var 0.39 0.57 0.70
## Proportion Explained 0.55 0.26 0.19
## Cumulative Proportion 0.55 0.81 1.00
##
## With factor correlations of
## MR1 MR2 MR3
## MR1 1.00 0.68 0.69
## MR2 0.68 1.00 0.31
## MR3 0.69 0.31 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 factors are sufficient.
##
## df null model = 66 with the objective function = 9.71 with Chi Square = 3194.86
## df of the model are 33 and the objective function was 0.35
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic n.obs is 335 with the empirical chi square 20.22 with prob < 0.96
## The total n.obs was 335 with Likelihood Chi Square = 113.7 with prob < 8.5e-11
##
## Tucker Lewis Index of factoring reliability = 0.948
## RMSEA index = 0.085 and the 90 % confidence intervals are 0.069 0.103
## BIC = -78.17
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of (regression) scores with factors 0.97 0.94 0.92
## Multiple R square of scores with factors 0.95 0.88 0.85
## Minimum correlation of possible factor scores 0.90 0.77 0.71
fa3<-fa(df[,1:12], nfactors=3, rotate="promax", fm="minres", weight=df$sweight)
fa3
## Factor Analysis using method = minres
## Call: fa(r = df[, 1:12], nfactors = 3, rotate = "promax", fm = "minres",
## weight = df$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## ssgs 0.75 0.25 -0.09 0.79 0.21 1.3
## ssar 0.32 0.44 0.22 0.75 0.25 2.3
## sswk 0.76 0.38 -0.25 0.82 0.18 1.7
## sspc 0.47 0.41 0.02 0.69 0.31 2.0
## ssno -0.16 0.89 -0.03 0.59 0.41 1.1
## sscs -0.09 0.64 0.13 0.44 0.56 1.1
## ssai 0.62 -0.04 0.03 0.38 0.62 1.0
## sssi 0.81 -0.33 0.15 0.51 0.49 1.4
## ssmk 0.20 0.63 0.16 0.80 0.20 1.3
## ssmc 0.46 0.09 0.37 0.69 0.31 2.0
## ssei 0.69 0.12 0.01 0.62 0.38 1.1
## ssao 0.04 0.15 0.69 0.67 0.33 1.1
##
## MR1 MR2 MR3
## SS loadings 3.80 2.88 1.06
## Proportion Var 0.32 0.24 0.09
## Cumulative Var 0.32 0.56 0.65
## Proportion Explained 0.49 0.37 0.14
## Cumulative Proportion 0.49 0.86 1.00
##
## With factor correlations of
## MR1 MR2 MR3
## MR1 1.00 0.70 0.68
## MR2 0.70 1.00 0.59
## MR3 0.68 0.59 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 3 factors are sufficient.
##
## df null model = 66 with the objective function = 8.65 with Chi Square = 2846.23
## df of the model are 33 and the objective function was 0.25
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic n.obs is 335 with the empirical chi square 25.42 with prob < 0.82
## The total n.obs was 335 with Likelihood Chi Square = 81.29 with prob < 5.9e-06
##
## Tucker Lewis Index of factoring reliability = 0.965
## RMSEA index = 0.066 and the 90 % confidence intervals are 0.048 0.084
## BIC = -110.58
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of (regression) scores with factors 0.96 0.94 0.88
## Multiple R square of scores with factors 0.92 0.89 0.78
## Minimum correlation of possible factor scores 0.84 0.78 0.55
fa4<-fa(dm[,1:12], nfactors=4, rotate="promax", fm="minres", weight=dm$sweight)
fa4
## Factor Analysis using method = minres
## Call: fa(r = dm[, 1:12], nfactors = 4, rotate = "promax", fm = "minres",
## weight = dm$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR4 MR2 MR3 h2 u2 com
## ssgs 0.13 0.78 0.10 -0.08 0.82 0.18 1.1
## ssar 0.54 0.17 -0.03 0.25 0.76 0.24 1.6
## sswk -0.10 0.87 0.12 0.06 0.82 0.18 1.1
## sspc 0.47 0.51 -0.15 0.04 0.77 0.23 2.2
## ssno -0.11 -0.08 0.04 0.98 0.75 0.25 1.0
## sscs 0.15 0.06 0.00 0.57 0.53 0.47 1.2
## ssai -0.10 0.05 0.83 0.07 0.68 0.32 1.0
## sssi 0.07 -0.04 0.83 -0.02 0.71 0.29 1.0
## ssmk 0.46 0.15 0.00 0.38 0.82 0.18 2.2
## ssmc 0.83 -0.12 0.33 -0.10 0.82 0.18 1.4
## ssei 0.20 0.30 0.46 0.00 0.72 0.28 2.1
## ssao 0.70 0.01 -0.03 0.05 0.52 0.48 1.0
##
## MR1 MR4 MR2 MR3
## SS loadings 2.66 2.29 2.03 1.75
## Proportion Var 0.22 0.19 0.17 0.15
## Cumulative Var 0.22 0.41 0.58 0.73
## Proportion Explained 0.30 0.26 0.23 0.20
## Cumulative Proportion 0.30 0.57 0.80 1.00
##
## With factor correlations of
## MR1 MR4 MR2 MR3
## MR1 1.00 0.84 0.60 0.70
## MR4 0.84 1.00 0.63 0.67
## MR2 0.60 0.63 1.00 0.31
## MR3 0.70 0.67 0.31 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 66 with the objective function = 9.71 with Chi Square = 3194.86
## df of the model are 24 and the objective function was 0.13
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic n.obs is 335 with the empirical chi square 6.55 with prob < 1
## The total n.obs was 335 with Likelihood Chi Square = 42.75 with prob < 0.011
##
## Tucker Lewis Index of factoring reliability = 0.983
## RMSEA index = 0.048 and the 90 % confidence intervals are 0.023 0.072
## BIC = -96.79
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR4 MR2 MR3
## Correlation of (regression) scores with factors 0.96 0.96 0.93 0.93
## Multiple R square of scores with factors 0.92 0.92 0.87 0.87
## Minimum correlation of possible factor scores 0.84 0.85 0.75 0.75
fa4<-fa(df[,1:12], nfactors=4, rotate="promax", fm="minres", weight=df$sweight)
fa4
## Factor Analysis using method = minres
## Call: fa(r = df[, 1:12], nfactors = 4, rotate = "promax", fm = "minres",
## weight = df$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR3 MR2 MR4 h2 u2 com
## ssgs 0.91 -0.02 -0.06 0.06 0.81 0.19 1.0
## ssar 0.50 0.37 0.12 -0.07 0.76 0.24 2.0
## sswk 1.01 -0.23 0.06 0.05 0.83 0.17 1.1
## sspc 0.65 0.12 0.11 -0.01 0.69 0.31 1.1
## ssno 0.03 -0.10 0.88 -0.02 0.70 0.30 1.0
## sscs -0.03 0.15 0.58 0.05 0.46 0.54 1.1
## ssai 0.03 -0.08 0.17 0.64 0.49 0.51 1.2
## sssi 0.04 0.13 -0.14 0.71 0.60 0.40 1.1
## ssmk 0.43 0.27 0.32 -0.07 0.79 0.21 2.7
## ssmc 0.33 0.51 -0.08 0.12 0.69 0.31 1.9
## ssei 0.60 0.06 -0.03 0.20 0.61 0.39 1.2
## ssao -0.15 0.88 0.03 0.04 0.66 0.34 1.1
##
## MR1 MR3 MR2 MR4
## SS loadings 3.71 1.66 1.53 1.21
## Proportion Var 0.31 0.14 0.13 0.10
## Cumulative Var 0.31 0.45 0.57 0.68
## Proportion Explained 0.46 0.20 0.19 0.15
## Cumulative Proportion 0.46 0.66 0.85 1.00
##
## With factor correlations of
## MR1 MR3 MR2 MR4
## MR1 1.00 0.80 0.71 0.71
## MR3 0.80 1.00 0.66 0.63
## MR2 0.71 0.66 1.00 0.41
## MR4 0.71 0.63 0.41 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 66 with the objective function = 8.65 with Chi Square = 2846.23
## df of the model are 24 and the objective function was 0.15
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic n.obs is 335 with the empirical chi square 8.83 with prob < 1
## The total n.obs was 335 with Likelihood Chi Square = 48.42 with prob < 0.0022
##
## Tucker Lewis Index of factoring reliability = 0.976
## RMSEA index = 0.055 and the 90 % confidence intervals are 0.032 0.078
## BIC = -91.12
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR3 MR2 MR4
## Correlation of (regression) scores with factors 0.97 0.93 0.91 0.88
## Multiple R square of scores with factors 0.95 0.87 0.84 0.77
## Minimum correlation of possible factor scores 0.90 0.73 0.67 0.55
fact3<- factanal(dm[,1:12], 3, rotation="promax")
print(fact3, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = dm[, 1:12], factors = 3, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.13 0.23 0.23 0.27 0.49 0.51 0.37 0.30 0.15 0.26 0.28 0.54
##
## Loadings:
## Factor1 Factor2 Factor3
## ssgs 0.80
## ssar 0.78
## sswk 0.21 0.67
## sspc 0.52 0.42
## ssno 0.86
## sscs 0.77
## ssai 0.85
## sssi 0.92
## ssmk 0.85
## ssmc 0.41 0.54
## ssei 0.46 0.35
## ssao 0.53
##
## Factor1 Factor2 Factor3
## SS loadings 3.45 2.14 1.41
## Proportion Var 0.29 0.18 0.12
## Cumulative Var 0.29 0.47 0.58
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.00 -0.57 -0.75
## Factor2 -0.57 1.00 0.72
## Factor3 -0.75 0.72 1.00
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 91.9 on 33 degrees of freedom.
## The p-value is 1.84e-07
fact3<- factanal(df[,1:12], 3, rotation="promax")
print(fact3, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = df[, 1:12], factors = 3, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.19 0.23 0.16 0.31 0.42 0.56 0.64 0.53 0.19 0.28 0.38 0.38
##
## Loadings:
## Factor1 Factor2 Factor3
## ssgs 0.83
## ssar 0.29 0.41 0.31
## sswk 0.92 0.29 -0.26
## sspc 0.51 0.34
## ssno 0.76
## sscs 0.56
## ssai 0.49
## sssi 0.53 -0.21 0.30
## ssmk 0.23 0.55 0.26
## ssmc 0.30 0.55
## ssei 0.68
## ssao 0.79
##
## Factor1 Factor2 Factor3
## SS loadings 3.03 1.67 1.32
## Proportion Var 0.25 0.14 0.11
## Cumulative Var 0.25 0.39 0.50
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.00 -0.57 -0.80
## Factor2 -0.57 1.00 0.57
## Factor3 -0.80 0.57 1.00
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 64.52 on 33 degrees of freedom.
## The p-value is 0.000839
fact4<- factanal(dm[,1:12], 4, rotation="promax")
print(fact4, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = dm[, 1:12], factors = 4, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.16 0.23 0.19 0.24 0.31 0.48 0.32 0.32 0.16 0.17 0.29 0.50
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## ssgs 0.80
## ssar 0.53 0.32
## sswk 0.84
## sspc 0.47 0.46
## ssno 0.94
## sscs 0.60
## ssai 0.82
## sssi 0.76
## ssmk 0.42 0.45
## ssmc 0.82 0.32
## ssei 0.37 0.39
## ssao 0.66
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 1.90 1.76 1.58 1.55
## Proportion Var 0.16 0.15 0.13 0.13
## Cumulative Var 0.16 0.30 0.44 0.57
##
## Factor Correlations:
## Factor1 Factor2 Factor3 Factor4
## Factor1 1.00 -0.57 0.82 -0.70
## Factor2 -0.57 1.00 -0.60 0.29
## Factor3 0.82 -0.60 1.00 -0.67
## Factor4 -0.70 0.29 -0.67 1.00
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 34.17 on 24 degrees of freedom.
## The p-value is 0.0817
fact4<- factanal(df[,1:12], 4, rotation="promax")
print(fact4, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = df[, 1:12], factors = 4, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.19 0.22 0.16 0.30 0.25 0.54 0.56 0.37 0.20 0.30 0.38 0.39
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## ssgs 0.78
## ssar 0.36 0.53
## sswk 0.92
## sspc 0.59 0.29
## ssno 0.93
## sscs 0.22 0.52
## ssai 0.56
## sssi 0.79
## ssmk 0.31 0.45 0.26
## ssmc 0.21 0.60
## ssei 0.50 0.28
## ssao 0.88
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 2.37 1.79 1.27 1.09
## Proportion Var 0.20 0.15 0.11 0.09
## Cumulative Var 0.20 0.35 0.45 0.54
##
## Factor Correlations:
## Factor1 Factor2 Factor3 Factor4
## Factor1 1.00 -0.69 0.81 0.71
## Factor2 -0.69 1.00 -0.70 -0.42
## Factor3 0.81 -0.70 1.00 0.66
## Factor4 0.71 -0.42 0.66 1.00
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 35.5 on 24 degrees of freedom.
## The p-value is 0.0613
mfa<-fa(r=dm, nfactors=4, max.iter=100, warnings=TRUE, rotate="none", fm="pa", weight=dm$sweight)
print(mfa, digits=2, cutoff=.10)
## Factor Analysis using method = pa
## Call: fa(r = dm, nfactors = 4, rotate = "none", max.iter = 100, warnings = TRUE,
## fm = "pa", weight = dm$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA2 PA3 PA4 h2 u2 com
## ssgs 0.87 0.07 -0.13 -0.25 0.84 0.16 1.2
## ssar 0.84 -0.22 -0.04 0.05 0.75 0.25 1.1
## sswk 0.85 0.03 -0.01 -0.28 0.80 0.20 1.2
## sspc 0.83 -0.19 -0.13 -0.11 0.76 0.24 1.2
## ssno 0.59 -0.43 0.28 0.10 0.62 0.38 2.4
## sscs 0.63 -0.33 0.22 0.07 0.57 0.43 1.8
## ssai 0.64 0.54 0.24 0.07 0.76 0.24 2.3
## sssi 0.63 0.51 0.04 0.12 0.67 0.33 2.0
## ssmk 0.87 -0.26 0.06 0.05 0.83 0.17 1.2
## ssmc 0.84 0.16 -0.21 0.21 0.82 0.18 1.3
## ssei 0.81 0.26 -0.04 -0.01 0.72 0.28 1.2
## ssao 0.68 -0.14 -0.21 0.15 0.54 0.46 1.4
## sweight 0.22 0.13 0.39 -0.11 0.22 0.78 2.0
##
## PA1 PA2 PA3 PA4
## SS loadings 7.03 1.13 0.46 0.27
## Proportion Var 0.54 0.09 0.04 0.02
## Cumulative Var 0.54 0.63 0.66 0.68
## Proportion Explained 0.79 0.13 0.05 0.03
## Cumulative Proportion 0.79 0.92 0.97 1.00
##
## Mean item complexity = 1.6
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 78 with the objective function = 9.87 0.1 with Chi Square = 3246.08
## df of the model are 32 and the objective function was 0.18
## 0.1
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
## 0.1
## The harmonic n.obs is 335 with the empirical chi square 10.67 with prob < 1
## 0.1The total n.obs was 335 with Likelihood Chi Square = 58.45 with prob < 0.0029
## 0.1
## Tucker Lewis Index of factoring reliability = 0.979
## RMSEA index = 0.05 and the 90 % confidence intervals are 0.029 0.07 0.1
## BIC = -127.6
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## PA1 PA2 PA3 PA4
## Correlation of (regression) scores with factors 0.98 0.89 0.75 0.74
## Multiple R square of scores with factors 0.97 0.80 0.56 0.55
## Minimum correlation of possible factor scores 0.93 0.59 0.13 0.10
ffa<-fa(r=df, nfactors=4, max.iter=100, warnings=TRUE, rotate="none", fm="pa", weight=df$sweight)
print(ffa, digits=2, cutoff=.10)
## Factor Analysis using method = pa
## Call: fa(r = df, nfactors = 4, rotate = "none", max.iter = 100, warnings = TRUE,
## fm = "pa", weight = df$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA2 PA3 PA4 h2 u2 com
## ssgs 0.87 0.11 0.05 -0.21 0.81 0.19 1.2
## ssar 0.86 -0.09 -0.16 -0.07 0.78 0.22 1.1
## sswk 0.86 0.05 0.16 -0.25 0.83 0.17 1.2
## sspc 0.82 -0.04 0.02 -0.08 0.69 0.31 1.0
## ssno 0.62 -0.45 0.15 0.07 0.61 0.39 2.0
## sscs 0.60 -0.33 0.10 0.20 0.51 0.49 1.9
## ssai 0.59 0.26 0.23 0.23 0.52 0.48 2.1
## sssi 0.59 0.44 0.04 0.16 0.56 0.44 2.0
## ssmk 0.87 -0.20 -0.04 -0.02 0.79 0.21 1.1
## ssmc 0.80 0.12 -0.18 0.05 0.69 0.31 1.2
## ssei 0.76 0.15 0.04 -0.07 0.61 0.39 1.1
## ssao 0.70 -0.02 -0.37 0.21 0.66 0.34 1.7
## sweight 0.11 -0.01 0.27 0.14 0.11 0.89 1.9
##
## PA1 PA2 PA3 PA4
## SS loadings 6.81 0.67 0.39 0.31
## Proportion Var 0.52 0.05 0.03 0.02
## Cumulative Var 0.52 0.58 0.61 0.63
## Proportion Explained 0.83 0.08 0.05 0.04
## Cumulative Proportion 0.83 0.92 0.96 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 78 with the objective function = 8.71 0.1 with Chi Square = 2863.27
## df of the model are 32 and the objective function was 0.16
## 0.1
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
## 0.1
## The harmonic n.obs is 335 with the empirical chi square 11.12 with prob < 1
## 0.1The total n.obs was 335 with Likelihood Chi Square = 53.43 with prob < 0.01
## 0.1
## Tucker Lewis Index of factoring reliability = 0.981
## RMSEA index = 0.045 and the 90 % confidence intervals are 0.022 0.065 0.1
## BIC = -132.62
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## PA1 PA2 PA3 PA4
## Correlation of (regression) scores with factors 0.98 0.79 0.71 0.70
## Multiple R square of scores with factors 0.96 0.63 0.51 0.48
## Minimum correlation of possible factor scores 0.92 0.26 0.02 -0.03
# all race sibling sample, includes misclassification because we treat all groups as one
m<- subset(d, sex==0)
f<- subset(d, sex==1)
dm<- dplyr::select(m, starts_with("ss"), sweight)
df<- dplyr::select(f, starts_with("ss"), sweight)
ev <- eigen(cor(dm)) # get eigenvalues
ev$values
## [1] 7.9802503 1.2554933 0.7879969 0.5627465 0.5063920 0.3884197
## [7] 0.3055296 0.2764156 0.2411000 0.2117469 0.1864291 0.1599634
## [13] 0.1375168
ev <- eigen(cor(df)) # get eigenvalues
ev$values
## [1] 7.9829611 1.0579828 0.6709219 0.5897234 0.5229690 0.4395253
## [7] 0.4025920 0.2882278 0.2708462 0.2406037 0.2069460 0.1673035
## [13] 0.1593974
fa3<-fa(dm[,1:12], nfactors=3, rotate="promax", fm="minres", weight=dm$sweight)
fa3
## Factor Analysis using method = minres
## Call: fa(r = dm[, 1:12], nfactors = 3, rotate = "promax", fm = "minres",
## weight = dm$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## ssgs 0.74 0.25 -0.06 0.81 0.193 1.2
## ssar 0.79 0.00 0.13 0.77 0.231 1.1
## sswk 0.68 0.24 0.02 0.76 0.236 1.2
## sspc 0.97 -0.08 -0.03 0.80 0.203 1.0
## ssno -0.07 0.08 0.98 0.92 0.081 1.0
## sscs 0.43 -0.07 0.38 0.49 0.509 2.0
## ssai -0.07 0.85 0.05 0.67 0.328 1.0
## sssi -0.07 0.91 0.01 0.75 0.250 1.0
## ssmk 0.74 -0.01 0.24 0.82 0.184 1.2
## ssmc 0.62 0.36 -0.07 0.77 0.232 1.6
## ssei 0.43 0.52 0.00 0.76 0.236 1.9
## ssao 0.75 -0.02 -0.02 0.52 0.482 1.0
##
## MR1 MR2 MR3
## SS loadings 4.98 2.49 1.37
## Proportion Var 0.41 0.21 0.11
## Cumulative Var 0.41 0.62 0.74
## Proportion Explained 0.56 0.28 0.15
## Cumulative Proportion 0.56 0.85 1.00
##
## With factor correlations of
## MR1 MR2 MR3
## MR1 1.00 0.71 0.65
## MR2 0.71 1.00 0.29
## MR3 0.65 0.29 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 factors are sufficient.
##
## df null model = 66 with the objective function = 10.73 with Chi Square = 7105.02
## df of the model are 33 and the objective function was 0.29
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic n.obs is 668 with the empirical chi square 27.35 with prob < 0.74
## The total n.obs was 668 with Likelihood Chi Square = 193.78 with prob < 1.2e-24
##
## Tucker Lewis Index of factoring reliability = 0.954
## RMSEA index = 0.085 and the 90 % confidence intervals are 0.074 0.097
## BIC = -20.87
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of (regression) scores with factors 0.98 0.95 0.96
## Multiple R square of scores with factors 0.96 0.90 0.93
## Minimum correlation of possible factor scores 0.91 0.80 0.86
fa3<-fa(df[,1:12], nfactors=3, rotate="promax", fm="minres", weight=df$sweight)
fa3
## Factor Analysis using method = minres
## Call: fa(r = df[, 1:12], nfactors = 3, rotate = "promax", fm = "minres",
## weight = df$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## ssgs 0.84 0.16 -0.06 0.82 0.18 1.1
## ssar 0.28 0.37 0.33 0.78 0.22 2.9
## sswk 0.84 0.31 -0.24 0.83 0.17 1.5
## sspc 0.49 0.34 0.10 0.73 0.27 1.9
## ssno -0.12 0.90 -0.04 0.63 0.37 1.0
## sscs -0.01 0.61 0.09 0.44 0.56 1.0
## ssai 0.62 -0.06 0.09 0.43 0.57 1.1
## sssi 0.77 -0.25 0.16 0.54 0.46 1.3
## ssmk 0.18 0.55 0.26 0.82 0.18 1.7
## ssmc 0.45 0.00 0.47 0.75 0.25 2.0
## ssei 0.70 0.12 0.04 0.67 0.33 1.1
## ssao 0.04 0.12 0.69 0.66 0.34 1.1
##
## MR1 MR2 MR3
## SS loadings 4.10 2.51 1.49
## Proportion Var 0.34 0.21 0.12
## Cumulative Var 0.34 0.55 0.68
## Proportion Explained 0.51 0.31 0.18
## Cumulative Proportion 0.51 0.82 1.00
##
## With factor correlations of
## MR1 MR2 MR3
## MR1 1.00 0.72 0.77
## MR2 0.72 1.00 0.68
## MR3 0.77 0.68 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 3 factors are sufficient.
##
## df null model = 66 with the objective function = 9.66 with Chi Square = 6399.49
## df of the model are 33 and the objective function was 0.16
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic n.obs is 668 with the empirical chi square 30.63 with prob < 0.59
## The total n.obs was 668 with Likelihood Chi Square = 105.74 with prob < 1.5e-09
##
## Tucker Lewis Index of factoring reliability = 0.977
## RMSEA index = 0.057 and the 90 % confidence intervals are 0.045 0.07
## BIC = -108.9
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of (regression) scores with factors 0.97 0.94 0.92
## Multiple R square of scores with factors 0.94 0.88 0.84
## Minimum correlation of possible factor scores 0.87 0.76 0.68
fa4<-fa(dm[,1:12], nfactors=4, rotate="promax", fm="minres", weight=dm$sweight)
fa4
## Factor Analysis using method = minres
## Call: fa(r = dm[, 1:12], nfactors = 4, rotate = "promax", fm = "minres",
## weight = dm$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR4 MR3 h2 u2 com
## ssgs 0.20 0.18 0.64 -0.05 0.84 0.155 1.4
## ssar 0.59 0.01 0.20 0.14 0.77 0.233 1.4
## sswk 0.02 0.12 0.80 0.03 0.86 0.144 1.1
## sspc 0.56 -0.10 0.45 -0.01 0.79 0.207 2.0
## ssno -0.11 0.06 -0.04 1.04 0.93 0.067 1.0
## sscs 0.39 -0.06 0.01 0.40 0.49 0.506 2.0
## ssai -0.05 0.83 0.01 0.05 0.67 0.329 1.0
## sssi -0.03 0.89 -0.01 0.01 0.75 0.247 1.0
## ssmk 0.58 0.00 0.14 0.26 0.82 0.183 1.5
## ssmc 0.67 0.40 -0.05 -0.08 0.80 0.196 1.7
## ssei 0.24 0.49 0.23 0.01 0.76 0.239 1.9
## ssao 0.89 0.02 -0.12 -0.05 0.58 0.419 1.0
##
## MR1 MR2 MR4 MR3
## SS loadings 3.24 2.34 2.00 1.49
## Proportion Var 0.27 0.20 0.17 0.12
## Cumulative Var 0.27 0.47 0.63 0.76
## Proportion Explained 0.36 0.26 0.22 0.16
## Cumulative Proportion 0.36 0.62 0.84 1.00
##
## With factor correlations of
## MR1 MR2 MR4 MR3
## MR1 1.00 0.66 0.84 0.69
## MR2 0.66 1.00 0.70 0.32
## MR4 0.84 0.70 1.00 0.62
## MR3 0.69 0.32 0.62 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 66 with the objective function = 10.73 with Chi Square = 7105.02
## df of the model are 24 and the objective function was 0.12
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic n.obs is 668 with the empirical chi square 9.63 with prob < 1
## The total n.obs was 668 with Likelihood Chi Square = 76.45 with prob < 2.2e-07
##
## Tucker Lewis Index of factoring reliability = 0.979
## RMSEA index = 0.057 and the 90 % confidence intervals are 0.043 0.072
## BIC = -79.65
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2 MR4 MR3
## Correlation of (regression) scores with factors 0.96 0.95 0.96 0.97
## Multiple R square of scores with factors 0.93 0.90 0.92 0.95
## Minimum correlation of possible factor scores 0.86 0.79 0.84 0.89
fa4<-fa(df[,1:12], nfactors=4, rotate="promax", fm="minres", weight=df$sweight)
fa4
## Factor Analysis using method = minres
## Call: fa(r = df[, 1:12], nfactors = 4, rotate = "promax", fm = "minres",
## weight = df$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR3 MR4 MR2 h2 u2 com
## ssgs 0.79 0.04 0.15 -0.05 0.82 0.18 1.1
## ssar 0.30 0.56 -0.03 0.11 0.79 0.21 1.6
## sswk 0.98 -0.17 0.07 0.05 0.86 0.14 1.1
## sspc 0.58 0.29 -0.03 0.06 0.75 0.25 1.5
## ssno -0.02 -0.11 0.00 0.97 0.78 0.22 1.0
## sscs 0.02 0.16 0.04 0.49 0.44 0.56 1.2
## ssai 0.02 -0.02 0.65 0.12 0.51 0.49 1.1
## sssi 0.05 0.07 0.73 -0.07 0.62 0.38 1.1
## ssmk 0.28 0.47 -0.06 0.27 0.82 0.18 2.4
## ssmc 0.13 0.61 0.23 -0.08 0.74 0.26 1.4
## ssei 0.53 0.13 0.22 0.00 0.67 0.33 1.5
## ssao -0.15 0.91 0.05 -0.01 0.66 0.34 1.1
##
## MR1 MR3 MR4 MR2
## SS loadings 3.06 2.43 1.49 1.48
## Proportion Var 0.25 0.20 0.12 0.12
## Cumulative Var 0.25 0.46 0.58 0.70
## Proportion Explained 0.36 0.29 0.18 0.17
## Cumulative Proportion 0.36 0.65 0.83 1.00
##
## With factor correlations of
## MR1 MR3 MR4 MR2
## MR1 1.00 0.84 0.76 0.71
## MR3 0.84 1.00 0.72 0.72
## MR4 0.76 0.72 1.00 0.48
## MR2 0.71 0.72 0.48 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 66 with the objective function = 9.66 with Chi Square = 6399.49
## df of the model are 24 and the objective function was 0.07
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic n.obs is 668 with the empirical chi square 7.57 with prob < 1
## The total n.obs was 668 with Likelihood Chi Square = 47.3 with prob < 0.0031
##
## Tucker Lewis Index of factoring reliability = 0.99
## RMSEA index = 0.038 and the 90 % confidence intervals are 0.022 0.054
## BIC = -108.81
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR3 MR4 MR2
## Correlation of (regression) scores with factors 0.97 0.95 0.90 0.93
## Multiple R square of scores with factors 0.94 0.91 0.81 0.87
## Minimum correlation of possible factor scores 0.89 0.82 0.63 0.74
fact3<- factanal(dm[,1:12], 3, rotation="promax")
print(fact3, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = dm[, 1:12], factors = 3, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.14 0.21 0.16 0.23 0.44 0.55 0.36 0.27 0.15 0.23 0.22 0.49
##
## Loadings:
## Factor1 Factor2 Factor3
## ssgs 0.21 0.27 0.54
## ssar 0.74
## sswk 0.24 0.57
## sspc 0.51 0.38
## ssno 0.88
## sscs 0.73
## ssai 0.86
## sssi 0.91
## ssmk 0.86
## ssmc 0.36 0.56
## ssei 0.21 0.55 0.22
## ssao 0.54 0.24
##
## Factor1 Factor2 Factor3
## SS loadings 3.42 2.42 0.82
## Proportion Var 0.29 0.20 0.07
## Cumulative Var 0.29 0.49 0.56
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.00 -0.73 0.74
## Factor2 -0.73 1.00 -0.64
## Factor3 0.74 -0.64 1.00
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 154.6 on 33 degrees of freedom.
## The p-value is 1.19e-17
fact3<- factanal(df[,1:12], 3, rotation="promax")
print(fact3, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = df[, 1:12], factors = 3, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.17 0.20 0.15 0.23 0.39 0.57 0.57 0.47 0.16 0.24 0.31 0.35
##
## Loadings:
## Factor1 Factor2 Factor3
## ssgs 0.83
## ssar 0.23 0.39 0.38
## sswk 0.91 0.26 -0.21
## sspc 0.51 0.32
## ssno 0.79
## sscs 0.54
## ssai 0.46 0.25
## sssi 0.59 0.26
## ssmk 0.55 0.32
## ssmc 0.29 0.61
## ssei 0.66
## ssao 0.72
##
## Factor1 Factor2 Factor3
## SS loadings 2.95 1.62 1.37
## Proportion Var 0.25 0.14 0.11
## Cumulative Var 0.25 0.38 0.49
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.00 -0.64 0.82
## Factor2 -0.64 1.00 -0.64
## Factor3 0.82 -0.64 1.00
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 75.31 on 33 degrees of freedom.
## The p-value is 3.76e-05
fact4<- factanal(dm[,1:12], 4, rotation="promax")
print(fact4, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = dm[, 1:12], factors = 4, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.15 0.21 0.14 0.22 0.06 0.54 0.35 0.27 0.18 0.21 0.22 0.43
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## ssgs 0.34 0.24 0.47
## ssar 0.77
## sswk 0.22 0.60
## sspc 0.66 0.30
## ssno 1.00
## sscs 0.43 0.35
## ssai 0.83
## sssi 0.87
## ssmk 0.74
## ssmc 0.61 0.42
## ssei 0.25 0.51
## ssao 0.87
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 3.11 1.98 1.17 0.73
## Proportion Var 0.26 0.17 0.10 0.06
## Cumulative Var 0.26 0.42 0.52 0.58
##
## Factor Correlations:
## Factor1 Factor2 Factor3 Factor4
## Factor1 1.00 -0.69 0.68 0.75
## Factor2 -0.69 1.00 -0.33 -0.54
## Factor3 0.68 -0.33 1.00 0.66
## Factor4 0.75 -0.54 0.66 1.00
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 72.21 on 24 degrees of freedom.
## The p-value is 1.01e-06
fact4<- factanal(df[,1:12], 4, rotation="promax")
print(fact4, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = df[, 1:12], factors = 4, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.17 0.19 0.14 0.22 0.17 0.56 0.52 0.40 0.17 0.26 0.30 0.35
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## ssgs 0.38 0.54
## ssar 0.60
## sswk 0.25 0.74
## sspc 0.38 0.45
## ssno 0.98
## sscs 0.21 0.46
## ssai 0.63
## sssi 0.77
## ssmk 0.61
## ssmc 0.56 0.40
## ssei 0.39 0.37
## ssao 0.84
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 2.00 1.56 1.27 1.24
## Proportion Var 0.17 0.13 0.11 0.10
## Cumulative Var 0.17 0.30 0.40 0.51
##
## Factor Correlations:
## Factor1 Factor2 Factor3 Factor4
## Factor1 1.00 -0.68 0.79 0.72
## Factor2 -0.68 1.00 -0.73 -0.53
## Factor3 0.79 -0.73 1.00 0.76
## Factor4 0.72 -0.53 0.76 1.00
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 28.74 on 24 degrees of freedom.
## The p-value is 0.23
mfa<-fa(r=dm, nfactors=4, max.iter=100, warnings=TRUE, rotate="none", fm="pa", weight=dm$sweight)
print(mfa, digits=2, cutoff=.10)
## Factor Analysis using method = pa
## Call: fa(r = dm, nfactors = 4, rotate = "none", max.iter = 100, warnings = TRUE,
## fm = "pa", weight = dm$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA2 PA3 PA4 h2 u2 com
## ssgs 0.89 0.09 -0.11 -0.20 0.86 0.14 1.1
## ssar 0.85 -0.18 -0.06 0.01 0.76 0.24 1.1
## sswk 0.88 0.03 -0.07 -0.24 0.83 0.17 1.2
## sspc 0.85 -0.16 -0.19 -0.07 0.79 0.21 1.2
## ssno 0.63 -0.52 0.36 0.00 0.80 0.20 2.6
## sscs 0.62 -0.34 0.11 0.07 0.51 0.49 1.7
## ssai 0.67 0.43 0.16 0.05 0.66 0.34 1.8
## sssi 0.70 0.50 0.18 0.07 0.78 0.22 2.0
## ssmk 0.87 -0.26 0.00 0.03 0.82 0.18 1.2
## ssmc 0.86 0.15 -0.10 0.16 0.80 0.20 1.2
## ssei 0.84 0.22 0.00 0.01 0.76 0.24 1.1
## ssao 0.70 -0.12 -0.21 0.24 0.61 0.39 1.5
## sweight 0.46 0.17 0.15 -0.07 0.27 0.73 1.6
##
## PA1 PA2 PA3 PA4
## SS loadings 7.65 1.06 0.33 0.20
## Proportion Var 0.59 0.08 0.03 0.02
## Cumulative Var 0.59 0.67 0.70 0.71
## Proportion Explained 0.83 0.11 0.04 0.02
## Cumulative Proportion 0.83 0.94 0.98 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 78 with the objective function = 11.02 0.1 with Chi Square = 7294.95
## df of the model are 32 and the objective function was 0.14
## 0.1
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
## 0.1
## The harmonic n.obs is 668 with the empirical chi square 13.2 with prob < 1
## 0.1The total n.obs was 668 with Likelihood Chi Square = 90.02 with prob < 2e-07
## 0.1
## Tucker Lewis Index of factoring reliability = 0.98
## RMSEA index = 0.052 and the 90 % confidence intervals are 0.04 0.065 0.1
## BIC = -118.12
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## PA1 PA2 PA3 PA4
## Correlation of (regression) scores with factors 0.99 0.90 0.75 0.69
## Multiple R square of scores with factors 0.97 0.81 0.57 0.48
## Minimum correlation of possible factor scores 0.94 0.62 0.14 -0.04
ffa<-fa(r=df, nfactors=4, max.iter=100, warnings=TRUE, rotate="none", fm="pa", weight=df$sweight)
print(ffa, digits=2, cutoff=.10)
## Factor Analysis using method = pa
## Call: fa(r = df, nfactors = 4, rotate = "none", max.iter = 100, warnings = TRUE,
## fm = "pa", weight = df$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA2 PA3 PA4 h2 u2 com
## ssgs 0.88 0.13 0.10 -0.16 0.83 0.17 1.1
## ssar 0.87 -0.10 -0.13 -0.04 0.78 0.22 1.1
## sswk 0.87 0.04 0.20 -0.23 0.85 0.15 1.2
## sspc 0.85 -0.05 -0.02 -0.14 0.75 0.25 1.1
## ssno 0.65 -0.53 0.20 0.19 0.77 0.23 2.3
## sscs 0.59 -0.27 0.04 0.10 0.44 0.56 1.5
## ssai 0.63 0.21 0.09 0.17 0.49 0.51 1.4
## sssi 0.67 0.40 0.07 0.23 0.67 0.33 1.9
## ssmk 0.88 -0.20 -0.07 -0.01 0.81 0.19 1.1
## ssmc 0.83 0.11 -0.18 0.03 0.73 0.27 1.1
## ssei 0.80 0.11 0.04 -0.08 0.67 0.33 1.1
## ssao 0.74 -0.04 -0.34 0.07 0.67 0.33 1.4
## sweight 0.43 0.24 0.09 0.11 0.27 0.73 1.8
##
## PA1 PA2 PA3 PA4
## SS loadings 7.49 0.71 0.29 0.25
## Proportion Var 0.58 0.05 0.02 0.02
## Cumulative Var 0.58 0.63 0.65 0.67
## Proportion Explained 0.86 0.08 0.03 0.03
## Cumulative Proportion 0.86 0.94 0.97 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 78 with the objective function = 9.92 0.1 with Chi Square = 6566.09
## df of the model are 32 and the objective function was 0.08
## 0.1
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.01
## 0.1
## The harmonic n.obs is 668 with the empirical chi square 8.95 with prob < 1
## 0.1The total n.obs was 668 with Likelihood Chi Square = 53.32 with prob < 0.01
## 0.1
## Tucker Lewis Index of factoring reliability = 0.992
## RMSEA index = 0.032 and the 90 % confidence intervals are 0.015 0.046 0.1
## BIC = -154.81
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## PA1 PA2 PA3 PA4
## Correlation of (regression) scores with factors 0.98 0.84 0.72 0.69
## Multiple R square of scores with factors 0.97 0.70 0.53 0.48
## Minimum correlation of possible factor scores 0.94 0.41 0.05 -0.05
# entire white sample
dw<- subset(dkw, bhw==3)
m<- subset(dw, sex==0)
f<- subset(dw, sex==1)
dm<- dplyr::select(m, starts_with("ss"), sweight)
df<- dplyr::select(f, starts_with("ss"), sweight)
ev <- eigen(cor(dm)) # get eigenvalues
ev$values
## [1] 7.1260114 1.4477942 0.9884043 0.6579831 0.5861015 0.4185320
## [7] 0.3490381 0.2928635 0.2809029 0.2482966 0.2430386 0.1831544
## [13] 0.1778793
ev <- eigen(cor(df)) # get eigenvalues
ev$values
## [1] 6.8928697 1.1431403 0.9841614 0.7371083 0.6107010 0.5205273
## [7] 0.4423190 0.3655205 0.3433207 0.2967630 0.2685093 0.2016999
## [13] 0.1933597
fa3<-fa(dm[,1:12], nfactors=3, rotate="promax", fm="minres", weight=dm$sweight)
fa3
## Factor Analysis using method = minres
## Call: fa(r = dm[, 1:12], nfactors = 3, rotate = "promax", fm = "minres",
## weight = dm$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## ssgs 0.84 0.13 -0.08 0.77 0.23 1.1
## ssar 0.72 -0.01 0.20 0.75 0.25 1.2
## sswk 0.77 0.11 -0.01 0.71 0.29 1.0
## sspc 0.95 -0.10 -0.02 0.76 0.24 1.0
## ssno -0.12 0.09 0.99 0.87 0.13 1.0
## sscs 0.28 -0.04 0.52 0.52 0.48 1.6
## ssai -0.08 0.82 0.06 0.61 0.39 1.0
## sssi -0.02 0.84 0.00 0.69 0.31 1.0
## ssmk 0.69 -0.06 0.30 0.78 0.22 1.4
## ssmc 0.62 0.31 -0.04 0.70 0.30 1.5
## ssei 0.49 0.46 -0.03 0.73 0.27 2.0
## ssao 0.70 -0.04 0.03 0.49 0.51 1.0
##
## MR1 MR2 MR3
## SS loadings 4.82 1.97 1.59
## Proportion Var 0.40 0.16 0.13
## Cumulative Var 0.40 0.57 0.70
## Proportion Explained 0.58 0.24 0.19
## Cumulative Proportion 0.58 0.81 1.00
##
## With factor correlations of
## MR1 MR2 MR3
## MR1 1.00 0.65 0.66
## MR2 0.65 1.00 0.23
## MR3 0.66 0.23 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 3 factors are sufficient.
##
## df null model = 66 with the objective function = 9.34 with Chi Square = 17419.3
## df of the model are 33 and the objective function was 0.31
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic n.obs is 1870 with the empirical chi square 123.48 with prob < 2.2e-12
## The total n.obs was 1870 with Likelihood Chi Square = 568.59 with prob < 7.5e-99
##
## Tucker Lewis Index of factoring reliability = 0.938
## RMSEA index = 0.093 and the 90 % confidence intervals are 0.087 0.1
## BIC = 319.98
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of (regression) scores with factors 0.97 0.93 0.95
## Multiple R square of scores with factors 0.95 0.86 0.90
## Minimum correlation of possible factor scores 0.90 0.72 0.81
fa3<-fa(df[,1:12], nfactors=3, rotate="promax", fm="minres", weight=df$sweight)
fa3
## Factor Analysis using method = minres
## Call: fa(r = df[, 1:12], nfactors = 3, rotate = "promax", fm = "minres",
## weight = df$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR3 MR2 h2 u2 com
## ssgs 0.80 0.04 0.07 0.76 0.24 1.0
## ssar 0.15 0.56 0.24 0.75 0.25 1.5
## sswk 0.80 -0.08 0.20 0.77 0.23 1.2
## sspc 0.49 0.23 0.20 0.70 0.30 1.8
## ssno -0.07 -0.13 0.99 0.76 0.24 1.0
## sscs -0.01 0.08 0.65 0.49 0.51 1.0
## ssai 0.66 -0.08 0.00 0.36 0.64 1.0
## sssi 0.65 0.13 -0.20 0.40 0.60 1.3
## ssmk 0.15 0.41 0.41 0.76 0.24 2.2
## ssmc 0.35 0.58 -0.09 0.67 0.33 1.7
## ssei 0.75 -0.01 0.03 0.59 0.41 1.0
## ssao -0.07 0.86 -0.04 0.60 0.40 1.0
##
## MR1 MR3 MR2
## SS loadings 3.55 2.07 2.00
## Proportion Var 0.30 0.17 0.17
## Cumulative Var 0.30 0.47 0.63
## Proportion Explained 0.47 0.27 0.26
## Cumulative Proportion 0.47 0.74 1.00
##
## With factor correlations of
## MR1 MR3 MR2
## MR1 1.00 0.79 0.63
## MR3 0.79 1.00 0.69
## MR2 0.63 0.69 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 factors are sufficient.
##
## df null model = 66 with the objective function = 8.05 with Chi Square = 14041.37
## df of the model are 33 and the objective function was 0.16
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic n.obs is 1751 with the empirical chi square 83.92 with prob < 2.6e-06
## The total n.obs was 1751 with Likelihood Chi Square = 271.7 with prob < 2.5e-39
##
## Tucker Lewis Index of factoring reliability = 0.966
## RMSEA index = 0.064 and the 90 % confidence intervals are 0.057 0.071
## BIC = 25.26
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR3 MR2
## Correlation of (regression) scores with factors 0.96 0.94 0.94
## Multiple R square of scores with factors 0.92 0.88 0.88
## Minimum correlation of possible factor scores 0.83 0.76 0.76
fa4<-fa(dm[,1:12], nfactors=4, rotate="promax", fm="minres", weight=dm$sweight)
fa4
## Factor Analysis using method = minres
## Call: fa(r = dm[, 1:12], nfactors = 4, rotate = "promax", fm = "minres",
## weight = dm$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 MR4 h2 u2 com
## ssgs 0.92 0.09 -0.07 -0.04 0.81 0.186 1.0
## ssar 0.43 0.01 0.21 0.30 0.74 0.260 2.3
## sswk 1.02 0.04 0.00 -0.19 0.79 0.205 1.1
## sspc 0.73 -0.10 0.01 0.22 0.75 0.246 1.2
## ssno -0.07 0.08 1.13 -0.20 0.96 0.041 1.1
## sscs 0.05 -0.05 0.50 0.25 0.52 0.483 1.5
## ssai 0.01 0.80 0.06 -0.09 0.60 0.396 1.0
## sssi -0.08 0.85 0.00 0.06 0.69 0.306 1.0
## ssmk 0.42 -0.05 0.31 0.28 0.77 0.226 2.7
## ssmc 0.15 0.36 -0.06 0.50 0.74 0.264 2.1
## ssei 0.45 0.45 -0.01 0.04 0.73 0.271 2.0
## ssao -0.08 -0.01 -0.06 0.92 0.66 0.339 1.0
##
## MR1 MR2 MR3 MR4
## SS loadings 3.41 1.95 1.74 1.68
## Proportion Var 0.28 0.16 0.14 0.14
## Cumulative Var 0.28 0.45 0.59 0.73
## Proportion Explained 0.39 0.22 0.20 0.19
## Cumulative Proportion 0.39 0.61 0.81 1.00
##
## With factor correlations of
## MR1 MR2 MR3 MR4
## MR1 1.00 0.66 0.67 0.83
## MR2 0.66 1.00 0.28 0.56
## MR3 0.67 0.28 1.00 0.68
## MR4 0.83 0.56 0.68 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 66 with the objective function = 9.34 with Chi Square = 17419.3
## df of the model are 24 and the objective function was 0.11
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic n.obs is 1870 with the empirical chi square 29.89 with prob < 0.19
## The total n.obs was 1870 with Likelihood Chi Square = 206.29 with prob < 6.3e-31
##
## Tucker Lewis Index of factoring reliability = 0.971
## RMSEA index = 0.064 and the 90 % confidence intervals are 0.056 0.072
## BIC = 25.48
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2 MR3 MR4
## Correlation of (regression) scores with factors 0.97 0.93 0.98 0.94
## Multiple R square of scores with factors 0.95 0.86 0.97 0.88
## Minimum correlation of possible factor scores 0.89 0.72 0.93 0.75
fa4<-fa(df[,1:12], nfactors=4, rotate="promax", fm="minres", weight=df$sweight)
fa4
## Factor Analysis using method = minres
## Call: fa(r = df[, 1:12], nfactors = 4, rotate = "promax", fm = "minres",
## weight = df$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR3 MR2 MR4 h2 u2 com
## ssgs 0.76 0.03 -0.07 0.18 0.77 0.23 1.1
## ssar 0.32 0.56 0.13 -0.08 0.76 0.24 1.8
## sswk 0.91 -0.13 0.01 0.11 0.81 0.19 1.1
## sspc 0.61 0.23 0.05 0.02 0.72 0.28 1.3
## ssno 0.01 -0.10 0.95 -0.01 0.79 0.21 1.0
## sscs -0.06 0.11 0.64 0.07 0.51 0.49 1.1
## ssai 0.04 -0.08 0.13 0.62 0.43 0.57 1.1
## sssi 0.00 0.16 -0.08 0.61 0.47 0.53 1.2
## ssmk 0.34 0.41 0.28 -0.09 0.77 0.23 2.8
## ssmc 0.10 0.60 -0.06 0.23 0.67 0.33 1.4
## ssei 0.46 0.02 0.00 0.34 0.58 0.42 1.9
## ssao -0.14 0.87 -0.01 0.04 0.60 0.40 1.1
##
## MR1 MR3 MR2 MR4
## SS loadings 2.83 2.12 1.57 1.38
## Proportion Var 0.24 0.18 0.13 0.12
## Cumulative Var 0.24 0.41 0.54 0.66
## Proportion Explained 0.36 0.27 0.20 0.17
## Cumulative Proportion 0.36 0.63 0.83 1.00
##
## With factor correlations of
## MR1 MR3 MR2 MR4
## MR1 1.00 0.81 0.67 0.73
## MR3 0.81 1.00 0.65 0.66
## MR2 0.67 0.65 1.00 0.40
## MR4 0.73 0.66 0.40 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 66 with the objective function = 8.05 with Chi Square = 14041.37
## df of the model are 24 and the objective function was 0.06
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic n.obs is 1751 with the empirical chi square 20.25 with prob < 0.68
## The total n.obs was 1751 with Likelihood Chi Square = 105.29 with prob < 3.7e-12
##
## Tucker Lewis Index of factoring reliability = 0.984
## RMSEA index = 0.044 and the 90 % confidence intervals are 0.036 0.053
## BIC = -73.94
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR3 MR2 MR4
## Correlation of (regression) scores with factors 0.96 0.94 0.93 0.88
## Multiple R square of scores with factors 0.93 0.88 0.87 0.77
## Minimum correlation of possible factor scores 0.86 0.77 0.73 0.55
fact3<- factanal(dm[,1:12], 3, rotation="promax")
print(fact3, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = dm[, 1:12], factors = 3, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.17 0.24 0.22 0.26 0.41 0.49 0.43 0.31 0.19 0.30 0.28 0.52
##
## Loadings:
## Factor1 Factor2 Factor3
## ssgs 0.77
## ssar 0.66 0.20
## sswk 0.80
## sspc 0.37 0.55
## ssno 0.91
## sscs 0.80
## ssai 0.84
## sssi 0.93
## ssmk 0.77
## ssmc 0.28 0.50
## ssei 0.48 0.38
## ssao 0.51 0.20
##
## Factor1 Factor2 Factor3
## SS loadings 2.99 2.12 1.82
## Proportion Var 0.25 0.18 0.15
## Cumulative Var 0.25 0.43 0.58
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.00 0.74 -0.75
## Factor2 0.74 1.00 -0.55
## Factor3 -0.75 -0.55 1.00
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 510.21 on 33 degrees of freedom.
## The p-value is 6.68e-87
fact3<- factanal(df[,1:12], 3, rotation="promax")
print(fact3, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = df[, 1:12], factors = 3, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.22 0.24 0.20 0.28 0.27 0.50 0.67 0.63 0.22 0.35 0.43 0.41
##
## Loadings:
## Factor1 Factor2 Factor3
## ssgs 0.87
## ssar 0.59 0.21
## sswk 0.93
## sspc 0.55 0.24
## ssno 0.86
## sscs 0.60
## ssai 0.58
## sssi 0.52 0.21
## ssmk 0.45 0.36
## ssmc 0.29 0.60
## ssei 0.73
## ssao 0.87
##
## Factor1 Factor2 Factor3
## SS loadings 3.21 1.80 1.35
## Proportion Var 0.27 0.15 0.11
## Cumulative Var 0.27 0.42 0.53
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.00 -0.55 0.82
## Factor2 -0.55 1.00 -0.61
## Factor3 0.82 -0.61 1.00
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 251.65 on 33 degrees of freedom.
## The p-value is 1.75e-35
fact4<- factanal(dm[,1:12], 4, rotation="promax")
print(fact4, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = dm[, 1:12], factors = 4, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.19 0.24 0.18 0.25 0.12 0.49 0.41 0.31 0.21 0.26 0.28 0.41
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## ssgs 0.76
## ssar 0.57 0.20
## sswk 0.95
## sspc 0.43 0.53
## ssno 1.03
## sscs 0.29 0.50
## ssai 0.80
## sssi 0.83
## ssmk 0.50 0.23 0.28
## ssmc 0.62 0.36
## ssei 0.39 0.45
## ssao 0.94
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 2.16 2.01 1.68 1.45
## Proportion Var 0.18 0.17 0.14 0.12
## Cumulative Var 0.18 0.35 0.49 0.61
##
## Factor Correlations:
## Factor1 Factor2 Factor3 Factor4
## Factor1 1.00 -0.63 0.63 -0.83
## Factor2 -0.63 1.00 -0.26 0.70
## Factor3 0.63 -0.26 1.00 -0.57
## Factor4 -0.83 0.70 -0.57 1.00
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 179.76 on 24 degrees of freedom.
## The p-value is 8.13e-26
fact4<- factanal(df[,1:12], 4, rotation="promax")
print(fact4, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = df[, 1:12], factors = 4, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.23 0.22 0.17 0.27 0.19 0.50 0.58 0.54 0.22 0.34 0.42 0.43
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## ssgs 0.39 0.51
## ssar 0.72
## sswk 0.32 0.68
## sspc 0.35 0.39
## ssno 0.94
## sscs 0.60
## ssai 0.66
## sssi 0.68
## ssmk 0.57 0.24
## ssmc 0.55 0.37
## ssei 0.52 0.28
## ssao 0.80
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 1.96 1.61 1.34 1.02
## Proportion Var 0.16 0.13 0.11 0.09
## Cumulative Var 0.16 0.30 0.41 0.49
##
## Factor Correlations:
## Factor1 Factor2 Factor3 Factor4
## Factor1 1.00 -0.62 0.72 0.64
## Factor2 -0.62 1.00 -0.67 -0.47
## Factor3 0.72 -0.67 1.00 0.72
## Factor4 0.64 -0.47 0.72 1.00
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 105.04 on 24 degrees of freedom.
## The p-value is 4.1e-12
mfa<-fa(r=dm, nfactors=4, max.iter=100, warnings=TRUE, rotate="none", fm="pa", weight=dm$sweight)
print(mfa, digits=2, cutoff=.10)
## Factor Analysis using method = pa
## Call: fa(r = dm, nfactors = 4, rotate = "none", max.iter = 100, warnings = TRUE,
## fm = "pa", weight = dm$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA2 PA3 PA4 h2 u2 com
## ssgs 0.87 0.08 -0.16 -0.19 0.818 0.18 1.2
## ssar 0.84 -0.17 -0.06 0.03 0.743 0.26 1.1
## sswk 0.84 0.03 -0.10 -0.27 0.796 0.20 1.2
## sspc 0.84 -0.11 -0.17 -0.07 0.750 0.25 1.1
## ssno 0.63 -0.50 0.33 -0.03 0.752 0.25 2.5
## sscs 0.62 -0.38 0.19 0.08 0.566 0.43 1.9
## ssai 0.58 0.50 0.26 0.02 0.652 0.35 2.4
## sssi 0.61 0.51 0.15 0.09 0.656 0.34 2.1
## ssmk 0.84 -0.26 0.01 0.01 0.783 0.22 1.2
## ssmc 0.82 0.18 -0.09 0.19 0.747 0.25 1.2
## ssei 0.81 0.28 0.02 -0.05 0.729 0.27 1.2
## ssao 0.70 -0.10 -0.20 0.32 0.644 0.36 1.6
## sweight 0.19 0.05 0.20 -0.05 0.082 0.92 2.3
##
## PA1 PA2 PA3 PA4
## SS loadings 6.90 1.14 0.39 0.28
## Proportion Var 0.53 0.09 0.03 0.02
## Cumulative Var 0.53 0.62 0.65 0.67
## Proportion Explained 0.79 0.13 0.05 0.03
## Cumulative Proportion 0.79 0.92 0.97 1.00
##
## Mean item complexity = 1.6
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 78 with the objective function = 9.43 0.1 with Chi Square = 17573.34
## df of the model are 32 and the objective function was 0.15
## 0.1
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
## 0.1
## The harmonic n.obs is 1870 with the empirical chi square 59.34 with prob < 0.0023
## 0.1The total n.obs was 1870 with Likelihood Chi Square = 273.84 with prob < 3.3e-40
## 0.1
## Tucker Lewis Index of factoring reliability = 0.966
## RMSEA index = 0.064 and the 90 % confidence intervals are 0.057 0.071 0.1
## BIC = 32.76
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## PA1 PA2 PA3 PA4
## Correlation of (regression) scores with factors 0.98 0.89 0.75 0.72
## Multiple R square of scores with factors 0.96 0.79 0.56 0.51
## Minimum correlation of possible factor scores 0.93 0.58 0.12 0.02
ffa<-fa(r=df, nfactors=4, max.iter=100, warnings=TRUE, rotate="none", fm="pa", weight=df$sweight)
## maximum iteration exceeded
print(ffa, digits=2, cutoff=.10)
## Factor Analysis using method = pa
## Call: fa(r = df, nfactors = 4, rotate = "none", max.iter = 100, warnings = TRUE,
## fm = "pa", weight = df$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA2 PA3 PA4 h2 u2 com
## ssgs 0.84 0.17 -0.03 -0.20 0.779 0.22 1.2
## ssar 0.84 -0.11 -0.15 0.07 0.753 0.25 1.1
## sswk 0.85 0.10 0.02 -0.28 0.814 0.19 1.2
## sspc 0.84 0.01 -0.05 -0.08 0.711 0.29 1.0
## ssno 0.63 -0.55 0.24 -0.03 0.765 0.24 2.3
## sscs 0.59 -0.36 0.14 0.06 0.503 0.50 1.8
## ssai 0.58 0.34 0.48 0.21 0.725 0.28 2.9
## sssi 0.56 0.29 0.01 0.04 0.397 0.60 1.5
## ssmk 0.85 -0.20 -0.07 0.03 0.760 0.24 1.1
## ssmc 0.78 0.13 -0.17 0.14 0.672 0.33 1.2
## ssei 0.73 0.18 0.03 -0.10 0.572 0.43 1.2
## ssao 0.68 -0.01 -0.25 0.29 0.611 0.39 1.6
## sweight -0.02 0.06 0.08 0.06 0.014 0.99 3.0
##
## PA1 PA2 PA3 PA4
## SS loadings 6.57 0.78 0.43 0.29
## Proportion Var 0.51 0.06 0.03 0.02
## Cumulative Var 0.51 0.57 0.60 0.62
## Proportion Explained 0.81 0.10 0.05 0.04
## Cumulative Proportion 0.81 0.91 0.96 1.00
##
## Mean item complexity = 1.6
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 78 with the objective function = 8.08 0.1 with Chi Square = 14092.64
## df of the model are 32 and the objective function was 0.1
## 0.1
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
## 0.1
## The harmonic n.obs is 1751 with the empirical chi square 56.62 with prob < 0.0046
## 0.1The total n.obs was 1751 with Likelihood Chi Square = 182.79 with prob < 4.8e-23
## 0.1
## Tucker Lewis Index of factoring reliability = 0.974
## RMSEA index = 0.052 and the 90 % confidence intervals are 0.045 0.059 0.1
## BIC = -56.18
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## PA1 PA2 PA3 PA4
## Correlation of (regression) scores with factors 0.98 0.85 0.77 0.72
## Multiple R square of scores with factors 0.96 0.73 0.60 0.52
## Minimum correlation of possible factor scores 0.92 0.46 0.20 0.04
# entire all race sample, includes misclassification because we treat all groups as one
m<- subset(dk, sex==0)
f<- subset(dk, sex==1)
dm<- dplyr::select(m, starts_with("ss"), sweight)
df<- dplyr::select(f, starts_with("ss"), sweight)
ev <- eigen(cor(dm)) # get eigenvalues
ev$values
## [1] 7.7727085 1.2792428 0.7730837 0.5883711 0.5558065 0.4010409
## [7] 0.3333736 0.2662284 0.2525010 0.2299792 0.2200691 0.1699923
## [13] 0.1576029
ev <- eigen(cor(df)) # get eigenvalues
ev$values
## [1] 7.5897674 1.1259045 0.7294491 0.6589891 0.5329967 0.4936741
## [7] 0.4021783 0.3217880 0.3055887 0.2588482 0.2258945 0.1842940
## [13] 0.1706274
fa3<-fa(dm[,1:12], nfactors=3, rotate="promax", fm="minres", weight=dm$sweight)
fa3
## Factor Analysis using method = minres
## Call: fa(r = dm[, 1:12], nfactors = 3, rotate = "promax", fm = "minres",
## weight = dm$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR3 MR2 h2 u2 com
## ssgs 0.71 0.27 -0.06 0.79 0.2144 1.3
## ssar 0.76 0.04 0.13 0.77 0.2308 1.1
## sswk 0.66 0.24 0.02 0.73 0.2663 1.3
## sspc 0.90 -0.01 -0.02 0.77 0.2272 1.0
## ssno -0.07 0.10 1.01 1.00 0.0043 1.0
## sscs 0.47 -0.10 0.36 0.49 0.5115 2.0
## ssai -0.09 0.83 0.07 0.62 0.3845 1.0
## sssi -0.02 0.85 0.00 0.70 0.3019 1.0
## ssmk 0.78 -0.05 0.20 0.80 0.2031 1.1
## ssmc 0.61 0.35 -0.07 0.73 0.2710 1.6
## ssei 0.42 0.52 0.00 0.75 0.2502 1.9
## ssao 0.78 -0.04 -0.05 0.52 0.4771 1.0
##
## MR1 MR3 MR2
## SS loadings 4.89 2.39 1.38
## Proportion Var 0.41 0.20 0.12
## Cumulative Var 0.41 0.61 0.72
## Proportion Explained 0.56 0.28 0.16
## Cumulative Proportion 0.56 0.84 1.00
##
## With factor correlations of
## MR1 MR3 MR2
## MR1 1.00 0.70 0.62
## MR3 0.70 1.00 0.24
## MR2 0.62 0.24 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 factors are sufficient.
##
## df null model = 66 with the objective function = 10.08 with Chi Square = 36130.37
## df of the model are 33 and the objective function was 0.32
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic n.obs is 3590 with the empirical chi square 212.07 with prob < 5e-28
## The total n.obs was 3590 with Likelihood Chi Square = 1129.31 with prob < 5.3e-216
##
## Tucker Lewis Index of factoring reliability = 0.939
## RMSEA index = 0.096 and the 90 % confidence intervals are 0.091 0.101
## BIC = 859.18
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR3 MR2
## Correlation of (regression) scores with factors 0.97 0.94 1.00
## Multiple R square of scores with factors 0.95 0.88 1.00
## Minimum correlation of possible factor scores 0.90 0.76 0.99
fa3<-fa(df[,1:12], nfactors=3, rotate="promax", fm="minres", weight=df$sweight)
fa3
## Factor Analysis using method = minres
## Call: fa(r = df[, 1:12], nfactors = 3, rotate = "promax", fm = "minres",
## weight = df$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## ssgs 0.84 0.06 0.01 0.79 0.21 1.0
## ssar 0.21 0.24 0.50 0.76 0.24 1.8
## sswk 0.84 0.17 -0.09 0.79 0.21 1.1
## sspc 0.53 0.19 0.22 0.74 0.26 1.6
## ssno -0.08 1.01 -0.13 0.77 0.23 1.0
## sscs 0.02 0.64 0.06 0.49 0.51 1.0
## ssai 0.68 -0.01 -0.05 0.40 0.60 1.0
## sssi 0.72 -0.18 0.10 0.47 0.53 1.2
## ssmk 0.17 0.42 0.39 0.79 0.21 2.3
## ssmc 0.39 -0.08 0.54 0.69 0.31 1.9
## ssei 0.77 0.06 -0.01 0.64 0.36 1.0
## ssao -0.04 -0.06 0.88 0.66 0.34 1.0
##
## MR1 MR2 MR3
## SS loadings 3.99 2.00 2.00
## Proportion Var 0.33 0.17 0.17
## Cumulative Var 0.33 0.50 0.67
## Proportion Explained 0.50 0.25 0.25
## Cumulative Proportion 0.50 0.75 1.00
##
## With factor correlations of
## MR1 MR2 MR3
## MR1 1.00 0.66 0.81
## MR2 0.66 1.00 0.71
## MR3 0.81 0.71 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 factors are sufficient.
##
## df null model = 66 with the objective function = 8.97 with Chi Square = 31376.06
## df of the model are 33 and the objective function was 0.15
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic n.obs is 3503 with the empirical chi square 127.28 with prob < 5.3e-13
## The total n.obs was 3503 with Likelihood Chi Square = 516.18 with prob < 4e-88
##
## Tucker Lewis Index of factoring reliability = 0.969
## RMSEA index = 0.065 and the 90 % confidence intervals are 0.06 0.07
## BIC = 246.86
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of (regression) scores with factors 0.97 0.94 0.94
## Multiple R square of scores with factors 0.93 0.89 0.88
## Minimum correlation of possible factor scores 0.86 0.77 0.77
fa4<-fa(dm[,1:12], nfactors=4, rotate="promax", fm="minres", weight=dm$sweight)
fa4
## Factor Analysis using method = minres
## Call: fa(r = dm[, 1:12], nfactors = 4, rotate = "promax", fm = "minres",
## weight = dm$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR3 MR2 MR4 h2 u2 com
## ssgs 0.88 0.14 -0.07 -0.03 0.83 0.170 1.1
## ssar 0.44 0.04 0.22 0.26 0.76 0.238 2.2
## sswk 1.01 0.07 0.01 -0.20 0.83 0.172 1.1
## sspc 0.72 -0.06 0.03 0.21 0.77 0.227 1.2
## ssno -0.07 0.07 1.11 -0.21 0.92 0.079 1.1
## sscs 0.02 -0.05 0.49 0.28 0.51 0.491 1.6
## ssai -0.03 0.82 0.06 -0.06 0.62 0.380 1.0
## sssi -0.02 0.85 -0.01 0.03 0.71 0.294 1.0
## ssmk 0.40 -0.03 0.32 0.28 0.79 0.209 2.8
## ssmc 0.15 0.39 -0.05 0.47 0.76 0.244 2.2
## ssei 0.41 0.46 0.00 0.07 0.75 0.253 2.0
## ssao -0.10 0.00 -0.06 0.95 0.69 0.311 1.0
##
## MR1 MR3 MR2 MR4
## SS loadings 3.35 2.15 1.74 1.69
## Proportion Var 0.28 0.18 0.14 0.14
## Cumulative Var 0.28 0.46 0.60 0.74
## Proportion Explained 0.38 0.24 0.19 0.19
## Cumulative Proportion 0.38 0.62 0.81 1.00
##
## With factor correlations of
## MR1 MR3 MR2 MR4
## MR1 1.00 0.73 0.70 0.84
## MR3 0.73 1.00 0.36 0.62
## MR2 0.70 0.36 1.00 0.70
## MR4 0.84 0.62 0.70 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 66 with the objective function = 10.08 with Chi Square = 36130.37
## df of the model are 24 and the objective function was 0.1
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic n.obs is 3590 with the empirical chi square 41.21 with prob < 0.016
## The total n.obs was 3590 with Likelihood Chi Square = 343.21 with prob < 3e-58
##
## Tucker Lewis Index of factoring reliability = 0.976
## RMSEA index = 0.061 and the 90 % confidence intervals are 0.055 0.067
## BIC = 146.74
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR3 MR2 MR4
## Correlation of (regression) scores with factors 0.97 0.94 0.97 0.94
## Multiple R square of scores with factors 0.95 0.88 0.94 0.88
## Minimum correlation of possible factor scores 0.90 0.75 0.89 0.77
fa4<-fa(df[,1:12], nfactors=4, rotate="promax", fm="minres", weight=df$sweight)
fa4
## Factor Analysis using method = minres
## Call: fa(r = df[, 1:12], nfactors = 4, rotate = "promax", fm = "minres",
## weight = df$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR3 MR4 MR2 h2 u2 com
## ssgs 0.66 0.02 0.31 -0.05 0.79 0.21 1.4
## ssar 0.35 0.52 -0.04 0.11 0.78 0.22 1.9
## sswk 0.83 -0.12 0.23 -0.01 0.84 0.16 1.2
## sspc 0.59 0.22 0.08 0.03 0.76 0.24 1.3
## ssno 0.00 -0.11 -0.03 0.98 0.81 0.19 1.0
## sscs -0.05 0.10 0.08 0.64 0.51 0.49 1.1
## ssai 0.06 -0.04 0.60 0.09 0.44 0.56 1.1
## sssi -0.03 0.12 0.70 -0.05 0.54 0.46 1.1
## ssmk 0.36 0.41 -0.08 0.27 0.80 0.20 2.8
## ssmc 0.03 0.57 0.32 -0.04 0.70 0.30 1.6
## ssei 0.41 0.02 0.40 0.03 0.63 0.37 2.0
## ssao -0.10 0.87 0.05 -0.03 0.65 0.35 1.0
##
## MR1 MR3 MR4 MR2
## SS loadings 2.66 2.10 1.90 1.58
## Proportion Var 0.22 0.18 0.16 0.13
## Cumulative Var 0.22 0.40 0.55 0.69
## Proportion Explained 0.32 0.26 0.23 0.19
## Cumulative Proportion 0.32 0.58 0.81 1.00
##
## With factor correlations of
## MR1 MR3 MR4 MR2
## MR1 1.00 0.81 0.75 0.71
## MR3 0.81 1.00 0.71 0.67
## MR4 0.75 0.71 1.00 0.48
## MR2 0.71 0.67 0.48 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 66 with the objective function = 8.97 with Chi Square = 31376.06
## df of the model are 24 and the objective function was 0.05
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.01
##
## The harmonic n.obs is 3503 with the empirical chi square 29.65 with prob < 0.2
## The total n.obs was 3503 with Likelihood Chi Square = 184.84 with prob < 8.7e-27
##
## Tucker Lewis Index of factoring reliability = 0.986
## RMSEA index = 0.044 and the 90 % confidence intervals are 0.038 0.05
## BIC = -11.03
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR3 MR4 MR2
## Correlation of (regression) scores with factors 0.96 0.94 0.91 0.94
## Multiple R square of scores with factors 0.93 0.89 0.83 0.88
## Minimum correlation of possible factor scores 0.85 0.78 0.65 0.76
fact3<- factanal(dm[,1:12], 3, rotation="promax")
print(fact3, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = dm[, 1:12], factors = 3, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.18 0.21 0.13 0.25 0.41 0.54 0.45 0.33 0.17 0.24 0.25 0.45
##
## Loadings:
## Factor1 Factor2 Factor3
## ssgs 0.24 0.43 0.37
## ssar 0.68 0.22
## sswk 0.23 0.28 0.56
## sspc 0.50 0.25 0.24
## ssno 0.87 -0.23
## sscs 0.75
## ssai 0.81
## sssi 0.88
## ssmk 0.80
## ssmc 0.29 0.68
## ssei 0.64
## ssao 0.55 0.38
##
## Factor1 Factor2 Factor3
## SS loadings 3.22 2.90 0.59
## Proportion Var 0.27 0.24 0.05
## Cumulative Var 0.27 0.51 0.56
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.00 -0.59 -0.59
## Factor2 -0.59 1.00 0.66
## Factor3 -0.59 0.66 1.00
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 808.18 on 33 degrees of freedom.
## The p-value is 1.61e-148
fact3<- factanal(df[,1:12], 3, rotation="promax")
print(fact3, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = df[, 1:12], factors = 3, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.20 0.23 0.17 0.24 0.26 0.50 0.62 0.54 0.20 0.32 0.35 0.33
##
## Loadings:
## Factor1 Factor2 Factor3
## ssgs 0.89
## ssar 0.26 0.46 0.25
## sswk 0.95
## sspc 0.60
## ssno 0.90
## sscs 0.61
## ssai 0.61
## sssi 0.63
## ssmk 0.23 0.37 0.40
## ssmc 0.33 0.58
## ssei 0.77
## ssao 0.89
##
## Factor1 Factor2 Factor3
## SS loadings 3.66 1.58 1.47
## Proportion Var 0.30 0.13 0.12
## Cumulative Var 0.30 0.44 0.56
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.00 0.63 -0.84
## Factor2 0.63 1.00 -0.66
## Factor3 -0.84 -0.66 1.00
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 458.77 on 33 degrees of freedom.
## The p-value is 1.92e-76
fact4<- factanal(dm[,1:12], 4, rotation="promax")
print(fact4, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = dm[, 1:12], factors = 4, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.17 0.22 0.15 0.23 0.15 0.51 0.40 0.30 0.20 0.24 0.25 0.35
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## ssgs 0.75
## ssar 0.31 0.39 0.21
## sswk 0.92
## sspc 0.58 0.32
## ssno 1.06
## sscs 0.30 0.51
## ssai 0.80
## sssi 0.82
## ssmk 0.29 0.39 0.31
## ssmc 0.56 0.39
## ssei 0.35 0.47
## ssao 0.98
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 2.08 1.85 1.75 1.55
## Proportion Var 0.17 0.15 0.15 0.13
## Cumulative Var 0.17 0.33 0.47 0.60
##
## Factor Correlations:
## Factor1 Factor2 Factor3 Factor4
## Factor1 1.00 -0.71 -0.71 -0.84
## Factor2 -0.71 1.00 0.41 0.74
## Factor3 -0.71 0.41 1.00 0.66
## Factor4 -0.84 0.74 0.66 1.00
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 287.88 on 24 degrees of freedom.
## The p-value is 4.58e-47
fact4<- factanal(df[,1:12], 4, rotation="promax")
print(fact4, digits=2, cutoff=0.2)
##
## Call:
## factanal(x = df[, 1:12], factors = 4, rotation = "promax")
##
## Uniquenesses:
## ssgs ssar sswk sspc ssno sscs ssai sssi ssmk ssmc ssei ssao
## 0.20 0.21 0.15 0.23 0.17 0.50 0.58 0.46 0.19 0.30 0.35 0.36
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## ssgs 0.61 0.39
## ssar 0.58 0.25
## sswk 0.59 0.53
## sspc 0.36 0.28 0.37
## ssno 0.99
## sscs 0.60
## ssai 0.60
## sssi 0.72
## ssmk 0.49 0.24 0.23
## ssmc 0.42 0.53
## ssei 0.60 0.26
## ssao 0.81
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 2.29 1.61 1.42 0.76
## Proportion Var 0.19 0.13 0.12 0.06
## Cumulative Var 0.19 0.33 0.44 0.51
##
## Factor Correlations:
## Factor1 Factor2 Factor3 Factor4
## Factor1 1.00 -0.63 0.63 0.48
## Factor2 -0.63 1.00 -0.69 -0.54
## Factor3 0.63 -0.69 1.00 0.74
## Factor4 0.48 -0.54 0.74 1.00
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 156.47 on 24 degrees of freedom.
## The p-value is 2.06e-21
mfa<-fa(r=dm[,1:12], nfactors=4, max.iter=100, warnings=TRUE, rotate="none", fm="pa", weight=dm$sweight)
print(mfa, digits=2, cutoff=.10)
## Factor Analysis using method = pa
## Call: fa(r = dm[, 1:12], nfactors = 4, rotate = "none", max.iter = 100,
## warnings = TRUE, fm = "pa", weight = dm$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA2 PA3 PA4 h2 u2 com
## ssgs 0.88 -0.12 -0.04 -0.21 0.83 0.17 1.2
## ssar 0.86 0.14 -0.05 -0.02 0.76 0.24 1.1
## sswk 0.86 -0.06 0.01 -0.28 0.83 0.17 1.2
## sspc 0.86 0.08 -0.13 -0.13 0.77 0.23 1.1
## ssno 0.65 0.58 0.35 0.07 0.88 0.12 2.6
## sscs 0.61 0.36 0.03 0.11 0.51 0.49 1.7
## ssai 0.62 -0.41 0.22 0.12 0.62 0.38 2.1
## sssi 0.67 -0.46 0.17 0.13 0.71 0.29 2.0
## ssmk 0.85 0.25 -0.04 0.00 0.79 0.21 1.2
## ssmc 0.84 -0.18 -0.10 0.13 0.76 0.24 1.2
## ssei 0.83 -0.24 0.05 -0.01 0.75 0.25 1.2
## ssao 0.71 0.09 -0.33 0.26 0.70 0.30 1.7
##
## PA1 PA2 PA3 PA4
## SS loadings 7.25 1.04 0.35 0.27
## Proportion Var 0.60 0.09 0.03 0.02
## Cumulative Var 0.60 0.69 0.72 0.74
## Proportion Explained 0.81 0.12 0.04 0.03
## Cumulative Proportion 0.81 0.93 0.97 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 66 with the objective function = 10.08 0.1 with Chi Square = 36130.37
## df of the model are 24 and the objective function was 0.09
## 0.1
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
## 0.1
## The harmonic n.obs is 3590 with the empirical chi square 41.4 with prob < 0.015
## 0.1The total n.obs was 3590 with Likelihood Chi Square = 339.36 with prob < 1.8e-57
## 0.1
## Tucker Lewis Index of factoring reliability = 0.976
## RMSEA index = 0.06 and the 90 % confidence intervals are 0.055 0.066 0.1
## BIC = 142.9
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## PA1 PA2 PA3 PA4
## Correlation of (regression) scores with factors 0.98 0.91 0.78 0.74
## Multiple R square of scores with factors 0.97 0.82 0.61 0.54
## Minimum correlation of possible factor scores 0.94 0.65 0.23 0.09
ffa<-fa(r=df[,1:12], nfactors=4, max.iter=100, warnings=TRUE, rotate="none", fm="pa", weight=df$sweight)
print(ffa, digits=2, cutoff=.10)
## Factor Analysis using method = pa
## Call: fa(r = df[, 1:12], nfactors = 4, rotate = "none", max.iter = 100,
## warnings = TRUE, fm = "pa", weight = df$sweight)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA2 PA3 PA4 h2 u2 com
## ssgs 0.86 -0.17 0.12 -0.10 0.80 0.20 1.1
## ssar 0.86 0.08 -0.15 -0.09 0.78 0.22 1.1
## sswk 0.87 -0.12 0.20 -0.17 0.83 0.17 1.2
## sspc 0.86 -0.02 0.01 -0.13 0.75 0.25 1.0
## ssno 0.64 0.57 0.14 0.09 0.76 0.24 2.1
## sscs 0.61 0.37 0.05 0.14 0.53 0.47 1.8
## ssai 0.60 -0.18 0.14 0.18 0.44 0.56 1.5
## sssi 0.63 -0.30 0.06 0.21 0.54 0.46 1.7
## ssmk 0.86 0.19 -0.10 -0.09 0.80 0.20 1.1
## ssmc 0.80 -0.15 -0.18 0.09 0.70 0.30 1.2
## ssei 0.77 -0.15 0.12 0.01 0.63 0.37 1.1
## ssao 0.72 -0.03 -0.36 0.05 0.65 0.35 1.5
##
## PA1 PA2 PA3 PA4
## SS loadings 7.00 0.72 0.31 0.19
## Proportion Var 0.58 0.06 0.03 0.02
## Cumulative Var 0.58 0.64 0.67 0.68
## Proportion Explained 0.85 0.09 0.04 0.02
## Cumulative Proportion 0.85 0.94 0.98 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 4 factors are sufficient.
##
## df null model = 66 with the objective function = 8.97 0.1 with Chi Square = 31376.06
## df of the model are 24 and the objective function was 0.05
## 0.1
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.01
## 0.1
## The harmonic n.obs is 3503 with the empirical chi square 30.06 with prob < 0.18
## 0.1The total n.obs was 3503 with Likelihood Chi Square = 186.87 with prob < 3.5e-27
## 0.1
## Tucker Lewis Index of factoring reliability = 0.986
## RMSEA index = 0.044 and the 90 % confidence intervals are 0.038 0.05 0.1
## BIC = -9
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## PA1 PA2 PA3 PA4
## Correlation of (regression) scores with factors 0.98 0.84 0.72 0.60
## Multiple R square of scores with factors 0.96 0.71 0.52 0.36
## Minimum correlation of possible factor scores 0.93 0.42 0.04 -0.28
# MGCFA USING SIBLING DATA
# WHITE RESPONDENTS
dw<- filter(ds, bhw==3)
nrow(dw) # N=670
## [1] 670
dgroup<- dplyr::select(dw, id, starts_with("ss"), asvab, efa, educ2011, T6665000, agec, age, agebin, agec2, sex, sexage, bhw, sweight)
fit<-lm(efa ~ sex + rcs(agec, 3) + sex*rcs(agec, 3), data=dgroup)
summary(fit)
##
## Call:
## lm(formula = efa ~ sex + rcs(agec, 3) + sex * rcs(agec, 3), data = dgroup)
##
## Residuals:
## Min 1Q Median 3Q Max
## -45.566 -7.201 0.721 8.433 34.519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 110.1806 1.9187 57.425 < 2e-16 ***
## sex -1.3067 2.7451 -0.476 0.634
## rcs(agec, 3)agec 6.8323 1.3492 5.064 5.33e-07 ***
## rcs(agec, 3)agec' -2.8192 1.6706 -1.688 0.092 .
## sex:rcs(agec, 3)agec -0.8730 1.8977 -0.460 0.646
## sex:rcs(agec, 3)agec' 0.2002 2.3373 0.086 0.932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.18 on 664 degrees of freedom
## Multiple R-squared: 0.1929, Adjusted R-squared: 0.1868
## F-statistic: 31.74 on 5 and 664 DF, p-value: < 2.2e-16
dgroup$pred1<-fitted(fit)
original_age_min <- 12
original_age_max <- 17
mean_centered_min <- min(dgroup$agec)
mean_centered_max <- max(dgroup$agec)
original_age_mean <- (original_age_min + original_age_max) / 2
mean_centered_age_mean <- (mean_centered_min + mean_centered_max) / 2
age_difference <- original_age_mean - mean_centered_age_mean
xyplot(dgroup$pred1 ~ dgroup$agec, data=dgroup, groups=sex, pch=19, type=c("p"), col=c('red', 'blue'), grid=TRUE, ylab="Predicted g", xlab="age", key=list(text=list(c("White Male", "White Female")), points=list(pch=c(19,19), col=c("red", "blue")), columns=2))
xyplot(dgroup$pred1 ~ dgroup$agec, data=dgroup, groups=sex, pch=19, type=c("p"), col=c('red', 'blue'), grid=TRUE, ylab="Predicted g", xlab="Age", key=list(text=list(c("White Male", "White Female")), points=list(pch=c(19,19), col=c("red", "blue")), columns=2), scales=list(x=list(at=seq(mean_centered_min, mean_centered_max), labels=seq(original_age_min, original_age_max))))
fit<-lm(asvab ~ sex + rcs(agec, 3) + sex*rcs(agec, 3), data=dgroup)
summary(fit)
##
## Call:
## lm(formula = asvab ~ sex + rcs(agec, 3) + sex * rcs(agec, 3),
## data = dgroup)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.9724 -10.8281 0.3089 11.9673 24.9765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 106.5771 2.0594 51.751 <2e-16 ***
## sex 1.1552 2.9465 0.392 0.695
## rcs(agec, 3)agec 1.8148 1.4482 1.253 0.211
## rcs(agec, 3)agec' -1.1537 1.7932 -0.643 0.520
## sex:rcs(agec, 3)agec -0.2895 2.0368 -0.142 0.887
## sex:rcs(agec, 3)agec' -0.2067 2.5087 -0.082 0.934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.15 on 664 degrees of freedom
## Multiple R-squared: 0.008656, Adjusted R-squared: 0.001191
## F-statistic: 1.16 on 5 and 664 DF, p-value: 0.3276
dgroup$pred2<-fitted(fit)
xyplot(dgroup$pred2 ~ dgroup$agec, data=dgroup, groups=sex, pch=19, type=c("p"), col=c('red', 'blue'), grid=TRUE, ylab="Predicted ASVAB", xlab="Age", key=list(text=list(c("White Male", "White Female")), points=list(pch=c(19,19), col=c("red", "blue")), columns=2), scales=list(x=list(at=seq(mean_centered_min, mean_centered_max), labels=seq(original_age_min, original_age_max))))
describeBy(dgroup$pred1, dgroup$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew
## X1 1 335 106.48 6.79 107.43 106.82 8.45 93.1 116.14 23.04 -0.36
## kurtosis se
## X1 -1.12 0.37
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 335 106.06 6.03 108.08 106.55 6.07 93.98 113.48 19.5 -0.56
## kurtosis se
## X1 -1.04 0.33
describeBy(dgroup$efa, dgroup$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew
## X1 1 335 106.48 15.16 107.64 107.17 14.11 63.82 139.83 76.01 -0.43
## kurtosis se
## X1 0.03 0.83
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 335 106.06 14.07 107.03 106.37 12.6 66.69 141.79 75.1 -0.25
## kurtosis se
## X1 0.06 0.77
describeBy(dgroup$asvab, dgroup$sex)
##
## Descriptive statistics by group
## INDICES: 0
## vars n mean sd median trimmed mad min max range skew
## V1 1 335 105.21 14.37 106.49 105.74 16.57 76.7 128.12 51.42 -0.25
## kurtosis se
## V1 -1.06 0.79
## ------------------------------------------------------
## INDICES: 1
## vars n mean sd median trimmed mad min max range skew
## V1 1 335 106.23 13.95 106.18 106.63 16.64 76.96 128.12 51.16 -0.18
## kurtosis se
## V1 -1.05 0.76
describeBy(dgroup$educ2011, dgroup$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 281 14.07 2.67 14 14.07 2.97 8 20 12 0.03 -0.68
## se
## X1 0.16
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 280 14.81 2.71 15 14.84 2.97 8 20 12 -0.16 -0.71
## se
## X1 0.16
cor(dgroup$efa, dgroup$asvab, use="pairwise.complete.obs", method="pearson")
## [,1]
## [1,] 0.8828078
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(agebin, sex) %>% summarise(MEAN = survey_mean(pred1), SD = survey_sd(pred1))
## # A tibble: 10 Ă— 5
## # Groups: agebin [5]
## agebin sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 12 0 96.5 0.235 1.99
## 2 12 1 96.6 0.225 1.82
## 3 13 0 103. 0.209 1.74
## 4 13 1 102. 0.194 1.49
## 5 14 0 108. 0.161 1.34
## 6 14 1 107. 0.142 1.11
## 7 15 0 112. 0.128 0.952
## 8 15 1 110. 0.0831 0.660
## 9 16 0 115. 0.0963 0.682
## 10 16 1 113. 0.0652 0.569
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(agebin, sex) %>% summarise(MEAN = survey_mean(efa), SD = survey_sd(efa))
## # A tibble: 10 Ă— 5
## # Groups: agebin [5]
## agebin sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 12 0 96.6 1.58 13.4
## 2 12 1 95.8 1.51 12.1
## 3 13 0 104. 1.72 14.3
## 4 13 1 103. 1.70 12.9
## 5 14 0 107. 1.62 13.6
## 6 14 1 108. 1.60 12.4
## 7 15 0 113. 1.65 12.5
## 8 15 1 112. 1.68 13.3
## 9 16 0 117. 1.96 13.8
## 10 16 1 112. 1.32 11.5
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(agebin, sex) %>% summarise(MEAN = survey_mean(asvab), SD = survey_sd(asvab))
## # A tibble: 10 Ă— 5
## # Groups: agebin [5]
## agebin sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 12 0 103. 1.68 14.3
## 2 12 1 104. 1.67 13.5
## 3 13 0 106. 1.76 14.6
## 4 13 1 107. 1.93 14.6
## 5 14 0 105. 1.70 14.2
## 6 14 1 109. 1.70 13.3
## 7 15 0 106. 1.91 14.4
## 8 15 1 108. 1.79 14.2
## 9 16 0 109. 2.04 14.3
## 10 16 1 106. 1.55 13.5
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(pred1), SD = survey_sd(pred1))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 107. 0.392 6.84
## 2 1 106. 0.342 6.07
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(efa), SD = survey_sd(efa))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 107. 0.863 15.2
## 2 1 107. 0.774 13.8
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(asvab, na.rm = TRUE), SD = survey_sd(asvab, na.rm = TRUE))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 106. 0.817 14.4
## 2 1 106. 0.775 13.8
dgroup %>% as_survey_design(ids = id, weights = T6665000) %>% group_by(sex) %>% summarise(MEAN = survey_mean(educ2011, na.rm = TRUE), SD = survey_sd(educ2011, na.rm = TRUE))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 14.1 0.162 2.69
## 2 1 14.8 0.164 2.72
# CORRELATED FACTOR MODEL
cf.model<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
'
cf.lv<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
verbal~~1*verbal
math~~1*math
'
cf.reduced<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
verbal~~1*verbal
math~~1*math
verbal~0*1
math~0*1
'
baseline<-cfa(cf.model, data=dgroup, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 373.206 46.000 0.000 0.946 0.103 0.045 16765.094
## bic
## 16963.414
Mc(baseline)
## [1] 0.7830577
configural<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 331.264 92.000 0.000 0.960 0.088 0.038 16403.455
## bic
## 16800.095
Mc(configural)
## [1] 0.8362545
summary(configural, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 88
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 331.264 299.933
## Degrees of freedom 92 92
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.104
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 130.642 118.286
## 0 200.622 181.647
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.817 0.041 19.853 0.000 0.736
## sswk 0.837 0.045 18.796 0.000 0.750
## sspc 0.761 0.042 18.063 0.000 0.678
## ssei 0.532 0.071 7.471 0.000 0.392
## math =~
## ssar 0.781 0.041 18.848 0.000 0.700
## ssmk 0.611 0.096 6.355 0.000 0.422
## ssmc 0.689 0.044 15.591 0.000 0.602
## ssao 0.651 0.041 15.792 0.000 0.570
## electronic =~
## ssai 0.522 0.044 11.851 0.000 0.436
## sssi 0.585 0.050 11.756 0.000 0.488
## ssei 0.169 0.068 2.496 0.013 0.036
## speed =~
## ssno 0.782 0.059 13.153 0.000 0.665
## sscs 0.652 0.050 13.119 0.000 0.555
## ssmk 0.304 0.100 3.044 0.002 0.108
## ci.upper Std.lv Std.all
##
## 0.898 0.817 0.898
## 0.924 0.837 0.892
## 0.843 0.761 0.832
## 0.671 0.532 0.620
##
## 0.862 0.781 0.895
## 0.799 0.611 0.635
## 0.775 0.689 0.803
## 0.731 0.651 0.706
##
## 0.608 0.522 0.690
## 0.683 0.585 0.751
## 0.301 0.169 0.197
##
## 0.899 0.782 0.792
## 0.749 0.652 0.695
## 0.499 0.304 0.316
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.922 0.019 49.140 0.000 0.885
## electronic 0.772 0.048 15.988 0.000 0.678
## speed 0.733 0.060 12.279 0.000 0.616
## math ~~
## electronic 0.741 0.053 13.937 0.000 0.637
## speed 0.755 0.061 12.435 0.000 0.636
## electronic ~~
## speed 0.450 0.094 4.775 0.000 0.265
## ci.upper Std.lv Std.all
##
## 0.959 0.922 0.922
## 0.867 0.772 0.772
## 0.850 0.733 0.733
##
## 0.845 0.741 0.741
## 0.874 0.755 0.755
##
## 0.634 0.450 0.450
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.378 0.051 7.429 0.000 0.278
## .sswk 0.382 0.052 7.278 0.000 0.279
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei 0.188 0.048 3.908 0.000 0.094
## .ssar 0.384 0.049 7.810 0.000 0.288
## .ssmk 0.448 0.054 8.275 0.000 0.342
## .ssmc 0.263 0.048 5.461 0.000 0.169
## .ssao 0.343 0.052 6.596 0.000 0.241
## .ssai 0.069 0.043 1.625 0.104 -0.014
## .sssi 0.163 0.044 3.736 0.000 0.078
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs 0.358 0.053 6.754 0.000 0.254
## ci.upper Std.lv Std.all
## 0.478 0.378 0.415
## 0.485 0.382 0.407
## 0.545 0.445 0.487
## 0.283 0.188 0.220
## 0.481 0.384 0.440
## 0.554 0.448 0.466
## 0.358 0.263 0.307
## 0.444 0.343 0.372
## 0.153 0.069 0.092
## 0.249 0.163 0.209
## 0.395 0.285 0.289
## 0.462 0.358 0.382
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.161 0.019 8.395 0.000 0.123
## .sswk 0.180 0.019 9.507 0.000 0.143
## .sspc 0.257 0.033 7.857 0.000 0.193
## .ssei 0.285 0.029 9.729 0.000 0.228
## .ssar 0.152 0.018 8.400 0.000 0.117
## .ssmk 0.181 0.023 7.900 0.000 0.136
## .ssmc 0.261 0.027 9.736 0.000 0.208
## .ssao 0.425 0.036 11.840 0.000 0.355
## .ssai 0.300 0.036 8.235 0.000 0.229
## .sssi 0.265 0.038 7.077 0.000 0.192
## .ssno 0.363 0.049 7.390 0.000 0.267
## .sscs 0.454 0.058 7.863 0.000 0.341
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.199 0.161 0.194
## 0.217 0.180 0.204
## 0.321 0.257 0.308
## 0.343 0.285 0.388
## 0.187 0.152 0.199
## 0.226 0.181 0.195
## 0.313 0.261 0.355
## 0.495 0.425 0.501
## 0.371 0.300 0.524
## 0.339 0.265 0.437
## 0.459 0.363 0.372
## 0.567 0.454 0.517
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.895 0.045 19.742 0.000 0.806
## sswk 0.884 0.046 19.306 0.000 0.794
## sspc 0.831 0.037 22.376 0.000 0.758
## ssei 0.537 0.071 7.574 0.000 0.398
## math =~
## ssar 0.850 0.050 17.117 0.000 0.753
## ssmk 0.601 0.083 7.270 0.000 0.439
## ssmc 0.819 0.052 15.718 0.000 0.717
## ssao 0.709 0.045 15.843 0.000 0.621
## electronic =~
## ssai 0.954 0.056 16.936 0.000 0.844
## sssi 0.857 0.054 15.943 0.000 0.752
## ssei 0.497 0.073 6.760 0.000 0.353
## speed =~
## ssno 0.844 0.066 12.887 0.000 0.716
## sscs 0.782 0.058 13.404 0.000 0.668
## ssmk 0.326 0.082 3.999 0.000 0.166
## ci.upper Std.lv Std.all
##
## 0.984 0.895 0.901
## 0.973 0.884 0.879
## 0.904 0.831 0.850
## 0.675 0.537 0.483
##
## 0.947 0.850 0.878
## 0.763 0.601 0.629
## 0.921 0.819 0.823
## 0.797 0.709 0.698
##
## 1.065 0.954 0.822
## 0.963 0.857 0.836
## 0.640 0.497 0.447
##
## 0.973 0.844 0.785
## 0.896 0.782 0.762
## 0.486 0.326 0.341
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.925 0.016 59.088 0.000 0.894
## electronic 0.683 0.043 15.964 0.000 0.599
## speed 0.710 0.047 14.942 0.000 0.617
## math ~~
## electronic 0.665 0.049 13.571 0.000 0.569
## speed 0.772 0.045 17.177 0.000 0.684
## electronic ~~
## speed 0.339 0.071 4.754 0.000 0.200
## ci.upper Std.lv Std.all
##
## 0.956 0.925 0.925
## 0.767 0.683 0.683
## 0.803 0.710 0.710
##
## 0.762 0.665 0.665
## 0.860 0.772 0.772
##
## 0.479 0.339 0.339
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.542 0.056 9.598 0.000 0.431
## .sswk 0.371 0.057 6.485 0.000 0.259
## .sspc 0.143 0.056 2.563 0.010 0.034
## .ssei 0.595 0.063 9.438 0.000 0.472
## .ssar 0.392 0.055 7.142 0.000 0.284
## .ssmk 0.259 0.054 4.760 0.000 0.152
## .ssmc 0.578 0.056 10.233 0.000 0.467
## .ssao 0.225 0.058 3.904 0.000 0.112
## .ssai 0.684 0.067 10.241 0.000 0.553
## .sssi 0.827 0.059 14.131 0.000 0.712
## .ssno 0.122 0.061 1.990 0.047 0.002
## .sscs -0.026 0.058 -0.447 0.655 -0.140
## ci.upper Std.lv Std.all
## 0.653 0.542 0.545
## 0.483 0.371 0.369
## 0.252 0.143 0.146
## 0.719 0.595 0.535
## 0.499 0.392 0.405
## 0.365 0.259 0.271
## 0.689 0.578 0.581
## 0.338 0.225 0.221
## 0.815 0.684 0.590
## 0.942 0.827 0.807
## 0.241 0.122 0.113
## 0.088 -0.026 -0.025
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.186 0.022 8.390 0.000 0.143
## .sswk 0.230 0.022 10.422 0.000 0.187
## .sspc 0.265 0.030 8.695 0.000 0.205
## .ssei 0.338 0.035 9.553 0.000 0.268
## .ssar 0.215 0.028 7.678 0.000 0.160
## .ssmk 0.142 0.017 8.165 0.000 0.108
## .ssmc 0.320 0.032 9.856 0.000 0.256
## .ssao 0.529 0.052 10.266 0.000 0.428
## .ssai 0.437 0.061 7.216 0.000 0.318
## .sssi 0.317 0.049 6.452 0.000 0.221
## .ssno 0.444 0.057 7.828 0.000 0.333
## .sscs 0.442 0.076 5.783 0.000 0.292
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.230 0.186 0.189
## 0.273 0.230 0.228
## 0.324 0.265 0.277
## 0.407 0.338 0.273
## 0.270 0.215 0.230
## 0.176 0.142 0.156
## 0.384 0.320 0.323
## 0.630 0.529 0.513
## 0.555 0.437 0.324
## 0.413 0.317 0.301
## 0.556 0.444 0.384
## 0.592 0.442 0.420
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
modificationIndices(configural, sort=T, maximum.number=30)
## lhs op rhs block group level mi epc sepc.lv sepc.all
## 226 electronic =~ ssmc 2 2 1 77.710 0.516 0.516 0.518
## 235 speed =~ ssmc 2 2 1 45.620 -0.557 -0.557 -0.559
## 234 speed =~ ssar 2 2 1 34.175 0.472 0.472 0.487
## 224 electronic =~ ssar 2 2 1 30.959 -0.307 -0.307 -0.317
## 215 math =~ sspc 2 2 1 27.425 0.743 0.743 0.761
## 223 electronic =~ sspc 2 2 1 26.619 -0.285 -0.285 -0.291
## 232 speed =~ sspc 2 2 1 26.460 0.305 0.305 0.312
## 115 math =~ sspc 1 1 1 18.776 0.559 0.559 0.611
## 126 electronic =~ ssmc 1 1 1 15.711 0.264 0.264 0.308
## 190 ssmc ~~ ssao 1 1 1 14.939 0.080 0.080 0.240
## 230 speed =~ ssgs 2 2 1 14.908 -0.218 -0.218 -0.220
## 292 ssmc ~~ sssi 2 2 1 14.676 0.090 0.090 0.281
## 114 math =~ sswk 1 1 1 14.124 -0.487 -0.487 -0.519
## 277 ssar ~~ ssmk 2 2 1 12.547 0.058 0.058 0.329
## 108 verbal =~ ssao 1 1 1 11.640 -0.520 -0.520 -0.564
## 239 ssgs ~~ sswk 2 2 1 9.765 0.061 0.061 0.297
## 135 speed =~ ssmc 1 1 1 9.318 -0.218 -0.218 -0.254
## 221 electronic =~ ssgs 2 2 1 9.147 0.160 0.160 0.161
## 293 ssmc ~~ ssno 2 2 1 9.125 -0.081 -0.081 -0.215
## 137 speed =~ ssai 1 1 1 8.987 0.180 0.180 0.238
## 162 sspc ~~ ssmk 1 1 1 8.931 0.044 0.044 0.204
## 139 ssgs ~~ sswk 1 1 1 8.908 0.049 0.049 0.286
## 109 verbal =~ ssai 1 1 1 8.833 0.500 0.500 0.660
## 110 verbal =~ sssi 1 1 1 8.833 -0.560 -0.560 -0.719
## 261 sspc ~~ ssar 2 2 1 8.658 0.049 0.049 0.205
## 132 speed =~ sspc 1 1 1 8.617 0.178 0.178 0.195
## 134 speed =~ ssar 1 1 1 8.383 0.213 0.213 0.244
## 171 ssei ~~ ssmc 1 1 1 8.365 0.048 0.048 0.177
## 138 speed =~ sssi 1 1 1 8.352 -0.193 -0.193 -0.247
## 140 ssgs ~~ sspc 1 1 1 8.235 -0.045 -0.045 -0.223
## sepc.nox
## 226 0.518
## 235 -0.559
## 234 0.487
## 224 -0.317
## 215 0.761
## 223 -0.291
## 232 0.312
## 115 0.611
## 126 0.308
## 190 0.240
## 230 -0.220
## 292 0.281
## 114 -0.519
## 277 0.329
## 108 -0.564
## 239 0.297
## 135 -0.254
## 221 0.161
## 293 -0.215
## 137 0.238
## 162 0.204
## 139 0.286
## 109 0.660
## 110 -0.719
## 261 0.205
## 132 0.195
## 134 0.244
## 171 0.177
## 138 -0.247
## 140 -0.223
metric<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 347.314 102.000 0.000 0.959 0.085 0.044 16399.505
## bic
## 16751.072
Mc(metric)
## [1] 0.8324821
summary(metric, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 76 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 92
## Number of equality constraints 14
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 347.314 310.218
## Degrees of freedom 102 102
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.120
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 140.877 125.830
## 0 206.437 184.387
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.819 0.038 21.715 0.000 0.745
## sswk (.p2.) 0.824 0.041 20.324 0.000 0.745
## sspc (.p3.) 0.761 0.036 21.361 0.000 0.691
## ssei (.p4.) 0.467 0.050 9.406 0.000 0.370
## math =~
## ssar (.p5.) 0.783 0.039 20.278 0.000 0.707
## ssmk (.p6.) 0.581 0.065 9.006 0.000 0.455
## ssmc (.p7.) 0.720 0.038 18.732 0.000 0.645
## ssao (.p8.) 0.652 0.037 17.845 0.000 0.581
## electronic =~
## ssai (.p9.) 0.541 0.038 14.181 0.000 0.466
## sssi (.10.) 0.518 0.042 12.253 0.000 0.436
## ssei (.11.) 0.280 0.039 7.245 0.000 0.204
## speed =~
## ssno (.12.) 0.776 0.050 15.651 0.000 0.679
## sscs (.13.) 0.686 0.041 16.641 0.000 0.605
## ssmk (.14.) 0.295 0.065 4.524 0.000 0.167
## ci.upper Std.lv Std.all
##
## 0.893 0.819 0.898
## 0.904 0.824 0.888
## 0.831 0.761 0.833
## 0.565 0.467 0.529
##
## 0.858 0.783 0.895
## 0.707 0.581 0.620
## 0.795 0.720 0.818
## 0.724 0.652 0.708
##
## 0.616 0.541 0.705
## 0.601 0.518 0.687
## 0.356 0.280 0.317
##
## 0.873 0.776 0.786
## 0.767 0.686 0.718
## 0.423 0.295 0.316
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.923 0.018 50.057 0.000 0.887
## electronic 0.790 0.049 16.219 0.000 0.694
## speed 0.743 0.052 14.232 0.000 0.640
## math ~~
## electronic 0.760 0.052 14.754 0.000 0.659
## speed 0.764 0.056 13.743 0.000 0.655
## electronic ~~
## speed 0.488 0.087 5.603 0.000 0.317
## ci.upper Std.lv Std.all
##
## 0.959 0.923 0.923
## 0.885 0.790 0.790
## 0.845 0.743 0.743
##
## 0.861 0.760 0.760
## 0.873 0.764 0.764
##
## 0.659 0.488 0.488
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.378 0.051 7.429 0.000 0.278
## .sswk 0.382 0.052 7.278 0.000 0.279
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei 0.188 0.048 3.908 0.000 0.094
## .ssar 0.384 0.049 7.810 0.000 0.288
## .ssmk 0.448 0.054 8.275 0.000 0.342
## .ssmc 0.263 0.048 5.461 0.000 0.169
## .ssao 0.343 0.052 6.596 0.000 0.241
## .ssai 0.069 0.043 1.625 0.104 -0.014
## .sssi 0.163 0.044 3.736 0.000 0.078
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs 0.358 0.053 6.754 0.000 0.254
## ci.upper Std.lv Std.all
## 0.478 0.378 0.415
## 0.485 0.382 0.411
## 0.545 0.445 0.487
## 0.283 0.188 0.213
## 0.481 0.384 0.439
## 0.554 0.448 0.479
## 0.358 0.263 0.299
## 0.444 0.343 0.372
## 0.153 0.069 0.090
## 0.249 0.163 0.216
## 0.395 0.285 0.289
## 0.462 0.358 0.375
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.161 0.019 8.441 0.000 0.124
## .sswk 0.182 0.019 9.510 0.000 0.144
## .sspc 0.256 0.031 8.126 0.000 0.194
## .ssei 0.278 0.030 9.401 0.000 0.220
## .ssar 0.153 0.018 8.292 0.000 0.117
## .ssmk 0.189 0.022 8.751 0.000 0.147
## .ssmc 0.257 0.027 9.645 0.000 0.205
## .ssao 0.424 0.036 11.921 0.000 0.354
## .ssai 0.296 0.035 8.406 0.000 0.227
## .sssi 0.300 0.035 8.699 0.000 0.233
## .ssno 0.373 0.049 7.609 0.000 0.277
## .sscs 0.442 0.055 8.006 0.000 0.333
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.198 0.161 0.194
## 0.219 0.182 0.211
## 0.318 0.256 0.306
## 0.336 0.278 0.356
## 0.189 0.153 0.200
## 0.232 0.189 0.216
## 0.309 0.257 0.331
## 0.494 0.424 0.499
## 0.365 0.296 0.503
## 0.368 0.300 0.528
## 0.470 0.373 0.383
## 0.550 0.442 0.484
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.819 0.038 21.715 0.000 0.745
## sswk (.p2.) 0.824 0.041 20.324 0.000 0.745
## sspc (.p3.) 0.761 0.036 21.361 0.000 0.691
## ssei (.p4.) 0.467 0.050 9.406 0.000 0.370
## math =~
## ssar (.p5.) 0.783 0.039 20.278 0.000 0.707
## ssmk (.p6.) 0.581 0.065 9.006 0.000 0.455
## ssmc (.p7.) 0.720 0.038 18.732 0.000 0.645
## ssao (.p8.) 0.652 0.037 17.845 0.000 0.581
## electronic =~
## ssai (.p9.) 0.541 0.038 14.181 0.000 0.466
## sssi (.10.) 0.518 0.042 12.253 0.000 0.436
## ssei (.11.) 0.280 0.039 7.245 0.000 0.204
## speed =~
## ssno (.12.) 0.776 0.050 15.651 0.000 0.679
## sscs (.13.) 0.686 0.041 16.641 0.000 0.605
## ssmk (.14.) 0.295 0.065 4.524 0.000 0.167
## ci.upper Std.lv Std.all
##
## 0.893 0.893 0.901
## 0.904 0.900 0.884
## 0.831 0.831 0.850
## 0.565 0.510 0.470
##
## 0.858 0.848 0.878
## 0.707 0.630 0.646
## 0.795 0.780 0.806
## 0.724 0.707 0.696
##
## 0.616 0.930 0.809
## 0.601 0.891 0.853
## 0.356 0.481 0.444
##
## 0.873 0.848 0.789
## 0.767 0.750 0.744
## 0.423 0.323 0.332
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 1.092 0.130 8.426 0.000 0.838
## electronic 1.287 0.191 6.742 0.000 0.913
## speed 0.836 0.111 7.501 0.000 0.618
## math ~~
## electronic 1.235 0.198 6.240 0.000 0.847
## speed 0.908 0.115 7.861 0.000 0.682
## electronic ~~
## speed 0.626 0.156 3.999 0.000 0.319
## ci.upper Std.lv Std.all
##
## 1.346 0.923 0.923
## 1.662 0.686 0.686
## 1.055 0.701 0.701
##
## 1.623 0.663 0.663
## 1.134 0.766 0.766
##
## 0.933 0.333 0.333
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.542 0.056 9.598 0.000 0.431
## .sswk 0.371 0.057 6.485 0.000 0.259
## .sspc 0.143 0.056 2.563 0.010 0.034
## .ssei 0.595 0.063 9.438 0.000 0.472
## .ssar 0.392 0.055 7.142 0.000 0.284
## .ssmk 0.259 0.054 4.760 0.000 0.152
## .ssmc 0.578 0.056 10.233 0.000 0.467
## .ssao 0.225 0.058 3.904 0.000 0.112
## .ssai 0.684 0.067 10.241 0.000 0.553
## .sssi 0.827 0.059 14.131 0.000 0.712
## .ssno 0.122 0.061 1.990 0.047 0.002
## .sscs -0.026 0.058 -0.447 0.655 -0.140
## ci.upper Std.lv Std.all
## 0.653 0.542 0.546
## 0.483 0.371 0.364
## 0.252 0.143 0.146
## 0.719 0.595 0.549
## 0.499 0.392 0.406
## 0.365 0.259 0.265
## 0.689 0.578 0.597
## 0.338 0.225 0.221
## 0.815 0.684 0.595
## 0.942 0.827 0.791
## 0.241 0.122 0.113
## 0.088 -0.026 -0.026
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.186 0.021 8.789 0.000 0.144
## .sswk 0.227 0.022 10.461 0.000 0.184
## .sspc 0.266 0.030 8.784 0.000 0.207
## .ssei 0.347 0.036 9.539 0.000 0.275
## .ssar 0.213 0.027 7.922 0.000 0.161
## .ssmk 0.137 0.017 8.239 0.000 0.104
## .ssmc 0.329 0.033 10.066 0.000 0.265
## .ssao 0.531 0.051 10.454 0.000 0.431
## .ssai 0.456 0.060 7.625 0.000 0.339
## .sssi 0.298 0.049 6.053 0.000 0.202
## .ssno 0.436 0.056 7.820 0.000 0.327
## .sscs 0.455 0.075 6.083 0.000 0.308
## verbal 1.191 0.141 8.437 0.000 0.914
## math 1.175 0.151 7.754 0.000 0.878
## electronic 2.955 0.493 5.994 0.000 1.989
## speed 1.196 0.195 6.115 0.000 0.812
## ci.upper Std.lv Std.all
## 0.227 0.186 0.189
## 0.269 0.227 0.219
## 0.325 0.266 0.278
## 0.418 0.347 0.295
## 0.266 0.213 0.229
## 0.170 0.137 0.144
## 0.393 0.329 0.351
## 0.630 0.531 0.515
## 0.573 0.456 0.345
## 0.395 0.298 0.273
## 0.545 0.436 0.377
## 0.601 0.455 0.447
## 1.467 1.000 1.000
## 1.471 1.000 1.000
## 3.921 1.000 1.000
## 1.579 1.000 1.000
lavTestScore(metric, release = 1:14)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 15.651 14 0.335
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p53. 0.023 1 0.881
## 2 .p2. == .p54. 0.921 1 0.337
## 3 .p3. == .p55. 0.001 1 0.973
## 4 .p4. == .p56. 2.728 1 0.099
## 5 .p5. == .p57. 0.049 1 0.825
## 6 .p6. == .p58. 4.443 1 0.035
## 7 .p7. == .p59. 3.147 1 0.076
## 8 .p8. == .p60. 0.001 1 0.974
## 9 .p9. == .p61. 1.137 1 0.286
## 10 .p10. == .p62. 6.132 1 0.013
## 11 .p11. == .p63. 4.888 1 0.027
## 12 .p12. == .p64. 0.003 1 0.956
## 13 .p13. == .p65. 1.932 1 0.164
## 14 .p14. == .p66. 3.845 1 0.050
scalar<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 500.667 110.000 0.000 0.935 0.103 0.052 16536.858
## bic
## 16852.367
Mc(scalar)
## [1] 0.7467846
summary(scalar, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 88 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 26
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 500.667 451.015
## Degrees of freedom 110 110
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.110
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 210.266 189.413
## 0 290.401 261.602
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.817 0.038 21.322 0.000 0.742
## sswk (.p2.) 0.826 0.041 20.300 0.000 0.747
## sspc (.p3.) 0.759 0.036 21.120 0.000 0.688
## ssei (.p4.) 0.444 0.045 9.973 0.000 0.357
## math =~
## ssar (.p5.) 0.781 0.039 20.070 0.000 0.705
## ssmk (.p6.) 0.515 0.076 6.775 0.000 0.366
## ssmc (.p7.) 0.722 0.040 18.065 0.000 0.644
## ssao (.p8.) 0.648 0.036 17.882 0.000 0.577
## electronic =~
## ssai (.p9.) 0.530 0.038 14.064 0.000 0.456
## sssi (.10.) 0.522 0.040 12.936 0.000 0.443
## ssei (.11.) 0.303 0.034 9.033 0.000 0.237
## speed =~
## ssno (.12.) 0.743 0.049 15.311 0.000 0.648
## sscs (.13.) 0.692 0.041 16.778 0.000 0.611
## ssmk (.14.) 0.368 0.078 4.715 0.000 0.215
## ci.upper Std.lv Std.all
##
## 0.892 0.817 0.893
## 0.906 0.826 0.890
## 0.829 0.759 0.820
## 0.531 0.444 0.503
##
## 0.858 0.781 0.894
## 0.664 0.515 0.548
## 0.800 0.722 0.811
## 0.720 0.648 0.703
##
## 0.604 0.530 0.694
## 0.601 0.522 0.689
## 0.369 0.303 0.343
##
## 0.838 0.743 0.757
## 0.773 0.692 0.717
## 0.522 0.368 0.392
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.922 0.019 48.335 0.000 0.885
## electronic 0.797 0.048 16.612 0.000 0.703
## speed 0.767 0.053 14.559 0.000 0.664
## math ~~
## electronic 0.768 0.050 15.219 0.000 0.669
## speed 0.784 0.058 13.620 0.000 0.672
## electronic ~~
## speed 0.512 0.087 5.900 0.000 0.342
## ci.upper Std.lv Std.all
##
## 0.959 0.922 0.922
## 0.891 0.797 0.797
## 0.870 0.767 0.767
##
## 0.867 0.768 0.768
## 0.897 0.784 0.784
##
## 0.682 0.512 0.512
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.454 0.050 9.095 0.000 0.356
## .sswk (.38.) 0.380 0.051 7.394 0.000 0.279
## .sspc (.39.) 0.304 0.051 5.960 0.000 0.204
## .ssei (.40.) 0.203 0.047 4.314 0.000 0.111
## .ssar (.41.) 0.366 0.050 7.329 0.000 0.268
## .ssmk (.42.) 0.412 0.054 7.581 0.000 0.306
## .ssmc (.43.) 0.376 0.047 7.975 0.000 0.284
## .ssao (.44.) 0.273 0.048 5.648 0.000 0.178
## .ssai (.45.) 0.053 0.041 1.289 0.197 -0.027
## .sssi (.46.) 0.171 0.041 4.150 0.000 0.090
## .ssno (.47.) 0.358 0.051 6.946 0.000 0.257
## .sscs (.48.) 0.320 0.051 6.246 0.000 0.219
## ci.upper Std.lv Std.all
## 0.552 0.454 0.496
## 0.480 0.380 0.409
## 0.404 0.304 0.328
## 0.295 0.203 0.230
## 0.464 0.366 0.419
## 0.519 0.412 0.438
## 0.469 0.376 0.423
## 0.367 0.273 0.296
## 0.132 0.053 0.069
## 0.252 0.171 0.226
## 0.459 0.358 0.365
## 0.420 0.320 0.331
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.170 0.021 8.256 0.000 0.129
## .sswk 0.180 0.019 9.325 0.000 0.142
## .sspc 0.281 0.036 7.773 0.000 0.210
## .ssei 0.276 0.030 9.258 0.000 0.217
## .ssar 0.153 0.019 7.985 0.000 0.115
## .ssmk 0.186 0.024 7.592 0.000 0.138
## .ssmc 0.272 0.030 9.176 0.000 0.214
## .ssao 0.429 0.036 12.050 0.000 0.359
## .ssai 0.302 0.035 8.713 0.000 0.234
## .sssi 0.301 0.035 8.659 0.000 0.233
## .ssno 0.410 0.054 7.582 0.000 0.304
## .sscs 0.452 0.058 7.819 0.000 0.338
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.210 0.170 0.203
## 0.218 0.180 0.209
## 0.352 0.281 0.328
## 0.334 0.276 0.354
## 0.190 0.153 0.200
## 0.234 0.186 0.210
## 0.330 0.272 0.343
## 0.499 0.429 0.505
## 0.371 0.302 0.518
## 0.369 0.301 0.525
## 0.516 0.410 0.426
## 0.565 0.452 0.485
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.817 0.038 21.322 0.000 0.742
## sswk (.p2.) 0.826 0.041 20.300 0.000 0.747
## sspc (.p3.) 0.759 0.036 21.120 0.000 0.688
## ssei (.p4.) 0.444 0.045 9.973 0.000 0.357
## math =~
## ssar (.p5.) 0.781 0.039 20.070 0.000 0.705
## ssmk (.p6.) 0.515 0.076 6.775 0.000 0.366
## ssmc (.p7.) 0.722 0.040 18.065 0.000 0.644
## ssao (.p8.) 0.648 0.036 17.882 0.000 0.577
## electronic =~
## ssai (.p9.) 0.530 0.038 14.064 0.000 0.456
## sssi (.10.) 0.522 0.040 12.936 0.000 0.443
## ssei (.11.) 0.303 0.034 9.033 0.000 0.237
## speed =~
## ssno (.12.) 0.743 0.049 15.311 0.000 0.648
## sscs (.13.) 0.692 0.041 16.778 0.000 0.611
## ssmk (.14.) 0.368 0.078 4.715 0.000 0.215
## ci.upper Std.lv Std.all
##
## 0.892 0.890 0.894
## 0.906 0.900 0.885
## 0.829 0.826 0.835
## 0.531 0.484 0.443
##
## 0.858 0.848 0.877
## 0.664 0.559 0.574
## 0.800 0.784 0.797
## 0.720 0.704 0.691
##
## 0.604 0.908 0.798
## 0.601 0.894 0.853
## 0.369 0.520 0.476
##
## 0.838 0.799 0.752
## 0.773 0.745 0.735
## 0.522 0.396 0.406
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 1.093 0.130 8.380 0.000 0.838
## electronic 1.293 0.191 6.759 0.000 0.918
## speed 0.852 0.111 7.703 0.000 0.635
## math ~~
## electronic 1.251 0.199 6.275 0.000 0.861
## speed 0.923 0.115 8.003 0.000 0.697
## electronic ~~
## speed 0.652 0.156 4.176 0.000 0.346
## ci.upper Std.lv Std.all
##
## 1.349 0.925 0.925
## 1.668 0.693 0.693
## 1.068 0.727 0.727
##
## 1.642 0.673 0.673
## 1.149 0.790 0.790
##
## 0.958 0.354 0.354
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.454 0.050 9.095 0.000 0.356
## .sswk (.38.) 0.380 0.051 7.394 0.000 0.279
## .sspc (.39.) 0.304 0.051 5.960 0.000 0.204
## .ssei (.40.) 0.203 0.047 4.314 0.000 0.111
## .ssar (.41.) 0.366 0.050 7.329 0.000 0.268
## .ssmk (.42.) 0.412 0.054 7.581 0.000 0.306
## .ssmc (.43.) 0.376 0.047 7.975 0.000 0.284
## .ssao (.44.) 0.273 0.048 5.648 0.000 0.178
## .ssai (.45.) 0.053 0.041 1.289 0.197 -0.027
## .sssi (.46.) 0.171 0.041 4.150 0.000 0.090
## .ssno (.47.) 0.358 0.051 6.946 0.000 0.257
## .sscs (.48.) 0.320 0.051 6.246 0.000 0.219
## verbal -0.007 0.090 -0.081 0.936 -0.183
## math 0.067 0.093 0.724 0.469 -0.115
## elctrnc 1.243 0.154 8.088 0.000 0.942
## speed -0.439 0.102 -4.311 0.000 -0.638
## ci.upper Std.lv Std.all
## 0.552 0.454 0.456
## 0.480 0.380 0.373
## 0.404 0.304 0.307
## 0.295 0.203 0.186
## 0.464 0.366 0.379
## 0.519 0.412 0.422
## 0.469 0.376 0.382
## 0.367 0.273 0.268
## 0.132 0.053 0.046
## 0.252 0.171 0.163
## 0.459 0.358 0.337
## 0.420 0.320 0.315
## 0.169 -0.007 -0.007
## 0.250 0.062 0.062
## 1.544 0.725 0.725
## -0.239 -0.408 -0.408
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.199 0.024 8.355 0.000 0.153
## .sswk 0.224 0.022 10.256 0.000 0.181
## .sspc 0.298 0.037 8.103 0.000 0.226
## .ssei 0.341 0.036 9.508 0.000 0.271
## .ssar 0.216 0.028 7.732 0.000 0.161
## .ssmk 0.131 0.019 6.814 0.000 0.093
## .ssmc 0.354 0.036 9.701 0.000 0.282
## .ssao 0.541 0.053 10.213 0.000 0.437
## .ssai 0.470 0.058 8.135 0.000 0.357
## .sssi 0.300 0.048 6.290 0.000 0.206
## .ssno 0.490 0.062 7.857 0.000 0.368
## .sscs 0.472 0.081 5.803 0.000 0.312
## verbal 1.186 0.141 8.394 0.000 0.909
## math 1.179 0.154 7.677 0.000 0.878
## electronic 2.935 0.487 6.033 0.000 1.981
## speed 1.158 0.191 6.072 0.000 0.784
## ci.upper Std.lv Std.all
## 0.246 0.199 0.201
## 0.267 0.224 0.217
## 0.369 0.298 0.303
## 0.411 0.341 0.286
## 0.270 0.216 0.231
## 0.168 0.131 0.137
## 0.425 0.354 0.365
## 0.645 0.541 0.522
## 0.583 0.470 0.363
## 0.393 0.300 0.273
## 0.612 0.490 0.434
## 0.631 0.472 0.460
## 1.463 1.000 1.000
## 1.480 1.000 1.000
## 3.888 1.000 1.000
## 1.531 1.000 1.000
lavTestScore(scalar, release = 15:26)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 147.83 12 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p37. == .p89. 42.854 1 0.000
## 2 .p38. == .p90. 0.033 1 0.856
## 3 .p39. == .p91. 64.944 1 0.000
## 4 .p40. == .p92. 1.342 1 0.247
## 5 .p41. == .p93. 3.702 1 0.054
## 6 .p42. == .p94. 9.638 1 0.002
## 7 .p43. == .p95. 50.311 1 0.000
## 8 .p44. == .p96. 10.343 1 0.001
## 9 .p45. == .p97. 1.784 1 0.182
## 10 .p46. == .p98. 0.352 1 0.553
## 11 .p47. == .p99. 18.731 1 0.000
## 12 .p48. == .p100. 4.001 1 0.045
scalar2<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("ssmc~1", "sspc~1", "ssno~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 366.230 107.000 0.000 0.957 0.085 0.045 16408.421
## bic
## 16737.452
Mc(scalar2)
## [1] 0.8238685
summary(scalar2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 87 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 23
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 366.230 327.346
## Degrees of freedom 107 107
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.119
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 148.593 132.817
## 0 217.637 194.529
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.820 0.038 21.693 0.000 0.746
## sswk (.p2.) 0.821 0.041 20.122 0.000 0.741
## sspc (.p3.) 0.762 0.036 21.392 0.000 0.692
## ssei (.p4.) 0.462 0.045 10.237 0.000 0.374
## math =~
## ssar (.p5.) 0.782 0.039 20.269 0.000 0.707
## ssmk (.p6.) 0.579 0.056 10.361 0.000 0.469
## ssmc (.p7.) 0.720 0.038 18.740 0.000 0.645
## ssao (.p8.) 0.653 0.036 18.006 0.000 0.582
## electronic =~
## ssai (.p9.) 0.533 0.038 14.115 0.000 0.459
## sssi (.10.) 0.525 0.041 12.936 0.000 0.446
## ssei (.11.) 0.286 0.034 8.469 0.000 0.220
## speed =~
## ssno (.12.) 0.776 0.050 15.671 0.000 0.679
## sscs (.13.) 0.686 0.040 17.000 0.000 0.607
## ssmk (.14.) 0.298 0.053 5.608 0.000 0.194
## ci.upper Std.lv Std.all
##
## 0.894 0.820 0.897
## 0.901 0.821 0.885
## 0.832 0.762 0.833
## 0.551 0.462 0.523
##
## 0.858 0.782 0.894
## 0.688 0.579 0.618
## 0.796 0.720 0.818
## 0.724 0.653 0.707
##
## 0.607 0.533 0.698
## 0.605 0.525 0.694
## 0.352 0.286 0.324
##
## 0.873 0.776 0.785
## 0.765 0.686 0.718
## 0.403 0.298 0.319
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.924 0.018 50.324 0.000 0.888
## electronic 0.791 0.048 16.462 0.000 0.697
## speed 0.744 0.051 14.667 0.000 0.645
## math ~~
## electronic 0.762 0.051 15.024 0.000 0.662
## speed 0.765 0.055 13.871 0.000 0.657
## electronic ~~
## speed 0.487 0.086 5.642 0.000 0.318
## ci.upper Std.lv Std.all
##
## 0.960 0.924 0.924
## 0.885 0.791 0.791
## 0.844 0.744 0.744
##
## 0.861 0.762 0.762
## 0.873 0.765 0.765
##
## 0.656 0.487 0.487
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.413 0.050 8.306 0.000 0.316
## .sswk (.38.) 0.340 0.051 6.667 0.000 0.240
## .sspc 0.445 0.051 8.701 0.000 0.345
## .ssei (.40.) 0.192 0.046 4.152 0.000 0.102
## .ssar (.41.) 0.398 0.049 8.121 0.000 0.302
## .ssmk (.42.) 0.447 0.052 8.566 0.000 0.345
## .ssmc 0.263 0.048 5.462 0.000 0.169
## .ssao (.44.) 0.301 0.048 6.304 0.000 0.207
## .ssai (.45.) 0.056 0.041 1.363 0.173 -0.024
## .sssi (.46.) 0.175 0.041 4.218 0.000 0.093
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs (.48.) 0.359 0.051 6.980 0.000 0.258
## ci.upper Std.lv Std.all
## 0.511 0.413 0.452
## 0.440 0.340 0.367
## 0.545 0.445 0.487
## 0.283 0.192 0.218
## 0.494 0.398 0.455
## 0.549 0.447 0.477
## 0.358 0.263 0.299
## 0.394 0.301 0.326
## 0.136 0.056 0.073
## 0.256 0.175 0.230
## 0.395 0.285 0.289
## 0.460 0.359 0.376
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.163 0.019 8.392 0.000 0.125
## .sswk 0.186 0.020 9.410 0.000 0.147
## .sspc 0.255 0.031 8.117 0.000 0.194
## .ssei 0.277 0.030 9.318 0.000 0.219
## .ssar 0.154 0.019 8.268 0.000 0.117
## .ssmk 0.189 0.021 8.894 0.000 0.148
## .ssmc 0.256 0.027 9.635 0.000 0.204
## .ssao 0.426 0.035 12.008 0.000 0.356
## .ssai 0.300 0.035 8.602 0.000 0.231
## .sssi 0.298 0.035 8.554 0.000 0.230
## .ssno 0.374 0.050 7.524 0.000 0.276
## .sscs 0.442 0.055 7.986 0.000 0.334
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.201 0.163 0.195
## 0.224 0.186 0.216
## 0.317 0.255 0.305
## 0.336 0.277 0.354
## 0.190 0.154 0.201
## 0.231 0.189 0.216
## 0.309 0.256 0.331
## 0.495 0.426 0.500
## 0.368 0.300 0.513
## 0.366 0.298 0.519
## 0.471 0.374 0.383
## 0.551 0.442 0.485
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.820 0.038 21.693 0.000 0.746
## sswk (.p2.) 0.821 0.041 20.122 0.000 0.741
## sspc (.p3.) 0.762 0.036 21.392 0.000 0.692
## ssei (.p4.) 0.462 0.045 10.237 0.000 0.374
## math =~
## ssar (.p5.) 0.782 0.039 20.269 0.000 0.707
## ssmk (.p6.) 0.579 0.056 10.361 0.000 0.469
## ssmc (.p7.) 0.720 0.038 18.740 0.000 0.645
## ssao (.p8.) 0.653 0.036 18.006 0.000 0.582
## electronic =~
## ssai (.p9.) 0.533 0.038 14.115 0.000 0.459
## sssi (.10.) 0.525 0.041 12.936 0.000 0.446
## ssei (.11.) 0.286 0.034 8.469 0.000 0.220
## speed =~
## ssno (.12.) 0.776 0.050 15.671 0.000 0.679
## sscs (.13.) 0.686 0.040 17.000 0.000 0.607
## ssmk (.14.) 0.298 0.053 5.608 0.000 0.194
## ci.upper Std.lv Std.all
##
## 0.894 0.894 0.899
## 0.901 0.896 0.881
## 0.832 0.831 0.850
## 0.551 0.504 0.465
##
## 0.858 0.848 0.878
## 0.688 0.627 0.643
## 0.796 0.781 0.806
## 0.724 0.708 0.696
##
## 0.607 0.913 0.800
## 0.605 0.900 0.857
## 0.352 0.490 0.451
##
## 0.873 0.848 0.789
## 0.765 0.749 0.743
## 0.403 0.326 0.334
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 1.093 0.130 8.436 0.000 0.839
## electronic 1.281 0.190 6.732 0.000 0.908
## speed 0.838 0.112 7.503 0.000 0.619
## math ~~
## electronic 1.232 0.196 6.273 0.000 0.847
## speed 0.908 0.115 7.858 0.000 0.681
## electronic ~~
## speed 0.623 0.156 3.990 0.000 0.317
## ci.upper Std.lv Std.all
##
## 1.348 0.925 0.925
## 1.654 0.686 0.686
## 1.056 0.702 0.702
##
## 1.618 0.664 0.664
## 1.134 0.766 0.766
##
## 0.928 0.333 0.333
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.413 0.050 8.306 0.000 0.316
## .sswk (.38.) 0.340 0.051 6.667 0.000 0.240
## .sspc 0.063 0.058 1.093 0.274 -0.050
## .ssei (.40.) 0.192 0.046 4.152 0.000 0.102
## .ssar (.41.) 0.398 0.049 8.121 0.000 0.302
## .ssmk (.42.) 0.447 0.052 8.566 0.000 0.345
## .ssmc 0.601 0.059 10.115 0.000 0.485
## .ssao (.44.) 0.301 0.048 6.304 0.000 0.207
## .ssai (.45.) 0.056 0.041 1.363 0.173 -0.024
## .sssi (.46.) 0.175 0.041 4.218 0.000 0.093
## .ssno 0.559 0.078 7.126 0.000 0.405
## .sscs (.48.) 0.359 0.051 6.980 0.000 0.258
## verbal 0.104 0.089 1.167 0.243 -0.071
## math -0.033 0.092 -0.356 0.722 -0.212
## elctrnc 1.220 0.152 8.027 0.000 0.922
## speed -0.564 0.108 -5.212 0.000 -0.776
## ci.upper Std.lv Std.all
## 0.511 0.413 0.416
## 0.440 0.340 0.334
## 0.176 0.063 0.065
## 0.283 0.192 0.177
## 0.494 0.398 0.412
## 0.549 0.447 0.459
## 0.718 0.601 0.621
## 0.394 0.301 0.296
## 0.136 0.056 0.049
## 0.256 0.175 0.166
## 0.713 0.559 0.520
## 0.460 0.359 0.356
## 0.279 0.096 0.096
## 0.147 -0.030 -0.030
## 1.518 0.713 0.713
## -0.352 -0.516 -0.516
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.189 0.022 8.663 0.000 0.146
## .sswk 0.232 0.022 10.470 0.000 0.189
## .sspc 0.265 0.030 8.746 0.000 0.205
## .ssei 0.345 0.036 9.643 0.000 0.275
## .ssar 0.214 0.027 7.985 0.000 0.162
## .ssmk 0.137 0.016 8.401 0.000 0.105
## .ssmc 0.328 0.032 10.161 0.000 0.265
## .ssao 0.534 0.052 10.324 0.000 0.432
## .ssai 0.467 0.058 8.006 0.000 0.353
## .sssi 0.293 0.048 6.088 0.000 0.199
## .ssno 0.437 0.055 7.947 0.000 0.329
## .sscs 0.455 0.073 6.244 0.000 0.312
## verbal 1.191 0.141 8.444 0.000 0.914
## math 1.175 0.151 7.763 0.000 0.878
## electronic 2.932 0.488 6.006 0.000 1.975
## speed 1.194 0.194 6.165 0.000 0.815
## ci.upper Std.lv Std.all
## 0.232 0.189 0.191
## 0.276 0.232 0.225
## 0.324 0.265 0.277
## 0.416 0.345 0.293
## 0.267 0.214 0.230
## 0.169 0.137 0.144
## 0.392 0.328 0.350
## 0.635 0.534 0.516
## 0.582 0.467 0.359
## 0.387 0.293 0.266
## 0.545 0.437 0.378
## 0.598 0.455 0.448
## 1.467 1.000 1.000
## 1.471 1.000 1.000
## 3.888 1.000 1.000
## 1.574 1.000 1.000
lavTestScore(scalar2, release = 15:23, standardized=T, epc=T) # with only ssmc and sspc the fit was not satisfactory and other subtests have similar X2 values, but ssno had the highest value in sepc.all earlier
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 18.763 9 0.027
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p37. == .p89. 12.088 1 0.001
## 2 .p38. == .p90. 13.259 1 0.000
## 3 .p40. == .p92. 0.098 1 0.754
## 4 .p41. == .p93. 2.713 1 0.100
## 5 .p42. == .p94. 0.014 1 0.906
## 6 .p44. == .p96. 3.917 1 0.048
## 7 .p45. == .p97. 1.187 1 0.276
## 8 .p46. == .p98. 0.775 1 0.379
## 9 .p48. == .p100. 0.014 1 0.906
##
## $epc
##
## expected parameter changes (epc) and expected parameter values (epv):
##
## lhs op rhs block group free label plabel est epc
## 1 verbal =~ ssgs 1 1 1 .p1. .p1. 0.820 -0.002
## 2 verbal =~ sswk 1 1 2 .p2. .p2. 0.821 0.002
## 3 verbal =~ sspc 1 1 3 .p3. .p3. 0.762 0.000
## 4 verbal =~ ssei 1 1 4 .p4. .p4. 0.462 0.005
## 5 math =~ ssar 1 1 5 .p5. .p5. 0.782 0.000
## 6 math =~ ssmk 1 1 6 .p6. .p6. 0.579 0.002
## 7 math =~ ssmc 1 1 7 .p7. .p7. 0.720 0.000
## 8 math =~ ssao 1 1 8 .p8. .p8. 0.653 -0.001
## 9 electronic =~ ssai 1 1 9 .p9. .p9. 0.533 0.008
## 10 electronic =~ sssi 1 1 10 .p10. .p10. 0.525 -0.007
## 11 electronic =~ ssei 1 1 11 .p11. .p11. 0.286 -0.006
## 12 speed =~ ssno 1 1 12 .p12. .p12. 0.776 0.000
## 13 speed =~ sscs 1 1 13 .p13. .p13. 0.686 0.001
## 14 speed =~ ssmk 1 1 14 .p14. .p14. 0.298 -0.002
## 15 ssgs ~~ ssgs 1 1 15 .p15. 0.163 0.000
## 16 sswk ~~ sswk 1 1 16 .p16. 0.186 0.000
## 17 sspc ~~ sspc 1 1 17 .p17. 0.255 0.000
## 18 ssei ~~ ssei 1 1 18 .p18. 0.277 0.001
## 19 ssar ~~ ssar 1 1 19 .p19. 0.154 0.000
## 20 ssmk ~~ ssmk 1 1 20 .p20. 0.189 0.000
## 21 ssmc ~~ ssmc 1 1 21 .p21. 0.256 0.000
## 22 ssao ~~ ssao 1 1 22 .p22. 0.426 0.000
## 23 ssai ~~ ssai 1 1 23 .p23. 0.300 -0.003
## 24 sssi ~~ sssi 1 1 24 .p24. 0.298 0.002
## 25 ssno ~~ ssno 1 1 25 .p25. 0.374 0.000
## 26 sscs ~~ sscs 1 1 26 .p26. 0.442 0.000
## 27 verbal ~~ verbal 1 1 0 .p27. 1.000 NA
## 28 math ~~ math 1 1 0 .p28. 1.000 NA
## 29 electronic ~~ electronic 1 1 0 .p29. 1.000 NA
## 30 speed ~~ speed 1 1 0 .p30. 1.000 NA
## 31 verbal ~~ math 1 1 27 .p31. 0.924 0.000
## 32 verbal ~~ electronic 1 1 28 .p32. 0.791 -0.001
## 33 verbal ~~ speed 1 1 29 .p33. 0.744 0.000
## 34 math ~~ electronic 1 1 30 .p34. 0.762 -0.001
## 35 math ~~ speed 1 1 31 .p35. 0.765 0.000
## 36 electronic ~~ speed 1 1 32 .p36. 0.487 -0.001
## 37 ssgs ~1 1 1 33 .p37. .p37. 0.413 -0.036
## 38 sswk ~1 1 1 34 .p38. .p38. 0.340 0.042
## 39 sspc ~1 1 1 35 .p39. 0.445 0.000
## 40 ssei ~1 1 1 36 .p40. .p40. 0.192 -0.004
## 41 ssar ~1 1 1 37 .p41. .p41. 0.398 -0.013
## 42 ssmk ~1 1 1 38 .p42. .p42. 0.447 0.001
## 43 ssmc ~1 1 1 39 .p43. 0.263 0.000
## 44 ssao ~1 1 1 40 .p44. .p44. 0.301 0.042
## 45 ssai ~1 1 1 41 .p45. .p45. 0.056 0.014
## 46 sssi ~1 1 1 42 .p46. .p46. 0.175 -0.011
## 47 ssno ~1 1 1 43 .p47. 0.285 0.000
## 48 sscs ~1 1 1 44 .p48. .p48. 0.359 -0.001
## 49 verbal ~1 1 1 0 .p49. 0.000 NA
## 50 math ~1 1 1 0 .p50. 0.000 NA
## 51 electronic ~1 1 1 0 .p51. 0.000 NA
## 52 speed ~1 1 1 0 .p52. 0.000 NA
## 53 verbal =~ ssgs 2 2 45 .p1. .p53. 0.820 -0.002
## 54 verbal =~ sswk 2 2 46 .p2. .p54. 0.821 0.002
## 55 verbal =~ sspc 2 2 47 .p3. .p55. 0.762 0.000
## 56 verbal =~ ssei 2 2 48 .p4. .p56. 0.462 0.005
## 57 math =~ ssar 2 2 49 .p5. .p57. 0.782 0.000
## 58 math =~ ssmk 2 2 50 .p6. .p58. 0.579 0.002
## 59 math =~ ssmc 2 2 51 .p7. .p59. 0.720 0.000
## 60 math =~ ssao 2 2 52 .p8. .p60. 0.653 -0.001
## 61 electronic =~ ssai 2 2 53 .p9. .p61. 0.533 0.008
## 62 electronic =~ sssi 2 2 54 .p10. .p62. 0.525 -0.007
## 63 electronic =~ ssei 2 2 55 .p11. .p63. 0.286 -0.006
## 64 speed =~ ssno 2 2 56 .p12. .p64. 0.776 0.000
## 65 speed =~ sscs 2 2 57 .p13. .p65. 0.686 0.001
## 66 speed =~ ssmk 2 2 58 .p14. .p66. 0.298 -0.002
## 67 ssgs ~~ ssgs 2 2 59 .p67. 0.189 0.001
## 68 sswk ~~ sswk 2 2 60 .p68. 0.232 0.000
## 69 sspc ~~ sspc 2 2 61 .p69. 0.265 0.000
## 70 ssei ~~ ssei 2 2 62 .p70. 0.345 0.001
## 71 ssar ~~ ssar 2 2 63 .p71. 0.214 0.000
## epv sepc.lv sepc.all sepc.nox
## 1 0.818 -0.002 -0.002 -0.002
## 2 0.823 0.002 0.003 0.003
## 3 0.762 0.000 0.000 0.000
## 4 0.467 0.005 0.006 0.006
## 5 0.782 0.000 0.000 0.000
## 6 0.580 0.002 0.002 0.002
## 7 0.720 0.000 0.000 0.000
## 8 0.652 -0.001 -0.001 -0.001
## 9 0.541 0.008 0.010 0.010
## 10 0.519 -0.007 -0.009 -0.009
## 11 0.280 -0.006 -0.007 -0.007
## 12 0.776 0.000 0.000 0.000
## 13 0.686 0.001 0.001 0.001
## 14 0.296 -0.002 -0.002 -0.002
## 15 0.164 0.163 0.195 0.195
## 16 0.185 -0.186 -0.216 -0.216
## 17 0.255 0.255 0.305 0.305
## 18 0.278 0.277 0.354 0.354
## 19 0.154 0.154 0.201 0.201
## 20 0.189 0.189 0.216 0.216
## 21 0.257 0.256 0.331 0.331
## 22 0.426 0.426 0.500 0.500
## 23 0.296 -0.300 -0.513 -0.513
## 24 0.300 0.298 0.519 0.519
## 25 0.373 -0.374 -0.383 -0.383
## 26 0.442 -0.442 -0.485 -0.485
## 27 NA NA NA NA
## 28 NA NA NA NA
## 29 NA NA NA NA
## 30 NA NA NA NA
## 31 0.924 0.000 0.000 0.000
## 32 0.790 -0.001 -0.001 -0.001
## 33 0.744 0.000 0.000 0.000
## 34 0.761 -0.001 -0.001 -0.001
## 35 0.765 0.000 0.000 0.000
## 36 0.486 -0.001 -0.001 -0.001
## 37 0.378 -0.036 -0.039 -0.039
## 38 0.382 0.042 0.045 0.045
## 39 0.445 0.000 0.000 0.000
## 40 0.188 -0.004 -0.005 -0.005
## 41 0.384 -0.013 -0.015 -0.015
## 42 0.448 0.001 0.001 0.001
## 43 0.263 0.000 0.000 0.000
## 44 0.343 0.042 0.045 0.045
## 45 0.069 0.014 0.018 0.018
## 46 0.163 -0.011 -0.015 -0.015
## 47 0.285 0.000 0.000 0.000
## 48 0.358 -0.001 -0.001 -0.001
## 49 NA NA NA NA
## 50 NA NA NA NA
## 51 NA NA NA NA
## 52 NA NA NA NA
## 53 0.818 -0.002 -0.002 -0.002
## 54 0.823 0.003 0.003 0.003
## 55 0.762 0.000 0.000 0.000
## 56 0.467 0.006 0.005 0.005
## 57 0.782 0.000 0.000 0.000
## 58 0.580 0.002 0.002 0.002
## 59 0.720 0.000 0.000 0.000
## 60 0.652 -0.001 -0.001 -0.001
## 61 0.541 0.014 0.012 0.012
## 62 0.519 -0.011 -0.011 -0.011
## 63 0.280 -0.010 -0.009 -0.009
## 64 0.776 0.000 0.000 0.000
## 65 0.686 0.001 0.001 0.001
## 66 0.296 -0.002 -0.002 -0.002
## 67 0.190 0.189 0.191 0.191
## 68 0.232 -0.232 -0.225 -0.225
## 69 0.265 0.265 0.277 0.277
## 70 0.346 0.345 0.293 0.293
## 71 0.214 0.214 0.230 0.230
## [ reached 'max' / getOption("max.print") -- omitted 33 rows ]
strict<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("ssmc~1", "sspc~1", "ssno~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 398.883 119.000 0.000 0.953 0.084 0.049 16417.074
## bic
## 16692.018
Mc(strict)
## [1] 0.811249
cf.cov<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("ssmc~1", "sspc~1", "ssno~1"))
fitMeasures(cf.cov, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 384.354 113.000 0.000 0.955 0.085 0.084 16414.545
## bic
## 16716.532
Mc(cf.cov)
## [1] 0.8164369
summary(cf.cov, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 65 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 29
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 384.354 342.964
## Degrees of freedom 113 113
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.121
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 153.607 137.065
## 0 230.747 205.898
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.852 0.032 27.020 0.000 0.790
## sswk (.p2.) 0.853 0.034 25.241 0.000 0.787
## sspc (.p3.) 0.794 0.029 27.497 0.000 0.737
## ssei (.p4.) 0.482 0.044 10.848 0.000 0.395
## math =~
## ssar (.p5.) 0.818 0.033 25.103 0.000 0.754
## ssmk (.p6.) 0.608 0.053 11.418 0.000 0.504
## ssmc (.p7.) 0.751 0.035 21.720 0.000 0.683
## ssao (.p8.) 0.683 0.032 21.422 0.000 0.621
## electronic =~
## ssai (.p9.) 0.587 0.037 15.902 0.000 0.514
## sssi (.10.) 0.589 0.039 15.006 0.000 0.512
## ssei (.11.) 0.316 0.037 8.657 0.000 0.245
## speed =~
## ssno (.12.) 0.796 0.047 17.053 0.000 0.704
## sscs (.13.) 0.703 0.041 17.046 0.000 0.623
## ssmk (.14.) 0.303 0.050 6.094 0.000 0.205
## ci.upper Std.lv Std.all
##
## 0.914 0.852 0.904
## 0.920 0.853 0.893
## 0.851 0.794 0.843
## 0.569 0.482 0.519
##
## 0.882 0.818 0.901
## 0.713 0.608 0.629
## 0.819 0.751 0.829
## 0.746 0.683 0.723
##
## 0.659 0.587 0.728
## 0.666 0.589 0.737
## 0.388 0.316 0.341
##
## 0.887 0.796 0.793
## 0.784 0.703 0.725
## 0.400 0.303 0.313
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.31.) 0.927 0.017 55.872 0.000 0.895
## elctrnc (.32.) 0.827 0.037 22.550 0.000 0.755
## speed (.33.) 0.742 0.044 16.804 0.000 0.655
## math ~~
## elctrnc (.34.) 0.795 0.040 20.088 0.000 0.717
## speed (.35.) 0.778 0.045 17.313 0.000 0.690
## electronic ~~
## speed (.36.) 0.480 0.071 6.738 0.000 0.341
## ci.upper Std.lv Std.all
##
## 0.960 0.927 0.927
## 0.899 0.827 0.827
## 0.829 0.742 0.742
##
## 0.872 0.795 0.795
## 0.866 0.778 0.778
##
## 0.620 0.480 0.480
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.413 0.050 8.303 0.000 0.316
## .sswk (.38.) 0.340 0.051 6.671 0.000 0.240
## .sspc 0.445 0.051 8.701 0.000 0.345
## .ssei (.40.) 0.193 0.046 4.163 0.000 0.102
## .ssar (.41.) 0.398 0.049 8.135 0.000 0.302
## .ssmk (.42.) 0.446 0.052 8.559 0.000 0.344
## .ssmc 0.263 0.048 5.461 0.000 0.169
## .ssao (.44.) 0.301 0.048 6.307 0.000 0.207
## .ssai (.45.) 0.058 0.041 1.413 0.158 -0.022
## .sssi (.46.) 0.172 0.041 4.151 0.000 0.091
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs (.48.) 0.360 0.051 6.995 0.000 0.259
## ci.upper Std.lv Std.all
## 0.511 0.413 0.438
## 0.440 0.340 0.356
## 0.545 0.445 0.472
## 0.284 0.193 0.208
## 0.494 0.398 0.439
## 0.549 0.446 0.461
## 0.358 0.263 0.291
## 0.394 0.301 0.319
## 0.138 0.058 0.072
## 0.253 0.172 0.215
## 0.395 0.285 0.284
## 0.461 0.360 0.371
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.163 0.019 8.436 0.000 0.125
## .sswk 0.185 0.020 9.374 0.000 0.147
## .sspc 0.256 0.031 8.142 0.000 0.195
## .ssei 0.277 0.030 9.392 0.000 0.220
## .ssar 0.155 0.018 8.405 0.000 0.119
## .ssmk 0.188 0.021 8.882 0.000 0.146
## .ssmc 0.257 0.026 9.752 0.000 0.206
## .ssao 0.425 0.035 12.027 0.000 0.356
## .ssai 0.305 0.035 8.795 0.000 0.237
## .sssi 0.291 0.034 8.508 0.000 0.224
## .ssno 0.374 0.050 7.514 0.000 0.276
## .sscs 0.446 0.056 7.992 0.000 0.336
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.201 0.163 0.183
## 0.224 0.185 0.203
## 0.318 0.256 0.289
## 0.335 0.277 0.322
## 0.191 0.155 0.188
## 0.229 0.188 0.200
## 0.309 0.257 0.313
## 0.495 0.425 0.477
## 0.373 0.305 0.470
## 0.358 0.291 0.456
## 0.472 0.374 0.371
## 0.555 0.446 0.474
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.852 0.032 27.020 0.000 0.790
## sswk (.p2.) 0.853 0.034 25.241 0.000 0.787
## sspc (.p3.) 0.794 0.029 27.497 0.000 0.737
## ssei (.p4.) 0.482 0.044 10.848 0.000 0.395
## math =~
## ssar (.p5.) 0.818 0.033 25.103 0.000 0.754
## ssmk (.p6.) 0.608 0.053 11.418 0.000 0.504
## ssmc (.p7.) 0.751 0.035 21.720 0.000 0.683
## ssao (.p8.) 0.683 0.032 21.422 0.000 0.621
## electronic =~
## ssai (.p9.) 0.587 0.037 15.902 0.000 0.514
## sssi (.10.) 0.589 0.039 15.006 0.000 0.512
## ssei (.11.) 0.316 0.037 8.657 0.000 0.245
## speed =~
## ssno (.12.) 0.796 0.047 17.053 0.000 0.704
## sscs (.13.) 0.703 0.041 17.046 0.000 0.623
## ssmk (.14.) 0.303 0.050 6.094 0.000 0.205
## ci.upper Std.lv Std.all
##
## 0.914 0.862 0.891
## 0.920 0.863 0.872
## 0.851 0.803 0.845
## 0.569 0.488 0.479
##
## 0.882 0.812 0.870
## 0.713 0.604 0.640
## 0.819 0.746 0.791
## 0.746 0.678 0.681
##
## 0.659 0.824 0.767
## 0.666 0.827 0.842
## 0.388 0.445 0.436
##
## 0.887 0.830 0.784
## 0.784 0.734 0.739
## 0.400 0.316 0.335
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.31.) 0.927 0.017 55.872 0.000 0.895
## elctrnc (.32.) 0.827 0.037 22.550 0.000 0.755
## speed (.33.) 0.742 0.044 16.804 0.000 0.655
## math ~~
## elctrnc (.34.) 0.795 0.040 20.088 0.000 0.717
## speed (.35.) 0.778 0.045 17.313 0.000 0.690
## electronic ~~
## speed (.36.) 0.480 0.071 6.738 0.000 0.341
## ci.upper Std.lv Std.all
##
## 0.960 0.923 0.923
## 0.899 0.582 0.582
## 0.829 0.703 0.703
##
## 0.872 0.570 0.570
## 0.866 0.751 0.751
##
## 0.620 0.328 0.328
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.413 0.050 8.303 0.000 0.316
## .sswk (.38.) 0.340 0.051 6.671 0.000 0.240
## .sspc 0.063 0.058 1.086 0.277 -0.051
## .ssei (.40.) 0.193 0.046 4.163 0.000 0.102
## .ssar (.41.) 0.398 0.049 8.135 0.000 0.302
## .ssmk (.42.) 0.446 0.052 8.559 0.000 0.344
## .ssmc 0.602 0.059 10.141 0.000 0.486
## .ssao (.44.) 0.301 0.048 6.307 0.000 0.207
## .ssai (.45.) 0.058 0.041 1.413 0.158 -0.022
## .sssi (.46.) 0.172 0.041 4.151 0.000 0.091
## .ssno 0.560 0.078 7.157 0.000 0.407
## .sscs (.48.) 0.360 0.051 6.995 0.000 0.259
## verbal 0.100 0.086 1.165 0.244 -0.069
## math -0.032 0.087 -0.364 0.716 -0.203
## elctrnc 1.100 0.138 7.956 0.000 0.829
## speed -0.551 0.106 -5.205 0.000 -0.759
## ci.upper Std.lv Std.all
## 0.511 0.413 0.427
## 0.440 0.340 0.344
## 0.176 0.063 0.066
## 0.284 0.193 0.189
## 0.494 0.398 0.427
## 0.549 0.446 0.473
## 0.718 0.602 0.638
## 0.394 0.301 0.302
## 0.138 0.058 0.054
## 0.253 0.172 0.175
## 0.714 0.560 0.529
## 0.461 0.360 0.362
## 0.269 0.099 0.099
## 0.139 -0.032 -0.032
## 1.370 0.783 0.783
## -0.344 -0.529 -0.529
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.192 0.022 8.706 0.000 0.149
## .sswk 0.235 0.023 10.431 0.000 0.191
## .sspc 0.258 0.029 8.806 0.000 0.200
## .ssei 0.350 0.036 9.656 0.000 0.279
## .ssar 0.211 0.027 7.959 0.000 0.159
## .ssmk 0.139 0.016 8.630 0.000 0.108
## .ssmc 0.332 0.032 10.373 0.000 0.270
## .ssao 0.532 0.052 10.312 0.000 0.431
## .ssai 0.476 0.060 7.883 0.000 0.358
## .sssi 0.280 0.049 5.730 0.000 0.184
## .ssno 0.432 0.055 7.853 0.000 0.324
## .sscs 0.448 0.071 6.298 0.000 0.308
## verbal 1.024 0.039 25.915 0.000 0.946
## math 0.986 0.040 24.926 0.000 0.909
## electronic 1.974 0.239 8.270 0.000 1.506
## speed 1.088 0.129 8.411 0.000 0.834
## ci.upper Std.lv Std.all
## 0.236 0.192 0.206
## 0.279 0.235 0.240
## 0.315 0.258 0.285
## 0.421 0.350 0.337
## 0.263 0.211 0.242
## 0.171 0.139 0.156
## 0.395 0.332 0.374
## 0.633 0.532 0.536
## 0.595 0.476 0.412
## 0.376 0.280 0.290
## 0.540 0.432 0.385
## 0.587 0.448 0.454
## 1.101 1.000 1.000
## 1.064 1.000 1.000
## 2.442 1.000 1.000
## 1.341 1.000 1.000
cf.vcov<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances", "lv.variances"), group.partial=c("ssmc~1", "sspc~1", "ssno~1"))
fitMeasures(cf.vcov, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 433.382 117.000 0.000 0.947 0.090 0.106 16455.573
## bic
## 16739.531
Mc(cf.vcov)
## [1] 0.7894185
summary(cf.vcov, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 51 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 92
## Number of equality constraints 29
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 433.382 385.769
## Degrees of freedom 117 117
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.123
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 183.784 163.593
## 0 249.597 222.175
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.857 0.031 28.034 0.000 0.797
## sswk (.p2.) 0.859 0.032 26.527 0.000 0.795
## sspc (.p3.) 0.795 0.028 28.299 0.000 0.740
## ssei (.p4.) 0.495 0.044 11.261 0.000 0.409
## math =~
## ssar (.p5.) 0.815 0.032 25.454 0.000 0.752
## ssmk (.p6.) 0.598 0.054 10.979 0.000 0.491
## ssmc (.p7.) 0.752 0.034 22.084 0.000 0.685
## ssao (.p8.) 0.680 0.030 22.466 0.000 0.621
## electronic =~
## ssai (.p9.) 0.707 0.038 18.805 0.000 0.633
## sssi (.10.) 0.729 0.035 20.556 0.000 0.660
## ssei (.11.) 0.376 0.043 8.766 0.000 0.292
## speed =~
## ssno (.12.) 0.811 0.044 18.551 0.000 0.725
## sscs (.13.) 0.717 0.037 19.186 0.000 0.643
## ssmk (.14.) 0.318 0.052 6.102 0.000 0.216
## ci.upper Std.lv Std.all
##
## 0.917 0.857 0.906
## 0.922 0.859 0.895
## 0.851 0.795 0.844
## 0.581 0.495 0.512
##
## 0.878 0.815 0.902
## 0.705 0.598 0.618
## 0.819 0.752 0.828
## 0.740 0.680 0.722
##
## 0.781 0.707 0.798
## 0.799 0.729 0.825
## 0.460 0.376 0.388
##
## 0.896 0.811 0.801
## 0.790 0.717 0.733
## 0.421 0.318 0.329
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.31.) 0.925 0.012 75.930 0.000 0.901
## elctrnc (.32.) 0.719 0.032 22.297 0.000 0.656
## speed (.33.) 0.723 0.037 19.687 0.000 0.651
## math ~~
## elctrnc (.34.) 0.694 0.036 19.230 0.000 0.624
## speed (.35.) 0.765 0.037 20.572 0.000 0.692
## electronic ~~
## speed (.36.) 0.384 0.057 6.727 0.000 0.272
## ci.upper Std.lv Std.all
##
## 0.948 0.925 0.925
## 0.782 0.719 0.719
## 0.795 0.723 0.723
##
## 0.765 0.694 0.694
## 0.837 0.765 0.765
##
## 0.495 0.384 0.384
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.412 0.050 8.272 0.000 0.315
## .sswk (.38.) 0.340 0.051 6.650 0.000 0.240
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei (.40.) 0.196 0.046 4.253 0.000 0.106
## .ssar (.41.) 0.397 0.049 8.119 0.000 0.301
## .ssmk (.42.) 0.448 0.052 8.583 0.000 0.346
## .ssmc 0.263 0.048 5.461 0.000 0.169
## .ssao (.44.) 0.300 0.048 6.293 0.000 0.207
## .ssai (.45.) 0.062 0.041 1.507 0.132 -0.019
## .sssi (.46.) 0.166 0.041 4.010 0.000 0.085
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs (.48.) 0.358 0.051 6.963 0.000 0.257
## ci.upper Std.lv Std.all
## 0.510 0.412 0.436
## 0.440 0.340 0.354
## 0.545 0.445 0.472
## 0.287 0.196 0.203
## 0.493 0.397 0.440
## 0.550 0.448 0.463
## 0.358 0.263 0.290
## 0.394 0.300 0.319
## 0.142 0.062 0.070
## 0.247 0.166 0.188
## 0.395 0.285 0.282
## 0.459 0.358 0.366
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.161 0.019 8.360 0.000 0.123
## .sswk 0.183 0.020 9.389 0.000 0.145
## .sspc 0.256 0.031 8.194 0.000 0.195
## .ssei 0.282 0.029 9.583 0.000 0.224
## .ssar 0.152 0.018 8.506 0.000 0.117
## .ssmk 0.187 0.021 8.697 0.000 0.145
## .ssmc 0.258 0.027 9.717 0.000 0.206
## .ssao 0.426 0.035 12.007 0.000 0.356
## .ssai 0.286 0.035 8.146 0.000 0.217
## .sssi 0.250 0.035 7.190 0.000 0.182
## .ssno 0.366 0.051 7.247 0.000 0.267
## .sscs 0.443 0.056 7.972 0.000 0.334
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.198 0.161 0.179
## 0.221 0.183 0.199
## 0.317 0.256 0.288
## 0.340 0.282 0.302
## 0.187 0.152 0.186
## 0.229 0.187 0.199
## 0.311 0.258 0.314
## 0.495 0.426 0.479
## 0.355 0.286 0.364
## 0.318 0.250 0.320
## 0.466 0.366 0.358
## 0.552 0.443 0.463
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.857 0.031 28.034 0.000 0.797
## sswk (.p2.) 0.859 0.032 26.527 0.000 0.795
## sspc (.p3.) 0.795 0.028 28.299 0.000 0.740
## ssei (.p4.) 0.495 0.044 11.261 0.000 0.409
## math =~
## ssar (.p5.) 0.815 0.032 25.454 0.000 0.752
## ssmk (.p6.) 0.598 0.054 10.979 0.000 0.491
## ssmc (.p7.) 0.752 0.034 22.084 0.000 0.685
## ssao (.p8.) 0.680 0.030 22.466 0.000 0.621
## electronic =~
## ssai (.p9.) 0.707 0.038 18.805 0.000 0.633
## sssi (.10.) 0.729 0.035 20.556 0.000 0.660
## ssei (.11.) 0.376 0.043 8.766 0.000 0.292
## speed =~
## ssno (.12.) 0.811 0.044 18.551 0.000 0.725
## sscs (.13.) 0.717 0.037 19.186 0.000 0.643
## ssmk (.14.) 0.318 0.052 6.102 0.000 0.216
## ci.upper Std.lv Std.all
##
## 0.917 0.857 0.891
## 0.922 0.859 0.871
## 0.851 0.795 0.838
## 0.581 0.495 0.492
##
## 0.878 0.815 0.868
## 0.705 0.598 0.634
## 0.819 0.752 0.798
## 0.740 0.680 0.682
##
## 0.781 0.707 0.697
## 0.799 0.729 0.785
## 0.460 0.376 0.374
##
## 0.896 0.811 0.772
## 0.790 0.717 0.727
## 0.421 0.318 0.338
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.31.) 0.925 0.012 75.930 0.000 0.901
## elctrnc (.32.) 0.719 0.032 22.297 0.000 0.656
## speed (.33.) 0.723 0.037 19.687 0.000 0.651
## math ~~
## elctrnc (.34.) 0.694 0.036 19.230 0.000 0.624
## speed (.35.) 0.765 0.037 20.572 0.000 0.692
## electronic ~~
## speed (.36.) 0.384 0.057 6.727 0.000 0.272
## ci.upper Std.lv Std.all
##
## 0.948 0.925 0.925
## 0.782 0.719 0.719
## 0.795 0.723 0.723
##
## 0.765 0.694 0.694
## 0.837 0.765 0.765
##
## 0.495 0.384 0.384
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.412 0.050 8.272 0.000 0.315
## .sswk (.38.) 0.340 0.051 6.650 0.000 0.240
## .sspc 0.062 0.058 1.070 0.284 -0.051
## .ssei (.40.) 0.196 0.046 4.253 0.000 0.106
## .ssar (.41.) 0.397 0.049 8.119 0.000 0.301
## .ssmk (.42.) 0.448 0.052 8.583 0.000 0.346
## .ssmc 0.601 0.060 10.084 0.000 0.484
## .ssao (.44.) 0.300 0.048 6.293 0.000 0.207
## .ssai (.45.) 0.062 0.041 1.507 0.132 -0.019
## .sssi (.46.) 0.166 0.041 4.010 0.000 0.085
## .ssno 0.557 0.078 7.127 0.000 0.404
## .sscs (.48.) 0.358 0.051 6.963 0.000 0.257
## verbal 0.101 0.086 1.180 0.238 -0.067
## math -0.030 0.088 -0.342 0.733 -0.202
## elctrnc 0.901 0.101 8.897 0.000 0.703
## speed -0.537 0.104 -5.161 0.000 -0.741
## ci.upper Std.lv Std.all
## 0.510 0.412 0.428
## 0.440 0.340 0.345
## 0.175 0.062 0.065
## 0.287 0.196 0.195
## 0.493 0.397 0.423
## 0.550 0.448 0.475
## 0.717 0.601 0.637
## 0.394 0.300 0.301
## 0.142 0.062 0.061
## 0.247 0.166 0.179
## 0.710 0.557 0.530
## 0.459 0.358 0.364
## 0.270 0.101 0.101
## 0.142 -0.030 -0.030
## 1.100 0.901 0.901
## -0.333 -0.537 -0.537
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.192 0.021 9.002 0.000 0.150
## .sswk 0.234 0.022 10.527 0.000 0.191
## .sspc 0.268 0.030 8.941 0.000 0.209
## .ssei 0.357 0.037 9.582 0.000 0.284
## .ssar 0.217 0.027 8.119 0.000 0.165
## .ssmk 0.139 0.016 8.508 0.000 0.107
## .ssmc 0.323 0.031 10.272 0.000 0.261
## .ssao 0.534 0.052 10.330 0.000 0.432
## .ssai 0.529 0.062 8.498 0.000 0.407
## .sssi 0.330 0.051 6.504 0.000 0.231
## .ssno 0.445 0.057 7.784 0.000 0.333
## .sscs 0.457 0.072 6.348 0.000 0.316
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.233 0.192 0.207
## 0.278 0.234 0.241
## 0.327 0.268 0.297
## 0.430 0.357 0.353
## 0.270 0.217 0.246
## 0.172 0.139 0.157
## 0.384 0.323 0.363
## 0.635 0.534 0.535
## 0.652 0.529 0.514
## 0.430 0.330 0.383
## 0.557 0.445 0.404
## 0.598 0.457 0.471
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
cf.cov2<-cfa(cf.lv, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("ssmc~1", "sspc~1", "ssno~1"))
fitMeasures(cf.cov2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 384.732 115.000 0.000 0.955 0.084 0.084 16410.923
## bic
## 16703.896
Mc(cf.cov2)
## [1] 0.817427
summary(cf.cov2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 58 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 94
## Number of equality constraints 29
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 384.732 343.639
## Degrees of freedom 115 115
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.120
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 153.845 137.413
## 0 230.887 206.226
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.856 0.031 27.912 0.000 0.796
## sswk (.p2.) 0.858 0.032 26.546 0.000 0.795
## sspc (.p3.) 0.798 0.028 28.571 0.000 0.743
## ssei (.p4.) 0.484 0.045 10.806 0.000 0.396
## math =~
## ssar (.p5.) 0.816 0.032 25.488 0.000 0.753
## ssmk (.p6.) 0.606 0.053 11.545 0.000 0.503
## ssmc (.p7.) 0.749 0.034 22.129 0.000 0.682
## ssao (.p8.) 0.681 0.030 22.532 0.000 0.622
## electronic =~
## ssai (.p9.) 0.587 0.037 15.938 0.000 0.515
## sssi (.10.) 0.589 0.039 15.051 0.000 0.512
## ssei (.11.) 0.317 0.037 8.666 0.000 0.245
## speed =~
## ssno (.12.) 0.795 0.047 17.061 0.000 0.704
## sscs (.13.) 0.703 0.041 17.049 0.000 0.622
## ssmk (.14.) 0.303 0.049 6.142 0.000 0.206
## ci.upper Std.lv Std.all
##
## 0.916 0.856 0.905
## 0.921 0.858 0.895
## 0.853 0.798 0.844
## 0.572 0.484 0.520
##
## 0.878 0.816 0.901
## 0.709 0.606 0.628
## 0.815 0.749 0.828
## 0.740 0.681 0.722
##
## 0.659 0.587 0.728
## 0.665 0.589 0.737
## 0.388 0.317 0.340
##
## 0.886 0.795 0.792
## 0.784 0.703 0.725
## 0.399 0.303 0.313
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.31.) 0.925 0.012 76.140 0.000 0.901
## elctrnc (.32.) 0.826 0.037 22.516 0.000 0.754
## speed (.33.) 0.738 0.044 16.731 0.000 0.652
## math ~~
## elctrnc (.34.) 0.796 0.039 20.439 0.000 0.719
## speed (.35.) 0.780 0.044 17.674 0.000 0.693
## electronic ~~
## speed (.36.) 0.481 0.071 6.756 0.000 0.341
## ci.upper Std.lv Std.all
##
## 0.949 0.925 0.925
## 0.898 0.826 0.826
## 0.825 0.738 0.738
##
## 0.872 0.796 0.796
## 0.866 0.780 0.780
##
## 0.621 0.481 0.481
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.413 0.050 8.301 0.000 0.315
## .sswk (.38.) 0.341 0.051 6.674 0.000 0.241
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei (.40.) 0.193 0.046 4.163 0.000 0.102
## .ssar (.41.) 0.398 0.049 8.140 0.000 0.302
## .ssmk (.42.) 0.446 0.052 8.557 0.000 0.344
## .ssmc 0.263 0.048 5.461 0.000 0.169
## .ssao (.44.) 0.301 0.048 6.303 0.000 0.207
## .ssai (.45.) 0.058 0.041 1.411 0.158 -0.022
## .sssi (.46.) 0.172 0.041 4.154 0.000 0.091
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs (.48.) 0.360 0.051 6.995 0.000 0.259
## ci.upper Std.lv Std.all
## 0.510 0.413 0.436
## 0.441 0.341 0.355
## 0.545 0.445 0.471
## 0.284 0.193 0.207
## 0.494 0.398 0.440
## 0.549 0.446 0.462
## 0.358 0.263 0.291
## 0.394 0.301 0.319
## 0.138 0.058 0.072
## 0.253 0.172 0.215
## 0.395 0.285 0.285
## 0.461 0.360 0.371
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.162 0.019 8.494 0.000 0.125
## .sswk 0.184 0.019 9.458 0.000 0.146
## .sspc 0.257 0.031 8.154 0.000 0.195
## .ssei 0.278 0.030 9.402 0.000 0.220
## .ssar 0.155 0.018 8.521 0.000 0.119
## .ssmk 0.188 0.021 8.857 0.000 0.146
## .ssmc 0.258 0.026 9.763 0.000 0.206
## .ssao 0.425 0.035 12.042 0.000 0.356
## .ssai 0.305 0.035 8.790 0.000 0.237
## .sssi 0.291 0.034 8.504 0.000 0.224
## .ssno 0.375 0.050 7.520 0.000 0.277
## .sscs 0.446 0.056 7.985 0.000 0.337
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.199 0.162 0.181
## 0.222 0.184 0.200
## 0.319 0.257 0.287
## 0.335 0.278 0.321
## 0.190 0.155 0.189
## 0.229 0.188 0.201
## 0.309 0.258 0.315
## 0.494 0.425 0.478
## 0.373 0.305 0.469
## 0.358 0.291 0.456
## 0.472 0.375 0.372
## 0.555 0.446 0.474
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.856 0.031 27.912 0.000 0.796
## sswk (.p2.) 0.858 0.032 26.546 0.000 0.795
## sspc (.p3.) 0.798 0.028 28.571 0.000 0.743
## ssei (.p4.) 0.484 0.045 10.806 0.000 0.396
## math =~
## ssar (.p5.) 0.816 0.032 25.488 0.000 0.753
## ssmk (.p6.) 0.606 0.053 11.545 0.000 0.503
## ssmc (.p7.) 0.749 0.034 22.129 0.000 0.682
## ssao (.p8.) 0.681 0.030 22.532 0.000 0.622
## electronic =~
## ssai (.p9.) 0.587 0.037 15.938 0.000 0.515
## sssi (.10.) 0.589 0.039 15.051 0.000 0.512
## ssei (.11.) 0.317 0.037 8.666 0.000 0.245
## speed =~
## ssno (.12.) 0.795 0.047 17.061 0.000 0.704
## sscs (.13.) 0.703 0.041 17.049 0.000 0.622
## ssmk (.14.) 0.303 0.049 6.142 0.000 0.206
## ci.upper Std.lv Std.all
##
## 0.916 0.856 0.889
## 0.921 0.858 0.870
## 0.853 0.798 0.844
## 0.572 0.484 0.476
##
## 0.878 0.816 0.872
## 0.709 0.606 0.641
## 0.815 0.749 0.792
## 0.740 0.681 0.683
##
## 0.659 0.825 0.767
## 0.665 0.827 0.842
## 0.388 0.445 0.437
##
## 0.886 0.830 0.784
## 0.784 0.735 0.740
## 0.399 0.316 0.335
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.31.) 0.925 0.012 76.140 0.000 0.901
## elctrnc (.32.) 0.826 0.037 22.516 0.000 0.754
## speed (.33.) 0.738 0.044 16.731 0.000 0.652
## math ~~
## elctrnc (.34.) 0.796 0.039 20.439 0.000 0.719
## speed (.35.) 0.780 0.044 17.674 0.000 0.693
## electronic ~~
## speed (.36.) 0.481 0.071 6.756 0.000 0.341
## ci.upper Std.lv Std.all
##
## 0.949 0.925 0.925
## 0.898 0.588 0.588
## 0.825 0.707 0.707
##
## 0.872 0.566 0.566
## 0.866 0.747 0.747
##
## 0.621 0.328 0.328
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.413 0.050 8.301 0.000 0.315
## .sswk (.38.) 0.341 0.051 6.674 0.000 0.241
## .sspc 0.063 0.058 1.088 0.276 -0.050
## .ssei (.40.) 0.193 0.046 4.163 0.000 0.102
## .ssar (.41.) 0.398 0.049 8.140 0.000 0.302
## .ssmk (.42.) 0.446 0.052 8.557 0.000 0.344
## .ssmc 0.602 0.059 10.144 0.000 0.486
## .ssao (.44.) 0.301 0.048 6.303 0.000 0.207
## .ssai (.45.) 0.058 0.041 1.411 0.158 -0.022
## .sssi (.46.) 0.172 0.041 4.154 0.000 0.091
## .ssno 0.560 0.078 7.159 0.000 0.407
## .sscs (.48.) 0.360 0.051 6.995 0.000 0.259
## verbal 0.100 0.086 1.163 0.245 -0.068
## math -0.032 0.088 -0.364 0.716 -0.203
## elctrnc 1.099 0.138 7.963 0.000 0.829
## speed -0.552 0.106 -5.204 0.000 -0.759
## ci.upper Std.lv Std.all
## 0.510 0.413 0.429
## 0.441 0.341 0.345
## 0.176 0.063 0.067
## 0.284 0.193 0.189
## 0.494 0.398 0.426
## 0.549 0.446 0.472
## 0.718 0.602 0.637
## 0.394 0.301 0.301
## 0.138 0.058 0.054
## 0.253 0.172 0.175
## 0.713 0.560 0.529
## 0.461 0.360 0.362
## 0.268 0.100 0.100
## 0.140 -0.032 -0.032
## 1.370 0.782 0.782
## -0.344 -0.528 -0.528
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.194 0.022 8.940 0.000 0.152
## .sswk 0.236 0.023 10.448 0.000 0.192
## .sspc 0.258 0.029 8.867 0.000 0.201
## .ssei 0.350 0.036 9.641 0.000 0.279
## .ssar 0.210 0.026 7.999 0.000 0.159
## .ssmk 0.139 0.016 8.672 0.000 0.108
## .ssmc 0.333 0.032 10.363 0.000 0.270
## .ssao 0.532 0.052 10.322 0.000 0.431
## .ssai 0.475 0.060 7.903 0.000 0.358
## .sssi 0.281 0.049 5.779 0.000 0.186
## .ssno 0.431 0.055 7.858 0.000 0.324
## .sscs 0.446 0.071 6.279 0.000 0.307
## electronic 1.975 0.238 8.308 0.000 1.509
## speed 1.091 0.129 8.458 0.000 0.838
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.237 0.194 0.209
## 0.281 0.236 0.243
## 0.315 0.258 0.288
## 0.422 0.350 0.338
## 0.262 0.210 0.240
## 0.171 0.139 0.156
## 0.395 0.333 0.372
## 0.633 0.532 0.534
## 0.593 0.475 0.411
## 0.377 0.281 0.291
## 0.539 0.431 0.385
## 0.586 0.446 0.453
## 2.441 1.000 1.000
## 1.344 1.000 1.000
reduced<-cfa(cf.reduced, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("ssmc~1", "sspc~1", "ssno~1"))
fitMeasures(reduced, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 391.261 117.000 0.000 0.954 0.084 0.085 16413.452
## bic
## 16697.411
Mc(reduced)
## [1] 0.8146647
summary(reduced, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 56 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 92
## Number of equality constraints 29
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 391.261 349.119
## Degrees of freedom 117 117
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.121
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 156.917 140.016
## 0 234.344 209.103
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.856 0.031 27.829 0.000 0.795
## sswk (.p2.) 0.861 0.032 26.977 0.000 0.798
## sspc (.p3.) 0.799 0.028 28.592 0.000 0.744
## ssei (.p4.) 0.477 0.044 10.769 0.000 0.390
## math =~
## ssar (.p5.) 0.816 0.032 25.498 0.000 0.753
## ssmk (.p6.) 0.594 0.051 11.555 0.000 0.493
## ssmc (.p7.) 0.749 0.034 22.140 0.000 0.683
## ssao (.p8.) 0.680 0.030 22.425 0.000 0.621
## electronic =~
## ssai (.p9.) 0.586 0.037 15.945 0.000 0.514
## sssi (.10.) 0.588 0.039 15.059 0.000 0.511
## ssei (.11.) 0.324 0.036 8.948 0.000 0.253
## speed =~
## ssno (.12.) 0.794 0.046 17.131 0.000 0.703
## sscs (.13.) 0.699 0.041 17.156 0.000 0.619
## ssmk (.14.) 0.317 0.047 6.687 0.000 0.224
## ci.upper Std.lv Std.all
##
## 0.916 0.856 0.904
## 0.923 0.861 0.896
## 0.853 0.799 0.844
## 0.564 0.477 0.513
##
## 0.879 0.816 0.901
## 0.694 0.594 0.614
## 0.816 0.749 0.828
## 0.739 0.680 0.721
##
## 0.658 0.586 0.727
## 0.664 0.588 0.736
## 0.395 0.324 0.348
##
## 0.885 0.794 0.791
## 0.779 0.699 0.722
## 0.410 0.317 0.328
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.33.) 0.923 0.012 75.054 0.000 0.899
## elctrnc (.34.) 0.827 0.037 22.569 0.000 0.755
## speed (.35.) 0.741 0.044 16.949 0.000 0.656
## math ~~
## elctrnc (.36.) 0.798 0.039 20.602 0.000 0.722
## speed (.37.) 0.781 0.044 17.682 0.000 0.694
## electronic ~~
## speed (.38.) 0.484 0.071 6.832 0.000 0.345
## ci.upper Std.lv Std.all
##
## 0.947 0.923 0.923
## 0.899 0.827 0.827
## 0.827 0.741 0.741
##
## 0.874 0.798 0.798
## 0.867 0.781 0.781
##
## 0.623 0.484 0.484
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 0.000 0.000
## math 0.000 0.000
## .ssgs (.39.) 0.453 0.038 11.951 0.000 0.379
## .sswk (.40.) 0.380 0.039 9.830 0.000 0.305
## .sspc 0.468 0.043 10.835 0.000 0.384
## .ssei (.42.) 0.217 0.039 5.507 0.000 0.140
## .ssar (.43.) 0.389 0.037 10.612 0.000 0.317
## .ssmk (.44.) 0.447 0.042 10.715 0.000 0.365
## .ssmc 0.269 0.041 6.528 0.000 0.188
## .ssao (.46.) 0.292 0.039 7.501 0.000 0.216
## .ssai (.47.) 0.068 0.038 1.782 0.075 -0.007
## .sssi (.48.) 0.182 0.038 4.728 0.000 0.107
## .ssno 0.293 0.052 5.608 0.000 0.190
## .sscs (.50.) 0.374 0.048 7.754 0.000 0.280
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.527 0.453 0.478
## 0.456 0.380 0.396
## 0.553 0.468 0.495
## 0.295 0.217 0.234
## 0.460 0.389 0.429
## 0.529 0.447 0.462
## 0.350 0.269 0.298
## 0.368 0.292 0.310
## 0.143 0.068 0.085
## 0.257 0.182 0.228
## 0.395 0.293 0.292
## 0.469 0.374 0.386
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.164 0.019 8.494 0.000 0.126
## .sswk 0.181 0.019 9.471 0.000 0.144
## .sspc 0.257 0.031 8.150 0.000 0.195
## .ssei 0.277 0.030 9.387 0.000 0.219
## .ssar 0.154 0.018 8.479 0.000 0.119
## .ssmk 0.187 0.021 8.719 0.000 0.145
## .ssmc 0.257 0.026 9.742 0.000 0.205
## .ssao 0.426 0.035 12.055 0.000 0.357
## .ssai 0.306 0.035 8.831 0.000 0.238
## .sssi 0.292 0.034 8.535 0.000 0.225
## .ssno 0.377 0.050 7.463 0.000 0.278
## .sscs 0.449 0.056 8.020 0.000 0.339
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.202 0.164 0.183
## 0.219 0.181 0.197
## 0.318 0.257 0.287
## 0.335 0.277 0.320
## 0.190 0.154 0.188
## 0.229 0.187 0.200
## 0.309 0.257 0.314
## 0.496 0.426 0.480
## 0.374 0.306 0.471
## 0.359 0.292 0.458
## 0.476 0.377 0.374
## 0.558 0.449 0.478
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.856 0.031 27.829 0.000 0.795
## sswk (.p2.) 0.861 0.032 26.977 0.000 0.798
## sspc (.p3.) 0.799 0.028 28.592 0.000 0.744
## ssei (.p4.) 0.477 0.044 10.769 0.000 0.390
## math =~
## ssar (.p5.) 0.816 0.032 25.498 0.000 0.753
## ssmk (.p6.) 0.594 0.051 11.555 0.000 0.493
## ssmc (.p7.) 0.749 0.034 22.140 0.000 0.683
## ssao (.p8.) 0.680 0.030 22.425 0.000 0.621
## electronic =~
## ssai (.p9.) 0.586 0.037 15.945 0.000 0.514
## sssi (.10.) 0.588 0.039 15.059 0.000 0.511
## ssei (.11.) 0.324 0.036 8.948 0.000 0.253
## speed =~
## ssno (.12.) 0.794 0.046 17.131 0.000 0.703
## sscs (.13.) 0.699 0.041 17.156 0.000 0.619
## ssmk (.14.) 0.317 0.047 6.687 0.000 0.224
## ci.upper Std.lv Std.all
##
## 0.916 0.856 0.887
## 0.923 0.861 0.872
## 0.853 0.799 0.844
## 0.564 0.477 0.468
##
## 0.879 0.816 0.872
## 0.694 0.594 0.628
## 0.816 0.749 0.793
## 0.739 0.680 0.681
##
## 0.658 0.823 0.767
## 0.664 0.825 0.840
## 0.395 0.455 0.447
##
## 0.885 0.828 0.782
## 0.779 0.730 0.736
## 0.410 0.331 0.350
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.33.) 0.923 0.012 75.054 0.000 0.899
## elctrnc (.34.) 0.827 0.037 22.569 0.000 0.755
## speed (.35.) 0.741 0.044 16.949 0.000 0.656
## math ~~
## elctrnc (.36.) 0.798 0.039 20.602 0.000 0.722
## speed (.37.) 0.781 0.044 17.682 0.000 0.694
## electronic ~~
## speed (.38.) 0.484 0.071 6.832 0.000 0.345
## ci.upper Std.lv Std.all
##
## 0.947 0.923 0.923
## 0.899 0.589 0.589
## 0.827 0.710 0.710
##
## 0.874 0.568 0.568
## 0.867 0.748 0.748
##
## 0.623 0.330 0.330
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 0.000 0.000
## math 0.000 0.000
## .ssgs (.39.) 0.453 0.038 11.951 0.000 0.379
## .sswk (.40.) 0.380 0.039 9.830 0.000 0.305
## .sspc 0.118 0.045 2.656 0.008 0.031
## .ssei (.42.) 0.217 0.039 5.507 0.000 0.140
## .ssar (.43.) 0.389 0.037 10.612 0.000 0.317
## .ssmk (.44.) 0.447 0.042 10.715 0.000 0.365
## .ssmc 0.572 0.047 12.243 0.000 0.480
## .ssao (.46.) 0.292 0.039 7.501 0.000 0.216
## .ssai (.47.) 0.068 0.038 1.782 0.075 -0.007
## .sssi (.48.) 0.182 0.038 4.728 0.000 0.107
## .ssno 0.587 0.076 7.776 0.000 0.439
## .sscs (.50.) 0.374 0.048 7.754 0.000 0.280
## elctrnc 1.066 0.118 9.053 0.000 0.835
## speed -0.596 0.090 -6.615 0.000 -0.773
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.527 0.453 0.470
## 0.456 0.380 0.385
## 0.205 0.118 0.125
## 0.295 0.217 0.213
## 0.460 0.389 0.415
## 0.529 0.447 0.473
## 0.663 0.572 0.605
## 0.368 0.292 0.293
## 0.143 0.068 0.063
## 0.257 0.182 0.185
## 0.735 0.587 0.555
## 0.469 0.374 0.377
## 1.296 0.759 0.759
## -0.420 -0.571 -0.571
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.198 0.022 8.884 0.000 0.154
## .sswk 0.233 0.022 10.469 0.000 0.189
## .sspc 0.257 0.029 8.850 0.000 0.200
## .ssei 0.349 0.036 9.584 0.000 0.277
## .ssar 0.209 0.026 7.954 0.000 0.158
## .ssmk 0.138 0.016 8.570 0.000 0.107
## .ssmc 0.332 0.032 10.334 0.000 0.269
## .ssao 0.534 0.052 10.288 0.000 0.433
## .ssai 0.476 0.060 7.954 0.000 0.359
## .sssi 0.284 0.048 5.862 0.000 0.189
## .ssno 0.435 0.055 7.890 0.000 0.327
## .sscs 0.450 0.071 6.358 0.000 0.312
## electronic 1.973 0.237 8.327 0.000 1.509
## speed 1.089 0.127 8.595 0.000 0.841
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.241 0.198 0.213
## 0.276 0.233 0.239
## 0.314 0.257 0.287
## 0.420 0.349 0.335
## 0.261 0.209 0.239
## 0.170 0.138 0.155
## 0.394 0.332 0.371
## 0.636 0.534 0.536
## 0.593 0.476 0.412
## 0.378 0.284 0.294
## 0.543 0.435 0.388
## 0.589 0.450 0.458
## 2.438 1.000 1.000
## 1.337 1.000 1.000
tests<-lavTestLRT(configural, metric, scalar2, cf.cov, cf.cov2, reduced)
Td=tests[2:6,"Chisq diff"]
Td
## [1] 12.7510927 17.1569424 15.6979297 0.3578327 5.5073741
dfd=tests[2:6,"Df diff"]
dfd
## [1] 10 5 6 2 2
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-335+ 335 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
## Warning in sqrt((ld) * G/(N - G)): NaNs produced
RMSEAD
## [1] 0.02869983 0.08532061 0.06956498 NaN 0.07246075
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] NA 0.06946337
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.04336727 0.13106688
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.02806026 0.11258857
RMSEA.CI(T=Td[4],df=dfd[4],N=N,G=2)
## [1] NA 0.06227855
RMSEA.CI(T=Td[5],df=dfd[5],N=N,G=2)
## [1] NA 0.1478016
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.762 0.737 0.235 0.118 0.015 0.001
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.996 0.995 0.922 0.856 0.627 0.332
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.985 0.981 0.813 0.694 0.386 0.133
round(pvals(T=Td[4],df=dfd[4],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.164 0.159 0.076 0.055 0.023 0.008
round(pvals(T=Td[5],df=dfd[5],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.936 0.930 0.774 0.699 0.517 0.327
tests<-lavTestLRT(configural, metric, scalar2, strict)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 12.75109 17.15694 25.93510
dfd=tests[2:4,"Df diff"]
dfd
## [1] 10 5 12
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-335+ 335 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.02869983 0.08532061 0.05896458
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] NA 0.06946337
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.04336727 0.13106688
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.02710490 0.09017467
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.762 0.737 0.235 0.118 0.015 0.001
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.996 0.995 0.922 0.856 0.627 0.332
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.989 0.986 0.716 0.518 0.145 0.014
tests<-lavTestLRT(configural, metric, scalar)
Td=tests[2:3,"Chisq diff"]
Td
## [1] 12.75109 155.04825
dfd=tests[2:3,"Df diff"]
dfd
## [1] 10 8
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-335+ 335 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.02869983 0.23459119
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] NA 0.06946337
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.2029950 0.2673459
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.762 0.737 0.235 0.118 0.015 0.001
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
# ONE FACTOR, just for checking if gap direction aligns with HOF
fmodel<-'
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
configural<-cfa(fmodel, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 845.152 108.000 0.000 0.877 0.143 0.063 16885.343
## bic
## 17209.867
Mc(configural)
## [1] 0.5764103
metric<-cfa(fmodel, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 875.029 119.000 0.000 0.874 0.138 0.077 16893.219
## bic
## 17168.163
Mc(metric)
## [1] 0.5683352
scalar<-cfa(fmodel, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1345.680 130.000 0.000 0.798 0.167 0.102 17341.871
## bic
## 17567.235
Mc(scalar)
## [1] 0.4030963
summary(scalar, standardized=T, ci=T) # g=-0.038
## lavaan 0.6-18 ended normally after 42 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 74
## Number of equality constraints 24
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1345.680 1201.213
## Degrees of freedom 130 130
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.120
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 487.066 434.777
## 0 858.614 766.436
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g =~
## ssgs (.p1.) 0.786 0.039 20.199 0.000 0.710
## ssar (.p2.) 0.751 0.039 19.446 0.000 0.675
## sswk (.p3.) 0.789 0.041 19.314 0.000 0.709
## sspc (.p4.) 0.749 0.035 21.213 0.000 0.680
## ssno (.p5.) 0.581 0.043 13.496 0.000 0.497
## sscs (.p6.) 0.556 0.038 14.473 0.000 0.481
## ssai (.p7.) 0.482 0.040 12.078 0.000 0.404
## sssi (.p8.) 0.479 0.040 11.868 0.000 0.400
## ssmk (.p9.) 0.781 0.038 20.746 0.000 0.708
## ssmc (.10.) 0.706 0.039 18.266 0.000 0.630
## ssei (.11.) 0.706 0.041 17.374 0.000 0.627
## ssao (.12.) 0.623 0.035 17.750 0.000 0.554
## ci.upper Std.lv Std.all
##
## 0.862 0.786 0.869
## 0.827 0.751 0.864
## 0.869 0.789 0.855
## 0.819 0.749 0.814
## 0.666 0.581 0.591
## 0.631 0.556 0.575
## 0.561 0.482 0.599
## 0.558 0.479 0.578
## 0.855 0.781 0.837
## 0.782 0.706 0.797
## 0.786 0.706 0.778
## 0.692 0.623 0.674
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.26.) 0.434 0.050 8.702 0.000 0.336
## .ssar (.27.) 0.374 0.048 7.713 0.000 0.279
## .sswk (.28.) 0.362 0.051 7.074 0.000 0.262
## .sspc (.29.) 0.284 0.052 5.449 0.000 0.182
## .ssno (.30.) 0.202 0.050 4.063 0.000 0.105
## .sscs (.31.) 0.167 0.049 3.448 0.001 0.072
## .ssai (.32.) 0.225 0.044 5.177 0.000 0.140
## .sssi (.33.) 0.374 0.049 7.562 0.000 0.277
## .ssmk (.34.) 0.331 0.053 6.214 0.000 0.227
## .ssmc (.35.) 0.385 0.046 8.392 0.000 0.295
## .ssei (.36.) 0.322 0.050 6.421 0.000 0.224
## .ssao (.37.) 0.278 0.047 5.907 0.000 0.186
## ci.upper Std.lv Std.all
## 0.532 0.434 0.480
## 0.469 0.374 0.430
## 0.463 0.362 0.393
## 0.387 0.284 0.309
## 0.300 0.202 0.205
## 0.262 0.167 0.173
## 0.311 0.225 0.280
## 0.471 0.374 0.451
## 0.435 0.331 0.355
## 0.475 0.385 0.435
## 0.421 0.322 0.355
## 0.370 0.278 0.301
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.199 0.021 9.454 0.000 0.158
## .ssar 0.192 0.019 10.285 0.000 0.155
## .sswk 0.228 0.023 9.999 0.000 0.183
## .sspc 0.286 0.036 7.980 0.000 0.216
## .ssno 0.630 0.079 8.012 0.000 0.476
## .sscs 0.627 0.066 9.455 0.000 0.497
## .ssai 0.417 0.042 10.029 0.000 0.335
## .sssi 0.458 0.046 9.978 0.000 0.368
## .ssmk 0.260 0.025 10.557 0.000 0.212
## .ssmc 0.286 0.028 10.255 0.000 0.231
## .ssei 0.325 0.035 9.331 0.000 0.257
## .ssao 0.466 0.037 12.607 0.000 0.394
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.241 0.199 0.244
## 0.228 0.192 0.254
## 0.273 0.228 0.268
## 0.356 0.286 0.337
## 0.784 0.630 0.651
## 0.757 0.627 0.670
## 0.498 0.417 0.642
## 0.548 0.458 0.666
## 0.309 0.260 0.299
## 0.341 0.286 0.365
## 0.394 0.325 0.395
## 0.539 0.466 0.546
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g =~
## ssgs (.p1.) 0.786 0.039 20.199 0.000 0.710
## ssar (.p2.) 0.751 0.039 19.446 0.000 0.675
## sswk (.p3.) 0.789 0.041 19.314 0.000 0.709
## sspc (.p4.) 0.749 0.035 21.213 0.000 0.680
## ssno (.p5.) 0.581 0.043 13.496 0.000 0.497
## sscs (.p6.) 0.556 0.038 14.473 0.000 0.481
## ssai (.p7.) 0.482 0.040 12.078 0.000 0.404
## sssi (.p8.) 0.479 0.040 11.868 0.000 0.400
## ssmk (.p9.) 0.781 0.038 20.746 0.000 0.708
## ssmc (.10.) 0.706 0.039 18.266 0.000 0.630
## ssei (.11.) 0.706 0.041 17.374 0.000 0.627
## ssao (.12.) 0.623 0.035 17.750 0.000 0.554
## ci.upper Std.lv Std.all
##
## 0.862 0.873 0.866
## 0.827 0.834 0.858
## 0.869 0.876 0.855
## 0.819 0.833 0.837
## 0.666 0.646 0.594
## 0.631 0.618 0.598
## 0.561 0.536 0.451
## 0.558 0.532 0.488
## 0.855 0.868 0.878
## 0.782 0.784 0.792
## 0.786 0.785 0.722
## 0.692 0.692 0.681
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.26.) 0.434 0.050 8.702 0.000 0.336
## .ssar (.27.) 0.374 0.048 7.713 0.000 0.279
## .sswk (.28.) 0.362 0.051 7.074 0.000 0.262
## .sspc (.29.) 0.284 0.052 5.449 0.000 0.182
## .ssno (.30.) 0.202 0.050 4.063 0.000 0.105
## .sscs (.31.) 0.167 0.049 3.448 0.001 0.072
## .ssai (.32.) 0.225 0.044 5.177 0.000 0.140
## .sssi (.33.) 0.374 0.049 7.562 0.000 0.277
## .ssmk (.34.) 0.331 0.053 6.214 0.000 0.227
## .ssmc (.35.) 0.385 0.046 8.392 0.000 0.295
## .ssei (.36.) 0.322 0.050 6.421 0.000 0.224
## .ssao (.37.) 0.278 0.047 5.907 0.000 0.186
## g 0.043 0.092 0.462 0.644 -0.138
## ci.upper Std.lv Std.all
## 0.532 0.434 0.431
## 0.469 0.374 0.385
## 0.463 0.362 0.354
## 0.387 0.284 0.286
## 0.300 0.202 0.186
## 0.262 0.167 0.162
## 0.311 0.225 0.190
## 0.471 0.374 0.343
## 0.435 0.331 0.335
## 0.475 0.385 0.389
## 0.421 0.322 0.297
## 0.370 0.278 0.274
## 0.224 0.038 0.038
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.254 0.025 10.175 0.000 0.205
## .ssar 0.250 0.027 9.241 0.000 0.197
## .sswk 0.282 0.027 10.550 0.000 0.230
## .sspc 0.297 0.033 8.976 0.000 0.232
## .ssno 0.764 0.086 8.853 0.000 0.594
## .sscs 0.685 0.084 8.170 0.000 0.521
## .ssai 1.122 0.121 9.256 0.000 0.885
## .sssi 0.903 0.104 8.653 0.000 0.698
## .ssmk 0.225 0.024 9.184 0.000 0.177
## .ssmc 0.366 0.034 10.835 0.000 0.300
## .ssei 0.565 0.067 8.392 0.000 0.433
## .ssao 0.553 0.052 10.559 0.000 0.450
## g 1.234 0.147 8.404 0.000 0.946
## ci.upper Std.lv Std.all
## 0.303 0.254 0.250
## 0.303 0.250 0.264
## 0.335 0.282 0.269
## 0.361 0.297 0.300
## 0.933 0.764 0.647
## 0.849 0.685 0.642
## 1.360 1.122 0.796
## 1.107 0.903 0.761
## 0.273 0.225 0.230
## 0.432 0.366 0.373
## 0.697 0.565 0.478
## 0.656 0.553 0.536
## 1.522 1.000 1.000
# HIGH ORDER FACTOR
hof.model<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
'
hof.lv<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
'
hof.weak<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
verbal~0*1
'
baseline<-cfa(hof.model, data=dgroup, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 437.113 48.000 0.000 0.936 0.110 0.054 16825.001
## bic
## 17014.307
Mc(baseline)
## [1] 0.7476525
configural<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 369.108 96.000 0.000 0.955 0.092 0.042 16433.298
## bic
## 16811.910
Mc(configural)
## [1] 0.8153676
summary(configural, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 112 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 84
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 369.108 332.122
## Degrees of freedom 96 96
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.111
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 143.679 129.281
## 0 225.429 202.840
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.201 0.066 3.049 0.002 0.072
## sswk 0.206 0.067 3.052 0.002 0.074
## sspc 0.187 0.059 3.171 0.002 0.072
## ssei 0.129 0.044 2.957 0.003 0.043
## math =~
## ssar 0.236 0.054 4.363 0.000 0.130
## ssmk 0.182 0.049 3.705 0.000 0.086
## ssmc 0.208 0.048 4.304 0.000 0.114
## ssao 0.196 0.049 4.033 0.000 0.101
## electronic =~
## ssai 0.339 0.039 8.650 0.000 0.263
## sssi 0.360 0.047 7.614 0.000 0.267
## ssei 0.113 0.042 2.667 0.008 0.030
## speed =~
## ssno 0.513 0.073 6.996 0.000 0.369
## sscs 0.432 0.062 7.022 0.000 0.312
## ssmk 0.209 0.048 4.312 0.000 0.114
## g =~
## verbal 3.947 1.355 2.913 0.004 1.291
## math 3.161 0.796 3.970 0.000 1.600
## electronic 1.228 0.193 6.368 0.000 0.850
## speed 1.149 0.208 5.518 0.000 0.741
## ci.upper Std.lv Std.all
##
## 0.330 0.817 0.897
## 0.338 0.837 0.892
## 0.303 0.762 0.834
## 0.214 0.524 0.611
##
## 0.342 0.782 0.895
## 0.278 0.602 0.626
## 0.303 0.691 0.806
## 0.292 0.651 0.706
##
## 0.416 0.538 0.711
## 0.452 0.570 0.730
## 0.196 0.179 0.209
##
## 0.656 0.781 0.791
## 0.553 0.658 0.702
## 0.304 0.318 0.331
##
## 6.602 0.969 0.969
## 4.721 0.953 0.953
## 1.605 0.775 0.775
## 1.557 0.754 0.754
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.378 0.051 7.429 0.000 0.278
## .sswk 0.382 0.052 7.278 0.000 0.279
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei 0.188 0.048 3.908 0.000 0.094
## .ssar 0.384 0.049 7.810 0.000 0.288
## .ssmk 0.448 0.054 8.275 0.000 0.342
## .ssmc 0.263 0.048 5.461 0.000 0.169
## .ssao 0.343 0.052 6.596 0.000 0.241
## .ssai 0.069 0.043 1.625 0.104 -0.014
## .sssi 0.163 0.044 3.736 0.000 0.078
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs 0.358 0.053 6.754 0.000 0.254
## ci.upper Std.lv Std.all
## 0.478 0.378 0.415
## 0.485 0.382 0.407
## 0.545 0.445 0.487
## 0.283 0.188 0.220
## 0.481 0.384 0.440
## 0.554 0.448 0.466
## 0.358 0.263 0.307
## 0.444 0.343 0.372
## 0.153 0.069 0.092
## 0.249 0.163 0.209
## 0.395 0.285 0.289
## 0.462 0.358 0.382
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.161 0.019 8.325 0.000 0.123
## .sswk 0.181 0.019 9.439 0.000 0.143
## .sspc 0.255 0.033 7.825 0.000 0.191
## .ssei 0.287 0.030 9.625 0.000 0.228
## .ssar 0.151 0.018 8.228 0.000 0.115
## .ssmk 0.184 0.022 8.206 0.000 0.140
## .ssmc 0.257 0.026 9.731 0.000 0.206
## .ssao 0.425 0.036 11.775 0.000 0.354
## .ssai 0.283 0.036 7.862 0.000 0.213
## .sssi 0.284 0.036 7.927 0.000 0.214
## .ssno 0.365 0.050 7.310 0.000 0.267
## .sscs 0.446 0.057 7.855 0.000 0.334
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.199 0.161 0.195
## 0.218 0.181 0.205
## 0.319 0.255 0.305
## 0.345 0.287 0.390
## 0.187 0.151 0.198
## 0.228 0.184 0.199
## 0.309 0.257 0.350
## 0.496 0.425 0.501
## 0.354 0.283 0.495
## 0.354 0.284 0.466
## 0.462 0.365 0.374
## 0.557 0.446 0.507
## 1.000 0.060 0.060
## 1.000 0.091 0.091
## 1.000 0.399 0.399
## 1.000 0.431 0.431
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.286 0.053 5.366 0.000 0.181
## sswk 0.282 0.051 5.584 0.000 0.183
## sspc 0.268 0.049 5.453 0.000 0.172
## ssei 0.170 0.039 4.397 0.000 0.094
## math =~
## ssar 0.163 0.080 2.039 0.041 0.006
## ssmk 0.115 0.058 1.971 0.049 0.001
## ssmc 0.158 0.078 2.040 0.041 0.006
## ssao 0.137 0.067 2.042 0.041 0.006
## electronic =~
## ssai 0.711 0.052 13.635 0.000 0.609
## sssi 0.631 0.047 13.438 0.000 0.539
## ssei 0.378 0.053 7.148 0.000 0.274
## speed =~
## ssno 0.564 0.069 8.171 0.000 0.429
## sscs 0.529 0.054 9.796 0.000 0.423
## ssmk 0.224 0.042 5.293 0.000 0.141
## g =~
## verbal 2.957 0.614 4.816 0.000 1.754
## math 5.103 2.606 1.958 0.050 -0.005
## electronic 0.909 0.113 8.046 0.000 0.687
## speed 1.119 0.151 7.415 0.000 0.824
## ci.upper Std.lv Std.all
##
## 0.390 0.892 0.898
## 0.381 0.881 0.876
## 0.365 0.837 0.857
## 0.246 0.531 0.479
##
## 0.320 0.849 0.876
## 0.229 0.596 0.624
## 0.310 0.823 0.827
## 0.268 0.711 0.700
##
## 0.813 0.961 0.828
## 0.723 0.853 0.832
## 0.481 0.510 0.461
##
## 0.700 0.847 0.787
## 0.635 0.794 0.774
## 0.307 0.336 0.352
##
## 4.161 0.947 0.947
## 10.211 0.981 0.981
## 1.130 0.673 0.673
## 1.415 0.746 0.746
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.542 0.056 9.598 0.000 0.431
## .sswk 0.371 0.057 6.485 0.000 0.259
## .sspc 0.143 0.056 2.563 0.010 0.034
## .ssei 0.595 0.063 9.438 0.000 0.472
## .ssar 0.392 0.055 7.142 0.000 0.284
## .ssmk 0.259 0.054 4.760 0.000 0.152
## .ssmc 0.578 0.056 10.233 0.000 0.467
## .ssao 0.225 0.058 3.904 0.000 0.112
## .ssai 0.684 0.067 10.241 0.000 0.553
## .sssi 0.827 0.059 14.131 0.000 0.712
## .ssno 0.122 0.061 1.990 0.047 0.002
## .sscs -0.026 0.058 -0.447 0.655 -0.140
## ci.upper Std.lv Std.all
## 0.653 0.542 0.545
## 0.483 0.371 0.369
## 0.252 0.143 0.146
## 0.719 0.595 0.537
## 0.499 0.392 0.405
## 0.365 0.259 0.271
## 0.689 0.578 0.581
## 0.338 0.225 0.221
## 0.815 0.684 0.590
## 0.942 0.827 0.807
## 0.241 0.122 0.113
## 0.088 -0.026 -0.025
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.191 0.023 8.414 0.000 0.146
## .sswk 0.235 0.023 10.318 0.000 0.190
## .sspc 0.254 0.029 8.697 0.000 0.197
## .ssei 0.339 0.036 9.434 0.000 0.269
## .ssar 0.218 0.028 7.773 0.000 0.163
## .ssmk 0.150 0.017 8.741 0.000 0.116
## .ssmc 0.314 0.031 10.091 0.000 0.253
## .ssao 0.526 0.052 10.201 0.000 0.425
## .ssai 0.424 0.064 6.673 0.000 0.299
## .sssi 0.324 0.049 6.553 0.000 0.227
## .ssno 0.440 0.058 7.579 0.000 0.326
## .sscs 0.422 0.073 5.756 0.000 0.278
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.235 0.191 0.193
## 0.280 0.235 0.232
## 0.311 0.254 0.266
## 0.410 0.339 0.277
## 0.273 0.218 0.232
## 0.183 0.150 0.164
## 0.374 0.314 0.316
## 0.627 0.526 0.510
## 0.549 0.424 0.315
## 0.421 0.324 0.308
## 0.553 0.440 0.380
## 0.566 0.422 0.401
## 1.000 0.103 0.103
## 1.000 0.037 0.037
## 1.000 0.548 0.548
## 1.000 0.444 0.444
## 1.000 1.000 1.000
#modificationIndices(configural, sort=T, maximum.number=30)
metric<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 392.776 109.000 0.000 0.953 0.088 0.057 16430.967
## bic
## 16750.984
Mc(metric)
## [1] 0.8088919
summary(metric, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 100 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 89
## Number of equality constraints 18
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 392.776 349.450
## Degrees of freedom 109 109
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.124
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 155.364 138.226
## 0 237.413 211.224
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.221 0.053 4.209 0.000 0.118
## sswk (.p2.) 0.223 0.053 4.208 0.000 0.119
## sspc (.p3.) 0.207 0.048 4.283 0.000 0.112
## ssei (.p4.) 0.126 0.032 3.905 0.000 0.063
## math =~
## ssar (.p5.) 0.220 0.052 4.231 0.000 0.118
## ssmk (.p6.) 0.162 0.041 3.937 0.000 0.081
## ssmc (.p7.) 0.203 0.048 4.211 0.000 0.108
## ssao (.p8.) 0.184 0.045 4.050 0.000 0.095
## electronic =~
## ssai (.p9.) 0.332 0.041 8.054 0.000 0.251
## sssi (.10.) 0.316 0.042 7.446 0.000 0.233
## ssei (.11.) 0.174 0.025 6.891 0.000 0.124
## speed =~
## ssno (.12.) 0.501 0.060 8.338 0.000 0.383
## sscs (.13.) 0.448 0.053 8.525 0.000 0.345
## ssmk (.14.) 0.196 0.031 6.266 0.000 0.135
## g =~
## verbal (.15.) 3.514 0.895 3.925 0.000 1.759
## math (.16.) 3.413 0.861 3.964 0.000 1.725
## elctrnc (.17.) 1.419 0.211 6.730 0.000 1.006
## speed (.18.) 1.163 0.170 6.854 0.000 0.831
## ci.upper Std.lv Std.all
##
## 0.324 0.808 0.896
## 0.327 0.814 0.885
## 0.302 0.756 0.832
## 0.190 0.461 0.515
##
## 0.322 0.783 0.894
## 0.243 0.576 0.618
## 0.297 0.722 0.821
## 0.273 0.653 0.708
##
## 0.412 0.576 0.733
## 0.399 0.549 0.704
## 0.223 0.301 0.336
##
## 0.618 0.768 0.782
## 0.551 0.687 0.722
## 0.258 0.301 0.323
##
## 5.268 0.962 0.962
## 5.100 0.960 0.960
## 1.832 0.817 0.817
## 1.496 0.758 0.758
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.378 0.051 7.429 0.000 0.278
## .sswk 0.382 0.052 7.278 0.000 0.279
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei 0.188 0.048 3.908 0.000 0.094
## .ssar 0.384 0.049 7.810 0.000 0.288
## .ssmk 0.448 0.054 8.275 0.000 0.342
## .ssmc 0.263 0.048 5.461 0.000 0.169
## .ssao 0.343 0.052 6.596 0.000 0.241
## .ssai 0.069 0.043 1.625 0.104 -0.014
## .sssi 0.163 0.044 3.736 0.000 0.078
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs 0.358 0.053 6.754 0.000 0.254
## ci.upper Std.lv Std.all
## 0.478 0.378 0.419
## 0.485 0.382 0.415
## 0.545 0.445 0.489
## 0.283 0.188 0.210
## 0.481 0.384 0.439
## 0.554 0.448 0.481
## 0.358 0.263 0.300
## 0.444 0.343 0.372
## 0.153 0.069 0.088
## 0.249 0.163 0.210
## 0.395 0.285 0.291
## 0.462 0.358 0.376
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.161 0.019 8.370 0.000 0.123
## .sswk 0.183 0.019 9.400 0.000 0.145
## .sspc 0.255 0.031 8.167 0.000 0.194
## .ssei 0.280 0.030 9.328 0.000 0.221
## .ssar 0.153 0.019 8.223 0.000 0.117
## .ssmk 0.193 0.021 9.099 0.000 0.152
## .ssmc 0.253 0.026 9.704 0.000 0.202
## .ssao 0.424 0.036 11.902 0.000 0.354
## .ssai 0.286 0.035 8.250 0.000 0.218
## .sssi 0.306 0.034 9.051 0.000 0.240
## .ssno 0.375 0.051 7.332 0.000 0.275
## .sscs 0.432 0.054 7.991 0.000 0.326
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.198 0.161 0.197
## 0.221 0.183 0.216
## 0.316 0.255 0.308
## 0.339 0.280 0.349
## 0.190 0.153 0.200
## 0.235 0.193 0.223
## 0.304 0.253 0.327
## 0.493 0.424 0.498
## 0.354 0.286 0.463
## 0.373 0.306 0.504
## 0.475 0.375 0.388
## 0.538 0.432 0.478
## 1.000 0.075 0.075
## 1.000 0.079 0.079
## 1.000 0.332 0.332
## 1.000 0.425 0.425
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.221 0.053 4.209 0.000 0.118
## sswk (.p2.) 0.223 0.053 4.208 0.000 0.119
## sspc (.p3.) 0.207 0.048 4.283 0.000 0.112
## ssei (.p4.) 0.126 0.032 3.905 0.000 0.063
## math =~
## ssar (.p5.) 0.220 0.052 4.231 0.000 0.118
## ssmk (.p6.) 0.162 0.041 3.937 0.000 0.081
## ssmc (.p7.) 0.203 0.048 4.211 0.000 0.108
## ssao (.p8.) 0.184 0.045 4.050 0.000 0.095
## electronic =~
## ssai (.p9.) 0.332 0.041 8.054 0.000 0.251
## sssi (.10.) 0.316 0.042 7.446 0.000 0.233
## ssei (.11.) 0.174 0.025 6.891 0.000 0.124
## speed =~
## ssno (.12.) 0.501 0.060 8.338 0.000 0.383
## sscs (.13.) 0.448 0.053 8.525 0.000 0.345
## ssmk (.14.) 0.196 0.031 6.266 0.000 0.135
## g =~
## verbal (.15.) 3.514 0.895 3.925 0.000 1.759
## math (.16.) 3.413 0.861 3.964 0.000 1.725
## elctrnc (.17.) 1.419 0.211 6.730 0.000 1.006
## speed (.18.) 1.163 0.170 6.854 0.000 0.831
## ci.upper Std.lv Std.all
##
## 0.324 0.903 0.900
## 0.327 0.910 0.884
## 0.302 0.846 0.860
## 0.190 0.516 0.493
##
## 0.322 0.849 0.879
## 0.243 0.624 0.639
## 0.297 0.782 0.807
## 0.273 0.708 0.698
##
## 0.412 0.879 0.795
## 0.399 0.837 0.838
## 0.223 0.460 0.439
##
## 0.618 0.860 0.795
## 0.551 0.769 0.759
## 0.258 0.337 0.345
##
## 5.268 0.951 0.951
## 5.100 0.978 0.978
## 1.832 0.592 0.592
## 1.496 0.748 0.748
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.542 0.056 9.598 0.000 0.431
## .sswk 0.371 0.057 6.485 0.000 0.259
## .sspc 0.143 0.056 2.563 0.010 0.034
## .ssei 0.595 0.063 9.438 0.000 0.472
## .ssar 0.392 0.055 7.142 0.000 0.284
## .ssmk 0.259 0.054 4.760 0.000 0.152
## .ssmc 0.578 0.056 10.233 0.000 0.467
## .ssao 0.225 0.058 3.904 0.000 0.112
## .ssai 0.684 0.067 10.241 0.000 0.553
## .sssi 0.827 0.059 14.131 0.000 0.712
## .ssno 0.122 0.061 1.990 0.047 0.002
## .sscs -0.026 0.058 -0.447 0.655 -0.140
## ci.upper Std.lv Std.all
## 0.653 0.542 0.540
## 0.483 0.371 0.360
## 0.252 0.143 0.145
## 0.719 0.595 0.569
## 0.499 0.392 0.406
## 0.365 0.259 0.265
## 0.689 0.578 0.596
## 0.338 0.225 0.222
## 0.815 0.684 0.619
## 0.942 0.827 0.828
## 0.241 0.122 0.112
## 0.088 -0.026 -0.026
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.192 0.022 8.832 0.000 0.149
## .sswk 0.233 0.022 10.372 0.000 0.189
## .sspc 0.252 0.029 8.808 0.000 0.196
## .ssei 0.350 0.037 9.484 0.000 0.278
## .ssar 0.212 0.027 7.876 0.000 0.160
## .ssmk 0.143 0.016 8.756 0.000 0.111
## .ssmc 0.327 0.032 10.314 0.000 0.265
## .ssao 0.528 0.051 10.401 0.000 0.428
## .ssai 0.450 0.063 7.081 0.000 0.325
## .sssi 0.296 0.050 5.869 0.000 0.197
## .ssno 0.430 0.056 7.725 0.000 0.321
## .sscs 0.437 0.071 6.153 0.000 0.298
## .verbal 1.604 0.792 2.024 0.043 0.051
## .math 0.642 0.511 1.256 0.209 -0.360
## .electronic 4.563 1.219 3.743 0.000 2.173
## .speed 1.298 0.364 3.565 0.000 0.584
## g 1.221 0.152 8.052 0.000 0.924
## ci.upper Std.lv Std.all
## 0.235 0.192 0.191
## 0.277 0.233 0.219
## 0.308 0.252 0.261
## 0.423 0.350 0.320
## 0.265 0.212 0.228
## 0.175 0.143 0.150
## 0.389 0.327 0.348
## 0.627 0.528 0.513
## 0.574 0.450 0.368
## 0.395 0.296 0.297
## 0.539 0.430 0.368
## 0.576 0.437 0.425
## 3.157 0.096 0.096
## 1.643 0.043 0.043
## 6.952 0.650 0.650
## 2.011 0.440 0.440
## 1.519 1.000 1.000
lavTestScore(metric, release = 1:18)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 23.232 18 0.182
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p53. 0.220 1 0.639
## 2 .p2. == .p54. 2.012 1 0.156
## 3 .p3. == .p55. 0.022 1 0.881
## 4 .p4. == .p56. 3.965 1 0.046
## 5 .p5. == .p57. 0.052 1 0.820
## 6 .p6. == .p58. 4.462 1 0.035
## 7 .p7. == .p59. 2.793 1 0.095
## 8 .p8. == .p60. 0.004 1 0.949
## 9 .p9. == .p61. 3.229 1 0.072
## 10 .p10. == .p62. 0.762 1 0.383
## 11 .p11. == .p63. 6.571 1 0.010
## 12 .p12. == .p64. 0.022 1 0.882
## 13 .p13. == .p65. 1.417 1 0.234
## 14 .p14. == .p66. 3.875 1 0.049
## 15 .p15. == .p67. 1.399 1 0.237
## 16 .p16. == .p68. 0.001 1 0.979
## 17 .p17. == .p69. 7.882 1 0.005
## 18 .p18. == .p70. 0.165 1 0.685
scalar<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 1.366400e-12) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 547.240 116.000 0.000 0.928 0.105 0.063 16571.431
## bic
## 16859.897
Mc(scalar)
## [1] 0.7244794
summary(scalar, standardized=T, ci=T) # -.102
## lavaan 0.6-18 ended normally after 120 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 94
## Number of equality constraints 30
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 547.240 485.895
## Degrees of freedom 116 116
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.126
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 225.586 200.298
## 0 321.654 285.597
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.217 0.054 3.984 0.000 0.110
## sswk (.p2.) 0.220 0.055 3.983 0.000 0.112
## sspc (.p3.) 0.203 0.050 4.068 0.000 0.105
## ssei (.p4.) 0.119 0.032 3.697 0.000 0.056
## math =~
## ssar (.p5.) 0.224 0.052 4.297 0.000 0.122
## ssmk (.p6.) 0.152 0.041 3.717 0.000 0.072
## ssmc (.p7.) 0.207 0.048 4.276 0.000 0.112
## ssao (.p8.) 0.186 0.045 4.124 0.000 0.098
## electronic =~
## ssai (.p9.) 0.320 0.040 8.023 0.000 0.242
## sssi (.10.) 0.315 0.041 7.610 0.000 0.234
## ssei (.11.) 0.184 0.025 7.491 0.000 0.136
## speed =~
## ssno (.12.) 0.467 0.058 8.065 0.000 0.353
## sscs (.13.) 0.440 0.054 8.096 0.000 0.334
## ssmk (.14.) 0.224 0.031 7.155 0.000 0.162
## g =~
## verbal (.15.) 3.578 0.959 3.732 0.000 1.699
## math (.16.) 3.345 0.832 4.020 0.000 1.714
## elctrnc (.17.) 1.446 0.216 6.692 0.000 1.022
## speed (.18.) 1.219 0.185 6.604 0.000 0.857
## ci.upper Std.lv Std.all
##
## 0.324 0.806 0.891
## 0.328 0.816 0.887
## 0.301 0.754 0.818
## 0.182 0.441 0.492
##
## 0.327 0.783 0.895
## 0.232 0.529 0.566
## 0.302 0.723 0.813
## 0.275 0.650 0.704
##
## 0.399 0.563 0.721
## 0.396 0.553 0.707
## 0.232 0.323 0.361
##
## 0.580 0.736 0.756
## 0.547 0.694 0.725
## 0.285 0.353 0.378
##
## 5.457 0.963 0.963
## 4.975 0.958 0.958
## 1.869 0.822 0.822
## 1.581 0.773 0.773
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.36.) 0.454 0.050 9.088 0.000 0.356
## .sswk (.37.) 0.381 0.051 7.421 0.000 0.281
## .sspc (.38.) 0.302 0.051 5.891 0.000 0.201
## .ssei (.39.) 0.203 0.047 4.317 0.000 0.111
## .ssar (.40.) 0.367 0.050 7.394 0.000 0.270
## .ssmk (.41.) 0.410 0.054 7.654 0.000 0.305
## .ssmc (.42.) 0.376 0.047 8.008 0.000 0.284
## .ssao (.43.) 0.273 0.048 5.691 0.000 0.179
## .ssai (.44.) 0.053 0.041 1.289 0.197 -0.027
## .sssi (.45.) 0.171 0.041 4.179 0.000 0.091
## .ssno (.46.) 0.358 0.052 6.943 0.000 0.257
## .sscs (.47.) 0.320 0.052 6.218 0.000 0.219
## ci.upper Std.lv Std.all
## 0.552 0.454 0.502
## 0.482 0.381 0.414
## 0.402 0.302 0.327
## 0.295 0.203 0.227
## 0.464 0.367 0.419
## 0.514 0.410 0.438
## 0.468 0.376 0.423
## 0.367 0.273 0.296
## 0.133 0.053 0.067
## 0.252 0.171 0.219
## 0.459 0.358 0.368
## 0.421 0.320 0.334
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.169 0.021 8.185 0.000 0.128
## .sswk 0.181 0.020 9.209 0.000 0.142
## .sspc 0.281 0.036 7.791 0.000 0.210
## .ssei 0.278 0.030 9.204 0.000 0.219
## .ssar 0.152 0.019 7.974 0.000 0.115
## .ssmk 0.192 0.023 8.365 0.000 0.147
## .ssmc 0.268 0.029 9.209 0.000 0.211
## .ssao 0.429 0.036 12.033 0.000 0.359
## .ssai 0.292 0.034 8.527 0.000 0.225
## .sssi 0.306 0.034 8.948 0.000 0.239
## .ssno 0.407 0.054 7.499 0.000 0.301
## .sscs 0.436 0.055 7.873 0.000 0.328
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.209 0.169 0.206
## 0.219 0.181 0.214
## 0.352 0.281 0.330
## 0.337 0.278 0.346
## 0.190 0.152 0.199
## 0.237 0.192 0.220
## 0.326 0.268 0.339
## 0.499 0.429 0.504
## 0.360 0.292 0.480
## 0.373 0.306 0.500
## 0.514 0.407 0.429
## 0.545 0.436 0.475
## 1.000 0.072 0.072
## 1.000 0.082 0.082
## 1.000 0.324 0.324
## 1.000 0.402 0.402
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.217 0.054 3.984 0.000 0.110
## sswk (.p2.) 0.220 0.055 3.983 0.000 0.112
## sspc (.p3.) 0.203 0.050 4.068 0.000 0.105
## ssei (.p4.) 0.119 0.032 3.697 0.000 0.056
## math =~
## ssar (.p5.) 0.224 0.052 4.297 0.000 0.122
## ssmk (.p6.) 0.152 0.041 3.717 0.000 0.072
## ssmc (.p7.) 0.207 0.048 4.276 0.000 0.112
## ssao (.p8.) 0.186 0.045 4.124 0.000 0.098
## electronic =~
## ssai (.p9.) 0.320 0.040 8.023 0.000 0.242
## sssi (.10.) 0.315 0.041 7.610 0.000 0.234
## ssei (.11.) 0.184 0.025 7.491 0.000 0.136
## speed =~
## ssno (.12.) 0.467 0.058 8.065 0.000 0.353
## sscs (.13.) 0.440 0.054 8.096 0.000 0.334
## ssmk (.14.) 0.224 0.031 7.155 0.000 0.162
## g =~
## verbal (.15.) 3.578 0.959 3.732 0.000 1.699
## math (.16.) 3.345 0.832 4.020 0.000 1.714
## elctrnc (.17.) 1.446 0.216 6.692 0.000 1.022
## speed (.18.) 1.219 0.185 6.604 0.000 0.857
## ci.upper Std.lv Std.all
##
## 0.324 0.900 0.893
## 0.328 0.910 0.885
## 0.301 0.842 0.846
## 0.182 0.492 0.468
##
## 0.327 0.849 0.879
## 0.232 0.573 0.585
## 0.302 0.783 0.795
## 0.275 0.705 0.693
##
## 0.399 0.858 0.782
## 0.396 0.843 0.841
## 0.232 0.492 0.468
##
## 0.580 0.820 0.765
## 0.547 0.773 0.758
## 0.285 0.393 0.401
##
## 5.457 0.953 0.953
## 4.975 0.976 0.976
## 1.869 0.596 0.596
## 1.581 0.767 0.767
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.36.) 0.454 0.050 9.088 0.000 0.356
## .sswk (.37.) 0.381 0.051 7.421 0.000 0.281
## .sspc (.38.) 0.302 0.051 5.891 0.000 0.201
## .ssei (.39.) 0.203 0.047 4.317 0.000 0.111
## .ssar (.40.) 0.367 0.050 7.394 0.000 0.270
## .ssmk (.41.) 0.410 0.054 7.654 0.000 0.305
## .ssmc (.42.) 0.376 0.047 8.008 0.000 0.284
## .ssao (.43.) 0.273 0.048 5.691 0.000 0.179
## .ssai (.44.) 0.053 0.041 1.289 0.197 -0.027
## .sssi (.45.) 0.171 0.041 4.179 0.000 0.091
## .ssno (.46.) 0.358 0.052 6.943 0.000 0.257
## .sscs (.47.) 0.320 0.052 6.218 0.000 0.219
## .verbal -0.446 0.147 -3.023 0.003 -0.735
## .math -0.149 0.143 -1.045 0.296 -0.430
## .elctrnc 1.895 0.281 6.735 0.000 1.343
## .speed -0.833 0.162 -5.148 0.000 -1.150
## g 0.112 0.088 1.269 0.204 -0.061
## ci.upper Std.lv Std.all
## 0.552 0.454 0.451
## 0.482 0.381 0.371
## 0.402 0.302 0.303
## 0.295 0.203 0.193
## 0.464 0.367 0.380
## 0.514 0.410 0.418
## 0.468 0.376 0.382
## 0.367 0.273 0.269
## 0.133 0.053 0.048
## 0.252 0.171 0.171
## 0.459 0.358 0.334
## 0.421 0.320 0.314
## -0.157 -0.108 -0.108
## 0.131 -0.040 -0.040
## 2.446 0.708 0.708
## -0.516 -0.474 -0.474
## 0.286 0.102 0.102
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.207 0.025 8.371 0.000 0.158
## .sswk 0.230 0.023 10.153 0.000 0.186
## .sspc 0.282 0.035 8.145 0.000 0.214
## .ssei 0.346 0.037 9.482 0.000 0.275
## .ssar 0.212 0.028 7.672 0.000 0.158
## .ssmk 0.140 0.017 8.033 0.000 0.106
## .ssmc 0.356 0.035 10.090 0.000 0.287
## .ssao 0.537 0.053 10.181 0.000 0.434
## .ssai 0.466 0.060 7.757 0.000 0.349
## .sssi 0.293 0.048 6.101 0.000 0.199
## .ssno 0.475 0.058 8.145 0.000 0.361
## .sscs 0.443 0.074 6.008 0.000 0.298
## .verbal 1.569 0.818 1.917 0.055 -0.035
## .math 0.665 0.517 1.287 0.198 -0.348
## .electronic 4.615 1.237 3.730 0.000 2.190
## .speed 1.271 0.354 3.587 0.000 0.577
## g 1.220 0.152 8.045 0.000 0.922
## ci.upper Std.lv Std.all
## 0.255 0.207 0.203
## 0.274 0.230 0.217
## 0.350 0.282 0.285
## 0.418 0.346 0.313
## 0.266 0.212 0.227
## 0.174 0.140 0.146
## 0.425 0.356 0.367
## 0.641 0.537 0.520
## 0.584 0.466 0.388
## 0.387 0.293 0.292
## 0.589 0.475 0.414
## 0.587 0.443 0.425
## 3.173 0.091 0.091
## 1.678 0.046 0.046
## 7.041 0.644 0.644
## 1.966 0.412 0.412
## 1.517 1.000 1.000
lavTestScore(scalar, release = 19:30)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 149.118 12 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p36. == .p88. 43.459 1 0.000
## 2 .p37. == .p89. 0.002 1 0.963
## 3 .p38. == .p90. 66.036 1 0.000
## 4 .p39. == .p91. 1.300 1 0.254
## 5 .p40. == .p92. 3.379 1 0.066
## 6 .p41. == .p93. 9.794 1 0.002
## 7 .p42. == .p94. 50.729 1 0.000
## 8 .p43. == .p95. 10.142 1 0.001
## 9 .p44. == .p96. 1.916 1 0.166
## 10 .p45. == .p97. 0.399 1 0.528
## 11 .p46. == .p98. 18.657 1 0.000
## 12 .p47. == .p99. 4.032 1 0.045
scalar2<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("ssmc~1", "sspc~1", "ssno ~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 411.525 113.000 0.000 0.950 0.089 0.057 16441.716
## bic
## 16743.704
Mc(scalar2)
## [1] 0.8000243
summary(scalar2, standardized=T, ci=T) # -.092
## lavaan 0.6-18 ended normally after 123 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 94
## Number of equality constraints 27
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 411.525 363.543
## Degrees of freedom 113 113
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.132
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 162.964 143.963
## 0 248.561 219.580
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.219 0.053 4.110 0.000 0.115
## sswk (.p2.) 0.219 0.053 4.108 0.000 0.115
## sspc (.p3.) 0.205 0.049 4.177 0.000 0.109
## ssei (.p4.) 0.124 0.032 3.849 0.000 0.061
## math =~
## ssar (.p5.) 0.219 0.052 4.199 0.000 0.117
## ssmk (.p6.) 0.161 0.041 3.934 0.000 0.081
## ssmc (.p7.) 0.202 0.048 4.185 0.000 0.107
## ssao (.p8.) 0.183 0.045 4.034 0.000 0.094
## electronic =~
## ssai (.p9.) 0.326 0.040 8.184 0.000 0.248
## sssi (.10.) 0.321 0.041 7.744 0.000 0.239
## ssei (.11.) 0.176 0.024 7.297 0.000 0.129
## speed =~
## ssno (.12.) 0.501 0.060 8.385 0.000 0.384
## sscs (.13.) 0.448 0.051 8.766 0.000 0.348
## ssmk (.14.) 0.197 0.028 7.007 0.000 0.142
## g =~
## verbal (.15.) 3.556 0.926 3.838 0.000 1.740
## math (.16.) 3.428 0.869 3.942 0.000 1.723
## elctrnc (.17.) 1.420 0.209 6.791 0.000 1.010
## speed (.18.) 1.163 0.169 6.894 0.000 0.832
## ci.upper Std.lv Std.all
##
## 0.323 0.809 0.895
## 0.324 0.810 0.883
## 0.301 0.757 0.832
## 0.187 0.458 0.511
##
## 0.321 0.782 0.894
## 0.241 0.575 0.617
## 0.297 0.722 0.821
## 0.272 0.654 0.708
##
## 0.404 0.566 0.725
## 0.402 0.557 0.711
## 0.223 0.306 0.341
##
## 0.618 0.768 0.782
## 0.548 0.687 0.722
## 0.252 0.302 0.325
##
## 5.372 0.963 0.963
## 5.132 0.960 0.960
## 1.830 0.818 0.818
## 1.493 0.758 0.758
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.36.) 0.413 0.050 8.297 0.000 0.315
## .sswk (.37.) 0.341 0.051 6.682 0.000 0.241
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei (.39.) 0.192 0.046 4.130 0.000 0.101
## .ssar (.40.) 0.398 0.049 8.136 0.000 0.302
## .ssmk (.41.) 0.447 0.052 8.599 0.000 0.345
## .ssmc 0.263 0.048 5.461 0.000 0.169
## .ssao (.43.) 0.300 0.048 6.304 0.000 0.207
## .ssai (.44.) 0.056 0.041 1.367 0.172 -0.024
## .sssi (.45.) 0.176 0.041 4.258 0.000 0.095
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs (.47.) 0.359 0.051 6.977 0.000 0.258
## ci.upper Std.lv Std.all
## 0.510 0.413 0.457
## 0.441 0.341 0.371
## 0.545 0.445 0.489
## 0.283 0.192 0.214
## 0.494 0.398 0.454
## 0.549 0.447 0.480
## 0.358 0.263 0.300
## 0.394 0.300 0.325
## 0.136 0.056 0.072
## 0.256 0.176 0.224
## 0.395 0.285 0.291
## 0.460 0.359 0.377
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.163 0.020 8.327 0.000 0.124
## .sswk 0.186 0.020 9.301 0.000 0.147
## .sspc 0.254 0.031 8.156 0.000 0.193
## .ssei 0.280 0.030 9.255 0.000 0.220
## .ssar 0.154 0.019 8.207 0.000 0.117
## .ssmk 0.193 0.021 9.206 0.000 0.152
## .ssmc 0.253 0.026 9.701 0.000 0.202
## .ssao 0.426 0.036 11.984 0.000 0.356
## .ssai 0.289 0.034 8.433 0.000 0.222
## .sssi 0.304 0.034 8.888 0.000 0.237
## .ssno 0.375 0.051 7.304 0.000 0.274
## .sscs 0.433 0.054 7.957 0.000 0.326
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.201 0.163 0.199
## 0.226 0.186 0.221
## 0.315 0.254 0.307
## 0.339 0.280 0.348
## 0.191 0.154 0.201
## 0.234 0.193 0.222
## 0.304 0.253 0.326
## 0.495 0.426 0.499
## 0.357 0.289 0.474
## 0.371 0.304 0.495
## 0.475 0.375 0.388
## 0.539 0.433 0.478
## 1.000 0.073 0.073
## 1.000 0.078 0.078
## 1.000 0.332 0.332
## 1.000 0.425 0.425
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.219 0.053 4.110 0.000 0.115
## sswk (.p2.) 0.219 0.053 4.108 0.000 0.115
## sspc (.p3.) 0.205 0.049 4.177 0.000 0.109
## ssei (.p4.) 0.124 0.032 3.849 0.000 0.061
## math =~
## ssar (.p5.) 0.219 0.052 4.199 0.000 0.117
## ssmk (.p6.) 0.161 0.041 3.934 0.000 0.081
## ssmc (.p7.) 0.202 0.048 4.185 0.000 0.107
## ssao (.p8.) 0.183 0.045 4.034 0.000 0.094
## electronic =~
## ssai (.p9.) 0.326 0.040 8.184 0.000 0.248
## sssi (.10.) 0.321 0.041 7.744 0.000 0.239
## ssei (.11.) 0.176 0.024 7.297 0.000 0.129
## speed =~
## ssno (.12.) 0.501 0.060 8.385 0.000 0.384
## sscs (.13.) 0.448 0.051 8.766 0.000 0.348
## ssmk (.14.) 0.197 0.028 7.007 0.000 0.142
## g =~
## verbal (.15.) 3.556 0.926 3.838 0.000 1.740
## math (.16.) 3.428 0.869 3.942 0.000 1.723
## elctrnc (.17.) 1.420 0.209 6.791 0.000 1.010
## speed (.18.) 1.163 0.169 6.894 0.000 0.832
## ci.upper Std.lv Std.all
##
## 0.323 0.904 0.898
## 0.324 0.906 0.880
## 0.301 0.846 0.861
## 0.187 0.512 0.489
##
## 0.321 0.848 0.878
## 0.241 0.623 0.638
## 0.297 0.782 0.807
## 0.272 0.709 0.697
##
## 0.404 0.863 0.785
## 0.402 0.848 0.845
## 0.223 0.466 0.444
##
## 0.618 0.860 0.795
## 0.548 0.769 0.758
## 0.252 0.338 0.346
##
## 5.372 0.951 0.951
## 5.132 0.979 0.979
## 1.830 0.593 0.593
## 1.493 0.749 0.749
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.36.) 0.413 0.050 8.297 0.000 0.315
## .sswk (.37.) 0.341 0.051 6.682 0.000 0.241
## .sspc 0.063 0.058 1.085 0.278 -0.051
## .ssei (.39.) 0.192 0.046 4.130 0.000 0.101
## .ssar (.40.) 0.398 0.049 8.136 0.000 0.302
## .ssmk (.41.) 0.447 0.052 8.599 0.000 0.345
## .ssmc 0.601 0.059 10.117 0.000 0.485
## .ssao (.43.) 0.300 0.048 6.304 0.000 0.207
## .ssai (.44.) 0.056 0.041 1.367 0.172 -0.024
## .sssi (.45.) 0.176 0.041 4.258 0.000 0.095
## .ssno 0.553 0.077 7.186 0.000 0.402
## .sscs (.47.) 0.359 0.051 6.977 0.000 0.258
## .verbal 0.029 0.138 0.208 0.835 -0.242
## .math -0.460 0.154 -2.991 0.003 -0.762
## .elctrnc 1.851 0.275 6.741 0.000 1.313
## .speed -0.979 0.161 -6.068 0.000 -1.295
## g 0.101 0.088 1.152 0.249 -0.071
## ci.upper Std.lv Std.all
## 0.510 0.413 0.410
## 0.441 0.341 0.331
## 0.177 0.063 0.064
## 0.283 0.192 0.183
## 0.494 0.398 0.412
## 0.549 0.447 0.458
## 0.717 0.601 0.620
## 0.394 0.300 0.296
## 0.136 0.056 0.051
## 0.256 0.176 0.175
## 0.704 0.553 0.511
## 0.460 0.359 0.354
## 0.299 0.007 0.007
## -0.159 -0.119 -0.119
## 2.390 0.700 0.700
## -0.663 -0.570 -0.570
## 0.273 0.092 0.092
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.196 0.022 8.711 0.000 0.152
## .sswk 0.238 0.023 10.386 0.000 0.193
## .sspc 0.251 0.029 8.774 0.000 0.195
## .ssei 0.350 0.036 9.630 0.000 0.279
## .ssar 0.213 0.027 7.903 0.000 0.160
## .ssmk 0.143 0.016 8.854 0.000 0.112
## .ssmc 0.327 0.032 10.345 0.000 0.265
## .ssao 0.531 0.052 10.274 0.000 0.429
## .ssai 0.465 0.061 7.605 0.000 0.345
## .sssi 0.287 0.049 5.883 0.000 0.192
## .ssno 0.430 0.055 7.756 0.000 0.322
## .sscs 0.437 0.070 6.284 0.000 0.301
## .verbal 1.616 0.816 1.981 0.048 0.017
## .math 0.633 0.512 1.236 0.216 -0.370
## .electronic 4.538 1.200 3.781 0.000 2.185
## .speed 1.296 0.364 3.564 0.000 0.583
## g 1.221 0.151 8.062 0.000 0.924
## ci.upper Std.lv Std.all
## 0.240 0.196 0.193
## 0.283 0.238 0.225
## 0.307 0.251 0.259
## 0.421 0.350 0.319
## 0.266 0.213 0.229
## 0.175 0.143 0.150
## 0.389 0.327 0.348
## 0.632 0.531 0.514
## 0.584 0.465 0.384
## 0.383 0.287 0.285
## 0.539 0.430 0.368
## 0.573 0.437 0.425
## 3.214 0.095 0.095
## 1.636 0.042 0.042
## 6.890 0.648 0.648
## 2.008 0.440 0.440
## 1.518 1.000 1.000
lavTestScore(scalar2, release = 19:27, standardized=T, epc=T) # with only ssmc and sspc the fit was not satisfactory and other subtests have similar X2 values, but ssno had the highest value in sepc.all earlier
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 18.609 9 0.029
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p36. == .p88. 11.942 1 0.001
## 2 .p37. == .p89. 12.920 1 0.000
## 3 .p39. == .p91. 0.060 1 0.806
## 4 .p40. == .p92. 2.605 1 0.107
## 5 .p41. == .p93. 0.004 1 0.947
## 6 .p43. == .p95. 3.970 1 0.046
## 7 .p44. == .p96. 1.247 1 0.264
## 8 .p45. == .p97. 0.898 1 0.343
## 9 .p47. == .p99. 0.004 1 0.947
##
## $epc
##
## expected parameter changes (epc) and expected parameter values (epv):
##
## lhs op rhs block group free label plabel est epc
## 1 verbal =~ ssgs 1 1 1 .p1. .p1. 0.219 -0.001
## 2 verbal =~ sswk 1 1 2 .p2. .p2. 0.219 0.001
## 3 verbal =~ sspc 1 1 3 .p3. .p3. 0.205 0.000
## 4 verbal =~ ssei 1 1 4 .p4. .p4. 0.124 0.001
## 5 math =~ ssar 1 1 5 .p5. .p5. 0.219 0.000
## 6 math =~ ssmk 1 1 6 .p6. .p6. 0.161 0.000
## 7 math =~ ssmc 1 1 7 .p7. .p7. 0.202 0.000
## 8 math =~ ssao 1 1 8 .p8. .p8. 0.183 0.000
## 9 electronic =~ ssai 1 1 9 .p9. .p9. 0.326 0.006
## 10 electronic =~ sssi 1 1 10 .p10. .p10. 0.321 -0.005
## 11 electronic =~ ssei 1 1 11 .p11. .p11. 0.176 -0.002
## 12 speed =~ ssno 1 1 12 .p12. .p12. 0.501 0.000
## 13 speed =~ sscs 1 1 13 .p13. .p13. 0.448 0.000
## 14 speed =~ ssmk 1 1 14 .p14. .p14. 0.197 -0.001
## 15 g =~ verbal 1 1 15 .p15. .p15. 3.556 0.002
## 16 g =~ math 1 1 16 .p16. .p16. 3.428 0.000
## 17 g =~ electronic 1 1 17 .p17. .p17. 1.420 -0.001
## 18 g =~ speed 1 1 18 .p18. .p18. 1.163 -0.001
## 19 ssgs ~~ ssgs 1 1 19 .p19. 0.163 0.000
## 20 sswk ~~ sswk 1 1 20 .p20. 0.186 0.000
## 21 sspc ~~ sspc 1 1 21 .p21. 0.254 0.000
## 22 ssei ~~ ssei 1 1 22 .p22. 0.280 0.000
## 23 ssar ~~ ssar 1 1 23 .p23. 0.154 0.000
## 24 ssmk ~~ ssmk 1 1 24 .p24. 0.193 0.000
## 25 ssmc ~~ ssmc 1 1 25 .p25. 0.253 0.000
## 26 ssao ~~ ssao 1 1 26 .p26. 0.426 0.000
## 27 ssai ~~ ssai 1 1 27 .p27. 0.289 -0.004
## 28 sssi ~~ sssi 1 1 28 .p28. 0.304 0.003
## 29 ssno ~~ ssno 1 1 29 .p29. 0.375 0.000
## 30 sscs ~~ sscs 1 1 30 .p30. 0.433 0.000
## 31 verbal ~~ verbal 1 1 0 .p31. 1.000 NA
## 32 math ~~ math 1 1 0 .p32. 1.000 NA
## 33 electronic ~~ electronic 1 1 0 .p33. 1.000 NA
## 34 speed ~~ speed 1 1 0 .p34. 1.000 NA
## 35 g ~~ g 1 1 0 .p35. 1.000 NA
## 36 ssgs ~1 1 1 31 .p36. .p36. 0.413 -0.035
## 37 sswk ~1 1 1 32 .p37. .p37. 0.341 0.041
## 38 sspc ~1 1 1 33 .p38. 0.445 0.000
## 39 ssei ~1 1 1 34 .p39. .p39. 0.192 -0.003
## 40 ssar ~1 1 1 35 .p40. .p40. 0.398 -0.013
## 41 ssmk ~1 1 1 36 .p41. .p41. 0.447 0.001
## 42 ssmc ~1 1 1 37 .p42. 0.263 0.000
## 43 ssao ~1 1 1 38 .p43. .p43. 0.300 0.042
## 44 ssai ~1 1 1 39 .p44. .p44. 0.056 0.013
## 45 sssi ~1 1 1 40 .p45. .p45. 0.176 -0.012
## 46 ssno ~1 1 1 41 .p46. 0.285 0.000
## 47 sscs ~1 1 1 42 .p47. .p47. 0.359 -0.001
## 48 verbal ~1 1 1 0 .p48. 0.000 NA
## 49 math ~1 1 1 0 .p49. 0.000 NA
## 50 electronic ~1 1 1 0 .p50. 0.000 NA
## 51 speed ~1 1 1 0 .p51. 0.000 NA
## 52 g ~1 1 1 0 .p52. 0.000 NA
## 53 verbal =~ ssgs 2 2 43 .p1. .p53. 0.219 -0.001
## 54 verbal =~ sswk 2 2 44 .p2. .p54. 0.219 0.001
## 55 verbal =~ sspc 2 2 45 .p3. .p55. 0.205 0.000
## 56 verbal =~ ssei 2 2 46 .p4. .p56. 0.124 0.001
## 57 math =~ ssar 2 2 47 .p5. .p57. 0.219 0.000
## 58 math =~ ssmk 2 2 48 .p6. .p58. 0.161 0.000
## 59 math =~ ssmc 2 2 49 .p7. .p59. 0.202 0.000
## 60 math =~ ssao 2 2 50 .p8. .p60. 0.183 0.000
## 61 electronic =~ ssai 2 2 51 .p9. .p61. 0.326 0.006
## 62 electronic =~ sssi 2 2 52 .p10. .p62. 0.321 -0.005
## 63 electronic =~ ssei 2 2 53 .p11. .p63. 0.176 -0.002
## 64 speed =~ ssno 2 2 54 .p12. .p64. 0.501 0.000
## 65 speed =~ sscs 2 2 55 .p13. .p65. 0.448 0.000
## 66 speed =~ ssmk 2 2 56 .p14. .p66. 0.197 -0.001
## 67 g =~ verbal 2 2 57 .p15. .p67. 3.556 0.002
## 68 g =~ math 2 2 58 .p16. .p68. 3.428 0.000
## 69 g =~ electronic 2 2 59 .p17. .p69. 1.420 -0.001
## 70 g =~ speed 2 2 60 .p18. .p70. 1.163 -0.001
## 71 ssgs ~~ ssgs 2 2 61 .p71. 0.196 0.001
## epv sepc.lv sepc.all sepc.nox
## 1 0.218 -0.003 -0.003 -0.003
## 2 0.220 0.002 0.002 0.002
## 3 0.205 0.000 0.000 0.000
## 4 0.125 0.003 0.003 0.003
## 5 0.219 0.000 0.000 0.000
## 6 0.161 0.001 0.001 0.001
## 7 0.202 0.000 0.000 0.000
## 8 0.183 -0.001 -0.001 -0.001
## 9 0.332 0.010 0.013 0.013
## 10 0.316 -0.008 -0.010 -0.010
## 11 0.174 -0.004 -0.004 -0.004
## 12 0.501 0.000 0.000 0.000
## 13 0.448 0.000 0.001 0.001
## 14 0.197 -0.001 -0.001 -0.001
## 15 3.558 0.000 0.000 0.000
## 16 3.427 0.000 0.000 0.000
## 17 1.418 -0.001 -0.001 -0.001
## 18 1.162 0.000 0.000 0.000
## 19 0.163 0.163 0.199 0.199
## 20 0.186 -0.186 -0.221 -0.221
## 21 0.254 0.254 0.307 0.307
## 22 0.280 0.280 0.348 0.348
## 23 0.154 -0.154 -0.201 -0.201
## 24 0.193 0.193 0.222 0.222
## 25 0.253 0.253 0.326 0.326
## 26 0.426 0.426 0.499 0.499
## 27 0.285 -0.289 -0.474 -0.474
## 28 0.307 0.304 0.495 0.495
## 29 0.375 -0.375 -0.388 -0.388
## 30 0.432 -0.433 -0.478 -0.478
## 31 NA NA NA NA
## 32 NA NA NA NA
## 33 NA NA NA NA
## 34 NA NA NA NA
## 35 NA NA NA NA
## 36 0.378 -0.035 -0.039 -0.039
## 37 0.382 0.041 0.045 0.045
## 38 0.445 0.000 0.000 0.000
## 39 0.188 -0.003 -0.004 -0.004
## 40 0.384 -0.013 -0.015 -0.015
## 41 0.448 0.001 0.001 0.001
## 42 0.263 0.000 0.000 0.000
## 43 0.343 0.042 0.046 0.046
## 44 0.069 0.013 0.017 0.017
## 45 0.163 -0.012 -0.016 -0.016
## 46 0.285 0.000 0.000 0.000
## 47 0.358 -0.001 -0.001 -0.001
## 48 NA NA NA NA
## 49 NA NA NA NA
## 50 NA NA NA NA
## 51 NA NA NA NA
## 52 NA NA NA NA
## 53 0.218 -0.003 -0.003 -0.003
## 54 0.220 0.002 0.002 0.002
## 55 0.205 0.000 0.000 0.000
## 56 0.125 0.003 0.003 0.003
## 57 0.219 0.000 0.000 0.000
## 58 0.161 0.001 0.001 0.001
## 59 0.202 0.000 0.000 0.000
## 60 0.183 -0.001 -0.001 -0.001
## 61 0.332 0.016 0.014 0.014
## 62 0.316 -0.012 -0.012 -0.012
## 63 0.174 -0.006 -0.005 -0.005
## 64 0.501 0.000 0.000 0.000
## 65 0.448 0.001 0.001 0.001
## 66 0.197 -0.001 -0.001 -0.001
## 67 3.558 0.000 0.000 0.000
## 68 3.427 0.000 0.000 0.000
## 69 1.418 -0.001 -0.001 -0.001
## 70 1.162 0.000 0.000 0.000
## 71 0.197 0.196 0.193 0.193
## [ reached 'max' / getOption("max.print") -- omitted 33 rows ]
strict<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("ssmc~1", "sspc~1", "ssno ~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 445.195 125.000 0.000 0.947 0.087 0.061 16451.386
## bic
## 16699.287
Mc(strict)
## [1] 0.7871716
latent<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("ssmc~1", "sspc~1", "ssno ~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 1.167720e-12) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 472.498 118.000 0.000 0.941 0.095 0.108 16492.689
## bic
## 16772.140
Mc(latent)
## [1] 0.7672474
summary(latent, standardized=T, ci=T) # -.065
## lavaan 0.6-18 ended normally after 78 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 89
## Number of equality constraints 27
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 472.498 416.062
## Degrees of freedom 118 118
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.136
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 197.409 173.831
## 0 275.088 242.232
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.241 0.043 5.607 0.000 0.157
## sswk (.p2.) 0.241 0.042 5.680 0.000 0.158
## sspc (.p3.) 0.225 0.039 5.764 0.000 0.148
## ssei (.p4.) 0.138 0.028 5.021 0.000 0.084
## math =~
## ssar (.p5.) 0.207 0.045 4.597 0.000 0.119
## ssmk (.p6.) 0.151 0.036 4.198 0.000 0.080
## ssmc (.p7.) 0.192 0.042 4.606 0.000 0.110
## ssao (.p8.) 0.173 0.039 4.463 0.000 0.097
## electronic =~
## ssai (.p9.) 0.502 0.034 14.634 0.000 0.435
## sssi (.10.) 0.508 0.034 14.791 0.000 0.440
## ssei (.11.) 0.269 0.030 9.027 0.000 0.210
## speed =~
## ssno (.12.) 0.538 0.049 10.893 0.000 0.442
## sscs (.13.) 0.481 0.040 12.032 0.000 0.403
## ssmk (.14.) 0.217 0.027 7.982 0.000 0.163
## g =~
## verbal (.15.) 3.414 0.653 5.226 0.000 2.134
## math (.16.) 3.807 0.887 4.290 0.000 2.067
## elctrnc (.17.) 1.017 0.099 10.305 0.000 0.823
## speed (.18.) 1.132 0.125 9.087 0.000 0.888
## ci.upper Std.lv Std.all
##
## 0.325 0.856 0.905
## 0.324 0.857 0.894
## 0.301 0.799 0.846
## 0.192 0.491 0.510
##
## 0.295 0.815 0.902
## 0.221 0.594 0.615
## 0.273 0.755 0.831
## 0.249 0.681 0.722
##
## 0.569 0.716 0.810
## 0.575 0.724 0.814
## 0.327 0.383 0.398
##
## 0.635 0.813 0.804
## 0.559 0.727 0.742
## 0.270 0.327 0.339
##
## 4.695 0.960 0.960
## 5.546 0.967 0.967
## 1.210 0.713 0.713
## 1.376 0.749 0.749
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.36.) 0.412 0.050 8.273 0.000 0.315
## .sswk (.37.) 0.340 0.051 6.663 0.000 0.240
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei (.39.) 0.194 0.046 4.206 0.000 0.104
## .ssar (.40.) 0.397 0.049 8.121 0.000 0.301
## .ssmk (.41.) 0.448 0.052 8.625 0.000 0.347
## .ssmc 0.263 0.048 5.461 0.000 0.169
## .ssao (.43.) 0.300 0.048 6.291 0.000 0.207
## .ssai (.44.) 0.060 0.041 1.466 0.143 -0.020
## .sssi (.45.) 0.169 0.041 4.105 0.000 0.089
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs (.47.) 0.358 0.051 6.957 0.000 0.257
## ci.upper Std.lv Std.all
## 0.510 0.412 0.436
## 0.440 0.340 0.355
## 0.545 0.445 0.471
## 0.285 0.194 0.201
## 0.493 0.397 0.440
## 0.550 0.448 0.464
## 0.358 0.263 0.290
## 0.393 0.300 0.318
## 0.140 0.060 0.068
## 0.250 0.169 0.191
## 0.395 0.285 0.282
## 0.458 0.358 0.365
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.161 0.020 8.263 0.000 0.123
## .sswk 0.185 0.020 9.369 0.000 0.146
## .sspc 0.254 0.031 8.172 0.000 0.193
## .ssei 0.283 0.030 9.462 0.000 0.224
## .ssar 0.152 0.018 8.241 0.000 0.116
## .ssmk 0.190 0.021 8.943 0.000 0.149
## .ssmc 0.254 0.026 9.656 0.000 0.203
## .ssao 0.426 0.036 11.959 0.000 0.356
## .ssai 0.268 0.035 7.579 0.000 0.199
## .sssi 0.266 0.036 7.469 0.000 0.196
## .ssno 0.361 0.052 6.967 0.000 0.259
## .sscs 0.431 0.055 7.826 0.000 0.323
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.200 0.161 0.180
## 0.223 0.185 0.201
## 0.314 0.254 0.284
## 0.342 0.283 0.305
## 0.188 0.152 0.186
## 0.232 0.190 0.204
## 0.306 0.254 0.309
## 0.496 0.426 0.479
## 0.337 0.268 0.343
## 0.336 0.266 0.337
## 0.462 0.361 0.353
## 0.539 0.431 0.449
## 1.000 0.079 0.079
## 1.000 0.065 0.065
## 1.000 0.492 0.492
## 1.000 0.438 0.438
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.241 0.043 5.607 0.000 0.157
## sswk (.p2.) 0.241 0.042 5.680 0.000 0.158
## sspc (.p3.) 0.225 0.039 5.764 0.000 0.148
## ssei (.p4.) 0.138 0.028 5.021 0.000 0.084
## math =~
## ssar (.p5.) 0.207 0.045 4.597 0.000 0.119
## ssmk (.p6.) 0.151 0.036 4.198 0.000 0.080
## ssmc (.p7.) 0.192 0.042 4.606 0.000 0.110
## ssao (.p8.) 0.173 0.039 4.463 0.000 0.097
## electronic =~
## ssai (.p9.) 0.502 0.034 14.634 0.000 0.435
## sssi (.10.) 0.508 0.034 14.791 0.000 0.440
## ssei (.11.) 0.269 0.030 9.027 0.000 0.210
## speed =~
## ssno (.12.) 0.538 0.049 10.893 0.000 0.442
## sscs (.13.) 0.481 0.040 12.032 0.000 0.403
## ssmk (.14.) 0.217 0.027 7.982 0.000 0.163
## g =~
## verbal (.15.) 3.414 0.653 5.226 0.000 2.134
## math (.16.) 3.807 0.887 4.290 0.000 2.067
## elctrnc (.17.) 1.017 0.099 10.305 0.000 0.823
## speed (.18.) 1.132 0.125 9.087 0.000 0.888
## ci.upper Std.lv Std.all
##
## 0.325 0.856 0.889
## 0.324 0.857 0.869
## 0.301 0.799 0.844
## 0.192 0.491 0.490
##
## 0.295 0.815 0.867
## 0.221 0.594 0.630
## 0.273 0.755 0.801
## 0.249 0.681 0.683
##
## 0.569 0.716 0.705
## 0.575 0.724 0.781
## 0.327 0.383 0.382
##
## 0.635 0.813 0.773
## 0.559 0.727 0.738
## 0.270 0.327 0.347
##
## 4.695 0.960 0.960
## 5.546 0.967 0.967
## 1.210 0.713 0.713
## 1.376 0.749 0.749
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.36.) 0.412 0.050 8.273 0.000 0.315
## .sswk (.37.) 0.340 0.051 6.663 0.000 0.240
## .sspc 0.062 0.058 1.071 0.284 -0.052
## .ssei (.39.) 0.194 0.046 4.206 0.000 0.104
## .ssar (.40.) 0.397 0.049 8.121 0.000 0.301
## .ssmk (.41.) 0.448 0.052 8.625 0.000 0.347
## .ssmc 0.600 0.060 10.063 0.000 0.483
## .ssao (.43.) 0.300 0.048 6.291 0.000 0.207
## .ssai (.44.) 0.060 0.041 1.466 0.143 -0.020
## .sssi (.45.) 0.169 0.041 4.105 0.000 0.089
## .ssno 0.551 0.077 7.189 0.000 0.400
## .sscs (.47.) 0.358 0.051 6.957 0.000 0.257
## .verbal 0.137 0.121 1.132 0.258 -0.100
## .math -0.362 0.120 -3.007 0.003 -0.598
## .elctrnc 1.214 0.123 9.843 0.000 0.972
## .speed -0.870 0.134 -6.477 0.000 -1.134
## g 0.065 0.082 0.787 0.431 -0.097
## ci.upper Std.lv Std.all
## 0.510 0.412 0.428
## 0.440 0.340 0.345
## 0.176 0.062 0.066
## 0.285 0.194 0.193
## 0.493 0.397 0.423
## 0.550 0.448 0.476
## 0.717 0.600 0.637
## 0.393 0.300 0.301
## 0.140 0.060 0.059
## 0.250 0.169 0.183
## 0.701 0.551 0.524
## 0.458 0.358 0.363
## 0.373 0.038 0.038
## -0.126 -0.092 -0.092
## 1.456 0.851 0.851
## -0.607 -0.576 -0.576
## 0.226 0.065 0.065
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.195 0.022 9.026 0.000 0.153
## .sswk 0.237 0.023 10.452 0.000 0.193
## .sspc 0.258 0.029 8.908 0.000 0.201
## .ssei 0.361 0.038 9.530 0.000 0.286
## .ssar 0.218 0.027 8.096 0.000 0.166
## .ssmk 0.147 0.016 8.951 0.000 0.115
## .ssmc 0.318 0.031 10.283 0.000 0.258
## .ssao 0.531 0.052 10.279 0.000 0.430
## .ssai 0.518 0.062 8.295 0.000 0.396
## .sssi 0.335 0.051 6.634 0.000 0.236
## .ssno 0.444 0.058 7.656 0.000 0.330
## .sscs 0.441 0.069 6.380 0.000 0.305
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.237 0.195 0.210
## 0.282 0.237 0.244
## 0.315 0.258 0.288
## 0.435 0.361 0.358
## 0.271 0.218 0.248
## 0.180 0.147 0.166
## 0.379 0.318 0.359
## 0.632 0.531 0.534
## 0.641 0.518 0.503
## 0.434 0.335 0.390
## 0.558 0.444 0.402
## 0.576 0.441 0.455
## 1.000 0.079 0.079
## 1.000 0.065 0.065
## 1.000 0.492 0.492
## 1.000 0.438 0.438
## 1.000 1.000 1.000
latent2<-cfa(hof.lv, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("ssmc~1", "sspc~1", "ssno ~1"))
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 412.930 114.000 0.000 0.950 0.088 0.057 16441.121
## bic
## 16738.601
Mc(latent2)
## [1] 0.7997824
summary(latent2, standardized=T, ci=T) # g -.093 Std.all
## lavaan 0.6-18 ended normally after 113 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 93
## Number of equality constraints 27
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 412.930 363.572
## Degrees of freedom 114 114
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.136
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 163.543 143.995
## 0 249.387 219.578
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.217 0.054 4.027 0.000 0.112
## sswk (.p2.) 0.218 0.054 4.024 0.000 0.112
## sspc (.p3.) 0.204 0.050 4.090 0.000 0.106
## ssei (.p4.) 0.123 0.033 3.779 0.000 0.059
## math =~
## ssar (.p5.) 0.220 0.052 4.196 0.000 0.117
## ssmk (.p6.) 0.161 0.041 3.923 0.000 0.081
## ssmc (.p7.) 0.203 0.048 4.182 0.000 0.108
## ssao (.p8.) 0.184 0.046 4.030 0.000 0.094
## electronic =~
## ssai (.p9.) 0.325 0.040 8.137 0.000 0.247
## sssi (.10.) 0.319 0.042 7.697 0.000 0.238
## ssei (.11.) 0.176 0.024 7.285 0.000 0.128
## speed =~
## ssno (.12.) 0.533 0.049 10.789 0.000 0.436
## sscs (.13.) 0.476 0.040 11.922 0.000 0.398
## ssmk (.14.) 0.211 0.027 7.768 0.000 0.158
## g =~
## verbal (.15.) 3.579 0.952 3.759 0.000 1.713
## math (.16.) 3.416 0.866 3.944 0.000 1.719
## elctrnc (.17.) 1.424 0.211 6.756 0.000 1.011
## speed (.18.) 1.093 0.127 8.606 0.000 0.844
## ci.upper Std.lv Std.all
##
## 0.323 0.808 0.895
## 0.324 0.809 0.882
## 0.301 0.756 0.832
## 0.187 0.457 0.511
##
## 0.323 0.783 0.894
## 0.242 0.574 0.614
## 0.298 0.722 0.821
## 0.273 0.654 0.708
##
## 0.403 0.565 0.724
## 0.401 0.556 0.710
## 0.223 0.305 0.341
##
## 0.630 0.790 0.795
## 0.554 0.705 0.732
## 0.264 0.313 0.334
##
## 5.446 0.963 0.963
## 5.114 0.960 0.960
## 1.837 0.818 0.818
## 1.342 0.738 0.738
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.36.) 0.413 0.050 8.295 0.000 0.315
## .sswk (.37.) 0.341 0.051 6.682 0.000 0.241
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei (.39.) 0.192 0.046 4.127 0.000 0.101
## .ssar (.40.) 0.398 0.049 8.127 0.000 0.302
## .ssmk (.41.) 0.448 0.052 8.610 0.000 0.346
## .ssmc 0.263 0.048 5.461 0.000 0.169
## .ssao (.43.) 0.300 0.048 6.300 0.000 0.207
## .ssai (.44.) 0.056 0.041 1.368 0.171 -0.024
## .sssi (.45.) 0.176 0.041 4.258 0.000 0.095
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs (.47.) 0.359 0.051 6.970 0.000 0.258
## ci.upper Std.lv Std.all
## 0.510 0.413 0.457
## 0.441 0.341 0.371
## 0.545 0.445 0.489
## 0.282 0.192 0.214
## 0.494 0.398 0.454
## 0.550 0.448 0.479
## 0.358 0.263 0.300
## 0.394 0.300 0.325
## 0.136 0.056 0.072
## 0.256 0.176 0.224
## 0.395 0.285 0.287
## 0.459 0.359 0.372
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .ssgs 0.162 0.020 8.318 0.000 0.124
## .sswk 0.187 0.020 9.296 0.000 0.147
## .sspc 0.254 0.031 8.165 0.000 0.193
## .ssei 0.279 0.030 9.258 0.000 0.220
## .ssar 0.154 0.019 8.174 0.000 0.117
## .ssmk 0.192 0.021 9.038 0.000 0.151
## .ssmc 0.252 0.026 9.699 0.000 0.201
## .ssao 0.426 0.036 11.983 0.000 0.356
## .ssai 0.290 0.034 8.448 0.000 0.222
## .sssi 0.304 0.034 8.889 0.000 0.237
## .ssno 0.363 0.052 6.974 0.000 0.261
## .sscs 0.430 0.055 7.849 0.000 0.323
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.456 0.456
## 0.201 0.162 0.199
## 0.226 0.187 0.222
## 0.315 0.254 0.308
## 0.339 0.279 0.348
## 0.191 0.154 0.201
## 0.234 0.192 0.220
## 0.303 0.252 0.326
## 0.495 0.426 0.499
## 0.357 0.290 0.476
## 0.371 0.304 0.496
## 0.466 0.363 0.368
## 0.538 0.430 0.464
## 1.000 0.072 0.072
## 1.000 0.079 0.079
## 1.000 0.330 0.330
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.217 0.054 4.027 0.000 0.112
## sswk (.p2.) 0.218 0.054 4.024 0.000 0.112
## sspc (.p3.) 0.204 0.050 4.090 0.000 0.106
## ssei (.p4.) 0.123 0.033 3.779 0.000 0.059
## math =~
## ssar (.p5.) 0.220 0.052 4.196 0.000 0.117
## ssmk (.p6.) 0.161 0.041 3.923 0.000 0.081
## ssmc (.p7.) 0.203 0.048 4.182 0.000 0.108
## ssao (.p8.) 0.184 0.046 4.030 0.000 0.094
## electronic =~
## ssai (.p9.) 0.325 0.040 8.137 0.000 0.247
## sssi (.10.) 0.319 0.042 7.697 0.000 0.238
## ssei (.11.) 0.176 0.024 7.285 0.000 0.128
## speed =~
## ssno (.12.) 0.533 0.049 10.789 0.000 0.436
## sscs (.13.) 0.476 0.040 11.922 0.000 0.398
## ssmk (.14.) 0.211 0.027 7.768 0.000 0.158
## g =~
## verbal (.15.) 3.579 0.952 3.759 0.000 1.713
## math (.16.) 3.416 0.866 3.944 0.000 1.719
## elctrnc (.17.) 1.424 0.211 6.756 0.000 1.011
## speed (.18.) 1.093 0.127 8.606 0.000 0.844
## ci.upper Std.lv Std.all
##
## 0.323 0.906 0.898
## 0.324 0.907 0.881
## 0.301 0.848 0.861
## 0.187 0.513 0.489
##
## 0.323 0.848 0.879
## 0.242 0.622 0.638
## 0.298 0.781 0.806
## 0.273 0.708 0.697
##
## 0.403 0.864 0.785
## 0.401 0.850 0.846
## 0.223 0.467 0.445
##
## 0.630 0.836 0.782
## 0.554 0.747 0.747
## 0.264 0.331 0.339
##
## 5.446 0.950 0.950
## 5.114 0.980 0.980
## 1.837 0.592 0.592
## 1.342 0.770 0.770
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.36.) 0.413 0.050 8.295 0.000 0.315
## .sswk (.37.) 0.341 0.051 6.682 0.000 0.241
## .sspc 0.063 0.058 1.086 0.277 -0.051
## .ssei (.39.) 0.192 0.046 4.127 0.000 0.101
## .ssar (.40.) 0.398 0.049 8.127 0.000 0.302
## .ssmk (.41.) 0.448 0.052 8.610 0.000 0.346
## .ssmc 0.601 0.059 10.095 0.000 0.484
## .ssao (.43.) 0.300 0.048 6.300 0.000 0.207
## .ssai (.44.) 0.056 0.041 1.368 0.171 -0.024
## .sssi (.45.) 0.176 0.041 4.258 0.000 0.095
## .ssno 0.553 0.077 7.190 0.000 0.402
## .sscs (.47.) 0.359 0.051 6.970 0.000 0.258
## .verbal 0.022 0.138 0.157 0.876 -0.249
## .math -0.464 0.155 -2.995 0.003 -0.767
## .elctrnc 1.855 0.276 6.729 0.000 1.315
## .speed -0.922 0.140 -6.571 0.000 -1.196
## g 0.103 0.088 1.170 0.242 -0.070
## ci.upper Std.lv Std.all
## 0.510 0.413 0.409
## 0.441 0.341 0.331
## 0.177 0.063 0.064
## 0.282 0.192 0.183
## 0.494 0.398 0.412
## 0.550 0.448 0.459
## 0.717 0.601 0.620
## 0.394 0.300 0.295
## 0.136 0.056 0.051
## 0.256 0.176 0.175
## 0.703 0.553 0.517
## 0.459 0.359 0.358
## 0.292 0.005 0.005
## -0.160 -0.120 -0.120
## 2.396 0.698 0.698
## -0.647 -0.588 -0.588
## 0.276 0.093 0.093
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .ssgs 0.197 0.023 8.706 0.000 0.152
## .sswk 0.238 0.023 10.380 0.000 0.193
## .sspc 0.250 0.028 8.793 0.000 0.194
## .ssei 0.350 0.036 9.624 0.000 0.279
## .ssar 0.212 0.027 7.932 0.000 0.159
## .ssmk 0.143 0.016 8.861 0.000 0.111
## .ssmc 0.329 0.032 10.367 0.000 0.267
## .ssao 0.531 0.052 10.286 0.000 0.430
## .ssai 0.465 0.061 7.596 0.000 0.345
## .sssi 0.287 0.049 5.878 0.000 0.191
## .ssno 0.444 0.058 7.679 0.000 0.331
## .sscs 0.443 0.069 6.388 0.000 0.307
## .verbal 1.706 0.856 1.994 0.046 0.029
## .math 0.584 0.491 1.188 0.235 -0.379
## .electronic 4.596 1.224 3.755 0.000 2.197
## g 1.222 0.152 8.064 0.000 0.925
## ci.upper Std.lv Std.all
## 1.000 0.407 0.407
## 0.241 0.197 0.193
## 0.283 0.238 0.224
## 0.306 0.250 0.258
## 0.421 0.350 0.318
## 0.264 0.212 0.228
## 0.175 0.143 0.151
## 0.391 0.329 0.350
## 0.632 0.531 0.514
## 0.585 0.465 0.384
## 0.383 0.287 0.285
## 0.557 0.444 0.389
## 0.579 0.443 0.443
## 3.384 0.098 0.098
## 1.547 0.039 0.039
## 6.995 0.650 0.650
## 1.519 1.000 1.000
weak<-cfa(hof.weak, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("ssmc~1", "sspc~1", "ssno ~1"))
fitMeasures(weak, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 412.930 115.000 0.000 0.950 0.088 0.057 16439.121
## bic
## 16732.094
Mc(weak)
## [1] 0.8003804
summary(weak, standardized=T, ci=T) # g -.099 Std.all
## lavaan 0.6-18 ended normally after 116 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 92
## Number of equality constraints 27
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 412.930 366.762
## Degrees of freedom 115 115
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.126
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 163.543 145.258
## 0 249.387 221.504
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.217 0.054 4.027 0.000 0.112
## sswk (.p2.) 0.218 0.054 4.024 0.000 0.112
## sspc (.p3.) 0.204 0.050 4.090 0.000 0.106
## ssei (.p4.) 0.123 0.033 3.779 0.000 0.059
## math =~
## ssar (.p5.) 0.220 0.052 4.196 0.000 0.117
## ssmk (.p6.) 0.161 0.041 3.923 0.000 0.081
## ssmc (.p7.) 0.203 0.048 4.182 0.000 0.108
## ssao (.p8.) 0.184 0.046 4.030 0.000 0.094
## electronic =~
## ssai (.p9.) 0.325 0.040 8.138 0.000 0.247
## sssi (.10.) 0.319 0.042 7.697 0.000 0.238
## ssei (.11.) 0.176 0.024 7.285 0.000 0.128
## speed =~
## ssno (.12.) 0.533 0.049 10.789 0.000 0.436
## sscs (.13.) 0.476 0.040 11.922 0.000 0.398
## ssmk (.14.) 0.211 0.027 7.768 0.000 0.158
## g =~
## verbal (.15.) 3.579 0.952 3.759 0.000 1.713
## math (.16.) 3.416 0.866 3.945 0.000 1.719
## elctrnc (.17.) 1.424 0.211 6.756 0.000 1.011
## speed (.18.) 1.093 0.127 8.606 0.000 0.844
## ci.upper Std.lv Std.all
##
## 0.323 0.808 0.895
## 0.324 0.809 0.882
## 0.301 0.756 0.832
## 0.187 0.457 0.511
##
## 0.323 0.783 0.894
## 0.242 0.574 0.614
## 0.298 0.722 0.821
## 0.273 0.654 0.708
##
## 0.403 0.565 0.724
## 0.401 0.556 0.710
## 0.223 0.305 0.341
##
## 0.630 0.790 0.795
## 0.554 0.705 0.732
## 0.264 0.313 0.334
##
## 5.446 0.963 0.963
## 5.113 0.960 0.960
## 1.837 0.818 0.818
## 1.342 0.738 0.738
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.37.) 0.413 0.050 8.295 0.000 0.315
## .sswk (.38.) 0.341 0.051 6.682 0.000 0.241
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei (.40.) 0.192 0.046 4.127 0.000 0.101
## .ssar (.41.) 0.398 0.049 8.127 0.000 0.302
## .ssmk (.42.) 0.448 0.052 8.610 0.000 0.346
## .ssmc 0.263 0.048 5.461 0.000 0.169
## .ssao (.44.) 0.300 0.048 6.300 0.000 0.207
## .ssai (.45.) 0.056 0.041 1.368 0.171 -0.024
## .sssi (.46.) 0.176 0.041 4.258 0.000 0.095
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs (.48.) 0.359 0.051 6.970 0.000 0.258
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.510 0.413 0.457
## 0.441 0.341 0.371
## 0.545 0.445 0.489
## 0.282 0.192 0.214
## 0.494 0.398 0.454
## 0.550 0.448 0.479
## 0.358 0.263 0.300
## 0.394 0.300 0.325
## 0.136 0.056 0.072
## 0.256 0.176 0.224
## 0.395 0.285 0.287
## 0.459 0.359 0.372
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .ssgs 0.162 0.020 8.318 0.000 0.124
## .sswk 0.187 0.020 9.296 0.000 0.147
## .sspc 0.254 0.031 8.165 0.000 0.193
## .ssei 0.279 0.030 9.258 0.000 0.220
## .ssar 0.154 0.019 8.174 0.000 0.117
## .ssmk 0.192 0.021 9.038 0.000 0.151
## .ssmc 0.252 0.026 9.699 0.000 0.201
## .ssao 0.426 0.036 11.983 0.000 0.356
## .ssai 0.290 0.034 8.448 0.000 0.222
## .sssi 0.304 0.034 8.889 0.000 0.237
## .ssno 0.363 0.052 6.974 0.000 0.261
## .sscs 0.430 0.055 7.849 0.000 0.323
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.456 0.456
## 0.201 0.162 0.199
## 0.226 0.187 0.222
## 0.315 0.254 0.308
## 0.339 0.279 0.348
## 0.191 0.154 0.201
## 0.234 0.192 0.220
## 0.303 0.252 0.326
## 0.495 0.426 0.499
## 0.357 0.290 0.476
## 0.371 0.304 0.496
## 0.466 0.363 0.368
## 0.538 0.430 0.464
## 1.000 0.072 0.072
## 1.000 0.079 0.079
## 1.000 0.330 0.330
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.217 0.054 4.027 0.000 0.112
## sswk (.p2.) 0.218 0.054 4.024 0.000 0.112
## sspc (.p3.) 0.204 0.050 4.090 0.000 0.106
## ssei (.p4.) 0.123 0.033 3.779 0.000 0.059
## math =~
## ssar (.p5.) 0.220 0.052 4.196 0.000 0.117
## ssmk (.p6.) 0.161 0.041 3.923 0.000 0.081
## ssmc (.p7.) 0.203 0.048 4.182 0.000 0.108
## ssao (.p8.) 0.184 0.046 4.030 0.000 0.094
## electronic =~
## ssai (.p9.) 0.325 0.040 8.138 0.000 0.247
## sssi (.10.) 0.319 0.042 7.697 0.000 0.238
## ssei (.11.) 0.176 0.024 7.285 0.000 0.128
## speed =~
## ssno (.12.) 0.533 0.049 10.789 0.000 0.436
## sscs (.13.) 0.476 0.040 11.922 0.000 0.398
## ssmk (.14.) 0.211 0.027 7.768 0.000 0.158
## g =~
## verbal (.15.) 3.579 0.952 3.759 0.000 1.713
## math (.16.) 3.416 0.866 3.945 0.000 1.719
## elctrnc (.17.) 1.424 0.211 6.756 0.000 1.011
## speed (.18.) 1.093 0.127 8.606 0.000 0.844
## ci.upper Std.lv Std.all
##
## 0.323 0.906 0.898
## 0.324 0.907 0.881
## 0.301 0.848 0.861
## 0.187 0.513 0.489
##
## 0.323 0.848 0.879
## 0.242 0.622 0.638
## 0.298 0.781 0.806
## 0.273 0.708 0.697
##
## 0.403 0.864 0.785
## 0.401 0.850 0.846
## 0.223 0.467 0.445
##
## 0.630 0.836 0.782
## 0.554 0.747 0.747
## 0.264 0.331 0.339
##
## 5.446 0.950 0.950
## 5.113 0.980 0.980
## 1.837 0.592 0.592
## 1.342 0.770 0.770
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.37.) 0.413 0.050 8.295 0.000 0.315
## .sswk (.38.) 0.341 0.051 6.682 0.000 0.241
## .sspc 0.063 0.058 1.086 0.277 -0.051
## .ssei (.40.) 0.192 0.046 4.127 0.000 0.101
## .ssar (.41.) 0.398 0.049 8.127 0.000 0.302
## .ssmk (.42.) 0.448 0.052 8.610 0.000 0.346
## .ssmc 0.601 0.059 10.095 0.000 0.484
## .ssao (.44.) 0.300 0.048 6.300 0.000 0.207
## .ssai (.45.) 0.056 0.041 1.368 0.171 -0.024
## .sssi (.46.) 0.176 0.041 4.258 0.000 0.095
## .ssno 0.553 0.077 7.190 0.000 0.402
## .sscs (.48.) 0.359 0.051 6.970 0.000 0.258
## .math -0.484 0.270 -1.793 0.073 -1.014
## .elctrnc 1.847 0.297 6.224 0.000 1.265
## .speed -0.928 0.147 -6.316 0.000 -1.216
## g 0.109 0.094 1.164 0.244 -0.075
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.510 0.413 0.409
## 0.441 0.341 0.331
## 0.177 0.063 0.064
## 0.282 0.192 0.183
## 0.494 0.398 0.412
## 0.550 0.448 0.459
## 0.717 0.601 0.620
## 0.394 0.300 0.295
## 0.136 0.056 0.051
## 0.256 0.176 0.175
## 0.703 0.553 0.517
## 0.459 0.359 0.358
## 0.045 -0.126 -0.126
## 2.428 0.694 0.694
## -0.640 -0.592 -0.592
## 0.293 0.099 0.099
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .ssgs 0.197 0.023 8.706 0.000 0.152
## .sswk 0.238 0.023 10.380 0.000 0.193
## .sspc 0.250 0.028 8.793 0.000 0.194
## .ssei 0.350 0.036 9.624 0.000 0.279
## .ssar 0.212 0.027 7.932 0.000 0.159
## .ssmk 0.143 0.016 8.861 0.000 0.111
## .ssmc 0.329 0.032 10.367 0.000 0.267
## .ssao 0.531 0.052 10.286 0.000 0.430
## .ssai 0.465 0.061 7.596 0.000 0.345
## .sssi 0.287 0.049 5.878 0.000 0.191
## .ssno 0.444 0.058 7.679 0.000 0.331
## .sscs 0.443 0.069 6.388 0.000 0.307
## .verbal 1.706 0.856 1.993 0.046 0.029
## .math 0.584 0.491 1.188 0.235 -0.379
## .electronic 4.596 1.224 3.755 0.000 2.197
## g 1.222 0.152 8.064 0.000 0.925
## ci.upper Std.lv Std.all
## 1.000 0.407 0.407
## 0.241 0.197 0.193
## 0.283 0.238 0.224
## 0.306 0.250 0.258
## 0.421 0.350 0.318
## 0.264 0.212 0.228
## 0.175 0.143 0.151
## 0.391 0.329 0.350
## 0.632 0.531 0.514
## 0.585 0.465 0.384
## 0.383 0.287 0.285
## 0.557 0.444 0.389
## 0.579 0.443 0.443
## 3.384 0.098 0.098
## 1.547 0.039 0.039
## 6.995 0.650 0.650
## 1.519 1.000 1.000
tests<-lavTestLRT(configural, metric, scalar2, latent2, weak)
## Warning: lavaan->lav_test_diff_SatorraBentler2001():
## scaling factor is negative
Td=tests[2:5,"Chisq diff"]
Td
## [1] 19.4453661 13.8879270 0.8992955 NA
dfd=tests[2:5,"Df diff"]
dfd
## [1] 13 4 1 1
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-335+ 335 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
## Warning in sqrt((ld) * G/(N - G)): NaNs produced
RMSEAD
## [1] 0.03852821 0.08602990 NaN NA
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] NA 0.07179863
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.03959879 0.13731095
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] NA 0.1416794
RMSEA.CI(T=Td[4],df=dfd[4],N=N,G=2)
## [1] NA NA
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.890 0.871 0.326 0.162 0.017 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.992 0.991 0.908 0.843 0.634 0.367
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.657 0.649 0.482 0.421 0.296 0.187
round(pvals(T=Td[4],df=dfd[4],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## NA NA NA NA NA NA
tests<-lavTestLRT(configural, metric, scalar2, latent)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 19.44537 13.88793 50.04972
dfd=tests[2:4,"Df diff"]
dfd
## [1] 13 4 5
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-335+ 335 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.03852821 0.08602990 0.16424335
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.03959879 0.13731095
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.1246301 0.2068771
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.992 0.991 0.908 0.843 0.634 0.367
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 1.000 1.000 0.996
tests<-lavTestLRT(configural, metric, scalar2, strict)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 19.44537 13.88793 26.97381
dfd=tests[2:4,"Df diff"]
dfd
## [1] 13 4 12
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-335+ 335 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.03852821 0.08602990 0.06112266
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] NA 0.07179863
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.03959879 0.13731095
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.02997141 0.09209679
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.890 0.871 0.326 0.162 0.017 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.992 0.991 0.908 0.843 0.634 0.367
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.992 0.990 0.754 0.564 0.172 0.018
tests<-lavTestLRT(configural, metric, scalar)
Td=tests[2:3,"Chisq diff"]
Td
## [1] 19.44537 132.98405
dfd=tests[2:3,"Df diff"]
dfd
## [1] 13 7
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-335+ 335 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.03852821 0.23213227
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] NA 0.07179863
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.1984324 0.2672276
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.890 0.871 0.326 0.162 0.017 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
hof.age<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
verbal~0*1
g ~ agec
'
hof.ageq<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
verbal~0*1
g ~ c(a,a)*agec
'
hof.age2<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
verbal~0*1
g ~ agec+agec2
'
hof.age2q<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
verbal~0*1
g ~ c(a,a)*agec+c(b,b)*agec2
'
sem.age<-sem(hof.age, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("ssmc~1", "sspc~1", "ssno ~1"))
fitMeasures(sem.age, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 552.412 137.000 0.000 0.934 0.095 0.062 1.024
## aic bic
## 16297.492 16599.480
Mc(sem.age)
## [1] 0.7331006
summary(sem.age, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 117 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 94
## Number of equality constraints 27
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 552.412 488.722
## Degrees of freedom 137 137
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.130
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 227.553 201.318
## 0 324.859 287.404
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.189 0.053 3.555 0.000 0.085
## sswk (.p2.) 0.190 0.054 3.543 0.000 0.085
## sspc (.p3.) 0.176 0.049 3.582 0.000 0.080
## ssei (.p4.) 0.107 0.031 3.412 0.001 0.045
## math =~
## ssar (.p5.) 0.254 0.042 6.071 0.000 0.172
## ssmk (.p6.) 0.188 0.035 5.449 0.000 0.120
## ssmc (.p7.) 0.234 0.039 6.044 0.000 0.158
## ssao (.p8.) 0.212 0.037 5.710 0.000 0.139
## electronic =~
## ssai (.p9.) 0.319 0.040 7.875 0.000 0.240
## sssi (.10.) 0.313 0.042 7.448 0.000 0.231
## ssei (.11.) 0.171 0.024 7.122 0.000 0.124
## speed =~
## ssno (.12.) 0.529 0.050 10.670 0.000 0.432
## sscs (.13.) 0.474 0.040 11.833 0.000 0.396
## ssmk (.14.) 0.208 0.027 7.834 0.000 0.156
## g =~
## verbal (.15.) 3.693 1.103 3.348 0.001 1.531
## math (.16.) 2.600 0.480 5.419 0.000 1.660
## elctrnc (.17.) 1.311 0.198 6.636 0.000 0.924
## speed (.18.) 0.983 0.115 8.516 0.000 0.756
## ci.upper Std.lv Std.all
##
## 0.293 0.807 0.894
## 0.295 0.810 0.884
## 0.273 0.753 0.829
## 0.168 0.456 0.509
##
## 0.336 0.784 0.893
## 0.256 0.581 0.621
## 0.310 0.724 0.821
## 0.285 0.655 0.709
##
## 0.398 0.568 0.726
## 0.395 0.557 0.710
## 0.219 0.305 0.341
##
## 0.626 0.788 0.793
## 0.553 0.706 0.733
## 0.260 0.310 0.332
##
## 5.856 0.972 0.972
## 3.541 0.946 0.946
## 1.698 0.827 0.827
## 1.209 0.741 0.741
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.337 0.044 7.694 0.000 0.251
## ci.upper Std.lv Std.all
##
## 0.423 0.300 0.455
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.39.) 0.391 0.045 8.603 0.000 0.302
## .sswk (.40.) 0.319 0.046 6.926 0.000 0.229
## .sspc 0.424 0.049 8.733 0.000 0.329
## .ssei (.42.) 0.173 0.043 4.025 0.000 0.089
## .ssar (.43.) 0.377 0.047 8.020 0.000 0.285
## .ssmk (.44.) 0.425 0.047 9.044 0.000 0.333
## .ssmc 0.244 0.047 5.243 0.000 0.153
## .ssao (.46.) 0.283 0.046 6.175 0.000 0.193
## .ssai (.47.) 0.042 0.038 1.099 0.272 -0.033
## .sssi (.48.) 0.163 0.039 4.140 0.000 0.086
## .ssno 0.269 0.053 5.082 0.000 0.165
## .sscs (.50.) 0.344 0.047 7.249 0.000 0.251
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.480 0.391 0.434
## 0.409 0.319 0.348
## 0.519 0.424 0.467
## 0.257 0.173 0.192
## 0.469 0.377 0.429
## 0.518 0.425 0.455
## 0.335 0.244 0.277
## 0.373 0.283 0.306
## 0.118 0.042 0.054
## 0.240 0.163 0.207
## 0.373 0.269 0.271
## 0.437 0.344 0.357
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .ssgs 0.164 0.019 8.559 0.000 0.126
## .sswk 0.183 0.020 9.305 0.000 0.144
## .sspc 0.257 0.031 8.340 0.000 0.197
## .ssei 0.278 0.030 9.261 0.000 0.219
## .ssar 0.156 0.019 8.052 0.000 0.118
## .ssmk 0.189 0.021 9.000 0.000 0.148
## .ssmc 0.253 0.026 9.563 0.000 0.201
## .ssao 0.425 0.036 11.943 0.000 0.355
## .ssai 0.289 0.034 8.490 0.000 0.223
## .sssi 0.306 0.034 8.980 0.000 0.239
## .ssno 0.366 0.053 6.958 0.000 0.263
## .sscs 0.429 0.055 7.853 0.000 0.322
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.451 0.451
## 0.201 0.164 0.201
## 0.221 0.183 0.218
## 0.318 0.257 0.312
## 0.337 0.278 0.346
## 0.194 0.156 0.203
## 0.230 0.189 0.216
## 0.305 0.253 0.326
## 0.494 0.425 0.497
## 0.356 0.289 0.473
## 0.373 0.306 0.497
## 0.469 0.366 0.370
## 0.536 0.429 0.462
## 1.000 0.055 0.055
## 1.000 0.105 0.105
## 1.000 0.316 0.316
## 1.000 0.793 0.793
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.189 0.053 3.555 0.000 0.085
## sswk (.p2.) 0.190 0.054 3.543 0.000 0.085
## sspc (.p3.) 0.176 0.049 3.582 0.000 0.080
## ssei (.p4.) 0.107 0.031 3.412 0.001 0.045
## math =~
## ssar (.p5.) 0.254 0.042 6.071 0.000 0.172
## ssmk (.p6.) 0.188 0.035 5.449 0.000 0.120
## ssmc (.p7.) 0.234 0.039 6.044 0.000 0.158
## ssao (.p8.) 0.212 0.037 5.710 0.000 0.139
## electronic =~
## ssai (.p9.) 0.319 0.040 7.875 0.000 0.240
## sssi (.10.) 0.313 0.042 7.448 0.000 0.231
## ssei (.11.) 0.171 0.024 7.122 0.000 0.124
## speed =~
## ssno (.12.) 0.529 0.050 10.670 0.000 0.432
## sscs (.13.) 0.474 0.040 11.833 0.000 0.396
## ssmk (.14.) 0.208 0.027 7.834 0.000 0.156
## g =~
## verbal (.15.) 3.693 1.103 3.348 0.001 1.531
## math (.16.) 2.600 0.480 5.419 0.000 1.660
## elctrnc (.17.) 1.311 0.198 6.636 0.000 0.924
## speed (.18.) 0.983 0.115 8.516 0.000 0.756
## ci.upper Std.lv Std.all
##
## 0.293 0.908 0.899
## 0.295 0.912 0.883
## 0.273 0.848 0.860
## 0.168 0.514 0.489
##
## 0.336 0.844 0.876
## 0.256 0.626 0.643
## 0.310 0.779 0.805
## 0.285 0.706 0.695
##
## 0.398 0.860 0.784
## 0.395 0.844 0.843
## 0.219 0.463 0.441
##
## 0.626 0.834 0.781
## 0.553 0.748 0.748
## 0.260 0.329 0.338
##
## 5.856 0.954 0.954
## 3.541 0.970 0.970
## 1.698 0.603 0.603
## 1.209 0.773 0.773
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.407 0.051 7.951 0.000 0.307
## ci.upper Std.lv Std.all
##
## 0.508 0.328 0.467
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.39.) 0.391 0.045 8.603 0.000 0.302
## .sswk (.40.) 0.319 0.046 6.926 0.000 0.229
## .sspc 0.043 0.054 0.796 0.426 -0.063
## .ssei (.42.) 0.173 0.043 4.025 0.000 0.089
## .ssar (.43.) 0.377 0.047 8.020 0.000 0.285
## .ssmk (.44.) 0.425 0.047 9.044 0.000 0.333
## .ssmc 0.582 0.058 10.082 0.000 0.469
## .ssao (.46.) 0.283 0.046 6.175 0.000 0.193
## .ssai (.47.) 0.042 0.038 1.099 0.272 -0.033
## .sssi (.48.) 0.163 0.039 4.140 0.000 0.086
## .ssno 0.535 0.073 7.310 0.000 0.392
## .sscs (.50.) 0.344 0.047 7.249 0.000 0.251
## .math -0.412 0.214 -1.931 0.053 -0.831
## .elctrnc 1.885 0.309 6.101 0.000 1.280
## .speed -0.932 0.148 -6.283 0.000 -1.222
## .g 0.202 0.096 2.098 0.036 0.013
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.480 0.391 0.387
## 0.409 0.319 0.309
## 0.150 0.043 0.044
## 0.257 0.173 0.164
## 0.469 0.377 0.391
## 0.518 0.425 0.437
## 0.695 0.582 0.602
## 0.373 0.283 0.279
## 0.118 0.042 0.039
## 0.240 0.163 0.163
## 0.679 0.535 0.501
## 0.437 0.344 0.344
## 0.006 -0.124 -0.124
## 2.491 0.699 0.699
## -0.641 -0.591 -0.591
## 0.390 0.163 0.163
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .ssgs 0.196 0.022 8.762 0.000 0.152
## .sswk 0.234 0.023 10.366 0.000 0.190
## .sspc 0.254 0.029 8.816 0.000 0.197
## .ssei 0.350 0.036 9.624 0.000 0.279
## .ssar 0.215 0.027 8.002 0.000 0.162
## .ssmk 0.139 0.016 8.836 0.000 0.109
## .ssmc 0.329 0.032 10.347 0.000 0.266
## .ssao 0.534 0.052 10.241 0.000 0.432
## .ssai 0.463 0.061 7.587 0.000 0.343
## .sssi 0.290 0.049 5.960 0.000 0.194
## .ssno 0.446 0.058 7.702 0.000 0.333
## .sscs 0.441 0.069 6.404 0.000 0.306
## .verbal 2.087 1.211 1.724 0.085 -0.286
## .math 0.645 0.358 1.804 0.071 -0.056
## .electronic 4.633 1.274 3.636 0.000 2.136
## .g 1.203 0.169 7.115 0.000 0.872
## ci.upper Std.lv Std.all
## 1.000 0.402 0.402
## 0.240 0.196 0.192
## 0.279 0.234 0.220
## 0.310 0.254 0.261
## 0.421 0.350 0.318
## 0.268 0.215 0.232
## 0.170 0.139 0.147
## 0.391 0.329 0.351
## 0.636 0.534 0.517
## 0.582 0.463 0.385
## 0.385 0.290 0.289
## 0.560 0.446 0.391
## 0.576 0.441 0.441
## 4.461 0.090 0.090
## 1.346 0.058 0.058
## 7.131 0.636 0.636
## 1.535 0.782 0.782
sem.ageq<-sem(hof.ageq, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("ssmc~1", "sspc~1", "ssno ~1"))
fitMeasures(sem.ageq, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 553.838 138.000 0.000 0.934 0.095 0.069 1.024
## aic bic
## 16296.918 16594.398
Mc(sem.ageq)
## [1] 0.7328674
summary(sem.ageq, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 117 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 94
## Number of equality constraints 28
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 553.838 490.020
## Degrees of freedom 138 138
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.130
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 227.992 201.720
## 0 325.846 288.299
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.187 0.053 3.531 0.000 0.083
## sswk (.p2.) 0.188 0.054 3.518 0.000 0.083
## sspc (.p3.) 0.175 0.049 3.557 0.000 0.079
## ssei (.p4.) 0.106 0.031 3.392 0.001 0.045
## math =~
## ssar (.p5.) 0.256 0.041 6.183 0.000 0.175
## ssmk (.p6.) 0.190 0.034 5.532 0.000 0.122
## ssmc (.p7.) 0.236 0.038 6.151 0.000 0.161
## ssao (.p8.) 0.214 0.037 5.802 0.000 0.142
## electronic =~
## ssai (.p9.) 0.319 0.041 7.865 0.000 0.239
## sssi (.10.) 0.313 0.042 7.434 0.000 0.230
## ssei (.11.) 0.171 0.024 7.119 0.000 0.124
## speed =~
## ssno (.12.) 0.529 0.050 10.662 0.000 0.432
## sscs (.13.) 0.474 0.040 11.825 0.000 0.396
## ssmk (.14.) 0.208 0.027 7.825 0.000 0.156
## g =~
## verbal (.15.) 3.728 1.121 3.325 0.001 1.531
## math (.16.) 2.586 0.469 5.514 0.000 1.667
## elctrnc (.17.) 1.311 0.198 6.623 0.000 0.923
## speed (.18.) 0.984 0.116 8.512 0.000 0.757
## ci.upper Std.lv Std.all
##
## 0.291 0.821 0.897
## 0.293 0.825 0.888
## 0.272 0.767 0.834
## 0.167 0.464 0.512
##
## 0.337 0.798 0.896
## 0.257 0.591 0.624
## 0.311 0.736 0.826
## 0.286 0.667 0.715
##
## 0.398 0.574 0.730
## 0.395 0.563 0.713
## 0.219 0.309 0.341
##
## 0.626 0.797 0.796
## 0.553 0.714 0.737
## 0.261 0.314 0.331
##
## 5.926 0.974 0.974
## 3.505 0.947 0.947
## 1.700 0.832 0.832
## 1.210 0.747 0.747
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.366 0.036 10.266 0.000 0.296
## ci.upper Std.lv Std.all
##
## 0.436 0.320 0.485
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.39.) 0.389 0.045 8.559 0.000 0.300
## .sswk (.40.) 0.317 0.046 6.895 0.000 0.227
## .sspc 0.423 0.049 8.687 0.000 0.327
## .ssei (.42.) 0.171 0.043 3.988 0.000 0.087
## .ssar (.43.) 0.375 0.047 7.946 0.000 0.283
## .ssmk (.44.) 0.424 0.047 9.005 0.000 0.331
## .ssmc 0.243 0.047 5.190 0.000 0.151
## .ssao (.46.) 0.282 0.046 6.122 0.000 0.191
## .ssai (.47.) 0.041 0.038 1.071 0.284 -0.034
## .sssi (.48.) 0.162 0.039 4.116 0.000 0.085
## .ssno 0.268 0.053 5.060 0.000 0.164
## .sscs (.50.) 0.343 0.047 7.247 0.000 0.250
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.479 0.389 0.426
## 0.407 0.317 0.341
## 0.518 0.423 0.460
## 0.255 0.171 0.188
## 0.468 0.375 0.421
## 0.516 0.424 0.447
## 0.334 0.243 0.272
## 0.372 0.282 0.302
## 0.116 0.041 0.052
## 0.239 0.162 0.205
## 0.371 0.268 0.268
## 0.435 0.343 0.354
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .ssgs 0.164 0.019 8.581 0.000 0.126
## .sswk 0.182 0.020 9.307 0.000 0.144
## .sspc 0.257 0.031 8.343 0.000 0.197
## .ssei 0.278 0.030 9.262 0.000 0.219
## .ssar 0.157 0.019 8.050 0.000 0.118
## .ssmk 0.189 0.021 9.006 0.000 0.148
## .ssmc 0.253 0.026 9.558 0.000 0.201
## .ssao 0.425 0.036 11.942 0.000 0.355
## .ssai 0.289 0.034 8.493 0.000 0.223
## .sssi 0.306 0.034 8.981 0.000 0.239
## .ssno 0.366 0.053 6.958 0.000 0.263
## .sscs 0.429 0.055 7.854 0.000 0.322
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.441 0.441
## 0.201 0.164 0.196
## 0.221 0.182 0.211
## 0.318 0.257 0.305
## 0.337 0.278 0.339
## 0.195 0.157 0.197
## 0.230 0.189 0.210
## 0.305 0.253 0.318
## 0.494 0.425 0.488
## 0.356 0.289 0.467
## 0.373 0.306 0.491
## 0.469 0.366 0.366
## 0.536 0.429 0.457
## 1.000 0.052 0.052
## 1.000 0.103 0.103
## 1.000 0.308 0.308
## 1.000 0.765 0.765
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.187 0.053 3.531 0.000 0.083
## sswk (.p2.) 0.188 0.054 3.518 0.000 0.083
## sspc (.p3.) 0.175 0.049 3.557 0.000 0.079
## ssei (.p4.) 0.106 0.031 3.392 0.001 0.045
## math =~
## ssar (.p5.) 0.256 0.041 6.183 0.000 0.175
## ssmk (.p6.) 0.190 0.034 5.532 0.000 0.122
## ssmc (.p7.) 0.236 0.038 6.151 0.000 0.161
## ssao (.p8.) 0.214 0.037 5.802 0.000 0.142
## electronic =~
## ssai (.p9.) 0.319 0.041 7.865 0.000 0.239
## sssi (.10.) 0.313 0.042 7.434 0.000 0.230
## ssei (.11.) 0.171 0.024 7.119 0.000 0.124
## speed =~
## ssno (.12.) 0.529 0.050 10.662 0.000 0.432
## sscs (.13.) 0.474 0.040 11.825 0.000 0.396
## ssmk (.14.) 0.208 0.027 7.825 0.000 0.156
## g =~
## verbal (.15.) 3.728 1.121 3.325 0.001 1.531
## math (.16.) 2.586 0.469 5.514 0.000 1.667
## elctrnc (.17.) 1.311 0.198 6.623 0.000 0.923
## speed (.18.) 0.984 0.116 8.512 0.000 0.757
## ci.upper Std.lv Std.all
##
## 0.291 0.892 0.895
## 0.293 0.896 0.880
## 0.272 0.833 0.856
## 0.167 0.504 0.485
##
## 0.337 0.828 0.873
## 0.257 0.614 0.641
## 0.311 0.764 0.800
## 0.286 0.693 0.688
##
## 0.398 0.855 0.782
## 0.395 0.838 0.842
## 0.219 0.460 0.443
##
## 0.626 0.825 0.777
## 0.553 0.739 0.744
## 0.261 0.325 0.339
##
## 5.926 0.952 0.952
## 3.505 0.970 0.970
## 1.700 0.594 0.594
## 1.210 0.767 0.767
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.366 0.036 10.266 0.000 0.296
## ci.upper Std.lv Std.all
##
## 0.436 0.301 0.429
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.39.) 0.389 0.045 8.559 0.000 0.300
## .sswk (.40.) 0.317 0.046 6.895 0.000 0.227
## .sspc 0.042 0.054 0.765 0.445 -0.065
## .ssei (.42.) 0.171 0.043 3.988 0.000 0.087
## .ssar (.43.) 0.375 0.047 7.946 0.000 0.283
## .ssmk (.44.) 0.424 0.047 9.005 0.000 0.331
## .ssmc 0.580 0.058 10.027 0.000 0.467
## .ssao (.46.) 0.282 0.046 6.122 0.000 0.191
## .ssai (.47.) 0.041 0.038 1.071 0.284 -0.034
## .sssi (.48.) 0.162 0.039 4.116 0.000 0.085
## .ssno 0.534 0.073 7.303 0.000 0.391
## .sscs (.50.) 0.343 0.047 7.247 0.000 0.250
## .math -0.409 0.211 -1.937 0.053 -0.824
## .elctrnc 1.888 0.310 6.097 0.000 1.281
## .speed -0.932 0.148 -6.278 0.000 -1.222
## .g 0.199 0.096 2.078 0.038 0.011
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.479 0.389 0.391
## 0.407 0.317 0.311
## 0.148 0.042 0.043
## 0.255 0.171 0.165
## 0.468 0.375 0.395
## 0.516 0.424 0.442
## 0.694 0.580 0.607
## 0.372 0.282 0.280
## 0.116 0.041 0.038
## 0.239 0.162 0.162
## 0.677 0.534 0.503
## 0.435 0.343 0.345
## 0.005 -0.126 -0.126
## 2.494 0.704 0.704
## -0.641 -0.598 -0.598
## 0.387 0.164 0.164
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .ssgs 0.196 0.022 8.760 0.000 0.152
## .sswk 0.235 0.023 10.359 0.000 0.190
## .sspc 0.254 0.029 8.808 0.000 0.197
## .ssei 0.350 0.036 9.622 0.000 0.279
## .ssar 0.215 0.027 7.994 0.000 0.162
## .ssmk 0.140 0.016 8.835 0.000 0.109
## .ssmc 0.329 0.032 10.336 0.000 0.267
## .ssao 0.534 0.052 10.246 0.000 0.432
## .ssai 0.463 0.061 7.589 0.000 0.343
## .sssi 0.290 0.049 5.956 0.000 0.194
## .ssno 0.446 0.058 7.701 0.000 0.333
## .sscs 0.441 0.069 6.403 0.000 0.306
## .verbal 2.115 1.237 1.710 0.087 -0.309
## .math 0.629 0.352 1.789 0.074 -0.060
## .electronic 4.660 1.283 3.632 0.000 2.145
## .g 1.204 0.169 7.116 0.000 0.873
## ci.upper Std.lv Std.all
## 1.000 0.412 0.412
## 0.240 0.196 0.198
## 0.279 0.235 0.226
## 0.310 0.254 0.268
## 0.421 0.350 0.325
## 0.267 0.215 0.239
## 0.171 0.140 0.152
## 0.391 0.329 0.360
## 0.636 0.534 0.527
## 0.582 0.463 0.388
## 0.385 0.290 0.292
## 0.560 0.446 0.396
## 0.577 0.441 0.447
## 4.539 0.093 0.093
## 1.319 0.060 0.060
## 7.174 0.647 0.647
## 1.536 0.816 0.816
sem.age2<-sem(hof.age2, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("ssmc~1", "sspc~1", "ssno ~1"))
fitMeasures(sem.age2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 589.641 159.000 0.000 0.932 0.090 0.059 1.086
## aic bic
## 16295.008 16606.010
Mc(sem.age2)
## [1] 0.7248036
summary(sem.age2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 119 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 27
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 589.641 525.462
## Degrees of freedom 159 159
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.122
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 246.106 219.319
## 0 343.535 306.143
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.190 0.053 3.574 0.000 0.086
## sswk (.p2.) 0.191 0.053 3.562 0.000 0.086
## sspc (.p3.) 0.177 0.049 3.599 0.000 0.081
## ssei (.p4.) 0.107 0.031 3.427 0.001 0.046
## math =~
## ssar (.p5.) 0.254 0.042 6.091 0.000 0.172
## ssmk (.p6.) 0.188 0.034 5.485 0.000 0.121
## ssmc (.p7.) 0.234 0.039 6.064 0.000 0.159
## ssao (.p8.) 0.212 0.037 5.733 0.000 0.140
## electronic =~
## ssai (.p9.) 0.319 0.040 7.887 0.000 0.240
## sssi (.10.) 0.313 0.042 7.460 0.000 0.231
## ssei (.11.) 0.171 0.024 7.126 0.000 0.124
## speed =~
## ssno (.12.) 0.529 0.050 10.656 0.000 0.432
## sscs (.13.) 0.474 0.040 11.811 0.000 0.395
## ssmk (.14.) 0.208 0.027 7.833 0.000 0.156
## g =~
## verbal (.15.) 3.647 1.086 3.358 0.001 1.519
## math (.16.) 2.578 0.474 5.443 0.000 1.650
## elctrnc (.17.) 1.299 0.196 6.634 0.000 0.915
## speed (.18.) 0.974 0.115 8.451 0.000 0.748
## ci.upper Std.lv Std.all
##
## 0.294 0.807 0.894
## 0.295 0.810 0.884
## 0.274 0.753 0.829
## 0.169 0.456 0.509
##
## 0.335 0.784 0.893
## 0.256 0.582 0.622
## 0.310 0.724 0.821
## 0.285 0.656 0.709
##
## 0.398 0.568 0.726
## 0.395 0.557 0.710
## 0.219 0.305 0.340
##
## 0.627 0.788 0.794
## 0.553 0.706 0.733
## 0.260 0.310 0.331
##
## 5.775 0.972 0.972
## 3.507 0.946 0.946
## 1.683 0.827 0.827
## 1.200 0.741 0.741
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.340 0.044 7.693 0.000 0.254
## agec2 -0.070 0.033 -2.115 0.034 -0.135
## ci.upper Std.lv Std.all
##
## 0.427 0.300 0.455
## -0.005 -0.062 -0.118
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.42.) 0.503 0.068 7.355 0.000 0.369
## .sswk (.43.) 0.431 0.069 6.247 0.000 0.296
## .sspc 0.529 0.067 7.943 0.000 0.398
## .ssei (.45.) 0.272 0.063 4.334 0.000 0.149
## .ssar (.46.) 0.483 0.066 7.350 0.000 0.354
## .ssmk (.47.) 0.537 0.069 7.813 0.000 0.402
## .ssmc 0.342 0.063 5.393 0.000 0.218
## .ssao (.49.) 0.372 0.061 6.105 0.000 0.252
## .ssai (.50.) 0.109 0.049 2.241 0.025 0.014
## .sssi (.51.) 0.229 0.050 4.614 0.000 0.131
## .ssno 0.352 0.065 5.384 0.000 0.224
## .sscs (.53.) 0.419 0.058 7.230 0.000 0.305
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.637 0.503 0.558
## 0.566 0.431 0.471
## 0.659 0.529 0.582
## 0.395 0.272 0.303
## 0.612 0.483 0.550
## 0.671 0.537 0.574
## 0.466 0.342 0.388
## 0.491 0.372 0.402
## 0.205 0.109 0.140
## 0.326 0.229 0.291
## 0.481 0.352 0.355
## 0.532 0.419 0.435
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .ssgs 0.163 0.019 8.544 0.000 0.126
## .sswk 0.183 0.020 9.326 0.000 0.145
## .sspc 0.257 0.031 8.381 0.000 0.197
## .ssei 0.279 0.030 9.271 0.000 0.220
## .ssar 0.157 0.019 8.079 0.000 0.119
## .ssmk 0.188 0.021 8.977 0.000 0.147
## .ssmc 0.254 0.026 9.579 0.000 0.202
## .ssao 0.425 0.036 11.943 0.000 0.355
## .ssai 0.289 0.034 8.488 0.000 0.223
## .sssi 0.306 0.034 8.979 0.000 0.239
## .ssno 0.365 0.053 6.953 0.000 0.262
## .sscs 0.430 0.055 7.863 0.000 0.322
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.451 0.451
## 0.201 0.163 0.201
## 0.221 0.183 0.218
## 0.318 0.257 0.312
## 0.337 0.279 0.347
## 0.194 0.157 0.203
## 0.229 0.188 0.215
## 0.306 0.254 0.326
## 0.494 0.425 0.497
## 0.356 0.289 0.473
## 0.373 0.306 0.496
## 0.468 0.365 0.370
## 0.537 0.430 0.463
## 1.000 0.055 0.055
## 1.000 0.105 0.105
## 1.000 0.316 0.316
## 1.000 0.779 0.779
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.190 0.053 3.574 0.000 0.086
## sswk (.p2.) 0.191 0.053 3.562 0.000 0.086
## sspc (.p3.) 0.177 0.049 3.599 0.000 0.081
## ssei (.p4.) 0.107 0.031 3.427 0.001 0.046
## math =~
## ssar (.p5.) 0.254 0.042 6.091 0.000 0.172
## ssmk (.p6.) 0.188 0.034 5.485 0.000 0.121
## ssmc (.p7.) 0.234 0.039 6.064 0.000 0.159
## ssao (.p8.) 0.212 0.037 5.733 0.000 0.140
## electronic =~
## ssai (.p9.) 0.319 0.040 7.887 0.000 0.240
## sssi (.10.) 0.313 0.042 7.460 0.000 0.231
## ssei (.11.) 0.171 0.024 7.126 0.000 0.124
## speed =~
## ssno (.12.) 0.529 0.050 10.656 0.000 0.432
## sscs (.13.) 0.474 0.040 11.811 0.000 0.395
## ssmk (.14.) 0.208 0.027 7.833 0.000 0.156
## g =~
## verbal (.15.) 3.647 1.086 3.358 0.001 1.519
## math (.16.) 2.578 0.474 5.443 0.000 1.650
## elctrnc (.17.) 1.299 0.196 6.634 0.000 0.915
## speed (.18.) 0.974 0.115 8.451 0.000 0.748
## ci.upper Std.lv Std.all
##
## 0.294 0.908 0.899
## 0.295 0.912 0.883
## 0.274 0.848 0.860
## 0.169 0.514 0.489
##
## 0.335 0.843 0.876
## 0.256 0.626 0.643
## 0.310 0.779 0.805
## 0.285 0.706 0.695
##
## 0.398 0.860 0.784
## 0.395 0.844 0.843
## 0.219 0.463 0.441
##
## 0.627 0.835 0.781
## 0.553 0.747 0.747
## 0.260 0.328 0.337
##
## 5.775 0.953 0.953
## 3.507 0.971 0.971
## 1.683 0.603 0.603
## 1.200 0.773 0.773
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.407 0.052 7.783 0.000 0.304
## agec2 -0.034 0.035 -0.967 0.334 -0.103
## ci.upper Std.lv Std.all
##
## 0.509 0.325 0.462
## 0.035 -0.027 -0.050
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.42.) 0.503 0.068 7.355 0.000 0.369
## .sswk (.43.) 0.431 0.069 6.247 0.000 0.296
## .sspc 0.148 0.072 2.048 0.041 0.006
## .ssei (.45.) 0.272 0.063 4.334 0.000 0.149
## .ssar (.46.) 0.483 0.066 7.350 0.000 0.354
## .ssmk (.47.) 0.537 0.069 7.813 0.000 0.402
## .ssmc 0.680 0.072 9.394 0.000 0.538
## .ssao (.49.) 0.372 0.061 6.105 0.000 0.252
## .ssai (.50.) 0.109 0.049 2.241 0.025 0.014
## .sssi (.51.) 0.229 0.050 4.614 0.000 0.131
## .ssno 0.619 0.084 7.355 0.000 0.454
## .sscs (.53.) 0.419 0.058 7.230 0.000 0.305
## .math -0.413 0.214 -1.936 0.053 -0.832
## .elctrnc 1.885 0.309 6.107 0.000 1.280
## .speed -0.932 0.148 -6.285 0.000 -1.223
## .g 0.111 0.139 0.793 0.428 -0.162
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.637 0.503 0.498
## 0.566 0.431 0.417
## 0.289 0.148 0.150
## 0.395 0.272 0.259
## 0.612 0.483 0.502
## 0.671 0.537 0.551
## 0.822 0.680 0.703
## 0.491 0.372 0.366
## 0.205 0.109 0.100
## 0.326 0.229 0.228
## 0.784 0.619 0.579
## 0.532 0.419 0.419
## 0.005 -0.124 -0.124
## 2.490 0.699 0.699
## -0.642 -0.591 -0.591
## 0.383 0.088 0.088
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .ssgs 0.196 0.022 8.756 0.000 0.152
## .sswk 0.235 0.023 10.371 0.000 0.190
## .sspc 0.254 0.029 8.816 0.000 0.197
## .ssei 0.350 0.036 9.626 0.000 0.279
## .ssar 0.216 0.027 8.029 0.000 0.163
## .ssmk 0.139 0.016 8.825 0.000 0.108
## .ssmc 0.329 0.032 10.337 0.000 0.266
## .ssao 0.534 0.052 10.242 0.000 0.432
## .ssai 0.463 0.061 7.588 0.000 0.343
## .sssi 0.289 0.049 5.949 0.000 0.194
## .ssno 0.446 0.058 7.696 0.000 0.333
## .sscs 0.442 0.069 6.405 0.000 0.307
## .verbal 2.094 1.209 1.732 0.083 -0.276
## .math 0.632 0.355 1.783 0.075 -0.063
## .electronic 4.633 1.273 3.640 0.000 2.139
## .g 1.221 0.173 7.056 0.000 0.882
## ci.upper Std.lv Std.all
## 1.000 0.402 0.402
## 0.240 0.196 0.192
## 0.279 0.235 0.220
## 0.310 0.254 0.261
## 0.422 0.350 0.318
## 0.268 0.216 0.233
## 0.170 0.139 0.147
## 0.391 0.329 0.351
## 0.636 0.534 0.517
## 0.582 0.463 0.385
## 0.385 0.289 0.289
## 0.560 0.446 0.390
## 0.577 0.442 0.442
## 4.464 0.091 0.091
## 1.327 0.057 0.057
## 7.128 0.637 0.637
## 1.560 0.779 0.779
sem.age2q<-sem(hof.age2q, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("ssmc~1", "sspc~1", "ssno ~1"))
fitMeasures(sem.age2q, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 591.443 161.000 0.000 0.932 0.089 0.064 1.083
## aic bic
## 16292.810 16594.797
Mc(sem.age2q)
## [1] 0.7249112
summary(sem.age2q, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 118 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 29
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 591.443 527.118
## Degrees of freedom 161 161
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.122
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 246.705 219.873
## 0 344.738 307.245
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.188 0.053 3.560 0.000 0.085
## sswk (.p2.) 0.189 0.053 3.547 0.000 0.085
## sspc (.p3.) 0.176 0.049 3.584 0.000 0.080
## ssei (.p4.) 0.106 0.031 3.417 0.001 0.045
## math =~
## ssar (.p5.) 0.255 0.041 6.183 0.000 0.174
## ssmk (.p6.) 0.189 0.034 5.551 0.000 0.123
## ssmc (.p7.) 0.236 0.038 6.152 0.000 0.161
## ssao (.p8.) 0.214 0.037 5.808 0.000 0.141
## electronic =~
## ssai (.p9.) 0.319 0.040 7.874 0.000 0.239
## sssi (.10.) 0.313 0.042 7.443 0.000 0.230
## ssei (.11.) 0.171 0.024 7.122 0.000 0.124
## speed =~
## ssno (.12.) 0.529 0.050 10.654 0.000 0.432
## sscs (.13.) 0.474 0.040 11.809 0.000 0.395
## ssmk (.14.) 0.208 0.027 7.824 0.000 0.156
## g =~
## verbal (.15.) 3.676 1.099 3.346 0.001 1.522
## math (.16.) 2.568 0.465 5.520 0.000 1.656
## elctrnc (.17.) 1.300 0.196 6.621 0.000 0.915
## speed (.18.) 0.975 0.115 8.451 0.000 0.749
## ci.upper Std.lv Std.all
##
## 0.292 0.818 0.896
## 0.294 0.821 0.887
## 0.272 0.763 0.833
## 0.168 0.462 0.511
##
## 0.336 0.795 0.895
## 0.256 0.590 0.624
## 0.311 0.734 0.824
## 0.286 0.665 0.714
##
## 0.398 0.573 0.729
## 0.395 0.562 0.713
## 0.219 0.308 0.341
##
## 0.627 0.795 0.796
## 0.553 0.712 0.736
## 0.260 0.313 0.331
##
## 5.829 0.973 0.973
## 3.479 0.947 0.947
## 1.685 0.831 0.831
## 1.201 0.746 0.746
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.366 0.036 10.187 0.000 0.296
## agec2 (b) -0.056 0.024 -2.293 0.022 -0.104
## ci.upper Std.lv Std.all
##
## 0.436 0.319 0.483
## -0.008 -0.049 -0.093
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.42.) 0.479 0.059 8.120 0.000 0.363
## .sswk (.43.) 0.407 0.059 6.853 0.000 0.290
## .sspc 0.506 0.059 8.579 0.000 0.390
## .ssei (.45.) 0.250 0.055 4.568 0.000 0.143
## .ssar (.46.) 0.460 0.058 7.931 0.000 0.346
## .ssmk (.47.) 0.513 0.059 8.624 0.000 0.396
## .ssmc 0.321 0.056 5.682 0.000 0.210
## .ssao (.49.) 0.353 0.055 6.443 0.000 0.245
## .ssai (.50.) 0.095 0.044 2.142 0.032 0.008
## .sssi (.51.) 0.214 0.045 4.772 0.000 0.126
## .ssno 0.334 0.060 5.573 0.000 0.217
## .sscs (.53.) 0.403 0.054 7.520 0.000 0.298
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.594 0.479 0.525
## 0.523 0.407 0.439
## 0.622 0.506 0.552
## 0.358 0.250 0.277
## 0.574 0.460 0.518
## 0.629 0.513 0.542
## 0.431 0.321 0.360
## 0.460 0.353 0.379
## 0.181 0.095 0.121
## 0.302 0.214 0.272
## 0.452 0.334 0.335
## 0.507 0.403 0.416
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .ssgs 0.163 0.019 8.567 0.000 0.126
## .sswk 0.183 0.020 9.321 0.000 0.144
## .sspc 0.258 0.031 8.377 0.000 0.197
## .ssei 0.278 0.030 9.270 0.000 0.220
## .ssar 0.157 0.019 8.071 0.000 0.119
## .ssmk 0.188 0.021 8.985 0.000 0.147
## .ssmc 0.254 0.027 9.571 0.000 0.202
## .ssao 0.425 0.036 11.942 0.000 0.355
## .ssai 0.289 0.034 8.492 0.000 0.223
## .sssi 0.306 0.034 8.978 0.000 0.239
## .ssno 0.365 0.053 6.954 0.000 0.262
## .sscs 0.429 0.055 7.862 0.000 0.322
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.443 0.443
## 0.201 0.163 0.196
## 0.221 0.183 0.213
## 0.318 0.258 0.307
## 0.337 0.278 0.341
## 0.195 0.157 0.199
## 0.229 0.188 0.210
## 0.306 0.254 0.320
## 0.494 0.425 0.490
## 0.356 0.289 0.469
## 0.373 0.306 0.492
## 0.468 0.365 0.366
## 0.536 0.429 0.459
## 1.000 0.053 0.053
## 1.000 0.103 0.103
## 1.000 0.310 0.310
## 1.000 0.758 0.758
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.188 0.053 3.560 0.000 0.085
## sswk (.p2.) 0.189 0.053 3.547 0.000 0.085
## sspc (.p3.) 0.176 0.049 3.584 0.000 0.080
## ssei (.p4.) 0.106 0.031 3.417 0.001 0.045
## math =~
## ssar (.p5.) 0.255 0.041 6.183 0.000 0.174
## ssmk (.p6.) 0.189 0.034 5.551 0.000 0.123
## ssmc (.p7.) 0.236 0.038 6.152 0.000 0.161
## ssao (.p8.) 0.214 0.037 5.808 0.000 0.141
## electronic =~
## ssai (.p9.) 0.319 0.040 7.874 0.000 0.239
## sssi (.10.) 0.313 0.042 7.443 0.000 0.230
## ssei (.11.) 0.171 0.024 7.122 0.000 0.124
## speed =~
## ssno (.12.) 0.529 0.050 10.654 0.000 0.432
## sscs (.13.) 0.474 0.040 11.809 0.000 0.395
## ssmk (.14.) 0.208 0.027 7.824 0.000 0.156
## g =~
## verbal (.15.) 3.676 1.099 3.346 0.001 1.522
## math (.16.) 2.568 0.465 5.520 0.000 1.656
## elctrnc (.17.) 1.300 0.196 6.621 0.000 0.915
## speed (.18.) 0.975 0.115 8.451 0.000 0.749
## ci.upper Std.lv Std.all
##
## 0.292 0.896 0.896
## 0.294 0.900 0.880
## 0.272 0.836 0.857
## 0.168 0.506 0.486
##
## 0.336 0.831 0.873
## 0.256 0.617 0.641
## 0.311 0.767 0.801
## 0.286 0.695 0.690
##
## 0.398 0.856 0.783
## 0.395 0.840 0.842
## 0.219 0.461 0.443
##
## 0.627 0.827 0.778
## 0.553 0.741 0.744
## 0.260 0.325 0.338
##
## 5.829 0.952 0.952
## 3.479 0.971 0.971
## 1.685 0.596 0.596
## 1.201 0.768 0.768
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.366 0.036 10.187 0.000 0.296
## agec2 (b) -0.056 0.024 -2.293 0.022 -0.104
## ci.upper Std.lv Std.all
##
## 0.436 0.297 0.423
## -0.008 -0.045 -0.084
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.42.) 0.479 0.059 8.120 0.000 0.363
## .sswk (.43.) 0.407 0.059 6.853 0.000 0.290
## .sspc 0.125 0.064 1.949 0.051 -0.001
## .ssei (.45.) 0.250 0.055 4.568 0.000 0.143
## .ssar (.46.) 0.460 0.058 7.931 0.000 0.346
## .ssmk (.47.) 0.513 0.059 8.624 0.000 0.396
## .ssmc 0.659 0.066 9.997 0.000 0.529
## .ssao (.49.) 0.353 0.055 6.443 0.000 0.245
## .ssai (.50.) 0.095 0.044 2.142 0.032 0.008
## .sssi (.51.) 0.214 0.045 4.772 0.000 0.126
## .ssno 0.601 0.081 7.445 0.000 0.443
## .sscs (.53.) 0.403 0.054 7.520 0.000 0.298
## .math -0.411 0.212 -1.941 0.052 -0.826
## .elctrnc 1.887 0.309 6.101 0.000 1.281
## .speed -0.932 0.148 -6.281 0.000 -1.223
## .g 0.185 0.097 1.919 0.055 -0.004
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.594 0.479 0.479
## 0.523 0.407 0.398
## 0.251 0.125 0.128
## 0.358 0.250 0.240
## 0.574 0.460 0.483
## 0.629 0.513 0.533
## 0.788 0.659 0.688
## 0.460 0.353 0.350
## 0.181 0.095 0.087
## 0.302 0.214 0.215
## 0.759 0.601 0.565
## 0.507 0.403 0.404
## 0.004 -0.126 -0.126
## 2.493 0.702 0.702
## -0.641 -0.597 -0.597
## 0.375 0.151 0.151
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .ssgs 0.196 0.022 8.752 0.000 0.152
## .sswk 0.235 0.023 10.372 0.000 0.191
## .sspc 0.254 0.029 8.808 0.000 0.197
## .ssei 0.350 0.036 9.630 0.000 0.279
## .ssar 0.216 0.027 8.023 0.000 0.163
## .ssmk 0.139 0.016 8.826 0.000 0.108
## .ssmc 0.329 0.032 10.332 0.000 0.266
## .ssao 0.533 0.052 10.239 0.000 0.431
## .ssai 0.463 0.061 7.593 0.000 0.344
## .sssi 0.289 0.049 5.945 0.000 0.194
## .ssno 0.446 0.058 7.694 0.000 0.332
## .sscs 0.442 0.069 6.404 0.000 0.307
## .verbal 2.128 1.235 1.723 0.085 -0.293
## .math 0.610 0.348 1.756 0.079 -0.071
## .electronic 4.659 1.282 3.635 0.000 2.147
## .g 1.223 0.173 7.050 0.000 0.883
## ci.upper Std.lv Std.all
## 1.000 0.410 0.410
## 0.240 0.196 0.196
## 0.279 0.235 0.225
## 0.310 0.254 0.266
## 0.422 0.350 0.324
## 0.268 0.216 0.238
## 0.170 0.139 0.151
## 0.391 0.329 0.358
## 0.635 0.533 0.525
## 0.583 0.463 0.387
## 0.384 0.289 0.291
## 0.560 0.446 0.395
## 0.577 0.442 0.446
## 4.549 0.094 0.094
## 1.291 0.058 0.058
## 7.172 0.645 0.645
## 1.563 0.807 0.807
# BIFACTOR MODEL (math not well defined because ar and wk are very small, mc strongly negative and ao near zero loading, but slightly better when removing mc)
bf.notworking<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
baseline<-cfa(bf.notworking, data=dgroup, meanstructure=T, sampling.weights="sweight", std.lv=T, orthogonal=T)
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 363.918 40.000 0.000 0.946 0.110 0.052 16767.806
## bic
## 16993.169
Mc(baseline)
## [1] 0.7849848
summary(baseline, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 53 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 50
##
## Number of observations 670
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 363.918 331.038
## Degrees of freedom 40 40
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.099
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.344 0.043 7.952 0.000 0.259
## sswk 0.432 0.052 8.360 0.000 0.330
## sspc 0.166 0.039 4.310 0.000 0.091
## ssei 0.202 0.044 4.549 0.000 0.115
## math =~
## ssar 0.098 0.065 1.501 0.133 -0.030
## ssmk 0.129 0.083 1.550 0.121 -0.034
## ssmc -0.438 0.294 -1.493 0.135 -1.014
## ssao -0.050 0.036 -1.418 0.156 -0.120
## electronic =~
## ssai 0.635 0.048 13.307 0.000 0.541
## sssi 0.623 0.045 13.733 0.000 0.534
## ssei 0.345 0.036 9.626 0.000 0.275
## speed =~
## ssno 0.666 0.085 7.840 0.000 0.499
## sscs 0.419 0.062 6.804 0.000 0.298
## ssmk 0.189 0.036 5.295 0.000 0.119
## g =~
## ssgs 0.784 0.033 23.621 0.000 0.719
## ssar 0.817 0.034 23.949 0.000 0.750
## sswk 0.772 0.036 21.731 0.000 0.703
## sspc 0.772 0.030 25.346 0.000 0.713
## ssno 0.583 0.041 14.333 0.000 0.503
## sscs 0.562 0.038 14.790 0.000 0.487
## ssai 0.525 0.042 12.472 0.000 0.442
## sssi 0.501 0.041 12.252 0.000 0.421
## ssmk 0.846 0.031 27.280 0.000 0.785
## ssmc 0.782 0.036 21.628 0.000 0.711
## ssei 0.725 0.038 18.923 0.000 0.650
## ssao 0.677 0.031 21.646 0.000 0.616
## ci.upper Std.lv Std.all
##
## 0.428 0.344 0.360
## 0.533 0.432 0.444
## 0.242 0.166 0.174
## 0.290 0.202 0.202
##
## 0.227 0.098 0.107
## 0.291 0.129 0.134
## 0.137 -0.438 -0.466
## 0.019 -0.050 -0.052
##
## 0.728 0.635 0.621
## 0.712 0.623 0.644
## 0.415 0.345 0.344
##
## 0.832 0.666 0.643
## 0.540 0.419 0.419
## 0.259 0.189 0.197
##
## 0.849 0.784 0.821
## 0.883 0.817 0.886
## 0.842 0.772 0.795
## 0.832 0.772 0.806
## 0.662 0.583 0.563
## 0.636 0.562 0.562
## 0.607 0.525 0.513
## 0.582 0.501 0.518
## 0.907 0.846 0.882
## 0.853 0.782 0.832
## 0.800 0.725 0.723
## 0.738 0.677 0.698
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.458 0.038 12.032 0.000 0.384
## .sswk 0.376 0.039 9.717 0.000 0.301
## .sspc 0.297 0.038 7.775 0.000 0.222
## .ssei 0.388 0.040 9.601 0.000 0.308
## .ssar 0.388 0.037 10.550 0.000 0.316
## .ssmk 0.355 0.039 9.223 0.000 0.280
## .ssmc 0.417 0.038 11.111 0.000 0.344
## .ssao 0.285 0.039 7.350 0.000 0.209
## .ssai 0.370 0.041 8.963 0.000 0.289
## .sssi 0.488 0.039 12.599 0.000 0.412
## .ssno 0.205 0.041 4.958 0.000 0.124
## .sscs 0.170 0.040 4.245 0.000 0.091
## ci.upper Std.lv Std.all
## 0.533 0.458 0.480
## 0.452 0.376 0.387
## 0.372 0.297 0.310
## 0.467 0.388 0.387
## 0.460 0.388 0.421
## 0.431 0.355 0.370
## 0.491 0.417 0.444
## 0.361 0.285 0.294
## 0.451 0.370 0.362
## 0.564 0.488 0.505
## 0.286 0.205 0.198
## 0.248 0.170 0.170
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.179 0.025 7.228 0.000 0.131
## .sswk 0.162 0.035 4.586 0.000 0.093
## .sspc 0.293 0.023 12.872 0.000 0.249
## .ssei 0.319 0.024 13.395 0.000 0.272
## .ssar 0.172 0.017 9.961 0.000 0.138
## .ssmk 0.153 0.018 8.617 0.000 0.118
## .ssmc 0.081 0.253 0.320 0.749 -0.415
## .ssao 0.481 0.033 14.781 0.000 0.417
## .ssai 0.368 0.044 8.383 0.000 0.282
## .sssi 0.296 0.040 7.393 0.000 0.217
## .ssno 0.288 0.094 3.064 0.002 0.104
## .sscs 0.510 0.055 9.220 0.000 0.401
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.228 0.179 0.196
## 0.231 0.162 0.171
## 0.338 0.293 0.320
## 0.366 0.319 0.318
## 0.206 0.172 0.203
## 0.188 0.153 0.166
## 0.577 0.081 0.091
## 0.545 0.481 0.511
## 0.454 0.368 0.351
## 0.374 0.296 0.316
## 0.473 0.288 0.269
## 0.618 0.510 0.509
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
bf.model<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
bf.lv<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
verbal~~1*verbal
math~~1*math
'
baseline<-cfa(bf.model, data=dgroup, meanstructure=T, sampling.weights="sweight", std.lv=T, orthogonal=T)
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 377.804 41.000 0.000 0.944 0.111 0.052 16779.691
## bic
## 17000.548
Mc(baseline)
## [1] 0.7774612
summary(baseline, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 43 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 49
##
## Number of observations 670
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 377.804 356.801
## Degrees of freedom 41 41
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.059
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.306 0.100 3.067 0.002 0.110
## sswk 0.370 0.135 2.743 0.006 0.106
## sspc 0.098 0.080 1.233 0.218 -0.058
## ssei 0.167 0.074 2.265 0.024 0.023
## math =~
## ssar 0.213 0.099 2.158 0.031 0.020
## ssmk 0.240 0.101 2.386 0.017 0.043
## ssao 0.091 0.059 1.531 0.126 -0.025
## electronic =~
## ssai 0.628 0.049 12.775 0.000 0.532
## sssi 0.621 0.046 13.395 0.000 0.530
## ssei 0.338 0.037 9.213 0.000 0.266
## speed =~
## ssno 0.643 0.074 8.696 0.000 0.498
## sscs 0.424 0.058 7.377 0.000 0.312
## ssmk 0.221 0.033 6.609 0.000 0.155
## g =~
## ssgs 0.804 0.034 23.836 0.000 0.738
## ssar 0.793 0.034 23.247 0.000 0.726
## sswk 0.797 0.037 21.355 0.000 0.724
## sspc 0.789 0.033 24.035 0.000 0.724
## ssno 0.584 0.041 14.397 0.000 0.505
## sscs 0.571 0.038 14.996 0.000 0.497
## ssai 0.532 0.043 12.419 0.000 0.448
## sssi 0.504 0.041 12.170 0.000 0.423
## ssmk 0.819 0.031 26.014 0.000 0.757
## ssmc 0.761 0.035 21.668 0.000 0.692
## ssei 0.741 0.040 18.652 0.000 0.663
## ssao 0.669 0.032 21.194 0.000 0.607
## ci.upper Std.lv Std.all
##
## 0.501 0.306 0.320
## 0.635 0.370 0.381
## 0.254 0.098 0.102
## 0.312 0.167 0.166
##
## 0.407 0.213 0.232
## 0.437 0.240 0.250
## 0.207 0.091 0.094
##
## 0.725 0.628 0.614
## 0.712 0.621 0.642
## 0.410 0.338 0.337
##
## 0.787 0.643 0.621
## 0.537 0.424 0.424
## 0.287 0.221 0.230
##
## 0.870 0.804 0.842
## 0.860 0.793 0.861
## 0.870 0.797 0.820
## 0.853 0.789 0.823
## 0.664 0.584 0.564
## 0.646 0.571 0.571
## 0.616 0.532 0.520
## 0.586 0.504 0.521
## 0.881 0.819 0.853
## 0.830 0.761 0.809
## 0.819 0.741 0.738
## 0.731 0.669 0.690
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.458 0.038 12.032 0.000 0.384
## .sswk 0.376 0.039 9.717 0.000 0.301
## .sspc 0.297 0.038 7.775 0.000 0.222
## .ssei 0.388 0.040 9.601 0.000 0.308
## .ssar 0.388 0.037 10.550 0.000 0.316
## .ssmk 0.355 0.039 9.223 0.000 0.280
## .ssao 0.285 0.039 7.350 0.000 0.209
## .ssai 0.370 0.041 8.963 0.000 0.289
## .sssi 0.488 0.039 12.599 0.000 0.412
## .ssno 0.205 0.041 4.958 0.000 0.124
## .sscs 0.170 0.040 4.245 0.000 0.091
## .ssmc 0.417 0.038 11.111 0.000 0.344
## ci.upper Std.lv Std.all
## 0.533 0.458 0.480
## 0.452 0.376 0.387
## 0.372 0.297 0.310
## 0.467 0.388 0.386
## 0.460 0.388 0.421
## 0.431 0.355 0.370
## 0.361 0.285 0.294
## 0.451 0.370 0.362
## 0.564 0.488 0.505
## 0.286 0.205 0.198
## 0.248 0.170 0.170
## 0.491 0.417 0.444
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.173 0.054 3.190 0.001 0.067
## .sswk 0.172 0.082 2.098 0.036 0.011
## .sspc 0.286 0.024 11.769 0.000 0.238
## .ssei 0.318 0.024 12.978 0.000 0.270
## .ssar 0.174 0.039 4.440 0.000 0.097
## .ssmk 0.144 0.047 3.066 0.002 0.052
## .ssao 0.486 0.032 15.364 0.000 0.424
## .ssai 0.368 0.044 8.321 0.000 0.281
## .sssi 0.295 0.040 7.300 0.000 0.216
## .ssno 0.317 0.075 4.194 0.000 0.169
## .sscs 0.495 0.052 9.575 0.000 0.394
## .ssmc 0.306 0.025 12.474 0.000 0.258
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.279 0.173 0.189
## 0.333 0.172 0.182
## 0.333 0.286 0.312
## 0.366 0.318 0.315
## 0.250 0.174 0.205
## 0.236 0.144 0.156
## 0.548 0.486 0.516
## 0.455 0.368 0.352
## 0.375 0.295 0.316
## 0.464 0.317 0.296
## 0.596 0.495 0.494
## 0.354 0.306 0.346
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
configural<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T)
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 299.869 82.000 0.000 0.964 0.089 0.038 16392.060
## bic
## 16833.773
Mc(configural)
## [1] 0.849734
summary(configural, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 70 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 299.869 273.379
## Degrees of freedom 82 82
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.097
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 113.565 103.532
## 0 186.304 169.846
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.218 0.070 3.122 0.002 0.081
## sswk 0.464 0.105 4.394 0.000 0.257
## sspc 0.097 0.042 2.338 0.019 0.016
## ssei 0.152 0.068 2.232 0.026 0.019
## math =~
## ssar 0.227 0.101 2.255 0.024 0.030
## ssmk 0.193 0.081 2.387 0.017 0.035
## ssao 0.132 0.082 1.618 0.106 -0.028
## electronic =~
## ssai 0.258 0.098 2.635 0.008 0.066
## sssi 0.478 0.173 2.755 0.006 0.138
## ssei 0.112 0.046 2.415 0.016 0.021
## speed =~
## ssno 0.693 0.154 4.498 0.000 0.391
## sscs 0.310 0.084 3.702 0.000 0.146
## ssmk 0.180 0.051 3.558 0.000 0.081
## g =~
## ssgs 0.775 0.045 17.370 0.000 0.688
## ssar 0.756 0.044 17.301 0.000 0.670
## sswk 0.772 0.048 16.060 0.000 0.678
## sspc 0.759 0.043 17.540 0.000 0.674
## ssno 0.567 0.058 9.810 0.000 0.453
## sscs 0.527 0.044 11.934 0.000 0.440
## ssai 0.419 0.045 9.362 0.000 0.331
## sssi 0.436 0.047 9.300 0.000 0.344
## ssmk 0.821 0.039 21.278 0.000 0.745
## ssmc 0.695 0.044 15.921 0.000 0.610
## ssei 0.636 0.047 13.420 0.000 0.544
## ssao 0.632 0.041 15.449 0.000 0.551
## ci.upper Std.lv Std.all
##
## 0.355 0.218 0.240
## 0.670 0.464 0.494
## 0.178 0.097 0.106
## 0.286 0.152 0.178
##
## 0.424 0.227 0.260
## 0.352 0.193 0.201
## 0.292 0.132 0.143
##
## 0.449 0.258 0.341
## 0.817 0.478 0.612
## 0.202 0.112 0.131
##
## 0.996 0.693 0.702
## 0.474 0.310 0.331
## 0.279 0.180 0.187
##
## 0.862 0.775 0.852
## 0.842 0.756 0.866
## 0.866 0.772 0.823
## 0.844 0.759 0.830
## 0.680 0.567 0.574
## 0.613 0.527 0.562
## 0.507 0.419 0.554
## 0.528 0.436 0.559
## 0.896 0.821 0.854
## 0.781 0.695 0.811
## 0.729 0.636 0.744
## 0.712 0.632 0.686
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.378 0.051 7.429 0.000 0.278
## .sswk 0.382 0.052 7.278 0.000 0.279
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei 0.188 0.048 3.908 0.000 0.094
## .ssar 0.384 0.049 7.810 0.000 0.288
## .ssmk 0.448 0.054 8.275 0.000 0.342
## .ssao 0.343 0.052 6.596 0.000 0.241
## .ssai 0.069 0.043 1.625 0.104 -0.014
## .sssi 0.163 0.044 3.736 0.000 0.078
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs 0.358 0.053 6.754 0.000 0.254
## .ssmc 0.263 0.048 5.461 0.000 0.169
## ci.upper Std.lv Std.all
## 0.478 0.378 0.415
## 0.485 0.382 0.407
## 0.545 0.445 0.487
## 0.283 0.188 0.220
## 0.481 0.384 0.440
## 0.554 0.448 0.467
## 0.444 0.343 0.372
## 0.153 0.069 0.092
## 0.249 0.163 0.209
## 0.395 0.285 0.289
## 0.462 0.358 0.382
## 0.358 0.263 0.307
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.180 0.024 7.464 0.000 0.133
## .sswk 0.070 0.092 0.762 0.446 -0.110
## .sspc 0.251 0.032 7.925 0.000 0.189
## .ssei 0.292 0.031 9.488 0.000 0.232
## .ssar 0.139 0.043 3.236 0.001 0.055
## .ssmk 0.179 0.032 5.623 0.000 0.117
## .ssao 0.432 0.036 11.847 0.000 0.361
## .ssai 0.330 0.050 6.588 0.000 0.232
## .sssi 0.190 0.162 1.173 0.241 -0.127
## .ssno 0.172 0.181 0.952 0.341 -0.183
## .sscs 0.505 0.064 7.849 0.000 0.379
## .ssmc 0.252 0.025 9.949 0.000 0.202
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.227 0.180 0.217
## 0.250 0.070 0.079
## 0.313 0.251 0.300
## 0.352 0.292 0.398
## 0.224 0.139 0.183
## 0.242 0.179 0.194
## 0.504 0.432 0.509
## 0.428 0.330 0.577
## 0.508 0.190 0.312
## 0.528 0.172 0.177
## 0.632 0.505 0.575
## 0.301 0.252 0.342
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.413 0.091 4.549 0.000 0.235
## sswk 0.309 0.103 3.001 0.003 0.107
## sspc 0.093 0.083 1.121 0.262 -0.070
## ssei 0.183 0.060 3.045 0.002 0.065
## math =~
## ssar 0.294 0.421 0.698 0.485 -0.531
## ssmk 0.215 0.307 0.702 0.483 -0.386
## ssao 0.036 0.081 0.447 0.655 -0.123
## electronic =~
## ssai 0.713 0.067 10.638 0.000 0.582
## sssi 0.624 0.063 9.868 0.000 0.500
## ssei 0.363 0.052 6.910 0.000 0.260
## speed =~
## ssno 0.693 0.112 6.215 0.000 0.474
## sscs 0.426 0.071 6.017 0.000 0.287
## ssmk 0.205 0.042 4.901 0.000 0.123
## g =~
## ssgs 0.832 0.048 17.160 0.000 0.737
## ssar 0.822 0.052 15.766 0.000 0.720
## sswk 0.821 0.053 15.377 0.000 0.717
## sspc 0.831 0.038 21.896 0.000 0.757
## ssno 0.598 0.056 10.694 0.000 0.489
## sscs 0.628 0.053 11.773 0.000 0.523
## ssai 0.646 0.065 9.872 0.000 0.518
## sssi 0.578 0.063 9.228 0.000 0.455
## ssmk 0.823 0.046 18.029 0.000 0.734
## ssmc 0.830 0.051 16.192 0.000 0.730
## ssei 0.846 0.056 15.001 0.000 0.735
## ssao 0.710 0.046 15.378 0.000 0.620
## ci.upper Std.lv Std.all
##
## 0.591 0.413 0.416
## 0.510 0.309 0.307
## 0.256 0.093 0.095
## 0.301 0.183 0.166
##
## 1.118 0.294 0.303
## 0.817 0.215 0.227
## 0.195 0.036 0.036
##
## 0.844 0.713 0.614
## 0.748 0.624 0.608
## 0.466 0.363 0.329
##
## 0.912 0.693 0.644
## 0.565 0.426 0.415
## 0.287 0.205 0.216
##
## 0.927 0.832 0.838
## 0.925 0.822 0.849
## 0.926 0.821 0.817
## 0.905 0.831 0.850
## 0.708 0.598 0.556
## 0.733 0.628 0.612
## 0.775 0.646 0.557
## 0.701 0.578 0.564
## 0.913 0.823 0.867
## 0.931 0.830 0.834
## 0.956 0.846 0.766
## 0.801 0.710 0.699
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.542 0.056 9.598 0.000 0.431
## .sswk 0.371 0.057 6.485 0.000 0.259
## .sspc 0.143 0.056 2.563 0.010 0.034
## .ssei 0.595 0.063 9.438 0.000 0.472
## .ssar 0.392 0.055 7.142 0.000 0.284
## .ssmk 0.259 0.054 4.760 0.000 0.152
## .ssao 0.225 0.058 3.904 0.000 0.112
## .ssai 0.684 0.067 10.241 0.000 0.553
## .sssi 0.827 0.059 14.131 0.000 0.712
## .ssno 0.122 0.061 1.990 0.047 0.002
## .sscs -0.026 0.058 -0.447 0.655 -0.140
## .ssmc 0.578 0.056 10.233 0.000 0.467
## ci.upper Std.lv Std.all
## 0.653 0.542 0.545
## 0.483 0.371 0.369
## 0.252 0.143 0.146
## 0.719 0.595 0.539
## 0.499 0.392 0.405
## 0.365 0.259 0.272
## 0.338 0.225 0.221
## 0.815 0.684 0.590
## 0.942 0.827 0.807
## 0.241 0.122 0.113
## 0.088 -0.026 -0.025
## 0.689 0.578 0.581
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.124 0.078 1.591 0.112 -0.029
## .sswk 0.241 0.043 5.551 0.000 0.156
## .sspc 0.256 0.028 9.222 0.000 0.202
## .ssei 0.339 0.035 9.588 0.000 0.270
## .ssar 0.175 0.244 0.719 0.472 -0.303
## .ssmk 0.136 0.130 1.053 0.292 -0.117
## .ssao 0.526 0.052 10.115 0.000 0.424
## .ssai 0.421 0.076 5.522 0.000 0.272
## .sssi 0.329 0.055 6.005 0.000 0.221
## .ssno 0.319 0.122 2.622 0.009 0.081
## .sscs 0.477 0.077 6.170 0.000 0.326
## .ssmc 0.302 0.031 9.839 0.000 0.241
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.276 0.124 0.125
## 0.326 0.241 0.238
## 0.310 0.256 0.268
## 0.409 0.339 0.278
## 0.654 0.175 0.187
## 0.390 0.136 0.151
## 0.627 0.526 0.509
## 0.571 0.421 0.313
## 0.436 0.329 0.312
## 0.558 0.319 0.276
## 0.629 0.477 0.453
## 0.362 0.302 0.304
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
#modificationIndices(configural, sort=T, maximum.number=30)
metric<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 329.237 102.000 0.000 0.962 0.082 0.054 16381.428
## bic
## 16732.996
Mc(metric)
## [1] 0.8438054
summary(metric, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 90 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 103
## Number of equality constraints 25
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 329.237 295.493
## Degrees of freedom 102 102
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.114
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 128.545 115.371
## 0 200.692 180.123
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.298 0.045 6.551 0.000 0.209
## sswk (.p2.) 0.365 0.084 4.368 0.000 0.201
## sspc (.p3.) 0.103 0.043 2.420 0.016 0.020
## ssei (.p4.) 0.171 0.043 3.982 0.000 0.087
## math =~
## ssar (.p5.) 0.258 0.139 1.862 0.063 -0.014
## ssmk (.p6.) 0.195 0.110 1.770 0.077 -0.021
## ssao (.p7.) 0.071 0.067 1.057 0.291 -0.061
## electronic =~
## ssai (.p8.) 0.342 0.044 7.727 0.000 0.255
## sssi (.p9.) 0.309 0.045 6.798 0.000 0.220
## ssei (.10.) 0.172 0.025 6.992 0.000 0.124
## speed =~
## ssno (.11.) 0.650 0.098 6.607 0.000 0.457
## sscs (.12.) 0.347 0.057 6.059 0.000 0.235
## ssmk (.13.) 0.182 0.032 5.759 0.000 0.120
## g =~
## ssgs (.14.) 0.759 0.039 19.237 0.000 0.682
## ssar (.15.) 0.749 0.040 18.668 0.000 0.670
## sswk (.16.) 0.754 0.043 17.611 0.000 0.670
## sspc (.17.) 0.752 0.037 20.354 0.000 0.679
## ssno (.18.) 0.554 0.044 12.700 0.000 0.469
## sscs (.19.) 0.549 0.038 14.477 0.000 0.474
## ssai (.20.) 0.466 0.037 12.515 0.000 0.393
## sssi (.21.) 0.452 0.038 11.822 0.000 0.377
## ssmk (.22.) 0.775 0.039 20.032 0.000 0.699
## ssmc (.23.) 0.719 0.038 18.965 0.000 0.645
## ssei (.24.) 0.679 0.040 16.966 0.000 0.601
## ssao (.25.) 0.638 0.036 17.523 0.000 0.566
## ci.upper Std.lv Std.all
##
## 0.387 0.298 0.329
## 0.529 0.365 0.396
## 0.187 0.103 0.114
## 0.255 0.171 0.191
##
## 0.530 0.258 0.297
## 0.410 0.195 0.210
## 0.203 0.071 0.077
##
## 0.428 0.342 0.436
## 0.398 0.309 0.395
## 0.220 0.172 0.192
##
## 0.843 0.650 0.664
## 0.459 0.347 0.363
## 0.243 0.182 0.196
##
## 0.836 0.759 0.839
## 0.827 0.749 0.863
## 0.837 0.754 0.817
## 0.824 0.752 0.826
## 0.640 0.554 0.566
## 0.623 0.549 0.575
## 0.539 0.466 0.595
## 0.527 0.452 0.578
## 0.851 0.775 0.837
## 0.794 0.719 0.823
## 0.758 0.679 0.760
## 0.709 0.638 0.694
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.378 0.051 7.429 0.000 0.278
## .sswk 0.382 0.052 7.278 0.000 0.279
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei 0.188 0.048 3.908 0.000 0.094
## .ssar 0.384 0.049 7.810 0.000 0.288
## .ssmk 0.448 0.054 8.275 0.000 0.342
## .ssao 0.343 0.052 6.596 0.000 0.241
## .ssai 0.069 0.043 1.625 0.104 -0.014
## .sssi 0.163 0.044 3.736 0.000 0.078
## .ssno 0.285 0.056 5.122 0.000 0.176
## .sscs 0.358 0.053 6.754 0.000 0.254
## .ssmc 0.263 0.048 5.461 0.000 0.169
## ci.upper Std.lv Std.all
## 0.478 0.378 0.418
## 0.485 0.382 0.414
## 0.545 0.445 0.489
## 0.283 0.188 0.211
## 0.481 0.384 0.443
## 0.554 0.448 0.484
## 0.444 0.343 0.373
## 0.153 0.069 0.088
## 0.249 0.163 0.209
## 0.395 0.285 0.291
## 0.462 0.358 0.375
## 0.358 0.263 0.302
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.154 0.026 5.872 0.000 0.103
## .sswk 0.149 0.042 3.561 0.000 0.067
## .sspc 0.253 0.030 8.396 0.000 0.194
## .ssei 0.278 0.030 9.348 0.000 0.220
## .ssar 0.125 0.071 1.779 0.075 -0.013
## .ssmk 0.185 0.042 4.460 0.000 0.104
## .ssao 0.433 0.035 12.276 0.000 0.364
## .ssai 0.280 0.037 7.620 0.000 0.208
## .sssi 0.311 0.035 8.818 0.000 0.242
## .ssno 0.230 0.100 2.297 0.022 0.034
## .sscs 0.490 0.057 8.539 0.000 0.377
## .ssmc 0.246 0.025 9.694 0.000 0.196
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.206 0.154 0.188
## 0.230 0.149 0.175
## 0.312 0.253 0.306
## 0.337 0.278 0.349
## 0.264 0.125 0.167
## 0.267 0.185 0.216
## 0.502 0.433 0.512
## 0.352 0.280 0.456
## 0.380 0.311 0.509
## 0.426 0.230 0.240
## 0.602 0.490 0.538
## 0.295 0.246 0.322
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.298 0.045 6.551 0.000 0.209
## sswk (.p2.) 0.365 0.084 4.368 0.000 0.201
## sspc (.p3.) 0.103 0.043 2.420 0.016 0.020
## ssei (.p4.) 0.171 0.043 3.982 0.000 0.087
## math =~
## ssar (.p5.) 0.258 0.139 1.862 0.063 -0.014
## ssmk (.p6.) 0.195 0.110 1.770 0.077 -0.021
## ssao (.p7.) 0.071 0.067 1.057 0.291 -0.061
## electronic =~
## ssai (.p8.) 0.342 0.044 7.727 0.000 0.255
## sssi (.p9.) 0.309 0.045 6.798 0.000 0.220
## ssei (.10.) 0.172 0.025 6.992 0.000 0.124
## speed =~
## ssno (.11.) 0.650 0.098 6.607 0.000 0.457
## sscs (.12.) 0.347 0.057 6.059 0.000 0.235
## ssmk (.13.) 0.182 0.032 5.759 0.000 0.120
## g =~
## ssgs (.14.) 0.759 0.039 19.237 0.000 0.682
## ssar (.15.) 0.749 0.040 18.668 0.000 0.670
## sswk (.16.) 0.754 0.043 17.611 0.000 0.670
## sspc (.17.) 0.752 0.037 20.354 0.000 0.679
## ssno (.18.) 0.554 0.044 12.700 0.000 0.469
## sscs (.19.) 0.549 0.038 14.477 0.000 0.474
## ssai (.20.) 0.466 0.037 12.515 0.000 0.393
## sssi (.21.) 0.452 0.038 11.822 0.000 0.377
## ssmk (.22.) 0.775 0.039 20.032 0.000 0.699
## ssmc (.23.) 0.719 0.038 18.965 0.000 0.645
## ssei (.24.) 0.679 0.040 16.966 0.000 0.601
## ssao (.25.) 0.638 0.036 17.523 0.000 0.566
## ci.upper Std.lv Std.all
##
## 0.387 0.322 0.323
## 0.529 0.395 0.385
## 0.187 0.112 0.114
## 0.255 0.185 0.177
##
## 0.530 0.264 0.271
## 0.410 0.199 0.203
## 0.203 0.073 0.071
##
## 0.428 0.724 0.654
## 0.398 0.655 0.657
## 0.220 0.365 0.349
##
## 0.843 0.725 0.668
## 0.459 0.387 0.383
## 0.243 0.202 0.207
##
## 0.836 0.844 0.845
## 0.827 0.833 0.855
## 0.837 0.838 0.818
## 0.824 0.836 0.852
## 0.640 0.616 0.568
## 0.623 0.611 0.604
## 0.539 0.519 0.468
## 0.527 0.503 0.504
## 0.851 0.862 0.880
## 0.794 0.800 0.820
## 0.758 0.756 0.724
## 0.709 0.709 0.697
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.542 0.056 9.598 0.000 0.431
## .sswk 0.371 0.057 6.485 0.000 0.259
## .sspc 0.143 0.056 2.563 0.010 0.034
## .ssei 0.595 0.063 9.438 0.000 0.472
## .ssar 0.392 0.055 7.142 0.000 0.284
## .ssmk 0.259 0.054 4.760 0.000 0.152
## .ssao 0.225 0.058 3.904 0.000 0.112
## .ssai 0.684 0.067 10.241 0.000 0.553
## .sssi 0.827 0.059 14.131 0.000 0.712
## .ssno 0.122 0.061 1.990 0.047 0.002
## .sscs -0.026 0.058 -0.447 0.655 -0.140
## .ssmc 0.578 0.056 10.233 0.000 0.467
## ci.upper Std.lv Std.all
## 0.653 0.542 0.542
## 0.483 0.371 0.362
## 0.252 0.143 0.145
## 0.719 0.595 0.571
## 0.499 0.392 0.403
## 0.365 0.259 0.264
## 0.338 0.225 0.221
## 0.815 0.684 0.618
## 0.942 0.827 0.829
## 0.241 0.122 0.112
## 0.088 -0.026 -0.026
## 0.689 0.578 0.592
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.181 0.037 4.943 0.000 0.109
## .sswk 0.192 0.043 4.484 0.000 0.108
## .sspc 0.251 0.027 9.409 0.000 0.199
## .ssei 0.350 0.037 9.581 0.000 0.279
## .ssar 0.185 0.076 2.417 0.016 0.035
## .ssmk 0.136 0.041 3.309 0.001 0.055
## .ssao 0.528 0.050 10.476 0.000 0.429
## .ssai 0.433 0.076 5.705 0.000 0.285
## .sssi 0.313 0.056 5.595 0.000 0.204
## .ssno 0.271 0.117 2.328 0.020 0.043
## .sscs 0.499 0.073 6.870 0.000 0.356
## .ssmc 0.313 0.031 10.193 0.000 0.253
## verbal 1.170 0.394 2.965 0.003 0.397
## math 1.044 0.552 1.891 0.059 -0.038
## electronic 4.494 1.156 3.889 0.000 2.230
## speed 1.242 0.339 3.660 0.000 0.577
## g 1.238 0.151 8.212 0.000 0.942
## ci.upper Std.lv Std.all
## 0.253 0.181 0.181
## 0.277 0.192 0.183
## 0.304 0.251 0.261
## 0.422 0.350 0.322
## 0.334 0.185 0.195
## 0.217 0.136 0.142
## 0.626 0.528 0.509
## 0.582 0.433 0.353
## 0.423 0.313 0.315
## 0.500 0.271 0.231
## 0.641 0.499 0.488
## 0.373 0.313 0.328
## 1.943 1.000 1.000
## 2.127 1.000 1.000
## 6.759 1.000 1.000
## 1.907 1.000 1.000
## 1.533 1.000 1.000
lavTestScore(metric, release = 1:25)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 27.955 25 0.31
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p70. 3.292 1 0.070
## 2 .p2. == .p71. 3.578 1 0.059
## 3 .p3. == .p72. 0.005 1 0.946
## 4 .p4. == .p73. 0.015 1 0.904
## 5 .p5. == .p74. 0.418 1 0.518
## 6 .p6. == .p75. 0.263 1 0.608
## 7 .p7. == .p76. 1.041 1 0.308
## 8 .p8. == .p77. 0.023 1 0.880
## 9 .p9. == .p78. 3.238 1 0.072
## 10 .p10. == .p79. 2.403 1 0.121
## 11 .p11. == .p80. 0.188 1 0.665
## 12 .p12. == .p81. 0.726 1 0.394
## 13 .p13. == .p82. 0.314 1 0.575
## 14 .p14. == .p83. 0.131 1 0.717
## 15 .p15. == .p84. 0.016 1 0.899
## 16 .p16. == .p85. 1.166 1 0.280
## 17 .p17. == .p86. 0.032 1 0.859
## 18 .p18. == .p87. 0.016 1 0.899
## 19 .p19. == .p88. 1.121 1 0.290
## 20 .p20. == .p89. 3.106 1 0.078
## 21 .p21. == .p90. 0.033 1 0.856
## 22 .p22. == .p91. 4.609 1 0.032
## 23 .p23. == .p92. 1.485 1 0.223
## 24 .p24. == .p93. 4.539 1 0.033
## 25 .p25. == .p94. 0.000 1 0.987
scalar<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 424.066 109.000 0.000 0.948 0.093 0.058 16462.257
## bic
## 16782.274
Mc(scalar)
## [1] 0.7901951
summary(scalar, standardized=T, ci=T) # -.432
## lavaan 0.6-18 ended normally after 120 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 108
## Number of equality constraints 37
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 424.066 380.459
## Degrees of freedom 109 109
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.115
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 171.130 153.532
## 0 252.936 226.926
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.065 0.043 1.486 0.137 -0.021
## sswk (.p2.) 0.115 0.067 1.722 0.085 -0.016
## sspc (.p3.) 0.185 0.091 2.036 0.042 0.007
## ssei (.p4.) 0.045 0.032 1.399 0.162 -0.018
## math =~
## ssar (.p5.) 0.273 0.040 6.901 0.000 0.196
## ssmk (.p6.) 0.257 0.041 6.328 0.000 0.177
## ssao (.p7.) 0.244 0.042 5.738 0.000 0.160
## electronic =~
## ssai (.p8.) 0.320 0.043 7.374 0.000 0.235
## sssi (.p9.) 0.320 0.047 6.754 0.000 0.227
## ssei (.10.) 0.170 0.025 6.722 0.000 0.120
## speed =~
## ssno (.11.) 0.476 0.064 7.490 0.000 0.351
## sscs (.12.) 0.480 0.060 8.053 0.000 0.363
## ssmk (.13.) 0.194 0.033 5.914 0.000 0.130
## g =~
## ssgs (.14.) 0.798 0.038 20.784 0.000 0.722
## ssar (.15.) 0.734 0.040 18.252 0.000 0.655
## sswk (.16.) 0.798 0.041 19.424 0.000 0.717
## sspc (.17.) 0.750 0.037 20.138 0.000 0.677
## ssno (.18.) 0.560 0.044 12.731 0.000 0.474
## sscs (.19.) 0.521 0.038 13.857 0.000 0.447
## ssai (.20.) 0.472 0.037 12.833 0.000 0.400
## sssi (.21.) 0.461 0.038 12.244 0.000 0.387
## ssmk (.22.) 0.758 0.039 19.489 0.000 0.682
## ssmc (.23.) 0.703 0.038 18.427 0.000 0.629
## ssei (.24.) 0.706 0.039 18.123 0.000 0.630
## ssao (.25.) 0.594 0.036 16.406 0.000 0.523
## ci.upper Std.lv Std.all
##
## 0.150 0.065 0.072
## 0.246 0.115 0.125
## 0.364 0.185 0.203
## 0.108 0.045 0.050
##
## 0.351 0.273 0.315
## 0.337 0.257 0.278
## 0.327 0.244 0.265
##
## 0.405 0.320 0.407
## 0.413 0.320 0.408
## 0.219 0.170 0.189
##
## 0.600 0.476 0.488
## 0.597 0.480 0.501
## 0.259 0.194 0.210
##
## 0.873 0.798 0.883
## 0.812 0.734 0.846
## 0.878 0.798 0.865
## 0.822 0.750 0.821
## 0.646 0.560 0.575
## 0.595 0.521 0.544
## 0.544 0.472 0.600
## 0.534 0.461 0.587
## 0.834 0.758 0.821
## 0.778 0.703 0.806
## 0.782 0.706 0.785
## 0.665 0.594 0.645
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.53.) 0.381 0.051 7.457 0.000 0.281
## .sswk (.54.) 0.387 0.052 7.384 0.000 0.284
## .sspc (.55.) 0.440 0.053 8.361 0.000 0.336
## .ssei (.56.) 0.190 0.048 3.947 0.000 0.096
## .ssar (.57.) 0.392 0.049 7.981 0.000 0.296
## .ssmk (.58.) 0.452 0.053 8.454 0.000 0.347
## .ssao (.59.) 0.308 0.052 5.912 0.000 0.206
## .ssai (.60.) 0.057 0.041 1.387 0.166 -0.024
## .sssi (.61.) 0.175 0.043 4.094 0.000 0.091
## .ssno (.62.) 0.330 0.053 6.203 0.000 0.226
## .sscs (.63.) 0.310 0.059 5.216 0.000 0.193
## .ssmc (.64.) 0.253 0.050 5.032 0.000 0.155
## ci.upper Std.lv Std.all
## 0.482 0.381 0.422
## 0.489 0.387 0.419
## 0.543 0.440 0.481
## 0.284 0.190 0.211
## 0.488 0.392 0.452
## 0.556 0.452 0.489
## 0.410 0.308 0.335
## 0.138 0.057 0.073
## 0.258 0.175 0.222
## 0.434 0.330 0.339
## 0.426 0.310 0.323
## 0.352 0.253 0.290
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.175 0.019 9.154 0.000 0.137
## .sswk 0.200 0.021 9.666 0.000 0.160
## .sspc 0.237 0.032 7.408 0.000 0.175
## .ssei 0.280 0.030 9.218 0.000 0.220
## .ssar 0.140 0.021 6.585 0.000 0.098
## .ssmk 0.175 0.020 8.564 0.000 0.135
## .ssao 0.435 0.039 11.252 0.000 0.359
## .ssai 0.293 0.035 8.270 0.000 0.224
## .sssi 0.302 0.036 8.355 0.000 0.231
## .ssno 0.409 0.059 6.885 0.000 0.292
## .sscs 0.415 0.068 6.147 0.000 0.283
## .ssmc 0.267 0.026 10.214 0.000 0.216
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.212 0.175 0.214
## 0.241 0.200 0.236
## 0.300 0.237 0.285
## 0.339 0.280 0.346
## 0.181 0.140 0.186
## 0.214 0.175 0.205
## 0.511 0.435 0.514
## 0.362 0.293 0.474
## 0.373 0.302 0.490
## 0.525 0.409 0.431
## 0.548 0.415 0.453
## 0.318 0.267 0.350
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.065 0.043 1.486 0.137 -0.021
## sswk (.p2.) 0.115 0.067 1.722 0.085 -0.016
## sspc (.p3.) 0.185 0.091 2.036 0.042 0.007
## ssei (.p4.) 0.045 0.032 1.399 0.162 -0.018
## math =~
## ssar (.p5.) 0.273 0.040 6.901 0.000 0.196
## ssmk (.p6.) 0.257 0.041 6.328 0.000 0.177
## ssao (.p7.) 0.244 0.042 5.738 0.000 0.160
## electronic =~
## ssai (.p8.) 0.320 0.043 7.374 0.000 0.235
## sssi (.p9.) 0.320 0.047 6.754 0.000 0.227
## ssei (.10.) 0.170 0.025 6.722 0.000 0.120
## speed =~
## ssno (.11.) 0.476 0.064 7.490 0.000 0.351
## sscs (.12.) 0.480 0.060 8.053 0.000 0.363
## ssmk (.13.) 0.194 0.033 5.914 0.000 0.130
## g =~
## ssgs (.14.) 0.798 0.038 20.784 0.000 0.722
## ssar (.15.) 0.734 0.040 18.252 0.000 0.655
## sswk (.16.) 0.798 0.041 19.424 0.000 0.717
## sspc (.17.) 0.750 0.037 20.138 0.000 0.677
## ssno (.18.) 0.560 0.044 12.731 0.000 0.474
## sscs (.19.) 0.521 0.038 13.857 0.000 0.447
## ssai (.20.) 0.472 0.037 12.833 0.000 0.400
## sssi (.21.) 0.461 0.038 12.244 0.000 0.387
## ssmk (.22.) 0.758 0.039 19.489 0.000 0.682
## ssmc (.23.) 0.703 0.038 18.427 0.000 0.629
## ssei (.24.) 0.706 0.039 18.123 0.000 0.630
## ssao (.25.) 0.594 0.036 16.406 0.000 0.523
## ci.upper Std.lv Std.all
##
## 0.150 0.059 0.059
## 0.246 0.105 0.102
## 0.364 0.168 0.172
## 0.108 0.041 0.039
##
## 0.351 0.280 0.288
## 0.337 0.264 0.270
## 0.327 0.250 0.245
##
## 0.405 0.671 0.614
## 0.413 0.672 0.672
## 0.219 0.356 0.340
##
## 0.600 0.549 0.508
## 0.597 0.553 0.540
## 0.259 0.224 0.229
##
## 0.873 0.887 0.885
## 0.812 0.816 0.838
## 0.878 0.887 0.865
## 0.822 0.833 0.850
## 0.646 0.623 0.577
## 0.595 0.579 0.566
## 0.544 0.525 0.480
## 0.534 0.512 0.513
## 0.834 0.843 0.863
## 0.778 0.782 0.803
## 0.782 0.785 0.749
## 0.665 0.660 0.648
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.53.) 0.381 0.051 7.457 0.000 0.281
## .sswk (.54.) 0.387 0.052 7.384 0.000 0.284
## .sspc (.55.) 0.440 0.053 8.361 0.000 0.336
## .ssei (.56.) 0.190 0.048 3.947 0.000 0.096
## .ssar (.57.) 0.392 0.049 7.981 0.000 0.296
## .ssmk (.58.) 0.452 0.053 8.454 0.000 0.347
## .ssao (.59.) 0.308 0.052 5.912 0.000 0.206
## .ssai (.60.) 0.057 0.041 1.387 0.166 -0.024
## .sssi (.61.) 0.175 0.043 4.094 0.000 0.091
## .ssno (.62.) 0.330 0.053 6.203 0.000 0.226
## .sscs (.63.) 0.310 0.059 5.216 0.000 0.193
## .ssmc (.64.) 0.253 0.050 5.032 0.000 0.155
## verbal -3.515 1.768 -1.988 0.047 -6.981
## math -1.330 0.303 -4.390 0.000 -1.924
## elctrnc 1.312 0.256 5.131 0.000 0.811
## speed -1.118 0.212 -5.273 0.000 -1.534
## g 0.480 0.115 4.165 0.000 0.254
## ci.upper Std.lv Std.all
## 0.482 0.381 0.381
## 0.489 0.387 0.377
## 0.543 0.440 0.448
## 0.284 0.190 0.181
## 0.488 0.392 0.403
## 0.556 0.452 0.462
## 0.410 0.308 0.302
## 0.138 0.057 0.052
## 0.258 0.175 0.175
## 0.434 0.330 0.306
## 0.426 0.310 0.302
## 0.352 0.253 0.260
## -0.050 -3.869 -3.869
## -0.736 -1.296 -1.296
## 1.814 0.626 0.626
## -0.702 -0.970 -0.970
## 0.706 0.432 0.432
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.214 0.022 9.742 0.000 0.171
## .sswk 0.253 0.025 10.279 0.000 0.205
## .sspc 0.238 0.034 6.914 0.000 0.171
## .ssei 0.353 0.037 9.453 0.000 0.279
## .ssar 0.203 0.028 7.283 0.000 0.149
## .ssmk 0.125 0.018 6.961 0.000 0.089
## .ssao 0.539 0.052 10.453 0.000 0.438
## .ssai 0.470 0.067 6.987 0.000 0.338
## .sssi 0.284 0.056 5.074 0.000 0.174
## .ssno 0.478 0.066 7.182 0.000 0.347
## .sscs 0.408 0.076 5.379 0.000 0.259
## .ssmc 0.336 0.033 10.257 0.000 0.272
## verbal 0.825 1.251 0.660 0.509 -1.626
## math 1.052 0.352 2.991 0.003 0.363
## electronic 4.395 1.203 3.653 0.000 2.037
## speed 1.329 0.347 3.831 0.000 0.649
## g 1.236 0.151 8.170 0.000 0.940
## ci.upper Std.lv Std.all
## 0.258 0.214 0.214
## 0.301 0.253 0.241
## 0.306 0.238 0.248
## 0.426 0.353 0.321
## 0.258 0.203 0.215
## 0.160 0.125 0.130
## 0.640 0.539 0.520
## 0.601 0.470 0.393
## 0.394 0.284 0.285
## 0.608 0.478 0.409
## 0.557 0.408 0.389
## 0.400 0.336 0.355
## 3.277 1.000 1.000
## 1.742 1.000 1.000
## 6.754 1.000 1.000
## 2.009 1.000 1.000
## 1.533 1.000 1.000
lavTestScore(scalar, release = 26:37)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 93.615 12 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p53. == .p122. 2.740 1 0.098
## 2 .p54. == .p123. 10.715 1 0.001
## 3 .p55. == .p124. 65.696 1 0.000
## 4 .p56. == .p125. 0.378 1 0.538
## 5 .p57. == .p126. 2.985 1 0.084
## 6 .p58. == .p127. 0.398 1 0.528
## 7 .p59. == .p128. 6.296 1 0.012
## 8 .p60. == .p129. 1.438 1 0.231
## 9 .p61. == .p130. 1.205 1 0.272
## 10 .p62. == .p131. 17.594 1 0.000
## 11 .p63. == .p132. 20.258 1 0.000
## 12 .p64. == .p133. 16.674 1 0.000
scalar2<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 339.060 107.000 0.000 0.961 0.080 0.054 16381.251
## bic
## 16710.282
Mc(scalar2)
## [1] 0.8407693
summary(scalar2, standardized=T, ci=T) # g -.416 Std.all
## lavaan 0.6-18 ended normally after 109 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 108
## Number of equality constraints 35
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 339.060 304.134
## Degrees of freedom 107 107
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.115
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 126.970 113.891
## 0 212.090 190.243
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.252 0.035 7.129 0.000 0.183
## sswk (.p2.) 0.414 0.057 7.325 0.000 0.304
## sspc (.p3.) 0.099 0.035 2.843 0.004 0.031
## ssei (.p4.) 0.152 0.038 3.966 0.000 0.077
## math =~
## ssar (.p5.) 0.189 0.041 4.635 0.000 0.109
## ssmk (.p6.) 0.232 0.048 4.876 0.000 0.139
## ssao (.p7.) 0.190 0.039 4.834 0.000 0.113
## electronic =~
## ssai (.p8.) 0.327 0.042 7.860 0.000 0.245
## sssi (.p9.) 0.328 0.045 7.287 0.000 0.240
## ssei (.10.) 0.172 0.024 7.084 0.000 0.124
## speed =~
## ssno (.11.) 0.669 0.101 6.604 0.000 0.471
## sscs (.12.) 0.338 0.057 5.965 0.000 0.227
## ssmk (.13.) 0.182 0.031 5.864 0.000 0.121
## g =~
## ssgs (.14.) 0.766 0.039 19.629 0.000 0.689
## ssar (.15.) 0.751 0.040 18.855 0.000 0.673
## sswk (.16.) 0.754 0.042 17.764 0.000 0.671
## sspc (.17.) 0.752 0.037 20.554 0.000 0.681
## ssno (.18.) 0.553 0.044 12.698 0.000 0.468
## sscs (.19.) 0.547 0.038 14.423 0.000 0.473
## ssai (.20.) 0.465 0.037 12.472 0.000 0.392
## sssi (.21.) 0.453 0.038 11.926 0.000 0.379
## ssmk (.22.) 0.775 0.039 19.970 0.000 0.699
## ssmc (.23.) 0.715 0.038 18.812 0.000 0.641
## ssei (.24.) 0.681 0.040 17.125 0.000 0.603
## ssao (.25.) 0.621 0.036 17.132 0.000 0.550
## ci.upper Std.lv Std.all
##
## 0.321 0.252 0.278
## 0.525 0.414 0.449
## 0.167 0.099 0.109
## 0.227 0.152 0.170
##
## 0.269 0.189 0.217
## 0.325 0.232 0.250
## 0.267 0.190 0.206
##
## 0.408 0.327 0.417
## 0.416 0.328 0.418
## 0.220 0.172 0.193
##
## 0.868 0.669 0.683
## 0.450 0.338 0.355
## 0.243 0.182 0.196
##
## 0.842 0.766 0.846
## 0.829 0.751 0.865
## 0.837 0.754 0.817
## 0.824 0.752 0.827
## 0.639 0.553 0.565
## 0.621 0.547 0.573
## 0.538 0.465 0.594
## 0.528 0.453 0.579
## 0.851 0.775 0.837
## 0.790 0.715 0.820
## 0.759 0.681 0.763
## 0.692 0.621 0.675
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.53.) 0.388 0.050 7.744 0.000 0.290
## .sswk (.54.) 0.378 0.053 7.197 0.000 0.275
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei (.56.) 0.186 0.047 3.983 0.000 0.095
## .ssar (.57.) 0.391 0.049 7.946 0.000 0.294
## .ssmk (.58.) 0.449 0.054 8.328 0.000 0.344
## .ssao (.59.) 0.322 0.054 6.006 0.000 0.217
## .ssai (.60.) 0.059 0.041 1.431 0.152 -0.022
## .sssi (.61.) 0.176 0.042 4.158 0.000 0.093
## .ssno (.62.) 0.285 0.056 5.112 0.000 0.176
## .sscs 0.358 0.053 6.754 0.000 0.254
## .ssmc (.64.) 0.256 0.048 5.348 0.000 0.162
## ci.upper Std.lv Std.all
## 0.486 0.388 0.429
## 0.481 0.378 0.410
## 0.545 0.445 0.489
## 0.278 0.186 0.208
## 0.487 0.391 0.450
## 0.555 0.449 0.485
## 0.427 0.322 0.350
## 0.139 0.059 0.075
## 0.258 0.176 0.224
## 0.394 0.285 0.291
## 0.462 0.358 0.375
## 0.350 0.256 0.294
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.170 0.020 8.517 0.000 0.131
## .sswk 0.111 0.034 3.281 0.001 0.045
## .sspc 0.252 0.030 8.335 0.000 0.192
## .ssei 0.281 0.030 9.422 0.000 0.222
## .ssar 0.154 0.019 7.943 0.000 0.116
## .ssmk 0.170 0.021 8.265 0.000 0.130
## .ssao 0.424 0.037 11.523 0.000 0.352
## .ssai 0.290 0.035 8.158 0.000 0.220
## .sssi 0.301 0.036 8.289 0.000 0.230
## .ssno 0.205 0.106 1.932 0.053 -0.003
## .sscs 0.497 0.057 8.671 0.000 0.385
## .ssmc 0.249 0.025 9.867 0.000 0.199
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.209 0.170 0.207
## 0.177 0.111 0.131
## 0.311 0.252 0.304
## 0.339 0.281 0.352
## 0.192 0.154 0.204
## 0.211 0.170 0.199
## 0.496 0.424 0.502
## 0.359 0.290 0.473
## 0.372 0.301 0.490
## 0.414 0.205 0.214
## 0.609 0.497 0.546
## 0.298 0.249 0.327
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.252 0.035 7.129 0.000 0.183
## sswk (.p2.) 0.414 0.057 7.325 0.000 0.304
## sspc (.p3.) 0.099 0.035 2.843 0.004 0.031
## ssei (.p4.) 0.152 0.038 3.966 0.000 0.077
## math =~
## ssar (.p5.) 0.189 0.041 4.635 0.000 0.109
## ssmk (.p6.) 0.232 0.048 4.876 0.000 0.139
## ssao (.p7.) 0.190 0.039 4.834 0.000 0.113
## electronic =~
## ssai (.p8.) 0.327 0.042 7.860 0.000 0.245
## sssi (.p9.) 0.328 0.045 7.287 0.000 0.240
## ssei (.10.) 0.172 0.024 7.084 0.000 0.124
## speed =~
## ssno (.11.) 0.669 0.101 6.604 0.000 0.471
## sscs (.12.) 0.338 0.057 5.965 0.000 0.227
## ssmk (.13.) 0.182 0.031 5.864 0.000 0.121
## g =~
## ssgs (.14.) 0.766 0.039 19.629 0.000 0.689
## ssar (.15.) 0.751 0.040 18.855 0.000 0.673
## sswk (.16.) 0.754 0.042 17.764 0.000 0.671
## sspc (.17.) 0.752 0.037 20.554 0.000 0.681
## ssno (.18.) 0.553 0.044 12.698 0.000 0.468
## sscs (.19.) 0.547 0.038 14.423 0.000 0.473
## ssai (.20.) 0.465 0.037 12.472 0.000 0.392
## sssi (.21.) 0.453 0.038 11.926 0.000 0.379
## ssmk (.22.) 0.775 0.039 19.970 0.000 0.699
## ssmc (.23.) 0.715 0.038 18.812 0.000 0.641
## ssei (.24.) 0.681 0.040 17.125 0.000 0.603
## ssao (.25.) 0.621 0.036 17.132 0.000 0.550
## ci.upper Std.lv Std.all
##
## 0.321 0.267 0.267
## 0.525 0.440 0.429
## 0.167 0.105 0.107
## 0.227 0.161 0.154
##
## 0.269 0.174 0.179
## 0.325 0.214 0.218
## 0.267 0.175 0.172
##
## 0.408 0.685 0.623
## 0.416 0.687 0.684
## 0.220 0.360 0.345
##
## 0.868 0.746 0.688
## 0.450 0.377 0.373
## 0.243 0.203 0.207
##
## 0.842 0.853 0.852
## 0.829 0.836 0.861
## 0.837 0.839 0.819
## 0.824 0.838 0.853
## 0.639 0.616 0.568
## 0.621 0.609 0.603
## 0.538 0.518 0.471
## 0.528 0.505 0.503
## 0.851 0.863 0.881
## 0.790 0.797 0.817
## 0.759 0.759 0.726
## 0.692 0.691 0.679
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.53.) 0.388 0.050 7.744 0.000 0.290
## .sswk (.54.) 0.378 0.053 7.197 0.000 0.275
## .sspc -0.122 0.068 -1.801 0.072 -0.255
## .ssei (.56.) 0.186 0.047 3.983 0.000 0.095
## .ssar (.57.) 0.391 0.049 7.946 0.000 0.294
## .ssmk (.58.) 0.449 0.054 8.328 0.000 0.344
## .ssao (.59.) 0.322 0.054 6.006 0.000 0.217
## .ssai (.60.) 0.059 0.041 1.431 0.152 -0.022
## .sssi (.61.) 0.176 0.042 4.158 0.000 0.093
## .ssno (.62.) 0.285 0.056 5.112 0.000 0.176
## .sscs -0.068 0.080 -0.841 0.400 -0.225
## .ssmc (.64.) 0.256 0.048 5.348 0.000 0.162
## verbal -0.847 0.194 -4.375 0.000 -1.227
## math -1.885 0.461 -4.091 0.000 -2.787
## elctrnc 1.311 0.234 5.606 0.000 0.853
## speed -0.626 0.157 -3.985 0.000 -0.935
## g 0.463 0.101 4.587 0.000 0.265
## ci.upper Std.lv Std.all
## 0.486 0.388 0.388
## 0.481 0.378 0.369
## 0.011 -0.122 -0.124
## 0.278 0.186 0.178
## 0.487 0.391 0.402
## 0.555 0.449 0.459
## 0.427 0.322 0.316
## 0.139 0.059 0.053
## 0.258 0.176 0.175
## 0.394 0.285 0.263
## 0.090 -0.068 -0.067
## 0.350 0.256 0.263
## -0.468 -0.799 -0.799
## -0.982 -2.041 -2.041
## 1.769 0.626 0.626
## -0.318 -0.562 -0.562
## 0.661 0.416 0.416
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.203 0.024 8.528 0.000 0.157
## .sswk 0.152 0.036 4.208 0.000 0.081
## .sspc 0.251 0.027 9.437 0.000 0.199
## .ssei 0.361 0.038 9.594 0.000 0.287
## .ssar 0.215 0.026 8.263 0.000 0.164
## .ssmk 0.129 0.020 6.335 0.000 0.089
## .ssao 0.528 0.050 10.532 0.000 0.430
## .ssai 0.473 0.068 6.932 0.000 0.339
## .sssi 0.280 0.056 4.969 0.000 0.170
## .ssno 0.240 0.124 1.928 0.054 -0.004
## .sscs 0.508 0.072 7.045 0.000 0.366
## .ssmc 0.316 0.031 10.229 0.000 0.256
## verbal 1.125 0.345 3.259 0.001 0.449
## math 0.852 0.476 1.791 0.073 -0.081
## electronic 4.393 1.134 3.872 0.000 2.170
## speed 1.243 0.334 3.717 0.000 0.588
## g 1.240 0.152 8.167 0.000 0.943
## ci.upper Std.lv Std.all
## 0.250 0.203 0.203
## 0.223 0.152 0.145
## 0.304 0.251 0.261
## 0.434 0.361 0.330
## 0.266 0.215 0.227
## 0.168 0.129 0.134
## 0.626 0.528 0.509
## 0.607 0.473 0.391
## 0.391 0.280 0.278
## 0.483 0.240 0.204
## 0.649 0.508 0.497
## 0.377 0.316 0.333
## 1.802 1.000 1.000
## 1.785 1.000 1.000
## 6.617 1.000 1.000
## 1.899 1.000 1.000
## 1.538 1.000 1.000
lavTestScore(scalar2, release = 26:35)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 9.286 10 0.505
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p53. == .p122. 1.830 1 0.176
## 2 .p54. == .p123. 1.734 1 0.188
## 3 .p56. == .p125. 0.028 1 0.868
## 4 .p57. == .p126. 3.526 1 0.060
## 5 .p58. == .p127. 0.245 1 0.621
## 6 .p59. == .p128. 5.730 1 0.017
## 7 .p60. == .p129. 1.055 1 0.304
## 8 .p61. == .p130. 1.278 1 0.258
## 9 .p62. == .p131. 0.245 1 0.621
## 10 .p64. == .p133. 2.491 1 0.114
strict<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 370.328 119.000 0.000 0.958 0.079 0.057 16388.518
## bic
## 16663.462
Mc(strict)
## [1] 0.8287487
summary(strict, standardized=T, ci=T) # g -.419 Std.all
## lavaan 0.6-18 ended normally after 104 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 108
## Number of equality constraints 47
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 370.328 329.819
## Degrees of freedom 119 119
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.123
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 144.819 128.977
## 0 225.509 200.841
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.239 0.036 6.637 0.000 0.168
## sswk (.p2.) 0.379 0.056 6.809 0.000 0.270
## sspc (.p3.) 0.090 0.033 2.769 0.006 0.026
## ssei (.p4.) 0.136 0.038 3.611 0.000 0.062
## math =~
## ssar (.p5.) 0.191 0.038 5.012 0.000 0.117
## ssmk (.p6.) 0.242 0.054 4.455 0.000 0.136
## ssao (.p7.) 0.194 0.036 5.394 0.000 0.124
## electronic =~
## ssai (.p8.) 0.315 0.047 6.678 0.000 0.223
## sssi (.p9.) 0.277 0.043 6.430 0.000 0.193
## ssei (.10.) 0.157 0.026 6.122 0.000 0.107
## speed =~
## ssno (.11.) 0.673 0.101 6.662 0.000 0.475
## sscs (.12.) 0.329 0.056 5.839 0.000 0.219
## ssmk (.13.) 0.176 0.028 6.300 0.000 0.121
## g =~
## ssgs (.14.) 0.765 0.039 19.511 0.000 0.689
## ssar (.15.) 0.750 0.040 18.916 0.000 0.672
## sswk (.16.) 0.753 0.042 17.777 0.000 0.670
## sspc (.17.) 0.753 0.036 20.691 0.000 0.682
## ssno (.18.) 0.553 0.043 12.781 0.000 0.469
## sscs (.19.) 0.547 0.038 14.421 0.000 0.473
## ssai (.20.) 0.465 0.037 12.411 0.000 0.391
## sssi (.21.) 0.456 0.038 11.947 0.000 0.381
## ssmk (.22.) 0.778 0.038 20.242 0.000 0.702
## ssmc (.23.) 0.717 0.038 18.844 0.000 0.642
## ssei (.24.) 0.685 0.040 17.132 0.000 0.607
## ssao (.25.) 0.620 0.036 17.010 0.000 0.549
## ci.upper Std.lv Std.all
##
## 0.310 0.239 0.263
## 0.489 0.379 0.412
## 0.154 0.090 0.099
## 0.210 0.136 0.150
##
## 0.266 0.191 0.216
## 0.349 0.242 0.264
## 0.265 0.194 0.205
##
## 0.408 0.315 0.390
## 0.362 0.277 0.353
## 0.207 0.157 0.172
##
## 0.871 0.673 0.686
## 0.439 0.329 0.344
## 0.231 0.176 0.192
##
## 0.842 0.765 0.842
## 0.827 0.750 0.847
## 0.836 0.753 0.818
## 0.824 0.753 0.829
## 0.638 0.553 0.564
## 0.621 0.547 0.572
## 0.538 0.465 0.576
## 0.530 0.456 0.581
## 0.853 0.778 0.847
## 0.791 0.717 0.803
## 0.764 0.685 0.752
## 0.692 0.620 0.654
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.53.) 0.392 0.050 7.779 0.000 0.293
## .sswk (.54.) 0.376 0.053 7.124 0.000 0.273
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei (.56.) 0.184 0.047 3.883 0.000 0.091
## .ssar (.57.) 0.390 0.050 7.852 0.000 0.293
## .ssmk (.58.) 0.451 0.054 8.359 0.000 0.345
## .ssao (.59.) 0.319 0.053 5.980 0.000 0.214
## .ssai (.60.) 0.046 0.041 1.118 0.264 -0.035
## .sssi (.61.) 0.192 0.042 4.539 0.000 0.109
## .ssno (.62.) 0.285 0.056 5.096 0.000 0.175
## .sscs 0.358 0.053 6.754 0.000 0.254
## .ssmc (.64.) 0.254 0.048 5.300 0.000 0.160
## ci.upper Std.lv Std.all
## 0.490 0.392 0.430
## 0.480 0.376 0.409
## 0.545 0.445 0.490
## 0.277 0.184 0.202
## 0.487 0.390 0.440
## 0.556 0.451 0.491
## 0.424 0.319 0.337
## 0.126 0.046 0.057
## 0.275 0.192 0.245
## 0.394 0.285 0.290
## 0.462 0.358 0.375
## 0.347 0.254 0.284
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.26.) 0.184 0.018 10.270 0.000 0.149
## .sswk (.27.) 0.137 0.032 4.263 0.000 0.074
## .sspc (.28.) 0.251 0.020 12.222 0.000 0.210
## .ssei (.29.) 0.317 0.024 13.276 0.000 0.270
## .ssar (.30.) 0.186 0.016 11.335 0.000 0.153
## .ssmk (.31.) 0.149 0.016 9.519 0.000 0.118
## .ssao (.32.) 0.476 0.031 15.139 0.000 0.414
## .ssai (.33.) 0.336 0.040 8.403 0.000 0.258
## .sssi (.34.) 0.331 0.032 10.467 0.000 0.269
## .ssno (.35.) 0.204 0.110 1.850 0.064 -0.012
## .sscs (.36.) 0.506 0.049 10.291 0.000 0.410
## .ssmc (.37.) 0.282 0.020 14.156 0.000 0.243
## verbal 1.000 1.000
## math 1.000 1.000
## elctrnc 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.219 0.184 0.223
## 0.199 0.137 0.161
## 0.291 0.251 0.303
## 0.363 0.317 0.382
## 0.218 0.186 0.237
## 0.180 0.149 0.177
## 0.537 0.476 0.530
## 0.415 0.336 0.516
## 0.393 0.331 0.538
## 0.420 0.204 0.212
## 0.602 0.506 0.554
## 0.321 0.282 0.355
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.239 0.036 6.637 0.000 0.168
## sswk (.p2.) 0.379 0.056 6.809 0.000 0.270
## sspc (.p3.) 0.090 0.033 2.769 0.006 0.026
## ssei (.p4.) 0.136 0.038 3.611 0.000 0.062
## math =~
## ssar (.p5.) 0.191 0.038 5.012 0.000 0.117
## ssmk (.p6.) 0.242 0.054 4.455 0.000 0.136
## ssao (.p7.) 0.194 0.036 5.394 0.000 0.124
## electronic =~
## ssai (.p8.) 0.315 0.047 6.678 0.000 0.223
## sssi (.p9.) 0.277 0.043 6.430 0.000 0.193
## ssei (.10.) 0.157 0.026 6.122 0.000 0.107
## speed =~
## ssno (.11.) 0.673 0.101 6.662 0.000 0.475
## sscs (.12.) 0.329 0.056 5.839 0.000 0.219
## ssmk (.13.) 0.176 0.028 6.300 0.000 0.121
## g =~
## ssgs (.14.) 0.765 0.039 19.511 0.000 0.689
## ssar (.15.) 0.750 0.040 18.916 0.000 0.672
## sswk (.16.) 0.753 0.042 17.777 0.000 0.670
## sspc (.17.) 0.753 0.036 20.691 0.000 0.682
## ssno (.18.) 0.553 0.043 12.781 0.000 0.469
## sscs (.19.) 0.547 0.038 14.421 0.000 0.473
## ssai (.20.) 0.465 0.037 12.411 0.000 0.391
## sssi (.21.) 0.456 0.038 11.947 0.000 0.381
## ssmk (.22.) 0.778 0.038 20.242 0.000 0.702
## ssmc (.23.) 0.717 0.038 18.844 0.000 0.642
## ssei (.24.) 0.685 0.040 17.132 0.000 0.607
## ssao (.25.) 0.620 0.036 17.010 0.000 0.549
## ci.upper Std.lv Std.all
##
## 0.310 0.290 0.290
## 0.489 0.460 0.448
## 0.154 0.109 0.111
## 0.210 0.165 0.160
##
## 0.266 0.165 0.172
## 0.349 0.208 0.210
## 0.265 0.167 0.169
##
## 0.408 0.749 0.693
## 0.362 0.659 0.651
## 0.207 0.373 0.361
##
## 0.871 0.767 0.708
## 0.439 0.375 0.372
## 0.231 0.201 0.202
##
## 0.842 0.854 0.855
## 0.827 0.836 0.876
## 0.836 0.840 0.818
## 0.824 0.840 0.854
## 0.638 0.617 0.570
## 0.621 0.610 0.605
## 0.538 0.519 0.480
## 0.530 0.508 0.502
## 0.853 0.867 0.874
## 0.791 0.799 0.833
## 0.764 0.765 0.740
## 0.692 0.692 0.698
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.53.) 0.392 0.050 7.779 0.000 0.293
## .sswk (.54.) 0.376 0.053 7.124 0.000 0.273
## .sspc -0.126 0.069 -1.831 0.067 -0.260
## .ssei (.56.) 0.184 0.047 3.883 0.000 0.091
## .ssar (.57.) 0.390 0.050 7.852 0.000 0.293
## .ssmk (.58.) 0.451 0.054 8.359 0.000 0.345
## .ssao (.59.) 0.319 0.053 5.980 0.000 0.214
## .ssai (.60.) 0.046 0.041 1.118 0.264 -0.035
## .sssi (.61.) 0.192 0.042 4.539 0.000 0.109
## .ssno (.62.) 0.285 0.056 5.096 0.000 0.175
## .sscs -0.076 0.080 -0.951 0.342 -0.232
## .ssmc (.64.) 0.254 0.048 5.300 0.000 0.160
## verbal -0.925 0.214 -4.321 0.000 -1.345
## math -1.849 0.463 -3.990 0.000 -2.757
## elctrnc 1.415 0.287 4.932 0.000 0.852
## speed -0.625 0.157 -3.979 0.000 -0.933
## g 0.467 0.102 4.592 0.000 0.268
## ci.upper Std.lv Std.all
## 0.490 0.392 0.392
## 0.480 0.376 0.366
## 0.009 -0.126 -0.128
## 0.277 0.184 0.178
## 0.487 0.390 0.408
## 0.556 0.451 0.454
## 0.424 0.319 0.322
## 0.126 0.046 0.042
## 0.275 0.192 0.190
## 0.394 0.285 0.263
## 0.081 -0.076 -0.075
## 0.347 0.254 0.264
## -0.506 -0.764 -0.764
## -0.941 -2.149 -2.149
## 1.977 0.596 0.596
## -0.317 -0.548 -0.548
## 0.666 0.419 0.419
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.26.) 0.184 0.018 10.270 0.000 0.149
## .sswk (.27.) 0.137 0.032 4.263 0.000 0.074
## .sspc (.28.) 0.251 0.020 12.222 0.000 0.210
## .ssei (.29.) 0.317 0.024 13.276 0.000 0.270
## .ssar (.30.) 0.186 0.016 11.335 0.000 0.153
## .ssmk (.31.) 0.149 0.016 9.519 0.000 0.118
## .ssao (.32.) 0.476 0.031 15.139 0.000 0.414
## .ssai (.33.) 0.336 0.040 8.403 0.000 0.258
## .sssi (.34.) 0.331 0.032 10.467 0.000 0.269
## .ssno (.35.) 0.204 0.110 1.850 0.064 -0.012
## .sscs (.36.) 0.506 0.049 10.291 0.000 0.410
## .ssmc (.37.) 0.282 0.020 14.156 0.000 0.243
## verbal 1.469 0.400 3.668 0.000 0.684
## math 0.740 0.398 1.857 0.063 -0.041
## elctrnc 5.643 1.672 3.376 0.001 2.367
## speed 1.301 0.308 4.224 0.000 0.697
## g 1.245 0.152 8.204 0.000 0.947
## ci.upper Std.lv Std.all
## 0.219 0.184 0.185
## 0.199 0.137 0.130
## 0.291 0.251 0.259
## 0.363 0.317 0.297
## 0.218 0.186 0.203
## 0.180 0.149 0.151
## 0.537 0.476 0.484
## 0.415 0.336 0.288
## 0.393 0.331 0.324
## 0.420 0.204 0.174
## 0.602 0.506 0.496
## 0.321 0.282 0.306
## 2.253 1.000 1.000
## 1.521 1.000 1.000
## 8.920 1.000 1.000
## 1.904 1.000 1.000
## 1.542 1.000 1.000
latent<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 396.825 112.000 0.000 0.953 0.087 0.106 16429.016
## bic
## 16735.510
Mc(latent)
## [1] 0.8082584
summary(latent, standardized=T, ci=T) # -.434
## lavaan 0.6-18 ended normally after 62 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 103
## Number of equality constraints 35
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 396.825 353.826
## Degrees of freedom 112 112
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.122
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 159.269 142.011
## 0 237.556 211.815
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.253 0.035 7.243 0.000 0.184
## sswk (.p2.) 0.431 0.044 9.798 0.000 0.345
## sspc (.p3.) 0.103 0.034 3.011 0.003 0.036
## ssei (.p4.) 0.150 0.039 3.843 0.000 0.074
## math =~
## ssar (.p5.) 0.189 0.036 5.240 0.000 0.118
## ssmk (.p6.) 0.230 0.042 5.512 0.000 0.149
## ssao (.p7.) 0.187 0.028 6.634 0.000 0.132
## electronic =~
## ssai (.p8.) 0.478 0.045 10.638 0.000 0.390
## sssi (.p9.) 0.532 0.044 12.116 0.000 0.446
## ssei (.10.) 0.254 0.033 7.654 0.000 0.189
## speed =~
## ssno (.11.) 0.706 0.090 7.859 0.000 0.530
## sscs (.12.) 0.360 0.056 6.442 0.000 0.250
## ssmk (.13.) 0.196 0.032 6.065 0.000 0.133
## g =~
## ssgs (.14.) 0.810 0.032 24.922 0.000 0.746
## ssar (.15.) 0.793 0.033 24.007 0.000 0.728
## sswk (.16.) 0.798 0.035 22.542 0.000 0.728
## sspc (.17.) 0.793 0.029 27.804 0.000 0.737
## ssno (.18.) 0.582 0.040 14.454 0.000 0.503
## sscs (.19.) 0.576 0.035 16.510 0.000 0.507
## ssai (.20.) 0.521 0.039 13.333 0.000 0.445
## sssi (.21.) 0.509 0.039 13.119 0.000 0.433
## ssmk (.22.) 0.817 0.030 27.043 0.000 0.758
## ssmc (.23.) 0.759 0.034 22.371 0.000 0.693
## ssei (.24.) 0.738 0.037 19.916 0.000 0.665
## ssao (.25.) 0.655 0.031 20.925 0.000 0.593
## ci.upper Std.lv Std.all
##
## 0.321 0.253 0.267
## 0.517 0.431 0.448
## 0.169 0.103 0.109
## 0.227 0.150 0.157
##
## 0.259 0.189 0.209
## 0.312 0.230 0.239
## 0.242 0.187 0.199
##
## 0.566 0.478 0.540
## 0.618 0.532 0.599
## 0.319 0.254 0.266
##
## 0.882 0.706 0.699
## 0.469 0.360 0.367
## 0.260 0.196 0.204
##
## 0.874 0.810 0.858
## 0.857 0.793 0.878
## 0.867 0.798 0.830
## 0.849 0.793 0.841
## 0.660 0.582 0.576
## 0.644 0.576 0.588
## 0.598 0.521 0.589
## 0.585 0.509 0.573
## 0.876 0.817 0.848
## 0.826 0.759 0.836
## 0.811 0.738 0.771
## 0.716 0.655 0.695
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.53.) 0.386 0.050 7.693 0.000 0.288
## .sswk (.54.) 0.379 0.052 7.224 0.000 0.276
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei (.56.) 0.188 0.046 4.045 0.000 0.097
## .ssar (.57.) 0.391 0.049 7.958 0.000 0.295
## .ssmk (.58.) 0.450 0.054 8.333 0.000 0.344
## .ssao (.59.) 0.320 0.053 6.007 0.000 0.216
## .ssai (.60.) 0.067 0.041 1.638 0.101 -0.013
## .sssi (.61.) 0.165 0.042 3.908 0.000 0.082
## .ssno (.62.) 0.285 0.056 5.113 0.000 0.176
## .sscs 0.358 0.053 6.754 0.000 0.254
## .ssmc (.64.) 0.257 0.048 5.360 0.000 0.163
## ci.upper Std.lv Std.all
## 0.484 0.386 0.409
## 0.482 0.379 0.394
## 0.545 0.445 0.472
## 0.279 0.188 0.196
## 0.487 0.391 0.433
## 0.555 0.450 0.466
## 0.424 0.320 0.339
## 0.148 0.067 0.076
## 0.248 0.165 0.186
## 0.394 0.285 0.282
## 0.462 0.358 0.365
## 0.351 0.257 0.283
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.172 0.020 8.612 0.000 0.133
## .sswk 0.103 0.033 3.154 0.002 0.039
## .sspc 0.250 0.030 8.308 0.000 0.191
## .ssei 0.284 0.029 9.685 0.000 0.227
## .ssar 0.152 0.019 7.863 0.000 0.114
## .ssmk 0.170 0.020 8.340 0.000 0.130
## .ssao 0.424 0.037 11.516 0.000 0.352
## .ssai 0.283 0.039 7.297 0.000 0.207
## .sssi 0.247 0.044 5.582 0.000 0.160
## .ssno 0.183 0.108 1.701 0.089 -0.028
## .sscs 0.499 0.058 8.575 0.000 0.385
## .ssmc 0.249 0.025 9.767 0.000 0.199
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.211 0.172 0.193
## 0.166 0.103 0.111
## 0.309 0.250 0.281
## 0.342 0.284 0.310
## 0.190 0.152 0.186
## 0.210 0.170 0.183
## 0.497 0.424 0.478
## 0.359 0.283 0.361
## 0.333 0.247 0.313
## 0.395 0.183 0.180
## 0.613 0.499 0.520
## 0.299 0.249 0.302
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.253 0.035 7.243 0.000 0.184
## sswk (.p2.) 0.431 0.044 9.798 0.000 0.345
## sspc (.p3.) 0.103 0.034 3.011 0.003 0.036
## ssei (.p4.) 0.150 0.039 3.843 0.000 0.074
## math =~
## ssar (.p5.) 0.189 0.036 5.240 0.000 0.118
## ssmk (.p6.) 0.230 0.042 5.512 0.000 0.149
## ssao (.p7.) 0.187 0.028 6.634 0.000 0.132
## electronic =~
## ssai (.p8.) 0.478 0.045 10.638 0.000 0.390
## sssi (.p9.) 0.532 0.044 12.116 0.000 0.446
## ssei (.10.) 0.254 0.033 7.654 0.000 0.189
## speed =~
## ssno (.11.) 0.706 0.090 7.859 0.000 0.530
## sscs (.12.) 0.360 0.056 6.442 0.000 0.250
## ssmk (.13.) 0.196 0.032 6.065 0.000 0.133
## g =~
## ssgs (.14.) 0.810 0.032 24.922 0.000 0.746
## ssar (.15.) 0.793 0.033 24.007 0.000 0.728
## sswk (.16.) 0.798 0.035 22.542 0.000 0.728
## sspc (.17.) 0.793 0.029 27.804 0.000 0.737
## ssno (.18.) 0.582 0.040 14.454 0.000 0.503
## sscs (.19.) 0.576 0.035 16.510 0.000 0.507
## ssai (.20.) 0.521 0.039 13.333 0.000 0.445
## sssi (.21.) 0.509 0.039 13.119 0.000 0.433
## ssmk (.22.) 0.817 0.030 27.043 0.000 0.758
## ssmc (.23.) 0.759 0.034 22.371 0.000 0.693
## ssei (.24.) 0.738 0.037 19.916 0.000 0.665
## ssao (.25.) 0.655 0.031 20.925 0.000 0.593
## ci.upper Std.lv Std.all
##
## 0.321 0.253 0.263
## 0.517 0.431 0.437
## 0.169 0.103 0.108
## 0.227 0.150 0.150
##
## 0.259 0.189 0.201
## 0.312 0.230 0.245
## 0.242 0.187 0.188
##
## 0.566 0.478 0.468
## 0.618 0.532 0.576
## 0.319 0.254 0.254
##
## 0.882 0.706 0.671
## 0.469 0.360 0.365
## 0.260 0.196 0.209
##
## 0.874 0.810 0.843
## 0.857 0.793 0.844
## 0.867 0.798 0.809
## 0.849 0.793 0.837
## 0.660 0.582 0.553
## 0.644 0.576 0.585
## 0.598 0.521 0.511
## 0.585 0.509 0.551
## 0.876 0.817 0.869
## 0.826 0.759 0.808
## 0.811 0.738 0.736
## 0.716 0.655 0.657
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.53.) 0.386 0.050 7.693 0.000 0.288
## .sswk (.54.) 0.379 0.052 7.224 0.000 0.276
## .sspc -0.118 0.067 -1.756 0.079 -0.250
## .ssei (.56.) 0.188 0.046 4.045 0.000 0.097
## .ssar (.57.) 0.391 0.049 7.958 0.000 0.295
## .ssmk (.58.) 0.450 0.054 8.333 0.000 0.344
## .ssao (.59.) 0.320 0.053 6.007 0.000 0.216
## .ssai (.60.) 0.067 0.041 1.638 0.101 -0.013
## .sssi (.61.) 0.165 0.042 3.908 0.000 0.082
## .ssno (.62.) 0.285 0.056 5.113 0.000 0.176
## .sscs -0.064 0.080 -0.805 0.421 -0.221
## .ssmc (.64.) 0.257 0.048 5.360 0.000 0.163
## verbal -0.813 0.173 -4.687 0.000 -1.152
## math -1.870 0.419 -4.458 0.000 -2.692
## elctrnc 0.826 0.113 7.312 0.000 0.604
## speed -0.588 0.139 -4.229 0.000 -0.860
## g 0.434 0.098 4.433 0.000 0.242
## ci.upper Std.lv Std.all
## 0.484 0.386 0.402
## 0.482 0.379 0.385
## 0.014 -0.118 -0.125
## 0.279 0.188 0.187
## 0.487 0.391 0.416
## 0.555 0.450 0.478
## 0.424 0.320 0.321
## 0.148 0.067 0.066
## 0.248 0.165 0.178
## 0.394 0.285 0.271
## 0.092 -0.064 -0.065
## 0.351 0.257 0.273
## -0.473 -0.813 -0.813
## -1.048 -1.870 -1.870
## 1.047 0.826 0.826
## -0.315 -0.588 -0.588
## 0.626 0.434 0.434
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.203 0.023 8.692 0.000 0.157
## .sswk 0.150 0.037 4.090 0.000 0.078
## .sspc 0.259 0.027 9.468 0.000 0.206
## .ssei 0.373 0.039 9.521 0.000 0.296
## .ssar 0.218 0.026 8.420 0.000 0.167
## .ssmk 0.126 0.019 6.747 0.000 0.089
## .ssao 0.530 0.050 10.527 0.000 0.431
## .ssai 0.541 0.065 8.290 0.000 0.413
## .sssi 0.312 0.056 5.550 0.000 0.202
## .ssno 0.270 0.117 2.318 0.020 0.042
## .sscs 0.508 0.071 7.109 0.000 0.368
## .ssmc 0.307 0.030 10.252 0.000 0.248
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.249 0.203 0.220
## 0.221 0.150 0.154
## 0.313 0.259 0.288
## 0.450 0.373 0.371
## 0.269 0.218 0.247
## 0.162 0.126 0.142
## 0.629 0.530 0.533
## 0.669 0.541 0.520
## 0.422 0.312 0.365
## 0.499 0.270 0.244
## 0.647 0.508 0.524
## 0.365 0.307 0.347
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
latent2<-cfa(bf.lv, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 339.294 109.000 0.000 0.962 0.079 0.054 16377.485
## bic
## 16697.502
Mc(latent2)
## [1] 0.8418795
summary(latent2, standardized=T, ci=T) # -.415
## lavaan 0.6-18 ended normally after 89 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 106
## Number of equality constraints 35
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 339.294 304.038
## Degrees of freedom 109 109
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.116
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 127.051 113.849
## 0 212.243 190.189
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.256 0.034 7.437 0.000 0.189
## sswk (.p2.) 0.427 0.043 9.908 0.000 0.343
## sspc (.p3.) 0.101 0.034 2.928 0.003 0.033
## ssei (.p4.) 0.156 0.038 4.062 0.000 0.080
## math =~
## ssar (.p5.) 0.184 0.036 5.034 0.000 0.112
## ssmk (.p6.) 0.225 0.042 5.331 0.000 0.142
## ssao (.p7.) 0.183 0.028 6.499 0.000 0.128
## electronic =~
## ssai (.p8.) 0.327 0.042 7.854 0.000 0.246
## sssi (.p9.) 0.328 0.045 7.302 0.000 0.240
## ssei (.10.) 0.172 0.024 7.076 0.000 0.124
## speed =~
## ssno (.11.) 0.669 0.101 6.599 0.000 0.470
## sscs (.12.) 0.339 0.057 5.980 0.000 0.228
## ssmk (.13.) 0.182 0.031 5.879 0.000 0.121
## g =~
## ssgs (.14.) 0.766 0.039 19.713 0.000 0.689
## ssar (.15.) 0.751 0.040 18.992 0.000 0.674
## sswk (.16.) 0.754 0.042 17.822 0.000 0.671
## sspc (.17.) 0.752 0.037 20.603 0.000 0.681
## ssno (.18.) 0.553 0.043 12.710 0.000 0.468
## sscs (.19.) 0.547 0.038 14.443 0.000 0.473
## ssai (.20.) 0.465 0.037 12.499 0.000 0.392
## sssi (.21.) 0.454 0.038 11.961 0.000 0.379
## ssmk (.22.) 0.774 0.039 20.063 0.000 0.699
## ssmc (.23.) 0.716 0.038 18.858 0.000 0.641
## ssei (.24.) 0.681 0.040 17.223 0.000 0.604
## ssao (.25.) 0.621 0.036 17.191 0.000 0.550
## ci.upper Std.lv Std.all
##
## 0.324 0.256 0.283
## 0.512 0.427 0.462
## 0.169 0.101 0.111
## 0.231 0.156 0.174
##
## 0.255 0.184 0.212
## 0.308 0.225 0.244
## 0.239 0.183 0.200
##
## 0.409 0.327 0.418
## 0.416 0.328 0.419
## 0.220 0.172 0.193
##
## 0.867 0.669 0.683
## 0.450 0.339 0.355
## 0.243 0.182 0.197
##
## 0.842 0.766 0.844
## 0.829 0.751 0.866
## 0.837 0.754 0.814
## 0.824 0.752 0.827
## 0.638 0.553 0.564
## 0.621 0.547 0.573
## 0.538 0.465 0.594
## 0.528 0.454 0.579
## 0.850 0.774 0.838
## 0.790 0.716 0.821
## 0.759 0.681 0.762
## 0.692 0.621 0.676
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.53.) 0.387 0.050 7.743 0.000 0.289
## .sswk (.54.) 0.379 0.053 7.212 0.000 0.276
## .sspc 0.445 0.051 8.700 0.000 0.345
## .ssei (.56.) 0.186 0.047 3.980 0.000 0.094
## .ssar (.57.) 0.391 0.049 7.954 0.000 0.295
## .ssmk (.58.) 0.449 0.054 8.329 0.000 0.344
## .ssao (.59.) 0.321 0.053 6.014 0.000 0.216
## .ssai (.60.) 0.059 0.041 1.432 0.152 -0.022
## .sssi (.61.) 0.176 0.042 4.158 0.000 0.093
## .ssno (.62.) 0.285 0.056 5.111 0.000 0.176
## .sscs 0.358 0.053 6.754 0.000 0.254
## .ssmc (.64.) 0.257 0.048 5.356 0.000 0.163
## ci.upper Std.lv Std.all
## 0.485 0.387 0.427
## 0.482 0.379 0.409
## 0.545 0.445 0.489
## 0.278 0.186 0.208
## 0.487 0.391 0.451
## 0.555 0.449 0.486
## 0.425 0.321 0.349
## 0.139 0.059 0.075
## 0.258 0.176 0.224
## 0.394 0.285 0.291
## 0.462 0.358 0.375
## 0.351 0.257 0.294
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.171 0.020 8.639 0.000 0.132
## .sswk 0.106 0.031 3.356 0.001 0.044
## .sspc 0.252 0.030 8.346 0.000 0.193
## .ssei 0.281 0.030 9.446 0.000 0.223
## .ssar 0.154 0.019 8.020 0.000 0.117
## .ssmk 0.171 0.020 8.371 0.000 0.131
## .ssao 0.424 0.037 11.562 0.000 0.352
## .ssai 0.290 0.036 8.153 0.000 0.220
## .sssi 0.301 0.036 8.279 0.000 0.230
## .ssno 0.206 0.106 1.946 0.052 -0.001
## .sscs 0.497 0.057 8.673 0.000 0.384
## .ssmc 0.248 0.025 9.902 0.000 0.199
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.209 0.171 0.208
## 0.167 0.106 0.123
## 0.311 0.252 0.304
## 0.339 0.281 0.352
## 0.192 0.154 0.205
## 0.212 0.171 0.201
## 0.496 0.424 0.503
## 0.359 0.290 0.472
## 0.372 0.301 0.490
## 0.414 0.206 0.215
## 0.609 0.497 0.545
## 0.298 0.248 0.327
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.256 0.034 7.437 0.000 0.189
## sswk (.p2.) 0.427 0.043 9.908 0.000 0.343
## sspc (.p3.) 0.101 0.034 2.928 0.003 0.033
## ssei (.p4.) 0.156 0.038 4.062 0.000 0.080
## math =~
## ssar (.p5.) 0.184 0.036 5.034 0.000 0.112
## ssmk (.p6.) 0.225 0.042 5.331 0.000 0.142
## ssao (.p7.) 0.183 0.028 6.499 0.000 0.128
## electronic =~
## ssai (.p8.) 0.327 0.042 7.854 0.000 0.246
## sssi (.p9.) 0.328 0.045 7.302 0.000 0.240
## ssei (.10.) 0.172 0.024 7.076 0.000 0.124
## speed =~
## ssno (.11.) 0.669 0.101 6.599 0.000 0.470
## sscs (.12.) 0.339 0.057 5.980 0.000 0.228
## ssmk (.13.) 0.182 0.031 5.879 0.000 0.121
## g =~
## ssgs (.14.) 0.766 0.039 19.713 0.000 0.689
## ssar (.15.) 0.751 0.040 18.992 0.000 0.674
## sswk (.16.) 0.754 0.042 17.822 0.000 0.671
## sspc (.17.) 0.752 0.037 20.603 0.000 0.681
## ssno (.18.) 0.553 0.043 12.710 0.000 0.468
## sscs (.19.) 0.547 0.038 14.443 0.000 0.473
## ssai (.20.) 0.465 0.037 12.499 0.000 0.392
## sssi (.21.) 0.454 0.038 11.961 0.000 0.379
## ssmk (.22.) 0.774 0.039 20.063 0.000 0.699
## ssmc (.23.) 0.716 0.038 18.858 0.000 0.641
## ssei (.24.) 0.681 0.040 17.223 0.000 0.604
## ssao (.25.) 0.621 0.036 17.191 0.000 0.550
## ci.upper Std.lv Std.all
##
## 0.324 0.256 0.257
## 0.512 0.427 0.419
## 0.169 0.101 0.103
## 0.231 0.156 0.149
##
## 0.255 0.184 0.189
## 0.308 0.225 0.230
## 0.239 0.183 0.180
##
## 0.409 0.684 0.622
## 0.416 0.686 0.684
## 0.220 0.360 0.344
##
## 0.867 0.746 0.688
## 0.450 0.378 0.374
## 0.243 0.203 0.207
##
## 0.842 0.853 0.854
## 0.829 0.837 0.859
## 0.837 0.840 0.822
## 0.824 0.838 0.854
## 0.638 0.616 0.568
## 0.621 0.609 0.603
## 0.538 0.519 0.472
## 0.528 0.505 0.504
## 0.850 0.863 0.879
## 0.790 0.797 0.817
## 0.759 0.759 0.727
## 0.692 0.692 0.678
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.53.) 0.387 0.050 7.743 0.000 0.289
## .sswk (.54.) 0.379 0.053 7.212 0.000 0.276
## .sspc -0.122 0.068 -1.799 0.072 -0.255
## .ssei (.56.) 0.186 0.047 3.980 0.000 0.094
## .ssar (.57.) 0.391 0.049 7.954 0.000 0.295
## .ssmk (.58.) 0.449 0.054 8.329 0.000 0.344
## .ssao (.59.) 0.321 0.053 6.014 0.000 0.216
## .ssai (.60.) 0.059 0.041 1.432 0.152 -0.022
## .sssi (.61.) 0.176 0.042 4.158 0.000 0.093
## .ssno (.62.) 0.285 0.056 5.111 0.000 0.176
## .sscs -0.067 0.080 -0.832 0.406 -0.224
## .ssmc (.64.) 0.257 0.048 5.356 0.000 0.163
## verbal -0.821 0.175 -4.703 0.000 -1.164
## math -1.934 0.446 -4.339 0.000 -2.807
## elctrnc 1.310 0.234 5.609 0.000 0.852
## speed -0.625 0.157 -3.974 0.000 -0.934
## g 0.462 0.101 4.568 0.000 0.264
## ci.upper Std.lv Std.all
## 0.485 0.387 0.388
## 0.482 0.379 0.371
## 0.011 -0.122 -0.124
## 0.278 0.186 0.178
## 0.487 0.391 0.401
## 0.555 0.449 0.458
## 0.425 0.321 0.314
## 0.139 0.059 0.053
## 0.258 0.176 0.175
## 0.394 0.285 0.263
## 0.090 -0.067 -0.066
## 0.351 0.257 0.263
## -0.479 -0.821 -0.821
## -1.060 -1.934 -1.934
## 1.768 0.627 0.627
## -0.317 -0.560 -0.560
## 0.660 0.415 0.415
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.203 0.023 8.739 0.000 0.158
## .sswk 0.155 0.035 4.381 0.000 0.086
## .sspc 0.251 0.027 9.409 0.000 0.199
## .ssei 0.360 0.038 9.596 0.000 0.287
## .ssar 0.215 0.026 8.319 0.000 0.164
## .ssmk 0.126 0.019 6.798 0.000 0.090
## .ssao 0.529 0.050 10.532 0.000 0.430
## .ssai 0.473 0.068 6.937 0.000 0.339
## .sssi 0.280 0.056 4.972 0.000 0.170
## .ssno 0.241 0.124 1.944 0.052 -0.002
## .sscs 0.507 0.072 7.034 0.000 0.366
## .ssmc 0.316 0.031 10.264 0.000 0.256
## electronic 4.361 1.125 3.878 0.000 2.157
## speed 1.245 0.335 3.721 0.000 0.589
## g 1.241 0.151 8.191 0.000 0.944
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.249 0.203 0.204
## 0.224 0.155 0.149
## 0.304 0.251 0.261
## 0.434 0.360 0.331
## 0.265 0.215 0.226
## 0.163 0.126 0.131
## 0.627 0.529 0.508
## 0.606 0.473 0.391
## 0.391 0.280 0.279
## 0.484 0.241 0.205
## 0.649 0.507 0.497
## 0.377 0.316 0.332
## 6.566 1.000 1.000
## 1.901 1.000 1.000
## 1.538 1.000 1.000
tests<-lavTestLRT(configural, metric, scalar2, latent)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 24.781016 8.708582 45.677432
dfd=tests[2:4,"Df diff"]
dfd
## [1] 20 5 5
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-335+ 335 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.02675296 0.04712439 0.15606968
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] NA 0.05686364
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] NA 0.09799123
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.1164270 0.1988718
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.790 0.757 0.115 0.032 0.001 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.879 0.866 0.533 0.398 0.165 0.043
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 1.000 0.999 0.989
tests<-lavTestLRT(configural, metric, scalar2, latent2)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 24.781016 8.708582 0.199330
dfd=tests[2:4,"Df diff"]
dfd
## [1] 20 5 2
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-335+ 335 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
## Warning in sqrt((ld) * G/(N - G)): NaNs produced
RMSEAD
## [1] 0.02675296 0.04712439 NaN
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] NA 0.09799123
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] NA 0.04486097
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.879 0.866 0.533 0.398 0.165 0.043
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.095 0.092 0.043 0.030 0.012 0.004
tests<-lavTestLRT(configural, metric, scalar2, strict)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 24.781016 8.708582 26.187076
dfd=tests[2:4,"Df diff"]
dfd
## [1] 20 5 12
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-335+ 335 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.02675296 0.04712439 0.05949529
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] NA 0.05686364
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] NA 0.09799123
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.02782329 0.09064581
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.790 0.757 0.115 0.032 0.001 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.879 0.866 0.533 0.398 0.165 0.043
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.990 0.987 0.726 0.529 0.151 0.015
tests<-lavTestLRT(configural, metric, scalar)
Td=tests[2:3,"Chisq diff"]
Td
## [1] 24.78102 84.60884
dfd=tests[2:3,"Df diff"]
dfd
## [1] 20 7
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-335+ 335 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.02675296 0.18219369
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] NA 0.05686364
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.1484763 0.2177790
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.790 0.757 0.115 0.032 0.001 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
bf.age<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
verbal~~1*verbal
math~~1*math
g ~ agec
'
bf.ageq<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
verbal~~1*verbal
math~~1*math
g ~ c(a,a)*agec
'
bf.age2<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
verbal~~1*verbal
math~~1*math
g ~ agec+agec2
'
bf.age2q<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssao
electronic =~ ssai + sssi + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
verbal~~1*verbal
math~~1*math
g ~ c(a,a)*agec+c(b,b)*agec2
'
sem.age<-sem(bf.age, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(sem.age, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 487.179 131.000 0.000 0.943 0.090 0.060 0.945
## aic bic
## 16244.260 16573.291
Mc(sem.age)
## [1] 0.7662836
summary(sem.age, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 86 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 108
## Number of equality constraints 35
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 487.179 434.280
## Degrees of freedom 131 131
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.122
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 198.167 176.650
## 0 289.012 257.630
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.250 0.034 7.249 0.000 0.182
## sswk (.p2.) 0.416 0.043 9.683 0.000 0.331
## sspc (.p3.) 0.098 0.035 2.806 0.005 0.030
## ssei (.p4.) 0.148 0.038 3.859 0.000 0.073
## math =~
## ssar (.p5.) 0.191 0.035 5.420 0.000 0.122
## ssmk (.p6.) 0.235 0.039 5.990 0.000 0.158
## ssao (.p7.) 0.190 0.029 6.598 0.000 0.134
## electronic =~
## ssai (.p8.) 0.321 0.042 7.660 0.000 0.239
## sssi (.p9.) 0.326 0.046 7.111 0.000 0.236
## ssei (.10.) 0.168 0.024 6.883 0.000 0.120
## speed =~
## ssno (.11.) 0.671 0.106 6.324 0.000 0.463
## sscs (.12.) 0.330 0.057 5.833 0.000 0.219
## ssmk (.13.) 0.175 0.031 5.704 0.000 0.115
## g =~
## ssgs (.14.) 0.691 0.036 18.954 0.000 0.619
## ssar (.15.) 0.671 0.039 17.096 0.000 0.594
## sswk (.16.) 0.683 0.038 17.848 0.000 0.608
## sspc (.17.) 0.676 0.035 19.379 0.000 0.607
## ssno (.18.) 0.500 0.040 12.345 0.000 0.420
## sscs (.19.) 0.496 0.034 14.415 0.000 0.428
## ssai (.20.) 0.426 0.033 12.882 0.000 0.361
## sssi (.21.) 0.412 0.033 12.313 0.000 0.347
## ssmk (.22.) 0.699 0.036 19.196 0.000 0.627
## ssmc (.23.) 0.642 0.036 17.752 0.000 0.572
## ssei (.24.) 0.617 0.037 16.646 0.000 0.545
## ssao (.25.) 0.555 0.035 15.740 0.000 0.486
## ci.upper Std.lv Std.all
##
## 0.317 0.250 0.276
## 0.500 0.416 0.449
## 0.167 0.098 0.108
## 0.223 0.148 0.166
##
## 0.261 0.191 0.221
## 0.312 0.235 0.255
## 0.247 0.190 0.207
##
## 0.404 0.321 0.409
## 0.415 0.326 0.415
## 0.216 0.168 0.188
##
## 0.879 0.671 0.686
## 0.441 0.330 0.346
## 0.235 0.175 0.189
##
## 0.762 0.767 0.846
## 0.748 0.746 0.859
## 0.758 0.758 0.820
## 0.744 0.750 0.825
## 0.579 0.555 0.567
## 0.563 0.550 0.577
## 0.491 0.473 0.602
## 0.478 0.458 0.583
## 0.770 0.776 0.840
## 0.713 0.713 0.817
## 0.690 0.685 0.766
## 0.624 0.616 0.672
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.318 0.044 7.316 0.000 0.233
## ci.upper Std.lv Std.all
##
## 0.404 0.287 0.435
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.51.) 0.367 0.046 7.929 0.000 0.276
## .sswk (.52.) 0.358 0.047 7.575 0.000 0.266
## .sspc 0.425 0.049 8.752 0.000 0.330
## .ssei (.54.) 0.167 0.043 3.864 0.000 0.082
## .ssar (.55.) 0.372 0.047 7.849 0.000 0.279
## .ssmk (.56.) 0.427 0.049 8.803 0.000 0.332
## .ssao (.57.) 0.305 0.052 5.914 0.000 0.204
## .ssai (.58.) 0.046 0.038 1.206 0.228 -0.029
## .sssi (.59.) 0.163 0.040 4.029 0.000 0.084
## .ssno (.60.) 0.270 0.053 5.095 0.000 0.166
## .sscs 0.343 0.050 6.903 0.000 0.246
## .ssmc (.62.) 0.237 0.046 5.136 0.000 0.146
## ci.upper Std.lv Std.all
## 0.457 0.367 0.405
## 0.451 0.358 0.387
## 0.520 0.425 0.467
## 0.252 0.167 0.187
## 0.465 0.372 0.429
## 0.522 0.427 0.462
## 0.406 0.305 0.332
## 0.122 0.046 0.059
## 0.242 0.163 0.207
## 0.375 0.270 0.276
## 0.441 0.343 0.360
## 0.327 0.237 0.271
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.171 0.019 8.764 0.000 0.133
## .sswk 0.107 0.031 3.506 0.000 0.047
## .sspc 0.254 0.030 8.567 0.000 0.196
## .ssei 0.281 0.030 9.455 0.000 0.222
## .ssar 0.160 0.020 8.118 0.000 0.122
## .ssmk 0.166 0.020 8.262 0.000 0.127
## .ssao 0.426 0.037 11.545 0.000 0.354
## .ssai 0.290 0.035 8.197 0.000 0.220
## .sssi 0.301 0.036 8.256 0.000 0.229
## .ssno 0.199 0.112 1.779 0.075 -0.020
## .sscs 0.497 0.057 8.693 0.000 0.385
## .ssmc 0.253 0.026 9.910 0.000 0.203
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.209 0.171 0.208
## 0.167 0.107 0.125
## 0.312 0.254 0.307
## 0.339 0.281 0.350
## 0.199 0.160 0.213
## 0.205 0.166 0.194
## 0.499 0.426 0.506
## 0.359 0.290 0.470
## 0.372 0.301 0.488
## 0.418 0.199 0.208
## 0.609 0.497 0.547
## 0.303 0.253 0.332
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.811 0.811
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.250 0.034 7.249 0.000 0.182
## sswk (.p2.) 0.416 0.043 9.683 0.000 0.331
## sspc (.p3.) 0.098 0.035 2.806 0.005 0.030
## ssei (.p4.) 0.148 0.038 3.859 0.000 0.073
## math =~
## ssar (.p5.) 0.191 0.035 5.420 0.000 0.122
## ssmk (.p6.) 0.235 0.039 5.990 0.000 0.158
## ssao (.p7.) 0.190 0.029 6.598 0.000 0.134
## electronic =~
## ssai (.p8.) 0.321 0.042 7.660 0.000 0.239
## sssi (.p9.) 0.326 0.046 7.111 0.000 0.236
## ssei (.10.) 0.168 0.024 6.883 0.000 0.120
## speed =~
## ssno (.11.) 0.671 0.106 6.324 0.000 0.463
## sscs (.12.) 0.330 0.057 5.833 0.000 0.219
## ssmk (.13.) 0.175 0.031 5.704 0.000 0.115
## g =~
## ssgs (.14.) 0.691 0.036 18.954 0.000 0.619
## ssar (.15.) 0.671 0.039 17.096 0.000 0.594
## sswk (.16.) 0.683 0.038 17.848 0.000 0.608
## sspc (.17.) 0.676 0.035 19.379 0.000 0.607
## ssno (.18.) 0.500 0.040 12.345 0.000 0.420
## sscs (.19.) 0.496 0.034 14.415 0.000 0.428
## ssai (.20.) 0.426 0.033 12.882 0.000 0.361
## sssi (.21.) 0.412 0.033 12.313 0.000 0.347
## ssmk (.22.) 0.699 0.036 19.196 0.000 0.627
## ssmc (.23.) 0.642 0.036 17.752 0.000 0.572
## ssei (.24.) 0.617 0.037 16.646 0.000 0.545
## ssao (.25.) 0.555 0.035 15.740 0.000 0.486
## ci.upper Std.lv Std.all
##
## 0.317 0.250 0.250
## 0.500 0.416 0.407
## 0.167 0.098 0.100
## 0.223 0.148 0.142
##
## 0.261 0.191 0.197
## 0.312 0.235 0.240
## 0.247 0.190 0.186
##
## 0.404 0.671 0.612
## 0.415 0.680 0.680
## 0.216 0.351 0.336
##
## 0.879 0.754 0.694
## 0.441 0.371 0.367
## 0.235 0.197 0.200
##
## 0.762 0.855 0.856
## 0.748 0.831 0.854
## 0.758 0.846 0.827
## 0.744 0.837 0.851
## 0.579 0.619 0.570
## 0.563 0.613 0.606
## 0.491 0.527 0.481
## 0.478 0.510 0.510
## 0.770 0.865 0.881
## 0.713 0.795 0.816
## 0.690 0.764 0.732
## 0.624 0.687 0.673
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.400 0.051 7.888 0.000 0.301
## ci.upper Std.lv Std.all
##
## 0.499 0.323 0.460
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.51.) 0.367 0.046 7.929 0.000 0.276
## .sswk (.52.) 0.358 0.047 7.575 0.000 0.266
## .sspc -0.143 0.066 -2.149 0.032 -0.273
## .ssei (.54.) 0.167 0.043 3.864 0.000 0.082
## .ssar (.55.) 0.372 0.047 7.849 0.000 0.279
## .ssmk (.56.) 0.427 0.049 8.803 0.000 0.332
## .ssao (.57.) 0.305 0.052 5.914 0.000 0.204
## .ssai (.58.) 0.046 0.038 1.206 0.228 -0.029
## .sssi (.59.) 0.163 0.040 4.029 0.000 0.084
## .ssno (.60.) 0.270 0.053 5.095 0.000 0.166
## .sscs -0.089 0.079 -1.121 0.262 -0.245
## .ssmc (.62.) 0.237 0.046 5.136 0.000 0.146
## verbal -0.856 0.182 -4.702 0.000 -1.212
## math -1.871 0.400 -4.676 0.000 -2.655
## elctrnc 1.315 0.239 5.496 0.000 0.846
## speed -0.629 0.162 -3.888 0.000 -0.946
## .g 0.595 0.106 5.593 0.000 0.386
## ci.upper Std.lv Std.all
## 0.457 0.367 0.367
## 0.451 0.358 0.351
## -0.013 -0.143 -0.145
## 0.252 0.167 0.160
## 0.465 0.372 0.382
## 0.522 0.427 0.435
## 0.406 0.305 0.299
## 0.122 0.046 0.042
## 0.242 0.163 0.163
## 0.375 0.270 0.249
## 0.067 -0.089 -0.088
## 0.327 0.237 0.243
## -0.499 -0.856 -0.856
## -1.087 -1.871 -1.871
## 1.784 0.630 0.630
## -0.312 -0.559 -0.559
## 0.803 0.480 0.480
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.204 0.023 8.820 0.000 0.159
## .sswk 0.157 0.034 4.547 0.000 0.089
## .sspc 0.256 0.027 9.455 0.000 0.203
## .ssei 0.361 0.038 9.616 0.000 0.288
## .ssar 0.220 0.026 8.486 0.000 0.169
## .ssmk 0.122 0.018 6.699 0.000 0.086
## .ssao 0.534 0.051 10.545 0.000 0.435
## .ssai 0.475 0.068 6.987 0.000 0.342
## .sssi 0.278 0.057 4.878 0.000 0.166
## .ssno 0.228 0.132 1.722 0.085 -0.032
## .sscs 0.509 0.072 7.046 0.000 0.368
## .ssmc 0.317 0.030 10.423 0.000 0.257
## electronic 4.356 1.151 3.783 0.000 2.099
## speed 1.263 0.345 3.662 0.000 0.587
## .g 1.208 0.164 7.367 0.000 0.887
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.250 0.204 0.205
## 0.224 0.157 0.150
## 0.309 0.256 0.265
## 0.435 0.361 0.331
## 0.271 0.220 0.232
## 0.157 0.122 0.126
## 0.633 0.534 0.512
## 0.608 0.475 0.395
## 0.390 0.278 0.278
## 0.488 0.228 0.193
## 0.651 0.509 0.498
## 0.376 0.317 0.334
## 6.612 1.000 1.000
## 1.940 1.000 1.000
## 1.530 0.788 0.788
sem.ageq<-sem(bf.ageq, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(sem.ageq, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 489.141 132.000 0.000 0.943 0.090 0.068 0.945
## aic bic
## 16244.221 16568.745
Mc(sem.ageq)
## [1] 0.7657332
summary(sem.ageq, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 92 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 108
## Number of equality constraints 36
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 489.141 436.046
## Degrees of freedom 132 132
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.122
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 198.782 177.205
## 0 290.359 258.841
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.249 0.034 7.234 0.000 0.182
## sswk (.p2.) 0.414 0.043 9.674 0.000 0.330
## sspc (.p3.) 0.097 0.035 2.769 0.006 0.028
## ssei (.p4.) 0.148 0.038 3.859 0.000 0.073
## math =~
## ssar (.p5.) 0.191 0.035 5.406 0.000 0.122
## ssmk (.p6.) 0.235 0.039 5.981 0.000 0.158
## ssao (.p7.) 0.190 0.029 6.575 0.000 0.133
## electronic =~
## ssai (.p8.) 0.321 0.042 7.653 0.000 0.239
## sssi (.p9.) 0.325 0.046 7.105 0.000 0.235
## ssei (.10.) 0.168 0.024 6.890 0.000 0.120
## speed =~
## ssno (.11.) 0.670 0.106 6.300 0.000 0.461
## sscs (.12.) 0.330 0.057 5.833 0.000 0.219
## ssmk (.13.) 0.175 0.031 5.705 0.000 0.115
## g =~
## ssgs (.14.) 0.692 0.037 18.943 0.000 0.620
## ssar (.15.) 0.672 0.039 17.087 0.000 0.595
## sswk (.16.) 0.684 0.038 17.867 0.000 0.609
## sspc (.17.) 0.677 0.035 19.380 0.000 0.609
## ssno (.18.) 0.501 0.041 12.350 0.000 0.421
## sscs (.19.) 0.496 0.034 14.407 0.000 0.429
## ssai (.20.) 0.426 0.033 12.841 0.000 0.361
## sssi (.21.) 0.412 0.034 12.295 0.000 0.346
## ssmk (.22.) 0.700 0.036 19.190 0.000 0.629
## ssmc (.23.) 0.643 0.036 17.710 0.000 0.572
## ssei (.24.) 0.618 0.037 16.604 0.000 0.545
## ssao (.25.) 0.556 0.035 15.707 0.000 0.487
## ci.upper Std.lv Std.all
##
## 0.317 0.249 0.271
## 0.498 0.414 0.442
## 0.165 0.097 0.105
## 0.223 0.148 0.163
##
## 0.261 0.191 0.217
## 0.312 0.235 0.250
## 0.247 0.190 0.205
##
## 0.403 0.321 0.406
## 0.415 0.325 0.411
## 0.216 0.168 0.185
##
## 0.878 0.670 0.680
## 0.441 0.330 0.344
## 0.235 0.175 0.186
##
## 0.763 0.784 0.852
## 0.749 0.762 0.864
## 0.759 0.776 0.826
## 0.746 0.767 0.831
## 0.580 0.567 0.576
## 0.564 0.562 0.586
## 0.491 0.482 0.610
## 0.478 0.467 0.591
## 0.772 0.793 0.845
## 0.714 0.729 0.822
## 0.691 0.700 0.773
## 0.625 0.630 0.680
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.352 0.035 10.032 0.000 0.283
## ci.upper Std.lv Std.all
##
## 0.420 0.310 0.470
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.51.) 0.364 0.046 7.878 0.000 0.274
## .sswk (.52.) 0.356 0.047 7.560 0.000 0.264
## .sspc 0.423 0.049 8.694 0.000 0.327
## .ssei (.54.) 0.165 0.043 3.821 0.000 0.081
## .ssar (.55.) 0.370 0.048 7.760 0.000 0.277
## .ssmk (.56.) 0.425 0.048 8.775 0.000 0.330
## .ssao (.57.) 0.303 0.052 5.862 0.000 0.202
## .ssai (.58.) 0.045 0.038 1.172 0.241 -0.030
## .sssi (.59.) 0.162 0.040 4.002 0.000 0.082
## .ssno (.60.) 0.269 0.053 5.074 0.000 0.165
## .sscs 0.342 0.050 6.890 0.000 0.244
## .ssmc (.62.) 0.235 0.046 5.068 0.000 0.144
## ci.upper Std.lv Std.all
## 0.455 0.364 0.396
## 0.448 0.356 0.379
## 0.518 0.423 0.458
## 0.250 0.165 0.183
## 0.464 0.370 0.420
## 0.520 0.425 0.453
## 0.405 0.303 0.327
## 0.120 0.045 0.057
## 0.241 0.162 0.204
## 0.373 0.269 0.273
## 0.439 0.342 0.356
## 0.326 0.235 0.265
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.171 0.019 8.777 0.000 0.133
## .sswk 0.107 0.030 3.522 0.000 0.048
## .sspc 0.254 0.030 8.572 0.000 0.196
## .ssei 0.281 0.030 9.457 0.000 0.222
## .ssar 0.161 0.020 8.132 0.000 0.122
## .ssmk 0.166 0.020 8.269 0.000 0.126
## .ssao 0.426 0.037 11.542 0.000 0.354
## .ssai 0.290 0.035 8.199 0.000 0.220
## .sssi 0.301 0.036 8.267 0.000 0.230
## .ssno 0.200 0.112 1.789 0.074 -0.019
## .sscs 0.497 0.057 8.696 0.000 0.385
## .ssmc 0.254 0.026 9.919 0.000 0.204
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.209 0.171 0.202
## 0.167 0.107 0.122
## 0.312 0.254 0.298
## 0.339 0.281 0.342
## 0.200 0.161 0.207
## 0.205 0.166 0.188
## 0.499 0.426 0.496
## 0.359 0.290 0.463
## 0.373 0.301 0.482
## 0.419 0.200 0.206
## 0.609 0.497 0.539
## 0.304 0.254 0.324
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.779 0.779
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.249 0.034 7.234 0.000 0.182
## sswk (.p2.) 0.414 0.043 9.674 0.000 0.330
## sspc (.p3.) 0.097 0.035 2.769 0.006 0.028
## ssei (.p4.) 0.148 0.038 3.859 0.000 0.073
## math =~
## ssar (.p5.) 0.191 0.035 5.406 0.000 0.122
## ssmk (.p6.) 0.235 0.039 5.981 0.000 0.158
## ssao (.p7.) 0.190 0.029 6.575 0.000 0.133
## electronic =~
## ssai (.p8.) 0.321 0.042 7.653 0.000 0.239
## sssi (.p9.) 0.325 0.046 7.105 0.000 0.235
## ssei (.10.) 0.168 0.024 6.890 0.000 0.120
## speed =~
## ssno (.11.) 0.670 0.106 6.300 0.000 0.461
## sscs (.12.) 0.330 0.057 5.833 0.000 0.219
## ssmk (.13.) 0.175 0.031 5.705 0.000 0.115
## g =~
## ssgs (.14.) 0.692 0.037 18.943 0.000 0.620
## ssar (.15.) 0.672 0.039 17.087 0.000 0.595
## sswk (.16.) 0.684 0.038 17.867 0.000 0.609
## sspc (.17.) 0.677 0.035 19.380 0.000 0.609
## ssno (.18.) 0.501 0.041 12.350 0.000 0.421
## sscs (.19.) 0.496 0.034 14.407 0.000 0.429
## ssai (.20.) 0.426 0.033 12.841 0.000 0.361
## sssi (.21.) 0.412 0.034 12.295 0.000 0.346
## ssmk (.22.) 0.700 0.036 19.190 0.000 0.629
## ssmc (.23.) 0.643 0.036 17.710 0.000 0.572
## ssei (.24.) 0.618 0.037 16.604 0.000 0.545
## ssao (.25.) 0.556 0.035 15.707 0.000 0.487
## ci.upper Std.lv Std.all
##
## 0.317 0.249 0.254
## 0.498 0.414 0.412
## 0.165 0.097 0.100
## 0.223 0.148 0.143
##
## 0.261 0.191 0.200
## 0.312 0.235 0.244
## 0.247 0.190 0.188
##
## 0.403 0.672 0.616
## 0.415 0.681 0.684
## 0.216 0.352 0.341
##
## 0.878 0.754 0.700
## 0.441 0.372 0.370
## 0.235 0.197 0.204
##
## 0.763 0.836 0.851
## 0.749 0.812 0.849
## 0.759 0.827 0.822
## 0.746 0.818 0.847
## 0.580 0.605 0.561
## 0.564 0.600 0.598
## 0.491 0.514 0.471
## 0.478 0.498 0.501
## 0.772 0.846 0.876
## 0.714 0.777 0.810
## 0.691 0.746 0.724
## 0.625 0.672 0.665
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.352 0.035 10.032 0.000 0.283
## ci.upper Std.lv Std.all
##
## 0.420 0.291 0.414
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.51.) 0.364 0.046 7.878 0.000 0.274
## .sswk (.52.) 0.356 0.047 7.560 0.000 0.264
## .sspc -0.146 0.067 -2.192 0.028 -0.276
## .ssei (.54.) 0.165 0.043 3.821 0.000 0.081
## .ssar (.55.) 0.370 0.048 7.760 0.000 0.277
## .ssmk (.56.) 0.425 0.048 8.775 0.000 0.330
## .ssao (.57.) 0.303 0.052 5.862 0.000 0.202
## .ssai (.58.) 0.045 0.038 1.172 0.241 -0.030
## .sssi (.59.) 0.162 0.040 4.002 0.000 0.082
## .ssno (.60.) 0.269 0.053 5.074 0.000 0.165
## .sscs -0.091 0.079 -1.140 0.254 -0.246
## .ssmc (.62.) 0.235 0.046 5.068 0.000 0.144
## verbal -0.859 0.183 -4.705 0.000 -1.217
## math -1.874 0.401 -4.668 0.000 -2.661
## elctrnc 1.317 0.240 5.495 0.000 0.847
## speed -0.630 0.162 -3.882 0.000 -0.949
## .g 0.592 0.107 5.555 0.000 0.383
## ci.upper Std.lv Std.all
## 0.455 0.364 0.371
## 0.448 0.356 0.354
## -0.015 -0.146 -0.151
## 0.250 0.165 0.160
## 0.464 0.370 0.387
## 0.520 0.425 0.440
## 0.405 0.303 0.300
## 0.120 0.045 0.041
## 0.241 0.162 0.162
## 0.373 0.269 0.249
## 0.065 -0.091 -0.090
## 0.326 0.235 0.245
## -0.501 -0.859 -0.859
## -1.087 -1.874 -1.874
## 1.787 0.629 0.629
## -0.312 -0.560 -0.560
## 0.800 0.490 0.490
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.204 0.023 8.823 0.000 0.159
## .sswk 0.157 0.034 4.574 0.000 0.090
## .sspc 0.255 0.027 9.432 0.000 0.202
## .ssei 0.361 0.038 9.612 0.000 0.287
## .ssar 0.219 0.026 8.471 0.000 0.169
## .ssmk 0.122 0.018 6.716 0.000 0.086
## .ssao 0.533 0.051 10.548 0.000 0.434
## .ssai 0.474 0.068 6.977 0.000 0.341
## .sssi 0.278 0.057 4.885 0.000 0.167
## .ssno 0.228 0.133 1.719 0.086 -0.032
## .sscs 0.509 0.072 7.040 0.000 0.367
## .ssmc 0.317 0.031 10.400 0.000 0.257
## electronic 4.384 1.160 3.781 0.000 2.111
## speed 1.269 0.347 3.662 0.000 0.590
## .g 1.210 0.164 7.363 0.000 0.888
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.249 0.204 0.211
## 0.225 0.157 0.155
## 0.308 0.255 0.273
## 0.435 0.361 0.339
## 0.270 0.219 0.240
## 0.158 0.122 0.131
## 0.632 0.533 0.523
## 0.608 0.474 0.398
## 0.390 0.278 0.281
## 0.487 0.228 0.196
## 0.651 0.509 0.506
## 0.377 0.317 0.345
## 6.657 1.000 1.000
## 1.948 1.000 1.000
## 1.532 0.828 0.828
sem.age2<-sem(bf.age2, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(sem.age2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 525.087 153.000 0.000 0.941 0.085 0.057 1.008
## aic bic
## 16242.454 16580.500
Mc(sem.age2)
## [1] 0.7572271
summary(sem.age2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 93 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 110
## Number of equality constraints 35
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 525.087 471.088
## Degrees of freedom 153 153
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.115
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 217.378 195.023
## 0 307.709 276.065
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.250 0.034 7.261 0.000 0.182
## sswk (.p2.) 0.416 0.043 9.691 0.000 0.332
## sspc (.p3.) 0.098 0.035 2.805 0.005 0.030
## ssei (.p4.) 0.149 0.038 3.886 0.000 0.074
## math =~
## ssar (.p5.) 0.190 0.036 5.343 0.000 0.120
## ssmk (.p6.) 0.234 0.039 5.927 0.000 0.156
## ssao (.p7.) 0.189 0.029 6.556 0.000 0.133
## electronic =~
## ssai (.p8.) 0.321 0.042 7.669 0.000 0.239
## sssi (.p9.) 0.325 0.046 7.117 0.000 0.236
## ssei (.10.) 0.168 0.024 6.894 0.000 0.120
## speed =~
## ssno (.11.) 0.670 0.106 6.312 0.000 0.462
## sscs (.12.) 0.330 0.057 5.832 0.000 0.219
## ssmk (.13.) 0.174 0.031 5.706 0.000 0.114
## g =~
## ssgs (.14.) 0.686 0.036 18.847 0.000 0.614
## ssar (.15.) 0.666 0.039 16.942 0.000 0.589
## sswk (.16.) 0.678 0.038 17.660 0.000 0.602
## sspc (.17.) 0.671 0.035 19.322 0.000 0.603
## ssno (.18.) 0.496 0.040 12.329 0.000 0.417
## sscs (.19.) 0.491 0.035 14.202 0.000 0.424
## ssai (.20.) 0.422 0.033 12.909 0.000 0.358
## sssi (.21.) 0.409 0.033 12.339 0.000 0.344
## ssmk (.22.) 0.694 0.037 19.009 0.000 0.623
## ssmc (.23.) 0.637 0.037 17.453 0.000 0.566
## ssei (.24.) 0.612 0.037 16.563 0.000 0.540
## ssao (.25.) 0.551 0.035 15.526 0.000 0.482
## ci.upper Std.lv Std.all
##
## 0.317 0.250 0.276
## 0.500 0.416 0.449
## 0.167 0.098 0.108
## 0.224 0.149 0.166
##
## 0.260 0.190 0.219
## 0.311 0.234 0.253
## 0.246 0.189 0.206
##
## 0.404 0.321 0.409
## 0.415 0.325 0.414
## 0.216 0.168 0.188
##
## 0.878 0.670 0.684
## 0.441 0.330 0.347
## 0.234 0.174 0.189
##
## 0.757 0.767 0.846
## 0.743 0.746 0.859
## 0.753 0.758 0.820
## 0.739 0.750 0.825
## 0.575 0.555 0.568
## 0.559 0.550 0.577
## 0.487 0.473 0.602
## 0.474 0.458 0.583
## 0.766 0.777 0.841
## 0.709 0.713 0.816
## 0.685 0.685 0.766
## 0.621 0.617 0.672
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.321 0.044 7.318 0.000 0.235
## agec2 -0.065 0.033 -1.978 0.048 -0.130
## ci.upper Std.lv Std.all
##
## 0.407 0.287 0.435
## -0.001 -0.058 -0.112
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.54.) 0.470 0.068 6.890 0.000 0.336
## .sswk (.55.) 0.460 0.068 6.722 0.000 0.326
## .sspc 0.526 0.068 7.761 0.000 0.393
## .ssei (.57.) 0.260 0.063 4.152 0.000 0.137
## .ssar (.58.) 0.473 0.065 7.224 0.000 0.344
## .ssmk (.59.) 0.532 0.071 7.493 0.000 0.393
## .ssao (.60.) 0.388 0.066 5.882 0.000 0.259
## .ssai (.61.) 0.110 0.049 2.248 0.025 0.014
## .sssi (.62.) 0.224 0.051 4.439 0.000 0.125
## .ssno (.63.) 0.345 0.064 5.355 0.000 0.219
## .sscs 0.417 0.060 6.960 0.000 0.300
## .ssmc (.65.) 0.333 0.065 5.152 0.000 0.206
## ci.upper Std.lv Std.all
## 0.604 0.470 0.518
## 0.595 0.460 0.498
## 0.659 0.526 0.578
## 0.382 0.260 0.290
## 0.601 0.473 0.545
## 0.671 0.532 0.575
## 0.517 0.388 0.423
## 0.206 0.110 0.140
## 0.323 0.224 0.286
## 0.472 0.345 0.353
## 0.535 0.417 0.438
## 0.459 0.333 0.381
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.170 0.019 8.757 0.000 0.132
## .sswk 0.107 0.031 3.522 0.000 0.048
## .sspc 0.254 0.029 8.617 0.000 0.196
## .ssei 0.281 0.030 9.461 0.000 0.223
## .ssar 0.161 0.020 8.159 0.000 0.122
## .ssmk 0.166 0.020 8.259 0.000 0.126
## .ssao 0.426 0.037 11.548 0.000 0.354
## .ssai 0.290 0.035 8.193 0.000 0.220
## .sssi 0.301 0.036 8.260 0.000 0.230
## .ssno 0.200 0.111 1.797 0.072 -0.018
## .sscs 0.497 0.057 8.693 0.000 0.385
## .ssmc 0.254 0.026 9.929 0.000 0.204
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.209 0.170 0.208
## 0.167 0.107 0.126
## 0.311 0.254 0.307
## 0.339 0.281 0.351
## 0.199 0.161 0.213
## 0.205 0.166 0.194
## 0.499 0.426 0.506
## 0.359 0.290 0.470
## 0.372 0.301 0.488
## 0.419 0.200 0.209
## 0.609 0.497 0.547
## 0.305 0.254 0.333
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.798 0.798
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.250 0.034 7.261 0.000 0.182
## sswk (.p2.) 0.416 0.043 9.691 0.000 0.332
## sspc (.p3.) 0.098 0.035 2.805 0.005 0.030
## ssei (.p4.) 0.149 0.038 3.886 0.000 0.074
## math =~
## ssar (.p5.) 0.190 0.036 5.343 0.000 0.120
## ssmk (.p6.) 0.234 0.039 5.927 0.000 0.156
## ssao (.p7.) 0.189 0.029 6.556 0.000 0.133
## electronic =~
## ssai (.p8.) 0.321 0.042 7.669 0.000 0.239
## sssi (.p9.) 0.325 0.046 7.117 0.000 0.236
## ssei (.10.) 0.168 0.024 6.894 0.000 0.120
## speed =~
## ssno (.11.) 0.670 0.106 6.312 0.000 0.462
## sscs (.12.) 0.330 0.057 5.832 0.000 0.219
## ssmk (.13.) 0.174 0.031 5.706 0.000 0.114
## g =~
## ssgs (.14.) 0.686 0.036 18.847 0.000 0.614
## ssar (.15.) 0.666 0.039 16.942 0.000 0.589
## sswk (.16.) 0.678 0.038 17.660 0.000 0.602
## sspc (.17.) 0.671 0.035 19.322 0.000 0.603
## ssno (.18.) 0.496 0.040 12.329 0.000 0.417
## sscs (.19.) 0.491 0.035 14.202 0.000 0.424
## ssai (.20.) 0.422 0.033 12.909 0.000 0.358
## sssi (.21.) 0.409 0.033 12.339 0.000 0.344
## ssmk (.22.) 0.694 0.037 19.009 0.000 0.623
## ssmc (.23.) 0.637 0.037 17.453 0.000 0.566
## ssei (.24.) 0.612 0.037 16.563 0.000 0.540
## ssao (.25.) 0.551 0.035 15.526 0.000 0.482
## ci.upper Std.lv Std.all
##
## 0.317 0.250 0.250
## 0.500 0.416 0.407
## 0.167 0.098 0.100
## 0.224 0.149 0.143
##
## 0.260 0.190 0.195
## 0.311 0.234 0.238
## 0.246 0.189 0.185
##
## 0.404 0.671 0.612
## 0.415 0.679 0.679
## 0.216 0.351 0.336
##
## 0.878 0.754 0.694
## 0.441 0.372 0.368
## 0.234 0.196 0.200
##
## 0.757 0.855 0.856
## 0.743 0.831 0.854
## 0.753 0.845 0.827
## 0.739 0.836 0.851
## 0.575 0.619 0.570
## 0.559 0.613 0.606
## 0.487 0.527 0.480
## 0.474 0.510 0.510
## 0.766 0.866 0.882
## 0.709 0.795 0.816
## 0.685 0.764 0.731
## 0.621 0.687 0.674
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.399 0.052 7.727 0.000 0.298
## agec2 -0.034 0.035 -0.984 0.325 -0.103
## ci.upper Std.lv Std.all
##
## 0.500 0.320 0.455
## 0.034 -0.028 -0.051
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.54.) 0.470 0.068 6.890 0.000 0.336
## .sswk (.55.) 0.460 0.068 6.722 0.000 0.326
## .sspc -0.041 0.081 -0.510 0.610 -0.200
## .ssei (.57.) 0.260 0.063 4.152 0.000 0.137
## .ssar (.58.) 0.473 0.065 7.224 0.000 0.344
## .ssmk (.59.) 0.532 0.071 7.493 0.000 0.393
## .ssao (.60.) 0.388 0.066 5.882 0.000 0.259
## .ssai (.61.) 0.110 0.049 2.248 0.025 0.014
## .sssi (.62.) 0.224 0.051 4.439 0.000 0.125
## .ssno (.63.) 0.345 0.064 5.355 0.000 0.219
## .sscs -0.014 0.088 -0.162 0.871 -0.187
## .ssmc (.65.) 0.333 0.065 5.152 0.000 0.206
## verbal -0.856 0.182 -4.704 0.000 -1.212
## math -1.886 0.406 -4.641 0.000 -2.683
## elctrnc 1.315 0.239 5.501 0.000 0.846
## speed -0.631 0.162 -3.884 0.000 -0.949
## .g 0.518 0.145 3.569 0.000 0.234
## ci.upper Std.lv Std.all
## 0.604 0.470 0.470
## 0.595 0.460 0.450
## 0.118 -0.041 -0.042
## 0.382 0.260 0.249
## 0.601 0.473 0.486
## 0.671 0.532 0.542
## 0.517 0.388 0.380
## 0.206 0.110 0.100
## 0.323 0.224 0.224
## 0.472 0.345 0.318
## 0.158 -0.014 -0.014
## 0.459 0.333 0.342
## -0.499 -0.856 -0.856
## -1.090 -1.886 -1.886
## 1.783 0.630 0.630
## -0.312 -0.560 -0.560
## 0.803 0.416 0.416
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.204 0.023 8.827 0.000 0.159
## .sswk 0.157 0.034 4.563 0.000 0.090
## .sspc 0.256 0.027 9.456 0.000 0.203
## .ssei 0.361 0.038 9.612 0.000 0.288
## .ssar 0.220 0.026 8.501 0.000 0.170
## .ssmk 0.122 0.018 6.715 0.000 0.086
## .ssao 0.533 0.051 10.541 0.000 0.434
## .ssai 0.475 0.068 6.983 0.000 0.341
## .sssi 0.278 0.057 4.890 0.000 0.167
## .ssno 0.228 0.132 1.726 0.084 -0.031
## .sscs 0.509 0.072 7.042 0.000 0.368
## .ssmc 0.317 0.030 10.404 0.000 0.257
## electronic 4.358 1.151 3.788 0.000 2.103
## speed 1.268 0.347 3.654 0.000 0.588
## .g 1.222 0.167 7.313 0.000 0.895
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.249 0.204 0.205
## 0.225 0.157 0.150
## 0.309 0.256 0.265
## 0.435 0.361 0.331
## 0.271 0.220 0.233
## 0.157 0.122 0.126
## 0.633 0.533 0.512
## 0.608 0.475 0.395
## 0.390 0.278 0.278
## 0.487 0.228 0.193
## 0.651 0.509 0.498
## 0.376 0.317 0.334
## 6.613 1.000 1.000
## 1.948 1.000 1.000
## 1.550 0.786 0.786
sem.age2q<-sem(bf.age2q, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(sem.age2q, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 527.225 155.000 0.000 0.941 0.085 0.063 1.005
## aic bic
## 16240.592 16569.623
Mc(sem.age2q)
## [1] 0.7571491
summary(sem.age2q, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 94 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 110
## Number of equality constraints 37
##
## Number of observations per group:
## 1 335
## 0 335
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 527.225 473.037
## Degrees of freedom 155 155
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.115
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 218.072 195.659
## 0 309.153 277.378
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.249 0.034 7.251 0.000 0.182
## sswk (.p2.) 0.415 0.043 9.693 0.000 0.331
## sspc (.p3.) 0.098 0.035 2.777 0.005 0.029
## ssei (.p4.) 0.149 0.038 3.886 0.000 0.074
## math =~
## ssar (.p5.) 0.190 0.036 5.336 0.000 0.120
## ssmk (.p6.) 0.234 0.039 5.921 0.000 0.156
## ssao (.p7.) 0.189 0.029 6.546 0.000 0.132
## electronic =~
## ssai (.p8.) 0.321 0.042 7.660 0.000 0.239
## sssi (.p9.) 0.325 0.046 7.109 0.000 0.235
## ssei (.10.) 0.168 0.024 6.901 0.000 0.120
## speed =~
## ssno (.11.) 0.668 0.106 6.296 0.000 0.460
## sscs (.12.) 0.330 0.057 5.836 0.000 0.219
## ssmk (.13.) 0.174 0.031 5.712 0.000 0.114
## g =~
## ssgs (.14.) 0.687 0.036 18.834 0.000 0.615
## ssar (.15.) 0.667 0.039 16.934 0.000 0.590
## sswk (.16.) 0.679 0.038 17.680 0.000 0.604
## sspc (.17.) 0.672 0.035 19.327 0.000 0.604
## ssno (.18.) 0.497 0.040 12.322 0.000 0.418
## sscs (.19.) 0.492 0.035 14.188 0.000 0.424
## ssai (.20.) 0.422 0.033 12.867 0.000 0.358
## sssi (.21.) 0.409 0.033 12.335 0.000 0.344
## ssmk (.22.) 0.695 0.037 19.005 0.000 0.624
## ssmc (.23.) 0.638 0.037 17.456 0.000 0.566
## ssei (.24.) 0.613 0.037 16.522 0.000 0.540
## ssao (.25.) 0.552 0.036 15.514 0.000 0.482
## ci.upper Std.lv Std.all
##
## 0.317 0.249 0.272
## 0.499 0.415 0.443
## 0.166 0.098 0.106
## 0.224 0.149 0.165
##
## 0.260 0.190 0.216
## 0.311 0.234 0.250
## 0.246 0.189 0.204
##
## 0.404 0.321 0.407
## 0.414 0.325 0.411
## 0.216 0.168 0.186
##
## 0.877 0.668 0.679
## 0.441 0.330 0.344
## 0.234 0.174 0.186
##
## 0.758 0.781 0.851
## 0.744 0.759 0.863
## 0.754 0.772 0.825
## 0.740 0.764 0.830
## 0.576 0.566 0.575
## 0.560 0.560 0.584
## 0.487 0.480 0.608
## 0.474 0.466 0.590
## 0.767 0.791 0.845
## 0.710 0.726 0.821
## 0.685 0.697 0.771
## 0.622 0.628 0.678
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.351 0.035 9.953 0.000 0.282
## agec2 (b) -0.053 0.024 -2.204 0.028 -0.101
## ci.upper Std.lv Std.all
##
## 0.421 0.309 0.468
## -0.006 -0.047 -0.090
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.54.) 0.449 0.059 7.610 0.000 0.334
## .sswk (.55.) 0.440 0.060 7.384 0.000 0.323
## .sspc 0.506 0.060 8.467 0.000 0.389
## .ssei (.57.) 0.241 0.055 4.396 0.000 0.134
## .ssar (.58.) 0.453 0.058 7.832 0.000 0.339
## .ssmk (.59.) 0.511 0.062 8.283 0.000 0.390
## .ssao (.60.) 0.371 0.060 6.214 0.000 0.254
## .ssai (.61.) 0.097 0.044 2.207 0.027 0.011
## .sssi (.62.) 0.212 0.046 4.584 0.000 0.121
## .ssno (.63.) 0.330 0.059 5.562 0.000 0.214
## .sscs 0.402 0.055 7.257 0.000 0.294
## .ssmc (.65.) 0.314 0.057 5.511 0.000 0.202
## ci.upper Std.lv Std.all
## 0.565 0.449 0.489
## 0.557 0.440 0.470
## 0.623 0.506 0.549
## 0.349 0.241 0.267
## 0.566 0.453 0.515
## 0.632 0.511 0.546
## 0.489 0.371 0.401
## 0.183 0.097 0.123
## 0.303 0.212 0.269
## 0.447 0.330 0.336
## 0.511 0.402 0.420
## 0.425 0.314 0.355
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.171 0.019 8.772 0.000 0.132
## .sswk 0.108 0.030 3.533 0.000 0.048
## .sspc 0.254 0.029 8.616 0.000 0.196
## .ssei 0.281 0.030 9.461 0.000 0.222
## .ssar 0.161 0.020 8.164 0.000 0.122
## .ssmk 0.165 0.020 8.265 0.000 0.126
## .ssao 0.427 0.037 11.546 0.000 0.354
## .ssai 0.289 0.035 8.194 0.000 0.220
## .sssi 0.301 0.036 8.271 0.000 0.230
## .ssno 0.201 0.111 1.808 0.071 -0.017
## .sscs 0.497 0.057 8.695 0.000 0.385
## .ssmc 0.255 0.026 9.936 0.000 0.205
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.209 0.171 0.202
## 0.167 0.108 0.123
## 0.312 0.254 0.299
## 0.339 0.281 0.343
## 0.200 0.161 0.208
## 0.204 0.165 0.189
## 0.499 0.427 0.498
## 0.359 0.289 0.464
## 0.373 0.301 0.483
## 0.419 0.201 0.208
## 0.609 0.497 0.540
## 0.305 0.255 0.326
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.773 0.773
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.249 0.034 7.251 0.000 0.182
## sswk (.p2.) 0.415 0.043 9.693 0.000 0.331
## sspc (.p3.) 0.098 0.035 2.777 0.005 0.029
## ssei (.p4.) 0.149 0.038 3.886 0.000 0.074
## math =~
## ssar (.p5.) 0.190 0.036 5.336 0.000 0.120
## ssmk (.p6.) 0.234 0.039 5.921 0.000 0.156
## ssao (.p7.) 0.189 0.029 6.546 0.000 0.132
## electronic =~
## ssai (.p8.) 0.321 0.042 7.660 0.000 0.239
## sssi (.p9.) 0.325 0.046 7.109 0.000 0.235
## ssei (.10.) 0.168 0.024 6.901 0.000 0.120
## speed =~
## ssno (.11.) 0.668 0.106 6.296 0.000 0.460
## sscs (.12.) 0.330 0.057 5.836 0.000 0.219
## ssmk (.13.) 0.174 0.031 5.712 0.000 0.114
## g =~
## ssgs (.14.) 0.687 0.036 18.834 0.000 0.615
## ssar (.15.) 0.667 0.039 16.934 0.000 0.590
## sswk (.16.) 0.679 0.038 17.680 0.000 0.604
## sspc (.17.) 0.672 0.035 19.327 0.000 0.604
## ssno (.18.) 0.497 0.040 12.322 0.000 0.418
## sscs (.19.) 0.492 0.035 14.188 0.000 0.424
## ssai (.20.) 0.422 0.033 12.867 0.000 0.358
## sssi (.21.) 0.409 0.033 12.335 0.000 0.344
## ssmk (.22.) 0.695 0.037 19.005 0.000 0.624
## ssmc (.23.) 0.638 0.037 17.456 0.000 0.566
## ssei (.24.) 0.613 0.037 16.522 0.000 0.540
## ssao (.25.) 0.552 0.036 15.514 0.000 0.482
## ci.upper Std.lv Std.all
##
## 0.317 0.249 0.253
## 0.499 0.415 0.411
## 0.166 0.098 0.101
## 0.224 0.149 0.144
##
## 0.260 0.190 0.198
## 0.311 0.234 0.241
## 0.246 0.189 0.187
##
## 0.404 0.673 0.616
## 0.414 0.680 0.683
## 0.216 0.352 0.341
##
## 0.877 0.754 0.698
## 0.441 0.372 0.371
## 0.234 0.197 0.203
##
## 0.758 0.839 0.852
## 0.744 0.815 0.850
## 0.754 0.830 0.822
## 0.740 0.821 0.847
## 0.576 0.608 0.563
## 0.560 0.602 0.599
## 0.487 0.516 0.472
## 0.474 0.500 0.502
## 0.767 0.850 0.878
## 0.710 0.780 0.811
## 0.685 0.749 0.725
## 0.622 0.675 0.667
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.351 0.035 9.953 0.000 0.282
## agec2 (b) -0.053 0.024 -2.204 0.028 -0.101
## ci.upper Std.lv Std.all
##
## 0.421 0.287 0.409
## -0.006 -0.044 -0.081
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.54.) 0.449 0.059 7.610 0.000 0.334
## .sswk (.55.) 0.440 0.060 7.384 0.000 0.323
## .sspc -0.062 0.074 -0.842 0.400 -0.208
## .ssei (.57.) 0.241 0.055 4.396 0.000 0.134
## .ssar (.58.) 0.453 0.058 7.832 0.000 0.339
## .ssmk (.59.) 0.511 0.062 8.283 0.000 0.390
## .ssao (.60.) 0.371 0.060 6.214 0.000 0.254
## .ssai (.61.) 0.097 0.044 2.207 0.027 0.011
## .sssi (.62.) 0.212 0.046 4.584 0.000 0.121
## .ssno (.63.) 0.330 0.059 5.562 0.000 0.214
## .sscs -0.029 0.084 -0.346 0.729 -0.193
## .ssmc (.65.) 0.314 0.057 5.511 0.000 0.202
## verbal -0.858 0.182 -4.708 0.000 -1.216
## math -1.887 0.407 -4.635 0.000 -2.685
## elctrnc 1.317 0.240 5.499 0.000 0.848
## speed -0.632 0.163 -3.879 0.000 -0.951
## .g 0.582 0.107 5.419 0.000 0.371
## ci.upper Std.lv Std.all
## 0.565 0.449 0.456
## 0.557 0.440 0.436
## 0.083 -0.062 -0.064
## 0.349 0.241 0.233
## 0.566 0.453 0.472
## 0.632 0.511 0.528
## 0.489 0.371 0.367
## 0.183 0.097 0.089
## 0.303 0.212 0.213
## 0.447 0.330 0.306
## 0.135 -0.029 -0.029
## 0.425 0.314 0.326
## -0.501 -0.858 -0.858
## -1.089 -1.887 -1.887
## 1.786 0.629 0.629
## -0.313 -0.560 -0.560
## 0.792 0.476 0.476
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.204 0.023 8.827 0.000 0.159
## .sswk 0.158 0.034 4.586 0.000 0.090
## .sspc 0.255 0.027 9.434 0.000 0.202
## .ssei 0.361 0.038 9.609 0.000 0.288
## .ssar 0.220 0.026 8.482 0.000 0.169
## .ssmk 0.122 0.018 6.726 0.000 0.086
## .ssao 0.533 0.051 10.535 0.000 0.434
## .ssai 0.474 0.068 6.972 0.000 0.341
## .sssi 0.279 0.057 4.900 0.000 0.167
## .ssno 0.228 0.132 1.728 0.084 -0.031
## .sscs 0.509 0.072 7.037 0.000 0.368
## .ssmc 0.317 0.030 10.391 0.000 0.257
## electronic 4.385 1.159 3.784 0.000 2.114
## speed 1.273 0.348 3.656 0.000 0.590
## .g 1.224 0.168 7.304 0.000 0.896
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.249 0.204 0.210
## 0.225 0.158 0.155
## 0.308 0.255 0.272
## 0.435 0.361 0.338
## 0.271 0.220 0.239
## 0.158 0.122 0.130
## 0.632 0.533 0.520
## 0.608 0.474 0.397
## 0.390 0.279 0.281
## 0.487 0.228 0.196
## 0.651 0.509 0.504
## 0.377 0.317 0.343
## 6.656 1.000 1.000
## 1.955 1.000 1.000
## 1.553 0.820 0.820
# ALL RACE RESPONDENTS
dgroup<- dplyr::select(ds, id, starts_with("ss"), asvab, efa, educ2011, T6665000, agec, age, agebin, agec2, sex, sexage, bhw, sweight)
nrow(dgroup) # N=1312
## [1] 1312
fit<-lm(efa ~ sex + rcs(agec, 3) + sex*rcs(agec, 3), data=dgroup)
summary(fit)
##
## Call:
## lm(formula = efa ~ sex + rcs(agec, 3) + sex * rcs(agec, 3), data = dgroup)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.359 -10.489 0.976 11.196 41.561
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 101.0457 1.5586 64.830 < 2e-16 ***
## sex -0.2699 2.2224 -0.121 0.903
## rcs(agec, 3)agec 4.9892 1.1394 4.379 1.29e-05 ***
## rcs(agec, 3)agec' -1.4447 1.3665 -1.057 0.291
## sex:rcs(agec, 3)agec -0.2351 1.5917 -0.148 0.883
## sex:rcs(agec, 3)agec' -0.1095 1.9188 -0.057 0.954
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.28 on 1306 degrees of freedom
## Multiple R-squared: 0.1105, Adjusted R-squared: 0.1071
## F-statistic: 32.44 on 5 and 1306 DF, p-value: < 2.2e-16
dgroup$pred1<-fitted(fit)
original_age_min <- 12
original_age_max <- 17
mean_centered_min <- min(dgroup$agec)
mean_centered_max <- max(dgroup$agec)
original_age_mean <- (original_age_min + original_age_max) / 2
mean_centered_age_mean <- (mean_centered_min + mean_centered_max) / 2
age_difference <- original_age_mean - mean_centered_age_mean
xyplot(dgroup$pred1 ~ dgroup$agec, data=dgroup, groups=sex, pch=19, type=c("p"), col=c('red', 'blue'), grid=TRUE, ylab="Predicted g", xlab="age", key=list(text=list(c("White Male", "White Female")), points=list(pch=c(19,19), col=c("red", "blue")), columns=2))
xyplot(dgroup$pred1 ~ dgroup$agec, data=dgroup, groups=sex, pch=19, type=c("p"), col=c('red', 'blue'), grid=TRUE, ylab="Predicted g", xlab="Age", key=list(text=list(c("White Male", "White Female")), points=list(pch=c(19,19), col=c("red", "blue")), columns=2), scales=list(x=list(at=seq(mean_centered_min, mean_centered_max), labels=seq(original_age_min, original_age_max))))
fit<-lm(asvab ~ sex + rcs(agec, 3) + sex*rcs(agec, 3), data=dgroup)
summary(fit)
##
## Call:
## lm(formula = asvab ~ sex + rcs(agec, 3) + sex * rcs(agec, 3),
## data = dgroup)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.025 -13.109 -1.783 12.655 30.010
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 98.59993 1.54121 63.975 <2e-16 ***
## sex 1.39321 2.19756 0.634 0.526
## rcs(agec, 3)agec 0.45553 1.12665 0.404 0.686
## rcs(agec, 3)agec' -0.02215 1.35119 -0.016 0.987
## sex:rcs(agec, 3)agec 0.15026 1.57391 0.095 0.924
## sex:rcs(agec, 3)agec' -0.45301 1.89731 -0.239 0.811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.11 on 1306 degrees of freedom
## Multiple R-squared: 0.002104, Adjusted R-squared: -0.001716
## F-statistic: 0.5508 on 5 and 1306 DF, p-value: 0.7378
dgroup$pred2<-fitted(fit)
xyplot(dgroup$pred2 ~ dgroup$agec, data=dgroup, groups=sex, pch=19, type=c("p"), col=c('red', 'blue'), grid=TRUE, ylab="Predicted ASVAB", xlab="Age", key=list(text=list(c("White Male", "White Female")), points=list(pch=c(19,19), col=c("red", "blue")), columns=2), scales=list(x=list(at=seq(mean_centered_min, mean_centered_max), labels=seq(original_age_min, original_age_max))))
describeBy(dgroup$pred1, dgroup$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew
## X1 1 656 99.53 5.49 100.29 99.79 6.48 88.57 107.69 19.12 -0.33
## kurtosis se
## X1 -1.07 0.21
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 656 99.17 5.25 100.11 99.48 6.21 88.89 106.44 17.55 -0.41
## kurtosis se
## X1 -1.1 0.21
describeBy(dgroup$efa, dgroup$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew
## X1 1 656 99.53 16.83 100.84 99.69 17.89 63.82 139.83 76.01 -0.08
## kurtosis se
## X1 -0.71 0.66
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 656 99.17 15.49 99.35 99.19 16.18 62.88 141.79 78.91 -0.01
## kurtosis se
## X1 -0.49 0.6
describeBy(dgroup$asvab, dgroup$sex)
##
## Descriptive statistics by group
## INDICES: 0
## vars n mean sd median trimmed mad min max range skew
## V1 1 656 98.58 15.28 96.33 97.93 18.96 76.7 128.12 51.42 0.29
## kurtosis se
## V1 -1.19 0.6
## ------------------------------------------------------
## INDICES: 1
## vars n mean sd median trimmed mad min max range skew
## V1 1 656 99.48 14.91 98.15 98.88 17.98 76.76 128.12 51.36 0.29
## kurtosis se
## V1 -1.1 0.58
describeBy(dgroup$educ2011, dgroup$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 547 13.29 2.83 13 13.27 2.97 6 20 14 0.13 -0.53
## se
## X1 0.12
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 570 14.01 2.86 14 14.02 2.97 6 20 14 0.02 -0.6
## se
## X1 0.12
cor(dgroup$efa, dgroup$asvab, use="pairwise.complete.obs", method="pearson")
## [,1]
## [1,] 0.9099632
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(agebin, sex) %>% summarise(MEAN = survey_mean(pred1), SD = survey_sd(pred1))
## # A tibble: 10 Ă— 5
## # Groups: agebin [5]
## agebin sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 12 0 91.0 0.140 1.41
## 2 12 1 91.1 0.139 1.40
## 3 13 0 95.9 0.123 1.34
## 4 13 1 95.6 0.129 1.32
## 5 14 0 100. 0.102 1.11
## 6 14 1 99.9 0.101 1.05
## 7 15 0 104. 0.0916 0.931
## 8 15 1 103. 0.0702 0.733
## 9 16 0 106. 0.0788 0.764
## 10 16 1 105. 0.0642 0.689
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(agebin, sex) %>% summarise(MEAN = survey_mean(efa), SD = survey_sd(efa))
## # A tibble: 10 Ă— 5
## # Groups: agebin [5]
## agebin sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 12 0 94.0 1.37 14.2
## 2 12 1 93.4 1.21 12.4
## 3 13 0 98.8 1.49 16.0
## 4 13 1 98.7 1.40 14.4
## 5 14 0 103. 1.37 15.7
## 6 14 1 104. 1.40 14.8
## 7 15 0 109. 1.35 14.8
## 8 15 1 108. 1.39 15.1
## 9 16 0 111. 1.59 15.9
## 10 16 1 109. 1.14 13.0
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(agebin, sex) %>% summarise(MEAN = survey_mean(asvab), SD = survey_sd(asvab))
## # A tibble: 10 Ă— 5
## # Groups: agebin [5]
## agebin sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 12 0 100. 1.43 14.7
## 2 12 1 101. 1.33 13.7
## 3 13 0 101. 1.46 15.6
## 4 13 1 102. 1.55 15.5
## 5 14 0 102. 1.37 15.2
## 6 14 1 105. 1.45 15.1
## 7 15 0 102. 1.44 15.1
## 8 15 1 103. 1.43 15.1
## 9 16 0 104. 1.59 15.7
## 10 16 1 103. 1.30 14.2
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(pred1), SD = survey_sd(pred1))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 99.4 0.247 5.57
## 2 1 99.3 0.236 5.34
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(efa), SD = survey_sd(efa))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 103. 0.709 16.5
## 2 1 103. 0.640 15.1
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(asvab, na.rm = TRUE), SD = survey_sd(asvab, na.rm = TRUE))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 102. 0.657 15.3
## 2 1 103. 0.631 14.7
dgroup %>% as_survey_design(ids = id, weights = T6665000) %>% group_by(sex) %>% summarise(MEAN = survey_mean(educ2011, na.rm = TRUE), SD = survey_sd(educ2011, na.rm = TRUE))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 13.7 0.129 2.79
## 2 1 14.4 0.130 2.81
# CORRELATED FACTOR MODEL
cf.model<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
'
cf.lv<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
verbal~~1*verbal
math~~1*math
'
cf.reduced<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
verbal~~1*verbal
math~~1*math
verbal~0*1
math~0*1
'
baseline<-cfa(cf.model, data=dgroup, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 257.691 44.000 0.000 0.984 0.061 0.020 32434.588
## bic
## 32672.836
Mc(baseline)
## [1] 0.9217335
configural<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 266.446 88.000 0.000 0.987 0.056 0.022 31916.836
## bic
## 32393.333
Mc(configural)
## [1] 0.934207
summary(configural, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 52 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 92
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 266.446 206.864
## Degrees of freedom 88 88
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.288
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 121.005 93.946
## 0 145.441 112.917
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.894 0.030 29.349 0.000 0.834
## sswk 0.913 0.033 27.793 0.000 0.848
## sspc 0.407 0.079 5.167 0.000 0.253
## ssei 0.586 0.074 7.917 0.000 0.441
## math =~
## ssar 0.856 0.031 27.409 0.000 0.795
## sspc 0.447 0.080 5.597 0.000 0.290
## ssmk 0.703 0.079 8.917 0.000 0.548
## ssmc 0.485 0.064 7.585 0.000 0.360
## ssao 0.739 0.031 24.124 0.000 0.679
## electronic =~
## ssai 0.573 0.033 17.610 0.000 0.509
## sssi 0.661 0.036 18.280 0.000 0.590
## ssmc 0.344 0.066 5.242 0.000 0.215
## ssei 0.181 0.073 2.467 0.014 0.037
## speed =~
## ssno 0.778 0.043 17.924 0.000 0.693
## sscs 0.680 0.039 17.404 0.000 0.603
## ssmk 0.279 0.080 3.470 0.001 0.121
## ci.upper Std.lv Std.all
##
## 0.953 0.894 0.916
## 0.977 0.913 0.910
## 0.561 0.407 0.423
## 0.731 0.586 0.647
##
## 0.917 0.856 0.907
## 0.603 0.447 0.465
## 0.857 0.703 0.684
## 0.610 0.485 0.523
## 0.799 0.739 0.757
##
## 0.636 0.573 0.707
## 0.732 0.661 0.779
## 0.472 0.344 0.370
## 0.325 0.181 0.200
##
## 0.863 0.778 0.797
## 0.757 0.680 0.700
## 0.436 0.279 0.271
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.894 0.016 54.821 0.000 0.862
## electronic 0.841 0.030 28.164 0.000 0.783
## speed 0.734 0.044 16.797 0.000 0.648
## math ~~
## electronic 0.774 0.037 21.195 0.000 0.703
## speed 0.796 0.044 18.092 0.000 0.709
## electronic ~~
## speed 0.538 0.062 8.622 0.000 0.416
## ci.upper Std.lv Std.all
##
## 0.925 0.894 0.894
## 0.900 0.841 0.841
## 0.819 0.734 0.734
##
## 0.846 0.774 0.774
## 0.882 0.796 0.796
##
## 0.660 0.538 0.538
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.139 0.042 3.332 0.001 0.057
## .sswk 0.154 0.043 3.607 0.000 0.070
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssei 0.000 0.039 0.009 0.993 -0.077
## .ssar 0.186 0.040 4.598 0.000 0.107
## .ssmk 0.241 0.044 5.433 0.000 0.154
## .ssmc 0.039 0.040 0.993 0.321 -0.038
## .ssao 0.171 0.042 4.054 0.000 0.088
## .ssai -0.108 0.035 -3.113 0.002 -0.176
## .sssi -0.068 0.036 -1.862 0.063 -0.139
## .ssno 0.175 0.043 4.060 0.000 0.090
## .sscs 0.245 0.043 5.752 0.000 0.162
## ci.upper Std.lv Std.all
## 0.220 0.139 0.142
## 0.238 0.154 0.154
## 0.333 0.253 0.263
## 0.077 0.000 0.000
## 0.265 0.186 0.197
## 0.327 0.241 0.234
## 0.117 0.039 0.042
## 0.253 0.171 0.175
## -0.040 -0.108 -0.134
## 0.004 -0.068 -0.080
## 0.259 0.175 0.179
## 0.329 0.245 0.253
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.153 0.015 10.348 0.000 0.124
## .sswk 0.173 0.016 11.087 0.000 0.142
## .sspc 0.234 0.022 10.766 0.000 0.191
## .ssei 0.266 0.022 12.014 0.000 0.222
## .ssar 0.157 0.015 10.359 0.000 0.127
## .ssmk 0.173 0.017 10.299 0.000 0.140
## .ssmc 0.250 0.019 13.149 0.000 0.212
## .ssao 0.408 0.028 14.568 0.000 0.353
## .ssai 0.328 0.028 11.674 0.000 0.273
## .sssi 0.284 0.028 9.993 0.000 0.228
## .ssno 0.348 0.038 9.242 0.000 0.274
## .sscs 0.480 0.054 8.835 0.000 0.374
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.182 0.153 0.161
## 0.203 0.173 0.172
## 0.276 0.234 0.253
## 0.309 0.266 0.324
## 0.187 0.157 0.177
## 0.206 0.173 0.164
## 0.287 0.250 0.290
## 0.463 0.408 0.428
## 0.383 0.328 0.500
## 0.340 0.284 0.394
## 0.421 0.348 0.365
## 0.587 0.480 0.509
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.995 0.033 30.614 0.000 0.932
## sswk 0.970 0.032 30.009 0.000 0.907
## sspc 0.361 0.084 4.306 0.000 0.196
## ssei 0.597 0.065 9.145 0.000 0.469
## math =~
## ssar 0.947 0.033 28.371 0.000 0.881
## sspc 0.539 0.084 6.415 0.000 0.375
## ssmk 0.730 0.061 11.929 0.000 0.610
## ssmc 0.567 0.040 14.200 0.000 0.488
## ssao 0.740 0.033 22.154 0.000 0.675
## electronic =~
## ssai 0.971 0.043 22.739 0.000 0.887
## sssi 0.968 0.040 24.056 0.000 0.889
## ssmc 0.474 0.042 11.208 0.000 0.391
## ssei 0.505 0.070 7.172 0.000 0.367
## speed =~
## ssno 0.878 0.047 18.752 0.000 0.786
## sscs 0.790 0.044 17.936 0.000 0.704
## ssmk 0.248 0.063 3.958 0.000 0.125
## ci.upper Std.lv Std.all
##
## 1.059 0.995 0.929
## 1.034 0.970 0.908
## 0.525 0.361 0.358
## 0.725 0.597 0.506
##
## 1.012 0.947 0.900
## 0.704 0.539 0.536
## 0.850 0.730 0.715
## 0.645 0.567 0.525
## 0.806 0.740 0.714
##
## 1.055 0.971 0.822
## 1.047 0.968 0.861
## 0.557 0.474 0.440
## 0.643 0.505 0.428
##
## 0.969 0.878 0.820
## 0.877 0.790 0.750
## 0.370 0.248 0.242
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.905 0.014 66.480 0.000 0.879
## electronic 0.773 0.026 30.178 0.000 0.723
## speed 0.697 0.035 19.829 0.000 0.628
## math ~~
## electronic 0.658 0.033 20.228 0.000 0.594
## speed 0.806 0.029 27.408 0.000 0.749
## electronic ~~
## speed 0.406 0.051 7.918 0.000 0.305
## ci.upper Std.lv Std.all
##
## 0.932 0.905 0.905
## 0.823 0.773 0.773
## 0.766 0.697 0.697
##
## 0.722 0.658 0.658
## 0.864 0.806 0.806
##
## 0.506 0.406 0.406
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.285 0.046 6.206 0.000 0.195
## .sswk 0.136 0.046 2.930 0.003 0.045
## .sspc -0.020 0.044 -0.450 0.652 -0.105
## .ssei 0.317 0.051 6.192 0.000 0.217
## .ssar 0.185 0.045 4.117 0.000 0.097
## .ssmk 0.094 0.044 2.150 0.032 0.008
## .ssmc 0.317 0.046 6.902 0.000 0.227
## .ssao 0.084 0.045 1.868 0.062 -0.004
## .ssai 0.427 0.052 8.172 0.000 0.324
## .sssi 0.489 0.049 10.035 0.000 0.394
## .ssno 0.022 0.047 0.466 0.641 -0.070
## .sscs -0.116 0.046 -2.517 0.012 -0.206
## ci.upper Std.lv Std.all
## 0.375 0.285 0.266
## 0.226 0.136 0.127
## 0.066 -0.020 -0.019
## 0.417 0.317 0.268
## 0.274 0.185 0.176
## 0.180 0.094 0.092
## 0.406 0.317 0.294
## 0.173 0.084 0.081
## 0.529 0.427 0.361
## 0.585 0.489 0.435
## 0.114 0.022 0.020
## -0.026 -0.116 -0.110
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.157 0.016 9.514 0.000 0.125
## .sswk 0.199 0.017 11.988 0.000 0.167
## .sspc 0.241 0.019 12.434 0.000 0.203
## .ssei 0.317 0.025 12.863 0.000 0.268
## .ssar 0.210 0.022 9.475 0.000 0.167
## .ssmk 0.158 0.014 11.706 0.000 0.132
## .ssmc 0.264 0.020 13.388 0.000 0.225
## .ssao 0.528 0.038 14.029 0.000 0.454
## .ssai 0.451 0.041 11.042 0.000 0.371
## .sssi 0.328 0.036 9.202 0.000 0.258
## .ssno 0.375 0.042 9.020 0.000 0.293
## .sscs 0.486 0.057 8.487 0.000 0.374
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.189 0.157 0.137
## 0.232 0.199 0.175
## 0.278 0.241 0.237
## 0.365 0.317 0.227
## 0.254 0.210 0.190
## 0.185 0.158 0.152
## 0.302 0.264 0.227
## 0.601 0.528 0.490
## 0.532 0.451 0.324
## 0.398 0.328 0.259
## 0.456 0.375 0.327
## 0.598 0.486 0.438
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
modificationIndices(configural, sort=T, maximum.number=30)
## lhs op rhs block group level mi epc sepc.lv sepc.all
## 192 ssmc ~~ ssao 1 1 1 30.119 0.079 0.079 0.247
## 290 ssmc ~~ ssao 2 2 1 16.235 0.067 0.067 0.180
## 224 electronic =~ sspc 2 2 1 15.818 -0.172 -0.172 -0.171
## 266 sspc ~~ sssi 2 2 1 11.870 -0.049 -0.049 -0.175
## 124 electronic =~ ssgs 1 1 1 11.688 0.220 0.220 0.225
## 268 sspc ~~ sscs 2 2 1 11.256 0.054 0.054 0.158
## 221 math =~ sscs 2 2 1 10.746 0.422 0.422 0.400
## 220 math =~ ssno 2 2 1 10.746 -0.468 -0.468 -0.438
## 293 ssmc ~~ ssno 2 2 1 10.627 -0.054 -0.054 -0.171
## 230 speed =~ ssgs 2 2 1 10.511 -0.131 -0.131 -0.123
## 298 ssao ~~ sscs 2 2 1 10.448 0.074 0.074 0.147
## 235 speed =~ ssmc 2 2 1 10.286 -0.202 -0.202 -0.187
## 131 electronic =~ sscs 1 1 1 9.405 0.170 0.170 0.175
## 288 ssmk ~~ ssno 2 2 1 9.283 0.062 0.062 0.255
## 297 ssao ~~ ssno 2 2 1 8.950 -0.066 -0.066 -0.148
## 282 ssar ~~ ssno 2 2 1 8.923 0.050 0.050 0.179
## 157 sswk ~~ ssao 1 1 1 8.658 -0.038 -0.038 -0.141
## 139 speed =~ ssai 1 1 1 8.529 0.131 0.131 0.162
## 213 verbal =~ ssno 2 2 1 8.413 -0.216 -0.216 -0.202
## 149 ssgs ~~ sssi 1 1 1 8.207 0.034 0.034 0.162
## 123 math =~ sscs 1 1 1 7.999 0.368 0.368 0.379
## 122 math =~ ssno 1 1 1 7.999 -0.421 -0.421 -0.431
## 214 verbal =~ sscs 2 2 1 7.897 0.190 0.190 0.181
## 132 speed =~ ssgs 1 1 1 7.772 -0.116 -0.116 -0.119
## 255 sswk ~~ ssao 2 2 1 7.765 -0.042 -0.042 -0.130
## 130 electronic =~ ssno 1 1 1 7.737 -0.171 -0.171 -0.175
## 272 ssei ~~ ssao 2 2 1 7.696 0.050 0.050 0.122
## 283 ssar ~~ sscs 2 2 1 7.610 -0.047 -0.047 -0.146
## 277 ssar ~~ ssmk 2 2 1 7.447 0.038 0.038 0.206
## 200 ssao ~~ sscs 1 1 1 7.414 0.054 0.054 0.122
## sepc.nox
## 192 0.247
## 290 0.180
## 224 -0.171
## 266 -0.175
## 124 0.225
## 268 0.158
## 221 0.400
## 220 -0.438
## 293 -0.171
## 230 -0.123
## 298 0.147
## 235 -0.187
## 131 0.175
## 288 0.255
## 297 -0.148
## 282 0.179
## 157 -0.141
## 139 0.162
## 213 -0.202
## 149 0.162
## 123 0.379
## 122 -0.431
## 214 0.181
## 132 -0.119
## 255 -0.130
## 130 -0.175
## 272 0.122
## 283 -0.146
## 277 0.206
## 200 0.122
metric<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 297.407 100.000 0.000 0.985 0.055 0.029 31923.798
## bic
## 32338.142
Mc(metric)
## [1] 0.9274756
summary(metric, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 77 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 16
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 297.407 228.974
## Degrees of freedom 100 100
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.299
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 140.271 107.994
## 0 157.137 120.980
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.900 0.029 31.218 0.000 0.843
## sswk (.p2.) 0.898 0.031 29.313 0.000 0.838
## sspc (.p3.) 0.363 0.055 6.612 0.000 0.256
## ssei (.p4.) 0.505 0.046 10.929 0.000 0.415
## math =~
## ssar (.p5.) 0.877 0.028 30.893 0.000 0.822
## sspc (.p6.) 0.484 0.058 8.398 0.000 0.371
## ssmk (.p7.) 0.706 0.049 14.285 0.000 0.609
## ssmc (.p8.) 0.518 0.031 16.599 0.000 0.457
## ssao (.p9.) 0.725 0.027 26.722 0.000 0.671
## electronic =~
## ssai (.10.) 0.588 0.028 21.294 0.000 0.534
## sssi (.11.) 0.613 0.030 20.194 0.000 0.553
## ssmc (.12.) 0.306 0.027 11.419 0.000 0.254
## ssei (.13.) 0.311 0.038 8.236 0.000 0.237
## speed =~
## ssno (.14.) 0.787 0.037 21.355 0.000 0.714
## sscs (.15.) 0.699 0.033 21.018 0.000 0.634
## ssmk (.16.) 0.240 0.048 5.006 0.000 0.146
## ci.upper Std.lv Std.all
##
## 0.957 0.900 0.919
## 0.958 0.898 0.907
## 0.471 0.363 0.380
## 0.596 0.505 0.539
##
## 0.933 0.877 0.912
## 0.597 0.484 0.506
## 0.803 0.706 0.703
## 0.579 0.518 0.557
## 0.778 0.725 0.749
##
## 0.642 0.588 0.717
## 0.672 0.613 0.743
## 0.359 0.306 0.329
## 0.385 0.311 0.332
##
## 0.859 0.787 0.802
## 0.764 0.699 0.713
## 0.334 0.240 0.239
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.895 0.016 56.339 0.000 0.864
## electronic 0.844 0.030 28.128 0.000 0.785
## speed 0.731 0.039 18.843 0.000 0.655
## math ~~
## electronic 0.786 0.033 23.544 0.000 0.721
## speed 0.798 0.041 19.373 0.000 0.718
## electronic ~~
## speed 0.551 0.059 9.376 0.000 0.436
## ci.upper Std.lv Std.all
##
## 0.926 0.895 0.895
## 0.903 0.844 0.844
## 0.807 0.731 0.731
##
## 0.852 0.786 0.786
## 0.879 0.798 0.798
##
## 0.666 0.551 0.551
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.139 0.042 3.332 0.001 0.057
## .sswk 0.154 0.043 3.607 0.000 0.070
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssei 0.000 0.039 0.009 0.993 -0.077
## .ssar 0.186 0.040 4.598 0.000 0.107
## .ssmk 0.241 0.044 5.433 0.000 0.154
## .ssmc 0.039 0.040 0.993 0.321 -0.038
## .ssao 0.171 0.042 4.054 0.000 0.088
## .ssai -0.108 0.035 -3.113 0.002 -0.176
## .sssi -0.068 0.036 -1.862 0.063 -0.139
## .ssno 0.175 0.043 4.060 0.000 0.090
## .sscs 0.245 0.043 5.752 0.000 0.162
## ci.upper Std.lv Std.all
## 0.220 0.139 0.142
## 0.238 0.154 0.156
## 0.333 0.253 0.264
## 0.077 0.000 0.000
## 0.265 0.186 0.193
## 0.327 0.241 0.240
## 0.117 0.039 0.042
## 0.253 0.171 0.177
## -0.040 -0.108 -0.132
## 0.004 -0.068 -0.082
## 0.259 0.175 0.178
## 0.329 0.245 0.251
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.150 0.015 10.195 0.000 0.121
## .sswk 0.174 0.016 11.093 0.000 0.143
## .sspc 0.234 0.021 10.931 0.000 0.192
## .ssei 0.260 0.022 11.708 0.000 0.217
## .ssar 0.156 0.015 10.433 0.000 0.127
## .ssmk 0.182 0.016 11.491 0.000 0.151
## .ssmc 0.252 0.019 13.599 0.000 0.216
## .ssao 0.411 0.028 14.872 0.000 0.357
## .ssai 0.326 0.027 12.242 0.000 0.274
## .sssi 0.305 0.027 11.101 0.000 0.251
## .ssno 0.344 0.037 9.310 0.000 0.271
## .sscs 0.472 0.052 8.997 0.000 0.369
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.179 0.150 0.156
## 0.204 0.174 0.177
## 0.276 0.234 0.256
## 0.304 0.260 0.297
## 0.185 0.156 0.169
## 0.213 0.182 0.181
## 0.289 0.252 0.292
## 0.465 0.411 0.439
## 0.378 0.326 0.486
## 0.359 0.305 0.448
## 0.416 0.344 0.357
## 0.575 0.472 0.492
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.900 0.029 31.218 0.000 0.843
## sswk (.p2.) 0.898 0.031 29.313 0.000 0.838
## sspc (.p3.) 0.363 0.055 6.612 0.000 0.256
## ssei (.p4.) 0.505 0.046 10.929 0.000 0.415
## math =~
## ssar (.p5.) 0.877 0.028 30.893 0.000 0.822
## sspc (.p6.) 0.484 0.058 8.398 0.000 0.371
## ssmk (.p7.) 0.706 0.049 14.285 0.000 0.609
## ssmc (.p8.) 0.518 0.031 16.599 0.000 0.457
## ssao (.p9.) 0.725 0.027 26.722 0.000 0.671
## electronic =~
## ssai (.10.) 0.588 0.028 21.294 0.000 0.534
## sssi (.11.) 0.613 0.030 20.194 0.000 0.553
## ssmc (.12.) 0.306 0.027 11.419 0.000 0.254
## ssei (.13.) 0.311 0.038 8.236 0.000 0.237
## speed =~
## ssno (.14.) 0.787 0.037 21.355 0.000 0.714
## sscs (.15.) 0.699 0.033 21.018 0.000 0.634
## ssmk (.16.) 0.240 0.048 5.006 0.000 0.146
## ci.upper Std.lv Std.all
##
## 0.957 0.990 0.928
## 0.958 0.988 0.913
## 0.471 0.400 0.395
## 0.596 0.556 0.484
##
## 0.933 0.917 0.892
## 0.597 0.506 0.500
## 0.803 0.739 0.709
## 0.579 0.542 0.503
## 0.778 0.758 0.723
##
## 0.642 0.953 0.814
## 0.672 0.993 0.868
## 0.359 0.497 0.461
## 0.385 0.504 0.438
##
## 0.859 0.871 0.818
## 0.764 0.774 0.741
## 0.334 0.266 0.255
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 1.040 0.083 12.527 0.000 0.877
## electronic 1.389 0.126 11.028 0.000 1.142
## speed 0.849 0.083 10.176 0.000 0.686
## math ~~
## electronic 1.127 0.109 10.355 0.000 0.913
## speed 0.930 0.084 11.066 0.000 0.765
## electronic ~~
## speed 0.738 0.110 6.728 0.000 0.523
## ci.upper Std.lv Std.all
##
## 1.202 0.903 0.903
## 1.636 0.778 0.778
## 1.013 0.697 0.697
##
## 1.340 0.664 0.664
## 1.094 0.803 0.803
##
## 0.953 0.411 0.411
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.285 0.046 6.206 0.000 0.195
## .sswk 0.136 0.046 2.930 0.003 0.045
## .sspc -0.020 0.044 -0.450 0.652 -0.105
## .ssei 0.317 0.051 6.192 0.000 0.217
## .ssar 0.185 0.045 4.117 0.000 0.097
## .ssmk 0.094 0.044 2.150 0.032 0.008
## .ssmc 0.317 0.046 6.902 0.000 0.227
## .ssao 0.084 0.045 1.868 0.062 -0.004
## .ssai 0.427 0.052 8.172 0.000 0.324
## .sssi 0.489 0.049 10.035 0.000 0.394
## .ssno 0.022 0.047 0.466 0.641 -0.070
## .sscs -0.116 0.046 -2.517 0.012 -0.206
## ci.upper Std.lv Std.all
## 0.375 0.285 0.267
## 0.226 0.136 0.125
## 0.066 -0.020 -0.019
## 0.417 0.317 0.276
## 0.274 0.185 0.180
## 0.180 0.094 0.091
## 0.406 0.317 0.294
## 0.173 0.084 0.080
## 0.529 0.427 0.364
## 0.585 0.489 0.428
## 0.114 0.022 0.021
## -0.026 -0.116 -0.111
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.158 0.016 9.870 0.000 0.127
## .sswk 0.196 0.016 12.009 0.000 0.164
## .sspc 0.241 0.019 12.403 0.000 0.203
## .ssei 0.323 0.025 12.855 0.000 0.274
## .ssar 0.215 0.022 9.722 0.000 0.172
## .ssmk 0.153 0.013 11.699 0.000 0.127
## .ssmc 0.262 0.020 13.399 0.000 0.224
## .ssao 0.526 0.037 14.121 0.000 0.453
## .ssai 0.462 0.041 11.372 0.000 0.383
## .sssi 0.322 0.035 9.116 0.000 0.253
## .ssno 0.376 0.040 9.284 0.000 0.296
## .sscs 0.493 0.056 8.735 0.000 0.382
## verbal 1.211 0.102 11.875 0.000 1.011
## math 1.094 0.091 12.035 0.000 0.916
## electronic 2.630 0.290 9.063 0.000 2.061
## speed 1.226 0.152 8.091 0.000 0.929
## ci.upper Std.lv Std.all
## 0.190 0.158 0.139
## 0.228 0.196 0.167
## 0.279 0.241 0.236
## 0.372 0.323 0.244
## 0.259 0.215 0.204
## 0.178 0.153 0.141
## 0.301 0.262 0.226
## 0.599 0.526 0.478
## 0.542 0.462 0.337
## 0.391 0.322 0.246
## 0.455 0.376 0.331
## 0.604 0.493 0.452
## 1.411 1.000 1.000
## 1.272 1.000 1.000
## 3.199 1.000 1.000
## 1.523 1.000 1.000
lavTestScore(metric, release = 1:16)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 31.224 16 0.013
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p55. 0.356 1 0.551
## 2 .p2. == .p56. 3.061 1 0.080
## 3 .p3. == .p57. 0.351 1 0.554
## 4 .p4. == .p58. 7.245 1 0.007
## 5 .p5. == .p59. 7.876 1 0.005
## 6 .p6. == .p60. 0.104 1 0.747
## 7 .p7. == .p61. 6.811 1 0.009
## 8 .p8. == .p62. 0.242 1 0.623
## 9 .p9. == .p63. 0.728 1 0.394
## 10 .p10. == .p64. 1.199 1 0.274
## 11 .p11. == .p65. 8.115 1 0.004
## 12 .p12. == .p66. 0.145 1 0.703
## 13 .p13. == .p67. 11.169 1 0.001
## 14 .p14. == .p68. 0.505 1 0.477
## 15 .p15. == .p69. 0.920 1 0.337
## 16 .p16. == .p70. 7.781 1 0.005
scalar<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 436.233 108.000 0.000 0.975 0.068 0.033 32046.624
## bic
## 32419.534
Mc(scalar)
## [1] 0.8823342
summary(scalar, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 92 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 100
## Number of equality constraints 28
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 436.233 338.217
## Degrees of freedom 108 108
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.290
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 207.173 160.624
## 0 229.060 177.593
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.900 0.029 31.215 0.000 0.844
## sswk (.p2.) 0.896 0.031 28.855 0.000 0.835
## sspc (.p3.) 0.276 0.069 4.007 0.000 0.141
## ssei (.p4.) 0.504 0.041 12.166 0.000 0.423
## math =~
## ssar (.p5.) 0.873 0.028 30.693 0.000 0.817
## sspc (.p6.) 0.573 0.071 8.053 0.000 0.434
## ssmk (.p7.) 0.708 0.048 14.822 0.000 0.615
## ssmc (.p8.) 0.502 0.028 18.220 0.000 0.448
## ssao (.p9.) 0.724 0.027 26.695 0.000 0.671
## electronic =~
## ssai (.10.) 0.586 0.027 21.458 0.000 0.533
## sssi (.11.) 0.611 0.029 20.721 0.000 0.553
## ssmc (.12.) 0.321 0.024 13.511 0.000 0.274
## ssei (.13.) 0.311 0.032 9.592 0.000 0.248
## speed =~
## ssno (.14.) 0.774 0.036 21.203 0.000 0.702
## sscs (.15.) 0.710 0.034 21.004 0.000 0.644
## ssmk (.16.) 0.236 0.046 5.132 0.000 0.146
## ci.upper Std.lv Std.all
##
## 0.957 0.900 0.918
## 0.957 0.896 0.906
## 0.411 0.276 0.285
## 0.585 0.504 0.539
##
## 0.928 0.873 0.908
## 0.713 0.573 0.592
## 0.802 0.708 0.705
## 0.556 0.502 0.541
## 0.778 0.724 0.748
##
## 0.640 0.586 0.715
## 0.669 0.611 0.741
## 0.367 0.321 0.345
## 0.375 0.311 0.332
##
## 0.845 0.774 0.792
## 0.777 0.710 0.718
## 0.326 0.236 0.235
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.899 0.017 54.264 0.000 0.866
## electronic 0.847 0.030 28.563 0.000 0.789
## speed 0.732 0.038 19.029 0.000 0.657
## math ~~
## electronic 0.793 0.032 24.390 0.000 0.729
## speed 0.805 0.041 19.573 0.000 0.724
## electronic ~~
## speed 0.556 0.058 9.529 0.000 0.442
## ci.upper Std.lv Std.all
##
## 0.931 0.899 0.899
## 0.905 0.847 0.847
## 0.807 0.732 0.732
##
## 0.856 0.793 0.793
## 0.885 0.805 0.805
##
## 0.671 0.556 0.556
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.39.) 0.186 0.041 4.519 0.000 0.105
## .sswk (.40.) 0.123 0.042 2.931 0.003 0.041
## .sspc (.41.) 0.143 0.041 3.489 0.000 0.063
## .ssei (.42.) 0.000 0.038 0.005 0.996 -0.074
## .ssar (.43.) 0.226 0.040 5.699 0.000 0.148
## .ssmk (.44.) 0.244 0.043 5.713 0.000 0.160
## .ssmc (.45.) 0.056 0.038 1.477 0.140 -0.018
## .ssao (.46.) 0.168 0.039 4.340 0.000 0.092
## .ssai (.47.) -0.113 0.033 -3.434 0.001 -0.177
## .sssi (.48.) -0.074 0.034 -2.189 0.029 -0.140
## .ssno (.49.) 0.218 0.041 5.386 0.000 0.139
## .sscs (.50.) 0.179 0.042 4.283 0.000 0.097
## ci.upper Std.lv Std.all
## 0.267 0.186 0.190
## 0.206 0.123 0.125
## 0.223 0.143 0.148
## 0.074 0.000 0.000
## 0.303 0.226 0.235
## 0.328 0.244 0.243
## 0.129 0.056 0.060
## 0.243 0.168 0.173
## -0.048 -0.113 -0.138
## -0.008 -0.074 -0.089
## 0.298 0.218 0.224
## 0.261 0.179 0.181
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.152 0.015 9.965 0.000 0.122
## .sswk 0.176 0.016 10.755 0.000 0.144
## .sspc 0.248 0.024 10.504 0.000 0.201
## .ssei 0.260 0.022 11.649 0.000 0.216
## .ssar 0.162 0.015 10.499 0.000 0.132
## .ssmk 0.182 0.016 11.507 0.000 0.151
## .ssmc 0.252 0.019 13.587 0.000 0.216
## .ssao 0.412 0.028 14.886 0.000 0.358
## .ssai 0.328 0.027 12.367 0.000 0.276
## .sssi 0.307 0.028 11.058 0.000 0.252
## .ssno 0.355 0.038 9.455 0.000 0.281
## .sscs 0.474 0.053 8.906 0.000 0.370
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.182 0.152 0.158
## 0.208 0.176 0.180
## 0.294 0.248 0.264
## 0.304 0.260 0.296
## 0.192 0.162 0.175
## 0.213 0.182 0.181
## 0.288 0.252 0.292
## 0.467 0.412 0.440
## 0.380 0.328 0.488
## 0.361 0.307 0.451
## 0.429 0.355 0.372
## 0.578 0.474 0.484
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.900 0.029 31.215 0.000 0.844
## sswk (.p2.) 0.896 0.031 28.855 0.000 0.835
## sspc (.p3.) 0.276 0.069 4.007 0.000 0.141
## ssei (.p4.) 0.504 0.041 12.166 0.000 0.423
## math =~
## ssar (.p5.) 0.873 0.028 30.693 0.000 0.817
## sspc (.p6.) 0.573 0.071 8.053 0.000 0.434
## ssmk (.p7.) 0.708 0.048 14.822 0.000 0.615
## ssmc (.p8.) 0.502 0.028 18.220 0.000 0.448
## ssao (.p9.) 0.724 0.027 26.695 0.000 0.671
## electronic =~
## ssai (.10.) 0.586 0.027 21.458 0.000 0.533
## sssi (.11.) 0.611 0.029 20.721 0.000 0.553
## ssmc (.12.) 0.321 0.024 13.511 0.000 0.274
## ssei (.13.) 0.311 0.032 9.592 0.000 0.248
## speed =~
## ssno (.14.) 0.774 0.036 21.203 0.000 0.702
## sscs (.15.) 0.710 0.034 21.004 0.000 0.644
## ssmk (.16.) 0.236 0.046 5.132 0.000 0.146
## ci.upper Std.lv Std.all
##
## 0.957 0.992 0.927
## 0.957 0.987 0.911
## 0.411 0.304 0.299
## 0.585 0.555 0.483
##
## 0.928 0.912 0.888
## 0.713 0.599 0.588
## 0.802 0.740 0.711
## 0.556 0.525 0.485
## 0.778 0.757 0.722
##
## 0.640 0.948 0.812
## 0.669 0.988 0.866
## 0.367 0.519 0.479
## 0.375 0.503 0.438
##
## 0.845 0.857 0.808
## 0.777 0.787 0.746
## 0.326 0.262 0.251
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 1.043 0.084 12.460 0.000 0.879
## electronic 1.396 0.126 11.039 0.000 1.148
## speed 0.851 0.084 10.179 0.000 0.687
## math ~~
## electronic 1.131 0.108 10.442 0.000 0.919
## speed 0.936 0.084 11.098 0.000 0.771
## electronic ~~
## speed 0.745 0.109 6.828 0.000 0.531
## ci.upper Std.lv Std.all
##
## 1.207 0.907 0.907
## 1.644 0.784 0.784
## 1.015 0.697 0.697
##
## 1.343 0.669 0.669
## 1.102 0.809 0.809
##
## 0.959 0.416 0.416
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.39.) 0.186 0.041 4.519 0.000 0.105
## .sswk (.40.) 0.123 0.042 2.931 0.003 0.041
## .sspc (.41.) 0.143 0.041 3.489 0.000 0.063
## .ssei (.42.) 0.000 0.038 0.005 0.996 -0.074
## .ssar (.43.) 0.226 0.040 5.699 0.000 0.148
## .ssmk (.44.) 0.244 0.043 5.713 0.000 0.160
## .ssmc (.45.) 0.056 0.038 1.477 0.140 -0.018
## .ssao (.46.) 0.168 0.039 4.340 0.000 0.092
## .ssai (.47.) -0.113 0.033 -3.434 0.001 -0.177
## .sssi (.48.) -0.074 0.034 -2.189 0.029 -0.140
## .ssno (.49.) 0.218 0.041 5.386 0.000 0.139
## .sscs (.50.) 0.179 0.042 4.283 0.000 0.097
## verbal 0.053 0.069 0.775 0.438 -0.081
## math -0.110 0.067 -1.629 0.103 -0.241
## elctrnc 0.932 0.103 9.086 0.000 0.731
## speed -0.316 0.077 -4.122 0.000 -0.467
## ci.upper Std.lv Std.all
## 0.267 0.186 0.174
## 0.206 0.123 0.114
## 0.223 0.143 0.140
## 0.074 0.000 0.000
## 0.303 0.226 0.220
## 0.328 0.244 0.234
## 0.129 0.056 0.051
## 0.243 0.168 0.160
## -0.048 -0.113 -0.097
## -0.008 -0.074 -0.065
## 0.298 0.218 0.206
## 0.261 0.179 0.170
## 0.188 0.048 0.048
## 0.022 -0.105 -0.105
## 1.133 0.576 0.576
## -0.166 -0.286 -0.286
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.162 0.017 9.522 0.000 0.128
## .sswk 0.198 0.017 11.685 0.000 0.165
## .sspc 0.256 0.022 11.839 0.000 0.213
## .ssei 0.323 0.025 13.010 0.000 0.274
## .ssar 0.223 0.023 9.709 0.000 0.178
## .ssmk 0.154 0.013 11.569 0.000 0.128
## .ssmc 0.262 0.020 13.274 0.000 0.223
## .ssao 0.526 0.037 14.129 0.000 0.453
## .ssai 0.465 0.040 11.518 0.000 0.386
## .sssi 0.326 0.034 9.509 0.000 0.258
## .ssno 0.390 0.041 9.450 0.000 0.309
## .sscs 0.494 0.058 8.478 0.000 0.380
## verbal 1.213 0.103 11.795 0.000 1.012
## math 1.091 0.090 12.076 0.000 0.914
## electronic 2.617 0.289 9.066 0.000 2.051
## speed 1.228 0.152 8.059 0.000 0.929
## ci.upper Std.lv Std.all
## 0.195 0.162 0.141
## 0.232 0.198 0.169
## 0.298 0.256 0.247
## 0.371 0.323 0.244
## 0.269 0.223 0.212
## 0.180 0.154 0.142
## 0.301 0.262 0.224
## 0.599 0.526 0.479
## 0.544 0.465 0.341
## 0.393 0.326 0.250
## 0.470 0.390 0.346
## 0.608 0.494 0.444
## 1.415 1.000 1.000
## 1.268 1.000 1.000
## 3.182 1.000 1.000
## 1.526 1.000 1.000
lavTestScore(scalar, release = 17:28)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 136.51 12 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p39. == .p93. 40.762 1 0.000
## 2 .p40. == .p94. 13.198 1 0.000
## 3 .p41. == .p95. 82.729 1 0.000
## 4 .p42. == .p96. 0.000 1 0.986
## 5 .p43. == .p97. 27.151 1 0.000
## 6 .p44. == .p98. 0.121 1 0.728
## 7 .p45. == .p99. 2.657 1 0.103
## 8 .p46. == .p100. 0.042 1 0.837
## 9 .p47. == .p101. 0.193 1 0.660
## 10 .p48. == .p102. 0.356 1 0.551
## 11 .p49. == .p103. 17.545 1 0.000
## 12 .p50. == .p104. 20.363 1 0.000
scalar2<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 351.237 107.000 0.000 0.982 0.059 0.031 31963.627
## bic
## 32341.717
Mc(scalar2)
## [1] 0.9110576
summary(scalar2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 93 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 100
## Number of equality constraints 27
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 351.237 271.359
## Degrees of freedom 107 107
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.294
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 165.151 127.593
## 0 186.086 143.766
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.901 0.029 31.348 0.000 0.844
## sswk (.p2.) 0.895 0.031 28.929 0.000 0.834
## sspc (.p3.) 0.365 0.056 6.569 0.000 0.256
## ssei (.p4.) 0.509 0.041 12.270 0.000 0.427
## math =~
## ssar (.p5.) 0.877 0.028 30.877 0.000 0.821
## sspc (.p6.) 0.483 0.058 8.285 0.000 0.368
## ssmk (.p7.) 0.688 0.050 13.809 0.000 0.590
## ssmc (.p8.) 0.513 0.028 18.327 0.000 0.458
## ssao (.p9.) 0.726 0.027 26.723 0.000 0.672
## electronic =~
## ssai (.10.) 0.587 0.027 21.472 0.000 0.534
## sssi (.11.) 0.612 0.030 20.695 0.000 0.554
## ssmc (.12.) 0.311 0.024 13.043 0.000 0.265
## ssei (.13.) 0.308 0.032 9.500 0.000 0.244
## speed =~
## ssno (.14.) 0.771 0.036 21.379 0.000 0.700
## sscs (.15.) 0.711 0.033 21.220 0.000 0.645
## ssmk (.16.) 0.261 0.048 5.402 0.000 0.166
## ci.upper Std.lv Std.all
##
## 0.957 0.901 0.918
## 0.955 0.895 0.905
## 0.473 0.365 0.381
## 0.590 0.509 0.543
##
## 0.933 0.877 0.912
## 0.597 0.483 0.505
## 0.785 0.688 0.684
## 0.568 0.513 0.552
## 0.779 0.726 0.749
##
## 0.641 0.587 0.717
## 0.670 0.612 0.742
## 0.358 0.311 0.335
## 0.371 0.308 0.328
##
## 0.842 0.771 0.789
## 0.776 0.711 0.718
## 0.355 0.261 0.259
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.896 0.016 56.215 0.000 0.864
## electronic 0.845 0.030 28.564 0.000 0.787
## speed 0.738 0.038 19.193 0.000 0.663
## math ~~
## electronic 0.787 0.033 23.862 0.000 0.722
## speed 0.803 0.041 19.634 0.000 0.723
## electronic ~~
## speed 0.558 0.058 9.586 0.000 0.444
## ci.upper Std.lv Std.all
##
## 0.927 0.896 0.896
## 0.903 0.845 0.845
## 0.814 0.738 0.738
##
## 0.851 0.787 0.787
## 0.884 0.803 0.803
##
## 0.672 0.558 0.558
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.39.) 0.175 0.041 4.292 0.000 0.095
## .sswk (.40.) 0.112 0.042 2.697 0.007 0.031
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssei (.42.) -0.002 0.038 -0.053 0.958 -0.076
## .ssar (.43.) 0.201 0.040 5.035 0.000 0.123
## .ssmk (.44.) 0.225 0.043 5.256 0.000 0.141
## .ssmc (.45.) 0.046 0.038 1.227 0.220 -0.028
## .ssao (.46.) 0.147 0.039 3.788 0.000 0.071
## .ssai (.47.) -0.109 0.033 -3.325 0.001 -0.174
## .sssi (.48.) -0.069 0.034 -2.057 0.040 -0.135
## .ssno (.49.) 0.226 0.041 5.575 0.000 0.147
## .sscs (.50.) 0.187 0.042 4.479 0.000 0.105
## ci.upper Std.lv Std.all
## 0.255 0.175 0.178
## 0.194 0.112 0.114
## 0.333 0.253 0.264
## 0.072 -0.002 -0.002
## 0.280 0.201 0.209
## 0.309 0.225 0.224
## 0.120 0.046 0.050
## 0.222 0.147 0.151
## -0.045 -0.109 -0.134
## -0.003 -0.069 -0.084
## 0.305 0.226 0.231
## 0.269 0.187 0.189
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.152 0.015 10.189 0.000 0.123
## .sswk 0.177 0.016 10.952 0.000 0.146
## .sspc 0.234 0.021 10.921 0.000 0.192
## .ssei 0.260 0.022 11.645 0.000 0.216
## .ssar 0.156 0.015 10.335 0.000 0.126
## .ssmk 0.182 0.016 11.278 0.000 0.150
## .ssmc 0.252 0.019 13.602 0.000 0.215
## .ssao 0.411 0.028 14.930 0.000 0.357
## .ssai 0.326 0.026 12.329 0.000 0.275
## .sssi 0.305 0.028 11.039 0.000 0.251
## .ssno 0.360 0.038 9.480 0.000 0.285
## .sscs 0.474 0.053 8.916 0.000 0.370
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.181 0.152 0.158
## 0.209 0.177 0.181
## 0.276 0.234 0.255
## 0.304 0.260 0.296
## 0.186 0.156 0.169
## 0.213 0.182 0.180
## 0.288 0.252 0.291
## 0.465 0.411 0.438
## 0.378 0.326 0.486
## 0.360 0.305 0.449
## 0.434 0.360 0.377
## 0.578 0.474 0.484
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.901 0.029 31.348 0.000 0.844
## sswk (.p2.) 0.895 0.031 28.929 0.000 0.834
## sspc (.p3.) 0.365 0.056 6.569 0.000 0.256
## ssei (.p4.) 0.509 0.041 12.270 0.000 0.427
## math =~
## ssar (.p5.) 0.877 0.028 30.877 0.000 0.821
## sspc (.p6.) 0.483 0.058 8.285 0.000 0.368
## ssmk (.p7.) 0.688 0.050 13.809 0.000 0.590
## ssmc (.p8.) 0.513 0.028 18.327 0.000 0.458
## ssao (.p9.) 0.726 0.027 26.723 0.000 0.672
## electronic =~
## ssai (.10.) 0.587 0.027 21.472 0.000 0.534
## sssi (.11.) 0.612 0.030 20.695 0.000 0.554
## ssmc (.12.) 0.311 0.024 13.043 0.000 0.265
## ssei (.13.) 0.308 0.032 9.500 0.000 0.244
## speed =~
## ssno (.14.) 0.771 0.036 21.379 0.000 0.700
## sscs (.15.) 0.711 0.033 21.220 0.000 0.645
## ssmk (.16.) 0.261 0.048 5.402 0.000 0.166
## ci.upper Std.lv Std.all
##
## 0.957 0.991 0.927
## 0.955 0.985 0.910
## 0.473 0.401 0.397
## 0.590 0.560 0.487
##
## 0.933 0.917 0.892
## 0.597 0.505 0.499
## 0.785 0.719 0.690
## 0.568 0.537 0.498
## 0.779 0.759 0.723
##
## 0.641 0.952 0.813
## 0.670 0.992 0.868
## 0.358 0.505 0.468
## 0.371 0.498 0.434
##
## 0.842 0.851 0.804
## 0.776 0.784 0.744
## 0.355 0.288 0.276
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 1.041 0.083 12.530 0.000 0.878
## electronic 1.390 0.126 11.040 0.000 1.144
## speed 0.855 0.083 10.265 0.000 0.692
## math ~~
## electronic 1.127 0.108 10.386 0.000 0.914
## speed 0.932 0.084 11.140 0.000 0.768
## electronic ~~
## speed 0.747 0.109 6.853 0.000 0.534
## ci.upper Std.lv Std.all
##
## 1.204 0.904 0.904
## 1.637 0.779 0.779
## 1.019 0.704 0.704
##
## 1.339 0.665 0.665
## 1.096 0.807 0.807
##
## 0.961 0.418 0.418
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.39.) 0.175 0.041 4.292 0.000 0.095
## .sswk (.40.) 0.112 0.042 2.697 0.007 0.031
## .sspc -0.028 0.044 -0.634 0.526 -0.113
## .ssei (.42.) -0.002 0.038 -0.053 0.958 -0.076
## .ssar (.43.) 0.201 0.040 5.035 0.000 0.123
## .ssmk (.44.) 0.225 0.043 5.256 0.000 0.141
## .ssmc (.45.) 0.046 0.038 1.227 0.220 -0.028
## .ssao (.46.) 0.147 0.039 3.788 0.000 0.071
## .ssai (.47.) -0.109 0.033 -3.325 0.001 -0.174
## .sssi (.48.) -0.069 0.034 -2.057 0.040 -0.135
## .ssno (.49.) 0.226 0.041 5.575 0.000 0.147
## .sscs (.50.) 0.187 0.042 4.479 0.000 0.105
## verbal 0.079 0.067 1.172 0.241 -0.053
## math -0.043 0.066 -0.645 0.519 -0.173
## elctrnc 0.916 0.102 8.962 0.000 0.715
## speed -0.339 0.077 -4.400 0.000 -0.490
## ci.upper Std.lv Std.all
## 0.255 0.175 0.164
## 0.194 0.112 0.104
## 0.058 -0.028 -0.027
## 0.072 -0.002 -0.002
## 0.280 0.201 0.196
## 0.309 0.225 0.216
## 0.120 0.046 0.043
## 0.222 0.147 0.140
## -0.045 -0.109 -0.094
## -0.003 -0.069 -0.061
## 0.305 0.226 0.214
## 0.269 0.187 0.177
## 0.211 0.072 0.072
## 0.087 -0.041 -0.041
## 1.116 0.565 0.565
## -0.188 -0.307 -0.307
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.161 0.017 9.742 0.000 0.129
## .sswk 0.200 0.017 11.997 0.000 0.167
## .sspc 0.241 0.019 12.374 0.000 0.203
## .ssei 0.323 0.025 12.991 0.000 0.274
## .ssar 0.216 0.022 9.701 0.000 0.172
## .ssmk 0.152 0.013 11.514 0.000 0.126
## .ssmc 0.261 0.020 13.331 0.000 0.223
## .ssao 0.526 0.038 14.009 0.000 0.453
## .ssai 0.463 0.040 11.477 0.000 0.384
## .sssi 0.323 0.034 9.402 0.000 0.255
## .ssno 0.397 0.042 9.512 0.000 0.315
## .sscs 0.495 0.058 8.541 0.000 0.381
## verbal 1.212 0.102 11.883 0.000 1.012
## math 1.094 0.091 12.000 0.000 0.915
## electronic 2.626 0.290 9.051 0.000 2.057
## speed 1.217 0.151 8.071 0.000 0.922
## ci.upper Std.lv Std.all
## 0.194 0.161 0.141
## 0.233 0.200 0.171
## 0.279 0.241 0.236
## 0.372 0.323 0.245
## 0.259 0.216 0.204
## 0.178 0.152 0.140
## 0.300 0.261 0.224
## 0.600 0.526 0.478
## 0.542 0.463 0.338
## 0.390 0.323 0.247
## 0.478 0.397 0.354
## 0.609 0.495 0.446
## 1.412 1.000 1.000
## 1.272 1.000 1.000
## 3.195 1.000 1.000
## 1.513 1.000 1.000
lavTestScore(scalar2, release = 17:27, standardized=T, epc=T)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 53.244 11 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p39. == .p93. 24.582 1 0.000
## 2 .p40. == .p94. 24.024 1 0.000
## 3 .p42. == .p96. 0.056 1 0.813
## 4 .p43. == .p97. 5.091 1 0.024
## 5 .p44. == .p98. 2.931 1 0.087
## 6 .p45. == .p99. 0.452 1 0.501
## 7 .p46. == .p100. 2.519 1 0.113
## 8 .p47. == .p101. 0.013 1 0.908
## 9 .p48. == .p102. 0.030 1 0.863
## 10 .p49. == .p103. 23.442 1 0.000
## 11 .p50. == .p104. 15.951 1 0.000
##
## $epc
##
## expected parameter changes (epc) and expected parameter values (epv):
##
## lhs op rhs block group free label plabel est epc
## 1 verbal =~ ssgs 1 1 1 .p1. .p1. 0.901 -0.001
## 2 verbal =~ sswk 1 1 2 .p2. .p2. 0.895 0.002
## 3 verbal =~ sspc 1 1 3 .p3. .p3. 0.365 0.000
## 4 verbal =~ ssei 1 1 4 .p4. .p4. 0.509 -0.002
## 5 math =~ ssar 1 1 5 .p5. .p5. 0.877 0.000
## 6 math =~ sspc 1 1 6 .p6. .p6. 0.483 0.000
## 7 math =~ ssmk 1 1 7 .p7. .p7. 0.688 0.017
## 8 math =~ ssmc 1 1 8 .p8. .p8. 0.513 0.006
## 9 math =~ ssao 1 1 9 .p9. .p9. 0.726 -0.001
## 10 electronic =~ ssai 1 1 10 .p10. .p10. 0.587 0.000
## 11 electronic =~ sssi 1 1 11 .p11. .p11. 0.612 0.000
## 12 electronic =~ ssmc 1 1 12 .p12. .p12. 0.311 -0.006
## 13 electronic =~ ssei 1 1 13 .p13. .p13. 0.308 0.002
## 14 speed =~ ssno 1 1 14 .p14. .p14. 0.771 0.014
## 15 speed =~ sscs 1 1 15 .p15. .p15. 0.711 -0.012
## 16 speed =~ ssmk 1 1 16 .p16. .p16. 0.261 -0.019
## 17 ssgs ~~ ssgs 1 1 17 .p17. 0.152 0.000
## 18 sswk ~~ sswk 1 1 18 .p18. 0.177 0.000
## 19 sspc ~~ sspc 1 1 19 .p19. 0.234 0.000
## 20 ssei ~~ ssei 1 1 20 .p20. 0.260 0.000
## 21 ssar ~~ ssar 1 1 21 .p21. 0.156 0.001
## 22 ssmk ~~ ssmk 1 1 22 .p22. 0.182 0.001
## 23 ssmc ~~ ssmc 1 1 23 .p23. 0.252 0.000
## 24 ssao ~~ ssao 1 1 24 .p24. 0.411 0.000
## 25 ssai ~~ ssai 1 1 25 .p25. 0.326 0.000
## 26 sssi ~~ sssi 1 1 26 .p26. 0.305 0.000
## 27 ssno ~~ ssno 1 1 27 .p27. 0.360 -0.009
## 28 sscs ~~ sscs 1 1 28 .p28. 0.474 0.004
## 29 verbal ~~ verbal 1 1 0 .p29. 1.000 NA
## 30 math ~~ math 1 1 0 .p30. 1.000 NA
## 31 electronic ~~ electronic 1 1 0 .p31. 1.000 NA
## 32 speed ~~ speed 1 1 0 .p32. 1.000 NA
## 33 verbal ~~ math 1 1 29 .p33. 0.896 0.000
## 34 verbal ~~ electronic 1 1 30 .p34. 0.845 -0.001
## 35 verbal ~~ speed 1 1 31 .p35. 0.738 -0.003
## 36 math ~~ electronic 1 1 32 .p36. 0.787 -0.001
## 37 math ~~ speed 1 1 33 .p37. 0.803 -0.002
## 38 electronic ~~ speed 1 1 34 .p38. 0.558 -0.004
## 39 ssgs ~1 1 1 35 .p39. .p39. 0.175 -0.036
## 40 sswk ~1 1 1 36 .p40. .p40. 0.112 0.042
## 41 sspc ~1 1 1 37 .p41. 0.253 0.000
## 42 ssei ~1 1 1 38 .p42. .p42. -0.002 0.002
## 43 ssar ~1 1 1 39 .p43. .p43. 0.201 -0.016
## 44 ssmk ~1 1 1 40 .p44. .p44. 0.225 0.015
## 45 ssmc ~1 1 1 41 .p45. .p45. 0.046 -0.007
## 46 ssao ~1 1 1 42 .p46. .p46. 0.147 0.024
## 47 ssai ~1 1 1 43 .p47. .p47. -0.109 0.001
## 48 sssi ~1 1 1 44 .p48. .p48. -0.069 0.002
## 49 ssno ~1 1 1 45 .p49. .p49. 0.226 -0.051
## 50 sscs ~1 1 1 46 .p50. .p50. 0.187 0.059
## 51 verbal ~1 1 1 0 .p51. 0.000 NA
## 52 math ~1 1 1 0 .p52. 0.000 NA
## 53 electronic ~1 1 1 0 .p53. 0.000 NA
## 54 speed ~1 1 1 0 .p54. 0.000 NA
## 55 verbal =~ ssgs 2 2 47 .p1. .p55. 0.901 -0.001
## 56 verbal =~ sswk 2 2 48 .p2. .p56. 0.895 0.002
## 57 verbal =~ sspc 2 2 49 .p3. .p57. 0.365 0.000
## 58 verbal =~ ssei 2 2 50 .p4. .p58. 0.509 -0.002
## 59 math =~ ssar 2 2 51 .p5. .p59. 0.877 0.000
## 60 math =~ sspc 2 2 52 .p6. .p60. 0.483 0.000
## 61 math =~ ssmk 2 2 53 .p7. .p61. 0.688 0.017
## 62 math =~ ssmc 2 2 54 .p8. .p62. 0.513 0.006
## 63 math =~ ssao 2 2 55 .p9. .p63. 0.726 -0.001
## 64 electronic =~ ssai 2 2 56 .p10. .p64. 0.587 0.000
## 65 electronic =~ sssi 2 2 57 .p11. .p65. 0.612 0.000
## 66 electronic =~ ssmc 2 2 58 .p12. .p66. 0.311 -0.006
## 67 electronic =~ ssei 2 2 59 .p13. .p67. 0.308 0.002
## 68 speed =~ ssno 2 2 60 .p14. .p68. 0.771 0.014
## 69 speed =~ sscs 2 2 61 .p15. .p69. 0.711 -0.012
## 70 speed =~ ssmk 2 2 62 .p16. .p70. 0.261 -0.019
## 71 ssgs ~~ ssgs 2 2 63 .p71. 0.161 0.000
## epv sepc.lv sepc.all sepc.nox
## 1 0.899 -0.001 -0.002 -0.002
## 2 0.897 0.002 0.002 0.002
## 3 0.364 0.000 0.000 0.000
## 4 0.506 -0.002 -0.002 -0.002
## 5 0.877 0.000 0.000 0.000
## 6 0.483 0.000 0.000 0.000
## 7 0.704 0.017 0.016 0.016
## 8 0.519 0.006 0.006 0.006
## 9 0.725 -0.001 -0.001 -0.001
## 10 0.588 0.000 0.000 0.000
## 11 0.613 0.000 0.001 0.001
## 12 0.306 -0.006 -0.006 -0.006
## 13 0.310 0.002 0.002 0.002
## 14 0.785 0.014 0.014 0.014
## 15 0.699 -0.012 -0.012 -0.012
## 16 0.241 -0.019 -0.019 -0.019
## 17 0.153 0.152 0.158 0.158
## 18 0.177 -0.177 -0.181 -0.181
## 19 0.234 0.234 0.255 0.255
## 20 0.260 -0.260 -0.296 -0.296
## 21 0.157 0.156 0.169 0.169
## 22 0.183 0.182 0.180 0.180
## 23 0.252 0.252 0.291 0.291
## 24 0.411 0.411 0.438 0.438
## 25 0.326 -0.326 -0.486 -0.486
## 26 0.305 -0.305 -0.449 -0.449
## 27 0.350 -0.360 -0.377 -0.377
## 28 0.478 0.474 0.484 0.484
## 29 NA NA NA NA
## 30 NA NA NA NA
## 31 NA NA NA NA
## 32 NA NA NA NA
## 33 0.896 0.000 0.000 0.000
## 34 0.845 -0.001 -0.001 -0.001
## 35 0.735 -0.003 -0.003 -0.003
## 36 0.786 -0.001 -0.001 -0.001
## 37 0.802 -0.002 -0.002 -0.002
## 38 0.554 -0.004 -0.004 -0.004
## 39 0.139 -0.036 -0.037 -0.037
## 40 0.154 0.042 0.042 0.042
## 41 0.253 0.000 0.000 0.000
## 42 0.000 0.002 0.002 0.002
## 43 0.186 -0.016 -0.016 -0.016
## 44 0.241 0.015 0.015 0.015
## 45 0.039 -0.007 -0.007 -0.007
## 46 0.171 0.024 0.025 0.025
## 47 -0.108 0.001 0.002 0.002
## 48 -0.068 0.002 0.002 0.002
## 49 0.175 -0.051 -0.053 -0.053
## 50 0.245 0.059 0.059 0.059
## 51 NA NA NA NA
## 52 NA NA NA NA
## 53 NA NA NA NA
## 54 NA NA NA NA
## 55 0.899 -0.002 -0.002 -0.002
## 56 0.897 0.002 0.002 0.002
## 57 0.364 0.000 0.000 0.000
## 58 0.506 -0.002 -0.002 -0.002
## 59 0.877 0.000 0.000 0.000
## 60 0.483 0.000 0.000 0.000
## 61 0.704 0.017 0.017 0.017
## 62 0.519 0.006 0.005 0.005
## 63 0.725 -0.001 -0.001 -0.001
## 64 0.588 0.000 0.000 0.000
## 65 0.613 0.001 0.001 0.001
## 66 0.306 -0.009 -0.009 -0.009
## 67 0.310 0.004 0.003 0.003
## 68 0.785 0.015 0.015 0.015
## 69 0.699 -0.013 -0.013 -0.013
## 70 0.241 -0.021 -0.020 -0.020
## 71 0.162 0.161 0.141 0.141
## [ reached 'max' / getOption("max.print") -- omitted 37 rows ]
strict<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("sspc~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 392.238 119.000 0.000 0.979 0.059 0.033 31980.629
## bic
## 32296.566
Mc(strict)
## [1] 0.9010362
cf.cov<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1"))
fitMeasures(cf.cov, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 397.168 113.000 0.000 0.979 0.062 0.083 31997.558
## bic
## 32344.572
Mc(cf.cov)
## [1] 0.8972881
summary(cf.cov, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 65 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 100
## Number of equality constraints 33
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 397.168 306.864
## Degrees of freedom 113 113
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.294
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 182.950 141.353
## 0 214.217 165.511
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.955 0.024 40.179 0.000 0.909
## sswk (.p2.) 0.950 0.025 38.056 0.000 0.901
## sspc (.p3.) 0.396 0.058 6.842 0.000 0.283
## ssei (.p4.) 0.534 0.042 12.555 0.000 0.450
## math =~
## ssar (.p5.) 0.899 0.024 37.782 0.000 0.853
## sspc (.p6.) 0.485 0.058 8.378 0.000 0.372
## ssmk (.p7.) 0.693 0.049 14.242 0.000 0.597
## ssmc (.p8.) 0.528 0.027 19.410 0.000 0.475
## ssao (.p9.) 0.744 0.024 31.626 0.000 0.697
## electronic =~
## ssai (.10.) 0.656 0.027 23.867 0.000 0.602
## sssi (.11.) 0.691 0.029 24.213 0.000 0.635
## ssmc (.12.) 0.348 0.026 13.544 0.000 0.298
## ssei (.13.) 0.350 0.036 9.784 0.000 0.280
## speed =~
## ssno (.14.) 0.792 0.035 22.649 0.000 0.723
## sscs (.15.) 0.730 0.033 21.849 0.000 0.664
## ssmk (.16.) 0.280 0.046 6.053 0.000 0.189
## ci.upper Std.lv Std.all
##
## 1.002 0.955 0.926
## 0.999 0.950 0.914
## 0.509 0.396 0.402
## 0.617 0.534 0.534
##
## 0.946 0.899 0.916
## 0.599 0.485 0.493
## 0.788 0.693 0.674
## 0.581 0.528 0.543
## 0.790 0.744 0.757
##
## 0.710 0.656 0.752
## 0.746 0.691 0.783
## 0.398 0.348 0.358
## 0.420 0.350 0.350
##
## 0.860 0.792 0.796
## 0.795 0.730 0.725
## 0.371 0.280 0.273
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.33.) 0.892 0.015 59.475 0.000 0.862
## elctrnc (.34.) 0.883 0.022 40.310 0.000 0.840
## speed (.35.) 0.732 0.034 21.449 0.000 0.665
## math ~~
## elctrnc (.36.) 0.794 0.027 28.924 0.000 0.740
## speed (.37.) 0.821 0.034 24.270 0.000 0.755
## electronic ~~
## speed (.38.) 0.554 0.050 11.191 0.000 0.457
## ci.upper Std.lv Std.all
##
## 0.921 0.892 0.892
## 0.926 0.883 0.883
## 0.798 0.732 0.732
##
## 0.848 0.794 0.794
## 0.888 0.821 0.821
##
## 0.651 0.554 0.554
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.39.) 0.175 0.041 4.294 0.000 0.095
## .sswk (.40.) 0.112 0.042 2.696 0.007 0.031
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssei (.42.) -0.003 0.038 -0.079 0.937 -0.077
## .ssar (.43.) 0.200 0.040 5.017 0.000 0.122
## .ssmk (.44.) 0.226 0.043 5.285 0.000 0.142
## .ssmc (.45.) 0.046 0.038 1.233 0.218 -0.027
## .ssao (.46.) 0.146 0.039 3.769 0.000 0.070
## .ssai (.47.) -0.108 0.033 -3.282 0.001 -0.172
## .sssi (.48.) -0.070 0.034 -2.084 0.037 -0.136
## .ssno (.49.) 0.226 0.040 5.586 0.000 0.147
## .sscs (.50.) 0.186 0.042 4.472 0.000 0.105
## ci.upper Std.lv Std.all
## 0.255 0.175 0.170
## 0.194 0.112 0.108
## 0.333 0.253 0.256
## 0.071 -0.003 -0.003
## 0.279 0.200 0.204
## 0.310 0.226 0.220
## 0.120 0.046 0.048
## 0.222 0.146 0.149
## -0.043 -0.108 -0.124
## -0.004 -0.070 -0.080
## 0.305 0.226 0.227
## 0.268 0.186 0.185
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.151 0.015 10.249 0.000 0.122
## .sswk 0.178 0.016 10.988 0.000 0.147
## .sspc 0.235 0.021 10.985 0.000 0.193
## .ssei 0.261 0.022 11.689 0.000 0.217
## .ssar 0.155 0.015 10.356 0.000 0.126
## .ssmk 0.180 0.016 11.091 0.000 0.148
## .ssmc 0.252 0.018 13.666 0.000 0.216
## .ssao 0.411 0.028 14.902 0.000 0.357
## .ssai 0.331 0.026 12.537 0.000 0.279
## .sssi 0.301 0.027 11.061 0.000 0.248
## .ssno 0.362 0.038 9.447 0.000 0.287
## .sscs 0.480 0.053 9.008 0.000 0.375
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.180 0.151 0.142
## 0.210 0.178 0.165
## 0.277 0.235 0.242
## 0.305 0.261 0.262
## 0.185 0.155 0.161
## 0.211 0.180 0.170
## 0.288 0.252 0.267
## 0.465 0.411 0.426
## 0.383 0.331 0.435
## 0.354 0.301 0.387
## 0.438 0.362 0.366
## 0.584 0.480 0.474
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.955 0.024 40.179 0.000 0.909
## sswk (.p2.) 0.950 0.025 38.056 0.000 0.901
## sspc (.p3.) 0.396 0.058 6.842 0.000 0.283
## ssei (.p4.) 0.534 0.042 12.555 0.000 0.450
## math =~
## ssar (.p5.) 0.899 0.024 37.782 0.000 0.853
## sspc (.p6.) 0.485 0.058 8.378 0.000 0.372
## ssmk (.p7.) 0.693 0.049 14.242 0.000 0.597
## ssmc (.p8.) 0.528 0.027 19.410 0.000 0.475
## ssao (.p9.) 0.744 0.024 31.626 0.000 0.697
## electronic =~
## ssai (.10.) 0.656 0.027 23.867 0.000 0.602
## sssi (.11.) 0.691 0.029 24.213 0.000 0.635
## ssmc (.12.) 0.348 0.026 13.544 0.000 0.298
## ssei (.13.) 0.350 0.036 9.784 0.000 0.280
## speed =~
## ssno (.14.) 0.792 0.035 22.649 0.000 0.723
## sscs (.15.) 0.730 0.033 21.849 0.000 0.664
## ssmk (.16.) 0.280 0.046 6.053 0.000 0.189
## ci.upper Std.lv Std.all
##
## 1.002 0.939 0.919
## 0.999 0.933 0.903
## 0.509 0.389 0.396
## 0.617 0.524 0.492
##
## 0.946 0.899 0.889
## 0.599 0.485 0.493
## 0.788 0.692 0.680
## 0.581 0.528 0.519
## 0.790 0.743 0.716
##
## 0.710 0.852 0.780
## 0.746 0.897 0.848
## 0.398 0.452 0.444
## 0.420 0.454 0.426
##
## 0.860 0.827 0.795
## 0.795 0.762 0.736
## 0.371 0.293 0.287
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.33.) 0.892 0.015 59.475 0.000 0.862
## elctrnc (.34.) 0.883 0.022 40.310 0.000 0.840
## speed (.35.) 0.732 0.034 21.449 0.000 0.665
## math ~~
## elctrnc (.36.) 0.794 0.027 28.924 0.000 0.740
## speed (.37.) 0.821 0.034 24.270 0.000 0.755
## electronic ~~
## speed (.38.) 0.554 0.050 11.191 0.000 0.457
## ci.upper Std.lv Std.all
##
## 0.921 0.907 0.907
## 0.926 0.691 0.691
## 0.798 0.712 0.712
##
## 0.848 0.611 0.611
## 0.888 0.786 0.786
##
## 0.651 0.408 0.408
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.39.) 0.175 0.041 4.294 0.000 0.095
## .sswk (.40.) 0.112 0.042 2.696 0.007 0.031
## .sspc -0.030 0.044 -0.678 0.497 -0.115
## .ssei (.42.) -0.003 0.038 -0.079 0.937 -0.077
## .ssar (.43.) 0.200 0.040 5.017 0.000 0.122
## .ssmk (.44.) 0.226 0.043 5.285 0.000 0.142
## .ssmc (.45.) 0.046 0.038 1.233 0.218 -0.027
## .ssao (.46.) 0.146 0.039 3.769 0.000 0.070
## .ssai (.47.) -0.108 0.033 -3.282 0.001 -0.172
## .sssi (.48.) -0.070 0.034 -2.084 0.037 -0.136
## .ssno (.49.) 0.226 0.040 5.586 0.000 0.147
## .sscs (.50.) 0.186 0.042 4.472 0.000 0.105
## verbal 0.074 0.063 1.167 0.243 -0.050
## math -0.040 0.065 -0.611 0.541 -0.166
## elctrnc 0.815 0.089 9.148 0.000 0.640
## speed -0.330 0.076 -4.349 0.000 -0.478
## ci.upper Std.lv Std.all
## 0.255 0.175 0.171
## 0.194 0.112 0.109
## 0.056 -0.030 -0.030
## 0.071 -0.003 -0.003
## 0.279 0.200 0.198
## 0.310 0.226 0.222
## 0.120 0.046 0.046
## 0.222 0.146 0.140
## -0.043 -0.108 -0.099
## -0.004 -0.070 -0.066
## 0.305 0.226 0.217
## 0.268 0.186 0.180
## 0.198 0.075 0.075
## 0.087 -0.040 -0.040
## 0.989 0.627 0.627
## -0.181 -0.316 -0.316
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.163 0.017 9.681 0.000 0.130
## .sswk 0.198 0.017 11.888 0.000 0.166
## .sspc 0.239 0.019 12.336 0.000 0.201
## .ssei 0.326 0.025 12.993 0.000 0.277
## .ssar 0.214 0.022 9.696 0.000 0.171
## .ssmk 0.154 0.013 11.583 0.000 0.128
## .ssmc 0.260 0.020 13.341 0.000 0.222
## .ssao 0.525 0.038 13.945 0.000 0.451
## .ssai 0.466 0.041 11.308 0.000 0.386
## .sssi 0.315 0.035 9.120 0.000 0.247
## .ssno 0.397 0.042 9.430 0.000 0.315
## .sscs 0.492 0.058 8.545 0.000 0.380
## verbal 0.966 0.030 32.700 0.000 0.908
## math 1.000 0.033 30.384 0.000 0.935
## electronic 1.688 0.133 12.662 0.000 1.427
## speed 1.091 0.096 11.405 0.000 0.904
## ci.upper Std.lv Std.all
## 0.196 0.163 0.156
## 0.231 0.198 0.185
## 0.277 0.239 0.247
## 0.376 0.326 0.287
## 0.257 0.214 0.209
## 0.180 0.154 0.148
## 0.299 0.260 0.252
## 0.599 0.525 0.487
## 0.547 0.466 0.391
## 0.383 0.315 0.281
## 0.480 0.397 0.367
## 0.605 0.492 0.459
## 1.024 1.000 1.000
## 1.064 1.000 1.000
## 1.949 1.000 1.000
## 1.279 1.000 1.000
cf.vcov<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances", "lv.variances"), group.partial=c("sspc~1"))
fitMeasures(cf.vcov, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 486.062 117.000 0.000 0.972 0.069 0.105 32078.452
## bic
## 32404.749
Mc(cf.vcov)
## [1] 0.8687013
summary(cf.vcov, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 55 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 33
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 486.062 372.838
## Degrees of freedom 117 117
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.304
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 241.853 185.515
## 0 244.209 187.323
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.947 0.022 42.310 0.000 0.903
## sswk (.p2.) 0.941 0.023 40.411 0.000 0.895
## sspc (.p3.) 0.387 0.057 6.771 0.000 0.275
## ssei (.p4.) 0.551 0.041 13.473 0.000 0.471
## math =~
## ssar (.p5.) 0.899 0.023 39.169 0.000 0.854
## sspc (.p6.) 0.490 0.058 8.481 0.000 0.376
## ssmk (.p7.) 0.697 0.048 14.472 0.000 0.602
## ssmc (.p8.) 0.520 0.028 18.751 0.000 0.466
## ssao (.p9.) 0.742 0.022 32.988 0.000 0.698
## electronic =~
## ssai (.10.) 0.768 0.028 27.258 0.000 0.712
## sssi (.11.) 0.819 0.027 30.413 0.000 0.766
## ssmc (.12.) 0.422 0.028 14.918 0.000 0.366
## ssei (.13.) 0.388 0.041 9.358 0.000 0.306
## speed =~
## ssno (.14.) 0.809 0.032 24.906 0.000 0.746
## sscs (.15.) 0.746 0.030 25.266 0.000 0.688
## ssmk (.16.) 0.282 0.048 5.879 0.000 0.188
## ci.upper Std.lv Std.all
##
## 0.991 0.947 0.925
## 0.986 0.941 0.913
## 0.499 0.387 0.395
## 0.631 0.551 0.535
##
## 0.945 0.899 0.917
## 0.603 0.490 0.499
## 0.791 0.697 0.676
## 0.574 0.520 0.518
## 0.786 0.742 0.755
##
## 0.823 0.768 0.808
## 0.871 0.819 0.846
## 0.477 0.422 0.421
## 0.469 0.388 0.376
##
## 0.873 0.809 0.807
## 0.804 0.746 0.735
## 0.376 0.282 0.274
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.33.) 0.899 0.010 85.874 0.000 0.879
## elctrnc (.34.) 0.797 0.019 41.627 0.000 0.759
## speed (.35.) 0.721 0.028 26.106 0.000 0.667
## math ~~
## elctrnc (.36.) 0.703 0.024 28.933 0.000 0.655
## speed (.37.) 0.806 0.026 30.989 0.000 0.755
## electronic ~~
## speed (.38.) 0.465 0.040 11.706 0.000 0.387
## ci.upper Std.lv Std.all
##
## 0.920 0.899 0.899
## 0.834 0.797 0.797
## 0.775 0.721 0.721
##
## 0.750 0.703 0.703
## 0.857 0.806 0.806
##
## 0.543 0.465 0.465
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.39.) 0.174 0.041 4.263 0.000 0.094
## .sswk (.40.) 0.112 0.042 2.675 0.007 0.030
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssei (.42.) 0.003 0.037 0.076 0.939 -0.071
## .ssar (.43.) 0.201 0.040 5.042 0.000 0.123
## .ssmk (.44.) 0.227 0.043 5.288 0.000 0.143
## .ssmc (.45.) 0.043 0.038 1.126 0.260 -0.032
## .ssao (.46.) 0.147 0.039 3.791 0.000 0.071
## .ssai (.47.) -0.107 0.033 -3.252 0.001 -0.171
## .sssi (.48.) -0.072 0.034 -2.127 0.033 -0.138
## .ssno (.49.) 0.224 0.041 5.534 0.000 0.145
## .sscs (.50.) 0.187 0.042 4.489 0.000 0.105
## ci.upper Std.lv Std.all
## 0.254 0.174 0.170
## 0.193 0.112 0.108
## 0.333 0.253 0.257
## 0.076 0.003 0.003
## 0.280 0.201 0.205
## 0.311 0.227 0.220
## 0.117 0.043 0.043
## 0.222 0.147 0.149
## -0.042 -0.107 -0.113
## -0.006 -0.072 -0.074
## 0.304 0.224 0.224
## 0.269 0.187 0.184
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.152 0.015 10.223 0.000 0.123
## .sswk 0.178 0.016 10.896 0.000 0.146
## .sspc 0.233 0.021 10.962 0.000 0.191
## .ssei 0.265 0.022 11.857 0.000 0.222
## .ssar 0.154 0.015 10.501 0.000 0.125
## .ssmk 0.180 0.016 11.115 0.000 0.148
## .ssmc 0.249 0.018 13.599 0.000 0.213
## .ssao 0.414 0.028 14.943 0.000 0.360
## .ssai 0.313 0.027 11.766 0.000 0.261
## .sssi 0.267 0.027 9.995 0.000 0.214
## .ssno 0.352 0.039 9.017 0.000 0.275
## .sscs 0.474 0.052 9.033 0.000 0.371
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.181 0.152 0.145
## 0.210 0.178 0.167
## 0.275 0.233 0.242
## 0.309 0.265 0.250
## 0.183 0.154 0.160
## 0.211 0.180 0.169
## 0.285 0.249 0.248
## 0.469 0.414 0.429
## 0.366 0.313 0.347
## 0.319 0.267 0.285
## 0.428 0.352 0.349
## 0.577 0.474 0.460
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.947 0.022 42.310 0.000 0.903
## sswk (.p2.) 0.941 0.023 40.411 0.000 0.895
## sspc (.p3.) 0.387 0.057 6.771 0.000 0.275
## ssei (.p4.) 0.551 0.041 13.473 0.000 0.471
## math =~
## ssar (.p5.) 0.899 0.023 39.169 0.000 0.854
## sspc (.p6.) 0.490 0.058 8.481 0.000 0.376
## ssmk (.p7.) 0.697 0.048 14.472 0.000 0.602
## ssmc (.p8.) 0.520 0.028 18.751 0.000 0.466
## ssao (.p9.) 0.742 0.022 32.988 0.000 0.698
## electronic =~
## ssai (.10.) 0.768 0.028 27.258 0.000 0.712
## sssi (.11.) 0.819 0.027 30.413 0.000 0.766
## ssmc (.12.) 0.422 0.028 14.918 0.000 0.366
## ssei (.13.) 0.388 0.041 9.358 0.000 0.306
## speed =~
## ssno (.14.) 0.809 0.032 24.906 0.000 0.746
## sscs (.15.) 0.746 0.030 25.266 0.000 0.688
## ssmk (.16.) 0.282 0.048 5.879 0.000 0.188
## ci.upper Std.lv Std.all
##
## 0.991 0.947 0.921
## 0.986 0.941 0.903
## 0.499 0.387 0.392
## 0.631 0.551 0.519
##
## 0.945 0.899 0.889
## 0.603 0.490 0.496
## 0.791 0.697 0.685
## 0.574 0.520 0.516
## 0.786 0.742 0.716
##
## 0.823 0.768 0.735
## 0.871 0.819 0.808
## 0.477 0.422 0.418
## 0.469 0.388 0.365
##
## 0.873 0.809 0.785
## 0.804 0.746 0.726
## 0.376 0.282 0.277
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.33.) 0.899 0.010 85.874 0.000 0.879
## elctrnc (.34.) 0.797 0.019 41.627 0.000 0.759
## speed (.35.) 0.721 0.028 26.106 0.000 0.667
## math ~~
## elctrnc (.36.) 0.703 0.024 28.933 0.000 0.655
## speed (.37.) 0.806 0.026 30.989 0.000 0.755
## electronic ~~
## speed (.38.) 0.465 0.040 11.706 0.000 0.387
## ci.upper Std.lv Std.all
##
## 0.920 0.899 0.899
## 0.834 0.797 0.797
## 0.775 0.721 0.721
##
## 0.750 0.703 0.703
## 0.857 0.806 0.806
##
## 0.543 0.465 0.465
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.39.) 0.174 0.041 4.263 0.000 0.094
## .sswk (.40.) 0.112 0.042 2.675 0.007 0.030
## .sspc -0.029 0.044 -0.661 0.508 -0.114
## .ssei (.42.) 0.003 0.037 0.076 0.939 -0.071
## .ssar (.43.) 0.201 0.040 5.042 0.000 0.123
## .ssmk (.44.) 0.227 0.043 5.288 0.000 0.143
## .ssmc (.45.) 0.043 0.038 1.126 0.260 -0.032
## .ssao (.46.) 0.147 0.039 3.791 0.000 0.071
## .ssai (.47.) -0.107 0.033 -3.252 0.001 -0.171
## .sssi (.48.) -0.072 0.034 -2.127 0.033 -0.138
## .ssno (.49.) 0.224 0.041 5.534 0.000 0.145
## .sscs (.50.) 0.187 0.042 4.489 0.000 0.105
## verbal 0.077 0.064 1.207 0.227 -0.048
## math -0.042 0.065 -0.651 0.515 -0.169
## elctrnc 0.692 0.069 9.979 0.000 0.556
## speed -0.322 0.075 -4.317 0.000 -0.468
## ci.upper Std.lv Std.all
## 0.254 0.174 0.169
## 0.193 0.112 0.107
## 0.057 -0.029 -0.029
## 0.076 0.003 0.003
## 0.280 0.201 0.199
## 0.311 0.227 0.223
## 0.117 0.043 0.042
## 0.222 0.147 0.141
## -0.042 -0.107 -0.102
## -0.006 -0.072 -0.071
## 0.304 0.224 0.218
## 0.269 0.187 0.182
## 0.202 0.077 0.077
## 0.085 -0.042 -0.042
## 0.828 0.692 0.692
## -0.176 -0.322 -0.322
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.161 0.016 10.000 0.000 0.130
## .sswk 0.201 0.017 12.110 0.000 0.169
## .sspc 0.244 0.020 12.378 0.000 0.205
## .ssei 0.333 0.026 12.789 0.000 0.282
## .ssar 0.214 0.022 9.881 0.000 0.172
## .ssmk 0.152 0.013 11.471 0.000 0.126
## .ssmc 0.261 0.020 13.210 0.000 0.222
## .ssao 0.525 0.038 13.960 0.000 0.451
## .ssai 0.501 0.043 11.569 0.000 0.416
## .sssi 0.357 0.037 9.752 0.000 0.285
## .ssno 0.408 0.044 9.296 0.000 0.322
## .sscs 0.500 0.058 8.619 0.000 0.386
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.193 0.161 0.153
## 0.234 0.201 0.185
## 0.283 0.244 0.250
## 0.384 0.333 0.295
## 0.257 0.214 0.209
## 0.178 0.152 0.147
## 0.299 0.261 0.256
## 0.598 0.525 0.488
## 0.586 0.501 0.460
## 0.429 0.357 0.348
## 0.494 0.408 0.384
## 0.614 0.500 0.473
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
cf.cov2<-cfa(cf.lv, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1"))
fitMeasures(cf.cov2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 399.089 115.000 0.000 0.979 0.061 0.082 31995.480
## bic
## 32332.135
Mc(cf.cov2)
## [1] 0.897315
summary(cf.cov2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 60 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 33
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 399.089 307.917
## Degrees of freedom 115 115
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.296
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 182.277 140.635
## 0 216.813 167.281
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.947 0.022 42.188 0.000 0.903
## sswk (.p2.) 0.941 0.023 40.486 0.000 0.895
## sspc (.p3.) 0.393 0.057 6.885 0.000 0.281
## ssei (.p4.) 0.529 0.042 12.555 0.000 0.447
## math =~
## ssar (.p5.) 0.899 0.023 39.168 0.000 0.854
## sspc (.p6.) 0.485 0.058 8.422 0.000 0.372
## ssmk (.p7.) 0.693 0.048 14.584 0.000 0.600
## ssmc (.p8.) 0.528 0.027 19.435 0.000 0.475
## ssao (.p9.) 0.743 0.022 33.109 0.000 0.699
## electronic =~
## ssai (.10.) 0.655 0.028 23.685 0.000 0.601
## sssi (.11.) 0.691 0.029 23.974 0.000 0.634
## ssmc (.12.) 0.347 0.026 13.448 0.000 0.297
## ssei (.13.) 0.349 0.036 9.822 0.000 0.280
## speed =~
## ssno (.14.) 0.792 0.035 22.662 0.000 0.724
## sscs (.15.) 0.730 0.033 21.854 0.000 0.665
## ssmk (.16.) 0.280 0.046 6.055 0.000 0.189
## ci.upper Std.lv Std.all
##
## 0.991 0.947 0.924
## 0.986 0.941 0.911
## 0.504 0.393 0.399
## 0.612 0.529 0.532
##
## 0.944 0.899 0.916
## 0.598 0.485 0.493
## 0.786 0.693 0.674
## 0.582 0.528 0.544
## 0.787 0.743 0.757
##
## 0.710 0.655 0.751
## 0.747 0.691 0.783
## 0.398 0.347 0.358
## 0.419 0.349 0.351
##
## 0.861 0.792 0.796
## 0.796 0.730 0.726
## 0.371 0.280 0.272
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.33.) 0.900 0.010 85.865 0.000 0.879
## elctrnc (.34.) 0.884 0.022 40.147 0.000 0.841
## speed (.35.) 0.738 0.034 21.988 0.000 0.672
## math ~~
## elctrnc (.36.) 0.794 0.027 29.691 0.000 0.742
## speed (.37.) 0.821 0.033 24.775 0.000 0.756
## electronic ~~
## speed (.38.) 0.553 0.050 11.177 0.000 0.456
## ci.upper Std.lv Std.all
##
## 0.920 0.900 0.900
## 0.927 0.884 0.884
## 0.803 0.738 0.738
##
## 0.847 0.794 0.794
## 0.886 0.821 0.821
##
## 0.650 0.553 0.553
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.39.) 0.175 0.041 4.304 0.000 0.096
## .sswk (.40.) 0.112 0.042 2.682 0.007 0.030
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssei (.42.) -0.003 0.038 -0.081 0.936 -0.077
## .ssar (.43.) 0.201 0.040 5.018 0.000 0.122
## .ssmk (.44.) 0.226 0.043 5.282 0.000 0.142
## .ssmc (.45.) 0.047 0.038 1.237 0.216 -0.027
## .ssao (.46.) 0.146 0.039 3.768 0.000 0.070
## .ssai (.47.) -0.108 0.033 -3.280 0.001 -0.172
## .sssi (.48.) -0.070 0.034 -2.088 0.037 -0.136
## .ssno (.49.) 0.226 0.040 5.586 0.000 0.147
## .sscs (.50.) 0.186 0.042 4.473 0.000 0.105
## ci.upper Std.lv Std.all
## 0.255 0.175 0.171
## 0.193 0.112 0.108
## 0.333 0.253 0.257
## 0.071 -0.003 -0.003
## 0.279 0.201 0.204
## 0.310 0.226 0.220
## 0.121 0.047 0.048
## 0.222 0.146 0.148
## -0.043 -0.108 -0.124
## -0.004 -0.070 -0.080
## 0.305 0.226 0.227
## 0.268 0.186 0.185
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.153 0.015 10.444 0.000 0.124
## .sswk 0.181 0.016 11.125 0.000 0.149
## .sspc 0.235 0.021 10.983 0.000 0.193
## .ssei 0.261 0.022 11.686 0.000 0.217
## .ssar 0.156 0.015 10.574 0.000 0.127
## .ssmk 0.180 0.016 11.148 0.000 0.148
## .ssmc 0.252 0.018 13.704 0.000 0.216
## .ssao 0.412 0.028 14.956 0.000 0.358
## .ssai 0.331 0.026 12.541 0.000 0.279
## .sssi 0.301 0.027 11.062 0.000 0.248
## .ssno 0.362 0.038 9.424 0.000 0.287
## .sscs 0.480 0.053 9.016 0.000 0.375
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.182 0.153 0.146
## 0.213 0.181 0.170
## 0.276 0.235 0.243
## 0.305 0.261 0.264
## 0.185 0.156 0.162
## 0.211 0.180 0.170
## 0.288 0.252 0.267
## 0.466 0.412 0.427
## 0.383 0.331 0.435
## 0.354 0.301 0.387
## 0.437 0.362 0.366
## 0.584 0.480 0.474
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.947 0.022 42.188 0.000 0.903
## sswk (.p2.) 0.941 0.023 40.486 0.000 0.895
## sspc (.p3.) 0.393 0.057 6.885 0.000 0.281
## ssei (.p4.) 0.529 0.042 12.555 0.000 0.447
## math =~
## ssar (.p5.) 0.899 0.023 39.168 0.000 0.854
## sspc (.p6.) 0.485 0.058 8.422 0.000 0.372
## ssmk (.p7.) 0.693 0.048 14.584 0.000 0.600
## ssmc (.p8.) 0.528 0.027 19.435 0.000 0.475
## ssao (.p9.) 0.743 0.022 33.109 0.000 0.699
## electronic =~
## ssai (.10.) 0.655 0.028 23.685 0.000 0.601
## sssi (.11.) 0.691 0.029 23.974 0.000 0.634
## ssmc (.12.) 0.347 0.026 13.448 0.000 0.297
## ssei (.13.) 0.349 0.036 9.822 0.000 0.280
## speed =~
## ssno (.14.) 0.792 0.035 22.662 0.000 0.724
## sscs (.15.) 0.730 0.033 21.854 0.000 0.665
## ssmk (.16.) 0.280 0.046 6.055 0.000 0.189
## ci.upper Std.lv Std.all
##
## 0.991 0.947 0.921
## 0.986 0.941 0.905
## 0.504 0.393 0.398
## 0.612 0.529 0.496
##
## 0.944 0.899 0.890
## 0.598 0.485 0.492
## 0.786 0.693 0.681
## 0.582 0.528 0.520
## 0.787 0.743 0.716
##
## 0.710 0.850 0.779
## 0.747 0.895 0.847
## 0.398 0.451 0.443
## 0.419 0.453 0.425
##
## 0.861 0.826 0.795
## 0.796 0.762 0.735
## 0.371 0.292 0.287
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.33.) 0.900 0.010 85.865 0.000 0.879
## elctrnc (.34.) 0.884 0.022 40.147 0.000 0.841
## speed (.35.) 0.738 0.034 21.988 0.000 0.672
## math ~~
## elctrnc (.36.) 0.794 0.027 29.691 0.000 0.742
## speed (.37.) 0.821 0.033 24.775 0.000 0.756
## electronic ~~
## speed (.38.) 0.553 0.050 11.177 0.000 0.456
## ci.upper Std.lv Std.all
##
## 0.920 0.900 0.900
## 0.927 0.682 0.682
## 0.803 0.707 0.707
##
## 0.847 0.613 0.613
## 0.886 0.787 0.787
##
## 0.650 0.409 0.409
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.39.) 0.175 0.041 4.304 0.000 0.096
## .sswk (.40.) 0.112 0.042 2.682 0.007 0.030
## .sspc -0.030 0.044 -0.681 0.496 -0.115
## .ssei (.42.) -0.003 0.038 -0.081 0.936 -0.077
## .ssar (.43.) 0.201 0.040 5.018 0.000 0.122
## .ssmk (.44.) 0.226 0.043 5.282 0.000 0.142
## .ssmc (.45.) 0.047 0.038 1.237 0.216 -0.027
## .ssao (.46.) 0.146 0.039 3.768 0.000 0.070
## .ssai (.47.) -0.108 0.033 -3.280 0.001 -0.172
## .sssi (.48.) -0.070 0.034 -2.088 0.037 -0.136
## .ssno (.49.) 0.226 0.040 5.586 0.000 0.147
## .sscs (.50.) 0.186 0.042 4.473 0.000 0.105
## verbal 0.075 0.064 1.169 0.242 -0.050
## math -0.039 0.065 -0.611 0.541 -0.166
## elctrnc 0.815 0.089 9.124 0.000 0.640
## speed -0.330 0.076 -4.349 0.000 -0.478
## ci.upper Std.lv Std.all
## 0.255 0.175 0.171
## 0.193 0.112 0.107
## 0.056 -0.030 -0.030
## 0.071 -0.003 -0.003
## 0.279 0.201 0.198
## 0.310 0.226 0.222
## 0.121 0.047 0.046
## 0.222 0.146 0.140
## -0.043 -0.108 -0.099
## -0.004 -0.070 -0.067
## 0.305 0.226 0.218
## 0.268 0.186 0.180
## 0.200 0.075 0.075
## 0.087 -0.039 -0.039
## 0.990 0.628 0.628
## -0.181 -0.316 -0.316
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.160 0.017 9.621 0.000 0.128
## .sswk 0.196 0.017 11.687 0.000 0.163
## .sspc 0.239 0.019 12.345 0.000 0.201
## .ssei 0.327 0.025 13.007 0.000 0.277
## .ssar 0.213 0.022 9.768 0.000 0.170
## .ssmk 0.153 0.013 11.522 0.000 0.127
## .ssmc 0.260 0.020 13.281 0.000 0.221
## .ssao 0.525 0.038 13.954 0.000 0.451
## .ssai 0.467 0.041 11.276 0.000 0.386
## .sssi 0.315 0.035 9.084 0.000 0.247
## .ssno 0.397 0.042 9.438 0.000 0.315
## .sscs 0.493 0.058 8.535 0.000 0.380
## electronic 1.682 0.135 12.419 0.000 1.416
## speed 1.088 0.095 11.418 0.000 0.901
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.193 0.160 0.152
## 0.229 0.196 0.181
## 0.277 0.239 0.246
## 0.376 0.327 0.287
## 0.256 0.213 0.209
## 0.179 0.153 0.147
## 0.298 0.260 0.251
## 0.598 0.525 0.487
## 0.548 0.467 0.393
## 0.383 0.315 0.282
## 0.480 0.397 0.368
## 0.606 0.493 0.459
## 1.947 1.000 1.000
## 1.275 1.000 1.000
reduced<-cfa(cf.reduced, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1"))
fitMeasures(reduced, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 409.332 117.000 0.000 0.978 0.062 0.082 32001.722
## bic
## 32328.019
Mc(reduced)
## [1] 0.8944986
summary(reduced, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 56 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 33
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 409.332 316.118
## Degrees of freedom 117 117
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.295
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 187.390 144.717
## 0 221.942 171.401
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.946 0.022 42.043 0.000 0.902
## sswk (.p2.) 0.943 0.023 40.950 0.000 0.898
## sspc (.p3.) 0.396 0.057 6.951 0.000 0.284
## ssei (.p4.) 0.523 0.042 12.560 0.000 0.442
## math =~
## ssar (.p5.) 0.900 0.023 39.158 0.000 0.855
## sspc (.p6.) 0.483 0.057 8.399 0.000 0.370
## ssmk (.p7.) 0.679 0.048 14.253 0.000 0.586
## ssmc (.p8.) 0.534 0.027 19.495 0.000 0.480
## ssao (.p9.) 0.743 0.022 33.067 0.000 0.699
## electronic =~
## ssai (.10.) 0.655 0.028 23.686 0.000 0.601
## sssi (.11.) 0.690 0.029 23.972 0.000 0.634
## ssmc (.12.) 0.341 0.026 13.210 0.000 0.291
## ssei (.13.) 0.356 0.035 10.119 0.000 0.287
## speed =~
## ssno (.14.) 0.789 0.035 22.675 0.000 0.721
## sscs (.15.) 0.729 0.033 22.002 0.000 0.664
## ssmk (.16.) 0.295 0.046 6.405 0.000 0.205
## ci.upper Std.lv Std.all
##
## 0.990 0.946 0.923
## 0.988 0.943 0.913
## 0.507 0.396 0.402
## 0.605 0.523 0.526
##
## 0.945 0.900 0.916
## 0.595 0.483 0.491
## 0.773 0.679 0.660
## 0.588 0.534 0.550
## 0.787 0.743 0.757
##
## 0.709 0.655 0.751
## 0.746 0.690 0.782
## 0.392 0.341 0.351
## 0.425 0.356 0.358
##
## 0.857 0.789 0.794
## 0.794 0.729 0.725
## 0.385 0.295 0.287
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.35.) 0.898 0.011 84.979 0.000 0.877
## elctrnc (.36.) 0.884 0.022 40.053 0.000 0.841
## speed (.37.) 0.741 0.033 22.148 0.000 0.675
## math ~~
## elctrnc (.38.) 0.795 0.027 29.751 0.000 0.743
## speed (.39.) 0.821 0.033 24.801 0.000 0.756
## electronic ~~
## speed (.40.) 0.557 0.049 11.261 0.000 0.460
## ci.upper Std.lv Std.all
##
## 0.919 0.898 0.898
## 0.927 0.884 0.884
## 0.806 0.741 0.741
##
## 0.848 0.795 0.795
## 0.886 0.821 0.821
##
## 0.654 0.557 0.557
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 0.000 0.000
## math 0.000 0.000
## .ssgs (.41.) 0.210 0.031 6.758 0.000 0.149
## .sswk (.42.) 0.146 0.032 4.628 0.000 0.084
## .sspc 0.260 0.034 7.590 0.000 0.193
## .ssei (.44.) 0.021 0.032 0.662 0.508 -0.041
## .ssar (.45.) 0.185 0.030 6.146 0.000 0.126
## .ssmk (.46.) 0.216 0.034 6.419 0.000 0.150
## .ssmc (.47.) 0.046 0.031 1.500 0.134 -0.014
## .ssao (.48.) 0.133 0.031 4.289 0.000 0.072
## .ssai (.49.) -0.094 0.031 -3.062 0.002 -0.154
## .sssi (.50.) -0.055 0.031 -1.801 0.072 -0.116
## .ssno (.51.) 0.228 0.038 6.065 0.000 0.154
## .sscs (.52.) 0.189 0.039 4.834 0.000 0.112
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.271 0.210 0.205
## 0.208 0.146 0.141
## 0.327 0.260 0.265
## 0.084 0.021 0.021
## 0.244 0.185 0.188
## 0.281 0.216 0.210
## 0.106 0.046 0.047
## 0.193 0.133 0.135
## -0.034 -0.094 -0.108
## 0.005 -0.055 -0.063
## 0.302 0.228 0.229
## 0.265 0.189 0.188
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.156 0.015 10.511 0.000 0.127
## .sswk 0.178 0.016 11.119 0.000 0.147
## .sspc 0.235 0.021 10.981 0.000 0.193
## .ssei 0.261 0.022 11.679 0.000 0.217
## .ssar 0.155 0.015 10.498 0.000 0.126
## .ssmk 0.180 0.016 10.970 0.000 0.148
## .ssmc 0.252 0.018 13.712 0.000 0.216
## .ssao 0.412 0.028 14.972 0.000 0.358
## .ssai 0.331 0.026 12.543 0.000 0.279
## .sssi 0.301 0.027 11.073 0.000 0.248
## .ssno 0.366 0.039 9.387 0.000 0.289
## .sscs 0.480 0.053 9.019 0.000 0.376
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.185 0.156 0.148
## 0.210 0.178 0.167
## 0.276 0.235 0.243
## 0.304 0.261 0.263
## 0.184 0.155 0.160
## 0.212 0.180 0.170
## 0.288 0.252 0.267
## 0.466 0.412 0.427
## 0.383 0.331 0.436
## 0.355 0.301 0.388
## 0.442 0.366 0.370
## 0.585 0.480 0.475
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.946 0.022 42.043 0.000 0.902
## sswk (.p2.) 0.943 0.023 40.950 0.000 0.898
## sspc (.p3.) 0.396 0.057 6.951 0.000 0.284
## ssei (.p4.) 0.523 0.042 12.560 0.000 0.442
## math =~
## ssar (.p5.) 0.900 0.023 39.158 0.000 0.855
## sspc (.p6.) 0.483 0.057 8.399 0.000 0.370
## ssmk (.p7.) 0.679 0.048 14.253 0.000 0.586
## ssmc (.p8.) 0.534 0.027 19.495 0.000 0.480
## ssao (.p9.) 0.743 0.022 33.067 0.000 0.699
## electronic =~
## ssai (.10.) 0.655 0.028 23.686 0.000 0.601
## sssi (.11.) 0.690 0.029 23.972 0.000 0.634
## ssmc (.12.) 0.341 0.026 13.210 0.000 0.291
## ssei (.13.) 0.356 0.035 10.119 0.000 0.287
## speed =~
## ssno (.14.) 0.789 0.035 22.675 0.000 0.721
## sscs (.15.) 0.729 0.033 22.002 0.000 0.664
## ssmk (.16.) 0.295 0.046 6.405 0.000 0.205
## ci.upper Std.lv Std.all
##
## 0.990 0.946 0.919
## 0.988 0.943 0.907
## 0.507 0.396 0.401
## 0.605 0.523 0.490
##
## 0.945 0.900 0.890
## 0.595 0.483 0.490
## 0.773 0.679 0.667
## 0.588 0.534 0.526
## 0.787 0.743 0.716
##
## 0.709 0.851 0.780
## 0.746 0.896 0.848
## 0.392 0.443 0.437
## 0.425 0.462 0.432
##
## 0.857 0.823 0.792
## 0.794 0.760 0.734
## 0.385 0.308 0.302
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.35.) 0.898 0.011 84.979 0.000 0.877
## elctrnc (.36.) 0.884 0.022 40.053 0.000 0.841
## speed (.37.) 0.741 0.033 22.148 0.000 0.675
## math ~~
## elctrnc (.38.) 0.795 0.027 29.751 0.000 0.743
## speed (.39.) 0.821 0.033 24.801 0.000 0.756
## electronic ~~
## speed (.40.) 0.557 0.049 11.261 0.000 0.460
## ci.upper Std.lv Std.all
##
## 0.919 0.898 0.898
## 0.927 0.681 0.681
## 0.806 0.711 0.711
##
## 0.848 0.612 0.612
## 0.886 0.788 0.788
##
## 0.654 0.411 0.411
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 0.000 0.000
## math 0.000 0.000
## .ssgs (.41.) 0.210 0.031 6.758 0.000 0.149
## .sswk (.42.) 0.146 0.032 4.628 0.000 0.084
## .sspc -0.027 0.034 -0.800 0.424 -0.094
## .ssei (.44.) 0.021 0.032 0.662 0.508 -0.041
## .ssar (.45.) 0.185 0.030 6.146 0.000 0.126
## .ssmk (.46.) 0.216 0.034 6.419 0.000 0.150
## .ssmc (.47.) 0.046 0.031 1.500 0.134 -0.014
## .ssao (.48.) 0.133 0.031 4.289 0.000 0.072
## .ssai (.49.) -0.094 0.031 -3.062 0.002 -0.154
## .sssi (.50.) -0.055 0.031 -1.801 0.072 -0.116
## .ssno (.51.) 0.228 0.038 6.065 0.000 0.154
## .sscs (.52.) 0.189 0.039 4.834 0.000 0.112
## elctrnc 0.772 0.071 10.936 0.000 0.634
## speed -0.337 0.061 -5.499 0.000 -0.456
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.271 0.210 0.204
## 0.208 0.146 0.141
## 0.040 -0.027 -0.028
## 0.084 0.021 0.020
## 0.244 0.185 0.183
## 0.281 0.216 0.212
## 0.106 0.046 0.045
## 0.193 0.133 0.128
## -0.034 -0.094 -0.086
## 0.005 -0.055 -0.052
## 0.302 0.228 0.219
## 0.265 0.189 0.182
## 0.910 0.594 0.594
## -0.217 -0.323 -0.323
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## .ssgs 0.164 0.017 9.650 0.000 0.130
## .sswk 0.193 0.017 11.668 0.000 0.160
## .sspc 0.239 0.019 12.323 0.000 0.201
## .ssei 0.325 0.025 12.957 0.000 0.276
## .ssar 0.212 0.022 9.712 0.000 0.169
## .ssmk 0.153 0.013 11.383 0.000 0.126
## .ssmc 0.260 0.020 13.315 0.000 0.221
## .ssao 0.525 0.038 13.907 0.000 0.451
## .ssai 0.467 0.041 11.269 0.000 0.385
## .sssi 0.315 0.035 9.082 0.000 0.247
## .ssno 0.403 0.042 9.494 0.000 0.319
## .sscs 0.494 0.058 8.563 0.000 0.381
## electronic 1.687 0.136 12.432 0.000 1.421
## speed 1.087 0.094 11.611 0.000 0.903
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.197 0.164 0.155
## 0.225 0.193 0.178
## 0.277 0.239 0.246
## 0.375 0.325 0.285
## 0.255 0.212 0.207
## 0.179 0.153 0.147
## 0.298 0.260 0.252
## 0.599 0.525 0.488
## 0.548 0.467 0.392
## 0.383 0.315 0.282
## 0.486 0.403 0.373
## 0.607 0.494 0.461
## 1.953 1.000 1.000
## 1.270 1.000 1.000
tests<-lavTestLRT(configural, metric, scalar2, cf.cov, cf.cov2, reduced)
Td=tests[2:6,"Chisq diff"]
Td
## [1] 22.461971 43.764021 35.528857 1.373784 8.364787
dfd=tests[2:6,"Df diff"]
dfd
## [1] 12 7 6 2 2
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-656+ 656 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
## Warning in sqrt((ld) * G/(N - G)): NaNs produced
RMSEAD
## [1] 0.03648341 0.08954510 0.08668162 NaN 0.06970378
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.01029143 0.05952129
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.06521271 0.11578118
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.0604624 0.1151812
RMSEA.CI(T=Td[4],df=dfd[4],N=N,G=2)
## [1] NA 0.06931366
RMSEA.CI(T=Td[5],df=dfd[5],N=N,G=2)
## [1] 0.02596873 0.12140610
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.967 0.951 0.184 0.046 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.995 0.976 0.757 0.272
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.988 0.953 0.687 0.236
round(pvals(T=Td[4],df=dfd[4],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.497 0.475 0.154 0.091 0.023 0.004
round(pvals(T=Td[5],df=dfd[5],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.985 0.980 0.805 0.697 0.429 0.188
tests<-lavTestLRT(configural, metric, scalar2, strict)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 22.46197 43.76402 27.51293
dfd=tests[2:4,"Df diff"]
dfd
## [1] 12 7 12
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-656+ 656 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.03648341 0.08954510 0.04442583
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.01029143 0.05952129
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.06521271 0.11578118
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.02241014 0.06648774
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.967 0.951 0.184 0.046 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.995 0.976 0.757 0.272
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.993 0.989 0.371 0.131 0.003 0.000
tests<-lavTestLRT(configural, metric, scalar)
Td=tests[2:3,"Chisq diff"]
Td
## [1] 22.46197 118.00064
dfd=tests[2:3,"Df diff"]
dfd
## [1] 12 8
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-656+ 656 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.03648341 0.14488785
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.01029143 0.05952129
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.1223681 0.1685197
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.967 0.951 0.184 0.046 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 1.000 1.000 0.999
# ONE FACTOR, just for checking if gap direction aligns with HOF
fmodel<-'
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
configural<-cfa(fmodel, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1513.542 108.000 0.000 0.894 0.141 0.055 33123.933
## bic
## 33496.843
Mc(configural)
## [1] 0.5850504
metric<-cfa(fmodel, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1592.678 119.000 0.000 0.889 0.137 0.074 33181.068
## bic
## 33497.006
Mc(metric)
## [1] 0.570043
scalar<-cfa(fmodel, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 2288.436 130.000 0.000 0.838 0.159 0.091 33854.826
## bic
## 34113.791
Mc(scalar)
## [1] 0.4390236
summary(scalar, standardized=T, ci=T) # g=-0.042
## lavaan 0.6-18 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 74
## Number of equality constraints 24
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2288.436 1743.936
## Degrees of freedom 130 130
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.312
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 843.486 642.790
## 0 1444.950 1101.145
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g =~
## ssgs (.p1.) 0.863 0.028 30.448 0.000 0.808
## ssar (.p2.) 0.819 0.029 28.563 0.000 0.763
## sswk (.p3.) 0.860 0.030 28.687 0.000 0.801
## sspc (.p4.) 0.799 0.027 29.662 0.000 0.746
## ssno (.p5.) 0.593 0.031 19.165 0.000 0.532
## sscs (.p6.) 0.568 0.030 18.965 0.000 0.509
## ssai (.p7.) 0.567 0.028 20.412 0.000 0.512
## sssi (.p8.) 0.600 0.029 21.032 0.000 0.544
## ssmk (.p9.) 0.838 0.029 28.497 0.000 0.781
## ssmc (.10.) 0.794 0.027 29.186 0.000 0.741
## ssei (.11.) 0.796 0.029 27.094 0.000 0.739
## ssao (.12.) 0.681 0.027 25.553 0.000 0.629
## ci.upper Std.lv Std.all
##
## 0.919 0.863 0.884
## 0.876 0.819 0.872
## 0.919 0.860 0.871
## 0.852 0.799 0.841
## 0.654 0.593 0.609
## 0.626 0.568 0.575
## 0.621 0.567 0.652
## 0.656 0.600 0.667
## 0.896 0.838 0.850
## 0.847 0.794 0.829
## 0.854 0.796 0.825
## 0.733 0.681 0.711
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.26.) 0.189 0.040 4.673 0.000 0.110
## .ssar (.27.) 0.169 0.039 4.280 0.000 0.092
## .sswk (.28.) 0.126 0.041 3.034 0.002 0.044
## .sspc (.29.) 0.102 0.042 2.461 0.014 0.021
## .ssno (.30.) 0.093 0.038 2.462 0.014 0.019
## .sscs (.31.) 0.065 0.039 1.676 0.094 -0.011
## .ssai (.32.) 0.048 0.034 1.421 0.155 -0.018
## .sssi (.33.) 0.113 0.038 3.006 0.003 0.039
## .ssmk (.34.) 0.146 0.043 3.419 0.001 0.062
## .ssmc (.35.) 0.147 0.037 3.931 0.000 0.074
## .ssei (.36.) 0.107 0.039 2.703 0.007 0.029
## .ssao (.37.) 0.118 0.038 3.094 0.002 0.043
## ci.upper Std.lv Std.all
## 0.268 0.189 0.194
## 0.246 0.169 0.180
## 0.207 0.126 0.127
## 0.183 0.102 0.108
## 0.168 0.093 0.096
## 0.140 0.065 0.066
## 0.115 0.048 0.056
## 0.187 0.113 0.126
## 0.230 0.146 0.148
## 0.221 0.147 0.154
## 0.184 0.107 0.110
## 0.192 0.118 0.123
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.209 0.017 12.532 0.000 0.176
## .ssar 0.211 0.016 13.270 0.000 0.180
## .sswk 0.236 0.019 12.486 0.000 0.199
## .sspc 0.264 0.026 10.341 0.000 0.214
## .ssno 0.597 0.057 10.394 0.000 0.484
## .sscs 0.653 0.056 11.677 0.000 0.543
## .ssai 0.434 0.033 13.223 0.000 0.370
## .sssi 0.448 0.035 12.987 0.000 0.381
## .ssmk 0.269 0.019 13.969 0.000 0.232
## .ssmc 0.288 0.021 13.809 0.000 0.247
## .ssei 0.298 0.025 11.889 0.000 0.249
## .ssao 0.452 0.028 15.869 0.000 0.396
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.241 0.209 0.219
## 0.243 0.211 0.239
## 0.273 0.236 0.242
## 0.314 0.264 0.293
## 0.709 0.597 0.629
## 0.762 0.653 0.670
## 0.498 0.434 0.575
## 0.516 0.448 0.555
## 0.307 0.269 0.277
## 0.328 0.288 0.313
## 0.347 0.298 0.319
## 0.508 0.452 0.494
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g =~
## ssgs (.p1.) 0.863 0.028 30.448 0.000 0.808
## ssar (.p2.) 0.819 0.029 28.563 0.000 0.763
## sswk (.p3.) 0.860 0.030 28.687 0.000 0.801
## sspc (.p4.) 0.799 0.027 29.662 0.000 0.746
## ssno (.p5.) 0.593 0.031 19.165 0.000 0.532
## sscs (.p6.) 0.568 0.030 18.965 0.000 0.509
## ssai (.p7.) 0.567 0.028 20.412 0.000 0.512
## sssi (.p8.) 0.600 0.029 21.032 0.000 0.544
## ssmk (.p9.) 0.838 0.029 28.497 0.000 0.781
## ssmc (.10.) 0.794 0.027 29.186 0.000 0.741
## ssei (.11.) 0.796 0.029 27.094 0.000 0.739
## ssao (.12.) 0.681 0.027 25.553 0.000 0.629
## ci.upper Std.lv Std.all
##
## 0.919 0.964 0.897
## 0.876 0.915 0.867
## 0.919 0.961 0.887
## 0.852 0.892 0.861
## 0.654 0.662 0.614
## 0.626 0.634 0.593
## 0.621 0.633 0.541
## 0.656 0.670 0.587
## 0.896 0.937 0.880
## 0.847 0.887 0.832
## 0.854 0.890 0.789
## 0.733 0.760 0.716
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.26.) 0.189 0.040 4.673 0.000 0.110
## .ssar (.27.) 0.169 0.039 4.280 0.000 0.092
## .sswk (.28.) 0.126 0.041 3.034 0.002 0.044
## .sspc (.29.) 0.102 0.042 2.461 0.014 0.021
## .ssno (.30.) 0.093 0.038 2.462 0.014 0.019
## .sscs (.31.) 0.065 0.039 1.676 0.094 -0.011
## .ssai (.32.) 0.048 0.034 1.421 0.155 -0.018
## .sssi (.33.) 0.113 0.038 3.006 0.003 0.039
## .ssmk (.34.) 0.146 0.043 3.419 0.001 0.062
## .ssmc (.35.) 0.147 0.037 3.931 0.000 0.074
## .ssei (.36.) 0.107 0.039 2.703 0.007 0.029
## .ssao (.37.) 0.118 0.038 3.094 0.002 0.043
## g 0.047 0.068 0.688 0.491 -0.087
## ci.upper Std.lv Std.all
## 0.268 0.189 0.176
## 0.246 0.169 0.160
## 0.207 0.126 0.116
## 0.183 0.102 0.099
## 0.168 0.093 0.087
## 0.140 0.065 0.061
## 0.115 0.048 0.041
## 0.187 0.113 0.099
## 0.230 0.146 0.137
## 0.221 0.147 0.138
## 0.184 0.107 0.094
## 0.192 0.118 0.111
## 0.181 0.042 0.042
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.226 0.018 12.500 0.000 0.191
## .ssar 0.277 0.022 12.366 0.000 0.233
## .sswk 0.250 0.019 13.195 0.000 0.213
## .sspc 0.277 0.025 11.219 0.000 0.229
## .ssno 0.724 0.064 11.352 0.000 0.599
## .sscs 0.741 0.066 11.304 0.000 0.612
## .ssai 0.967 0.085 11.423 0.000 0.801
## .sssi 0.854 0.076 11.247 0.000 0.705
## .ssmk 0.256 0.020 13.077 0.000 0.218
## .ssmc 0.349 0.025 14.188 0.000 0.300
## .ssei 0.481 0.045 10.586 0.000 0.392
## .ssao 0.549 0.039 14.181 0.000 0.473
## g 1.248 0.099 12.631 0.000 1.054
## ci.upper Std.lv Std.all
## 0.262 0.226 0.196
## 0.321 0.277 0.249
## 0.287 0.250 0.213
## 0.326 0.277 0.258
## 0.849 0.724 0.623
## 0.869 0.741 0.648
## 1.133 0.967 0.707
## 1.002 0.854 0.656
## 0.295 0.256 0.226
## 0.397 0.349 0.307
## 0.570 0.481 0.378
## 0.625 0.549 0.487
## 1.441 1.000 1.000
# HIGH ORDER FACTOR
hof.model<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
'
hof.lv<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
verbal~~1*verbal
math~~1*math
'
hof.weak<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
verbal~~1*verbal
math~~1*math
verbal~0*1
'
baseline<-cfa(hof.model, data=dgroup, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 443.805 46.000 0.000 0.970 0.081 0.038 32616.702
## bic
## 32844.592
Mc(baseline)
## [1] 0.8592305
configural<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 402.032 92.000 0.000 0.977 0.072 0.032 32044.423
## bic
## 32500.202
Mc(configural)
## [1] 0.8884804
summary(configural, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 110 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 88
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 402.032 309.815
## Degrees of freedom 92 92
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.298
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 162.732 125.405
## 0 239.300 184.410
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.247 0.049 5.052 0.000 0.151
## sswk 0.253 0.051 4.992 0.000 0.154
## sspc 0.119 0.029 4.143 0.000 0.063
## ssei 0.154 0.032 4.847 0.000 0.092
## math =~
## ssar 0.309 0.040 7.657 0.000 0.230
## sspc 0.153 0.032 4.734 0.000 0.090
## ssmk 0.243 0.041 5.921 0.000 0.162
## ssmc 0.180 0.032 5.576 0.000 0.116
## ssao 0.267 0.037 7.258 0.000 0.195
## electronic =~
## ssai 0.315 0.036 8.853 0.000 0.246
## sssi 0.355 0.042 8.545 0.000 0.274
## ssmc 0.179 0.034 5.251 0.000 0.112
## ssei 0.118 0.035 3.417 0.001 0.051
## speed =~
## ssno 0.490 0.054 9.157 0.000 0.385
## sscs 0.436 0.048 9.168 0.000 0.343
## ssmk 0.204 0.036 5.739 0.000 0.135
## g =~
## verbal 3.470 0.744 4.661 0.000 2.011
## math 2.586 0.383 6.753 0.000 1.836
## electronic 1.549 0.206 7.529 0.000 1.146
## speed 1.224 0.168 7.272 0.000 0.894
## ci.upper Std.lv Std.all
##
## 0.343 0.892 0.914
## 0.353 0.915 0.912
## 0.175 0.430 0.447
## 0.216 0.556 0.614
##
## 0.388 0.858 0.909
## 0.216 0.425 0.442
## 0.323 0.672 0.657
## 0.243 0.498 0.536
## 0.340 0.741 0.759
##
## 0.385 0.582 0.718
## 0.437 0.655 0.771
## 0.245 0.329 0.355
## 0.186 0.219 0.241
##
## 0.595 0.775 0.794
## 0.529 0.689 0.710
## 0.274 0.323 0.316
##
## 4.928 0.961 0.961
## 3.337 0.933 0.933
## 1.953 0.840 0.840
## 1.554 0.774 0.774
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.139 0.042 3.332 0.001 0.057
## .sswk 0.154 0.043 3.607 0.000 0.070
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssei 0.000 0.039 0.009 0.993 -0.077
## .ssar 0.186 0.040 4.598 0.000 0.107
## .ssmk 0.241 0.044 5.433 0.000 0.154
## .ssmc 0.039 0.040 0.993 0.321 -0.038
## .ssao 0.171 0.042 4.054 0.000 0.088
## .ssai -0.108 0.035 -3.113 0.002 -0.176
## .sssi -0.068 0.036 -1.862 0.063 -0.139
## .ssno 0.175 0.043 4.060 0.000 0.090
## .sscs 0.245 0.043 5.752 0.000 0.162
## ci.upper Std.lv Std.all
## 0.220 0.139 0.142
## 0.238 0.154 0.154
## 0.333 0.253 0.263
## 0.077 0.000 0.000
## 0.265 0.186 0.197
## 0.327 0.241 0.235
## 0.117 0.039 0.042
## 0.253 0.171 0.175
## -0.040 -0.108 -0.134
## 0.004 -0.068 -0.080
## 0.259 0.175 0.179
## 0.329 0.245 0.253
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.156 0.015 10.407 0.000 0.127
## .sswk 0.169 0.015 10.947 0.000 0.139
## .sspc 0.233 0.022 10.670 0.000 0.190
## .ssei 0.266 0.022 11.928 0.000 0.222
## .ssar 0.154 0.016 9.546 0.000 0.122
## .ssmk 0.178 0.016 10.900 0.000 0.146
## .ssmc 0.249 0.019 13.351 0.000 0.212
## .ssao 0.405 0.028 14.414 0.000 0.350
## .ssai 0.317 0.028 11.186 0.000 0.262
## .sssi 0.292 0.029 10.220 0.000 0.236
## .ssno 0.352 0.038 9.178 0.000 0.277
## .sscs 0.468 0.053 8.755 0.000 0.363
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.186 0.156 0.164
## 0.199 0.169 0.168
## 0.275 0.233 0.251
## 0.309 0.266 0.325
## 0.185 0.154 0.173
## 0.210 0.178 0.169
## 0.285 0.249 0.289
## 0.460 0.405 0.424
## 0.373 0.317 0.484
## 0.348 0.292 0.405
## 0.427 0.352 0.369
## 0.572 0.468 0.496
## 1.000 0.077 0.077
## 1.000 0.130 0.130
## 1.000 0.294 0.294
## 1.000 0.400 0.400
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.218 0.080 2.727 0.006 0.061
## sswk 0.214 0.078 2.736 0.006 0.061
## sspc 0.093 0.038 2.483 0.013 0.020
## ssei 0.124 0.048 2.589 0.010 0.030
## math =~
## ssar 0.349 0.046 7.646 0.000 0.260
## sspc 0.175 0.038 4.598 0.000 0.101
## ssmk 0.245 0.037 6.580 0.000 0.172
## ssmc 0.216 0.031 7.057 0.000 0.156
## ssao 0.275 0.037 7.525 0.000 0.203
## electronic =~
## ssai 0.648 0.041 15.972 0.000 0.568
## sssi 0.642 0.041 15.731 0.000 0.562
## ssmc 0.301 0.031 9.654 0.000 0.240
## ssei 0.367 0.044 8.250 0.000 0.280
## speed =~
## ssno 0.589 0.054 10.947 0.000 0.483
## sscs 0.533 0.042 12.644 0.000 0.451
## ssmk 0.230 0.031 7.345 0.000 0.169
## g =~
## verbal 4.446 1.713 2.595 0.009 1.088
## math 2.520 0.392 6.433 0.000 1.752
## electronic 1.121 0.102 11.009 0.000 0.922
## speed 1.102 0.127 8.695 0.000 0.853
## ci.upper Std.lv Std.all
##
## 0.374 0.992 0.926
## 0.366 0.973 0.911
## 0.167 0.426 0.423
## 0.217 0.563 0.478
##
## 0.439 0.947 0.900
## 0.250 0.476 0.472
## 0.317 0.663 0.654
## 0.276 0.586 0.541
## 0.347 0.746 0.719
##
## 0.727 0.973 0.824
## 0.722 0.965 0.858
## 0.363 0.453 0.418
## 0.454 0.551 0.468
##
## 0.694 0.876 0.819
## 0.616 0.793 0.753
## 0.291 0.342 0.337
##
## 7.804 0.976 0.976
## 3.288 0.929 0.929
## 1.321 0.746 0.746
## 1.350 0.741 0.741
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.285 0.046 6.206 0.000 0.195
## .sswk 0.136 0.046 2.930 0.003 0.045
## .sspc -0.020 0.044 -0.450 0.652 -0.105
## .ssei 0.317 0.051 6.192 0.000 0.217
## .ssar 0.185 0.045 4.117 0.000 0.097
## .ssmk 0.094 0.044 2.150 0.032 0.008
## .ssmc 0.317 0.046 6.902 0.000 0.227
## .ssao 0.084 0.045 1.868 0.062 -0.004
## .ssai 0.427 0.052 8.172 0.000 0.324
## .sssi 0.489 0.049 10.035 0.000 0.394
## .ssno 0.022 0.047 0.466 0.641 -0.070
## .sscs -0.116 0.046 -2.517 0.012 -0.206
## ci.upper Std.lv Std.all
## 0.375 0.285 0.266
## 0.226 0.136 0.127
## 0.066 -0.020 -0.019
## 0.417 0.317 0.269
## 0.274 0.185 0.176
## 0.180 0.094 0.093
## 0.406 0.317 0.292
## 0.173 0.084 0.081
## 0.529 0.427 0.361
## 0.585 0.489 0.435
## 0.114 0.022 0.020
## -0.026 -0.116 -0.110
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.163 0.017 9.394 0.000 0.129
## .sswk 0.195 0.016 11.822 0.000 0.162
## .sspc 0.239 0.020 12.131 0.000 0.200
## .ssei 0.313 0.025 12.532 0.000 0.264
## .ssar 0.211 0.023 9.059 0.000 0.165
## .ssmk 0.160 0.013 11.873 0.000 0.133
## .ssmc 0.256 0.019 13.286 0.000 0.218
## .ssao 0.520 0.038 13.759 0.000 0.446
## .ssai 0.448 0.042 10.592 0.000 0.365
## .sssi 0.334 0.037 9.071 0.000 0.262
## .ssno 0.378 0.044 8.541 0.000 0.291
## .sscs 0.481 0.057 8.477 0.000 0.370
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.198 0.163 0.142
## 0.227 0.195 0.170
## 0.278 0.239 0.236
## 0.362 0.313 0.226
## 0.256 0.211 0.190
## 0.186 0.160 0.155
## 0.293 0.256 0.218
## 0.594 0.520 0.483
## 0.531 0.448 0.321
## 0.406 0.334 0.264
## 0.465 0.378 0.330
## 0.593 0.481 0.433
## 1.000 0.048 0.048
## 1.000 0.136 0.136
## 1.000 0.443 0.443
## 1.000 0.452 0.452
## 1.000 1.000 1.000
#modificationIndices(configural, sort=T, maximum.number=30)
metric<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 451.166 107.000 0.000 0.974 0.070 0.049 32063.556
## bic
## 32441.645
Mc(metric)
## [1] 0.8769891
summary(metric, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 102 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 93
## Number of equality constraints 20
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 451.166 345.739
## Degrees of freedom 107 107
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.305
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 186.427 142.863
## 0 264.739 202.876
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.250 0.042 5.980 0.000 0.168
## sswk (.p2.) 0.251 0.042 5.934 0.000 0.168
## sspc (.p3.) 0.113 0.023 4.930 0.000 0.068
## ssei (.p4.) 0.136 0.025 5.337 0.000 0.086
## math =~
## ssar (.p5.) 0.320 0.038 8.467 0.000 0.246
## sspc (.p6.) 0.162 0.026 6.127 0.000 0.110
## ssmk (.p7.) 0.238 0.033 7.259 0.000 0.174
## ssmc (.p8.) 0.196 0.024 8.071 0.000 0.148
## ssao (.p9.) 0.266 0.033 8.163 0.000 0.202
## electronic =~
## ssai (.10.) 0.306 0.035 8.760 0.000 0.238
## sssi (.11.) 0.319 0.037 8.548 0.000 0.246
## ssmc (.12.) 0.151 0.020 7.452 0.000 0.111
## ssei (.13.) 0.174 0.021 8.156 0.000 0.132
## speed =~
## ssno (.14.) 0.496 0.045 10.989 0.000 0.408
## sscs (.15.) 0.446 0.040 11.184 0.000 0.368
## ssmk (.16.) 0.196 0.024 8.259 0.000 0.149
## g =~
## verbal (.17.) 3.432 0.615 5.580 0.000 2.227
## math (.18.) 2.499 0.341 7.339 0.000 1.832
## elctrnc (.19.) 1.769 0.222 7.954 0.000 1.333
## speed (.20.) 1.207 0.136 8.852 0.000 0.940
## ci.upper Std.lv Std.all
##
## 0.332 0.895 0.917
## 0.334 0.897 0.908
## 0.157 0.402 0.425
## 0.186 0.485 0.510
##
## 0.395 0.863 0.911
## 0.213 0.435 0.459
## 0.302 0.640 0.650
## 0.244 0.528 0.561
## 0.329 0.715 0.746
##
## 0.375 0.623 0.741
## 0.392 0.648 0.758
## 0.191 0.307 0.326
## 0.215 0.353 0.370
##
## 0.585 0.778 0.796
## 0.524 0.699 0.717
## 0.242 0.307 0.311
##
## 4.638 0.960 0.960
## 3.166 0.928 0.928
## 2.205 0.871 0.871
## 1.474 0.770 0.770
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.139 0.042 3.332 0.001 0.057
## .sswk 0.154 0.043 3.607 0.000 0.070
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssei 0.000 0.039 0.009 0.993 -0.077
## .ssar 0.186 0.040 4.598 0.000 0.107
## .ssmk 0.241 0.044 5.433 0.000 0.154
## .ssmc 0.039 0.040 0.993 0.321 -0.038
## .ssao 0.171 0.042 4.054 0.000 0.088
## .ssai -0.108 0.035 -3.113 0.002 -0.176
## .sssi -0.068 0.036 -1.862 0.063 -0.139
## .ssno 0.175 0.043 4.060 0.000 0.090
## .sscs 0.245 0.043 5.752 0.000 0.162
## ci.upper Std.lv Std.all
## 0.220 0.139 0.142
## 0.238 0.154 0.156
## 0.333 0.253 0.267
## 0.077 0.000 0.000
## 0.265 0.186 0.196
## 0.327 0.241 0.244
## 0.117 0.039 0.042
## 0.253 0.171 0.178
## -0.040 -0.108 -0.129
## 0.004 -0.068 -0.079
## 0.259 0.175 0.179
## 0.329 0.245 0.252
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.153 0.015 10.330 0.000 0.124
## .sswk 0.171 0.016 10.968 0.000 0.140
## .sspc 0.234 0.021 10.939 0.000 0.192
## .ssei 0.260 0.023 11.568 0.000 0.216
## .ssar 0.152 0.016 9.487 0.000 0.120
## .ssmk 0.187 0.016 11.843 0.000 0.156
## .ssmc 0.251 0.018 13.568 0.000 0.214
## .ssao 0.406 0.028 14.717 0.000 0.352
## .ssai 0.319 0.027 12.013 0.000 0.267
## .sssi 0.310 0.028 11.219 0.000 0.256
## .ssno 0.350 0.039 8.983 0.000 0.273
## .sscs 0.462 0.051 8.968 0.000 0.361
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.181 0.153 0.160
## 0.201 0.171 0.175
## 0.276 0.234 0.261
## 0.304 0.260 0.287
## 0.183 0.152 0.169
## 0.218 0.187 0.192
## 0.287 0.251 0.283
## 0.461 0.406 0.443
## 0.371 0.319 0.452
## 0.364 0.310 0.425
## 0.426 0.350 0.366
## 0.563 0.462 0.486
## 1.000 0.078 0.078
## 1.000 0.138 0.138
## 1.000 0.242 0.242
## 1.000 0.407 0.407
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.250 0.042 5.980 0.000 0.168
## sswk (.p2.) 0.251 0.042 5.934 0.000 0.168
## sspc (.p3.) 0.113 0.023 4.930 0.000 0.068
## ssei (.p4.) 0.136 0.025 5.337 0.000 0.086
## math =~
## ssar (.p5.) 0.320 0.038 8.467 0.000 0.246
## sspc (.p6.) 0.162 0.026 6.127 0.000 0.110
## ssmk (.p7.) 0.238 0.033 7.259 0.000 0.174
## ssmc (.p8.) 0.196 0.024 8.071 0.000 0.148
## ssao (.p9.) 0.266 0.033 8.163 0.000 0.202
## electronic =~
## ssai (.10.) 0.306 0.035 8.760 0.000 0.238
## sssi (.11.) 0.319 0.037 8.548 0.000 0.246
## ssmc (.12.) 0.151 0.020 7.452 0.000 0.111
## ssei (.13.) 0.174 0.021 8.156 0.000 0.132
## speed =~
## ssno (.14.) 0.496 0.045 10.989 0.000 0.408
## sscs (.15.) 0.446 0.040 11.184 0.000 0.368
## ssmk (.16.) 0.196 0.024 8.259 0.000 0.149
## g =~
## verbal (.17.) 3.432 0.615 5.580 0.000 2.227
## math (.18.) 2.499 0.341 7.339 0.000 1.832
## elctrnc (.19.) 1.769 0.222 7.954 0.000 1.333
## speed (.20.) 1.207 0.136 8.852 0.000 0.940
## ci.upper Std.lv Std.all
##
## 0.332 0.990 0.925
## 0.334 0.992 0.915
## 0.157 0.445 0.436
## 0.186 0.537 0.487
##
## 0.395 0.936 0.896
## 0.213 0.472 0.463
## 0.302 0.694 0.662
## 0.244 0.572 0.546
## 0.329 0.775 0.733
##
## 0.375 0.892 0.796
## 0.392 0.929 0.854
## 0.191 0.440 0.420
## 0.215 0.505 0.458
##
## 0.585 0.874 0.817
## 0.524 0.786 0.748
## 0.242 0.345 0.328
##
## 4.638 0.961 0.961
## 3.166 0.948 0.948
## 2.205 0.673 0.673
## 1.474 0.759 0.759
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.285 0.046 6.206 0.000 0.195
## .sswk 0.136 0.046 2.930 0.003 0.045
## .sspc -0.020 0.044 -0.450 0.652 -0.105
## .ssei 0.317 0.051 6.192 0.000 0.217
## .ssar 0.185 0.045 4.117 0.000 0.097
## .ssmk 0.094 0.044 2.150 0.032 0.008
## .ssmc 0.317 0.046 6.902 0.000 0.227
## .ssao 0.084 0.045 1.868 0.062 -0.004
## .ssai 0.427 0.052 8.172 0.000 0.324
## .sssi 0.489 0.049 10.035 0.000 0.394
## .ssno 0.022 0.047 0.466 0.641 -0.070
## .sscs -0.116 0.046 -2.517 0.012 -0.206
## ci.upper Std.lv Std.all
## 0.375 0.285 0.266
## 0.226 0.136 0.125
## 0.066 -0.020 -0.019
## 0.417 0.317 0.287
## 0.274 0.185 0.178
## 0.180 0.094 0.090
## 0.406 0.317 0.302
## 0.173 0.084 0.080
## 0.529 0.427 0.381
## 0.585 0.489 0.450
## 0.114 0.022 0.020
## -0.026 -0.116 -0.110
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.166 0.017 9.782 0.000 0.133
## .sswk 0.191 0.016 11.631 0.000 0.159
## .sspc 0.237 0.019 12.173 0.000 0.199
## .ssei 0.322 0.026 12.584 0.000 0.272
## .ssar 0.215 0.023 9.517 0.000 0.170
## .ssmk 0.156 0.013 11.836 0.000 0.130
## .ssmc 0.256 0.019 13.304 0.000 0.218
## .ssao 0.519 0.037 13.899 0.000 0.445
## .ssai 0.461 0.042 10.847 0.000 0.378
## .sssi 0.319 0.037 8.698 0.000 0.247
## .ssno 0.380 0.042 9.014 0.000 0.298
## .sscs 0.486 0.056 8.762 0.000 0.378
## .verbal 1.187 0.511 2.325 0.020 0.187
## .math 0.870 0.284 3.068 0.002 0.314
## .electronic 4.642 1.118 4.150 0.000 2.450
## .speed 1.316 0.280 4.702 0.000 0.768
## g 1.226 0.103 11.902 0.000 1.024
## ci.upper Std.lv Std.all
## 0.200 0.166 0.145
## 0.223 0.191 0.162
## 0.275 0.237 0.228
## 0.372 0.322 0.265
## 0.259 0.215 0.197
## 0.182 0.156 0.142
## 0.294 0.256 0.233
## 0.592 0.519 0.463
## 0.544 0.461 0.367
## 0.390 0.319 0.270
## 0.463 0.380 0.332
## 0.595 0.486 0.441
## 2.188 0.076 0.076
## 1.425 0.102 0.102
## 6.834 0.548 0.548
## 1.865 0.424 0.424
## 1.428 1.000 1.000
lavTestScore(metric, release = 1:20)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 47.626 20 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p55. 0.063 1 0.802
## 2 .p2. == .p56. 3.089 1 0.079
## 3 .p3. == .p57. 0.927 1 0.336
## 4 .p4. == .p58. 10.663 1 0.001
## 5 .p5. == .p59. 2.364 1 0.124
## 6 .p6. == .p60. 0.693 1 0.405
## 7 .p7. == .p61. 10.629 1 0.001
## 8 .p8. == .p62. 1.327 1 0.249
## 9 .p9. == .p63. 1.512 1 0.219
## 10 .p10. == .p64. 4.408 1 0.036
## 11 .p11. == .p65. 0.552 1 0.457
## 12 .p12. == .p66. 0.108 1 0.743
## 13 .p13. == .p67. 15.273 1 0.000
## 14 .p14. == .p68. 0.417 1 0.518
## 15 .p15. == .p69. 0.572 1 0.450
## 16 .p16. == .p70. 9.221 1 0.002
## 17 .p17. == .p71. 0.165 1 0.685
## 18 .p18. == .p72. 3.896 1 0.048
## 19 .p19. == .p73. 19.380 1 0.000
## 20 .p20. == .p74. 0.536 1 0.464
scalar<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 1.250407e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 595.690 114.000 0.000 0.964 0.080 0.052 32194.080
## bic
## 32535.914
Mc(scalar)
## [1] 0.8321765
summary(scalar, standardized=T, ci=T) # g -.085 Std.all
## lavaan 0.6-18 ended normally after 121 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 32
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 595.690 455.269
## Degrees of freedom 114 114
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.308
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 255.040 194.920
## 0 340.649 260.349
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.254 0.041 6.166 0.000 0.173
## sswk (.p2.) 0.255 0.042 6.113 0.000 0.173
## sspc (.p3.) 0.091 0.024 3.770 0.000 0.044
## ssei (.p4.) 0.139 0.025 5.534 0.000 0.089
## math =~
## ssar (.p5.) 0.312 0.041 7.590 0.000 0.231
## sspc (.p6.) 0.187 0.028 6.610 0.000 0.132
## ssmk (.p7.) 0.233 0.035 6.700 0.000 0.165
## ssmc (.p8.) 0.184 0.025 7.336 0.000 0.135
## ssao (.p9.) 0.259 0.035 7.386 0.000 0.190
## electronic =~
## ssai (.10.) 0.304 0.034 8.858 0.000 0.237
## sssi (.11.) 0.316 0.036 8.703 0.000 0.245
## ssmc (.12.) 0.160 0.020 8.105 0.000 0.121
## ssei (.13.) 0.171 0.021 8.315 0.000 0.131
## speed =~
## ssno (.14.) 0.486 0.044 11.018 0.000 0.400
## sscs (.15.) 0.452 0.041 11.076 0.000 0.372
## ssmk (.16.) 0.191 0.023 8.143 0.000 0.145
## g =~
## verbal (.17.) 3.376 0.588 5.744 0.000 2.224
## math (.18.) 2.570 0.386 6.662 0.000 1.814
## elctrnc (.19.) 1.784 0.223 8.000 0.000 1.347
## speed (.20.) 1.218 0.138 8.819 0.000 0.947
## ci.upper Std.lv Std.all
##
## 0.335 0.896 0.915
## 0.336 0.897 0.908
## 0.139 0.321 0.336
## 0.188 0.488 0.513
##
## 0.392 0.859 0.909
## 0.243 0.517 0.540
## 0.302 0.643 0.653
## 0.233 0.507 0.540
## 0.328 0.715 0.746
##
## 0.371 0.621 0.739
## 0.387 0.646 0.757
## 0.199 0.327 0.348
## 0.211 0.350 0.367
##
## 0.573 0.767 0.788
## 0.532 0.712 0.723
## 0.237 0.301 0.306
##
## 4.528 0.959 0.959
## 3.326 0.932 0.932
## 2.221 0.872 0.872
## 1.489 0.773 0.773
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.190 0.041 4.627 0.000 0.110
## .sswk (.39.) 0.128 0.042 3.030 0.002 0.045
## .sspc (.40.) 0.137 0.041 3.346 0.001 0.057
## .ssei (.41.) -0.005 0.038 -0.119 0.905 -0.079
## .ssar (.42.) 0.220 0.040 5.550 0.000 0.143
## .ssmk (.43.) 0.247 0.043 5.773 0.000 0.163
## .ssmc (.44.) 0.059 0.038 1.562 0.118 -0.015
## .ssao (.45.) 0.164 0.039 4.221 0.000 0.088
## .ssai (.46.) -0.113 0.033 -3.420 0.001 -0.177
## .sssi (.47.) -0.073 0.034 -2.170 0.030 -0.139
## .ssno (.48.) 0.217 0.040 5.389 0.000 0.138
## .sscs (.49.) 0.180 0.042 4.295 0.000 0.098
## ci.upper Std.lv Std.all
## 0.271 0.190 0.195
## 0.210 0.128 0.129
## 0.217 0.137 0.143
## 0.070 -0.005 -0.005
## 0.298 0.220 0.233
## 0.331 0.247 0.251
## 0.133 0.059 0.063
## 0.240 0.164 0.171
## -0.048 -0.113 -0.134
## -0.007 -0.073 -0.085
## 0.296 0.217 0.223
## 0.261 0.180 0.182
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.156 0.015 10.067 0.000 0.125
## .sswk 0.171 0.016 10.636 0.000 0.140
## .sspc 0.250 0.024 10.504 0.000 0.203
## .ssei 0.260 0.023 11.509 0.000 0.216
## .ssar 0.156 0.016 9.470 0.000 0.124
## .ssmk 0.187 0.016 11.911 0.000 0.157
## .ssmc 0.250 0.018 13.534 0.000 0.214
## .ssao 0.408 0.028 14.734 0.000 0.353
## .ssai 0.321 0.026 12.156 0.000 0.269
## .sssi 0.311 0.028 11.171 0.000 0.257
## .ssno 0.360 0.040 9.058 0.000 0.282
## .sscs 0.462 0.052 8.858 0.000 0.360
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.186 0.156 0.163
## 0.203 0.171 0.176
## 0.296 0.250 0.272
## 0.304 0.260 0.287
## 0.188 0.156 0.174
## 0.218 0.187 0.193
## 0.286 0.250 0.283
## 0.462 0.408 0.444
## 0.372 0.321 0.454
## 0.366 0.311 0.427
## 0.438 0.360 0.380
## 0.565 0.462 0.477
## 1.000 0.081 0.081
## 1.000 0.131 0.131
## 1.000 0.239 0.239
## 1.000 0.403 0.403
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.254 0.041 6.166 0.000 0.173
## sswk (.p2.) 0.255 0.042 6.113 0.000 0.173
## sspc (.p3.) 0.091 0.024 3.770 0.000 0.044
## ssei (.p4.) 0.139 0.025 5.534 0.000 0.089
## math =~
## ssar (.p5.) 0.312 0.041 7.590 0.000 0.231
## sspc (.p6.) 0.187 0.028 6.610 0.000 0.132
## ssmk (.p7.) 0.233 0.035 6.700 0.000 0.165
## ssmc (.p8.) 0.184 0.025 7.336 0.000 0.135
## ssao (.p9.) 0.259 0.035 7.386 0.000 0.190
## electronic =~
## ssai (.10.) 0.304 0.034 8.858 0.000 0.237
## sssi (.11.) 0.316 0.036 8.703 0.000 0.245
## ssmc (.12.) 0.160 0.020 8.105 0.000 0.121
## ssei (.13.) 0.171 0.021 8.315 0.000 0.131
## speed =~
## ssno (.14.) 0.486 0.044 11.018 0.000 0.400
## sscs (.15.) 0.452 0.041 11.076 0.000 0.372
## ssmk (.16.) 0.191 0.023 8.143 0.000 0.145
## g =~
## verbal (.17.) 3.376 0.588 5.744 0.000 2.224
## math (.18.) 2.570 0.386 6.662 0.000 1.814
## elctrnc (.19.) 1.784 0.223 8.000 0.000 1.347
## speed (.20.) 1.218 0.138 8.819 0.000 0.947
## ci.upper Std.lv Std.all
##
## 0.335 0.991 0.923
## 0.336 0.992 0.915
## 0.139 0.356 0.346
## 0.188 0.540 0.490
##
## 0.392 0.931 0.893
## 0.243 0.560 0.545
## 0.302 0.697 0.665
## 0.233 0.550 0.523
## 0.328 0.775 0.732
##
## 0.371 0.888 0.793
## 0.387 0.924 0.852
## 0.199 0.467 0.444
## 0.211 0.500 0.454
##
## 0.573 0.861 0.808
## 0.532 0.799 0.754
## 0.237 0.338 0.323
##
## 4.528 0.959 0.959
## 3.326 0.952 0.952
## 2.221 0.675 0.675
## 1.489 0.762 0.762
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.190 0.041 4.627 0.000 0.110
## .sswk (.39.) 0.128 0.042 3.030 0.002 0.045
## .sspc (.40.) 0.137 0.041 3.346 0.001 0.057
## .ssei (.41.) -0.005 0.038 -0.119 0.905 -0.079
## .ssar (.42.) 0.220 0.040 5.550 0.000 0.143
## .ssmk (.43.) 0.247 0.043 5.773 0.000 0.163
## .ssmc (.44.) 0.059 0.038 1.562 0.118 -0.015
## .ssao (.45.) 0.164 0.039 4.221 0.000 0.088
## .ssai (.46.) -0.113 0.033 -3.420 0.001 -0.177
## .sssi (.47.) -0.073 0.034 -2.170 0.030 -0.139
## .ssno (.48.) 0.217 0.040 5.389 0.000 0.138
## .sscs (.49.) 0.180 0.042 4.295 0.000 0.098
## .verbal -0.169 0.108 -1.562 0.118 -0.382
## .math -0.512 0.123 -4.157 0.000 -0.753
## .elctrnc 1.628 0.203 8.017 0.000 1.230
## .speed -0.613 0.107 -5.734 0.000 -0.822
## g 0.094 0.067 1.403 0.161 -0.037
## ci.upper Std.lv Std.all
## 0.271 0.190 0.177
## 0.210 0.128 0.118
## 0.217 0.137 0.133
## 0.070 -0.005 -0.004
## 0.298 0.220 0.211
## 0.331 0.247 0.236
## 0.133 0.059 0.056
## 0.240 0.164 0.155
## -0.048 -0.113 -0.101
## -0.007 -0.073 -0.067
## 0.296 0.217 0.204
## 0.261 0.180 0.169
## 0.043 -0.043 -0.043
## -0.271 -0.171 -0.171
## 2.026 0.557 0.557
## -0.403 -0.346 -0.346
## 0.225 0.085 0.085
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.170 0.018 9.286 0.000 0.134
## .sswk 0.190 0.017 11.308 0.000 0.157
## .sspc 0.254 0.022 11.531 0.000 0.211
## .ssei 0.323 0.025 12.837 0.000 0.274
## .ssar 0.221 0.023 9.531 0.000 0.175
## .ssmk 0.157 0.013 11.754 0.000 0.131
## .ssmc 0.255 0.019 13.167 0.000 0.217
## .ssao 0.519 0.037 13.886 0.000 0.446
## .ssai 0.464 0.042 11.056 0.000 0.382
## .sssi 0.323 0.035 9.191 0.000 0.254
## .ssno 0.393 0.043 9.191 0.000 0.309
## .sscs 0.486 0.057 8.474 0.000 0.374
## .verbal 1.218 0.510 2.391 0.017 0.219
## .math 0.846 0.293 2.887 0.004 0.272
## .electronic 4.646 1.117 4.159 0.000 2.456
## .speed 1.313 0.283 4.634 0.000 0.758
## g 1.225 0.103 11.878 0.000 1.023
## ci.upper Std.lv Std.all
## 0.206 0.170 0.148
## 0.223 0.190 0.162
## 0.297 0.254 0.240
## 0.372 0.323 0.266
## 0.266 0.221 0.203
## 0.184 0.157 0.143
## 0.293 0.255 0.231
## 0.592 0.519 0.464
## 0.547 0.464 0.371
## 0.392 0.323 0.275
## 0.476 0.393 0.346
## 0.598 0.486 0.432
## 2.217 0.080 0.080
## 1.420 0.095 0.095
## 6.836 0.544 0.544
## 1.869 0.420 0.420
## 1.427 1.000 1.000
lavTestScore(scalar, release = 21:32)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 142.339 12 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p38. == .p92. 45.713 1 0.000
## 2 .p39. == .p93. 10.064 1 0.002
## 3 .p40. == .p94. 89.630 1 0.000
## 4 .p41. == .p95. 0.240 1 0.624
## 5 .p42. == .p96. 22.873 1 0.000
## 6 .p43. == .p97. 0.470 1 0.493
## 7 .p44. == .p98. 3.989 1 0.046
## 8 .p45. == .p99. 0.223 1 0.637
## 9 .p46. == .p100. 0.165 1 0.685
## 10 .p47. == .p101. 0.258 1 0.612
## 11 .p48. == .p102. 15.116 1 0.000
## 12 .p49. == .p103. 20.467 1 0.000
scalar2<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 3.008805e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 503.732 113.000 0.000 0.971 0.073 0.050 32104.122
## bic
## 32451.136
Mc(scalar2)
## [1] 0.8615513
summary(scalar2, standardized=T, ci=T) # -.105
## lavaan 0.6-18 ended normally after 123 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 31
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 503.732 383.548
## Degrees of freedom 113 113
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.313
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 210.470 160.254
## 0 293.262 223.293
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.246 0.043 5.762 0.000 0.162
## sswk (.p2.) 0.246 0.043 5.716 0.000 0.162
## sspc (.p3.) 0.112 0.023 4.856 0.000 0.067
## ssei (.p4.) 0.136 0.026 5.265 0.000 0.085
## math =~
## ssar (.p5.) 0.323 0.038 8.568 0.000 0.249
## sspc (.p6.) 0.161 0.026 6.089 0.000 0.109
## ssmk (.p7.) 0.236 0.033 7.245 0.000 0.173
## ssmc (.p8.) 0.194 0.024 8.231 0.000 0.148
## ssao (.p9.) 0.268 0.032 8.265 0.000 0.205
## electronic =~
## ssai (.10.) 0.307 0.034 8.989 0.000 0.240
## sssi (.11.) 0.320 0.036 8.820 0.000 0.249
## ssmc (.12.) 0.156 0.019 8.046 0.000 0.118
## ssei (.13.) 0.170 0.020 8.302 0.000 0.130
## speed =~
## ssno (.14.) 0.482 0.044 11.066 0.000 0.396
## sscs (.15.) 0.450 0.041 11.082 0.000 0.370
## ssmk (.16.) 0.200 0.023 8.693 0.000 0.155
## g =~
## verbal (.17.) 3.499 0.649 5.392 0.000 2.227
## math (.18.) 2.473 0.334 7.412 0.000 1.819
## elctrnc (.19.) 1.767 0.217 8.130 0.000 1.341
## speed (.20.) 1.224 0.139 8.840 0.000 0.953
## ci.upper Std.lv Std.all
##
## 0.330 0.896 0.915
## 0.330 0.895 0.906
## 0.157 0.408 0.431
## 0.186 0.493 0.518
##
## 0.397 0.863 0.912
## 0.213 0.430 0.454
## 0.300 0.631 0.640
## 0.241 0.518 0.551
## 0.332 0.715 0.746
##
## 0.374 0.623 0.741
## 0.391 0.649 0.759
## 0.194 0.317 0.337
## 0.210 0.344 0.362
##
## 0.567 0.761 0.783
## 0.529 0.711 0.723
## 0.246 0.317 0.322
##
## 4.771 0.962 0.962
## 3.127 0.927 0.927
## 2.193 0.870 0.870
## 1.496 0.775 0.775
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.177 0.041 4.339 0.000 0.097
## .sswk (.39.) 0.114 0.042 2.737 0.006 0.032
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssei (.41.) -0.007 0.038 -0.186 0.852 -0.081
## .ssar (.42.) 0.197 0.040 4.927 0.000 0.118
## .ssmk (.43.) 0.231 0.043 5.375 0.000 0.147
## .ssmc (.44.) 0.049 0.038 1.310 0.190 -0.024
## .ssao (.45.) 0.143 0.039 3.693 0.000 0.067
## .ssai (.46.) -0.109 0.033 -3.306 0.001 -0.173
## .sssi (.47.) -0.068 0.034 -2.033 0.042 -0.134
## .ssno (.48.) 0.225 0.040 5.605 0.000 0.147
## .sscs (.49.) 0.188 0.042 4.517 0.000 0.106
## ci.upper Std.lv Std.all
## 0.256 0.177 0.181
## 0.196 0.114 0.116
## 0.333 0.253 0.267
## 0.067 -0.007 -0.007
## 0.275 0.197 0.208
## 0.315 0.231 0.234
## 0.123 0.049 0.052
## 0.219 0.143 0.149
## -0.044 -0.109 -0.129
## -0.002 -0.068 -0.080
## 0.304 0.225 0.232
## 0.270 0.188 0.191
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.155 0.015 10.345 0.000 0.126
## .sswk 0.174 0.016 10.845 0.000 0.143
## .sspc 0.234 0.021 10.924 0.000 0.192
## .ssei 0.260 0.023 11.517 0.000 0.216
## .ssar 0.151 0.016 9.408 0.000 0.119
## .ssmk 0.186 0.016 11.752 0.000 0.155
## .ssmc 0.250 0.018 13.559 0.000 0.214
## .ssao 0.407 0.028 14.786 0.000 0.353
## .ssai 0.319 0.026 12.106 0.000 0.267
## .sssi 0.310 0.028 11.138 0.000 0.255
## .ssno 0.366 0.040 9.114 0.000 0.287
## .sscs 0.461 0.052 8.856 0.000 0.359
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.185 0.155 0.162
## 0.206 0.174 0.179
## 0.276 0.234 0.261
## 0.305 0.260 0.287
## 0.182 0.151 0.168
## 0.217 0.186 0.192
## 0.286 0.250 0.283
## 0.461 0.407 0.443
## 0.371 0.319 0.451
## 0.364 0.310 0.424
## 0.444 0.366 0.387
## 0.563 0.461 0.477
## 1.000 0.076 0.076
## 1.000 0.141 0.141
## 1.000 0.243 0.243
## 1.000 0.400 0.400
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.246 0.043 5.762 0.000 0.162
## sswk (.p2.) 0.246 0.043 5.716 0.000 0.162
## sspc (.p3.) 0.112 0.023 4.856 0.000 0.067
## ssei (.p4.) 0.136 0.026 5.265 0.000 0.085
## math =~
## ssar (.p5.) 0.323 0.038 8.568 0.000 0.249
## sspc (.p6.) 0.161 0.026 6.089 0.000 0.109
## ssmk (.p7.) 0.236 0.033 7.245 0.000 0.173
## ssmc (.p8.) 0.194 0.024 8.231 0.000 0.148
## ssao (.p9.) 0.268 0.032 8.265 0.000 0.205
## electronic =~
## ssai (.10.) 0.307 0.034 8.989 0.000 0.240
## sssi (.11.) 0.320 0.036 8.820 0.000 0.249
## ssmc (.12.) 0.156 0.019 8.046 0.000 0.118
## ssei (.13.) 0.170 0.020 8.302 0.000 0.130
## speed =~
## ssno (.14.) 0.482 0.044 11.066 0.000 0.396
## sscs (.15.) 0.450 0.041 11.082 0.000 0.370
## ssmk (.16.) 0.200 0.023 8.693 0.000 0.155
## g =~
## verbal (.17.) 3.499 0.649 5.392 0.000 2.227
## math (.18.) 2.473 0.334 7.412 0.000 1.819
## elctrnc (.19.) 1.767 0.217 8.130 0.000 1.341
## speed (.20.) 1.224 0.139 8.840 0.000 0.953
## ci.upper Std.lv Std.all
##
## 0.330 0.990 0.923
## 0.330 0.989 0.913
## 0.157 0.451 0.442
## 0.186 0.545 0.495
##
## 0.397 0.936 0.897
## 0.213 0.466 0.457
## 0.300 0.685 0.652
## 0.241 0.563 0.536
## 0.332 0.776 0.733
##
## 0.374 0.891 0.795
## 0.391 0.928 0.854
## 0.194 0.454 0.432
## 0.210 0.493 0.447
##
## 0.567 0.855 0.804
## 0.529 0.799 0.753
## 0.246 0.356 0.339
##
## 4.771 0.963 0.963
## 3.127 0.946 0.946
## 2.193 0.674 0.674
## 1.496 0.764 0.764
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.177 0.041 4.339 0.000 0.097
## .sswk (.39.) 0.114 0.042 2.737 0.006 0.032
## .sspc -0.036 0.043 -0.841 0.401 -0.121
## .ssei (.41.) -0.007 0.038 -0.186 0.852 -0.081
## .ssar (.42.) 0.197 0.040 4.927 0.000 0.118
## .ssmk (.43.) 0.231 0.043 5.375 0.000 0.147
## .ssmc (.44.) 0.049 0.038 1.310 0.190 -0.024
## .ssao (.45.) 0.143 0.039 3.693 0.000 0.067
## .ssai (.46.) -0.109 0.033 -3.306 0.001 -0.173
## .sssi (.47.) -0.068 0.034 -2.033 0.042 -0.134
## .ssno (.48.) 0.225 0.040 5.605 0.000 0.147
## .sscs (.49.) 0.188 0.042 4.517 0.000 0.106
## .verbal -0.138 0.088 -1.567 0.117 -0.310
## .math -0.372 0.095 -3.900 0.000 -0.558
## .elctrnc 1.542 0.196 7.864 0.000 1.158
## .speed -0.682 0.111 -6.164 0.000 -0.898
## g 0.117 0.066 1.755 0.079 -0.014
## ci.upper Std.lv Std.all
## 0.256 0.177 0.165
## 0.196 0.114 0.105
## 0.049 -0.036 -0.036
## 0.067 -0.007 -0.006
## 0.275 0.197 0.188
## 0.315 0.231 0.220
## 0.123 0.049 0.047
## 0.219 0.143 0.135
## -0.044 -0.109 -0.097
## -0.002 -0.068 -0.063
## 0.304 0.225 0.212
## 0.270 0.188 0.177
## 0.035 -0.034 -0.034
## -0.185 -0.128 -0.128
## 1.927 0.531 0.531
## -0.465 -0.384 -0.384
## 0.247 0.105 0.105
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.170 0.018 9.646 0.000 0.135
## .sswk 0.195 0.017 11.663 0.000 0.162
## .sspc 0.237 0.019 12.147 0.000 0.199
## .ssei 0.324 0.025 12.830 0.000 0.274
## .ssar 0.214 0.022 9.523 0.000 0.170
## .ssmk 0.156 0.013 11.747 0.000 0.130
## .ssmc 0.255 0.019 13.207 0.000 0.217
## .ssao 0.520 0.038 13.782 0.000 0.446
## .ssai 0.462 0.042 10.996 0.000 0.379
## .sssi 0.319 0.035 9.035 0.000 0.250
## .ssno 0.401 0.043 9.335 0.000 0.317
## .sscs 0.485 0.057 8.537 0.000 0.374
## .verbal 1.182 0.529 2.236 0.025 0.146
## .math 0.881 0.282 3.122 0.002 0.328
## .electronic 4.605 1.098 4.195 0.000 2.454
## .speed 1.312 0.280 4.686 0.000 0.763
## g 1.226 0.103 11.904 0.000 1.024
## ci.upper Std.lv Std.all
## 0.204 0.170 0.147
## 0.228 0.195 0.166
## 0.275 0.237 0.227
## 0.373 0.324 0.267
## 0.258 0.214 0.196
## 0.181 0.156 0.141
## 0.293 0.255 0.231
## 0.594 0.520 0.463
## 0.544 0.462 0.368
## 0.388 0.319 0.270
## 0.485 0.401 0.354
## 0.597 0.485 0.432
## 2.219 0.073 0.073
## 1.434 0.105 0.105
## 6.757 0.546 0.546
## 1.861 0.416 0.416
## 1.428 1.000 1.000
lavTestScore(scalar2, release = 21:31, standardized=T, epc=T) # others have low and similar chi-square, but ssno and sscs have the highest value in sepc.all
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 52.047 11 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p38. == .p92. 25.826 1 0.000
## 2 .p39. == .p93. 22.929 1 0.000
## 3 .p41. == .p95. 0.527 1 0.468
## 4 .p42. == .p96. 2.767 1 0.096
## 5 .p43. == .p97. 1.163 1 0.281
## 6 .p44. == .p98. 0.982 1 0.322
## 7 .p45. == .p99. 3.426 1 0.064
## 8 .p46. == .p100. 0.004 1 0.949
## 9 .p47. == .p101. 0.006 1 0.940
## 10 .p48. == .p102. 20.700 1 0.000
## 11 .p49. == .p103. 15.759 1 0.000
##
## $epc
##
## expected parameter changes (epc) and expected parameter values (epv):
##
## lhs op rhs block group free label plabel est epc
## 1 verbal =~ ssgs 1 1 1 .p1. .p1. 0.246 0.001
## 2 verbal =~ sswk 1 1 2 .p2. .p2. 0.246 0.002
## 3 verbal =~ sspc 1 1 3 .p3. .p3. 0.112 0.000
## 4 verbal =~ ssei 1 1 4 .p4. .p4. 0.136 -0.001
## 5 math =~ ssar 1 1 5 .p5. .p5. 0.323 -0.003
## 6 math =~ sspc 1 1 6 .p6. .p6. 0.161 -0.001
## 7 math =~ ssmk 1 1 7 .p7. .p7. 0.236 0.001
## 8 math =~ ssmc 1 1 8 .p8. .p8. 0.194 0.002
## 9 math =~ ssao 1 1 9 .p9. .p9. 0.268 -0.003
## 10 electronic =~ ssai 1 1 10 .p10. .p10. 0.307 0.000
## 11 electronic =~ sssi 1 1 11 .p11. .p11. 0.320 0.001
## 12 electronic =~ ssmc 1 1 12 .p12. .p12. 0.156 -0.005
## 13 electronic =~ ssei 1 1 13 .p13. .p13. 0.170 0.004
## 14 speed =~ ssno 1 1 14 .p14. .p14. 0.482 0.011
## 15 speed =~ sscs 1 1 15 .p15. .p15. 0.450 -0.006
## 16 speed =~ ssmk 1 1 16 .p16. .p16. 0.200 -0.006
## 17 g =~ verbal 1 1 17 .p17. .p17. 3.499 -0.027
## 18 g =~ math 1 1 18 .p18. .p18. 2.473 0.026
## 19 g =~ electronic 1 1 19 .p19. .p19. 1.767 -0.003
## 20 g =~ speed 1 1 20 .p20. .p20. 1.224 -0.009
## 21 ssgs ~~ ssgs 1 1 21 .p21. 0.155 0.000
## 22 sswk ~~ sswk 1 1 22 .p22. 0.174 -0.001
## 23 sspc ~~ sspc 1 1 23 .p23. 0.234 0.000
## 24 ssei ~~ ssei 1 1 24 .p24. 0.260 -0.001
## 25 ssar ~~ ssar 1 1 25 .p25. 0.151 0.001
## 26 ssmk ~~ ssmk 1 1 26 .p26. 0.186 0.001
## 27 ssmc ~~ ssmc 1 1 27 .p27. 0.250 0.000
## 28 ssao ~~ ssao 1 1 28 .p28. 0.407 0.000
## 29 ssai ~~ ssai 1 1 29 .p29. 0.319 0.000
## 30 sssi ~~ sssi 1 1 30 .p30. 0.310 0.000
## 31 ssno ~~ ssno 1 1 31 .p31. 0.366 -0.009
## 32 sscs ~~ sscs 1 1 32 .p32. 0.461 0.005
## 33 verbal ~~ verbal 1 1 0 .p33. 1.000 NA
## 34 math ~~ math 1 1 0 .p34. 1.000 NA
## 35 electronic ~~ electronic 1 1 0 .p35. 1.000 NA
## 36 speed ~~ speed 1 1 0 .p36. 1.000 NA
## 37 g ~~ g 1 1 0 .p37. 1.000 NA
## 38 ssgs ~1 1 1 33 .p38. .p38. 0.177 -0.038
## 39 sswk ~1 1 1 34 .p39. .p39. 0.114 0.040
## 40 sspc ~1 1 1 35 .p40. 0.253 0.000
## 41 ssei ~1 1 1 36 .p41. .p41. -0.007 0.007
## 42 ssar ~1 1 1 37 .p42. .p42. 0.197 -0.011
## 43 ssmk ~1 1 1 38 .p43. .p43. 0.231 0.010
## 44 ssmc ~1 1 1 39 .p44. .p44. 0.049 -0.010
## 45 ssao ~1 1 1 40 .p45. .p45. 0.143 0.028
## 46 ssai ~1 1 1 41 .p46. .p46. -0.109 0.001
## 47 sssi ~1 1 1 42 .p47. .p47. -0.068 0.001
## 48 ssno ~1 1 1 43 .p48. .p48. 0.225 -0.051
## 49 sscs ~1 1 1 44 .p49. .p49. 0.188 0.058
## 50 verbal ~1 1 1 0 .p50. 0.000 NA
## 51 math ~1 1 1 0 .p51. 0.000 NA
## 52 electronic ~1 1 1 0 .p52. 0.000 NA
## 53 speed ~1 1 1 0 .p53. 0.000 NA
## 54 g ~1 1 1 0 .p54. 0.000 NA
## 55 verbal =~ ssgs 2 2 45 .p1. .p55. 0.246 0.001
## 56 verbal =~ sswk 2 2 46 .p2. .p56. 0.246 0.002
## 57 verbal =~ sspc 2 2 47 .p3. .p57. 0.112 0.000
## 58 verbal =~ ssei 2 2 48 .p4. .p58. 0.136 -0.001
## 59 math =~ ssar 2 2 49 .p5. .p59. 0.323 -0.003
## 60 math =~ sspc 2 2 50 .p6. .p60. 0.161 -0.001
## 61 math =~ ssmk 2 2 51 .p7. .p61. 0.236 0.001
## 62 math =~ ssmc 2 2 52 .p8. .p62. 0.194 0.002
## 63 math =~ ssao 2 2 53 .p9. .p63. 0.268 -0.003
## 64 electronic =~ ssai 2 2 54 .p10. .p64. 0.307 0.000
## 65 electronic =~ sssi 2 2 55 .p11. .p65. 0.320 0.001
## 66 electronic =~ ssmc 2 2 56 .p12. .p66. 0.156 -0.005
## 67 electronic =~ ssei 2 2 57 .p13. .p67. 0.170 0.004
## 68 speed =~ ssno 2 2 58 .p14. .p68. 0.482 0.011
## 69 speed =~ sscs 2 2 59 .p15. .p69. 0.450 -0.006
## 70 speed =~ ssmk 2 2 60 .p16. .p70. 0.200 -0.006
## 71 g =~ verbal 2 2 61 .p17. .p71. 3.499 -0.027
## epv sepc.lv sepc.all sepc.nox
## 1 0.247 0.005 0.005 0.005
## 2 0.248 0.008 0.008 0.008
## 3 0.112 0.000 0.000 0.000
## 4 0.135 -0.003 -0.003 -0.003
## 5 0.320 -0.008 -0.009 -0.009
## 6 0.161 -0.001 -0.002 -0.002
## 7 0.237 0.003 0.003 0.003
## 8 0.196 0.005 0.006 0.006
## 9 0.265 -0.007 -0.007 -0.007
## 10 0.307 0.001 0.001 0.001
## 11 0.320 0.001 0.001 0.001
## 12 0.151 -0.011 -0.011 -0.011
## 13 0.174 0.008 0.008 0.008
## 14 0.493 0.017 0.018 0.018
## 15 0.444 -0.010 -0.010 -0.010
## 16 0.195 -0.009 -0.009 -0.009
## 17 3.472 -0.007 -0.007 -0.007
## 18 2.499 0.010 0.010 0.010
## 19 1.764 -0.002 -0.002 -0.002
## 20 1.216 -0.005 -0.005 -0.005
## 21 0.155 0.155 0.162 0.162
## 22 0.174 -0.174 -0.179 -0.179
## 23 0.234 0.234 0.261 0.261
## 24 0.260 -0.260 -0.287 -0.287
## 25 0.152 0.151 0.168 0.168
## 26 0.187 0.186 0.192 0.192
## 27 0.251 0.250 0.283 0.283
## 28 0.408 0.407 0.443 0.443
## 29 0.319 -0.319 -0.451 -0.451
## 30 0.309 -0.310 -0.424 -0.424
## 31 0.356 -0.366 -0.387 -0.387
## 32 0.467 0.461 0.477 0.477
## 33 NA NA NA NA
## 34 NA NA NA NA
## 35 NA NA NA NA
## 36 NA NA NA NA
## 37 NA NA NA NA
## 38 0.139 -0.038 -0.039 -0.039
## 39 0.154 0.040 0.040 0.040
## 40 0.253 0.000 0.000 0.000
## 41 0.000 0.007 0.008 0.008
## 42 0.186 -0.011 -0.012 -0.012
## 43 0.241 0.010 0.010 0.010
## 44 0.039 -0.010 -0.011 -0.011
## 45 0.171 0.028 0.029 0.029
## 46 -0.108 0.001 0.001 0.001
## 47 -0.068 0.001 0.001 0.001
## 48 0.175 -0.051 -0.052 -0.052
## 49 0.245 0.058 0.058 0.058
## 50 NA NA NA NA
## 51 NA NA NA NA
## 52 NA NA NA NA
## 53 NA NA NA NA
## 54 NA NA NA NA
## 55 0.247 0.005 0.005 0.005
## 56 0.248 0.009 0.008 0.008
## 57 0.112 0.000 0.000 0.000
## 58 0.135 -0.003 -0.003 -0.003
## 59 0.320 -0.009 -0.008 -0.008
## 60 0.161 -0.002 -0.002 -0.002
## 61 0.237 0.003 0.003 0.003
## 62 0.196 0.006 0.005 0.005
## 63 0.265 -0.008 -0.007 -0.007
## 64 0.307 0.001 0.001 0.001
## 65 0.320 0.002 0.001 0.001
## 66 0.151 -0.015 -0.015 -0.015
## 67 0.174 0.011 0.010 0.010
## 68 0.493 0.019 0.018 0.018
## 69 0.444 -0.011 -0.010 -0.010
## 70 0.195 -0.010 -0.009 -0.009
## 71 3.472 -0.007 -0.007 -0.007
## [ reached 'max' / getOption("max.print") -- omitted 37 rows ]
strict<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("sspc~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 2.212536e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 547.144 125.000 0.000 0.968 0.072 0.053 32123.535
## bic
## 32408.397
Mc(strict)
## [1] 0.8512913
summary(strict, standardized=T, ci=T) # -.109
## lavaan 0.6-18 ended normally after 119 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 43
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 547.144 410.927
## Degrees of freedom 125 125
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.331
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 237.448 178.332
## 0 309.697 232.595
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.240 0.044 5.507 0.000 0.155
## sswk (.p2.) 0.240 0.044 5.474 0.000 0.154
## sspc (.p3.) 0.108 0.023 4.686 0.000 0.063
## ssei (.p4.) 0.132 0.026 5.039 0.000 0.081
## math =~
## ssar (.p5.) 0.310 0.040 7.836 0.000 0.232
## sspc (.p6.) 0.156 0.027 5.797 0.000 0.103
## ssmk (.p7.) 0.227 0.034 6.614 0.000 0.160
## ssmc (.p8.) 0.186 0.024 7.629 0.000 0.138
## ssao (.p9.) 0.255 0.034 7.600 0.000 0.189
## electronic =~
## ssai (.10.) 0.292 0.036 8.093 0.000 0.221
## sssi (.11.) 0.298 0.038 7.937 0.000 0.224
## ssmc (.12.) 0.146 0.020 7.324 0.000 0.107
## ssei (.13.) 0.162 0.021 7.531 0.000 0.120
## speed =~
## ssno (.14.) 0.479 0.044 10.844 0.000 0.393
## sscs (.15.) 0.448 0.041 10.834 0.000 0.367
## ssmk (.16.) 0.198 0.023 8.494 0.000 0.152
## g =~
## verbal (.17.) 3.584 0.691 5.188 0.000 2.230
## math (.18.) 2.597 0.378 6.876 0.000 1.856
## elctrnc (.19.) 1.880 0.253 7.435 0.000 1.385
## speed (.20.) 1.232 0.142 8.669 0.000 0.953
## ci.upper Std.lv Std.all
##
## 0.326 0.895 0.912
## 0.326 0.892 0.901
## 0.153 0.402 0.424
## 0.183 0.492 0.508
##
## 0.387 0.861 0.896
## 0.208 0.433 0.458
## 0.294 0.632 0.645
## 0.234 0.517 0.551
## 0.320 0.709 0.721
##
## 0.363 0.622 0.711
## 0.372 0.635 0.744
## 0.185 0.310 0.330
## 0.204 0.344 0.356
##
## 0.566 0.761 0.776
## 0.529 0.710 0.719
## 0.244 0.315 0.321
##
## 4.939 0.963 0.963
## 3.337 0.933 0.933
## 2.376 0.883 0.883
## 1.511 0.776 0.776
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.179 0.041 4.372 0.000 0.098
## .sswk (.39.) 0.112 0.042 2.697 0.007 0.031
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssei (.41.) -0.010 0.038 -0.252 0.801 -0.084
## .ssar (.42.) 0.199 0.040 4.947 0.000 0.120
## .ssmk (.43.) 0.231 0.043 5.389 0.000 0.147
## .ssmc (.44.) 0.051 0.038 1.346 0.178 -0.023
## .ssao (.45.) 0.139 0.039 3.589 0.000 0.063
## .ssai (.46.) -0.112 0.033 -3.373 0.001 -0.177
## .sssi (.47.) -0.066 0.034 -1.954 0.051 -0.131
## .ssno (.48.) 0.228 0.040 5.633 0.000 0.149
## .sscs (.49.) 0.187 0.041 4.523 0.000 0.106
## ci.upper Std.lv Std.all
## 0.259 0.179 0.182
## 0.193 0.112 0.113
## 0.333 0.253 0.267
## 0.065 -0.010 -0.010
## 0.278 0.199 0.207
## 0.315 0.231 0.236
## 0.124 0.051 0.054
## 0.214 0.139 0.141
## -0.047 -0.112 -0.128
## 0.000 -0.066 -0.077
## 0.307 0.228 0.233
## 0.267 0.187 0.189
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.21.) 0.162 0.012 13.728 0.000 0.139
## .sswk (.22.) 0.185 0.012 15.805 0.000 0.162
## .sspc (.23.) 0.235 0.015 16.188 0.000 0.206
## .ssei (.24.) 0.290 0.017 17.190 0.000 0.257
## .ssar (.25.) 0.182 0.014 13.142 0.000 0.155
## .ssmk (.26.) 0.172 0.011 16.031 0.000 0.151
## .ssmc (.27.) 0.254 0.013 18.906 0.000 0.228
## .ssao (.28.) 0.464 0.024 19.533 0.000 0.418
## .ssai (.29.) 0.379 0.024 15.514 0.000 0.331
## .sssi (.30.) 0.324 0.023 14.122 0.000 0.279
## .ssno (.31.) 0.382 0.030 12.739 0.000 0.323
## .sscs (.32.) 0.472 0.039 12.008 0.000 0.395
## .verbal 1.000 1.000
## .math 1.000 1.000
## .elctrnc 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.185 0.162 0.168
## 0.208 0.185 0.189
## 0.263 0.235 0.262
## 0.323 0.290 0.309
## 0.209 0.182 0.197
## 0.193 0.172 0.180
## 0.281 0.254 0.288
## 0.511 0.464 0.480
## 0.427 0.379 0.495
## 0.369 0.324 0.446
## 0.441 0.382 0.398
## 0.549 0.472 0.483
## 1.000 0.072 0.072
## 1.000 0.129 0.129
## 1.000 0.221 0.221
## 1.000 0.397 0.397
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.240 0.044 5.507 0.000 0.155
## sswk (.p2.) 0.240 0.044 5.474 0.000 0.154
## sspc (.p3.) 0.108 0.023 4.686 0.000 0.063
## ssei (.p4.) 0.132 0.026 5.039 0.000 0.081
## math =~
## ssar (.p5.) 0.310 0.040 7.836 0.000 0.232
## sspc (.p6.) 0.156 0.027 5.797 0.000 0.103
## ssmk (.p7.) 0.227 0.034 6.614 0.000 0.160
## ssmc (.p8.) 0.186 0.024 7.629 0.000 0.138
## ssao (.p9.) 0.255 0.034 7.600 0.000 0.189
## electronic =~
## ssai (.10.) 0.292 0.036 8.093 0.000 0.221
## sssi (.11.) 0.298 0.038 7.937 0.000 0.224
## ssmc (.12.) 0.146 0.020 7.324 0.000 0.107
## ssei (.13.) 0.162 0.021 7.531 0.000 0.120
## speed =~
## ssno (.14.) 0.479 0.044 10.844 0.000 0.393
## sscs (.15.) 0.448 0.041 10.834 0.000 0.367
## ssmk (.16.) 0.198 0.023 8.494 0.000 0.152
## g =~
## verbal (.17.) 3.584 0.691 5.188 0.000 2.230
## math (.18.) 2.597 0.378 6.876 0.000 1.856
## elctrnc (.19.) 1.880 0.253 7.435 0.000 1.385
## speed (.20.) 1.232 0.142 8.669 0.000 0.953
## ci.upper Std.lv Std.all
##
## 0.326 0.992 0.927
## 0.326 0.989 0.917
## 0.153 0.446 0.436
## 0.183 0.545 0.499
##
## 0.387 0.944 0.911
## 0.208 0.475 0.465
## 0.294 0.692 0.651
## 0.234 0.567 0.539
## 0.320 0.777 0.752
##
## 0.363 0.907 0.828
## 0.372 0.926 0.852
## 0.185 0.453 0.431
## 0.204 0.502 0.460
##
## 0.566 0.858 0.811
## 0.529 0.801 0.759
## 0.244 0.355 0.334
##
## 4.939 0.961 0.961
## 3.337 0.942 0.942
## 2.376 0.670 0.670
## 1.511 0.762 0.762
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.179 0.041 4.372 0.000 0.098
## .sswk (.39.) 0.112 0.042 2.697 0.007 0.031
## .sspc -0.036 0.043 -0.832 0.405 -0.121
## .ssei (.41.) -0.010 0.038 -0.252 0.801 -0.084
## .ssar (.42.) 0.199 0.040 4.947 0.000 0.120
## .ssmk (.43.) 0.231 0.043 5.389 0.000 0.147
## .ssmc (.44.) 0.051 0.038 1.346 0.178 -0.023
## .ssao (.45.) 0.139 0.039 3.589 0.000 0.063
## .ssai (.46.) -0.112 0.033 -3.373 0.001 -0.177
## .sssi (.47.) -0.066 0.034 -1.954 0.051 -0.131
## .ssno (.48.) 0.228 0.040 5.633 0.000 0.149
## .sscs (.49.) 0.187 0.041 4.523 0.000 0.106
## .verbal -0.156 0.098 -1.591 0.112 -0.347
## .math -0.398 0.104 -3.816 0.000 -0.602
## .elctrnc 1.630 0.224 7.271 0.000 1.190
## .speed -0.690 0.114 -6.037 0.000 -0.914
## g 0.120 0.067 1.793 0.073 -0.011
## ci.upper Std.lv Std.all
## 0.259 0.179 0.167
## 0.193 0.112 0.104
## 0.049 -0.036 -0.035
## 0.065 -0.010 -0.009
## 0.278 0.199 0.192
## 0.315 0.231 0.217
## 0.124 0.051 0.048
## 0.214 0.139 0.134
## -0.047 -0.112 -0.102
## 0.000 -0.066 -0.060
## 0.307 0.228 0.216
## 0.267 0.187 0.177
## 0.036 -0.038 -0.038
## -0.194 -0.130 -0.130
## 2.069 0.525 0.525
## -0.466 -0.386 -0.386
## 0.252 0.109 0.109
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.21.) 0.162 0.012 13.728 0.000 0.139
## .sswk (.22.) 0.185 0.012 15.805 0.000 0.162
## .sspc (.23.) 0.235 0.015 16.188 0.000 0.206
## .ssei (.24.) 0.290 0.017 17.190 0.000 0.257
## .ssar (.25.) 0.182 0.014 13.142 0.000 0.155
## .ssmk (.26.) 0.172 0.011 16.031 0.000 0.151
## .ssmc (.27.) 0.254 0.013 18.906 0.000 0.228
## .ssao (.28.) 0.464 0.024 19.533 0.000 0.418
## .ssai (.29.) 0.379 0.024 15.514 0.000 0.331
## .sssi (.30.) 0.324 0.023 14.122 0.000 0.279
## .ssno (.31.) 0.382 0.030 12.739 0.000 0.323
## .sscs (.32.) 0.472 0.039 12.008 0.000 0.395
## .verbal 1.290 0.554 2.327 0.020 0.204
## .math 1.041 0.329 3.168 0.002 0.397
## .elctrnc 5.322 1.395 3.816 0.000 2.589
## .speed 1.345 0.298 4.506 0.000 0.760
## g 1.225 0.103 11.932 0.000 1.023
## ci.upper Std.lv Std.all
## 0.185 0.162 0.141
## 0.208 0.185 0.159
## 0.263 0.235 0.225
## 0.323 0.290 0.243
## 0.209 0.182 0.170
## 0.193 0.172 0.152
## 0.281 0.254 0.230
## 0.511 0.464 0.435
## 0.427 0.379 0.315
## 0.369 0.324 0.274
## 0.441 0.382 0.342
## 0.549 0.472 0.424
## 2.377 0.076 0.076
## 1.685 0.112 0.112
## 8.055 0.551 0.551
## 1.930 0.420 0.420
## 1.426 1.000 1.000
latent<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("sspc~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 1.212352e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 618.717 118.000 0.000 0.962 0.080 0.108 32209.107
## bic
## 32530.224
Mc(latent)
## [1] 0.8261595
summary(latent, standardized=T, ci=T) # -.068
## lavaan 0.6-18 ended normally after 76 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 93
## Number of equality constraints 31
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 618.717 469.124
## Degrees of freedom 118 118
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.319
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 281.350 213.326
## 0 337.366 255.798
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.230 0.045 5.097 0.000 0.141
## sswk (.p2.) 0.229 0.045 5.085 0.000 0.141
## sspc (.p3.) 0.105 0.023 4.575 0.000 0.060
## ssei (.p4.) 0.129 0.027 4.765 0.000 0.076
## math =~
## ssar (.p5.) 0.334 0.030 11.030 0.000 0.274
## sspc (.p6.) 0.164 0.025 6.568 0.000 0.115
## ssmk (.p7.) 0.243 0.028 8.596 0.000 0.188
## ssmc (.p8.) 0.196 0.020 9.865 0.000 0.157
## ssao (.p9.) 0.276 0.026 10.627 0.000 0.225
## electronic =~
## ssai (.10.) 0.483 0.028 17.514 0.000 0.429
## sssi (.11.) 0.510 0.028 18.043 0.000 0.455
## ssmc (.12.) 0.258 0.019 13.217 0.000 0.219
## ssei (.13.) 0.260 0.026 10.162 0.000 0.210
## speed =~
## ssno (.14.) 0.523 0.037 14.225 0.000 0.451
## sscs (.15.) 0.488 0.033 14.846 0.000 0.424
## ssmk (.16.) 0.221 0.022 10.013 0.000 0.178
## g =~
## verbal (.17.) 3.989 0.832 4.793 0.000 2.358
## math (.18.) 2.509 0.266 9.428 0.000 1.987
## elctrnc (.19.) 1.246 0.088 14.162 0.000 1.073
## speed (.20.) 1.176 0.104 11.322 0.000 0.972
## ci.upper Std.lv Std.all
##
## 0.318 0.944 0.922
## 0.318 0.943 0.915
## 0.151 0.434 0.441
## 0.182 0.531 0.518
##
## 0.393 0.901 0.920
## 0.214 0.444 0.452
## 0.299 0.657 0.641
## 0.235 0.530 0.527
## 0.327 0.746 0.759
##
## 0.538 0.772 0.814
## 0.566 0.815 0.841
## 0.296 0.411 0.409
## 0.310 0.415 0.405
##
## 0.595 0.807 0.806
## 0.553 0.754 0.743
## 0.265 0.342 0.334
##
## 5.620 0.970 0.970
## 3.031 0.929 0.929
## 1.418 0.780 0.780
## 1.379 0.762 0.762
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.176 0.041 4.329 0.000 0.097
## .sswk (.39.) 0.114 0.042 2.727 0.006 0.032
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssei (.41.) -0.003 0.037 -0.083 0.934 -0.076
## .ssar (.42.) 0.197 0.040 4.945 0.000 0.119
## .ssmk (.43.) 0.232 0.043 5.408 0.000 0.148
## .ssmc (.44.) 0.045 0.038 1.173 0.241 -0.030
## .ssao (.45.) 0.143 0.039 3.709 0.000 0.068
## .ssai (.46.) -0.107 0.033 -3.255 0.001 -0.172
## .sssi (.47.) -0.070 0.034 -2.067 0.039 -0.136
## .ssno (.48.) 0.223 0.040 5.533 0.000 0.144
## .sscs (.49.) 0.188 0.042 4.510 0.000 0.106
## ci.upper Std.lv Std.all
## 0.256 0.176 0.172
## 0.196 0.114 0.110
## 0.333 0.253 0.257
## 0.070 -0.003 -0.003
## 0.275 0.197 0.201
## 0.316 0.232 0.227
## 0.119 0.045 0.044
## 0.219 0.143 0.146
## -0.043 -0.107 -0.113
## -0.004 -0.070 -0.072
## 0.302 0.223 0.222
## 0.269 0.188 0.185
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.157 0.015 10.308 0.000 0.127
## .sswk 0.173 0.016 10.797 0.000 0.142
## .sspc 0.233 0.021 10.906 0.000 0.191
## .ssei 0.264 0.023 11.686 0.000 0.220
## .ssar 0.148 0.016 9.392 0.000 0.117
## .ssmk 0.183 0.016 11.430 0.000 0.151
## .ssmc 0.247 0.018 13.448 0.000 0.211
## .ssao 0.410 0.028 14.756 0.000 0.355
## .ssai 0.303 0.027 11.214 0.000 0.250
## .sssi 0.276 0.027 10.071 0.000 0.222
## .ssno 0.350 0.040 8.685 0.000 0.271
## .sscs 0.459 0.053 8.712 0.000 0.356
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.187 0.157 0.150
## 0.204 0.173 0.163
## 0.274 0.233 0.241
## 0.309 0.264 0.251
## 0.179 0.148 0.154
## 0.214 0.183 0.174
## 0.282 0.247 0.243
## 0.464 0.410 0.424
## 0.356 0.303 0.337
## 0.329 0.276 0.293
## 0.429 0.350 0.350
## 0.563 0.459 0.447
## 1.000 0.059 0.059
## 1.000 0.137 0.137
## 1.000 0.392 0.392
## 1.000 0.420 0.420
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.230 0.045 5.097 0.000 0.141
## sswk (.p2.) 0.229 0.045 5.085 0.000 0.141
## sspc (.p3.) 0.105 0.023 4.575 0.000 0.060
## ssei (.p4.) 0.129 0.027 4.765 0.000 0.076
## math =~
## ssar (.p5.) 0.334 0.030 11.030 0.000 0.274
## sspc (.p6.) 0.164 0.025 6.568 0.000 0.115
## ssmk (.p7.) 0.243 0.028 8.596 0.000 0.188
## ssmc (.p8.) 0.196 0.020 9.865 0.000 0.157
## ssao (.p9.) 0.276 0.026 10.627 0.000 0.225
## electronic =~
## ssai (.10.) 0.483 0.028 17.514 0.000 0.429
## sssi (.11.) 0.510 0.028 18.043 0.000 0.455
## ssmc (.12.) 0.258 0.019 13.217 0.000 0.219
## ssei (.13.) 0.260 0.026 10.162 0.000 0.210
## speed =~
## ssno (.14.) 0.523 0.037 14.225 0.000 0.451
## sscs (.15.) 0.488 0.033 14.846 0.000 0.424
## ssmk (.16.) 0.221 0.022 10.013 0.000 0.178
## g =~
## verbal (.17.) 3.989 0.832 4.793 0.000 2.358
## math (.18.) 2.509 0.266 9.428 0.000 1.987
## elctrnc (.19.) 1.246 0.088 14.162 0.000 1.073
## speed (.20.) 1.176 0.104 11.322 0.000 0.972
## ci.upper Std.lv Std.all
##
## 0.318 0.944 0.918
## 0.318 0.943 0.905
## 0.151 0.434 0.440
## 0.182 0.531 0.502
##
## 0.393 0.901 0.889
## 0.214 0.444 0.450
## 0.299 0.657 0.649
## 0.235 0.530 0.524
## 0.327 0.746 0.719
##
## 0.538 0.772 0.739
## 0.566 0.815 0.804
## 0.296 0.411 0.407
## 0.310 0.415 0.392
##
## 0.595 0.807 0.781
## 0.553 0.754 0.732
## 0.265 0.342 0.338
##
## 5.620 0.970 0.970
## 3.031 0.929 0.929
## 1.418 0.780 0.780
## 1.379 0.762 0.762
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.176 0.041 4.329 0.000 0.097
## .sswk (.39.) 0.114 0.042 2.727 0.006 0.032
## .sspc -0.037 0.043 -0.846 0.397 -0.122
## .ssei (.41.) -0.003 0.037 -0.083 0.934 -0.076
## .ssar (.42.) 0.197 0.040 4.945 0.000 0.119
## .ssmk (.43.) 0.232 0.043 5.408 0.000 0.148
## .ssmc (.44.) 0.045 0.038 1.173 0.241 -0.030
## .ssao (.45.) 0.143 0.039 3.709 0.000 0.068
## .ssai (.46.) -0.107 0.033 -3.255 0.001 -0.172
## .sssi (.47.) -0.070 0.034 -2.067 0.039 -0.136
## .ssno (.48.) 0.223 0.040 5.533 0.000 0.144
## .sscs (.49.) 0.188 0.042 4.510 0.000 0.106
## .verbal 0.024 0.058 0.422 0.673 -0.089
## .math -0.257 0.079 -3.252 0.001 -0.412
## .elctrnc 1.016 0.086 11.746 0.000 0.846
## .speed -0.574 0.092 -6.260 0.000 -0.754
## g 0.068 0.061 1.118 0.263 -0.051
## ci.upper Std.lv Std.all
## 0.256 0.176 0.172
## 0.196 0.114 0.109
## 0.048 -0.037 -0.037
## 0.070 -0.003 -0.003
## 0.275 0.197 0.195
## 0.316 0.232 0.229
## 0.119 0.045 0.044
## 0.219 0.143 0.138
## -0.043 -0.107 -0.103
## -0.004 -0.070 -0.069
## 0.302 0.223 0.215
## 0.269 0.188 0.183
## 0.137 0.006 0.006
## -0.102 -0.095 -0.095
## 1.185 0.636 0.636
## -0.394 -0.372 -0.372
## 0.187 0.068 0.068
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.167 0.017 9.945 0.000 0.134
## .sswk 0.196 0.016 11.915 0.000 0.164
## .sspc 0.241 0.020 12.138 0.000 0.202
## .ssei 0.332 0.026 12.607 0.000 0.281
## .ssar 0.214 0.022 9.609 0.000 0.171
## .ssmk 0.157 0.013 11.802 0.000 0.131
## .ssmc 0.255 0.019 13.147 0.000 0.217
## .ssao 0.519 0.038 13.755 0.000 0.445
## .ssai 0.496 0.044 11.302 0.000 0.410
## .sssi 0.362 0.037 9.727 0.000 0.289
## .ssno 0.417 0.045 9.253 0.000 0.329
## .sscs 0.491 0.057 8.638 0.000 0.380
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.200 0.167 0.158
## 0.228 0.196 0.181
## 0.280 0.241 0.248
## 0.384 0.332 0.296
## 0.258 0.214 0.209
## 0.183 0.157 0.154
## 0.293 0.255 0.250
## 0.593 0.519 0.483
## 0.582 0.496 0.454
## 0.435 0.362 0.353
## 0.506 0.417 0.390
## 0.602 0.491 0.464
## 1.000 0.059 0.059
## 1.000 0.137 0.137
## 1.000 0.392 0.392
## 1.000 0.420 0.420
## 1.000 1.000 1.000
latent2<-cfa(hof.lv, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 4.942165e-14) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 503.989 115.000 0.000 0.971 0.072 0.050 32100.379
## bic
## 32437.034
Mc(latent2)
## [1] 0.8621242
summary(latent2, standardized=T, ci=T) # g -.093 Std.all
## lavaan 0.6-18 ended normally after 103 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 31
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 503.989 383.237
## Degrees of freedom 115 115
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.315
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 210.821 160.310
## 0 293.168 222.927
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.251 0.040 6.308 0.000 0.173
## sswk (.p2.) 0.251 0.040 6.273 0.000 0.172
## sspc (.p3.) 0.115 0.021 5.395 0.000 0.073
## ssei (.p4.) 0.138 0.025 5.566 0.000 0.089
## math =~
## ssar (.p5.) 0.319 0.031 10.218 0.000 0.258
## sspc (.p6.) 0.158 0.024 6.589 0.000 0.111
## ssmk (.p7.) 0.233 0.028 8.284 0.000 0.178
## ssmc (.p8.) 0.191 0.020 9.539 0.000 0.152
## ssao (.p9.) 0.264 0.027 9.859 0.000 0.212
## electronic =~
## ssai (.10.) 0.307 0.034 9.023 0.000 0.240
## sssi (.11.) 0.320 0.036 8.854 0.000 0.249
## ssmc (.12.) 0.156 0.019 8.077 0.000 0.118
## ssei (.13.) 0.170 0.020 8.295 0.000 0.130
## speed =~
## ssno (.14.) 0.481 0.044 11.051 0.000 0.396
## sscs (.15.) 0.449 0.041 11.066 0.000 0.370
## ssmk (.16.) 0.201 0.023 8.749 0.000 0.156
## g =~
## verbal (.17.) 3.435 0.587 5.855 0.000 2.285
## math (.18.) 2.506 0.293 8.553 0.000 1.932
## elctrnc (.19.) 1.769 0.216 8.172 0.000 1.345
## speed (.20.) 1.223 0.139 8.797 0.000 0.951
## ci.upper Std.lv Std.all
##
## 0.329 0.898 0.916
## 0.329 0.897 0.907
## 0.156 0.410 0.433
## 0.187 0.494 0.518
##
## 0.380 0.860 0.911
## 0.205 0.427 0.451
## 0.288 0.628 0.638
## 0.231 0.516 0.549
## 0.317 0.712 0.745
##
## 0.374 0.624 0.741
## 0.390 0.649 0.759
## 0.194 0.318 0.338
## 0.210 0.345 0.362
##
## 0.567 0.760 0.783
## 0.529 0.710 0.723
## 0.246 0.317 0.322
##
## 4.585 0.960 0.960
## 3.080 0.929 0.929
## 2.193 0.871 0.871
## 1.496 0.774 0.774
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.177 0.041 4.337 0.000 0.097
## .sswk (.39.) 0.114 0.042 2.741 0.006 0.033
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssei (.41.) -0.007 0.038 -0.185 0.853 -0.081
## .ssar (.42.) 0.197 0.040 4.928 0.000 0.118
## .ssmk (.43.) 0.231 0.043 5.378 0.000 0.147
## .ssmc (.44.) 0.049 0.038 1.305 0.192 -0.025
## .ssao (.45.) 0.143 0.039 3.693 0.000 0.067
## .ssai (.46.) -0.109 0.033 -3.305 0.001 -0.173
## .sssi (.47.) -0.068 0.034 -2.032 0.042 -0.134
## .ssno (.48.) 0.225 0.040 5.606 0.000 0.147
## .sscs (.49.) 0.188 0.042 4.515 0.000 0.106
## ci.upper Std.lv Std.all
## 0.256 0.177 0.180
## 0.196 0.114 0.115
## 0.333 0.253 0.267
## 0.067 -0.007 -0.007
## 0.275 0.197 0.209
## 0.315 0.231 0.235
## 0.123 0.049 0.052
## 0.218 0.143 0.149
## -0.044 -0.109 -0.129
## -0.002 -0.068 -0.080
## 0.304 0.225 0.232
## 0.269 0.188 0.191
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.155 0.015 10.343 0.000 0.126
## .sswk 0.174 0.016 10.887 0.000 0.143
## .sspc 0.234 0.021 10.930 0.000 0.192
## .ssei 0.261 0.023 11.526 0.000 0.216
## .ssar 0.151 0.016 9.556 0.000 0.120
## .ssmk 0.187 0.016 11.771 0.000 0.155
## .ssmc 0.250 0.018 13.563 0.000 0.214
## .ssao 0.407 0.028 14.805 0.000 0.354
## .ssai 0.319 0.026 12.081 0.000 0.267
## .sssi 0.310 0.028 11.136 0.000 0.255
## .ssno 0.366 0.040 9.112 0.000 0.287
## .sscs 0.462 0.052 8.859 0.000 0.359
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.078 0.078
## 1.000 0.137 0.137
## 0.184 0.155 0.161
## 0.205 0.174 0.178
## 0.276 0.234 0.261
## 0.305 0.261 0.287
## 0.182 0.151 0.170
## 0.218 0.187 0.193
## 0.286 0.250 0.283
## 0.461 0.407 0.445
## 0.371 0.319 0.451
## 0.364 0.310 0.423
## 0.444 0.366 0.387
## 0.564 0.462 0.478
## 1.000 0.242 0.242
## 1.000 0.401 0.401
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.251 0.040 6.308 0.000 0.173
## sswk (.p2.) 0.251 0.040 6.273 0.000 0.172
## sspc (.p3.) 0.115 0.021 5.395 0.000 0.073
## ssei (.p4.) 0.138 0.025 5.566 0.000 0.089
## math =~
## ssar (.p5.) 0.319 0.031 10.218 0.000 0.258
## sspc (.p6.) 0.158 0.024 6.589 0.000 0.111
## ssmk (.p7.) 0.233 0.028 8.284 0.000 0.178
## ssmc (.p8.) 0.191 0.020 9.539 0.000 0.152
## ssao (.p9.) 0.264 0.027 9.859 0.000 0.212
## electronic =~
## ssai (.10.) 0.307 0.034 9.023 0.000 0.240
## sssi (.11.) 0.320 0.036 8.854 0.000 0.249
## ssmc (.12.) 0.156 0.019 8.077 0.000 0.118
## ssei (.13.) 0.170 0.020 8.295 0.000 0.130
## speed =~
## ssno (.14.) 0.481 0.044 11.051 0.000 0.396
## sscs (.15.) 0.449 0.041 11.066 0.000 0.370
## ssmk (.16.) 0.201 0.023 8.749 0.000 0.156
## g =~
## verbal (.17.) 3.435 0.587 5.855 0.000 2.285
## math (.18.) 2.506 0.293 8.553 0.000 1.932
## elctrnc (.19.) 1.769 0.216 8.172 0.000 1.345
## speed (.20.) 1.223 0.139 8.797 0.000 0.951
## ci.upper Std.lv Std.all
##
## 0.329 0.988 0.923
## 0.329 0.987 0.913
## 0.156 0.451 0.442
## 0.187 0.544 0.494
##
## 0.380 0.941 0.898
## 0.205 0.467 0.457
## 0.288 0.687 0.653
## 0.231 0.565 0.537
## 0.317 0.780 0.734
##
## 0.374 0.890 0.795
## 0.390 0.927 0.854
## 0.194 0.454 0.431
## 0.210 0.492 0.447
##
## 0.567 0.857 0.804
## 0.529 0.800 0.754
## 0.246 0.357 0.340
##
## 4.585 0.967 0.967
## 3.080 0.941 0.941
## 2.193 0.676 0.676
## 1.496 0.762 0.762
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.177 0.041 4.337 0.000 0.097
## .sswk (.39.) 0.114 0.042 2.741 0.006 0.033
## .sspc -0.037 0.043 -0.844 0.399 -0.122
## .ssei (.41.) -0.007 0.038 -0.185 0.853 -0.081
## .ssar (.42.) 0.197 0.040 4.928 0.000 0.118
## .ssmk (.43.) 0.231 0.043 5.378 0.000 0.147
## .ssmc (.44.) 0.049 0.038 1.305 0.192 -0.025
## .ssao (.45.) 0.143 0.039 3.693 0.000 0.067
## .ssai (.46.) -0.109 0.033 -3.305 0.001 -0.173
## .sssi (.47.) -0.068 0.034 -2.032 0.042 -0.134
## .ssno (.48.) 0.225 0.040 5.606 0.000 0.147
## .sscs (.49.) 0.188 0.042 4.515 0.000 0.106
## .verbal -0.090 0.085 -1.052 0.293 -0.257
## .math -0.343 0.091 -3.772 0.000 -0.521
## .elctrnc 1.565 0.198 7.924 0.000 1.178
## .speed -0.666 0.110 -6.073 0.000 -0.880
## g 0.103 0.066 1.559 0.119 -0.026
## ci.upper Std.lv Std.all
## 0.256 0.177 0.165
## 0.196 0.114 0.106
## 0.048 -0.037 -0.036
## 0.067 -0.007 -0.006
## 0.275 0.197 0.188
## 0.315 0.231 0.219
## 0.123 0.049 0.047
## 0.218 0.143 0.134
## -0.044 -0.109 -0.097
## -0.002 -0.068 -0.063
## 0.304 0.225 0.212
## 0.269 0.188 0.177
## 0.077 -0.023 -0.023
## -0.165 -0.116 -0.116
## 1.952 0.540 0.540
## -0.451 -0.374 -0.374
## 0.233 0.093 0.093
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.170 0.017 9.872 0.000 0.136
## .sswk 0.195 0.017 11.692 0.000 0.162
## .sspc 0.237 0.020 12.145 0.000 0.199
## .ssei 0.324 0.025 12.823 0.000 0.274
## .ssar 0.213 0.022 9.534 0.000 0.169
## .ssmk 0.156 0.013 11.758 0.000 0.130
## .ssmc 0.255 0.019 13.208 0.000 0.217
## .ssao 0.520 0.038 13.775 0.000 0.446
## .ssai 0.462 0.042 11.009 0.000 0.379
## .sssi 0.319 0.035 9.045 0.000 0.250
## .ssno 0.401 0.043 9.314 0.000 0.316
## .sscs 0.485 0.057 8.529 0.000 0.374
## .electronic 4.567 1.081 4.227 0.000 2.450
## .speed 1.331 0.275 4.846 0.000 0.793
## g 1.229 0.102 12.049 0.000 1.029
## ci.upper Std.lv Std.all
## 1.000 0.065 0.065
## 1.000 0.115 0.115
## 0.204 0.170 0.148
## 0.228 0.195 0.167
## 0.275 0.237 0.227
## 0.373 0.324 0.267
## 0.257 0.213 0.194
## 0.181 0.156 0.140
## 0.293 0.255 0.231
## 0.594 0.520 0.461
## 0.544 0.462 0.368
## 0.388 0.319 0.271
## 0.485 0.401 0.353
## 0.597 0.485 0.431
## 6.685 0.543 0.543
## 1.870 0.420 0.420
## 1.428 1.000 1.000
weak<-cfa(hof.weak, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1"))
fitMeasures(weak, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 503.989 116.000 0.000 0.971 0.071 0.050 32098.379
## bic
## 32429.855
Mc(weak)
## [1] 0.8624531
summary(weak, standardized=T, ci=T) # g -.070 Std.all
## lavaan 0.6-18 ended normally after 106 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 95
## Number of equality constraints 31
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 503.989 386.569
## Degrees of freedom 116 116
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.304
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 210.821 161.704
## 0 293.168 224.865
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.251 0.040 6.308 0.000 0.173
## sswk (.p2.) 0.251 0.040 6.273 0.000 0.172
## sspc (.p3.) 0.115 0.021 5.395 0.000 0.073
## ssei (.p4.) 0.138 0.025 5.566 0.000 0.089
## math =~
## ssar (.p5.) 0.319 0.031 10.218 0.000 0.258
## sspc (.p6.) 0.158 0.024 6.589 0.000 0.111
## ssmk (.p7.) 0.233 0.028 8.284 0.000 0.178
## ssmc (.p8.) 0.191 0.020 9.539 0.000 0.152
## ssao (.p9.) 0.264 0.027 9.859 0.000 0.212
## electronic =~
## ssai (.10.) 0.307 0.034 9.022 0.000 0.240
## sssi (.11.) 0.320 0.036 8.854 0.000 0.249
## ssmc (.12.) 0.156 0.019 8.077 0.000 0.118
## ssei (.13.) 0.170 0.020 8.295 0.000 0.130
## speed =~
## ssno (.14.) 0.481 0.044 11.051 0.000 0.396
## sscs (.15.) 0.449 0.041 11.066 0.000 0.370
## ssmk (.16.) 0.201 0.023 8.749 0.000 0.156
## g =~
## verbal (.17.) 3.435 0.587 5.855 0.000 2.285
## math (.18.) 2.506 0.293 8.553 0.000 1.932
## elctrnc (.19.) 1.769 0.216 8.171 0.000 1.345
## speed (.20.) 1.223 0.139 8.797 0.000 0.951
## ci.upper Std.lv Std.all
##
## 0.329 0.898 0.916
## 0.329 0.897 0.907
## 0.156 0.410 0.433
## 0.187 0.494 0.518
##
## 0.380 0.860 0.911
## 0.205 0.427 0.451
## 0.288 0.628 0.638
## 0.231 0.516 0.549
## 0.317 0.712 0.745
##
## 0.374 0.624 0.741
## 0.390 0.649 0.759
## 0.194 0.318 0.338
## 0.210 0.345 0.362
##
## 0.567 0.760 0.783
## 0.529 0.710 0.723
## 0.246 0.317 0.322
##
## 4.585 0.960 0.960
## 3.080 0.929 0.929
## 2.193 0.871 0.871
## 1.496 0.774 0.774
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.39.) 0.177 0.041 4.337 0.000 0.097
## .sswk (.40.) 0.114 0.042 2.741 0.006 0.033
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssei (.42.) -0.007 0.038 -0.185 0.853 -0.081
## .ssar (.43.) 0.197 0.040 4.928 0.000 0.118
## .ssmk (.44.) 0.231 0.043 5.378 0.000 0.147
## .ssmc (.45.) 0.049 0.038 1.305 0.192 -0.025
## .ssao (.46.) 0.143 0.039 3.693 0.000 0.067
## .ssai (.47.) -0.109 0.033 -3.305 0.001 -0.173
## .sssi (.48.) -0.068 0.034 -2.032 0.042 -0.134
## .ssno (.49.) 0.225 0.040 5.606 0.000 0.147
## .sscs (.50.) 0.188 0.042 4.515 0.000 0.106
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.256 0.177 0.180
## 0.196 0.114 0.115
## 0.333 0.253 0.267
## 0.067 -0.007 -0.007
## 0.275 0.197 0.209
## 0.315 0.231 0.235
## 0.123 0.049 0.052
## 0.218 0.143 0.149
## -0.044 -0.109 -0.129
## -0.002 -0.068 -0.080
## 0.304 0.225 0.232
## 0.269 0.188 0.191
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.155 0.015 10.343 0.000 0.126
## .sswk 0.174 0.016 10.887 0.000 0.143
## .sspc 0.234 0.021 10.930 0.000 0.192
## .ssei 0.261 0.023 11.526 0.000 0.216
## .ssar 0.151 0.016 9.556 0.000 0.120
## .ssmk 0.187 0.016 11.771 0.000 0.155
## .ssmc 0.250 0.018 13.563 0.000 0.214
## .ssao 0.407 0.028 14.805 0.000 0.354
## .ssai 0.319 0.026 12.081 0.000 0.267
## .sssi 0.310 0.028 11.137 0.000 0.255
## .ssno 0.366 0.040 9.112 0.000 0.287
## .sscs 0.462 0.052 8.859 0.000 0.359
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.078 0.078
## 1.000 0.137 0.137
## 0.184 0.155 0.161
## 0.205 0.174 0.178
## 0.276 0.234 0.261
## 0.305 0.261 0.287
## 0.182 0.151 0.170
## 0.218 0.187 0.193
## 0.286 0.250 0.283
## 0.461 0.407 0.445
## 0.371 0.319 0.451
## 0.364 0.310 0.423
## 0.444 0.366 0.387
## 0.564 0.462 0.478
## 1.000 0.242 0.242
## 1.000 0.401 0.401
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.251 0.040 6.308 0.000 0.173
## sswk (.p2.) 0.251 0.040 6.273 0.000 0.172
## sspc (.p3.) 0.115 0.021 5.395 0.000 0.073
## ssei (.p4.) 0.138 0.025 5.566 0.000 0.089
## math =~
## ssar (.p5.) 0.319 0.031 10.218 0.000 0.258
## sspc (.p6.) 0.158 0.024 6.589 0.000 0.111
## ssmk (.p7.) 0.233 0.028 8.284 0.000 0.178
## ssmc (.p8.) 0.191 0.020 9.539 0.000 0.152
## ssao (.p9.) 0.264 0.027 9.859 0.000 0.212
## electronic =~
## ssai (.10.) 0.307 0.034 9.022 0.000 0.240
## sssi (.11.) 0.320 0.036 8.854 0.000 0.249
## ssmc (.12.) 0.156 0.019 8.077 0.000 0.118
## ssei (.13.) 0.170 0.020 8.295 0.000 0.130
## speed =~
## ssno (.14.) 0.481 0.044 11.051 0.000 0.396
## sscs (.15.) 0.449 0.041 11.066 0.000 0.370
## ssmk (.16.) 0.201 0.023 8.749 0.000 0.156
## g =~
## verbal (.17.) 3.435 0.587 5.855 0.000 2.285
## math (.18.) 2.506 0.293 8.553 0.000 1.932
## elctrnc (.19.) 1.769 0.216 8.171 0.000 1.345
## speed (.20.) 1.223 0.139 8.797 0.000 0.951
## ci.upper Std.lv Std.all
##
## 0.329 0.988 0.923
## 0.329 0.987 0.913
## 0.156 0.451 0.442
## 0.187 0.544 0.494
##
## 0.380 0.941 0.898
## 0.205 0.467 0.457
## 0.288 0.687 0.653
## 0.231 0.565 0.537
## 0.317 0.780 0.734
##
## 0.374 0.890 0.795
## 0.390 0.927 0.854
## 0.194 0.454 0.431
## 0.210 0.492 0.447
##
## 0.567 0.857 0.804
## 0.529 0.800 0.754
## 0.246 0.357 0.340
##
## 4.585 0.967 0.967
## 3.080 0.941 0.941
## 2.193 0.676 0.676
## 1.496 0.762 0.762
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.39.) 0.177 0.041 4.337 0.000 0.097
## .sswk (.40.) 0.114 0.042 2.741 0.006 0.033
## .sspc -0.037 0.043 -0.844 0.399 -0.122
## .ssei (.42.) -0.007 0.038 -0.185 0.853 -0.081
## .ssar (.43.) 0.197 0.040 4.928 0.000 0.118
## .ssmk (.44.) 0.231 0.043 5.378 0.000 0.147
## .ssmc (.45.) 0.049 0.038 1.305 0.192 -0.025
## .ssao (.46.) 0.143 0.039 3.693 0.000 0.067
## .ssai (.47.) -0.109 0.033 -3.305 0.001 -0.173
## .sssi (.48.) -0.068 0.034 -2.032 0.042 -0.134
## .ssno (.49.) 0.225 0.040 5.606 0.000 0.147
## .sscs (.50.) 0.188 0.042 4.515 0.000 0.106
## .math -0.278 0.117 -2.372 0.018 -0.507
## .elctrnc 1.611 0.231 6.969 0.000 1.158
## .speed -0.634 0.115 -5.519 0.000 -0.859
## g 0.077 0.070 1.102 0.270 -0.060
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.256 0.177 0.165
## 0.196 0.114 0.106
## 0.048 -0.037 -0.036
## 0.067 -0.007 -0.006
## 0.275 0.197 0.188
## 0.315 0.231 0.219
## 0.123 0.049 0.047
## 0.218 0.143 0.134
## -0.044 -0.109 -0.097
## -0.002 -0.068 -0.063
## 0.304 0.225 0.212
## 0.269 0.188 0.177
## -0.048 -0.094 -0.094
## 2.064 0.556 0.556
## -0.409 -0.356 -0.356
## 0.214 0.070 0.070
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.170 0.017 9.872 0.000 0.136
## .sswk 0.195 0.017 11.692 0.000 0.162
## .sspc 0.237 0.020 12.145 0.000 0.199
## .ssei 0.324 0.025 12.823 0.000 0.274
## .ssar 0.213 0.022 9.534 0.000 0.169
## .ssmk 0.156 0.013 11.758 0.000 0.130
## .ssmc 0.255 0.019 13.208 0.000 0.217
## .ssao 0.520 0.038 13.775 0.000 0.446
## .ssai 0.462 0.042 11.009 0.000 0.379
## .sssi 0.319 0.035 9.045 0.000 0.250
## .ssno 0.401 0.043 9.314 0.000 0.316
## .sscs 0.485 0.057 8.529 0.000 0.374
## .electronic 4.567 1.081 4.227 0.000 2.450
## .speed 1.331 0.275 4.846 0.000 0.793
## g 1.229 0.102 12.049 0.000 1.029
## ci.upper Std.lv Std.all
## 1.000 0.065 0.065
## 1.000 0.115 0.115
## 0.204 0.170 0.148
## 0.228 0.195 0.167
## 0.275 0.237 0.227
## 0.373 0.324 0.267
## 0.257 0.213 0.194
## 0.181 0.156 0.140
## 0.293 0.255 0.231
## 0.594 0.520 0.461
## 0.544 0.462 0.368
## 0.388 0.319 0.271
## 0.485 0.401 0.353
## 0.597 0.485 0.431
## 6.685 0.543 0.543
## 1.870 0.420 0.420
## 1.428 1.000 1.000
tests<-lavTestLRT(configural, metric, scalar2, latent2, weak)
## Warning: lavaan->lav_test_diff_SatorraBentler2001():
## scaling factor is negative
Td=tests[2:5,"Chisq diff"]
Td
## [1] 36.4066524 35.9193435 0.1818753 NA
dfd=tests[2:5,"Df diff"]
dfd
## [1] 15 6 2 1
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-656+ 656 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
## Warning in sqrt((ld) * G/(N - G)): NaNs produced
RMSEAD
## [1] 0.04667755 0.08725288 NaN NA
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.02748806 0.06618290
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.06104503 0.11573345
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] NA 0.02971612
RMSEA.CI(T=Td[4],df=dfd[4],N=N,G=2)
## [1] NA NA
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.998 0.997 0.420 0.139 0.002 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.989 0.956 0.699 0.247
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.087 0.082 0.018 0.009 0.002 0.000
round(pvals(T=Td[4],df=dfd[4],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## NA NA NA NA NA NA
tests<-lavTestLRT(configural, metric, scalar2, latent)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 36.40665 35.91934 79.63929
dfd=tests[2:4,"Df diff"]
dfd
## [1] 15 6 5
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-656+ 656 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.04667755 0.08725288 0.15096564
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.06104503 0.11573345
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.1227380 0.1809753
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.989 0.956 0.699 0.247
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 1.000 1.000 0.998
tests<-lavTestLRT(configural, metric, scalar2, strict)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 36.40665 35.91934 28.89718
dfd=tests[2:4,"Df diff"]
dfd
## [1] 15 6 12
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-656+ 656 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.04667755 0.08725288 0.04636560
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.02748806 0.06618290
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.06104503 0.11573345
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.02482250 0.06823879
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.998 0.997 0.420 0.139 0.002 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.989 0.956 0.699 0.247
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.996 0.993 0.426 0.164 0.005 0.000
tests<-lavTestLRT(configural, metric, scalar)
Td=tests[2:3,"Chisq diff"]
Td
## [1] 36.40665 106.11388
dfd=tests[2:3,"Df diff"]
dfd
## [1] 15 7
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-656+ 656 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.04667755 0.14702716
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.02748806 0.06618290
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.1230112 0.1723126
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.998 0.997 0.420 0.139 0.002 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 1.000 1.000 0.999
hof.age<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
verbal~~1*verbal
math~~1*math
verbal~0*1
g ~ agec
'
hof.ageq<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
verbal~~1*verbal
math~~1*math
verbal~0*1
g ~ c(a,a)*agec
'
hof.age2<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
verbal~~1*verbal
math~~1*math
verbal~0*1
g ~ agec + agec2
'
hof.age2q<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
verbal~~1*verbal
math~~1*math
verbal~0*1
g ~ c(a,a)*agec + c(b,b)*agec2
'
sem.age<-sem(hof.age, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1"))
fitMeasures(sem.age, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 689.232 138.000 0.000 0.960 0.078 0.052 0.626
## aic bic
## 31891.132 32232.966
Mc(sem.age)
## [1] 0.8103951
summary(sem.age, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 108 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 97
## Number of equality constraints 31
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 689.232 530.431
## Degrees of freedom 138 138
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.299
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 284.327 218.817
## 0 404.905 311.614
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.250 0.035 7.090 0.000 0.181
## sswk (.p2.) 0.250 0.035 7.061 0.000 0.181
## sspc (.p3.) 0.114 0.020 5.606 0.000 0.074
## ssei (.p4.) 0.138 0.022 6.203 0.000 0.094
## math =~
## ssar (.p5.) 0.328 0.027 12.310 0.000 0.276
## sspc (.p6.) 0.163 0.024 6.859 0.000 0.116
## ssmk (.p7.) 0.241 0.025 9.681 0.000 0.192
## ssmc (.p8.) 0.196 0.018 10.916 0.000 0.161
## ssao (.p9.) 0.272 0.023 11.789 0.000 0.227
## electronic =~
## ssai (.10.) 0.305 0.034 8.958 0.000 0.239
## sssi (.11.) 0.317 0.036 8.771 0.000 0.247
## ssmc (.12.) 0.156 0.020 7.989 0.000 0.118
## ssei (.13.) 0.168 0.020 8.259 0.000 0.128
## speed =~
## ssno (.14.) 0.475 0.044 10.867 0.000 0.389
## sscs (.15.) 0.444 0.041 10.910 0.000 0.364
## ssmk (.16.) 0.197 0.023 8.689 0.000 0.153
## g =~
## verbal (.17.) 3.147 0.480 6.561 0.000 2.207
## math (.18.) 2.211 0.223 9.924 0.000 1.774
## elctrnc (.19.) 1.632 0.201 8.107 0.000 1.237
## speed (.20.) 1.137 0.130 8.775 0.000 0.883
## ci.upper Std.lv Std.all
##
## 0.319 0.896 0.915
## 0.320 0.898 0.908
## 0.154 0.411 0.434
## 0.181 0.494 0.518
##
## 0.380 0.859 0.911
## 0.209 0.427 0.451
## 0.289 0.631 0.641
## 0.232 0.514 0.547
## 0.317 0.712 0.745
##
## 0.372 0.625 0.743
## 0.388 0.650 0.759
## 0.194 0.319 0.340
## 0.208 0.345 0.362
##
## 0.560 0.758 0.781
## 0.524 0.709 0.722
## 0.242 0.315 0.320
##
## 4.087 0.960 0.960
## 2.648 0.924 0.924
## 2.027 0.873 0.873
## 1.391 0.780 0.780
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.300 0.031 9.669 0.000 0.239
## ci.upper Std.lv Std.all
##
## 0.360 0.274 0.408
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.41.) 0.162 0.038 4.313 0.000 0.088
## .sswk (.42.) 0.100 0.038 2.617 0.009 0.025
## .sspc 0.239 0.039 6.117 0.000 0.162
## .ssei (.44.) -0.020 0.035 -0.578 0.563 -0.088
## .ssar (.45.) 0.183 0.038 4.805 0.000 0.108
## .ssmk (.46.) 0.216 0.039 5.517 0.000 0.139
## .ssmc (.47.) 0.036 0.036 0.994 0.320 -0.035
## .ssao (.48.) 0.131 0.037 3.541 0.000 0.059
## .ssai (.49.) -0.119 0.031 -3.828 0.000 -0.179
## .sssi (.50.) -0.078 0.032 -2.439 0.015 -0.141
## .ssno (.51.) 0.215 0.038 5.733 0.000 0.142
## .sscs (.52.) 0.179 0.039 4.594 0.000 0.102
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.236 0.162 0.165
## 0.175 0.100 0.101
## 0.315 0.239 0.252
## 0.048 -0.020 -0.021
## 0.258 0.183 0.194
## 0.293 0.216 0.220
## 0.106 0.036 0.038
## 0.204 0.131 0.137
## -0.058 -0.119 -0.141
## -0.015 -0.078 -0.091
## 0.289 0.215 0.222
## 0.255 0.179 0.182
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.157 0.015 10.509 0.000 0.128
## .sswk 0.171 0.016 10.843 0.000 0.140
## .sspc 0.234 0.021 11.025 0.000 0.193
## .ssei 0.259 0.023 11.512 0.000 0.215
## .ssar 0.152 0.016 9.560 0.000 0.121
## .ssmk 0.184 0.016 11.683 0.000 0.153
## .ssmc 0.251 0.019 13.546 0.000 0.215
## .ssao 0.407 0.028 14.798 0.000 0.353
## .ssai 0.318 0.026 12.095 0.000 0.267
## .sssi 0.311 0.028 11.175 0.000 0.256
## .ssno 0.368 0.040 9.145 0.000 0.289
## .sscs 0.461 0.052 8.854 0.000 0.359
## .electronic 1.000 1.000
## .speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.078 0.078
## 1.000 0.146 0.146
## 0.187 0.157 0.164
## 0.202 0.171 0.175
## 0.276 0.234 0.262
## 0.304 0.259 0.286
## 0.183 0.152 0.171
## 0.215 0.184 0.190
## 0.288 0.251 0.285
## 0.461 0.407 0.445
## 0.370 0.318 0.448
## 0.365 0.311 0.424
## 0.447 0.368 0.390
## 0.563 0.461 0.478
## 1.000 0.238 0.238
## 1.000 0.392 0.392
## 1.000 0.834 0.834
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.250 0.035 7.090 0.000 0.181
## sswk (.p2.) 0.250 0.035 7.061 0.000 0.181
## sspc (.p3.) 0.114 0.020 5.606 0.000 0.074
## ssei (.p4.) 0.138 0.022 6.203 0.000 0.094
## math =~
## ssar (.p5.) 0.328 0.027 12.310 0.000 0.276
## sspc (.p6.) 0.163 0.024 6.859 0.000 0.116
## ssmk (.p7.) 0.241 0.025 9.681 0.000 0.192
## ssmc (.p8.) 0.196 0.018 10.916 0.000 0.161
## ssao (.p9.) 0.272 0.023 11.789 0.000 0.227
## electronic =~
## ssai (.10.) 0.305 0.034 8.958 0.000 0.239
## sssi (.11.) 0.317 0.036 8.771 0.000 0.247
## ssmc (.12.) 0.156 0.020 7.989 0.000 0.118
## ssei (.13.) 0.168 0.020 8.259 0.000 0.128
## speed =~
## ssno (.14.) 0.475 0.044 10.867 0.000 0.389
## sscs (.15.) 0.444 0.041 10.910 0.000 0.364
## ssmk (.16.) 0.197 0.023 8.689 0.000 0.153
## g =~
## verbal (.17.) 3.147 0.480 6.561 0.000 2.207
## math (.18.) 2.211 0.223 9.924 0.000 1.774
## elctrnc (.19.) 1.632 0.201 8.107 0.000 1.237
## speed (.20.) 1.137 0.130 8.775 0.000 0.883
## ci.upper Std.lv Std.all
##
## 0.319 0.987 0.922
## 0.320 0.989 0.914
## 0.154 0.452 0.443
## 0.181 0.544 0.494
##
## 0.380 0.940 0.897
## 0.209 0.467 0.457
## 0.289 0.690 0.656
## 0.232 0.563 0.535
## 0.317 0.779 0.734
##
## 0.372 0.889 0.795
## 0.388 0.925 0.853
## 0.194 0.454 0.432
## 0.208 0.491 0.446
##
## 0.560 0.856 0.803
## 0.524 0.801 0.755
## 0.242 0.356 0.338
##
## 4.087 0.967 0.967
## 2.648 0.937 0.937
## 2.027 0.681 0.681
## 1.391 0.766 0.766
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.336 0.036 9.241 0.000 0.265
## ci.upper Std.lv Std.all
##
## 0.408 0.277 0.394
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.41.) 0.162 0.038 4.313 0.000 0.088
## .sswk (.42.) 0.100 0.038 2.617 0.009 0.025
## .sspc -0.050 0.041 -1.211 0.226 -0.130
## .ssei (.44.) -0.020 0.035 -0.578 0.563 -0.088
## .ssar (.45.) 0.183 0.038 4.805 0.000 0.108
## .ssmk (.46.) 0.216 0.039 5.517 0.000 0.139
## .ssmc (.47.) 0.036 0.036 0.994 0.320 -0.035
## .ssao (.48.) 0.131 0.037 3.541 0.000 0.059
## .ssai (.49.) -0.119 0.031 -3.828 0.000 -0.179
## .sssi (.50.) -0.078 0.032 -2.439 0.015 -0.141
## .ssno (.51.) 0.215 0.038 5.733 0.000 0.142
## .sscs (.52.) 0.179 0.039 4.594 0.000 0.102
## .math -0.267 0.113 -2.370 0.018 -0.488
## .elctrnc 1.623 0.234 6.935 0.000 1.164
## .speed -0.642 0.117 -5.480 0.000 -0.872
## .g 0.110 0.071 1.548 0.122 -0.029
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.236 0.162 0.151
## 0.175 0.100 0.092
## 0.031 -0.050 -0.049
## 0.048 -0.020 -0.018
## 0.258 0.183 0.175
## 0.293 0.216 0.206
## 0.106 0.036 0.034
## 0.204 0.131 0.124
## -0.058 -0.119 -0.106
## -0.015 -0.078 -0.072
## 0.289 0.215 0.202
## 0.255 0.179 0.168
## -0.046 -0.093 -0.093
## 2.082 0.557 0.557
## -0.412 -0.356 -0.356
## 0.248 0.090 0.090
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.173 0.017 9.996 0.000 0.139
## .sswk 0.193 0.017 11.650 0.000 0.160
## .sspc 0.237 0.020 12.123 0.000 0.199
## .ssei 0.324 0.025 12.794 0.000 0.274
## .ssar 0.214 0.023 9.529 0.000 0.170
## .ssmk 0.152 0.013 11.684 0.000 0.127
## .ssmc 0.256 0.019 13.210 0.000 0.218
## .ssao 0.520 0.038 13.721 0.000 0.446
## .ssai 0.460 0.042 10.991 0.000 0.378
## .sssi 0.320 0.035 9.081 0.000 0.251
## .ssno 0.403 0.043 9.357 0.000 0.319
## .sscs 0.485 0.057 8.552 0.000 0.374
## .electronic 4.550 1.087 4.187 0.000 2.420
## .speed 1.344 0.282 4.768 0.000 0.791
## .g 1.246 0.109 11.436 0.000 1.033
## ci.upper Std.lv Std.all
## 1.000 0.064 0.064
## 1.000 0.122 0.122
## 0.207 0.173 0.151
## 0.225 0.193 0.165
## 0.276 0.237 0.228
## 0.373 0.324 0.267
## 0.259 0.214 0.195
## 0.178 0.152 0.138
## 0.294 0.256 0.232
## 0.595 0.520 0.461
## 0.543 0.460 0.368
## 0.389 0.320 0.272
## 0.488 0.403 0.355
## 0.596 0.485 0.431
## 6.680 0.536 0.536
## 1.896 0.413 0.413
## 1.460 0.844 0.844
sem.ageq<-sem(hof.ageq, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1"))
fitMeasures(sem.ageq, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 689.988 139.000 0.000 0.960 0.078 0.055 0.625
## aic bic
## 31889.888 32226.543
Mc(sem.ageq)
## [1] 0.8104705
summary(sem.ageq, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 100 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 97
## Number of equality constraints 32
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 689.988 531.460
## Degrees of freedom 139 139
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.298
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 284.534 219.161
## 0 405.454 312.299
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.249 0.035 7.063 0.000 0.180
## sswk (.p2.) 0.250 0.035 7.032 0.000 0.180
## sspc (.p3.) 0.114 0.020 5.594 0.000 0.074
## ssei (.p4.) 0.137 0.022 6.185 0.000 0.094
## math =~
## ssar (.p5.) 0.328 0.027 12.321 0.000 0.276
## sspc (.p6.) 0.163 0.024 6.858 0.000 0.116
## ssmk (.p7.) 0.241 0.025 9.683 0.000 0.192
## ssmc (.p8.) 0.197 0.018 10.919 0.000 0.161
## ssao (.p9.) 0.272 0.023 11.794 0.000 0.227
## electronic =~
## ssai (.10.) 0.305 0.034 8.963 0.000 0.239
## sssi (.11.) 0.317 0.036 8.775 0.000 0.247
## ssmc (.12.) 0.156 0.020 7.990 0.000 0.118
## ssei (.13.) 0.169 0.020 8.263 0.000 0.129
## speed =~
## ssno (.14.) 0.474 0.044 10.858 0.000 0.389
## sscs (.15.) 0.444 0.041 10.903 0.000 0.364
## ssmk (.16.) 0.197 0.023 8.692 0.000 0.153
## g =~
## verbal (.17.) 3.157 0.483 6.539 0.000 2.211
## math (.18.) 2.209 0.222 9.932 0.000 1.773
## elctrnc (.19.) 1.631 0.201 8.111 0.000 1.237
## speed (.20.) 1.138 0.130 8.770 0.000 0.884
## ci.upper Std.lv Std.all
##
## 0.318 0.904 0.916
## 0.319 0.906 0.910
## 0.154 0.414 0.435
## 0.181 0.498 0.520
##
## 0.380 0.866 0.912
## 0.210 0.430 0.451
## 0.290 0.635 0.642
## 0.232 0.519 0.549
## 0.317 0.718 0.747
##
## 0.372 0.629 0.745
## 0.388 0.654 0.761
## 0.194 0.321 0.340
## 0.208 0.347 0.362
##
## 0.560 0.762 0.782
## 0.524 0.713 0.724
## 0.242 0.317 0.320
##
## 4.103 0.961 0.961
## 2.646 0.925 0.925
## 2.025 0.874 0.874
## 1.393 0.783 0.783
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.315 0.025 12.757 0.000 0.267
## ci.upper Std.lv Std.all
##
## 0.363 0.285 0.425
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.41.) 0.161 0.038 4.290 0.000 0.088
## .sswk (.42.) 0.099 0.038 2.598 0.009 0.024
## .sspc 0.238 0.039 6.097 0.000 0.162
## .ssei (.44.) -0.021 0.035 -0.599 0.549 -0.089
## .ssar (.45.) 0.182 0.038 4.779 0.000 0.108
## .ssmk (.46.) 0.216 0.039 5.500 0.000 0.139
## .ssmc (.47.) 0.035 0.036 0.973 0.330 -0.036
## .ssao (.48.) 0.131 0.037 3.520 0.000 0.058
## .ssai (.49.) -0.119 0.031 -3.843 0.000 -0.180
## .sssi (.50.) -0.079 0.032 -2.454 0.014 -0.141
## .ssno (.51.) 0.215 0.038 5.725 0.000 0.141
## .sscs (.52.) 0.178 0.039 4.587 0.000 0.102
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.235 0.161 0.163
## 0.174 0.099 0.099
## 0.315 0.238 0.250
## 0.047 -0.021 -0.022
## 0.257 0.182 0.192
## 0.292 0.216 0.218
## 0.106 0.035 0.037
## 0.204 0.131 0.136
## -0.058 -0.119 -0.141
## -0.016 -0.079 -0.091
## 0.288 0.215 0.220
## 0.254 0.178 0.181
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.157 0.015 10.523 0.000 0.128
## .sswk 0.171 0.016 10.844 0.000 0.140
## .sspc 0.234 0.021 11.026 0.000 0.193
## .ssei 0.259 0.023 11.512 0.000 0.215
## .ssar 0.152 0.016 9.560 0.000 0.121
## .ssmk 0.184 0.016 11.688 0.000 0.153
## .ssmc 0.251 0.019 13.545 0.000 0.215
## .ssao 0.407 0.028 14.798 0.000 0.353
## .ssai 0.318 0.026 12.096 0.000 0.266
## .sssi 0.311 0.028 11.176 0.000 0.256
## .ssno 0.368 0.040 9.147 0.000 0.289
## .sscs 0.461 0.052 8.855 0.000 0.359
## .electronic 1.000 1.000
## .speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.076 0.076
## 1.000 0.144 0.144
## 0.187 0.157 0.162
## 0.202 0.171 0.173
## 0.276 0.234 0.258
## 0.304 0.259 0.282
## 0.183 0.152 0.169
## 0.215 0.184 0.188
## 0.288 0.251 0.281
## 0.461 0.407 0.441
## 0.370 0.318 0.445
## 0.365 0.311 0.421
## 0.447 0.368 0.388
## 0.563 0.461 0.476
## 1.000 0.235 0.235
## 1.000 0.387 0.387
## 1.000 0.819 0.819
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.249 0.035 7.063 0.000 0.180
## sswk (.p2.) 0.250 0.035 7.032 0.000 0.180
## sspc (.p3.) 0.114 0.020 5.594 0.000 0.074
## ssei (.p4.) 0.137 0.022 6.185 0.000 0.094
## math =~
## ssar (.p5.) 0.328 0.027 12.321 0.000 0.276
## sspc (.p6.) 0.163 0.024 6.858 0.000 0.116
## ssmk (.p7.) 0.241 0.025 9.683 0.000 0.192
## ssmc (.p8.) 0.197 0.018 10.919 0.000 0.161
## ssao (.p9.) 0.272 0.023 11.794 0.000 0.227
## electronic =~
## ssai (.10.) 0.305 0.034 8.963 0.000 0.239
## sssi (.11.) 0.317 0.036 8.775 0.000 0.247
## ssmc (.12.) 0.156 0.020 7.990 0.000 0.118
## ssei (.13.) 0.169 0.020 8.263 0.000 0.129
## speed =~
## ssno (.14.) 0.474 0.044 10.858 0.000 0.389
## sscs (.15.) 0.444 0.041 10.903 0.000 0.364
## ssmk (.16.) 0.197 0.023 8.692 0.000 0.153
## g =~
## verbal (.17.) 3.157 0.483 6.539 0.000 2.211
## math (.18.) 2.209 0.222 9.932 0.000 1.773
## elctrnc (.19.) 1.631 0.201 8.111 0.000 1.237
## speed (.20.) 1.138 0.130 8.770 0.000 0.884
## ci.upper Std.lv Std.all
##
## 0.318 0.979 0.920
## 0.319 0.980 0.913
## 0.154 0.449 0.442
## 0.181 0.539 0.493
##
## 0.380 0.932 0.896
## 0.210 0.463 0.456
## 0.290 0.684 0.655
## 0.232 0.558 0.534
## 0.317 0.773 0.731
##
## 0.372 0.886 0.794
## 0.388 0.921 0.852
## 0.194 0.452 0.433
## 0.208 0.489 0.446
##
## 0.560 0.851 0.802
## 0.524 0.797 0.753
## 0.242 0.354 0.339
##
## 4.103 0.967 0.967
## 2.646 0.936 0.936
## 2.025 0.677 0.677
## 1.393 0.763 0.763
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.315 0.025 12.757 0.000 0.267
## ci.upper Std.lv Std.all
##
## 0.363 0.262 0.373
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.41.) 0.161 0.038 4.290 0.000 0.088
## .sswk (.42.) 0.099 0.038 2.598 0.009 0.024
## .sspc -0.050 0.041 -1.229 0.219 -0.131
## .ssei (.44.) -0.021 0.035 -0.599 0.549 -0.089
## .ssar (.45.) 0.182 0.038 4.779 0.000 0.108
## .ssmk (.46.) 0.216 0.039 5.500 0.000 0.139
## .ssmc (.47.) 0.035 0.036 0.973 0.330 -0.036
## .ssao (.48.) 0.131 0.037 3.520 0.000 0.058
## .ssai (.49.) -0.119 0.031 -3.843 0.000 -0.180
## .sssi (.50.) -0.079 0.032 -2.454 0.014 -0.141
## .ssno (.51.) 0.215 0.038 5.725 0.000 0.141
## .sscs (.52.) 0.178 0.039 4.587 0.000 0.102
## .math -0.267 0.112 -2.370 0.018 -0.487
## .elctrnc 1.623 0.234 6.938 0.000 1.165
## .speed -0.642 0.117 -5.477 0.000 -0.872
## .g 0.110 0.071 1.554 0.120 -0.029
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.235 0.161 0.152
## 0.174 0.099 0.092
## 0.030 -0.050 -0.050
## 0.047 -0.021 -0.019
## 0.257 0.182 0.175
## 0.292 0.216 0.206
## 0.106 0.035 0.034
## 0.204 0.131 0.124
## -0.058 -0.119 -0.107
## -0.016 -0.079 -0.073
## 0.288 0.215 0.202
## 0.254 0.178 0.168
## -0.046 -0.094 -0.094
## 2.082 0.560 0.560
## -0.412 -0.358 -0.358
## 0.249 0.091 0.091
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.173 0.017 9.992 0.000 0.139
## .sswk 0.193 0.017 11.647 0.000 0.160
## .sspc 0.237 0.020 12.123 0.000 0.199
## .ssei 0.324 0.025 12.795 0.000 0.274
## .ssar 0.214 0.023 9.526 0.000 0.170
## .ssmk 0.153 0.013 11.692 0.000 0.127
## .ssmc 0.256 0.019 13.213 0.000 0.218
## .ssao 0.520 0.038 13.724 0.000 0.446
## .ssai 0.461 0.042 10.993 0.000 0.378
## .sssi 0.320 0.035 9.082 0.000 0.251
## .ssno 0.403 0.043 9.354 0.000 0.319
## .sscs 0.485 0.057 8.550 0.000 0.374
## .electronic 4.554 1.088 4.187 0.000 2.422
## .speed 1.347 0.282 4.772 0.000 0.794
## .g 1.247 0.109 11.436 0.000 1.033
## ci.upper Std.lv Std.all
## 1.000 0.065 0.065
## 1.000 0.124 0.124
## 0.206 0.173 0.153
## 0.225 0.193 0.167
## 0.276 0.237 0.231
## 0.373 0.324 0.270
## 0.258 0.214 0.198
## 0.178 0.153 0.140
## 0.294 0.256 0.234
## 0.595 0.520 0.465
## 0.543 0.461 0.370
## 0.389 0.320 0.274
## 0.487 0.403 0.357
## 0.596 0.485 0.433
## 6.686 0.542 0.542
## 1.901 0.418 0.418
## 1.460 0.861 0.861
sem.age2<-sem(hof.age2, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1"))
fitMeasures(sem.age2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 728.395 160.000 0.000 0.959 0.074 0.049 0.659
## aic bic
## 31891.007 32243.200
Mc(sem.age2)
## [1] 0.8051078
summary(sem.age2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 106 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 99
## Number of equality constraints 31
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 728.395 562.674
## Degrees of freedom 160 160
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.295
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 298.953 230.937
## 0 429.442 331.737
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.251 0.035 7.157 0.000 0.182
## sswk (.p2.) 0.251 0.035 7.125 0.000 0.182
## sspc (.p3.) 0.115 0.020 5.621 0.000 0.075
## ssei (.p4.) 0.138 0.022 6.243 0.000 0.095
## math =~
## ssar (.p5.) 0.327 0.027 12.235 0.000 0.275
## sspc (.p6.) 0.162 0.024 6.844 0.000 0.116
## ssmk (.p7.) 0.240 0.025 9.655 0.000 0.191
## ssmc (.p8.) 0.196 0.018 10.875 0.000 0.161
## ssao (.p9.) 0.271 0.023 11.737 0.000 0.226
## electronic =~
## ssai (.10.) 0.306 0.034 8.977 0.000 0.239
## sssi (.11.) 0.318 0.036 8.791 0.000 0.247
## ssmc (.12.) 0.156 0.020 8.001 0.000 0.118
## ssei (.13.) 0.169 0.020 8.270 0.000 0.129
## speed =~
## ssno (.14.) 0.474 0.044 10.839 0.000 0.388
## sscs (.15.) 0.444 0.041 10.880 0.000 0.364
## ssmk (.16.) 0.197 0.023 8.687 0.000 0.153
## g =~
## verbal (.17.) 3.123 0.472 6.610 0.000 2.197
## math (.18.) 2.212 0.224 9.895 0.000 1.774
## elctrnc (.19.) 1.625 0.200 8.112 0.000 1.232
## speed (.20.) 1.136 0.130 8.752 0.000 0.881
## ci.upper Std.lv Std.all
##
## 0.320 0.896 0.915
## 0.320 0.898 0.908
## 0.155 0.411 0.434
## 0.182 0.494 0.518
##
## 0.379 0.859 0.910
## 0.209 0.427 0.451
## 0.289 0.631 0.641
## 0.231 0.515 0.548
## 0.316 0.712 0.745
##
## 0.373 0.626 0.743
## 0.389 0.650 0.759
## 0.194 0.319 0.340
## 0.209 0.345 0.362
##
## 0.560 0.758 0.781
## 0.523 0.709 0.722
## 0.241 0.315 0.320
##
## 4.049 0.960 0.960
## 2.650 0.925 0.925
## 2.017 0.872 0.872
## 1.390 0.780 0.780
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.300 0.031 9.582 0.000 0.239
## agec2 -0.041 0.025 -1.652 0.099 -0.089
## ci.upper Std.lv Std.all
##
## 0.361 0.273 0.407
## 0.008 -0.037 -0.070
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.44.) 0.233 0.057 4.062 0.000 0.121
## .sswk (.45.) 0.171 0.058 2.958 0.003 0.058
## .sspc 0.304 0.055 5.514 0.000 0.196
## .ssei (.47.) 0.044 0.053 0.839 0.401 -0.059
## .ssar (.48.) 0.249 0.055 4.529 0.000 0.141
## .ssmk (.49.) 0.285 0.058 4.900 0.000 0.171
## .ssmc (.50.) 0.098 0.052 1.880 0.060 -0.004
## .ssao (.51.) 0.186 0.051 3.678 0.000 0.087
## .ssai (.52.) -0.073 0.041 -1.781 0.075 -0.154
## .sssi (.53.) -0.031 0.044 -0.712 0.476 -0.116
## .ssno (.54.) 0.264 0.049 5.417 0.000 0.169
## .sscs (.55.) 0.225 0.048 4.722 0.000 0.131
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.346 0.233 0.238
## 0.285 0.171 0.173
## 0.412 0.304 0.321
## 0.147 0.044 0.046
## 0.357 0.249 0.264
## 0.399 0.285 0.290
## 0.201 0.098 0.105
## 0.285 0.186 0.195
## 0.007 -0.073 -0.087
## 0.054 -0.031 -0.036
## 0.360 0.264 0.272
## 0.318 0.225 0.229
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.157 0.015 10.493 0.000 0.128
## .sswk 0.171 0.016 10.854 0.000 0.141
## .sspc 0.234 0.021 11.035 0.000 0.193
## .ssei 0.259 0.023 11.515 0.000 0.215
## .ssar 0.152 0.016 9.583 0.000 0.121
## .ssmk 0.184 0.016 11.671 0.000 0.153
## .ssmc 0.251 0.019 13.549 0.000 0.215
## .ssao 0.407 0.028 14.799 0.000 0.353
## .ssai 0.318 0.026 12.088 0.000 0.266
## .sssi 0.311 0.028 11.175 0.000 0.256
## .ssno 0.368 0.040 9.145 0.000 0.289
## .sscs 0.461 0.052 8.861 0.000 0.359
## .electronic 1.000 1.000
## .speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.078 0.078
## 1.000 0.145 0.145
## 0.186 0.157 0.163
## 0.202 0.171 0.175
## 0.276 0.234 0.262
## 0.304 0.259 0.286
## 0.183 0.152 0.171
## 0.215 0.184 0.190
## 0.288 0.251 0.285
## 0.461 0.407 0.445
## 0.369 0.318 0.448
## 0.365 0.311 0.424
## 0.447 0.368 0.390
## 0.563 0.461 0.479
## 1.000 0.239 0.239
## 1.000 0.391 0.391
## 1.000 0.829 0.829
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.251 0.035 7.157 0.000 0.182
## sswk (.p2.) 0.251 0.035 7.125 0.000 0.182
## sspc (.p3.) 0.115 0.020 5.621 0.000 0.075
## ssei (.p4.) 0.138 0.022 6.243 0.000 0.095
## math =~
## ssar (.p5.) 0.327 0.027 12.235 0.000 0.275
## sspc (.p6.) 0.162 0.024 6.844 0.000 0.116
## ssmk (.p7.) 0.240 0.025 9.655 0.000 0.191
## ssmc (.p8.) 0.196 0.018 10.875 0.000 0.161
## ssao (.p9.) 0.271 0.023 11.737 0.000 0.226
## electronic =~
## ssai (.10.) 0.306 0.034 8.977 0.000 0.239
## sssi (.11.) 0.318 0.036 8.791 0.000 0.247
## ssmc (.12.) 0.156 0.020 8.001 0.000 0.118
## ssei (.13.) 0.169 0.020 8.270 0.000 0.129
## speed =~
## ssno (.14.) 0.474 0.044 10.839 0.000 0.388
## sscs (.15.) 0.444 0.041 10.880 0.000 0.364
## ssmk (.16.) 0.197 0.023 8.687 0.000 0.153
## g =~
## verbal (.17.) 3.123 0.472 6.610 0.000 2.197
## math (.18.) 2.212 0.224 9.895 0.000 1.774
## elctrnc (.19.) 1.625 0.200 8.112 0.000 1.232
## speed (.20.) 1.136 0.130 8.752 0.000 0.881
## ci.upper Std.lv Std.all
##
## 0.320 0.987 0.922
## 0.320 0.989 0.914
## 0.155 0.452 0.443
## 0.182 0.544 0.494
##
## 0.379 0.940 0.897
## 0.209 0.467 0.457
## 0.289 0.690 0.656
## 0.231 0.563 0.536
## 0.316 0.779 0.734
##
## 0.373 0.889 0.795
## 0.389 0.924 0.853
## 0.194 0.454 0.432
## 0.209 0.491 0.446
##
## 0.560 0.856 0.803
## 0.523 0.801 0.754
## 0.241 0.356 0.338
##
## 4.049 0.967 0.967
## 2.650 0.938 0.938
## 2.017 0.681 0.681
## 1.390 0.766 0.766
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.336 0.037 9.146 0.000 0.264
## agec2 -0.018 0.026 -0.708 0.479 -0.069
## ci.upper Std.lv Std.all
##
## 0.408 0.276 0.393
## 0.032 -0.015 -0.028
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.44.) 0.233 0.057 4.062 0.000 0.121
## .sswk (.45.) 0.171 0.058 2.958 0.003 0.058
## .sspc 0.016 0.057 0.276 0.782 -0.096
## .ssei (.47.) 0.044 0.053 0.839 0.401 -0.059
## .ssar (.48.) 0.249 0.055 4.529 0.000 0.141
## .ssmk (.49.) 0.285 0.058 4.900 0.000 0.171
## .ssmc (.50.) 0.098 0.052 1.880 0.060 -0.004
## .ssao (.51.) 0.186 0.051 3.678 0.000 0.087
## .ssai (.52.) -0.073 0.041 -1.781 0.075 -0.154
## .sssi (.53.) -0.031 0.044 -0.712 0.476 -0.116
## .ssno (.54.) 0.264 0.049 5.417 0.000 0.169
## .sscs (.55.) 0.225 0.048 4.722 0.000 0.131
## .math -0.268 0.113 -2.372 0.018 -0.490
## .elctrnc 1.621 0.233 6.944 0.000 1.163
## .speed -0.643 0.117 -5.478 0.000 -0.873
## .g 0.056 0.104 0.540 0.589 -0.147
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.346 0.233 0.218
## 0.285 0.171 0.158
## 0.128 0.016 0.015
## 0.147 0.044 0.040
## 0.357 0.249 0.238
## 0.399 0.285 0.271
## 0.201 0.098 0.094
## 0.285 0.186 0.175
## 0.007 -0.073 -0.066
## 0.054 -0.031 -0.029
## 0.360 0.264 0.248
## 0.318 0.225 0.212
## -0.047 -0.093 -0.093
## 2.078 0.557 0.557
## -0.413 -0.356 -0.356
## 0.259 0.046 0.046
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.173 0.017 9.995 0.000 0.139
## .sswk 0.193 0.017 11.650 0.000 0.160
## .sspc 0.237 0.020 12.121 0.000 0.199
## .ssei 0.324 0.025 12.795 0.000 0.274
## .ssar 0.215 0.022 9.540 0.000 0.171
## .ssmk 0.152 0.013 11.680 0.000 0.127
## .ssmc 0.256 0.019 13.202 0.000 0.218
## .ssao 0.520 0.038 13.721 0.000 0.446
## .ssai 0.460 0.042 10.991 0.000 0.378
## .sssi 0.320 0.035 9.080 0.000 0.251
## .ssno 0.403 0.043 9.357 0.000 0.319
## .sscs 0.485 0.057 8.553 0.000 0.374
## .electronic 4.539 1.082 4.195 0.000 2.419
## .speed 1.345 0.283 4.760 0.000 0.791
## .g 1.252 0.110 11.399 0.000 1.037
## ci.upper Std.lv Std.all
## 1.000 0.065 0.065
## 1.000 0.121 0.121
## 0.206 0.173 0.150
## 0.225 0.193 0.165
## 0.276 0.237 0.228
## 0.373 0.324 0.267
## 0.259 0.215 0.195
## 0.178 0.152 0.138
## 0.294 0.256 0.232
## 0.595 0.520 0.461
## 0.543 0.460 0.368
## 0.389 0.320 0.272
## 0.488 0.403 0.355
## 0.597 0.485 0.431
## 6.660 0.537 0.537
## 1.899 0.413 0.413
## 1.468 0.844 0.844
sem.age2q<-sem(hof.age2q, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1"))
fitMeasures(sem.age2q, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 729.579 162.000 0.000 0.959 0.073 0.052 0.657
## aic bic
## 31888.191 32230.026
Mc(sem.age2q)
## [1] 0.8053583
summary(sem.age2q, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 102 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 99
## Number of equality constraints 33
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 729.579 564.268
## Degrees of freedom 162 162
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.293
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 299.358 231.528
## 0 430.221 332.740
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.250 0.035 7.130 0.000 0.181
## sswk (.p2.) 0.251 0.035 7.098 0.000 0.181
## sspc (.p3.) 0.115 0.020 5.609 0.000 0.075
## ssei (.p4.) 0.138 0.022 6.226 0.000 0.094
## math =~
## ssar (.p5.) 0.327 0.027 12.250 0.000 0.275
## sspc (.p6.) 0.163 0.024 6.845 0.000 0.116
## ssmk (.p7.) 0.240 0.025 9.660 0.000 0.192
## ssmc (.p8.) 0.196 0.018 10.884 0.000 0.161
## ssao (.p9.) 0.271 0.023 11.749 0.000 0.226
## electronic =~
## ssai (.10.) 0.306 0.034 8.978 0.000 0.239
## sssi (.11.) 0.318 0.036 8.791 0.000 0.247
## ssmc (.12.) 0.156 0.020 7.999 0.000 0.118
## ssei (.13.) 0.169 0.020 8.271 0.000 0.129
## speed =~
## ssno (.14.) 0.474 0.044 10.837 0.000 0.388
## sscs (.15.) 0.443 0.041 10.878 0.000 0.363
## ssmk (.16.) 0.197 0.023 8.691 0.000 0.153
## g =~
## verbal (.17.) 3.133 0.475 6.591 0.000 2.201
## math (.18.) 2.210 0.223 9.902 0.000 1.773
## elctrnc (.19.) 1.625 0.200 8.114 0.000 1.232
## speed (.20.) 1.136 0.130 8.751 0.000 0.882
## ci.upper Std.lv Std.all
##
## 0.319 0.903 0.916
## 0.320 0.905 0.909
## 0.155 0.414 0.434
## 0.181 0.498 0.520
##
## 0.380 0.865 0.911
## 0.209 0.430 0.451
## 0.289 0.635 0.642
## 0.231 0.518 0.549
## 0.317 0.717 0.747
##
## 0.372 0.629 0.745
## 0.389 0.654 0.761
## 0.194 0.321 0.340
## 0.209 0.347 0.362
##
## 0.560 0.761 0.782
## 0.523 0.712 0.724
## 0.241 0.317 0.320
##
## 4.065 0.961 0.961
## 2.647 0.926 0.926
## 2.017 0.874 0.874
## 1.391 0.783 0.783
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.315 0.025 12.659 0.000 0.266
## agec2 (b) -0.031 0.018 -1.743 0.081 -0.067
## ci.upper Std.lv Std.all
##
## 0.364 0.285 0.424
## 0.004 -0.028 -0.054
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.44.) 0.216 0.049 4.387 0.000 0.120
## .sswk (.45.) 0.154 0.050 3.109 0.002 0.057
## .sspc 0.288 0.048 5.972 0.000 0.194
## .ssei (.47.) 0.029 0.045 0.629 0.529 -0.060
## .ssar (.48.) 0.233 0.048 4.867 0.000 0.139
## .ssmk (.49.) 0.268 0.050 5.365 0.000 0.170
## .ssmc (.50.) 0.083 0.046 1.823 0.068 -0.006
## .ssao (.51.) 0.173 0.045 3.839 0.000 0.085
## .ssai (.52.) -0.084 0.037 -2.307 0.021 -0.156
## .sssi (.53.) -0.043 0.039 -1.098 0.272 -0.118
## .ssno (.54.) 0.252 0.044 5.728 0.000 0.166
## .sscs (.55.) 0.213 0.044 4.884 0.000 0.128
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.313 0.216 0.219
## 0.251 0.154 0.155
## 0.383 0.288 0.303
## 0.117 0.029 0.030
## 0.327 0.233 0.245
## 0.366 0.268 0.271
## 0.173 0.083 0.088
## 0.261 0.173 0.180
## -0.013 -0.084 -0.100
## 0.033 -0.043 -0.049
## 0.339 0.252 0.259
## 0.299 0.213 0.217
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.157 0.015 10.510 0.000 0.128
## .sswk 0.171 0.016 10.851 0.000 0.140
## .sspc 0.234 0.021 11.035 0.000 0.193
## .ssei 0.259 0.023 11.515 0.000 0.215
## .ssar 0.152 0.016 9.578 0.000 0.121
## .ssmk 0.184 0.016 11.678 0.000 0.153
## .ssmc 0.251 0.019 13.546 0.000 0.215
## .ssao 0.407 0.028 14.799 0.000 0.353
## .ssai 0.318 0.026 12.090 0.000 0.266
## .sssi 0.311 0.028 11.175 0.000 0.256
## .ssno 0.368 0.040 9.147 0.000 0.289
## .sscs 0.461 0.052 8.860 0.000 0.359
## .electronic 1.000 1.000
## .speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.077 0.077
## 1.000 0.143 0.143
## 0.186 0.157 0.162
## 0.202 0.171 0.173
## 0.276 0.234 0.259
## 0.304 0.259 0.283
## 0.184 0.152 0.169
## 0.215 0.184 0.188
## 0.288 0.251 0.282
## 0.461 0.407 0.442
## 0.369 0.318 0.446
## 0.365 0.311 0.421
## 0.447 0.368 0.388
## 0.563 0.461 0.476
## 1.000 0.236 0.236
## 1.000 0.387 0.387
## 1.000 0.817 0.817
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.250 0.035 7.130 0.000 0.181
## sswk (.p2.) 0.251 0.035 7.098 0.000 0.181
## sspc (.p3.) 0.115 0.020 5.609 0.000 0.075
## ssei (.p4.) 0.138 0.022 6.226 0.000 0.094
## math =~
## ssar (.p5.) 0.327 0.027 12.250 0.000 0.275
## sspc (.p6.) 0.163 0.024 6.845 0.000 0.116
## ssmk (.p7.) 0.240 0.025 9.660 0.000 0.192
## ssmc (.p8.) 0.196 0.018 10.884 0.000 0.161
## ssao (.p9.) 0.271 0.023 11.749 0.000 0.226
## electronic =~
## ssai (.10.) 0.306 0.034 8.978 0.000 0.239
## sssi (.11.) 0.318 0.036 8.791 0.000 0.247
## ssmc (.12.) 0.156 0.020 7.999 0.000 0.118
## ssei (.13.) 0.169 0.020 8.271 0.000 0.129
## speed =~
## ssno (.14.) 0.474 0.044 10.837 0.000 0.388
## sscs (.15.) 0.443 0.041 10.878 0.000 0.363
## ssmk (.16.) 0.197 0.023 8.691 0.000 0.153
## g =~
## verbal (.17.) 3.133 0.475 6.591 0.000 2.201
## math (.18.) 2.210 0.223 9.902 0.000 1.773
## elctrnc (.19.) 1.625 0.200 8.114 0.000 1.232
## speed (.20.) 1.136 0.130 8.751 0.000 0.882
## ci.upper Std.lv Std.all
##
## 0.319 0.980 0.921
## 0.320 0.981 0.913
## 0.155 0.449 0.442
## 0.181 0.540 0.493
##
## 0.380 0.933 0.896
## 0.209 0.464 0.457
## 0.289 0.685 0.655
## 0.231 0.559 0.535
## 0.317 0.774 0.732
##
## 0.372 0.886 0.794
## 0.389 0.921 0.852
## 0.194 0.452 0.432
## 0.209 0.489 0.446
##
## 0.560 0.852 0.802
## 0.523 0.797 0.753
## 0.241 0.354 0.339
##
## 4.065 0.967 0.967
## 2.647 0.936 0.936
## 2.017 0.677 0.677
## 1.391 0.763 0.763
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.315 0.025 12.659 0.000 0.266
## agec2 (b) -0.031 0.018 -1.743 0.081 -0.067
## ci.upper Std.lv Std.all
##
## 0.364 0.261 0.371
## 0.004 -0.026 -0.048
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.44.) 0.216 0.049 4.387 0.000 0.120
## .sswk (.45.) 0.154 0.050 3.109 0.002 0.057
## .sspc -0.000 0.050 -0.003 0.998 -0.098
## .ssei (.47.) 0.029 0.045 0.629 0.529 -0.060
## .ssar (.48.) 0.233 0.048 4.867 0.000 0.139
## .ssmk (.49.) 0.268 0.050 5.365 0.000 0.170
## .ssmc (.50.) 0.083 0.046 1.823 0.068 -0.006
## .ssao (.51.) 0.173 0.045 3.839 0.000 0.085
## .ssai (.52.) -0.084 0.037 -2.307 0.021 -0.156
## .sssi (.53.) -0.043 0.039 -1.098 0.272 -0.118
## .ssno (.54.) 0.252 0.044 5.728 0.000 0.166
## .sscs (.55.) 0.213 0.044 4.884 0.000 0.128
## .math -0.268 0.113 -2.372 0.018 -0.489
## .elctrnc 1.621 0.233 6.944 0.000 1.164
## .speed -0.643 0.117 -5.476 0.000 -0.873
## .g 0.104 0.071 1.467 0.142 -0.035
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.313 0.216 0.203
## 0.251 0.154 0.143
## 0.097 -0.000 -0.000
## 0.117 0.029 0.026
## 0.327 0.233 0.224
## 0.366 0.268 0.256
## 0.173 0.083 0.079
## 0.261 0.173 0.163
## -0.013 -0.084 -0.076
## 0.033 -0.043 -0.039
## 0.339 0.252 0.237
## 0.299 0.213 0.202
## -0.047 -0.094 -0.094
## 2.079 0.559 0.559
## -0.413 -0.358 -0.358
## 0.244 0.086 0.086
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.172 0.017 9.992 0.000 0.139
## .sswk 0.193 0.017 11.648 0.000 0.161
## .sspc 0.237 0.020 12.120 0.000 0.199
## .ssei 0.324 0.025 12.799 0.000 0.274
## .ssar 0.215 0.023 9.537 0.000 0.171
## .ssmk 0.152 0.013 11.688 0.000 0.127
## .ssmc 0.256 0.019 13.211 0.000 0.218
## .ssao 0.520 0.038 13.720 0.000 0.446
## .ssai 0.461 0.042 10.994 0.000 0.378
## .sssi 0.320 0.035 9.081 0.000 0.251
## .ssno 0.403 0.043 9.352 0.000 0.318
## .sscs 0.485 0.057 8.551 0.000 0.374
## .electronic 4.545 1.084 4.193 0.000 2.421
## .speed 1.349 0.283 4.766 0.000 0.794
## .g 1.253 0.110 11.392 0.000 1.037
## ci.upper Std.lv Std.all
## 1.000 0.065 0.065
## 1.000 0.123 0.123
## 0.206 0.172 0.152
## 0.225 0.193 0.167
## 0.276 0.237 0.230
## 0.373 0.324 0.270
## 0.259 0.215 0.198
## 0.178 0.152 0.139
## 0.294 0.256 0.234
## 0.594 0.520 0.465
## 0.543 0.461 0.370
## 0.389 0.320 0.274
## 0.487 0.403 0.357
## 0.597 0.485 0.433
## 6.670 0.541 0.541
## 1.904 0.417 0.417
## 1.468 0.858 0.858
# BIFACTOR MODEL (verbal is ill defined due to wk having high loadings and negative variance, but then ei has negative variance, and finally with only gs and pc, the gs test has negative loading)
bf.notworking<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
baseline<-cfa(bf.notworking, data=dgroup, meanstructure=T, sampling.weights="sweight", std.lv=T, orthogonal=T)
## Warning: lavaan->lav_object_post_check():
## some estimated ov variances are negative
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 408.592 38.000 0.000 0.972 0.086 0.038 32597.489
## bic
## 32866.813
Mc(baseline)
## [1] 0.8681946
summary(baseline, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 72 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 52
##
## Number of observations 1312
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 408.592 350.120
## Degrees of freedom 38 38
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.167
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.062 0.330 0.189 0.850 -0.584
## sswk 0.484 2.049 0.236 0.813 -3.533
## sspc 0.086 0.457 0.188 0.851 -0.810
## ssei 0.006 0.120 0.054 0.957 -0.228
## math =~
## ssar 0.316 0.050 6.356 0.000 0.219
## sspc 0.234 0.077 3.038 0.002 0.083
## ssmk 0.304 0.039 7.826 0.000 0.228
## ssmc 0.238 0.041 5.749 0.000 0.157
## ssao 0.338 0.050 6.817 0.000 0.241
## electronic =~
## ssai 0.563 0.038 14.832 0.000 0.488
## sssi 0.600 0.036 16.857 0.000 0.530
## ssmc 0.301 0.027 11.288 0.000 0.249
## ssei 0.303 0.031 9.668 0.000 0.242
## speed =~
## ssno 0.670 0.056 12.016 0.000 0.561
## sscs 0.424 0.043 9.881 0.000 0.340
## ssmk 0.232 0.029 8.117 0.000 0.176
## g =~
## ssgs 0.933 0.028 33.683 0.000 0.879
## ssar 0.834 0.030 27.704 0.000 0.775
## sswk 0.923 0.033 27.697 0.000 0.858
## sspc 0.818 0.039 20.730 0.000 0.741
## ssno 0.596 0.032 18.749 0.000 0.534
## sscs 0.571 0.030 19.028 0.000 0.512
## ssai 0.620 0.031 20.164 0.000 0.560
## sssi 0.638 0.030 21.597 0.000 0.580
## ssmk 0.850 0.027 31.311 0.000 0.797
## ssmc 0.804 0.025 31.734 0.000 0.754
## ssei 0.864 0.030 28.536 0.000 0.805
## ssao 0.672 0.028 24.131 0.000 0.618
## ci.upper Std.lv Std.all
##
## 0.708 0.062 0.061
## 4.500 0.484 0.467
## 0.982 0.086 0.086
## 0.241 0.006 0.006
##
## 0.414 0.316 0.317
## 0.385 0.234 0.235
## 0.380 0.304 0.299
## 0.319 0.238 0.233
## 0.435 0.338 0.335
##
## 0.637 0.563 0.538
## 0.669 0.600 0.580
## 0.353 0.301 0.295
## 0.364 0.303 0.285
##
## 0.780 0.670 0.653
## 0.508 0.424 0.412
## 0.289 0.232 0.228
##
## 0.987 0.933 0.909
## 0.893 0.834 0.835
## 0.989 0.923 0.891
## 0.896 0.818 0.823
## 0.658 0.596 0.581
## 0.630 0.571 0.555
## 0.681 0.620 0.593
## 0.696 0.638 0.617
## 0.903 0.850 0.835
## 0.853 0.804 0.788
## 0.923 0.864 0.813
## 0.727 0.672 0.667
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.211 0.031 6.806 0.000 0.151
## .sswk 0.145 0.031 4.602 0.000 0.083
## .sspc 0.117 0.030 3.869 0.000 0.058
## .ssei 0.158 0.033 4.834 0.000 0.094
## .ssar 0.186 0.030 6.138 0.000 0.126
## .ssmk 0.168 0.031 5.367 0.000 0.107
## .ssmc 0.177 0.031 5.791 0.000 0.117
## .ssao 0.128 0.031 4.135 0.000 0.067
## .ssai 0.158 0.033 4.852 0.000 0.094
## .sssi 0.210 0.032 6.615 0.000 0.148
## .ssno 0.099 0.032 3.093 0.002 0.036
## .sscs 0.066 0.032 2.069 0.039 0.003
## ci.upper Std.lv Std.all
## 0.272 0.211 0.206
## 0.207 0.145 0.140
## 0.176 0.117 0.118
## 0.222 0.158 0.149
## 0.245 0.186 0.186
## 0.229 0.168 0.165
## 0.237 0.177 0.174
## 0.188 0.128 0.127
## 0.222 0.158 0.151
## 0.272 0.210 0.203
## 0.161 0.099 0.096
## 0.128 0.066 0.064
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.180 0.022 8.121 0.000 0.137
## .sswk -0.013 2.020 -0.007 0.995 -3.972
## .sspc 0.257 0.058 4.454 0.000 0.144
## .ssei 0.292 0.023 12.562 0.000 0.247
## .ssar 0.203 0.017 12.257 0.000 0.170
## .ssmk 0.167 0.014 12.082 0.000 0.140
## .ssmc 0.247 0.016 15.190 0.000 0.215
## .ssao 0.450 0.027 16.786 0.000 0.398
## .ssai 0.393 0.028 14.032 0.000 0.338
## .sssi 0.303 0.027 11.106 0.000 0.249
## .ssno 0.250 0.058 4.269 0.000 0.135
## .sscs 0.552 0.043 12.821 0.000 0.468
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.223 0.180 0.171
## 3.946 -0.013 -0.012
## 0.370 0.257 0.260
## 0.338 0.292 0.259
## 0.235 0.203 0.203
## 0.194 0.167 0.161
## 0.279 0.247 0.237
## 0.503 0.450 0.443
## 0.448 0.393 0.359
## 0.356 0.303 0.283
## 0.364 0.250 0.237
## 0.637 0.552 0.522
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
bf.model<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
bf.lv<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
'
baseline<-cfa(bf.model, data=dgroup, meanstructure=T, sampling.weights="sweight", std.lv=T, orthogonal=T)
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 430.323 42.000 0.000 0.971 0.084 0.038 32611.220
## bic
## 32859.827
Mc(baseline)
## [1] 0.8623433
summary(baseline, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 40 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 48
##
## Number of observations 1312
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 430.323 327.798
## Degrees of freedom 42 42
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.313
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar 0.342 0.030 11.595 0.000 0.284
## sspc 0.214 0.029 7.393 0.000 0.158
## ssmk 0.320 0.027 11.997 0.000 0.268
## ssmc 0.251 0.032 7.896 0.000 0.189
## ssao 0.358 0.038 9.409 0.000 0.283
## electronic =~
## ssai 0.565 0.037 15.398 0.000 0.493
## sssi 0.602 0.034 17.935 0.000 0.536
## ssmc 0.308 0.025 12.258 0.000 0.259
## ssei 0.312 0.030 10.292 0.000 0.253
## speed =~
## ssno 0.673 0.054 12.462 0.000 0.567
## sscs 0.431 0.040 10.712 0.000 0.352
## ssmk 0.236 0.025 9.331 0.000 0.187
## g =~
## ssgs 0.941 0.023 41.571 0.000 0.897
## ssar 0.823 0.025 33.236 0.000 0.775
## sswk 0.943 0.023 40.803 0.000 0.898
## sspc 0.829 0.021 39.085 0.000 0.787
## ssno 0.590 0.030 19.921 0.000 0.532
## sscs 0.567 0.028 20.035 0.000 0.512
## ssai 0.618 0.030 20.906 0.000 0.560
## sssi 0.635 0.027 23.156 0.000 0.581
## ssmk 0.842 0.024 35.675 0.000 0.795
## ssmc 0.796 0.024 33.068 0.000 0.749
## ssei 0.859 0.026 32.593 0.000 0.808
## ssao 0.662 0.025 26.610 0.000 0.613
## ci.upper Std.lv Std.all
##
## 0.400 0.342 0.343
## 0.271 0.214 0.216
## 0.373 0.320 0.315
## 0.314 0.251 0.247
## 0.432 0.358 0.355
##
## 0.637 0.565 0.540
## 0.667 0.602 0.582
## 0.357 0.308 0.302
## 0.372 0.312 0.294
##
## 0.778 0.673 0.655
## 0.510 0.431 0.419
## 0.286 0.236 0.233
##
## 0.986 0.941 0.917
## 0.872 0.823 0.824
## 0.989 0.943 0.910
## 0.870 0.829 0.834
## 0.648 0.590 0.575
## 0.623 0.567 0.551
## 0.676 0.618 0.591
## 0.689 0.635 0.614
## 0.888 0.842 0.828
## 0.843 0.796 0.781
## 0.911 0.859 0.808
## 0.711 0.662 0.656
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.186 0.030 6.138 0.000 0.126
## .sspc 0.117 0.030 3.869 0.000 0.058
## .ssmk 0.168 0.031 5.367 0.000 0.107
## .ssmc 0.177 0.031 5.791 0.000 0.117
## .ssao 0.128 0.031 4.135 0.000 0.067
## .ssai 0.158 0.033 4.852 0.000 0.094
## .sssi 0.210 0.032 6.615 0.000 0.148
## .ssei 0.158 0.033 4.834 0.000 0.094
## .ssno 0.099 0.032 3.093 0.002 0.036
## .sscs 0.066 0.032 2.069 0.039 0.003
## .ssgs 0.211 0.031 6.806 0.000 0.151
## .sswk 0.145 0.031 4.602 0.000 0.083
## ci.upper Std.lv Std.all
## 0.245 0.186 0.186
## 0.176 0.117 0.118
## 0.229 0.168 0.165
## 0.237 0.177 0.174
## 0.188 0.128 0.127
## 0.222 0.158 0.151
## 0.272 0.210 0.203
## 0.222 0.158 0.149
## 0.161 0.099 0.096
## 0.128 0.066 0.064
## 0.272 0.211 0.206
## 0.207 0.145 0.140
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.203 0.016 12.325 0.000 0.171
## .sspc 0.254 0.016 16.111 0.000 0.223
## .ssmk 0.165 0.013 12.452 0.000 0.139
## .ssmc 0.246 0.016 15.484 0.000 0.215
## .ssao 0.451 0.027 16.865 0.000 0.399
## .ssai 0.394 0.028 14.149 0.000 0.339
## .sssi 0.304 0.027 11.355 0.000 0.252
## .ssei 0.295 0.017 17.294 0.000 0.261
## .ssno 0.254 0.057 4.486 0.000 0.143
## .sscs 0.551 0.043 12.926 0.000 0.467
## .ssgs 0.169 0.012 14.178 0.000 0.145
## .sswk 0.184 0.011 15.966 0.000 0.161
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.235 0.203 0.204
## 0.285 0.254 0.258
## 0.191 0.165 0.160
## 0.278 0.246 0.237
## 0.504 0.451 0.444
## 0.448 0.394 0.360
## 0.357 0.304 0.284
## 0.328 0.295 0.261
## 0.364 0.254 0.241
## 0.634 0.551 0.520
## 0.192 0.169 0.160
## 0.206 0.184 0.171
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
configural<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T)
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 387.962 84.000 0.000 0.977 0.074 0.031 32046.352
## bic
## 32543.566
Mc(configural)
## [1] 0.8905398
summary(configural, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 48 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 387.962 302.452
## Degrees of freedom 84 84
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.283
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 160.132 124.837
## 0 227.830 177.615
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar 0.321 0.042 7.623 0.000 0.239
## sspc 0.169 0.041 4.116 0.000 0.088
## ssmk 0.306 0.042 7.314 0.000 0.224
## ssmc 0.243 0.045 5.387 0.000 0.155
## ssao 0.399 0.058 6.882 0.000 0.285
## electronic =~
## ssai 0.301 0.053 5.645 0.000 0.196
## sssi 0.384 0.061 6.290 0.000 0.264
## ssmc 0.182 0.042 4.330 0.000 0.100
## ssei 0.103 0.038 2.717 0.007 0.029
## speed =~
## ssno 0.628 0.087 7.197 0.000 0.457
## sscs 0.356 0.060 5.936 0.000 0.238
## ssmk 0.217 0.041 5.235 0.000 0.136
## g =~
## ssgs 0.889 0.031 29.062 0.000 0.829
## ssar 0.782 0.033 23.497 0.000 0.717
## sswk 0.910 0.033 27.751 0.000 0.846
## sspc 0.815 0.030 27.087 0.000 0.756
## ssno 0.570 0.041 13.973 0.000 0.490
## sscs 0.539 0.036 15.045 0.000 0.469
## ssai 0.489 0.032 15.455 0.000 0.427
## sssi 0.547 0.034 16.107 0.000 0.480
## ssmk 0.849 0.032 26.934 0.000 0.787
## ssmc 0.725 0.032 22.446 0.000 0.661
## ssei 0.737 0.033 22.136 0.000 0.672
## ssao 0.650 0.032 20.075 0.000 0.586
## ci.upper Std.lv Std.all
##
## 0.404 0.321 0.341
## 0.249 0.169 0.176
## 0.388 0.306 0.300
## 0.331 0.243 0.262
## 0.512 0.399 0.408
##
## 0.405 0.301 0.372
## 0.504 0.384 0.452
## 0.265 0.182 0.196
## 0.178 0.103 0.114
##
## 0.799 0.628 0.644
## 0.473 0.356 0.366
## 0.298 0.217 0.212
##
## 0.949 0.889 0.911
## 0.848 0.782 0.829
## 0.975 0.910 0.908
## 0.874 0.815 0.848
## 0.650 0.570 0.585
## 0.610 0.539 0.556
## 0.551 0.489 0.604
## 0.613 0.547 0.644
## 0.911 0.849 0.832
## 0.788 0.725 0.782
## 0.802 0.737 0.814
## 0.713 0.650 0.665
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.186 0.040 4.598 0.000 0.107
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssmk 0.241 0.044 5.433 0.000 0.154
## .ssmc 0.039 0.040 0.993 0.321 -0.038
## .ssao 0.171 0.042 4.054 0.000 0.088
## .ssai -0.108 0.035 -3.113 0.002 -0.176
## .sssi -0.068 0.036 -1.862 0.063 -0.139
## .ssei 0.000 0.039 0.009 0.993 -0.077
## .ssno 0.175 0.043 4.060 0.000 0.090
## .sscs 0.245 0.043 5.752 0.000 0.162
## .ssgs 0.139 0.042 3.332 0.001 0.057
## .sswk 0.154 0.043 3.607 0.000 0.070
## ci.upper Std.lv Std.all
## 0.265 0.186 0.197
## 0.333 0.253 0.263
## 0.327 0.241 0.236
## 0.117 0.039 0.042
## 0.253 0.171 0.175
## -0.040 -0.108 -0.134
## 0.004 -0.068 -0.080
## 0.077 0.000 0.000
## 0.259 0.175 0.179
## 0.329 0.245 0.253
## 0.220 0.139 0.142
## 0.238 0.154 0.154
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.174 0.021 8.445 0.000 0.134
## .sspc 0.231 0.022 10.722 0.000 0.189
## .ssmk 0.179 0.021 8.669 0.000 0.139
## .ssmc 0.242 0.023 10.684 0.000 0.198
## .ssao 0.374 0.040 9.368 0.000 0.296
## .ssai 0.326 0.031 10.581 0.000 0.266
## .sssi 0.275 0.044 6.305 0.000 0.189
## .ssei 0.266 0.022 11.889 0.000 0.222
## .ssno 0.232 0.087 2.669 0.008 0.062
## .sscs 0.525 0.058 8.974 0.000 0.410
## .ssgs 0.162 0.015 11.111 0.000 0.134
## .sswk 0.177 0.015 11.515 0.000 0.147
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.215 0.174 0.196
## 0.274 0.231 0.250
## 0.220 0.179 0.172
## 0.286 0.242 0.282
## 0.452 0.374 0.392
## 0.386 0.326 0.497
## 0.360 0.275 0.381
## 0.310 0.266 0.325
## 0.403 0.232 0.244
## 0.640 0.525 0.557
## 0.191 0.162 0.170
## 0.207 0.177 0.176
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar 0.362 0.046 7.856 0.000 0.272
## sspc 0.198 0.043 4.554 0.000 0.113
## ssmk 0.305 0.035 8.645 0.000 0.236
## ssmc 0.273 0.053 5.139 0.000 0.169
## ssao 0.299 0.057 5.230 0.000 0.187
## electronic =~
## ssai 0.620 0.051 12.123 0.000 0.520
## sssi 0.652 0.048 13.559 0.000 0.557
## ssmc 0.314 0.036 8.631 0.000 0.242
## ssei 0.358 0.045 7.966 0.000 0.270
## speed =~
## ssno 0.737 0.078 9.425 0.000 0.584
## sscs 0.436 0.052 8.370 0.000 0.334
## ssmk 0.237 0.032 7.362 0.000 0.174
## g =~
## ssgs 0.990 0.033 30.228 0.000 0.925
## ssar 0.864 0.036 23.845 0.000 0.793
## sswk 0.972 0.032 30.039 0.000 0.908
## sspc 0.859 0.028 30.135 0.000 0.803
## ssno 0.611 0.042 14.522 0.000 0.528
## sscs 0.607 0.041 14.640 0.000 0.526
## ssai 0.741 0.045 16.324 0.000 0.652
## sssi 0.720 0.042 17.336 0.000 0.639
## ssmk 0.845 0.034 24.896 0.000 0.778
## ssmc 0.868 0.035 24.896 0.000 0.799
## ssei 0.976 0.039 25.215 0.000 0.900
## ssao 0.685 0.037 18.571 0.000 0.613
## ci.upper Std.lv Std.all
##
## 0.453 0.362 0.344
## 0.283 0.198 0.197
## 0.374 0.305 0.302
## 0.377 0.273 0.252
## 0.411 0.299 0.288
##
## 0.720 0.620 0.525
## 0.746 0.652 0.579
## 0.385 0.314 0.290
## 0.445 0.358 0.303
##
## 0.890 0.737 0.689
## 0.538 0.436 0.413
## 0.299 0.237 0.234
##
## 1.054 0.990 0.924
## 0.935 0.864 0.821
## 1.035 0.972 0.910
## 0.914 0.859 0.853
## 0.693 0.611 0.571
## 0.688 0.607 0.576
## 0.830 0.741 0.628
## 0.802 0.720 0.640
## 0.911 0.845 0.836
## 0.936 0.868 0.802
## 1.052 0.976 0.827
## 0.757 0.685 0.660
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.185 0.045 4.117 0.000 0.097
## .sspc -0.020 0.044 -0.450 0.652 -0.105
## .ssmk 0.094 0.044 2.150 0.032 0.008
## .ssmc 0.317 0.046 6.902 0.000 0.227
## .ssao 0.084 0.045 1.868 0.062 -0.004
## .ssai 0.427 0.052 8.172 0.000 0.324
## .sssi 0.489 0.049 10.035 0.000 0.394
## .ssei 0.317 0.051 6.192 0.000 0.217
## .ssno 0.022 0.047 0.466 0.641 -0.070
## .sscs -0.116 0.046 -2.517 0.012 -0.206
## .ssgs 0.285 0.046 6.206 0.000 0.195
## .sswk 0.136 0.046 2.930 0.003 0.045
## ci.upper Std.lv Std.all
## 0.274 0.185 0.176
## 0.066 -0.020 -0.019
## 0.180 0.094 0.093
## 0.406 0.317 0.292
## 0.173 0.084 0.081
## 0.529 0.427 0.361
## 0.585 0.489 0.435
## 0.417 0.317 0.268
## 0.114 0.022 0.020
## -0.026 -0.116 -0.110
## 0.375 0.285 0.266
## 0.226 0.136 0.127
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.229 0.030 7.679 0.000 0.170
## .sspc 0.237 0.020 11.993 0.000 0.198
## .ssmk 0.158 0.018 8.692 0.000 0.122
## .ssmc 0.246 0.024 10.069 0.000 0.198
## .ssao 0.517 0.040 13.088 0.000 0.440
## .ssai 0.460 0.046 10.106 0.000 0.371
## .sssi 0.322 0.041 7.896 0.000 0.242
## .ssei 0.313 0.025 12.526 0.000 0.264
## .ssno 0.229 0.089 2.567 0.010 0.054
## .sscs 0.552 0.059 9.384 0.000 0.437
## .ssgs 0.168 0.017 9.986 0.000 0.135
## .sswk 0.197 0.017 11.911 0.000 0.165
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.287 0.229 0.207
## 0.276 0.237 0.234
## 0.193 0.158 0.155
## 0.294 0.246 0.210
## 0.595 0.517 0.481
## 0.550 0.460 0.330
## 0.402 0.322 0.255
## 0.362 0.313 0.224
## 0.404 0.229 0.200
## 0.668 0.552 0.497
## 0.201 0.168 0.147
## 0.229 0.197 0.173
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
#modificationIndices(configural, sort=T, maximum.number=30)
metric<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 444.634 104.000 0.000 0.974 0.071 0.050 32063.025
## bic
## 32456.652
Mc(metric)
## [1] 0.878171
summary(metric, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 67 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 100
## Number of equality constraints 24
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 444.634 342.479
## Degrees of freedom 104 104
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.298
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 187.636 144.527
## 0 256.998 197.952
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.339 0.034 9.948 0.000 0.273
## sspc (.p2.) 0.184 0.030 6.164 0.000 0.126
## ssmk (.p3.) 0.311 0.033 9.426 0.000 0.246
## ssmc (.p4.) 0.258 0.036 7.223 0.000 0.188
## ssao (.p5.) 0.360 0.049 7.395 0.000 0.265
## electronic =~
## ssai (.p6.) 0.312 0.036 8.694 0.000 0.242
## sssi (.p7.) 0.327 0.039 8.387 0.000 0.251
## ssmc (.p8.) 0.160 0.022 7.234 0.000 0.117
## ssei (.p9.) 0.174 0.022 8.085 0.000 0.132
## speed =~
## ssno (.10.) 0.636 0.061 10.379 0.000 0.516
## sscs (.11.) 0.364 0.041 8.841 0.000 0.283
## ssmk (.12.) 0.208 0.025 8.295 0.000 0.159
## g =~
## ssgs (.13.) 0.889 0.029 31.082 0.000 0.833
## ssar (.14.) 0.779 0.029 26.512 0.000 0.721
## sswk (.15.) 0.891 0.030 29.570 0.000 0.832
## sspc (.16.) 0.792 0.027 29.855 0.000 0.740
## ssno (.17.) 0.562 0.031 18.160 0.000 0.501
## sscs (.18.) 0.545 0.029 18.590 0.000 0.488
## ssai (.19.) 0.545 0.026 20.641 0.000 0.494
## sssi (.20.) 0.568 0.028 20.604 0.000 0.514
## ssmk (.21.) 0.798 0.029 27.339 0.000 0.741
## ssmc (.22.) 0.739 0.028 26.505 0.000 0.684
## ssei (.23.) 0.789 0.029 27.250 0.000 0.733
## ssao (.24.) 0.631 0.028 22.707 0.000 0.577
## ci.upper Std.lv Std.all
##
## 0.406 0.339 0.360
## 0.243 0.184 0.195
## 0.375 0.311 0.317
## 0.328 0.258 0.274
## 0.456 0.360 0.376
##
## 0.382 0.312 0.368
## 0.404 0.327 0.381
## 0.204 0.160 0.170
## 0.216 0.174 0.182
##
## 0.756 0.636 0.654
## 0.445 0.364 0.373
## 0.258 0.208 0.213
##
## 0.945 0.889 0.912
## 0.836 0.779 0.825
## 0.950 0.891 0.903
## 0.844 0.792 0.838
## 0.623 0.562 0.578
## 0.602 0.545 0.558
## 0.597 0.545 0.644
## 0.622 0.568 0.661
## 0.856 0.798 0.815
## 0.794 0.739 0.787
## 0.846 0.789 0.826
## 0.686 0.631 0.659
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.186 0.040 4.598 0.000 0.107
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssmk 0.241 0.044 5.433 0.000 0.154
## .ssmc 0.039 0.040 0.993 0.321 -0.038
## .ssao 0.171 0.042 4.054 0.000 0.088
## .ssai -0.108 0.035 -3.113 0.002 -0.176
## .sssi -0.068 0.036 -1.862 0.063 -0.139
## .ssei 0.000 0.039 0.009 0.993 -0.077
## .ssno 0.175 0.043 4.060 0.000 0.090
## .sscs 0.245 0.043 5.752 0.000 0.162
## .ssgs 0.139 0.042 3.332 0.001 0.057
## .sswk 0.154 0.043 3.607 0.000 0.070
## ci.upper Std.lv Std.all
## 0.265 0.186 0.197
## 0.333 0.253 0.267
## 0.327 0.241 0.246
## 0.117 0.039 0.042
## 0.253 0.171 0.178
## -0.040 -0.108 -0.128
## 0.004 -0.068 -0.079
## 0.077 0.000 0.000
## 0.259 0.175 0.180
## 0.329 0.245 0.251
## 0.220 0.139 0.142
## 0.238 0.154 0.156
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.169 0.019 8.896 0.000 0.132
## .sspc 0.233 0.021 11.040 0.000 0.191
## .ssmk 0.183 0.018 10.408 0.000 0.149
## .ssmc 0.244 0.020 12.038 0.000 0.205
## .ssao 0.389 0.033 11.773 0.000 0.324
## .ssai 0.321 0.027 12.047 0.000 0.269
## .sssi 0.308 0.028 10.881 0.000 0.252
## .ssei 0.260 0.023 11.470 0.000 0.215
## .ssno 0.225 0.063 3.599 0.000 0.102
## .sscs 0.524 0.053 9.830 0.000 0.419
## .ssgs 0.159 0.014 11.178 0.000 0.131
## .sswk 0.180 0.016 11.515 0.000 0.149
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.206 0.169 0.190
## 0.274 0.233 0.260
## 0.218 0.183 0.191
## 0.284 0.244 0.277
## 0.453 0.389 0.424
## 0.374 0.321 0.449
## 0.363 0.308 0.417
## 0.304 0.260 0.284
## 0.348 0.225 0.238
## 0.628 0.524 0.549
## 0.187 0.159 0.168
## 0.210 0.180 0.184
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.339 0.034 9.948 0.000 0.273
## sspc (.p2.) 0.184 0.030 6.164 0.000 0.126
## ssmk (.p3.) 0.311 0.033 9.426 0.000 0.246
## ssmc (.p4.) 0.258 0.036 7.223 0.000 0.188
## ssao (.p5.) 0.360 0.049 7.395 0.000 0.265
## electronic =~
## ssai (.p6.) 0.312 0.036 8.694 0.000 0.242
## sssi (.p7.) 0.327 0.039 8.387 0.000 0.251
## ssmc (.p8.) 0.160 0.022 7.234 0.000 0.117
## ssei (.p9.) 0.174 0.022 8.085 0.000 0.132
## speed =~
## ssno (.10.) 0.636 0.061 10.379 0.000 0.516
## sscs (.11.) 0.364 0.041 8.841 0.000 0.283
## ssmk (.12.) 0.208 0.025 8.295 0.000 0.159
## g =~
## ssgs (.13.) 0.889 0.029 31.082 0.000 0.833
## ssar (.14.) 0.779 0.029 26.512 0.000 0.721
## sswk (.15.) 0.891 0.030 29.570 0.000 0.832
## sspc (.16.) 0.792 0.027 29.855 0.000 0.740
## ssno (.17.) 0.562 0.031 18.160 0.000 0.501
## sscs (.18.) 0.545 0.029 18.590 0.000 0.488
## ssai (.19.) 0.545 0.026 20.641 0.000 0.494
## sssi (.20.) 0.568 0.028 20.604 0.000 0.514
## ssmk (.21.) 0.798 0.029 27.339 0.000 0.741
## ssmc (.22.) 0.739 0.028 26.505 0.000 0.684
## ssei (.23.) 0.789 0.029 27.250 0.000 0.733
## ssao (.24.) 0.631 0.028 22.707 0.000 0.577
## ci.upper Std.lv Std.all
##
## 0.406 0.332 0.317
## 0.243 0.180 0.177
## 0.375 0.304 0.291
## 0.328 0.252 0.241
## 0.456 0.353 0.333
##
## 0.382 0.642 0.576
## 0.404 0.674 0.623
## 0.204 0.330 0.315
## 0.216 0.358 0.324
##
## 0.756 0.736 0.684
## 0.445 0.421 0.401
## 0.258 0.241 0.230
##
## 0.945 0.988 0.922
## 0.836 0.865 0.826
## 0.950 0.991 0.914
## 0.844 0.881 0.862
## 0.623 0.625 0.581
## 0.602 0.606 0.577
## 0.597 0.606 0.544
## 0.622 0.631 0.584
## 0.856 0.887 0.848
## 0.794 0.821 0.785
## 0.846 0.878 0.794
## 0.686 0.702 0.663
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.185 0.045 4.117 0.000 0.097
## .sspc -0.020 0.044 -0.450 0.652 -0.105
## .ssmk 0.094 0.044 2.150 0.032 0.008
## .ssmc 0.317 0.046 6.902 0.000 0.227
## .ssao 0.084 0.045 1.868 0.062 -0.004
## .ssai 0.427 0.052 8.172 0.000 0.324
## .sssi 0.489 0.049 10.035 0.000 0.394
## .ssei 0.317 0.051 6.192 0.000 0.217
## .ssno 0.022 0.047 0.466 0.641 -0.070
## .sscs -0.116 0.046 -2.517 0.012 -0.206
## .ssgs 0.285 0.046 6.206 0.000 0.195
## .sswk 0.136 0.046 2.930 0.003 0.045
## ci.upper Std.lv Std.all
## 0.274 0.185 0.177
## 0.066 -0.020 -0.019
## 0.180 0.094 0.090
## 0.406 0.317 0.303
## 0.173 0.084 0.080
## 0.529 0.427 0.383
## 0.585 0.489 0.453
## 0.417 0.317 0.287
## 0.114 0.022 0.020
## -0.026 -0.116 -0.110
## 0.375 0.285 0.266
## 0.226 0.136 0.125
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.238 0.025 9.443 0.000 0.189
## .sspc 0.236 0.020 12.064 0.000 0.197
## .ssmk 0.156 0.016 9.839 0.000 0.125
## .ssmc 0.248 0.021 11.619 0.000 0.206
## .ssao 0.504 0.039 13.048 0.000 0.428
## .ssai 0.462 0.045 10.182 0.000 0.373
## .sssi 0.317 0.040 7.980 0.000 0.239
## .ssei 0.323 0.026 12.566 0.000 0.273
## .ssno 0.225 0.077 2.906 0.004 0.073
## .sscs 0.557 0.056 9.950 0.000 0.447
## .ssgs 0.173 0.017 10.463 0.000 0.140
## .sswk 0.194 0.016 11.975 0.000 0.163
## math 0.958 0.196 4.879 0.000 0.573
## electronic 4.242 0.976 4.346 0.000 2.329
## speed 1.338 0.241 5.561 0.000 0.866
## g 1.236 0.101 12.224 0.000 1.038
## ci.upper Std.lv Std.all
## 0.287 0.238 0.217
## 0.274 0.236 0.226
## 0.188 0.156 0.143
## 0.289 0.248 0.226
## 0.579 0.504 0.450
## 0.551 0.462 0.372
## 0.394 0.317 0.271
## 0.373 0.323 0.264
## 0.377 0.225 0.195
## 0.666 0.557 0.506
## 0.205 0.173 0.150
## 0.226 0.194 0.165
## 1.343 1.000 1.000
## 6.155 1.000 1.000
## 1.809 1.000 1.000
## 1.434 1.000 1.000
lavTestScore(metric, release = 1:24)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 56.247 24 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p63. 0.904 1 0.342
## 2 .p2. == .p64. 0.136 1 0.713
## 3 .p3. == .p65. 0.579 1 0.447
## 4 .p4. == .p66. 2.077 1 0.149
## 5 .p5. == .p67. 3.684 1 0.055
## 6 .p6. == .p68. 0.005 1 0.943
## 7 .p7. == .p69. 2.123 1 0.145
## 8 .p8. == .p70. 2.003 1 0.157
## 9 .p9. == .p71. 7.841 1 0.005
## 10 .p10. == .p72. 0.066 1 0.797
## 11 .p11. == .p73. 0.259 1 0.610
## 12 .p12. == .p74. 0.492 1 0.483
## 13 .p13. == .p75. 0.014 1 0.905
## 14 .p14. == .p76. 1.291 1 0.256
## 15 .p15. == .p77. 3.390 1 0.066
## 16 .p16. == .p78. 1.073 1 0.300
## 17 .p17. == .p79. 0.198 1 0.657
## 18 .p18. == .p80. 0.386 1 0.535
## 19 .p19. == .p81. 7.141 1 0.008
## 20 .p20. == .p82. 0.040 1 0.842
## 21 .p21. == .p83. 8.812 1 0.003
## 22 .p22. == .p84. 1.039 1 0.308
## 23 .p23. == .p85. 10.891 1 0.001
## 24 .p24. == .p86. 0.210 1 0.647
metric2<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"), group.partial=c("g=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 433.505 103.000 0.000 0.975 0.070 0.046 32053.896
## bic
## 32452.702
Mc(metric2)
## [1] 0.88157
scalar<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 599.248 112.000 0.000 0.963 0.081 0.054 32201.638
## bic
## 32553.831
Mc(scalar)
## [1] 0.8304143
summary(scalar, standardized=T, ci=T) # -.034
## lavaan 0.6-18 ended normally after 85 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 104
## Number of equality constraints 36
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 599.248 465.292
## Degrees of freedom 112 112
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.288
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 261.907 203.360
## 0 337.341 261.932
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.326 0.035 9.206 0.000 0.256
## sspc (.p2.) 0.223 0.032 6.949 0.000 0.160
## ssmk (.p3.) 0.306 0.035 8.684 0.000 0.237
## ssmc (.p4.) 0.261 0.034 7.656 0.000 0.194
## ssao (.p5.) 0.362 0.046 7.846 0.000 0.272
## electronic =~
## ssai (.p6.) 0.308 0.034 9.060 0.000 0.241
## sssi (.p7.) 0.322 0.036 8.997 0.000 0.252
## ssmc (.p8.) 0.174 0.022 7.828 0.000 0.130
## ssei (.p9.) 0.172 0.020 8.499 0.000 0.133
## speed =~
## ssno (.10.) 0.569 0.057 9.909 0.000 0.456
## sscs (.11.) 0.418 0.047 8.947 0.000 0.327
## ssmk (.12.) 0.218 0.024 9.178 0.000 0.171
## g =~
## ssgs (.13.) 0.889 0.029 30.997 0.000 0.833
## ssar (.14.) 0.779 0.029 26.449 0.000 0.721
## sswk (.15.) 0.891 0.030 29.331 0.000 0.831
## sspc (.16.) 0.784 0.027 29.092 0.000 0.732
## ssno (.17.) 0.563 0.031 18.131 0.000 0.502
## sscs (.18.) 0.540 0.030 18.284 0.000 0.482
## ssai (.19.) 0.546 0.026 20.682 0.000 0.495
## sssi (.20.) 0.569 0.028 20.628 0.000 0.515
## ssmk (.21.) 0.798 0.029 27.232 0.000 0.740
## ssmc (.22.) 0.736 0.028 26.371 0.000 0.681
## ssei (.23.) 0.790 0.029 27.234 0.000 0.733
## ssao (.24.) 0.629 0.028 22.662 0.000 0.575
## ci.upper Std.lv Std.all
##
## 0.395 0.326 0.346
## 0.286 0.223 0.233
## 0.375 0.306 0.313
## 0.327 0.261 0.278
## 0.453 0.362 0.379
##
## 0.375 0.308 0.364
## 0.392 0.322 0.375
## 0.217 0.174 0.185
## 0.212 0.172 0.181
##
## 0.681 0.569 0.586
## 0.510 0.418 0.424
## 0.264 0.218 0.223
##
## 0.946 0.889 0.911
## 0.837 0.779 0.827
## 0.950 0.891 0.903
## 0.837 0.784 0.821
## 0.624 0.563 0.580
## 0.598 0.540 0.547
## 0.598 0.546 0.645
## 0.624 0.569 0.663
## 0.855 0.798 0.815
## 0.791 0.736 0.784
## 0.846 0.790 0.827
## 0.684 0.629 0.658
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.218 0.039 5.526 0.000 0.141
## .sspc (.48.) 0.140 0.041 3.422 0.001 0.060
## .ssmk (.49.) 0.251 0.043 5.775 0.000 0.166
## .ssmc (.50.) 0.058 0.038 1.529 0.126 -0.016
## .ssao (.51.) 0.175 0.040 4.384 0.000 0.097
## .ssai (.52.) -0.113 0.033 -3.399 0.001 -0.178
## .sssi (.53.) -0.074 0.034 -2.157 0.031 -0.142
## .ssei (.54.) -0.003 0.038 -0.078 0.938 -0.077
## .ssno (.55.) 0.210 0.042 5.002 0.000 0.128
## .sscs (.56.) 0.149 0.046 3.255 0.001 0.059
## .ssgs (.57.) 0.193 0.041 4.691 0.000 0.112
## .sswk (.58.) 0.129 0.042 3.072 0.002 0.047
## ci.upper Std.lv Std.all
## 0.295 0.218 0.231
## 0.220 0.140 0.146
## 0.336 0.251 0.256
## 0.133 0.058 0.062
## 0.253 0.175 0.183
## -0.048 -0.113 -0.133
## -0.007 -0.074 -0.087
## 0.071 -0.003 -0.003
## 0.292 0.210 0.217
## 0.239 0.149 0.151
## 0.273 0.193 0.197
## 0.212 0.129 0.131
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.174 0.018 9.444 0.000 0.138
## .sspc 0.248 0.023 10.599 0.000 0.202
## .ssmk 0.180 0.017 10.395 0.000 0.146
## .ssmc 0.241 0.021 11.716 0.000 0.201
## .ssao 0.388 0.033 11.822 0.000 0.324
## .ssai 0.323 0.026 12.316 0.000 0.272
## .sssi 0.310 0.028 11.071 0.000 0.255
## .ssei 0.259 0.023 11.387 0.000 0.214
## .ssno 0.300 0.057 5.303 0.000 0.189
## .sscs 0.507 0.059 8.583 0.000 0.391
## .ssgs 0.163 0.015 10.941 0.000 0.134
## .sswk 0.180 0.016 11.283 0.000 0.149
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.210 0.174 0.196
## 0.293 0.248 0.271
## 0.214 0.180 0.188
## 0.281 0.241 0.274
## 0.453 0.388 0.424
## 0.375 0.323 0.451
## 0.365 0.310 0.420
## 0.303 0.259 0.284
## 0.411 0.300 0.319
## 0.622 0.507 0.520
## 0.192 0.163 0.171
## 0.211 0.180 0.185
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.326 0.035 9.206 0.000 0.256
## sspc (.p2.) 0.223 0.032 6.949 0.000 0.160
## ssmk (.p3.) 0.306 0.035 8.684 0.000 0.237
## ssmc (.p4.) 0.261 0.034 7.656 0.000 0.194
## ssao (.p5.) 0.362 0.046 7.846 0.000 0.272
## electronic =~
## ssai (.p6.) 0.308 0.034 9.060 0.000 0.241
## sssi (.p7.) 0.322 0.036 8.997 0.000 0.252
## ssmc (.p8.) 0.174 0.022 7.828 0.000 0.130
## ssei (.p9.) 0.172 0.020 8.499 0.000 0.133
## speed =~
## ssno (.10.) 0.569 0.057 9.909 0.000 0.456
## sscs (.11.) 0.418 0.047 8.947 0.000 0.327
## ssmk (.12.) 0.218 0.024 9.178 0.000 0.171
## g =~
## ssgs (.13.) 0.889 0.029 30.997 0.000 0.833
## ssar (.14.) 0.779 0.029 26.449 0.000 0.721
## sswk (.15.) 0.891 0.030 29.331 0.000 0.831
## sspc (.16.) 0.784 0.027 29.092 0.000 0.732
## ssno (.17.) 0.563 0.031 18.131 0.000 0.502
## sscs (.18.) 0.540 0.030 18.284 0.000 0.482
## ssai (.19.) 0.546 0.026 20.682 0.000 0.495
## sssi (.20.) 0.569 0.028 20.628 0.000 0.515
## ssmk (.21.) 0.798 0.029 27.232 0.000 0.740
## ssmc (.22.) 0.736 0.028 26.371 0.000 0.681
## ssei (.23.) 0.790 0.029 27.234 0.000 0.733
## ssao (.24.) 0.629 0.028 22.662 0.000 0.575
## ci.upper Std.lv Std.all
##
## 0.395 0.319 0.305
## 0.286 0.218 0.212
## 0.375 0.300 0.287
## 0.327 0.256 0.243
## 0.453 0.355 0.336
##
## 0.375 0.631 0.568
## 0.392 0.659 0.612
## 0.217 0.356 0.339
## 0.212 0.353 0.320
##
## 0.681 0.660 0.616
## 0.510 0.486 0.456
## 0.264 0.253 0.242
##
## 0.946 0.990 0.921
## 0.837 0.867 0.828
## 0.950 0.991 0.914
## 0.837 0.873 0.847
## 0.624 0.627 0.585
## 0.598 0.601 0.565
## 0.598 0.608 0.547
## 0.624 0.634 0.588
## 0.855 0.888 0.849
## 0.791 0.819 0.779
## 0.846 0.879 0.796
## 0.684 0.700 0.662
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.218 0.039 5.526 0.000 0.141
## .sspc (.48.) 0.140 0.041 3.422 0.001 0.060
## .ssmk (.49.) 0.251 0.043 5.775 0.000 0.166
## .ssmc (.50.) 0.058 0.038 1.529 0.126 -0.016
## .ssao (.51.) 0.175 0.040 4.384 0.000 0.097
## .ssai (.52.) -0.113 0.033 -3.399 0.001 -0.178
## .sssi (.53.) -0.074 0.034 -2.157 0.031 -0.142
## .ssei (.54.) -0.003 0.038 -0.078 0.938 -0.077
## .ssno (.55.) 0.210 0.042 5.002 0.000 0.128
## .sscs (.56.) 0.149 0.046 3.255 0.001 0.059
## .ssgs (.57.) 0.193 0.041 4.691 0.000 0.112
## .sswk (.58.) 0.129 0.042 3.072 0.002 0.047
## math -0.328 0.122 -2.696 0.007 -0.567
## elctrnc 1.708 0.230 7.425 0.000 1.257
## speed -0.435 0.117 -3.711 0.000 -0.665
## g 0.037 0.069 0.541 0.589 -0.098
## ci.upper Std.lv Std.all
## 0.295 0.218 0.208
## 0.220 0.140 0.136
## 0.336 0.251 0.240
## 0.133 0.058 0.056
## 0.253 0.175 0.165
## -0.048 -0.113 -0.102
## -0.007 -0.074 -0.069
## 0.071 -0.003 -0.003
## 0.292 0.210 0.196
## 0.239 0.149 0.140
## 0.273 0.193 0.179
## 0.212 0.129 0.119
## -0.090 -0.335 -0.335
## 2.159 0.834 0.834
## -0.205 -0.375 -0.375
## 0.173 0.034 0.034
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.244 0.025 9.898 0.000 0.196
## .sspc 0.251 0.022 11.375 0.000 0.208
## .ssmk 0.152 0.015 9.971 0.000 0.122
## .ssmc 0.242 0.022 11.156 0.000 0.200
## .ssao 0.503 0.038 13.246 0.000 0.429
## .ssai 0.466 0.042 11.001 0.000 0.383
## .sssi 0.325 0.035 9.229 0.000 0.256
## .ssei 0.323 0.025 12.800 0.000 0.274
## .ssno 0.319 0.066 4.855 0.000 0.190
## .sscs 0.537 0.061 8.854 0.000 0.418
## .ssgs 0.176 0.018 10.033 0.000 0.142
## .sswk 0.193 0.016 11.768 0.000 0.161
## math 0.961 0.201 4.784 0.000 0.568
## electronic 4.198 0.963 4.362 0.000 2.312
## speed 1.347 0.247 5.443 0.000 0.862
## g 1.238 0.102 12.159 0.000 1.039
## ci.upper Std.lv Std.all
## 0.292 0.244 0.222
## 0.295 0.251 0.237
## 0.182 0.152 0.139
## 0.285 0.242 0.219
## 0.578 0.503 0.449
## 0.549 0.466 0.378
## 0.394 0.325 0.280
## 0.373 0.323 0.265
## 0.448 0.319 0.278
## 0.656 0.537 0.473
## 0.211 0.176 0.153
## 0.226 0.193 0.164
## 1.355 1.000 1.000
## 6.085 1.000 1.000
## 1.832 1.000 1.000
## 1.438 1.000 1.000
lavTestScore(scalar, release = 25:36)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 152.695 12 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p47. == .p109. 14.892 1 0.000
## 2 .p48. == .p110. 86.856 1 0.000
## 3 .p49. == .p111. 1.449 1 0.229
## 4 .p50. == .p112. 4.231 1 0.040
## 5 .p51. == .p113. 0.075 1 0.784
## 6 .p52. == .p114. 0.284 1 0.594
## 7 .p53. == .p115. 0.614 1 0.433
## 8 .p54. == .p116. 0.119 1 0.730
## 9 .p55. == .p117. 21.278 1 0.000
## 10 .p56. == .p118. 37.221 1 0.000
## 11 .p57. == .p119. 46.224 1 0.000
## 12 .p58. == .p120. 8.223 1 0.004
scalar2<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 478.345 110.000 0.000 0.972 0.071 0.051 32084.736
## bic
## 32447.287
Mc(scalar2)
## [1] 0.8689388
summary(scalar2, standardized=T, ci=T) # g -.070 Std.all
## lavaan 0.6-18 ended normally after 84 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 104
## Number of equality constraints 34
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 478.345 369.471
## Degrees of freedom 110 110
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.295
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 202.083 156.087
## 0 276.263 213.384
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.331 0.033 9.974 0.000 0.266
## sspc (.p2.) 0.183 0.030 6.086 0.000 0.124
## ssmk (.p3.) 0.316 0.033 9.589 0.000 0.252
## ssmc (.p4.) 0.255 0.035 7.211 0.000 0.186
## ssao (.p5.) 0.364 0.047 7.688 0.000 0.271
## electronic =~
## ssai (.p6.) 0.311 0.034 9.097 0.000 0.244
## sssi (.p7.) 0.326 0.036 9.013 0.000 0.255
## ssmc (.p8.) 0.168 0.021 7.827 0.000 0.126
## ssei (.p9.) 0.170 0.020 8.407 0.000 0.130
## speed =~
## ssno (.10.) 0.616 0.060 10.340 0.000 0.499
## sscs (.11.) 0.372 0.041 9.081 0.000 0.292
## ssmk (.12.) 0.218 0.025 8.678 0.000 0.168
## g =~
## ssgs (.13.) 0.890 0.029 31.152 0.000 0.834
## ssar (.14.) 0.780 0.029 26.560 0.000 0.722
## sswk (.15.) 0.888 0.030 29.233 0.000 0.829
## sspc (.16.) 0.792 0.027 29.851 0.000 0.740
## ssno (.17.) 0.563 0.031 18.159 0.000 0.502
## sscs (.18.) 0.545 0.029 18.582 0.000 0.488
## ssai (.19.) 0.545 0.026 20.651 0.000 0.494
## sssi (.20.) 0.568 0.028 20.575 0.000 0.514
## ssmk (.21.) 0.797 0.029 27.306 0.000 0.740
## ssmc (.22.) 0.739 0.028 26.510 0.000 0.684
## ssei (.23.) 0.790 0.029 27.258 0.000 0.733
## ssao (.24.) 0.631 0.028 22.744 0.000 0.576
## ci.upper Std.lv Std.all
##
## 0.396 0.331 0.351
## 0.242 0.183 0.194
## 0.381 0.316 0.322
## 0.324 0.255 0.271
## 0.457 0.364 0.380
##
## 0.378 0.311 0.368
## 0.397 0.326 0.379
## 0.210 0.168 0.179
## 0.210 0.170 0.178
##
## 0.732 0.616 0.634
## 0.452 0.372 0.381
## 0.267 0.218 0.222
##
## 0.946 0.890 0.911
## 0.837 0.780 0.827
## 0.948 0.888 0.901
## 0.844 0.792 0.838
## 0.624 0.563 0.579
## 0.603 0.545 0.558
## 0.597 0.545 0.645
## 0.622 0.568 0.662
## 0.855 0.797 0.813
## 0.793 0.739 0.787
## 0.847 0.790 0.827
## 0.685 0.631 0.658
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.202 0.040 5.072 0.000 0.124
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssmk (.49.) 0.221 0.043 5.083 0.000 0.136
## .ssmc (.50.) 0.053 0.038 1.407 0.159 -0.021
## .ssao (.51.) 0.159 0.039 4.046 0.000 0.082
## .ssai (.52.) -0.110 0.033 -3.310 0.001 -0.175
## .sssi (.53.) -0.071 0.034 -2.059 0.040 -0.138
## .ssei (.54.) -0.007 0.038 -0.180 0.857 -0.081
## .ssno (.55.) 0.184 0.043 4.289 0.000 0.100
## .sscs 0.245 0.043 5.752 0.000 0.162
## .ssgs (.57.) 0.175 0.041 4.312 0.000 0.096
## .sswk (.58.) 0.112 0.042 2.695 0.007 0.031
## ci.upper Std.lv Std.all
## 0.280 0.202 0.214
## 0.333 0.253 0.267
## 0.306 0.221 0.225
## 0.128 0.053 0.057
## 0.237 0.159 0.166
## -0.045 -0.110 -0.130
## -0.003 -0.071 -0.083
## 0.067 -0.007 -0.007
## 0.269 0.184 0.190
## 0.329 0.245 0.251
## 0.255 0.175 0.180
## 0.194 0.112 0.114
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.171 0.018 9.331 0.000 0.135
## .sspc 0.232 0.021 11.030 0.000 0.191
## .ssmk 0.180 0.018 10.070 0.000 0.145
## .ssmc 0.243 0.020 11.971 0.000 0.203
## .ssao 0.387 0.033 11.692 0.000 0.323
## .ssai 0.322 0.026 12.199 0.000 0.270
## .sssi 0.308 0.028 10.968 0.000 0.253
## .ssei 0.260 0.023 11.407 0.000 0.215
## .ssno 0.249 0.059 4.185 0.000 0.132
## .sscs 0.518 0.053 9.818 0.000 0.414
## .ssgs 0.162 0.015 11.147 0.000 0.133
## .sswk 0.183 0.016 11.366 0.000 0.152
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.207 0.171 0.193
## 0.274 0.232 0.260
## 0.214 0.180 0.186
## 0.283 0.243 0.276
## 0.452 0.387 0.422
## 0.373 0.322 0.449
## 0.363 0.308 0.418
## 0.304 0.260 0.285
## 0.365 0.249 0.263
## 0.621 0.518 0.543
## 0.190 0.162 0.170
## 0.215 0.183 0.188
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.331 0.033 9.974 0.000 0.266
## sspc (.p2.) 0.183 0.030 6.086 0.000 0.124
## ssmk (.p3.) 0.316 0.033 9.589 0.000 0.252
## ssmc (.p4.) 0.255 0.035 7.211 0.000 0.186
## ssao (.p5.) 0.364 0.047 7.688 0.000 0.271
## electronic =~
## ssai (.p6.) 0.311 0.034 9.097 0.000 0.244
## sssi (.p7.) 0.326 0.036 9.013 0.000 0.255
## ssmc (.p8.) 0.168 0.021 7.827 0.000 0.126
## ssei (.p9.) 0.170 0.020 8.407 0.000 0.130
## speed =~
## ssno (.10.) 0.616 0.060 10.340 0.000 0.499
## sscs (.11.) 0.372 0.041 9.081 0.000 0.292
## ssmk (.12.) 0.218 0.025 8.678 0.000 0.168
## g =~
## ssgs (.13.) 0.890 0.029 31.152 0.000 0.834
## ssar (.14.) 0.780 0.029 26.560 0.000 0.722
## sswk (.15.) 0.888 0.030 29.233 0.000 0.829
## sspc (.16.) 0.792 0.027 29.851 0.000 0.740
## ssno (.17.) 0.563 0.031 18.159 0.000 0.502
## sscs (.18.) 0.545 0.029 18.582 0.000 0.488
## ssai (.19.) 0.545 0.026 20.651 0.000 0.494
## sssi (.20.) 0.568 0.028 20.575 0.000 0.514
## ssmk (.21.) 0.797 0.029 27.306 0.000 0.740
## ssmc (.22.) 0.739 0.028 26.510 0.000 0.684
## ssei (.23.) 0.790 0.029 27.258 0.000 0.733
## ssao (.24.) 0.631 0.028 22.744 0.000 0.576
## ci.upper Std.lv Std.all
##
## 0.396 0.322 0.307
## 0.242 0.178 0.175
## 0.381 0.308 0.294
## 0.324 0.248 0.236
## 0.457 0.354 0.334
##
## 0.378 0.639 0.574
## 0.397 0.669 0.619
## 0.210 0.345 0.328
## 0.210 0.349 0.316
##
## 0.732 0.711 0.662
## 0.452 0.430 0.409
## 0.267 0.251 0.240
##
## 0.946 0.990 0.921
## 0.837 0.868 0.828
## 0.948 0.988 0.912
## 0.844 0.881 0.863
## 0.624 0.626 0.583
## 0.603 0.606 0.578
## 0.597 0.607 0.545
## 0.622 0.632 0.585
## 0.855 0.887 0.847
## 0.793 0.822 0.783
## 0.847 0.879 0.796
## 0.685 0.701 0.663
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.202 0.040 5.072 0.000 0.124
## .sspc -0.026 0.044 -0.596 0.551 -0.112
## .ssmk (.49.) 0.221 0.043 5.083 0.000 0.136
## .ssmc (.50.) 0.053 0.038 1.407 0.159 -0.021
## .ssao (.51.) 0.159 0.039 4.046 0.000 0.082
## .ssai (.52.) -0.110 0.033 -3.310 0.001 -0.175
## .sssi (.53.) -0.071 0.034 -2.059 0.040 -0.138
## .ssei (.54.) -0.007 0.038 -0.180 0.857 -0.081
## .ssno (.55.) 0.184 0.043 4.289 0.000 0.100
## .sscs -0.027 0.052 -0.525 0.600 -0.129
## .ssgs (.57.) 0.175 0.041 4.312 0.000 0.096
## .sswk (.58.) 0.112 0.042 2.695 0.007 0.031
## math -0.300 0.096 -3.114 0.002 -0.489
## elctrnc 1.595 0.219 7.276 0.000 1.166
## speed -0.351 0.099 -3.555 0.000 -0.545
## g 0.078 0.068 1.147 0.251 -0.055
## ci.upper Std.lv Std.all
## 0.280 0.202 0.193
## 0.060 -0.026 -0.025
## 0.306 0.221 0.211
## 0.128 0.053 0.051
## 0.237 0.159 0.151
## -0.045 -0.110 -0.099
## -0.003 -0.071 -0.066
## 0.067 -0.007 -0.006
## 0.269 0.184 0.172
## 0.075 -0.027 -0.026
## 0.255 0.175 0.163
## 0.194 0.112 0.104
## -0.111 -0.309 -0.309
## 2.025 0.777 0.777
## -0.158 -0.304 -0.304
## 0.210 0.070 0.070
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.241 0.024 9.940 0.000 0.193
## .sspc 0.236 0.020 12.035 0.000 0.197
## .ssmk 0.152 0.016 9.593 0.000 0.121
## .ssmc 0.246 0.021 11.456 0.000 0.204
## .ssao 0.503 0.038 13.109 0.000 0.428
## .ssai 0.464 0.043 10.841 0.000 0.380
## .sssi 0.320 0.036 8.927 0.000 0.250
## .ssei 0.324 0.025 12.825 0.000 0.275
## .ssno 0.256 0.071 3.588 0.000 0.116
## .sscs 0.549 0.055 9.916 0.000 0.441
## .ssgs 0.176 0.017 10.341 0.000 0.142
## .sswk 0.198 0.017 11.980 0.000 0.166
## math 0.947 0.193 4.895 0.000 0.568
## electronic 4.215 0.959 4.394 0.000 2.335
## speed 1.335 0.241 5.539 0.000 0.863
## g 1.238 0.101 12.233 0.000 1.039
## ci.upper Std.lv Std.all
## 0.288 0.241 0.220
## 0.274 0.236 0.226
## 0.183 0.152 0.139
## 0.288 0.246 0.223
## 0.579 0.503 0.449
## 0.547 0.464 0.374
## 0.390 0.320 0.274
## 0.374 0.324 0.266
## 0.396 0.256 0.222
## 0.658 0.549 0.499
## 0.209 0.176 0.152
## 0.231 0.198 0.169
## 1.326 1.000 1.000
## 6.095 1.000 1.000
## 1.808 1.000 1.000
## 1.436 1.000 1.000
lavTestScore(scalar2, release = 25:34)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 33.727 10 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p47. == .p109. 4.032 1 0.045
## 2 .p49. == .p111. 5.618 1 0.018
## 3 .p50. == .p112. 2.168 1 0.141
## 4 .p51. == .p113. 0.684 1 0.408
## 5 .p52. == .p114. 0.034 1 0.853
## 6 .p53. == .p115. 0.142 1 0.707
## 7 .p54. == .p116. 0.520 1 0.471
## 8 .p55. == .p117. 5.618 1 0.018
## 9 .p57. == .p119. 22.918 1 0.000
## 10 .p58. == .p120. 23.346 1 0.000
strict<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 522.558 122.000 0.000 0.970 0.071 0.053 32104.949
## bic
## 32405.348
Mc(strict)
## [1] 0.8583286
summary(strict, standardized=T, ci=T) # g -.069 Std.all
## lavaan 0.6-18 ended normally after 83 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 104
## Number of equality constraints 46
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 522.558 398.950
## Degrees of freedom 122 122
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.310
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 228.829 174.701
## 0 293.730 224.250
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.325 0.032 10.017 0.000 0.262
## sspc (.p2.) 0.181 0.030 6.070 0.000 0.123
## ssmk (.p3.) 0.310 0.036 8.643 0.000 0.240
## ssmc (.p4.) 0.245 0.035 7.046 0.000 0.177
## ssao (.p5.) 0.344 0.045 7.571 0.000 0.255
## electronic =~
## ssai (.p6.) 0.298 0.036 8.357 0.000 0.228
## sssi (.p7.) 0.300 0.038 7.927 0.000 0.226
## ssmc (.p8.) 0.155 0.022 7.074 0.000 0.112
## ssei (.p9.) 0.162 0.021 7.610 0.000 0.120
## speed =~
## ssno (.10.) 0.618 0.059 10.420 0.000 0.502
## sscs (.11.) 0.372 0.042 8.946 0.000 0.290
## ssmk (.12.) 0.216 0.024 9.134 0.000 0.170
## g =~
## ssgs (.13.) 0.889 0.029 31.050 0.000 0.833
## ssar (.14.) 0.782 0.029 26.775 0.000 0.724
## sswk (.15.) 0.887 0.030 29.115 0.000 0.827
## sspc (.16.) 0.792 0.026 29.884 0.000 0.740
## ssno (.17.) 0.562 0.031 18.152 0.000 0.502
## sscs (.18.) 0.545 0.029 18.530 0.000 0.487
## ssai (.19.) 0.549 0.027 20.466 0.000 0.496
## sssi (.20.) 0.567 0.028 20.571 0.000 0.513
## ssmk (.21.) 0.799 0.029 27.341 0.000 0.742
## ssmc (.22.) 0.738 0.028 26.454 0.000 0.684
## ssei (.23.) 0.793 0.029 27.554 0.000 0.736
## ssao (.24.) 0.631 0.028 22.498 0.000 0.576
## ci.upper Std.lv Std.all
##
## 0.389 0.325 0.339
## 0.240 0.181 0.192
## 0.381 0.310 0.319
## 0.314 0.245 0.262
## 0.434 0.344 0.351
##
## 0.369 0.298 0.340
## 0.374 0.300 0.349
## 0.198 0.155 0.166
## 0.203 0.162 0.166
##
## 0.734 0.618 0.635
## 0.453 0.372 0.378
## 0.263 0.216 0.222
##
## 0.945 0.889 0.908
## 0.839 0.782 0.814
## 0.946 0.887 0.897
## 0.844 0.792 0.838
## 0.623 0.562 0.578
## 0.603 0.545 0.554
## 0.601 0.549 0.626
## 0.621 0.567 0.660
## 0.856 0.799 0.821
## 0.793 0.738 0.788
## 0.849 0.793 0.816
## 0.686 0.631 0.642
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.205 0.040 5.111 0.000 0.126
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssmk (.49.) 0.222 0.043 5.126 0.000 0.137
## .ssmc (.50.) 0.055 0.038 1.448 0.148 -0.019
## .ssao (.51.) 0.156 0.039 3.964 0.000 0.079
## .ssai (.52.) -0.116 0.033 -3.462 0.001 -0.181
## .sssi (.53.) -0.066 0.035 -1.906 0.057 -0.134
## .ssei (.54.) -0.009 0.038 -0.248 0.804 -0.084
## .ssno (.55.) 0.184 0.043 4.283 0.000 0.100
## .sscs 0.245 0.043 5.752 0.000 0.162
## .ssgs (.57.) 0.177 0.041 4.343 0.000 0.097
## .sswk (.58.) 0.111 0.042 2.669 0.008 0.029
## ci.upper Std.lv Std.all
## 0.284 0.205 0.214
## 0.333 0.253 0.267
## 0.307 0.222 0.228
## 0.129 0.055 0.059
## 0.233 0.156 0.159
## -0.050 -0.116 -0.132
## 0.002 -0.066 -0.077
## 0.065 -0.009 -0.010
## 0.269 0.184 0.190
## 0.329 0.245 0.249
## 0.257 0.177 0.181
## 0.192 0.111 0.112
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.25.) 0.205 0.017 12.252 0.000 0.172
## .sspc (.26.) 0.233 0.014 16.242 0.000 0.205
## .ssmk (.27.) 0.166 0.014 12.069 0.000 0.139
## .ssmc (.28.) 0.248 0.016 15.258 0.000 0.216
## .ssao (.29.) 0.449 0.027 16.558 0.000 0.396
## .ssai (.30.) 0.378 0.026 14.606 0.000 0.328
## .sssi (.31.) 0.325 0.024 13.625 0.000 0.279
## .ssei (.32.) 0.289 0.017 17.133 0.000 0.256
## .ssno (.33.) 0.249 0.058 4.296 0.000 0.136
## .sscs (.34.) 0.534 0.040 13.203 0.000 0.454
## .ssgs (.35.) 0.168 0.011 14.752 0.000 0.146
## .sswk (.36.) 0.191 0.012 16.307 0.000 0.168
## math 1.000 1.000
## elctrnc 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.238 0.205 0.222
## 0.262 0.233 0.261
## 0.193 0.166 0.175
## 0.279 0.248 0.282
## 0.502 0.449 0.465
## 0.429 0.378 0.492
## 0.372 0.325 0.442
## 0.322 0.289 0.306
## 0.363 0.249 0.263
## 0.613 0.534 0.551
## 0.191 0.168 0.176
## 0.214 0.191 0.196
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.325 0.032 10.017 0.000 0.262
## sspc (.p2.) 0.181 0.030 6.070 0.000 0.123
## ssmk (.p3.) 0.310 0.036 8.643 0.000 0.240
## ssmc (.p4.) 0.245 0.035 7.046 0.000 0.177
## ssao (.p5.) 0.344 0.045 7.571 0.000 0.255
## electronic =~
## ssai (.p6.) 0.298 0.036 8.357 0.000 0.228
## sssi (.p7.) 0.300 0.038 7.927 0.000 0.226
## ssmc (.p8.) 0.155 0.022 7.074 0.000 0.112
## ssei (.p9.) 0.162 0.021 7.610 0.000 0.120
## speed =~
## ssno (.10.) 0.618 0.059 10.420 0.000 0.502
## sscs (.11.) 0.372 0.042 8.946 0.000 0.290
## ssmk (.12.) 0.216 0.024 9.134 0.000 0.170
## g =~
## ssgs (.13.) 0.889 0.029 31.050 0.000 0.833
## ssar (.14.) 0.782 0.029 26.775 0.000 0.724
## sswk (.15.) 0.887 0.030 29.115 0.000 0.827
## sspc (.16.) 0.792 0.026 29.884 0.000 0.740
## ssno (.17.) 0.562 0.031 18.152 0.000 0.502
## sscs (.18.) 0.545 0.029 18.530 0.000 0.487
## ssai (.19.) 0.549 0.027 20.466 0.000 0.496
## sssi (.20.) 0.567 0.028 20.571 0.000 0.513
## ssmk (.21.) 0.799 0.029 27.341 0.000 0.742
## ssmc (.22.) 0.738 0.028 26.454 0.000 0.684
## ssei (.23.) 0.793 0.029 27.554 0.000 0.736
## ssao (.24.) 0.631 0.028 22.498 0.000 0.576
## ci.upper Std.lv Std.all
##
## 0.389 0.334 0.322
## 0.240 0.186 0.182
## 0.381 0.318 0.301
## 0.314 0.252 0.239
## 0.434 0.353 0.342
##
## 0.369 0.662 0.607
## 0.374 0.666 0.616
## 0.198 0.344 0.327
## 0.203 0.359 0.328
##
## 0.734 0.714 0.665
## 0.453 0.430 0.412
## 0.263 0.250 0.236
##
## 0.945 0.990 0.924
## 0.839 0.871 0.840
## 0.946 0.988 0.914
## 0.844 0.882 0.862
## 0.623 0.626 0.584
## 0.603 0.607 0.582
## 0.601 0.611 0.560
## 0.621 0.632 0.585
## 0.856 0.890 0.840
## 0.793 0.823 0.782
## 0.849 0.883 0.807
## 0.686 0.703 0.680
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.205 0.040 5.111 0.000 0.126
## .sspc -0.025 0.044 -0.581 0.561 -0.111
## .ssmk (.49.) 0.222 0.043 5.126 0.000 0.137
## .ssmc (.50.) 0.055 0.038 1.448 0.148 -0.019
## .ssao (.51.) 0.156 0.039 3.964 0.000 0.079
## .ssai (.52.) -0.116 0.033 -3.462 0.001 -0.181
## .sssi (.53.) -0.066 0.035 -1.906 0.057 -0.134
## .ssei (.54.) -0.009 0.038 -0.248 0.804 -0.084
## .ssno (.55.) 0.184 0.043 4.283 0.000 0.100
## .sscs -0.028 0.052 -0.535 0.593 -0.130
## .ssgs (.57.) 0.177 0.041 4.343 0.000 0.097
## .sswk (.58.) 0.111 0.042 2.669 0.008 0.029
## math -0.306 0.099 -3.091 0.002 -0.501
## elctrnc 1.700 0.255 6.659 0.000 1.200
## speed -0.349 0.098 -3.554 0.000 -0.542
## g 0.077 0.068 1.142 0.254 -0.055
## ci.upper Std.lv Std.all
## 0.284 0.205 0.198
## 0.060 -0.025 -0.025
## 0.307 0.222 0.210
## 0.129 0.055 0.052
## 0.233 0.156 0.151
## -0.050 -0.116 -0.106
## 0.002 -0.066 -0.061
## 0.065 -0.009 -0.009
## 0.269 0.184 0.172
## 0.074 -0.028 -0.027
## 0.257 0.177 0.165
## 0.192 0.111 0.103
## -0.112 -0.299 -0.299
## 2.200 0.766 0.766
## -0.157 -0.302 -0.302
## 0.210 0.069 0.069
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.25.) 0.205 0.017 12.252 0.000 0.172
## .sspc (.26.) 0.233 0.014 16.242 0.000 0.205
## .ssmk (.27.) 0.166 0.014 12.069 0.000 0.139
## .ssmc (.28.) 0.248 0.016 15.258 0.000 0.216
## .ssao (.29.) 0.449 0.027 16.558 0.000 0.396
## .ssai (.30.) 0.378 0.026 14.606 0.000 0.328
## .sssi (.31.) 0.325 0.024 13.625 0.000 0.279
## .ssei (.32.) 0.289 0.017 17.133 0.000 0.256
## .ssno (.33.) 0.249 0.058 4.296 0.000 0.136
## .sscs (.34.) 0.534 0.040 13.203 0.000 0.454
## .ssgs (.35.) 0.168 0.011 14.752 0.000 0.146
## .sswk (.36.) 0.191 0.012 16.307 0.000 0.168
## math 1.053 0.220 4.776 0.000 0.621
## elctrnc 4.923 1.241 3.968 0.000 2.491
## speed 1.335 0.235 5.687 0.000 0.875
## g 1.241 0.102 12.226 0.000 1.042
## ci.upper Std.lv Std.all
## 0.238 0.205 0.191
## 0.262 0.233 0.223
## 0.193 0.166 0.148
## 0.279 0.248 0.224
## 0.502 0.449 0.420
## 0.429 0.378 0.318
## 0.372 0.325 0.279
## 0.322 0.289 0.241
## 0.363 0.249 0.217
## 0.613 0.534 0.491
## 0.191 0.168 0.147
## 0.214 0.191 0.164
## 1.485 1.000 1.000
## 7.355 1.000 1.000
## 1.795 1.000 1.000
## 1.440 1.000 1.000
latent<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 586.893 114.000 0.000 0.964 0.080 0.108 32185.283
## bic
## 32527.118
Mc(latent)
## [1] 0.8349731
summary(latent, standardized=T, ci=T) # -.075
## lavaan 0.6-18 ended normally after 52 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 100
## Number of equality constraints 34
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 586.893 450.032
## Degrees of freedom 114 114
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.304
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 269.344 206.534
## 0 317.549 243.498
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.330 0.029 11.221 0.000 0.273
## sspc (.p2.) 0.186 0.028 6.541 0.000 0.130
## ssmk (.p3.) 0.314 0.027 11.752 0.000 0.262
## ssmc (.p4.) 0.252 0.033 7.718 0.000 0.188
## ssao (.p5.) 0.360 0.039 9.347 0.000 0.285
## electronic =~
## ssai (.p6.) 0.472 0.033 14.517 0.000 0.408
## sssi (.p7.) 0.509 0.030 16.784 0.000 0.450
## ssmc (.p8.) 0.271 0.023 12.031 0.000 0.227
## ssei (.p9.) 0.253 0.026 9.805 0.000 0.202
## speed =~
## ssno (.10.) 0.661 0.057 11.591 0.000 0.549
## sscs (.11.) 0.403 0.039 10.336 0.000 0.327
## ssmk (.12.) 0.237 0.025 9.429 0.000 0.188
## g =~
## ssgs (.13.) 0.941 0.022 41.854 0.000 0.897
## ssar (.14.) 0.824 0.025 33.395 0.000 0.775
## sswk (.15.) 0.940 0.023 40.426 0.000 0.895
## sspc (.16.) 0.836 0.021 40.337 0.000 0.796
## ssno (.17.) 0.591 0.029 20.086 0.000 0.534
## sscs (.18.) 0.573 0.028 20.834 0.000 0.519
## ssai (.19.) 0.608 0.027 22.145 0.000 0.554
## sssi (.20.) 0.637 0.027 23.661 0.000 0.584
## ssmk (.21.) 0.842 0.023 35.918 0.000 0.796
## ssmc (.22.) 0.798 0.024 33.456 0.000 0.751
## ssei (.23.) 0.854 0.026 32.629 0.000 0.803
## ssao (.24.) 0.665 0.024 27.246 0.000 0.617
## ci.upper Std.lv Std.all
##
## 0.388 0.330 0.337
## 0.242 0.186 0.189
## 0.367 0.314 0.308
## 0.316 0.252 0.251
## 0.436 0.360 0.367
##
## 0.536 0.472 0.497
## 0.569 0.509 0.526
## 0.316 0.271 0.270
## 0.303 0.253 0.246
##
## 0.773 0.661 0.657
## 0.479 0.403 0.401
## 0.286 0.237 0.232
##
## 0.985 0.941 0.920
## 0.872 0.824 0.841
## 0.986 0.940 0.912
## 0.877 0.836 0.852
## 0.649 0.591 0.587
## 0.627 0.573 0.570
## 0.662 0.608 0.641
## 0.689 0.637 0.657
## 0.888 0.842 0.824
## 0.845 0.798 0.794
## 0.906 0.854 0.831
## 0.713 0.665 0.678
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.203 0.040 5.104 0.000 0.125
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssmk (.49.) 0.222 0.043 5.119 0.000 0.137
## .ssmc (.50.) 0.048 0.038 1.250 0.211 -0.027
## .ssao (.51.) 0.160 0.039 4.073 0.000 0.083
## .ssai (.52.) -0.106 0.033 -3.193 0.001 -0.171
## .sssi (.53.) -0.073 0.034 -2.133 0.033 -0.140
## .ssei (.54.) -0.003 0.037 -0.073 0.942 -0.076
## .ssno (.55.) 0.183 0.043 4.260 0.000 0.099
## .sscs 0.245 0.043 5.752 0.000 0.162
## .ssgs (.57.) 0.175 0.041 4.290 0.000 0.095
## .sswk (.58.) 0.112 0.042 2.689 0.007 0.030
## ci.upper Std.lv Std.all
## 0.281 0.203 0.207
## 0.333 0.253 0.257
## 0.307 0.222 0.218
## 0.123 0.048 0.048
## 0.238 0.160 0.163
## -0.041 -0.106 -0.112
## -0.006 -0.073 -0.075
## 0.071 -0.003 -0.003
## 0.267 0.183 0.182
## 0.329 0.245 0.244
## 0.255 0.175 0.171
## 0.194 0.112 0.109
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.171 0.018 9.407 0.000 0.136
## .sspc 0.230 0.021 10.992 0.000 0.189
## .ssmk 0.179 0.018 10.014 0.000 0.144
## .ssmc 0.237 0.020 11.725 0.000 0.198
## .ssao 0.391 0.032 12.027 0.000 0.327
## .ssai 0.307 0.027 11.195 0.000 0.254
## .sssi 0.274 0.029 9.503 0.000 0.217
## .ssei 0.264 0.023 11.608 0.000 0.220
## .ssno 0.227 0.062 3.674 0.000 0.106
## .sscs 0.519 0.054 9.635 0.000 0.413
## .ssgs 0.161 0.015 10.779 0.000 0.132
## .sswk 0.179 0.016 11.133 0.000 0.147
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.207 0.171 0.179
## 0.271 0.230 0.239
## 0.214 0.179 0.172
## 0.277 0.237 0.234
## 0.454 0.391 0.406
## 0.361 0.307 0.342
## 0.330 0.274 0.292
## 0.309 0.264 0.250
## 0.348 0.227 0.224
## 0.625 0.519 0.514
## 0.190 0.161 0.154
## 0.210 0.179 0.168
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.330 0.029 11.221 0.000 0.273
## sspc (.p2.) 0.186 0.028 6.541 0.000 0.130
## ssmk (.p3.) 0.314 0.027 11.752 0.000 0.262
## ssmc (.p4.) 0.252 0.033 7.718 0.000 0.188
## ssao (.p5.) 0.360 0.039 9.347 0.000 0.285
## electronic =~
## ssai (.p6.) 0.472 0.033 14.517 0.000 0.408
## sssi (.p7.) 0.509 0.030 16.784 0.000 0.450
## ssmc (.p8.) 0.271 0.023 12.031 0.000 0.227
## ssei (.p9.) 0.253 0.026 9.805 0.000 0.202
## speed =~
## ssno (.10.) 0.661 0.057 11.591 0.000 0.549
## sscs (.11.) 0.403 0.039 10.336 0.000 0.327
## ssmk (.12.) 0.237 0.025 9.429 0.000 0.188
## g =~
## ssgs (.13.) 0.941 0.022 41.854 0.000 0.897
## ssar (.14.) 0.824 0.025 33.395 0.000 0.775
## sswk (.15.) 0.940 0.023 40.426 0.000 0.895
## sspc (.16.) 0.836 0.021 40.337 0.000 0.796
## ssno (.17.) 0.591 0.029 20.086 0.000 0.534
## sscs (.18.) 0.573 0.028 20.834 0.000 0.519
## ssai (.19.) 0.608 0.027 22.145 0.000 0.554
## sssi (.20.) 0.637 0.027 23.661 0.000 0.584
## ssmk (.21.) 0.842 0.023 35.918 0.000 0.796
## ssmc (.22.) 0.798 0.024 33.456 0.000 0.751
## ssei (.23.) 0.854 0.026 32.629 0.000 0.803
## ssao (.24.) 0.665 0.024 27.246 0.000 0.617
## ci.upper Std.lv Std.all
##
## 0.388 0.330 0.326
## 0.242 0.186 0.188
## 0.367 0.314 0.312
## 0.316 0.252 0.250
## 0.436 0.360 0.348
##
## 0.536 0.472 0.451
## 0.569 0.509 0.503
## 0.316 0.271 0.269
## 0.303 0.253 0.238
##
## 0.773 0.661 0.638
## 0.479 0.403 0.395
## 0.286 0.237 0.235
##
## 0.985 0.941 0.915
## 0.872 0.824 0.812
## 0.986 0.940 0.903
## 0.877 0.836 0.847
## 0.649 0.591 0.570
## 0.627 0.573 0.562
## 0.662 0.608 0.582
## 0.689 0.637 0.629
## 0.888 0.842 0.836
## 0.845 0.798 0.790
## 0.906 0.854 0.805
## 0.713 0.665 0.641
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.203 0.040 5.104 0.000 0.125
## .sspc -0.024 0.044 -0.560 0.575 -0.110
## .ssmk (.49.) 0.222 0.043 5.119 0.000 0.137
## .ssmc (.50.) 0.048 0.038 1.250 0.211 -0.027
## .ssao (.51.) 0.160 0.039 4.073 0.000 0.083
## .ssai (.52.) -0.106 0.033 -3.193 0.001 -0.171
## .sssi (.53.) -0.073 0.034 -2.133 0.033 -0.140
## .ssei (.54.) -0.003 0.037 -0.073 0.942 -0.076
## .ssno (.55.) 0.183 0.043 4.260 0.000 0.099
## .sscs -0.027 0.052 -0.515 0.606 -0.129
## .ssgs (.57.) 0.175 0.041 4.290 0.000 0.095
## .sswk (.58.) 0.112 0.042 2.689 0.007 0.030
## math -0.312 0.093 -3.348 0.001 -0.495
## elctrnc 1.025 0.092 11.144 0.000 0.845
## speed -0.327 0.090 -3.620 0.000 -0.504
## g 0.075 0.064 1.178 0.239 -0.050
## ci.upper Std.lv Std.all
## 0.281 0.203 0.200
## 0.061 -0.024 -0.025
## 0.307 0.222 0.221
## 0.123 0.048 0.048
## 0.238 0.160 0.155
## -0.041 -0.106 -0.102
## -0.006 -0.073 -0.072
## 0.071 -0.003 -0.003
## 0.267 0.183 0.177
## 0.075 -0.027 -0.026
## 0.255 0.175 0.170
## 0.194 0.112 0.108
## -0.129 -0.312 -0.312
## 1.205 1.025 1.025
## -0.150 -0.327 -0.327
## 0.200 0.075 0.075
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.240 0.024 9.990 0.000 0.193
## .sspc 0.240 0.020 12.053 0.000 0.201
## .ssmk 0.151 0.016 9.710 0.000 0.120
## .ssmc 0.246 0.021 11.481 0.000 0.204
## .ssao 0.503 0.038 13.106 0.000 0.428
## .ssai 0.501 0.044 11.354 0.000 0.414
## .sssi 0.360 0.037 9.741 0.000 0.288
## .ssei 0.332 0.026 12.586 0.000 0.280
## .ssno 0.288 0.068 4.262 0.000 0.155
## .sscs 0.550 0.055 9.948 0.000 0.442
## .ssgs 0.173 0.016 10.544 0.000 0.141
## .sswk 0.200 0.016 12.179 0.000 0.168
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.287 0.240 0.234
## 0.279 0.240 0.247
## 0.181 0.151 0.149
## 0.288 0.246 0.241
## 0.578 0.503 0.468
## 0.587 0.501 0.458
## 0.433 0.360 0.352
## 0.383 0.332 0.295
## 0.420 0.288 0.268
## 0.658 0.550 0.528
## 0.205 0.173 0.163
## 0.232 0.200 0.184
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
latent2<-cfa(bf.lv, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 478.459 111.000 0.000 0.972 0.071 0.051 32082.849
## bic
## 32440.222
Mc(latent2)
## [1] 0.8692326
summary(latent2, standardized=T, ci=T) # -.070
## lavaan 0.6-18 ended normally after 77 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 103
## Number of equality constraints 34
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 478.459 368.833
## Degrees of freedom 111 111
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.297
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 202.257 155.915
## 0 276.202 212.918
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.327 0.030 11.032 0.000 0.269
## sspc (.p2.) 0.181 0.029 6.326 0.000 0.125
## ssmk (.p3.) 0.312 0.027 11.541 0.000 0.259
## ssmc (.p4.) 0.252 0.034 7.504 0.000 0.186
## ssao (.p5.) 0.359 0.040 9.043 0.000 0.281
## electronic =~
## ssai (.p6.) 0.311 0.034 9.093 0.000 0.244
## sssi (.p7.) 0.326 0.036 9.008 0.000 0.255
## ssmc (.p8.) 0.168 0.021 7.848 0.000 0.126
## ssei (.p9.) 0.170 0.020 8.408 0.000 0.130
## speed =~
## ssno (.10.) 0.616 0.060 10.340 0.000 0.499
## sscs (.11.) 0.372 0.041 9.078 0.000 0.292
## ssmk (.12.) 0.218 0.025 8.667 0.000 0.168
## g =~
## ssgs (.13.) 0.890 0.029 31.202 0.000 0.834
## ssar (.14.) 0.780 0.029 26.679 0.000 0.723
## sswk (.15.) 0.888 0.030 29.260 0.000 0.829
## sspc (.16.) 0.792 0.026 29.908 0.000 0.740
## ssno (.17.) 0.563 0.031 18.151 0.000 0.502
## sscs (.18.) 0.545 0.029 18.579 0.000 0.488
## ssai (.19.) 0.546 0.026 20.694 0.000 0.494
## sssi (.20.) 0.568 0.028 20.616 0.000 0.514
## ssmk (.21.) 0.798 0.029 27.419 0.000 0.740
## ssmc (.22.) 0.739 0.028 26.585 0.000 0.684
## ssei (.23.) 0.790 0.029 27.281 0.000 0.733
## ssao (.24.) 0.631 0.028 22.897 0.000 0.577
## ci.upper Std.lv Std.all
##
## 0.386 0.327 0.347
## 0.237 0.181 0.192
## 0.365 0.312 0.318
## 0.318 0.252 0.268
## 0.437 0.359 0.375
##
## 0.378 0.311 0.368
## 0.396 0.326 0.379
## 0.210 0.168 0.179
## 0.210 0.170 0.178
##
## 0.732 0.616 0.634
## 0.452 0.372 0.381
## 0.267 0.218 0.222
##
## 0.946 0.890 0.911
## 0.838 0.780 0.828
## 0.948 0.888 0.901
## 0.844 0.792 0.838
## 0.624 0.563 0.579
## 0.603 0.545 0.558
## 0.597 0.546 0.645
## 0.622 0.568 0.662
## 0.855 0.798 0.814
## 0.793 0.739 0.787
## 0.847 0.790 0.827
## 0.685 0.631 0.660
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.202 0.040 5.075 0.000 0.124
## .sspc 0.253 0.041 6.138 0.000 0.172
## .ssmk (.49.) 0.221 0.043 5.078 0.000 0.136
## .ssmc (.50.) 0.053 0.038 1.404 0.160 -0.021
## .ssao (.51.) 0.159 0.039 4.043 0.000 0.082
## .ssai (.52.) -0.110 0.033 -3.309 0.001 -0.175
## .sssi (.53.) -0.071 0.034 -2.058 0.040 -0.138
## .ssei (.54.) -0.007 0.038 -0.180 0.857 -0.081
## .ssno (.55.) 0.184 0.043 4.289 0.000 0.100
## .sscs 0.245 0.043 5.752 0.000 0.162
## .ssgs (.57.) 0.176 0.041 4.313 0.000 0.096
## .sswk (.58.) 0.112 0.042 2.693 0.007 0.031
## ci.upper Std.lv Std.all
## 0.280 0.202 0.214
## 0.333 0.253 0.267
## 0.306 0.221 0.225
## 0.128 0.053 0.057
## 0.237 0.159 0.167
## -0.045 -0.110 -0.130
## -0.003 -0.071 -0.083
## 0.067 -0.007 -0.007
## 0.269 0.184 0.190
## 0.329 0.245 0.252
## 0.255 0.176 0.180
## 0.194 0.112 0.114
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.172 0.018 9.346 0.000 0.136
## .sspc 0.232 0.021 11.029 0.000 0.191
## .ssmk 0.180 0.018 10.202 0.000 0.145
## .ssmc 0.243 0.020 11.989 0.000 0.203
## .ssao 0.388 0.033 11.863 0.000 0.324
## .ssai 0.322 0.026 12.188 0.000 0.270
## .sssi 0.308 0.028 10.963 0.000 0.253
## .ssei 0.260 0.023 11.411 0.000 0.215
## .ssno 0.248 0.059 4.184 0.000 0.132
## .sscs 0.517 0.053 9.816 0.000 0.414
## .ssgs 0.162 0.015 11.142 0.000 0.133
## .sswk 0.184 0.016 11.384 0.000 0.152
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.208 0.172 0.193
## 0.274 0.232 0.260
## 0.215 0.180 0.187
## 0.283 0.243 0.276
## 0.452 0.388 0.424
## 0.373 0.322 0.449
## 0.363 0.308 0.418
## 0.304 0.260 0.285
## 0.365 0.248 0.263
## 0.621 0.517 0.543
## 0.190 0.162 0.170
## 0.215 0.184 0.189
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.327 0.030 11.032 0.000 0.269
## sspc (.p2.) 0.181 0.029 6.326 0.000 0.125
## ssmk (.p3.) 0.312 0.027 11.541 0.000 0.259
## ssmc (.p4.) 0.252 0.034 7.504 0.000 0.186
## ssao (.p5.) 0.359 0.040 9.043 0.000 0.281
## electronic =~
## ssai (.p6.) 0.311 0.034 9.093 0.000 0.244
## sssi (.p7.) 0.326 0.036 9.008 0.000 0.255
## ssmc (.p8.) 0.168 0.021 7.848 0.000 0.126
## ssei (.p9.) 0.170 0.020 8.408 0.000 0.130
## speed =~
## ssno (.10.) 0.616 0.060 10.340 0.000 0.499
## sscs (.11.) 0.372 0.041 9.078 0.000 0.292
## ssmk (.12.) 0.218 0.025 8.667 0.000 0.168
## g =~
## ssgs (.13.) 0.890 0.029 31.202 0.000 0.834
## ssar (.14.) 0.780 0.029 26.679 0.000 0.723
## sswk (.15.) 0.888 0.030 29.260 0.000 0.829
## sspc (.16.) 0.792 0.026 29.908 0.000 0.740
## ssno (.17.) 0.563 0.031 18.151 0.000 0.502
## sscs (.18.) 0.545 0.029 18.579 0.000 0.488
## ssai (.19.) 0.546 0.026 20.694 0.000 0.494
## sssi (.20.) 0.568 0.028 20.616 0.000 0.514
## ssmk (.21.) 0.798 0.029 27.419 0.000 0.740
## ssmc (.22.) 0.739 0.028 26.585 0.000 0.684
## ssei (.23.) 0.790 0.029 27.281 0.000 0.733
## ssao (.24.) 0.631 0.028 22.897 0.000 0.577
## ci.upper Std.lv Std.all
##
## 0.386 0.327 0.312
## 0.237 0.181 0.177
## 0.365 0.312 0.298
## 0.318 0.252 0.240
## 0.437 0.359 0.338
##
## 0.378 0.639 0.574
## 0.396 0.668 0.619
## 0.210 0.345 0.328
## 0.210 0.349 0.316
##
## 0.732 0.711 0.662
## 0.452 0.430 0.409
## 0.267 0.251 0.240
##
## 0.946 0.990 0.921
## 0.838 0.868 0.827
## 0.948 0.988 0.912
## 0.844 0.881 0.862
## 0.624 0.626 0.583
## 0.603 0.606 0.578
## 0.597 0.607 0.545
## 0.622 0.632 0.585
## 0.855 0.887 0.846
## 0.793 0.822 0.782
## 0.847 0.879 0.796
## 0.685 0.702 0.662
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.202 0.040 5.075 0.000 0.124
## .sspc -0.026 0.044 -0.595 0.552 -0.112
## .ssmk (.49.) 0.221 0.043 5.078 0.000 0.136
## .ssmc (.50.) 0.053 0.038 1.404 0.160 -0.021
## .ssao (.51.) 0.159 0.039 4.043 0.000 0.082
## .ssai (.52.) -0.110 0.033 -3.309 0.001 -0.175
## .sssi (.53.) -0.071 0.034 -2.058 0.040 -0.138
## .ssei (.54.) -0.007 0.038 -0.180 0.857 -0.081
## .ssno (.55.) 0.184 0.043 4.289 0.000 0.100
## .sscs -0.027 0.052 -0.523 0.601 -0.129
## .ssgs (.57.) 0.176 0.041 4.313 0.000 0.096
## .sswk (.58.) 0.112 0.042 2.693 0.007 0.031
## math -0.304 0.094 -3.232 0.001 -0.488
## elctrnc 1.596 0.219 7.275 0.000 1.166
## speed -0.351 0.099 -3.555 0.000 -0.545
## g 0.078 0.068 1.146 0.252 -0.055
## ci.upper Std.lv Std.all
## 0.280 0.202 0.192
## 0.060 -0.026 -0.025
## 0.306 0.221 0.211
## 0.128 0.053 0.051
## 0.237 0.159 0.150
## -0.045 -0.110 -0.099
## -0.003 -0.071 -0.066
## 0.067 -0.007 -0.006
## 0.269 0.184 0.172
## 0.075 -0.027 -0.026
## 0.255 0.176 0.163
## 0.194 0.112 0.104
## -0.120 -0.304 -0.304
## 2.026 0.777 0.777
## -0.158 -0.304 -0.304
## 0.210 0.070 0.070
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.240 0.024 9.948 0.000 0.193
## .sspc 0.236 0.020 12.041 0.000 0.197
## .ssmk 0.152 0.016 9.564 0.000 0.121
## .ssmc 0.245 0.021 11.437 0.000 0.203
## .ssao 0.503 0.038 13.096 0.000 0.428
## .ssai 0.464 0.043 10.846 0.000 0.380
## .sssi 0.320 0.036 8.932 0.000 0.250
## .ssei 0.324 0.025 12.823 0.000 0.275
## .ssno 0.256 0.072 3.586 0.000 0.116
## .sscs 0.549 0.055 9.924 0.000 0.441
## .ssgs 0.175 0.017 10.336 0.000 0.142
## .sswk 0.198 0.017 11.930 0.000 0.165
## electronic 4.214 0.959 4.395 0.000 2.335
## speed 1.335 0.241 5.536 0.000 0.863
## g 1.237 0.101 12.254 0.000 1.039
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.288 0.240 0.218
## 0.274 0.236 0.225
## 0.183 0.152 0.138
## 0.288 0.245 0.222
## 0.579 0.503 0.448
## 0.547 0.464 0.374
## 0.390 0.320 0.274
## 0.374 0.324 0.266
## 0.397 0.256 0.222
## 0.658 0.549 0.499
## 0.209 0.175 0.152
## 0.230 0.198 0.168
## 6.093 1.000 1.000
## 1.808 1.000 1.000
## 1.435 1.000 1.000
tests<-lavTestLRT(configural, metric2, scalar2, latent2)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 33.54426955 35.38968692 0.07199855
dfd=tests[2:4,"Df diff"]
dfd
## [1] 19 7 1
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-656+ 656 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
## Warning in sqrt((ld) * G/(N - G)): NaNs produced
RMSEAD
## [1] 0.03418602 0.07868838 NaN
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.01326882 0.05280246
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.05410812 0.10528511
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] NA 0.06713763
RMSEA.CI(T=Td[4],df=dfd[4],N=N,G=2)
## [1] NA NA
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.979 0.965 0.085 0.009 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.971 0.899 0.500 0.097
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.212 0.205 0.095 0.067 0.027 0.009
round(pvals(T=Td[4],df=dfd[4],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## NA NA NA NA NA NA
tests<-lavTestLRT(configural, metric2, scalar2, latent)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 33.54427 35.38969 69.42108
dfd=tests[2:4,"Df diff"]
dfd
## [1] 19 7 4
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-656+ 656 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.03418602 0.07868838 0.15801859
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.05410812 0.10528511
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.1266066 0.1915948
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.971 0.899 0.500 0.097
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 1.000 1.000 0.999
tests<-lavTestLRT(configural, metric2, scalar2, strict)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 33.54427 35.38969 30.51764
dfd=tests[2:4,"Df diff"]
dfd
## [1] 19 7 12
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-656+ 656 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.03418602 0.07868838 0.04853796
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.01326882 0.05280246
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.05410812 0.10528511
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.02742186 0.07021796
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.979 0.965 0.085 0.009 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.971 0.899 0.500 0.097
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.998 0.996 0.492 0.208 0.007 0.000
tests<-lavTestLRT(configural, metric2, scalar)
Td=tests[2:3,"Chisq diff"]
Td
## [1] 33.54427 139.41970
dfd=tests[2:3,"Df diff"]
dfd
## [1] 19 9
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-656+ 656 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.03418602 0.14874065
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.01326882 0.05280246
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.1274716 0.1709583
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.979 0.965 0.085 0.009 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
bf.age<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
g ~ agec
'
bf.ageq<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
g ~ c(a,a)*agec
'
bf.age2<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
g ~ agec+agec2
'
bf.age2q<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
g ~ c(a,a)*agec+c(b,b)*agec2
'
sem.age<-sem(bf.age, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(sem.age, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 668.641 133.000 0.000 0.961 0.078 0.053 0.618
## aic bic
## 31880.541 32248.272
Mc(sem.age)
## [1] 0.8152281
summary(sem.age, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 76 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 105
## Number of equality constraints 34
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 668.641 517.098
## Degrees of freedom 133 133
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.293
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 276.039 213.476
## 0 392.603 303.622
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.330 0.030 11.065 0.000 0.271
## sspc (.p2.) 0.184 0.029 6.422 0.000 0.128
## ssmk (.p3.) 0.306 0.027 11.441 0.000 0.254
## ssmc (.p4.) 0.259 0.033 7.798 0.000 0.194
## ssao (.p5.) 0.363 0.039 9.238 0.000 0.286
## electronic =~
## ssai (.p6.) 0.309 0.034 9.085 0.000 0.242
## sssi (.p7.) 0.326 0.036 8.992 0.000 0.255
## ssmc (.p8.) 0.170 0.022 7.840 0.000 0.127
## ssei (.p9.) 0.169 0.020 8.390 0.000 0.130
## speed =~
## ssno (.10.) 0.614 0.060 10.162 0.000 0.495
## sscs (.11.) 0.368 0.041 8.973 0.000 0.287
## ssmk (.12.) 0.214 0.025 8.578 0.000 0.165
## g =~
## ssgs (.13.) 0.812 0.027 29.577 0.000 0.759
## ssar (.14.) 0.713 0.028 25.173 0.000 0.657
## sswk (.15.) 0.814 0.028 28.689 0.000 0.758
## sspc (.16.) 0.724 0.025 28.767 0.000 0.675
## ssno (.17.) 0.517 0.029 18.001 0.000 0.461
## sscs (.18.) 0.501 0.027 18.728 0.000 0.449
## ssai (.19.) 0.502 0.024 20.786 0.000 0.455
## sssi (.20.) 0.520 0.025 20.602 0.000 0.471
## ssmk (.21.) 0.732 0.027 27.161 0.000 0.679
## ssmc (.22.) 0.675 0.026 25.843 0.000 0.624
## ssei (.23.) 0.724 0.028 26.114 0.000 0.669
## ssao (.24.) 0.576 0.026 22.048 0.000 0.525
## ci.upper Std.lv Std.all
##
## 0.388 0.330 0.350
## 0.240 0.184 0.194
## 0.359 0.306 0.312
## 0.323 0.259 0.275
## 0.440 0.363 0.379
##
## 0.376 0.309 0.365
## 0.397 0.326 0.379
## 0.212 0.170 0.181
## 0.209 0.169 0.177
##
## 0.732 0.614 0.632
## 0.448 0.368 0.377
## 0.263 0.214 0.219
##
## 0.866 0.888 0.909
## 0.768 0.779 0.827
## 0.870 0.890 0.902
## 0.774 0.792 0.837
## 0.574 0.565 0.582
## 0.554 0.548 0.561
## 0.550 0.549 0.648
## 0.570 0.568 0.662
## 0.785 0.800 0.816
## 0.726 0.737 0.785
## 0.778 0.791 0.828
## 0.628 0.630 0.658
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.296 0.031 9.660 0.000 0.236
## ci.upper Std.lv Std.all
##
## 0.355 0.270 0.403
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.189 0.038 4.990 0.000 0.115
## .sspc 0.239 0.039 6.119 0.000 0.162
## .ssmk (.48.) 0.206 0.040 5.178 0.000 0.128
## .ssmc (.49.) 0.040 0.036 1.105 0.269 -0.031
## .ssao (.50.) 0.149 0.038 3.925 0.000 0.075
## .ssai (.51.) -0.119 0.031 -3.795 0.000 -0.180
## .sssi (.52.) -0.082 0.033 -2.482 0.013 -0.146
## .ssei (.53.) -0.020 0.035 -0.566 0.572 -0.088
## .ssno (.54.) 0.175 0.041 4.298 0.000 0.095
## .sscs 0.236 0.040 5.854 0.000 0.157
## .ssgs (.56.) 0.161 0.038 4.282 0.000 0.087
## .sswk (.57.) 0.098 0.038 2.564 0.010 0.023
## ci.upper Std.lv Std.all
## 0.263 0.189 0.200
## 0.315 0.239 0.253
## 0.284 0.206 0.210
## 0.111 0.040 0.043
## 0.224 0.149 0.156
## -0.057 -0.119 -0.140
## -0.017 -0.082 -0.095
## 0.049 -0.020 -0.021
## 0.255 0.175 0.180
## 0.315 0.236 0.242
## 0.235 0.161 0.165
## 0.173 0.098 0.099
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.172 0.019 9.272 0.000 0.136
## .sspc 0.233 0.021 11.122 0.000 0.192
## .ssmk 0.181 0.017 10.369 0.000 0.147
## .ssmc 0.242 0.020 11.851 0.000 0.202
## .ssao 0.387 0.033 11.790 0.000 0.322
## .ssai 0.322 0.026 12.237 0.000 0.270
## .sssi 0.308 0.028 10.953 0.000 0.253
## .ssei 0.258 0.023 11.407 0.000 0.214
## .ssno 0.247 0.060 4.123 0.000 0.130
## .sscs 0.517 0.053 9.824 0.000 0.414
## .ssgs 0.165 0.014 11.423 0.000 0.137
## .sswk 0.181 0.016 11.397 0.000 0.150
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.209 0.172 0.194
## 0.274 0.233 0.261
## 0.215 0.181 0.188
## 0.282 0.242 0.275
## 0.451 0.387 0.422
## 0.373 0.322 0.448
## 0.364 0.308 0.418
## 0.303 0.258 0.283
## 0.365 0.247 0.262
## 0.621 0.517 0.543
## 0.194 0.165 0.173
## 0.212 0.181 0.186
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.837 0.837
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.330 0.030 11.065 0.000 0.271
## sspc (.p2.) 0.184 0.029 6.422 0.000 0.128
## ssmk (.p3.) 0.306 0.027 11.441 0.000 0.254
## ssmc (.p4.) 0.259 0.033 7.798 0.000 0.194
## ssao (.p5.) 0.363 0.039 9.238 0.000 0.286
## electronic =~
## ssai (.p6.) 0.309 0.034 9.085 0.000 0.242
## sssi (.p7.) 0.326 0.036 8.992 0.000 0.255
## ssmc (.p8.) 0.170 0.022 7.840 0.000 0.127
## ssei (.p9.) 0.169 0.020 8.390 0.000 0.130
## speed =~
## ssno (.10.) 0.614 0.060 10.162 0.000 0.495
## sscs (.11.) 0.368 0.041 8.973 0.000 0.287
## ssmk (.12.) 0.214 0.025 8.578 0.000 0.165
## g =~
## ssgs (.13.) 0.812 0.027 29.577 0.000 0.759
## ssar (.14.) 0.713 0.028 25.173 0.000 0.657
## sswk (.15.) 0.814 0.028 28.689 0.000 0.758
## sspc (.16.) 0.724 0.025 28.767 0.000 0.675
## ssno (.17.) 0.517 0.029 18.001 0.000 0.461
## sscs (.18.) 0.501 0.027 18.728 0.000 0.449
## ssai (.19.) 0.502 0.024 20.786 0.000 0.455
## sssi (.20.) 0.520 0.025 20.602 0.000 0.471
## ssmk (.21.) 0.732 0.027 27.161 0.000 0.679
## ssmc (.22.) 0.675 0.026 25.843 0.000 0.624
## ssei (.23.) 0.724 0.028 26.114 0.000 0.669
## ssao (.24.) 0.576 0.026 22.048 0.000 0.525
## ci.upper Std.lv Std.all
##
## 0.388 0.330 0.314
## 0.240 0.184 0.180
## 0.359 0.306 0.292
## 0.323 0.259 0.246
## 0.440 0.363 0.342
##
## 0.376 0.632 0.568
## 0.397 0.666 0.618
## 0.212 0.347 0.330
## 0.209 0.346 0.313
##
## 0.732 0.711 0.662
## 0.448 0.426 0.406
## 0.263 0.248 0.237
##
## 0.866 0.987 0.919
## 0.768 0.866 0.826
## 0.870 0.989 0.913
## 0.774 0.880 0.862
## 0.574 0.629 0.585
## 0.554 0.609 0.580
## 0.550 0.610 0.549
## 0.570 0.632 0.586
## 0.785 0.890 0.848
## 0.726 0.820 0.781
## 0.778 0.880 0.797
## 0.628 0.701 0.661
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.326 0.036 9.014 0.000 0.255
## ci.upper Std.lv Std.all
##
## 0.397 0.268 0.382
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.189 0.038 4.990 0.000 0.115
## .sspc -0.038 0.041 -0.919 0.358 -0.119
## .ssmk (.48.) 0.206 0.040 5.178 0.000 0.128
## .ssmc (.49.) 0.040 0.036 1.105 0.269 -0.031
## .ssao (.50.) 0.149 0.038 3.925 0.000 0.075
## .ssai (.51.) -0.119 0.031 -3.795 0.000 -0.180
## .sssi (.52.) -0.082 0.033 -2.482 0.013 -0.146
## .ssei (.53.) -0.020 0.035 -0.566 0.572 -0.088
## .ssno (.54.) 0.175 0.041 4.298 0.000 0.095
## .sscs -0.037 0.050 -0.736 0.462 -0.135
## .ssgs (.56.) 0.161 0.038 4.282 0.000 0.087
## .sswk (.57.) 0.098 0.038 2.564 0.010 0.023
## math -0.301 0.094 -3.197 0.001 -0.485
## elctrnc 1.603 0.220 7.280 0.000 1.172
## speed -0.352 0.100 -3.512 0.000 -0.549
## .g 0.109 0.068 1.590 0.112 -0.025
## ci.upper Std.lv Std.all
## 0.263 0.189 0.180
## 0.043 -0.038 -0.037
## 0.284 0.206 0.196
## 0.111 0.040 0.038
## 0.224 0.149 0.141
## -0.057 -0.119 -0.107
## -0.017 -0.082 -0.076
## 0.049 -0.020 -0.018
## 0.255 0.175 0.163
## 0.061 -0.037 -0.035
## 0.235 0.161 0.150
## 0.173 0.098 0.090
## -0.116 -0.301 -0.301
## 2.035 0.784 0.784
## -0.156 -0.304 -0.304
## 0.243 0.090 0.090
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.241 0.024 9.915 0.000 0.193
## .sspc 0.236 0.020 12.010 0.000 0.197
## .ssmk 0.153 0.016 9.809 0.000 0.123
## .ssmc 0.244 0.022 11.267 0.000 0.201
## .ssao 0.502 0.038 13.081 0.000 0.427
## .ssai 0.465 0.042 10.947 0.000 0.382
## .sssi 0.319 0.036 8.913 0.000 0.249
## .ssei 0.324 0.025 12.787 0.000 0.274
## .ssno 0.254 0.073 3.501 0.000 0.112
## .sscs 0.550 0.055 9.945 0.000 0.441
## .ssgs 0.180 0.017 10.542 0.000 0.146
## .sswk 0.197 0.016 11.935 0.000 0.164
## electronic 4.183 0.954 4.386 0.000 2.314
## speed 1.342 0.245 5.476 0.000 0.862
## .g 1.262 0.107 11.767 0.000 1.051
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.289 0.241 0.219
## 0.274 0.236 0.225
## 0.184 0.153 0.139
## 0.286 0.244 0.221
## 0.577 0.502 0.446
## 0.548 0.465 0.376
## 0.390 0.319 0.275
## 0.374 0.324 0.266
## 0.397 0.254 0.220
## 0.658 0.550 0.499
## 0.213 0.180 0.156
## 0.229 0.197 0.167
## 6.052 1.000 1.000
## 1.823 1.000 1.000
## 1.472 0.854 0.854
sem.ageq<-sem(bf.ageq, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(sem.ageq, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 669.169 134.000 0.000 0.961 0.078 0.055 0.617
## aic bic
## 31879.069 32241.621
Mc(sem.ageq)
## [1] 0.815375
summary(sem.ageq, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 78 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 105
## Number of equality constraints 35
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 669.169 517.921
## Degrees of freedom 134 134
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.292
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 276.192 213.766
## 0 392.977 304.155
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.330 0.030 11.071 0.000 0.271
## sspc (.p2.) 0.184 0.029 6.423 0.000 0.128
## ssmk (.p3.) 0.306 0.027 11.444 0.000 0.254
## ssmc (.p4.) 0.259 0.033 7.801 0.000 0.194
## ssao (.p5.) 0.363 0.039 9.240 0.000 0.286
## electronic =~
## ssai (.p6.) 0.309 0.034 9.087 0.000 0.242
## sssi (.p7.) 0.326 0.036 8.991 0.000 0.255
## ssmc (.p8.) 0.170 0.022 7.839 0.000 0.127
## ssei (.p9.) 0.169 0.020 8.393 0.000 0.130
## speed =~
## ssno (.10.) 0.613 0.060 10.160 0.000 0.495
## sscs (.11.) 0.367 0.041 8.973 0.000 0.287
## ssmk (.12.) 0.214 0.025 8.580 0.000 0.165
## g =~
## ssgs (.13.) 0.813 0.027 29.563 0.000 0.759
## ssar (.14.) 0.713 0.028 25.166 0.000 0.657
## sswk (.15.) 0.814 0.028 28.676 0.000 0.759
## sspc (.16.) 0.725 0.025 28.762 0.000 0.675
## ssno (.17.) 0.517 0.029 18.003 0.000 0.461
## sscs (.18.) 0.501 0.027 18.729 0.000 0.449
## ssai (.19.) 0.502 0.024 20.784 0.000 0.455
## sssi (.20.) 0.520 0.025 20.593 0.000 0.471
## ssmk (.21.) 0.732 0.027 27.153 0.000 0.680
## ssmc (.22.) 0.675 0.026 25.836 0.000 0.624
## ssei (.23.) 0.724 0.028 26.093 0.000 0.669
## ssao (.24.) 0.577 0.026 22.048 0.000 0.525
## ci.upper Std.lv Std.all
##
## 0.388 0.330 0.348
## 0.240 0.184 0.193
## 0.359 0.306 0.311
## 0.324 0.259 0.274
## 0.440 0.363 0.378
##
## 0.376 0.309 0.364
## 0.397 0.326 0.378
## 0.212 0.170 0.180
## 0.209 0.169 0.176
##
## 0.732 0.613 0.630
## 0.448 0.367 0.376
## 0.263 0.214 0.218
##
## 0.866 0.894 0.910
## 0.768 0.785 0.828
## 0.870 0.896 0.903
## 0.774 0.797 0.839
## 0.574 0.569 0.585
## 0.554 0.552 0.564
## 0.549 0.553 0.650
## 0.570 0.572 0.664
## 0.785 0.806 0.818
## 0.726 0.743 0.788
## 0.778 0.797 0.830
## 0.628 0.634 0.661
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.308 0.024 12.646 0.000 0.260
## ci.upper Std.lv Std.all
##
## 0.356 0.280 0.417
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.188 0.038 4.970 0.000 0.114
## .sspc 0.238 0.039 6.103 0.000 0.162
## .ssmk (.48.) 0.205 0.040 5.167 0.000 0.127
## .ssmc (.49.) 0.040 0.036 1.089 0.276 -0.032
## .ssao (.50.) 0.149 0.038 3.909 0.000 0.074
## .ssai (.51.) -0.119 0.031 -3.811 0.000 -0.180
## .sssi (.52.) -0.082 0.033 -2.492 0.013 -0.146
## .ssei (.53.) -0.020 0.035 -0.584 0.559 -0.089
## .ssno (.54.) 0.175 0.041 4.292 0.000 0.095
## .sscs 0.236 0.040 5.848 0.000 0.157
## .ssgs (.56.) 0.160 0.038 4.263 0.000 0.087
## .sswk (.57.) 0.097 0.038 2.547 0.011 0.022
## ci.upper Std.lv Std.all
## 0.263 0.188 0.199
## 0.315 0.238 0.251
## 0.283 0.205 0.208
## 0.111 0.040 0.042
## 0.223 0.149 0.155
## -0.058 -0.119 -0.140
## -0.018 -0.082 -0.095
## 0.048 -0.020 -0.021
## 0.254 0.175 0.179
## 0.315 0.236 0.241
## 0.234 0.160 0.163
## 0.172 0.097 0.098
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.173 0.019 9.270 0.000 0.136
## .sspc 0.233 0.021 11.123 0.000 0.192
## .ssmk 0.181 0.017 10.374 0.000 0.147
## .ssmc 0.242 0.020 11.851 0.000 0.202
## .ssao 0.387 0.033 11.792 0.000 0.322
## .ssai 0.322 0.026 12.238 0.000 0.270
## .sssi 0.309 0.028 10.955 0.000 0.253
## .ssei 0.258 0.023 11.408 0.000 0.214
## .ssno 0.248 0.060 4.129 0.000 0.130
## .sscs 0.517 0.053 9.825 0.000 0.414
## .ssgs 0.165 0.014 11.437 0.000 0.137
## .sswk 0.181 0.016 11.394 0.000 0.150
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.209 0.173 0.192
## 0.274 0.233 0.258
## 0.215 0.181 0.187
## 0.282 0.242 0.272
## 0.451 0.387 0.420
## 0.373 0.322 0.445
## 0.364 0.309 0.416
## 0.303 0.258 0.280
## 0.365 0.248 0.261
## 0.621 0.517 0.541
## 0.194 0.165 0.171
## 0.212 0.181 0.184
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.826 0.826
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.330 0.030 11.071 0.000 0.271
## sspc (.p2.) 0.184 0.029 6.423 0.000 0.128
## ssmk (.p3.) 0.306 0.027 11.444 0.000 0.254
## ssmc (.p4.) 0.259 0.033 7.801 0.000 0.194
## ssao (.p5.) 0.363 0.039 9.240 0.000 0.286
## electronic =~
## ssai (.p6.) 0.309 0.034 9.087 0.000 0.242
## sssi (.p7.) 0.326 0.036 8.991 0.000 0.255
## ssmc (.p8.) 0.170 0.022 7.839 0.000 0.127
## ssei (.p9.) 0.169 0.020 8.393 0.000 0.130
## speed =~
## ssno (.10.) 0.613 0.060 10.160 0.000 0.495
## sscs (.11.) 0.367 0.041 8.973 0.000 0.287
## ssmk (.12.) 0.214 0.025 8.580 0.000 0.165
## g =~
## ssgs (.13.) 0.813 0.027 29.563 0.000 0.759
## ssar (.14.) 0.713 0.028 25.166 0.000 0.657
## sswk (.15.) 0.814 0.028 28.676 0.000 0.759
## sspc (.16.) 0.725 0.025 28.762 0.000 0.675
## ssno (.17.) 0.517 0.029 18.003 0.000 0.461
## sscs (.18.) 0.501 0.027 18.729 0.000 0.449
## ssai (.19.) 0.502 0.024 20.784 0.000 0.455
## sssi (.20.) 0.520 0.025 20.593 0.000 0.471
## ssmk (.21.) 0.732 0.027 27.153 0.000 0.680
## ssmc (.22.) 0.675 0.026 25.836 0.000 0.624
## ssei (.23.) 0.724 0.028 26.093 0.000 0.669
## ssao (.24.) 0.577 0.026 22.048 0.000 0.525
## ci.upper Std.lv Std.all
##
## 0.388 0.330 0.316
## 0.240 0.184 0.181
## 0.359 0.306 0.293
## 0.324 0.259 0.247
## 0.440 0.363 0.343
##
## 0.376 0.632 0.570
## 0.397 0.667 0.620
## 0.212 0.347 0.332
## 0.209 0.346 0.315
##
## 0.732 0.711 0.663
## 0.448 0.426 0.407
## 0.263 0.249 0.238
##
## 0.866 0.980 0.918
## 0.768 0.860 0.824
## 0.870 0.982 0.911
## 0.774 0.874 0.860
## 0.574 0.624 0.582
## 0.554 0.604 0.577
## 0.549 0.606 0.546
## 0.570 0.627 0.583
## 0.785 0.883 0.846
## 0.726 0.814 0.778
## 0.778 0.873 0.795
## 0.628 0.695 0.658
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.308 0.024 12.646 0.000 0.260
## ci.upper Std.lv Std.all
##
## 0.356 0.256 0.364
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.188 0.038 4.970 0.000 0.114
## .sspc -0.039 0.041 -0.933 0.351 -0.119
## .ssmk (.48.) 0.205 0.040 5.167 0.000 0.127
## .ssmc (.49.) 0.040 0.036 1.089 0.276 -0.032
## .ssao (.50.) 0.149 0.038 3.909 0.000 0.074
## .ssai (.51.) -0.119 0.031 -3.811 0.000 -0.180
## .sssi (.52.) -0.082 0.033 -2.492 0.013 -0.146
## .ssei (.53.) -0.020 0.035 -0.584 0.559 -0.089
## .ssno (.54.) 0.175 0.041 4.292 0.000 0.095
## .sscs -0.037 0.050 -0.744 0.457 -0.136
## .ssgs (.56.) 0.160 0.038 4.263 0.000 0.087
## .sswk (.57.) 0.097 0.038 2.547 0.011 0.022
## math -0.300 0.094 -3.196 0.001 -0.485
## elctrnc 1.604 0.220 7.281 0.000 1.172
## speed -0.352 0.100 -3.512 0.000 -0.549
## .g 0.109 0.068 1.595 0.111 -0.025
## ci.upper Std.lv Std.all
## 0.263 0.188 0.180
## 0.042 -0.039 -0.038
## 0.283 0.205 0.196
## 0.111 0.040 0.038
## 0.223 0.149 0.141
## -0.058 -0.119 -0.107
## -0.018 -0.082 -0.076
## 0.048 -0.020 -0.019
## 0.254 0.175 0.163
## 0.061 -0.037 -0.036
## 0.234 0.160 0.150
## 0.172 0.097 0.090
## -0.116 -0.300 -0.300
## 2.035 0.784 0.784
## -0.156 -0.304 -0.304
## 0.243 0.091 0.091
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.241 0.024 9.914 0.000 0.193
## .sspc 0.236 0.020 12.011 0.000 0.197
## .ssmk 0.153 0.016 9.810 0.000 0.123
## .ssmc 0.244 0.022 11.271 0.000 0.201
## .ssao 0.502 0.038 13.082 0.000 0.427
## .ssai 0.465 0.042 10.943 0.000 0.382
## .sssi 0.319 0.036 8.914 0.000 0.249
## .ssei 0.324 0.025 12.789 0.000 0.274
## .ssno 0.254 0.073 3.500 0.000 0.112
## .sscs 0.550 0.055 9.944 0.000 0.441
## .ssgs 0.179 0.017 10.540 0.000 0.146
## .sswk 0.197 0.016 11.935 0.000 0.164
## electronic 4.187 0.955 4.385 0.000 2.316
## speed 1.344 0.245 5.479 0.000 0.863
## .g 1.262 0.107 11.767 0.000 1.052
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.289 0.241 0.221
## 0.274 0.236 0.228
## 0.184 0.153 0.141
## 0.286 0.244 0.223
## 0.577 0.502 0.449
## 0.548 0.465 0.377
## 0.390 0.319 0.276
## 0.374 0.324 0.269
## 0.396 0.254 0.221
## 0.658 0.550 0.501
## 0.213 0.179 0.157
## 0.229 0.197 0.169
## 6.058 1.000 1.000
## 1.825 1.000 1.000
## 1.472 0.867 0.867
sem.age2<-sem(bf.age2, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(sem.age2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 708.357 155.000 0.000 0.960 0.074 0.050 0.651
## aic bic
## 31880.969 32259.059
Mc(sem.age2)
## [1] 0.8097386
summary(sem.age2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 80 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 107
## Number of equality constraints 34
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 708.357 549.571
## Degrees of freedom 155 155
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.289
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 291.067 225.822
## 0 417.289 323.750
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.330 0.030 11.034 0.000 0.271
## sspc (.p2.) 0.184 0.029 6.407 0.000 0.127
## ssmk (.p3.) 0.306 0.027 11.415 0.000 0.253
## ssmc (.p4.) 0.259 0.033 7.789 0.000 0.193
## ssao (.p5.) 0.363 0.039 9.223 0.000 0.286
## electronic =~
## ssai (.p6.) 0.309 0.034 9.094 0.000 0.243
## sssi (.p7.) 0.326 0.036 8.999 0.000 0.255
## ssmc (.p8.) 0.170 0.022 7.840 0.000 0.127
## ssei (.p9.) 0.169 0.020 8.393 0.000 0.130
## speed =~
## ssno (.10.) 0.613 0.060 10.152 0.000 0.495
## sscs (.11.) 0.368 0.041 8.970 0.000 0.287
## ssmk (.12.) 0.214 0.025 8.581 0.000 0.165
## g =~
## ssgs (.13.) 0.810 0.027 29.569 0.000 0.757
## ssar (.14.) 0.711 0.028 25.144 0.000 0.656
## sswk (.15.) 0.812 0.028 28.597 0.000 0.756
## sspc (.16.) 0.723 0.025 28.829 0.000 0.673
## ssno (.17.) 0.516 0.029 18.028 0.000 0.460
## sscs (.18.) 0.500 0.027 18.656 0.000 0.447
## ssai (.19.) 0.501 0.024 20.815 0.000 0.454
## sssi (.20.) 0.519 0.025 20.578 0.000 0.469
## ssmk (.21.) 0.731 0.027 27.102 0.000 0.678
## ssmc (.22.) 0.673 0.026 25.703 0.000 0.622
## ssei (.23.) 0.722 0.028 26.155 0.000 0.668
## ssao (.24.) 0.575 0.026 21.952 0.000 0.524
## ci.upper Std.lv Std.all
##
## 0.388 0.330 0.350
## 0.240 0.184 0.194
## 0.358 0.306 0.312
## 0.324 0.259 0.275
## 0.440 0.363 0.379
##
## 0.376 0.309 0.365
## 0.397 0.326 0.380
## 0.212 0.170 0.181
## 0.209 0.169 0.177
##
## 0.732 0.613 0.631
## 0.448 0.368 0.377
## 0.263 0.214 0.219
##
## 0.864 0.888 0.909
## 0.766 0.779 0.827
## 0.867 0.889 0.902
## 0.772 0.792 0.838
## 0.572 0.565 0.582
## 0.552 0.548 0.561
## 0.548 0.549 0.647
## 0.568 0.568 0.662
## 0.783 0.800 0.816
## 0.724 0.737 0.785
## 0.776 0.791 0.828
## 0.626 0.630 0.659
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.296 0.031 9.573 0.000 0.235
## agec2 -0.038 0.024 -1.570 0.117 -0.086
## ci.upper Std.lv Std.all
##
## 0.356 0.270 0.402
## 0.010 -0.035 -0.066
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.49.) 0.249 0.054 4.655 0.000 0.144
## .sspc 0.301 0.055 5.485 0.000 0.193
## .ssmk (.51.) 0.268 0.057 4.676 0.000 0.156
## .ssmc (.52.) 0.097 0.052 1.893 0.058 -0.003
## .ssao (.53.) 0.198 0.050 3.945 0.000 0.100
## .ssai (.54.) -0.076 0.041 -1.830 0.067 -0.157
## .sssi (.55.) -0.037 0.044 -0.850 0.396 -0.123
## .ssei (.56.) 0.042 0.053 0.793 0.428 -0.061
## .ssno (.57.) 0.219 0.050 4.338 0.000 0.120
## .sscs 0.279 0.047 5.872 0.000 0.186
## .ssgs (.59.) 0.230 0.058 3.968 0.000 0.116
## .sswk (.60.) 0.167 0.058 2.860 0.004 0.053
## ci.upper Std.lv Std.all
## 0.355 0.249 0.265
## 0.408 0.301 0.318
## 0.380 0.268 0.273
## 0.198 0.097 0.104
## 0.297 0.198 0.207
## 0.005 -0.076 -0.090
## 0.049 -0.037 -0.043
## 0.145 0.042 0.044
## 0.318 0.219 0.226
## 0.372 0.279 0.286
## 0.344 0.230 0.236
## 0.281 0.167 0.169
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.173 0.019 9.268 0.000 0.136
## .sspc 0.233 0.021 11.132 0.000 0.192
## .ssmk 0.181 0.017 10.375 0.000 0.147
## .ssmc 0.242 0.020 11.839 0.000 0.202
## .ssao 0.387 0.033 11.785 0.000 0.322
## .ssai 0.322 0.026 12.235 0.000 0.270
## .sssi 0.308 0.028 10.953 0.000 0.253
## .ssei 0.258 0.023 11.408 0.000 0.214
## .ssno 0.248 0.060 4.128 0.000 0.130
## .sscs 0.517 0.053 9.826 0.000 0.414
## .ssgs 0.165 0.014 11.409 0.000 0.137
## .sswk 0.181 0.016 11.426 0.000 0.150
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.209 0.173 0.194
## 0.274 0.233 0.261
## 0.215 0.181 0.188
## 0.282 0.242 0.275
## 0.451 0.387 0.422
## 0.373 0.322 0.448
## 0.364 0.308 0.418
## 0.303 0.258 0.283
## 0.365 0.248 0.262
## 0.621 0.517 0.543
## 0.194 0.165 0.173
## 0.213 0.181 0.187
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.833 0.833
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.330 0.030 11.034 0.000 0.271
## sspc (.p2.) 0.184 0.029 6.407 0.000 0.127
## ssmk (.p3.) 0.306 0.027 11.415 0.000 0.253
## ssmc (.p4.) 0.259 0.033 7.789 0.000 0.193
## ssao (.p5.) 0.363 0.039 9.223 0.000 0.286
## electronic =~
## ssai (.p6.) 0.309 0.034 9.094 0.000 0.243
## sssi (.p7.) 0.326 0.036 8.999 0.000 0.255
## ssmc (.p8.) 0.170 0.022 7.840 0.000 0.127
## ssei (.p9.) 0.169 0.020 8.393 0.000 0.130
## speed =~
## ssno (.10.) 0.613 0.060 10.152 0.000 0.495
## sscs (.11.) 0.368 0.041 8.970 0.000 0.287
## ssmk (.12.) 0.214 0.025 8.581 0.000 0.165
## g =~
## ssgs (.13.) 0.810 0.027 29.569 0.000 0.757
## ssar (.14.) 0.711 0.028 25.144 0.000 0.656
## sswk (.15.) 0.812 0.028 28.597 0.000 0.756
## sspc (.16.) 0.723 0.025 28.829 0.000 0.673
## ssno (.17.) 0.516 0.029 18.028 0.000 0.460
## sscs (.18.) 0.500 0.027 18.656 0.000 0.447
## ssai (.19.) 0.501 0.024 20.815 0.000 0.454
## sssi (.20.) 0.519 0.025 20.578 0.000 0.469
## ssmk (.21.) 0.731 0.027 27.102 0.000 0.678
## ssmc (.22.) 0.673 0.026 25.703 0.000 0.622
## ssei (.23.) 0.722 0.028 26.155 0.000 0.668
## ssao (.24.) 0.575 0.026 21.952 0.000 0.524
## ci.upper Std.lv Std.all
##
## 0.388 0.330 0.314
## 0.240 0.184 0.180
## 0.358 0.306 0.291
## 0.324 0.259 0.246
## 0.440 0.363 0.342
##
## 0.376 0.632 0.568
## 0.397 0.666 0.618
## 0.212 0.347 0.330
## 0.209 0.346 0.313
##
## 0.732 0.711 0.662
## 0.448 0.426 0.406
## 0.263 0.248 0.237
##
## 0.864 0.987 0.919
## 0.766 0.866 0.826
## 0.867 0.989 0.912
## 0.772 0.881 0.862
## 0.572 0.629 0.585
## 0.552 0.609 0.580
## 0.548 0.610 0.549
## 0.568 0.632 0.586
## 0.783 0.890 0.848
## 0.724 0.820 0.781
## 0.776 0.880 0.797
## 0.626 0.701 0.661
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.326 0.037 8.925 0.000 0.254
## agec2 -0.015 0.026 -0.575 0.565 -0.065
## ci.upper Std.lv Std.all
##
## 0.398 0.268 0.381
## 0.036 -0.012 -0.022
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.49.) 0.249 0.054 4.655 0.000 0.144
## .sspc 0.024 0.057 0.414 0.679 -0.088
## .ssmk (.51.) 0.268 0.057 4.676 0.000 0.156
## .ssmc (.52.) 0.097 0.052 1.893 0.058 -0.003
## .ssao (.53.) 0.198 0.050 3.945 0.000 0.100
## .ssai (.54.) -0.076 0.041 -1.830 0.067 -0.157
## .sssi (.55.) -0.037 0.044 -0.850 0.396 -0.123
## .ssei (.56.) 0.042 0.053 0.793 0.428 -0.061
## .ssno (.57.) 0.219 0.050 4.338 0.000 0.120
## .sscs 0.006 0.058 0.098 0.922 -0.108
## .ssgs (.59.) 0.230 0.058 3.968 0.000 0.116
## .sswk (.60.) 0.167 0.058 2.860 0.004 0.053
## math -0.301 0.094 -3.198 0.001 -0.485
## elctrnc 1.602 0.220 7.284 0.000 1.171
## speed -0.352 0.100 -3.512 0.000 -0.549
## .g 0.054 0.102 0.532 0.595 -0.145
## ci.upper Std.lv Std.all
## 0.355 0.249 0.238
## 0.135 0.024 0.023
## 0.380 0.268 0.255
## 0.198 0.097 0.093
## 0.297 0.198 0.187
## 0.005 -0.076 -0.068
## 0.049 -0.037 -0.035
## 0.145 0.042 0.038
## 0.318 0.219 0.204
## 0.120 0.006 0.005
## 0.344 0.230 0.214
## 0.281 0.167 0.154
## -0.116 -0.301 -0.301
## 2.034 0.784 0.784
## -0.156 -0.304 -0.304
## 0.253 0.044 0.044
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.241 0.024 9.910 0.000 0.193
## .sspc 0.236 0.020 12.009 0.000 0.197
## .ssmk 0.153 0.016 9.813 0.000 0.123
## .ssmc 0.244 0.022 11.264 0.000 0.201
## .ssao 0.502 0.038 13.077 0.000 0.427
## .ssai 0.465 0.042 10.946 0.000 0.382
## .sssi 0.319 0.036 8.914 0.000 0.249
## .ssei 0.324 0.025 12.788 0.000 0.275
## .ssno 0.254 0.073 3.501 0.000 0.112
## .sscs 0.550 0.055 9.944 0.000 0.441
## .ssgs 0.180 0.017 10.544 0.000 0.146
## .sswk 0.197 0.016 11.941 0.000 0.164
## electronic 4.177 0.952 4.389 0.000 2.312
## speed 1.344 0.245 5.474 0.000 0.863
## .g 1.267 0.108 11.747 0.000 1.056
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.289 0.241 0.219
## 0.274 0.236 0.225
## 0.184 0.153 0.139
## 0.286 0.244 0.221
## 0.577 0.502 0.446
## 0.548 0.465 0.376
## 0.390 0.319 0.275
## 0.374 0.324 0.266
## 0.396 0.254 0.220
## 0.658 0.550 0.499
## 0.213 0.180 0.156
## 0.229 0.197 0.167
## 6.042 1.000 1.000
## 1.825 1.000 1.000
## 1.479 0.854 0.854
sem.age2q<-sem(bf.age2q, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "sscs~1"))
fitMeasures(sem.age2q, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 709.363 157.000 0.000 0.960 0.073 0.052 0.649
## aic bic
## 31877.975 32245.706
Mc(sem.age2q)
## [1] 0.8100456
summary(sem.age2q, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 80 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 107
## Number of equality constraints 36
##
## Number of observations per group:
## 1 656
## 0 656
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 709.363 550.968
## Degrees of freedom 157 157
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.287
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 291.434 226.359
## 0 417.929 324.609
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.330 0.030 11.053 0.000 0.271
## sspc (.p2.) 0.184 0.029 6.410 0.000 0.127
## ssmk (.p3.) 0.306 0.027 11.431 0.000 0.254
## ssmc (.p4.) 0.258 0.033 7.789 0.000 0.193
## ssao (.p5.) 0.363 0.039 9.222 0.000 0.286
## electronic =~
## ssai (.p6.) 0.309 0.034 9.093 0.000 0.243
## sssi (.p7.) 0.326 0.036 8.994 0.000 0.255
## ssmc (.p8.) 0.170 0.022 7.839 0.000 0.127
## ssei (.p9.) 0.169 0.020 8.394 0.000 0.130
## speed =~
## ssno (.10.) 0.613 0.060 10.153 0.000 0.495
## sscs (.11.) 0.367 0.041 8.972 0.000 0.287
## ssmk (.12.) 0.214 0.025 8.584 0.000 0.165
## g =~
## ssgs (.13.) 0.811 0.027 29.554 0.000 0.757
## ssar (.14.) 0.711 0.028 25.132 0.000 0.656
## sswk (.15.) 0.812 0.028 28.587 0.000 0.757
## sspc (.16.) 0.723 0.025 28.824 0.000 0.674
## ssno (.17.) 0.516 0.029 18.021 0.000 0.460
## sscs (.18.) 0.500 0.027 18.653 0.000 0.447
## ssai (.19.) 0.501 0.024 20.804 0.000 0.454
## sssi (.20.) 0.519 0.025 20.587 0.000 0.469
## ssmk (.21.) 0.731 0.027 27.095 0.000 0.678
## ssmc (.22.) 0.673 0.026 25.737 0.000 0.622
## ssei (.23.) 0.722 0.028 26.131 0.000 0.668
## ssao (.24.) 0.575 0.026 21.968 0.000 0.524
## ci.upper Std.lv Std.all
##
## 0.388 0.330 0.349
## 0.240 0.184 0.193
## 0.358 0.306 0.311
## 0.323 0.258 0.274
## 0.440 0.363 0.378
##
## 0.376 0.309 0.364
## 0.397 0.326 0.378
## 0.212 0.170 0.180
## 0.209 0.169 0.176
##
## 0.731 0.613 0.630
## 0.448 0.367 0.376
## 0.263 0.214 0.218
##
## 0.864 0.893 0.910
## 0.767 0.784 0.828
## 0.868 0.895 0.903
## 0.772 0.797 0.839
## 0.572 0.569 0.584
## 0.552 0.551 0.563
## 0.548 0.552 0.650
## 0.568 0.572 0.664
## 0.784 0.805 0.818
## 0.725 0.742 0.787
## 0.776 0.796 0.829
## 0.627 0.634 0.661
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.308 0.025 12.548 0.000 0.260
## agec2 (b) -0.028 0.018 -1.598 0.110 -0.063
## ci.upper Std.lv Std.all
##
## 0.356 0.280 0.417
## 0.006 -0.026 -0.049
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.49.) 0.233 0.047 4.955 0.000 0.141
## .sspc 0.284 0.048 5.899 0.000 0.190
## .ssmk (.51.) 0.251 0.050 5.050 0.000 0.154
## .ssmc (.52.) 0.082 0.045 1.811 0.070 -0.007
## .ssao (.53.) 0.185 0.045 4.102 0.000 0.097
## .ssai (.54.) -0.087 0.037 -2.380 0.017 -0.159
## .sssi (.55.) -0.049 0.040 -1.239 0.215 -0.127
## .ssei (.56.) 0.025 0.045 0.559 0.576 -0.064
## .ssno (.57.) 0.207 0.046 4.478 0.000 0.117
## .sscs 0.267 0.044 6.038 0.000 0.181
## .ssgs (.59.) 0.212 0.050 4.261 0.000 0.114
## .sswk (.60.) 0.149 0.050 2.975 0.003 0.051
## ci.upper Std.lv Std.all
## 0.326 0.233 0.247
## 0.378 0.284 0.299
## 0.349 0.251 0.255
## 0.171 0.082 0.087
## 0.273 0.185 0.193
## -0.015 -0.087 -0.103
## 0.029 -0.049 -0.057
## 0.114 0.025 0.026
## 0.298 0.207 0.213
## 0.354 0.267 0.273
## 0.309 0.212 0.216
## 0.246 0.149 0.150
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.172 0.019 9.263 0.000 0.136
## .sspc 0.233 0.021 11.132 0.000 0.192
## .ssmk 0.181 0.017 10.379 0.000 0.147
## .ssmc 0.242 0.020 11.849 0.000 0.202
## .ssao 0.387 0.033 11.791 0.000 0.322
## .ssai 0.322 0.026 12.235 0.000 0.270
## .sssi 0.309 0.028 10.954 0.000 0.253
## .ssei 0.258 0.023 11.408 0.000 0.214
## .ssno 0.248 0.060 4.134 0.000 0.130
## .sscs 0.517 0.053 9.826 0.000 0.414
## .ssgs 0.165 0.014 11.425 0.000 0.137
## .sswk 0.181 0.016 11.414 0.000 0.150
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.209 0.172 0.193
## 0.274 0.233 0.259
## 0.215 0.181 0.187
## 0.282 0.242 0.273
## 0.451 0.387 0.420
## 0.373 0.322 0.445
## 0.364 0.309 0.416
## 0.303 0.258 0.281
## 0.365 0.248 0.262
## 0.620 0.517 0.541
## 0.194 0.165 0.172
## 0.212 0.181 0.184
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.824 0.824
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.330 0.030 11.053 0.000 0.271
## sspc (.p2.) 0.184 0.029 6.410 0.000 0.127
## ssmk (.p3.) 0.306 0.027 11.431 0.000 0.254
## ssmc (.p4.) 0.258 0.033 7.789 0.000 0.193
## ssao (.p5.) 0.363 0.039 9.222 0.000 0.286
## electronic =~
## ssai (.p6.) 0.309 0.034 9.093 0.000 0.243
## sssi (.p7.) 0.326 0.036 8.994 0.000 0.255
## ssmc (.p8.) 0.170 0.022 7.839 0.000 0.127
## ssei (.p9.) 0.169 0.020 8.394 0.000 0.130
## speed =~
## ssno (.10.) 0.613 0.060 10.153 0.000 0.495
## sscs (.11.) 0.367 0.041 8.972 0.000 0.287
## ssmk (.12.) 0.214 0.025 8.584 0.000 0.165
## g =~
## ssgs (.13.) 0.811 0.027 29.554 0.000 0.757
## ssar (.14.) 0.711 0.028 25.132 0.000 0.656
## sswk (.15.) 0.812 0.028 28.587 0.000 0.757
## sspc (.16.) 0.723 0.025 28.824 0.000 0.674
## ssno (.17.) 0.516 0.029 18.021 0.000 0.460
## sscs (.18.) 0.500 0.027 18.653 0.000 0.447
## ssai (.19.) 0.501 0.024 20.804 0.000 0.454
## sssi (.20.) 0.519 0.025 20.587 0.000 0.469
## ssmk (.21.) 0.731 0.027 27.095 0.000 0.678
## ssmc (.22.) 0.673 0.026 25.737 0.000 0.622
## ssei (.23.) 0.722 0.028 26.131 0.000 0.668
## ssao (.24.) 0.575 0.026 21.968 0.000 0.524
## ci.upper Std.lv Std.all
##
## 0.388 0.330 0.316
## 0.240 0.184 0.180
## 0.358 0.306 0.293
## 0.323 0.258 0.247
## 0.440 0.363 0.343
##
## 0.376 0.632 0.570
## 0.397 0.667 0.619
## 0.212 0.347 0.332
## 0.209 0.346 0.315
##
## 0.731 0.711 0.663
## 0.448 0.426 0.407
## 0.263 0.249 0.238
##
## 0.864 0.981 0.918
## 0.767 0.861 0.824
## 0.868 0.983 0.911
## 0.772 0.875 0.860
## 0.572 0.625 0.583
## 0.552 0.605 0.578
## 0.548 0.606 0.546
## 0.568 0.628 0.584
## 0.784 0.885 0.847
## 0.725 0.815 0.779
## 0.776 0.874 0.795
## 0.627 0.696 0.658
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.308 0.025 12.548 0.000 0.260
## agec2 (b) -0.028 0.018 -1.598 0.110 -0.063
## ci.upper Std.lv Std.all
##
## 0.356 0.255 0.363
## 0.006 -0.023 -0.043
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.49.) 0.233 0.047 4.955 0.000 0.141
## .sspc 0.007 0.050 0.144 0.886 -0.091
## .ssmk (.51.) 0.251 0.050 5.050 0.000 0.154
## .ssmc (.52.) 0.082 0.045 1.811 0.070 -0.007
## .ssao (.53.) 0.185 0.045 4.102 0.000 0.097
## .ssai (.54.) -0.087 0.037 -2.380 0.017 -0.159
## .sssi (.55.) -0.049 0.040 -1.239 0.215 -0.127
## .ssei (.56.) 0.025 0.045 0.559 0.576 -0.064
## .ssno (.57.) 0.207 0.046 4.478 0.000 0.117
## .sscs -0.006 0.054 -0.103 0.918 -0.112
## .ssgs (.59.) 0.212 0.050 4.261 0.000 0.114
## .sswk (.60.) 0.149 0.050 2.975 0.003 0.051
## math -0.301 0.094 -3.196 0.001 -0.485
## elctrnc 1.603 0.220 7.282 0.000 1.171
## speed -0.353 0.100 -3.513 0.000 -0.549
## .g 0.104 0.069 1.513 0.130 -0.031
## ci.upper Std.lv Std.all
## 0.326 0.233 0.223
## 0.105 0.007 0.007
## 0.349 0.251 0.241
## 0.171 0.082 0.079
## 0.273 0.185 0.175
## -0.015 -0.087 -0.079
## 0.029 -0.049 -0.046
## 0.114 0.025 0.023
## 0.298 0.207 0.193
## 0.101 -0.006 -0.005
## 0.309 0.212 0.198
## 0.246 0.149 0.138
## -0.116 -0.301 -0.301
## 2.034 0.784 0.784
## -0.156 -0.304 -0.304
## 0.239 0.086 0.086
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.241 0.024 9.907 0.000 0.193
## .sspc 0.236 0.020 12.008 0.000 0.197
## .ssmk 0.153 0.016 9.813 0.000 0.123
## .ssmc 0.244 0.022 11.273 0.000 0.201
## .ssao 0.502 0.038 13.082 0.000 0.427
## .ssai 0.465 0.042 10.940 0.000 0.382
## .sssi 0.319 0.036 8.916 0.000 0.249
## .ssei 0.324 0.025 12.793 0.000 0.275
## .ssno 0.254 0.073 3.503 0.000 0.112
## .sscs 0.550 0.055 9.941 0.000 0.441
## .ssgs 0.179 0.017 10.545 0.000 0.146
## .sswk 0.197 0.016 11.946 0.000 0.165
## electronic 4.182 0.953 4.387 0.000 2.313
## speed 1.345 0.246 5.477 0.000 0.864
## .g 1.268 0.108 11.740 0.000 1.056
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.289 0.241 0.221
## 0.274 0.236 0.228
## 0.184 0.153 0.141
## 0.286 0.244 0.223
## 0.577 0.502 0.449
## 0.548 0.465 0.377
## 0.390 0.319 0.276
## 0.374 0.324 0.268
## 0.396 0.254 0.221
## 0.658 0.550 0.501
## 0.213 0.179 0.157
## 0.229 0.197 0.169
## 6.050 1.000 1.000
## 1.827 1.000 1.000
## 1.479 0.865 0.865
# MGCFA USING FULL DATA, NOT JUST SIBLING
# WHITE RESPONDENTS
dw<- filter(dk, bhw==3)
nrow(dw) # N=3659
## [1] 3659
dgroup<- dplyr::select(dw, id, starts_with("ss"), asvab, efa, educ2011, T6665000, agec, age, agebin, agec2, sex, sexage, bhw, sweight)
original_age_min <- 12
original_age_max <- 17
mean_centered_min <- min(dgroup$agec)
mean_centered_max <- max(dgroup$agec)
original_age_mean <- (original_age_min + original_age_max) / 2
mean_centered_age_mean <- (mean_centered_min + mean_centered_max) / 2
age_difference <- original_age_mean - mean_centered_age_mean
# Lynn's hypothesis validated
fit<-lm(efa ~ sex + rcs(agec, 3) + sex*rcs(agec, 3), data=dgroup)
summary(fit)
##
## Call:
## lm(formula = efa ~ sex + rcs(agec, 3) + sex * rcs(agec, 3), data = dgroup)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.544 -7.528 1.081 8.934 42.180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 108.9037 0.8099 134.459 <2e-16 ***
## sex -0.7228 1.1631 -0.621 0.5344
## rcs(agec, 3)agec 5.1097 0.5615 9.100 <2e-16 ***
## rcs(agec, 3)agec' -1.7739 0.6963 -2.548 0.0109 *
## sex:rcs(agec, 3)agec -0.1566 0.8107 -0.193 0.8468
## sex:rcs(agec, 3)agec' -0.3932 0.9993 -0.393 0.6940
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.9 on 3653 degrees of freedom
## Multiple R-squared: 0.1368, Adjusted R-squared: 0.1356
## F-statistic: 115.7 on 5 and 3653 DF, p-value: < 2.2e-16
dgroup$pred1<-fitted(fit)
xyplot(dgroup$pred1 ~ dgroup$agec, data=dgroup, groups=sex, pch=19, type=c("p"), col=c('red', 'blue'), grid=TRUE, ylab="Predicted g", xlab="age", key=list(text=list(c("White Male", "White Female")), points=list(pch=c(19,19), col=c("red", "blue")), columns=2))
xyplot(dgroup$pred1 ~ dgroup$agec, data=dgroup, groups=sex, pch=19, type=c("p"), col=c('red', 'blue'), grid=TRUE, ylab="Predicted g", xlab="Age", key=list(text=list(c("White Male", "White Female")), points=list(pch=c(19,19), col=c("red", "blue")), columns=2), scales=list(x=list(at=seq(mean_centered_min, mean_centered_max), labels=seq(original_age_min, original_age_max))))
# Lynn's hypothesis not validated
fit<-lm(asvab ~ sex + rcs(agec, 3) + sex*rcs(agec, 3), data=dgroup)
summary(fit)
##
## Call:
## lm(formula = asvab ~ sex + rcs(agec, 3) + sex * rcs(agec, 3),
## data = dgroup)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.628 -11.208 1.253 12.232 22.893
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 105.20064 0.89087 118.088 <2e-16 ***
## sex 1.65608 1.27934 1.294 0.196
## rcs(agec, 3)agec -0.01539 0.61764 -0.025 0.980
## rcs(agec, 3)agec' 0.09021 0.76590 0.118 0.906
## sex:rcs(agec, 3)agec 0.27152 0.89169 0.305 0.761
## sex:rcs(agec, 3)agec' -0.62267 1.09913 -0.567 0.571
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.19 on 3653 degrees of freedom
## Multiple R-squared: 0.00147, Adjusted R-squared: 0.0001034
## F-statistic: 1.076 on 5 and 3653 DF, p-value: 0.3717
dgroup$pred2<-fitted(fit)
xyplot(dgroup$pred2 ~ dgroup$agec, data=dgroup, groups=sex, pch=19, type=c("p"), col=c('red', 'blue'), grid=TRUE, ylab="Predicted ASVAB", xlab="Age", key=list(text=list(c("White Male", "White Female")), points=list(pch=c(19,19), col=c("red", "blue")), columns=2), scales=list(x=list(at=seq(mean_centered_min, mean_centered_max), labels=seq(original_age_min, original_age_max))))
describeBy(dgroup$pred1, dgroup$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew
## X1 1 1889 106.52 5.41 107.27 106.76 6.58 96.13 114.6 18.47 -0.32
## kurtosis se
## X1 -1.11 0.12
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 1770 105.72 4.79 106.96 106.1 5.19 95.8 112.02 16.23 -0.56
## kurtosis se
## X1 -0.93 0.11
describeBy(dgroup$efa, dgroup$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew
## X1 1 1889 106.52 14.7 107.7 107.09 14.58 63.82 146.25 82.43 -0.35
## kurtosis se
## X1 -0.14 0.34
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 1770 105.72 12.94 107.08 106.3 12.44 65.48 141.79 76.31 -0.41
## kurtosis se
## X1 0.05 0.31
describeBy(dgroup$asvab, dgroup$sex)
##
## Descriptive statistics by group
## INDICES: 0
## vars n mean sd median trimmed mad min max range skew
## V1 1 1889 105.29 14.65 106.61 105.77 18.28 76.7 128.12 51.42 -0.22
## kurtosis se
## V1 -1.13 0.34
## ------------------------------------------------------
## INDICES: 1
## vars n mean sd median trimmed mad min max range skew
## V1 1 1770 106.28 13.68 107.62 106.85 16.21 76.7 128.12 51.42 -0.28
## kurtosis se
## V1 -0.96 0.33
describeBy(dgroup$educ2011, dgroup$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 1527 13.95 2.85 14 13.95 2.97 6 20 14 0.06 -0.59
## se
## X1 0.07
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 1439 14.64 4.17 15 14.62 2.97 6 95 89 9.85 189.68
## se
## X1 0.11
cor(dgroup$efa, dgroup$asvab, use="pairwise.complete.obs", method="pearson")
## [,1]
## [1,] 0.8840502
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(agebin, sex) %>% summarise(MEAN = survey_mean(pred1), SD = survey_sd(pred1))
## # A tibble: 10 Ă— 5
## # Groups: agebin [5]
## agebin sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 12 0 98.5 0.0763 1.49
## 2 12 1 98.0 0.0818 1.50
## 3 13 0 103. 0.0696 1.35
## 4 13 1 103. 0.0729 1.31
## 5 14 0 108. 0.0561 1.07
## 6 14 1 107. 0.0486 0.907
## 7 15 0 111. 0.0453 0.841
## 8 15 1 109. 0.0299 0.565
## 9 16 0 113. 0.0405 0.695
## 10 16 1 111. 0.0283 0.497
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(agebin, sex) %>% summarise(MEAN = survey_mean(efa), SD = survey_sd(efa))
## # A tibble: 10 Ă— 5
## # Groups: agebin [5]
## agebin sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 12 0 98.7 0.680 13.3
## 2 12 1 98.1 0.649 11.8
## 3 13 0 104. 0.685 13.4
## 4 13 1 103. 0.680 12.2
## 5 14 0 106. 0.737 14.0
## 6 14 1 107. 0.629 11.7
## 7 15 0 113. 0.700 13.0
## 8 15 1 110. 0.657 12.5
## 9 16 0 113. 0.838 14.4
## 10 16 1 112. 0.670 11.8
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(agebin, sex) %>% summarise(MEAN = survey_mean(asvab), SD = survey_sd(asvab))
## # A tibble: 10 Ă— 5
## # Groups: agebin [5]
## agebin sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 12 0 105. 0.744 14.5
## 2 12 1 106. 0.744 13.6
## 3 13 0 106. 0.753 14.7
## 4 13 1 107. 0.769 13.8
## 5 14 0 104. 0.766 14.6
## 6 14 1 107. 0.713 13.3
## 7 15 0 107. 0.768 14.3
## 8 15 1 106. 0.722 13.7
## 9 16 0 106. 0.857 14.7
## 10 16 1 106. 0.779 13.7
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(pred1), SD = survey_sd(pred1))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 107. 0.133 5.48
## 2 1 106. 0.122 4.86
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(efa), SD = survey_sd(efa))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 107. 0.352 14.7
## 2 1 106. 0.319 12.9
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(asvab, na.rm = TRUE), SD = survey_sd(asvab, na.rm = TRUE))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 106. 0.348 14.6
## 2 1 107. 0.334 13.6
dgroup %>% as_survey_design(ids = id, weights = T6665000) %>% group_by(sex) %>% summarise(MEAN = survey_mean(educ2011, na.rm = TRUE), SD = survey_sd(educ2011, na.rm = TRUE))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 13.9 0.0737 2.86
## 2 1 14.7 0.116 4.27
# CORRELATED FACTOR MODEL
cf.model<-' # model produces negative loadings for ssar and ssmk if they load on verbal
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
'
cf.lv<-' # model produces negative loadings for ssar and ssmk if they load on verbal
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
verbal~~1*verbal
math~~1*math
speed~~1*speed
'
cf.reduced<-' # model produces negative loadings for ssar and ssmk if they load on verbal
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
verbal~~1*verbal
math~~1*math
speed~~1*speed
verbal~0*1
math~0*1
'
baseline<-cfa(cf.model, data=dgroup, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1095.379 45.000 0.000 0.967 0.080 0.030 88880.415
## bic
## 89159.638
Mc(baseline)
## [1] 0.8662577
configural<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 917.557 90.000 0.000 0.974 0.071 0.026 86682.687
## bic
## 87241.132
Mc(configural)
## [1] 0.893047
summary(configural, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 47 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 90
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 917.557 809.914
## Degrees of freedom 90 90
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.133
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 375.668 331.597
## 0 541.889 478.317
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.734 0.017 43.597 0.000 0.701
## sswk 0.778 0.018 43.663 0.000 0.744
## sspc 0.751 0.018 42.638 0.000 0.716
## ssei 0.365 0.041 8.906 0.000 0.284
## math =~
## ssar 0.747 0.018 41.341 0.000 0.712
## ssmk 0.628 0.029 21.591 0.000 0.571
## ssmc 0.402 0.031 13.182 0.000 0.343
## ssao 0.640 0.018 35.022 0.000 0.605
## electronic =~
## ssai 0.471 0.021 22.868 0.000 0.431
## sssi 0.508 0.021 24.217 0.000 0.467
## ssmc 0.292 0.030 9.868 0.000 0.234
## ssei 0.244 0.043 5.703 0.000 0.160
## speed =~
## ssno 0.786 0.023 33.661 0.000 0.741
## sscs 0.670 0.022 29.921 0.000 0.626
## ssmk 0.231 0.029 7.943 0.000 0.174
## ci.upper Std.lv Std.all
##
## 0.767 0.734 0.871
## 0.813 0.778 0.883
## 0.785 0.751 0.853
## 0.445 0.365 0.476
##
## 0.782 0.747 0.895
## 0.685 0.628 0.689
## 0.462 0.402 0.496
## 0.676 0.640 0.707
##
## 0.512 0.471 0.638
## 0.549 0.508 0.676
## 0.350 0.292 0.360
## 0.328 0.244 0.319
##
## 0.832 0.786 0.831
## 0.714 0.670 0.739
## 0.288 0.231 0.253
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.886 0.010 92.499 0.000 0.867
## electronic 0.835 0.020 41.476 0.000 0.796
## speed 0.676 0.024 27.607 0.000 0.628
## math ~~
## electronic 0.722 0.025 28.960 0.000 0.673
## speed 0.717 0.025 28.371 0.000 0.667
## electronic ~~
## speed 0.452 0.038 11.749 0.000 0.376
## ci.upper Std.lv Std.all
##
## 0.905 0.886 0.886
## 0.874 0.835 0.835
## 0.724 0.676 0.676
##
## 0.770 0.722 0.722
## 0.766 0.717 0.717
##
## 0.527 0.452 0.452
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.331 0.021 15.977 0.000 0.291
## .sswk 0.379 0.022 17.461 0.000 0.337
## .sspc 0.453 0.022 20.981 0.000 0.411
## .ssei 0.139 0.019 7.329 0.000 0.102
## .ssar 0.327 0.021 15.677 0.000 0.286
## .ssmk 0.382 0.022 16.962 0.000 0.337
## .ssmc 0.235 0.020 11.729 0.000 0.196
## .ssao 0.356 0.022 15.988 0.000 0.312
## .ssai 0.055 0.018 3.026 0.002 0.019
## .sssi 0.059 0.019 3.200 0.001 0.023
## .ssno 0.244 0.023 10.435 0.000 0.198
## .sscs 0.358 0.023 15.788 0.000 0.313
## ci.upper Std.lv Std.all
## 0.372 0.331 0.393
## 0.422 0.379 0.430
## 0.495 0.453 0.515
## 0.176 0.139 0.182
## 0.368 0.327 0.392
## 0.426 0.382 0.419
## 0.274 0.235 0.289
## 0.399 0.356 0.392
## 0.091 0.055 0.075
## 0.096 0.059 0.079
## 0.290 0.244 0.258
## 0.402 0.358 0.395
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.171 0.009 19.938 0.000 0.155
## .sswk 0.172 0.008 20.364 0.000 0.156
## .sspc 0.211 0.012 17.352 0.000 0.187
## .ssei 0.245 0.011 22.061 0.000 0.224
## .ssar 0.139 0.008 16.665 0.000 0.122
## .ssmk 0.174 0.008 20.606 0.000 0.158
## .ssmc 0.241 0.012 19.891 0.000 0.217
## .ssao 0.411 0.017 23.542 0.000 0.377
## .ssai 0.323 0.016 19.851 0.000 0.291
## .sssi 0.306 0.016 19.606 0.000 0.276
## .ssno 0.277 0.020 14.088 0.000 0.238
## .sscs 0.372 0.020 18.405 0.000 0.333
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.188 0.171 0.241
## 0.189 0.172 0.221
## 0.235 0.211 0.272
## 0.267 0.245 0.418
## 0.155 0.139 0.199
## 0.191 0.174 0.210
## 0.265 0.241 0.366
## 0.446 0.411 0.501
## 0.355 0.323 0.592
## 0.337 0.306 0.543
## 0.316 0.277 0.309
## 0.412 0.372 0.453
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.875 0.019 47.095 0.000 0.839
## sswk 0.822 0.019 44.262 0.000 0.785
## sspc 0.842 0.016 53.394 0.000 0.811
## ssei 0.557 0.027 20.398 0.000 0.503
## math =~
## ssar 0.850 0.019 43.624 0.000 0.812
## ssmk 0.668 0.030 22.206 0.000 0.609
## ssmc 0.494 0.022 22.971 0.000 0.452
## ssao 0.720 0.018 40.162 0.000 0.685
## electronic =~
## ssai 0.846 0.026 32.200 0.000 0.795
## sssi 0.822 0.022 37.196 0.000 0.778
## ssmc 0.411 0.022 18.927 0.000 0.368
## ssei 0.470 0.029 16.441 0.000 0.414
## speed =~
## ssno 0.894 0.023 38.482 0.000 0.849
## sscs 0.771 0.022 34.864 0.000 0.727
## ssmk 0.234 0.030 7.887 0.000 0.176
## ci.upper Std.lv Std.all
##
## 0.912 0.875 0.899
## 0.858 0.822 0.877
## 0.873 0.842 0.856
## 0.610 0.557 0.507
##
## 0.889 0.850 0.893
## 0.727 0.668 0.697
## 0.536 0.494 0.518
## 0.755 0.720 0.709
##
## 0.898 0.846 0.768
## 0.865 0.822 0.830
## 0.453 0.411 0.431
## 0.526 0.470 0.428
##
## 0.940 0.894 0.840
## 0.814 0.771 0.770
## 0.293 0.234 0.244
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.910 0.008 116.104 0.000 0.894
## electronic 0.670 0.019 36.043 0.000 0.633
## speed 0.679 0.021 31.679 0.000 0.637
## math ~~
## electronic 0.562 0.023 24.919 0.000 0.518
## speed 0.781 0.019 40.167 0.000 0.743
## electronic ~~
## speed 0.286 0.030 9.680 0.000 0.228
## ci.upper Std.lv Std.all
##
## 0.925 0.910 0.910
## 0.706 0.670 0.670
## 0.721 0.679 0.679
##
## 0.607 0.562 0.562
## 0.819 0.781 0.781
##
## 0.343 0.286 0.286
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.523 0.023 22.328 0.000 0.477
## .sswk 0.392 0.022 17.468 0.000 0.348
## .sspc 0.211 0.024 8.959 0.000 0.165
## .ssei 0.582 0.026 22.070 0.000 0.531
## .ssar 0.395 0.023 17.329 0.000 0.350
## .ssmk 0.242 0.023 10.519 0.000 0.197
## .ssmc 0.563 0.023 24.735 0.000 0.518
## .ssao 0.214 0.024 8.814 0.000 0.166
## .ssai 0.614 0.027 23.150 0.000 0.562
## .sssi 0.769 0.024 32.369 0.000 0.723
## .ssno 0.096 0.026 3.771 0.000 0.046
## .sscs 0.007 0.024 0.306 0.759 -0.040
## ci.upper Std.lv Std.all
## 0.569 0.523 0.537
## 0.436 0.392 0.419
## 0.257 0.211 0.215
## 0.634 0.582 0.531
## 0.440 0.395 0.415
## 0.287 0.242 0.252
## 0.608 0.563 0.591
## 0.262 0.214 0.210
## 0.666 0.614 0.557
## 0.816 0.769 0.777
## 0.146 0.096 0.090
## 0.054 0.007 0.007
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.182 0.009 20.793 0.000 0.165
## .sswk 0.202 0.010 20.128 0.000 0.182
## .sspc 0.260 0.013 20.719 0.000 0.235
## .ssei 0.322 0.016 20.410 0.000 0.292
## .ssar 0.184 0.010 17.592 0.000 0.164
## .ssmk 0.173 0.009 19.961 0.000 0.156
## .ssmc 0.267 0.013 21.107 0.000 0.242
## .ssao 0.515 0.019 26.863 0.000 0.477
## .ssai 0.498 0.026 19.431 0.000 0.448
## .sssi 0.305 0.019 16.278 0.000 0.268
## .ssno 0.335 0.023 14.796 0.000 0.290
## .sscs 0.409 0.024 17.366 0.000 0.362
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.199 0.182 0.192
## 0.221 0.202 0.230
## 0.284 0.260 0.268
## 0.353 0.322 0.268
## 0.205 0.184 0.203
## 0.190 0.173 0.188
## 0.292 0.267 0.294
## 0.552 0.515 0.498
## 0.548 0.498 0.410
## 0.342 0.305 0.311
## 0.379 0.335 0.295
## 0.455 0.409 0.408
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
modificationIndices(configural, sort=T, maximum.number=30)
## lhs op rhs block group level mi epc sepc.lv
## 216 math =~ sspc 2 2 1 157.277 0.613 0.613
## 117 math =~ sspc 1 1 1 95.777 0.404 0.404
## 290 ssmc ~~ ssao 2 2 1 86.736 0.092 0.092
## 191 ssmc ~~ ssao 1 1 1 75.835 0.075 0.075
## 232 speed =~ sspc 2 2 1 75.715 0.199 0.199
## 224 electronic =~ sspc 2 2 1 65.207 -0.183 -0.183
## 298 ssao ~~ sscs 2 2 1 59.592 0.098 0.098
## 240 ssgs ~~ sspc 2 2 1 50.916 -0.056 -0.056
## 230 speed =~ ssgs 2 2 1 50.229 -0.152 -0.152
## 297 ssao ~~ ssno 2 2 1 49.848 -0.090 -0.090
## 141 ssgs ~~ sspc 1 1 1 48.234 -0.046 -0.046
## 215 math =~ sswk 2 2 1 46.511 -0.312 -0.312
## 282 ssar ~~ ssno 2 2 1 45.795 0.065 0.065
## 116 math =~ sswk 1 1 1 45.612 -0.276 -0.276
## 222 electronic =~ ssgs 2 2 1 43.597 0.141 0.141
## 239 ssgs ~~ sswk 2 2 1 42.181 0.048 0.048
## 140 ssgs ~~ sswk 1 1 1 35.819 0.040 0.040
## 261 sspc ~~ ssar 2 2 1 35.324 0.040 0.040
## 133 speed =~ sspc 1 1 1 32.560 0.125 0.125
## 123 electronic =~ ssgs 1 1 1 30.769 0.201 0.201
## 131 speed =~ ssgs 1 1 1 30.655 -0.113 -0.113
## 255 sswk ~~ ssao 2 2 1 29.592 -0.048 -0.048
## 293 ssmc ~~ ssno 2 2 1 26.549 -0.049 -0.049
## 135 speed =~ ssar 1 1 1 26.168 0.171 0.171
## 167 sspc ~~ sssi 1 1 1 26.163 -0.038 -0.038
## 122 math =~ sscs 1 1 1 25.588 0.313 0.313
## 121 math =~ ssno 1 1 1 25.588 -0.368 -0.368
## 214 math =~ ssgs 2 2 1 24.084 -0.232 -0.232
## 234 speed =~ ssar 2 2 1 24.004 0.198 0.198
## 212 verbal =~ ssno 2 2 1 23.677 -0.207 -0.207
## sepc.all sepc.nox
## 216 0.623 0.623
## 117 0.459 0.459
## 290 0.248 0.248
## 191 0.237 0.237
## 232 0.203 0.203
## 224 -0.186 -0.186
## 298 0.213 0.213
## 240 -0.255 -0.255
## 230 -0.156 -0.156
## 297 -0.217 -0.217
## 141 -0.241 -0.241
## 215 -0.333 -0.333
## 282 0.262 0.262
## 116 -0.313 -0.313
## 222 0.145 0.145
## 239 0.252 0.252
## 140 0.233 0.233
## 261 0.183 0.183
## 133 0.142 0.142
## 123 0.239 0.239
## 131 -0.135 -0.135
## 255 -0.148 -0.148
## 293 -0.165 -0.165
## 135 0.205 0.205
## 167 -0.150 -0.150
## 122 0.346 0.346
## 121 -0.389 -0.389
## 214 -0.238 -0.238
## 234 0.208 0.208
## 212 -0.194 -0.194
metric<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1026.433 101.000 0.000 0.971 0.071 0.038 86769.563
## bic
## 87259.754
Mc(metric)
## [1] 0.8811791
summary(metric, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 77 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 94
## Number of equality constraints 15
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1026.433 899.320
## Degrees of freedom 101 101
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.141
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 434.639 380.813
## 0 591.794 518.507
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.750 0.015 49.191 0.000 0.720
## sswk (.p2.) 0.744 0.016 45.379 0.000 0.711
## sspc (.p3.) 0.742 0.016 46.919 0.000 0.711
## ssei (.p4.) 0.394 0.019 20.261 0.000 0.356
## math =~
## ssar (.p5.) 0.760 0.016 46.145 0.000 0.727
## ssmk (.p6.) 0.617 0.022 27.750 0.000 0.573
## ssmc (.p7.) 0.437 0.017 25.894 0.000 0.404
## ssao (.p8.) 0.648 0.016 41.647 0.000 0.617
## electronic =~
## ssai (.p9.) 0.474 0.017 28.632 0.000 0.441
## sssi (.10.) 0.469 0.017 27.480 0.000 0.436
## ssmc (.11.) 0.242 0.013 18.144 0.000 0.215
## ssei (.12.) 0.283 0.016 17.422 0.000 0.252
## speed =~
## ssno (.13.) 0.790 0.021 38.459 0.000 0.750
## sscs (.14.) 0.678 0.019 36.408 0.000 0.642
## ssmk (.15.) 0.217 0.020 10.845 0.000 0.177
## ci.upper Std.lv Std.all
##
## 0.780 0.750 0.876
## 0.776 0.744 0.869
## 0.773 0.742 0.850
## 0.432 0.394 0.485
##
## 0.792 0.760 0.899
## 0.660 0.617 0.690
## 0.470 0.437 0.544
## 0.678 0.648 0.711
##
## 0.506 0.474 0.643
## 0.503 0.469 0.641
## 0.268 0.242 0.301
## 0.315 0.283 0.349
##
## 0.831 0.790 0.832
## 0.715 0.678 0.745
## 0.256 0.217 0.242
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.890 0.009 96.263 0.000 0.872
## electronic 0.827 0.019 42.542 0.000 0.789
## speed 0.677 0.023 29.024 0.000 0.632
## math ~~
## electronic 0.711 0.024 29.523 0.000 0.664
## speed 0.720 0.025 28.923 0.000 0.671
## electronic ~~
## speed 0.447 0.037 12.067 0.000 0.375
## ci.upper Std.lv Std.all
##
## 0.908 0.890 0.890
## 0.865 0.827 0.827
## 0.723 0.677 0.677
##
## 0.758 0.711 0.711
## 0.769 0.720 0.720
##
## 0.520 0.447 0.447
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.331 0.021 15.977 0.000 0.291
## .sswk 0.379 0.022 17.461 0.000 0.337
## .sspc 0.453 0.022 20.981 0.000 0.411
## .ssei 0.139 0.019 7.329 0.000 0.102
## .ssar 0.327 0.021 15.677 0.000 0.286
## .ssmk 0.382 0.022 16.962 0.000 0.337
## .ssmc 0.235 0.020 11.729 0.000 0.196
## .ssao 0.356 0.022 15.988 0.000 0.312
## .ssai 0.055 0.018 3.026 0.002 0.019
## .sssi 0.059 0.019 3.200 0.001 0.023
## .ssno 0.244 0.023 10.435 0.000 0.198
## .sscs 0.358 0.023 15.788 0.000 0.313
## ci.upper Std.lv Std.all
## 0.372 0.331 0.387
## 0.422 0.379 0.443
## 0.495 0.453 0.519
## 0.176 0.139 0.171
## 0.368 0.327 0.387
## 0.426 0.382 0.427
## 0.274 0.235 0.292
## 0.399 0.356 0.390
## 0.091 0.055 0.075
## 0.096 0.059 0.081
## 0.290 0.244 0.257
## 0.402 0.358 0.393
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.170 0.009 19.954 0.000 0.153
## .sswk 0.179 0.009 20.939 0.000 0.163
## .sspc 0.212 0.012 18.085 0.000 0.189
## .ssei 0.240 0.011 21.954 0.000 0.218
## .ssar 0.137 0.008 16.738 0.000 0.121
## .ssmk 0.180 0.008 21.614 0.000 0.164
## .ssmc 0.245 0.012 20.337 0.000 0.222
## .ssao 0.411 0.017 23.936 0.000 0.377
## .ssai 0.318 0.015 20.815 0.000 0.288
## .sssi 0.317 0.015 20.917 0.000 0.287
## .ssno 0.277 0.018 15.187 0.000 0.241
## .sscs 0.368 0.019 19.383 0.000 0.331
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.187 0.170 0.232
## 0.196 0.179 0.245
## 0.235 0.212 0.278
## 0.261 0.240 0.363
## 0.153 0.137 0.192
## 0.196 0.180 0.225
## 0.269 0.245 0.381
## 0.445 0.411 0.495
## 0.348 0.318 0.586
## 0.346 0.317 0.590
## 0.313 0.277 0.307
## 0.406 0.368 0.445
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.750 0.015 49.191 0.000 0.720
## sswk (.p2.) 0.744 0.016 45.379 0.000 0.711
## sspc (.p3.) 0.742 0.016 46.919 0.000 0.711
## ssei (.p4.) 0.394 0.019 20.261 0.000 0.356
## math =~
## ssar (.p5.) 0.760 0.016 46.145 0.000 0.727
## ssmk (.p6.) 0.617 0.022 27.750 0.000 0.573
## ssmc (.p7.) 0.437 0.017 25.894 0.000 0.404
## ssao (.p8.) 0.648 0.016 41.647 0.000 0.617
## electronic =~
## ssai (.p9.) 0.474 0.017 28.632 0.000 0.441
## sssi (.10.) 0.469 0.017 27.480 0.000 0.436
## ssmc (.11.) 0.242 0.013 18.144 0.000 0.215
## ssei (.12.) 0.283 0.016 17.422 0.000 0.252
## speed =~
## ssno (.13.) 0.790 0.021 38.459 0.000 0.750
## sscs (.14.) 0.678 0.019 36.408 0.000 0.642
## ssmk (.15.) 0.217 0.020 10.845 0.000 0.177
## ci.upper Std.lv Std.all
##
## 0.780 0.861 0.895
## 0.776 0.854 0.887
## 0.773 0.852 0.859
## 0.432 0.453 0.430
##
## 0.792 0.835 0.888
## 0.660 0.678 0.697
## 0.470 0.481 0.501
## 0.678 0.712 0.704
##
## 0.506 0.843 0.765
## 0.503 0.835 0.833
## 0.268 0.430 0.448
## 0.315 0.504 0.480
##
## 0.831 0.890 0.838
## 0.715 0.764 0.766
## 0.256 0.244 0.251
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 1.147 0.058 19.642 0.000 1.033
## electronic 1.403 0.089 15.803 0.000 1.229
## speed 0.877 0.051 17.300 0.000 0.778
## math ~~
## electronic 1.137 0.081 14.050 0.000 0.978
## speed 0.964 0.052 18.453 0.000 0.862
## electronic ~~
## speed 0.604 0.069 8.742 0.000 0.469
## ci.upper Std.lv Std.all
##
## 1.262 0.909 0.909
## 1.577 0.687 0.687
## 0.977 0.679 0.679
##
## 1.295 0.581 0.581
## 1.067 0.779 0.779
##
## 0.740 0.301 0.301
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.523 0.023 22.328 0.000 0.477
## .sswk 0.392 0.022 17.468 0.000 0.348
## .sspc 0.211 0.024 8.959 0.000 0.165
## .ssei 0.582 0.026 22.070 0.000 0.531
## .ssar 0.395 0.023 17.329 0.000 0.350
## .ssmk 0.242 0.023 10.519 0.000 0.197
## .ssmc 0.563 0.023 24.735 0.000 0.518
## .ssao 0.214 0.024 8.814 0.000 0.166
## .ssai 0.614 0.027 23.150 0.000 0.562
## .sssi 0.769 0.024 32.369 0.000 0.723
## .ssno 0.096 0.026 3.771 0.000 0.046
## .sscs 0.007 0.024 0.306 0.759 -0.040
## ci.upper Std.lv Std.all
## 0.569 0.523 0.544
## 0.436 0.392 0.408
## 0.257 0.211 0.213
## 0.634 0.582 0.554
## 0.440 0.395 0.420
## 0.287 0.242 0.249
## 0.608 0.563 0.586
## 0.262 0.214 0.212
## 0.666 0.614 0.557
## 0.816 0.769 0.767
## 0.146 0.096 0.091
## 0.054 0.007 0.007
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.185 0.009 21.433 0.000 0.168
## .sswk 0.198 0.010 19.860 0.000 0.178
## .sspc 0.257 0.012 20.744 0.000 0.233
## .ssei 0.333 0.017 20.025 0.000 0.300
## .ssar 0.187 0.011 17.789 0.000 0.166
## .ssmk 0.170 0.009 19.824 0.000 0.153
## .ssmc 0.266 0.013 21.102 0.000 0.241
## .ssao 0.516 0.019 27.264 0.000 0.478
## .ssai 0.505 0.025 20.140 0.000 0.456
## .sssi 0.308 0.019 16.598 0.000 0.271
## .ssno 0.336 0.022 15.424 0.000 0.293
## .sscs 0.411 0.023 17.683 0.000 0.366
## verbal 1.318 0.068 19.266 0.000 1.184
## math 1.209 0.065 18.539 0.000 1.081
## electronic 3.168 0.244 12.984 0.000 2.690
## speed 1.268 0.081 15.665 0.000 1.109
## ci.upper Std.lv Std.all
## 0.202 0.185 0.200
## 0.217 0.198 0.213
## 0.282 0.257 0.262
## 0.366 0.333 0.301
## 0.208 0.187 0.211
## 0.186 0.170 0.179
## 0.290 0.266 0.288
## 0.553 0.516 0.504
## 0.555 0.505 0.415
## 0.344 0.308 0.306
## 0.378 0.336 0.298
## 0.457 0.411 0.414
## 1.452 1.000 1.000
## 1.337 1.000 1.000
## 3.647 1.000 1.000
## 1.426 1.000 1.000
lavTestScore(metric, release = 1:15)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 108.848 15 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p54. 6.939 1 0.008
## 2 .p2. == .p55. 28.977 1 0.000
## 3 .p3. == .p56. 1.539 1 0.215
## 4 .p4. == .p57. 67.273 1 0.000
## 5 .p5. == .p58. 8.535 1 0.003
## 6 .p6. == .p59. 10.160 1 0.001
## 7 .p7. == .p60. 0.837 1 0.360
## 8 .p8. == .p61. 0.507 1 0.476
## 9 .p9. == .p62. 0.084 1 0.773
## 10 .p10. == .p63. 14.689 1 0.000
## 11 .p11. == .p64. 4.139 1 0.042
## 12 .p12. == .p65. 61.275 1 0.000
## 13 .p13. == .p66. 0.753 1 0.385
## 14 .p14. == .p67. 0.660 1 0.416
## 15 .p15. == .p68. 9.943 1 0.002
scalar<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1611.265 109.000 0.000 0.953 0.087 0.044 87338.396
## bic
## 87778.947
Mc(scalar)
## [1] 0.8143706
summary(scalar, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 92 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 27
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1611.265 1417.421
## Degrees of freedom 109 109
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.137
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 699.681 615.505
## 0 911.585 801.916
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.749 0.016 47.877 0.000 0.718
## sswk (.p2.) 0.745 0.016 45.272 0.000 0.713
## sspc (.p3.) 0.738 0.016 46.658 0.000 0.707
## ssei (.p4.) 0.380 0.017 22.704 0.000 0.347
## math =~
## ssar (.p5.) 0.759 0.017 45.623 0.000 0.726
## ssmk (.p6.) 0.595 0.022 26.467 0.000 0.551
## ssmc (.p7.) 0.437 0.015 28.299 0.000 0.407
## ssao (.p8.) 0.649 0.015 41.954 0.000 0.618
## electronic =~
## ssai (.p9.) 0.456 0.016 28.801 0.000 0.425
## sssi (.10.) 0.479 0.017 28.925 0.000 0.447
## ssmc (.11.) 0.241 0.012 20.749 0.000 0.218
## ssei (.12.) 0.299 0.014 22.035 0.000 0.272
## speed =~
## ssno (.13.) 0.771 0.021 37.551 0.000 0.731
## sscs (.14.) 0.690 0.019 37.024 0.000 0.654
## ssmk (.15.) 0.242 0.020 11.878 0.000 0.202
## ci.upper Std.lv Std.all
##
## 0.780 0.749 0.870
## 0.777 0.745 0.870
## 0.769 0.738 0.838
## 0.412 0.380 0.467
##
## 0.791 0.759 0.898
## 0.640 0.595 0.665
## 0.468 0.437 0.544
## 0.679 0.649 0.710
##
## 0.487 0.456 0.624
## 0.512 0.479 0.650
## 0.264 0.241 0.300
## 0.325 0.299 0.368
##
## 0.811 0.771 0.816
## 0.727 0.690 0.751
## 0.282 0.242 0.270
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.890 0.009 94.677 0.000 0.872
## electronic 0.832 0.019 43.145 0.000 0.794
## speed 0.687 0.023 29.483 0.000 0.642
## math ~~
## electronic 0.716 0.024 30.016 0.000 0.670
## speed 0.725 0.025 29.084 0.000 0.676
## electronic ~~
## speed 0.456 0.037 12.363 0.000 0.384
## ci.upper Std.lv Std.all
##
## 0.909 0.890 0.890
## 0.870 0.832 0.832
## 0.733 0.687 0.687
##
## 0.763 0.716 0.716
## 0.774 0.725 0.725
##
## 0.528 0.456 0.456
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.418 0.021 20.281 0.000 0.378
## .sswk (.39.) 0.379 0.021 18.120 0.000 0.338
## .sspc (.40.) 0.336 0.022 15.556 0.000 0.294
## .ssei (.41.) 0.148 0.019 7.958 0.000 0.111
## .ssar (.42.) 0.360 0.021 17.575 0.000 0.320
## .ssmk (.43.) 0.356 0.022 16.330 0.000 0.313
## .ssmc (.44.) 0.233 0.019 12.254 0.000 0.196
## .ssao (.45.) 0.295 0.021 14.391 0.000 0.255
## .ssai (.46.) 0.025 0.017 1.474 0.140 -0.008
## .sssi (.47.) 0.081 0.018 4.547 0.000 0.046
## .ssno (.48.) 0.298 0.023 13.153 0.000 0.253
## .sscs (.49.) 0.303 0.022 13.805 0.000 0.260
## ci.upper Std.lv Std.all
## 0.458 0.418 0.486
## 0.420 0.379 0.443
## 0.379 0.336 0.382
## 0.184 0.148 0.182
## 0.400 0.360 0.426
## 0.398 0.356 0.397
## 0.271 0.233 0.291
## 0.335 0.295 0.323
## 0.058 0.025 0.034
## 0.116 0.081 0.110
## 0.342 0.298 0.315
## 0.346 0.303 0.330
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.180 0.009 19.359 0.000 0.162
## .sswk 0.178 0.009 20.557 0.000 0.161
## .sspc 0.231 0.013 17.717 0.000 0.205
## .ssei 0.238 0.011 21.682 0.000 0.216
## .ssar 0.139 0.008 16.475 0.000 0.122
## .ssmk 0.181 0.008 21.319 0.000 0.164
## .ssmc 0.245 0.012 20.376 0.000 0.221
## .ssao 0.414 0.017 24.108 0.000 0.380
## .ssai 0.325 0.015 21.481 0.000 0.296
## .sssi 0.315 0.015 20.658 0.000 0.285
## .ssno 0.298 0.018 16.230 0.000 0.262
## .sscs 0.368 0.019 18.932 0.000 0.329
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.199 0.180 0.243
## 0.195 0.178 0.243
## 0.256 0.231 0.297
## 0.259 0.238 0.361
## 0.155 0.139 0.194
## 0.197 0.181 0.225
## 0.268 0.245 0.379
## 0.447 0.414 0.496
## 0.355 0.325 0.610
## 0.345 0.315 0.578
## 0.334 0.298 0.334
## 0.406 0.368 0.436
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.749 0.016 47.877 0.000 0.718
## sswk (.p2.) 0.745 0.016 45.272 0.000 0.713
## sspc (.p3.) 0.738 0.016 46.658 0.000 0.707
## ssei (.p4.) 0.380 0.017 22.704 0.000 0.347
## math =~
## ssar (.p5.) 0.759 0.017 45.623 0.000 0.726
## ssmk (.p6.) 0.595 0.022 26.467 0.000 0.551
## ssmc (.p7.) 0.437 0.015 28.299 0.000 0.407
## ssao (.p8.) 0.649 0.015 41.954 0.000 0.618
## electronic =~
## ssai (.p9.) 0.456 0.016 28.801 0.000 0.425
## sssi (.10.) 0.479 0.017 28.925 0.000 0.447
## ssmc (.11.) 0.241 0.012 20.749 0.000 0.218
## ssei (.12.) 0.299 0.014 22.035 0.000 0.272
## speed =~
## ssno (.13.) 0.771 0.021 37.551 0.000 0.731
## sscs (.14.) 0.690 0.019 37.024 0.000 0.654
## ssmk (.15.) 0.242 0.020 11.878 0.000 0.202
## ci.upper Std.lv Std.all
##
## 0.780 0.858 0.888
## 0.777 0.854 0.888
## 0.769 0.846 0.847
## 0.412 0.435 0.412
##
## 0.791 0.834 0.886
## 0.640 0.655 0.671
## 0.468 0.481 0.501
## 0.679 0.713 0.703
##
## 0.487 0.808 0.745
## 0.512 0.849 0.838
## 0.264 0.427 0.445
## 0.325 0.529 0.500
##
## 0.811 0.866 0.821
## 0.727 0.775 0.770
## 0.282 0.272 0.279
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 1.146 0.059 19.543 0.000 1.031
## electronic 1.405 0.089 15.836 0.000 1.231
## speed 0.886 0.051 17.365 0.000 0.786
## math ~~
## electronic 1.145 0.081 14.155 0.000 0.986
## speed 0.967 0.052 18.504 0.000 0.864
## electronic ~~
## speed 0.617 0.069 8.977 0.000 0.482
## ci.upper Std.lv Std.all
##
## 1.261 0.910 0.910
## 1.579 0.692 0.692
## 0.986 0.688 0.688
##
## 1.303 0.588 0.588
## 1.069 0.783 0.783
##
## 0.751 0.310 0.310
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.418 0.021 20.281 0.000 0.378
## .sswk (.39.) 0.379 0.021 18.120 0.000 0.338
## .sspc (.40.) 0.336 0.022 15.556 0.000 0.294
## .ssei (.41.) 0.148 0.019 7.958 0.000 0.111
## .ssar (.42.) 0.360 0.021 17.575 0.000 0.320
## .ssmk (.43.) 0.356 0.022 16.330 0.000 0.313
## .ssmc (.44.) 0.233 0.019 12.254 0.000 0.196
## .ssao (.45.) 0.295 0.021 14.391 0.000 0.255
## .ssai (.46.) 0.025 0.017 1.474 0.140 -0.008
## .sssi (.47.) 0.081 0.018 4.547 0.000 0.046
## .ssno (.48.) 0.298 0.023 13.153 0.000 0.253
## .sscs (.49.) 0.303 0.022 13.805 0.000 0.260
## verbal 0.018 0.040 0.446 0.655 -0.061
## math -0.013 0.039 -0.322 0.747 -0.089
## elctrnc 1.394 0.070 19.810 0.000 1.256
## speed -0.342 0.044 -7.737 0.000 -0.429
## ci.upper Std.lv Std.all
## 0.458 0.418 0.432
## 0.420 0.379 0.394
## 0.379 0.336 0.336
## 0.184 0.148 0.140
## 0.400 0.360 0.383
## 0.398 0.356 0.365
## 0.271 0.233 0.243
## 0.335 0.295 0.291
## 0.058 0.025 0.023
## 0.116 0.081 0.080
## 0.342 0.298 0.282
## 0.346 0.303 0.301
## 0.097 0.016 0.016
## 0.064 -0.011 -0.011
## 1.532 0.787 0.787
## -0.255 -0.304 -0.304
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.197 0.010 20.552 0.000 0.178
## .sswk 0.196 0.010 19.507 0.000 0.176
## .sspc 0.283 0.015 19.359 0.000 0.254
## .ssei 0.330 0.016 20.324 0.000 0.298
## .ssar 0.191 0.011 17.720 0.000 0.170
## .ssmk 0.170 0.009 19.560 0.000 0.153
## .ssmc 0.265 0.013 21.001 0.000 0.240
## .ssao 0.520 0.019 26.838 0.000 0.482
## .ssai 0.524 0.024 21.622 0.000 0.476
## .sssi 0.305 0.018 16.838 0.000 0.270
## .ssno 0.362 0.023 16.055 0.000 0.317
## .sscs 0.412 0.024 17.151 0.000 0.365
## verbal 1.314 0.069 19.153 0.000 1.180
## math 1.208 0.066 18.434 0.000 1.080
## electronic 3.137 0.241 13.009 0.000 2.664
## speed 1.262 0.081 15.647 0.000 1.104
## ci.upper Std.lv Std.all
## 0.216 0.197 0.211
## 0.215 0.196 0.212
## 0.312 0.283 0.283
## 0.362 0.330 0.295
## 0.212 0.191 0.215
## 0.187 0.170 0.179
## 0.290 0.265 0.288
## 0.558 0.520 0.506
## 0.571 0.524 0.445
## 0.341 0.305 0.298
## 0.406 0.362 0.325
## 0.459 0.412 0.406
## 1.448 1.000 1.000
## 1.337 1.000 1.000
## 3.609 1.000 1.000
## 1.420 1.000 1.000
lavTestScore(scalar, release = 16:27)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 567.473 12 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p38. == .p91. 240.970 1 0.000
## 2 .p39. == .p92. 0.000 1 0.988
## 3 .p40. == .p93. 312.536 1 0.000
## 4 .p41. == .p94. 3.262 1 0.071
## 5 .p42. == .p95. 74.621 1 0.000
## 6 .p43. == .p96. 21.632 1 0.000
## 7 .p44. == .p97. 0.048 1 0.826
## 8 .p45. == .p98. 40.704 1 0.000
## 9 .p46. == .p99. 24.780 1 0.000
## 10 .p47. == .p100. 12.978 1 0.000
## 11 .p48. == .p101. 87.287 1 0.000
## 12 .p49. == .p102. 51.234 1 0.000
scalar2<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1120.390 106.000 0.000 0.968 0.072 0.040 86853.521
## bic
## 87312.687
Mc(scalar2)
## [1] 0.8705295
summary(scalar2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 89 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 24
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1120.390 981.668
## Degrees of freedom 106 106
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.141
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 470.307 412.075
## 0 650.083 569.592
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.750 0.015 49.235 0.000 0.720
## sswk (.p2.) 0.744 0.016 45.457 0.000 0.712
## sspc (.p3.) 0.743 0.016 47.057 0.000 0.712
## ssei (.p4.) 0.380 0.017 22.430 0.000 0.347
## math =~
## ssar (.p5.) 0.759 0.017 45.766 0.000 0.727
## ssmk (.p6.) 0.605 0.020 29.613 0.000 0.565
## ssmc (.p7.) 0.439 0.016 28.302 0.000 0.409
## ssao (.p8.) 0.647 0.015 41.944 0.000 0.617
## electronic =~
## ssai (.p9.) 0.457 0.016 28.835 0.000 0.426
## sssi (.10.) 0.480 0.017 28.933 0.000 0.448
## ssmc (.11.) 0.239 0.012 20.615 0.000 0.217
## ssei (.12.) 0.298 0.014 21.711 0.000 0.271
## speed =~
## ssno (.13.) 0.790 0.020 38.555 0.000 0.750
## sscs (.14.) 0.675 0.018 36.553 0.000 0.639
## ssmk (.15.) 0.231 0.017 13.236 0.000 0.197
## ci.upper Std.lv Std.all
##
## 0.780 0.750 0.876
## 0.776 0.744 0.869
## 0.773 0.743 0.850
## 0.413 0.380 0.468
##
## 0.792 0.759 0.898
## 0.645 0.605 0.676
## 0.469 0.439 0.546
## 0.677 0.647 0.709
##
## 0.488 0.457 0.625
## 0.513 0.480 0.650
## 0.262 0.239 0.298
## 0.325 0.298 0.367
##
## 0.830 0.790 0.832
## 0.712 0.675 0.743
## 0.266 0.231 0.258
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.890 0.009 96.040 0.000 0.872
## electronic 0.829 0.019 43.086 0.000 0.791
## speed 0.679 0.023 29.276 0.000 0.634
## math ~~
## electronic 0.715 0.024 29.999 0.000 0.669
## speed 0.719 0.025 28.662 0.000 0.670
## electronic ~~
## speed 0.449 0.037 12.178 0.000 0.377
## ci.upper Std.lv Std.all
##
## 0.908 0.890 0.890
## 0.866 0.829 0.829
## 0.725 0.679 0.679
##
## 0.762 0.715 0.715
## 0.769 0.719 0.719
##
## 0.521 0.449 0.449
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.331 0.021 15.977 0.000 0.291
## .sswk (.39.) 0.376 0.022 17.425 0.000 0.334
## .sspc 0.453 0.022 20.981 0.000 0.411
## .ssei (.41.) 0.147 0.019 7.922 0.000 0.111
## .ssar (.42.) 0.353 0.020 17.243 0.000 0.313
## .ssmk (.43.) 0.371 0.022 16.997 0.000 0.328
## .ssmc (.44.) 0.231 0.019 12.123 0.000 0.194
## .ssao (.45.) 0.289 0.021 14.088 0.000 0.249
## .ssai (.46.) 0.026 0.017 1.522 0.128 -0.007
## .sssi (.47.) 0.082 0.018 4.612 0.000 0.047
## .ssno 0.244 0.023 10.434 0.000 0.198
## .sscs (.49.) 0.365 0.022 16.519 0.000 0.322
## ci.upper Std.lv Std.all
## 0.372 0.331 0.387
## 0.418 0.376 0.439
## 0.495 0.453 0.519
## 0.184 0.147 0.181
## 0.394 0.353 0.418
## 0.414 0.371 0.415
## 0.268 0.231 0.287
## 0.329 0.289 0.316
## 0.059 0.026 0.035
## 0.117 0.082 0.111
## 0.290 0.244 0.257
## 0.408 0.365 0.401
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.170 0.009 19.935 0.000 0.153
## .sswk 0.179 0.009 20.934 0.000 0.162
## .sspc 0.212 0.012 18.106 0.000 0.189
## .ssei 0.239 0.011 21.751 0.000 0.217
## .ssar 0.138 0.008 16.615 0.000 0.122
## .ssmk 0.180 0.008 21.496 0.000 0.163
## .ssmc 0.245 0.012 20.412 0.000 0.222
## .ssao 0.415 0.017 24.174 0.000 0.382
## .ssai 0.325 0.015 21.444 0.000 0.295
## .sssi 0.314 0.015 20.627 0.000 0.285
## .ssno 0.278 0.018 15.212 0.000 0.242
## .sscs 0.371 0.019 19.493 0.000 0.333
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.187 0.170 0.232
## 0.196 0.179 0.245
## 0.235 0.212 0.278
## 0.260 0.239 0.362
## 0.155 0.138 0.193
## 0.196 0.180 0.225
## 0.269 0.245 0.380
## 0.449 0.415 0.498
## 0.355 0.325 0.609
## 0.344 0.314 0.577
## 0.313 0.278 0.308
## 0.408 0.371 0.448
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.750 0.015 49.235 0.000 0.720
## sswk (.p2.) 0.744 0.016 45.457 0.000 0.712
## sspc (.p3.) 0.743 0.016 47.057 0.000 0.712
## ssei (.p4.) 0.380 0.017 22.430 0.000 0.347
## math =~
## ssar (.p5.) 0.759 0.017 45.766 0.000 0.727
## ssmk (.p6.) 0.605 0.020 29.613 0.000 0.565
## ssmc (.p7.) 0.439 0.016 28.302 0.000 0.409
## ssao (.p8.) 0.647 0.015 41.944 0.000 0.617
## electronic =~
## ssai (.p9.) 0.457 0.016 28.835 0.000 0.426
## sssi (.10.) 0.480 0.017 28.933 0.000 0.448
## ssmc (.11.) 0.239 0.012 20.615 0.000 0.217
## ssei (.12.) 0.298 0.014 21.711 0.000 0.271
## speed =~
## ssno (.13.) 0.790 0.020 38.555 0.000 0.750
## sscs (.14.) 0.675 0.018 36.553 0.000 0.639
## ssmk (.15.) 0.231 0.017 13.236 0.000 0.197
## ci.upper Std.lv Std.all
##
## 0.780 0.860 0.894
## 0.776 0.853 0.887
## 0.773 0.852 0.859
## 0.413 0.436 0.412
##
## 0.792 0.835 0.887
## 0.645 0.665 0.683
## 0.469 0.483 0.504
## 0.677 0.712 0.702
##
## 0.488 0.809 0.746
## 0.513 0.850 0.839
## 0.262 0.424 0.443
## 0.325 0.528 0.500
##
## 0.830 0.889 0.837
## 0.712 0.760 0.763
## 0.266 0.260 0.267
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 1.147 0.058 19.642 0.000 1.032
## electronic 1.399 0.088 15.819 0.000 1.226
## speed 0.878 0.051 17.334 0.000 0.779
## math ~~
## electronic 1.142 0.081 14.139 0.000 0.983
## speed 0.962 0.052 18.446 0.000 0.860
## electronic ~~
## speed 0.608 0.069 8.866 0.000 0.473
## ci.upper Std.lv Std.all
##
## 1.261 0.910 0.910
## 1.572 0.689 0.689
## 0.977 0.681 0.681
##
## 1.300 0.586 0.586
## 1.064 0.778 0.778
##
## 0.742 0.305 0.305
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.503 0.026 19.487 0.000 0.453
## .sswk (.39.) 0.376 0.022 17.425 0.000 0.334
## .sspc 0.192 0.027 7.168 0.000 0.139
## .ssei (.41.) 0.147 0.019 7.922 0.000 0.111
## .ssar (.42.) 0.353 0.020 17.243 0.000 0.313
## .ssmk (.43.) 0.371 0.022 16.997 0.000 0.328
## .ssmc (.44.) 0.231 0.019 12.123 0.000 0.194
## .ssao (.45.) 0.289 0.021 14.088 0.000 0.249
## .ssai (.46.) 0.026 0.017 1.522 0.128 -0.007
## .sssi (.47.) 0.082 0.018 4.612 0.000 0.047
## .ssno 0.524 0.034 15.437 0.000 0.457
## .sscs (.49.) 0.365 0.022 16.519 0.000 0.322
## verbal 0.027 0.042 0.636 0.525 -0.055
## math 0.009 0.039 0.223 0.823 -0.068
## elctrnc 1.387 0.070 19.722 0.000 1.249
## speed -0.541 0.049 -11.007 0.000 -0.638
## ci.upper Std.lv Std.all
## 0.554 0.503 0.523
## 0.418 0.376 0.391
## 0.244 0.192 0.193
## 0.184 0.147 0.140
## 0.394 0.353 0.375
## 0.414 0.371 0.381
## 0.268 0.231 0.241
## 0.329 0.289 0.285
## 0.059 0.026 0.024
## 0.117 0.082 0.081
## 0.591 0.524 0.494
## 0.408 0.365 0.367
## 0.108 0.023 0.023
## 0.085 0.008 0.008
## 1.525 0.783 0.783
## -0.445 -0.481 -0.481
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.185 0.009 21.423 0.000 0.168
## .sswk 0.197 0.010 19.825 0.000 0.178
## .sspc 0.257 0.012 20.712 0.000 0.233
## .ssei 0.330 0.016 20.348 0.000 0.299
## .ssar 0.189 0.011 17.772 0.000 0.168
## .ssmk 0.169 0.009 19.767 0.000 0.153
## .ssmc 0.266 0.013 21.041 0.000 0.241
## .ssao 0.522 0.019 26.799 0.000 0.484
## .ssai 0.523 0.024 21.583 0.000 0.475
## .sssi 0.304 0.018 16.728 0.000 0.269
## .ssno 0.337 0.022 15.491 0.000 0.295
## .sscs 0.414 0.023 17.906 0.000 0.368
## verbal 1.315 0.068 19.305 0.000 1.182
## math 1.209 0.065 18.476 0.000 1.081
## electronic 3.137 0.241 13.021 0.000 2.665
## speed 1.265 0.080 15.723 0.000 1.107
## ci.upper Std.lv Std.all
## 0.202 0.185 0.200
## 0.217 0.197 0.213
## 0.281 0.257 0.262
## 0.362 0.330 0.296
## 0.210 0.189 0.213
## 0.186 0.169 0.179
## 0.290 0.266 0.289
## 0.561 0.522 0.508
## 0.570 0.523 0.444
## 0.340 0.304 0.296
## 0.380 0.337 0.299
## 0.459 0.414 0.418
## 1.449 1.000 1.000
## 1.337 1.000 1.000
## 3.609 1.000 1.000
## 1.423 1.000 1.000
lavTestScore(scalar2, release = 16:24, standardized=T, epc=T)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 93.675 9 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p39. == .p92. 3.080 1 0.079
## 2 .p41. == .p94. 3.080 1 0.079
## 3 .p42. == .p95. 51.021 1 0.000
## 4 .p43. == .p96. 4.350 1 0.037
## 5 .p44. == .p97. 0.442 1 0.506
## 6 .p45. == .p98. 49.754 1 0.000
## 7 .p46. == .p99. 23.504 1 0.000
## 8 .p47. == .p100. 14.549 1 0.000
## 9 .p49. == .p102. 4.350 1 0.037
##
## $epc
##
## expected parameter changes (epc) and expected parameter values (epv):
##
## lhs op rhs block group free label plabel est epc
## 1 verbal =~ ssgs 1 1 1 .p1. .p1. 0.750 0.000
## 2 verbal =~ sswk 1 1 2 .p2. .p2. 0.744 0.000
## 3 verbal =~ sspc 1 1 3 .p3. .p3. 0.743 0.000
## 4 verbal =~ ssei 1 1 4 .p4. .p4. 0.380 0.014
## 5 math =~ ssar 1 1 5 .p5. .p5. 0.759 -0.001
## 6 math =~ ssmk 1 1 6 .p6. .p6. 0.605 0.013
## 7 math =~ ssmc 1 1 7 .p7. .p7. 0.439 -0.003
## 8 math =~ ssao 1 1 8 .p8. .p8. 0.647 0.000
## 9 electronic =~ ssai 1 1 9 .p9. .p9. 0.457 0.016
## 10 electronic =~ sssi 1 1 10 .p10. .p10. 0.480 -0.013
## 11 electronic =~ ssmc 1 1 11 .p11. .p11. 0.239 0.003
## 12 electronic =~ ssei 1 1 12 .p12. .p12. 0.298 -0.015
## 13 speed =~ ssno 1 1 13 .p13. .p13. 0.790 0.001
## 14 speed =~ sscs 1 1 14 .p14. .p14. 0.675 0.003
## 15 speed =~ ssmk 1 1 15 .p15. .p15. 0.231 -0.016
## 16 ssgs ~~ ssgs 1 1 16 .p16. 0.170 0.000
## 17 sswk ~~ sswk 1 1 17 .p17. 0.179 0.000
## 18 sspc ~~ sspc 1 1 18 .p18. 0.212 0.000
## 19 ssei ~~ ssei 1 1 19 .p19. 0.239 0.001
## 20 ssar ~~ ssar 1 1 20 .p20. 0.138 0.001
## 21 ssmk ~~ ssmk 1 1 21 .p21. 0.180 0.000
## 22 ssmc ~~ ssmc 1 1 22 .p22. 0.245 0.000
## 23 ssao ~~ ssao 1 1 23 .p23. 0.415 0.000
## 24 ssai ~~ ssai 1 1 24 .p24. 0.325 -0.005
## 25 sssi ~~ sssi 1 1 25 .p25. 0.314 0.003
## 26 ssno ~~ ssno 1 1 26 .p26. 0.278 -0.002
## 27 sscs ~~ sscs 1 1 27 .p27. 0.371 -0.001
## 28 verbal ~~ verbal 1 1 0 .p28. 1.000 NA
## 29 math ~~ math 1 1 0 .p29. 1.000 NA
## 30 electronic ~~ electronic 1 1 0 .p30. 1.000 NA
## 31 speed ~~ speed 1 1 0 .p31. 1.000 NA
## 32 verbal ~~ math 1 1 28 .p32. 0.890 0.000
## 33 verbal ~~ electronic 1 1 29 .p33. 0.829 -0.002
## 34 verbal ~~ speed 1 1 30 .p34. 0.679 -0.002
## 35 math ~~ electronic 1 1 31 .p35. 0.715 -0.003
## 36 math ~~ speed 1 1 32 .p36. 0.719 0.001
## 37 electronic ~~ speed 1 1 33 .p37. 0.449 -0.005
## 38 ssgs ~1 1 1 34 .p38. 0.331 0.000
## 39 sswk ~1 1 1 35 .p39. .p39. 0.376 0.003
## 40 sspc ~1 1 1 36 .p40. 0.453 0.000
## 41 ssei ~1 1 1 37 .p41. .p41. 0.147 -0.008
## 42 ssar ~1 1 1 38 .p42. .p42. 0.353 -0.026
## 43 ssmk ~1 1 1 39 .p43. .p43. 0.371 0.010
## 44 ssmc ~1 1 1 40 .p44. .p44. 0.231 0.004
## 45 ssao ~1 1 1 41 .p45. .p45. 0.289 0.067
## 46 ssai ~1 1 1 42 .p46. .p46. 0.026 0.029
## 47 sssi ~1 1 1 43 .p47. .p47. 0.082 -0.023
## 48 ssno ~1 1 1 44 .p48. 0.244 0.000
## 49 sscs ~1 1 1 45 .p49. .p49. 0.365 -0.007
## 50 verbal ~1 1 1 0 .p50. 0.000 NA
## 51 math ~1 1 1 0 .p51. 0.000 NA
## 52 electronic ~1 1 1 0 .p52. 0.000 NA
## 53 speed ~1 1 1 0 .p53. 0.000 NA
## 54 verbal =~ ssgs 2 2 46 .p1. .p54. 0.750 0.000
## 55 verbal =~ sswk 2 2 47 .p2. .p55. 0.744 0.000
## 56 verbal =~ sspc 2 2 48 .p3. .p56. 0.743 0.000
## 57 verbal =~ ssei 2 2 49 .p4. .p57. 0.380 0.014
## 58 math =~ ssar 2 2 50 .p5. .p58. 0.759 -0.001
## 59 math =~ ssmk 2 2 51 .p6. .p59. 0.605 0.013
## 60 math =~ ssmc 2 2 52 .p7. .p60. 0.439 -0.003
## 61 math =~ ssao 2 2 53 .p8. .p61. 0.647 0.000
## 62 electronic =~ ssai 2 2 54 .p9. .p62. 0.457 0.016
## 63 electronic =~ sssi 2 2 55 .p10. .p63. 0.480 -0.013
## 64 electronic =~ ssmc 2 2 56 .p11. .p64. 0.239 0.003
## 65 electronic =~ ssei 2 2 57 .p12. .p65. 0.298 -0.015
## 66 speed =~ ssno 2 2 58 .p13. .p66. 0.790 0.001
## 67 speed =~ sscs 2 2 59 .p14. .p67. 0.675 0.003
## 68 speed =~ ssmk 2 2 60 .p15. .p68. 0.231 -0.016
## 69 ssgs ~~ ssgs 2 2 61 .p69. 0.185 0.000
## 70 sswk ~~ sswk 2 2 62 .p70. 0.197 0.000
## 71 sspc ~~ sspc 2 2 63 .p71. 0.257 0.000
## epv sepc.lv sepc.all sepc.nox
## 1 0.750 0.000 0.000 0.000
## 2 0.744 0.000 0.000 0.000
## 3 0.742 0.000 0.000 0.000
## 4 0.394 0.014 0.018 0.018
## 5 0.759 -0.001 -0.001 -0.001
## 6 0.618 0.013 0.015 0.015
## 7 0.436 -0.003 -0.003 -0.003
## 8 0.647 0.000 0.000 0.000
## 9 0.473 0.016 0.022 0.022
## 10 0.468 -0.013 -0.017 -0.017
## 11 0.242 0.003 0.003 0.003
## 12 0.283 -0.015 -0.019 -0.019
## 13 0.791 0.001 0.001 0.001
## 14 0.678 0.003 0.003 0.003
## 15 0.216 -0.016 -0.018 -0.018
## 16 0.170 0.170 0.232 0.232
## 17 0.179 0.179 0.245 0.245
## 18 0.212 0.212 0.278 0.278
## 19 0.240 0.239 0.362 0.362
## 20 0.139 0.138 0.193 0.193
## 21 0.180 -0.180 -0.225 -0.225
## 22 0.245 -0.245 -0.380 -0.380
## 23 0.416 0.415 0.498 0.498
## 24 0.320 -0.325 -0.609 -0.609
## 25 0.317 0.314 0.577 0.577
## 26 0.276 -0.278 -0.308 -0.308
## 27 0.369 -0.371 -0.448 -0.448
## 28 NA NA NA NA
## 29 NA NA NA NA
## 30 NA NA NA NA
## 31 NA NA NA NA
## 32 0.890 0.000 0.000 0.000
## 33 0.827 -0.002 -0.002 -0.002
## 34 0.677 -0.002 -0.002 -0.002
## 35 0.713 -0.003 -0.003 -0.003
## 36 0.721 0.001 0.001 0.001
## 37 0.444 -0.005 -0.005 -0.005
## 38 0.331 0.000 0.000 0.000
## 39 0.379 0.003 0.004 0.004
## 40 0.453 0.000 0.000 0.000
## 41 0.139 -0.008 -0.010 -0.010
## 42 0.327 -0.026 -0.031 -0.031
## 43 0.382 0.010 0.012 0.012
## 44 0.235 0.004 0.005 0.005
## 45 0.356 0.067 0.073 0.073
## 46 0.055 0.029 0.040 0.040
## 47 0.059 -0.023 -0.031 -0.031
## 48 0.244 0.000 0.000 0.000
## 49 0.358 -0.007 -0.008 -0.008
## 50 NA NA NA NA
## 51 NA NA NA NA
## 52 NA NA NA NA
## 53 NA NA NA NA
## 54 0.750 0.000 0.000 0.000
## 55 0.744 0.000 0.000 0.000
## 56 0.742 0.000 0.000 0.000
## 57 0.394 0.017 0.016 0.016
## 58 0.759 -0.001 -0.001 -0.001
## 59 0.618 0.014 0.015 0.015
## 60 0.436 -0.003 -0.003 -0.003
## 61 0.647 0.000 0.000 0.000
## 62 0.473 0.029 0.027 0.027
## 63 0.468 -0.022 -0.022 -0.022
## 64 0.242 0.005 0.005 0.005
## 65 0.283 -0.027 -0.026 -0.026
## 66 0.791 0.001 0.001 0.001
## 67 0.678 0.003 0.003 0.003
## 68 0.216 -0.018 -0.018 -0.018
## 69 0.185 0.185 0.200 0.200
## 70 0.198 0.197 0.213 0.213
## 71 0.257 0.257 0.262 0.262
## [ reached 'max' / getOption("max.print") -- omitted 35 rows ]
strict<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1293.661 118.000 0.000 0.963 0.074 0.044 87002.792
## bic
## 87387.498
Mc(strict)
## [1] 0.8515498
cf.cov<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(cf.cov, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1224.419 112.000 0.000 0.965 0.074 0.095 86945.549
## bic
## 87367.486
Mc(cf.cov)
## [1] 0.8589429
summary(cf.cov, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 63 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 30
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1224.419 1072.364
## Degrees of freedom 112 112
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.142
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 509.027 445.813
## 0 715.392 626.551
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.806 0.013 59.815 0.000 0.780
## sswk (.p2.) 0.800 0.014 58.431 0.000 0.774
## sspc (.p3.) 0.800 0.013 63.479 0.000 0.775
## ssei (.p4.) 0.410 0.018 23.269 0.000 0.376
## math =~
## ssar (.p5.) 0.805 0.014 57.319 0.000 0.777
## ssmk (.p6.) 0.639 0.020 32.719 0.000 0.601
## ssmc (.p7.) 0.467 0.015 30.851 0.000 0.438
## ssao (.p8.) 0.686 0.014 50.220 0.000 0.659
## electronic =~
## ssai (.p9.) 0.509 0.016 32.662 0.000 0.479
## sssi (.10.) 0.541 0.016 34.650 0.000 0.511
## ssmc (.11.) 0.268 0.012 21.882 0.000 0.244
## ssei (.12.) 0.331 0.015 22.192 0.000 0.302
## speed =~
## ssno (.13.) 0.829 0.020 40.536 0.000 0.789
## sscs (.14.) 0.709 0.019 37.803 0.000 0.673
## ssmk (.15.) 0.246 0.018 13.842 0.000 0.211
## ci.upper Std.lv Std.all
##
## 0.833 0.806 0.891
## 0.827 0.800 0.884
## 0.824 0.800 0.866
## 0.445 0.410 0.473
##
## 0.832 0.805 0.908
## 0.677 0.639 0.679
## 0.497 0.467 0.551
## 0.713 0.686 0.729
##
## 0.540 0.509 0.665
## 0.572 0.541 0.696
## 0.292 0.268 0.315
## 0.361 0.331 0.382
##
## 0.869 0.829 0.843
## 0.746 0.709 0.758
## 0.280 0.246 0.261
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.32.) 0.896 0.008 113.833 0.000 0.881
## elctrnc (.33.) 0.866 0.015 58.121 0.000 0.836
## speed (.34.) 0.691 0.019 36.395 0.000 0.654
## math ~~
## elctrnc (.35.) 0.742 0.019 39.758 0.000 0.705
## speed (.36.) 0.755 0.019 39.057 0.000 0.717
## electronic ~~
## speed (.37.) 0.452 0.030 15.134 0.000 0.394
## ci.upper Std.lv Std.all
##
## 0.911 0.896 0.896
## 0.895 0.866 0.866
## 0.728 0.691 0.691
##
## 0.778 0.742 0.742
## 0.793 0.755 0.755
##
## 0.511 0.452 0.452
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.331 0.021 15.977 0.000 0.291
## .sswk (.39.) 0.376 0.022 17.416 0.000 0.333
## .sspc 0.453 0.022 20.981 0.000 0.411
## .ssei (.41.) 0.149 0.019 7.987 0.000 0.112
## .ssar (.42.) 0.353 0.020 17.231 0.000 0.313
## .ssmk (.43.) 0.372 0.022 17.021 0.000 0.329
## .ssmc (.44.) 0.231 0.019 12.120 0.000 0.194
## .ssao (.45.) 0.289 0.021 14.074 0.000 0.248
## .ssai (.46.) 0.027 0.017 1.575 0.115 -0.007
## .sssi (.47.) 0.080 0.018 4.493 0.000 0.045
## .ssno 0.244 0.023 10.435 0.000 0.198
## .sscs (.49.) 0.365 0.022 16.517 0.000 0.322
## ci.upper Std.lv Std.all
## 0.372 0.331 0.366
## 0.418 0.376 0.415
## 0.495 0.453 0.491
## 0.185 0.149 0.171
## 0.393 0.353 0.398
## 0.414 0.372 0.395
## 0.268 0.231 0.272
## 0.329 0.289 0.307
## 0.060 0.027 0.035
## 0.115 0.080 0.103
## 0.290 0.244 0.249
## 0.408 0.365 0.390
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.169 0.008 19.997 0.000 0.153
## .sswk 0.179 0.009 20.948 0.000 0.162
## .sspc 0.214 0.012 18.163 0.000 0.191
## .ssei 0.240 0.011 21.848 0.000 0.218
## .ssar 0.139 0.008 16.772 0.000 0.122
## .ssmk 0.179 0.008 21.276 0.000 0.162
## .ssmc 0.244 0.012 20.452 0.000 0.221
## .ssao 0.416 0.017 24.211 0.000 0.382
## .ssai 0.327 0.015 21.593 0.000 0.297
## .sssi 0.312 0.015 20.688 0.000 0.283
## .ssno 0.280 0.018 15.385 0.000 0.244
## .sscs 0.373 0.019 19.567 0.000 0.335
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.186 0.169 0.207
## 0.196 0.179 0.219
## 0.237 0.214 0.251
## 0.261 0.240 0.319
## 0.155 0.139 0.176
## 0.195 0.179 0.202
## 0.268 0.244 0.340
## 0.450 0.416 0.469
## 0.356 0.327 0.557
## 0.342 0.312 0.516
## 0.316 0.280 0.289
## 0.410 0.373 0.426
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.806 0.013 59.815 0.000 0.780
## sswk (.p2.) 0.800 0.014 58.431 0.000 0.774
## sspc (.p3.) 0.800 0.013 63.479 0.000 0.775
## ssei (.p4.) 0.410 0.018 23.269 0.000 0.376
## math =~
## ssar (.p5.) 0.805 0.014 57.319 0.000 0.777
## ssmk (.p6.) 0.639 0.020 32.719 0.000 0.601
## ssmc (.p7.) 0.467 0.015 30.851 0.000 0.438
## ssao (.p8.) 0.686 0.014 50.220 0.000 0.659
## electronic =~
## ssai (.p9.) 0.509 0.016 32.662 0.000 0.479
## sssi (.10.) 0.541 0.016 34.650 0.000 0.511
## ssmc (.11.) 0.268 0.012 21.882 0.000 0.244
## ssei (.12.) 0.331 0.015 22.192 0.000 0.302
## speed =~
## ssno (.13.) 0.829 0.020 40.536 0.000 0.789
## sscs (.14.) 0.709 0.019 37.803 0.000 0.673
## ssmk (.15.) 0.246 0.018 13.842 0.000 0.211
## ci.upper Std.lv Std.all
##
## 0.833 0.808 0.881
## 0.827 0.802 0.874
## 0.824 0.801 0.847
## 0.445 0.411 0.417
##
## 0.832 0.793 0.877
## 0.677 0.630 0.676
## 0.497 0.461 0.510
## 0.713 0.676 0.683
##
## 0.540 0.741 0.716
## 0.572 0.787 0.824
## 0.292 0.389 0.431
## 0.361 0.482 0.488
##
## 0.869 0.851 0.827
## 0.746 0.729 0.751
## 0.280 0.252 0.271
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.32.) 0.896 0.008 113.833 0.000 0.881
## elctrnc (.33.) 0.866 0.015 58.121 0.000 0.836
## speed (.34.) 0.691 0.019 36.395 0.000 0.654
## math ~~
## elctrnc (.35.) 0.742 0.019 39.758 0.000 0.705
## speed (.36.) 0.755 0.019 39.057 0.000 0.717
## electronic ~~
## speed (.37.) 0.452 0.030 15.134 0.000 0.394
## ci.upper Std.lv Std.all
##
## 0.911 0.907 0.907
## 0.895 0.594 0.594
## 0.728 0.671 0.671
##
## 0.778 0.517 0.517
## 0.793 0.746 0.746
##
## 0.511 0.303 0.303
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.502 0.026 19.492 0.000 0.452
## .sswk (.39.) 0.376 0.022 17.416 0.000 0.333
## .sspc 0.191 0.027 7.137 0.000 0.138
## .ssei (.41.) 0.149 0.019 7.987 0.000 0.112
## .ssar (.42.) 0.353 0.020 17.231 0.000 0.313
## .ssmk (.43.) 0.372 0.022 17.021 0.000 0.329
## .ssmc (.44.) 0.231 0.019 12.120 0.000 0.194
## .ssao (.45.) 0.289 0.021 14.074 0.000 0.248
## .ssai (.46.) 0.027 0.017 1.575 0.115 -0.007
## .sssi (.47.) 0.080 0.018 4.493 0.000 0.045
## .ssno 0.523 0.034 15.439 0.000 0.456
## .sscs (.49.) 0.365 0.022 16.517 0.000 0.322
## verbal 0.026 0.039 0.663 0.507 -0.050
## math 0.009 0.037 0.242 0.808 -0.063
## elctrnc 1.239 0.062 19.973 0.000 1.118
## speed -0.515 0.047 -11.003 0.000 -0.606
## ci.upper Std.lv Std.all
## 0.553 0.502 0.548
## 0.418 0.376 0.410
## 0.243 0.191 0.202
## 0.185 0.149 0.151
## 0.393 0.353 0.390
## 0.414 0.372 0.399
## 0.268 0.231 0.256
## 0.329 0.289 0.292
## 0.060 0.027 0.026
## 0.115 0.080 0.084
## 0.589 0.523 0.508
## 0.408 0.365 0.376
## 0.102 0.026 0.026
## 0.081 0.009 0.009
## 1.361 0.852 0.852
## -0.423 -0.501 -0.501
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.188 0.009 21.407 0.000 0.171
## .sswk 0.198 0.010 19.706 0.000 0.178
## .sspc 0.252 0.012 20.603 0.000 0.228
## .ssei 0.337 0.016 20.533 0.000 0.305
## .ssar 0.189 0.011 17.740 0.000 0.168
## .ssmk 0.171 0.009 20.009 0.000 0.154
## .ssmc 0.266 0.013 21.082 0.000 0.241
## .ssao 0.522 0.019 26.800 0.000 0.484
## .ssai 0.523 0.024 21.474 0.000 0.475
## .sssi 0.293 0.018 16.359 0.000 0.258
## .ssno 0.336 0.022 15.311 0.000 0.293
## .sscs 0.410 0.023 17.742 0.000 0.364
## verbal 1.004 0.018 54.429 0.000 0.968
## math 0.972 0.020 48.566 0.000 0.933
## electronic 2.115 0.120 17.567 0.000 1.879
## speed 1.055 0.057 18.444 0.000 0.943
## ci.upper Std.lv Std.all
## 0.205 0.188 0.224
## 0.218 0.198 0.235
## 0.276 0.252 0.282
## 0.369 0.337 0.346
## 0.210 0.189 0.231
## 0.188 0.171 0.197
## 0.291 0.266 0.327
## 0.560 0.522 0.533
## 0.571 0.523 0.488
## 0.328 0.293 0.321
## 0.379 0.336 0.317
## 0.455 0.410 0.436
## 1.040 1.000 1.000
## 1.011 1.000 1.000
## 2.351 1.000 1.000
## 1.167 1.000 1.000
cf.vcov<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances", "lv.variances"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(cf.vcov, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1539.591 116.000 0.000 0.955 0.082 0.119 87252.721
## bic
## 87649.838
Mc(cf.vcov)
## [1] 0.8231754
summary(cf.vcov, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 50 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 94
## Number of equality constraints 30
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1539.591 1338.945
## Degrees of freedom 116 116
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.150
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 709.400 616.948
## 0 830.191 721.997
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.808 0.013 63.318 0.000 0.783
## sswk (.p2.) 0.802 0.013 61.974 0.000 0.777
## sspc (.p3.) 0.800 0.012 67.222 0.000 0.776
## ssei (.p4.) 0.413 0.018 22.929 0.000 0.378
## math =~
## ssar (.p5.) 0.800 0.013 59.701 0.000 0.774
## ssmk (.p6.) 0.635 0.019 33.312 0.000 0.597
## ssmc (.p7.) 0.455 0.015 29.880 0.000 0.425
## ssao (.p8.) 0.681 0.013 52.730 0.000 0.656
## electronic =~
## ssai (.p9.) 0.631 0.017 37.092 0.000 0.598
## sssi (.10.) 0.685 0.015 46.182 0.000 0.656
## ssmc (.11.) 0.348 0.014 25.023 0.000 0.321
## ssei (.12.) 0.413 0.018 23.095 0.000 0.378
## speed =~
## ssno (.13.) 0.840 0.017 50.726 0.000 0.807
## sscs (.14.) 0.719 0.016 45.915 0.000 0.689
## ssmk (.15.) 0.249 0.018 13.865 0.000 0.214
## ci.upper Std.lv Std.all
##
## 0.833 0.808 0.891
## 0.827 0.802 0.886
## 0.823 0.800 0.868
## 0.448 0.413 0.452
##
## 0.826 0.800 0.907
## 0.672 0.635 0.676
## 0.485 0.455 0.520
## 0.706 0.681 0.725
##
## 0.664 0.631 0.750
## 0.715 0.685 0.792
## 0.375 0.348 0.397
## 0.448 0.413 0.452
##
## 0.872 0.840 0.848
## 0.750 0.719 0.763
## 0.284 0.249 0.265
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.32.) 0.900 0.006 148.699 0.000 0.888
## elctrnc (.33.) 0.735 0.014 51.426 0.000 0.707
## speed (.34.) 0.681 0.016 42.336 0.000 0.649
## math ~~
## elctrnc (.35.) 0.629 0.017 36.691 0.000 0.595
## speed (.36.) 0.751 0.016 47.252 0.000 0.720
## electronic ~~
## speed (.37.) 0.354 0.024 15.004 0.000 0.308
## ci.upper Std.lv Std.all
##
## 0.912 0.900 0.900
## 0.763 0.735 0.735
## 0.713 0.681 0.681
##
## 0.662 0.629 0.629
## 0.782 0.751 0.751
##
## 0.400 0.354 0.354
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.331 0.021 15.977 0.000 0.291
## .sswk (.39.) 0.375 0.022 17.336 0.000 0.333
## .sspc 0.453 0.022 20.981 0.000 0.411
## .ssei (.41.) 0.150 0.018 8.115 0.000 0.114
## .ssar (.42.) 0.354 0.020 17.291 0.000 0.314
## .ssmk (.43.) 0.372 0.022 17.049 0.000 0.329
## .ssmc (.44.) 0.226 0.019 11.724 0.000 0.188
## .ssao (.45.) 0.289 0.020 14.104 0.000 0.249
## .ssai (.46.) 0.033 0.017 1.932 0.053 -0.000
## .sssi (.47.) 0.075 0.018 4.246 0.000 0.041
## .ssno 0.244 0.023 10.435 0.000 0.198
## .sscs (.49.) 0.364 0.022 16.496 0.000 0.321
## ci.upper Std.lv Std.all
## 0.372 0.331 0.365
## 0.417 0.375 0.414
## 0.495 0.453 0.492
## 0.186 0.150 0.164
## 0.394 0.354 0.401
## 0.415 0.372 0.397
## 0.264 0.226 0.258
## 0.329 0.289 0.308
## 0.066 0.033 0.039
## 0.110 0.075 0.087
## 0.290 0.244 0.247
## 0.408 0.364 0.387
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.170 0.009 19.806 0.000 0.153
## .sswk 0.177 0.008 20.941 0.000 0.160
## .sspc 0.209 0.012 18.001 0.000 0.186
## .ssei 0.242 0.011 21.335 0.000 0.220
## .ssar 0.138 0.008 17.025 0.000 0.122
## .ssmk 0.179 0.008 21.295 0.000 0.162
## .ssmc 0.240 0.012 20.395 0.000 0.217
## .ssao 0.419 0.017 24.286 0.000 0.385
## .ssai 0.309 0.016 19.774 0.000 0.278
## .sssi 0.279 0.015 18.881 0.000 0.250
## .ssno 0.276 0.018 14.942 0.000 0.240
## .sscs 0.371 0.019 19.563 0.000 0.334
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.186 0.170 0.206
## 0.193 0.177 0.216
## 0.232 0.209 0.247
## 0.264 0.242 0.290
## 0.154 0.138 0.178
## 0.195 0.179 0.203
## 0.263 0.240 0.313
## 0.453 0.419 0.475
## 0.340 0.309 0.437
## 0.308 0.279 0.373
## 0.312 0.276 0.281
## 0.408 0.371 0.418
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.808 0.013 63.318 0.000 0.783
## sswk (.p2.) 0.802 0.013 61.974 0.000 0.777
## sspc (.p3.) 0.800 0.012 67.222 0.000 0.776
## ssei (.p4.) 0.413 0.018 22.929 0.000 0.378
## math =~
## ssar (.p5.) 0.800 0.013 59.701 0.000 0.774
## ssmk (.p6.) 0.635 0.019 33.312 0.000 0.597
## ssmc (.p7.) 0.455 0.015 29.880 0.000 0.425
## ssao (.p8.) 0.681 0.013 52.730 0.000 0.656
## electronic =~
## ssai (.p9.) 0.631 0.017 37.092 0.000 0.598
## sssi (.10.) 0.685 0.015 46.182 0.000 0.656
## ssmc (.11.) 0.348 0.014 25.023 0.000 0.321
## ssei (.12.) 0.413 0.018 23.095 0.000 0.378
## speed =~
## ssno (.13.) 0.840 0.017 50.726 0.000 0.807
## sscs (.14.) 0.719 0.016 45.915 0.000 0.689
## ssmk (.15.) 0.249 0.018 13.865 0.000 0.214
## ci.upper Std.lv Std.all
##
## 0.833 0.808 0.882
## 0.827 0.802 0.874
## 0.823 0.800 0.842
## 0.448 0.413 0.428
##
## 0.826 0.800 0.880
## 0.672 0.635 0.680
## 0.485 0.455 0.511
## 0.706 0.681 0.687
##
## 0.664 0.631 0.641
## 0.715 0.685 0.759
## 0.375 0.348 0.391
## 0.448 0.413 0.427
##
## 0.872 0.840 0.821
## 0.750 0.719 0.746
## 0.284 0.249 0.267
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.32.) 0.900 0.006 148.699 0.000 0.888
## elctrnc (.33.) 0.735 0.014 51.426 0.000 0.707
## speed (.34.) 0.681 0.016 42.336 0.000 0.649
## math ~~
## elctrnc (.35.) 0.629 0.017 36.691 0.000 0.595
## speed (.36.) 0.751 0.016 47.252 0.000 0.720
## electronic ~~
## speed (.37.) 0.354 0.024 15.004 0.000 0.308
## ci.upper Std.lv Std.all
##
## 0.912 0.900 0.900
## 0.763 0.735 0.735
## 0.713 0.681 0.681
##
## 0.662 0.629 0.629
## 0.782 0.751 0.751
##
## 0.400 0.354 0.354
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.501 0.026 19.333 0.000 0.450
## .sswk (.39.) 0.375 0.022 17.336 0.000 0.333
## .sspc 0.190 0.027 7.057 0.000 0.137
## .ssei (.41.) 0.150 0.018 8.115 0.000 0.114
## .ssar (.42.) 0.354 0.020 17.291 0.000 0.314
## .ssmk (.43.) 0.372 0.022 17.049 0.000 0.329
## .ssmc (.44.) 0.226 0.019 11.724 0.000 0.188
## .ssao (.45.) 0.289 0.020 14.104 0.000 0.249
## .ssai (.46.) 0.033 0.017 1.932 0.053 -0.000
## .sssi (.47.) 0.075 0.018 4.246 0.000 0.041
## .ssno 0.522 0.034 15.417 0.000 0.455
## .sscs (.49.) 0.364 0.022 16.496 0.000 0.321
## verbal 0.027 0.039 0.698 0.485 -0.049
## math 0.007 0.037 0.180 0.857 -0.066
## elctrnc 0.984 0.043 22.837 0.000 0.900
## speed -0.506 0.046 -11.119 0.000 -0.596
## ci.upper Std.lv Std.all
## 0.552 0.501 0.547
## 0.417 0.375 0.409
## 0.242 0.190 0.200
## 0.186 0.150 0.155
## 0.394 0.354 0.389
## 0.415 0.372 0.399
## 0.264 0.226 0.254
## 0.329 0.289 0.291
## 0.066 0.033 0.033
## 0.110 0.075 0.083
## 0.588 0.522 0.510
## 0.408 0.364 0.378
## 0.104 0.027 0.027
## 0.079 0.007 0.007
## 1.069 0.984 0.984
## -0.417 -0.506 -0.506
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.186 0.009 21.827 0.000 0.169
## .sswk 0.199 0.010 20.421 0.000 0.180
## .sspc 0.261 0.012 21.015 0.000 0.237
## .ssei 0.341 0.016 20.695 0.000 0.309
## .ssar 0.187 0.011 17.788 0.000 0.167
## .ssmk 0.170 0.009 19.852 0.000 0.153
## .ssmc 0.267 0.013 20.802 0.000 0.242
## .ssao 0.520 0.019 26.791 0.000 0.482
## .ssai 0.570 0.026 22.188 0.000 0.520
## .sssi 0.346 0.019 18.366 0.000 0.309
## .ssno 0.341 0.023 15.100 0.000 0.297
## .sscs 0.413 0.023 17.895 0.000 0.368
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.202 0.186 0.221
## 0.219 0.199 0.237
## 0.286 0.261 0.290
## 0.374 0.341 0.366
## 0.208 0.187 0.226
## 0.187 0.170 0.195
## 0.292 0.267 0.336
## 0.558 0.520 0.528
## 0.621 0.570 0.589
## 0.383 0.346 0.424
## 0.386 0.341 0.326
## 0.458 0.413 0.444
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
cf.cov2<-cfa(cf.lv, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(cf.cov2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1228.407 115.000 0.000 0.965 0.073 0.095 86943.538
## bic
## 87346.859
Mc(cf.cov2)
## [1] 0.8588268
summary(cf.cov2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 60 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 95
## Number of equality constraints 30
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1228.407 1070.080
## Degrees of freedom 115 115
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.148
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 512.616 446.546
## 0 715.791 623.534
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.807 0.013 63.107 0.000 0.782
## sswk (.p2.) 0.801 0.013 62.053 0.000 0.776
## sspc (.p3.) 0.800 0.012 67.376 0.000 0.777
## ssei (.p4.) 0.410 0.018 23.170 0.000 0.376
## math =~
## ssar (.p5.) 0.800 0.013 59.647 0.000 0.773
## ssmk (.p6.) 0.635 0.019 33.343 0.000 0.597
## ssmc (.p7.) 0.464 0.015 31.403 0.000 0.435
## ssao (.p8.) 0.681 0.013 52.770 0.000 0.656
## electronic =~
## ssai (.p9.) 0.509 0.016 32.720 0.000 0.479
## sssi (.10.) 0.542 0.016 34.717 0.000 0.511
## ssmc (.11.) 0.268 0.012 21.953 0.000 0.244
## ssei (.12.) 0.331 0.015 22.258 0.000 0.302
## speed =~
## ssno (.13.) 0.840 0.017 50.573 0.000 0.807
## sscs (.14.) 0.720 0.016 45.880 0.000 0.689
## ssmk (.15.) 0.249 0.018 13.869 0.000 0.213
## ci.upper Std.lv Std.all
##
## 0.832 0.807 0.891
## 0.827 0.801 0.884
## 0.824 0.800 0.866
## 0.445 0.410 0.473
##
## 0.826 0.800 0.906
## 0.672 0.635 0.676
## 0.492 0.464 0.548
## 0.706 0.681 0.726
##
## 0.540 0.509 0.665
## 0.572 0.542 0.696
## 0.292 0.268 0.317
## 0.361 0.331 0.382
##
## 0.872 0.840 0.848
## 0.750 0.720 0.763
## 0.284 0.249 0.265
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.32.) 0.901 0.006 149.380 0.000 0.889
## elctrnc (.33.) 0.866 0.015 58.164 0.000 0.837
## speed (.34.) 0.681 0.016 42.336 0.000 0.650
## math ~~
## elctrnc (.35.) 0.746 0.018 40.839 0.000 0.710
## speed (.36.) 0.750 0.016 47.241 0.000 0.719
## electronic ~~
## speed (.37.) 0.443 0.027 16.135 0.000 0.389
## ci.upper Std.lv Std.all
##
## 0.913 0.901 0.901
## 0.895 0.866 0.866
## 0.713 0.681 0.681
##
## 0.782 0.746 0.746
## 0.782 0.750 0.750
##
## 0.497 0.443 0.443
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.331 0.021 15.977 0.000 0.291
## .sswk (.39.) 0.376 0.022 17.415 0.000 0.333
## .sspc 0.453 0.022 20.981 0.000 0.411
## .ssei (.41.) 0.149 0.019 7.989 0.000 0.112
## .ssar (.42.) 0.354 0.020 17.253 0.000 0.313
## .ssmk (.43.) 0.372 0.022 17.017 0.000 0.329
## .ssmc (.44.) 0.231 0.019 12.103 0.000 0.193
## .ssao (.45.) 0.289 0.021 14.072 0.000 0.248
## .ssai (.46.) 0.027 0.017 1.578 0.114 -0.006
## .sssi (.47.) 0.080 0.018 4.498 0.000 0.045
## .ssno 0.244 0.023 10.435 0.000 0.198
## .sscs (.49.) 0.365 0.022 16.518 0.000 0.322
## ci.upper Std.lv Std.all
## 0.372 0.331 0.366
## 0.418 0.376 0.414
## 0.495 0.453 0.490
## 0.185 0.149 0.171
## 0.394 0.354 0.400
## 0.414 0.372 0.396
## 0.268 0.231 0.273
## 0.329 0.289 0.307
## 0.060 0.027 0.035
## 0.115 0.080 0.103
## 0.290 0.244 0.247
## 0.408 0.365 0.387
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.169 0.008 20.139 0.000 0.153
## .sswk 0.180 0.008 21.206 0.000 0.163
## .sspc 0.213 0.012 18.172 0.000 0.190
## .ssei 0.240 0.011 21.864 0.000 0.219
## .ssar 0.140 0.008 17.154 0.000 0.124
## .ssmk 0.179 0.008 21.345 0.000 0.163
## .ssmc 0.244 0.012 20.479 0.000 0.221
## .ssao 0.417 0.017 24.308 0.000 0.383
## .ssai 0.327 0.015 21.593 0.000 0.297
## .sssi 0.312 0.015 20.680 0.000 0.283
## .ssno 0.275 0.018 14.894 0.000 0.239
## .sscs 0.372 0.019 19.507 0.000 0.335
## electronic 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.186 0.169 0.206
## 0.196 0.180 0.219
## 0.237 0.213 0.250
## 0.262 0.240 0.319
## 0.156 0.140 0.180
## 0.196 0.179 0.203
## 0.268 0.244 0.341
## 0.451 0.417 0.473
## 0.356 0.327 0.557
## 0.342 0.312 0.516
## 0.311 0.275 0.281
## 0.409 0.372 0.418
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.807 0.013 63.107 0.000 0.782
## sswk (.p2.) 0.801 0.013 62.053 0.000 0.776
## sspc (.p3.) 0.800 0.012 67.376 0.000 0.777
## ssei (.p4.) 0.410 0.018 23.170 0.000 0.376
## math =~
## ssar (.p5.) 0.800 0.013 59.647 0.000 0.773
## ssmk (.p6.) 0.635 0.019 33.343 0.000 0.597
## ssmc (.p7.) 0.464 0.015 31.403 0.000 0.435
## ssao (.p8.) 0.681 0.013 52.770 0.000 0.656
## electronic =~
## ssai (.p9.) 0.509 0.016 32.720 0.000 0.479
## sssi (.10.) 0.542 0.016 34.717 0.000 0.511
## ssmc (.11.) 0.268 0.012 21.953 0.000 0.244
## ssei (.12.) 0.331 0.015 22.258 0.000 0.302
## speed =~
## ssno (.13.) 0.840 0.017 50.573 0.000 0.807
## sscs (.14.) 0.720 0.016 45.880 0.000 0.689
## ssmk (.15.) 0.249 0.018 13.869 0.000 0.213
## ci.upper Std.lv Std.all
##
## 0.832 0.807 0.881
## 0.827 0.801 0.875
## 0.824 0.800 0.847
## 0.445 0.410 0.416
##
## 0.826 0.800 0.880
## 0.672 0.635 0.680
## 0.492 0.464 0.513
## 0.706 0.681 0.686
##
## 0.540 0.741 0.716
## 0.572 0.787 0.824
## 0.292 0.390 0.431
## 0.361 0.482 0.489
##
## 0.872 0.840 0.821
## 0.750 0.720 0.747
## 0.284 0.249 0.266
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.32.) 0.901 0.006 149.380 0.000 0.889
## elctrnc (.33.) 0.866 0.015 58.164 0.000 0.837
## speed (.34.) 0.681 0.016 42.336 0.000 0.650
## math ~~
## elctrnc (.35.) 0.746 0.018 40.839 0.000 0.710
## speed (.36.) 0.750 0.016 47.241 0.000 0.719
## electronic ~~
## speed (.37.) 0.443 0.027 16.135 0.000 0.389
## ci.upper Std.lv Std.all
##
## 0.913 0.901 0.901
## 0.895 0.595 0.595
## 0.713 0.681 0.681
##
## 0.782 0.513 0.513
## 0.782 0.750 0.750
##
## 0.497 0.305 0.305
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.502 0.026 19.492 0.000 0.452
## .sswk (.39.) 0.376 0.022 17.415 0.000 0.333
## .sspc 0.191 0.027 7.138 0.000 0.138
## .ssei (.41.) 0.149 0.019 7.989 0.000 0.112
## .ssar (.42.) 0.354 0.020 17.253 0.000 0.313
## .ssmk (.43.) 0.372 0.022 17.017 0.000 0.329
## .ssmc (.44.) 0.231 0.019 12.103 0.000 0.193
## .ssao (.45.) 0.289 0.021 14.072 0.000 0.248
## .ssai (.46.) 0.027 0.017 1.578 0.114 -0.006
## .sssi (.47.) 0.080 0.018 4.498 0.000 0.045
## .ssno 0.523 0.034 15.443 0.000 0.456
## .sscs (.49.) 0.365 0.022 16.518 0.000 0.322
## verbal 0.026 0.039 0.663 0.507 -0.050
## math 0.009 0.037 0.239 0.811 -0.064
## elctrnc 1.239 0.062 20.005 0.000 1.117
## speed -0.507 0.046 -11.138 0.000 -0.597
## ci.upper Std.lv Std.all
## 0.553 0.502 0.548
## 0.418 0.376 0.410
## 0.243 0.191 0.202
## 0.185 0.149 0.151
## 0.394 0.354 0.389
## 0.414 0.372 0.398
## 0.268 0.231 0.255
## 0.329 0.289 0.291
## 0.060 0.027 0.026
## 0.115 0.080 0.084
## 0.589 0.523 0.511
## 0.408 0.365 0.379
## 0.102 0.026 0.026
## 0.081 0.009 0.009
## 1.360 0.852 0.852
## -0.418 -0.507 -0.507
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.188 0.009 21.525 0.000 0.171
## .sswk 0.197 0.010 19.935 0.000 0.177
## .sspc 0.253 0.012 20.618 0.000 0.229
## .ssei 0.337 0.016 20.510 0.000 0.305
## .ssar 0.186 0.010 17.775 0.000 0.166
## .ssmk 0.170 0.009 19.905 0.000 0.153
## .ssmc 0.266 0.013 21.036 0.000 0.241
## .ssao 0.521 0.019 26.810 0.000 0.483
## .ssai 0.523 0.024 21.491 0.000 0.475
## .sssi 0.293 0.018 16.392 0.000 0.258
## .ssno 0.342 0.023 15.105 0.000 0.298
## .sscs 0.411 0.023 17.846 0.000 0.366
## electronic 2.114 0.120 17.631 0.000 1.879
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.205 0.188 0.224
## 0.216 0.197 0.235
## 0.277 0.253 0.283
## 0.369 0.337 0.346
## 0.207 0.186 0.226
## 0.187 0.170 0.195
## 0.291 0.266 0.325
## 0.559 0.521 0.529
## 0.571 0.523 0.488
## 0.328 0.293 0.321
## 0.386 0.342 0.327
## 0.456 0.411 0.442
## 2.349 1.000 1.000
reduced<-cfa(cf.reduced, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(reduced, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1229.035 117.000 0.000 0.965 0.072 0.095 86940.166
## bic
## 87331.077
Mc(reduced)
## [1] 0.8589879
summary(reduced, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 55 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 93
## Number of equality constraints 30
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1229.035 1070.838
## Degrees of freedom 117 117
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.148
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 512.736 446.739
## 0 716.299 624.100
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.807 0.013 63.121 0.000 0.782
## sswk (.p2.) 0.801 0.013 62.131 0.000 0.776
## sspc (.p3.) 0.800 0.012 67.381 0.000 0.777
## ssei (.p4.) 0.409 0.018 23.337 0.000 0.375
## math =~
## ssar (.p5.) 0.800 0.013 59.700 0.000 0.773
## ssmk (.p6.) 0.634 0.019 33.642 0.000 0.597
## ssmc (.p7.) 0.464 0.015 31.450 0.000 0.435
## ssao (.p8.) 0.681 0.013 52.856 0.000 0.656
## electronic =~
## ssai (.p9.) 0.509 0.016 32.713 0.000 0.479
## sssi (.10.) 0.541 0.016 34.705 0.000 0.511
## ssmc (.11.) 0.268 0.012 21.957 0.000 0.244
## ssei (.12.) 0.333 0.015 22.647 0.000 0.304
## speed =~
## ssno (.13.) 0.840 0.017 50.567 0.000 0.807
## sscs (.14.) 0.719 0.016 45.883 0.000 0.689
## ssmk (.15.) 0.250 0.018 14.156 0.000 0.215
## ci.upper Std.lv Std.all
##
## 0.832 0.807 0.891
## 0.827 0.801 0.884
## 0.824 0.800 0.866
## 0.443 0.409 0.471
##
## 0.826 0.800 0.906
## 0.671 0.634 0.675
## 0.493 0.464 0.548
## 0.706 0.681 0.726
##
## 0.540 0.509 0.665
## 0.572 0.541 0.696
## 0.292 0.268 0.316
## 0.362 0.333 0.383
##
## 0.872 0.840 0.848
## 0.750 0.719 0.763
## 0.284 0.250 0.266
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.34.) 0.901 0.006 149.278 0.000 0.889
## elctrnc (.35.) 0.866 0.015 58.228 0.000 0.837
## speed (.36.) 0.682 0.016 42.412 0.000 0.650
## math ~~
## elctrnc (.37.) 0.746 0.018 40.932 0.000 0.710
## speed (.38.) 0.750 0.016 47.151 0.000 0.719
## electronic ~~
## speed (.39.) 0.444 0.027 16.172 0.000 0.390
## ci.upper Std.lv Std.all
##
## 0.913 0.901 0.901
## 0.895 0.866 0.866
## 0.713 0.682 0.682
##
## 0.782 0.746 0.746
## 0.782 0.750 0.750
##
## 0.498 0.444 0.444
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 0.000 0.000
## math 0.000 0.000
## .ssgs 0.338 0.017 19.667 0.000 0.304
## .sswk (.41.) 0.386 0.016 24.686 0.000 0.355
## .sspc 0.460 0.018 25.651 0.000 0.425
## .ssei (.43.) 0.155 0.016 9.593 0.000 0.124
## .ssar (.44.) 0.357 0.016 23.033 0.000 0.327
## .ssmk (.45.) 0.376 0.017 21.978 0.000 0.342
## .ssmc (.46.) 0.235 0.016 14.835 0.000 0.204
## .ssao (.47.) 0.292 0.017 17.442 0.000 0.259
## .ssai (.48.) 0.031 0.016 1.911 0.056 -0.001
## .sssi (.49.) 0.084 0.017 5.065 0.000 0.051
## .ssno 0.248 0.022 11.394 0.000 0.205
## .sscs (.51.) 0.369 0.021 17.816 0.000 0.328
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.372 0.338 0.373
## 0.417 0.386 0.426
## 0.495 0.460 0.498
## 0.187 0.155 0.179
## 0.388 0.357 0.405
## 0.409 0.376 0.401
## 0.266 0.235 0.278
## 0.325 0.292 0.311
## 0.062 0.031 0.040
## 0.116 0.084 0.108
## 0.291 0.248 0.251
## 0.409 0.369 0.391
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.169 0.008 20.139 0.000 0.153
## .sswk 0.180 0.008 21.198 0.000 0.163
## .sspc 0.213 0.012 18.171 0.000 0.190
## .ssei 0.240 0.011 21.850 0.000 0.219
## .ssar 0.140 0.008 17.080 0.000 0.124
## .ssmk 0.179 0.008 21.340 0.000 0.163
## .ssmc 0.245 0.012 20.489 0.000 0.221
## .ssao 0.417 0.017 24.336 0.000 0.383
## .ssai 0.327 0.015 21.598 0.000 0.297
## .sssi 0.312 0.015 20.684 0.000 0.283
## .ssno 0.275 0.018 14.889 0.000 0.239
## .sscs 0.372 0.019 19.517 0.000 0.335
## electronic 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.186 0.169 0.206
## 0.196 0.180 0.219
## 0.237 0.213 0.250
## 0.262 0.240 0.318
## 0.156 0.140 0.180
## 0.196 0.179 0.204
## 0.268 0.245 0.341
## 0.451 0.417 0.473
## 0.357 0.327 0.558
## 0.342 0.312 0.516
## 0.312 0.275 0.281
## 0.409 0.372 0.418
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.807 0.013 63.121 0.000 0.782
## sswk (.p2.) 0.801 0.013 62.131 0.000 0.776
## sspc (.p3.) 0.800 0.012 67.381 0.000 0.777
## ssei (.p4.) 0.409 0.018 23.337 0.000 0.375
## math =~
## ssar (.p5.) 0.800 0.013 59.700 0.000 0.773
## ssmk (.p6.) 0.634 0.019 33.642 0.000 0.597
## ssmc (.p7.) 0.464 0.015 31.450 0.000 0.435
## ssao (.p8.) 0.681 0.013 52.856 0.000 0.656
## electronic =~
## ssai (.p9.) 0.509 0.016 32.713 0.000 0.479
## sssi (.10.) 0.541 0.016 34.705 0.000 0.511
## ssmc (.11.) 0.268 0.012 21.957 0.000 0.244
## ssei (.12.) 0.333 0.015 22.647 0.000 0.304
## speed =~
## ssno (.13.) 0.840 0.017 50.567 0.000 0.807
## sscs (.14.) 0.719 0.016 45.883 0.000 0.689
## ssmk (.15.) 0.250 0.018 14.156 0.000 0.215
## ci.upper Std.lv Std.all
##
## 0.832 0.807 0.881
## 0.827 0.801 0.875
## 0.824 0.800 0.847
## 0.443 0.409 0.414
##
## 0.826 0.800 0.880
## 0.671 0.634 0.679
## 0.493 0.464 0.513
## 0.706 0.681 0.686
##
## 0.540 0.741 0.715
## 0.572 0.787 0.824
## 0.292 0.389 0.430
## 0.362 0.484 0.490
##
## 0.872 0.840 0.821
## 0.750 0.719 0.747
## 0.284 0.250 0.267
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.34.) 0.901 0.006 149.278 0.000 0.889
## elctrnc (.35.) 0.866 0.015 58.228 0.000 0.837
## speed (.36.) 0.682 0.016 42.412 0.000 0.650
## math ~~
## elctrnc (.37.) 0.746 0.018 40.932 0.000 0.710
## speed (.38.) 0.750 0.016 47.151 0.000 0.719
## electronic ~~
## speed (.39.) 0.444 0.027 16.172 0.000 0.390
## ci.upper Std.lv Std.all
##
## 0.913 0.901 0.901
## 0.895 0.596 0.596
## 0.713 0.682 0.682
##
## 0.782 0.513 0.513
## 0.782 0.750 0.750
##
## 0.498 0.305 0.305
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 0.000 0.000
## math 0.000 0.000
## .ssgs 0.516 0.019 27.746 0.000 0.480
## .sswk (.41.) 0.386 0.016 24.686 0.000 0.355
## .sspc 0.205 0.019 10.784 0.000 0.168
## .ssei (.43.) 0.155 0.016 9.593 0.000 0.124
## .ssar (.44.) 0.357 0.016 23.033 0.000 0.327
## .ssmk (.45.) 0.376 0.017 21.978 0.000 0.342
## .ssmc (.46.) 0.235 0.016 14.835 0.000 0.204
## .ssao (.47.) 0.292 0.017 17.442 0.000 0.259
## .ssai (.48.) 0.031 0.016 1.911 0.056 -0.001
## .sssi (.49.) 0.084 0.017 5.065 0.000 0.051
## .ssno 0.527 0.032 16.299 0.000 0.464
## .sscs (.51.) 0.369 0.021 17.816 0.000 0.328
## elctrnc 1.224 0.055 22.294 0.000 1.117
## speed -0.518 0.038 -13.502 0.000 -0.593
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.553 0.516 0.564
## 0.417 0.386 0.421
## 0.242 0.205 0.217
## 0.187 0.155 0.157
## 0.388 0.357 0.393
## 0.409 0.376 0.403
## 0.266 0.235 0.260
## 0.325 0.292 0.294
## 0.062 0.031 0.030
## 0.116 0.084 0.088
## 0.591 0.527 0.515
## 0.409 0.369 0.382
## 1.332 0.842 0.842
## -0.442 -0.518 -0.518
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.188 0.009 21.525 0.000 0.171
## .sswk 0.197 0.010 19.933 0.000 0.177
## .sspc 0.253 0.012 20.623 0.000 0.229
## .ssei 0.336 0.016 20.504 0.000 0.304
## .ssar 0.186 0.010 17.927 0.000 0.166
## .ssmk 0.170 0.009 19.892 0.000 0.153
## .ssmc 0.266 0.013 21.022 0.000 0.241
## .ssao 0.521 0.019 26.829 0.000 0.483
## .ssai 0.523 0.024 21.509 0.000 0.475
## .sssi 0.293 0.018 16.428 0.000 0.258
## .ssno 0.342 0.023 15.115 0.000 0.298
## .sscs 0.411 0.023 17.859 0.000 0.366
## electronic 2.114 0.120 17.637 0.000 1.879
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.205 0.188 0.224
## 0.216 0.197 0.235
## 0.277 0.253 0.283
## 0.369 0.336 0.346
## 0.207 0.186 0.226
## 0.187 0.170 0.195
## 0.291 0.266 0.325
## 0.559 0.521 0.529
## 0.571 0.523 0.488
## 0.328 0.293 0.321
## 0.386 0.342 0.327
## 0.456 0.411 0.443
## 2.348 1.000 1.000
tests<-lavTestLRT(configural, metric, scalar2, cf.cov, cf.cov2, reduced)
Td=tests[2:6,"Chisq diff"]
Td
## [1] 89.9528865 82.3674282 90.4367701 2.8941629 0.5534058
dfd=tests[2:6,"Df diff"]
dfd
## [1] 11 5 6 3 2
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
## Warning in sqrt((ld) * G/(N - G)): NaNs produced
RMSEAD
## [1] 0.06265276 0.09199127 0.08772890 NaN NaN
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.05102160 0.07494727
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.07511388 0.10996169
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.07226633 0.10413636
RMSEA.CI(T=Td[4],df=dfd[4],N=N,G=2)
## [1] NA 0.03889373
RMSEA.CI(T=Td[5],df=dfd[5],N=N,G=2)
## [1] NA 0.03142063
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.963 0.662 0.010 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 0.999 0.882 0.242
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 0.998 0.802 0.112
round(pvals(T=Td[4],df=dfd[4],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.592 0.512 0.008 0.001 0.000 0.000
round(pvals(T=Td[5],df=dfd[5],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.242 0.206 0.004 0.001 0.000 0.000
tests<-lavTestLRT(configural, metric, scalar2, strict)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 89.95289 82.36743 135.50078
dfd=tests[2:4,"Df diff"]
dfd
## [1] 11 5 12
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.06265276 0.09199127 0.07502340
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.05102160 0.07494727
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.07511388 0.10996169
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.06393755 0.08663601
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.963 0.662 0.010 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 0.999 0.882 0.242
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 0.987 0.249 0.000
tests<-lavTestLRT(configural, metric, scalar)
Td=tests[2:3,"Chisq diff"]
Td
## [1] 89.95289 542.07374
dfd=tests[2:3,"Df diff"]
dfd
## [1] 11 8
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.06265276 0.19107688
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.05102160 0.07494727
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.1775845 0.2048696
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.963 0.662 0.010 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
# ONE FACTOR, just for checking if gap direction aligns with HOF
fmodel<-'
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
configural<-cfa(fmodel, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 4430.975 108.000 0.000 0.864 0.148 0.066 90160.105
## bic
## 90606.861
Mc(configural)
## [1] 0.5538324
metric<-cfa(fmodel, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 4602.582 119.000 0.000 0.859 0.144 0.079 90309.712
## bic
## 90688.214
Mc(metric)
## [1] 0.5418067
scalar<-cfa(fmodel, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 7155.640 130.000 0.000 0.778 0.172 0.105 92840.771
## bic
## 93151.018
Mc(scalar)
## [1] 0.3827736
summary(scalar, standardized=T, ci=T) # -0.082
## lavaan 0.6-18 ended normally after 42 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 74
## Number of equality constraints 24
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 7155.640 6228.649
## Degrees of freedom 130 130
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.149
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 2599.517 2262.758
## 0 4556.123 3965.892
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g =~
## ssgs (.p1.) 0.722 0.015 47.295 0.000 0.692
## ssar (.p2.) 0.702 0.016 43.412 0.000 0.670
## sswk (.p3.) 0.710 0.016 44.259 0.000 0.678
## sspc (.p4.) 0.724 0.015 46.857 0.000 0.694
## ssno (.p5.) 0.555 0.017 31.847 0.000 0.521
## sscs (.p6.) 0.511 0.016 31.658 0.000 0.479
## ssai (.p7.) 0.444 0.016 27.481 0.000 0.412
## sssi (.p8.) 0.451 0.017 27.187 0.000 0.418
## ssmk (.p9.) 0.722 0.016 44.694 0.000 0.690
## ssmc (.10.) 0.645 0.016 40.525 0.000 0.613
## ssei (.11.) 0.641 0.016 40.163 0.000 0.610
## ssao (.12.) 0.601 0.015 40.910 0.000 0.572
## ci.upper Std.lv Std.all
##
## 0.752 0.722 0.846
## 0.734 0.702 0.844
## 0.741 0.710 0.833
## 0.754 0.724 0.827
## 0.589 0.555 0.586
## 0.543 0.511 0.554
## 0.475 0.444 0.571
## 0.483 0.451 0.559
## 0.754 0.722 0.817
## 0.676 0.645 0.774
## 0.673 0.641 0.762
## 0.630 0.601 0.663
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.26.) 0.390 0.020 19.218 0.000 0.350
## .ssar (.27.) 0.327 0.020 16.237 0.000 0.288
## .sswk (.28.) 0.353 0.021 17.048 0.000 0.312
## .sspc (.29.) 0.309 0.022 14.115 0.000 0.266
## .ssno (.30.) 0.152 0.021 7.407 0.000 0.112
## .sscs (.31.) 0.168 0.020 8.331 0.000 0.129
## .ssai (.32.) 0.206 0.018 11.573 0.000 0.171
## .sssi (.33.) 0.290 0.021 13.663 0.000 0.249
## .ssmk (.34.) 0.277 0.022 12.805 0.000 0.235
## .ssmc (.35.) 0.354 0.019 18.672 0.000 0.317
## .ssei (.36.) 0.276 0.019 14.208 0.000 0.238
## .ssao (.37.) 0.263 0.020 13.031 0.000 0.224
## ci.upper Std.lv Std.all
## 0.429 0.390 0.457
## 0.367 0.327 0.393
## 0.393 0.353 0.414
## 0.352 0.309 0.353
## 0.192 0.152 0.161
## 0.208 0.168 0.182
## 0.241 0.206 0.265
## 0.332 0.290 0.360
## 0.319 0.277 0.313
## 0.391 0.354 0.425
## 0.315 0.276 0.328
## 0.303 0.263 0.290
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.206 0.009 21.929 0.000 0.188
## .ssar 0.199 0.009 22.759 0.000 0.182
## .sswk 0.222 0.010 22.889 0.000 0.203
## .sspc 0.243 0.013 19.412 0.000 0.218
## .ssno 0.590 0.030 19.518 0.000 0.531
## .sscs 0.590 0.026 22.687 0.000 0.539
## .ssai 0.407 0.017 23.388 0.000 0.373
## .sssi 0.446 0.020 22.272 0.000 0.406
## .ssmk 0.259 0.010 24.798 0.000 0.238
## .ssmc 0.278 0.014 20.233 0.000 0.251
## .ssei 0.298 0.013 22.827 0.000 0.272
## .ssao 0.460 0.018 25.654 0.000 0.425
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.225 0.206 0.284
## 0.217 0.199 0.288
## 0.241 0.222 0.306
## 0.267 0.243 0.316
## 0.649 0.590 0.657
## 0.641 0.590 0.693
## 0.441 0.407 0.674
## 0.485 0.446 0.687
## 0.279 0.259 0.332
## 0.305 0.278 0.401
## 0.323 0.298 0.420
## 0.495 0.460 0.560
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g =~
## ssgs (.p1.) 0.722 0.015 47.295 0.000 0.692
## ssar (.p2.) 0.702 0.016 43.412 0.000 0.670
## sswk (.p3.) 0.710 0.016 44.259 0.000 0.678
## sspc (.p4.) 0.724 0.015 46.857 0.000 0.694
## ssno (.p5.) 0.555 0.017 31.847 0.000 0.521
## sscs (.p6.) 0.511 0.016 31.658 0.000 0.479
## ssai (.p7.) 0.444 0.016 27.481 0.000 0.412
## sssi (.p8.) 0.451 0.017 27.187 0.000 0.418
## ssmk (.p9.) 0.722 0.016 44.694 0.000 0.690
## ssmc (.10.) 0.645 0.016 40.525 0.000 0.613
## ssei (.11.) 0.641 0.016 40.163 0.000 0.610
## ssao (.12.) 0.601 0.015 40.910 0.000 0.572
## ci.upper Std.lv Std.all
##
## 0.752 0.843 0.867
## 0.734 0.819 0.857
## 0.741 0.828 0.857
## 0.754 0.845 0.842
## 0.589 0.648 0.606
## 0.543 0.596 0.587
## 0.475 0.518 0.456
## 0.483 0.526 0.490
## 0.754 0.842 0.852
## 0.676 0.752 0.783
## 0.673 0.748 0.710
## 0.630 0.701 0.687
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.26.) 0.390 0.020 19.218 0.000 0.350
## .ssar (.27.) 0.327 0.020 16.237 0.000 0.288
## .sswk (.28.) 0.353 0.021 17.048 0.000 0.312
## .sspc (.29.) 0.309 0.022 14.115 0.000 0.266
## .ssno (.30.) 0.152 0.021 7.407 0.000 0.112
## .sscs (.31.) 0.168 0.020 8.331 0.000 0.129
## .ssai (.32.) 0.206 0.018 11.573 0.000 0.171
## .sssi (.33.) 0.290 0.021 13.663 0.000 0.249
## .ssmk (.34.) 0.277 0.022 12.805 0.000 0.235
## .ssmc (.35.) 0.354 0.019 18.672 0.000 0.317
## .ssei (.36.) 0.276 0.019 14.208 0.000 0.238
## .ssao (.37.) 0.263 0.020 13.031 0.000 0.224
## g 0.095 0.041 2.339 0.019 0.015
## ci.upper Std.lv Std.all
## 0.429 0.390 0.401
## 0.367 0.327 0.343
## 0.393 0.353 0.366
## 0.352 0.309 0.308
## 0.192 0.152 0.142
## 0.208 0.168 0.165
## 0.241 0.206 0.181
## 0.332 0.290 0.271
## 0.319 0.277 0.280
## 0.391 0.354 0.369
## 0.315 0.276 0.262
## 0.303 0.263 0.258
## 0.176 0.082 0.082
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.235 0.010 23.093 0.000 0.215
## .ssar 0.242 0.011 22.502 0.000 0.221
## .sswk 0.247 0.011 23.038 0.000 0.226
## .sspc 0.293 0.014 20.998 0.000 0.265
## .ssno 0.722 0.035 20.796 0.000 0.654
## .sscs 0.677 0.031 21.542 0.000 0.615
## .ssai 1.022 0.048 21.186 0.000 0.928
## .sssi 0.873 0.039 22.306 0.000 0.796
## .ssmk 0.267 0.012 22.207 0.000 0.243
## .ssmc 0.357 0.016 22.344 0.000 0.325
## .ssei 0.550 0.026 20.741 0.000 0.498
## .ssao 0.550 0.020 27.779 0.000 0.511
## g 1.361 0.070 19.579 0.000 1.225
## ci.upper Std.lv Std.all
## 0.255 0.235 0.249
## 0.263 0.242 0.266
## 0.268 0.247 0.265
## 0.320 0.293 0.291
## 0.790 0.722 0.633
## 0.738 0.677 0.656
## 1.117 1.022 0.792
## 0.949 0.873 0.760
## 0.290 0.267 0.273
## 0.388 0.357 0.387
## 0.601 0.550 0.495
## 0.589 0.550 0.528
## 1.497 1.000 1.000
# HIGH ORDER FACTOR
hof.model<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
'
hof.lv<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
math~~1*math
'
hof.weak<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
math~~1*math
verbal~0*1
math~0*1
g~0*1
'
hof.weak2<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
math~~1*math
verbal~0*1
math~0*1
'
baseline<-cfa(hof.model, data=dgroup, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1514.370 47.000 0.000 0.954 0.092 0.044 89295.407
## bic
## 89562.219
Mc(baseline)
## [1] 0.8182642
configural<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1240.765 94.000 0.000 0.964 0.082 0.036 86997.895
## bic
## 87531.521
Mc(configural)
## [1] 0.8549199
summary(configural, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 116 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 86
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1240.765 1092.323
## Degrees of freedom 94 94
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.136
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 473.208 416.594
## 0 767.557 675.728
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.129 0.042 3.101 0.002 0.047
## sswk 0.137 0.044 3.089 0.002 0.050
## sspc 0.133 0.043 3.104 0.002 0.049
## ssei 0.063 0.023 2.735 0.006 0.018
## math =~
## ssar 0.322 0.020 16.425 0.000 0.283
## ssmk 0.257 0.019 13.522 0.000 0.220
## ssmc 0.179 0.016 10.999 0.000 0.147
## ssao 0.278 0.018 15.147 0.000 0.242
## electronic =~
## ssai 0.275 0.020 13.644 0.000 0.235
## sssi 0.289 0.021 13.648 0.000 0.248
## ssmc 0.158 0.017 9.082 0.000 0.124
## ssei 0.147 0.023 6.509 0.000 0.103
## speed =~
## ssno 0.565 0.029 19.366 0.000 0.508
## sscs 0.484 0.024 20.001 0.000 0.436
## ssmk 0.201 0.017 11.802 0.000 0.167
## g =~
## verbal 5.590 1.856 3.012 0.003 1.953
## math 2.095 0.160 13.103 0.000 1.781
## electronic 1.432 0.113 12.649 0.000 1.210
## speed 0.966 0.070 13.714 0.000 0.828
## ci.upper Std.lv Std.all
##
## 0.211 0.732 0.869
## 0.224 0.779 0.883
## 0.216 0.753 0.856
## 0.107 0.355 0.464
##
## 0.360 0.746 0.894
## 0.295 0.597 0.659
## 0.211 0.416 0.513
## 0.314 0.645 0.712
##
## 0.314 0.480 0.650
## 0.331 0.505 0.673
## 0.192 0.276 0.340
## 0.191 0.257 0.336
##
## 0.622 0.785 0.830
## 0.531 0.672 0.742
## 0.234 0.279 0.308
##
## 9.227 0.984 0.984
## 2.408 0.902 0.902
## 1.654 0.820 0.820
## 1.104 0.695 0.695
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.331 0.021 15.977 0.000 0.291
## .sswk 0.379 0.022 17.461 0.000 0.337
## .sspc 0.453 0.022 20.981 0.000 0.411
## .ssei 0.139 0.019 7.329 0.000 0.102
## .ssar 0.327 0.021 15.677 0.000 0.286
## .ssmk 0.382 0.022 16.962 0.000 0.337
## .ssmc 0.235 0.020 11.729 0.000 0.196
## .ssao 0.356 0.022 15.988 0.000 0.312
## .ssai 0.055 0.018 3.026 0.002 0.019
## .sssi 0.059 0.019 3.200 0.001 0.023
## .ssno 0.244 0.023 10.435 0.000 0.198
## .sscs 0.358 0.023 15.788 0.000 0.313
## ci.upper Std.lv Std.all
## 0.372 0.331 0.393
## 0.422 0.379 0.430
## 0.495 0.453 0.515
## 0.176 0.139 0.182
## 0.368 0.327 0.392
## 0.426 0.382 0.421
## 0.274 0.235 0.289
## 0.399 0.356 0.392
## 0.091 0.055 0.075
## 0.096 0.059 0.079
## 0.290 0.244 0.258
## 0.402 0.358 0.395
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.174 0.009 19.885 0.000 0.157
## .sswk 0.171 0.008 20.273 0.000 0.155
## .sspc 0.207 0.012 17.300 0.000 0.184
## .ssei 0.247 0.011 21.644 0.000 0.224
## .ssar 0.140 0.009 16.158 0.000 0.123
## .ssmk 0.177 0.008 21.087 0.000 0.161
## .ssmc 0.240 0.012 19.911 0.000 0.216
## .ssao 0.406 0.017 23.294 0.000 0.371
## .ssai 0.315 0.016 19.164 0.000 0.283
## .sssi 0.309 0.016 19.620 0.000 0.278
## .ssno 0.279 0.021 13.574 0.000 0.239
## .sscs 0.369 0.020 18.155 0.000 0.329
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.191 0.174 0.245
## 0.188 0.171 0.220
## 0.231 0.207 0.268
## 0.269 0.247 0.421
## 0.157 0.140 0.200
## 0.194 0.177 0.216
## 0.264 0.240 0.364
## 0.440 0.406 0.494
## 0.347 0.315 0.578
## 0.340 0.309 0.548
## 0.319 0.279 0.311
## 0.409 0.369 0.449
## 1.000 0.031 0.031
## 1.000 0.186 0.186
## 1.000 0.328 0.328
## 1.000 0.517 0.517
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.263 0.033 7.930 0.000 0.198
## sswk 0.248 0.032 7.859 0.000 0.186
## sspc 0.256 0.033 7.874 0.000 0.192
## ssei 0.164 0.023 7.223 0.000 0.120
## math =~
## ssar 0.233 0.038 6.174 0.000 0.159
## ssmk 0.168 0.028 6.016 0.000 0.113
## ssmc 0.143 0.023 6.158 0.000 0.098
## ssao 0.199 0.033 6.124 0.000 0.136
## electronic =~
## ssai 0.667 0.025 26.717 0.000 0.618
## sssi 0.647 0.022 29.302 0.000 0.603
## ssmc 0.296 0.018 16.651 0.000 0.261
## ssei 0.390 0.022 17.588 0.000 0.347
## speed =~
## ssno 0.604 0.029 21.106 0.000 0.548
## sscs 0.526 0.024 22.089 0.000 0.480
## ssmk 0.210 0.018 11.889 0.000 0.175
## g =~
## verbal 3.161 0.444 7.116 0.000 2.291
## math 3.498 0.614 5.700 0.000 2.295
## electronic 0.791 0.050 15.881 0.000 0.693
## speed 1.084 0.073 14.879 0.000 0.941
## ci.upper Std.lv Std.all
##
## 0.328 0.871 0.895
## 0.310 0.822 0.877
## 0.320 0.850 0.863
## 0.209 0.545 0.500
##
## 0.307 0.848 0.890
## 0.223 0.611 0.639
## 0.189 0.520 0.545
## 0.263 0.726 0.714
##
## 0.716 0.851 0.772
## 0.690 0.824 0.833
## 0.331 0.378 0.395
## 0.434 0.498 0.456
##
## 0.660 0.891 0.837
## 0.573 0.776 0.775
## 0.244 0.309 0.323
##
## 4.032 0.953 0.953
## 4.701 0.961 0.961
## 0.888 0.620 0.620
## 1.227 0.735 0.735
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.523 0.023 22.328 0.000 0.477
## .sswk 0.392 0.022 17.468 0.000 0.348
## .sspc 0.211 0.024 8.959 0.000 0.165
## .ssei 0.582 0.026 22.070 0.000 0.531
## .ssar 0.395 0.023 17.329 0.000 0.350
## .ssmk 0.242 0.023 10.519 0.000 0.197
## .ssmc 0.563 0.023 24.735 0.000 0.518
## .ssao 0.214 0.024 8.814 0.000 0.166
## .ssai 0.614 0.027 23.150 0.000 0.562
## .sssi 0.769 0.024 32.369 0.000 0.723
## .ssno 0.096 0.026 3.771 0.000 0.046
## .sscs 0.007 0.024 0.306 0.759 -0.040
## ci.upper Std.lv Std.all
## 0.569 0.523 0.537
## 0.436 0.392 0.419
## 0.257 0.211 0.215
## 0.634 0.582 0.534
## 0.440 0.395 0.415
## 0.287 0.242 0.253
## 0.608 0.563 0.589
## 0.262 0.214 0.210
## 0.666 0.614 0.557
## 0.816 0.769 0.777
## 0.146 0.096 0.090
## 0.054 0.007 0.007
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.189 0.009 20.539 0.000 0.171
## .sswk 0.202 0.010 19.584 0.000 0.182
## .sspc 0.247 0.012 20.102 0.000 0.223
## .ssei 0.324 0.016 20.498 0.000 0.293
## .ssar 0.189 0.011 17.060 0.000 0.167
## .ssmk 0.179 0.009 20.515 0.000 0.162
## .ssmc 0.264 0.013 21.044 0.000 0.240
## .ssao 0.506 0.019 26.613 0.000 0.469
## .ssai 0.490 0.026 18.542 0.000 0.438
## .sssi 0.300 0.019 15.640 0.000 0.263
## .ssno 0.340 0.023 14.528 0.000 0.294
## .sscs 0.400 0.024 16.802 0.000 0.353
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.207 0.189 0.200
## 0.222 0.202 0.230
## 0.272 0.247 0.255
## 0.355 0.324 0.272
## 0.211 0.189 0.208
## 0.196 0.179 0.196
## 0.289 0.264 0.290
## 0.544 0.506 0.490
## 0.542 0.490 0.404
## 0.338 0.300 0.307
## 0.386 0.340 0.300
## 0.447 0.400 0.399
## 1.000 0.091 0.091
## 1.000 0.076 0.076
## 1.000 0.615 0.615
## 1.000 0.460 0.460
## 1.000 1.000 1.000
#modificationIndices(configural, sort=T, maximum.number=30)
metric<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1372.664 108.000 0.000 0.960 0.080 0.050 87101.795
## bic
## 87548.551
Mc(metric)
## [1] 0.841253
summary(metric, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 108 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 91
## Number of equality constraints 19
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1372.664 1195.798
## Degrees of freedom 108 108
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.148
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 540.291 470.674
## 0 832.374 725.123
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.168 0.028 6.024 0.000 0.113
## sswk (.p2.) 0.167 0.028 5.989 0.000 0.113
## sspc (.p3.) 0.168 0.028 6.017 0.000 0.113
## ssei (.p4.) 0.089 0.016 5.685 0.000 0.058
## math =~
## ssar (.p5.) 0.307 0.018 16.732 0.000 0.271
## ssmk (.p6.) 0.233 0.016 14.851 0.000 0.202
## ssmc (.p7.) 0.185 0.012 15.359 0.000 0.162
## ssao (.p8.) 0.264 0.017 15.989 0.000 0.232
## electronic =~
## ssai (.p9.) 0.280 0.017 16.271 0.000 0.246
## sssi (.10.) 0.277 0.017 16.033 0.000 0.244
## ssmc (.11.) 0.131 0.010 13.472 0.000 0.112
## ssei (.12.) 0.168 0.011 14.648 0.000 0.146
## speed =~
## ssno (.13.) 0.559 0.026 21.875 0.000 0.509
## sscs (.14.) 0.484 0.022 22.236 0.000 0.441
## ssmk (.15.) 0.194 0.012 15.619 0.000 0.170
## g =~
## verbal (.16.) 4.281 0.747 5.730 0.000 2.817
## math (.17.) 2.252 0.158 14.279 0.000 1.943
## elctrnc (.18.) 1.469 0.100 14.660 0.000 1.273
## speed (.19.) 0.999 0.060 16.756 0.000 0.882
## ci.upper Std.lv Std.all
##
## 0.223 0.739 0.872
## 0.222 0.736 0.867
## 0.223 0.739 0.851
## 0.120 0.392 0.477
##
## 0.343 0.756 0.897
## 0.264 0.574 0.645
## 0.209 0.457 0.560
## 0.297 0.651 0.715
##
## 0.314 0.498 0.666
## 0.311 0.493 0.660
## 0.150 0.233 0.286
## 0.191 0.299 0.364
##
## 0.609 0.790 0.830
## 0.526 0.684 0.749
## 0.218 0.274 0.308
##
## 5.746 0.974 0.974
## 2.561 0.914 0.914
## 1.666 0.827 0.827
## 1.116 0.707 0.707
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.331 0.021 15.977 0.000 0.291
## .sswk 0.379 0.022 17.461 0.000 0.337
## .sspc 0.453 0.022 20.981 0.000 0.411
## .ssei 0.139 0.019 7.329 0.000 0.102
## .ssar 0.327 0.021 15.677 0.000 0.286
## .ssmk 0.382 0.022 16.962 0.000 0.337
## .ssmc 0.235 0.020 11.729 0.000 0.196
## .ssao 0.356 0.022 15.988 0.000 0.312
## .ssai 0.055 0.018 3.026 0.002 0.019
## .sssi 0.059 0.019 3.200 0.001 0.023
## .ssno 0.244 0.023 10.435 0.000 0.198
## .sscs 0.358 0.023 15.788 0.000 0.313
## ci.upper Std.lv Std.all
## 0.372 0.331 0.391
## 0.422 0.379 0.447
## 0.495 0.453 0.522
## 0.176 0.139 0.169
## 0.368 0.327 0.388
## 0.426 0.382 0.429
## 0.274 0.235 0.287
## 0.399 0.356 0.391
## 0.091 0.055 0.074
## 0.096 0.059 0.080
## 0.290 0.244 0.257
## 0.402 0.358 0.392
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.172 0.009 19.850 0.000 0.155
## .sswk 0.179 0.009 20.811 0.000 0.162
## .sspc 0.209 0.012 18.035 0.000 0.186
## .ssei 0.244 0.011 22.021 0.000 0.222
## .ssar 0.139 0.008 16.536 0.000 0.123
## .ssmk 0.183 0.008 22.124 0.000 0.167
## .ssmc 0.242 0.012 20.227 0.000 0.219
## .ssao 0.404 0.017 23.717 0.000 0.371
## .ssai 0.311 0.015 20.409 0.000 0.281
## .sssi 0.316 0.015 20.887 0.000 0.286
## .ssno 0.282 0.019 14.922 0.000 0.245
## .sscs 0.365 0.019 19.310 0.000 0.328
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.188 0.172 0.239
## 0.196 0.179 0.248
## 0.231 0.209 0.276
## 0.265 0.244 0.360
## 0.156 0.139 0.196
## 0.200 0.183 0.232
## 0.266 0.242 0.363
## 0.438 0.404 0.488
## 0.341 0.311 0.557
## 0.345 0.316 0.565
## 0.319 0.282 0.311
## 0.402 0.365 0.438
## 1.000 0.052 0.052
## 1.000 0.165 0.165
## 1.000 0.317 0.317
## 1.000 0.501 0.501
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.168 0.028 6.024 0.000 0.113
## sswk (.p2.) 0.167 0.028 5.989 0.000 0.113
## sspc (.p3.) 0.168 0.028 6.017 0.000 0.113
## ssei (.p4.) 0.089 0.016 5.685 0.000 0.058
## math =~
## ssar (.p5.) 0.307 0.018 16.732 0.000 0.271
## ssmk (.p6.) 0.233 0.016 14.851 0.000 0.202
## ssmc (.p7.) 0.185 0.012 15.359 0.000 0.162
## ssao (.p8.) 0.264 0.017 15.989 0.000 0.232
## electronic =~
## ssai (.p9.) 0.280 0.017 16.271 0.000 0.246
## sssi (.10.) 0.277 0.017 16.033 0.000 0.244
## ssmc (.11.) 0.131 0.010 13.472 0.000 0.112
## ssei (.12.) 0.168 0.011 14.648 0.000 0.146
## speed =~
## ssno (.13.) 0.559 0.026 21.875 0.000 0.509
## sscs (.14.) 0.484 0.022 22.236 0.000 0.441
## ssmk (.15.) 0.194 0.012 15.619 0.000 0.170
## g =~
## verbal (.16.) 4.281 0.747 5.730 0.000 2.817
## math (.17.) 2.252 0.158 14.279 0.000 1.943
## elctrnc (.18.) 1.469 0.100 14.660 0.000 1.273
## speed (.19.) 0.999 0.060 16.756 0.000 0.882
## ci.upper Std.lv Std.all
##
## 0.223 0.866 0.892
## 0.222 0.862 0.888
## 0.223 0.866 0.868
## 0.120 0.459 0.450
##
## 0.343 0.835 0.886
## 0.264 0.634 0.653
## 0.209 0.505 0.538
## 0.297 0.719 0.710
##
## 0.314 0.815 0.757
## 0.311 0.808 0.829
## 0.150 0.382 0.408
## 0.191 0.490 0.480
##
## 0.609 0.886 0.836
## 0.526 0.767 0.771
## 0.218 0.308 0.317
##
## 5.746 0.960 0.960
## 2.561 0.956 0.956
## 1.666 0.583 0.583
## 1.116 0.728 0.728
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.523 0.023 22.328 0.000 0.477
## .sswk 0.392 0.022 17.468 0.000 0.348
## .sspc 0.211 0.024 8.959 0.000 0.165
## .ssei 0.582 0.026 22.070 0.000 0.531
## .ssar 0.395 0.023 17.329 0.000 0.350
## .ssmk 0.242 0.023 10.519 0.000 0.197
## .ssmc 0.563 0.023 24.735 0.000 0.518
## .ssao 0.214 0.024 8.814 0.000 0.166
## .ssai 0.614 0.027 23.150 0.000 0.562
## .sssi 0.769 0.024 32.369 0.000 0.723
## .ssno 0.096 0.026 3.771 0.000 0.046
## .sscs 0.007 0.024 0.306 0.759 -0.040
## ci.upper Std.lv Std.all
## 0.569 0.523 0.539
## 0.436 0.392 0.404
## 0.257 0.211 0.212
## 0.634 0.582 0.570
## 0.440 0.395 0.419
## 0.287 0.242 0.249
## 0.608 0.563 0.600
## 0.262 0.214 0.211
## 0.666 0.614 0.570
## 0.816 0.769 0.790
## 0.146 0.096 0.091
## 0.054 0.007 0.007
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.193 0.009 21.395 0.000 0.176
## .sswk 0.199 0.010 19.392 0.000 0.179
## .sspc 0.244 0.012 20.217 0.000 0.221
## .ssei 0.338 0.017 20.349 0.000 0.306
## .ssar 0.191 0.011 17.437 0.000 0.169
## .ssmk 0.175 0.009 20.409 0.000 0.159
## .ssmc 0.264 0.013 21.066 0.000 0.239
## .ssao 0.508 0.019 27.060 0.000 0.471
## .ssai 0.496 0.026 19.343 0.000 0.446
## .sssi 0.296 0.019 15.947 0.000 0.260
## .ssno 0.339 0.022 15.056 0.000 0.295
## .sscs 0.402 0.023 17.183 0.000 0.356
## .verbal 2.059 0.659 3.124 0.002 0.767
## .math 0.643 0.145 4.432 0.000 0.359
## .electronic 5.593 0.723 7.732 0.000 4.175
## .speed 1.183 0.130 9.123 0.000 0.929
## g 1.334 0.071 18.673 0.000 1.194
## ci.upper Std.lv Std.all
## 0.211 0.193 0.205
## 0.219 0.199 0.211
## 0.268 0.244 0.246
## 0.371 0.338 0.325
## 0.212 0.191 0.215
## 0.192 0.175 0.186
## 0.288 0.264 0.300
## 0.545 0.508 0.495
## 0.547 0.496 0.428
## 0.333 0.296 0.312
## 0.383 0.339 0.301
## 0.448 0.402 0.406
## 3.351 0.078 0.078
## 0.927 0.087 0.087
## 7.011 0.660 0.660
## 1.438 0.471 0.471
## 1.474 1.000 1.000
lavTestScore(metric, release = 1:19)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 131.374 19 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p54. 1.622 1 0.203
## 2 .p2. == .p55. 38.056 1 0.000
## 3 .p3. == .p56. 2.745 1 0.098
## 4 .p4. == .p57. 75.460 1 0.000
## 5 .p5. == .p58. 4.718 1 0.030
## 6 .p6. == .p59. 11.862 1 0.001
## 7 .p7. == .p60. 0.188 1 0.664
## 8 .p8. == .p61. 0.275 1 0.600
## 9 .p9. == .p62. 0.509 1 0.476
## 10 .p10. == .p63. 3.081 1 0.079
## 11 .p11. == .p64. 0.342 1 0.559
## 12 .p12. == .p65. 69.487 1 0.000
## 13 .p13. == .p66. 0.914 1 0.339
## 14 .p14. == .p67. 1.033 1 0.309
## 15 .p15. == .p68. 7.753 1 0.005
## 16 .p16. == .p69. 7.645 1 0.006
## 17 .p17. == .p70. 0.001 1 0.970
## 18 .p18. == .p71. 22.548 1 0.000
## 19 .p19. == .p72. 0.055 1 0.814
metric2<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"), group.partial=c("electronic=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1304.120 107.000 0.000 0.962 0.078 0.041 87035.251
## bic
## 87488.212
Mc(metric2)
## [1] 0.8490558
scalar<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 8.270929e-14) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1878.781 114.000 0.000 0.944 0.092 0.047 87595.911
## bic
## 88005.437
Mc(scalar)
## [1] 0.7856671
summary(scalar, standardized=T, ci=T) # -.095
## lavaan 0.6-18 ended normally after 125 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 30
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1878.781 1628.436
## Degrees of freedom 114 114
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.154
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 769.326 666.815
## 0 1109.455 961.622
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.167 0.029 5.826 0.000 0.111
## sswk (.p2.) 0.167 0.029 5.794 0.000 0.111
## sspc (.p3.) 0.167 0.029 5.823 0.000 0.111
## ssei (.p4.) 0.100 0.018 5.622 0.000 0.065
## math =~
## ssar (.p5.) 0.307 0.019 16.345 0.000 0.270
## ssmk (.p6.) 0.228 0.016 14.211 0.000 0.196
## ssmc (.p7.) 0.183 0.012 15.116 0.000 0.159
## ssao (.p8.) 0.264 0.017 15.616 0.000 0.231
## electronic =~
## ssai (.p9.) 0.285 0.017 16.875 0.000 0.251
## sssi (.10.) 0.302 0.018 16.794 0.000 0.267
## ssmc (.11.) 0.144 0.010 14.890 0.000 0.125
## ssei 0.097 0.015 6.595 0.000 0.068
## speed =~
## ssno (.13.) 0.539 0.025 21.623 0.000 0.490
## sscs (.14.) 0.488 0.022 21.720 0.000 0.444
## ssmk (.15.) 0.203 0.012 16.737 0.000 0.180
## g =~
## verbal (.16.) 4.340 0.782 5.547 0.000 2.807
## math (.17.) 2.259 0.161 14.058 0.000 1.944
## elctrnc (.18.) 1.436 0.097 14.849 0.000 1.247
## speed (.19.) 1.021 0.062 16.608 0.000 0.901
## ci.upper Std.lv Std.all
##
## 0.224 0.745 0.868
## 0.224 0.745 0.871
## 0.223 0.742 0.841
## 0.134 0.443 0.574
##
## 0.344 0.758 0.896
## 0.259 0.562 0.630
## 0.206 0.451 0.548
## 0.298 0.653 0.715
##
## 0.318 0.498 0.666
## 0.338 0.529 0.694
## 0.163 0.252 0.306
## 0.125 0.169 0.219
##
## 0.588 0.771 0.814
## 0.532 0.698 0.757
## 0.227 0.291 0.325
##
## 5.873 0.974 0.974
## 2.574 0.914 0.914
## 1.626 0.821 0.821
## 1.142 0.715 0.715
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.417 0.020 20.402 0.000 0.377
## .sswk (.38.) 0.380 0.021 18.249 0.000 0.339
## .sspc (.39.) 0.335 0.022 15.573 0.000 0.293
## .ssei (.40.) 0.142 0.019 7.604 0.000 0.106
## .ssar (.41.) 0.354 0.020 17.385 0.000 0.314
## .ssmk (.42.) 0.359 0.022 16.517 0.000 0.316
## .ssmc (.43.) 0.237 0.019 12.543 0.000 0.200
## .ssao (.44.) 0.290 0.020 14.158 0.000 0.250
## .ssai (.45.) 0.025 0.017 1.506 0.132 -0.008
## .sssi (.46.) 0.077 0.018 4.381 0.000 0.043
## .ssno (.47.) 0.297 0.022 13.259 0.000 0.253
## .sscs (.48.) 0.302 0.022 13.849 0.000 0.260
## ci.upper Std.lv Std.all
## 0.457 0.417 0.486
## 0.421 0.380 0.444
## 0.377 0.335 0.380
## 0.179 0.142 0.184
## 0.394 0.354 0.419
## 0.401 0.359 0.402
## 0.274 0.237 0.288
## 0.330 0.290 0.317
## 0.059 0.025 0.034
## 0.112 0.077 0.101
## 0.341 0.297 0.313
## 0.345 0.302 0.328
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.181 0.009 19.282 0.000 0.163
## .sswk 0.176 0.009 20.534 0.000 0.159
## .sspc 0.227 0.013 17.600 0.000 0.202
## .ssei 0.252 0.011 23.085 0.000 0.230
## .ssar 0.141 0.009 16.334 0.000 0.124
## .ssmk 0.184 0.008 21.850 0.000 0.167
## .ssmc 0.240 0.012 20.057 0.000 0.216
## .ssao 0.409 0.017 23.910 0.000 0.375
## .ssai 0.311 0.015 20.380 0.000 0.281
## .sssi 0.301 0.015 19.817 0.000 0.271
## .ssno 0.303 0.019 15.962 0.000 0.266
## .sscs 0.362 0.019 18.776 0.000 0.324
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.200 0.181 0.246
## 0.193 0.176 0.241
## 0.253 0.227 0.292
## 0.273 0.252 0.422
## 0.158 0.141 0.197
## 0.200 0.184 0.230
## 0.263 0.240 0.354
## 0.442 0.409 0.489
## 0.341 0.311 0.557
## 0.330 0.301 0.518
## 0.341 0.303 0.338
## 0.400 0.362 0.427
## 1.000 0.050 0.050
## 1.000 0.164 0.164
## 1.000 0.326 0.326
## 1.000 0.489 0.489
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.167 0.029 5.826 0.000 0.111
## sswk (.p2.) 0.167 0.029 5.794 0.000 0.111
## sspc (.p3.) 0.167 0.029 5.823 0.000 0.111
## ssei (.p4.) 0.100 0.018 5.622 0.000 0.065
## math =~
## ssar (.p5.) 0.307 0.019 16.345 0.000 0.270
## ssmk (.p6.) 0.228 0.016 14.211 0.000 0.196
## ssmc (.p7.) 0.183 0.012 15.116 0.000 0.159
## ssao (.p8.) 0.264 0.017 15.616 0.000 0.231
## electronic =~
## ssai (.p9.) 0.285 0.017 16.875 0.000 0.251
## sssi (.10.) 0.302 0.018 16.794 0.000 0.267
## ssmc (.11.) 0.144 0.010 14.890 0.000 0.125
## ssei 0.192 0.013 14.326 0.000 0.166
## speed =~
## ssno (.13.) 0.539 0.025 21.623 0.000 0.490
## sscs (.14.) 0.488 0.022 21.720 0.000 0.444
## ssmk (.15.) 0.203 0.012 16.737 0.000 0.180
## g =~
## verbal (.16.) 4.340 0.782 5.547 0.000 2.807
## math (.17.) 2.259 0.161 14.058 0.000 1.944
## elctrnc (.18.) 1.436 0.097 14.849 0.000 1.247
## speed (.19.) 1.021 0.062 16.608 0.000 0.901
## ci.upper Std.lv Std.all
##
## 0.224 0.856 0.883
## 0.224 0.856 0.887
## 0.223 0.853 0.854
## 0.134 0.509 0.472
##
## 0.344 0.833 0.884
## 0.259 0.618 0.637
## 0.206 0.495 0.527
## 0.298 0.717 0.707
##
## 0.318 0.773 0.730
## 0.338 0.821 0.834
## 0.163 0.391 0.416
## 0.218 0.522 0.484
##
## 0.588 0.861 0.819
## 0.532 0.780 0.777
## 0.227 0.325 0.335
##
## 5.873 0.966 0.966
## 2.574 0.948 0.948
## 1.626 0.603 0.603
## 1.142 0.728 0.728
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.417 0.020 20.402 0.000 0.377
## .sswk (.38.) 0.380 0.021 18.249 0.000 0.339
## .sspc (.39.) 0.335 0.022 15.573 0.000 0.293
## .ssei (.40.) 0.142 0.019 7.604 0.000 0.106
## .ssar (.41.) 0.354 0.020 17.385 0.000 0.314
## .ssmk (.42.) 0.359 0.022 16.517 0.000 0.316
## .ssmc (.43.) 0.237 0.019 12.543 0.000 0.200
## .ssao (.44.) 0.290 0.020 14.158 0.000 0.250
## .ssai (.45.) 0.025 0.017 1.506 0.132 -0.008
## .sssi (.46.) 0.077 0.018 4.381 0.000 0.043
## .ssno (.47.) 0.297 0.022 13.259 0.000 0.253
## .sscs (.48.) 0.302 0.022 13.849 0.000 0.260
## .verbal -0.413 0.052 -7.971 0.000 -0.515
## .math -0.240 0.062 -3.876 0.000 -0.361
## .elctrnc 2.073 0.135 15.356 0.000 1.808
## .speed -0.598 0.060 -10.056 0.000 -0.715
## g 0.109 0.040 2.725 0.006 0.030
## ci.upper Std.lv Std.all
## 0.457 0.417 0.430
## 0.421 0.380 0.394
## 0.377 0.335 0.335
## 0.179 0.142 0.132
## 0.394 0.354 0.376
## 0.401 0.359 0.370
## 0.274 0.237 0.252
## 0.330 0.290 0.286
## 0.059 0.025 0.024
## 0.112 0.077 0.078
## 0.341 0.297 0.282
## 0.345 0.302 0.301
## -0.312 -0.081 -0.081
## -0.119 -0.088 -0.088
## 2.337 0.763 0.763
## -0.482 -0.375 -0.375
## 0.187 0.095 0.095
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.207 0.010 20.663 0.000 0.187
## .sswk 0.198 0.010 19.114 0.000 0.177
## .sspc 0.270 0.014 19.017 0.000 0.243
## .ssei 0.321 0.016 20.576 0.000 0.290
## .ssar 0.193 0.011 17.361 0.000 0.171
## .ssmk 0.175 0.009 20.176 0.000 0.158
## .ssmc 0.263 0.013 20.872 0.000 0.238
## .ssao 0.515 0.019 26.598 0.000 0.477
## .ssai 0.523 0.025 21.287 0.000 0.475
## .sssi 0.294 0.018 16.335 0.000 0.259
## .ssno 0.365 0.023 15.832 0.000 0.320
## .sscs 0.398 0.024 16.594 0.000 0.351
## .verbal 1.733 0.593 2.925 0.003 0.572
## .math 0.745 0.149 4.990 0.000 0.452
## .electronic 4.697 0.608 7.724 0.000 3.505
## .speed 1.199 0.132 9.101 0.000 0.941
## g 1.297 0.069 18.782 0.000 1.162
## ci.upper Std.lv Std.all
## 0.227 0.207 0.220
## 0.218 0.198 0.212
## 0.298 0.270 0.271
## 0.351 0.321 0.276
## 0.215 0.193 0.218
## 0.192 0.175 0.186
## 0.288 0.263 0.298
## 0.553 0.515 0.500
## 0.572 0.523 0.467
## 0.329 0.294 0.304
## 0.410 0.365 0.330
## 0.445 0.398 0.396
## 2.895 0.066 0.066
## 1.038 0.101 0.101
## 5.888 0.637 0.637
## 1.458 0.470 0.470
## 1.433 1.000 1.000
lavTestScore(scalar, release = 19:30)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 556.944 12 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p37. == .p90. 243.241 1 0.000
## 2 .p38. == .p91. 0.221 1 0.638
## 3 .p39. == .p92. 312.317 1 0.000
## 4 .p40. == .p93. 0.893 1 0.345
## 5 .p41. == .p94. 57.868 1 0.000
## 6 .p42. == .p95. 13.363 1 0.000
## 7 .p43. == .p96. 0.520 1 0.471
## 8 .p44. == .p97. 46.686 1 0.000
## 9 .p45. == .p98. 23.192 1 0.000
## 10 .p46. == .p99. 11.775 1 0.001
## 11 .p47. == .p100. 81.594 1 0.000
## 12 .p48. == .p101. 49.774 1 0.000
scalar2<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1")) # not freeing gs leads to poor fit
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 4.770394e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1390.367 111.000 0.000 0.960 0.079 0.043 87113.497
## bic
## 87541.638
Mc(scalar2)
## [1] 0.8395641
summary(scalar2, standardized=T, ci=T) # -.083
## lavaan 0.6-18 ended normally after 127 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 27
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1390.367 1200.624
## Degrees of freedom 111 111
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.158
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 541.284 467.416
## 0 849.082 733.209
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.172 0.028 6.213 0.000 0.118
## sswk (.p2.) 0.171 0.028 6.177 0.000 0.117
## sspc (.p3.) 0.172 0.028 6.199 0.000 0.118
## ssei (.p4.) 0.102 0.017 5.978 0.000 0.069
## math =~
## ssar (.p5.) 0.304 0.019 16.111 0.000 0.267
## ssmk (.p6.) 0.230 0.016 14.428 0.000 0.198
## ssmc (.p7.) 0.182 0.012 14.959 0.000 0.158
## ssao (.p8.) 0.261 0.017 15.403 0.000 0.228
## electronic =~
## ssai (.p9.) 0.286 0.017 16.993 0.000 0.253
## sssi (.10.) 0.304 0.018 16.904 0.000 0.269
## ssmc (.11.) 0.143 0.010 14.856 0.000 0.124
## ssei 0.097 0.015 6.599 0.000 0.068
## speed =~
## ssno (.13.) 0.558 0.026 21.803 0.000 0.508
## sscs (.14.) 0.482 0.022 22.429 0.000 0.440
## ssmk (.15.) 0.197 0.011 17.167 0.000 0.174
## g =~
## verbal (.16.) 4.221 0.717 5.890 0.000 2.816
## math (.17.) 2.285 0.164 13.906 0.000 1.963
## elctrnc (.18.) 1.427 0.096 14.921 0.000 1.239
## speed (.19.) 1.004 0.060 16.716 0.000 0.886
## ci.upper Std.lv Std.all
##
## 0.226 0.746 0.875
## 0.226 0.744 0.870
## 0.226 0.746 0.853
## 0.136 0.443 0.574
##
## 0.341 0.759 0.896
## 0.261 0.573 0.642
## 0.206 0.454 0.551
## 0.295 0.652 0.713
##
## 0.319 0.498 0.666
## 0.339 0.529 0.695
## 0.162 0.249 0.302
## 0.126 0.169 0.219
##
## 0.608 0.791 0.830
## 0.525 0.684 0.749
## 0.219 0.279 0.313
##
## 5.626 0.973 0.973
## 2.607 0.916 0.916
## 1.614 0.819 0.819
## 1.122 0.709 0.709
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.330 0.021 16.008 0.000 0.289
## .sswk (.38.) 0.376 0.021 17.638 0.000 0.334
## .sspc 0.452 0.022 21.000 0.000 0.409
## .ssei (.40.) 0.142 0.019 7.564 0.000 0.105
## .ssar (.41.) 0.348 0.020 17.035 0.000 0.308
## .ssmk (.42.) 0.376 0.022 17.316 0.000 0.334
## .ssmc (.43.) 0.235 0.019 12.435 0.000 0.198
## .ssao (.44.) 0.284 0.021 13.843 0.000 0.244
## .ssai (.45.) 0.027 0.017 1.570 0.117 -0.007
## .sssi (.46.) 0.079 0.018 4.466 0.000 0.044
## .ssno 0.243 0.023 10.402 0.000 0.197
## .sscs (.48.) 0.360 0.022 16.418 0.000 0.317
## ci.upper Std.lv Std.all
## 0.370 0.330 0.386
## 0.418 0.376 0.440
## 0.494 0.452 0.516
## 0.179 0.142 0.184
## 0.388 0.348 0.411
## 0.419 0.376 0.422
## 0.272 0.235 0.285
## 0.324 0.284 0.311
## 0.060 0.027 0.035
## 0.113 0.079 0.103
## 0.289 0.243 0.255
## 0.402 0.360 0.394
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.171 0.009 19.874 0.000 0.154
## .sswk 0.177 0.008 20.919 0.000 0.161
## .sspc 0.209 0.012 17.979 0.000 0.186
## .ssei 0.253 0.011 23.140 0.000 0.231
## .ssar 0.141 0.009 16.493 0.000 0.124
## .ssmk 0.183 0.008 22.067 0.000 0.167
## .ssmc 0.240 0.012 20.093 0.000 0.217
## .ssao 0.410 0.017 23.981 0.000 0.377
## .ssai 0.311 0.015 20.348 0.000 0.281
## .sssi 0.300 0.015 19.778 0.000 0.271
## .ssno 0.282 0.019 14.942 0.000 0.245
## .sscs 0.366 0.019 19.335 0.000 0.329
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.188 0.171 0.235
## 0.194 0.177 0.243
## 0.231 0.209 0.273
## 0.274 0.253 0.423
## 0.157 0.141 0.196
## 0.199 0.183 0.230
## 0.264 0.240 0.355
## 0.444 0.410 0.491
## 0.341 0.311 0.556
## 0.330 0.300 0.517
## 0.319 0.282 0.311
## 0.403 0.366 0.439
## 1.000 0.053 0.053
## 1.000 0.161 0.161
## 1.000 0.329 0.329
## 1.000 0.498 0.498
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.172 0.028 6.213 0.000 0.118
## sswk (.p2.) 0.171 0.028 6.177 0.000 0.117
## sspc (.p3.) 0.172 0.028 6.199 0.000 0.118
## ssei (.p4.) 0.102 0.017 5.978 0.000 0.069
## math =~
## ssar (.p5.) 0.304 0.019 16.111 0.000 0.267
## ssmk (.p6.) 0.230 0.016 14.428 0.000 0.198
## ssmc (.p7.) 0.182 0.012 14.959 0.000 0.158
## ssao (.p8.) 0.261 0.017 15.403 0.000 0.228
## electronic =~
## ssai (.p9.) 0.286 0.017 16.993 0.000 0.253
## sssi (.10.) 0.304 0.018 16.904 0.000 0.269
## ssmc (.11.) 0.143 0.010 14.856 0.000 0.124
## ssei 0.192 0.013 14.313 0.000 0.166
## speed =~
## ssno (.13.) 0.558 0.026 21.803 0.000 0.508
## sscs (.14.) 0.482 0.022 22.429 0.000 0.440
## ssmk (.15.) 0.197 0.011 17.167 0.000 0.174
## g =~
## verbal (.16.) 4.221 0.717 5.890 0.000 2.816
## math (.17.) 2.285 0.164 13.906 0.000 1.963
## elctrnc (.18.) 1.427 0.096 14.921 0.000 1.239
## speed (.19.) 1.004 0.060 16.716 0.000 0.886
## ci.upper Std.lv Std.all
##
## 0.226 0.859 0.890
## 0.226 0.855 0.886
## 0.226 0.858 0.866
## 0.136 0.510 0.474
##
## 0.341 0.833 0.885
## 0.261 0.629 0.650
## 0.206 0.498 0.531
## 0.295 0.716 0.706
##
## 0.319 0.775 0.731
## 0.339 0.823 0.836
## 0.162 0.387 0.412
## 0.219 0.520 0.483
##
## 0.608 0.884 0.835
## 0.525 0.764 0.769
## 0.219 0.312 0.322
##
## 5.626 0.964 0.964
## 2.607 0.951 0.951
## 1.614 0.601 0.601
## 1.122 0.723 0.723
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.505 0.025 19.968 0.000 0.455
## .sswk (.38.) 0.376 0.021 17.638 0.000 0.334
## .sspc 0.193 0.026 7.343 0.000 0.141
## .ssei (.40.) 0.142 0.019 7.564 0.000 0.105
## .ssar (.41.) 0.348 0.020 17.035 0.000 0.308
## .ssmk (.42.) 0.376 0.022 17.316 0.000 0.334
## .ssmc (.43.) 0.235 0.019 12.435 0.000 0.198
## .ssao (.44.) 0.284 0.021 13.843 0.000 0.244
## .ssai (.45.) 0.027 0.017 1.570 0.117 -0.007
## .sssi (.46.) 0.079 0.018 4.466 0.000 0.044
## .ssno 0.507 0.033 15.458 0.000 0.443
## .sscs (.48.) 0.360 0.022 16.418 0.000 0.317
## .verbal -0.292 0.053 -5.508 0.000 -0.396
## .math -0.156 0.062 -2.512 0.012 -0.278
## .elctrnc 2.074 0.135 15.413 0.000 1.810
## .speed -0.831 0.065 -12.718 0.000 -0.959
## g 0.094 0.039 2.427 0.015 0.018
## ci.upper Std.lv Std.all
## 0.554 0.505 0.523
## 0.418 0.376 0.390
## 0.244 0.193 0.195
## 0.179 0.142 0.132
## 0.388 0.348 0.369
## 0.419 0.376 0.389
## 0.272 0.235 0.250
## 0.324 0.284 0.280
## 0.060 0.027 0.025
## 0.113 0.079 0.080
## 0.572 0.507 0.479
## 0.402 0.360 0.362
## -0.188 -0.058 -0.058
## -0.034 -0.057 -0.057
## 2.338 0.766 0.766
## -0.703 -0.525 -0.525
## 0.171 0.083 0.083
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.194 0.009 21.542 0.000 0.176
## .sswk 0.199 0.010 19.472 0.000 0.179
## .sspc 0.246 0.012 20.321 0.000 0.222
## .ssei 0.322 0.016 20.603 0.000 0.291
## .ssar 0.192 0.011 17.477 0.000 0.171
## .ssmk 0.175 0.009 20.394 0.000 0.158
## .ssmc 0.264 0.013 20.917 0.000 0.239
## .ssao 0.517 0.019 26.567 0.000 0.478
## .ssai 0.522 0.025 21.238 0.000 0.474
## .sssi 0.292 0.018 16.199 0.000 0.257
## .ssno 0.338 0.023 15.019 0.000 0.294
## .sscs 0.403 0.023 17.374 0.000 0.358
## .verbal 1.749 0.558 3.132 0.002 0.654
## .math 0.724 0.149 4.850 0.000 0.431
## .electronic 4.690 0.604 7.770 0.000 3.507
## .speed 1.199 0.131 9.178 0.000 0.943
## g 1.299 0.069 18.814 0.000 1.164
## ci.upper Std.lv Std.all
## 0.212 0.194 0.208
## 0.220 0.199 0.214
## 0.269 0.246 0.250
## 0.352 0.322 0.277
## 0.214 0.192 0.217
## 0.192 0.175 0.186
## 0.288 0.264 0.299
## 0.555 0.517 0.502
## 0.570 0.522 0.465
## 0.328 0.292 0.302
## 0.382 0.338 0.302
## 0.448 0.403 0.408
## 2.843 0.070 0.070
## 1.016 0.096 0.096
## 5.873 0.639 0.639
## 1.456 0.478 0.478
## 1.435 1.000 1.000
lavTestScore(scalar2, release = 19:27, standardized=T, epc=T)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 85.672 9 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p38. == .p91. 0.660 1 0.417
## 2 .p40. == .p93. 0.660 1 0.417
## 3 .p41. == .p94. 35.106 1 0.000
## 4 .p42. == .p95. 0.483 1 0.487
## 5 .p43. == .p96. 0.096 1 0.757
## 6 .p44. == .p97. 56.852 1 0.000
## 7 .p45. == .p98. 21.964 1 0.000
## 8 .p46. == .p99. 13.436 1 0.000
## 9 .p48. == .p101. 0.483 1 0.487
##
## $epc
##
## expected parameter changes (epc) and expected parameter values (epv):
##
## lhs op rhs block group free label plabel est epc
## 1 verbal =~ ssgs 1 1 1 .p1. .p1. 0.172 0.001
## 2 verbal =~ sswk 1 1 2 .p2. .p2. 0.171 0.001
## 3 verbal =~ sspc 1 1 3 .p3. .p3. 0.172 0.001
## 4 verbal =~ ssei 1 1 4 .p4. .p4. 0.102 0.001
## 5 math =~ ssar 1 1 5 .p5. .p5. 0.304 -0.001
## 6 math =~ ssmk 1 1 6 .p6. .p6. 0.230 0.000
## 7 math =~ ssmc 1 1 7 .p7. .p7. 0.182 0.000
## 8 math =~ ssao 1 1 8 .p8. .p8. 0.261 0.000
## 9 electronic =~ ssai 1 1 9 .p9. .p9. 0.286 0.010
## 10 electronic =~ sssi 1 1 10 .p10. .p10. 0.304 -0.008
## 11 electronic =~ ssmc 1 1 11 .p11. .p11. 0.143 -0.002
## 12 electronic =~ ssei 1 1 12 .p12. 0.097 -0.002
## 13 speed =~ ssno 1 1 13 .p13. .p13. 0.558 0.001
## 14 speed =~ sscs 1 1 14 .p14. .p14. 0.482 0.001
## 15 speed =~ ssmk 1 1 15 .p15. .p15. 0.197 -0.002
## 16 g =~ verbal 1 1 16 .p16. .p16. 4.221 -0.015
## 17 g =~ math 1 1 17 .p17. .p17. 2.285 0.007
## 18 g =~ electronic 1 1 18 .p18. .p18. 1.427 0.003
## 19 g =~ speed 1 1 19 .p19. .p19. 1.004 -0.001
## 20 ssgs ~~ ssgs 1 1 20 .p20. 0.171 0.000
## 21 sswk ~~ sswk 1 1 21 .p21. 0.177 0.000
## 22 sspc ~~ sspc 1 1 22 .p22. 0.209 0.000
## 23 ssei ~~ ssei 1 1 23 .p23. 0.253 0.000
## 24 ssar ~~ ssar 1 1 24 .p24. 0.141 0.000
## 25 ssmk ~~ ssmk 1 1 25 .p25. 0.183 0.000
## 26 ssmc ~~ ssmc 1 1 26 .p26. 0.240 0.000
## 27 ssao ~~ ssao 1 1 27 .p27. 0.410 0.000
## 28 ssai ~~ ssai 1 1 28 .p28. 0.311 -0.006
## 29 sssi ~~ sssi 1 1 29 .p29. 0.300 0.005
## 30 ssno ~~ ssno 1 1 30 .p30. 0.282 0.000
## 31 sscs ~~ sscs 1 1 31 .p31. 0.366 0.000
## 32 verbal ~~ verbal 1 1 0 .p32. 1.000 NA
## 33 math ~~ math 1 1 0 .p33. 1.000 NA
## 34 electronic ~~ electronic 1 1 0 .p34. 1.000 NA
## 35 speed ~~ speed 1 1 0 .p35. 1.000 NA
## 36 g ~~ g 1 1 0 .p36. 1.000 NA
## 37 ssgs ~1 1 1 32 .p37. 0.330 0.002
## 38 sswk ~1 1 1 33 .p38. .p38. 0.376 0.003
## 39 sspc ~1 1 1 34 .p39. 0.452 0.002
## 40 ssei ~1 1 1 35 .p40. .p40. 0.142 -0.003
## 41 ssar ~1 1 1 36 .p41. .p41. 0.348 -0.021
## 42 ssmk ~1 1 1 37 .p42. .p42. 0.376 0.005
## 43 ssmc ~1 1 1 38 .p43. .p43. 0.235 0.000
## 44 ssao ~1 1 1 39 .p44. .p44. 0.284 0.072
## 45 ssai ~1 1 1 40 .p45. .p45. 0.027 0.029
## 46 sssi ~1 1 1 41 .p46. .p46. 0.079 -0.019
## 47 ssno ~1 1 1 42 .p47. 0.243 0.001
## 48 sscs ~1 1 1 43 .p48. .p48. 0.360 -0.002
## 49 verbal ~1 1 1 0 .p49. 0.000 NA
## 50 math ~1 1 1 0 .p50. 0.000 NA
## 51 electronic ~1 1 1 0 .p51. 0.000 NA
## 52 speed ~1 1 1 0 .p52. 0.000 NA
## 53 g ~1 1 1 0 .p53. 0.000 NA
## 54 verbal =~ ssgs 2 2 44 .p1. .p54. 0.172 0.001
## 55 verbal =~ sswk 2 2 45 .p2. .p55. 0.171 0.001
## 56 verbal =~ sspc 2 2 46 .p3. .p56. 0.172 0.001
## 57 verbal =~ ssei 2 2 47 .p4. .p57. 0.102 0.001
## 58 math =~ ssar 2 2 48 .p5. .p58. 0.304 -0.001
## 59 math =~ ssmk 2 2 49 .p6. .p59. 0.230 0.000
## 60 math =~ ssmc 2 2 50 .p7. .p60. 0.182 0.000
## 61 math =~ ssao 2 2 51 .p8. .p61. 0.261 0.000
## 62 electronic =~ ssai 2 2 52 .p9. .p62. 0.286 0.010
## 63 electronic =~ sssi 2 2 53 .p10. .p63. 0.304 -0.008
## 64 electronic =~ ssmc 2 2 54 .p11. .p64. 0.143 -0.002
## 65 electronic =~ ssei 2 2 55 .p65. 0.192 -0.004
## 66 speed =~ ssno 2 2 56 .p13. .p66. 0.558 0.001
## 67 speed =~ sscs 2 2 57 .p14. .p67. 0.482 0.001
## 68 speed =~ ssmk 2 2 58 .p15. .p68. 0.197 -0.002
## 69 g =~ verbal 2 2 59 .p16. .p69. 4.221 -0.015
## 70 g =~ math 2 2 60 .p17. .p70. 2.285 0.007
## 71 g =~ electronic 2 2 61 .p18. .p71. 1.427 0.003
## epv sepc.lv sepc.all sepc.nox
## 1 0.173 0.002 0.003 0.003
## 2 0.172 0.002 0.003 0.003
## 3 0.172 0.002 0.003 0.003
## 4 0.103 0.005 0.007 0.007
## 5 0.303 -0.003 -0.003 -0.003
## 6 0.230 0.001 0.001 0.001
## 7 0.182 0.001 0.001 0.001
## 8 0.261 -0.001 -0.001 -0.001
## 9 0.296 0.017 0.023 0.023
## 10 0.295 -0.015 -0.019 -0.019
## 11 0.141 -0.003 -0.004 -0.004
## 12 0.095 -0.004 -0.005 -0.005
## 13 0.559 0.001 0.001 0.001
## 14 0.483 0.001 0.002 0.002
## 15 0.194 -0.003 -0.004 -0.004
## 16 4.207 -0.003 -0.003 -0.003
## 17 2.292 0.003 0.003 0.003
## 18 1.430 0.002 0.002 0.002
## 19 1.003 -0.001 -0.001 -0.001
## 20 0.171 0.171 0.235 0.235
## 21 0.178 0.177 0.243 0.243
## 22 0.209 0.209 0.273 0.273
## 23 0.253 0.253 0.423 0.423
## 24 0.141 0.141 0.196 0.196
## 25 0.183 0.183 0.230 0.230
## 26 0.240 0.240 0.355 0.355
## 27 0.410 -0.410 -0.491 -0.491
## 28 0.305 -0.311 -0.556 -0.556
## 29 0.306 0.300 0.517 0.517
## 30 0.282 -0.282 -0.311 -0.311
## 31 0.365 -0.366 -0.439 -0.439
## 32 NA NA NA NA
## 33 NA NA NA NA
## 34 NA NA NA NA
## 35 NA NA NA NA
## 36 NA NA NA NA
## 37 0.331 0.002 0.002 0.002
## 38 0.379 0.003 0.004 0.004
## 39 0.453 0.002 0.002 0.002
## 40 0.139 -0.003 -0.003 -0.003
## 41 0.327 -0.021 -0.024 -0.024
## 42 0.382 0.005 0.006 0.006
## 43 0.235 0.000 0.000 0.000
## 44 0.356 0.072 0.078 0.078
## 45 0.055 0.029 0.038 0.038
## 46 0.059 -0.019 -0.025 -0.025
## 47 0.244 0.001 0.001 0.001
## 48 0.358 -0.002 -0.002 -0.002
## 49 NA NA NA NA
## 50 NA NA NA NA
## 51 NA NA NA NA
## 52 NA NA NA NA
## 53 NA NA NA NA
## 54 0.173 0.003 0.003 0.003
## 55 0.172 0.003 0.003 0.003
## 56 0.172 0.003 0.003 0.003
## 57 0.103 0.006 0.006 0.006
## 58 0.303 -0.003 -0.003 -0.003
## 59 0.230 0.001 0.001 0.001
## 60 0.182 0.001 0.001 0.001
## 61 0.261 -0.001 -0.001 -0.001
## 62 0.296 0.027 0.025 0.025
## 63 0.295 -0.023 -0.023 -0.023
## 64 0.141 -0.005 -0.006 -0.006
## 65 0.188 -0.012 -0.011 -0.011
## 66 0.559 0.001 0.001 0.001
## 67 0.483 0.002 0.002 0.002
## 68 0.194 -0.004 -0.004 -0.004
## 69 4.207 -0.003 -0.003 -0.003
## 70 2.292 0.003 0.003 0.003
## 71 1.430 0.001 0.001 0.001
## [ reached 'max' / getOption("max.print") -- omitted 35 rows ]
strict<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 1.078965e-12) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1560.720 123.000 0.000 0.955 0.080 0.047 87259.850
## bic
## 87613.532
Mc(strict)
## [1] 0.8215872
summary(strict, standardized=T, ci=T) # -.095
## lavaan 0.6-18 ended normally after 127 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 39
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1560.720 1334.585
## Degrees of freedom 123 123
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.169
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 649.640 555.513
## 0 911.079 779.072
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.167 0.029 5.866 0.000 0.112
## sswk (.p2.) 0.166 0.028 5.836 0.000 0.110
## sspc (.p3.) 0.167 0.029 5.858 0.000 0.111
## ssei (.p4.) 0.100 0.018 5.674 0.000 0.066
## math =~
## ssar (.p5.) 0.285 0.020 14.138 0.000 0.245
## ssmk (.p6.) 0.215 0.017 12.745 0.000 0.182
## ssmc (.p7.) 0.170 0.013 13.373 0.000 0.145
## ssao (.p8.) 0.244 0.018 13.616 0.000 0.209
## electronic =~
## ssai (.p9.) 0.265 0.019 14.079 0.000 0.228
## sssi (.10.) 0.272 0.020 13.478 0.000 0.232
## ssmc (.11.) 0.129 0.010 12.439 0.000 0.108
## ssei 0.083 0.014 5.847 0.000 0.055
## speed =~
## ssno (.13.) 0.548 0.026 21.169 0.000 0.498
## sscs (.14.) 0.473 0.022 21.659 0.000 0.431
## ssmk (.15.) 0.192 0.012 16.319 0.000 0.169
## g =~
## verbal (.16.) 4.339 0.776 5.592 0.000 2.818
## math (.17.) 2.451 0.197 12.457 0.000 2.065
## elctrnc (.18.) 1.576 0.126 12.524 0.000 1.330
## speed (.19.) 1.025 0.063 16.320 0.000 0.902
## ci.upper Std.lv Std.all
##
## 0.223 0.746 0.868
## 0.222 0.740 0.862
## 0.223 0.744 0.842
## 0.135 0.447 0.566
##
## 0.324 0.754 0.879
## 0.248 0.569 0.642
## 0.195 0.451 0.546
## 0.279 0.647 0.688
##
## 0.302 0.494 0.614
## 0.311 0.508 0.673
## 0.149 0.240 0.290
## 0.111 0.155 0.197
##
## 0.599 0.785 0.816
## 0.516 0.678 0.738
## 0.215 0.275 0.310
##
## 5.860 0.974 0.974
## 2.836 0.926 0.926
## 1.823 0.844 0.844
## 1.148 0.716 0.716
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.329 0.021 16.003 0.000 0.289
## .sswk (.38.) 0.375 0.021 17.655 0.000 0.333
## .sspc 0.451 0.021 21.009 0.000 0.409
## .ssei (.40.) 0.143 0.019 7.586 0.000 0.106
## .ssar (.41.) 0.351 0.021 17.103 0.000 0.311
## .ssmk (.42.) 0.376 0.022 17.306 0.000 0.333
## .ssmc (.43.) 0.236 0.019 12.530 0.000 0.199
## .ssao (.44.) 0.274 0.020 13.541 0.000 0.235
## .ssai (.45.) 0.011 0.017 0.673 0.501 -0.022
## .sssi (.46.) 0.084 0.018 4.767 0.000 0.049
## .ssno 0.243 0.023 10.395 0.000 0.197
## .sscs (.48.) 0.360 0.022 16.428 0.000 0.317
## ci.upper Std.lv Std.all
## 0.369 0.329 0.383
## 0.417 0.375 0.437
## 0.493 0.451 0.511
## 0.180 0.143 0.181
## 0.391 0.351 0.409
## 0.418 0.376 0.424
## 0.273 0.236 0.286
## 0.314 0.274 0.292
## 0.045 0.011 0.014
## 0.118 0.084 0.111
## 0.289 0.243 0.252
## 0.403 0.360 0.391
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.20.) 0.182 0.006 28.601 0.000 0.170
## .sswk (.21.) 0.189 0.007 27.852 0.000 0.176
## .sspc (.22.) 0.227 0.009 26.479 0.000 0.211
## .ssei (.23.) 0.285 0.009 30.204 0.000 0.266
## .ssar (.24.) 0.167 0.007 23.871 0.000 0.153
## .ssmk (.25.) 0.180 0.006 29.513 0.000 0.168
## .ssmc (.26.) 0.253 0.009 29.005 0.000 0.236
## .ssao (.27.) 0.465 0.013 35.640 0.000 0.439
## .ssai (.28.) 0.403 0.015 27.599 0.000 0.375
## .sssi (.29.) 0.311 0.012 25.629 0.000 0.287
## .ssno (.30.) 0.310 0.016 19.823 0.000 0.279
## .sscs (.31.) 0.385 0.015 24.847 0.000 0.354
## .verbal 1.000 1.000
## .math 1.000 1.000
## .elctrnc 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.195 0.182 0.247
## 0.203 0.189 0.257
## 0.244 0.227 0.291
## 0.303 0.285 0.457
## 0.181 0.167 0.227
## 0.192 0.180 0.228
## 0.270 0.253 0.370
## 0.490 0.465 0.526
## 0.432 0.403 0.623
## 0.335 0.311 0.547
## 0.340 0.310 0.334
## 0.415 0.385 0.456
## 1.000 0.050 0.050
## 1.000 0.143 0.143
## 1.000 0.287 0.287
## 1.000 0.488 0.488
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.167 0.029 5.866 0.000 0.112
## sswk (.p2.) 0.166 0.028 5.836 0.000 0.110
## sspc (.p3.) 0.167 0.029 5.858 0.000 0.111
## ssei (.p4.) 0.100 0.018 5.674 0.000 0.066
## math =~
## ssar (.p5.) 0.285 0.020 14.138 0.000 0.245
## ssmk (.p6.) 0.215 0.017 12.745 0.000 0.182
## ssmc (.p7.) 0.170 0.013 13.373 0.000 0.145
## ssao (.p8.) 0.244 0.018 13.616 0.000 0.209
## electronic =~
## ssai (.p9.) 0.265 0.019 14.079 0.000 0.228
## sssi (.10.) 0.272 0.020 13.478 0.000 0.232
## ssmc (.11.) 0.129 0.010 12.439 0.000 0.108
## ssei 0.174 0.014 12.240 0.000 0.146
## speed =~
## ssno (.13.) 0.548 0.026 21.169 0.000 0.498
## sscs (.14.) 0.473 0.022 21.659 0.000 0.431
## ssmk (.15.) 0.192 0.012 16.319 0.000 0.169
## g =~
## verbal (.16.) 4.339 0.776 5.592 0.000 2.818
## math (.17.) 2.451 0.197 12.457 0.000 2.065
## elctrnc (.18.) 1.576 0.126 12.524 0.000 1.330
## speed (.19.) 1.025 0.063 16.320 0.000 0.902
## ci.upper Std.lv Std.all
##
## 0.223 0.862 0.896
## 0.222 0.855 0.891
## 0.223 0.859 0.874
## 0.135 0.516 0.484
##
## 0.324 0.840 0.899
## 0.248 0.634 0.651
## 0.195 0.503 0.537
## 0.279 0.721 0.726
##
## 0.302 0.800 0.783
## 0.311 0.822 0.827
## 0.149 0.389 0.415
## 0.201 0.525 0.493
##
## 0.599 0.891 0.848
## 0.516 0.770 0.779
## 0.215 0.312 0.321
##
## 5.860 0.961 0.961
## 2.836 0.947 0.947
## 1.823 0.594 0.594
## 1.148 0.718 0.718
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.503 0.025 19.938 0.000 0.454
## .sswk (.38.) 0.375 0.021 17.655 0.000 0.333
## .sspc 0.192 0.026 7.312 0.000 0.140
## .ssei (.40.) 0.143 0.019 7.586 0.000 0.106
## .ssar (.41.) 0.351 0.021 17.103 0.000 0.311
## .ssmk (.42.) 0.376 0.022 17.306 0.000 0.333
## .ssmc (.43.) 0.236 0.019 12.530 0.000 0.199
## .ssao (.44.) 0.274 0.020 13.541 0.000 0.235
## .ssai (.45.) 0.011 0.017 0.673 0.501 -0.022
## .sssi (.46.) 0.084 0.018 4.767 0.000 0.049
## .ssno 0.508 0.033 15.461 0.000 0.444
## .sscs (.48.) 0.360 0.022 16.428 0.000 0.317
## .verbal -0.351 0.065 -5.393 0.000 -0.478
## .math -0.198 0.070 -2.837 0.005 -0.335
## .elctrnc 2.260 0.172 13.104 0.000 1.922
## .speed -0.862 0.070 -12.303 0.000 -0.999
## g 0.108 0.039 2.754 0.006 0.031
## ci.upper Std.lv Std.all
## 0.553 0.503 0.523
## 0.417 0.375 0.391
## 0.243 0.192 0.195
## 0.180 0.143 0.134
## 0.391 0.351 0.376
## 0.418 0.376 0.386
## 0.273 0.236 0.252
## 0.314 0.274 0.277
## 0.045 0.011 0.011
## 0.118 0.084 0.084
## 0.573 0.508 0.484
## 0.403 0.360 0.364
## -0.223 -0.068 -0.068
## -0.061 -0.067 -0.067
## 2.598 0.748 0.748
## -0.725 -0.530 -0.530
## 0.185 0.095 0.095
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.20.) 0.182 0.006 28.601 0.000 0.170
## .sswk (.21.) 0.189 0.007 27.852 0.000 0.176
## .sspc (.22.) 0.227 0.009 26.479 0.000 0.211
## .ssei (.23.) 0.285 0.009 30.204 0.000 0.266
## .ssar (.24.) 0.167 0.007 23.871 0.000 0.153
## .ssmk (.25.) 0.180 0.006 29.513 0.000 0.168
## .ssmc (.26.) 0.253 0.009 29.005 0.000 0.236
## .ssao (.27.) 0.465 0.013 35.640 0.000 0.439
## .ssai (.28.) 0.403 0.015 27.599 0.000 0.375
## .sssi (.29.) 0.311 0.012 25.629 0.000 0.287
## .ssno (.30.) 0.310 0.016 19.823 0.000 0.279
## .sscs (.31.) 0.385 0.015 24.847 0.000 0.354
## .verbal 2.026 0.645 3.141 0.002 0.762
## .math 0.904 0.174 5.193 0.000 0.563
## .elctrnc 5.912 0.907 6.518 0.000 4.134
## .speed 1.279 0.141 9.060 0.000 1.002
## g 1.298 0.069 18.864 0.000 1.163
## ci.upper Std.lv Std.all
## 0.195 0.182 0.197
## 0.203 0.189 0.206
## 0.244 0.227 0.235
## 0.303 0.285 0.250
## 0.181 0.167 0.191
## 0.192 0.180 0.189
## 0.270 0.253 0.288
## 0.490 0.465 0.472
## 0.432 0.403 0.387
## 0.335 0.311 0.315
## 0.340 0.310 0.281
## 0.415 0.385 0.394
## 3.291 0.077 0.077
## 1.246 0.104 0.104
## 7.690 0.647 0.647
## 1.555 0.484 0.484
## 1.433 1.000 1.000
latent<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 5.834047e-14) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1706.346 116.000 0.000 0.950 0.087 0.102 87419.477
## bic
## 87816.593
Mc(latent)
## [1] 0.8046248
summary(latent, standardized=T, ci=T) # -.039
## lavaan 0.6-18 ended normally after 84 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 91
## Number of equality constraints 27
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1706.346 1468.065
## Degrees of freedom 116 116
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.162
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 714.251 614.510
## 0 992.095 853.555
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.187 0.027 6.922 0.000 0.134
## sswk (.p2.) 0.186 0.027 6.879 0.000 0.133
## sspc (.p3.) 0.186 0.027 6.900 0.000 0.133
## ssei (.p4.) 0.107 0.016 6.532 0.000 0.075
## math =~
## ssar (.p5.) 0.294 0.018 15.956 0.000 0.258
## ssmk (.p6.) 0.221 0.016 14.244 0.000 0.191
## ssmc (.p7.) 0.173 0.012 14.968 0.000 0.151
## ssao (.p8.) 0.252 0.016 15.344 0.000 0.220
## electronic =~
## ssai (.p9.) 0.451 0.015 29.151 0.000 0.421
## sssi (.10.) 0.492 0.015 31.948 0.000 0.462
## ssmc (.11.) 0.237 0.011 22.440 0.000 0.216
## ssei 0.152 0.017 9.043 0.000 0.119
## speed =~
## ssno (.13.) 0.588 0.021 28.514 0.000 0.548
## sscs (.14.) 0.509 0.017 30.101 0.000 0.476
## ssmk (.15.) 0.210 0.011 19.237 0.000 0.188
## g =~
## verbal (.16.) 4.194 0.642 6.530 0.000 2.935
## math (.17.) 2.523 0.185 13.650 0.000 2.161
## elctrnc (.18.) 0.985 0.044 22.315 0.000 0.898
## speed (.19.) 1.015 0.051 19.980 0.000 0.915
## ci.upper Std.lv Std.all
##
## 0.239 0.804 0.889
## 0.239 0.802 0.887
## 0.239 0.803 0.871
## 0.139 0.462 0.575
##
## 0.330 0.798 0.906
## 0.252 0.601 0.644
## 0.196 0.470 0.535
## 0.284 0.685 0.729
##
## 0.481 0.633 0.757
## 0.522 0.691 0.802
## 0.257 0.332 0.378
## 0.185 0.213 0.266
##
## 0.629 0.838 0.849
## 0.542 0.725 0.768
## 0.231 0.299 0.321
##
## 5.453 0.973 0.973
## 2.885 0.930 0.930
## 1.071 0.702 0.702
## 1.114 0.712 0.712
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.336 0.021 16.350 0.000 0.296
## .sswk (.38.) 0.388 0.021 18.115 0.000 0.346
## .sspc 0.458 0.022 21.268 0.000 0.416
## .ssei (.40.) 0.135 0.019 7.248 0.000 0.099
## .ssar (.41.) 0.354 0.020 17.333 0.000 0.314
## .ssmk (.42.) 0.384 0.022 17.620 0.000 0.341
## .ssmc (.43.) 0.240 0.019 12.602 0.000 0.203
## .ssao (.44.) 0.289 0.021 14.084 0.000 0.249
## .ssai (.45.) 0.044 0.017 2.576 0.010 0.010
## .sssi (.46.) 0.086 0.018 4.883 0.000 0.052
## .ssno 0.248 0.023 10.592 0.000 0.202
## .sscs (.48.) 0.363 0.022 16.573 0.000 0.320
## ci.upper Std.lv Std.all
## 0.376 0.336 0.372
## 0.430 0.388 0.429
## 0.500 0.458 0.497
## 0.172 0.135 0.168
## 0.394 0.354 0.402
## 0.426 0.384 0.411
## 0.278 0.240 0.273
## 0.329 0.289 0.308
## 0.077 0.044 0.052
## 0.121 0.086 0.100
## 0.294 0.248 0.252
## 0.406 0.363 0.385
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.172 0.009 19.769 0.000 0.155
## .sswk 0.175 0.008 20.940 0.000 0.159
## .sspc 0.206 0.012 17.832 0.000 0.183
## .ssei 0.252 0.011 23.151 0.000 0.231
## .ssar 0.139 0.008 16.558 0.000 0.122
## .ssmk 0.181 0.008 21.830 0.000 0.165
## .ssmc 0.238 0.012 20.018 0.000 0.214
## .ssao 0.413 0.017 24.009 0.000 0.379
## .ssai 0.299 0.016 18.172 0.000 0.267
## .sssi 0.265 0.015 17.128 0.000 0.235
## .ssno 0.271 0.019 14.230 0.000 0.234
## .sscs 0.365 0.019 19.091 0.000 0.328
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.189 0.172 0.210
## 0.192 0.175 0.214
## 0.228 0.206 0.242
## 0.273 0.252 0.390
## 0.155 0.139 0.179
## 0.198 0.181 0.209
## 0.261 0.238 0.307
## 0.446 0.413 0.468
## 0.332 0.299 0.428
## 0.295 0.265 0.357
## 0.309 0.271 0.279
## 0.403 0.365 0.410
## 1.000 0.054 0.054
## 1.000 0.136 0.136
## 1.000 0.508 0.508
## 1.000 0.493 0.493
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.187 0.027 6.922 0.000 0.134
## sswk (.p2.) 0.186 0.027 6.879 0.000 0.133
## sspc (.p3.) 0.186 0.027 6.900 0.000 0.133
## ssei (.p4.) 0.107 0.016 6.532 0.000 0.075
## math =~
## ssar (.p5.) 0.294 0.018 15.956 0.000 0.258
## ssmk (.p6.) 0.221 0.016 14.244 0.000 0.191
## ssmc (.p7.) 0.173 0.012 14.968 0.000 0.151
## ssao (.p8.) 0.252 0.016 15.344 0.000 0.220
## electronic =~
## ssai (.p9.) 0.451 0.015 29.151 0.000 0.421
## sssi (.10.) 0.492 0.015 31.948 0.000 0.462
## ssmc (.11.) 0.237 0.011 22.440 0.000 0.216
## ssei 0.342 0.014 24.190 0.000 0.315
## speed =~
## ssno (.13.) 0.588 0.021 28.514 0.000 0.548
## sscs (.14.) 0.509 0.017 30.101 0.000 0.476
## ssmk (.15.) 0.210 0.011 19.237 0.000 0.188
## g =~
## verbal (.16.) 4.194 0.642 6.530 0.000 2.935
## math (.17.) 2.523 0.185 13.650 0.000 2.161
## elctrnc (.18.) 0.985 0.044 22.315 0.000 0.898
## speed (.19.) 1.015 0.051 19.980 0.000 0.915
## ci.upper Std.lv Std.all
##
## 0.239 0.804 0.878
## 0.239 0.802 0.874
## 0.239 0.803 0.848
## 0.139 0.462 0.448
##
## 0.330 0.798 0.876
## 0.252 0.601 0.646
## 0.196 0.470 0.526
## 0.284 0.685 0.690
##
## 0.481 0.633 0.643
## 0.522 0.691 0.761
## 0.257 0.332 0.371
## 0.370 0.480 0.466
##
## 0.629 0.838 0.817
## 0.542 0.725 0.752
## 0.231 0.299 0.321
##
## 5.453 0.973 0.973
## 2.885 0.930 0.930
## 1.071 0.702 0.702
## 1.114 0.712 0.712
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.522 0.025 20.538 0.000 0.472
## .sswk (.38.) 0.388 0.021 18.115 0.000 0.346
## .sspc 0.210 0.026 8.027 0.000 0.159
## .ssei (.40.) 0.135 0.019 7.248 0.000 0.099
## .ssar (.41.) 0.354 0.020 17.333 0.000 0.314
## .ssmk (.42.) 0.384 0.022 17.620 0.000 0.341
## .ssmc (.43.) 0.240 0.019 12.602 0.000 0.203
## .ssao (.44.) 0.289 0.021 14.084 0.000 0.249
## .ssai (.45.) 0.044 0.017 2.576 0.010 0.010
## .sssi (.46.) 0.086 0.018 4.883 0.000 0.052
## .ssno 0.511 0.033 15.595 0.000 0.447
## .sscs (.48.) 0.363 0.022 16.573 0.000 0.320
## .verbal -0.160 0.040 -3.968 0.000 -0.239
## .math -0.060 0.055 -1.076 0.282 -0.168
## .elctrnc 1.297 0.053 24.525 0.000 1.193
## .speed -0.744 0.055 -13.647 0.000 -0.851
## g 0.039 0.035 1.106 0.269 -0.030
## ci.upper Std.lv Std.all
## 0.572 0.522 0.570
## 0.430 0.388 0.422
## 0.262 0.210 0.222
## 0.172 0.135 0.131
## 0.394 0.354 0.388
## 0.426 0.384 0.413
## 0.278 0.240 0.269
## 0.329 0.289 0.291
## 0.077 0.044 0.044
## 0.121 0.086 0.095
## 0.575 0.511 0.498
## 0.406 0.363 0.377
## -0.081 -0.037 -0.037
## 0.049 -0.022 -0.022
## 1.400 0.924 0.924
## -0.637 -0.522 -0.522
## 0.109 0.039 0.039
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.192 0.009 21.848 0.000 0.175
## .sswk 0.199 0.010 20.029 0.000 0.179
## .sspc 0.251 0.012 20.678 0.000 0.227
## .ssei 0.315 0.015 20.352 0.000 0.284
## .ssar 0.193 0.011 17.664 0.000 0.171
## .ssmk 0.176 0.009 20.421 0.000 0.160
## .ssmc 0.266 0.013 20.761 0.000 0.241
## .ssao 0.515 0.019 26.550 0.000 0.477
## .ssai 0.570 0.026 22.034 0.000 0.519
## .sssi 0.347 0.019 18.414 0.000 0.310
## .ssno 0.351 0.023 15.191 0.000 0.306
## .sscs 0.405 0.023 17.487 0.000 0.359
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.210 0.192 0.229
## 0.218 0.199 0.236
## 0.275 0.251 0.281
## 0.345 0.315 0.296
## 0.214 0.193 0.232
## 0.193 0.176 0.204
## 0.291 0.266 0.332
## 0.553 0.515 0.524
## 0.621 0.570 0.587
## 0.384 0.347 0.421
## 0.396 0.351 0.333
## 0.450 0.405 0.435
## 1.000 0.054 0.054
## 1.000 0.136 0.136
## 1.000 0.508 0.508
## 1.000 0.493 0.493
## 1.000 1.000 1.000
latent2<-cfa(hof.lv, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 4.572000e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1399.587 113.000 0.000 0.959 0.079 0.043 87118.717
## bic
## 87534.449
Mc(latent2)
## [1] 0.838736
summary(latent2, standardized=T, ci=T) # -.076
## lavaan 0.6-18 ended normally after 110 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 94
## Number of equality constraints 27
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1399.587 1204.307
## Degrees of freedom 113 113
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.162
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 545.617 469.489
## 0 853.970 734.818
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.177 0.027 6.519 0.000 0.123
## sswk (.p2.) 0.176 0.027 6.479 0.000 0.123
## sspc (.p3.) 0.176 0.027 6.502 0.000 0.123
## ssei (.p4.) 0.105 0.017 6.247 0.000 0.072
## math =~
## ssar (.p5.) 0.289 0.018 16.323 0.000 0.254
## ssmk (.p6.) 0.218 0.015 14.545 0.000 0.188
## ssmc (.p7.) 0.172 0.011 15.255 0.000 0.150
## ssao (.p8.) 0.248 0.016 15.633 0.000 0.217
## electronic =~
## ssai (.p9.) 0.285 0.017 16.853 0.000 0.252
## sssi (.10.) 0.303 0.018 16.764 0.000 0.268
## ssmc (.11.) 0.143 0.010 14.811 0.000 0.124
## ssei 0.096 0.015 6.522 0.000 0.067
## speed =~
## ssno (.13.) 0.585 0.021 28.331 0.000 0.544
## sscs (.14.) 0.506 0.017 29.925 0.000 0.473
## ssmk (.15.) 0.207 0.011 19.021 0.000 0.186
## g =~
## verbal (.16.) 4.118 0.667 6.175 0.000 2.811
## math (.17.) 2.403 0.176 13.670 0.000 2.059
## elctrnc (.18.) 1.434 0.096 14.866 0.000 1.245
## speed (.19.) 0.957 0.050 19.071 0.000 0.859
## ci.upper Std.lv Std.all
##
## 0.230 0.748 0.876
## 0.229 0.746 0.870
## 0.230 0.748 0.854
## 0.138 0.445 0.575
##
## 0.323 0.751 0.893
## 0.247 0.566 0.636
## 0.194 0.448 0.546
## 0.279 0.645 0.709
##
## 0.318 0.498 0.666
## 0.338 0.530 0.695
## 0.162 0.250 0.305
## 0.125 0.167 0.217
##
## 0.625 0.809 0.840
## 0.539 0.700 0.757
## 0.228 0.286 0.321
##
## 5.425 0.972 0.972
## 2.748 0.923 0.923
## 1.623 0.820 0.820
## 1.056 0.691 0.691
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.330 0.021 16.009 0.000 0.289
## .sswk (.38.) 0.376 0.021 17.639 0.000 0.334
## .sspc 0.452 0.022 21.001 0.000 0.409
## .ssei (.40.) 0.142 0.019 7.562 0.000 0.105
## .ssar (.41.) 0.348 0.020 17.064 0.000 0.308
## .ssmk (.42.) 0.377 0.022 17.328 0.000 0.334
## .ssmc (.43.) 0.234 0.019 12.395 0.000 0.197
## .ssao (.44.) 0.284 0.021 13.836 0.000 0.244
## .ssai (.45.) 0.027 0.017 1.579 0.114 -0.006
## .sssi (.46.) 0.079 0.018 4.474 0.000 0.044
## .ssno 0.243 0.023 10.402 0.000 0.197
## .sscs (.48.) 0.359 0.022 16.417 0.000 0.317
## ci.upper Std.lv Std.all
## 0.370 0.330 0.386
## 0.418 0.376 0.439
## 0.494 0.452 0.515
## 0.179 0.142 0.183
## 0.388 0.348 0.414
## 0.419 0.377 0.423
## 0.271 0.234 0.286
## 0.324 0.284 0.312
## 0.060 0.027 0.036
## 0.113 0.079 0.103
## 0.289 0.243 0.252
## 0.402 0.359 0.389
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.171 0.009 19.846 0.000 0.154
## .sswk 0.178 0.008 20.966 0.000 0.161
## .sspc 0.208 0.012 17.960 0.000 0.186
## .ssei 0.253 0.011 23.172 0.000 0.231
## .ssar 0.143 0.009 16.836 0.000 0.127
## .ssmk 0.184 0.008 22.054 0.000 0.167
## .ssmc 0.240 0.012 20.098 0.000 0.216
## .ssao 0.411 0.017 24.069 0.000 0.378
## .ssai 0.311 0.015 20.377 0.000 0.281
## .sssi 0.301 0.015 19.778 0.000 0.271
## .ssno 0.273 0.019 14.295 0.000 0.236
## .sscs 0.365 0.019 19.091 0.000 0.327
## .verbal 1.000 1.000
## .electronic 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.522 0.522
## 1.000 0.148 0.148
## 0.187 0.171 0.233
## 0.194 0.178 0.242
## 0.231 0.208 0.271
## 0.274 0.253 0.423
## 0.160 0.143 0.202
## 0.200 0.184 0.232
## 0.263 0.240 0.357
## 0.445 0.411 0.497
## 0.341 0.311 0.556
## 0.330 0.301 0.517
## 0.311 0.273 0.295
## 0.402 0.365 0.426
## 1.000 0.056 0.056
## 1.000 0.327 0.327
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.177 0.027 6.519 0.000 0.123
## sswk (.p2.) 0.176 0.027 6.479 0.000 0.123
## sspc (.p3.) 0.176 0.027 6.502 0.000 0.123
## ssei (.p4.) 0.105 0.017 6.247 0.000 0.072
## math =~
## ssar (.p5.) 0.289 0.018 16.323 0.000 0.254
## ssmk (.p6.) 0.218 0.015 14.545 0.000 0.188
## ssmc (.p7.) 0.172 0.011 15.255 0.000 0.150
## ssao (.p8.) 0.248 0.016 15.633 0.000 0.217
## electronic =~
## ssai (.p9.) 0.285 0.017 16.853 0.000 0.252
## sssi (.10.) 0.303 0.018 16.764 0.000 0.268
## ssmc (.11.) 0.143 0.010 14.811 0.000 0.124
## ssei 0.192 0.013 14.209 0.000 0.165
## speed =~
## ssno (.13.) 0.585 0.021 28.331 0.000 0.544
## sscs (.14.) 0.506 0.017 29.925 0.000 0.473
## ssmk (.15.) 0.207 0.011 19.021 0.000 0.186
## g =~
## verbal (.16.) 4.118 0.667 6.175 0.000 2.811
## math (.17.) 2.403 0.176 13.670 0.000 2.059
## elctrnc (.18.) 1.434 0.096 14.866 0.000 1.245
## speed (.19.) 0.957 0.050 19.071 0.000 0.859
## ci.upper Std.lv Std.all
##
## 0.230 0.856 0.889
## 0.229 0.853 0.886
## 0.230 0.855 0.865
## 0.138 0.509 0.472
##
## 0.323 0.841 0.889
## 0.247 0.634 0.655
## 0.194 0.502 0.533
## 0.279 0.722 0.709
##
## 0.318 0.774 0.731
## 0.338 0.822 0.836
## 0.162 0.388 0.412
## 0.218 0.521 0.483
##
## 0.625 0.865 0.826
## 0.539 0.749 0.762
## 0.228 0.306 0.316
##
## 5.425 0.969 0.969
## 2.748 0.939 0.939
## 1.623 0.602 0.602
## 1.056 0.737 0.737
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.505 0.025 19.973 0.000 0.455
## .sswk (.38.) 0.376 0.021 17.639 0.000 0.334
## .sspc 0.193 0.026 7.345 0.000 0.141
## .ssei (.40.) 0.142 0.019 7.562 0.000 0.105
## .ssar (.41.) 0.348 0.020 17.064 0.000 0.308
## .ssmk (.42.) 0.377 0.022 17.328 0.000 0.334
## .ssmc (.43.) 0.234 0.019 12.395 0.000 0.197
## .ssao (.44.) 0.284 0.021 13.836 0.000 0.244
## .ssai (.45.) 0.027 0.017 1.579 0.114 -0.006
## .sssi (.46.) 0.079 0.018 4.474 0.000 0.044
## .ssno 0.507 0.033 15.465 0.000 0.442
## .sscs (.48.) 0.359 0.022 16.417 0.000 0.317
## .verbal -0.252 0.053 -4.737 0.000 -0.356
## .math -0.145 0.062 -2.328 0.020 -0.267
## .elctrnc 2.090 0.136 15.404 0.000 1.824
## .speed -0.785 0.057 -13.782 0.000 -0.896
## g 0.086 0.039 2.239 0.025 0.011
## ci.upper Std.lv Std.all
## 0.554 0.505 0.524
## 0.418 0.376 0.391
## 0.244 0.193 0.195
## 0.179 0.142 0.132
## 0.388 0.348 0.368
## 0.419 0.377 0.389
## 0.271 0.234 0.249
## 0.324 0.284 0.279
## 0.060 0.027 0.025
## 0.113 0.079 0.080
## 0.571 0.507 0.483
## 0.402 0.359 0.366
## -0.148 -0.052 -0.052
## -0.023 -0.050 -0.050
## 2.356 0.770 0.770
## -0.673 -0.530 -0.530
## 0.162 0.076 0.076
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.194 0.009 21.564 0.000 0.177
## .sswk 0.199 0.010 19.439 0.000 0.179
## .sspc 0.246 0.012 20.375 0.000 0.223
## .ssei 0.321 0.016 20.578 0.000 0.291
## .ssar 0.188 0.011 17.351 0.000 0.167
## .ssmk 0.174 0.009 20.335 0.000 0.157
## .ssmc 0.263 0.013 20.886 0.000 0.239
## .ssao 0.516 0.019 26.544 0.000 0.477
## .ssai 0.522 0.025 21.252 0.000 0.474
## .sssi 0.293 0.018 16.201 0.000 0.257
## .ssno 0.350 0.023 15.230 0.000 0.305
## .sscs 0.404 0.023 17.516 0.000 0.359
## .verbal 1.453 0.412 3.531 0.000 0.647
## .electronic 4.701 0.610 7.711 0.000 3.506
## g 1.299 0.069 18.803 0.000 1.164
## ci.upper Std.lv Std.all
## 1.000 0.457 0.457
## 1.000 0.118 0.118
## 0.212 0.194 0.210
## 0.219 0.199 0.215
## 0.270 0.246 0.252
## 0.352 0.321 0.277
## 0.210 0.188 0.210
## 0.191 0.174 0.185
## 0.288 0.263 0.297
## 0.554 0.516 0.497
## 0.571 0.522 0.466
## 0.328 0.293 0.302
## 0.395 0.350 0.318
## 0.450 0.404 0.419
## 2.260 0.062 0.062
## 5.896 0.638 0.638
## 1.435 1.000 1.000
standardizedSolution(latent2) # get the correct SEs for standardized solution
## lhs op rhs group label est.std se z pvalue
## 1 verbal =~ ssgs 1 .p1. 0.876 0.007 130.288 0.000
## 2 verbal =~ sswk 1 .p2. 0.870 0.007 123.084 0.000
## 3 verbal =~ sspc 1 .p3. 0.854 0.009 97.859 0.000
## 4 verbal =~ ssei 1 .p4. 0.575 0.023 25.444 0.000
## 5 math =~ ssar 1 .p5. 0.893 0.007 136.292 0.000
## 6 math =~ ssmk 1 .p6. 0.636 0.016 38.759 0.000
## 7 math =~ ssmc 1 .p7. 0.546 0.015 36.439 0.000
## 8 math =~ ssao 1 .p8. 0.709 0.012 60.603 0.000
## 9 electronic =~ ssai 1 .p9. 0.666 0.016 41.253 0.000
## 10 electronic =~ sssi 1 .p10. 0.695 0.015 45.001 0.000
## 11 electronic =~ ssmc 1 .p11. 0.305 0.014 21.762 0.000
## 12 electronic =~ ssei 1 0.217 0.029 7.524 0.000
## 13 speed =~ ssno 1 .p13. 0.840 0.011 75.116 0.000
## 14 speed =~ sscs 1 .p14. 0.757 0.013 60.223 0.000
## 15 speed =~ ssmk 1 .p15. 0.321 0.017 18.611 0.000
## 16 g =~ verbal 1 .p16. 0.972 0.009 110.870 0.000
## 17 g =~ math 1 .p17. 0.923 0.010 92.621 0.000
## 18 g =~ electronic 1 .p18. 0.820 0.018 45.423 0.000
## 19 g =~ speed 1 .p19. 0.691 0.019 36.545 0.000
## 20 speed ~~ speed 1 0.522 0.026 19.942 0.000
## 21 math ~~ math 1 0.148 0.018 8.018 0.000
## 22 ssgs ~~ ssgs 1 0.233 0.012 19.844 0.000
## 23 sswk ~~ sswk 1 0.242 0.012 19.676 0.000
## 24 sspc ~~ sspc 1 0.271 0.015 18.234 0.000
## 25 ssei ~~ ssei 1 0.423 0.018 24.161 0.000
## 26 ssar ~~ ssar 1 0.202 0.012 17.303 0.000
## 27 ssmk ~~ ssmk 1 0.232 0.011 20.937 0.000
## 28 ssmc ~~ ssmc 1 0.357 0.016 22.633 0.000
## 29 ssao ~~ ssao 1 0.497 0.017 29.972 0.000
## 30 ssai ~~ ssai 1 0.556 0.022 25.873 0.000
## 31 sssi ~~ sssi 1 0.517 0.021 24.118 0.000
## 32 ssno ~~ ssno 1 0.295 0.019 15.684 0.000
## 33 sscs ~~ sscs 1 0.426 0.019 22.375 0.000
## 34 verbal ~~ verbal 1 0.056 0.017 3.269 0.001
## 35 electronic ~~ electronic 1 0.327 0.030 11.050 0.000
## 36 g ~~ g 1 1.000 0.000 NA NA
## 37 ssgs ~1 1 0.386 0.026 15.032 0.000
## 38 sswk ~1 1 .p38. 0.439 0.027 16.237 0.000
## 39 sspc ~1 1 0.515 0.028 18.459 0.000
## 40 ssei ~1 1 .p40. 0.183 0.024 7.491 0.000
## 41 ssar ~1 1 .p41. 0.414 0.027 15.164 0.000
## 42 ssmk ~1 1 .p42. 0.423 0.027 15.832 0.000
## 43 ssmc ~1 1 .p43. 0.286 0.025 11.243 0.000
## 44 ssao ~1 1 .p44. 0.312 0.024 13.046 0.000
## 45 ssai ~1 1 .p45. 0.036 0.023 1.574 0.116
## 46 sssi ~1 1 .p46. 0.103 0.023 4.471 0.000
## 47 ssno ~1 1 0.252 0.025 9.953 0.000
## 48 sscs ~1 1 .p48. 0.389 0.025 15.624 0.000
## 49 verbal ~1 1 0.000 0.000 NA NA
## 50 math ~1 1 0.000 0.000 NA NA
## 51 electronic ~1 1 0.000 0.000 NA NA
## 52 speed ~1 1 0.000 0.000 NA NA
## 53 g ~1 1 0.000 0.000 NA NA
## 54 verbal =~ ssgs 2 .p1. 0.889 0.006 145.559 0.000
## 55 verbal =~ sswk 2 .p2. 0.886 0.006 141.062 0.000
## 56 verbal =~ sspc 2 .p3. 0.865 0.007 121.707 0.000
## 57 verbal =~ ssei 2 .p4. 0.472 0.017 28.611 0.000
## 58 math =~ ssar 2 .p5. 0.889 0.007 134.561 0.000
## 59 math =~ ssmk 2 .p6. 0.655 0.017 38.974 0.000
## 60 math =~ ssmc 2 .p7. 0.533 0.015 35.651 0.000
## 61 math =~ ssao 2 .p8. 0.709 0.011 62.229 0.000
## 62 electronic =~ ssai 2 .p9. 0.731 0.013 55.757 0.000
## 63 electronic =~ sssi 2 .p10. 0.836 0.011 78.036 0.000
## 64 electronic =~ ssmc 2 .p11. 0.412 0.016 25.662 0.000
## 65 electronic =~ ssei 2 0.483 0.017 28.936 0.000
## 66 speed =~ ssno 2 .p13. 0.826 0.012 71.610 0.000
## 67 speed =~ sscs 2 .p14. 0.762 0.013 59.671 0.000
## 68 speed =~ ssmk 2 .p15. 0.316 0.017 18.447 0.000
## 69 g =~ verbal 2 .p16. 0.969 0.009 112.910 0.000
## 70 g =~ math 2 .p17. 0.939 0.008 118.307 0.000
## 71 g =~ electronic 2 .p18. 0.602 0.019 32.399 0.000
## 72 g =~ speed 2 .p19. 0.737 0.017 42.921 0.000
## 73 speed ~~ speed 2 0.457 0.025 18.027 0.000
## 74 math ~~ math 2 0.118 0.015 7.883 0.000
## 75 ssgs ~~ ssgs 2 0.210 0.011 19.313 0.000
## 76 sswk ~~ sswk 2 0.215 0.011 19.267 0.000
## 77 sspc ~~ sspc 2 0.252 0.012 20.486 0.000
## 78 ssei ~~ ssei 2 0.277 0.014 20.445 0.000
## 79 ssar ~~ ssar 2 0.210 0.012 17.912 0.000
## 80 ssmk ~~ ssmk 2 0.185 0.009 20.148 0.000
## 81 ssmc ~~ ssmc 2 0.297 0.013 22.654 0.000
## 82 ssao ~~ ssao 2 0.497 0.016 30.740 0.000
## 83 ssai ~~ ssai 2 0.466 0.019 24.316 0.000
## 84 sssi ~~ sssi 2 0.302 0.018 16.872 0.000
## 85 ssno ~~ ssno 2 0.318 0.019 16.733 0.000
## 86 sscs ~~ sscs 2 0.419 0.019 21.521 0.000
## 87 verbal ~~ verbal 2 0.062 0.017 3.724 0.000
## 88 electronic ~~ electronic 2 0.638 0.022 28.517 0.000
## 89 g ~~ g 2 1.000 0.000 NA NA
## 90 ssgs ~1 2 0.524 0.027 19.389 0.000
## ci.lower ci.upper
## 1 0.862 0.889
## 2 0.857 0.884
## 3 0.836 0.871
## 4 0.531 0.620
## 5 0.880 0.906
## 6 0.604 0.668
## 7 0.517 0.576
## 8 0.686 0.732
## 9 0.634 0.698
## 10 0.664 0.725
## 11 0.277 0.332
## 12 0.160 0.273
## 13 0.818 0.862
## 14 0.733 0.782
## 15 0.288 0.355
## 16 0.955 0.989
## 17 0.904 0.943
## 18 0.785 0.856
## 19 0.654 0.729
## 20 0.471 0.573
## 21 0.112 0.184
## 22 0.210 0.257
## 23 0.218 0.266
## 24 0.242 0.301
## 25 0.389 0.458
## 26 0.180 0.225
## 27 0.210 0.253
## 28 0.326 0.388
## 29 0.465 0.530
## 30 0.514 0.599
## 31 0.475 0.559
## 32 0.258 0.331
## 33 0.389 0.464
## 34 0.022 0.089
## 35 0.269 0.385
## 36 1.000 1.000
## 37 0.335 0.436
## 38 0.386 0.492
## 39 0.461 0.570
## 40 0.135 0.231
## 41 0.360 0.467
## 42 0.370 0.475
## 43 0.236 0.336
## 44 0.265 0.359
## 45 -0.009 0.080
## 46 0.058 0.149
## 47 0.203 0.302
## 48 0.340 0.437
## 49 0.000 0.000
## 50 0.000 0.000
## 51 0.000 0.000
## 52 0.000 0.000
## 53 0.000 0.000
## 54 0.877 0.901
## 55 0.874 0.899
## 56 0.851 0.879
## 57 0.440 0.505
## 58 0.876 0.902
## 59 0.622 0.688
## 60 0.504 0.562
## 61 0.687 0.732
## 62 0.705 0.757
## 63 0.815 0.857
## 64 0.381 0.444
## 65 0.451 0.516
## 66 0.803 0.848
## 67 0.737 0.787
## 68 0.282 0.349
## 69 0.952 0.985
## 70 0.924 0.955
## 71 0.565 0.638
## 72 0.704 0.771
## 73 0.407 0.506
## 74 0.088 0.147
## 75 0.188 0.231
## 76 0.193 0.236
## 77 0.228 0.276
## 78 0.250 0.304
## 79 0.187 0.233
## 80 0.167 0.203
## 81 0.272 0.323
## 82 0.465 0.529
## 83 0.428 0.503
## 84 0.267 0.337
## 85 0.281 0.356
## 86 0.381 0.457
## 87 0.029 0.094
## 88 0.594 0.682
## 89 1.000 1.000
## 90 0.471 0.577
## [ reached 'max' / getOption("max.print") -- omitted 16 rows ]
weak<-cfa(hof.weak, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(weak, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1400.043 116.000 0.000 0.959 0.078 0.043 87113.174
## bic
## 87510.290
Mc(weak)
## [1] 0.8390276
weak2<-cfa(hof.weak2, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(weak2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1399.588 115.000 0.000 0.959 0.078 0.043 87114.718
## bic
## 87518.039
Mc(weak2)
## [1] 0.8389652
summary(weak2, standardized=T, ci=T) # -.023
## lavaan 0.6-18 ended normally after 104 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 92
## Number of equality constraints 27
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1399.588 1214.395
## Degrees of freedom 115 115
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.152
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 545.625 473.428
## 0 853.963 740.967
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.177 0.027 6.518 0.000 0.123
## sswk (.p2.) 0.176 0.027 6.478 0.000 0.123
## sspc (.p3.) 0.176 0.027 6.502 0.000 0.123
## ssei (.p4.) 0.105 0.017 6.248 0.000 0.072
## math =~
## ssar (.p5.) 0.288 0.018 16.317 0.000 0.254
## ssmk (.p6.) 0.218 0.015 14.555 0.000 0.188
## ssmc (.p7.) 0.172 0.011 15.249 0.000 0.150
## ssao (.p8.) 0.248 0.016 15.630 0.000 0.217
## electronic =~
## ssai (.p9.) 0.285 0.017 16.859 0.000 0.252
## sssi (.10.) 0.303 0.018 16.770 0.000 0.268
## ssmc (.11.) 0.143 0.010 14.797 0.000 0.124
## ssei 0.096 0.015 6.551 0.000 0.067
## speed =~
## ssno (.13.) 0.585 0.021 28.339 0.000 0.544
## sscs (.14.) 0.506 0.017 29.977 0.000 0.473
## ssmk (.15.) 0.207 0.011 19.083 0.000 0.186
## g =~
## verbal (.16.) 4.118 0.667 6.174 0.000 2.810
## math (.17.) 2.403 0.176 13.665 0.000 2.059
## elctrnc (.18.) 1.434 0.096 14.869 0.000 1.245
## speed (.19.) 0.957 0.050 19.077 0.000 0.859
## ci.upper Std.lv Std.all
##
## 0.230 0.748 0.876
## 0.229 0.746 0.870
## 0.230 0.748 0.854
## 0.138 0.445 0.576
##
## 0.323 0.751 0.893
## 0.247 0.566 0.636
## 0.194 0.448 0.546
## 0.279 0.645 0.709
##
## 0.318 0.498 0.666
## 0.338 0.529 0.695
## 0.162 0.250 0.305
## 0.124 0.167 0.217
##
## 0.625 0.809 0.840
## 0.539 0.700 0.757
## 0.228 0.286 0.321
##
## 5.425 0.972 0.972
## 2.748 0.923 0.923
## 1.623 0.820 0.820
## 1.056 0.691 0.691
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .math 0.000 0.000
## .ssgs 0.330 0.021 16.023 0.000 0.289
## .sswk (.40.) 0.376 0.021 18.216 0.000 0.335
## .sspc 0.452 0.022 20.999 0.000 0.409
## .ssei (.42.) 0.142 0.019 7.605 0.000 0.105
## .ssar (.43.) 0.348 0.020 17.660 0.000 0.310
## .ssmk (.44.) 0.377 0.021 17.613 0.000 0.335
## .ssmc (.45.) 0.234 0.019 12.531 0.000 0.198
## .ssao (.46.) 0.284 0.020 14.272 0.000 0.245
## .ssai (.47.) 0.027 0.017 1.578 0.115 -0.006
## .sssi (.48.) 0.079 0.018 4.471 0.000 0.044
## .ssno 0.243 0.023 10.406 0.000 0.197
## .sscs (.50.) 0.359 0.022 16.409 0.000 0.316
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.370 0.330 0.386
## 0.416 0.376 0.439
## 0.494 0.452 0.515
## 0.178 0.142 0.183
## 0.387 0.348 0.414
## 0.419 0.377 0.423
## 0.271 0.234 0.286
## 0.323 0.284 0.312
## 0.060 0.027 0.036
## 0.113 0.079 0.103
## 0.289 0.243 0.252
## 0.402 0.359 0.389
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.171 0.009 19.846 0.000 0.154
## .sswk 0.178 0.008 20.956 0.000 0.161
## .sspc 0.208 0.012 17.960 0.000 0.186
## .ssei 0.253 0.011 23.172 0.000 0.231
## .ssar 0.143 0.009 16.809 0.000 0.127
## .ssmk 0.184 0.008 22.054 0.000 0.167
## .ssmc 0.240 0.012 20.102 0.000 0.216
## .ssao 0.411 0.017 24.094 0.000 0.378
## .ssai 0.311 0.015 20.377 0.000 0.281
## .sssi 0.301 0.015 19.779 0.000 0.271
## .ssno 0.273 0.019 14.293 0.000 0.236
## .sscs 0.364 0.019 19.097 0.000 0.327
## .verbal 1.000 1.000
## .electronic 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.522 0.522
## 1.000 0.148 0.148
## 0.187 0.171 0.233
## 0.194 0.178 0.242
## 0.231 0.208 0.271
## 0.274 0.253 0.423
## 0.160 0.143 0.203
## 0.200 0.184 0.232
## 0.263 0.240 0.357
## 0.445 0.411 0.497
## 0.341 0.311 0.556
## 0.330 0.301 0.517
## 0.311 0.273 0.295
## 0.402 0.364 0.426
## 1.000 0.056 0.056
## 1.000 0.327 0.327
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.177 0.027 6.518 0.000 0.123
## sswk (.p2.) 0.176 0.027 6.478 0.000 0.123
## sspc (.p3.) 0.176 0.027 6.502 0.000 0.123
## ssei (.p4.) 0.105 0.017 6.248 0.000 0.072
## math =~
## ssar (.p5.) 0.288 0.018 16.317 0.000 0.254
## ssmk (.p6.) 0.218 0.015 14.555 0.000 0.188
## ssmc (.p7.) 0.172 0.011 15.249 0.000 0.150
## ssao (.p8.) 0.248 0.016 15.630 0.000 0.217
## electronic =~
## ssai (.p9.) 0.285 0.017 16.859 0.000 0.252
## sssi (.10.) 0.303 0.018 16.770 0.000 0.268
## ssmc (.11.) 0.143 0.010 14.797 0.000 0.124
## ssei 0.192 0.013 14.326 0.000 0.165
## speed =~
## ssno (.13.) 0.585 0.021 28.339 0.000 0.544
## sscs (.14.) 0.506 0.017 29.977 0.000 0.473
## ssmk (.15.) 0.207 0.011 19.083 0.000 0.186
## g =~
## verbal (.16.) 4.118 0.667 6.174 0.000 2.810
## math (.17.) 2.403 0.176 13.665 0.000 2.059
## elctrnc (.18.) 1.434 0.096 14.869 0.000 1.245
## speed (.19.) 0.957 0.050 19.077 0.000 0.859
## ci.upper Std.lv Std.all
##
## 0.230 0.856 0.889
## 0.229 0.853 0.886
## 0.230 0.855 0.865
## 0.138 0.509 0.472
##
## 0.323 0.841 0.889
## 0.247 0.634 0.655
## 0.194 0.502 0.533
## 0.279 0.722 0.709
##
## 0.318 0.774 0.731
## 0.338 0.822 0.836
## 0.162 0.388 0.412
## 0.218 0.521 0.483
##
## 0.625 0.865 0.826
## 0.539 0.749 0.762
## 0.228 0.306 0.316
##
## 5.425 0.969 0.969
## 2.748 0.939 0.939
## 1.623 0.602 0.602
## 1.056 0.737 0.737
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .math 0.000 0.000
## .ssgs 0.504 0.023 21.686 0.000 0.459
## .sswk (.40.) 0.376 0.021 18.216 0.000 0.335
## .sspc 0.193 0.024 8.173 0.000 0.146
## .ssei (.42.) 0.142 0.019 7.605 0.000 0.105
## .ssar (.43.) 0.348 0.020 17.660 0.000 0.310
## .ssmk (.44.) 0.377 0.021 17.613 0.000 0.335
## .ssmc (.45.) 0.234 0.019 12.531 0.000 0.198
## .ssao (.46.) 0.284 0.020 14.272 0.000 0.245
## .ssai (.47.) 0.027 0.017 1.578 0.115 -0.006
## .sssi (.48.) 0.079 0.018 4.471 0.000 0.044
## .ssno 0.507 0.033 15.518 0.000 0.443
## .sscs (.50.) 0.359 0.022 16.409 0.000 0.316
## .elctrnc 2.177 0.151 14.395 0.000 1.881
## .speed -0.726 0.056 -12.995 0.000 -0.836
## g 0.026 0.039 0.658 0.510 -0.051
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.550 0.504 0.524
## 0.416 0.376 0.391
## 0.239 0.193 0.195
## 0.178 0.142 0.132
## 0.387 0.348 0.368
## 0.419 0.377 0.389
## 0.271 0.234 0.249
## 0.323 0.284 0.279
## 0.060 0.027 0.025
## 0.113 0.079 0.080
## 0.571 0.507 0.483
## 0.402 0.359 0.366
## 2.474 0.802 0.802
## -0.617 -0.491 -0.491
## 0.102 0.023 0.023
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.194 0.009 21.564 0.000 0.177
## .sswk 0.199 0.010 19.442 0.000 0.179
## .sspc 0.246 0.012 20.380 0.000 0.223
## .ssei 0.321 0.016 20.605 0.000 0.291
## .ssar 0.188 0.011 17.411 0.000 0.167
## .ssmk 0.174 0.009 20.335 0.000 0.157
## .ssmc 0.263 0.013 20.875 0.000 0.239
## .ssao 0.516 0.019 26.577 0.000 0.478
## .ssai 0.522 0.025 21.250 0.000 0.474
## .sssi 0.292 0.018 16.205 0.000 0.257
## .ssno 0.350 0.023 15.232 0.000 0.305
## .sscs 0.404 0.023 17.521 0.000 0.359
## .verbal 1.453 0.411 3.532 0.000 0.647
## .electronic 4.701 0.609 7.719 0.000 3.507
## g 1.299 0.069 18.803 0.000 1.164
## ci.upper Std.lv Std.all
## 1.000 0.457 0.457
## 1.000 0.118 0.118
## 0.212 0.194 0.210
## 0.219 0.199 0.215
## 0.270 0.246 0.252
## 0.352 0.321 0.277
## 0.210 0.188 0.210
## 0.191 0.174 0.185
## 0.288 0.263 0.297
## 0.554 0.516 0.497
## 0.571 0.522 0.466
## 0.328 0.292 0.302
## 0.395 0.350 0.318
## 0.450 0.404 0.419
## 2.260 0.062 0.062
## 5.895 0.638 0.638
## 1.435 1.000 1.000
tests<-lavTestLRT(configural, metric2, scalar2, latent2, weak2)
Td=tests[2:5,"Chisq diff"]
Td
## [1] 51.437397711 59.937104017 6.630671675 0.001238102
dfd=tests[2:5,"Df diff"]
dfd
## [1] 13 4 2 2
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
## Warning in sqrt((ld) * G/(N - G)): NaNs produced
RMSEAD
## [1] 0.04021220 0.08745256 0.03558439 NaN
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.02904082 0.05203700
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.06868184 0.10766399
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.007745589 0.067192916
RMSEA.CI(T=Td[4],df=dfd[4],N=N,G=2)
## [1] NA NA
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.089 0.002 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.999 0.991 0.756 0.160
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.964 0.940 0.264 0.111 0.008 0.000
round(pvals(T=Td[4],df=dfd[4],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.001 0.001 0.000 0.000 0.000 0.000
tests<-lavTestLRT(configural, metric2, scalar2, latent)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 51.4374 59.9371 251.3403
dfd=tests[2:4,"Df diff"]
dfd
## [1] 13 4 5
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.04021220 0.08745256 0.16414786
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.06868184 0.10766399
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.1471822 0.1817226
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.999 0.991 0.756 0.160
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
tests<-lavTestLRT(configural, metric2, scalar2, strict)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 51.4374 59.9371 133.6165
dfd=tests[2:4,"Df diff"]
dfd
## [1] 13 4 12
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.04021220 0.08745256 0.07444889
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.02904082 0.05203700
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.06868184 0.10766399
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.06336094 0.08606703
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.089 0.002 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.999 0.991 0.756 0.160
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 0.983 0.223 0.000
tests<-lavTestLRT(configural, metric2, scalar)
Td=tests[2:3,"Chisq diff"]
Td
## [1] 51.4374 460.2914
dfd=tests[2:3,"Df diff"]
dfd
## [1] 13 7
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.0402122 0.1881881
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.02904082 0.05203700
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.1737819 0.2029519
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.089 0.002 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
tests<-lavTestLRT(configural, metric)
Td=tests[2,"Chisq diff"]
Td
## [1] 107.3616
dfd=tests[2,"Df diff"]
dfd
## [1] 14
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.06039107
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.05002088 0.07129083
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.950 0.544 0.001 0.000
hof.age<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
math~~1*math
g ~agec
'
hof.ageq<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
math~~1*math
g ~ c(a,a)*agec
'
hof.age2<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
math~~1*math
g ~agec + agec2
'
hof.age2q<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
math~~1*math
g ~c(a,a)*agec + c(b,b)*agec2
'
sem.age<-sem(hof.age, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 9.384075e-15) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(sem.age, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 2104.721 135.000 0.000 0.940 0.089 0.050 0.613
## aic bic
## 86582.056 87010.197
Mc(sem.age)
## [1] 0.7639639
summary(sem.age, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 109 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 27
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2104.721 1806.761
## Degrees of freedom 135 135
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.165
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 832.899 714.987
## 0 1271.823 1091.774
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.173 0.024 7.162 0.000 0.126
## sswk (.p2.) 0.173 0.024 7.116 0.000 0.125
## sspc (.p3.) 0.172 0.024 7.136 0.000 0.125
## ssei (.p4.) 0.103 0.015 6.910 0.000 0.074
## math =~
## ssar (.p5.) 0.297 0.015 20.258 0.000 0.269
## ssmk (.p6.) 0.226 0.013 17.893 0.000 0.202
## ssmc (.p7.) 0.177 0.010 17.976 0.000 0.158
## ssao (.p8.) 0.256 0.013 19.103 0.000 0.229
## electronic =~
## ssai (.p9.) 0.281 0.017 16.650 0.000 0.248
## sssi (.10.) 0.298 0.018 16.548 0.000 0.263
## ssmc (.11.) 0.141 0.010 14.607 0.000 0.122
## ssei 0.094 0.014 6.497 0.000 0.065
## speed =~
## ssno (.13.) 0.581 0.020 28.356 0.000 0.541
## sscs (.14.) 0.503 0.017 30.040 0.000 0.471
## ssmk (.15.) 0.204 0.011 18.910 0.000 0.183
## g =~
## verbal (.16.) 3.865 0.570 6.782 0.000 2.748
## math (.17.) 2.129 0.130 16.336 0.000 1.873
## elctrnc (.18.) 1.352 0.091 14.905 0.000 1.174
## speed (.19.) 0.889 0.046 19.259 0.000 0.799
## ci.upper Std.lv Std.all
##
## 0.220 0.748 0.875
## 0.220 0.747 0.872
## 0.220 0.745 0.851
## 0.132 0.446 0.577
##
## 0.326 0.750 0.892
## 0.251 0.571 0.641
## 0.196 0.446 0.544
## 0.282 0.645 0.709
##
## 0.314 0.500 0.668
## 0.334 0.531 0.695
## 0.160 0.251 0.306
## 0.122 0.167 0.216
##
## 0.621 0.808 0.839
## 0.536 0.700 0.758
## 0.225 0.284 0.318
##
## 4.982 0.973 0.973
## 2.384 0.918 0.918
## 1.529 0.827 0.827
## 0.980 0.695 0.695
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.296 0.020 14.738 0.000 0.257
## ci.upper Std.lv Std.all
##
## 0.336 0.272 0.394
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.334 0.020 17.091 0.000 0.296
## .sswk (.40.) 0.380 0.020 19.246 0.000 0.341
## .sspc 0.456 0.021 22.065 0.000 0.415
## .ssei (.42.) 0.146 0.017 8.350 0.000 0.112
## .ssar (.43.) 0.353 0.020 17.903 0.000 0.314
## .ssmk (.44.) 0.381 0.020 18.998 0.000 0.341
## .ssmc (.45.) 0.238 0.018 13.047 0.000 0.202
## .ssao (.46.) 0.288 0.020 14.348 0.000 0.248
## .ssai (.47.) 0.029 0.016 1.787 0.074 -0.003
## .sssi (.48.) 0.081 0.017 4.796 0.000 0.048
## .ssno 0.247 0.022 11.014 0.000 0.203
## .sscs (.50.) 0.363 0.021 17.508 0.000 0.322
## ci.upper Std.lv Std.all
## 0.372 0.334 0.391
## 0.419 0.380 0.444
## 0.496 0.456 0.521
## 0.180 0.146 0.189
## 0.391 0.353 0.419
## 0.420 0.381 0.427
## 0.274 0.238 0.290
## 0.327 0.288 0.316
## 0.060 0.029 0.038
## 0.115 0.081 0.107
## 0.290 0.247 0.256
## 0.403 0.363 0.392
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.171 0.009 20.052 0.000 0.154
## .sswk 0.175 0.008 20.778 0.000 0.159
## .sspc 0.212 0.012 18.125 0.000 0.189
## .ssei 0.251 0.011 23.173 0.000 0.230
## .ssar 0.145 0.009 16.913 0.000 0.128
## .ssmk 0.180 0.008 21.808 0.000 0.164
## .ssmc 0.241 0.012 20.099 0.000 0.218
## .ssao 0.411 0.017 24.007 0.000 0.378
## .ssai 0.310 0.015 20.352 0.000 0.280
## .sssi 0.302 0.015 19.855 0.000 0.272
## .ssno 0.275 0.019 14.398 0.000 0.238
## .sscs 0.364 0.019 19.091 0.000 0.327
## .verbal 1.000 1.000
## .electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.517 0.517
## 1.000 0.157 0.157
## 0.187 0.171 0.234
## 0.192 0.175 0.239
## 0.235 0.212 0.276
## 0.273 0.251 0.421
## 0.162 0.145 0.205
## 0.196 0.180 0.227
## 0.265 0.241 0.358
## 0.445 0.411 0.497
## 0.340 0.310 0.553
## 0.332 0.302 0.517
## 0.313 0.275 0.297
## 0.401 0.364 0.426
## 1.000 0.054 0.054
## 1.000 0.316 0.316
## 1.000 0.845 0.845
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.173 0.024 7.162 0.000 0.126
## sswk (.p2.) 0.173 0.024 7.116 0.000 0.125
## sspc (.p3.) 0.172 0.024 7.136 0.000 0.125
## ssei (.p4.) 0.103 0.015 6.910 0.000 0.074
## math =~
## ssar (.p5.) 0.297 0.015 20.258 0.000 0.269
## ssmk (.p6.) 0.226 0.013 17.893 0.000 0.202
## ssmc (.p7.) 0.177 0.010 17.976 0.000 0.158
## ssao (.p8.) 0.256 0.013 19.103 0.000 0.229
## electronic =~
## ssai (.p9.) 0.281 0.017 16.650 0.000 0.248
## sssi (.10.) 0.298 0.018 16.548 0.000 0.263
## ssmc (.11.) 0.141 0.010 14.607 0.000 0.122
## ssei 0.188 0.013 14.069 0.000 0.162
## speed =~
## ssno (.13.) 0.581 0.020 28.356 0.000 0.541
## sscs (.14.) 0.503 0.017 30.040 0.000 0.471
## ssmk (.15.) 0.204 0.011 18.910 0.000 0.183
## g =~
## verbal (.16.) 3.865 0.570 6.782 0.000 2.748
## math (.17.) 2.129 0.130 16.336 0.000 1.873
## elctrnc (.18.) 1.352 0.091 14.905 0.000 1.174
## speed (.19.) 0.889 0.046 19.259 0.000 0.799
## ci.upper Std.lv Std.all
##
## 0.220 0.857 0.890
## 0.220 0.855 0.888
## 0.220 0.853 0.862
## 0.132 0.511 0.474
##
## 0.326 0.839 0.887
## 0.251 0.639 0.659
## 0.196 0.499 0.531
## 0.282 0.722 0.709
##
## 0.314 0.772 0.730
## 0.334 0.819 0.834
## 0.160 0.387 0.412
## 0.215 0.517 0.480
##
## 0.621 0.864 0.824
## 0.536 0.749 0.763
## 0.225 0.303 0.313
##
## 4.982 0.968 0.968
## 2.384 0.935 0.935
## 1.529 0.611 0.611
## 0.980 0.741 0.741
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.329 0.022 15.001 0.000 0.286
## ci.upper Std.lv Std.all
##
## 0.372 0.265 0.382
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.509 0.024 21.229 0.000 0.462
## .sswk (.40.) 0.380 0.020 19.246 0.000 0.341
## .sspc 0.197 0.025 7.887 0.000 0.148
## .ssei (.42.) 0.146 0.017 8.350 0.000 0.112
## .ssar (.43.) 0.353 0.020 17.903 0.000 0.314
## .ssmk (.44.) 0.381 0.020 18.998 0.000 0.341
## .ssmc (.45.) 0.238 0.018 13.047 0.000 0.202
## .ssao (.46.) 0.288 0.020 14.348 0.000 0.248
## .ssai (.47.) 0.029 0.016 1.787 0.074 -0.003
## .sssi (.48.) 0.081 0.017 4.796 0.000 0.048
## .ssno 0.510 0.032 16.159 0.000 0.448
## .sscs (.50.) 0.363 0.021 17.508 0.000 0.322
## .verbal -0.248 0.052 -4.794 0.000 -0.349
## .math -0.140 0.061 -2.284 0.022 -0.259
## .elctrnc 2.123 0.138 15.345 0.000 1.852
## .speed -0.788 0.057 -13.828 0.000 -0.900
## .g 0.111 0.038 2.873 0.004 0.035
## ci.upper Std.lv Std.all
## 0.555 0.509 0.528
## 0.419 0.380 0.395
## 0.246 0.197 0.199
## 0.180 0.146 0.135
## 0.391 0.353 0.373
## 0.420 0.381 0.393
## 0.274 0.238 0.253
## 0.327 0.288 0.283
## 0.060 0.029 0.027
## 0.115 0.081 0.083
## 0.572 0.510 0.486
## 0.403 0.363 0.369
## -0.147 -0.050 -0.050
## -0.020 -0.049 -0.049
## 2.394 0.774 0.774
## -0.677 -0.530 -0.530
## 0.186 0.089 0.089
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.194 0.009 21.632 0.000 0.176
## .sswk 0.196 0.010 19.428 0.000 0.176
## .sspc 0.251 0.012 20.477 0.000 0.227
## .ssei 0.320 0.016 20.632 0.000 0.290
## .ssar 0.191 0.011 17.508 0.000 0.170
## .ssmk 0.170 0.009 20.034 0.000 0.154
## .ssmc 0.265 0.013 20.920 0.000 0.240
## .ssao 0.516 0.020 26.466 0.000 0.478
## .ssai 0.522 0.025 21.254 0.000 0.474
## .sssi 0.294 0.018 16.286 0.000 0.259
## .ssno 0.352 0.023 15.312 0.000 0.307
## .sscs 0.404 0.023 17.518 0.000 0.359
## .verbal 1.551 0.442 3.506 0.000 0.684
## .electronic 4.720 0.622 7.593 0.000 3.502
## .g 1.312 0.076 17.283 0.000 1.163
## ci.upper Std.lv Std.all
## 1.000 0.452 0.452
## 1.000 0.126 0.126
## 0.211 0.194 0.209
## 0.215 0.196 0.211
## 0.275 0.251 0.256
## 0.351 0.320 0.276
## 0.212 0.191 0.213
## 0.187 0.170 0.181
## 0.290 0.265 0.299
## 0.554 0.516 0.498
## 0.571 0.522 0.467
## 0.330 0.294 0.305
## 0.397 0.352 0.321
## 0.449 0.404 0.419
## 2.418 0.063 0.063
## 5.939 0.627 0.627
## 1.461 0.854 0.854
sem.ageq<-sem(hof.ageq, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 1.677521e-14) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(sem.ageq, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 2106.273 136.000 0.000 0.940 0.089 0.052 0.613
## aic bic
## 86581.607 87003.544
Mc(sem.ageq)
## [1] 0.7639063
summary(sem.ageq, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 105 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 28
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2106.273 1808.572
## Degrees of freedom 136 136
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.165
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 833.455 715.654
## 0 1272.819 1092.918
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.173 0.024 7.157 0.000 0.126
## sswk (.p2.) 0.173 0.024 7.110 0.000 0.125
## sspc (.p3.) 0.172 0.024 7.130 0.000 0.125
## ssei (.p4.) 0.103 0.015 6.906 0.000 0.074
## math =~
## ssar (.p5.) 0.298 0.015 20.240 0.000 0.269
## ssmk (.p6.) 0.226 0.013 17.882 0.000 0.202
## ssmc (.p7.) 0.177 0.010 17.959 0.000 0.158
## ssao (.p8.) 0.256 0.013 19.082 0.000 0.230
## electronic =~
## ssai (.p9.) 0.281 0.017 16.646 0.000 0.248
## sssi (.10.) 0.298 0.018 16.543 0.000 0.263
## ssmc (.11.) 0.141 0.010 14.603 0.000 0.122
## ssei 0.093 0.014 6.483 0.000 0.065
## speed =~
## ssno (.13.) 0.581 0.020 28.340 0.000 0.541
## sscs (.14.) 0.504 0.017 30.024 0.000 0.471
## ssmk (.15.) 0.204 0.011 18.906 0.000 0.183
## g =~
## verbal (.16.) 3.869 0.571 6.777 0.000 2.750
## math (.17.) 2.128 0.130 16.316 0.000 1.873
## elctrnc (.18.) 1.352 0.091 14.899 0.000 1.174
## speed (.19.) 0.889 0.046 19.238 0.000 0.799
## ci.upper Std.lv Std.all
##
## 0.220 0.754 0.877
## 0.220 0.752 0.874
## 0.220 0.751 0.852
## 0.132 0.449 0.579
##
## 0.326 0.755 0.893
## 0.251 0.575 0.642
## 0.196 0.449 0.545
## 0.282 0.649 0.711
##
## 0.314 0.503 0.670
## 0.334 0.533 0.696
## 0.160 0.252 0.306
## 0.122 0.167 0.215
##
## 0.621 0.811 0.840
## 0.536 0.703 0.759
## 0.225 0.285 0.318
##
## 4.988 0.973 0.973
## 2.384 0.919 0.919
## 1.529 0.829 0.829
## 0.980 0.698 0.698
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.310 0.016 19.731 0.000 0.280
## ci.upper Std.lv Std.all
##
## 0.341 0.283 0.409
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.334 0.020 17.100 0.000 0.296
## .sswk (.40.) 0.380 0.020 19.276 0.000 0.342
## .sspc 0.456 0.021 22.053 0.000 0.416
## .ssei (.42.) 0.146 0.017 8.367 0.000 0.112
## .ssar (.43.) 0.353 0.020 17.898 0.000 0.314
## .ssmk (.44.) 0.381 0.020 19.041 0.000 0.342
## .ssmc (.45.) 0.238 0.018 13.048 0.000 0.202
## .ssao (.46.) 0.288 0.020 14.346 0.000 0.249
## .ssai (.47.) 0.029 0.016 1.797 0.072 -0.003
## .sssi (.48.) 0.082 0.017 4.806 0.000 0.048
## .ssno 0.247 0.022 11.030 0.000 0.203
## .sscs (.50.) 0.363 0.021 17.545 0.000 0.322
## ci.upper Std.lv Std.all
## 0.372 0.334 0.389
## 0.419 0.380 0.442
## 0.497 0.456 0.518
## 0.180 0.146 0.188
## 0.392 0.353 0.417
## 0.420 0.381 0.425
## 0.274 0.238 0.289
## 0.327 0.288 0.315
## 0.060 0.029 0.039
## 0.115 0.082 0.107
## 0.291 0.247 0.255
## 0.403 0.363 0.392
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.171 0.009 20.065 0.000 0.154
## .sswk 0.175 0.008 20.774 0.000 0.158
## .sspc 0.212 0.012 18.121 0.000 0.189
## .ssei 0.251 0.011 23.177 0.000 0.230
## .ssar 0.145 0.009 16.928 0.000 0.128
## .ssmk 0.180 0.008 21.811 0.000 0.164
## .ssmc 0.241 0.012 20.100 0.000 0.218
## .ssao 0.411 0.017 24.004 0.000 0.378
## .ssai 0.310 0.015 20.348 0.000 0.280
## .sssi 0.302 0.015 19.857 0.000 0.272
## .ssno 0.275 0.019 14.401 0.000 0.238
## .sscs 0.364 0.019 19.091 0.000 0.327
## .verbal 1.000 1.000
## .electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.513 0.513
## 1.000 0.155 0.155
## 0.187 0.171 0.231
## 0.191 0.175 0.236
## 0.235 0.212 0.273
## 0.272 0.251 0.417
## 0.162 0.145 0.203
## 0.196 0.180 0.225
## 0.265 0.241 0.355
## 0.445 0.411 0.494
## 0.340 0.310 0.551
## 0.332 0.302 0.515
## 0.313 0.275 0.295
## 0.401 0.364 0.424
## 1.000 0.053 0.053
## 1.000 0.313 0.313
## 1.000 0.832 0.832
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.173 0.024 7.157 0.000 0.126
## sswk (.p2.) 0.173 0.024 7.110 0.000 0.125
## sspc (.p3.) 0.172 0.024 7.130 0.000 0.125
## ssei (.p4.) 0.103 0.015 6.906 0.000 0.074
## math =~
## ssar (.p5.) 0.298 0.015 20.240 0.000 0.269
## ssmk (.p6.) 0.226 0.013 17.882 0.000 0.202
## ssmc (.p7.) 0.177 0.010 17.959 0.000 0.158
## ssao (.p8.) 0.256 0.013 19.082 0.000 0.230
## electronic =~
## ssai (.p9.) 0.281 0.017 16.646 0.000 0.248
## sssi (.10.) 0.298 0.018 16.543 0.000 0.263
## ssmc (.11.) 0.141 0.010 14.603 0.000 0.122
## ssei 0.188 0.013 14.068 0.000 0.162
## speed =~
## ssno (.13.) 0.581 0.020 28.340 0.000 0.541
## sscs (.14.) 0.504 0.017 30.024 0.000 0.471
## ssmk (.15.) 0.204 0.011 18.906 0.000 0.183
## g =~
## verbal (.16.) 3.869 0.571 6.777 0.000 2.750
## math (.17.) 2.128 0.130 16.316 0.000 1.873
## elctrnc (.18.) 1.352 0.091 14.899 0.000 1.174
## speed (.19.) 0.889 0.046 19.238 0.000 0.799
## ci.upper Std.lv Std.all
##
## 0.220 0.850 0.888
## 0.220 0.849 0.887
## 0.220 0.847 0.861
## 0.132 0.507 0.473
##
## 0.326 0.834 0.886
## 0.251 0.635 0.658
## 0.196 0.496 0.529
## 0.282 0.717 0.706
##
## 0.314 0.770 0.729
## 0.334 0.817 0.833
## 0.160 0.386 0.413
## 0.215 0.516 0.481
##
## 0.621 0.861 0.823
## 0.536 0.746 0.761
## 0.225 0.302 0.313
##
## 4.988 0.968 0.968
## 2.384 0.934 0.934
## 1.529 0.607 0.607
## 0.980 0.738 0.738
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.310 0.016 19.731 0.000 0.280
## ci.upper Std.lv Std.all
##
## 0.341 0.252 0.364
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.509 0.024 21.247 0.000 0.462
## .sswk (.40.) 0.380 0.020 19.276 0.000 0.342
## .sspc 0.197 0.025 7.901 0.000 0.148
## .ssei (.42.) 0.146 0.017 8.367 0.000 0.112
## .ssar (.43.) 0.353 0.020 17.898 0.000 0.314
## .ssmk (.44.) 0.381 0.020 19.041 0.000 0.342
## .ssmc (.45.) 0.238 0.018 13.048 0.000 0.202
## .ssao (.46.) 0.288 0.020 14.346 0.000 0.249
## .ssai (.47.) 0.029 0.016 1.797 0.072 -0.003
## .sssi (.48.) 0.082 0.017 4.806 0.000 0.048
## .ssno 0.510 0.032 16.186 0.000 0.448
## .sscs (.50.) 0.363 0.021 17.545 0.000 0.322
## .verbal -0.213 0.051 -4.157 0.000 -0.314
## .math -0.120 0.061 -1.982 0.047 -0.239
## .elctrnc 2.136 0.139 15.373 0.000 1.864
## .speed -0.780 0.057 -13.746 0.000 -0.892
## .g 0.100 0.038 2.604 0.009 0.025
## ci.upper Std.lv Std.all
## 0.556 0.509 0.531
## 0.419 0.380 0.397
## 0.246 0.197 0.200
## 0.180 0.146 0.136
## 0.392 0.353 0.375
## 0.420 0.381 0.395
## 0.274 0.238 0.254
## 0.327 0.288 0.284
## 0.060 0.029 0.027
## 0.115 0.082 0.083
## 0.572 0.510 0.488
## 0.403 0.363 0.370
## -0.113 -0.043 -0.043
## -0.001 -0.043 -0.043
## 2.408 0.780 0.780
## -0.669 -0.527 -0.527
## 0.175 0.081 0.081
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.194 0.009 21.634 0.000 0.176
## .sswk 0.196 0.010 19.427 0.000 0.176
## .sspc 0.250 0.012 20.456 0.000 0.226
## .ssei 0.320 0.016 20.626 0.000 0.290
## .ssar 0.191 0.011 17.496 0.000 0.169
## .ssmk 0.170 0.008 20.059 0.000 0.154
## .ssmc 0.265 0.013 20.922 0.000 0.240
## .ssao 0.516 0.019 26.469 0.000 0.478
## .ssai 0.522 0.025 21.254 0.000 0.474
## .sssi 0.294 0.018 16.286 0.000 0.259
## .ssno 0.352 0.023 15.310 0.000 0.307
## .sscs 0.404 0.023 17.516 0.000 0.359
## .verbal 1.544 0.441 3.497 0.000 0.679
## .electronic 4.729 0.623 7.593 0.000 3.509
## .g 1.312 0.076 17.284 0.000 1.164
## ci.upper Std.lv Std.all
## 1.000 0.455 0.455
## 1.000 0.127 0.127
## 0.211 0.194 0.211
## 0.215 0.196 0.213
## 0.274 0.250 0.259
## 0.351 0.320 0.278
## 0.212 0.191 0.215
## 0.187 0.170 0.184
## 0.290 0.265 0.302
## 0.554 0.516 0.501
## 0.570 0.522 0.469
## 0.329 0.294 0.306
## 0.397 0.352 0.322
## 0.449 0.404 0.421
## 2.409 0.064 0.064
## 5.950 0.631 0.631
## 1.461 0.868 0.868
sem.age2<-sem(hof.age2, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 7.343210e-14) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(sem.age2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 2200.368 157.000 0.000 0.938 0.084 0.048 0.640
## aic bic
## 86566.867 87007.418
Mc(sem.age2)
## [1] 0.7563121
summary(sem.age2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 113 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 27
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2200.368 1899.988
## Degrees of freedom 157 157
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.158
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 889.284 767.885
## 0 1311.084 1132.104
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.176 0.024 7.466 0.000 0.130
## sswk (.p2.) 0.176 0.024 7.415 0.000 0.130
## sspc (.p3.) 0.176 0.024 7.435 0.000 0.129
## ssei (.p4.) 0.104 0.015 7.174 0.000 0.076
## math =~
## ssar (.p5.) 0.295 0.015 20.036 0.000 0.266
## ssmk (.p6.) 0.225 0.013 17.785 0.000 0.200
## ssmc (.p7.) 0.176 0.010 17.835 0.000 0.156
## ssao (.p8.) 0.254 0.013 18.921 0.000 0.228
## electronic =~
## ssai (.p9.) 0.281 0.017 16.695 0.000 0.248
## sssi (.10.) 0.299 0.018 16.593 0.000 0.263
## ssmc (.11.) 0.141 0.010 14.647 0.000 0.122
## ssei 0.096 0.014 6.767 0.000 0.068
## speed =~
## ssno (.13.) 0.580 0.020 28.321 0.000 0.540
## sscs (.14.) 0.503 0.017 29.961 0.000 0.470
## ssmk (.15.) 0.203 0.011 18.839 0.000 0.182
## g =~
## verbal (.16.) 3.773 0.535 7.054 0.000 2.725
## math (.17.) 2.136 0.132 16.204 0.000 1.878
## elctrnc (.18.) 1.343 0.090 14.916 0.000 1.166
## speed (.19.) 0.888 0.046 19.266 0.000 0.798
## ci.upper Std.lv Std.all
##
## 0.223 0.748 0.875
## 0.223 0.747 0.872
## 0.222 0.745 0.851
## 0.133 0.442 0.572
##
## 0.324 0.750 0.891
## 0.250 0.571 0.642
## 0.195 0.446 0.543
## 0.280 0.645 0.709
##
## 0.315 0.500 0.668
## 0.334 0.530 0.695
## 0.160 0.251 0.306
## 0.124 0.171 0.222
##
## 0.620 0.808 0.839
## 0.536 0.700 0.758
## 0.224 0.283 0.317
##
## 4.822 0.972 0.972
## 2.395 0.919 0.919
## 1.519 0.826 0.826
## 0.978 0.696 0.696
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.293 0.020 14.586 0.000 0.254
## agec2 -0.048 0.014 -3.409 0.001 -0.075
## ci.upper Std.lv Std.all
##
## 0.333 0.268 0.388
## -0.020 -0.044 -0.083
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.400 0.027 14.825 0.000 0.347
## .sswk (.43.) 0.445 0.027 16.413 0.000 0.392
## .sspc 0.521 0.028 18.758 0.000 0.467
## .ssei (.45.) 0.200 0.023 8.581 0.000 0.154
## .ssar (.46.) 0.415 0.026 15.922 0.000 0.364
## .ssmk (.47.) 0.446 0.027 16.374 0.000 0.393
## .ssmc (.48.) 0.293 0.024 12.381 0.000 0.247
## .ssao (.49.) 0.342 0.025 13.481 0.000 0.292
## .ssai (.50.) 0.065 0.019 3.378 0.001 0.027
## .sssi (.51.) 0.120 0.020 5.979 0.000 0.081
## .ssno 0.297 0.027 11.216 0.000 0.245
## .sscs (.53.) 0.407 0.024 16.837 0.000 0.359
## ci.upper Std.lv Std.all
## 0.453 0.400 0.468
## 0.498 0.445 0.520
## 0.576 0.521 0.596
## 0.245 0.200 0.258
## 0.466 0.415 0.494
## 0.499 0.446 0.501
## 0.340 0.293 0.358
## 0.391 0.342 0.376
## 0.103 0.065 0.087
## 0.159 0.120 0.157
## 0.349 0.297 0.309
## 0.454 0.407 0.440
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.171 0.009 20.034 0.000 0.154
## .sswk 0.175 0.008 20.771 0.000 0.159
## .sspc 0.212 0.012 18.172 0.000 0.189
## .ssei 0.251 0.011 23.157 0.000 0.230
## .ssar 0.146 0.009 16.953 0.000 0.129
## .ssmk 0.180 0.008 21.767 0.000 0.164
## .ssmc 0.241 0.012 20.091 0.000 0.218
## .ssao 0.411 0.017 24.010 0.000 0.378
## .ssai 0.310 0.015 20.346 0.000 0.281
## .sssi 0.302 0.015 19.848 0.000 0.272
## .ssno 0.275 0.019 14.414 0.000 0.238
## .sscs 0.364 0.019 19.100 0.000 0.327
## .verbal 1.000 1.000
## .electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.515 0.515
## 1.000 0.155 0.155
## 0.187 0.171 0.234
## 0.192 0.175 0.239
## 0.235 0.212 0.276
## 0.272 0.251 0.420
## 0.162 0.146 0.206
## 0.196 0.180 0.227
## 0.265 0.241 0.359
## 0.445 0.411 0.497
## 0.340 0.310 0.554
## 0.332 0.302 0.518
## 0.313 0.275 0.297
## 0.401 0.364 0.426
## 1.000 0.056 0.056
## 1.000 0.317 0.317
## 1.000 0.838 0.838
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.176 0.024 7.466 0.000 0.130
## sswk (.p2.) 0.176 0.024 7.415 0.000 0.130
## sspc (.p3.) 0.176 0.024 7.435 0.000 0.129
## ssei (.p4.) 0.104 0.015 7.174 0.000 0.076
## math =~
## ssar (.p5.) 0.295 0.015 20.036 0.000 0.266
## ssmk (.p6.) 0.225 0.013 17.785 0.000 0.200
## ssmc (.p7.) 0.176 0.010 17.835 0.000 0.156
## ssao (.p8.) 0.254 0.013 18.921 0.000 0.228
## electronic =~
## ssai (.p9.) 0.281 0.017 16.695 0.000 0.248
## sssi (.10.) 0.299 0.018 16.593 0.000 0.263
## ssmc (.11.) 0.141 0.010 14.647 0.000 0.122
## ssei 0.191 0.014 14.140 0.000 0.164
## speed =~
## ssno (.13.) 0.580 0.020 28.321 0.000 0.540
## sscs (.14.) 0.503 0.017 29.961 0.000 0.470
## ssmk (.15.) 0.203 0.011 18.839 0.000 0.182
## g =~
## verbal (.16.) 3.773 0.535 7.054 0.000 2.725
## math (.17.) 2.136 0.132 16.204 0.000 1.878
## elctrnc (.18.) 1.343 0.090 14.916 0.000 1.166
## speed (.19.) 0.888 0.046 19.266 0.000 0.798
## ci.upper Std.lv Std.all
##
## 0.223 0.857 0.890
## 0.223 0.855 0.888
## 0.222 0.853 0.862
## 0.133 0.506 0.470
##
## 0.324 0.839 0.887
## 0.250 0.639 0.660
## 0.195 0.499 0.530
## 0.280 0.722 0.709
##
## 0.315 0.770 0.729
## 0.334 0.818 0.833
## 0.160 0.387 0.411
## 0.217 0.523 0.485
##
## 0.620 0.864 0.824
## 0.536 0.749 0.762
## 0.224 0.303 0.312
##
## 4.822 0.967 0.967
## 2.395 0.936 0.936
## 1.519 0.611 0.611
## 0.978 0.742 0.742
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.325 0.022 14.621 0.000 0.282
## agec2 -0.036 0.016 -2.285 0.022 -0.067
## ci.upper Std.lv Std.all
##
## 0.369 0.261 0.377
## -0.005 -0.029 -0.055
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.572 0.030 18.948 0.000 0.513
## .sswk (.43.) 0.445 0.027 16.413 0.000 0.392
## .sspc 0.260 0.031 8.432 0.000 0.200
## .ssei (.45.) 0.200 0.023 8.581 0.000 0.154
## .ssar (.46.) 0.415 0.026 15.922 0.000 0.364
## .ssmk (.47.) 0.446 0.027 16.374 0.000 0.393
## .ssmc (.48.) 0.293 0.024 12.381 0.000 0.247
## .ssao (.49.) 0.342 0.025 13.481 0.000 0.292
## .ssai (.50.) 0.065 0.019 3.378 0.001 0.027
## .sssi (.51.) 0.120 0.020 5.979 0.000 0.081
## .ssno 0.561 0.035 16.006 0.000 0.492
## .sscs (.53.) 0.407 0.024 16.837 0.000 0.359
## .verbal -0.203 0.052 -3.893 0.000 -0.305
## .math -0.125 0.061 -2.067 0.039 -0.244
## .elctrnc 2.139 0.138 15.459 0.000 1.868
## .speed -0.783 0.057 -13.788 0.000 -0.894
## .g 0.079 0.055 1.438 0.150 -0.029
## ci.upper Std.lv Std.all
## 0.632 0.572 0.594
## 0.498 0.445 0.462
## 0.321 0.260 0.263
## 0.245 0.200 0.185
## 0.466 0.415 0.439
## 0.499 0.446 0.460
## 0.340 0.293 0.312
## 0.391 0.342 0.335
## 0.103 0.065 0.062
## 0.159 0.120 0.122
## 0.630 0.561 0.535
## 0.454 0.407 0.414
## -0.101 -0.042 -0.042
## -0.006 -0.044 -0.044
## 2.410 0.781 0.781
## -0.672 -0.525 -0.525
## 0.188 0.064 0.064
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.193 0.009 21.633 0.000 0.176
## .sswk 0.195 0.010 19.418 0.000 0.176
## .sspc 0.251 0.012 20.478 0.000 0.227
## .ssei 0.320 0.016 20.520 0.000 0.289
## .ssar 0.191 0.011 17.517 0.000 0.170
## .ssmk 0.170 0.009 20.001 0.000 0.153
## .ssmc 0.265 0.013 20.934 0.000 0.240
## .ssao 0.516 0.020 26.466 0.000 0.478
## .ssai 0.523 0.025 21.285 0.000 0.475
## .sssi 0.295 0.018 16.337 0.000 0.260
## .ssno 0.352 0.023 15.319 0.000 0.307
## .sscs 0.404 0.023 17.531 0.000 0.359
## .verbal 1.544 0.425 3.630 0.000 0.711
## .electronic 4.695 0.617 7.608 0.000 3.486
## .g 1.319 0.077 17.221 0.000 1.169
## ci.upper Std.lv Std.all
## 1.000 0.450 0.450
## 1.000 0.124 0.124
## 0.211 0.193 0.209
## 0.215 0.195 0.211
## 0.275 0.251 0.256
## 0.350 0.320 0.275
## 0.212 0.191 0.213
## 0.187 0.170 0.181
## 0.290 0.265 0.300
## 0.554 0.516 0.498
## 0.571 0.523 0.468
## 0.331 0.295 0.306
## 0.397 0.352 0.320
## 0.449 0.404 0.419
## 2.378 0.065 0.065
## 5.905 0.627 0.627
## 1.469 0.851 0.851
sem.age2q<-sem(hof.age2q, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 6.084044e-14) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(sem.age2q, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 2202.134 159.000 0.000 0.938 0.084 0.049 0.640
## aic bic
## 86564.633 86992.774
Mc(sem.age2q)
## [1] 0.7563362
summary(sem.age2q, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 111 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 29
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2202.134 1902.277
## Degrees of freedom 159 159
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.158
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 890.113 768.910
## 0 1312.021 1133.368
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.176 0.024 7.462 0.000 0.130
## sswk (.p2.) 0.176 0.024 7.411 0.000 0.129
## sspc (.p3.) 0.175 0.024 7.431 0.000 0.129
## ssei (.p4.) 0.104 0.015 7.174 0.000 0.076
## math =~
## ssar (.p5.) 0.295 0.015 20.023 0.000 0.266
## ssmk (.p6.) 0.225 0.013 17.780 0.000 0.200
## ssmc (.p7.) 0.176 0.010 17.819 0.000 0.156
## ssao (.p8.) 0.254 0.013 18.905 0.000 0.228
## electronic =~
## ssai (.p9.) 0.281 0.017 16.688 0.000 0.248
## sssi (.10.) 0.299 0.018 16.584 0.000 0.263
## ssmc (.11.) 0.141 0.010 14.641 0.000 0.122
## ssei 0.096 0.014 6.722 0.000 0.068
## speed =~
## ssno (.13.) 0.580 0.020 28.309 0.000 0.540
## sscs (.14.) 0.503 0.017 29.948 0.000 0.470
## ssmk (.15.) 0.203 0.011 18.835 0.000 0.182
## g =~
## verbal (.16.) 3.777 0.536 7.050 0.000 2.727
## math (.17.) 2.137 0.132 16.193 0.000 1.878
## elctrnc (.18.) 1.343 0.090 14.908 0.000 1.166
## speed (.19.) 0.888 0.046 19.254 0.000 0.798
## ci.upper Std.lv Std.all
##
## 0.222 0.753 0.877
## 0.222 0.751 0.874
## 0.222 0.750 0.852
## 0.133 0.445 0.574
##
## 0.324 0.754 0.892
## 0.250 0.575 0.642
## 0.195 0.448 0.544
## 0.280 0.649 0.711
##
## 0.314 0.502 0.669
## 0.334 0.533 0.696
## 0.160 0.252 0.306
## 0.124 0.171 0.221
##
## 0.620 0.810 0.839
## 0.536 0.703 0.759
## 0.224 0.284 0.317
##
## 4.826 0.972 0.972
## 2.396 0.920 0.920
## 1.520 0.828 0.828
## 0.979 0.699 0.699
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.307 0.016 19.449 0.000 0.276
## agec2 (b) -0.043 0.011 -4.068 0.000 -0.064
## ci.upper Std.lv Std.all
##
## 0.338 0.279 0.404
## -0.022 -0.039 -0.074
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.393 0.024 16.391 0.000 0.346
## .sswk (.43.) 0.438 0.024 18.140 0.000 0.391
## .sspc 0.515 0.025 20.635 0.000 0.466
## .ssei (.45.) 0.194 0.021 9.264 0.000 0.153
## .ssar (.46.) 0.409 0.024 17.406 0.000 0.363
## .ssmk (.47.) 0.439 0.024 18.086 0.000 0.392
## .ssmc (.48.) 0.288 0.021 13.403 0.000 0.246
## .ssao (.49.) 0.336 0.023 14.472 0.000 0.291
## .ssai (.50.) 0.062 0.018 3.437 0.001 0.027
## .sssi (.51.) 0.116 0.019 6.216 0.000 0.079
## .ssno 0.292 0.025 11.763 0.000 0.244
## .sscs (.53.) 0.402 0.023 17.676 0.000 0.358
## ci.upper Std.lv Std.all
## 0.440 0.393 0.458
## 0.486 0.438 0.510
## 0.564 0.515 0.585
## 0.236 0.194 0.251
## 0.455 0.409 0.484
## 0.487 0.439 0.491
## 0.330 0.288 0.350
## 0.382 0.336 0.369
## 0.097 0.062 0.082
## 0.153 0.116 0.152
## 0.341 0.292 0.303
## 0.447 0.402 0.435
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.171 0.009 20.048 0.000 0.154
## .sswk 0.175 0.008 20.768 0.000 0.158
## .sspc 0.212 0.012 18.163 0.000 0.189
## .ssei 0.251 0.011 23.162 0.000 0.230
## .ssar 0.146 0.009 16.968 0.000 0.129
## .ssmk 0.180 0.008 21.781 0.000 0.164
## .ssmc 0.241 0.012 20.093 0.000 0.218
## .ssao 0.411 0.017 24.006 0.000 0.378
## .ssai 0.310 0.015 20.346 0.000 0.280
## .sssi 0.302 0.015 19.849 0.000 0.272
## .ssno 0.275 0.019 14.418 0.000 0.238
## .sscs 0.364 0.019 19.100 0.000 0.327
## .verbal 1.000 1.000
## .electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.512 0.512
## 1.000 0.153 0.153
## 0.187 0.171 0.231
## 0.191 0.175 0.236
## 0.235 0.212 0.274
## 0.272 0.251 0.418
## 0.163 0.146 0.204
## 0.196 0.180 0.225
## 0.265 0.241 0.356
## 0.445 0.411 0.495
## 0.340 0.310 0.552
## 0.332 0.302 0.516
## 0.313 0.275 0.296
## 0.401 0.364 0.424
## 1.000 0.055 0.055
## 1.000 0.314 0.314
## 1.000 0.827 0.827
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.176 0.024 7.462 0.000 0.130
## sswk (.p2.) 0.176 0.024 7.411 0.000 0.129
## sspc (.p3.) 0.175 0.024 7.431 0.000 0.129
## ssei (.p4.) 0.104 0.015 7.174 0.000 0.076
## math =~
## ssar (.p5.) 0.295 0.015 20.023 0.000 0.266
## ssmk (.p6.) 0.225 0.013 17.780 0.000 0.200
## ssmc (.p7.) 0.176 0.010 17.819 0.000 0.156
## ssao (.p8.) 0.254 0.013 18.905 0.000 0.228
## electronic =~
## ssai (.p9.) 0.281 0.017 16.688 0.000 0.248
## sssi (.10.) 0.299 0.018 16.584 0.000 0.263
## ssmc (.11.) 0.141 0.010 14.641 0.000 0.122
## ssei 0.191 0.013 14.138 0.000 0.164
## speed =~
## ssno (.13.) 0.580 0.020 28.309 0.000 0.540
## sscs (.14.) 0.503 0.017 29.948 0.000 0.470
## ssmk (.15.) 0.203 0.011 18.835 0.000 0.182
## g =~
## verbal (.16.) 3.777 0.536 7.050 0.000 2.727
## math (.17.) 2.137 0.132 16.193 0.000 1.878
## elctrnc (.18.) 1.343 0.090 14.908 0.000 1.166
## speed (.19.) 0.888 0.046 19.254 0.000 0.798
## ci.upper Std.lv Std.all
##
## 0.222 0.851 0.888
## 0.222 0.850 0.887
## 0.222 0.848 0.861
## 0.133 0.504 0.469
##
## 0.324 0.834 0.886
## 0.250 0.636 0.659
## 0.195 0.496 0.529
## 0.280 0.717 0.707
##
## 0.314 0.769 0.728
## 0.334 0.816 0.832
## 0.160 0.386 0.412
## 0.217 0.521 0.485
##
## 0.620 0.861 0.823
## 0.536 0.747 0.761
## 0.224 0.301 0.313
##
## 4.826 0.966 0.966
## 2.396 0.935 0.935
## 1.520 0.608 0.608
## 0.979 0.739 0.739
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.307 0.016 19.449 0.000 0.276
## agec2 (b) -0.043 0.011 -4.068 0.000 -0.064
## ci.upper Std.lv Std.all
##
## 0.338 0.248 0.358
## -0.022 -0.035 -0.065
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.566 0.028 20.389 0.000 0.512
## .sswk (.43.) 0.438 0.024 18.140 0.000 0.391
## .sspc 0.254 0.029 8.894 0.000 0.198
## .ssei (.45.) 0.194 0.021 9.264 0.000 0.153
## .ssar (.46.) 0.409 0.024 17.406 0.000 0.363
## .ssmk (.47.) 0.439 0.024 18.086 0.000 0.392
## .ssmc (.48.) 0.288 0.021 13.403 0.000 0.246
## .ssao (.49.) 0.336 0.023 14.472 0.000 0.291
## .ssai (.50.) 0.062 0.018 3.437 0.001 0.027
## .sssi (.51.) 0.116 0.019 6.216 0.000 0.079
## .ssno 0.556 0.034 16.463 0.000 0.490
## .sscs (.53.) 0.402 0.023 17.676 0.000 0.358
## .verbal -0.218 0.052 -4.212 0.000 -0.320
## .math -0.133 0.061 -2.197 0.028 -0.252
## .elctrnc 2.134 0.138 15.438 0.000 1.863
## .speed -0.786 0.057 -13.820 0.000 -0.898
## .g 0.106 0.038 2.753 0.006 0.030
## ci.upper Std.lv Std.all
## 0.621 0.566 0.591
## 0.486 0.438 0.458
## 0.310 0.254 0.258
## 0.236 0.194 0.181
## 0.455 0.409 0.434
## 0.487 0.439 0.456
## 0.330 0.288 0.307
## 0.382 0.336 0.331
## 0.097 0.062 0.058
## 0.153 0.116 0.118
## 0.622 0.556 0.532
## 0.447 0.402 0.410
## -0.117 -0.045 -0.045
## -0.014 -0.047 -0.047
## 2.404 0.781 0.781
## -0.675 -0.529 -0.529
## 0.181 0.085 0.085
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .speed 1.000 1.000
## .math 1.000 1.000
## .ssgs 0.194 0.009 21.637 0.000 0.176
## .sswk 0.195 0.010 19.418 0.000 0.176
## .sspc 0.251 0.012 20.460 0.000 0.227
## .ssei 0.320 0.016 20.523 0.000 0.289
## .ssar 0.191 0.011 17.504 0.000 0.169
## .ssmk 0.170 0.008 20.023 0.000 0.153
## .ssmc 0.265 0.013 20.935 0.000 0.240
## .ssao 0.516 0.019 26.468 0.000 0.478
## .ssai 0.523 0.025 21.285 0.000 0.475
## .sssi 0.295 0.018 16.339 0.000 0.260
## .ssno 0.352 0.023 15.315 0.000 0.307
## .sscs 0.404 0.023 17.530 0.000 0.359
## .verbal 1.543 0.426 3.625 0.000 0.708
## .electronic 4.707 0.619 7.607 0.000 3.494
## .g 1.319 0.077 17.221 0.000 1.169
## ci.upper Std.lv Std.all
## 1.000 0.453 0.453
## 1.000 0.125 0.125
## 0.211 0.194 0.211
## 0.215 0.195 0.213
## 0.275 0.251 0.258
## 0.350 0.320 0.277
## 0.212 0.191 0.215
## 0.187 0.170 0.183
## 0.290 0.265 0.302
## 0.554 0.516 0.501
## 0.571 0.523 0.469
## 0.330 0.295 0.307
## 0.397 0.352 0.322
## 0.449 0.404 0.420
## 2.377 0.066 0.066
## 5.920 0.631 0.631
## 1.469 0.863 0.863
# BIFACTOR MODEL (math ill defined due to ao having negative variance, but then mc has negative loading and ar and mk very small; correlated residuals between mc and ao substantially improves fit)
bf.notworking<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
baseline<-cfa(bf.notworking, data=dgroup, meanstructure=T, sampling.weights="sweight", std.lv=T, orthogonal=T)
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -1.980381e-07) is smaller than zero. This may be a symptom that
## the model is not identified.
## Warning: lavaan->lav_object_post_check():
## some estimated ov variances are negative
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1183.982 39.000 0.000 0.964 0.090 0.044 88981.019
## bic
## 89297.471
Mc(baseline)
## [1] 0.8551282
summary(baseline, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 6958 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 51
##
## Number of observations 3659
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1183.982 1014.501
## Degrees of freedom 39 39
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.167
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.329 0.019 17.418 0.000 0.292
## sswk 0.418 0.022 18.921 0.000 0.375
## sspc 0.174 0.016 11.122 0.000 0.144
## ssei 0.212 0.020 10.365 0.000 0.172
## math =~
## ssar 0.000 0.000 0.838 0.402 -0.000
## ssmk 0.001 0.000 2.822 0.005 0.000
## ssmc 0.004 0.000 9.156 0.000 0.004
## ssao 16.470 0.002 9752.380 0.000 16.467
## electronic =~
## ssai 0.600 0.021 28.728 0.000 0.559
## sssi 0.645 0.017 38.231 0.000 0.612
## ssmc 0.309 0.013 23.894 0.000 0.283
## ssei 0.393 0.016 24.772 0.000 0.361
## speed =~
## ssno 0.701 0.036 19.517 0.000 0.630
## sscs 0.408 0.026 15.982 0.000 0.358
## ssmk 0.176 0.013 13.786 0.000 0.151
## g =~
## ssgs 0.734 0.014 51.542 0.000 0.706
## ssar 0.797 0.014 58.504 0.000 0.771
## sswk 0.716 0.015 49.398 0.000 0.688
## sspc 0.778 0.012 62.883 0.000 0.753
## ssno 0.606 0.017 35.690 0.000 0.573
## sscs 0.557 0.016 35.757 0.000 0.527
## ssai 0.436 0.018 24.667 0.000 0.402
## sssi 0.428 0.017 24.734 0.000 0.394
## ssmk 0.812 0.012 65.846 0.000 0.788
## ssmc 0.676 0.015 45.409 0.000 0.647
## ssei 0.654 0.017 38.980 0.000 0.622
## ssao 0.665 0.014 48.434 0.000 0.638
## ci.upper Std.lv Std.all
##
## 0.366 0.329 0.359
## 0.461 0.418 0.459
## 0.205 0.174 0.185
## 0.252 0.212 0.221
##
## 0.001 0.000 0.000
## 0.002 0.001 0.001
## 0.005 0.004 0.005
## 16.474 16.470 17.036
##
## 0.641 0.600 0.612
## 0.678 0.645 0.679
## 0.334 0.309 0.342
## 0.424 0.393 0.409
##
## 0.771 0.701 0.693
## 0.459 0.408 0.420
## 0.201 0.176 0.188
##
## 0.762 0.734 0.801
## 0.824 0.797 0.889
## 0.745 0.716 0.787
## 0.802 0.778 0.825
## 0.639 0.606 0.599
## 0.588 0.557 0.574
## 0.471 0.436 0.445
## 0.462 0.428 0.451
## 0.836 0.812 0.865
## 0.705 0.676 0.750
## 0.687 0.654 0.682
## 0.692 0.665 0.687
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.429 0.016 27.179 0.000 0.398
## .sswk 0.386 0.016 24.681 0.000 0.355
## .sspc 0.330 0.016 20.454 0.000 0.298
## .ssei 0.365 0.017 21.667 0.000 0.332
## .ssar 0.362 0.015 23.338 0.000 0.331
## .ssmk 0.310 0.016 19.252 0.000 0.279
## .ssmc 0.402 0.015 25.959 0.000 0.372
## .ssao 0.283 0.017 17.147 0.000 0.251
## .ssai 0.340 0.017 20.076 0.000 0.307
## .sssi 0.421 0.016 25.642 0.000 0.389
## .ssno 0.169 0.017 9.716 0.000 0.135
## .sscs 0.179 0.017 10.711 0.000 0.147
## ci.upper Std.lv Std.all
## 0.460 0.429 0.468
## 0.417 0.386 0.424
## 0.362 0.330 0.350
## 0.398 0.365 0.380
## 0.392 0.362 0.403
## 0.342 0.310 0.331
## 0.432 0.402 0.446
## 0.316 0.283 0.293
## 0.373 0.340 0.346
## 0.453 0.421 0.443
## 0.203 0.169 0.167
## 0.212 0.179 0.185
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.193 0.010 19.885 0.000 0.174
## .sswk 0.141 0.015 9.340 0.000 0.111
## .sspc 0.253 0.008 30.327 0.000 0.237
## .ssei 0.292 0.011 27.018 0.000 0.271
## .ssar 0.169 0.007 24.267 0.000 0.156
## .ssmk 0.190 0.007 28.713 0.000 0.177
## .ssmc 0.260 0.009 29.098 0.000 0.243
## .ssao -270.776 0.057 -4792.256 0.000 -270.887
## .ssai 0.413 0.017 24.959 0.000 0.380
## .sssi 0.303 0.014 20.991 0.000 0.275
## .ssno 0.165 0.043 3.815 0.000 0.080
## .sscs 0.466 0.020 23.913 0.000 0.428
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.212 0.193 0.230
## 0.170 0.141 0.170
## 0.269 0.253 0.285
## 0.313 0.292 0.318
## 0.183 0.169 0.210
## 0.203 0.190 0.216
## 0.278 0.260 0.321
## -270.665 -270.776 -289.708
## 0.445 0.413 0.428
## 0.331 0.303 0.336
## 0.250 0.165 0.161
## 0.505 0.466 0.494
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
bf.model<-'
verbal =~ ssgs + sswk + sspc + ssei
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
bf.lv<-'
verbal =~ ssgs + sswk + sspc + ssei
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
verbal~~1*verbal
speed~~1*speed
'
bf.weak<-'
verbal =~ ssgs + sswk + sspc + ssei
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
verbal~~1*verbal
speed~~1*speed
speed~0*1
'
baseline<-cfa(bf.model, data=dgroup, meanstructure=T, sampling.weights="sweight", std.lv=T, orthogonal=T)
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1301.887 43.000 0.000 0.961 0.089 0.045 89090.924
## bic
## 89382.556
Mc(baseline)
## [1] 0.8419176
summary(baseline, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 35 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
##
## Number of observations 3659
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1301.887 1145.848
## Degrees of freedom 43 43
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.136
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.334 0.018 18.575 0.000 0.299
## sswk 0.427 0.021 20.725 0.000 0.387
## sspc 0.181 0.015 12.321 0.000 0.152
## ssei 0.217 0.020 10.958 0.000 0.178
## electronic =~
## ssai 0.599 0.021 28.655 0.000 0.558
## sssi 0.648 0.017 38.182 0.000 0.615
## ssmc 0.303 0.013 23.138 0.000 0.278
## ssei 0.392 0.016 24.750 0.000 0.361
## speed =~
## ssno 0.701 0.035 19.954 0.000 0.632
## sscs 0.412 0.025 16.411 0.000 0.362
## ssmk 0.180 0.013 14.159 0.000 0.155
## g =~
## ssgs 0.732 0.014 51.782 0.000 0.704
## ssar 0.795 0.014 58.710 0.000 0.769
## sswk 0.712 0.014 49.675 0.000 0.684
## sspc 0.776 0.012 63.118 0.000 0.752
## ssno 0.601 0.017 35.430 0.000 0.568
## sscs 0.558 0.016 35.985 0.000 0.527
## ssai 0.433 0.018 24.662 0.000 0.399
## sssi 0.427 0.017 24.821 0.000 0.394
## ssmk 0.812 0.012 66.024 0.000 0.788
## ssmc 0.684 0.015 46.265 0.000 0.655
## ssei 0.653 0.017 39.172 0.000 0.620
## ssao 0.685 0.013 53.992 0.000 0.660
## ci.upper Std.lv Std.all
##
## 0.369 0.334 0.364
## 0.468 0.427 0.469
## 0.210 0.181 0.192
## 0.255 0.217 0.226
##
## 0.640 0.599 0.611
## 0.681 0.648 0.682
## 0.329 0.303 0.336
## 0.423 0.392 0.409
##
## 0.770 0.701 0.693
## 0.461 0.412 0.424
## 0.205 0.180 0.192
##
## 0.759 0.732 0.798
## 0.822 0.795 0.886
## 0.740 0.712 0.782
## 0.800 0.776 0.824
## 0.634 0.601 0.594
## 0.588 0.558 0.574
## 0.468 0.433 0.441
## 0.461 0.427 0.450
## 0.836 0.812 0.865
## 0.713 0.684 0.759
## 0.685 0.653 0.681
## 0.710 0.685 0.709
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.429 0.016 27.179 0.000 0.398
## .sswk 0.386 0.016 24.681 0.000 0.355
## .sspc 0.330 0.016 20.454 0.000 0.298
## .ssei 0.365 0.017 21.667 0.000 0.332
## .ssai 0.340 0.017 20.076 0.000 0.307
## .sssi 0.421 0.016 25.642 0.000 0.389
## .ssmc 0.402 0.015 25.959 0.000 0.372
## .ssno 0.169 0.017 9.716 0.000 0.135
## .sscs 0.179 0.017 10.711 0.000 0.147
## .ssmk 0.310 0.016 19.252 0.000 0.279
## .ssar 0.362 0.015 23.338 0.000 0.331
## .ssao 0.283 0.017 17.147 0.000 0.251
## ci.upper Std.lv Std.all
## 0.460 0.429 0.468
## 0.417 0.386 0.424
## 0.362 0.330 0.350
## 0.398 0.365 0.380
## 0.373 0.340 0.346
## 0.453 0.421 0.443
## 0.432 0.402 0.446
## 0.203 0.169 0.167
## 0.212 0.179 0.185
## 0.342 0.310 0.331
## 0.392 0.362 0.403
## 0.316 0.283 0.293
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.194 0.009 20.551 0.000 0.175
## .sswk 0.139 0.015 9.537 0.000 0.111
## .sspc 0.253 0.008 30.513 0.000 0.236
## .ssei 0.292 0.011 26.909 0.000 0.271
## .ssai 0.416 0.017 25.061 0.000 0.384
## .sssi 0.299 0.015 20.375 0.000 0.271
## .ssmc 0.252 0.009 29.101 0.000 0.235
## .ssno 0.170 0.042 4.045 0.000 0.088
## .sscs 0.463 0.019 24.046 0.000 0.426
## .ssmk 0.189 0.006 29.522 0.000 0.176
## .ssar 0.173 0.007 26.020 0.000 0.160
## .ssao 0.465 0.013 36.106 0.000 0.440
## verbal 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.212 0.194 0.231
## 0.168 0.139 0.168
## 0.269 0.253 0.285
## 0.314 0.292 0.318
## 0.449 0.416 0.432
## 0.328 0.299 0.332
## 0.269 0.252 0.310
## 0.252 0.170 0.166
## 0.501 0.463 0.491
## 0.201 0.189 0.214
## 0.186 0.173 0.214
## 0.490 0.465 0.498
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
configural<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T)
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1072.823 86.000 0.000 0.969 0.079 0.036 86845.954
## bic
## 87429.219
Mc(configural)
## [1] 0.8738158
summary(configural, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 48 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 94
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1072.823 947.980
## Degrees of freedom 86 86
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.132
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 440.970 389.655
## 0 631.853 558.325
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.294 0.020 14.884 0.000 0.255
## sswk 0.419 0.024 17.612 0.000 0.372
## sspc 0.189 0.019 9.832 0.000 0.151
## ssei 0.202 0.022 9.338 0.000 0.160
## electronic =~
## ssai 0.283 0.033 8.540 0.000 0.218
## sssi 0.342 0.035 9.637 0.000 0.272
## ssmc 0.148 0.023 6.294 0.000 0.102
## ssei 0.174 0.025 6.939 0.000 0.125
## speed =~
## ssno 0.746 0.068 11.028 0.000 0.613
## sscs 0.339 0.038 8.989 0.000 0.265
## ssmk 0.150 0.019 7.841 0.000 0.113
## g =~
## ssgs 0.668 0.018 36.815 0.000 0.632
## ssar 0.734 0.018 40.193 0.000 0.698
## sswk 0.693 0.020 35.340 0.000 0.654
## sspc 0.725 0.018 40.662 0.000 0.690
## ssno 0.542 0.023 23.753 0.000 0.497
## sscs 0.508 0.021 24.613 0.000 0.468
## ssai 0.370 0.019 19.419 0.000 0.333
## sssi 0.391 0.020 19.604 0.000 0.352
## ssmk 0.785 0.018 44.509 0.000 0.750
## ssmc 0.630 0.019 33.142 0.000 0.593
## ssei 0.513 0.019 27.293 0.000 0.476
## ssao 0.640 0.018 35.619 0.000 0.604
## ci.upper Std.lv Std.all
##
## 0.333 0.294 0.349
## 0.465 0.419 0.475
## 0.227 0.189 0.215
## 0.245 0.202 0.266
##
## 0.347 0.283 0.383
## 0.411 0.342 0.455
## 0.194 0.148 0.182
## 0.223 0.174 0.229
##
## 0.878 0.746 0.788
## 0.413 0.339 0.374
## 0.188 0.150 0.165
##
## 0.703 0.668 0.792
## 0.770 0.734 0.880
## 0.731 0.693 0.785
## 0.760 0.725 0.824
## 0.587 0.542 0.573
## 0.549 0.508 0.561
## 0.408 0.370 0.502
## 0.430 0.391 0.520
## 0.820 0.785 0.862
## 0.668 0.630 0.777
## 0.550 0.513 0.675
## 0.675 0.640 0.706
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.331 0.021 15.977 0.000 0.291
## .sswk 0.379 0.022 17.461 0.000 0.337
## .sspc 0.453 0.022 20.981 0.000 0.411
## .ssei 0.139 0.019 7.329 0.000 0.102
## .ssai 0.055 0.018 3.026 0.002 0.019
## .sssi 0.059 0.019 3.200 0.001 0.023
## .ssmc 0.235 0.020 11.729 0.000 0.196
## .ssno 0.244 0.023 10.435 0.000 0.198
## .sscs 0.358 0.023 15.788 0.000 0.313
## .ssmk 0.382 0.022 16.962 0.000 0.337
## .ssar 0.327 0.021 15.677 0.000 0.286
## .ssao 0.356 0.022 15.988 0.000 0.312
## ci.upper Std.lv Std.all
## 0.372 0.331 0.393
## 0.422 0.379 0.430
## 0.495 0.453 0.515
## 0.176 0.139 0.183
## 0.091 0.055 0.075
## 0.096 0.059 0.079
## 0.274 0.235 0.289
## 0.290 0.244 0.258
## 0.402 0.358 0.395
## 0.426 0.382 0.419
## 0.368 0.327 0.392
## 0.399 0.356 0.392
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.178 0.010 18.712 0.000 0.159
## .sswk 0.123 0.016 7.889 0.000 0.093
## .sspc 0.213 0.011 19.456 0.000 0.192
## .ssei 0.244 0.012 19.595 0.000 0.219
## .ssai 0.328 0.020 16.408 0.000 0.289
## .sssi 0.295 0.022 13.236 0.000 0.251
## .ssmc 0.239 0.012 20.026 0.000 0.216
## .ssno 0.046 0.091 0.502 0.616 -0.133
## .sscs 0.448 0.025 18.041 0.000 0.399
## .ssmk 0.191 0.009 21.741 0.000 0.174
## .ssar 0.157 0.008 19.180 0.000 0.141
## .ssao 0.413 0.017 24.049 0.000 0.379
## verbal 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.197 0.178 0.251
## 0.154 0.123 0.158
## 0.235 0.213 0.275
## 0.268 0.244 0.421
## 0.367 0.328 0.602
## 0.338 0.295 0.522
## 0.262 0.239 0.363
## 0.225 0.046 0.051
## 0.497 0.448 0.546
## 0.208 0.191 0.230
## 0.173 0.157 0.226
## 0.446 0.413 0.502
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.363 0.023 15.620 0.000 0.317
## sswk 0.412 0.025 16.676 0.000 0.363
## sspc 0.181 0.021 8.755 0.000 0.141
## ssei 0.250 0.026 9.781 0.000 0.200
## electronic =~
## ssai 0.679 0.030 23.015 0.000 0.622
## sssi 0.670 0.023 28.842 0.000 0.624
## ssmc 0.301 0.018 16.247 0.000 0.264
## ssei 0.401 0.022 18.226 0.000 0.357
## speed =~
## ssno 0.766 0.054 14.088 0.000 0.659
## sscs 0.389 0.034 11.345 0.000 0.322
## ssmk 0.180 0.018 10.004 0.000 0.145
## g =~
## ssgs 0.797 0.020 38.883 0.000 0.757
## ssar 0.844 0.020 42.612 0.000 0.805
## sswk 0.737 0.021 35.860 0.000 0.696
## sspc 0.828 0.016 50.848 0.000 0.796
## ssno 0.647 0.025 26.202 0.000 0.599
## sscs 0.608 0.022 27.529 0.000 0.564
## ssai 0.507 0.027 18.965 0.000 0.455
## sssi 0.483 0.025 19.495 0.000 0.435
## ssmk 0.835 0.017 48.411 0.000 0.801
## ssmc 0.744 0.021 34.965 0.000 0.703
## ssei 0.789 0.025 31.992 0.000 0.740
## ssao 0.730 0.018 41.217 0.000 0.695
## ci.upper Std.lv Std.all
##
## 0.408 0.363 0.372
## 0.460 0.412 0.439
## 0.222 0.181 0.184
## 0.300 0.250 0.231
##
## 0.737 0.679 0.617
## 0.715 0.670 0.676
## 0.337 0.301 0.315
## 0.444 0.401 0.371
##
## 0.872 0.766 0.719
## 0.456 0.389 0.388
## 0.216 0.180 0.188
##
## 0.837 0.797 0.819
## 0.882 0.844 0.886
## 0.777 0.737 0.787
## 0.860 0.828 0.841
## 0.695 0.647 0.607
## 0.651 0.608 0.607
## 0.560 0.507 0.460
## 0.532 0.483 0.488
## 0.869 0.835 0.871
## 0.786 0.744 0.781
## 0.837 0.789 0.730
## 0.764 0.730 0.718
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.523 0.023 22.328 0.000 0.477
## .sswk 0.392 0.022 17.468 0.000 0.348
## .sspc 0.211 0.024 8.959 0.000 0.165
## .ssei 0.582 0.026 22.070 0.000 0.531
## .ssai 0.614 0.027 23.150 0.000 0.562
## .sssi 0.769 0.024 32.369 0.000 0.723
## .ssmc 0.563 0.023 24.735 0.000 0.518
## .ssno 0.096 0.026 3.771 0.000 0.046
## .sscs 0.007 0.024 0.306 0.759 -0.040
## .ssmk 0.242 0.023 10.519 0.000 0.197
## .ssar 0.395 0.023 17.329 0.000 0.350
## .ssao 0.214 0.024 8.814 0.000 0.166
## ci.upper Std.lv Std.all
## 0.569 0.523 0.537
## 0.436 0.392 0.419
## 0.257 0.211 0.215
## 0.634 0.582 0.539
## 0.666 0.614 0.557
## 0.816 0.769 0.777
## 0.608 0.563 0.591
## 0.146 0.096 0.090
## 0.054 0.007 0.007
## 0.287 0.242 0.252
## 0.440 0.395 0.415
## 0.262 0.214 0.210
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.181 0.012 14.524 0.000 0.157
## .sswk 0.165 0.016 10.019 0.000 0.133
## .sspc 0.250 0.011 22.604 0.000 0.228
## .ssei 0.322 0.016 19.641 0.000 0.290
## .ssai 0.495 0.028 17.649 0.000 0.440
## .sssi 0.299 0.021 14.127 0.000 0.257
## .ssmc 0.263 0.012 21.381 0.000 0.239
## .ssno 0.130 0.074 1.761 0.078 -0.015
## .sscs 0.482 0.027 17.721 0.000 0.429
## .ssmk 0.190 0.009 20.767 0.000 0.172
## .ssar 0.196 0.011 18.555 0.000 0.175
## .ssao 0.501 0.019 26.672 0.000 0.464
## verbal 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.206 0.181 0.191
## 0.197 0.165 0.188
## 0.272 0.250 0.258
## 0.354 0.322 0.276
## 0.550 0.495 0.408
## 0.340 0.299 0.305
## 0.288 0.263 0.290
## 0.274 0.130 0.114
## 0.535 0.482 0.481
## 0.208 0.190 0.206
## 0.217 0.196 0.216
## 0.538 0.501 0.485
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
#modificationIndices(configural, sort=T, maximum.number=30)
metric<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1206.610 105.000 0.000 0.965 0.076 0.051 86941.740
## bic
## 87407.111
Mc(metric)
## [1] 0.8602129
summary(metric, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 69 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 23
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1206.610 1052.744
## Degrees of freedom 105 105
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.146
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 507.690 442.949
## 0 698.920 609.794
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.312 0.015 20.283 0.000 0.282
## sswk (.p2.) 0.388 0.022 17.827 0.000 0.346
## sspc (.p3.) 0.177 0.014 12.181 0.000 0.148
## ssei (.p4.) 0.211 0.016 13.254 0.000 0.180
## electronic =~
## ssai (.p5.) 0.303 0.018 17.025 0.000 0.268
## sssi (.p6.) 0.300 0.018 16.455 0.000 0.264
## ssmc (.p7.) 0.137 0.010 14.003 0.000 0.118
## ssei (.p8.) 0.185 0.012 15.775 0.000 0.162
## speed =~
## ssno (.p9.) 0.726 0.047 15.347 0.000 0.634
## sscs (.10.) 0.349 0.027 13.127 0.000 0.297
## ssmk (.11.) 0.158 0.013 12.196 0.000 0.132
## g =~
## ssgs (.12.) 0.684 0.015 44.336 0.000 0.654
## ssar (.13.) 0.738 0.016 45.213 0.000 0.706
## sswk (.14.) 0.667 0.016 40.602 0.000 0.634
## sspc (.15.) 0.726 0.016 46.718 0.000 0.695
## ssno (.16.) 0.557 0.017 31.947 0.000 0.522
## sscs (.17.) 0.523 0.016 32.618 0.000 0.491
## ssai (.18.) 0.390 0.015 25.337 0.000 0.359
## sssi (.19.) 0.388 0.015 25.552 0.000 0.358
## ssmk (.20.) 0.755 0.016 46.131 0.000 0.723
## ssmc (.21.) 0.634 0.016 40.164 0.000 0.603
## ssei (.22.) 0.591 0.015 38.482 0.000 0.560
## ssao (.23.) 0.640 0.015 42.438 0.000 0.610
## ci.upper Std.lv Std.all
##
## 0.343 0.312 0.365
## 0.431 0.388 0.452
## 0.205 0.177 0.201
## 0.242 0.211 0.258
##
## 0.338 0.303 0.405
## 0.336 0.300 0.402
## 0.156 0.137 0.169
## 0.207 0.185 0.226
##
## 0.819 0.726 0.761
## 0.401 0.349 0.381
## 0.183 0.158 0.177
##
## 0.714 0.684 0.799
## 0.770 0.738 0.880
## 0.699 0.667 0.776
## 0.756 0.726 0.826
## 0.591 0.557 0.583
## 0.554 0.523 0.571
## 0.420 0.390 0.521
## 0.418 0.388 0.520
## 0.787 0.755 0.849
## 0.665 0.634 0.780
## 0.621 0.591 0.722
## 0.669 0.640 0.706
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.331 0.021 15.977 0.000 0.291
## .sswk 0.379 0.022 17.461 0.000 0.337
## .sspc 0.453 0.022 20.981 0.000 0.411
## .ssei 0.139 0.019 7.329 0.000 0.102
## .ssai 0.055 0.018 3.026 0.002 0.019
## .sssi 0.059 0.019 3.200 0.001 0.023
## .ssmc 0.235 0.020 11.729 0.000 0.196
## .ssno 0.244 0.023 10.435 0.000 0.198
## .sscs 0.358 0.023 15.788 0.000 0.313
## .ssmk 0.382 0.022 16.962 0.000 0.337
## .ssar 0.327 0.021 15.677 0.000 0.286
## .ssao 0.356 0.022 15.988 0.000 0.312
## ci.upper Std.lv Std.all
## 0.372 0.331 0.386
## 0.422 0.379 0.441
## 0.495 0.453 0.516
## 0.176 0.139 0.170
## 0.091 0.055 0.074
## 0.096 0.059 0.080
## 0.274 0.235 0.289
## 0.290 0.244 0.256
## 0.402 0.358 0.391
## 0.426 0.382 0.429
## 0.368 0.327 0.390
## 0.399 0.356 0.392
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.168 0.009 18.417 0.000 0.150
## .sswk 0.143 0.013 11.368 0.000 0.119
## .sspc 0.213 0.011 19.809 0.000 0.192
## .ssei 0.241 0.011 21.478 0.000 0.219
## .ssai 0.316 0.016 20.053 0.000 0.285
## .sssi 0.316 0.016 20.372 0.000 0.286
## .ssmc 0.240 0.012 20.194 0.000 0.217
## .ssno 0.074 0.057 1.306 0.191 -0.037
## .sscs 0.442 0.021 20.846 0.000 0.400
## .ssmk 0.197 0.009 22.946 0.000 0.180
## .ssar 0.158 0.008 19.429 0.000 0.142
## .ssao 0.413 0.017 24.383 0.000 0.379
## verbal 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.186 0.168 0.229
## 0.168 0.143 0.194
## 0.234 0.213 0.277
## 0.263 0.241 0.361
## 0.347 0.316 0.565
## 0.347 0.316 0.568
## 0.263 0.240 0.363
## 0.185 0.074 0.081
## 0.483 0.442 0.528
## 0.213 0.197 0.249
## 0.174 0.158 0.225
## 0.446 0.413 0.502
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.312 0.015 20.283 0.000 0.282
## sswk (.p2.) 0.388 0.022 17.827 0.000 0.346
## sspc (.p3.) 0.177 0.014 12.181 0.000 0.148
## ssei (.p4.) 0.211 0.016 13.254 0.000 0.180
## electronic =~
## ssai (.p5.) 0.303 0.018 17.025 0.000 0.268
## sssi (.p6.) 0.300 0.018 16.455 0.000 0.264
## ssmc (.p7.) 0.137 0.010 14.003 0.000 0.118
## ssei (.p8.) 0.185 0.012 15.775 0.000 0.162
## speed =~
## ssno (.p9.) 0.726 0.047 15.347 0.000 0.634
## sscs (.10.) 0.349 0.027 13.127 0.000 0.297
## ssmk (.11.) 0.158 0.013 12.196 0.000 0.132
## g =~
## ssgs (.12.) 0.684 0.015 44.336 0.000 0.654
## ssar (.13.) 0.738 0.016 45.213 0.000 0.706
## sswk (.14.) 0.667 0.016 40.602 0.000 0.634
## sspc (.15.) 0.726 0.016 46.718 0.000 0.695
## ssno (.16.) 0.557 0.017 31.947 0.000 0.522
## sscs (.17.) 0.523 0.016 32.618 0.000 0.491
## ssai (.18.) 0.390 0.015 25.337 0.000 0.359
## sssi (.19.) 0.388 0.015 25.552 0.000 0.358
## ssmk (.20.) 0.755 0.016 46.131 0.000 0.723
## ssmc (.21.) 0.634 0.016 40.164 0.000 0.603
## ssei (.22.) 0.591 0.015 38.482 0.000 0.560
## ssao (.23.) 0.640 0.015 42.438 0.000 0.610
## ci.upper Std.lv Std.all
##
## 0.343 0.349 0.364
## 0.431 0.435 0.454
## 0.205 0.197 0.200
## 0.242 0.236 0.234
##
## 0.338 0.684 0.635
## 0.336 0.677 0.694
## 0.156 0.309 0.330
## 0.207 0.417 0.413
##
## 0.819 0.784 0.742
## 0.401 0.376 0.379
## 0.183 0.170 0.174
##
## 0.714 0.779 0.812
## 0.770 0.840 0.885
## 0.699 0.759 0.793
## 0.756 0.827 0.839
## 0.591 0.634 0.600
## 0.554 0.595 0.600
## 0.420 0.444 0.412
## 0.418 0.442 0.453
## 0.787 0.860 0.880
## 0.665 0.722 0.770
## 0.621 0.673 0.668
## 0.669 0.729 0.717
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.523 0.023 22.328 0.000 0.477
## .sswk 0.392 0.022 17.468 0.000 0.348
## .sspc 0.211 0.024 8.959 0.000 0.165
## .ssei 0.582 0.026 22.070 0.000 0.531
## .ssai 0.614 0.027 23.150 0.000 0.562
## .sssi 0.769 0.024 32.369 0.000 0.723
## .ssmc 0.563 0.023 24.735 0.000 0.518
## .ssno 0.096 0.026 3.771 0.000 0.046
## .sscs 0.007 0.024 0.306 0.759 -0.040
## .ssmk 0.242 0.023 10.519 0.000 0.197
## .ssar 0.395 0.023 17.329 0.000 0.350
## .ssao 0.214 0.024 8.814 0.000 0.166
## ci.upper Std.lv Std.all
## 0.569 0.523 0.545
## 0.436 0.392 0.410
## 0.257 0.211 0.214
## 0.634 0.582 0.578
## 0.666 0.614 0.570
## 0.816 0.769 0.788
## 0.608 0.563 0.600
## 0.146 0.096 0.091
## 0.054 0.007 0.007
## 0.287 0.242 0.248
## 0.440 0.395 0.416
## 0.262 0.214 0.211
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.192 0.011 17.989 0.000 0.171
## .sswk 0.152 0.014 10.633 0.000 0.124
## .sspc 0.250 0.011 22.590 0.000 0.228
## .ssei 0.334 0.017 19.583 0.000 0.300
## .ssai 0.496 0.028 17.910 0.000 0.442
## .sssi 0.298 0.021 14.436 0.000 0.258
## .ssmc 0.263 0.012 21.366 0.000 0.239
## .ssno 0.099 0.065 1.524 0.127 -0.028
## .sscs 0.489 0.025 19.228 0.000 0.439
## .ssmk 0.187 0.009 20.963 0.000 0.169
## .ssar 0.195 0.010 19.133 0.000 0.175
## .ssao 0.502 0.019 27.089 0.000 0.465
## verbal 1.251 0.127 9.853 0.000 1.002
## electronic 5.095 0.602 8.467 0.000 3.916
## speed 1.165 0.127 9.170 0.000 0.916
## g 1.298 0.067 19.333 0.000 1.166
## ci.upper Std.lv Std.all
## 0.213 0.192 0.208
## 0.180 0.152 0.166
## 0.271 0.250 0.257
## 0.367 0.334 0.329
## 0.550 0.496 0.427
## 0.339 0.298 0.313
## 0.288 0.263 0.299
## 0.227 0.099 0.089
## 0.539 0.489 0.496
## 0.204 0.187 0.196
## 0.215 0.195 0.216
## 0.538 0.502 0.486
## 1.500 1.000 1.000
## 6.275 1.000 1.000
## 1.414 1.000 1.000
## 1.429 1.000 1.000
lavTestScore(metric, release = 1:23)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 130.089 23 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p62. 2.623 1 0.105
## 2 .p2. == .p63. 6.099 1 0.014
## 3 .p3. == .p64. 0.770 1 0.380
## 4 .p4. == .p65. 2.758 1 0.097
## 5 .p5. == .p66. 0.358 1 0.549
## 6 .p6. == .p67. 2.111 1 0.146
## 7 .p7. == .p68. 0.000 1 0.999
## 8 .p8. == .p69. 0.780 1 0.377
## 9 .p9. == .p70. 0.165 1 0.685
## 10 .p10. == .p71. 0.020 1 0.889
## 11 .p11. == .p72. 0.010 1 0.921
## 12 .p12. == .p73. 5.460 1 0.019
## 13 .p13. == .p74. 0.545 1 0.460
## 14 .p14. == .p75. 32.159 1 0.000
## 15 .p15. == .p76. 0.051 1 0.821
## 16 .p16. == .p77. 3.062 1 0.080
## 17 .p17. == .p78. 0.688 1 0.407
## 18 .p18. == .p79. 0.537 1 0.464
## 19 .p19. == .p80. 3.686 1 0.055
## 20 .p20. == .p81. 17.679 1 0.000
## 21 .p21. == .p82. 0.189 1 0.664
## 22 .p22. == .p83. 80.671 1 0.000
## 23 .p23. == .p84. 0.001 1 0.977
metric2<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"), group.partial=c("g=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1124.635 104.000 0.000 0.968 0.073 0.041 86861.765
## bic
## 87333.341
Mc(metric2)
## [1] 0.8697867
scalar<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1678.713 112.000 0.000 0.951 0.087 0.046 87399.843
## bic
## 87821.779
Mc(scalar)
## [1] 0.8072283
summary(scalar, standardized=T, ci=T) # +.119
## lavaan 0.6-18 ended normally after 91 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 102
## Number of equality constraints 34
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1678.713 1545.603
## Degrees of freedom 112 112
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.086
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 740.277 681.579
## 0 938.435 864.024
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.362 0.023 15.578 0.000 0.316
## sswk (.p2.) 0.280 0.059 4.740 0.000 0.164
## sspc (.p3.) 0.074 0.049 1.502 0.133 -0.023
## ssei (.p4.) 0.198 0.034 5.839 0.000 0.131
## electronic =~
## ssai (.p5.) 0.275 0.017 16.633 0.000 0.243
## sssi (.p6.) 0.311 0.018 17.048 0.000 0.276
## ssmc (.p7.) 0.150 0.009 15.915 0.000 0.132
## ssei (.p8.) 0.173 0.011 15.288 0.000 0.151
## speed =~
## ssno (.p9.) 0.639 0.056 11.388 0.000 0.529
## sscs (.10.) 0.401 0.035 11.514 0.000 0.333
## ssmk (.11.) 0.179 0.012 14.547 0.000 0.155
## g =~
## ssgs (.12.) 0.693 0.017 39.780 0.000 0.659
## ssar (.13.) 0.728 0.018 40.667 0.000 0.693
## sswk (.14.) 0.688 0.019 36.972 0.000 0.651
## sspc (.15.) 0.747 0.017 44.392 0.000 0.714
## ssno (.16.) 0.555 0.018 31.028 0.000 0.520
## sscs (.17.) 0.526 0.016 32.994 0.000 0.495
## ssai (.18.) 0.405 0.016 25.928 0.000 0.375
## sssi (.19.) 0.402 0.015 26.094 0.000 0.372
## ssmk (.20.) 0.753 0.017 44.670 0.000 0.720
## ssmc (.21.) 0.639 0.016 40.566 0.000 0.608
## ssei 0.526 0.020 26.333 0.000 0.487
## ssao (.23.) 0.639 0.015 41.532 0.000 0.609
## ci.upper Std.lv Std.all
##
## 0.407 0.362 0.420
## 0.396 0.280 0.326
## 0.171 0.074 0.084
## 0.264 0.198 0.258
##
## 0.308 0.275 0.366
## 0.347 0.311 0.414
## 0.169 0.150 0.184
## 0.195 0.173 0.226
##
## 0.748 0.639 0.671
## 0.470 0.401 0.433
## 0.203 0.179 0.200
##
## 0.727 0.693 0.805
## 0.763 0.728 0.867
## 0.724 0.688 0.801
## 0.780 0.747 0.841
## 0.590 0.555 0.583
## 0.557 0.526 0.567
## 0.436 0.405 0.538
## 0.432 0.402 0.534
## 0.786 0.753 0.844
## 0.669 0.639 0.781
## 0.565 0.526 0.687
## 0.669 0.639 0.702
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.46.) 0.362 0.033 11.065 0.000 0.298
## .sswk (.47.) 0.347 0.021 16.753 0.000 0.306
## .sspc (.48.) 0.366 0.028 13.218 0.000 0.311
## .ssei (.49.) 0.137 0.019 7.365 0.000 0.101
## .ssai (.50.) 0.035 0.017 2.033 0.042 0.001
## .sssi (.51.) 0.065 0.018 3.602 0.000 0.030
## .ssmc (.52.) 0.260 0.019 13.393 0.000 0.222
## .ssno (.53.) 0.273 0.027 10.122 0.000 0.220
## .sscs (.54.) 0.263 0.025 10.545 0.000 0.214
## .ssmk (.55.) 0.382 0.022 17.103 0.000 0.338
## .ssar (.56.) 0.403 0.022 18.503 0.000 0.361
## .ssao (.57.) 0.330 0.022 15.191 0.000 0.288
## ci.upper Std.lv Std.all
## 0.426 0.362 0.421
## 0.388 0.347 0.404
## 0.420 0.366 0.412
## 0.174 0.137 0.179
## 0.068 0.035 0.046
## 0.100 0.065 0.086
## 0.298 0.260 0.317
## 0.326 0.273 0.287
## 0.312 0.263 0.284
## 0.425 0.382 0.428
## 0.446 0.403 0.480
## 0.373 0.330 0.363
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.130 0.029 4.422 0.000 0.072
## .sswk 0.187 0.020 9.266 0.000 0.147
## .sspc 0.225 0.013 17.878 0.000 0.200
## .ssei 0.240 0.013 18.779 0.000 0.215
## .ssai 0.327 0.015 21.224 0.000 0.297
## .sssi 0.308 0.016 19.788 0.000 0.277
## .ssmc 0.239 0.012 19.879 0.000 0.215
## .ssno 0.191 0.058 3.291 0.001 0.077
## .sscs 0.422 0.028 15.105 0.000 0.367
## .ssmk 0.197 0.010 19.929 0.000 0.177
## .ssar 0.176 0.012 14.665 0.000 0.152
## .ssao 0.421 0.018 23.742 0.000 0.386
## verbal 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.187 0.130 0.175
## 0.226 0.187 0.253
## 0.250 0.225 0.285
## 0.265 0.240 0.410
## 0.357 0.327 0.576
## 0.338 0.308 0.543
## 0.262 0.239 0.357
## 0.304 0.191 0.210
## 0.477 0.422 0.491
## 0.216 0.197 0.247
## 0.199 0.176 0.249
## 0.455 0.421 0.507
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.362 0.023 15.578 0.000 0.316
## sswk (.p2.) 0.280 0.059 4.740 0.000 0.164
## sspc (.p3.) 0.074 0.049 1.502 0.133 -0.023
## ssei (.p4.) 0.198 0.034 5.839 0.000 0.131
## electronic =~
## ssai (.p5.) 0.275 0.017 16.633 0.000 0.243
## sssi (.p6.) 0.311 0.018 17.048 0.000 0.276
## ssmc (.p7.) 0.150 0.009 15.915 0.000 0.132
## ssei (.p8.) 0.173 0.011 15.288 0.000 0.151
## speed =~
## ssno (.p9.) 0.639 0.056 11.388 0.000 0.529
## sscs (.10.) 0.401 0.035 11.514 0.000 0.333
## ssmk (.11.) 0.179 0.012 14.547 0.000 0.155
## g =~
## ssgs (.12.) 0.693 0.017 39.780 0.000 0.659
## ssar (.13.) 0.728 0.018 40.667 0.000 0.693
## sswk (.14.) 0.688 0.019 36.972 0.000 0.651
## sspc (.15.) 0.747 0.017 44.392 0.000 0.714
## ssno (.16.) 0.555 0.018 31.028 0.000 0.520
## sscs (.17.) 0.526 0.016 32.994 0.000 0.495
## ssai (.18.) 0.405 0.016 25.928 0.000 0.375
## sssi (.19.) 0.402 0.015 26.094 0.000 0.372
## ssmk (.20.) 0.753 0.017 44.670 0.000 0.720
## ssmc (.21.) 0.639 0.016 40.566 0.000 0.608
## ssei 0.690 0.021 32.573 0.000 0.649
## ssao (.23.) 0.639 0.015 41.532 0.000 0.609
## ci.upper Std.lv Std.all
##
## 0.407 0.417 0.433
## 0.396 0.323 0.338
## 0.171 0.086 0.086
## 0.264 0.228 0.214
##
## 0.308 0.615 0.580
## 0.347 0.696 0.708
## 0.169 0.336 0.355
## 0.195 0.386 0.362
##
## 0.748 0.697 0.661
## 0.470 0.438 0.435
## 0.203 0.195 0.200
##
## 0.727 0.783 0.814
## 0.763 0.823 0.870
## 0.724 0.778 0.813
## 0.780 0.845 0.852
## 0.590 0.628 0.596
## 0.557 0.595 0.591
## 0.436 0.459 0.432
## 0.432 0.455 0.463
## 0.786 0.852 0.874
## 0.669 0.722 0.763
## 0.732 0.781 0.732
## 0.669 0.723 0.712
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.46.) 0.362 0.033 11.065 0.000 0.298
## .sswk (.47.) 0.347 0.021 16.753 0.000 0.306
## .sspc (.48.) 0.366 0.028 13.218 0.000 0.311
## .ssei (.49.) 0.137 0.019 7.365 0.000 0.101
## .ssai (.50.) 0.035 0.017 2.033 0.042 0.001
## .sssi (.51.) 0.065 0.018 3.602 0.000 0.030
## .ssmc (.52.) 0.260 0.019 13.393 0.000 0.222
## .ssno (.53.) 0.273 0.027 10.122 0.000 0.220
## .sscs (.54.) 0.263 0.025 10.545 0.000 0.214
## .ssmk (.55.) 0.382 0.022 17.103 0.000 0.338
## .ssar (.56.) 0.403 0.022 18.503 0.000 0.361
## .ssao (.57.) 0.330 0.022 15.191 0.000 0.288
## verbal 0.619 0.185 3.340 0.001 0.256
## elctrnc 2.422 0.178 13.573 0.000 2.072
## speed -0.210 0.094 -2.225 0.026 -0.394
## g -0.134 0.048 -2.770 0.006 -0.229
## ci.upper Std.lv Std.all
## 0.426 0.362 0.376
## 0.388 0.347 0.363
## 0.420 0.366 0.369
## 0.174 0.137 0.129
## 0.068 0.035 0.033
## 0.100 0.065 0.066
## 0.298 0.260 0.274
## 0.326 0.273 0.259
## 0.312 0.263 0.262
## 0.425 0.382 0.391
## 0.446 0.403 0.426
## 0.373 0.330 0.325
## 0.982 0.537 0.537
## 2.771 1.084 1.084
## -0.025 -0.192 -0.192
## -0.039 -0.119 -0.119
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.139 0.041 3.365 0.001 0.058
## .sswk 0.206 0.025 8.169 0.000 0.157
## .sspc 0.262 0.014 19.302 0.000 0.236
## .ssei 0.328 0.017 19.166 0.000 0.294
## .ssai 0.536 0.025 21.644 0.000 0.488
## .sssi 0.274 0.018 15.067 0.000 0.239
## .ssmc 0.263 0.013 21.001 0.000 0.238
## .ssno 0.230 0.071 3.251 0.001 0.091
## .sscs 0.466 0.033 14.292 0.000 0.402
## .ssmk 0.187 0.010 18.039 0.000 0.167
## .ssar 0.217 0.016 13.952 0.000 0.186
## .ssao 0.509 0.019 26.741 0.000 0.471
## verbal 1.329 0.154 8.637 0.000 1.028
## electronic 4.991 0.609 8.200 0.000 3.798
## speed 1.191 0.134 8.895 0.000 0.928
## g 1.279 0.065 19.651 0.000 1.152
## ci.upper Std.lv Std.all
## 0.219 0.139 0.150
## 0.256 0.206 0.225
## 0.289 0.262 0.267
## 0.361 0.328 0.288
## 0.585 0.536 0.477
## 0.310 0.274 0.284
## 0.287 0.263 0.293
## 0.369 0.230 0.207
## 0.530 0.466 0.461
## 0.207 0.187 0.197
## 0.247 0.217 0.242
## 0.546 0.509 0.493
## 1.631 1.000 1.000
## 6.184 1.000 1.000
## 1.453 1.000 1.000
## 1.407 1.000 1.000
lavTestScore(scalar, release = 23:34)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 524.159 12 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p46. == .p107. 129.051 1 0.000
## 2 .p47. == .p108. 49.011 1 0.000
## 3 .p48. == .p109. 202.291 1 0.000
## 4 .p49. == .p110. 0.381 1 0.537
## 5 .p50. == .p111. 18.846 1 0.000
## 6 .p51. == .p112. 1.255 1 0.263
## 7 .p52. == .p113. 19.844 1 0.000
## 8 .p53. == .p114. 87.563 1 0.000
## 9 .p54. == .p115. 100.288 1 0.000
## 10 .p55. == .p116. 0.046 1 0.830
## 11 .p56. == .p117. 211.140 1 0.000
## 12 .p57. == .p118. 7.413 1 0.006
scalar2<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1162.513 108.000 0.000 0.967 0.073 0.042 86891.644
## bic
## 87338.400
Mc(scalar2)
## [1] 0.8657683
summary(scalar2, standardized=T, ci=T) # +.137
## lavaan 0.6-18 ended normally after 88 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 102
## Number of equality constraints 30
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1162.513 1015.719
## Degrees of freedom 108 108
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.145
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 477.458 417.168
## 0 685.055 598.551
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.313 0.015 20.612 0.000 0.283
## sswk (.p2.) 0.394 0.021 18.709 0.000 0.353
## sspc (.p3.) 0.179 0.014 12.407 0.000 0.150
## ssei (.p4.) 0.217 0.016 13.728 0.000 0.186
## electronic =~
## ssai (.p5.) 0.278 0.015 18.044 0.000 0.248
## sssi (.p6.) 0.313 0.017 18.379 0.000 0.279
## ssmc (.p7.) 0.157 0.009 16.658 0.000 0.138
## ssei (.p8.) 0.190 0.011 17.235 0.000 0.169
## speed =~
## ssno (.p9.) 0.724 0.048 15.236 0.000 0.631
## sscs (.10.) 0.349 0.027 13.079 0.000 0.297
## ssmk (.11.) 0.157 0.013 12.140 0.000 0.131
## g =~
## ssgs (.12.) 0.686 0.015 44.398 0.000 0.656
## ssar (.13.) 0.740 0.016 45.619 0.000 0.708
## sswk (.14.) 0.669 0.016 40.853 0.000 0.637
## sspc (.15.) 0.728 0.015 47.011 0.000 0.697
## ssno (.16.) 0.559 0.017 32.175 0.000 0.525
## sscs (.17.) 0.524 0.016 32.817 0.000 0.493
## ssai (.18.) 0.401 0.015 26.099 0.000 0.371
## sssi (.19.) 0.397 0.015 25.986 0.000 0.367
## ssmk (.20.) 0.758 0.016 46.617 0.000 0.726
## ssmc (.21.) 0.636 0.016 40.197 0.000 0.605
## ssei 0.512 0.018 28.350 0.000 0.477
## ssao (.23.) 0.643 0.015 42.926 0.000 0.613
## ci.upper Std.lv Std.all
##
## 0.342 0.313 0.364
## 0.435 0.394 0.457
## 0.207 0.179 0.203
## 0.248 0.217 0.284
##
## 0.309 0.278 0.370
## 0.346 0.313 0.416
## 0.175 0.157 0.192
## 0.212 0.190 0.249
##
## 0.818 0.724 0.758
## 0.401 0.349 0.381
## 0.182 0.157 0.176
##
## 0.716 0.686 0.799
## 0.772 0.740 0.882
## 0.701 0.669 0.776
## 0.758 0.728 0.827
## 0.593 0.559 0.585
## 0.556 0.524 0.573
## 0.431 0.401 0.532
## 0.427 0.397 0.529
## 0.790 0.758 0.850
## 0.667 0.636 0.779
## 0.548 0.512 0.672
## 0.672 0.643 0.707
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.328 0.021 15.918 0.000 0.288
## .sswk (.47.) 0.374 0.021 17.515 0.000 0.332
## .sspc 0.450 0.022 20.911 0.000 0.408
## .ssei (.49.) 0.143 0.019 7.608 0.000 0.106
## .ssai (.50.) 0.029 0.017 1.690 0.091 -0.005
## .sssi (.51.) 0.059 0.018 3.321 0.001 0.024
## .ssmc (.52.) 0.253 0.019 13.340 0.000 0.216
## .ssno (.53.) 0.242 0.023 10.348 0.000 0.196
## .sscs 0.355 0.023 15.705 0.000 0.311
## .ssmk (.55.) 0.375 0.022 17.058 0.000 0.332
## .ssar 0.324 0.021 15.550 0.000 0.283
## .ssao (.57.) 0.335 0.021 16.140 0.000 0.294
## ci.upper Std.lv Std.all
## 0.369 0.328 0.382
## 0.416 0.374 0.434
## 0.492 0.450 0.511
## 0.180 0.143 0.188
## 0.063 0.029 0.038
## 0.094 0.059 0.079
## 0.291 0.253 0.310
## 0.288 0.242 0.253
## 0.400 0.355 0.388
## 0.418 0.375 0.421
## 0.364 0.324 0.386
## 0.376 0.335 0.369
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.170 0.009 18.647 0.000 0.152
## .sswk 0.140 0.012 11.379 0.000 0.116
## .sspc 0.213 0.011 19.736 0.000 0.192
## .ssei 0.236 0.011 21.156 0.000 0.214
## .ssai 0.329 0.015 21.324 0.000 0.299
## .sssi 0.310 0.015 20.033 0.000 0.279
## .ssmc 0.237 0.012 19.981 0.000 0.214
## .ssno 0.076 0.057 1.334 0.182 -0.036
## .sscs 0.442 0.021 20.816 0.000 0.400
## .ssmk 0.196 0.009 22.912 0.000 0.179
## .ssar 0.157 0.008 19.306 0.000 0.141
## .ssao 0.413 0.017 24.395 0.000 0.380
## verbal 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.187 0.170 0.230
## 0.164 0.140 0.189
## 0.234 0.213 0.275
## 0.258 0.236 0.406
## 0.359 0.329 0.580
## 0.340 0.310 0.548
## 0.261 0.237 0.356
## 0.187 0.076 0.083
## 0.483 0.442 0.527
## 0.213 0.196 0.247
## 0.173 0.157 0.223
## 0.446 0.413 0.500
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.313 0.015 20.612 0.000 0.283
## sswk (.p2.) 0.394 0.021 18.709 0.000 0.353
## sspc (.p3.) 0.179 0.014 12.407 0.000 0.150
## ssei (.p4.) 0.217 0.016 13.728 0.000 0.186
## electronic =~
## ssai (.p5.) 0.278 0.015 18.044 0.000 0.248
## sssi (.p6.) 0.313 0.017 18.379 0.000 0.279
## ssmc (.p7.) 0.157 0.009 16.658 0.000 0.138
## ssei (.p8.) 0.190 0.011 17.235 0.000 0.169
## speed =~
## ssno (.p9.) 0.724 0.048 15.236 0.000 0.631
## sscs (.10.) 0.349 0.027 13.079 0.000 0.297
## ssmk (.11.) 0.157 0.013 12.140 0.000 0.131
## g =~
## ssgs (.12.) 0.686 0.015 44.398 0.000 0.656
## ssar (.13.) 0.740 0.016 45.619 0.000 0.708
## sswk (.14.) 0.669 0.016 40.853 0.000 0.637
## sspc (.15.) 0.728 0.015 47.011 0.000 0.697
## ssno (.16.) 0.559 0.017 32.175 0.000 0.525
## sscs (.17.) 0.524 0.016 32.817 0.000 0.493
## ssai (.18.) 0.401 0.015 26.099 0.000 0.371
## sssi (.19.) 0.397 0.015 25.986 0.000 0.367
## ssmk (.20.) 0.758 0.016 46.617 0.000 0.726
## ssmc (.21.) 0.636 0.016 40.197 0.000 0.605
## ssei 0.680 0.020 33.252 0.000 0.639
## ssao (.23.) 0.643 0.015 42.926 0.000 0.613
## ci.upper Std.lv Std.all
##
## 0.342 0.345 0.360
## 0.435 0.434 0.454
## 0.207 0.197 0.200
## 0.248 0.239 0.223
##
## 0.309 0.613 0.579
## 0.346 0.689 0.702
## 0.175 0.345 0.364
## 0.212 0.419 0.391
##
## 0.818 0.782 0.741
## 0.401 0.376 0.380
## 0.182 0.169 0.173
##
## 0.716 0.778 0.812
## 0.772 0.839 0.885
## 0.701 0.758 0.793
## 0.758 0.825 0.838
## 0.593 0.634 0.600
## 0.556 0.594 0.599
## 0.431 0.454 0.429
## 0.427 0.451 0.459
## 0.790 0.859 0.880
## 0.667 0.721 0.760
## 0.720 0.770 0.718
## 0.672 0.728 0.716
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.531 0.025 21.479 0.000 0.483
## .sswk (.47.) 0.374 0.021 17.515 0.000 0.332
## .sspc 0.268 0.024 11.001 0.000 0.220
## .ssei (.49.) 0.143 0.019 7.608 0.000 0.106
## .ssai (.50.) 0.029 0.017 1.690 0.091 -0.005
## .sssi (.51.) 0.059 0.018 3.321 0.001 0.024
## .ssmc (.52.) 0.253 0.019 13.340 0.000 0.216
## .ssno (.53.) 0.242 0.023 10.348 0.000 0.196
## .sscs 0.117 0.024 4.900 0.000 0.070
## .ssmk (.55.) 0.375 0.022 17.058 0.000 0.332
## .ssar 0.510 0.026 19.572 0.000 0.459
## .ssao (.57.) 0.335 0.021 16.140 0.000 0.294
## verbal 0.315 0.063 4.972 0.000 0.191
## elctrnc 2.463 0.155 15.907 0.000 2.160
## speed -0.082 0.044 -1.848 0.065 -0.169
## g -0.155 0.042 -3.670 0.000 -0.238
## ci.upper Std.lv Std.all
## 0.580 0.531 0.554
## 0.416 0.374 0.391
## 0.316 0.268 0.272
## 0.180 0.143 0.133
## 0.063 0.029 0.027
## 0.094 0.059 0.060
## 0.291 0.253 0.267
## 0.288 0.242 0.229
## 0.164 0.117 0.118
## 0.418 0.375 0.385
## 0.561 0.510 0.538
## 0.376 0.335 0.329
## 0.439 0.286 0.286
## 2.767 1.119 1.119
## 0.005 -0.076 -0.076
## -0.072 -0.137 -0.137
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.194 0.010 18.709 0.000 0.174
## .sswk 0.151 0.014 10.775 0.000 0.124
## .sspc 0.250 0.011 22.608 0.000 0.228
## .ssei 0.323 0.016 20.533 0.000 0.293
## .ssai 0.537 0.025 21.808 0.000 0.489
## .sssi 0.286 0.018 15.871 0.000 0.251
## .ssmc 0.261 0.012 20.975 0.000 0.237
## .ssno 0.101 0.065 1.554 0.120 -0.027
## .sscs 0.489 0.025 19.176 0.000 0.439
## .ssmk 0.186 0.009 21.035 0.000 0.168
## .ssar 0.194 0.010 19.064 0.000 0.174
## .ssao 0.503 0.019 27.052 0.000 0.467
## verbal 1.217 0.121 10.043 0.000 0.979
## electronic 4.849 0.561 8.650 0.000 3.751
## speed 1.165 0.128 9.114 0.000 0.914
## g 1.285 0.066 19.508 0.000 1.156
## ci.upper Std.lv Std.all
## 0.214 0.194 0.211
## 0.179 0.151 0.165
## 0.272 0.250 0.258
## 0.354 0.323 0.281
## 0.586 0.537 0.480
## 0.321 0.286 0.297
## 0.285 0.261 0.290
## 0.229 0.101 0.091
## 0.539 0.489 0.497
## 0.203 0.186 0.195
## 0.214 0.194 0.216
## 0.540 0.503 0.487
## 1.454 1.000 1.000
## 5.948 1.000 1.000
## 1.415 1.000 1.000
## 1.414 1.000 1.000
lavTestScore(scalar2, release = 23:30)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 36.366 8 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p47. == .p108. 1.946 1 0.163
## 2 .p49. == .p110. 1.946 1 0.163
## 3 .p50. == .p111. 24.995 1 0.000
## 4 .p51. == .p112. 0.128 1 0.720
## 5 .p52. == .p113. 17.811 1 0.000
## 6 .p53. == .p114. 0.453 1 0.501
## 7 .p55. == .p116. 0.453 1 0.501
## 8 .p57. == .p118. 4.215 1 0.040
strict<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1322.067 120.000 0.000 0.962 0.074 0.046 87027.197
## bic
## 87399.494
Mc(strict)
## [1] 0.8484819
summary(strict, standardized=T, ci=T) # +.137
## lavaan 0.6-18 ended normally after 87 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 102
## Number of equality constraints 42
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1322.067 1145.128
## Degrees of freedom 120 120
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.155
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 571.521 495.032
## 0 750.546 650.097
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.307 0.015 20.702 0.000 0.278
## sswk (.p2.) 0.376 0.022 16.990 0.000 0.332
## sspc (.p3.) 0.171 0.014 11.762 0.000 0.142
## ssei (.p4.) 0.213 0.015 13.853 0.000 0.183
## electronic =~
## ssai (.p5.) 0.264 0.017 15.821 0.000 0.231
## sssi (.p6.) 0.280 0.019 15.086 0.000 0.244
## ssmc (.p7.) 0.143 0.010 14.278 0.000 0.123
## ssei (.p8.) 0.176 0.012 15.071 0.000 0.153
## speed =~
## ssno (.p9.) 0.719 0.048 14.999 0.000 0.625
## sscs (.10.) 0.343 0.025 13.778 0.000 0.295
## ssmk (.11.) 0.155 0.012 12.541 0.000 0.130
## g =~
## ssgs (.12.) 0.687 0.015 44.328 0.000 0.657
## ssar (.13.) 0.740 0.016 45.757 0.000 0.708
## sswk (.14.) 0.668 0.016 40.723 0.000 0.636
## sspc (.15.) 0.728 0.016 46.880 0.000 0.697
## ssno (.16.) 0.559 0.017 32.195 0.000 0.525
## sscs (.17.) 0.524 0.016 32.570 0.000 0.493
## ssai (.18.) 0.405 0.016 26.117 0.000 0.375
## sssi (.19.) 0.398 0.015 25.974 0.000 0.368
## ssmk (.20.) 0.757 0.016 46.399 0.000 0.725
## ssmc (.21.) 0.635 0.016 40.116 0.000 0.604
## ssei 0.513 0.018 28.627 0.000 0.478
## ssao (.23.) 0.642 0.015 42.494 0.000 0.612
## ci.upper Std.lv Std.all
##
## 0.336 0.307 0.355
## 0.419 0.376 0.438
## 0.199 0.171 0.192
## 0.243 0.213 0.272
##
## 0.297 0.264 0.327
## 0.317 0.280 0.377
## 0.162 0.143 0.174
## 0.198 0.176 0.224
##
## 0.813 0.719 0.752
## 0.392 0.343 0.370
## 0.179 0.155 0.174
##
## 0.717 0.687 0.796
## 0.771 0.740 0.870
## 0.700 0.668 0.778
## 0.758 0.728 0.819
## 0.593 0.559 0.585
## 0.556 0.524 0.565
## 0.435 0.405 0.502
## 0.428 0.398 0.535
## 0.788 0.757 0.852
## 0.666 0.635 0.774
## 0.548 0.513 0.654
## 0.671 0.642 0.688
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.329 0.021 15.973 0.000 0.289
## .sswk (.47.) 0.375 0.021 17.615 0.000 0.334
## .sspc 0.451 0.021 20.974 0.000 0.409
## .ssei (.49.) 0.143 0.019 7.576 0.000 0.106
## .ssai (.50.) 0.011 0.017 0.647 0.518 -0.023
## .sssi (.51.) 0.069 0.018 3.872 0.000 0.034
## .ssmc (.52.) 0.257 0.019 13.470 0.000 0.219
## .ssno (.53.) 0.243 0.023 10.379 0.000 0.197
## .sscs 0.356 0.023 15.732 0.000 0.312
## .ssmk (.55.) 0.376 0.022 17.103 0.000 0.333
## .ssar 0.325 0.021 15.617 0.000 0.284
## .ssao (.57.) 0.333 0.021 16.088 0.000 0.293
## ci.upper Std.lv Std.all
## 0.369 0.329 0.381
## 0.417 0.375 0.437
## 0.493 0.451 0.507
## 0.180 0.143 0.182
## 0.045 0.011 0.014
## 0.104 0.069 0.093
## 0.294 0.257 0.313
## 0.289 0.243 0.254
## 0.400 0.356 0.384
## 0.419 0.376 0.423
## 0.365 0.325 0.382
## 0.374 0.333 0.357
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.24.) 0.179 0.008 22.648 0.000 0.164
## .sswk (.25.) 0.150 0.011 13.111 0.000 0.128
## .sspc (.26.) 0.231 0.008 29.702 0.000 0.216
## .ssei (.27.) 0.276 0.010 28.150 0.000 0.257
## .ssai (.28.) 0.418 0.015 28.194 0.000 0.389
## .sssi (.29.) 0.316 0.012 25.580 0.000 0.292
## .ssmc (.30.) 0.250 0.009 28.957 0.000 0.233
## .ssno (.31.) 0.084 0.059 1.421 0.155 -0.032
## .sscs (.32.) 0.467 0.019 25.109 0.000 0.430
## .ssmk (.33.) 0.192 0.006 30.104 0.000 0.180
## .ssar (.34.) 0.176 0.007 26.519 0.000 0.163
## .ssao (.35.) 0.459 0.013 35.958 0.000 0.434
## verbal 1.000 1.000
## elctrnc 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.195 0.179 0.241
## 0.173 0.150 0.204
## 0.247 0.231 0.293
## 0.295 0.276 0.448
## 0.447 0.418 0.641
## 0.340 0.316 0.571
## 0.267 0.250 0.371
## 0.200 0.084 0.092
## 0.503 0.467 0.543
## 0.205 0.192 0.244
## 0.189 0.176 0.243
## 0.484 0.459 0.527
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.307 0.015 20.702 0.000 0.278
## sswk (.p2.) 0.376 0.022 16.990 0.000 0.332
## sspc (.p3.) 0.171 0.014 11.762 0.000 0.142
## ssei (.p4.) 0.213 0.015 13.853 0.000 0.183
## electronic =~
## ssai (.p5.) 0.264 0.017 15.821 0.000 0.231
## sssi (.p6.) 0.280 0.019 15.086 0.000 0.244
## ssmc (.p7.) 0.143 0.010 14.278 0.000 0.123
## ssei (.p8.) 0.176 0.012 15.071 0.000 0.153
## speed =~
## ssno (.p9.) 0.719 0.048 14.999 0.000 0.625
## sscs (.10.) 0.343 0.025 13.778 0.000 0.295
## ssmk (.11.) 0.155 0.012 12.541 0.000 0.130
## g =~
## ssgs (.12.) 0.687 0.015 44.328 0.000 0.657
## ssar (.13.) 0.740 0.016 45.757 0.000 0.708
## sswk (.14.) 0.668 0.016 40.723 0.000 0.636
## sspc (.15.) 0.728 0.016 46.880 0.000 0.697
## ssno (.16.) 0.559 0.017 32.195 0.000 0.525
## sscs (.17.) 0.524 0.016 32.570 0.000 0.493
## ssai (.18.) 0.405 0.016 26.117 0.000 0.375
## sssi (.19.) 0.398 0.015 25.974 0.000 0.368
## ssmk (.20.) 0.757 0.016 46.399 0.000 0.725
## ssmc (.21.) 0.635 0.016 40.116 0.000 0.604
## ssei 0.678 0.020 33.124 0.000 0.638
## ssao (.23.) 0.642 0.015 42.494 0.000 0.612
## ci.upper Std.lv Std.all
##
## 0.336 0.358 0.374
## 0.419 0.439 0.458
## 0.199 0.199 0.204
## 0.243 0.249 0.236
##
## 0.297 0.645 0.631
## 0.317 0.685 0.688
## 0.162 0.348 0.369
## 0.198 0.429 0.406
##
## 0.813 0.792 0.750
## 0.392 0.378 0.385
## 0.179 0.170 0.174
##
## 0.717 0.781 0.815
## 0.771 0.841 0.895
## 0.700 0.759 0.792
## 0.758 0.827 0.846
## 0.593 0.635 0.601
## 0.556 0.596 0.606
## 0.435 0.460 0.450
## 0.428 0.452 0.455
## 0.788 0.860 0.877
## 0.666 0.722 0.764
## 0.718 0.770 0.730
## 0.671 0.729 0.733
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.530 0.025 21.277 0.000 0.481
## .sswk (.47.) 0.375 0.021 17.615 0.000 0.334
## .sspc 0.269 0.024 11.069 0.000 0.221
## .ssei (.49.) 0.143 0.019 7.576 0.000 0.106
## .ssai (.50.) 0.011 0.017 0.647 0.518 -0.023
## .sssi (.51.) 0.069 0.018 3.872 0.000 0.034
## .ssmc (.52.) 0.257 0.019 13.470 0.000 0.219
## .ssno (.53.) 0.243 0.023 10.379 0.000 0.197
## .sscs 0.117 0.024 4.903 0.000 0.071
## .ssmk (.55.) 0.376 0.022 17.103 0.000 0.333
## .ssar 0.510 0.026 19.605 0.000 0.459
## .ssao (.57.) 0.333 0.021 16.088 0.000 0.293
## verbal 0.326 0.067 4.894 0.000 0.196
## elctrnc 2.678 0.194 13.785 0.000 2.297
## speed -0.083 0.045 -1.867 0.062 -0.171
## g -0.156 0.042 -3.672 0.000 -0.239
## ci.upper Std.lv Std.all
## 0.579 0.530 0.553
## 0.417 0.375 0.391
## 0.316 0.269 0.275
## 0.180 0.143 0.135
## 0.045 0.011 0.011
## 0.104 0.069 0.070
## 0.294 0.257 0.272
## 0.289 0.243 0.230
## 0.164 0.117 0.120
## 0.419 0.376 0.383
## 0.561 0.510 0.543
## 0.374 0.333 0.335
## 0.457 0.279 0.279
## 3.059 1.097 1.097
## 0.004 -0.076 -0.076
## -0.073 -0.137 -0.137
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.24.) 0.179 0.008 22.648 0.000 0.164
## .sswk (.25.) 0.150 0.011 13.111 0.000 0.128
## .sspc (.26.) 0.231 0.008 29.702 0.000 0.216
## .ssei (.27.) 0.276 0.010 28.150 0.000 0.257
## .ssai (.28.) 0.418 0.015 28.194 0.000 0.389
## .sssi (.29.) 0.316 0.012 25.580 0.000 0.292
## .ssmc (.30.) 0.250 0.009 28.957 0.000 0.233
## .ssno (.31.) 0.084 0.059 1.421 0.155 -0.032
## .sscs (.32.) 0.467 0.019 25.109 0.000 0.430
## .ssmk (.33.) 0.192 0.006 30.104 0.000 0.180
## .ssar (.34.) 0.176 0.007 26.519 0.000 0.163
## .ssao (.35.) 0.459 0.013 35.958 0.000 0.434
## verbal 1.366 0.136 10.009 0.000 1.098
## elctrnc 5.962 0.804 7.419 0.000 4.387
## speed 1.213 0.102 11.841 0.000 1.012
## g 1.291 0.066 19.474 0.000 1.161
## ci.upper Std.lv Std.all
## 0.195 0.179 0.196
## 0.173 0.150 0.163
## 0.247 0.231 0.242
## 0.295 0.276 0.247
## 0.447 0.418 0.400
## 0.340 0.316 0.319
## 0.267 0.250 0.280
## 0.200 0.084 0.076
## 0.503 0.467 0.484
## 0.205 0.192 0.200
## 0.189 0.176 0.199
## 0.484 0.459 0.463
## 1.633 1.000 1.000
## 7.536 1.000 1.000
## 1.414 1.000 1.000
## 1.421 1.000 1.000
latent<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1536.901 112.000 0.000 0.955 0.083 0.104 87258.031
## bic
## 87679.968
Mc(latent)
## [1] 0.823028
summary(latent, standardized=T, ci=T) # +.148
## lavaan 0.6-18 ended normally after 59 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 30
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1536.901 1335.948
## Degrees of freedom 112 112
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.150
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 685.947 596.258
## 0 850.954 739.690
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.325 0.015 21.589 0.000 0.296
## sswk (.p2.) 0.417 0.017 24.384 0.000 0.384
## sspc (.p3.) 0.190 0.014 13.614 0.000 0.162
## ssei (.p4.) 0.222 0.017 13.249 0.000 0.189
## electronic =~
## ssai (.p5.) 0.441 0.018 24.797 0.000 0.406
## sssi (.p6.) 0.516 0.016 32.350 0.000 0.484
## ssmc (.p7.) 0.263 0.011 24.859 0.000 0.242
## ssei (.p8.) 0.311 0.013 24.529 0.000 0.286
## speed =~
## ssno (.p9.) 0.750 0.042 17.835 0.000 0.668
## sscs (.10.) 0.366 0.025 14.453 0.000 0.317
## ssmk (.11.) 0.166 0.013 12.681 0.000 0.140
## g =~
## ssgs (.12.) 0.735 0.014 52.874 0.000 0.707
## ssar (.13.) 0.792 0.013 58.788 0.000 0.765
## sswk (.14.) 0.716 0.014 50.157 0.000 0.688
## sspc (.15.) 0.777 0.012 64.139 0.000 0.753
## ssno (.16.) 0.596 0.017 35.389 0.000 0.563
## sscs (.17.) 0.560 0.015 36.982 0.000 0.530
## ssai (.18.) 0.438 0.016 26.815 0.000 0.406
## sssi (.19.) 0.440 0.016 27.459 0.000 0.409
## ssmk (.20.) 0.810 0.012 66.001 0.000 0.786
## ssmc (.21.) 0.688 0.014 47.877 0.000 0.659
## ssei 0.551 0.018 30.404 0.000 0.516
## ssao (.23.) 0.687 0.013 54.483 0.000 0.662
## ci.upper Std.lv Std.all
##
## 0.355 0.325 0.360
## 0.451 0.417 0.460
## 0.217 0.190 0.205
## 0.255 0.222 0.269
##
## 0.476 0.441 0.526
## 0.547 0.516 0.601
## 0.284 0.263 0.298
## 0.336 0.311 0.376
##
## 0.833 0.750 0.758
## 0.416 0.366 0.388
## 0.192 0.166 0.177
##
## 0.762 0.735 0.812
## 0.818 0.792 0.899
## 0.744 0.716 0.790
## 0.801 0.777 0.842
## 0.629 0.596 0.602
## 0.589 0.560 0.593
## 0.470 0.438 0.522
## 0.472 0.440 0.513
## 0.835 0.810 0.865
## 0.716 0.688 0.780
## 0.587 0.551 0.668
## 0.712 0.687 0.731
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.327 0.021 15.829 0.000 0.286
## .sswk (.47.) 0.373 0.022 17.340 0.000 0.331
## .sspc 0.449 0.022 20.797 0.000 0.406
## .ssei (.49.) 0.143 0.019 7.610 0.000 0.106
## .ssai (.50.) 0.037 0.017 2.141 0.032 0.003
## .sssi (.51.) 0.054 0.018 3.040 0.002 0.019
## .ssmc (.52.) 0.247 0.019 12.827 0.000 0.210
## .ssno (.53.) 0.241 0.023 10.297 0.000 0.195
## .sscs 0.355 0.023 15.654 0.000 0.310
## .ssmk (.55.) 0.376 0.022 17.024 0.000 0.332
## .ssar 0.322 0.021 15.419 0.000 0.281
## .ssao (.57.) 0.335 0.021 16.112 0.000 0.294
## ci.upper Std.lv Std.all
## 0.367 0.327 0.361
## 0.415 0.373 0.412
## 0.491 0.449 0.486
## 0.180 0.143 0.173
## 0.070 0.037 0.044
## 0.089 0.054 0.063
## 0.285 0.247 0.280
## 0.287 0.241 0.244
## 0.399 0.355 0.376
## 0.419 0.376 0.401
## 0.363 0.322 0.366
## 0.376 0.335 0.357
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.174 0.009 18.676 0.000 0.155
## .sswk 0.135 0.012 10.937 0.000 0.110
## .sspc 0.212 0.011 19.770 0.000 0.191
## .ssei 0.232 0.011 20.232 0.000 0.210
## .ssai 0.318 0.016 19.918 0.000 0.286
## .sssi 0.276 0.015 17.902 0.000 0.246
## .ssmc 0.235 0.012 20.022 0.000 0.212
## .ssno 0.062 0.056 1.092 0.275 -0.049
## .sscs 0.444 0.021 20.793 0.000 0.402
## .ssmk 0.193 0.008 22.675 0.000 0.176
## .ssar 0.149 0.008 18.657 0.000 0.133
## .ssao 0.412 0.017 24.188 0.000 0.378
## verbal 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.192 0.174 0.212
## 0.159 0.135 0.164
## 0.233 0.212 0.249
## 0.255 0.232 0.341
## 0.349 0.318 0.451
## 0.306 0.276 0.375
## 0.259 0.235 0.303
## 0.172 0.062 0.063
## 0.486 0.444 0.498
## 0.209 0.193 0.220
## 0.165 0.149 0.192
## 0.445 0.412 0.466
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.325 0.015 21.589 0.000 0.296
## sswk (.p2.) 0.417 0.017 24.384 0.000 0.384
## sspc (.p3.) 0.190 0.014 13.614 0.000 0.162
## ssei (.p4.) 0.222 0.017 13.249 0.000 0.189
## electronic =~
## ssai (.p5.) 0.441 0.018 24.797 0.000 0.406
## sssi (.p6.) 0.516 0.016 32.350 0.000 0.484
## ssmc (.p7.) 0.263 0.011 24.859 0.000 0.242
## ssei (.p8.) 0.311 0.013 24.529 0.000 0.286
## speed =~
## ssno (.p9.) 0.750 0.042 17.835 0.000 0.668
## sscs (.10.) 0.366 0.025 14.453 0.000 0.317
## ssmk (.11.) 0.166 0.013 12.681 0.000 0.140
## g =~
## ssgs (.12.) 0.735 0.014 52.874 0.000 0.707
## ssar (.13.) 0.792 0.013 58.788 0.000 0.765
## sswk (.14.) 0.716 0.014 50.157 0.000 0.688
## sspc (.15.) 0.777 0.012 64.139 0.000 0.753
## ssno (.16.) 0.596 0.017 35.389 0.000 0.563
## sscs (.17.) 0.560 0.015 36.982 0.000 0.530
## ssai (.18.) 0.438 0.016 26.815 0.000 0.406
## sssi (.19.) 0.440 0.016 27.459 0.000 0.409
## ssmk (.20.) 0.810 0.012 66.001 0.000 0.786
## ssmc (.21.) 0.688 0.014 47.877 0.000 0.659
## ssei 0.743 0.020 37.451 0.000 0.704
## ssao (.23.) 0.687 0.013 54.483 0.000 0.662
## ci.upper Std.lv Std.all
##
## 0.355 0.325 0.355
## 0.451 0.417 0.456
## 0.217 0.190 0.201
## 0.255 0.222 0.218
##
## 0.476 0.441 0.447
## 0.547 0.516 0.577
## 0.284 0.263 0.294
## 0.336 0.311 0.306
##
## 0.833 0.750 0.733
## 0.416 0.366 0.379
## 0.192 0.166 0.178
##
## 0.762 0.735 0.802
## 0.818 0.792 0.870
## 0.744 0.716 0.782
## 0.801 0.777 0.822
## 0.629 0.596 0.582
## 0.589 0.560 0.580
## 0.470 0.438 0.445
## 0.472 0.440 0.493
## 0.835 0.810 0.867
## 0.716 0.688 0.768
## 0.782 0.743 0.732
## 0.712 0.687 0.695
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.532 0.025 21.458 0.000 0.484
## .sswk (.47.) 0.373 0.022 17.340 0.000 0.331
## .sspc 0.268 0.025 10.929 0.000 0.220
## .ssei (.49.) 0.143 0.019 7.610 0.000 0.106
## .ssai (.50.) 0.037 0.017 2.141 0.032 0.003
## .sssi (.51.) 0.054 0.018 3.040 0.002 0.019
## .ssmc (.52.) 0.247 0.019 12.827 0.000 0.210
## .ssno (.53.) 0.241 0.023 10.297 0.000 0.195
## .sscs 0.118 0.024 4.917 0.000 0.071
## .ssmk (.55.) 0.376 0.022 17.024 0.000 0.332
## .ssar 0.512 0.026 19.534 0.000 0.461
## .ssao (.57.) 0.335 0.021 16.112 0.000 0.294
## verbal 0.306 0.059 5.169 0.000 0.190
## elctrnc 1.520 0.063 24.178 0.000 1.397
## speed -0.076 0.042 -1.784 0.074 -0.158
## g -0.148 0.039 -3.761 0.000 -0.225
## ci.upper Std.lv Std.all
## 0.581 0.532 0.581
## 0.415 0.373 0.407
## 0.316 0.268 0.284
## 0.180 0.143 0.141
## 0.070 0.037 0.037
## 0.089 0.054 0.060
## 0.285 0.247 0.276
## 0.287 0.241 0.236
## 0.165 0.118 0.122
## 0.419 0.376 0.402
## 0.563 0.512 0.563
## 0.376 0.335 0.339
## 0.422 0.306 0.306
## 1.643 1.520 1.520
## 0.007 -0.076 -0.076
## -0.071 -0.148 -0.148
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.193 0.010 19.343 0.000 0.174
## .sswk 0.151 0.014 11.011 0.000 0.124
## .sspc 0.253 0.011 22.674 0.000 0.231
## .ssei 0.332 0.016 21.088 0.000 0.301
## .ssai 0.586 0.026 22.286 0.000 0.534
## .sssi 0.338 0.019 17.573 0.000 0.300
## .ssmc 0.259 0.013 20.602 0.000 0.234
## .ssno 0.130 0.060 2.169 0.030 0.012
## .sscs 0.485 0.025 19.378 0.000 0.436
## .ssmk 0.190 0.009 21.468 0.000 0.172
## .ssar 0.201 0.010 19.508 0.000 0.181
## .ssao 0.504 0.019 27.187 0.000 0.468
## verbal 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.213 0.193 0.230
## 0.178 0.151 0.181
## 0.275 0.253 0.283
## 0.363 0.332 0.322
## 0.637 0.586 0.602
## 0.376 0.338 0.424
## 0.284 0.259 0.323
## 0.247 0.130 0.124
## 0.534 0.485 0.520
## 0.207 0.190 0.217
## 0.221 0.201 0.243
## 0.541 0.504 0.517
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
latent2<-cfa(bf.lv, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1170.838 110.000 0.000 0.967 0.073 0.042 86895.969
## bic
## 87330.315
Mc(latent2)
## [1] 0.8650201
summary(latent2, standardized=T, ci=T) # +.137
## lavaan 0.6-18 ended normally after 79 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 100
## Number of equality constraints 30
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1170.838 1017.580
## Degrees of freedom 110 110
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.151
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 480.865 417.922
## 0 689.973 599.658
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.326 0.015 21.735 0.000 0.297
## sswk (.p2.) 0.416 0.017 24.550 0.000 0.383
## sspc (.p3.) 0.188 0.014 13.522 0.000 0.161
## ssei (.p4.) 0.225 0.017 13.629 0.000 0.193
## electronic =~
## ssai (.p5.) 0.279 0.015 18.037 0.000 0.248
## sssi (.p6.) 0.313 0.017 18.388 0.000 0.279
## ssmc (.p7.) 0.156 0.009 16.644 0.000 0.138
## ssei (.p8.) 0.191 0.011 17.359 0.000 0.169
## speed =~
## ssno (.p9.) 0.753 0.043 17.419 0.000 0.668
## sscs (.10.) 0.362 0.026 14.112 0.000 0.312
## ssmk (.11.) 0.163 0.013 12.416 0.000 0.137
## g =~
## ssgs (.12.) 0.685 0.015 44.385 0.000 0.655
## ssar (.13.) 0.740 0.016 45.582 0.000 0.708
## sswk (.14.) 0.668 0.016 40.863 0.000 0.636
## sspc (.15.) 0.727 0.015 47.021 0.000 0.697
## ssno (.16.) 0.558 0.017 32.137 0.000 0.524
## sscs (.17.) 0.524 0.016 32.789 0.000 0.493
## ssai (.18.) 0.400 0.015 26.074 0.000 0.370
## sssi (.19.) 0.397 0.015 25.991 0.000 0.367
## ssmk (.20.) 0.757 0.016 46.567 0.000 0.725
## ssmc (.21.) 0.636 0.016 40.178 0.000 0.605
## ssei 0.511 0.018 28.304 0.000 0.476
## ssao (.23.) 0.642 0.015 42.897 0.000 0.613
## ci.upper Std.lv Std.all
##
## 0.356 0.326 0.378
## 0.450 0.416 0.479
## 0.216 0.188 0.214
## 0.258 0.225 0.295
##
## 0.309 0.279 0.370
## 0.346 0.313 0.416
## 0.175 0.156 0.192
## 0.212 0.191 0.250
##
## 0.838 0.753 0.779
## 0.412 0.362 0.392
## 0.189 0.163 0.183
##
## 0.716 0.685 0.793
## 0.771 0.740 0.882
## 0.701 0.668 0.769
## 0.757 0.727 0.825
## 0.592 0.558 0.578
## 0.556 0.524 0.568
## 0.431 0.400 0.532
## 0.427 0.397 0.529
## 0.789 0.757 0.848
## 0.667 0.636 0.779
## 0.546 0.511 0.669
## 0.672 0.642 0.708
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.328 0.021 15.918 0.000 0.288
## .sswk (.47.) 0.374 0.021 17.517 0.000 0.333
## .sspc 0.450 0.022 20.912 0.000 0.408
## .ssei (.49.) 0.143 0.019 7.606 0.000 0.106
## .ssai (.50.) 0.029 0.017 1.688 0.091 -0.005
## .sssi (.51.) 0.059 0.018 3.322 0.001 0.024
## .ssmc (.52.) 0.253 0.019 13.339 0.000 0.216
## .ssno (.53.) 0.242 0.023 10.350 0.000 0.196
## .sscs 0.355 0.023 15.705 0.000 0.311
## .ssmk (.55.) 0.375 0.022 17.058 0.000 0.332
## .ssar 0.324 0.021 15.551 0.000 0.283
## .ssao (.57.) 0.335 0.021 16.144 0.000 0.294
## ci.upper Std.lv Std.all
## 0.369 0.328 0.380
## 0.416 0.374 0.431
## 0.492 0.450 0.511
## 0.180 0.143 0.187
## 0.062 0.029 0.038
## 0.094 0.059 0.079
## 0.291 0.253 0.311
## 0.288 0.242 0.251
## 0.400 0.355 0.385
## 0.418 0.375 0.420
## 0.365 0.324 0.386
## 0.376 0.335 0.369
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.170 0.009 18.631 0.000 0.152
## .sswk 0.135 0.012 11.131 0.000 0.111
## .sspc 0.213 0.011 19.730 0.000 0.192
## .ssei 0.236 0.011 21.155 0.000 0.214
## .ssai 0.330 0.015 21.340 0.000 0.299
## .sssi 0.310 0.015 20.033 0.000 0.279
## .ssmc 0.237 0.012 19.964 0.000 0.214
## .ssno 0.054 0.059 0.929 0.353 -0.060
## .sscs 0.446 0.022 20.689 0.000 0.403
## .ssmk 0.197 0.009 23.043 0.000 0.180
## .ssar 0.156 0.008 19.192 0.000 0.140
## .ssao 0.411 0.017 24.337 0.000 0.378
## electronic 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.188 0.170 0.228
## 0.159 0.135 0.179
## 0.234 0.213 0.274
## 0.258 0.236 0.404
## 0.360 0.330 0.581
## 0.340 0.310 0.548
## 0.260 0.237 0.356
## 0.169 0.054 0.058
## 0.488 0.446 0.523
## 0.213 0.197 0.247
## 0.172 0.156 0.221
## 0.444 0.411 0.499
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.326 0.015 21.735 0.000 0.297
## sswk (.p2.) 0.416 0.017 24.550 0.000 0.383
## sspc (.p3.) 0.188 0.014 13.522 0.000 0.161
## ssei (.p4.) 0.225 0.017 13.629 0.000 0.193
## electronic =~
## ssai (.p5.) 0.279 0.015 18.037 0.000 0.248
## sssi (.p6.) 0.313 0.017 18.388 0.000 0.279
## ssmc (.p7.) 0.156 0.009 16.644 0.000 0.138
## ssei (.p8.) 0.191 0.011 17.359 0.000 0.169
## speed =~
## ssno (.p9.) 0.753 0.043 17.419 0.000 0.668
## sscs (.10.) 0.362 0.026 14.112 0.000 0.312
## ssmk (.11.) 0.163 0.013 12.416 0.000 0.137
## g =~
## ssgs (.12.) 0.685 0.015 44.385 0.000 0.655
## ssar (.13.) 0.740 0.016 45.582 0.000 0.708
## sswk (.14.) 0.668 0.016 40.863 0.000 0.636
## sspc (.15.) 0.727 0.015 47.021 0.000 0.697
## ssno (.16.) 0.558 0.017 32.137 0.000 0.524
## sscs (.17.) 0.524 0.016 32.789 0.000 0.493
## ssai (.18.) 0.400 0.015 26.074 0.000 0.370
## sssi (.19.) 0.397 0.015 25.991 0.000 0.367
## ssmk (.20.) 0.757 0.016 46.567 0.000 0.725
## ssmc (.21.) 0.636 0.016 40.178 0.000 0.605
## ssei 0.680 0.020 33.268 0.000 0.640
## ssao (.23.) 0.642 0.015 42.897 0.000 0.613
## ci.upper Std.lv Std.all
##
## 0.356 0.326 0.343
## 0.450 0.416 0.438
## 0.216 0.188 0.192
## 0.258 0.225 0.210
##
## 0.309 0.613 0.579
## 0.346 0.688 0.701
## 0.175 0.344 0.363
## 0.212 0.420 0.392
##
## 0.838 0.753 0.720
## 0.412 0.362 0.367
## 0.189 0.163 0.167
##
## 0.716 0.778 0.817
## 0.771 0.840 0.885
## 0.701 0.759 0.799
## 0.757 0.825 0.839
## 0.592 0.634 0.606
## 0.556 0.595 0.604
## 0.431 0.455 0.430
## 0.427 0.451 0.460
## 0.789 0.859 0.881
## 0.667 0.721 0.760
## 0.720 0.772 0.721
## 0.672 0.729 0.716
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.532 0.025 21.681 0.000 0.484
## .sswk (.47.) 0.374 0.021 17.517 0.000 0.333
## .sspc 0.268 0.024 11.004 0.000 0.220
## .ssei (.49.) 0.143 0.019 7.606 0.000 0.106
## .ssai (.50.) 0.029 0.017 1.688 0.091 -0.005
## .sssi (.51.) 0.059 0.018 3.322 0.001 0.024
## .ssmc (.52.) 0.253 0.019 13.339 0.000 0.216
## .ssno (.53.) 0.242 0.023 10.350 0.000 0.196
## .sscs 0.117 0.024 4.899 0.000 0.070
## .ssmk (.55.) 0.375 0.022 17.058 0.000 0.332
## .ssar 0.510 0.026 19.567 0.000 0.459
## .ssao (.57.) 0.335 0.021 16.144 0.000 0.294
## verbal 0.297 0.058 5.084 0.000 0.183
## elctrnc 2.462 0.155 15.920 0.000 2.159
## speed -0.079 0.042 -1.871 0.061 -0.162
## g -0.155 0.042 -3.667 0.000 -0.238
## ci.upper Std.lv Std.all
## 0.581 0.532 0.559
## 0.416 0.374 0.394
## 0.316 0.268 0.273
## 0.180 0.143 0.134
## 0.062 0.029 0.027
## 0.094 0.059 0.060
## 0.291 0.253 0.267
## 0.288 0.242 0.232
## 0.164 0.117 0.119
## 0.418 0.375 0.385
## 0.561 0.510 0.537
## 0.376 0.335 0.329
## 0.412 0.297 0.297
## 2.766 1.119 1.119
## 0.004 -0.079 -0.079
## -0.072 -0.137 -0.137
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.196 0.010 19.525 0.000 0.176
## .sswk 0.153 0.014 11.190 0.000 0.127
## .sspc 0.250 0.011 22.583 0.000 0.228
## .ssei 0.324 0.016 20.589 0.000 0.293
## .ssai 0.537 0.025 21.812 0.000 0.489
## .sssi 0.286 0.018 15.872 0.000 0.250
## .ssmc 0.262 0.012 21.016 0.000 0.237
## .ssno 0.124 0.062 2.023 0.043 0.004
## .sscs 0.486 0.025 19.462 0.000 0.437
## .ssmk 0.186 0.009 21.234 0.000 0.168
## .ssar 0.195 0.010 19.344 0.000 0.176
## .ssao 0.505 0.019 27.108 0.000 0.468
## electronic 4.842 0.559 8.667 0.000 3.747
## g 1.288 0.066 19.519 0.000 1.159
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.216 0.196 0.216
## 0.180 0.153 0.170
## 0.272 0.250 0.259
## 0.355 0.324 0.283
## 0.586 0.537 0.480
## 0.321 0.286 0.297
## 0.286 0.262 0.291
## 0.245 0.124 0.114
## 0.534 0.486 0.500
## 0.203 0.186 0.195
## 0.215 0.195 0.217
## 0.541 0.505 0.487
## 5.937 1.000 1.000
## 1.418 1.000 1.000
weak<-cfa(bf.weak, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(weak, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1174.987 111.000 0.000 0.966 0.072 0.043 86898.117
## bic
## 87326.258
Mc(weak)
## [1] 0.864648
summary(weak, standardized=T, ci=T) # +.150
## lavaan 0.6-18 ended normally after 78 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 99
## Number of equality constraints 30
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1174.987 1021.114
## Degrees of freedom 111 111
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.151
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 482.796 419.570
## 0 692.191 601.544
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.327 0.015 21.777 0.000 0.297
## sswk (.p2.) 0.416 0.017 24.591 0.000 0.383
## sspc (.p3.) 0.188 0.014 13.527 0.000 0.161
## ssei (.p4.) 0.226 0.016 13.680 0.000 0.193
## electronic =~
## ssai (.p5.) 0.278 0.015 18.036 0.000 0.248
## sssi (.p6.) 0.312 0.017 18.395 0.000 0.279
## ssmc (.p7.) 0.158 0.009 16.769 0.000 0.139
## ssei (.p8.) 0.191 0.011 17.351 0.000 0.169
## speed =~
## ssno (.p9.) 0.754 0.043 17.359 0.000 0.669
## sscs (.10.) 0.361 0.026 14.028 0.000 0.311
## ssmk (.11.) 0.162 0.013 12.357 0.000 0.137
## g =~
## ssgs (.12.) 0.685 0.015 44.376 0.000 0.655
## ssar (.13.) 0.740 0.016 45.599 0.000 0.708
## sswk (.14.) 0.668 0.016 40.863 0.000 0.636
## sspc (.15.) 0.727 0.015 47.021 0.000 0.697
## ssno (.16.) 0.561 0.017 32.597 0.000 0.527
## sscs (.17.) 0.525 0.016 33.023 0.000 0.494
## ssai (.18.) 0.401 0.015 26.087 0.000 0.370
## sssi (.19.) 0.397 0.015 25.984 0.000 0.367
## ssmk (.20.) 0.757 0.016 46.612 0.000 0.725
## ssmc (.21.) 0.635 0.016 40.139 0.000 0.604
## ssei 0.511 0.018 28.292 0.000 0.476
## ssao (.23.) 0.642 0.015 42.828 0.000 0.613
## ci.upper Std.lv Std.all
##
## 0.356 0.327 0.378
## 0.449 0.416 0.479
## 0.216 0.188 0.214
## 0.258 0.226 0.295
##
## 0.308 0.278 0.369
## 0.346 0.312 0.415
## 0.176 0.158 0.193
## 0.212 0.191 0.250
##
## 0.839 0.754 0.780
## 0.412 0.361 0.391
## 0.188 0.162 0.182
##
## 0.716 0.685 0.793
## 0.772 0.740 0.882
## 0.700 0.668 0.769
## 0.757 0.727 0.825
## 0.594 0.561 0.580
## 0.557 0.525 0.569
## 0.431 0.401 0.532
## 0.427 0.397 0.528
## 0.789 0.757 0.849
## 0.666 0.635 0.779
## 0.546 0.511 0.668
## 0.671 0.642 0.708
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## speed 0.000 0.000
## .ssgs 0.328 0.021 15.923 0.000 0.288
## .sswk (.48.) 0.375 0.021 17.520 0.000 0.333
## .sspc 0.450 0.022 20.915 0.000 0.408
## .ssei (.50.) 0.143 0.019 7.598 0.000 0.106
## .ssai (.51.) 0.029 0.017 1.681 0.093 -0.005
## .sssi (.52.) 0.059 0.018 3.289 0.001 0.024
## .ssmc (.53.) 0.255 0.019 13.438 0.000 0.218
## .ssno (.54.) 0.218 0.020 10.734 0.000 0.178
## .sscs 0.345 0.022 15.554 0.000 0.301
## .ssmk (.56.) 0.375 0.022 17.026 0.000 0.331
## .ssar 0.324 0.021 15.555 0.000 0.283
## .ssao (.58.) 0.339 0.020 16.570 0.000 0.299
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.369 0.328 0.380
## 0.416 0.375 0.431
## 0.492 0.450 0.511
## 0.180 0.143 0.187
## 0.062 0.029 0.038
## 0.094 0.059 0.078
## 0.292 0.255 0.312
## 0.258 0.218 0.225
## 0.388 0.345 0.374
## 0.418 0.375 0.420
## 0.365 0.324 0.386
## 0.379 0.339 0.374
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.170 0.009 18.632 0.000 0.152
## .sswk 0.135 0.012 11.165 0.000 0.111
## .sspc 0.213 0.011 19.731 0.000 0.192
## .ssei 0.236 0.011 21.151 0.000 0.214
## .ssai 0.330 0.015 21.366 0.000 0.300
## .sssi 0.310 0.015 20.061 0.000 0.280
## .ssmc 0.237 0.012 19.952 0.000 0.214
## .ssno 0.053 0.059 0.890 0.374 -0.063
## .sscs 0.446 0.022 20.667 0.000 0.404
## .ssmk 0.197 0.009 23.062 0.000 0.180
## .ssar 0.156 0.008 19.185 0.000 0.140
## .ssao 0.411 0.017 24.325 0.000 0.378
## electronic 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.188 0.170 0.228
## 0.159 0.135 0.179
## 0.234 0.213 0.274
## 0.258 0.236 0.404
## 0.360 0.330 0.581
## 0.340 0.310 0.548
## 0.260 0.237 0.356
## 0.169 0.053 0.056
## 0.488 0.446 0.523
## 0.213 0.197 0.247
## 0.171 0.156 0.221
## 0.444 0.411 0.499
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.327 0.015 21.777 0.000 0.297
## sswk (.p2.) 0.416 0.017 24.591 0.000 0.383
## sspc (.p3.) 0.188 0.014 13.527 0.000 0.161
## ssei (.p4.) 0.226 0.016 13.680 0.000 0.193
## electronic =~
## ssai (.p5.) 0.278 0.015 18.036 0.000 0.248
## sssi (.p6.) 0.312 0.017 18.395 0.000 0.279
## ssmc (.p7.) 0.158 0.009 16.769 0.000 0.139
## ssei (.p8.) 0.191 0.011 17.351 0.000 0.169
## speed =~
## ssno (.p9.) 0.754 0.043 17.359 0.000 0.669
## sscs (.10.) 0.361 0.026 14.028 0.000 0.311
## ssmk (.11.) 0.162 0.013 12.357 0.000 0.137
## g =~
## ssgs (.12.) 0.685 0.015 44.376 0.000 0.655
## ssar (.13.) 0.740 0.016 45.599 0.000 0.708
## sswk (.14.) 0.668 0.016 40.863 0.000 0.636
## sspc (.15.) 0.727 0.015 47.021 0.000 0.697
## ssno (.16.) 0.561 0.017 32.597 0.000 0.527
## sscs (.17.) 0.525 0.016 33.023 0.000 0.494
## ssai (.18.) 0.401 0.015 26.087 0.000 0.370
## sssi (.19.) 0.397 0.015 25.984 0.000 0.367
## ssmk (.20.) 0.757 0.016 46.612 0.000 0.725
## ssmc (.21.) 0.635 0.016 40.139 0.000 0.604
## ssei 0.680 0.020 33.279 0.000 0.640
## ssao (.23.) 0.642 0.015 42.828 0.000 0.613
## ci.upper Std.lv Std.all
##
## 0.356 0.327 0.343
## 0.449 0.416 0.438
## 0.216 0.188 0.192
## 0.258 0.226 0.211
##
## 0.308 0.612 0.579
## 0.346 0.687 0.700
## 0.176 0.347 0.365
## 0.212 0.420 0.392
##
## 0.839 0.754 0.720
## 0.412 0.361 0.367
## 0.188 0.162 0.167
##
## 0.716 0.778 0.816
## 0.772 0.840 0.885
## 0.700 0.759 0.799
## 0.757 0.825 0.839
## 0.594 0.636 0.608
## 0.557 0.596 0.605
## 0.431 0.455 0.430
## 0.427 0.451 0.460
## 0.789 0.860 0.882
## 0.666 0.721 0.759
## 0.720 0.771 0.721
## 0.671 0.729 0.716
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## speed 0.000 0.000
## .ssgs 0.535 0.025 21.635 0.000 0.486
## .sswk (.48.) 0.375 0.021 17.520 0.000 0.333
## .sspc 0.275 0.024 11.401 0.000 0.227
## .ssei (.50.) 0.143 0.019 7.598 0.000 0.106
## .ssai (.51.) 0.029 0.017 1.681 0.093 -0.005
## .sssi (.52.) 0.059 0.018 3.289 0.001 0.024
## .ssmc (.53.) 0.255 0.019 13.438 0.000 0.218
## .ssno (.54.) 0.218 0.020 10.734 0.000 0.178
## .sscs 0.107 0.023 4.595 0.000 0.061
## .ssmk (.56.) 0.375 0.022 17.026 0.000 0.331
## .ssar 0.521 0.025 21.054 0.000 0.472
## .ssao (.58.) 0.339 0.020 16.570 0.000 0.299
## verbal 0.321 0.056 5.699 0.000 0.211
## elctrnc 2.489 0.156 15.921 0.000 2.182
## g -0.170 0.042 -4.100 0.000 -0.252
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.583 0.535 0.561
## 0.416 0.375 0.394
## 0.322 0.275 0.279
## 0.180 0.143 0.133
## 0.062 0.029 0.027
## 0.094 0.059 0.060
## 0.292 0.255 0.268
## 0.258 0.218 0.208
## 0.153 0.107 0.109
## 0.418 0.375 0.384
## 0.569 0.521 0.549
## 0.379 0.339 0.333
## 0.431 0.321 0.321
## 2.795 1.131 1.131
## -0.089 -0.150 -0.150
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.196 0.010 19.523 0.000 0.176
## .sswk 0.154 0.014 11.220 0.000 0.127
## .sspc 0.250 0.011 22.583 0.000 0.228
## .ssei 0.324 0.016 20.604 0.000 0.293
## .ssai 0.538 0.025 21.827 0.000 0.489
## .sssi 0.287 0.018 15.961 0.000 0.252
## .ssmc 0.261 0.012 20.986 0.000 0.237
## .ssno 0.122 0.062 1.972 0.049 0.001
## .sscs 0.486 0.025 19.474 0.000 0.437
## .ssmk 0.186 0.009 21.233 0.000 0.168
## .ssar 0.195 0.010 19.346 0.000 0.175
## .ssao 0.505 0.019 27.128 0.000 0.468
## electronic 4.841 0.559 8.667 0.000 3.747
## g 1.288 0.066 19.520 0.000 1.159
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.216 0.196 0.216
## 0.180 0.154 0.170
## 0.272 0.250 0.259
## 0.355 0.324 0.283
## 0.586 0.538 0.481
## 0.322 0.287 0.298
## 0.286 0.261 0.290
## 0.244 0.122 0.112
## 0.535 0.486 0.500
## 0.203 0.186 0.195
## 0.215 0.195 0.217
## 0.541 0.505 0.487
## 5.936 1.000 1.000
## 1.417 1.000 1.000
standardizedSolution(weak) # get the correct SEs for standardized solution
## lhs op rhs group label est.std se z pvalue
## 1 verbal =~ ssgs 1 .p1. 0.378 0.017 21.831 0.000
## 2 verbal =~ sswk 1 .p2. 0.479 0.020 24.284 0.000
## 3 verbal =~ sspc 1 .p3. 0.214 0.016 13.366 0.000
## 4 verbal =~ ssei 1 .p4. 0.295 0.022 13.638 0.000
## 5 electronic =~ ssai 1 .p5. 0.369 0.020 18.835 0.000
## 6 electronic =~ sssi 1 .p6. 0.415 0.022 19.308 0.000
## 7 electronic =~ ssmc 1 .p7. 0.193 0.012 16.774 0.000
## 8 electronic =~ ssei 1 .p8. 0.250 0.015 16.738 0.000
## 9 speed =~ ssno 1 .p9. 0.780 0.043 18.156 0.000
## 10 speed =~ sscs 1 .p10. 0.391 0.027 14.611 0.000
## 11 speed =~ ssmk 1 .p11. 0.182 0.015 12.167 0.000
## 12 g =~ ssgs 1 .p12. 0.793 0.009 86.221 0.000
## 13 g =~ ssar 1 .p13. 0.882 0.007 132.627 0.000
## 14 g =~ sswk 1 .p14. 0.769 0.010 76.315 0.000
## 15 g =~ sspc 1 .p15. 0.825 0.009 92.681 0.000
## 16 g =~ ssno 1 .p16. 0.580 0.016 36.780 0.000
## 17 g =~ sscs 1 .p17. 0.569 0.015 38.521 0.000
## 18 g =~ ssai 1 .p18. 0.532 0.017 31.600 0.000
## 19 g =~ sssi 1 .p19. 0.528 0.017 31.931 0.000
## 20 g =~ ssmk 1 .p20. 0.849 0.007 113.887 0.000
## 21 g =~ ssmc 1 .p21. 0.779 0.011 73.454 0.000
## 22 g =~ ssei 1 0.668 0.015 45.471 0.000
## 23 g =~ ssao 1 .p23. 0.708 0.012 59.053 0.000
## 24 verbal ~~ verbal 1 1.000 0.000 NA NA
## 25 speed ~~ speed 1 1.000 0.000 NA NA
## 26 speed ~1 1 0.000 0.000 NA NA
## 27 ssgs ~~ ssgs 1 0.228 0.012 18.729 0.000
## 28 sswk ~~ sswk 1 0.179 0.016 11.117 0.000
## 29 sspc ~~ sspc 1 0.274 0.014 20.124 0.000
## 30 ssei ~~ ssei 1 0.404 0.017 23.681 0.000
## 31 ssai ~~ ssai 1 0.581 0.020 28.901 0.000
## 32 sssi ~~ sssi 1 0.548 0.022 25.446 0.000
## 33 ssmc ~~ ssmc 1 0.356 0.016 22.168 0.000
## 34 ssno ~~ ssno 1 0.056 0.063 0.890 0.374
## 35 sscs ~~ sscs 1 0.523 0.021 24.402 0.000
## 36 ssmk ~~ ssmk 1 0.247 0.012 21.131 0.000
## 37 ssar ~~ ssar 1 0.221 0.012 18.850 0.000
## 38 ssao ~~ ssao 1 0.499 0.017 29.434 0.000
## 39 electronic ~~ electronic 1 1.000 0.000 NA NA
## 40 g ~~ g 1 1.000 0.000 NA NA
## 41 verbal ~~ electronic 1 0.000 0.000 NA NA
## 42 verbal ~~ speed 1 0.000 0.000 NA NA
## 43 verbal ~~ g 1 0.000 0.000 NA NA
## 44 electronic ~~ speed 1 0.000 0.000 NA NA
## 45 electronic ~~ g 1 0.000 0.000 NA NA
## 46 speed ~~ g 1 0.000 0.000 NA NA
## 47 ssgs ~1 1 0.380 0.025 15.042 0.000
## 48 sswk ~1 1 .p48. 0.431 0.026 16.307 0.000
## 49 sspc ~1 1 0.511 0.028 18.453 0.000
## 50 ssei ~1 1 .p50. 0.187 0.025 7.497 0.000
## 51 ssai ~1 1 .p51. 0.038 0.023 1.676 0.094
## 52 sssi ~1 1 .p52. 0.078 0.024 3.291 0.001
## 53 ssmc ~1 1 .p53. 0.312 0.026 12.059 0.000
## 54 ssno ~1 1 .p54. 0.225 0.022 10.387 0.000
## 55 sscs ~1 1 0.374 0.025 14.726 0.000
## 56 ssmk ~1 1 .p56. 0.420 0.027 15.601 0.000
## 57 ssar ~1 1 0.386 0.028 13.933 0.000
## 58 ssao ~1 1 .p58. 0.374 0.024 15.403 0.000
## 59 verbal ~1 1 0.000 0.000 NA NA
## 60 electronic ~1 1 0.000 0.000 NA NA
## 61 g ~1 1 0.000 0.000 NA NA
## 62 verbal =~ ssgs 2 .p1. 0.343 0.016 20.945 0.000
## 63 verbal =~ sswk 2 .p2. 0.438 0.019 23.386 0.000
## 64 verbal =~ sspc 2 .p3. 0.192 0.014 13.301 0.000
## 65 verbal =~ ssei 2 .p4. 0.211 0.016 13.237 0.000
## 66 electronic =~ ssai 2 .p5. 0.579 0.018 32.230 0.000
## 67 electronic =~ sssi 2 .p6. 0.700 0.015 46.132 0.000
## 68 electronic =~ ssmc 2 .p7. 0.365 0.014 26.020 0.000
## 69 electronic =~ ssei 2 .p8. 0.392 0.016 25.036 0.000
## 70 speed =~ ssno 2 .p9. 0.720 0.041 17.448 0.000
## 71 speed =~ sscs 2 .p10. 0.367 0.026 14.346 0.000
## 72 speed =~ ssmk 2 .p11. 0.167 0.014 12.254 0.000
## 73 g =~ ssgs 2 .p12. 0.816 0.008 96.213 0.000
## 74 g =~ ssar 2 .p13. 0.885 0.006 141.196 0.000
## 75 g =~ sswk 2 .p14. 0.799 0.009 90.760 0.000
## 76 g =~ sspc 2 .p15. 0.839 0.008 109.099 0.000
## 77 g =~ ssno 2 .p16. 0.608 0.015 39.698 0.000
## 78 g =~ sscs 2 .p17. 0.605 0.014 42.736 0.000
## 79 g =~ ssai 2 .p18. 0.430 0.016 26.959 0.000
## 80 g =~ sssi 2 .p19. 0.460 0.016 28.878 0.000
## 81 g =~ ssmk 2 .p20. 0.882 0.006 150.819 0.000
## 82 g =~ ssmc 2 .p21. 0.759 0.010 77.455 0.000
## 83 g =~ ssei 2 0.721 0.012 60.852 0.000
## 84 g =~ ssao 2 .p23. 0.716 0.011 65.057 0.000
## 85 verbal ~~ verbal 2 1.000 0.000 NA NA
## 86 speed ~~ speed 2 1.000 0.000 NA NA
## 87 speed ~1 2 0.000 0.000 NA NA
## 88 ssgs ~~ ssgs 2 0.216 0.012 18.256 0.000
## 89 sswk ~~ sswk 2 0.170 0.015 11.469 0.000
## 90 sspc ~~ sspc 2 0.259 0.012 22.279 0.000
## ci.lower ci.upper
## 1 0.344 0.412
## 2 0.440 0.518
## 3 0.182 0.245
## 4 0.253 0.338
## 5 0.331 0.408
## 6 0.373 0.457
## 7 0.171 0.216
## 8 0.220 0.279
## 9 0.696 0.864
## 10 0.339 0.444
## 11 0.153 0.211
## 12 0.775 0.811
## 13 0.869 0.895
## 14 0.749 0.789
## 15 0.807 0.842
## 16 0.549 0.610
## 17 0.540 0.598
## 18 0.499 0.565
## 19 0.496 0.561
## 20 0.834 0.863
## 21 0.758 0.800
## 22 0.640 0.697
## 23 0.684 0.731
## 24 1.000 1.000
## 25 1.000 1.000
## 26 0.000 0.000
## 27 0.204 0.252
## 28 0.147 0.210
## 29 0.247 0.301
## 30 0.370 0.437
## 31 0.542 0.620
## 32 0.506 0.590
## 33 0.325 0.388
## 34 -0.068 0.180
## 35 0.481 0.565
## 36 0.224 0.270
## 37 0.198 0.244
## 38 0.466 0.532
## 39 1.000 1.000
## 40 1.000 1.000
## 41 0.000 0.000
## 42 0.000 0.000
## 43 0.000 0.000
## 44 0.000 0.000
## 45 0.000 0.000
## 46 0.000 0.000
## 47 0.330 0.429
## 48 0.379 0.483
## 49 0.456 0.565
## 50 0.138 0.236
## 51 -0.006 0.083
## 52 0.032 0.125
## 53 0.262 0.363
## 54 0.183 0.268
## 55 0.324 0.423
## 56 0.367 0.472
## 57 0.332 0.441
## 58 0.326 0.422
## 59 0.000 0.000
## 60 0.000 0.000
## 61 0.000 0.000
## 62 0.311 0.375
## 63 0.402 0.475
## 64 0.163 0.220
## 65 0.180 0.242
## 66 0.543 0.614
## 67 0.671 0.730
## 68 0.338 0.393
## 69 0.362 0.423
## 70 0.639 0.801
## 71 0.316 0.417
## 72 0.140 0.193
## 73 0.800 0.833
## 74 0.873 0.897
## 75 0.781 0.816
## 76 0.824 0.854
## 77 0.578 0.638
## 78 0.577 0.633
## 79 0.399 0.461
## 80 0.429 0.491
## 81 0.870 0.893
## 82 0.740 0.778
## 83 0.697 0.744
## 84 0.694 0.738
## 85 1.000 1.000
## 86 1.000 1.000
## 87 0.000 0.000
## 88 0.193 0.239
## 89 0.141 0.199
## 90 0.236 0.281
## [ reached 'max' / getOption("max.print") -- omitted 32 rows ]
tests<-lavTestLRT(configural, metric2, scalar2, latent2, weak)
Td=tests[2:5,"Chisq diff"]
Td
## [1] 42.642165 34.339526 5.627269 3.577741
dfd=tests[2:5,"Df diff"]
dfd
## [1] 18 4 2 1
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.02736250 0.06440609 0.03149397 0.03754673
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.01679632 0.03806134
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.04561580 0.08504943
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] NA 0.0636532
RMSEA.CI(T=Td[4],df=dfd[4],N=N,G=2)
## [1] NA 0.08268906
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.999 0.994 0.000 0.000 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.900 0.675 0.111 0.002
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.940 0.908 0.200 0.076 0.004 0.000
round(pvals(T=Td[4],df=dfd[4],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.941 0.918 0.403 0.250 0.063 0.009
tests<-lavTestLRT(configural, metric2, scalar2, latent)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 42.64217 34.33953 285.86824
dfd=tests[2:4,"Df diff"]
dfd
## [1] 18 4 4
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.02736250 0.06440609 0.19631143
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.04561580 0.08504943
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.1773467 0.2159103
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.900 0.675 0.111 0.002
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
tests<-lavTestLRT(configural, metric2, scalar2, strict)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 42.64217 34.33953 128.21316
dfd=tests[2:4,"Df diff"]
dfd
## [1] 18 4 12
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.02736250 0.06440609 0.07277623
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.01679632 0.03806134
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.04561580 0.08504943
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.06168172 0.08441105
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.999 0.994 0.000 0.000 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.900 0.675 0.111 0.002
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 0.970 0.158 0.000
tests<-lavTestLRT(configural, metric2, scalar)
Td=tests[2:3,"Chisq diff"]
Td
## [1] 42.64217 1809.64665
dfd=tests[2:3,"Df diff"]
dfd
## [1] 18 8
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.0273625 0.3509476
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.01679632 0.03806134
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.3373854 0.3646079
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.999 0.994 0.000 0.000 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
tests<-lavTestLRT(configural, metric)
Td=tests[2,"Chisq diff"]
Td
## [1] 110.4191
dfd=tests[2,"Df diff"]
dfd
## [1] 19
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.05129725
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.04225580 0.06076186
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.610 0.066 0.000 0.000
bf.age<-'
verbal =~ ssgs + sswk + sspc + ssei
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
verbal~~1*verbal
speed~~1*speed
speed~0*1
g ~ agec
'
bf.ageq<-'
verbal =~ ssgs + sswk + sspc + ssei
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
verbal~~1*verbal
speed~~1*speed
speed~0*1
g ~ c(a,a)*agec
'
bf.age2<-'
verbal =~ ssgs + sswk + sspc + ssei
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
verbal~~1*verbal
speed~~1*speed
speed~0*1
g ~ agec+agec2
'
bf.age2q<-'
verbal =~ ssgs + sswk + sspc + ssei
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
verbal~~1*verbal
speed~~1*speed
speed~0*1
g ~ c(a,a)*agec+c(b,b)*agec2
'
sem.age<-sem(bf.age, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(sem.age, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 1944.887 133.000 0.000 0.945 0.086 0.051 0.570
## aic bic
## 86426.222 86866.773
Mc(sem.age)
## [1] 0.7806246
summary(sem.age, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 76 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 101
## Number of equality constraints 30
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1944.887 1683.942
## Degrees of freedom 133 133
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.155
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 808.432 699.965
## 0 1136.455 983.977
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.323 0.015 21.339 0.000 0.293
## sswk (.p2.) 0.409 0.017 23.887 0.000 0.375
## sspc (.p3.) 0.187 0.014 13.235 0.000 0.159
## ssei (.p4.) 0.220 0.017 13.316 0.000 0.188
## electronic =~
## ssai (.p5.) 0.273 0.015 17.782 0.000 0.243
## sssi (.p6.) 0.308 0.017 18.091 0.000 0.275
## ssmc (.p7.) 0.155 0.009 16.522 0.000 0.136
## ssei (.p8.) 0.188 0.011 17.172 0.000 0.167
## speed =~
## ssno (.p9.) 0.771 0.047 16.243 0.000 0.678
## sscs (.10.) 0.349 0.027 13.116 0.000 0.297
## ssmk (.11.) 0.153 0.013 11.517 0.000 0.127
## g =~
## ssgs (.12.) 0.640 0.015 42.757 0.000 0.610
## ssar (.13.) 0.685 0.016 42.441 0.000 0.653
## sswk (.14.) 0.626 0.016 40.305 0.000 0.595
## sspc (.15.) 0.676 0.015 44.143 0.000 0.646
## ssno (.16.) 0.524 0.016 32.034 0.000 0.492
## sscs (.17.) 0.492 0.015 32.646 0.000 0.463
## ssai (.18.) 0.378 0.014 26.573 0.000 0.350
## sssi (.19.) 0.373 0.014 26.027 0.000 0.345
## ssmk (.20.) 0.709 0.016 45.400 0.000 0.678
## ssmc (.21.) 0.590 0.016 38.030 0.000 0.560
## ssei 0.480 0.017 28.379 0.000 0.447
## ssao (.23.) 0.596 0.015 39.696 0.000 0.566
## ci.upper Std.lv Std.all
##
## 0.353 0.323 0.374
## 0.442 0.409 0.471
## 0.214 0.187 0.212
## 0.253 0.220 0.288
##
## 0.304 0.273 0.363
## 0.342 0.308 0.410
## 0.173 0.155 0.190
## 0.210 0.188 0.246
##
## 0.864 0.771 0.797
## 0.401 0.349 0.378
## 0.179 0.153 0.172
##
## 0.669 0.687 0.795
## 0.716 0.736 0.877
## 0.656 0.672 0.774
## 0.706 0.726 0.824
## 0.556 0.563 0.582
## 0.522 0.529 0.573
## 0.406 0.406 0.539
## 0.401 0.401 0.532
## 0.739 0.761 0.853
## 0.621 0.634 0.777
## 0.513 0.515 0.673
## 0.625 0.640 0.705
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.272 0.020 13.694 0.000 0.233
## ci.upper Std.lv Std.all
##
## 0.311 0.253 0.366
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## speed 0.000 0.000
## .ssgs 0.332 0.020 16.995 0.000 0.294
## .sswk (.47.) 0.379 0.020 19.029 0.000 0.340
## .sspc 0.454 0.021 22.003 0.000 0.414
## .ssei (.49.) 0.146 0.018 8.305 0.000 0.112
## .ssai (.50.) 0.032 0.016 1.957 0.050 -0.000
## .sssi (.51.) 0.060 0.017 3.472 0.001 0.026
## .ssmc (.52.) 0.259 0.018 14.223 0.000 0.223
## .ssno (.53.) 0.221 0.019 11.436 0.000 0.183
## .sscs 0.348 0.021 16.436 0.000 0.307
## .ssmk (.55.) 0.379 0.020 18.622 0.000 0.339
## .ssar 0.328 0.020 16.284 0.000 0.289
## .ssao (.57.) 0.343 0.020 17.374 0.000 0.304
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.371 0.332 0.385
## 0.418 0.379 0.436
## 0.495 0.454 0.516
## 0.181 0.146 0.191
## 0.064 0.032 0.043
## 0.094 0.060 0.080
## 0.295 0.259 0.317
## 0.259 0.221 0.229
## 0.390 0.348 0.377
## 0.419 0.379 0.425
## 0.368 0.328 0.391
## 0.382 0.343 0.378
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.170 0.009 18.557 0.000 0.152
## .sswk 0.136 0.012 11.287 0.000 0.112
## .sspc 0.215 0.011 19.811 0.000 0.193
## .ssei 0.236 0.011 21.234 0.000 0.214
## .ssai 0.329 0.015 21.370 0.000 0.299
## .sssi 0.311 0.015 20.063 0.000 0.280
## .ssmc 0.240 0.012 20.026 0.000 0.216
## .ssno 0.024 0.067 0.356 0.722 -0.108
## .sscs 0.451 0.022 20.754 0.000 0.408
## .ssmk 0.193 0.008 22.765 0.000 0.176
## .ssar 0.163 0.008 19.621 0.000 0.146
## .ssao 0.415 0.017 24.315 0.000 0.381
## electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.188 0.170 0.228
## 0.159 0.136 0.180
## 0.236 0.215 0.276
## 0.258 0.236 0.403
## 0.359 0.329 0.578
## 0.341 0.311 0.548
## 0.263 0.240 0.360
## 0.156 0.024 0.026
## 0.493 0.451 0.529
## 0.209 0.193 0.242
## 0.179 0.163 0.231
## 0.448 0.415 0.503
## 1.000 1.000 1.000
## 1.000 0.866 0.866
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.323 0.015 21.339 0.000 0.293
## sswk (.p2.) 0.409 0.017 23.887 0.000 0.375
## sspc (.p3.) 0.187 0.014 13.235 0.000 0.159
## ssei (.p4.) 0.220 0.017 13.316 0.000 0.188
## electronic =~
## ssai (.p5.) 0.273 0.015 17.782 0.000 0.243
## sssi (.p6.) 0.308 0.017 18.091 0.000 0.275
## ssmc (.p7.) 0.155 0.009 16.522 0.000 0.136
## ssei (.p8.) 0.188 0.011 17.172 0.000 0.167
## speed =~
## ssno (.p9.) 0.771 0.047 16.243 0.000 0.678
## sscs (.10.) 0.349 0.027 13.116 0.000 0.297
## ssmk (.11.) 0.153 0.013 11.517 0.000 0.127
## g =~
## ssgs (.12.) 0.640 0.015 42.757 0.000 0.610
## ssar (.13.) 0.685 0.016 42.441 0.000 0.653
## sswk (.14.) 0.626 0.016 40.305 0.000 0.595
## sspc (.15.) 0.676 0.015 44.143 0.000 0.646
## ssno (.16.) 0.524 0.016 32.034 0.000 0.492
## sscs (.17.) 0.492 0.015 32.646 0.000 0.463
## ssai (.18.) 0.378 0.014 26.573 0.000 0.350
## sssi (.19.) 0.373 0.014 26.027 0.000 0.345
## ssmk (.20.) 0.709 0.016 45.400 0.000 0.678
## ssmc (.21.) 0.590 0.016 38.030 0.000 0.560
## ssei 0.636 0.019 32.748 0.000 0.598
## ssao (.23.) 0.596 0.015 39.696 0.000 0.566
## ci.upper Std.lv Std.all
##
## 0.353 0.323 0.339
## 0.442 0.409 0.430
## 0.214 0.187 0.190
## 0.253 0.220 0.206
##
## 0.304 0.605 0.572
## 0.342 0.682 0.697
## 0.173 0.343 0.361
## 0.210 0.417 0.389
##
## 0.864 0.771 0.737
## 0.401 0.349 0.354
## 0.179 0.153 0.157
##
## 0.669 0.780 0.818
## 0.716 0.835 0.880
## 0.656 0.762 0.802
## 0.706 0.824 0.838
## 0.556 0.639 0.610
## 0.522 0.600 0.609
## 0.406 0.461 0.436
## 0.401 0.455 0.464
## 0.739 0.864 0.885
## 0.621 0.719 0.759
## 0.674 0.776 0.724
## 0.625 0.726 0.714
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.306 0.021 14.363 0.000 0.264
## ci.upper Std.lv Std.all
##
## 0.348 0.251 0.362
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## speed 0.000 0.000
## .ssgs 0.538 0.024 22.739 0.000 0.492
## .sswk (.47.) 0.379 0.020 19.029 0.000 0.340
## .sspc 0.278 0.023 12.208 0.000 0.233
## .ssei (.49.) 0.146 0.018 8.305 0.000 0.112
## .ssai (.50.) 0.032 0.016 1.957 0.050 -0.000
## .sssi (.51.) 0.060 0.017 3.472 0.001 0.026
## .ssmc (.52.) 0.259 0.018 14.223 0.000 0.223
## .ssno (.53.) 0.221 0.019 11.436 0.000 0.183
## .sscs 0.111 0.023 4.877 0.000 0.066
## .ssmk (.55.) 0.379 0.020 18.622 0.000 0.339
## .ssar 0.524 0.023 22.443 0.000 0.479
## .ssao (.57.) 0.343 0.020 17.374 0.000 0.304
## verbal 0.328 0.058 5.698 0.000 0.215
## elctrnc 2.527 0.161 15.734 0.000 2.212
## .g -0.166 0.042 -3.958 0.000 -0.248
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.584 0.538 0.565
## 0.418 0.379 0.398
## 0.322 0.278 0.283
## 0.181 0.146 0.136
## 0.064 0.032 0.030
## 0.094 0.060 0.061
## 0.295 0.259 0.273
## 0.259 0.221 0.211
## 0.155 0.111 0.112
## 0.419 0.379 0.389
## 0.570 0.524 0.553
## 0.382 0.343 0.337
## 0.441 0.328 0.328
## 2.841 1.142 1.142
## -0.084 -0.136 -0.136
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.196 0.010 19.416 0.000 0.176
## .sswk 0.155 0.014 11.325 0.000 0.128
## .sspc 0.253 0.011 22.614 0.000 0.231
## .ssei 0.324 0.016 20.643 0.000 0.293
## .ssai 0.538 0.025 21.875 0.000 0.490
## .sssi 0.286 0.018 15.911 0.000 0.251
## .ssmc 0.265 0.013 21.136 0.000 0.240
## .ssno 0.094 0.070 1.334 0.182 -0.044
## .sscs 0.491 0.025 19.557 0.000 0.442
## .ssmk 0.182 0.009 21.001 0.000 0.165
## .ssar 0.202 0.010 19.783 0.000 0.182
## .ssao 0.508 0.019 27.099 0.000 0.471
## electronic 4.892 0.573 8.537 0.000 3.769
## .g 1.291 0.070 18.405 0.000 1.153
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.215 0.196 0.216
## 0.182 0.155 0.171
## 0.274 0.253 0.261
## 0.354 0.324 0.282
## 0.587 0.538 0.482
## 0.322 0.286 0.299
## 0.289 0.265 0.294
## 0.231 0.094 0.085
## 0.540 0.491 0.505
## 0.199 0.182 0.192
## 0.222 0.202 0.225
## 0.545 0.508 0.491
## 6.015 1.000 1.000
## 1.428 0.869 0.869
sem.ageq<-sem(bf.ageq, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(sem.ageq, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 1946.709 134.000 0.000 0.945 0.086 0.053 0.570
## aic bic
## 86426.044 86860.390
Mc(sem.ageq)
## [1] 0.7805369
summary(sem.ageq, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 75 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 101
## Number of equality constraints 31
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1946.709 1685.771
## Degrees of freedom 134 134
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.155
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 809.092 700.641
## 0 1137.617 985.130
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.323 0.015 21.339 0.000 0.293
## sswk (.p2.) 0.409 0.017 23.878 0.000 0.375
## sspc (.p3.) 0.187 0.014 13.222 0.000 0.159
## ssei (.p4.) 0.220 0.017 13.306 0.000 0.188
## electronic =~
## ssai (.p5.) 0.273 0.015 17.778 0.000 0.243
## sssi (.p6.) 0.308 0.017 18.082 0.000 0.275
## ssmc (.p7.) 0.155 0.009 16.517 0.000 0.136
## ssei (.p8.) 0.188 0.011 17.172 0.000 0.167
## speed =~
## ssno (.p9.) 0.771 0.048 16.231 0.000 0.678
## sscs (.10.) 0.349 0.027 13.106 0.000 0.297
## ssmk (.11.) 0.153 0.013 11.508 0.000 0.127
## g =~
## ssgs (.12.) 0.640 0.015 42.691 0.000 0.610
## ssar (.13.) 0.685 0.016 42.409 0.000 0.653
## sswk (.14.) 0.626 0.016 40.267 0.000 0.595
## sspc (.15.) 0.676 0.015 44.102 0.000 0.646
## ssno (.16.) 0.524 0.016 32.020 0.000 0.492
## sscs (.17.) 0.493 0.015 32.627 0.000 0.463
## ssai (.18.) 0.378 0.014 26.564 0.000 0.350
## sssi (.19.) 0.373 0.014 26.011 0.000 0.345
## ssmk (.20.) 0.709 0.016 45.351 0.000 0.678
## ssmc (.21.) 0.590 0.016 37.990 0.000 0.560
## ssei 0.480 0.017 28.383 0.000 0.447
## ssao (.23.) 0.596 0.015 39.650 0.000 0.566
## ci.upper Std.lv Std.all
##
## 0.353 0.323 0.372
## 0.442 0.409 0.468
## 0.214 0.187 0.211
## 0.253 0.220 0.287
##
## 0.303 0.273 0.362
## 0.342 0.308 0.409
## 0.173 0.155 0.189
## 0.210 0.188 0.245
##
## 0.865 0.771 0.795
## 0.401 0.349 0.377
## 0.179 0.153 0.171
##
## 0.669 0.693 0.798
## 0.717 0.742 0.878
## 0.656 0.677 0.776
## 0.706 0.732 0.826
## 0.556 0.568 0.585
## 0.522 0.533 0.576
## 0.406 0.409 0.542
## 0.401 0.404 0.535
## 0.739 0.767 0.856
## 0.621 0.639 0.780
## 0.513 0.520 0.676
## 0.625 0.645 0.708
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.287 0.015 18.758 0.000 0.257
## ci.upper Std.lv Std.all
##
## 0.317 0.265 0.383
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## speed 0.000 0.000
## .ssgs 0.333 0.020 17.017 0.000 0.294
## .sswk (.47.) 0.379 0.020 19.073 0.000 0.340
## .sspc 0.455 0.021 21.998 0.000 0.414
## .ssei (.49.) 0.146 0.018 8.329 0.000 0.112
## .ssai (.50.) 0.032 0.016 1.966 0.049 0.000
## .sssi (.51.) 0.060 0.017 3.484 0.000 0.026
## .ssmc (.52.) 0.259 0.018 14.229 0.000 0.223
## .ssno (.53.) 0.222 0.019 11.458 0.000 0.184
## .sscs 0.348 0.021 16.467 0.000 0.307
## .ssmk (.55.) 0.380 0.020 18.672 0.000 0.340
## .ssar 0.328 0.020 16.275 0.000 0.289
## .ssao (.57.) 0.343 0.020 17.383 0.000 0.304
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.371 0.333 0.383
## 0.418 0.379 0.434
## 0.495 0.455 0.513
## 0.181 0.146 0.191
## 0.064 0.032 0.043
## 0.094 0.060 0.080
## 0.295 0.259 0.316
## 0.259 0.222 0.228
## 0.390 0.348 0.376
## 0.419 0.380 0.423
## 0.368 0.328 0.389
## 0.382 0.343 0.376
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.170 0.009 18.553 0.000 0.152
## .sswk 0.136 0.012 11.288 0.000 0.112
## .sspc 0.215 0.011 19.807 0.000 0.194
## .ssei 0.236 0.011 21.237 0.000 0.214
## .ssai 0.329 0.015 21.369 0.000 0.299
## .sssi 0.311 0.015 20.067 0.000 0.280
## .ssmc 0.240 0.012 20.034 0.000 0.216
## .ssno 0.024 0.067 0.354 0.723 -0.108
## .sscs 0.451 0.022 20.757 0.000 0.408
## .ssmk 0.192 0.008 22.776 0.000 0.176
## .ssar 0.163 0.008 19.672 0.000 0.147
## .ssao 0.415 0.017 24.305 0.000 0.381
## electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.188 0.170 0.225
## 0.159 0.136 0.178
## 0.236 0.215 0.273
## 0.258 0.236 0.400
## 0.359 0.329 0.576
## 0.341 0.311 0.546
## 0.263 0.240 0.357
## 0.156 0.024 0.025
## 0.493 0.451 0.526
## 0.209 0.192 0.239
## 0.179 0.163 0.228
## 0.448 0.415 0.499
## 1.000 1.000 1.000
## 1.000 0.853 0.853
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.323 0.015 21.339 0.000 0.293
## sswk (.p2.) 0.409 0.017 23.878 0.000 0.375
## sspc (.p3.) 0.187 0.014 13.222 0.000 0.159
## ssei (.p4.) 0.220 0.017 13.306 0.000 0.188
## electronic =~
## ssai (.p5.) 0.273 0.015 17.778 0.000 0.243
## sssi (.p6.) 0.308 0.017 18.082 0.000 0.275
## ssmc (.p7.) 0.155 0.009 16.517 0.000 0.136
## ssei (.p8.) 0.188 0.011 17.172 0.000 0.167
## speed =~
## ssno (.p9.) 0.771 0.048 16.231 0.000 0.678
## sscs (.10.) 0.349 0.027 13.106 0.000 0.297
## ssmk (.11.) 0.153 0.013 11.508 0.000 0.127
## g =~
## ssgs (.12.) 0.640 0.015 42.691 0.000 0.610
## ssar (.13.) 0.685 0.016 42.409 0.000 0.653
## sswk (.14.) 0.626 0.016 40.267 0.000 0.595
## sspc (.15.) 0.676 0.015 44.102 0.000 0.646
## ssno (.16.) 0.524 0.016 32.020 0.000 0.492
## sscs (.17.) 0.493 0.015 32.627 0.000 0.463
## ssai (.18.) 0.378 0.014 26.564 0.000 0.350
## sssi (.19.) 0.373 0.014 26.011 0.000 0.345
## ssmk (.20.) 0.709 0.016 45.351 0.000 0.678
## ssmc (.21.) 0.590 0.016 37.990 0.000 0.560
## ssei 0.636 0.019 32.709 0.000 0.598
## ssao (.23.) 0.596 0.015 39.650 0.000 0.566
## ci.upper Std.lv Std.all
##
## 0.353 0.323 0.341
## 0.442 0.409 0.432
## 0.214 0.187 0.191
## 0.253 0.220 0.206
##
## 0.303 0.605 0.574
## 0.342 0.683 0.698
## 0.173 0.343 0.363
## 0.210 0.417 0.391
##
## 0.865 0.771 0.739
## 0.401 0.349 0.355
## 0.179 0.153 0.158
##
## 0.669 0.774 0.816
## 0.717 0.828 0.879
## 0.656 0.757 0.800
## 0.706 0.817 0.836
## 0.556 0.634 0.607
## 0.522 0.596 0.606
## 0.406 0.457 0.433
## 0.401 0.451 0.461
## 0.739 0.857 0.884
## 0.621 0.714 0.756
## 0.675 0.769 0.721
## 0.625 0.720 0.711
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.287 0.015 18.758 0.000 0.257
## ci.upper Std.lv Std.all
##
## 0.317 0.237 0.342
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## speed 0.000 0.000
## .ssgs 0.538 0.024 22.763 0.000 0.492
## .sswk (.47.) 0.379 0.020 19.073 0.000 0.340
## .sspc 0.278 0.023 12.236 0.000 0.233
## .ssei (.49.) 0.146 0.018 8.329 0.000 0.112
## .ssai (.50.) 0.032 0.016 1.966 0.049 0.000
## .sssi (.51.) 0.060 0.017 3.484 0.000 0.026
## .ssmc (.52.) 0.259 0.018 14.229 0.000 0.223
## .ssno (.53.) 0.222 0.019 11.458 0.000 0.184
## .sscs 0.111 0.023 4.888 0.000 0.066
## .ssmk (.55.) 0.380 0.020 18.672 0.000 0.340
## .ssar 0.525 0.023 22.482 0.000 0.479
## .ssao (.57.) 0.343 0.020 17.383 0.000 0.304
## verbal 0.329 0.058 5.700 0.000 0.216
## elctrnc 2.528 0.161 15.735 0.000 2.213
## .g -0.168 0.042 -4.017 0.000 -0.250
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.584 0.538 0.568
## 0.418 0.379 0.400
## 0.322 0.278 0.284
## 0.181 0.146 0.137
## 0.064 0.032 0.031
## 0.094 0.060 0.062
## 0.295 0.259 0.274
## 0.259 0.222 0.212
## 0.155 0.111 0.113
## 0.419 0.380 0.391
## 0.570 0.525 0.557
## 0.382 0.343 0.339
## 0.441 0.329 0.329
## 2.843 1.142 1.142
## -0.086 -0.139 -0.139
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.196 0.010 19.409 0.000 0.176
## .sswk 0.155 0.014 11.330 0.000 0.128
## .sspc 0.252 0.011 22.593 0.000 0.231
## .ssei 0.324 0.016 20.641 0.000 0.293
## .ssai 0.538 0.025 21.872 0.000 0.490
## .sssi 0.286 0.018 15.913 0.000 0.251
## .ssmc 0.264 0.013 21.139 0.000 0.240
## .ssno 0.093 0.070 1.329 0.184 -0.044
## .sscs 0.491 0.025 19.551 0.000 0.442
## .ssmk 0.183 0.009 21.033 0.000 0.166
## .ssar 0.202 0.010 19.775 0.000 0.182
## .ssao 0.508 0.019 27.101 0.000 0.471
## electronic 4.904 0.574 8.538 0.000 3.778
## .g 1.291 0.070 18.405 0.000 1.154
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.215 0.196 0.218
## 0.182 0.155 0.173
## 0.274 0.252 0.264
## 0.354 0.324 0.284
## 0.587 0.538 0.483
## 0.322 0.286 0.300
## 0.289 0.264 0.297
## 0.231 0.093 0.086
## 0.540 0.491 0.507
## 0.200 0.183 0.194
## 0.222 0.202 0.227
## 0.544 0.508 0.495
## 6.030 1.000 1.000
## 1.428 0.883 0.883
sem.age2<-sem(bf.age2, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(sem.age2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 2038.654 155.000 0.000 0.943 0.082 0.049 0.597
## aic bic
## 86409.153 86862.114
Mc(sem.age2)
## [1] 0.7730044
summary(sem.age2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 73 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 103
## Number of equality constraints 30
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2038.654 1773.559
## Degrees of freedom 155 155
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.149
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 864.052 751.695
## 0 1174.602 1021.864
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.323 0.015 21.461 0.000 0.294
## sswk (.p2.) 0.408 0.017 23.812 0.000 0.375
## sspc (.p3.) 0.188 0.014 13.270 0.000 0.160
## ssei (.p4.) 0.221 0.017 13.379 0.000 0.189
## electronic =~
## ssai (.p5.) 0.273 0.015 17.736 0.000 0.242
## sssi (.p6.) 0.307 0.017 18.071 0.000 0.274
## ssmc (.p7.) 0.155 0.009 16.521 0.000 0.136
## ssei (.p8.) 0.191 0.011 17.185 0.000 0.169
## speed =~
## ssno (.p9.) 0.772 0.048 16.151 0.000 0.678
## sscs (.10.) 0.348 0.027 13.052 0.000 0.296
## ssmk (.11.) 0.152 0.013 11.445 0.000 0.126
## g =~
## ssgs (.12.) 0.636 0.015 42.535 0.000 0.607
## ssar (.13.) 0.681 0.016 42.237 0.000 0.650
## sswk (.14.) 0.623 0.016 40.102 0.000 0.592
## sspc (.15.) 0.672 0.015 43.983 0.000 0.642
## ssno (.16.) 0.522 0.016 32.090 0.000 0.490
## sscs (.17.) 0.490 0.015 32.576 0.000 0.461
## ssai (.18.) 0.376 0.014 26.434 0.000 0.348
## sssi (.19.) 0.371 0.014 26.034 0.000 0.343
## ssmk (.20.) 0.706 0.016 45.340 0.000 0.675
## ssmc (.21.) 0.587 0.015 37.962 0.000 0.557
## ssei 0.478 0.017 28.423 0.000 0.445
## ssao (.23.) 0.593 0.015 39.516 0.000 0.563
## ci.upper Std.lv Std.all
##
## 0.353 0.323 0.374
## 0.442 0.408 0.470
## 0.215 0.188 0.213
## 0.254 0.221 0.289
##
## 0.303 0.273 0.361
## 0.340 0.307 0.408
## 0.173 0.155 0.190
## 0.212 0.191 0.249
##
## 0.865 0.772 0.798
## 0.401 0.348 0.377
## 0.178 0.152 0.171
##
## 0.666 0.687 0.795
## 0.713 0.735 0.877
## 0.653 0.672 0.774
## 0.702 0.726 0.823
## 0.554 0.563 0.582
## 0.520 0.529 0.573
## 0.404 0.406 0.538
## 0.399 0.401 0.532
## 0.736 0.761 0.854
## 0.618 0.634 0.777
## 0.511 0.516 0.674
## 0.622 0.640 0.705
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.269 0.020 13.563 0.000 0.230
## agec2 -0.048 0.014 -3.470 0.001 -0.076
## ci.upper Std.lv Std.all
##
## 0.307 0.249 0.360
## -0.021 -0.045 -0.085
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## speed 0.000 0.000
## .ssgs 0.396 0.026 15.044 0.000 0.344
## .sswk (.50.) 0.440 0.026 16.717 0.000 0.388
## .sspc 0.522 0.028 18.681 0.000 0.467
## .ssei (.52.) 0.196 0.022 8.738 0.000 0.152
## .ssai (.53.) 0.069 0.020 3.522 0.000 0.031
## .sssi (.54.) 0.097 0.020 4.812 0.000 0.057
## .ssmc (.55.) 0.317 0.024 13.208 0.000 0.270
## .ssno (.56.) 0.274 0.024 11.204 0.000 0.226
## .sscs 0.397 0.025 15.751 0.000 0.348
## .ssmk (.58.) 0.450 0.029 15.761 0.000 0.394
## .ssar 0.396 0.027 14.641 0.000 0.343
## .ssao (.60.) 0.402 0.026 15.402 0.000 0.351
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.448 0.396 0.458
## 0.492 0.440 0.507
## 0.576 0.522 0.592
## 0.240 0.196 0.256
## 0.107 0.069 0.091
## 0.136 0.097 0.128
## 0.364 0.317 0.388
## 0.322 0.274 0.283
## 0.447 0.397 0.430
## 0.506 0.450 0.505
## 0.449 0.396 0.472
## 0.453 0.402 0.443
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.170 0.009 18.551 0.000 0.152
## .sswk 0.136 0.012 11.375 0.000 0.113
## .sspc 0.215 0.011 19.852 0.000 0.194
## .ssei 0.235 0.011 21.142 0.000 0.214
## .ssai 0.330 0.015 21.370 0.000 0.299
## .sssi 0.311 0.015 20.113 0.000 0.281
## .ssmc 0.240 0.012 20.013 0.000 0.216
## .ssno 0.023 0.068 0.340 0.734 -0.110
## .sscs 0.451 0.022 20.738 0.000 0.408
## .ssmk 0.192 0.008 22.706 0.000 0.175
## .ssar 0.163 0.008 19.651 0.000 0.147
## .ssao 0.414 0.017 24.312 0.000 0.381
## electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.188 0.170 0.228
## 0.160 0.136 0.181
## 0.236 0.215 0.277
## 0.257 0.235 0.401
## 0.360 0.330 0.580
## 0.342 0.311 0.550
## 0.263 0.240 0.360
## 0.156 0.023 0.025
## 0.493 0.451 0.529
## 0.208 0.192 0.241
## 0.179 0.163 0.232
## 0.448 0.414 0.503
## 1.000 1.000 1.000
## 1.000 0.859 0.859
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.323 0.015 21.461 0.000 0.294
## sswk (.p2.) 0.408 0.017 23.812 0.000 0.375
## sspc (.p3.) 0.188 0.014 13.270 0.000 0.160
## ssei (.p4.) 0.221 0.017 13.379 0.000 0.189
## electronic =~
## ssai (.p5.) 0.273 0.015 17.736 0.000 0.242
## sssi (.p6.) 0.307 0.017 18.071 0.000 0.274
## ssmc (.p7.) 0.155 0.009 16.521 0.000 0.136
## ssei (.p8.) 0.191 0.011 17.185 0.000 0.169
## speed =~
## ssno (.p9.) 0.772 0.048 16.151 0.000 0.678
## sscs (.10.) 0.348 0.027 13.052 0.000 0.296
## ssmk (.11.) 0.152 0.013 11.445 0.000 0.126
## g =~
## ssgs (.12.) 0.636 0.015 42.535 0.000 0.607
## ssar (.13.) 0.681 0.016 42.237 0.000 0.650
## sswk (.14.) 0.623 0.016 40.102 0.000 0.592
## sspc (.15.) 0.672 0.015 43.983 0.000 0.642
## ssno (.16.) 0.522 0.016 32.090 0.000 0.490
## sscs (.17.) 0.490 0.015 32.576 0.000 0.461
## ssai (.18.) 0.376 0.014 26.434 0.000 0.348
## sssi (.19.) 0.371 0.014 26.034 0.000 0.343
## ssmk (.20.) 0.706 0.016 45.340 0.000 0.675
## ssmc (.21.) 0.587 0.015 37.962 0.000 0.557
## ssei 0.631 0.019 32.632 0.000 0.593
## ssao (.23.) 0.593 0.015 39.516 0.000 0.563
## ci.upper Std.lv Std.all
##
## 0.353 0.323 0.339
## 0.442 0.408 0.429
## 0.215 0.188 0.191
## 0.254 0.221 0.207
##
## 0.303 0.603 0.571
## 0.340 0.680 0.695
## 0.173 0.342 0.361
## 0.212 0.422 0.394
##
## 0.865 0.772 0.737
## 0.401 0.348 0.353
## 0.178 0.152 0.156
##
## 0.666 0.779 0.818
## 0.713 0.835 0.880
## 0.653 0.762 0.802
## 0.702 0.824 0.838
## 0.554 0.639 0.610
## 0.520 0.600 0.609
## 0.404 0.461 0.436
## 0.399 0.455 0.465
## 0.736 0.864 0.886
## 0.618 0.719 0.759
## 0.669 0.773 0.722
## 0.622 0.726 0.714
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.303 0.022 13.987 0.000 0.260
## agec2 -0.040 0.015 -2.564 0.010 -0.070
## ci.upper Std.lv Std.all
##
## 0.345 0.247 0.356
## -0.009 -0.032 -0.061
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## speed 0.000 0.000
## .ssgs 0.600 0.030 20.209 0.000 0.542
## .sswk (.50.) 0.440 0.026 16.717 0.000 0.388
## .sspc 0.344 0.030 11.603 0.000 0.286
## .ssei (.52.) 0.196 0.022 8.738 0.000 0.152
## .ssai (.53.) 0.069 0.020 3.522 0.000 0.031
## .sssi (.54.) 0.097 0.020 4.812 0.000 0.057
## .ssmc (.55.) 0.317 0.024 13.208 0.000 0.270
## .ssno (.56.) 0.274 0.024 11.204 0.000 0.226
## .sscs 0.160 0.027 5.978 0.000 0.107
## .ssmk (.58.) 0.450 0.029 15.761 0.000 0.394
## .ssar 0.593 0.030 19.521 0.000 0.533
## .ssao (.60.) 0.402 0.026 15.402 0.000 0.351
## verbal 0.333 0.058 5.744 0.000 0.220
## elctrnc 2.543 0.162 15.731 0.000 2.226
## .g -0.185 0.059 -3.126 0.002 -0.302
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.658 0.600 0.630
## 0.492 0.440 0.463
## 0.402 0.344 0.350
## 0.240 0.196 0.183
## 0.107 0.069 0.065
## 0.136 0.097 0.099
## 0.364 0.317 0.334
## 0.322 0.274 0.261
## 0.212 0.160 0.162
## 0.506 0.450 0.461
## 0.652 0.593 0.625
## 0.453 0.402 0.395
## 0.447 0.333 0.333
## 2.860 1.149 1.149
## -0.069 -0.151 -0.151
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.196 0.010 19.418 0.000 0.176
## .sswk 0.155 0.014 11.384 0.000 0.129
## .sspc 0.253 0.011 22.611 0.000 0.231
## .ssei 0.322 0.016 20.483 0.000 0.291
## .ssai 0.539 0.025 21.894 0.000 0.491
## .sssi 0.289 0.018 16.022 0.000 0.253
## .ssmc 0.264 0.013 21.140 0.000 0.240
## .ssno 0.093 0.071 1.311 0.190 -0.046
## .sscs 0.491 0.025 19.541 0.000 0.442
## .ssmk 0.182 0.009 20.946 0.000 0.165
## .ssar 0.202 0.010 19.776 0.000 0.182
## .ssao 0.508 0.019 27.093 0.000 0.471
## electronic 4.900 0.574 8.533 0.000 3.775
## .g 1.298 0.071 18.344 0.000 1.159
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.215 0.196 0.215
## 0.182 0.155 0.172
## 0.275 0.253 0.262
## 0.353 0.322 0.281
## 0.587 0.539 0.483
## 0.324 0.289 0.302
## 0.289 0.264 0.294
## 0.231 0.093 0.084
## 0.540 0.491 0.505
## 0.199 0.182 0.191
## 0.222 0.202 0.225
## 0.545 0.508 0.491
## 6.026 1.000 1.000
## 1.436 0.865 0.865
sem.age2q<-sem(bf.age2q, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(sem.age2q, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 2040.520 157.000 0.000 0.943 0.081 0.050 0.596
## aic bic
## 86407.019 86847.570
Mc(sem.age2q)
## [1] 0.7730186
summary(sem.age2q, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 79 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 103
## Number of equality constraints 32
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2040.520 1775.825
## Degrees of freedom 157 157
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.149
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 864.842 752.655
## 0 1175.678 1023.170
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.323 0.015 21.459 0.000 0.294
## sswk (.p2.) 0.408 0.017 23.817 0.000 0.374
## sspc (.p3.) 0.187 0.014 13.264 0.000 0.160
## ssei (.p4.) 0.221 0.017 13.369 0.000 0.189
## electronic =~
## ssai (.p5.) 0.273 0.015 17.740 0.000 0.242
## sssi (.p6.) 0.307 0.017 18.068 0.000 0.274
## ssmc (.p7.) 0.155 0.009 16.516 0.000 0.136
## ssei (.p8.) 0.190 0.011 17.194 0.000 0.169
## speed =~
## ssno (.p9.) 0.772 0.048 16.142 0.000 0.678
## sscs (.10.) 0.348 0.027 13.045 0.000 0.296
## ssmk (.11.) 0.152 0.013 11.438 0.000 0.126
## g =~
## ssgs (.12.) 0.637 0.015 42.479 0.000 0.607
## ssar (.13.) 0.682 0.016 42.223 0.000 0.650
## sswk (.14.) 0.623 0.016 40.079 0.000 0.592
## sspc (.15.) 0.673 0.015 43.957 0.000 0.643
## ssno (.16.) 0.522 0.016 32.084 0.000 0.490
## sscs (.17.) 0.490 0.015 32.557 0.000 0.461
## ssai (.18.) 0.376 0.014 26.434 0.000 0.348
## sssi (.19.) 0.371 0.014 26.022 0.000 0.343
## ssmk (.20.) 0.706 0.016 45.304 0.000 0.675
## ssmc (.21.) 0.587 0.015 37.924 0.000 0.557
## ssei 0.478 0.017 28.422 0.000 0.445
## ssao (.23.) 0.593 0.015 39.489 0.000 0.563
## ci.upper Std.lv Std.all
##
## 0.353 0.323 0.373
## 0.442 0.408 0.468
## 0.215 0.187 0.212
## 0.254 0.221 0.288
##
## 0.303 0.273 0.361
## 0.340 0.307 0.407
## 0.173 0.155 0.189
## 0.212 0.190 0.248
##
## 0.865 0.772 0.796
## 0.401 0.348 0.377
## 0.178 0.152 0.170
##
## 0.666 0.692 0.797
## 0.713 0.740 0.878
## 0.653 0.677 0.776
## 0.703 0.731 0.825
## 0.554 0.567 0.585
## 0.520 0.533 0.576
## 0.404 0.409 0.541
## 0.399 0.403 0.535
## 0.736 0.767 0.856
## 0.618 0.638 0.779
## 0.511 0.520 0.676
## 0.622 0.644 0.707
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.284 0.015 18.493 0.000 0.254
## agec2 (b) -0.045 0.010 -4.291 0.000 -0.065
## ci.upper Std.lv Std.all
##
## 0.314 0.261 0.377
## -0.024 -0.041 -0.078
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## speed 0.000 0.000
## .ssgs 0.391 0.024 16.601 0.000 0.345
## .sswk (.50.) 0.436 0.024 18.362 0.000 0.389
## .sspc 0.517 0.025 20.668 0.000 0.468
## .ssei (.52.) 0.192 0.020 9.417 0.000 0.152
## .ssai (.53.) 0.066 0.018 3.630 0.000 0.030
## .sssi (.54.) 0.094 0.019 4.963 0.000 0.057
## .ssmc (.55.) 0.312 0.022 14.458 0.000 0.270
## .ssno (.56.) 0.270 0.023 11.980 0.000 0.226
## .sscs 0.394 0.024 16.684 0.000 0.347
## .ssmk (.58.) 0.445 0.025 17.634 0.000 0.395
## .ssar 0.391 0.024 16.118 0.000 0.344
## .ssao (.60.) 0.398 0.024 16.877 0.000 0.352
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.437 0.391 0.451
## 0.482 0.436 0.499
## 0.566 0.517 0.584
## 0.232 0.192 0.250
## 0.102 0.066 0.088
## 0.131 0.094 0.125
## 0.355 0.312 0.381
## 0.314 0.270 0.278
## 0.440 0.394 0.426
## 0.494 0.445 0.497
## 0.439 0.391 0.464
## 0.444 0.398 0.437
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.170 0.009 18.550 0.000 0.152
## .sswk 0.136 0.012 11.371 0.000 0.113
## .sspc 0.215 0.011 19.845 0.000 0.194
## .ssei 0.235 0.011 21.152 0.000 0.214
## .ssai 0.329 0.015 21.376 0.000 0.299
## .sssi 0.311 0.015 20.116 0.000 0.281
## .ssmc 0.240 0.012 20.021 0.000 0.216
## .ssno 0.023 0.068 0.340 0.734 -0.110
## .sscs 0.451 0.022 20.740 0.000 0.408
## .ssmk 0.192 0.008 22.728 0.000 0.175
## .ssar 0.163 0.008 19.704 0.000 0.147
## .ssao 0.415 0.017 24.303 0.000 0.381
## electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.188 0.170 0.226
## 0.160 0.136 0.179
## 0.236 0.215 0.274
## 0.257 0.235 0.399
## 0.360 0.329 0.577
## 0.342 0.311 0.548
## 0.263 0.240 0.357
## 0.156 0.023 0.025
## 0.493 0.451 0.527
## 0.208 0.192 0.239
## 0.180 0.163 0.229
## 0.448 0.415 0.500
## 1.000 1.000 1.000
## 1.000 0.847 0.847
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.323 0.015 21.459 0.000 0.294
## sswk (.p2.) 0.408 0.017 23.817 0.000 0.374
## sspc (.p3.) 0.187 0.014 13.264 0.000 0.160
## ssei (.p4.) 0.221 0.017 13.369 0.000 0.189
## electronic =~
## ssai (.p5.) 0.273 0.015 17.740 0.000 0.242
## sssi (.p6.) 0.307 0.017 18.068 0.000 0.274
## ssmc (.p7.) 0.155 0.009 16.516 0.000 0.136
## ssei (.p8.) 0.190 0.011 17.194 0.000 0.169
## speed =~
## ssno (.p9.) 0.772 0.048 16.142 0.000 0.678
## sscs (.10.) 0.348 0.027 13.045 0.000 0.296
## ssmk (.11.) 0.152 0.013 11.438 0.000 0.126
## g =~
## ssgs (.12.) 0.637 0.015 42.479 0.000 0.607
## ssar (.13.) 0.682 0.016 42.223 0.000 0.650
## sswk (.14.) 0.623 0.016 40.079 0.000 0.592
## sspc (.15.) 0.673 0.015 43.957 0.000 0.643
## ssno (.16.) 0.522 0.016 32.084 0.000 0.490
## sscs (.17.) 0.490 0.015 32.557 0.000 0.461
## ssai (.18.) 0.376 0.014 26.434 0.000 0.348
## sssi (.19.) 0.371 0.014 26.022 0.000 0.343
## ssmk (.20.) 0.706 0.016 45.304 0.000 0.675
## ssmc (.21.) 0.587 0.015 37.924 0.000 0.557
## ssei 0.631 0.019 32.557 0.000 0.593
## ssao (.23.) 0.593 0.015 39.489 0.000 0.563
## ci.upper Std.lv Std.all
##
## 0.353 0.323 0.341
## 0.442 0.408 0.431
## 0.215 0.187 0.192
## 0.254 0.221 0.207
##
## 0.303 0.604 0.573
## 0.340 0.680 0.696
## 0.173 0.343 0.363
## 0.212 0.422 0.395
##
## 0.865 0.772 0.739
## 0.401 0.348 0.354
## 0.178 0.152 0.157
##
## 0.666 0.774 0.816
## 0.713 0.829 0.879
## 0.653 0.757 0.800
## 0.703 0.818 0.836
## 0.554 0.635 0.608
## 0.520 0.596 0.606
## 0.404 0.457 0.434
## 0.399 0.451 0.462
## 0.736 0.858 0.884
## 0.618 0.714 0.756
## 0.669 0.768 0.719
## 0.622 0.721 0.711
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.284 0.015 18.493 0.000 0.254
## agec2 (b) -0.045 0.010 -4.291 0.000 -0.065
## ci.upper Std.lv Std.all
##
## 0.314 0.233 0.336
## -0.024 -0.037 -0.069
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## speed 0.000 0.000
## .ssgs 0.595 0.027 21.860 0.000 0.542
## .sswk (.50.) 0.436 0.024 18.362 0.000 0.389
## .sspc 0.339 0.027 12.632 0.000 0.287
## .ssei (.52.) 0.192 0.020 9.417 0.000 0.152
## .ssai (.53.) 0.066 0.018 3.630 0.000 0.030
## .sssi (.54.) 0.094 0.019 4.963 0.000 0.057
## .ssmc (.55.) 0.312 0.022 14.458 0.000 0.270
## .ssno (.56.) 0.270 0.023 11.980 0.000 0.226
## .sscs 0.156 0.025 6.203 0.000 0.107
## .ssmk (.58.) 0.445 0.025 17.634 0.000 0.395
## .ssar 0.588 0.028 21.374 0.000 0.534
## .ssao (.60.) 0.398 0.024 16.877 0.000 0.352
## verbal 0.333 0.058 5.746 0.000 0.219
## elctrnc 2.543 0.162 15.740 0.000 2.226
## .g -0.169 0.042 -4.032 0.000 -0.251
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.649 0.595 0.628
## 0.482 0.436 0.460
## 0.392 0.339 0.347
## 0.232 0.192 0.180
## 0.102 0.066 0.063
## 0.131 0.094 0.096
## 0.355 0.312 0.331
## 0.314 0.270 0.258
## 0.205 0.156 0.159
## 0.494 0.445 0.458
## 0.642 0.588 0.624
## 0.444 0.398 0.393
## 0.447 0.333 0.333
## 2.860 1.148 1.148
## -0.087 -0.139 -0.139
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.196 0.010 19.413 0.000 0.176
## .sswk 0.155 0.014 11.386 0.000 0.129
## .sspc 0.253 0.011 22.592 0.000 0.231
## .ssei 0.322 0.016 20.487 0.000 0.292
## .ssai 0.539 0.025 21.893 0.000 0.491
## .sssi 0.289 0.018 16.026 0.000 0.253
## .ssmc 0.264 0.013 21.139 0.000 0.240
## .ssno 0.093 0.071 1.309 0.191 -0.046
## .sscs 0.491 0.025 19.535 0.000 0.442
## .ssmk 0.182 0.009 20.979 0.000 0.165
## .ssar 0.202 0.010 19.766 0.000 0.182
## .ssao 0.508 0.019 27.095 0.000 0.471
## electronic 4.911 0.576 8.534 0.000 3.783
## .g 1.298 0.071 18.345 0.000 1.159
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.215 0.196 0.217
## 0.182 0.155 0.173
## 0.275 0.253 0.264
## 0.353 0.322 0.283
## 0.587 0.539 0.484
## 0.324 0.289 0.302
## 0.289 0.264 0.296
## 0.231 0.093 0.085
## 0.540 0.491 0.507
## 0.199 0.182 0.193
## 0.222 0.202 0.227
## 0.544 0.508 0.494
## 6.039 1.000 1.000
## 1.437 0.878 0.878
# BIFACTOR WITH VERBAL REMOVED, WORSE FIT BUT KEEP THE NATURE OF G
bf.model<-'
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
bf.lv<-'
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
'
baseline<-cfa(bf.model, data=dgroup, meanstructure=T, sampling.weights="sweight", std.lv=T, orthogonal=T)
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1480.110 43.000 0.000 0.955 0.096 0.044 89269.147
## bic
## 89560.779
Mc(baseline)
## [1] 0.8216557
summary(baseline, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 38 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
##
## Number of observations 3659
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1480.110 1313.795
## Degrees of freedom 43 43
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.127
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar 0.284 0.020 14.095 0.000 0.245
## ssmk 0.260 0.019 13.663 0.000 0.223
## ssmc 0.214 0.021 9.971 0.000 0.172
## ssao 0.369 0.030 12.420 0.000 0.311
## electronic =~
## ssai 0.567 0.021 26.730 0.000 0.525
## sssi 0.620 0.017 36.569 0.000 0.587
## ssmc 0.294 0.013 22.533 0.000 0.269
## ssei 0.371 0.016 23.793 0.000 0.341
## speed =~
## ssno 0.712 0.028 25.788 0.000 0.658
## sscs 0.458 0.022 20.960 0.000 0.415
## ssmk 0.230 0.013 18.301 0.000 0.205
## g =~
## ssgs 0.805 0.013 61.740 0.000 0.779
## ssar 0.731 0.015 50.383 0.000 0.703
## sswk 0.799 0.013 61.520 0.000 0.773
## sspc 0.797 0.012 66.103 0.000 0.773
## ssno 0.566 0.017 32.782 0.000 0.532
## sscs 0.526 0.016 33.646 0.000 0.495
## ssai 0.477 0.017 28.059 0.000 0.443
## sssi 0.468 0.016 28.413 0.000 0.436
## ssmk 0.752 0.013 56.963 0.000 0.726
## ssmc 0.663 0.015 45.363 0.000 0.635
## ssei 0.720 0.016 45.655 0.000 0.689
## ssao 0.607 0.014 43.834 0.000 0.580
## ci.upper Std.lv Std.all
##
## 0.324 0.284 0.317
## 0.297 0.260 0.279
## 0.255 0.214 0.236
## 0.427 0.369 0.382
##
## 0.608 0.567 0.577
## 0.653 0.620 0.653
## 0.320 0.294 0.326
## 0.402 0.371 0.381
##
## 0.766 0.712 0.704
## 0.501 0.458 0.471
## 0.254 0.230 0.247
##
## 0.830 0.805 0.878
## 0.759 0.731 0.815
## 0.824 0.799 0.878
## 0.820 0.797 0.845
## 0.600 0.566 0.560
## 0.557 0.526 0.541
## 0.510 0.477 0.486
## 0.500 0.468 0.493
## 0.777 0.752 0.807
## 0.692 0.663 0.734
## 0.751 0.720 0.739
## 0.634 0.607 0.628
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.362 0.015 23.338 0.000 0.331
## .ssmk 0.310 0.016 19.252 0.000 0.279
## .ssmc 0.402 0.015 25.959 0.000 0.372
## .ssao 0.283 0.017 17.147 0.000 0.251
## .ssai 0.340 0.017 20.076 0.000 0.307
## .sssi 0.421 0.016 25.642 0.000 0.389
## .ssei 0.365 0.017 21.667 0.000 0.332
## .ssno 0.169 0.017 9.716 0.000 0.135
## .sscs 0.179 0.017 10.711 0.000 0.147
## .ssgs 0.429 0.016 27.179 0.000 0.398
## .sswk 0.386 0.016 24.681 0.000 0.355
## .sspc 0.330 0.016 20.454 0.000 0.298
## ci.upper Std.lv Std.all
## 0.392 0.362 0.403
## 0.342 0.310 0.333
## 0.432 0.402 0.445
## 0.316 0.283 0.293
## 0.373 0.340 0.346
## 0.453 0.421 0.443
## 0.398 0.365 0.374
## 0.203 0.169 0.167
## 0.212 0.179 0.185
## 0.460 0.429 0.468
## 0.417 0.386 0.424
## 0.362 0.330 0.350
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.190 0.010 19.212 0.000 0.171
## .ssmk 0.182 0.008 21.862 0.000 0.166
## .ssmc 0.244 0.010 24.173 0.000 0.224
## .ssao 0.429 0.020 21.851 0.000 0.391
## .ssai 0.415 0.016 25.237 0.000 0.383
## .sssi 0.298 0.015 20.245 0.000 0.270
## .ssei 0.294 0.010 28.295 0.000 0.274
## .ssno 0.195 0.031 6.260 0.000 0.134
## .sscs 0.458 0.018 24.878 0.000 0.422
## .ssgs 0.193 0.007 28.157 0.000 0.180
## .sswk 0.190 0.007 27.683 0.000 0.177
## .sspc 0.253 0.009 27.444 0.000 0.235
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.209 0.190 0.236
## 0.198 0.182 0.210
## 0.264 0.244 0.299
## 0.468 0.429 0.460
## 0.447 0.415 0.431
## 0.327 0.298 0.331
## 0.314 0.294 0.309
## 0.256 0.195 0.191
## 0.494 0.458 0.485
## 0.206 0.193 0.230
## 0.204 0.190 0.230
## 0.272 0.253 0.285
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
configural<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T)
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1192.621 86.000 0.000 0.965 0.084 0.034 86965.752
## bic
## 87549.017
Mc(configural)
## [1] 0.8596238
summary(configural, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 45 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 94
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1192.621 1075.657
## Degrees of freedom 86 86
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.109
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 437.374 394.479
## 0 755.247 681.178
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar 0.297 0.024 12.225 0.000 0.249
## ssmk 0.265 0.022 11.831 0.000 0.221
## ssmc 0.214 0.025 8.519 0.000 0.165
## ssao 0.383 0.035 10.828 0.000 0.314
## electronic =~
## ssai 0.243 0.034 7.173 0.000 0.177
## sssi 0.328 0.037 8.752 0.000 0.255
## ssmc 0.161 0.024 6.598 0.000 0.113
## ssei 0.145 0.026 5.592 0.000 0.094
## speed =~
## ssno 0.707 0.047 14.983 0.000 0.615
## sscs 0.388 0.032 12.039 0.000 0.325
## ssmk 0.195 0.019 10.393 0.000 0.159
## g =~
## ssgs 0.731 0.017 43.384 0.000 0.698
## ssar 0.670 0.019 34.381 0.000 0.631
## sswk 0.777 0.018 43.592 0.000 0.742
## sspc 0.753 0.017 43.166 0.000 0.719
## ssno 0.524 0.023 23.097 0.000 0.479
## sscs 0.485 0.021 23.182 0.000 0.444
## ssai 0.398 0.018 21.691 0.000 0.362
## sssi 0.407 0.019 21.052 0.000 0.369
## ssmk 0.726 0.019 38.586 0.000 0.689
## ssmc 0.596 0.019 31.289 0.000 0.559
## ssei 0.564 0.017 32.502 0.000 0.530
## ssao 0.555 0.019 28.532 0.000 0.517
## ci.upper Std.lv Std.all
##
## 0.344 0.297 0.355
## 0.308 0.265 0.292
## 0.263 0.214 0.264
## 0.453 0.383 0.423
##
## 0.310 0.243 0.330
## 0.402 0.328 0.437
## 0.208 0.161 0.198
## 0.195 0.145 0.189
##
## 0.799 0.707 0.747
## 0.451 0.388 0.428
## 0.232 0.195 0.216
##
## 0.764 0.731 0.868
## 0.708 0.670 0.802
## 0.812 0.777 0.881
## 0.787 0.753 0.856
## 0.568 0.524 0.553
## 0.526 0.485 0.535
## 0.434 0.398 0.539
## 0.445 0.407 0.542
## 0.763 0.726 0.801
## 0.633 0.596 0.735
## 0.598 0.564 0.736
## 0.593 0.555 0.612
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.327 0.021 15.677 0.000 0.286
## .ssmk 0.382 0.022 16.962 0.000 0.337
## .ssmc 0.235 0.020 11.729 0.000 0.196
## .ssao 0.356 0.022 15.988 0.000 0.312
## .ssai 0.055 0.018 3.026 0.002 0.019
## .sssi 0.059 0.019 3.200 0.001 0.023
## .ssei 0.139 0.019 7.329 0.000 0.102
## .ssno 0.244 0.023 10.435 0.000 0.198
## .sscs 0.358 0.023 15.788 0.000 0.313
## .ssgs 0.331 0.021 15.977 0.000 0.291
## .sswk 0.379 0.022 17.461 0.000 0.337
## .sspc 0.453 0.022 20.981 0.000 0.411
## ci.upper Std.lv Std.all
## 0.368 0.327 0.392
## 0.426 0.382 0.421
## 0.274 0.235 0.289
## 0.399 0.356 0.392
## 0.091 0.055 0.075
## 0.096 0.059 0.079
## 0.176 0.139 0.182
## 0.290 0.244 0.258
## 0.402 0.358 0.395
## 0.372 0.331 0.393
## 0.422 0.379 0.430
## 0.495 0.453 0.515
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.160 0.012 13.202 0.000 0.136
## .ssmk 0.186 0.010 17.848 0.000 0.165
## .ssmc 0.231 0.013 17.860 0.000 0.206
## .ssao 0.367 0.025 14.894 0.000 0.319
## .ssai 0.327 0.019 17.688 0.000 0.291
## .sssi 0.291 0.023 12.524 0.000 0.245
## .ssei 0.247 0.012 21.172 0.000 0.225
## .ssno 0.121 0.055 2.197 0.028 0.013
## .sscs 0.435 0.023 18.661 0.000 0.390
## .ssgs 0.175 0.009 20.210 0.000 0.158
## .sswk 0.174 0.008 20.631 0.000 0.157
## .sspc 0.207 0.012 17.485 0.000 0.184
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.184 0.160 0.230
## 0.206 0.186 0.226
## 0.257 0.231 0.352
## 0.415 0.367 0.447
## 0.364 0.327 0.601
## 0.336 0.291 0.515
## 0.270 0.247 0.422
## 0.230 0.121 0.136
## 0.481 0.435 0.530
## 0.192 0.175 0.247
## 0.190 0.174 0.223
## 0.230 0.207 0.267
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar 0.296 0.034 8.787 0.000 0.230
## ssmk 0.268 0.032 8.365 0.000 0.205
## ssmc 0.197 0.042 4.699 0.000 0.115
## ssao 0.325 0.056 5.831 0.000 0.216
## electronic =~
## ssai 0.646 0.030 21.464 0.000 0.587
## sssi 0.640 0.023 27.471 0.000 0.594
## ssmc 0.289 0.018 15.735 0.000 0.253
## ssei 0.377 0.022 17.169 0.000 0.334
## speed =~
## ssno 0.769 0.041 18.581 0.000 0.688
## sscs 0.449 0.030 15.056 0.000 0.391
## ssmk 0.229 0.018 12.879 0.000 0.194
## g =~
## ssgs 0.869 0.019 46.111 0.000 0.832
## ssar 0.780 0.021 37.091 0.000 0.739
## sswk 0.819 0.019 43.795 0.000 0.782
## sspc 0.849 0.016 54.333 0.000 0.819
## ssno 0.603 0.025 24.063 0.000 0.554
## sscs 0.573 0.022 26.095 0.000 0.530
## ssai 0.549 0.026 21.311 0.000 0.498
## sssi 0.524 0.023 22.368 0.000 0.478
## ssmk 0.777 0.018 42.949 0.000 0.741
## ssmc 0.727 0.021 34.673 0.000 0.686
## ssei 0.858 0.023 36.842 0.000 0.813
## ssao 0.663 0.019 34.092 0.000 0.625
## ci.upper Std.lv Std.all
##
## 0.362 0.296 0.310
## 0.330 0.268 0.282
## 0.279 0.197 0.206
## 0.435 0.325 0.320
##
## 0.705 0.646 0.586
## 0.685 0.640 0.646
## 0.325 0.289 0.303
## 0.421 0.377 0.344
##
## 0.850 0.769 0.722
## 0.508 0.449 0.449
## 0.264 0.229 0.241
##
## 0.906 0.869 0.892
## 0.822 0.780 0.819
## 0.856 0.819 0.874
## 0.880 0.849 0.863
## 0.652 0.603 0.566
## 0.616 0.573 0.572
## 0.599 0.549 0.498
## 0.570 0.524 0.530
## 0.812 0.777 0.818
## 0.768 0.727 0.761
## 0.904 0.858 0.782
## 0.701 0.663 0.652
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.395 0.023 17.329 0.000 0.350
## .ssmk 0.242 0.023 10.519 0.000 0.197
## .ssmc 0.563 0.023 24.735 0.000 0.518
## .ssao 0.214 0.024 8.814 0.000 0.166
## .ssai 0.614 0.027 23.150 0.000 0.562
## .sssi 0.769 0.024 32.369 0.000 0.723
## .ssei 0.582 0.026 22.070 0.000 0.531
## .ssno 0.096 0.026 3.771 0.000 0.046
## .sscs 0.007 0.024 0.306 0.759 -0.040
## .ssgs 0.523 0.023 22.328 0.000 0.477
## .sswk 0.392 0.022 17.468 0.000 0.348
## .sspc 0.211 0.024 8.959 0.000 0.165
## ci.upper Std.lv Std.all
## 0.440 0.395 0.415
## 0.287 0.242 0.255
## 0.608 0.563 0.590
## 0.262 0.214 0.210
## 0.666 0.614 0.557
## 0.816 0.769 0.777
## 0.634 0.582 0.531
## 0.146 0.096 0.090
## 0.054 0.007 0.007
## 0.569 0.523 0.537
## 0.436 0.392 0.419
## 0.257 0.211 0.215
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.211 0.018 11.774 0.000 0.176
## .ssmk 0.175 0.015 12.069 0.000 0.147
## .ssmc 0.261 0.016 15.896 0.000 0.229
## .ssao 0.488 0.031 15.629 0.000 0.426
## .ssai 0.495 0.028 17.644 0.000 0.440
## .sssi 0.296 0.021 13.919 0.000 0.254
## .ssei 0.325 0.016 20.409 0.000 0.294
## .ssno 0.180 0.053 3.409 0.001 0.076
## .sscs 0.472 0.027 17.444 0.000 0.419
## .ssgs 0.193 0.009 20.969 0.000 0.175
## .sswk 0.206 0.010 20.118 0.000 0.186
## .sspc 0.247 0.012 20.495 0.000 0.224
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.246 0.211 0.233
## 0.203 0.175 0.194
## 0.293 0.261 0.286
## 0.549 0.488 0.472
## 0.550 0.495 0.408
## 0.338 0.296 0.302
## 0.357 0.325 0.270
## 0.283 0.180 0.158
## 0.525 0.472 0.471
## 0.211 0.193 0.204
## 0.227 0.206 0.235
## 0.271 0.247 0.255
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
#modificationIndices(configural, sort=T, maximum.number=30)
metric<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1327.495 105.000 0.000 0.961 0.080 0.049 87062.626
## bic
## 87527.996
Mc(metric)
## [1] 0.846116
summary(metric, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 68 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 23
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1327.495 1164.419
## Degrees of freedom 105 105
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.140
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 504.345 442.388
## 0 823.151 722.031
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.299 0.022 13.691 0.000 0.256
## ssmk (.p2.) 0.269 0.020 13.211 0.000 0.229
## ssmc (.p3.) 0.213 0.023 9.084 0.000 0.167
## ssao (.p4.) 0.370 0.034 10.995 0.000 0.304
## electronic =~
## ssai (.p5.) 0.278 0.018 15.535 0.000 0.243
## sssi (.p6.) 0.278 0.019 14.863 0.000 0.241
## ssmc (.p7.) 0.129 0.010 12.698 0.000 0.109
## ssei (.p8.) 0.168 0.012 14.556 0.000 0.145
## speed =~
## ssno (.p9.) 0.699 0.035 20.178 0.000 0.631
## sscs (.10.) 0.393 0.023 16.998 0.000 0.348
## ssmk (.11.) 0.201 0.013 15.960 0.000 0.176
## g =~
## ssgs (.12.) 0.741 0.015 48.920 0.000 0.712
## ssar (.13.) 0.672 0.016 40.977 0.000 0.640
## sswk (.14.) 0.738 0.016 45.349 0.000 0.706
## sspc (.15.) 0.743 0.016 47.131 0.000 0.712
## ssno (.16.) 0.523 0.017 30.140 0.000 0.489
## sscs (.17.) 0.491 0.016 30.857 0.000 0.460
## ssai (.18.) 0.416 0.015 27.999 0.000 0.387
## sssi (.19.) 0.410 0.015 28.066 0.000 0.381
## ssmk (.20.) 0.694 0.016 42.878 0.000 0.662
## ssmc (.21.) 0.604 0.015 39.711 0.000 0.574
## ssei (.22.) 0.640 0.015 43.299 0.000 0.611
## ssao (.23.) 0.564 0.015 37.307 0.000 0.534
## ci.upper Std.lv Std.all
##
## 0.341 0.299 0.357
## 0.309 0.269 0.305
## 0.259 0.213 0.261
## 0.435 0.370 0.407
##
## 0.314 0.278 0.372
## 0.314 0.278 0.370
## 0.149 0.129 0.159
## 0.191 0.168 0.204
##
## 0.767 0.699 0.738
## 0.439 0.393 0.432
## 0.226 0.201 0.228
##
## 0.771 0.741 0.872
## 0.704 0.672 0.803
## 0.770 0.738 0.866
## 0.774 0.743 0.852
## 0.557 0.523 0.552
## 0.523 0.491 0.540
## 0.445 0.416 0.555
## 0.439 0.410 0.547
## 0.726 0.694 0.786
## 0.634 0.604 0.741
## 0.669 0.640 0.776
## 0.593 0.564 0.620
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.327 0.021 15.677 0.000 0.286
## .ssmk 0.382 0.022 16.962 0.000 0.337
## .ssmc 0.235 0.020 11.729 0.000 0.196
## .ssao 0.356 0.022 15.988 0.000 0.312
## .ssai 0.055 0.018 3.026 0.002 0.019
## .sssi 0.059 0.019 3.200 0.001 0.023
## .ssei 0.139 0.019 7.329 0.000 0.102
## .ssno 0.244 0.023 10.435 0.000 0.198
## .sscs 0.358 0.023 15.788 0.000 0.313
## .ssgs 0.331 0.021 15.977 0.000 0.291
## .sswk 0.379 0.022 17.461 0.000 0.337
## .sspc 0.453 0.022 20.981 0.000 0.411
## ci.upper Std.lv Std.all
## 0.368 0.327 0.391
## 0.426 0.382 0.432
## 0.274 0.235 0.288
## 0.399 0.356 0.391
## 0.091 0.055 0.074
## 0.096 0.059 0.079
## 0.176 0.139 0.169
## 0.290 0.244 0.258
## 0.402 0.358 0.393
## 0.372 0.331 0.389
## 0.422 0.379 0.445
## 0.495 0.453 0.520
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.159 0.012 13.724 0.000 0.136
## .ssmk 0.185 0.010 18.692 0.000 0.166
## .ssmc 0.237 0.013 18.965 0.000 0.213
## .ssao 0.372 0.023 15.959 0.000 0.326
## .ssai 0.311 0.015 20.333 0.000 0.281
## .sssi 0.317 0.015 20.680 0.000 0.287
## .ssei 0.243 0.011 21.912 0.000 0.221
## .ssno 0.134 0.036 3.692 0.000 0.063
## .sscs 0.431 0.020 21.417 0.000 0.392
## .ssgs 0.174 0.008 20.421 0.000 0.157
## .sswk 0.182 0.009 21.359 0.000 0.165
## .sspc 0.209 0.011 18.238 0.000 0.186
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.182 0.159 0.227
## 0.205 0.185 0.238
## 0.262 0.237 0.357
## 0.418 0.372 0.450
## 0.341 0.311 0.554
## 0.347 0.317 0.564
## 0.264 0.243 0.357
## 0.205 0.134 0.150
## 0.471 0.431 0.521
## 0.190 0.174 0.240
## 0.199 0.182 0.250
## 0.231 0.209 0.274
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.299 0.022 13.691 0.000 0.256
## ssmk (.p2.) 0.269 0.020 13.211 0.000 0.229
## ssmc (.p3.) 0.213 0.023 9.084 0.000 0.167
## ssao (.p4.) 0.370 0.034 10.995 0.000 0.304
## electronic =~
## ssai (.p5.) 0.278 0.018 15.535 0.000 0.243
## sssi (.p6.) 0.278 0.019 14.863 0.000 0.241
## ssmc (.p7.) 0.129 0.010 12.698 0.000 0.109
## ssei (.p8.) 0.168 0.012 14.556 0.000 0.145
## speed =~
## ssno (.p9.) 0.699 0.035 20.178 0.000 0.631
## sscs (.10.) 0.393 0.023 16.998 0.000 0.348
## ssmk (.11.) 0.201 0.013 15.960 0.000 0.176
## g =~
## ssgs (.12.) 0.741 0.015 48.920 0.000 0.712
## ssar (.13.) 0.672 0.016 40.977 0.000 0.640
## sswk (.14.) 0.738 0.016 45.349 0.000 0.706
## sspc (.15.) 0.743 0.016 47.131 0.000 0.712
## ssno (.16.) 0.523 0.017 30.140 0.000 0.489
## sscs (.17.) 0.491 0.016 30.857 0.000 0.460
## ssai (.18.) 0.416 0.015 27.999 0.000 0.387
## sssi (.19.) 0.410 0.015 28.066 0.000 0.381
## ssmk (.20.) 0.694 0.016 42.878 0.000 0.662
## ssmc (.21.) 0.604 0.015 39.711 0.000 0.574
## ssei (.22.) 0.640 0.015 43.299 0.000 0.611
## ssao (.23.) 0.564 0.015 37.307 0.000 0.534
## ci.upper Std.lv Std.all
##
## 0.341 0.284 0.299
## 0.309 0.256 0.264
## 0.259 0.202 0.216
## 0.435 0.351 0.347
##
## 0.314 0.650 0.606
## 0.314 0.648 0.668
## 0.149 0.302 0.322
## 0.191 0.392 0.384
##
## 0.767 0.780 0.732
## 0.439 0.439 0.440
## 0.226 0.225 0.231
##
## 0.771 0.858 0.888
## 0.704 0.778 0.820
## 0.770 0.854 0.884
## 0.774 0.860 0.867
## 0.557 0.605 0.568
## 0.523 0.569 0.570
## 0.445 0.482 0.449
## 0.439 0.475 0.490
## 0.726 0.804 0.828
## 0.634 0.699 0.746
## 0.669 0.741 0.726
## 0.593 0.653 0.644
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.395 0.023 17.329 0.000 0.350
## .ssmk 0.242 0.023 10.519 0.000 0.197
## .ssmc 0.563 0.023 24.735 0.000 0.518
## .ssao 0.214 0.024 8.814 0.000 0.166
## .ssai 0.614 0.027 23.150 0.000 0.562
## .sssi 0.769 0.024 32.369 0.000 0.723
## .ssei 0.582 0.026 22.070 0.000 0.531
## .ssno 0.096 0.026 3.771 0.000 0.046
## .sscs 0.007 0.024 0.306 0.759 -0.040
## .ssgs 0.523 0.023 22.328 0.000 0.477
## .sswk 0.392 0.022 17.468 0.000 0.348
## .sspc 0.211 0.024 8.959 0.000 0.165
## ci.upper Std.lv Std.all
## 0.440 0.395 0.416
## 0.287 0.242 0.249
## 0.608 0.563 0.601
## 0.262 0.214 0.211
## 0.666 0.614 0.572
## 0.816 0.769 0.793
## 0.634 0.582 0.570
## 0.146 0.096 0.090
## 0.054 0.007 0.007
## 0.569 0.523 0.541
## 0.436 0.392 0.406
## 0.257 0.211 0.213
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.215 0.013 16.163 0.000 0.189
## .ssmk 0.179 0.011 16.789 0.000 0.159
## .ssmc 0.258 0.014 18.916 0.000 0.231
## .ssao 0.477 0.024 20.275 0.000 0.430
## .ssai 0.497 0.028 17.934 0.000 0.443
## .sssi 0.295 0.021 14.204 0.000 0.254
## .ssei 0.339 0.017 20.249 0.000 0.306
## .ssno 0.160 0.045 3.580 0.000 0.072
## .sscs 0.479 0.025 19.078 0.000 0.429
## .ssgs 0.197 0.009 21.866 0.000 0.179
## .sswk 0.204 0.010 19.962 0.000 0.184
## .sspc 0.244 0.012 20.590 0.000 0.221
## math 0.902 0.102 8.827 0.000 0.702
## electronic 5.453 0.709 7.686 0.000 4.062
## speed 1.246 0.117 10.669 0.000 1.017
## g 1.341 0.069 19.320 0.000 1.205
## ci.upper Std.lv Std.all
## 0.241 0.215 0.239
## 0.200 0.179 0.191
## 0.284 0.258 0.293
## 0.523 0.477 0.465
## 0.551 0.497 0.431
## 0.336 0.295 0.314
## 0.372 0.339 0.325
## 0.248 0.160 0.141
## 0.528 0.479 0.481
## 0.215 0.197 0.211
## 0.224 0.204 0.218
## 0.267 0.244 0.248
## 1.103 1.000 1.000
## 6.843 1.000 1.000
## 1.475 1.000 1.000
## 1.477 1.000 1.000
lavTestScore(metric, release = 1:23)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 131.561 23 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p62. 0.015 1 0.902
## 2 .p2. == .p63. 0.005 1 0.946
## 3 .p3. == .p64. 0.271 1 0.603
## 4 .p4. == .p65. 0.433 1 0.511
## 5 .p5. == .p66. 0.810 1 0.368
## 6 .p6. == .p67. 1.664 1 0.197
## 7 .p7. == .p68. 1.589 1 0.207
## 8 .p8. == .p69. 2.102 1 0.147
## 9 .p9. == .p70. 0.115 1 0.734
## 10 .p10. == .p71. 0.009 1 0.926
## 11 .p11. == .p72. 0.024 1 0.878
## 12 .p12. == .p73. 2.096 1 0.148
## 13 .p13. == .p74. 1.160 1 0.281
## 14 .p14. == .p75. 32.105 1 0.000
## 15 .p15. == .p76. 1.848 1 0.174
## 16 .p16. == .p77. 0.830 1 0.362
## 17 .p17. == .p78. 0.546 1 0.460
## 18 .p18. == .p79. 0.641 1 0.424
## 19 .p19. == .p80. 1.617 1 0.204
## 20 .p20. == .p81. 13.978 1 0.000
## 21 .p21. == .p82. 0.062 1 0.803
## 22 .p22. == .p83. 78.693 1 0.000
## 23 .p23. == .p84. 1.315 1 0.252
metric2<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"), group.partial=c("g=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1247.730 104.000 0.000 0.964 0.078 0.040 86984.861
## bic
## 87456.436
Mc(metric2)
## [1] 0.8552746
scalar<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1855.499 112.000 0.000 0.945 0.092 0.046 87576.629
## bic
## 87998.565
Mc(scalar)
## [1] 0.7879559
summary(scalar, standardized=T, ci=T) # -.023
## lavaan 0.6-18 ended normally after 86 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 102
## Number of equality constraints 34
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1855.499 1647.212
## Degrees of freedom 112 112
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.126
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 743.109 659.693
## 0 1112.390 987.520
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.288 0.026 10.992 0.000 0.236
## ssmk (.p2.) 0.263 0.023 11.267 0.000 0.217
## ssmc (.p3.) 0.217 0.025 8.574 0.000 0.167
## ssao (.p4.) 0.376 0.040 9.496 0.000 0.298
## electronic =~
## ssai (.p5.) 0.257 0.016 16.300 0.000 0.226
## sssi (.p6.) 0.293 0.018 16.507 0.000 0.258
## ssmc (.p7.) 0.138 0.010 14.056 0.000 0.118
## ssei (.p8.) 0.172 0.011 15.882 0.000 0.151
## speed =~
## ssno (.p9.) 0.595 0.032 18.587 0.000 0.532
## sscs (.10.) 0.460 0.025 18.361 0.000 0.411
## ssmk (.11.) 0.222 0.012 18.088 0.000 0.198
## g =~
## ssgs (.12.) 0.749 0.016 47.942 0.000 0.719
## ssar (.13.) 0.681 0.017 41.022 0.000 0.648
## sswk (.14.) 0.748 0.016 45.909 0.000 0.716
## sspc (.15.) 0.746 0.016 47.800 0.000 0.715
## ssno (.16.) 0.531 0.018 30.241 0.000 0.496
## sscs (.17.) 0.493 0.016 31.167 0.000 0.462
## ssai (.18.) 0.428 0.015 28.670 0.000 0.399
## sssi (.19.) 0.423 0.015 28.473 0.000 0.394
## ssmk (.20.) 0.703 0.016 43.342 0.000 0.671
## ssmc (.21.) 0.613 0.015 39.840 0.000 0.583
## ssei 0.569 0.017 33.028 0.000 0.535
## ssao (.23.) 0.568 0.015 37.619 0.000 0.539
## ci.upper Std.lv Std.all
##
## 0.339 0.288 0.341
## 0.309 0.263 0.296
## 0.266 0.217 0.264
## 0.453 0.376 0.411
##
## 0.288 0.257 0.341
## 0.328 0.293 0.387
## 0.157 0.138 0.167
## 0.194 0.172 0.223
##
## 0.658 0.595 0.630
## 0.509 0.460 0.499
## 0.246 0.222 0.249
##
## 0.780 0.749 0.869
## 0.713 0.681 0.808
## 0.779 0.748 0.870
## 0.777 0.746 0.843
## 0.565 0.531 0.562
## 0.524 0.493 0.535
## 0.457 0.428 0.567
## 0.452 0.423 0.559
## 0.735 0.703 0.790
## 0.643 0.613 0.746
## 0.602 0.569 0.737
## 0.598 0.568 0.622
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.360 0.020 17.856 0.000 0.320
## .ssmk (.47.) 0.362 0.022 16.414 0.000 0.319
## .ssmc (.48.) 0.238 0.019 12.478 0.000 0.201
## .ssao (.49.) 0.302 0.022 13.438 0.000 0.258
## .ssai (.50.) 0.031 0.017 1.820 0.069 -0.002
## .sssi (.51.) 0.063 0.018 3.511 0.000 0.028
## .ssei (.52.) 0.145 0.019 7.678 0.000 0.108
## .ssno (.53.) 0.288 0.023 12.291 0.000 0.242
## .sscs (.54.) 0.277 0.025 11.275 0.000 0.229
## .ssgs (.55.) 0.410 0.021 19.928 0.000 0.370
## .sswk (.56.) 0.373 0.021 17.811 0.000 0.332
## .sspc (.57.) 0.329 0.022 15.195 0.000 0.287
## ci.upper Std.lv Std.all
## 0.399 0.360 0.427
## 0.405 0.362 0.407
## 0.276 0.238 0.290
## 0.346 0.302 0.330
## 0.065 0.031 0.041
## 0.098 0.063 0.083
## 0.182 0.145 0.188
## 0.334 0.288 0.305
## 0.325 0.277 0.301
## 0.451 0.410 0.476
## 0.415 0.373 0.435
## 0.372 0.329 0.372
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.164 0.013 12.641 0.000 0.138
## .ssmk 0.180 0.010 17.237 0.000 0.159
## .ssmc 0.234 0.013 17.995 0.000 0.208
## .ssao 0.372 0.027 13.615 0.000 0.318
## .ssai 0.321 0.015 21.399 0.000 0.291
## .sssi 0.309 0.015 20.105 0.000 0.279
## .ssei 0.242 0.011 22.045 0.000 0.220
## .ssno 0.257 0.028 9.194 0.000 0.202
## .sscs 0.395 0.023 17.098 0.000 0.350
## .ssgs 0.182 0.009 19.666 0.000 0.164
## .sswk 0.179 0.009 21.044 0.000 0.162
## .sspc 0.227 0.013 17.629 0.000 0.202
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.189 0.164 0.231
## 0.200 0.180 0.227
## 0.259 0.234 0.346
## 0.425 0.372 0.445
## 0.350 0.321 0.563
## 0.339 0.309 0.538
## 0.263 0.242 0.407
## 0.312 0.257 0.288
## 0.441 0.395 0.465
## 0.200 0.182 0.245
## 0.196 0.179 0.243
## 0.252 0.227 0.290
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.288 0.026 10.992 0.000 0.236
## ssmk (.p2.) 0.263 0.023 11.267 0.000 0.217
## ssmc (.p3.) 0.217 0.025 8.574 0.000 0.167
## ssao (.p4.) 0.376 0.040 9.496 0.000 0.298
## electronic =~
## ssai (.p5.) 0.257 0.016 16.300 0.000 0.226
## sssi (.p6.) 0.293 0.018 16.507 0.000 0.258
## ssmc (.p7.) 0.138 0.010 14.056 0.000 0.118
## ssei (.p8.) 0.172 0.011 15.882 0.000 0.151
## speed =~
## ssno (.p9.) 0.595 0.032 18.587 0.000 0.532
## sscs (.10.) 0.460 0.025 18.361 0.000 0.411
## ssmk (.11.) 0.222 0.012 18.088 0.000 0.198
## g =~
## ssgs (.12.) 0.749 0.016 47.942 0.000 0.719
## ssar (.13.) 0.681 0.017 41.022 0.000 0.648
## sswk (.14.) 0.748 0.016 45.909 0.000 0.716
## sspc (.15.) 0.746 0.016 47.800 0.000 0.715
## ssno (.16.) 0.531 0.018 30.241 0.000 0.496
## sscs (.17.) 0.493 0.016 31.167 0.000 0.462
## ssai (.18.) 0.428 0.015 28.670 0.000 0.399
## sssi (.19.) 0.423 0.015 28.473 0.000 0.394
## ssmk (.20.) 0.703 0.016 43.342 0.000 0.671
## ssmc (.21.) 0.613 0.015 39.840 0.000 0.583
## ssei 0.736 0.021 35.528 0.000 0.695
## ssao (.23.) 0.568 0.015 37.619 0.000 0.539
## ci.upper Std.lv Std.all
##
## 0.339 0.277 0.293
## 0.309 0.253 0.262
## 0.266 0.209 0.222
## 0.453 0.362 0.357
##
## 0.288 0.586 0.555
## 0.328 0.667 0.684
## 0.157 0.313 0.334
## 0.194 0.393 0.362
##
## 0.658 0.668 0.632
## 0.509 0.517 0.512
## 0.246 0.249 0.257
##
## 0.780 0.850 0.881
## 0.713 0.772 0.817
## 0.779 0.848 0.884
## 0.777 0.846 0.851
## 0.565 0.602 0.570
## 0.524 0.559 0.555
## 0.457 0.485 0.460
## 0.452 0.480 0.492
## 0.735 0.797 0.825
## 0.643 0.696 0.741
## 0.777 0.835 0.770
## 0.598 0.645 0.636
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.360 0.020 17.856 0.000 0.320
## .ssmk (.47.) 0.362 0.022 16.414 0.000 0.319
## .ssmc (.48.) 0.238 0.019 12.478 0.000 0.201
## .ssao (.49.) 0.302 0.022 13.438 0.000 0.258
## .ssai (.50.) 0.031 0.017 1.820 0.069 -0.002
## .sssi (.51.) 0.063 0.018 3.511 0.000 0.028
## .ssei (.52.) 0.145 0.019 7.678 0.000 0.108
## .ssno (.53.) 0.288 0.023 12.291 0.000 0.242
## .sscs (.54.) 0.277 0.025 11.275 0.000 0.229
## .ssgs (.55.) 0.410 0.021 19.928 0.000 0.370
## .sswk (.56.) 0.373 0.021 17.811 0.000 0.332
## .sspc (.57.) 0.329 0.022 15.195 0.000 0.287
## math -0.109 0.068 -1.590 0.112 -0.242
## elctrnc 2.355 0.159 14.823 0.000 2.044
## speed -0.436 0.065 -6.717 0.000 -0.563
## g 0.027 0.040 0.667 0.505 -0.052
## ci.upper Std.lv Std.all
## 0.399 0.360 0.381
## 0.405 0.362 0.375
## 0.276 0.238 0.254
## 0.346 0.302 0.298
## 0.065 0.031 0.030
## 0.098 0.063 0.064
## 0.182 0.145 0.133
## 0.334 0.288 0.272
## 0.325 0.277 0.275
## 0.451 0.410 0.425
## 0.415 0.373 0.389
## 0.372 0.329 0.331
## 0.025 -0.113 -0.113
## 2.667 1.033 1.033
## -0.309 -0.388 -0.388
## 0.105 0.023 0.023
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.220 0.015 14.963 0.000 0.191
## .ssmk 0.172 0.011 15.456 0.000 0.150
## .ssmc 0.255 0.014 17.652 0.000 0.227
## .ssao 0.480 0.028 17.326 0.000 0.426
## .ssai 0.534 0.024 21.865 0.000 0.486
## .sssi 0.276 0.018 15.122 0.000 0.241
## .ssei 0.325 0.016 20.781 0.000 0.294
## .ssno 0.308 0.036 8.548 0.000 0.237
## .sscs 0.436 0.027 15.939 0.000 0.383
## .ssgs 0.208 0.010 21.179 0.000 0.189
## .sswk 0.202 0.010 19.838 0.000 0.182
## .sspc 0.273 0.014 19.194 0.000 0.245
## math 0.927 0.107 8.692 0.000 0.718
## electronic 5.195 0.664 7.819 0.000 3.893
## speed 1.261 0.124 10.198 0.000 1.018
## g 1.287 0.067 19.298 0.000 1.156
## ci.upper Std.lv Std.all
## 0.249 0.220 0.246
## 0.194 0.172 0.184
## 0.283 0.255 0.290
## 0.534 0.480 0.468
## 0.582 0.534 0.480
## 0.312 0.276 0.290
## 0.355 0.325 0.276
## 0.378 0.308 0.276
## 0.490 0.436 0.429
## 0.227 0.208 0.224
## 0.222 0.202 0.219
## 0.301 0.273 0.276
## 1.136 1.000 1.000
## 6.497 1.000 1.000
## 1.503 1.000 1.000
## 1.418 1.000 1.000
lavTestScore(scalar, release = 23:34)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 585.564 12 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p46. == .p107. 57.072 1 0.000
## 2 .p47. == .p108. 7.509 1 0.006
## 3 .p48. == .p109. 2.047 1 0.153
## 4 .p49. == .p110. 38.739 1 0.000
## 5 .p50. == .p111. 18.497 1 0.000
## 6 .p51. == .p112. 1.922 1 0.166
## 7 .p52. == .p113. 3.639 1 0.056
## 8 .p53. == .p114. 113.514 1 0.000
## 9 .p54. == .p115. 90.197 1 0.000
## 10 .p55. == .p116. 221.371 1 0.000
## 11 .p56. == .p117. 0.026 1 0.872
## 12 .p57. == .p118. 327.223 1 0.000
scalar2<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "sspc~1", "ssno~1", "sswk~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1302.004 109.000 0.000 0.962 0.077 0.041 87029.135
## bic
## 87469.686
Mc(scalar2)
## [1] 0.8495336
summary(scalar2, standardized=T, ci=T) # -.217
## lavaan 0.6-18 ended normally after 91 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 102
## Number of equality constraints 31
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1302.004 1142.761
## Degrees of freedom 109 109
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.139
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 481.379 422.503
## 0 820.626 720.258
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.273 0.023 11.721 0.000 0.227
## ssmk (.p2.) 0.246 0.024 10.371 0.000 0.200
## ssmc (.p3.) 0.218 0.019 11.301 0.000 0.181
## ssao (.p4.) 0.415 0.031 13.498 0.000 0.355
## electronic =~
## ssai (.p5.) 0.265 0.016 16.501 0.000 0.234
## sssi (.p6.) 0.305 0.018 16.494 0.000 0.269
## ssmc (.p7.) 0.137 0.010 13.817 0.000 0.118
## ssei (.p8.) 0.156 0.010 14.882 0.000 0.135
## speed =~
## ssno (.p9.) 0.698 0.035 19.868 0.000 0.629
## sscs (.10.) 0.399 0.022 18.036 0.000 0.356
## ssmk (.11.) 0.195 0.012 16.292 0.000 0.172
## g =~
## ssgs (.12.) 0.749 0.015 49.419 0.000 0.720
## ssar (.13.) 0.683 0.016 41.617 0.000 0.651
## sswk (.14.) 0.746 0.016 46.017 0.000 0.714
## sspc (.15.) 0.751 0.016 48.003 0.000 0.720
## ssno (.16.) 0.529 0.017 30.371 0.000 0.495
## sscs (.17.) 0.494 0.016 31.159 0.000 0.463
## ssai (.18.) 0.425 0.015 28.518 0.000 0.396
## sssi (.19.) 0.424 0.015 28.606 0.000 0.395
## ssmk (.20.) 0.706 0.016 43.385 0.000 0.674
## ssmc (.21.) 0.615 0.015 40.066 0.000 0.585
## ssei 0.568 0.017 32.993 0.000 0.534
## ssao (.23.) 0.563 0.015 37.841 0.000 0.534
## ci.upper Std.lv Std.all
##
## 0.318 0.273 0.324
## 0.293 0.246 0.277
## 0.256 0.218 0.266
## 0.475 0.415 0.454
##
## 0.297 0.265 0.351
## 0.341 0.305 0.402
## 0.157 0.137 0.167
## 0.176 0.156 0.202
##
## 0.767 0.698 0.736
## 0.443 0.399 0.437
## 0.219 0.195 0.220
##
## 0.779 0.749 0.874
## 0.715 0.683 0.811
## 0.778 0.746 0.869
## 0.781 0.751 0.855
## 0.563 0.529 0.557
## 0.525 0.494 0.541
## 0.454 0.425 0.564
## 0.453 0.424 0.558
## 0.738 0.706 0.795
## 0.645 0.615 0.748
## 0.602 0.568 0.738
## 0.592 0.563 0.615
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.346 0.020 17.073 0.000 0.306
## .ssmk (.47.) 0.400 0.022 18.421 0.000 0.357
## .ssmc (.48.) 0.251 0.019 13.312 0.000 0.214
## .ssao (.49.) 0.325 0.024 13.794 0.000 0.279
## .ssai (.50.) 0.042 0.017 2.477 0.013 0.009
## .sssi (.51.) 0.081 0.018 4.471 0.000 0.046
## .ssei (.52.) 0.130 0.019 7.010 0.000 0.094
## .ssno 0.250 0.023 10.679 0.000 0.204
## .sscs (.54.) 0.351 0.022 15.917 0.000 0.307
## .ssgs (.55.) 0.338 0.020 16.903 0.000 0.299
## .sswk 0.387 0.022 17.942 0.000 0.344
## .sspc 0.461 0.021 21.459 0.000 0.419
## ci.upper Std.lv Std.all
## 0.385 0.346 0.410
## 0.442 0.400 0.450
## 0.288 0.251 0.305
## 0.371 0.325 0.355
## 0.075 0.042 0.056
## 0.117 0.081 0.107
## 0.166 0.130 0.169
## 0.295 0.250 0.263
## 0.394 0.351 0.384
## 0.378 0.338 0.395
## 0.429 0.387 0.450
## 0.503 0.461 0.525
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.168 0.011 14.805 0.000 0.146
## .ssmk 0.192 0.010 18.998 0.000 0.172
## .ssmc 0.232 0.012 18.865 0.000 0.208
## .ssao 0.348 0.027 13.127 0.000 0.296
## .ssai 0.318 0.015 21.130 0.000 0.289
## .sssi 0.303 0.016 19.411 0.000 0.273
## .ssei 0.246 0.011 22.441 0.000 0.224
## .ssno 0.133 0.037 3.635 0.000 0.061
## .sscs 0.431 0.020 21.086 0.000 0.391
## .ssgs 0.174 0.009 20.279 0.000 0.157
## .sswk 0.181 0.008 21.334 0.000 0.164
## .sspc 0.207 0.011 18.164 0.000 0.185
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.190 0.168 0.237
## 0.212 0.192 0.243
## 0.256 0.232 0.343
## 0.400 0.348 0.416
## 0.348 0.318 0.559
## 0.334 0.303 0.527
## 0.267 0.246 0.415
## 0.205 0.133 0.148
## 0.471 0.431 0.516
## 0.190 0.174 0.236
## 0.198 0.181 0.245
## 0.230 0.207 0.269
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.273 0.023 11.721 0.000 0.227
## ssmk (.p2.) 0.246 0.024 10.371 0.000 0.200
## ssmc (.p3.) 0.218 0.019 11.301 0.000 0.181
## ssao (.p4.) 0.415 0.031 13.498 0.000 0.355
## electronic =~
## ssai (.p5.) 0.265 0.016 16.501 0.000 0.234
## sssi (.p6.) 0.305 0.018 16.494 0.000 0.269
## ssmc (.p7.) 0.137 0.010 13.817 0.000 0.118
## ssei (.p8.) 0.156 0.010 14.882 0.000 0.135
## speed =~
## ssno (.p9.) 0.698 0.035 19.868 0.000 0.629
## sscs (.10.) 0.399 0.022 18.036 0.000 0.356
## ssmk (.11.) 0.195 0.012 16.292 0.000 0.172
## g =~
## ssgs (.12.) 0.749 0.015 49.419 0.000 0.720
## ssar (.13.) 0.683 0.016 41.617 0.000 0.651
## sswk (.14.) 0.746 0.016 46.017 0.000 0.714
## sspc (.15.) 0.751 0.016 48.003 0.000 0.720
## ssno (.16.) 0.529 0.017 30.371 0.000 0.495
## sscs (.17.) 0.494 0.016 31.159 0.000 0.463
## ssai (.18.) 0.425 0.015 28.518 0.000 0.396
## sssi (.19.) 0.424 0.015 28.606 0.000 0.395
## ssmk (.20.) 0.706 0.016 43.385 0.000 0.674
## ssmc (.21.) 0.615 0.015 40.066 0.000 0.585
## ssei 0.734 0.021 35.724 0.000 0.694
## ssao (.23.) 0.563 0.015 37.841 0.000 0.534
## ci.upper Std.lv Std.all
##
## 0.318 0.263 0.278
## 0.293 0.238 0.246
## 0.256 0.211 0.225
## 0.475 0.401 0.396
##
## 0.297 0.597 0.563
## 0.341 0.686 0.700
## 0.157 0.309 0.329
## 0.176 0.350 0.326
##
## 0.767 0.784 0.738
## 0.443 0.449 0.450
## 0.219 0.219 0.227
##
## 0.779 0.851 0.886
## 0.715 0.775 0.820
## 0.778 0.847 0.882
## 0.781 0.852 0.864
## 0.563 0.600 0.565
## 0.525 0.561 0.562
## 0.454 0.483 0.456
## 0.453 0.481 0.491
## 0.738 0.801 0.829
## 0.645 0.698 0.744
## 0.775 0.833 0.776
## 0.592 0.639 0.632
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.346 0.020 17.073 0.000 0.306
## .ssmk (.47.) 0.400 0.022 18.421 0.000 0.357
## .ssmc (.48.) 0.251 0.019 13.312 0.000 0.214
## .ssao (.49.) 0.325 0.024 13.794 0.000 0.279
## .ssai (.50.) 0.042 0.017 2.477 0.013 0.009
## .sssi (.51.) 0.081 0.018 4.471 0.000 0.046
## .ssei (.52.) 0.130 0.019 7.010 0.000 0.094
## .ssno 0.756 0.069 10.922 0.000 0.620
## .sscs (.54.) 0.351 0.022 15.917 0.000 0.307
## .ssgs (.55.) 0.338 0.020 16.903 0.000 0.299
## .sswk 0.208 0.024 8.699 0.000 0.161
## .sspc 0.026 0.025 1.031 0.303 -0.024
## math -0.492 0.067 -7.377 0.000 -0.623
## elctrnc 1.866 0.132 14.110 0.000 1.607
## speed -1.132 0.089 -12.708 0.000 -1.306
## g 0.247 0.039 6.251 0.000 0.169
## ci.upper Std.lv Std.all
## 0.385 0.346 0.365
## 0.442 0.400 0.414
## 0.288 0.251 0.268
## 0.371 0.325 0.321
## 0.075 0.042 0.040
## 0.117 0.081 0.083
## 0.166 0.130 0.121
## 0.892 0.756 0.712
## 0.394 0.351 0.351
## 0.378 0.338 0.353
## 0.255 0.208 0.217
## 0.076 0.026 0.026
## -0.362 -0.510 -0.510
## 2.125 0.829 0.829
## -0.957 -1.007 -1.007
## 0.324 0.217 0.217
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.224 0.013 17.383 0.000 0.199
## .ssmk 0.187 0.011 17.322 0.000 0.166
## .ssmc 0.254 0.013 18.829 0.000 0.227
## .ssao 0.454 0.027 17.135 0.000 0.402
## .ssai 0.532 0.025 21.183 0.000 0.483
## .sssi 0.259 0.020 13.153 0.000 0.220
## .ssei 0.336 0.016 21.492 0.000 0.305
## .ssno 0.154 0.045 3.386 0.001 0.065
## .sscs 0.480 0.025 18.917 0.000 0.430
## .ssgs 0.197 0.009 22.011 0.000 0.180
## .sswk 0.204 0.010 20.111 0.000 0.184
## .sspc 0.247 0.012 20.798 0.000 0.224
## math 0.932 0.105 8.831 0.000 0.725
## electronic 5.062 0.639 7.927 0.000 3.810
## speed 1.262 0.119 10.612 0.000 1.029
## g 1.289 0.066 19.451 0.000 1.159
## ci.upper Std.lv Std.all
## 0.249 0.224 0.250
## 0.208 0.187 0.200
## 0.280 0.254 0.288
## 0.506 0.454 0.444
## 0.582 0.532 0.475
## 0.297 0.259 0.269
## 0.367 0.336 0.291
## 0.243 0.154 0.136
## 0.530 0.480 0.482
## 0.215 0.197 0.214
## 0.224 0.204 0.222
## 0.271 0.247 0.254
## 1.138 1.000 1.000
## 6.313 1.000 1.000
## 1.495 1.000 1.000
## 1.418 1.000 1.000
lavTestScore(scalar2, release = 23:31)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 53.544 9 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p46. == .p107. 6.786 1 0.009
## 2 .p47. == .p108. 6.490 1 0.011
## 3 .p48. == .p109. 3.688 1 0.055
## 4 .p49. == .p110. 33.090 1 0.000
## 5 .p50. == .p111. 10.581 1 0.001
## 6 .p51. == .p112. 13.753 1 0.000
## 7 .p52. == .p113. 7.976 1 0.005
## 8 .p54. == .p115. 6.490 1 0.011
## 9 .p55. == .p116. 0.006 1 0.937
strict<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("g=~ssei", "sspc~1", "ssno~1", "sswk~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1487.113 121.000 0.000 0.957 0.079 0.045 87190.244
## bic
## 87556.336
Mc(strict)
## [1] 0.8296681
summary(strict, standardized=T, ci=T) # -.214
## lavaan 0.6-18 ended normally after 92 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 102
## Number of equality constraints 43
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1487.113 1298.248
## Degrees of freedom 121 121
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.145
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 590.902 515.857
## 0 896.212 782.392
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.256 0.026 9.999 0.000 0.206
## ssmk (.p2.) 0.231 0.026 8.799 0.000 0.180
## ssmc (.p3.) 0.209 0.019 10.883 0.000 0.171
## ssao (.p4.) 0.395 0.031 12.816 0.000 0.335
## electronic =~
## ssai (.p5.) 0.255 0.018 14.537 0.000 0.221
## sssi (.p6.) 0.265 0.020 12.997 0.000 0.225
## ssmc (.p7.) 0.125 0.011 11.764 0.000 0.104
## ssei (.p8.) 0.146 0.011 12.976 0.000 0.124
## speed =~
## ssno (.p9.) 0.696 0.035 19.644 0.000 0.627
## sscs (.10.) 0.394 0.021 18.994 0.000 0.354
## ssmk (.11.) 0.193 0.012 16.409 0.000 0.170
## g =~
## ssgs (.12.) 0.749 0.015 49.329 0.000 0.720
## ssar (.13.) 0.683 0.016 41.782 0.000 0.651
## sswk (.14.) 0.743 0.016 45.815 0.000 0.711
## sspc (.15.) 0.749 0.016 47.868 0.000 0.718
## ssno (.16.) 0.528 0.017 30.335 0.000 0.494
## sscs (.17.) 0.494 0.016 31.064 0.000 0.463
## ssai (.18.) 0.426 0.015 28.218 0.000 0.396
## sssi (.19.) 0.426 0.015 28.682 0.000 0.397
## ssmk (.20.) 0.705 0.016 43.159 0.000 0.673
## ssmc (.21.) 0.615 0.015 39.917 0.000 0.585
## ssei 0.564 0.017 32.782 0.000 0.531
## ssao (.23.) 0.564 0.015 37.662 0.000 0.535
## ci.upper Std.lv Std.all
##
## 0.306 0.256 0.300
## 0.283 0.231 0.262
## 0.246 0.209 0.253
## 0.455 0.395 0.422
##
## 0.290 0.255 0.318
## 0.305 0.265 0.353
## 0.146 0.125 0.152
## 0.168 0.146 0.184
##
## 0.765 0.696 0.733
## 0.435 0.394 0.426
## 0.216 0.193 0.219
##
## 0.779 0.749 0.867
## 0.715 0.683 0.800
## 0.775 0.743 0.860
## 0.780 0.749 0.843
## 0.562 0.528 0.556
## 0.525 0.494 0.533
## 0.455 0.426 0.530
## 0.456 0.426 0.568
## 0.737 0.705 0.799
## 0.645 0.615 0.746
## 0.598 0.564 0.713
## 0.593 0.564 0.603
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.348 0.020 17.086 0.000 0.308
## .ssmk (.47.) 0.401 0.022 18.461 0.000 0.358
## .ssmc (.48.) 0.255 0.019 13.414 0.000 0.217
## .ssao (.49.) 0.321 0.024 13.157 0.000 0.273
## .ssai (.50.) 0.025 0.017 1.447 0.148 -0.009
## .sssi (.51.) 0.096 0.018 5.277 0.000 0.060
## .ssei (.52.) 0.126 0.019 6.757 0.000 0.089
## .ssno 0.251 0.023 10.721 0.000 0.205
## .sscs (.54.) 0.351 0.022 15.944 0.000 0.308
## .ssgs (.55.) 0.340 0.020 17.014 0.000 0.301
## .sswk 0.388 0.022 18.023 0.000 0.346
## .sspc 0.462 0.021 21.539 0.000 0.420
## ci.upper Std.lv Std.all
## 0.388 0.348 0.408
## 0.443 0.401 0.454
## 0.292 0.255 0.309
## 0.369 0.321 0.343
## 0.058 0.025 0.031
## 0.131 0.096 0.127
## 0.162 0.126 0.159
## 0.296 0.251 0.264
## 0.394 0.351 0.379
## 0.380 0.340 0.394
## 0.430 0.388 0.449
## 0.504 0.462 0.520
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.24.) 0.198 0.011 18.287 0.000 0.176
## .ssmk (.25.) 0.191 0.009 20.874 0.000 0.173
## .ssmc (.26.) 0.242 0.010 23.869 0.000 0.222
## .ssao (.27.) 0.400 0.026 15.485 0.000 0.350
## .ssai (.28.) 0.400 0.015 26.195 0.000 0.370
## .sssi (.29.) 0.312 0.013 24.022 0.000 0.286
## .ssei (.30.) 0.286 0.010 30.002 0.000 0.268
## .ssno (.31.) 0.138 0.038 3.622 0.000 0.063
## .sscs (.32.) 0.458 0.018 25.662 0.000 0.423
## .ssgs (.33.) 0.185 0.006 29.013 0.000 0.173
## .sswk (.34.) 0.194 0.007 28.313 0.000 0.180
## .sspc (.35.) 0.228 0.008 27.062 0.000 0.211
## math 1.000 1.000
## elctrnc 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.219 0.198 0.271
## 0.209 0.191 0.245
## 0.262 0.242 0.357
## 0.451 0.400 0.458
## 0.430 0.400 0.619
## 0.337 0.312 0.553
## 0.305 0.286 0.457
## 0.213 0.138 0.153
## 0.493 0.458 0.534
## 0.198 0.185 0.248
## 0.207 0.194 0.260
## 0.244 0.228 0.289
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.256 0.026 9.999 0.000 0.206
## ssmk (.p2.) 0.231 0.026 8.799 0.000 0.180
## ssmc (.p3.) 0.209 0.019 10.883 0.000 0.171
## ssao (.p4.) 0.395 0.031 12.816 0.000 0.335
## electronic =~
## ssai (.p5.) 0.255 0.018 14.537 0.000 0.221
## sssi (.p6.) 0.265 0.020 12.997 0.000 0.225
## ssmc (.p7.) 0.125 0.011 11.764 0.000 0.104
## ssei (.p8.) 0.146 0.011 12.976 0.000 0.124
## speed =~
## ssno (.p9.) 0.696 0.035 19.644 0.000 0.627
## sscs (.10.) 0.394 0.021 18.994 0.000 0.354
## ssmk (.11.) 0.193 0.012 16.409 0.000 0.170
## g =~
## ssgs (.12.) 0.749 0.015 49.329 0.000 0.720
## ssar (.13.) 0.683 0.016 41.782 0.000 0.651
## sswk (.14.) 0.743 0.016 45.815 0.000 0.711
## sspc (.15.) 0.749 0.016 47.868 0.000 0.718
## ssno (.16.) 0.528 0.017 30.335 0.000 0.494
## sscs (.17.) 0.494 0.016 31.064 0.000 0.463
## ssai (.18.) 0.426 0.015 28.218 0.000 0.396
## sssi (.19.) 0.426 0.015 28.682 0.000 0.397
## ssmk (.20.) 0.705 0.016 43.159 0.000 0.673
## ssmc (.21.) 0.615 0.015 39.917 0.000 0.585
## ssei 0.735 0.021 35.557 0.000 0.694
## ssao (.23.) 0.564 0.015 37.662 0.000 0.535
## ci.upper Std.lv Std.all
##
## 0.306 0.274 0.293
## 0.283 0.247 0.255
## 0.246 0.223 0.238
## 0.455 0.422 0.424
##
## 0.290 0.643 0.628
## 0.305 0.667 0.670
## 0.146 0.315 0.335
## 0.168 0.367 0.347
##
## 0.765 0.794 0.747
## 0.435 0.450 0.455
## 0.216 0.220 0.226
##
## 0.779 0.853 0.893
## 0.715 0.778 0.830
## 0.775 0.846 0.887
## 0.780 0.853 0.873
## 0.562 0.601 0.565
## 0.525 0.562 0.569
## 0.455 0.485 0.473
## 0.456 0.485 0.487
## 0.737 0.803 0.826
## 0.645 0.700 0.746
## 0.775 0.836 0.790
## 0.593 0.642 0.645
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.348 0.020 17.086 0.000 0.308
## .ssmk (.47.) 0.401 0.022 18.461 0.000 0.358
## .ssmc (.48.) 0.255 0.019 13.414 0.000 0.217
## .ssao (.49.) 0.321 0.024 13.157 0.000 0.273
## .ssai (.50.) 0.025 0.017 1.447 0.148 -0.009
## .sssi (.51.) 0.096 0.018 5.277 0.000 0.060
## .ssei (.52.) 0.126 0.019 6.757 0.000 0.089
## .ssno 0.766 0.070 10.901 0.000 0.628
## .sscs (.54.) 0.351 0.022 15.944 0.000 0.308
## .ssgs (.55.) 0.340 0.020 17.014 0.000 0.301
## .sswk 0.212 0.024 8.873 0.000 0.165
## .sspc 0.029 0.025 1.157 0.247 -0.020
## math -0.516 0.073 -7.072 0.000 -0.660
## elctrnc 2.037 0.167 12.174 0.000 1.709
## speed -1.146 0.089 -12.814 0.000 -1.321
## g 0.243 0.039 6.173 0.000 0.166
## ci.upper Std.lv Std.all
## 0.388 0.348 0.372
## 0.443 0.401 0.412
## 0.292 0.255 0.271
## 0.369 0.321 0.322
## 0.058 0.025 0.024
## 0.131 0.096 0.096
## 0.162 0.126 0.119
## 0.903 0.766 0.720
## 0.394 0.351 0.355
## 0.380 0.340 0.356
## 0.258 0.212 0.222
## 0.078 0.029 0.030
## -0.373 -0.483 -0.483
## 2.366 0.809 0.809
## -0.971 -1.005 -1.005
## 0.320 0.214 0.214
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.24.) 0.198 0.011 18.287 0.000 0.176
## .ssmk (.25.) 0.191 0.009 20.874 0.000 0.173
## .ssmc (.26.) 0.242 0.010 23.869 0.000 0.222
## .ssao (.27.) 0.400 0.026 15.485 0.000 0.350
## .ssai (.28.) 0.400 0.015 26.195 0.000 0.370
## .sssi (.29.) 0.312 0.013 24.022 0.000 0.286
## .ssei (.30.) 0.286 0.010 30.002 0.000 0.268
## .ssno (.31.) 0.138 0.038 3.622 0.000 0.063
## .sscs (.32.) 0.458 0.018 25.662 0.000 0.423
## .ssgs (.33.) 0.185 0.006 29.013 0.000 0.173
## .sswk (.34.) 0.194 0.007 28.313 0.000 0.180
## .sspc (.35.) 0.228 0.008 27.062 0.000 0.211
## math 1.144 0.130 8.820 0.000 0.890
## elctrnc 6.342 0.971 6.529 0.000 4.438
## speed 1.300 0.110 11.864 0.000 1.085
## g 1.296 0.067 19.441 0.000 1.165
## ci.upper Std.lv Std.all
## 0.219 0.198 0.225
## 0.209 0.191 0.202
## 0.262 0.242 0.275
## 0.451 0.400 0.404
## 0.430 0.400 0.381
## 0.337 0.312 0.314
## 0.305 0.286 0.255
## 0.213 0.138 0.122
## 0.493 0.458 0.469
## 0.198 0.185 0.203
## 0.207 0.194 0.213
## 0.244 0.228 0.239
## 1.398 1.000 1.000
## 8.246 1.000 1.000
## 1.515 1.000 1.000
## 1.426 1.000 1.000
latent<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("g=~ssei", "sspc~1", "ssno~1", "sswk~1"))
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1668.171 113.000 0.000 0.951 0.087 0.105 87387.301
## bic
## 87803.033
Mc(latent)
## [1] 0.8085027
summary(latent, standardized=T, ci=T) # -.233
## lavaan 0.6-18 ended normally after 54 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 31
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1668.171 1457.052
## Degrees of freedom 113 113
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.145
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 686.393 599.525
## 0 981.778 857.527
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.270 0.021 12.590 0.000 0.228
## ssmk (.p2.) 0.243 0.022 11.087 0.000 0.200
## ssmc (.p3.) 0.217 0.019 11.684 0.000 0.181
## ssao (.p4.) 0.408 0.027 15.033 0.000 0.355
## electronic =~
## ssai (.p5.) 0.421 0.019 22.348 0.000 0.384
## sssi (.p6.) 0.521 0.016 32.295 0.000 0.489
## ssmc (.p7.) 0.239 0.013 19.026 0.000 0.215
## ssei (.p8.) 0.251 0.014 18.246 0.000 0.224
## speed =~
## ssno (.p9.) 0.739 0.031 23.535 0.000 0.677
## sscs (.10.) 0.427 0.021 20.679 0.000 0.387
## ssmk (.11.) 0.208 0.012 17.288 0.000 0.185
## g =~
## ssgs (.12.) 0.802 0.013 62.403 0.000 0.777
## ssar (.13.) 0.731 0.014 51.065 0.000 0.703
## sswk (.14.) 0.799 0.013 61.596 0.000 0.774
## sspc (.15.) 0.802 0.012 68.170 0.000 0.779
## ssno (.16.) 0.565 0.017 33.261 0.000 0.531
## sscs (.17.) 0.528 0.015 34.936 0.000 0.498
## ssai (.18.) 0.472 0.016 30.045 0.000 0.441
## sssi (.19.) 0.471 0.015 30.658 0.000 0.441
## ssmk (.20.) 0.755 0.013 57.547 0.000 0.730
## ssmc (.21.) 0.666 0.014 46.802 0.000 0.638
## ssei 0.610 0.017 35.637 0.000 0.577
## ssao (.23.) 0.602 0.014 44.110 0.000 0.575
## ci.upper Std.lv Std.all
##
## 0.312 0.270 0.307
## 0.286 0.243 0.262
## 0.254 0.217 0.247
## 0.462 0.408 0.435
##
## 0.458 0.421 0.500
## 0.553 0.521 0.601
## 0.264 0.239 0.272
## 0.278 0.251 0.304
##
## 0.800 0.739 0.747
## 0.467 0.427 0.451
## 0.232 0.208 0.224
##
## 0.828 0.802 0.886
## 0.759 0.731 0.831
## 0.825 0.799 0.884
## 0.826 0.802 0.872
## 0.598 0.565 0.571
## 0.557 0.528 0.558
## 0.502 0.472 0.559
## 0.502 0.471 0.544
## 0.781 0.755 0.812
## 0.693 0.666 0.756
## 0.644 0.610 0.739
## 0.628 0.602 0.641
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.347 0.020 17.073 0.000 0.307
## .ssmk (.47.) 0.400 0.022 18.403 0.000 0.358
## .ssmc (.48.) 0.247 0.019 12.907 0.000 0.210
## .ssao (.49.) 0.326 0.024 13.805 0.000 0.280
## .ssai (.50.) 0.052 0.017 3.028 0.002 0.018
## .sssi (.51.) 0.072 0.018 4.006 0.000 0.037
## .ssei (.52.) 0.133 0.019 7.154 0.000 0.096
## .ssno 0.250 0.023 10.670 0.000 0.204
## .sscs (.54.) 0.350 0.022 15.867 0.000 0.307
## .ssgs (.55.) 0.338 0.020 16.774 0.000 0.298
## .sswk 0.387 0.022 17.874 0.000 0.345
## .sspc 0.461 0.022 21.372 0.000 0.419
## ci.upper Std.lv Std.all
## 0.386 0.347 0.394
## 0.443 0.400 0.430
## 0.285 0.247 0.281
## 0.373 0.326 0.348
## 0.085 0.052 0.061
## 0.108 0.072 0.083
## 0.169 0.133 0.161
## 0.296 0.250 0.253
## 0.393 0.350 0.370
## 0.377 0.338 0.373
## 0.429 0.387 0.428
## 0.503 0.461 0.501
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.166 0.011 15.214 0.000 0.145
## .ssmk 0.192 0.010 19.324 0.000 0.172
## .ssmc 0.227 0.012 18.590 0.000 0.203
## .ssao 0.352 0.025 13.866 0.000 0.302
## .ssai 0.312 0.016 19.515 0.000 0.280
## .sssi 0.259 0.016 15.771 0.000 0.226
## .ssei 0.246 0.011 22.122 0.000 0.224
## .ssno 0.113 0.038 2.941 0.003 0.038
## .sscs 0.435 0.021 20.624 0.000 0.394
## .ssgs 0.176 0.009 19.919 0.000 0.158
## .sswk 0.179 0.009 21.086 0.000 0.163
## .sspc 0.204 0.011 17.978 0.000 0.181
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.187 0.166 0.215
## 0.211 0.192 0.221
## 0.251 0.227 0.294
## 0.402 0.352 0.400
## 0.343 0.312 0.438
## 0.291 0.259 0.344
## 0.268 0.246 0.361
## 0.188 0.113 0.115
## 0.476 0.435 0.486
## 0.193 0.176 0.214
## 0.196 0.179 0.219
## 0.226 0.204 0.240
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.270 0.021 12.590 0.000 0.228
## ssmk (.p2.) 0.243 0.022 11.087 0.000 0.200
## ssmc (.p3.) 0.217 0.019 11.684 0.000 0.181
## ssao (.p4.) 0.408 0.027 15.033 0.000 0.355
## electronic =~
## ssai (.p5.) 0.421 0.019 22.348 0.000 0.384
## sssi (.p6.) 0.521 0.016 32.295 0.000 0.489
## ssmc (.p7.) 0.239 0.013 19.026 0.000 0.215
## ssei (.p8.) 0.251 0.014 18.246 0.000 0.224
## speed =~
## ssno (.p9.) 0.739 0.031 23.535 0.000 0.677
## sscs (.10.) 0.427 0.021 20.679 0.000 0.387
## ssmk (.11.) 0.208 0.012 17.288 0.000 0.185
## g =~
## ssgs (.12.) 0.802 0.013 62.403 0.000 0.777
## ssar (.13.) 0.731 0.014 51.065 0.000 0.703
## sswk (.14.) 0.799 0.013 61.596 0.000 0.774
## sspc (.15.) 0.802 0.012 68.170 0.000 0.779
## ssno (.16.) 0.565 0.017 33.261 0.000 0.531
## sscs (.17.) 0.528 0.015 34.936 0.000 0.498
## ssai (.18.) 0.472 0.016 30.045 0.000 0.441
## sssi (.19.) 0.471 0.015 30.658 0.000 0.441
## ssmk (.20.) 0.755 0.013 57.547 0.000 0.730
## ssmc (.21.) 0.666 0.014 46.802 0.000 0.638
## ssei 0.806 0.019 41.462 0.000 0.768
## ssao (.23.) 0.602 0.014 44.110 0.000 0.575
## ci.upper Std.lv Std.all
##
## 0.312 0.270 0.296
## 0.286 0.243 0.262
## 0.254 0.217 0.243
## 0.462 0.408 0.412
##
## 0.458 0.421 0.426
## 0.553 0.521 0.582
## 0.264 0.239 0.268
## 0.278 0.251 0.244
##
## 0.800 0.739 0.721
## 0.467 0.427 0.441
## 0.232 0.208 0.225
##
## 0.828 0.802 0.876
## 0.759 0.731 0.801
## 0.825 0.799 0.871
## 0.826 0.802 0.847
## 0.598 0.565 0.551
## 0.557 0.528 0.545
## 0.502 0.472 0.477
## 0.502 0.471 0.527
## 0.781 0.755 0.814
## 0.693 0.666 0.744
## 0.844 0.806 0.785
## 0.628 0.602 0.607
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.347 0.020 17.073 0.000 0.307
## .ssmk (.47.) 0.400 0.022 18.403 0.000 0.358
## .ssmc (.48.) 0.247 0.019 12.907 0.000 0.210
## .ssao (.49.) 0.326 0.024 13.805 0.000 0.280
## .ssai (.50.) 0.052 0.017 3.028 0.002 0.018
## .sssi (.51.) 0.072 0.018 4.006 0.000 0.037
## .ssei (.52.) 0.133 0.019 7.154 0.000 0.096
## .ssno 0.747 0.068 10.948 0.000 0.614
## .sscs (.54.) 0.350 0.022 15.867 0.000 0.307
## .ssgs (.55.) 0.338 0.020 16.774 0.000 0.298
## .sswk 0.206 0.024 8.560 0.000 0.159
## .sspc 0.024 0.025 0.962 0.336 -0.025
## math -0.511 0.066 -7.776 0.000 -0.640
## elctrnc 1.108 0.052 21.445 0.000 1.007
## speed -1.059 0.077 -13.787 0.000 -1.210
## g 0.233 0.037 6.209 0.000 0.159
## ci.upper Std.lv Std.all
## 0.386 0.347 0.380
## 0.443 0.400 0.432
## 0.285 0.247 0.276
## 0.373 0.326 0.329
## 0.085 0.052 0.052
## 0.108 0.072 0.081
## 0.169 0.133 0.129
## 0.881 0.747 0.730
## 0.393 0.350 0.362
## 0.377 0.338 0.369
## 0.253 0.206 0.225
## 0.074 0.024 0.026
## -0.382 -0.511 -0.511
## 1.210 1.108 1.108
## -0.909 -1.059 -1.059
## 0.306 0.233 0.233
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.225 0.013 17.183 0.000 0.199
## .ssmk 0.187 0.011 17.338 0.000 0.166
## .ssmc 0.253 0.014 18.185 0.000 0.226
## .ssao 0.452 0.026 17.240 0.000 0.401
## .ssai 0.578 0.026 22.206 0.000 0.527
## .sssi 0.307 0.020 15.513 0.000 0.268
## .ssei 0.342 0.016 21.659 0.000 0.311
## .ssno 0.185 0.043 4.351 0.000 0.102
## .sscs 0.476 0.025 19.165 0.000 0.427
## .ssgs 0.195 0.009 22.247 0.000 0.177
## .sswk 0.204 0.010 20.629 0.000 0.184
## .sspc 0.255 0.012 21.124 0.000 0.231
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.250 0.225 0.270
## 0.208 0.187 0.218
## 0.280 0.253 0.316
## 0.504 0.452 0.461
## 0.629 0.578 0.591
## 0.346 0.307 0.384
## 0.373 0.342 0.325
## 0.268 0.185 0.176
## 0.524 0.476 0.508
## 0.212 0.195 0.232
## 0.223 0.204 0.242
## 0.278 0.255 0.283
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
latent2<-cfa(bf.lv, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "sspc~1", "ssno~1", "sswk~1"))
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr aic
## 1302.451 110.000 0.000 0.962 0.077 0.041 87027.581
## bic
## 87461.927
Mc(latent2)
## [1] 0.8495978
summary(latent2, standardized=T, ci=T) # -.217
## lavaan 0.6-18 ended normally after 87 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 101
## Number of equality constraints 31
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1302.451 1143.488
## Degrees of freedom 110 110
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.139
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 481.662 422.876
## 0 820.788 720.612
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.269 0.022 12.304 0.000 0.226
## ssmk (.p2.) 0.242 0.022 10.835 0.000 0.198
## ssmc (.p3.) 0.215 0.019 11.577 0.000 0.179
## ssao (.p4.) 0.409 0.028 14.340 0.000 0.353
## electronic =~
## ssai (.p5.) 0.265 0.016 16.500 0.000 0.234
## sssi (.p6.) 0.305 0.018 16.492 0.000 0.269
## ssmc (.p7.) 0.138 0.010 13.827 0.000 0.118
## ssei (.p8.) 0.156 0.010 14.879 0.000 0.135
## speed =~
## ssno (.p9.) 0.698 0.035 19.868 0.000 0.629
## sscs (.10.) 0.399 0.022 18.026 0.000 0.356
## ssmk (.11.) 0.195 0.012 16.262 0.000 0.172
## g =~
## ssgs (.12.) 0.750 0.015 49.464 0.000 0.720
## ssar (.13.) 0.683 0.016 41.616 0.000 0.651
## sswk (.14.) 0.746 0.016 46.059 0.000 0.714
## sspc (.15.) 0.751 0.016 48.016 0.000 0.720
## ssno (.16.) 0.529 0.017 30.360 0.000 0.495
## sscs (.17.) 0.494 0.016 31.157 0.000 0.463
## ssai (.18.) 0.425 0.015 28.522 0.000 0.396
## sssi (.19.) 0.424 0.015 28.622 0.000 0.395
## ssmk (.20.) 0.706 0.016 43.360 0.000 0.674
## ssmc (.21.) 0.615 0.015 40.048 0.000 0.585
## ssei 0.568 0.017 32.971 0.000 0.534
## ssao (.23.) 0.563 0.015 37.814 0.000 0.534
## ci.upper Std.lv Std.all
##
## 0.311 0.269 0.319
## 0.286 0.242 0.273
## 0.252 0.215 0.262
## 0.464 0.409 0.448
##
## 0.297 0.265 0.351
## 0.341 0.305 0.402
## 0.157 0.138 0.167
## 0.176 0.156 0.202
##
## 0.767 0.698 0.736
## 0.443 0.399 0.437
## 0.219 0.195 0.220
##
## 0.779 0.750 0.874
## 0.715 0.683 0.812
## 0.778 0.746 0.868
## 0.781 0.751 0.855
## 0.563 0.529 0.557
## 0.525 0.494 0.541
## 0.455 0.425 0.564
## 0.453 0.424 0.559
## 0.738 0.706 0.795
## 0.645 0.615 0.748
## 0.602 0.568 0.738
## 0.592 0.563 0.617
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.346 0.020 17.077 0.000 0.306
## .ssmk (.47.) 0.400 0.022 18.421 0.000 0.357
## .ssmc (.48.) 0.251 0.019 13.302 0.000 0.214
## .ssao (.49.) 0.325 0.024 13.771 0.000 0.279
## .ssai (.50.) 0.042 0.017 2.477 0.013 0.009
## .sssi (.51.) 0.081 0.018 4.473 0.000 0.046
## .ssei (.52.) 0.130 0.019 7.010 0.000 0.094
## .ssno 0.250 0.023 10.680 0.000 0.204
## .sscs (.54.) 0.351 0.022 15.919 0.000 0.307
## .ssgs (.55.) 0.338 0.020 16.904 0.000 0.299
## .sswk 0.387 0.022 17.943 0.000 0.344
## .sspc 0.461 0.021 21.460 0.000 0.419
## ci.upper Std.lv Std.all
## 0.385 0.346 0.411
## 0.442 0.400 0.450
## 0.288 0.251 0.306
## 0.371 0.325 0.356
## 0.075 0.042 0.056
## 0.117 0.081 0.107
## 0.166 0.130 0.169
## 0.295 0.250 0.263
## 0.394 0.351 0.384
## 0.378 0.338 0.395
## 0.429 0.387 0.450
## 0.503 0.461 0.525
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.168 0.011 14.900 0.000 0.146
## .ssmk 0.192 0.010 19.098 0.000 0.173
## .ssmc 0.232 0.012 18.853 0.000 0.208
## .ssao 0.350 0.026 13.287 0.000 0.298
## .ssai 0.318 0.015 21.132 0.000 0.289
## .sssi 0.303 0.016 19.410 0.000 0.273
## .ssei 0.246 0.011 22.443 0.000 0.225
## .ssno 0.133 0.037 3.629 0.000 0.061
## .sscs 0.431 0.020 21.084 0.000 0.391
## .ssgs 0.174 0.009 20.311 0.000 0.157
## .sswk 0.181 0.008 21.415 0.000 0.165
## .sspc 0.207 0.011 18.193 0.000 0.185
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.190 0.168 0.238
## 0.212 0.192 0.244
## 0.256 0.232 0.343
## 0.401 0.350 0.419
## 0.348 0.318 0.559
## 0.334 0.303 0.527
## 0.267 0.246 0.415
## 0.205 0.133 0.148
## 0.471 0.431 0.516
## 0.191 0.174 0.236
## 0.198 0.181 0.246
## 0.230 0.207 0.269
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.269 0.022 12.304 0.000 0.226
## ssmk (.p2.) 0.242 0.022 10.835 0.000 0.198
## ssmc (.p3.) 0.215 0.019 11.577 0.000 0.179
## ssao (.p4.) 0.409 0.028 14.340 0.000 0.353
## electronic =~
## ssai (.p5.) 0.265 0.016 16.500 0.000 0.234
## sssi (.p6.) 0.305 0.018 16.492 0.000 0.269
## ssmc (.p7.) 0.138 0.010 13.827 0.000 0.118
## ssei (.p8.) 0.156 0.010 14.879 0.000 0.135
## speed =~
## ssno (.p9.) 0.698 0.035 19.868 0.000 0.629
## sscs (.10.) 0.399 0.022 18.026 0.000 0.356
## ssmk (.11.) 0.195 0.012 16.262 0.000 0.172
## g =~
## ssgs (.12.) 0.750 0.015 49.464 0.000 0.720
## ssar (.13.) 0.683 0.016 41.616 0.000 0.651
## sswk (.14.) 0.746 0.016 46.059 0.000 0.714
## sspc (.15.) 0.751 0.016 48.016 0.000 0.720
## ssno (.16.) 0.529 0.017 30.360 0.000 0.495
## sscs (.17.) 0.494 0.016 31.157 0.000 0.463
## ssai (.18.) 0.425 0.015 28.522 0.000 0.396
## sssi (.19.) 0.424 0.015 28.622 0.000 0.395
## ssmk (.20.) 0.706 0.016 43.360 0.000 0.674
## ssmc (.21.) 0.615 0.015 40.048 0.000 0.585
## ssei 0.735 0.021 35.765 0.000 0.694
## ssao (.23.) 0.563 0.015 37.814 0.000 0.534
## ci.upper Std.lv Std.all
##
## 0.311 0.269 0.284
## 0.286 0.242 0.250
## 0.252 0.215 0.229
## 0.464 0.409 0.403
##
## 0.297 0.596 0.563
## 0.341 0.686 0.700
## 0.157 0.309 0.329
## 0.176 0.350 0.326
##
## 0.767 0.785 0.738
## 0.443 0.449 0.450
## 0.219 0.219 0.227
##
## 0.779 0.851 0.887
## 0.715 0.775 0.819
## 0.778 0.847 0.882
## 0.781 0.852 0.864
## 0.563 0.600 0.565
## 0.525 0.561 0.562
## 0.455 0.483 0.456
## 0.453 0.481 0.491
## 0.738 0.801 0.829
## 0.645 0.698 0.743
## 0.775 0.834 0.776
## 0.592 0.639 0.630
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.346 0.020 17.077 0.000 0.306
## .ssmk (.47.) 0.400 0.022 18.421 0.000 0.357
## .ssmc (.48.) 0.251 0.019 13.302 0.000 0.214
## .ssao (.49.) 0.325 0.024 13.771 0.000 0.279
## .ssai (.50.) 0.042 0.017 2.477 0.013 0.009
## .sssi (.51.) 0.081 0.018 4.473 0.000 0.046
## .ssei (.52.) 0.130 0.019 7.010 0.000 0.094
## .ssno 0.757 0.069 10.919 0.000 0.621
## .sscs (.54.) 0.351 0.022 15.919 0.000 0.307
## .ssgs (.55.) 0.338 0.020 16.904 0.000 0.299
## .sswk 0.208 0.024 8.701 0.000 0.161
## .sspc 0.026 0.025 1.032 0.302 -0.023
## math -0.500 0.065 -7.640 0.000 -0.629
## elctrnc 1.866 0.132 14.109 0.000 1.607
## speed -1.132 0.089 -12.707 0.000 -1.307
## g 0.247 0.039 6.251 0.000 0.169
## ci.upper Std.lv Std.all
## 0.385 0.346 0.365
## 0.442 0.400 0.413
## 0.288 0.251 0.267
## 0.371 0.325 0.320
## 0.075 0.042 0.040
## 0.117 0.081 0.083
## 0.166 0.130 0.121
## 0.893 0.757 0.712
## 0.394 0.351 0.351
## 0.378 0.338 0.353
## 0.255 0.208 0.217
## 0.076 0.026 0.026
## -0.372 -0.500 -0.500
## 2.125 0.830 0.830
## -0.958 -1.008 -1.008
## 0.324 0.217 0.217
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.224 0.013 17.204 0.000 0.198
## .ssmk 0.186 0.011 17.127 0.000 0.165
## .ssmc 0.253 0.014 18.738 0.000 0.227
## .ssao 0.453 0.027 16.903 0.000 0.400
## .ssai 0.532 0.025 21.187 0.000 0.483
## .sssi 0.259 0.020 13.164 0.000 0.220
## .ssei 0.336 0.016 21.483 0.000 0.305
## .ssno 0.154 0.046 3.373 0.001 0.064
## .sscs 0.480 0.025 18.927 0.000 0.431
## .ssgs 0.197 0.009 21.993 0.000 0.179
## .sswk 0.204 0.010 20.220 0.000 0.184
## .sspc 0.248 0.012 20.820 0.000 0.224
## electronic 5.060 0.638 7.926 0.000 3.808
## speed 1.263 0.119 10.624 0.000 1.030
## g 1.288 0.066 19.473 0.000 1.158
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.249 0.224 0.249
## 0.208 0.186 0.199
## 0.280 0.253 0.287
## 0.505 0.453 0.441
## 0.581 0.532 0.475
## 0.298 0.259 0.270
## 0.366 0.336 0.291
## 0.243 0.154 0.136
## 0.530 0.480 0.482
## 0.214 0.197 0.214
## 0.223 0.204 0.221
## 0.271 0.248 0.254
## 6.311 1.000 1.000
## 1.496 1.000 1.000
## 1.418 1.000 1.000
standardizedSolution(latent2) # get the correct SEs for standardized solution
## lhs op rhs group label est.std se z pvalue
## 1 math =~ ssar 1 .p1. 0.319 0.026 12.062 0.000
## 2 math =~ ssmk 1 .p2. 0.273 0.026 10.699 0.000
## 3 math =~ ssmc 1 .p3. 0.262 0.022 11.781 0.000
## 4 math =~ ssao 1 .p4. 0.448 0.031 14.419 0.000
## 5 electronic =~ ssai 1 .p5. 0.351 0.020 17.196 0.000
## 6 electronic =~ sssi 1 .p6. 0.402 0.023 17.205 0.000
## 7 electronic =~ ssmc 1 .p7. 0.167 0.012 13.914 0.000
## 8 electronic =~ ssei 1 .p8. 0.202 0.014 14.727 0.000
## 9 speed =~ ssno 1 .p9. 0.736 0.032 22.967 0.000
## 10 speed =~ sscs 1 .p10. 0.437 0.022 19.531 0.000
## 11 speed =~ ssmk 1 .p11. 0.220 0.014 15.994 0.000
## 12 g =~ ssgs 1 .p12. 0.874 0.007 131.126 0.000
## 13 g =~ ssar 1 .p13. 0.812 0.009 92.705 0.000
## 14 g =~ sswk 1 .p14. 0.868 0.007 123.029 0.000
## 15 g =~ sspc 1 .p15. 0.855 0.009 99.321 0.000
## 16 g =~ ssno 1 .p16. 0.557 0.017 32.350 0.000
## 17 g =~ sscs 1 .p17. 0.541 0.015 35.573 0.000
## 18 g =~ ssai 1 .p18. 0.564 0.016 35.718 0.000
## 19 g =~ sssi 1 .p19. 0.559 0.015 36.412 0.000
## 20 g =~ ssmk 1 .p20. 0.795 0.009 87.481 0.000
## 21 g =~ ssmc 1 .p21. 0.748 0.011 69.847 0.000
## 22 g =~ ssei 1 0.738 0.012 61.035 0.000
## 23 g =~ ssao 1 .p23. 0.617 0.013 47.196 0.000
## 24 math ~~ math 1 1.000 0.000 NA NA
## 25 ssar ~~ ssar 1 0.238 0.015 15.392 0.000
## 26 ssmk ~~ ssmk 1 0.244 0.013 18.905 0.000
## 27 ssmc ~~ ssmc 1 0.343 0.017 20.286 0.000
## 28 ssao ~~ ssao 1 0.419 0.030 14.170 0.000
## 29 ssai ~~ ssai 1 0.559 0.020 27.795 0.000
## 30 sssi ~~ sssi 1 0.527 0.022 23.895 0.000
## 31 ssei ~~ ssei 1 0.415 0.017 23.890 0.000
## 32 ssno ~~ ssno 1 0.148 0.041 3.594 0.000
## 33 sscs ~~ sscs 1 0.516 0.020 25.309 0.000
## 34 ssgs ~~ ssgs 1 0.236 0.012 20.283 0.000
## 35 sswk ~~ sswk 1 0.246 0.012 20.053 0.000
## 36 sspc ~~ sspc 1 0.269 0.015 18.269 0.000
## 37 electronic ~~ electronic 1 1.000 0.000 NA NA
## 38 speed ~~ speed 1 1.000 0.000 NA NA
## 39 g ~~ g 1 1.000 0.000 NA NA
## 40 math ~~ electronic 1 0.000 0.000 NA NA
## 41 math ~~ speed 1 0.000 0.000 NA NA
## 42 math ~~ g 1 0.000 0.000 NA NA
## 43 electronic ~~ speed 1 0.000 0.000 NA NA
## 44 electronic ~~ g 1 0.000 0.000 NA NA
## 45 speed ~~ g 1 0.000 0.000 NA NA
## 46 ssar ~1 1 .p46. 0.411 0.027 15.267 0.000
## 47 ssmk ~1 1 .p47. 0.450 0.027 16.827 0.000
## 48 ssmc ~1 1 .p48. 0.306 0.025 12.061 0.000
## 49 ssao ~1 1 .p49. 0.356 0.027 13.112 0.000
## 50 ssai ~1 1 .p50. 0.056 0.023 2.465 0.014
## 51 sssi ~1 1 .p51. 0.107 0.024 4.479 0.000
## 52 ssei ~1 1 .p52. 0.169 0.024 6.906 0.000
## 53 ssno ~1 1 0.263 0.026 10.100 0.000
## 54 sscs ~1 1 .p54. 0.384 0.025 15.112 0.000
## 55 ssgs ~1 1 .p55. 0.395 0.025 15.808 0.000
## 56 sswk ~1 1 0.450 0.027 16.547 0.000
## 57 sspc ~1 1 0.525 0.028 18.859 0.000
## 58 math ~1 1 0.000 0.000 NA NA
## 59 electronic ~1 1 0.000 0.000 NA NA
## 60 speed ~1 1 0.000 0.000 NA NA
## 61 g ~1 1 0.000 0.000 NA NA
## 62 math =~ ssar 2 .p1. 0.284 0.023 12.131 0.000
## 63 math =~ ssmk 2 .p2. 0.250 0.024 10.643 0.000
## 64 math =~ ssmc 2 .p3. 0.229 0.020 11.718 0.000
## 65 math =~ ssao 2 .p4. 0.403 0.028 14.386 0.000
## 66 electronic =~ ssai 2 .p5. 0.563 0.020 27.909 0.000
## 67 electronic =~ sssi 2 .p6. 0.700 0.016 43.178 0.000
## 68 electronic =~ ssmc 2 .p7. 0.329 0.016 20.215 0.000
## 69 electronic =~ ssei 2 .p8. 0.326 0.018 18.454 0.000
## 70 speed =~ ssno 2 .p9. 0.738 0.031 23.999 0.000
## 71 speed =~ sscs 2 .p10. 0.450 0.022 20.554 0.000
## 72 speed =~ ssmk 2 .p11. 0.227 0.014 15.796 0.000
## 73 g =~ ssgs 2 .p12. 0.887 0.006 142.492 0.000
## 74 g =~ ssar 2 .p13. 0.819 0.008 98.232 0.000
## 75 g =~ sswk 2 .p14. 0.882 0.006 139.487 0.000
## 76 g =~ sspc 2 .p15. 0.864 0.007 121.671 0.000
## 77 g =~ ssno 2 .p16. 0.565 0.017 34.129 0.000
## 78 g =~ sscs 2 .p17. 0.562 0.015 38.389 0.000
## 79 g =~ ssai 2 .p18. 0.456 0.016 28.766 0.000
## 80 g =~ sssi 2 .p19. 0.491 0.016 31.656 0.000
## 81 g =~ ssmk 2 .p20. 0.829 0.007 111.841 0.000
## 82 g =~ ssmc 2 .p21. 0.743 0.011 70.515 0.000
## 83 g =~ ssei 2 0.776 0.011 70.665 0.000
## 84 g =~ ssao 2 .p23. 0.630 0.013 48.902 0.000
## 85 math ~~ math 2 1.000 0.000 NA NA
## 86 ssar ~~ ssar 2 0.249 0.014 17.743 0.000
## 87 ssmk ~~ ssmk 2 0.199 0.011 17.391 0.000
## 88 ssmc ~~ ssmc 2 0.287 0.015 19.605 0.000
## 89 ssao ~~ ssao 2 0.441 0.025 17.963 0.000
## 90 ssai ~~ ssai 2 0.475 0.020 23.396 0.000
## ci.lower ci.upper
## 1 0.268 0.371
## 2 0.223 0.323
## 3 0.218 0.305
## 4 0.387 0.508
## 5 0.311 0.391
## 6 0.356 0.447
## 7 0.144 0.191
## 8 0.175 0.229
## 9 0.673 0.799
## 10 0.393 0.481
## 11 0.193 0.247
## 12 0.861 0.887
## 13 0.795 0.830
## 14 0.855 0.882
## 15 0.838 0.872
## 16 0.524 0.591
## 17 0.511 0.571
## 18 0.533 0.595
## 19 0.529 0.589
## 20 0.778 0.813
## 21 0.727 0.769
## 22 0.714 0.761
## 23 0.591 0.642
## 24 1.000 1.000
## 25 0.208 0.268
## 26 0.219 0.270
## 27 0.310 0.377
## 28 0.361 0.478
## 29 0.519 0.598
## 30 0.483 0.570
## 31 0.381 0.449
## 32 0.067 0.228
## 33 0.476 0.556
## 34 0.213 0.259
## 35 0.222 0.270
## 36 0.240 0.298
## 37 1.000 1.000
## 38 1.000 1.000
## 39 1.000 1.000
## 40 0.000 0.000
## 41 0.000 0.000
## 42 0.000 0.000
## 43 0.000 0.000
## 44 0.000 0.000
## 45 0.000 0.000
## 46 0.358 0.464
## 47 0.398 0.503
## 48 0.256 0.355
## 49 0.303 0.409
## 50 0.011 0.100
## 51 0.060 0.154
## 52 0.121 0.217
## 53 0.212 0.314
## 54 0.334 0.434
## 55 0.346 0.443
## 56 0.397 0.503
## 57 0.470 0.579
## 58 0.000 0.000
## 59 0.000 0.000
## 60 0.000 0.000
## 61 0.000 0.000
## 62 0.238 0.329
## 63 0.204 0.297
## 64 0.191 0.267
## 65 0.348 0.458
## 66 0.524 0.603
## 67 0.668 0.731
## 68 0.297 0.361
## 69 0.291 0.361
## 70 0.678 0.799
## 71 0.407 0.493
## 72 0.199 0.255
## 73 0.874 0.899
## 74 0.802 0.835
## 75 0.870 0.895
## 76 0.850 0.877
## 77 0.532 0.597
## 78 0.533 0.591
## 79 0.425 0.487
## 80 0.461 0.521
## 81 0.814 0.843
## 82 0.722 0.764
## 83 0.755 0.798
## 84 0.605 0.655
## 85 1.000 1.000
## 86 0.222 0.277
## 87 0.177 0.222
## 88 0.258 0.316
## 89 0.393 0.489
## 90 0.435 0.515
## [ reached 'max' / getOption("max.print") -- omitted 32 rows ]
tests<-lavTestLRT(configural, metric2, scalar2, latent2)
Td=tests[2:5,"Chisq diff"]
Td
## [1] 42.8916991 47.5201179 0.4049352 NA
dfd=tests[2:5,"Df diff"]
dfd
## [1] 18 5 1 NA
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
## Warning in sqrt((ld) * G/(N - G)): NaNs produced
RMSEAD
## [1] 0.02750069 0.06819692 NaN NA
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.01695667 0.03818834
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.05129138 0.08651128
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] NA 0.05292329
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.999 0.994 0.000 0.000 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.961 0.799 0.151 0.002
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.475 0.439 0.064 0.026 0.003 0.000
tests<-lavTestLRT(configural, metric2, scalar2, latent)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 42.89170 47.52012 282.53642
dfd=tests[2:4,"Df diff"]
dfd
## [1] 18 5 4
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.02750069 0.06819692 0.19514773
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.05129138 0.08651128
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.1761846 0.2147493
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.961 0.799 0.151 0.002
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
tests<-lavTestLRT(configural, metric2, scalar2, strict)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 42.89170 47.52012 154.11205
dfd=tests[2:4,"Df diff"]
dfd
## [1] 18 5 12
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.02750069 0.06819692 0.08047803
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.01695667 0.03818834
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.05129138 0.08651128
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.06940886 0.09204285
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.999 0.994 0.000 0.000 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.961 0.799 0.151 0.002
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 0.999 0.544 0.003
tests<-lavTestLRT(configural, metric2, scalar)
Td=tests[2:3,"Chisq diff"]
Td
## [1] 42.8917 632.7873
dfd=tests[2:3,"Df diff"]
dfd
## [1] 18 8
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.02750069 0.20666818
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.01695667 0.03818834
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.1931671 0.2204393
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.999 0.994 0.000 0.000 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
tests<-lavTestLRT(configural, metric)
Td=tests[2,"Chisq diff"]
Td
## [1] 105.224
dfd=tests[2,"Df diff"]
dfd
## [1] 19
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-1770+ 1889 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.04981841
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.04075397 0.05931140
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.507 0.039 0.000 0.000
bf.age<-'
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
g ~ agec
'
bf.ageq<-'
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
g ~ c(a,a)*agec
'
bf.age2<-'
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
g ~ agec+agec2
'
bf.age2q<-'
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
g ~ c(a,a)*agec+c(b,b)*agec2
'
sem.age<-sem(bf.age, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "sspc~1", "ssno~1", "sswk~1"))
fitMeasures(sem.age, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 2011.670 132.000 0.000 0.943 0.088 0.049 0.589
## aic bic
## 86495.004 86941.760
Mc(sem.age)
## [1] 0.7734255
summary(sem.age, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 85 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 103
## Number of equality constraints 31
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2011.670 1755.899
## Degrees of freedom 132 132
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.146
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 769.735 671.868
## 0 1241.935 1084.031
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.272 0.021 12.794 0.000 0.230
## ssmk (.p2.) 0.239 0.021 11.195 0.000 0.197
## ssmc (.p3.) 0.221 0.018 12.220 0.000 0.186
## ssao (.p4.) 0.414 0.027 15.124 0.000 0.360
## electronic =~
## ssai (.p5.) 0.261 0.016 16.362 0.000 0.230
## sssi (.p6.) 0.303 0.019 16.316 0.000 0.267
## ssmc (.p7.) 0.138 0.010 13.695 0.000 0.118
## ssei (.p8.) 0.153 0.010 14.725 0.000 0.132
## speed =~
## ssno (.p9.) 0.700 0.036 19.521 0.000 0.630
## sscs (.10.) 0.394 0.022 17.742 0.000 0.350
## ssmk (.11.) 0.192 0.012 16.112 0.000 0.169
## g =~
## ssgs (.12.) 0.690 0.015 45.749 0.000 0.660
## ssar (.13.) 0.627 0.016 38.783 0.000 0.595
## sswk (.14.) 0.688 0.016 43.467 0.000 0.657
## sspc (.15.) 0.689 0.016 43.741 0.000 0.658
## ssno (.16.) 0.488 0.016 29.630 0.000 0.456
## sscs (.17.) 0.457 0.015 30.574 0.000 0.428
## ssai (.18.) 0.396 0.014 28.765 0.000 0.369
## sssi (.19.) 0.392 0.014 28.248 0.000 0.365
## ssmk (.20.) 0.652 0.016 41.727 0.000 0.622
## ssmc (.21.) 0.565 0.015 37.432 0.000 0.536
## ssei 0.525 0.016 32.344 0.000 0.493
## ssao (.23.) 0.517 0.015 35.420 0.000 0.488
## ci.upper Std.lv Std.all
##
## 0.314 0.272 0.323
## 0.281 0.239 0.269
## 0.257 0.221 0.269
## 0.468 0.414 0.453
##
## 0.293 0.261 0.346
## 0.340 0.303 0.399
## 0.157 0.138 0.167
## 0.173 0.153 0.198
##
## 0.770 0.700 0.738
## 0.437 0.394 0.431
## 0.215 0.192 0.216
##
## 0.719 0.749 0.873
## 0.659 0.681 0.809
## 0.719 0.747 0.870
## 0.720 0.748 0.852
## 0.521 0.530 0.559
## 0.486 0.496 0.544
## 0.423 0.430 0.569
## 0.420 0.426 0.561
## 0.683 0.708 0.798
## 0.595 0.614 0.746
## 0.557 0.570 0.740
## 0.545 0.561 0.614
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.293 0.020 14.818 0.000 0.254
## ci.upper Std.lv Std.all
##
## 0.332 0.270 0.390
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.45.) 0.349 0.019 17.935 0.000 0.311
## .ssmk (.46.) 0.401 0.020 20.164 0.000 0.362
## .ssmc (.47.) 0.254 0.018 13.930 0.000 0.218
## .ssao (.48.) 0.328 0.023 14.212 0.000 0.283
## .ssai (.49.) 0.044 0.016 2.740 0.006 0.013
## .sssi (.50.) 0.082 0.018 4.653 0.000 0.047
## .ssei (.51.) 0.133 0.017 7.719 0.000 0.099
## .ssno 0.252 0.022 11.243 0.000 0.208
## .sscs (.53.) 0.354 0.021 16.984 0.000 0.313
## .ssgs (.54.) 0.341 0.019 18.043 0.000 0.304
## .sswk 0.390 0.020 19.540 0.000 0.351
## .sspc 0.464 0.021 22.495 0.000 0.423
## ci.upper Std.lv Std.all
## 0.387 0.349 0.415
## 0.440 0.401 0.452
## 0.290 0.254 0.309
## 0.373 0.328 0.359
## 0.076 0.044 0.059
## 0.116 0.082 0.107
## 0.167 0.133 0.173
## 0.296 0.252 0.266
## 0.395 0.354 0.388
## 0.378 0.341 0.398
## 0.429 0.390 0.454
## 0.504 0.464 0.528
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.170 0.011 15.109 0.000 0.148
## .ssmk 0.192 0.010 19.586 0.000 0.173
## .ssmc 0.232 0.012 18.893 0.000 0.208
## .ssao 0.347 0.026 13.532 0.000 0.297
## .ssai 0.318 0.015 21.162 0.000 0.288
## .sssi 0.304 0.016 19.397 0.000 0.273
## .ssei 0.245 0.011 22.467 0.000 0.223
## .ssno 0.128 0.038 3.406 0.001 0.054
## .sscs 0.432 0.020 21.171 0.000 0.392
## .ssgs 0.174 0.008 20.660 0.000 0.158
## .sswk 0.179 0.008 21.301 0.000 0.163
## .sspc 0.211 0.011 18.405 0.000 0.189
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.192 0.170 0.240
## 0.211 0.192 0.243
## 0.256 0.232 0.343
## 0.398 0.347 0.417
## 0.347 0.318 0.556
## 0.334 0.304 0.526
## 0.266 0.245 0.413
## 0.202 0.128 0.142
## 0.472 0.432 0.518
## 0.191 0.174 0.237
## 0.196 0.179 0.243
## 0.234 0.211 0.274
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.848 0.848
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.272 0.021 12.794 0.000 0.230
## ssmk (.p2.) 0.239 0.021 11.195 0.000 0.197
## ssmc (.p3.) 0.221 0.018 12.220 0.000 0.186
## ssao (.p4.) 0.414 0.027 15.124 0.000 0.360
## electronic =~
## ssai (.p5.) 0.261 0.016 16.362 0.000 0.230
## sssi (.p6.) 0.303 0.019 16.316 0.000 0.267
## ssmc (.p7.) 0.138 0.010 13.695 0.000 0.118
## ssei (.p8.) 0.153 0.010 14.725 0.000 0.132
## speed =~
## ssno (.p9.) 0.700 0.036 19.521 0.000 0.630
## sscs (.10.) 0.394 0.022 17.742 0.000 0.350
## ssmk (.11.) 0.192 0.012 16.112 0.000 0.169
## g =~
## ssgs (.12.) 0.690 0.015 45.749 0.000 0.660
## ssar (.13.) 0.627 0.016 38.783 0.000 0.595
## sswk (.14.) 0.688 0.016 43.467 0.000 0.657
## sspc (.15.) 0.689 0.016 43.741 0.000 0.658
## ssno (.16.) 0.488 0.016 29.630 0.000 0.456
## sscs (.17.) 0.457 0.015 30.574 0.000 0.428
## ssai (.18.) 0.396 0.014 28.765 0.000 0.369
## sssi (.19.) 0.392 0.014 28.248 0.000 0.365
## ssmk (.20.) 0.652 0.016 41.727 0.000 0.622
## ssmc (.21.) 0.565 0.015 37.432 0.000 0.536
## ssei 0.679 0.020 34.545 0.000 0.641
## ssao (.23.) 0.517 0.015 35.420 0.000 0.488
## ci.upper Std.lv Std.all
##
## 0.314 0.272 0.287
## 0.281 0.239 0.247
## 0.257 0.221 0.236
## 0.468 0.414 0.408
##
## 0.293 0.588 0.556
## 0.340 0.682 0.697
## 0.157 0.309 0.329
## 0.173 0.343 0.320
##
## 0.770 0.789 0.742
## 0.437 0.444 0.445
## 0.215 0.216 0.223
##
## 0.719 0.850 0.886
## 0.659 0.772 0.816
## 0.719 0.848 0.884
## 0.720 0.849 0.861
## 0.521 0.602 0.566
## 0.486 0.563 0.564
## 0.423 0.488 0.462
## 0.420 0.484 0.494
## 0.683 0.804 0.831
## 0.595 0.696 0.741
## 0.718 0.837 0.779
## 0.545 0.637 0.628
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.320 0.021 14.936 0.000 0.278
## ci.upper Std.lv Std.all
##
## 0.362 0.260 0.375
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.45.) 0.349 0.019 17.935 0.000 0.311
## .ssmk (.46.) 0.401 0.020 20.164 0.000 0.362
## .ssmc (.47.) 0.254 0.018 13.930 0.000 0.218
## .ssao (.48.) 0.328 0.023 14.212 0.000 0.283
## .ssai (.49.) 0.044 0.016 2.740 0.006 0.013
## .sssi (.50.) 0.082 0.018 4.653 0.000 0.047
## .ssei (.51.) 0.133 0.017 7.719 0.000 0.099
## .ssno 0.777 0.071 10.898 0.000 0.638
## .sscs (.53.) 0.354 0.021 16.984 0.000 0.313
## .ssgs (.54.) 0.341 0.019 18.043 0.000 0.304
## .sswk 0.210 0.023 9.133 0.000 0.165
## .sspc 0.029 0.024 1.185 0.236 -0.019
## math -0.499 0.064 -7.799 0.000 -0.625
## elctrnc 1.881 0.134 14.010 0.000 1.618
## speed -1.158 0.092 -12.620 0.000 -1.337
## .g 0.289 0.041 7.120 0.000 0.209
## ci.upper Std.lv Std.all
## 0.387 0.349 0.369
## 0.440 0.401 0.415
## 0.290 0.254 0.270
## 0.373 0.328 0.324
## 0.076 0.044 0.042
## 0.116 0.082 0.083
## 0.167 0.133 0.124
## 0.917 0.777 0.731
## 0.395 0.354 0.354
## 0.378 0.341 0.355
## 0.255 0.210 0.219
## 0.076 0.029 0.029
## -0.374 -0.499 -0.499
## 2.145 0.837 0.837
## -0.978 -1.027 -1.027
## 0.368 0.234 0.234
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.225 0.013 17.395 0.000 0.200
## .ssmk 0.186 0.011 17.646 0.000 0.166
## .ssmc 0.253 0.013 18.709 0.000 0.226
## .ssao 0.451 0.026 17.223 0.000 0.400
## .ssai 0.534 0.025 21.338 0.000 0.485
## .sssi 0.258 0.020 13.127 0.000 0.220
## .ssei 0.335 0.016 21.584 0.000 0.305
## .ssno 0.146 0.047 3.113 0.002 0.054
## .sscs 0.482 0.025 18.994 0.000 0.432
## .ssgs 0.198 0.009 22.188 0.000 0.180
## .sswk 0.202 0.010 20.329 0.000 0.183
## .sspc 0.253 0.012 20.959 0.000 0.229
## electronic 5.055 0.644 7.845 0.000 3.792
## speed 1.270 0.121 10.538 0.000 1.034
## .g 1.305 0.073 17.945 0.000 1.163
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.251 0.225 0.251
## 0.207 0.186 0.199
## 0.279 0.253 0.286
## 0.502 0.451 0.439
## 0.583 0.534 0.478
## 0.297 0.258 0.270
## 0.365 0.335 0.291
## 0.238 0.146 0.129
## 0.532 0.482 0.484
## 0.215 0.198 0.215
## 0.221 0.202 0.219
## 0.276 0.253 0.260
## 6.318 1.000 1.000
## 1.507 1.000 1.000
## 1.448 0.860 0.860
sem.ageq<-sem(bf.ageq, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "sspc~1", "ssno~1", "sswk~1"))
fitMeasures(sem.ageq, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 2012.824 133.000 0.000 0.943 0.088 0.050 0.589
## aic bic
## 86494.159 86934.710
Mc(sem.ageq)
## [1] 0.7734092
summary(sem.ageq, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 85 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 103
## Number of equality constraints 32
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2012.824 1757.114
## Degrees of freedom 133 133
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.146
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 770.167 672.324
## 0 1242.657 1084.789
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.272 0.021 12.796 0.000 0.230
## ssmk (.p2.) 0.239 0.021 11.196 0.000 0.197
## ssmc (.p3.) 0.221 0.018 12.228 0.000 0.186
## ssao (.p4.) 0.414 0.027 15.130 0.000 0.360
## electronic =~
## ssai (.p5.) 0.261 0.016 16.361 0.000 0.230
## sssi (.p6.) 0.303 0.019 16.310 0.000 0.267
## ssmc (.p7.) 0.137 0.010 13.695 0.000 0.118
## ssei (.p8.) 0.153 0.010 14.729 0.000 0.132
## speed =~
## ssno (.p9.) 0.700 0.036 19.509 0.000 0.630
## sscs (.10.) 0.394 0.022 17.743 0.000 0.350
## ssmk (.11.) 0.192 0.012 16.114 0.000 0.168
## g =~
## ssgs (.12.) 0.690 0.015 45.687 0.000 0.660
## ssar (.13.) 0.627 0.016 38.751 0.000 0.595
## sswk (.14.) 0.688 0.016 43.428 0.000 0.657
## sspc (.15.) 0.689 0.016 43.712 0.000 0.658
## ssno (.16.) 0.488 0.016 29.616 0.000 0.456
## sscs (.17.) 0.457 0.015 30.555 0.000 0.428
## ssai (.18.) 0.396 0.014 28.762 0.000 0.369
## sssi (.19.) 0.392 0.014 28.237 0.000 0.365
## ssmk (.20.) 0.652 0.016 41.680 0.000 0.622
## ssmc (.21.) 0.565 0.015 37.397 0.000 0.536
## ssei 0.525 0.016 32.341 0.000 0.493
## ssao (.23.) 0.517 0.015 35.383 0.000 0.488
## ci.upper Std.lv Std.all
##
## 0.314 0.272 0.322
## 0.281 0.239 0.268
## 0.257 0.221 0.268
## 0.468 0.414 0.452
##
## 0.293 0.261 0.345
## 0.339 0.303 0.398
## 0.157 0.137 0.167
## 0.173 0.153 0.198
##
## 0.770 0.700 0.737
## 0.437 0.394 0.430
## 0.215 0.192 0.215
##
## 0.720 0.754 0.875
## 0.659 0.685 0.811
## 0.720 0.752 0.872
## 0.720 0.753 0.853
## 0.521 0.534 0.562
## 0.487 0.500 0.546
## 0.423 0.433 0.571
## 0.420 0.429 0.563
## 0.683 0.713 0.800
## 0.595 0.618 0.748
## 0.557 0.574 0.742
## 0.545 0.565 0.617
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.305 0.015 19.811 0.000 0.275
## ci.upper Std.lv Std.all
##
## 0.335 0.279 0.403
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.45.) 0.349 0.019 17.937 0.000 0.311
## .ssmk (.46.) 0.402 0.020 20.209 0.000 0.363
## .ssmc (.47.) 0.254 0.018 13.932 0.000 0.218
## .ssao (.48.) 0.328 0.023 14.212 0.000 0.283
## .ssai (.49.) 0.044 0.016 2.749 0.006 0.013
## .sssi (.50.) 0.082 0.018 4.664 0.000 0.047
## .ssei (.51.) 0.133 0.017 7.733 0.000 0.100
## .ssno 0.252 0.022 11.259 0.000 0.208
## .sscs (.53.) 0.354 0.021 17.017 0.000 0.313
## .ssgs (.54.) 0.341 0.019 18.057 0.000 0.304
## .sswk 0.390 0.020 19.563 0.000 0.351
## .sspc 0.464 0.021 22.482 0.000 0.423
## ci.upper Std.lv Std.all
## 0.387 0.349 0.413
## 0.440 0.402 0.451
## 0.290 0.254 0.308
## 0.373 0.328 0.359
## 0.076 0.044 0.059
## 0.116 0.082 0.107
## 0.167 0.133 0.173
## 0.296 0.252 0.265
## 0.395 0.354 0.387
## 0.378 0.341 0.396
## 0.429 0.390 0.452
## 0.504 0.464 0.526
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.170 0.011 15.117 0.000 0.148
## .ssmk 0.192 0.010 19.593 0.000 0.172
## .ssmc 0.232 0.012 18.895 0.000 0.208
## .ssao 0.347 0.026 13.532 0.000 0.297
## .ssai 0.318 0.015 21.161 0.000 0.288
## .sssi 0.304 0.016 19.404 0.000 0.273
## .ssei 0.245 0.011 22.468 0.000 0.223
## .ssno 0.128 0.038 3.411 0.001 0.055
## .sscs 0.432 0.020 21.173 0.000 0.392
## .ssgs 0.175 0.008 20.677 0.000 0.158
## .sswk 0.179 0.008 21.301 0.000 0.162
## .sspc 0.211 0.011 18.402 0.000 0.189
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.192 0.170 0.238
## 0.211 0.192 0.241
## 0.256 0.232 0.340
## 0.398 0.347 0.415
## 0.347 0.318 0.554
## 0.334 0.304 0.524
## 0.266 0.245 0.410
## 0.202 0.128 0.142
## 0.472 0.432 0.516
## 0.191 0.175 0.235
## 0.195 0.179 0.240
## 0.234 0.211 0.272
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.837 0.837
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.272 0.021 12.796 0.000 0.230
## ssmk (.p2.) 0.239 0.021 11.196 0.000 0.197
## ssmc (.p3.) 0.221 0.018 12.228 0.000 0.186
## ssao (.p4.) 0.414 0.027 15.130 0.000 0.360
## electronic =~
## ssai (.p5.) 0.261 0.016 16.361 0.000 0.230
## sssi (.p6.) 0.303 0.019 16.310 0.000 0.267
## ssmc (.p7.) 0.137 0.010 13.695 0.000 0.118
## ssei (.p8.) 0.153 0.010 14.729 0.000 0.132
## speed =~
## ssno (.p9.) 0.700 0.036 19.509 0.000 0.630
## sscs (.10.) 0.394 0.022 17.743 0.000 0.350
## ssmk (.11.) 0.192 0.012 16.114 0.000 0.168
## g =~
## ssgs (.12.) 0.690 0.015 45.687 0.000 0.660
## ssar (.13.) 0.627 0.016 38.751 0.000 0.595
## sswk (.14.) 0.688 0.016 43.428 0.000 0.657
## sspc (.15.) 0.689 0.016 43.712 0.000 0.658
## ssno (.16.) 0.488 0.016 29.616 0.000 0.456
## sscs (.17.) 0.457 0.015 30.555 0.000 0.428
## ssai (.18.) 0.396 0.014 28.762 0.000 0.369
## sssi (.19.) 0.392 0.014 28.237 0.000 0.365
## ssmk (.20.) 0.652 0.016 41.680 0.000 0.622
## ssmc (.21.) 0.565 0.015 37.397 0.000 0.536
## ssei 0.679 0.020 34.491 0.000 0.641
## ssao (.23.) 0.517 0.015 35.383 0.000 0.488
## ci.upper Std.lv Std.all
##
## 0.314 0.272 0.289
## 0.281 0.239 0.248
## 0.257 0.221 0.237
## 0.468 0.414 0.410
##
## 0.293 0.588 0.557
## 0.339 0.682 0.698
## 0.157 0.309 0.331
## 0.173 0.344 0.321
##
## 0.770 0.789 0.744
## 0.437 0.444 0.446
## 0.215 0.216 0.224
##
## 0.720 0.845 0.885
## 0.659 0.767 0.814
## 0.720 0.843 0.882
## 0.720 0.843 0.859
## 0.521 0.598 0.563
## 0.487 0.560 0.562
## 0.423 0.485 0.459
## 0.420 0.480 0.492
## 0.683 0.799 0.829
## 0.595 0.692 0.739
## 0.718 0.832 0.777
## 0.545 0.632 0.626
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.305 0.015 19.811 0.000 0.275
## ci.upper Std.lv Std.all
##
## 0.335 0.249 0.359
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.45.) 0.349 0.019 17.937 0.000 0.311
## .ssmk (.46.) 0.402 0.020 20.209 0.000 0.363
## .ssmc (.47.) 0.254 0.018 13.932 0.000 0.218
## .ssao (.48.) 0.328 0.023 14.212 0.000 0.283
## .ssai (.49.) 0.044 0.016 2.749 0.006 0.013
## .sssi (.50.) 0.082 0.018 4.664 0.000 0.047
## .ssei (.51.) 0.133 0.017 7.733 0.000 0.100
## .ssno 0.778 0.071 10.903 0.000 0.638
## .sscs (.53.) 0.354 0.021 17.017 0.000 0.313
## .ssgs (.54.) 0.341 0.019 18.057 0.000 0.304
## .sswk 0.210 0.023 9.149 0.000 0.165
## .sspc 0.029 0.024 1.196 0.232 -0.019
## math -0.499 0.064 -7.799 0.000 -0.624
## elctrnc 1.882 0.134 14.012 0.000 1.619
## speed -1.158 0.092 -12.619 0.000 -1.338
## .g 0.287 0.040 7.113 0.000 0.208
## ci.upper Std.lv Std.all
## 0.387 0.349 0.370
## 0.440 0.402 0.417
## 0.290 0.254 0.272
## 0.373 0.328 0.325
## 0.076 0.044 0.042
## 0.116 0.082 0.084
## 0.167 0.133 0.125
## 0.917 0.778 0.733
## 0.395 0.354 0.355
## 0.378 0.341 0.358
## 0.255 0.210 0.220
## 0.077 0.029 0.030
## -0.374 -0.499 -0.499
## 2.146 0.837 0.837
## -0.978 -1.027 -1.027
## 0.367 0.235 0.235
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.225 0.013 17.392 0.000 0.200
## .ssmk 0.186 0.011 17.659 0.000 0.166
## .ssmc 0.253 0.013 18.715 0.000 0.226
## .ssao 0.451 0.026 17.223 0.000 0.399
## .ssai 0.534 0.025 21.332 0.000 0.485
## .sssi 0.258 0.020 13.129 0.000 0.220
## .ssei 0.335 0.016 21.578 0.000 0.305
## .ssno 0.146 0.047 3.111 0.002 0.054
## .sscs 0.482 0.025 18.991 0.000 0.433
## .ssgs 0.198 0.009 22.180 0.000 0.180
## .sswk 0.202 0.010 20.322 0.000 0.183
## .sspc 0.252 0.012 20.933 0.000 0.229
## electronic 5.063 0.645 7.844 0.000 3.798
## speed 1.271 0.121 10.538 0.000 1.035
## .g 1.305 0.073 17.940 0.000 1.162
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.251 0.225 0.254
## 0.207 0.186 0.201
## 0.279 0.253 0.288
## 0.502 0.451 0.441
## 0.583 0.534 0.479
## 0.297 0.258 0.271
## 0.365 0.335 0.293
## 0.238 0.146 0.130
## 0.532 0.482 0.486
## 0.215 0.198 0.217
## 0.221 0.202 0.221
## 0.276 0.252 0.262
## 6.328 1.000 1.000
## 1.508 1.000 1.000
## 1.448 0.871 0.871
sem.age2<-sem(bf.age2, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "sspc~1", "ssno~1", "sswk~1"))
fitMeasures(sem.age2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 2106.007 154.000 0.000 0.941 0.083 0.046 0.616
## aic bic
## 86478.506 86937.672
Mc(sem.age2)
## [1] 0.7658159
summary(sem.age2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 90 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 105
## Number of equality constraints 31
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2106.007 1844.847
## Degrees of freedom 154 154
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.142
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 826.516 724.022
## 0 1279.491 1120.825
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.271 0.021 12.815 0.000 0.230
## ssmk (.p2.) 0.238 0.021 11.175 0.000 0.196
## ssmc (.p3.) 0.222 0.018 12.328 0.000 0.187
## ssao (.p4.) 0.415 0.027 15.301 0.000 0.361
## electronic =~
## ssai (.p5.) 0.261 0.016 16.340 0.000 0.230
## sssi (.p6.) 0.302 0.019 16.323 0.000 0.266
## ssmc (.p7.) 0.138 0.010 13.701 0.000 0.118
## ssei (.p8.) 0.155 0.010 14.839 0.000 0.135
## speed =~
## ssno (.p9.) 0.700 0.036 19.484 0.000 0.629
## sscs (.10.) 0.393 0.022 17.718 0.000 0.350
## ssmk (.11.) 0.192 0.012 16.099 0.000 0.168
## g =~
## ssgs (.12.) 0.687 0.015 45.417 0.000 0.657
## ssar (.13.) 0.625 0.016 38.645 0.000 0.593
## sswk (.14.) 0.686 0.016 43.190 0.000 0.655
## sspc (.15.) 0.686 0.016 43.555 0.000 0.655
## ssno (.16.) 0.487 0.016 29.687 0.000 0.455
## sscs (.17.) 0.456 0.015 30.514 0.000 0.426
## ssai (.18.) 0.394 0.014 28.630 0.000 0.367
## sssi (.19.) 0.391 0.014 28.247 0.000 0.364
## ssmk (.20.) 0.650 0.016 41.708 0.000 0.620
## ssmc (.21.) 0.563 0.015 37.330 0.000 0.533
## ssei 0.522 0.016 32.173 0.000 0.490
## ssao (.23.) 0.515 0.015 35.286 0.000 0.486
## ci.upper Std.lv Std.all
##
## 0.313 0.271 0.322
## 0.279 0.238 0.268
## 0.257 0.222 0.270
## 0.468 0.415 0.454
##
## 0.292 0.261 0.345
## 0.338 0.302 0.397
## 0.157 0.138 0.167
## 0.176 0.155 0.202
##
## 0.770 0.700 0.738
## 0.437 0.393 0.431
## 0.215 0.192 0.216
##
## 0.717 0.750 0.873
## 0.656 0.681 0.810
## 0.717 0.748 0.870
## 0.717 0.749 0.852
## 0.519 0.531 0.560
## 0.485 0.497 0.544
## 0.421 0.430 0.569
## 0.418 0.427 0.561
## 0.681 0.710 0.799
## 0.592 0.614 0.746
## 0.553 0.569 0.740
## 0.543 0.562 0.615
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.289 0.020 14.650 0.000 0.251
## agec2 -0.052 0.014 -3.743 0.000 -0.079
## ci.upper Std.lv Std.all
##
## 0.328 0.265 0.383
## -0.025 -0.048 -0.090
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.48.) 0.415 0.025 16.354 0.000 0.365
## .ssmk (.49.) 0.470 0.027 17.704 0.000 0.418
## .ssmc (.50.) 0.313 0.023 13.340 0.000 0.267
## .ssao (.51.) 0.384 0.027 14.002 0.000 0.330
## .ssai (.52.) 0.085 0.020 4.370 0.000 0.047
## .sssi (.53.) 0.122 0.020 5.992 0.000 0.082
## .ssei (.54.) 0.192 0.023 8.384 0.000 0.147
## .ssno 0.304 0.026 11.690 0.000 0.253
## .sscs (.56.) 0.402 0.024 16.590 0.000 0.355
## .ssgs (.57.) 0.413 0.026 15.675 0.000 0.361
## .sswk 0.463 0.028 16.811 0.000 0.409
## .sspc 0.537 0.028 19.200 0.000 0.482
## ci.upper Std.lv Std.all
## 0.465 0.415 0.493
## 0.522 0.470 0.530
## 0.359 0.313 0.381
## 0.438 0.384 0.420
## 0.124 0.085 0.113
## 0.162 0.122 0.161
## 0.237 0.192 0.249
## 0.354 0.304 0.320
## 0.450 0.402 0.441
## 0.464 0.413 0.481
## 0.517 0.463 0.538
## 0.591 0.537 0.611
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.170 0.011 15.199 0.000 0.148
## .ssmk 0.192 0.010 19.678 0.000 0.173
## .ssmc 0.232 0.012 18.901 0.000 0.208
## .ssao 0.347 0.026 13.566 0.000 0.297
## .ssai 0.318 0.015 21.165 0.000 0.289
## .sssi 0.304 0.016 19.467 0.000 0.273
## .ssei 0.244 0.011 22.434 0.000 0.223
## .ssno 0.128 0.038 3.399 0.001 0.054
## .sscs 0.432 0.020 21.172 0.000 0.392
## .ssgs 0.175 0.008 20.667 0.000 0.158
## .sswk 0.179 0.008 21.321 0.000 0.163
## .sspc 0.211 0.011 18.482 0.000 0.189
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.192 0.170 0.241
## 0.211 0.192 0.243
## 0.256 0.232 0.342
## 0.397 0.347 0.416
## 0.348 0.318 0.557
## 0.335 0.304 0.527
## 0.266 0.244 0.412
## 0.202 0.128 0.142
## 0.472 0.432 0.518
## 0.191 0.175 0.237
## 0.196 0.179 0.243
## 0.234 0.211 0.274
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.840 0.840
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.271 0.021 12.815 0.000 0.230
## ssmk (.p2.) 0.238 0.021 11.175 0.000 0.196
## ssmc (.p3.) 0.222 0.018 12.328 0.000 0.187
## ssao (.p4.) 0.415 0.027 15.301 0.000 0.361
## electronic =~
## ssai (.p5.) 0.261 0.016 16.340 0.000 0.230
## sssi (.p6.) 0.302 0.019 16.323 0.000 0.266
## ssmc (.p7.) 0.138 0.010 13.701 0.000 0.118
## ssei (.p8.) 0.155 0.010 14.839 0.000 0.135
## speed =~
## ssno (.p9.) 0.700 0.036 19.484 0.000 0.629
## sscs (.10.) 0.393 0.022 17.718 0.000 0.350
## ssmk (.11.) 0.192 0.012 16.099 0.000 0.168
## g =~
## ssgs (.12.) 0.687 0.015 45.417 0.000 0.657
## ssar (.13.) 0.625 0.016 38.645 0.000 0.593
## sswk (.14.) 0.686 0.016 43.190 0.000 0.655
## sspc (.15.) 0.686 0.016 43.555 0.000 0.655
## ssno (.16.) 0.487 0.016 29.687 0.000 0.455
## sscs (.17.) 0.456 0.015 30.514 0.000 0.426
## ssai (.18.) 0.394 0.014 28.630 0.000 0.367
## sssi (.19.) 0.391 0.014 28.247 0.000 0.364
## ssmk (.20.) 0.650 0.016 41.708 0.000 0.620
## ssmc (.21.) 0.563 0.015 37.330 0.000 0.533
## ssei 0.678 0.020 34.493 0.000 0.639
## ssao (.23.) 0.515 0.015 35.286 0.000 0.486
## ci.upper Std.lv Std.all
##
## 0.313 0.271 0.286
## 0.279 0.238 0.246
## 0.257 0.222 0.236
## 0.468 0.415 0.409
##
## 0.292 0.588 0.556
## 0.338 0.680 0.695
## 0.157 0.310 0.330
## 0.176 0.349 0.324
##
## 0.770 0.789 0.742
## 0.437 0.444 0.444
## 0.215 0.216 0.223
##
## 0.717 0.850 0.886
## 0.656 0.772 0.816
## 0.717 0.848 0.883
## 0.717 0.849 0.860
## 0.519 0.602 0.566
## 0.485 0.563 0.564
## 0.421 0.487 0.461
## 0.418 0.483 0.494
## 0.681 0.804 0.831
## 0.592 0.696 0.741
## 0.716 0.838 0.779
## 0.543 0.637 0.628
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.318 0.022 14.564 0.000 0.275
## agec2 -0.031 0.016 -1.954 0.051 -0.061
## ci.upper Std.lv Std.all
##
## 0.360 0.257 0.370
## 0.000 -0.025 -0.046
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.48.) 0.415 0.025 16.354 0.000 0.365
## .ssmk (.49.) 0.470 0.027 17.704 0.000 0.418
## .ssmc (.50.) 0.313 0.023 13.340 0.000 0.267
## .ssao (.51.) 0.384 0.027 14.002 0.000 0.330
## .ssai (.52.) 0.085 0.020 4.370 0.000 0.047
## .sssi (.53.) 0.122 0.020 5.992 0.000 0.082
## .ssei (.54.) 0.192 0.023 8.384 0.000 0.147
## .ssno 0.832 0.073 11.342 0.000 0.688
## .sscs (.56.) 0.402 0.024 16.590 0.000 0.355
## .ssgs (.57.) 0.413 0.026 15.675 0.000 0.361
## .sswk 0.280 0.029 9.648 0.000 0.223
## .sspc 0.099 0.030 3.297 0.001 0.040
## math -0.510 0.064 -7.972 0.000 -0.635
## elctrnc 1.887 0.135 14.000 0.000 1.623
## speed -1.165 0.092 -12.623 0.000 -1.346
## .g 0.252 0.057 4.416 0.000 0.140
## ci.upper Std.lv Std.all
## 0.465 0.415 0.439
## 0.522 0.470 0.486
## 0.359 0.313 0.334
## 0.438 0.384 0.379
## 0.124 0.085 0.081
## 0.162 0.122 0.125
## 0.237 0.192 0.178
## 0.976 0.832 0.783
## 0.450 0.402 0.403
## 0.464 0.413 0.430
## 0.337 0.280 0.292
## 0.157 0.099 0.100
## -0.384 -0.510 -0.510
## 2.151 0.839 0.839
## -0.984 -1.034 -1.034
## 0.363 0.203 0.203
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.225 0.013 17.464 0.000 0.200
## .ssmk 0.186 0.011 17.749 0.000 0.166
## .ssmc 0.252 0.013 18.704 0.000 0.226
## .ssao 0.450 0.026 17.277 0.000 0.399
## .ssai 0.534 0.025 21.387 0.000 0.485
## .sssi 0.261 0.020 13.309 0.000 0.222
## .ssei 0.334 0.016 21.461 0.000 0.303
## .ssno 0.146 0.047 3.100 0.002 0.054
## .sscs 0.482 0.025 18.998 0.000 0.433
## .ssgs 0.198 0.009 22.213 0.000 0.180
## .sswk 0.202 0.010 20.337 0.000 0.183
## .sspc 0.253 0.012 20.979 0.000 0.229
## electronic 5.062 0.645 7.848 0.000 3.798
## speed 1.271 0.121 10.528 0.000 1.034
## .g 1.311 0.073 17.884 0.000 1.168
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.251 0.225 0.252
## 0.207 0.186 0.199
## 0.279 0.252 0.286
## 0.501 0.450 0.438
## 0.583 0.534 0.478
## 0.299 0.261 0.273
## 0.364 0.334 0.288
## 0.238 0.146 0.129
## 0.532 0.482 0.484
## 0.215 0.198 0.215
## 0.222 0.202 0.219
## 0.276 0.253 0.260
## 6.327 1.000 1.000
## 1.507 1.000 1.000
## 1.455 0.858 0.858
sem.age2q<-sem(bf.age2q, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "sspc~1", "ssno~1", "sswk~1"))
fitMeasures(sem.age2q, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr ecvi
## 2108.224 156.000 0.000 0.941 0.083 0.048 0.616
## aic bic
## 86476.723 86923.479
Mc(sem.age2q)
## [1] 0.7657932
summary(sem.age2q, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 88 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 105
## Number of equality constraints 33
##
## Number of observations per group:
## 1 1770
## 0 1889
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2108.224 1847.113
## Degrees of freedom 156 156
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.141
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 827.097 724.658
## 0 1281.127 1122.455
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.271 0.021 12.803 0.000 0.230
## ssmk (.p2.) 0.238 0.021 11.169 0.000 0.196
## ssmc (.p3.) 0.222 0.018 12.320 0.000 0.187
## ssao (.p4.) 0.415 0.027 15.276 0.000 0.361
## electronic =~
## ssai (.p5.) 0.261 0.016 16.343 0.000 0.230
## sssi (.p6.) 0.302 0.019 16.315 0.000 0.266
## ssmc (.p7.) 0.138 0.010 13.700 0.000 0.118
## ssei (.p8.) 0.155 0.010 14.822 0.000 0.134
## speed =~
## ssno (.p9.) 0.700 0.036 19.476 0.000 0.629
## sscs (.10.) 0.393 0.022 17.721 0.000 0.350
## ssmk (.11.) 0.192 0.012 16.101 0.000 0.168
## g =~
## ssgs (.12.) 0.687 0.015 45.373 0.000 0.657
## ssar (.13.) 0.625 0.016 38.629 0.000 0.593
## sswk (.14.) 0.686 0.016 43.168 0.000 0.655
## sspc (.15.) 0.686 0.016 43.542 0.000 0.655
## ssno (.16.) 0.487 0.016 29.677 0.000 0.455
## sscs (.17.) 0.456 0.015 30.500 0.000 0.426
## ssai (.18.) 0.394 0.014 28.637 0.000 0.367
## sssi (.19.) 0.391 0.014 28.244 0.000 0.364
## ssmk (.20.) 0.650 0.016 41.673 0.000 0.620
## ssmc (.21.) 0.563 0.015 37.310 0.000 0.533
## ssei 0.522 0.016 32.182 0.000 0.490
## ssao (.23.) 0.515 0.015 35.260 0.000 0.486
## ci.upper Std.lv Std.all
##
## 0.313 0.271 0.321
## 0.280 0.238 0.267
## 0.257 0.222 0.269
## 0.468 0.415 0.453
##
## 0.292 0.261 0.345
## 0.338 0.302 0.397
## 0.157 0.138 0.167
## 0.175 0.155 0.201
##
## 0.770 0.700 0.737
## 0.437 0.393 0.430
## 0.215 0.192 0.215
##
## 0.717 0.753 0.874
## 0.656 0.685 0.811
## 0.717 0.752 0.871
## 0.717 0.752 0.853
## 0.519 0.534 0.562
## 0.485 0.499 0.546
## 0.421 0.432 0.571
## 0.418 0.429 0.563
## 0.681 0.713 0.801
## 0.593 0.617 0.748
## 0.554 0.572 0.741
## 0.544 0.564 0.617
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.302 0.015 19.509 0.000 0.271
## agec2 (b) -0.043 0.010 -4.112 0.000 -0.063
## ci.upper Std.lv Std.all
##
## 0.332 0.275 0.398
## -0.022 -0.039 -0.074
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.48.) 0.404 0.023 17.638 0.000 0.359
## .ssmk (.49.) 0.458 0.024 19.285 0.000 0.412
## .ssmc (.50.) 0.303 0.021 14.186 0.000 0.261
## .ssao (.51.) 0.374 0.026 14.545 0.000 0.324
## .ssai (.52.) 0.078 0.018 4.322 0.000 0.043
## .sssi (.53.) 0.115 0.019 6.010 0.000 0.078
## .ssei (.54.) 0.182 0.021 8.816 0.000 0.141
## .ssno 0.295 0.024 12.034 0.000 0.247
## .sscs (.56.) 0.394 0.023 17.268 0.000 0.349
## .ssgs (.57.) 0.400 0.023 17.160 0.000 0.355
## .sswk 0.450 0.024 18.376 0.000 0.402
## .sspc 0.524 0.025 20.951 0.000 0.475
## ci.upper Std.lv Std.all
## 0.448 0.404 0.478
## 0.505 0.458 0.515
## 0.345 0.303 0.367
## 0.425 0.374 0.409
## 0.114 0.078 0.103
## 0.153 0.115 0.152
## 0.222 0.182 0.236
## 0.343 0.295 0.310
## 0.439 0.394 0.431
## 0.446 0.400 0.465
## 0.498 0.450 0.522
## 0.573 0.524 0.594
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.170 0.011 15.193 0.000 0.148
## .ssmk 0.192 0.010 19.675 0.000 0.173
## .ssmc 0.232 0.012 18.900 0.000 0.208
## .ssao 0.347 0.026 13.554 0.000 0.297
## .ssai 0.318 0.015 21.166 0.000 0.289
## .sssi 0.304 0.016 19.464 0.000 0.273
## .ssei 0.244 0.011 22.440 0.000 0.223
## .ssno 0.128 0.038 3.404 0.001 0.054
## .sscs 0.432 0.020 21.172 0.000 0.392
## .ssgs 0.175 0.008 20.683 0.000 0.158
## .sswk 0.179 0.008 21.318 0.000 0.163
## .sspc 0.212 0.011 18.467 0.000 0.189
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.192 0.170 0.239
## 0.211 0.192 0.242
## 0.256 0.232 0.340
## 0.397 0.347 0.414
## 0.348 0.318 0.555
## 0.335 0.304 0.525
## 0.266 0.244 0.410
## 0.202 0.128 0.142
## 0.472 0.432 0.517
## 0.191 0.175 0.235
## 0.196 0.179 0.241
## 0.234 0.212 0.272
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.832 0.832
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.271 0.021 12.803 0.000 0.230
## ssmk (.p2.) 0.238 0.021 11.169 0.000 0.196
## ssmc (.p3.) 0.222 0.018 12.320 0.000 0.187
## ssao (.p4.) 0.415 0.027 15.276 0.000 0.361
## electronic =~
## ssai (.p5.) 0.261 0.016 16.343 0.000 0.230
## sssi (.p6.) 0.302 0.019 16.315 0.000 0.266
## ssmc (.p7.) 0.138 0.010 13.700 0.000 0.118
## ssei (.p8.) 0.155 0.010 14.822 0.000 0.134
## speed =~
## ssno (.p9.) 0.700 0.036 19.476 0.000 0.629
## sscs (.10.) 0.393 0.022 17.721 0.000 0.350
## ssmk (.11.) 0.192 0.012 16.101 0.000 0.168
## g =~
## ssgs (.12.) 0.687 0.015 45.373 0.000 0.657
## ssar (.13.) 0.625 0.016 38.629 0.000 0.593
## sswk (.14.) 0.686 0.016 43.168 0.000 0.655
## sspc (.15.) 0.686 0.016 43.542 0.000 0.655
## ssno (.16.) 0.487 0.016 29.677 0.000 0.455
## sscs (.17.) 0.456 0.015 30.500 0.000 0.426
## ssai (.18.) 0.394 0.014 28.637 0.000 0.367
## sssi (.19.) 0.391 0.014 28.244 0.000 0.364
## ssmk (.20.) 0.650 0.016 41.673 0.000 0.620
## ssmc (.21.) 0.563 0.015 37.310 0.000 0.533
## ssei 0.678 0.020 34.478 0.000 0.639
## ssao (.23.) 0.515 0.015 35.260 0.000 0.486
## ci.upper Std.lv Std.all
##
## 0.313 0.271 0.287
## 0.280 0.238 0.247
## 0.257 0.222 0.237
## 0.468 0.415 0.410
##
## 0.292 0.588 0.557
## 0.338 0.680 0.696
## 0.157 0.310 0.331
## 0.175 0.348 0.325
##
## 0.770 0.789 0.743
## 0.437 0.444 0.445
## 0.215 0.216 0.224
##
## 0.717 0.845 0.885
## 0.656 0.769 0.815
## 0.717 0.844 0.883
## 0.717 0.845 0.859
## 0.519 0.599 0.564
## 0.485 0.561 0.562
## 0.421 0.485 0.459
## 0.418 0.481 0.492
## 0.681 0.800 0.830
## 0.593 0.693 0.740
## 0.716 0.834 0.777
## 0.544 0.634 0.626
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.302 0.015 19.509 0.000 0.271
## agec2 (b) -0.043 0.010 -4.112 0.000 -0.063
## ci.upper Std.lv Std.all
##
## 0.332 0.245 0.353
## -0.022 -0.035 -0.065
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.48.) 0.404 0.023 17.638 0.000 0.359
## .ssmk (.49.) 0.458 0.024 19.285 0.000 0.412
## .ssmc (.50.) 0.303 0.021 14.186 0.000 0.261
## .ssao (.51.) 0.374 0.026 14.545 0.000 0.324
## .ssai (.52.) 0.078 0.018 4.322 0.000 0.043
## .sssi (.53.) 0.115 0.019 6.010 0.000 0.078
## .ssei (.54.) 0.182 0.021 8.816 0.000 0.141
## .ssno 0.823 0.072 11.351 0.000 0.681
## .sscs (.56.) 0.394 0.023 17.268 0.000 0.349
## .ssgs (.57.) 0.400 0.023 17.160 0.000 0.355
## .sswk 0.268 0.026 10.167 0.000 0.216
## .sspc 0.087 0.028 3.148 0.002 0.033
## math -0.508 0.064 -7.947 0.000 -0.633
## elctrnc 1.887 0.135 14.004 0.000 1.623
## speed -1.164 0.092 -12.625 0.000 -1.345
## .g 0.293 0.041 7.239 0.000 0.214
## ci.upper Std.lv Std.all
## 0.448 0.404 0.428
## 0.505 0.458 0.475
## 0.345 0.303 0.324
## 0.425 0.374 0.370
## 0.114 0.078 0.074
## 0.153 0.115 0.118
## 0.222 0.182 0.169
## 0.965 0.823 0.775
## 0.439 0.394 0.395
## 0.446 0.400 0.419
## 0.319 0.268 0.280
## 0.141 0.087 0.088
## -0.383 -0.508 -0.508
## 2.151 0.838 0.838
## -0.984 -1.033 -1.033
## 0.373 0.238 0.238
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.225 0.013 17.453 0.000 0.200
## .ssmk 0.186 0.011 17.752 0.000 0.166
## .ssmc 0.252 0.013 18.705 0.000 0.226
## .ssao 0.450 0.026 17.262 0.000 0.399
## .ssai 0.534 0.025 21.370 0.000 0.485
## .sssi 0.260 0.020 13.288 0.000 0.222
## .ssei 0.334 0.016 21.471 0.000 0.303
## .ssno 0.146 0.047 3.103 0.002 0.054
## .sscs 0.482 0.025 18.996 0.000 0.433
## .ssgs 0.198 0.009 22.213 0.000 0.180
## .sswk 0.202 0.010 20.336 0.000 0.183
## .sspc 0.252 0.012 20.960 0.000 0.229
## electronic 5.069 0.646 7.845 0.000 3.803
## speed 1.271 0.121 10.529 0.000 1.035
## .g 1.312 0.073 17.884 0.000 1.168
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.251 0.225 0.253
## 0.207 0.186 0.200
## 0.279 0.252 0.288
## 0.501 0.450 0.440
## 0.583 0.534 0.479
## 0.299 0.260 0.273
## 0.364 0.334 0.290
## 0.238 0.146 0.129
## 0.532 0.482 0.486
## 0.215 0.198 0.217
## 0.222 0.202 0.221
## 0.276 0.252 0.261
## 6.336 1.000 1.000
## 1.508 1.000 1.000
## 1.456 0.866 0.866
# CROSS VALIDATION
set.seed(123) # For reproducibility, set seed if needed
split_indices <- sample(1:nrow(dgroup), size = nrow(dgroup) / 2)
dhalf1 <- dgroup[split_indices, ]
dhalf2 <- dgroup[-split_indices, ]
# WHITE GROUP
# CORRELATED FACTOR MODEL
cf.model<-' # model produces negative loadings for ssar and ssmk if they load on verbal
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
'
cf.lv<-' # model produces negative loadings for ssar and ssmk if they load on verbal
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
verbal~~1*verbal
math~~1*math
speed~~1*speed
'
cf.reduced<-' # model produces negative loadings for ssar and ssmk if they load on verbal
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
verbal~~1*verbal
math~~1*math
speed~~1*speed
verbal~0*1
math~0*1
'
baseline<-cfa(cf.model, data=dhalf1, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 511.208 45.000 0.000 0.970 0.880 0.075 0.028
## aic bic
## 44152.998 44401.016
configural<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 468.833 90.000 0.000 0.976 0.902 0.068 0.026
## aic bic
## 43126.404 43622.441
metric<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 522.504 101.000 0.000 0.973 0.891 0.068 0.037
## aic bic
## 43158.075 43593.485
scalar<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 851.631 109.000 0.000 0.953 0.816 0.086 0.043
## aic bic
## 43471.202 43862.520
scalar2<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 582.135 106.000 0.000 0.970 0.878 0.070 0.039
## aic bic
## 43207.706 43615.559
strict<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 663.993 118.000 0.000 0.965 0.861 0.071 0.044
## aic bic
## 43265.564 43607.278
cf.cov<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(cf.cov, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 631.248 112.000 0.000 0.967 0.868 0.071 0.096
## aic bic
## 43244.819 43619.603
cf.vcov<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances", "lv.variances"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(cf.vcov, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 748.427 116.000 0.000 0.960 0.841 0.077 0.117
## aic bic
## 43353.998 43706.736
cf.cov2<-cfa(cf.lv, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(cf.cov2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 636.153 115.000 0.000 0.967 0.867 0.070 0.096
## aic bic
## 43243.724 43601.973
reduced<-cfa(cf.reduced, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(reduced, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 639.232 117.000 0.000 0.967 0.867 0.070 0.097
## aic bic
## 43242.803 43590.030
baseline<-cfa(cf.model, data=dhalf2, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 651.576 45.000 0.000 0.963 0.847 0.086 0.033
## aic bic
## 44752.807 45000.850
configural<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 577.863 90.000 0.000 0.969 0.875 0.077 0.029
## aic bic
## 43621.439 44117.526
metric<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 640.528 101.000 0.000 0.966 0.863 0.076 0.042
## aic bic
## 43662.104 44097.557
scalar<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 906.271 109.000 0.000 0.950 0.804 0.089 0.047
## aic bic
## 43911.847 44303.204
scalar2<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 682.445 106.000 0.000 0.964 0.854 0.077 0.043
## aic bic
## 43694.021 44101.914
strict<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 778.667 118.000 0.000 0.959 0.835 0.078 0.048
## aic bic
## 43766.243 44107.991
cf.cov<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(cf.cov, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 738.379 112.000 0.000 0.961 0.843 0.078 0.093
## aic bic
## 43737.955 44112.776
cf.vcov<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances", "lv.variances"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(cf.vcov, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 940.375 116.000 0.000 0.948 0.798 0.088 0.120
## aic bic
## 43931.951 44284.724
cf.cov2<-cfa(cf.lv, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(cf.cov2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 738.958 115.000 0.000 0.961 0.843 0.077 0.093
## aic bic
## 43732.534 44090.819
reduced<-cfa(cf.reduced, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(reduced, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 746.594 117.000 0.000 0.961 0.842 0.077 0.094
## aic bic
## 43736.170 44083.431
# HIGH ORDER FACTOR
hof.model<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
'
hof.lv<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
math~~1*math
'
hof.weak<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
math~~1*math
verbal~0*1
math~0*1
g~0*1
'
hof.weak2<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
speed~~1*speed
math~~1*math
math~0*1
'
baseline<-cfa(hof.model, data=dhalf1, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 684.808 47.000 0.000 0.960 0.840 0.086 0.041
## aic bic
## 44322.598 44559.594
configural<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 599.417 94.000 0.000 0.968 0.871 0.077 0.034
## aic bic
## 43248.988 43722.979
metric<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 670.368 108.000 0.000 0.964 0.857 0.075 0.050
## aic bic
## 43291.939 43688.769
metric2<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"), group.partial=c("electronic=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 647.218 107.000 0.000 0.966 0.863 0.074 0.043
## aic bic
## 43270.789 43673.130
scalar<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 4.775225e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 970.292 114.000 0.000 0.945 0.791 0.091 0.049
## aic bic
## 43579.863 43943.623
scalar2<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1")) # not freeing gs leads to poor fit
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 6.247374e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 704.133 111.000 0.000 0.962 0.850 0.076 0.045
## aic bic
## 43319.704 43699.999
strict<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 785.167 123.000 0.000 0.958 0.834 0.077 0.049
## aic bic
## 43376.738 43690.895
latent<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 1.507965e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 826.693 116.000 0.000 0.955 0.823 0.082 0.102
## aic bic
## 43432.264 43785.002
latent2<-cfa(hof.lv, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 2.944642e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 711.503 113.000 0.000 0.962 0.849 0.076 0.046
## aic bic
## 43323.074 43692.346
weak<-cfa(hof.weak, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(weak, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 714.945 116.000 0.000 0.962 0.849 0.075 0.047
## aic bic
## 43320.516 43673.254
weak2<-cfa(hof.weak2, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(weak2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 711.503 114.000 0.000 0.962 0.849 0.076 0.046
## aic bic
## 43321.074 43684.835
baseline<-cfa(hof.model, data=dhalf2, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 896.658 47.000 0.000 0.948 0.793 0.099 0.048
## aic bic
## 44993.889 45230.908
configural<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 768.588 94.000 0.000 0.958 0.832 0.089 0.039
## aic bic
## 43804.164 44278.202
metric<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 842.410 108.000 0.000 0.954 0.818 0.086 0.052
## aic bic
## 43849.986 44246.855
metric2<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"), group.partial=c("electronic=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 797.832 107.000 0.000 0.957 0.828 0.084 0.043
## aic bic
## 43807.408 44209.789
scalar<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 2.740575e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1058.965 114.000 0.000 0.941 0.772 0.095 0.048
## aic bic
## 44054.541 44418.338
scalar2<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1")) # not freeing gs leads to poor fit
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 5.500076e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 833.556 111.000 0.000 0.955 0.821 0.084 0.044
## aic bic
## 43835.132 44215.465
strict<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 1.880095e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 928.841 123.000 0.000 0.950 0.802 0.085 0.048
## aic bic
## 43906.417 44220.605
latent<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 7.341806e-14) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1028.122 116.000 0.000 0.943 0.779 0.093 0.101
## aic bic
## 44019.698 44372.471
latent2<-cfa(hof.lv, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 1.708777e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 836.420 113.000 0.000 0.955 0.821 0.084 0.045
## aic bic
## 43833.996 44203.305
weak<-cfa(hof.weak, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(weak, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 843.394 116.000 0.000 0.955 0.820 0.083 0.047
## aic bic
## 43834.970 44187.743
weak2<-cfa(hof.weak2, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1", "ssgs~1"))
fitMeasures(weak2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 836.420 114.000 0.000 0.955 0.821 0.083 0.045
## aic bic
## 43831.996 44195.793
# BIFACTOR MODEL
bf.model<-'
verbal =~ ssgs + sswk + sspc + ssei
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
bf.lv<-'
verbal =~ ssgs + sswk + sspc + ssei
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
verbal~~1*verbal
speed~~1*speed
'
bf.weak<-'
verbal =~ ssgs + sswk + sspc + ssei
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
verbal~~1*verbal
speed~~1*speed
speed~0*1
'
baseline<-cfa(bf.model, data=dhalf1, meanstructure=T, sampling.weights="sweight", std.lv=T, orthogonal=T)
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 592.574 43.000 0.000 0.965 0.861 0.084 0.041
## aic bic
## 44238.364 44497.406
configural<-cfa(bf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T)
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 528.801 86.000 0.000 0.972 0.886 0.075 0.034
## aic bic
## 43194.372 43712.456
metric<-cfa(bf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 608.016 105.000 0.000 0.968 0.872 0.072 0.053
## aic bic
## 43235.587 43648.951
metric2<-cfa(bf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"), group.partial=c("g=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 574.906 104.000 0.000 0.970 0.879 0.070 0.043
## aic bic
## 43204.477 43623.353
scalar<-cfa(bf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 913.644 112.000 0.000 0.949 0.803 0.088 0.050
## aic bic
## 43527.215 43901.999
scalar2<-cfa(bf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 608.038 108.000 0.000 0.968 0.872 0.071 0.045
## aic bic
## 43229.609 43626.439
strict<-cfa(bf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 679.836 120.000 0.000 0.964 0.858 0.071 0.050
## aic bic
## 43277.407 43608.098
latent<-cfa(bf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 747.295 112.000 0.000 0.960 0.841 0.079 0.103
## aic bic
## 43360.866 43735.649
latent2<-cfa(bf.lv, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 614.093 110.000 0.000 0.968 0.871 0.071 0.046
## aic bic
## 43231.664 43617.471
weak<-cfa(bf.weak, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(weak, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 621.359 111.000 0.000 0.967 0.870 0.071 0.046
## aic bic
## 43236.930 43617.225
baseline<-cfa(bf.model, data=dhalf2, meanstructure=T, sampling.weights="sweight", std.lv=T, orthogonal=T)
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 766.212 43.000 0.000 0.955 0.821 0.096 0.049
## aic bic
## 44871.442 45130.510
configural<-cfa(bf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T)
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 652.273 86.000 0.000 0.965 0.857 0.085 0.039
## aic bic
## 43703.849 44221.984
metric<-cfa(bf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 731.611 105.000 0.000 0.961 0.843 0.081 0.053
## aic bic
## 43745.187 44158.592
metric2<-cfa(bf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"), group.partial=c("g=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 684.385 104.000 0.000 0.964 0.853 0.078 0.043
## aic bic
## 43699.961 44118.878
scalar<-cfa(bf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 909.010 112.000 0.000 0.950 0.804 0.088 0.047
## aic bic
## 43908.586 44283.407
scalar2<-cfa(bf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 693.054 108.000 0.000 0.963 0.852 0.077 0.043
## aic bic
## 43700.630 44097.499
strict<-cfa(bf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 785.946 120.000 0.000 0.958 0.834 0.078 0.047
## aic bic
## 43769.522 44100.246
latent<-cfa(bf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 931.586 112.000 0.000 0.949 0.799 0.089 0.104
## aic bic
## 43931.162 44305.983
latent2<-cfa(bf.lv, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 695.692 110.000 0.000 0.963 0.852 0.076 0.044
## aic bic
## 43699.268 44085.113
weak<-cfa(bf.weak, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("g=~ssei", "ssar~1", "ssgs~1", "sscs~1", "sspc~1"))
fitMeasures(weak, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 695.744 111.000 0.000 0.963 0.852 0.076 0.044
## aic bic
## 43697.320 44077.653
# ALL RACE RESPONDENTS
nrow(dk) # N=7093
## [1] 7093
dgroup<- dplyr::select(dk, id, starts_with("ss"), asvab, efa, educ2011, T6665000, agec, age, agebin, agec2, sex, sexage, bhw, sweight)
original_age_min <- 12
original_age_max <- 17
mean_centered_min <- min(dgroup$agec)
mean_centered_max <- max(dgroup$agec)
original_age_mean <- (original_age_min + original_age_max) / 2
mean_centered_age_mean <- (mean_centered_min + mean_centered_max) / 2
age_difference <- original_age_mean - mean_centered_age_mean
fit<-lm(efa ~ sex + rcs(agec, 3) + sex*rcs(agec, 3), data=dgroup)
summary(fit)
##
## Call:
## lm(formula = efa ~ sex + rcs(agec, 3) + sex * rcs(agec, 3), data = dgroup)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.83 -10.08 0.90 10.82 47.86
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 101.4169 0.6657 152.339 <2e-16 ***
## sex 1.5202 0.9479 1.604 0.1088
## rcs(agec, 3)agec 4.2752 0.4658 9.179 <2e-16 ***
## rcs(agec, 3)agec' -0.8342 0.5752 -1.450 0.1470
## sex:rcs(agec, 3)agec 0.8792 0.6635 1.325 0.1852
## sex:rcs(agec, 3)agec' -1.8867 0.8142 -2.317 0.0205 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.61 on 7087 degrees of freedom
## Multiple R-squared: 0.09974, Adjusted R-squared: 0.0991
## F-statistic: 157 on 5 and 7087 DF, p-value: < 2.2e-16
dgroup$pred1<-fitted(fit)
xyplot(dgroup$pred1 ~ dgroup$agec, data=dgroup, groups=sex, pch=19, type=c("p"), col=c('red', 'blue'), grid=TRUE, ylab="Predicted g", xlab="age", key=list(text=list(c("White Male", "White Female")), points=list(pch=c(19,19), col=c("red", "blue")), columns=2))
xyplot(dgroup$pred1 ~ dgroup$agec, data=dgroup, groups=sex, pch=19, type=c("p"), col=c('red', 'blue'), grid=TRUE, ylab="Predicted g", xlab="Age", key=list(text=list(c("White Male", "White Female")), points=list(pch=c(19,19), col=c("red", "blue")), columns=2), scales=list(x=list(at=seq(mean_centered_min, mean_centered_max), labels=seq(original_age_min, original_age_max))))
fit<-lm(asvab ~ sex + rcs(agec, 3) + sex*rcs(agec, 3), data=dgroup)
summary(fit)
##
## Call:
## lm(formula = asvab ~ sex + rcs(agec, 3) + sex * rcs(agec, 3),
## data = dgroup)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.447 -13.284 -1.264 12.622 29.012
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 98.6849 0.6830 144.490 < 2e-16 ***
## sex 3.1734 0.9725 3.263 0.00111 **
## rcs(agec, 3)agec -0.4421 0.4778 -0.925 0.35486
## rcs(agec, 3)agec' 0.7646 0.5901 1.296 0.19517
## sex:rcs(agec, 3)agec 1.1153 0.6807 1.639 0.10136
## sex:rcs(agec, 3)agec' -2.0014 0.8353 -2.396 0.01660 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.99 on 7087 degrees of freedom
## Multiple R-squared: 0.002427, Adjusted R-squared: 0.001723
## F-statistic: 3.448 on 5 and 7087 DF, p-value: 0.004096
dgroup$pred2<-fitted(fit)
xyplot(dgroup$pred2 ~ dgroup$agec, data=dgroup, groups=sex, pch=19, type=c("p"), col=c('red', 'blue'), grid=TRUE, ylab="Predicted ASVAB", xlab="Age", key=list(text=list(c("White Male", "White Female")), points=list(pch=c(19,19), col=c("red", "blue")), columns=2), scales=list(x=list(at=seq(mean_centered_min, mean_centered_max), labels=seq(original_age_min, original_age_max))))
describeBy(dgroup$pred1, dgroup$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew
## X1 1 3590 100.12 5.15 100.34 100.22 6.37 90.73 108.62 17.89 -0.14
## kurtosis se
## X1 -1.14 0.09
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 3503 99.87 4.55 101.4 100.33 4.28 90.05 105.19 15.14 -0.68
## kurtosis se
## X1 -0.83 0.08
describeBy(dgroup$efa, dgroup$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew
## X1 1 3590 100.12 16.2 100.81 100.33 17.63 61.4 146.25 84.85 -0.08
## kurtosis se
## X1 -0.62 0.27
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew
## X1 1 3503 99.87 14.52 100.59 100.2 15.45 58.73 142.07 83.34 -0.17
## kurtosis se
## X1 -0.49 0.25
describeBy(dgroup$asvab, dgroup$sex)
##
## Descriptive statistics by group
## INDICES: 0
## vars n mean sd median trimmed mad min max range skew
## V1 1 3590 99.49 15.29 97.82 98.93 19.4 76.7 128.12 51.42 0.25
## kurtosis se
## V1 -1.2 0.26
## ------------------------------------------------------
## INDICES: 1
## vars n mean sd median trimmed mad min max range skew
## V1 1 3503 100.52 14.68 99.72 100.19 18.61 76.7 128.12 51.42 0.15
## kurtosis se
## V1 -1.17 0.25
describeBy(dgroup$educ2011, dgroup$sex)
##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 2960 13.42 3.54 13 13.32 2.97 6 95 89 8.36 189.11
## se
## X1 0.07
## ------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 2950 14.13 3.89 14 14.08 2.97 6 95 89 9.07 187.2
## se
## X1 0.07
cor(dgroup$efa, dgroup$asvab, use="pairwise.complete.obs", method="pearson")
## [,1]
## [1,] 0.904695
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(agebin, sex) %>% summarise(MEAN = survey_mean(pred1), SD = survey_sd(pred1))
## # A tibble: 10 Ă— 5
## # Groups: agebin [5]
## agebin sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 12 0 92.7 0.0500 1.23
## 2 12 1 92.4 0.0652 1.54
## 3 13 0 96.9 0.0463 1.17
## 4 13 1 97.3 0.0567 1.35
## 5 14 0 101. 0.0426 1.04
## 6 14 1 101. 0.0361 0.866
## 7 15 0 104. 0.0398 0.957
## 8 15 1 103. 0.0175 0.433
## 9 16 0 107. 0.0393 0.872
## 10 16 1 105. 0.0139 0.314
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(agebin, sex) %>% summarise(MEAN = survey_mean(efa), SD = survey_sd(efa))
## # A tibble: 10 Ă— 5
## # Groups: agebin [5]
## agebin sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 12 0 95.3 0.567 14.3
## 2 12 1 95.2 0.519 12.7
## 3 13 0 100. 0.569 14.8
## 4 13 1 99.7 0.538 13.1
## 5 14 0 103. 0.597 15.0
## 6 14 1 104. 0.542 13.4
## 7 15 0 108. 0.595 15.1
## 8 15 1 107. 0.541 13.7
## 9 16 0 110. 0.681 15.5
## 10 16 1 108. 0.568 13.5
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(agebin, sex) %>% summarise(MEAN = survey_mean(asvab), SD = survey_sd(asvab))
## # A tibble: 10 Ă— 5
## # Groups: agebin [5]
## agebin sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 12 0 102. 0.611 15.3
## 2 12 1 103. 0.591 14.3
## 3 13 0 102. 0.600 15.3
## 4 13 1 103. 0.599 14.4
## 5 14 0 102. 0.611 15.1
## 6 14 1 104. 0.596 14.5
## 7 15 0 103. 0.620 15.2
## 8 15 1 103. 0.578 14.4
## 9 16 0 103. 0.682 15.3
## 10 16 1 103. 0.635 14.6
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(pred1), SD = survey_sd(pred1))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 100. 0.0991 5.25
## 2 1 99.8 0.0884 4.58
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(efa), SD = survey_sd(efa))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 103. 0.289 15.9
## 2 1 103. 0.260 14.1
dgroup %>% as_survey_design(ids = id, weights = sweight) %>% group_by(sex) %>% summarise(MEAN = survey_mean(asvab, na.rm = TRUE), SD = survey_sd(asvab, na.rm = TRUE))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 102. 0.280 15.2
## 2 1 103. 0.269 14.4
dgroup %>% as_survey_design(ids = id, weights = T6665000) %>% group_by(sex) %>% summarise(MEAN = survey_mean(educ2011, na.rm = TRUE), SD = survey_sd(educ2011, na.rm = TRUE))
## # A tibble: 2 Ă— 4
## sex MEAN MEAN_se SD
## <dbl> <dbl> <dbl> <dbl>
## 1 0 13.7 0.0594 3.18
## 2 1 14.4 0.0862 4.08
# CORRELATED FACTOR MODEL
cf.model<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
'
cf.lv<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
verbal~~1*verbal
math~~1*math
speed~~1*speed
'
cf.reduced<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
verbal~~1*verbal
math~~1*math
speed~~1*speed
math~0*1
'
baseline<-cfa(cf.model, data=dgroup, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2017.025 45.000 0.000 0.971 0.079 0.027
## aic bic
## 174173.099 174482.108
Mc(baseline)
## [1] 0.8702005
configural<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 1738.406 90.000 0.000 0.976 0.072 0.025
## aic bic
## 170596.159 171214.177
Mc(configural)
## [1] 0.890283
summary(configural, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 53 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 90
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1738.406 1335.833
## Degrees of freedom 90 90
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.301
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 697.088 535.659
## 0 1041.319 800.174
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.811 0.013 64.756 0.000 0.786
## sswk 0.846 0.013 63.955 0.000 0.820
## sspc 0.807 0.012 64.690 0.000 0.783
## ssei 0.448 0.035 12.958 0.000 0.380
## math =~
## ssar 0.822 0.013 61.236 0.000 0.796
## ssmk 0.666 0.023 28.961 0.000 0.621
## ssmc 0.416 0.026 15.693 0.000 0.364
## ssao 0.706 0.013 52.306 0.000 0.680
## electronic =~
## ssai 0.526 0.015 35.224 0.000 0.496
## sssi 0.586 0.015 39.006 0.000 0.556
## ssmc 0.342 0.026 13.011 0.000 0.291
## ssei 0.217 0.036 6.010 0.000 0.146
## speed =~
## ssno 0.787 0.017 45.112 0.000 0.753
## sscs 0.692 0.017 40.259 0.000 0.659
## ssmk 0.253 0.023 10.846 0.000 0.207
## ci.upper Std.lv Std.all
##
## 0.835 0.811 0.888
## 0.872 0.846 0.897
## 0.831 0.807 0.869
## 0.516 0.448 0.551
##
## 0.848 0.822 0.902
## 0.711 0.666 0.691
## 0.468 0.416 0.474
## 0.733 0.706 0.740
##
## 0.555 0.526 0.667
## 0.615 0.586 0.728
## 0.394 0.342 0.390
## 0.288 0.217 0.267
##
## 0.821 0.787 0.832
## 0.726 0.692 0.743
## 0.298 0.253 0.262
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.902 0.006 141.766 0.000 0.890
## electronic 0.869 0.012 72.888 0.000 0.846
## speed 0.692 0.018 38.613 0.000 0.657
## math ~~
## electronic 0.770 0.016 49.234 0.000 0.740
## speed 0.736 0.018 41.238 0.000 0.701
## electronic ~~
## speed 0.505 0.027 18.895 0.000 0.453
## ci.upper Std.lv Std.all
##
## 0.915 0.902 0.902
## 0.893 0.869 0.869
## 0.727 0.692 0.692
##
## 0.801 0.770 0.770
## 0.771 0.736 0.736
##
## 0.557 0.505 0.505
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.120 0.017 7.097 0.000 0.087
## .sswk 0.181 0.017 10.369 0.000 0.147
## .sspc 0.284 0.017 16.543 0.000 0.251
## .ssei -0.010 0.015 -0.667 0.505 -0.040
## .ssar 0.148 0.017 8.728 0.000 0.115
## .ssmk 0.224 0.018 12.435 0.000 0.189
## .ssmc 0.039 0.016 2.369 0.018 0.007
## .ssao 0.198 0.018 11.088 0.000 0.163
## .ssai -0.097 0.015 -6.622 0.000 -0.126
## .sssi -0.131 0.015 -8.757 0.000 -0.160
## .ssno 0.173 0.018 9.602 0.000 0.138
## .sscs 0.271 0.018 15.206 0.000 0.236
## ci.upper Std.lv Std.all
## 0.153 0.120 0.131
## 0.216 0.181 0.192
## 0.318 0.284 0.306
## 0.020 -0.010 -0.013
## 0.181 0.148 0.162
## 0.260 0.224 0.233
## 0.070 0.039 0.044
## 0.232 0.198 0.207
## -0.069 -0.097 -0.124
## -0.102 -0.131 -0.163
## 0.209 0.173 0.183
## 0.306 0.271 0.291
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.176 0.007 27.031 0.000 0.163
## .sswk 0.174 0.007 26.100 0.000 0.161
## .sspc 0.211 0.009 23.690 0.000 0.193
## .ssei 0.243 0.008 29.205 0.000 0.227
## .ssar 0.155 0.007 22.647 0.000 0.141
## .ssmk 0.174 0.007 26.349 0.000 0.161
## .ssmc 0.262 0.009 27.674 0.000 0.243
## .ssao 0.413 0.013 30.748 0.000 0.386
## .ssai 0.345 0.012 27.688 0.000 0.321
## .sssi 0.304 0.012 25.371 0.000 0.281
## .ssno 0.276 0.015 18.687 0.000 0.247
## .sscs 0.389 0.017 23.121 0.000 0.356
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.189 0.176 0.211
## 0.187 0.174 0.195
## 0.228 0.211 0.244
## 0.259 0.243 0.368
## 0.168 0.155 0.186
## 0.187 0.174 0.187
## 0.280 0.262 0.339
## 0.439 0.413 0.453
## 0.370 0.345 0.556
## 0.328 0.304 0.470
## 0.305 0.276 0.308
## 0.422 0.389 0.448
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.940 0.014 68.260 0.000 0.913
## sswk 0.902 0.014 66.100 0.000 0.875
## sspc 0.884 0.012 76.053 0.000 0.861
## ssei 0.578 0.025 23.283 0.000 0.530
## math =~
## ssar 0.926 0.014 64.705 0.000 0.898
## ssmk 0.697 0.025 28.312 0.000 0.649
## ssmc 0.512 0.019 27.065 0.000 0.475
## ssao 0.740 0.014 53.116 0.000 0.713
## electronic =~
## ssai 0.873 0.020 44.004 0.000 0.834
## sssi 0.898 0.017 52.887 0.000 0.865
## ssmc 0.462 0.019 23.889 0.000 0.424
## ssei 0.468 0.026 17.720 0.000 0.417
## speed =~
## ssno 0.884 0.018 48.274 0.000 0.848
## sscs 0.765 0.017 44.247 0.000 0.731
## ssmk 0.256 0.024 10.473 0.000 0.208
## ci.upper Std.lv Std.all
##
## 0.967 0.940 0.908
## 0.928 0.902 0.894
## 0.907 0.884 0.867
## 0.627 0.578 0.511
##
## 0.954 0.926 0.904
## 0.745 0.697 0.693
## 0.549 0.512 0.492
## 0.768 0.740 0.718
##
## 0.912 0.873 0.776
## 0.931 0.898 0.842
## 0.500 0.462 0.445
## 0.520 0.468 0.414
##
## 0.920 0.884 0.835
## 0.799 0.765 0.751
## 0.304 0.256 0.255
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.918 0.006 164.431 0.000 0.907
## electronic 0.756 0.012 62.775 0.000 0.732
## speed 0.701 0.016 43.593 0.000 0.670
## math ~~
## electronic 0.659 0.015 43.953 0.000 0.630
## speed 0.797 0.015 53.894 0.000 0.768
## electronic ~~
## speed 0.371 0.022 17.001 0.000 0.329
## ci.upper Std.lv Std.all
##
## 0.929 0.918 0.918
## 0.780 0.756 0.756
## 0.733 0.701 0.701
##
## 0.689 0.659 0.659
## 0.826 0.797 0.797
##
## 0.414 0.371 0.371
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.276 0.019 14.542 0.000 0.239
## .sswk 0.179 0.018 9.735 0.000 0.143
## .sspc 0.041 0.019 2.207 0.027 0.005
## .ssei 0.339 0.021 16.134 0.000 0.298
## .ssar 0.194 0.019 10.391 0.000 0.157
## .ssmk 0.087 0.019 4.675 0.000 0.050
## .ssmc 0.322 0.019 17.179 0.000 0.286
## .ssao 0.081 0.019 4.256 0.000 0.044
## .ssai 0.382 0.021 18.202 0.000 0.341
## .sssi 0.482 0.020 24.659 0.000 0.443
## .ssno -0.002 0.020 -0.083 0.934 -0.040
## .sscs -0.080 0.019 -4.255 0.000 -0.117
## ci.upper Std.lv Std.all
## 0.313 0.276 0.267
## 0.215 0.179 0.178
## 0.078 0.041 0.041
## 0.380 0.339 0.299
## 0.230 0.194 0.189
## 0.123 0.087 0.086
## 0.359 0.322 0.310
## 0.119 0.081 0.079
## 0.423 0.382 0.340
## 0.520 0.482 0.452
## 0.037 -0.002 -0.002
## -0.043 -0.080 -0.079
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.188 0.007 27.287 0.000 0.175
## .sswk 0.205 0.008 26.082 0.000 0.189
## .sspc 0.258 0.010 26.522 0.000 0.239
## .ssei 0.317 0.012 26.918 0.000 0.294
## .ssar 0.193 0.008 23.203 0.000 0.176
## .ssmk 0.176 0.007 25.284 0.000 0.163
## .ssmc 0.293 0.011 27.752 0.000 0.272
## .ssao 0.516 0.015 34.945 0.000 0.487
## .ssai 0.503 0.019 26.251 0.000 0.465
## .sssi 0.331 0.016 21.016 0.000 0.300
## .ssno 0.340 0.019 18.238 0.000 0.304
## .sscs 0.452 0.019 23.583 0.000 0.414
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.202 0.188 0.176
## 0.220 0.205 0.201
## 0.277 0.258 0.248
## 0.341 0.317 0.248
## 0.209 0.193 0.183
## 0.190 0.176 0.174
## 0.314 0.293 0.271
## 0.545 0.516 0.485
## 0.541 0.503 0.398
## 0.362 0.331 0.291
## 0.377 0.340 0.303
## 0.489 0.452 0.436
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
modificationIndices(configural, sort=T, maximum.number=30)
## lhs op rhs block group level mi epc sepc.lv
## 216 math =~ sspc 2 2 1 299.856 0.628 0.628
## 290 ssmc ~~ ssao 2 2 1 185.967 0.101 0.101
## 232 speed =~ sspc 2 2 1 185.864 0.231 0.231
## 117 math =~ sspc 1 1 1 179.167 0.423 0.423
## 191 ssmc ~~ ssao 1 1 1 173.781 0.085 0.085
## 224 electronic =~ sspc 2 2 1 130.599 -0.216 -0.216
## 230 speed =~ ssgs 2 2 1 126.137 -0.181 -0.181
## 298 ssao ~~ sscs 2 2 1 109.091 0.098 0.098
## 222 electronic =~ ssgs 2 2 1 101.842 0.184 0.184
## 297 ssao ~~ ssno 2 2 1 94.332 -0.089 -0.089
## 240 ssgs ~~ sspc 2 2 1 94.083 -0.054 -0.054
## 239 ssgs ~~ sswk 2 2 1 93.655 0.053 0.053
## 255 sswk ~~ ssao 2 2 1 84.813 -0.059 -0.059
## 141 ssgs ~~ sspc 1 1 1 81.030 -0.043 -0.043
## 116 math =~ sswk 1 1 1 81.009 -0.284 -0.284
## 215 math =~ sswk 2 2 1 75.968 -0.305 -0.305
## 282 ssar ~~ ssno 2 2 1 73.566 0.062 0.062
## 133 speed =~ sspc 1 1 1 68.588 0.132 0.132
## 123 electronic =~ ssgs 1 1 1 62.669 0.237 0.237
## 140 ssgs ~~ sswk 1 1 1 58.318 0.037 0.037
## 214 math =~ ssgs 2 2 1 58.275 -0.272 -0.272
## 167 sspc ~~ sssi 1 1 1 56.719 -0.041 -0.041
## 130 electronic =~ sscs 1 1 1 54.834 0.163 0.163
## 135 speed =~ ssar 1 1 1 51.303 0.185 0.185
## 122 math =~ sscs 1 1 1 49.793 0.323 0.323
## 121 math =~ ssno 1 1 1 49.792 -0.368 -0.368
## 113 verbal =~ ssno 1 1 1 48.652 -0.229 -0.229
## 293 ssmc ~~ ssno 2 2 1 47.858 -0.050 -0.050
## 131 speed =~ ssgs 1 1 1 47.851 -0.105 -0.105
## 129 electronic =~ ssno 1 1 1 47.282 -0.169 -0.169
## sepc.all sepc.nox
## 216 0.616 0.616
## 290 0.259 0.259
## 232 0.226 0.226
## 117 0.456 0.456
## 191 0.258 0.258
## 224 -0.212 -0.212
## 230 -0.175 -0.175
## 298 0.203 0.203
## 222 0.177 0.177
## 297 -0.212 -0.212
## 240 -0.243 -0.243
## 239 0.270 0.270
## 255 -0.180 -0.180
## 141 -0.222 -0.222
## 116 -0.301 -0.301
## 215 -0.302 -0.302
## 282 0.242 0.242
## 133 0.142 0.142
## 123 0.260 0.260
## 140 0.211 0.211
## 214 -0.263 -0.263
## 167 -0.161 -0.161
## 130 0.175 0.175
## 135 0.203 0.203
## 122 0.347 0.347
## 121 -0.389 -0.389
## 113 -0.242 -0.242
## 293 -0.158 -0.158
## 131 -0.115 -0.115
## 129 -0.178 -0.178
metric<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 1914.821 101.000 0.000 0.973 0.071 0.034
## aic bic
## 170750.574 171293.056
Mc(metric)
## [1] 0.8799608
summary(metric, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 79 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 94
## Number of equality constraints 15
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1914.821 1470.052
## Degrees of freedom 101 101
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.303
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 793.001 608.805
## 0 1121.820 861.247
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.823 0.011 72.139 0.000 0.801
## sswk (.p2.) 0.822 0.012 67.947 0.000 0.798
## sspc (.p3.) 0.796 0.011 69.890 0.000 0.773
## ssei (.p4.) 0.424 0.018 23.542 0.000 0.389
## math =~
## ssar (.p5.) 0.839 0.012 68.414 0.000 0.815
## ssmk (.p6.) 0.655 0.018 37.148 0.000 0.621
## ssmc (.p7.) 0.454 0.014 31.681 0.000 0.426
## ssao (.p8.) 0.696 0.012 59.580 0.000 0.673
## electronic =~
## ssai (.p9.) 0.523 0.012 43.773 0.000 0.500
## sssi (.10.) 0.548 0.013 43.044 0.000 0.523
## ssmc (.11.) 0.292 0.012 24.058 0.000 0.269
## ssei (.12.) 0.305 0.015 20.101 0.000 0.275
## speed =~
## ssno (.13.) 0.796 0.015 51.622 0.000 0.766
## sscs (.14.) 0.695 0.015 47.893 0.000 0.666
## ssmk (.15.) 0.241 0.017 14.511 0.000 0.208
## ci.upper Std.lv Std.all
##
## 0.846 0.823 0.892
## 0.845 0.822 0.889
## 0.818 0.796 0.866
## 0.459 0.424 0.495
##
## 0.863 0.839 0.907
## 0.690 0.655 0.691
## 0.482 0.454 0.520
## 0.719 0.696 0.734
##
## 0.547 0.523 0.666
## 0.573 0.548 0.698
## 0.316 0.292 0.335
## 0.334 0.305 0.356
##
## 0.826 0.796 0.837
## 0.723 0.695 0.745
## 0.273 0.241 0.254
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.905 0.006 145.765 0.000 0.893
## electronic 0.869 0.012 72.428 0.000 0.846
## speed 0.693 0.017 40.579 0.000 0.660
## math ~~
## electronic 0.769 0.015 50.955 0.000 0.740
## speed 0.738 0.018 41.967 0.000 0.704
## electronic ~~
## speed 0.508 0.026 19.675 0.000 0.458
## ci.upper Std.lv Std.all
##
## 0.917 0.905 0.905
## 0.893 0.869 0.869
## 0.727 0.693 0.693
##
## 0.799 0.769 0.769
## 0.773 0.738 0.738
##
## 0.559 0.508 0.508
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.120 0.017 7.097 0.000 0.087
## .sswk 0.181 0.017 10.369 0.000 0.147
## .sspc 0.284 0.017 16.543 0.000 0.251
## .ssei -0.010 0.015 -0.667 0.505 -0.040
## .ssar 0.148 0.017 8.728 0.000 0.115
## .ssmk 0.224 0.018 12.435 0.000 0.189
## .ssmc 0.039 0.016 2.369 0.018 0.007
## .ssao 0.198 0.018 11.088 0.000 0.163
## .ssai -0.097 0.015 -6.622 0.000 -0.126
## .sssi -0.131 0.015 -8.757 0.000 -0.160
## .ssno 0.173 0.018 9.602 0.000 0.138
## .sscs 0.271 0.018 15.206 0.000 0.236
## ci.upper Std.lv Std.all
## 0.153 0.120 0.130
## 0.216 0.181 0.196
## 0.318 0.284 0.309
## 0.020 -0.010 -0.012
## 0.181 0.148 0.160
## 0.260 0.224 0.237
## 0.070 0.039 0.044
## 0.232 0.198 0.208
## -0.069 -0.097 -0.124
## -0.102 -0.131 -0.167
## 0.209 0.173 0.182
## 0.306 0.271 0.290
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.175 0.006 27.074 0.000 0.162
## .sswk 0.178 0.007 26.530 0.000 0.165
## .sspc 0.212 0.009 24.515 0.000 0.195
## .ssei 0.236 0.008 28.346 0.000 0.220
## .ssar 0.152 0.007 22.587 0.000 0.139
## .ssmk 0.179 0.006 27.655 0.000 0.166
## .ssmc 0.267 0.010 28.015 0.000 0.248
## .ssao 0.414 0.013 31.516 0.000 0.389
## .ssai 0.344 0.012 28.804 0.000 0.321
## .sssi 0.316 0.012 26.897 0.000 0.293
## .ssno 0.272 0.014 19.518 0.000 0.244
## .sscs 0.388 0.016 24.247 0.000 0.357
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.187 0.175 0.205
## 0.191 0.178 0.209
## 0.229 0.212 0.251
## 0.253 0.236 0.322
## 0.165 0.152 0.178
## 0.192 0.179 0.199
## 0.285 0.267 0.350
## 0.440 0.414 0.461
## 0.368 0.344 0.557
## 0.340 0.316 0.513
## 0.299 0.272 0.300
## 0.419 0.388 0.446
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.823 0.011 72.139 0.000 0.801
## sswk (.p2.) 0.822 0.012 67.947 0.000 0.798
## sspc (.p3.) 0.796 0.011 69.890 0.000 0.773
## ssei (.p4.) 0.424 0.018 23.542 0.000 0.389
## math =~
## ssar (.p5.) 0.839 0.012 68.414 0.000 0.815
## ssmk (.p6.) 0.655 0.018 37.148 0.000 0.621
## ssmc (.p7.) 0.454 0.014 31.681 0.000 0.426
## ssao (.p8.) 0.696 0.012 59.580 0.000 0.673
## electronic =~
## ssai (.p9.) 0.523 0.012 43.773 0.000 0.500
## sssi (.10.) 0.548 0.013 43.044 0.000 0.523
## ssmc (.11.) 0.292 0.012 24.058 0.000 0.269
## ssei (.12.) 0.305 0.015 20.101 0.000 0.275
## speed =~
## ssno (.13.) 0.796 0.015 51.622 0.000 0.766
## sscs (.14.) 0.695 0.015 47.893 0.000 0.666
## ssmk (.15.) 0.241 0.017 14.511 0.000 0.208
## ci.upper Std.lv Std.all
##
## 0.846 0.928 0.905
## 0.845 0.926 0.900
## 0.818 0.896 0.871
## 0.459 0.478 0.439
##
## 0.863 0.906 0.898
## 0.690 0.708 0.694
## 0.482 0.490 0.469
## 0.719 0.752 0.723
##
## 0.547 0.871 0.773
## 0.573 0.913 0.844
## 0.316 0.487 0.466
## 0.334 0.507 0.466
##
## 0.826 0.875 0.830
## 0.723 0.763 0.750
## 0.273 0.264 0.259
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 1.116 0.039 28.793 0.000 1.040
## electronic 1.443 0.058 24.839 0.000 1.329
## speed 0.868 0.036 23.916 0.000 0.797
## math ~~
## electronic 1.213 0.053 22.808 0.000 1.109
## speed 0.945 0.037 25.243 0.000 0.871
## electronic ~~
## speed 0.705 0.048 14.607 0.000 0.611
## ci.upper Std.lv Std.all
##
## 1.192 0.917 0.917
## 1.557 0.769 0.769
## 0.939 0.701 0.701
##
## 1.317 0.674 0.674
## 1.018 0.796 0.796
##
## 0.800 0.386 0.386
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.276 0.019 14.542 0.000 0.239
## .sswk 0.179 0.018 9.735 0.000 0.143
## .sspc 0.041 0.019 2.207 0.027 0.005
## .ssei 0.339 0.021 16.134 0.000 0.298
## .ssar 0.194 0.019 10.391 0.000 0.157
## .ssmk 0.087 0.019 4.675 0.000 0.050
## .ssmc 0.322 0.019 17.179 0.000 0.286
## .ssao 0.081 0.019 4.256 0.000 0.044
## .ssai 0.382 0.021 18.202 0.000 0.341
## .sssi 0.482 0.020 24.659 0.000 0.443
## .ssno -0.002 0.020 -0.083 0.934 -0.040
## .sscs -0.080 0.019 -4.255 0.000 -0.117
## ci.upper Std.lv Std.all
## 0.313 0.276 0.269
## 0.215 0.179 0.174
## 0.078 0.041 0.040
## 0.380 0.339 0.311
## 0.230 0.194 0.192
## 0.123 0.087 0.085
## 0.359 0.322 0.309
## 0.119 0.081 0.078
## 0.423 0.382 0.339
## 0.520 0.482 0.446
## 0.037 -0.002 -0.002
## -0.043 -0.080 -0.079
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.190 0.007 27.905 0.000 0.177
## .sswk 0.201 0.008 25.914 0.000 0.186
## .sspc 0.256 0.010 26.583 0.000 0.237
## .ssei 0.326 0.012 26.362 0.000 0.301
## .ssar 0.197 0.008 23.735 0.000 0.180
## .ssmk 0.173 0.007 25.163 0.000 0.159
## .ssmc 0.291 0.010 27.824 0.000 0.271
## .ssao 0.514 0.015 35.261 0.000 0.486
## .ssai 0.510 0.019 26.932 0.000 0.473
## .sssi 0.335 0.016 21.529 0.000 0.305
## .ssno 0.345 0.018 19.189 0.000 0.310
## .sscs 0.452 0.019 23.742 0.000 0.415
## verbal 1.269 0.045 28.261 0.000 1.181
## math 1.167 0.044 26.808 0.000 1.082
## electronic 2.772 0.144 19.238 0.000 2.489
## speed 1.207 0.059 20.466 0.000 1.091
## ci.upper Std.lv Std.all
## 0.204 0.190 0.181
## 0.217 0.201 0.190
## 0.275 0.256 0.242
## 0.350 0.326 0.275
## 0.213 0.197 0.193
## 0.186 0.173 0.166
## 0.312 0.291 0.267
## 0.543 0.514 0.477
## 0.547 0.510 0.402
## 0.366 0.335 0.287
## 0.380 0.345 0.311
## 0.489 0.452 0.437
## 1.357 1.000 1.000
## 1.253 1.000 1.000
## 3.054 1.000 1.000
## 1.322 1.000 1.000
lavTestScore(metric, release = 1:15)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 177.5 15 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p54. 8.649 1 0.003
## 2 .p2. == .p55. 31.188 1 0.000
## 3 .p3. == .p56. 5.380 1 0.020
## 4 .p4. == .p57. 106.431 1 0.000
## 5 .p5. == .p58. 25.697 1 0.000
## 6 .p6. == .p59. 18.386 1 0.000
## 7 .p7. == .p60. 0.217 1 0.641
## 8 .p8. == .p61. 1.899 1 0.168
## 9 .p9. == .p62. 0.316 1 0.574
## 10 .p10. == .p63. 26.769 1 0.000
## 11 .p11. == .p64. 3.950 1 0.047
## 12 .p12. == .p65. 111.522 1 0.000
## 13 .p13. == .p66. 4.075 1 0.044
## 14 .p14. == .p67. 0.126 1 0.722
## 15 .p15. == .p68. 17.578 1 0.000
scalar<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2799.154 109.000 0.000 0.960 0.083 0.039
## aic bic
## 171618.906 172106.454
Mc(scalar)
## [1] 0.8272394
summary(scalar, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 88 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 27
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2799.154 2158.109
## Degrees of freedom 109 109
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.297
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1197.880 923.549
## 0 1601.274 1234.560
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.822 0.012 71.254 0.000 0.799
## sswk (.p2.) 0.823 0.012 67.888 0.000 0.799
## sspc (.p3.) 0.794 0.012 68.848 0.000 0.771
## ssei (.p4.) 0.409 0.015 27.249 0.000 0.379
## math =~
## ssar (.p5.) 0.838 0.012 68.108 0.000 0.814
## ssmk (.p6.) 0.636 0.018 36.337 0.000 0.602
## ssmc (.p7.) 0.458 0.013 35.565 0.000 0.432
## ssao (.p8.) 0.697 0.012 59.628 0.000 0.674
## electronic =~
## ssai (.p9.) 0.513 0.012 43.773 0.000 0.490
## sssi (.10.) 0.554 0.012 44.576 0.000 0.529
## ssmc (.11.) 0.288 0.011 27.158 0.000 0.267
## ssei (.12.) 0.319 0.012 26.191 0.000 0.295
## speed =~
## ssno (.13.) 0.781 0.015 50.978 0.000 0.751
## sscs (.14.) 0.706 0.015 48.464 0.000 0.677
## ssmk (.15.) 0.262 0.016 15.975 0.000 0.230
## ci.upper Std.lv Std.all
##
## 0.844 0.822 0.887
## 0.846 0.823 0.890
## 0.817 0.794 0.857
## 0.438 0.409 0.478
##
## 0.862 0.838 0.906
## 0.671 0.636 0.671
## 0.483 0.458 0.524
## 0.719 0.697 0.734
##
## 0.536 0.513 0.656
## 0.578 0.554 0.702
## 0.309 0.288 0.330
## 0.343 0.319 0.373
##
## 0.811 0.781 0.824
## 0.734 0.706 0.750
## 0.294 0.262 0.276
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.905 0.006 143.911 0.000 0.893
## electronic 0.873 0.012 73.400 0.000 0.850
## speed 0.701 0.017 41.247 0.000 0.668
## math ~~
## electronic 0.773 0.015 51.716 0.000 0.743
## speed 0.742 0.018 42.188 0.000 0.708
## electronic ~~
## speed 0.517 0.026 20.204 0.000 0.467
## ci.upper Std.lv Std.all
##
## 0.917 0.905 0.905
## 0.896 0.873 0.873
## 0.735 0.701 0.701
##
## 0.802 0.773 0.773
## 0.777 0.742 0.742
##
## 0.567 0.517 0.517
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.199 0.017 12.027 0.000 0.167
## .sswk (.39.) 0.183 0.017 10.856 0.000 0.150
## .sspc (.40.) 0.175 0.017 10.214 0.000 0.142
## .ssei (.41.) -0.002 0.015 -0.156 0.876 -0.032
## .ssar (.42.) 0.175 0.017 10.514 0.000 0.143
## .ssmk (.43.) 0.206 0.017 11.804 0.000 0.172
## .ssmc (.44.) 0.034 0.015 2.211 0.027 0.004
## .ssao (.45.) 0.151 0.016 9.217 0.000 0.119
## .ssai (.46.) -0.121 0.014 -8.911 0.000 -0.148
## .sssi (.47.) -0.115 0.014 -8.104 0.000 -0.142
## .ssno (.48.) 0.217 0.017 12.474 0.000 0.183
## .sscs (.49.) 0.221 0.017 12.851 0.000 0.187
## ci.upper Std.lv Std.all
## 0.232 0.199 0.215
## 0.217 0.183 0.199
## 0.209 0.175 0.189
## 0.027 -0.002 -0.003
## 0.208 0.175 0.189
## 0.241 0.206 0.217
## 0.065 0.034 0.039
## 0.183 0.151 0.159
## -0.094 -0.121 -0.155
## -0.087 -0.115 -0.145
## 0.251 0.217 0.229
## 0.255 0.221 0.235
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.184 0.007 26.562 0.000 0.170
## .sswk 0.177 0.007 26.158 0.000 0.164
## .sspc 0.228 0.009 24.067 0.000 0.209
## .ssei 0.235 0.008 28.100 0.000 0.218
## .ssar 0.153 0.007 22.397 0.000 0.140
## .ssmk 0.179 0.007 27.291 0.000 0.166
## .ssmc 0.267 0.009 28.075 0.000 0.248
## .ssao 0.416 0.013 31.691 0.000 0.390
## .ssai 0.348 0.012 29.209 0.000 0.325
## .sssi 0.316 0.012 26.664 0.000 0.293
## .ssno 0.288 0.014 20.555 0.000 0.261
## .sscs 0.387 0.016 23.775 0.000 0.355
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.197 0.184 0.214
## 0.191 0.177 0.208
## 0.246 0.228 0.265
## 0.251 0.235 0.321
## 0.167 0.153 0.179
## 0.192 0.179 0.199
## 0.285 0.267 0.350
## 0.441 0.416 0.461
## 0.372 0.348 0.570
## 0.339 0.316 0.508
## 0.316 0.288 0.321
## 0.419 0.387 0.437
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.822 0.012 71.254 0.000 0.799
## sswk (.p2.) 0.823 0.012 67.888 0.000 0.799
## sspc (.p3.) 0.794 0.012 68.848 0.000 0.771
## ssei (.p4.) 0.409 0.015 27.249 0.000 0.379
## math =~
## ssar (.p5.) 0.838 0.012 68.108 0.000 0.814
## ssmk (.p6.) 0.636 0.018 36.337 0.000 0.602
## ssmc (.p7.) 0.458 0.013 35.565 0.000 0.432
## ssao (.p8.) 0.697 0.012 59.628 0.000 0.674
## electronic =~
## ssai (.p9.) 0.513 0.012 43.773 0.000 0.490
## sssi (.10.) 0.554 0.012 44.576 0.000 0.529
## ssmc (.11.) 0.288 0.011 27.158 0.000 0.267
## ssei (.12.) 0.319 0.012 26.191 0.000 0.295
## speed =~
## ssno (.13.) 0.781 0.015 50.978 0.000 0.751
## sscs (.14.) 0.706 0.015 48.464 0.000 0.677
## ssmk (.15.) 0.262 0.016 15.975 0.000 0.230
## ci.upper Std.lv Std.all
##
## 0.844 0.925 0.900
## 0.846 0.926 0.900
## 0.817 0.894 0.861
## 0.438 0.460 0.421
##
## 0.862 0.906 0.897
## 0.671 0.688 0.673
## 0.483 0.494 0.474
## 0.719 0.753 0.723
##
## 0.536 0.853 0.764
## 0.578 0.921 0.847
## 0.309 0.479 0.459
## 0.343 0.531 0.486
##
## 0.811 0.855 0.817
## 0.734 0.773 0.754
## 0.294 0.287 0.281
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 1.116 0.039 28.731 0.000 1.040
## electronic 1.446 0.058 24.861 0.000 1.332
## speed 0.874 0.036 23.993 0.000 0.802
## math ~~
## electronic 1.221 0.053 22.953 0.000 1.117
## speed 0.947 0.037 25.311 0.000 0.873
## electronic ~~
## speed 0.715 0.048 14.898 0.000 0.621
## ci.upper Std.lv Std.all
##
## 1.192 0.918 0.918
## 1.560 0.772 0.772
## 0.945 0.708 0.708
##
## 1.326 0.680 0.680
## 1.020 0.800 0.800
##
## 0.809 0.392 0.392
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.199 0.017 12.027 0.000 0.167
## .sswk (.39.) 0.183 0.017 10.856 0.000 0.150
## .sspc (.40.) 0.175 0.017 10.214 0.000 0.142
## .ssei (.41.) -0.002 0.015 -0.156 0.876 -0.032
## .ssar (.42.) 0.175 0.017 10.514 0.000 0.143
## .ssmk (.43.) 0.206 0.017 11.804 0.000 0.172
## .ssmc (.44.) 0.034 0.015 2.211 0.027 0.004
## .ssao (.45.) 0.151 0.016 9.217 0.000 0.119
## .ssai (.46.) -0.121 0.014 -8.911 0.000 -0.148
## .sssi (.47.) -0.115 0.014 -8.104 0.000 -0.142
## .ssno (.48.) 0.217 0.017 12.474 0.000 0.183
## .sscs (.49.) 0.221 0.017 12.851 0.000 0.187
## verbal -0.008 0.030 -0.277 0.782 -0.066
## math -0.019 0.029 -0.643 0.521 -0.076
## elctrnc 1.047 0.047 22.488 0.000 0.956
## speed -0.347 0.033 -10.570 0.000 -0.412
## ci.upper Std.lv Std.all
## 0.232 0.199 0.194
## 0.217 0.183 0.178
## 0.209 0.175 0.169
## 0.027 -0.002 -0.002
## 0.208 0.175 0.174
## 0.241 0.206 0.202
## 0.065 0.034 0.033
## 0.183 0.151 0.145
## -0.094 -0.121 -0.108
## -0.087 -0.115 -0.105
## 0.251 0.217 0.207
## 0.255 0.221 0.215
## 0.050 -0.007 -0.007
## 0.038 -0.017 -0.017
## 1.138 0.629 0.629
## -0.283 -0.317 -0.317
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.201 0.007 26.899 0.000 0.186
## .sswk 0.200 0.008 25.525 0.000 0.185
## .sspc 0.278 0.011 24.982 0.000 0.256
## .ssei 0.323 0.012 26.693 0.000 0.299
## .ssar 0.199 0.008 23.676 0.000 0.183
## .ssmk 0.172 0.007 24.886 0.000 0.159
## .ssmc 0.292 0.010 27.909 0.000 0.271
## .ssao 0.517 0.015 34.895 0.000 0.488
## .ssai 0.519 0.019 27.927 0.000 0.482
## .sssi 0.335 0.015 22.136 0.000 0.305
## .ssno 0.365 0.018 19.980 0.000 0.329
## .sscs 0.454 0.020 23.166 0.000 0.415
## verbal 1.267 0.045 28.175 0.000 1.179
## math 1.167 0.044 26.698 0.000 1.081
## electronic 2.766 0.143 19.310 0.000 2.485
## speed 1.200 0.059 20.436 0.000 1.085
## ci.upper Std.lv Std.all
## 0.215 0.201 0.190
## 0.215 0.200 0.189
## 0.299 0.278 0.258
## 0.347 0.323 0.270
## 0.215 0.199 0.195
## 0.186 0.172 0.165
## 0.312 0.292 0.268
## 0.546 0.517 0.477
## 0.555 0.519 0.416
## 0.364 0.335 0.283
## 0.401 0.365 0.333
## 0.492 0.454 0.431
## 1.355 1.000 1.000
## 1.253 1.000 1.000
## 3.047 1.000 1.000
## 1.315 1.000 1.000
lavTestScore(scalar, release = 16:27)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 861.007 12 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p38. == .p91. 380.487 1 0.000
## 2 .p39. == .p92. 0.302 1 0.583
## 3 .p40. == .p93. 525.281 1 0.000
## 4 .p41. == .p94. 4.845 1 0.028
## 5 .p42. == .p95. 86.592 1 0.000
## 6 .p43. == .p96. 21.035 1 0.000
## 7 .p44. == .p97. 0.937 1 0.333
## 8 .p45. == .p98. 47.331 1 0.000
## 9 .p46. == .p99. 23.463 1 0.000
## 10 .p47. == .p100. 13.978 1 0.000
## 11 .p48. == .p101. 120.317 1 0.000
## 12 .p49. == .p102. 76.967 1 0.000
scalar2<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2131.637 107.000 0.000 0.970 0.073 0.036
## aic bic
## 170955.389 171456.670
Mc(scalar2)
## [1] 0.8669787
summary(scalar2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 96 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 25
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2131.637 1639.773
## Degrees of freedom 107 107
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.300
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 894.030 687.737
## 0 1237.607 952.036
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.824 0.011 72.172 0.000 0.802
## sswk (.p2.) 0.819 0.012 67.432 0.000 0.795
## sspc (.p3.) 0.796 0.011 69.954 0.000 0.773
## ssei (.p4.) 0.428 0.015 27.759 0.000 0.398
## math =~
## ssar (.p5.) 0.838 0.012 68.205 0.000 0.814
## ssmk (.p6.) 0.649 0.016 40.572 0.000 0.617
## ssmc (.p7.) 0.460 0.013 35.630 0.000 0.435
## ssao (.p8.) 0.696 0.012 59.632 0.000 0.673
## electronic =~
## ssai (.p9.) 0.515 0.012 43.828 0.000 0.492
## sssi (.10.) 0.556 0.013 44.502 0.000 0.532
## ssmc (.11.) 0.287 0.011 26.959 0.000 0.266
## ssei (.12.) 0.302 0.012 24.146 0.000 0.277
## speed =~
## ssno (.13.) 0.796 0.015 51.706 0.000 0.766
## sscs (.14.) 0.693 0.014 48.178 0.000 0.665
## ssmk (.15.) 0.248 0.014 17.245 0.000 0.220
## ci.upper Std.lv Std.all
##
## 0.846 0.824 0.891
## 0.842 0.819 0.887
## 0.818 0.796 0.866
## 0.458 0.428 0.499
##
## 0.863 0.838 0.906
## 0.680 0.649 0.684
## 0.485 0.460 0.527
## 0.718 0.696 0.733
##
## 0.538 0.515 0.658
## 0.581 0.556 0.705
## 0.308 0.287 0.328
## 0.326 0.302 0.352
##
## 0.826 0.796 0.836
## 0.722 0.693 0.743
## 0.276 0.248 0.262
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.906 0.006 146.578 0.000 0.894
## electronic 0.868 0.012 72.529 0.000 0.845
## speed 0.695 0.017 40.992 0.000 0.662
## math ~~
## electronic 0.769 0.015 50.990 0.000 0.739
## speed 0.738 0.018 41.740 0.000 0.704
## electronic ~~
## speed 0.507 0.026 19.695 0.000 0.457
## ci.upper Std.lv Std.all
##
## 0.918 0.906 0.906
## 0.892 0.868 0.868
## 0.728 0.695 0.695
##
## 0.798 0.769 0.769
## 0.773 0.738 0.738
##
## 0.557 0.507 0.507
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.157 0.017 9.513 0.000 0.125
## .sswk (.39.) 0.143 0.017 8.448 0.000 0.110
## .sspc 0.284 0.017 16.543 0.000 0.251
## .ssei (.41.) -0.011 0.015 -0.733 0.464 -0.040
## .ssar (.42.) 0.169 0.017 10.108 0.000 0.136
## .ssmk (.43.) 0.220 0.018 12.541 0.000 0.185
## .ssmc (.44.) 0.034 0.015 2.170 0.030 0.003
## .ssao (.45.) 0.145 0.016 8.863 0.000 0.113
## .ssai (.46.) -0.118 0.014 -8.671 0.000 -0.144
## .sssi (.47.) -0.110 0.014 -7.789 0.000 -0.138
## .ssno 0.173 0.018 9.602 0.000 0.138
## .sscs (.49.) 0.274 0.017 15.774 0.000 0.240
## ci.upper Std.lv Std.all
## 0.190 0.157 0.170
## 0.176 0.143 0.155
## 0.318 0.284 0.309
## 0.018 -0.011 -0.013
## 0.201 0.169 0.182
## 0.254 0.220 0.232
## 0.064 0.034 0.038
## 0.177 0.145 0.153
## -0.091 -0.118 -0.151
## -0.083 -0.110 -0.140
## 0.209 0.173 0.182
## 0.308 0.274 0.294
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.177 0.007 26.959 0.000 0.164
## .sswk 0.181 0.007 26.306 0.000 0.168
## .sspc 0.211 0.009 24.516 0.000 0.194
## .ssei 0.236 0.008 28.303 0.000 0.220
## .ssar 0.153 0.007 22.607 0.000 0.140
## .ssmk 0.179 0.006 27.581 0.000 0.166
## .ssmc 0.266 0.009 28.088 0.000 0.248
## .ssao 0.417 0.013 31.774 0.000 0.391
## .ssai 0.346 0.012 29.050 0.000 0.323
## .sssi 0.314 0.012 26.479 0.000 0.291
## .ssno 0.272 0.014 19.509 0.000 0.245
## .sscs 0.389 0.016 24.296 0.000 0.358
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.190 0.177 0.207
## 0.195 0.181 0.213
## 0.228 0.211 0.250
## 0.253 0.236 0.322
## 0.166 0.153 0.179
## 0.192 0.179 0.199
## 0.285 0.266 0.349
## 0.443 0.417 0.463
## 0.370 0.346 0.566
## 0.337 0.314 0.504
## 0.300 0.272 0.301
## 0.420 0.389 0.447
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.824 0.011 72.172 0.000 0.802
## sswk (.p2.) 0.819 0.012 67.432 0.000 0.795
## sspc (.p3.) 0.796 0.011 69.954 0.000 0.773
## ssei (.p4.) 0.428 0.015 27.759 0.000 0.398
## math =~
## ssar (.p5.) 0.838 0.012 68.205 0.000 0.814
## ssmk (.p6.) 0.649 0.016 40.572 0.000 0.617
## ssmc (.p7.) 0.460 0.013 35.630 0.000 0.435
## ssao (.p8.) 0.696 0.012 59.632 0.000 0.673
## electronic =~
## ssai (.p9.) 0.515 0.012 43.828 0.000 0.492
## sssi (.10.) 0.556 0.013 44.502 0.000 0.532
## ssmc (.11.) 0.287 0.011 26.959 0.000 0.266
## ssei (.12.) 0.302 0.012 24.146 0.000 0.277
## speed =~
## ssno (.13.) 0.796 0.015 51.706 0.000 0.766
## sscs (.14.) 0.693 0.014 48.178 0.000 0.665
## ssmk (.15.) 0.248 0.014 17.245 0.000 0.220
## ci.upper Std.lv Std.all
##
## 0.846 0.929 0.904
## 0.842 0.923 0.898
## 0.818 0.897 0.871
## 0.458 0.482 0.444
##
## 0.863 0.906 0.898
## 0.680 0.701 0.687
## 0.485 0.497 0.477
## 0.718 0.752 0.722
##
## 0.538 0.857 0.766
## 0.581 0.925 0.850
## 0.308 0.477 0.458
## 0.326 0.502 0.461
##
## 0.826 0.874 0.829
## 0.722 0.761 0.749
## 0.276 0.272 0.267
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 1.119 0.039 28.803 0.000 1.043
## electronic 1.439 0.058 24.813 0.000 1.325
## speed 0.870 0.036 23.942 0.000 0.799
## math ~~
## electronic 1.211 0.053 22.862 0.000 1.107
## speed 0.944 0.037 25.247 0.000 0.871
## electronic ~~
## speed 0.701 0.048 14.670 0.000 0.607
## ci.upper Std.lv Std.all
##
## 1.195 0.918 0.918
## 1.553 0.768 0.768
## 0.941 0.703 0.703
##
## 1.315 0.674 0.674
## 1.017 0.796 0.796
##
## 0.795 0.384 0.384
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.157 0.017 9.513 0.000 0.125
## .sswk (.39.) 0.143 0.017 8.448 0.000 0.110
## .sspc -0.035 0.020 -1.777 0.076 -0.073
## .ssei (.41.) -0.011 0.015 -0.733 0.464 -0.040
## .ssar (.42.) 0.169 0.017 10.108 0.000 0.136
## .ssmk (.43.) 0.220 0.018 12.541 0.000 0.185
## .ssmc (.44.) 0.034 0.015 2.170 0.030 0.003
## .ssao (.45.) 0.145 0.016 8.863 0.000 0.113
## .ssai (.46.) -0.118 0.014 -8.671 0.000 -0.144
## .sssi (.47.) -0.110 0.014 -7.789 0.000 -0.138
## .ssno 0.410 0.026 15.655 0.000 0.359
## .sscs (.49.) 0.274 0.017 15.774 0.000 0.240
## verbal 0.096 0.030 3.236 0.001 0.038
## math -0.000 0.029 -0.015 0.988 -0.057
## elctrnc 1.027 0.046 22.224 0.000 0.936
## speed -0.517 0.037 -14.116 0.000 -0.589
## ci.upper Std.lv Std.all
## 0.190 0.157 0.153
## 0.176 0.143 0.139
## 0.004 -0.035 -0.034
## 0.018 -0.011 -0.010
## 0.201 0.169 0.167
## 0.254 0.220 0.215
## 0.064 0.034 0.032
## 0.177 0.145 0.139
## -0.091 -0.118 -0.105
## -0.083 -0.110 -0.101
## 0.462 0.410 0.389
## 0.308 0.274 0.270
## 0.154 0.085 0.085
## 0.056 -0.000 -0.000
## 1.117 0.617 0.617
## -0.446 -0.471 -0.471
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.193 0.007 27.688 0.000 0.179
## .sswk 0.205 0.008 26.010 0.000 0.189
## .sspc 0.256 0.010 26.528 0.000 0.237
## .ssei 0.327 0.012 27.077 0.000 0.303
## .ssar 0.198 0.008 23.766 0.000 0.182
## .ssmk 0.173 0.007 25.182 0.000 0.159
## .ssmc 0.292 0.010 27.920 0.000 0.271
## .ssao 0.518 0.015 34.861 0.000 0.489
## .ssai 0.516 0.019 27.749 0.000 0.479
## .sssi 0.329 0.015 21.706 0.000 0.300
## .ssno 0.346 0.018 19.263 0.000 0.311
## .sscs 0.453 0.019 23.974 0.000 0.416
## verbal 1.271 0.045 28.290 0.000 1.183
## math 1.168 0.044 26.771 0.000 1.083
## electronic 2.766 0.144 19.209 0.000 2.484
## speed 1.205 0.059 20.504 0.000 1.090
## ci.upper Std.lv Std.all
## 0.207 0.193 0.183
## 0.220 0.205 0.194
## 0.274 0.256 0.241
## 0.350 0.327 0.276
## 0.214 0.198 0.194
## 0.186 0.173 0.166
## 0.312 0.292 0.269
## 0.548 0.518 0.479
## 0.552 0.516 0.413
## 0.359 0.329 0.278
## 0.381 0.346 0.312
## 0.490 0.453 0.439
## 1.359 1.000 1.000
## 1.254 1.000 1.000
## 3.048 1.000 1.000
## 1.321 1.000 1.000
lavTestScore(scalar2, release = 16:25, standardized=T, epc=T)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 215.798 10 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p38. == .p91. 112.596 1 0.000
## 2 .p39. == .p92. 111.585 1 0.000
## 3 .p41. == .p94. 0.031 1 0.860
## 4 .p42. == .p95. 53.032 1 0.000
## 5 .p43. == .p96. 1.756 1 0.185
## 6 .p44. == .p97. 1.226 1 0.268
## 7 .p45. == .p98. 59.985 1 0.000
## 8 .p46. == .p99. 17.426 1 0.000
## 9 .p47. == .p100. 22.013 1 0.000
## 10 .p49. == .p102. 1.756 1 0.185
##
## $epc
##
## expected parameter changes (epc) and expected parameter values (epv):
##
## lhs op rhs block group free label plabel est epc
## 1 verbal =~ ssgs 1 1 1 .p1. .p1. 0.824 -0.002
## 2 verbal =~ sswk 1 1 2 .p2. .p2. 0.819 0.002
## 3 verbal =~ sspc 1 1 3 .p3. .p3. 0.796 0.000
## 4 verbal =~ ssei 1 1 4 .p4. .p4. 0.428 -0.003
## 5 math =~ ssar 1 1 5 .p5. .p5. 0.838 0.000
## 6 math =~ ssmk 1 1 6 .p6. .p6. 0.649 0.007
## 7 math =~ ssmc 1 1 7 .p7. .p7. 0.460 -0.007
## 8 math =~ ssao 1 1 8 .p8. .p8. 0.696 0.000
## 9 electronic =~ ssai 1 1 9 .p9. .p9. 0.515 0.009
## 10 electronic =~ sssi 1 1 10 .p10. .p10. 0.556 -0.008
## 11 electronic =~ ssmc 1 1 11 .p11. .p11. 0.287 0.006
## 12 electronic =~ ssei 1 1 12 .p12. .p12. 0.302 0.003
## 13 speed =~ ssno 1 1 13 .p13. .p13. 0.796 0.001
## 14 speed =~ sscs 1 1 14 .p14. .p14. 0.693 0.001
## 15 speed =~ ssmk 1 1 15 .p15. .p15. 0.248 -0.008
## 16 ssgs ~~ ssgs 1 1 16 .p16. 0.177 0.000
## 17 sswk ~~ sswk 1 1 17 .p17. 0.181 0.000
## 18 sspc ~~ sspc 1 1 18 .p18. 0.211 0.000
## 19 ssei ~~ ssei 1 1 19 .p19. 0.236 0.000
## 20 ssar ~~ ssar 1 1 20 .p20. 0.153 0.000
## 21 ssmk ~~ ssmk 1 1 21 .p21. 0.179 0.000
## 22 ssmc ~~ ssmc 1 1 22 .p22. 0.266 0.000
## 23 ssao ~~ ssao 1 1 23 .p23. 0.417 0.000
## 24 ssai ~~ ssai 1 1 24 .p24. 0.346 -0.002
## 25 sssi ~~ sssi 1 1 25 .p25. 0.314 0.003
## 26 ssno ~~ ssno 1 1 26 .p26. 0.272 -0.001
## 27 sscs ~~ sscs 1 1 27 .p27. 0.389 -0.001
## 28 verbal ~~ verbal 1 1 0 .p28. 1.000 NA
## 29 math ~~ math 1 1 0 .p29. 1.000 NA
## 30 electronic ~~ electronic 1 1 0 .p30. 1.000 NA
## 31 speed ~~ speed 1 1 0 .p31. 1.000 NA
## 32 verbal ~~ math 1 1 28 .p32. 0.906 0.000
## 33 verbal ~~ electronic 1 1 29 .p33. 0.868 0.001
## 34 verbal ~~ speed 1 1 30 .p34. 0.695 -0.001
## 35 math ~~ electronic 1 1 31 .p35. 0.769 0.001
## 36 math ~~ speed 1 1 32 .p36. 0.738 0.000
## 37 electronic ~~ speed 1 1 33 .p37. 0.507 0.000
## 38 ssgs ~1 1 1 34 .p38. .p38. 0.157 -0.038
## 39 sswk ~1 1 1 35 .p39. .p39. 0.143 0.039
## 40 sspc ~1 1 1 36 .p40. 0.284 0.000
## 41 ssei ~1 1 1 37 .p41. .p41. -0.011 0.001
## 42 ssar ~1 1 1 38 .p42. .p42. 0.169 -0.021
## 43 ssmk ~1 1 1 39 .p43. .p43. 0.220 0.005
## 44 ssmc ~1 1 1 40 .p44. .p44. 0.034 0.005
## 45 ssao ~1 1 1 41 .p45. .p45. 0.145 0.053
## 46 ssai ~1 1 1 42 .p46. .p46. -0.118 0.020
## 47 sssi ~1 1 1 43 .p47. .p47. -0.110 -0.021
## 48 ssno ~1 1 1 44 .p48. 0.173 0.000
## 49 sscs ~1 1 1 45 .p49. .p49. 0.274 -0.004
## 50 verbal ~1 1 1 0 .p50. 0.000 NA
## 51 math ~1 1 1 0 .p51. 0.000 NA
## 52 electronic ~1 1 1 0 .p52. 0.000 NA
## 53 speed ~1 1 1 0 .p53. 0.000 NA
## 54 verbal =~ ssgs 2 2 46 .p1. .p54. 0.824 -0.002
## 55 verbal =~ sswk 2 2 47 .p2. .p55. 0.819 0.002
## 56 verbal =~ sspc 2 2 48 .p3. .p56. 0.796 0.000
## 57 verbal =~ ssei 2 2 49 .p4. .p57. 0.428 -0.003
## 58 math =~ ssar 2 2 50 .p5. .p58. 0.838 0.000
## 59 math =~ ssmk 2 2 51 .p6. .p59. 0.649 0.007
## 60 math =~ ssmc 2 2 52 .p7. .p60. 0.460 -0.007
## 61 math =~ ssao 2 2 53 .p8. .p61. 0.696 0.000
## 62 electronic =~ ssai 2 2 54 .p9. .p62. 0.515 0.009
## 63 electronic =~ sssi 2 2 55 .p10. .p63. 0.556 -0.008
## 64 electronic =~ ssmc 2 2 56 .p11. .p64. 0.287 0.006
## 65 electronic =~ ssei 2 2 57 .p12. .p65. 0.302 0.003
## 66 speed =~ ssno 2 2 58 .p13. .p66. 0.796 0.001
## 67 speed =~ sscs 2 2 59 .p14. .p67. 0.693 0.001
## 68 speed =~ ssmk 2 2 60 .p15. .p68. 0.248 -0.008
## 69 ssgs ~~ ssgs 2 2 61 .p69. 0.193 0.000
## 70 sswk ~~ sswk 2 2 62 .p70. 0.205 0.000
## 71 sspc ~~ sspc 2 2 63 .p71. 0.256 0.000
## epv sepc.lv sepc.all sepc.nox
## 1 0.822 -0.002 -0.002 -0.002
## 2 0.821 0.002 0.002 0.002
## 3 0.796 0.000 0.000 0.000
## 4 0.425 -0.003 -0.004 -0.004
## 5 0.838 0.000 0.000 0.000
## 6 0.655 0.007 0.007 0.007
## 7 0.454 -0.007 -0.007 -0.007
## 8 0.696 0.000 0.000 0.000
## 9 0.524 0.009 0.011 0.011
## 10 0.548 -0.008 -0.011 -0.011
## 11 0.293 0.006 0.007 0.007
## 12 0.304 0.003 0.003 0.003
## 13 0.796 0.001 0.001 0.001
## 14 0.695 0.001 0.001 0.001
## 15 0.240 -0.008 -0.008 -0.008
## 16 0.177 0.177 0.207 0.207
## 17 0.181 -0.181 -0.213 -0.213
## 18 0.211 -0.211 -0.250 -0.250
## 19 0.236 -0.236 -0.322 -0.322
## 20 0.153 0.153 0.179 0.179
## 21 0.179 0.179 0.199 0.199
## 22 0.266 -0.266 -0.349 -0.349
## 23 0.417 0.417 0.463 0.463
## 24 0.345 -0.346 -0.566 -0.566
## 25 0.317 0.314 0.504 0.504
## 26 0.271 -0.272 -0.301 -0.301
## 27 0.388 -0.389 -0.447 -0.447
## 28 NA NA NA NA
## 29 NA NA NA NA
## 30 NA NA NA NA
## 31 NA NA NA NA
## 32 0.906 0.000 0.000 0.000
## 33 0.870 0.001 0.001 0.001
## 34 0.694 -0.001 -0.001 -0.001
## 35 0.770 0.001 0.001 0.001
## 36 0.739 0.000 0.000 0.000
## 37 0.507 0.000 0.000 0.000
## 38 0.120 -0.038 -0.041 -0.041
## 39 0.181 0.039 0.042 0.042
## 40 0.284 0.000 0.000 0.000
## 41 -0.010 0.001 0.001 0.001
## 42 0.148 -0.021 -0.022 -0.022
## 43 0.224 0.005 0.005 0.005
## 44 0.039 0.005 0.006 0.006
## 45 0.198 0.053 0.056 0.056
## 46 -0.097 0.020 0.026 0.026
## 47 -0.131 -0.021 -0.026 -0.026
## 48 0.173 0.000 0.000 0.000
## 49 0.271 -0.004 -0.004 -0.004
## 50 NA NA NA NA
## 51 NA NA NA NA
## 52 NA NA NA NA
## 53 NA NA NA NA
## 54 0.822 -0.002 -0.002 -0.002
## 55 0.821 0.002 0.002 0.002
## 56 0.796 0.000 0.000 0.000
## 57 0.425 -0.004 -0.003 -0.003
## 58 0.838 0.000 0.000 0.000
## 59 0.655 0.007 0.007 0.007
## 60 0.454 -0.007 -0.007 -0.007
## 61 0.696 0.000 0.000 0.000
## 62 0.524 0.015 0.013 0.013
## 63 0.548 -0.014 -0.013 -0.013
## 64 0.293 0.010 0.010 0.010
## 65 0.304 0.004 0.004 0.004
## 66 0.796 0.001 0.001 0.001
## 67 0.695 0.001 0.001 0.001
## 68 0.240 -0.009 -0.009 -0.009
## 69 0.193 0.193 0.183 0.183
## 70 0.204 -0.205 -0.194 -0.194
## 71 0.255 -0.256 -0.241 -0.241
## [ reached 'max' / getOption("max.print") -- omitted 35 rows ]
strict<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2432.221 119.000 0.000 0.966 0.074 0.039
## aic bic
## 171231.974 171650.853
Mc(strict)
## [1] 0.8495176
cf.cov<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(cf.cov, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2366.862 113.000 0.000 0.967 0.075 0.094
## aic bic
## 171178.615 171638.695
Mc(cf.cov)
## [1] 0.8530802
summary(cf.cov, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 65 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 98
## Number of equality constraints 31
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2366.862 1821.326
## Degrees of freedom 113 113
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.300
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 982.231 755.837
## 0 1384.632 1065.489
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.880 0.010 88.892 0.000 0.861
## sswk (.p2.) 0.875 0.010 87.006 0.000 0.855
## sspc (.p3.) 0.851 0.009 94.357 0.000 0.833
## ssei (.p4.) 0.461 0.016 28.861 0.000 0.430
## math =~
## ssar (.p5.) 0.878 0.010 84.774 0.000 0.858
## ssmk (.p6.) 0.677 0.015 44.066 0.000 0.647
## ssmc (.p7.) 0.484 0.013 37.599 0.000 0.459
## ssao (.p8.) 0.728 0.010 71.529 0.000 0.708
## electronic =~
## ssai (.p9.) 0.582 0.012 49.298 0.000 0.559
## sssi (.10.) 0.636 0.012 53.464 0.000 0.612
## ssmc (.11.) 0.325 0.012 28.051 0.000 0.302
## ssei (.12.) 0.337 0.014 24.341 0.000 0.310
## speed =~
## ssno (.13.) 0.832 0.015 53.960 0.000 0.801
## sscs (.14.) 0.725 0.014 50.390 0.000 0.697
## ssmk (.15.) 0.262 0.014 18.051 0.000 0.233
## ci.upper Std.lv Std.all
##
## 0.900 0.880 0.903
## 0.894 0.875 0.899
## 0.869 0.851 0.879
## 0.492 0.461 0.502
##
## 0.899 0.878 0.913
## 0.708 0.677 0.685
## 0.509 0.484 0.524
## 0.748 0.728 0.748
##
## 0.605 0.582 0.702
## 0.659 0.636 0.752
## 0.347 0.325 0.351
## 0.364 0.337 0.367
##
## 0.862 0.832 0.846
## 0.753 0.725 0.757
## 0.290 0.262 0.265
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.32.) 0.907 0.006 164.362 0.000 0.896
## elctrnc (.33.) 0.898 0.009 98.442 0.000 0.880
## speed (.34.) 0.702 0.014 50.236 0.000 0.674
## math ~~
## elctrnc (.35.) 0.790 0.012 67.084 0.000 0.767
## speed (.36.) 0.768 0.014 55.265 0.000 0.741
## electronic ~~
## speed (.37.) 0.504 0.021 24.313 0.000 0.463
## ci.upper Std.lv Std.all
##
## 0.918 0.907 0.907
## 0.916 0.898 0.898
## 0.729 0.702 0.702
##
## 0.813 0.790 0.790
## 0.796 0.768 0.768
##
## 0.545 0.504 0.504
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.157 0.017 9.480 0.000 0.124
## .sswk (.39.) 0.143 0.017 8.438 0.000 0.109
## .sspc 0.284 0.017 16.543 0.000 0.251
## .ssei (.41.) -0.009 0.015 -0.629 0.529 -0.038
## .ssar (.42.) 0.168 0.017 10.099 0.000 0.136
## .ssmk (.43.) 0.220 0.018 12.562 0.000 0.186
## .ssmc (.44.) 0.034 0.015 2.171 0.030 0.003
## .ssao (.45.) 0.145 0.016 8.851 0.000 0.113
## .ssai (.46.) -0.117 0.014 -8.642 0.000 -0.144
## .sssi (.47.) -0.112 0.014 -7.934 0.000 -0.140
## .ssno 0.173 0.018 9.602 0.000 0.138
## .sscs (.49.) 0.274 0.017 15.761 0.000 0.240
## ci.upper Std.lv Std.all
## 0.189 0.157 0.161
## 0.176 0.143 0.147
## 0.318 0.284 0.293
## 0.020 -0.009 -0.010
## 0.201 0.168 0.175
## 0.254 0.220 0.223
## 0.064 0.034 0.036
## 0.177 0.145 0.148
## -0.091 -0.117 -0.141
## -0.085 -0.112 -0.133
## 0.209 0.173 0.176
## 0.308 0.274 0.286
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.176 0.007 26.989 0.000 0.163
## .sswk 0.181 0.007 26.314 0.000 0.168
## .sspc 0.214 0.009 24.647 0.000 0.197
## .ssei 0.238 0.008 28.417 0.000 0.222
## .ssar 0.154 0.007 22.803 0.000 0.140
## .ssmk 0.178 0.007 27.265 0.000 0.165
## .ssmc 0.265 0.009 28.180 0.000 0.247
## .ssao 0.418 0.013 31.858 0.000 0.392
## .ssai 0.348 0.012 29.205 0.000 0.325
## .sssi 0.311 0.012 26.524 0.000 0.288
## .ssno 0.274 0.014 19.687 0.000 0.247
## .sscs 0.392 0.016 24.455 0.000 0.361
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## ci.upper Std.lv Std.all
## 0.189 0.176 0.185
## 0.195 0.181 0.191
## 0.231 0.214 0.228
## 0.254 0.238 0.282
## 0.167 0.154 0.166
## 0.190 0.178 0.182
## 0.284 0.265 0.311
## 0.444 0.418 0.441
## 0.372 0.348 0.507
## 0.334 0.311 0.435
## 0.302 0.274 0.284
## 0.424 0.392 0.427
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.880 0.010 88.892 0.000 0.861
## sswk (.p2.) 0.875 0.010 87.006 0.000 0.855
## sspc (.p3.) 0.851 0.009 94.357 0.000 0.833
## ssei (.p4.) 0.461 0.016 28.861 0.000 0.430
## math =~
## ssar (.p5.) 0.878 0.010 84.774 0.000 0.858
## ssmk (.p6.) 0.677 0.015 44.066 0.000 0.647
## ssmc (.p7.) 0.484 0.013 37.599 0.000 0.459
## ssao (.p8.) 0.728 0.010 71.529 0.000 0.708
## electronic =~
## ssai (.p9.) 0.582 0.012 49.298 0.000 0.559
## sssi (.10.) 0.636 0.012 53.464 0.000 0.612
## ssmc (.11.) 0.325 0.012 28.051 0.000 0.302
## ssei (.12.) 0.337 0.014 24.341 0.000 0.310
## speed =~
## ssno (.13.) 0.832 0.015 53.960 0.000 0.801
## sscs (.14.) 0.725 0.014 50.390 0.000 0.697
## ssmk (.15.) 0.262 0.014 18.051 0.000 0.233
## ci.upper Std.lv Std.all
##
## 0.900 0.877 0.893
## 0.894 0.872 0.887
## 0.869 0.848 0.861
## 0.492 0.459 0.455
##
## 0.899 0.871 0.891
## 0.708 0.671 0.683
## 0.509 0.480 0.491
## 0.748 0.722 0.708
##
## 0.605 0.769 0.731
## 0.659 0.840 0.831
## 0.347 0.429 0.439
## 0.364 0.446 0.441
##
## 0.862 0.840 0.820
## 0.753 0.732 0.738
## 0.290 0.264 0.269
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.32.) 0.907 0.006 164.362 0.000 0.896
## elctrnc (.33.) 0.898 0.009 98.442 0.000 0.880
## speed (.34.) 0.702 0.014 50.236 0.000 0.674
## math ~~
## elctrnc (.35.) 0.790 0.012 67.084 0.000 0.767
## speed (.36.) 0.768 0.014 55.265 0.000 0.741
## electronic ~~
## speed (.37.) 0.504 0.021 24.313 0.000 0.463
## ci.upper Std.lv Std.all
##
## 0.918 0.918 0.918
## 0.916 0.682 0.682
## 0.729 0.697 0.697
##
## 0.813 0.603 0.603
## 0.796 0.768 0.768
##
## 0.545 0.378 0.378
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.157 0.017 9.480 0.000 0.124
## .sswk (.39.) 0.143 0.017 8.438 0.000 0.109
## .sspc -0.035 0.020 -1.807 0.071 -0.073
## .ssei (.41.) -0.009 0.015 -0.629 0.529 -0.038
## .ssar (.42.) 0.168 0.017 10.099 0.000 0.136
## .ssmk (.43.) 0.220 0.018 12.562 0.000 0.186
## .ssmc (.44.) 0.034 0.015 2.171 0.030 0.003
## .ssao (.45.) 0.145 0.016 8.851 0.000 0.113
## .ssai (.46.) -0.117 0.014 -8.642 0.000 -0.144
## .sssi (.47.) -0.112 0.014 -7.934 0.000 -0.140
## .ssno 0.409 0.026 15.630 0.000 0.358
## .sscs (.49.) 0.274 0.017 15.761 0.000 0.240
## verbal 0.090 0.028 3.262 0.001 0.036
## math 0.000 0.028 0.005 0.996 -0.054
## elctrnc 0.906 0.040 22.908 0.000 0.829
## speed -0.494 0.035 -13.938 0.000 -0.564
## ci.upper Std.lv Std.all
## 0.189 0.157 0.160
## 0.176 0.143 0.145
## 0.003 -0.035 -0.036
## 0.020 -0.009 -0.009
## 0.201 0.168 0.172
## 0.254 0.220 0.224
## 0.064 0.034 0.034
## 0.177 0.145 0.142
## -0.091 -0.117 -0.111
## -0.085 -0.112 -0.111
## 0.461 0.409 0.400
## 0.308 0.274 0.276
## 0.144 0.090 0.090
## 0.054 0.000 0.000
## 0.984 0.685 0.685
## -0.425 -0.490 -0.490
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.196 0.007 27.733 0.000 0.182
## .sswk 0.206 0.008 25.841 0.000 0.190
## .sspc 0.250 0.009 26.519 0.000 0.232
## .ssei 0.333 0.012 27.174 0.000 0.309
## .ssar 0.197 0.008 23.645 0.000 0.180
## .ssmk 0.175 0.007 25.515 0.000 0.161
## .ssmc 0.293 0.010 28.065 0.000 0.273
## .ssao 0.518 0.015 34.840 0.000 0.489
## .ssai 0.515 0.019 27.450 0.000 0.478
## .sssi 0.317 0.015 21.083 0.000 0.288
## .ssno 0.344 0.018 19.085 0.000 0.308
## .sscs 0.448 0.019 23.715 0.000 0.411
## verbal 0.993 0.012 80.822 0.000 0.969
## math 0.982 0.014 70.286 0.000 0.955
## electronic 1.747 0.066 26.589 0.000 1.618
## speed 1.019 0.041 24.995 0.000 0.939
## ci.upper Std.lv Std.all
## 0.209 0.196 0.203
## 0.221 0.206 0.213
## 0.269 0.250 0.258
## 0.357 0.333 0.326
## 0.213 0.197 0.206
## 0.188 0.175 0.180
## 0.314 0.293 0.307
## 0.548 0.518 0.499
## 0.551 0.515 0.465
## 0.347 0.317 0.310
## 0.379 0.344 0.328
## 0.486 0.448 0.456
## 1.018 1.000 1.000
## 1.010 1.000 1.000
## 1.876 1.000 1.000
## 1.099 1.000 1.000
cf.vcov<-cfa(cf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances", "lv.variances"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(cf.vcov, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2834.448 117.000 0.000 0.960 0.081 0.114
## aic bic
## 171638.200 172070.813
Mc(cf.vcov)
## [1] 0.8256491
cf.cov2<-cfa(cf.lv, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(cf.cov2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2371.017 116.000 0.000 0.967 0.074 0.094
## aic bic
## 171176.770 171616.249
Mc(cf.cov2)
## [1] 0.8530107
summary(cf.cov2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 60 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 95
## Number of equality constraints 31
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2371.017 1814.487
## Degrees of freedom 116 116
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.307
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 984.551 753.455
## 0 1386.466 1061.032
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.879 0.009 92.854 0.000 0.860
## sswk (.p2.) 0.873 0.010 91.146 0.000 0.854
## sspc (.p3.) 0.849 0.009 99.150 0.000 0.833
## ssei (.p4.) 0.461 0.016 28.827 0.000 0.429
## math =~
## ssar (.p5.) 0.875 0.010 88.324 0.000 0.855
## ssmk (.p6.) 0.675 0.015 44.656 0.000 0.645
## ssmc (.p7.) 0.482 0.013 38.043 0.000 0.457
## ssao (.p8.) 0.725 0.010 74.517 0.000 0.706
## electronic =~
## ssai (.p9.) 0.582 0.012 49.241 0.000 0.559
## sssi (.10.) 0.636 0.012 53.391 0.000 0.612
## ssmc (.11.) 0.325 0.012 28.077 0.000 0.302
## ssei (.12.) 0.337 0.014 24.401 0.000 0.310
## speed =~
## ssno (.13.) 0.835 0.013 65.533 0.000 0.810
## sscs (.14.) 0.729 0.012 60.232 0.000 0.705
## ssmk (.15.) 0.262 0.015 18.042 0.000 0.234
## ci.upper Std.lv Std.all
##
## 0.897 0.879 0.902
## 0.892 0.873 0.898
## 0.866 0.849 0.879
## 0.492 0.461 0.502
##
## 0.894 0.875 0.912
## 0.705 0.675 0.684
## 0.507 0.482 0.522
## 0.744 0.725 0.746
##
## 0.605 0.582 0.702
## 0.659 0.636 0.752
## 0.347 0.325 0.352
## 0.364 0.337 0.367
##
## 0.860 0.835 0.848
## 0.752 0.729 0.759
## 0.291 0.262 0.266
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.32.) 0.913 0.004 219.913 0.000 0.905
## elctrnc (.33.) 0.898 0.009 98.442 0.000 0.880
## speed (.34.) 0.700 0.012 58.813 0.000 0.676
## math ~~
## elctrnc (.35.) 0.793 0.012 68.790 0.000 0.770
## speed (.36.) 0.768 0.012 66.274 0.000 0.745
## electronic ~~
## speed (.37.) 0.500 0.019 26.310 0.000 0.463
## ci.upper Std.lv Std.all
##
## 0.921 0.913 0.913
## 0.916 0.898 0.898
## 0.723 0.700 0.700
##
## 0.816 0.793 0.793
## 0.790 0.768 0.768
##
## 0.538 0.500 0.500
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.157 0.017 9.486 0.000 0.125
## .sswk (.39.) 0.142 0.017 8.425 0.000 0.109
## .sspc 0.284 0.017 16.543 0.000 0.251
## .ssei (.41.) -0.009 0.015 -0.625 0.532 -0.038
## .ssar (.42.) 0.169 0.017 10.115 0.000 0.136
## .ssmk (.43.) 0.220 0.018 12.557 0.000 0.186
## .ssmc (.44.) 0.034 0.015 2.170 0.030 0.003
## .ssao (.45.) 0.144 0.016 8.848 0.000 0.112
## .ssai (.46.) -0.117 0.014 -8.643 0.000 -0.144
## .sssi (.47.) -0.112 0.014 -7.938 0.000 -0.140
## .ssno 0.173 0.018 9.602 0.000 0.138
## .sscs (.49.) 0.274 0.017 15.764 0.000 0.240
## ci.upper Std.lv Std.all
## 0.189 0.157 0.161
## 0.175 0.142 0.146
## 0.318 0.284 0.294
## 0.020 -0.009 -0.010
## 0.201 0.169 0.176
## 0.254 0.220 0.223
## 0.064 0.034 0.036
## 0.176 0.144 0.149
## -0.091 -0.117 -0.141
## -0.085 -0.112 -0.133
## 0.209 0.173 0.176
## 0.308 0.274 0.285
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.177 0.006 27.214 0.000 0.164
## .sswk 0.182 0.007 26.580 0.000 0.169
## .sspc 0.213 0.009 24.727 0.000 0.196
## .ssei 0.238 0.008 28.434 0.000 0.222
## .ssar 0.155 0.007 23.406 0.000 0.142
## .ssmk 0.178 0.007 27.362 0.000 0.165
## .ssmc 0.266 0.009 28.228 0.000 0.247
## .ssao 0.419 0.013 31.974 0.000 0.393
## .ssai 0.348 0.012 29.202 0.000 0.325
## .sssi 0.311 0.012 26.522 0.000 0.288
## .ssno 0.273 0.014 19.222 0.000 0.245
## .sscs 0.392 0.016 24.532 0.000 0.361
## electronic 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.189 0.177 0.186
## 0.196 0.182 0.193
## 0.230 0.213 0.228
## 0.255 0.238 0.283
## 0.168 0.155 0.169
## 0.191 0.178 0.183
## 0.284 0.266 0.312
## 0.444 0.419 0.443
## 0.372 0.348 0.507
## 0.334 0.311 0.435
## 0.301 0.273 0.281
## 0.423 0.392 0.425
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.879 0.009 92.854 0.000 0.860
## sswk (.p2.) 0.873 0.010 91.146 0.000 0.854
## sspc (.p3.) 0.849 0.009 99.150 0.000 0.833
## ssei (.p4.) 0.461 0.016 28.827 0.000 0.429
## math =~
## ssar (.p5.) 0.875 0.010 88.324 0.000 0.855
## ssmk (.p6.) 0.675 0.015 44.656 0.000 0.645
## ssmc (.p7.) 0.482 0.013 38.043 0.000 0.457
## ssao (.p8.) 0.725 0.010 74.517 0.000 0.706
## electronic =~
## ssai (.p9.) 0.582 0.012 49.241 0.000 0.559
## sssi (.10.) 0.636 0.012 53.391 0.000 0.612
## ssmc (.11.) 0.325 0.012 28.077 0.000 0.302
## ssei (.12.) 0.337 0.014 24.401 0.000 0.310
## speed =~
## ssno (.13.) 0.835 0.013 65.533 0.000 0.810
## sscs (.14.) 0.729 0.012 60.232 0.000 0.705
## ssmk (.15.) 0.262 0.015 18.042 0.000 0.234
## ci.upper Std.lv Std.all
##
## 0.897 0.879 0.894
## 0.892 0.873 0.888
## 0.866 0.849 0.861
## 0.492 0.461 0.456
##
## 0.894 0.875 0.893
## 0.705 0.675 0.685
## 0.507 0.482 0.493
## 0.744 0.725 0.710
##
## 0.605 0.769 0.731
## 0.659 0.840 0.830
## 0.347 0.429 0.438
## 0.364 0.445 0.440
##
## 0.860 0.835 0.818
## 0.752 0.729 0.736
## 0.291 0.262 0.266
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.32.) 0.913 0.004 219.913 0.000 0.905
## elctrnc (.33.) 0.898 0.009 98.442 0.000 0.880
## speed (.34.) 0.700 0.012 58.813 0.000 0.676
## math ~~
## elctrnc (.35.) 0.793 0.012 68.790 0.000 0.770
## speed (.36.) 0.768 0.012 66.274 0.000 0.745
## electronic ~~
## speed (.37.) 0.500 0.019 26.310 0.000 0.463
## ci.upper Std.lv Std.all
##
## 0.921 0.913 0.913
## 0.916 0.680 0.680
## 0.723 0.700 0.700
##
## 0.816 0.600 0.600
## 0.790 0.768 0.768
##
## 0.538 0.379 0.379
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.38.) 0.157 0.017 9.486 0.000 0.125
## .sswk (.39.) 0.142 0.017 8.425 0.000 0.109
## .sspc -0.035 0.020 -1.808 0.071 -0.074
## .ssei (.41.) -0.009 0.015 -0.625 0.532 -0.038
## .ssar (.42.) 0.169 0.017 10.115 0.000 0.136
## .ssmk (.43.) 0.220 0.018 12.557 0.000 0.186
## .ssmc (.44.) 0.034 0.015 2.170 0.030 0.003
## .ssao (.45.) 0.144 0.016 8.848 0.000 0.112
## .ssai (.46.) -0.117 0.014 -8.643 0.000 -0.144
## .sssi (.47.) -0.112 0.014 -7.938 0.000 -0.140
## .ssno 0.409 0.026 15.630 0.000 0.358
## .sscs (.49.) 0.274 0.017 15.764 0.000 0.240
## verbal 0.090 0.028 3.263 0.001 0.036
## math 0.000 0.028 0.004 0.997 -0.054
## elctrnc 0.906 0.040 22.909 0.000 0.829
## speed -0.492 0.035 -14.027 0.000 -0.561
## ci.upper Std.lv Std.all
## 0.189 0.157 0.160
## 0.175 0.142 0.145
## 0.003 -0.035 -0.036
## 0.020 -0.009 -0.009
## 0.201 0.169 0.172
## 0.254 0.220 0.223
## 0.064 0.034 0.034
## 0.176 0.144 0.141
## -0.091 -0.117 -0.112
## -0.085 -0.112 -0.111
## 0.461 0.409 0.401
## 0.308 0.274 0.277
## 0.145 0.090 0.090
## 0.054 0.000 0.000
## 0.984 0.686 0.686
## -0.423 -0.492 -0.492
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.195 0.007 27.815 0.000 0.181
## .sswk 0.204 0.008 26.082 0.000 0.189
## .sspc 0.251 0.009 26.482 0.000 0.232
## .ssei 0.333 0.012 27.168 0.000 0.309
## .ssar 0.195 0.008 23.749 0.000 0.178
## .ssmk 0.174 0.007 25.416 0.000 0.160
## .ssmc 0.293 0.010 28.019 0.000 0.272
## .ssao 0.518 0.015 34.843 0.000 0.488
## .ssai 0.515 0.019 27.451 0.000 0.478
## .sssi 0.317 0.015 21.080 0.000 0.288
## .ssno 0.345 0.018 18.909 0.000 0.310
## .sscs 0.449 0.019 23.824 0.000 0.412
## electronic 1.744 0.066 26.555 0.000 1.616
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.208 0.195 0.201
## 0.219 0.204 0.211
## 0.269 0.251 0.258
## 0.356 0.333 0.326
## 0.211 0.195 0.203
## 0.187 0.174 0.179
## 0.313 0.293 0.306
## 0.547 0.518 0.496
## 0.551 0.515 0.466
## 0.347 0.317 0.310
## 0.381 0.345 0.331
## 0.486 0.449 0.458
## 1.873 1.000 1.000
reduced<-cfa(cf.reduced, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(reduced, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2371.017 117.000 0.000 0.967 0.074 0.094
## aic bic
## 171174.770 171607.383
Mc(reduced)
## [1] 0.8530708
summary(reduced, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 58 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 94
## Number of equality constraints 31
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2371.017 1815.809
## Degrees of freedom 117 117
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.306
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 984.550 754.003
## 0 1386.467 1061.805
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.879 0.009 92.853 0.000 0.860
## sswk (.p2.) 0.873 0.010 91.146 0.000 0.854
## sspc (.p3.) 0.849 0.009 99.151 0.000 0.833
## ssei (.p4.) 0.461 0.016 28.826 0.000 0.429
## math =~
## ssar (.p5.) 0.875 0.010 88.369 0.000 0.855
## ssmk (.p6.) 0.675 0.015 44.743 0.000 0.645
## ssmc (.p7.) 0.482 0.013 38.089 0.000 0.457
## ssao (.p8.) 0.725 0.010 74.552 0.000 0.706
## electronic =~
## ssai (.p9.) 0.582 0.012 49.241 0.000 0.559
## sssi (.10.) 0.636 0.012 53.389 0.000 0.612
## ssmc (.11.) 0.325 0.012 28.114 0.000 0.302
## ssei (.12.) 0.337 0.014 24.401 0.000 0.310
## speed =~
## ssno (.13.) 0.835 0.013 65.533 0.000 0.810
## sscs (.14.) 0.729 0.012 60.181 0.000 0.705
## ssmk (.15.) 0.262 0.014 18.120 0.000 0.234
## ci.upper Std.lv Std.all
##
## 0.897 0.879 0.902
## 0.892 0.873 0.898
## 0.866 0.849 0.879
## 0.492 0.461 0.502
##
## 0.894 0.875 0.912
## 0.704 0.675 0.684
## 0.507 0.482 0.522
## 0.744 0.725 0.746
##
## 0.605 0.582 0.702
## 0.659 0.636 0.752
## 0.347 0.325 0.352
## 0.364 0.337 0.367
##
## 0.860 0.835 0.848
## 0.752 0.729 0.759
## 0.291 0.262 0.266
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.33.) 0.913 0.004 219.913 0.000 0.905
## elctrnc (.34.) 0.898 0.009 98.439 0.000 0.880
## speed (.35.) 0.700 0.012 58.852 0.000 0.676
## math ~~
## elctrnc (.36.) 0.793 0.012 68.793 0.000 0.770
## speed (.37.) 0.768 0.012 66.246 0.000 0.745
## electronic ~~
## speed (.38.) 0.500 0.019 26.314 0.000 0.463
## ci.upper Std.lv Std.all
##
## 0.921 0.913 0.913
## 0.916 0.898 0.898
## 0.723 0.700 0.700
##
## 0.816 0.793 0.793
## 0.790 0.768 0.768
##
## 0.538 0.500 0.500
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 0.000 0.000
## .ssgs (.39.) 0.157 0.014 10.927 0.000 0.129
## .sswk (.40.) 0.142 0.014 9.890 0.000 0.114
## .sspc 0.284 0.015 19.375 0.000 0.255
## .ssei (.42.) -0.009 0.013 -0.689 0.491 -0.035
## .ssar (.43.) 0.169 0.013 13.350 0.000 0.144
## .ssmk (.44.) 0.220 0.014 16.052 0.000 0.193
## .ssmc (.45.) 0.034 0.013 2.603 0.009 0.008
## .ssao (.46.) 0.144 0.013 10.944 0.000 0.119
## .ssai (.47.) -0.117 0.013 -9.110 0.000 -0.142
## .sssi (.48.) -0.112 0.013 -8.480 0.000 -0.138
## .ssno 0.173 0.017 10.295 0.000 0.140
## .sscs (.50.) 0.274 0.016 16.711 0.000 0.242
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.185 0.157 0.161
## 0.170 0.142 0.146
## 0.313 0.284 0.294
## 0.017 -0.009 -0.010
## 0.193 0.169 0.176
## 0.247 0.220 0.223
## 0.059 0.034 0.036
## 0.170 0.144 0.149
## -0.092 -0.117 -0.141
## -0.086 -0.112 -0.133
## 0.206 0.173 0.176
## 0.306 0.274 0.286
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.177 0.006 27.214 0.000 0.164
## .sswk 0.182 0.007 26.579 0.000 0.169
## .sspc 0.213 0.009 24.727 0.000 0.196
## .ssei 0.238 0.008 28.434 0.000 0.222
## .ssar 0.155 0.007 23.342 0.000 0.142
## .ssmk 0.178 0.007 27.364 0.000 0.165
## .ssmc 0.266 0.009 28.235 0.000 0.247
## .ssao 0.419 0.013 31.985 0.000 0.393
## .ssai 0.348 0.012 29.198 0.000 0.325
## .sssi 0.311 0.012 26.521 0.000 0.288
## .ssno 0.273 0.014 19.215 0.000 0.245
## .sscs 0.392 0.016 24.526 0.000 0.361
## electronic 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.189 0.177 0.186
## 0.196 0.182 0.193
## 0.230 0.213 0.228
## 0.255 0.238 0.283
## 0.168 0.155 0.169
## 0.191 0.178 0.183
## 0.284 0.266 0.312
## 0.444 0.419 0.443
## 0.372 0.348 0.507
## 0.334 0.311 0.435
## 0.301 0.273 0.281
## 0.423 0.392 0.425
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.879 0.009 92.853 0.000 0.860
## sswk (.p2.) 0.873 0.010 91.146 0.000 0.854
## sspc (.p3.) 0.849 0.009 99.151 0.000 0.833
## ssei (.p4.) 0.461 0.016 28.826 0.000 0.429
## math =~
## ssar (.p5.) 0.875 0.010 88.369 0.000 0.855
## ssmk (.p6.) 0.675 0.015 44.743 0.000 0.645
## ssmc (.p7.) 0.482 0.013 38.089 0.000 0.457
## ssao (.p8.) 0.725 0.010 74.552 0.000 0.706
## electronic =~
## ssai (.p9.) 0.582 0.012 49.241 0.000 0.559
## sssi (.10.) 0.636 0.012 53.389 0.000 0.612
## ssmc (.11.) 0.325 0.012 28.114 0.000 0.302
## ssei (.12.) 0.337 0.014 24.401 0.000 0.310
## speed =~
## ssno (.13.) 0.835 0.013 65.533 0.000 0.810
## sscs (.14.) 0.729 0.012 60.181 0.000 0.705
## ssmk (.15.) 0.262 0.014 18.120 0.000 0.234
## ci.upper Std.lv Std.all
##
## 0.897 0.879 0.894
## 0.892 0.873 0.888
## 0.866 0.849 0.861
## 0.492 0.461 0.456
##
## 0.894 0.875 0.893
## 0.704 0.675 0.685
## 0.507 0.482 0.493
## 0.744 0.725 0.710
##
## 0.605 0.769 0.731
## 0.659 0.840 0.830
## 0.347 0.429 0.438
## 0.364 0.445 0.440
##
## 0.860 0.835 0.818
## 0.752 0.729 0.736
## 0.291 0.262 0.266
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math (.33.) 0.913 0.004 219.913 0.000 0.905
## elctrnc (.34.) 0.898 0.009 98.439 0.000 0.880
## speed (.35.) 0.700 0.012 58.852 0.000 0.676
## math ~~
## elctrnc (.36.) 0.793 0.012 68.793 0.000 0.770
## speed (.37.) 0.768 0.012 66.246 0.000 0.745
## electronic ~~
## speed (.38.) 0.500 0.019 26.314 0.000 0.463
## ci.upper Std.lv Std.all
##
## 0.921 0.913 0.913
## 0.916 0.680 0.680
## 0.723 0.700 0.700
##
## 0.816 0.600 0.600
## 0.790 0.768 0.768
##
## 0.538 0.379 0.379
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 0.000 0.000
## .ssgs (.39.) 0.157 0.014 10.927 0.000 0.129
## .sswk (.40.) 0.142 0.014 9.890 0.000 0.114
## .sspc -0.035 0.018 -1.941 0.052 -0.071
## .ssei (.42.) -0.009 0.013 -0.689 0.491 -0.035
## .ssar (.43.) 0.169 0.013 13.350 0.000 0.144
## .ssmk (.44.) 0.220 0.014 16.052 0.000 0.193
## .ssmc (.45.) 0.034 0.013 2.603 0.009 0.008
## .ssao (.46.) 0.144 0.013 10.944 0.000 0.119
## .ssai (.47.) -0.117 0.013 -9.110 0.000 -0.142
## .sssi (.48.) -0.112 0.013 -8.480 0.000 -0.138
## .ssno 0.409 0.025 16.120 0.000 0.360
## .sscs (.50.) 0.274 0.016 16.711 0.000 0.242
## verbal 0.090 0.017 5.164 0.000 0.056
## elctrnc 0.906 0.035 26.242 0.000 0.838
## speed -0.492 0.030 -16.465 0.000 -0.551
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.185 0.157 0.160
## 0.170 0.142 0.145
## 0.000 -0.035 -0.036
## 0.017 -0.009 -0.009
## 0.193 0.169 0.172
## 0.247 0.220 0.223
## 0.059 0.034 0.034
## 0.170 0.144 0.141
## -0.092 -0.117 -0.112
## -0.086 -0.112 -0.111
## 0.459 0.409 0.401
## 0.306 0.274 0.277
## 0.125 0.090 0.090
## 0.974 0.686 0.686
## -0.433 -0.492 -0.492
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal 1.000 1.000
## math 1.000 1.000
## speed 1.000 1.000
## .ssgs 0.195 0.007 27.815 0.000 0.181
## .sswk 0.204 0.008 26.081 0.000 0.189
## .sspc 0.251 0.009 26.483 0.000 0.232
## .ssei 0.333 0.012 27.168 0.000 0.309
## .ssar 0.195 0.008 23.864 0.000 0.179
## .ssmk 0.174 0.007 25.416 0.000 0.160
## .ssmc 0.293 0.010 28.000 0.000 0.272
## .ssao 0.518 0.015 34.849 0.000 0.488
## .ssai 0.515 0.019 27.458 0.000 0.478
## .sssi 0.317 0.015 21.087 0.000 0.288
## .ssno 0.345 0.018 18.916 0.000 0.310
## .sscs 0.449 0.019 23.821 0.000 0.412
## electronic 1.744 0.066 26.559 0.000 1.616
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 0.208 0.195 0.201
## 0.219 0.204 0.211
## 0.269 0.251 0.258
## 0.356 0.333 0.326
## 0.211 0.195 0.203
## 0.187 0.174 0.179
## 0.313 0.293 0.306
## 0.547 0.518 0.496
## 0.551 0.515 0.466
## 0.347 0.317 0.310
## 0.381 0.345 0.331
## 0.486 0.449 0.458
## 1.873 1.000 1.000
tests<-lavTestLRT(configural, metric, scalar2, cf.cov, cf.cov2, reduced)
Td=tests[2:6,"Chisq diff"]
Td
## [1] 1.344341e+02 1.725852e+02 1.820876e+02 2.634084e+00 1.234371e-05
dfd=tests[2:6,"Df diff"]
dfd
## [1] 11 6 6 3 1
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-3503+ 3590 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
## Warning in sqrt((ld) * G/(N - G)): NaNs produced
RMSEAD
## [1] 0.05625777 0.08849196 0.09098085 NaN NaN
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.04796441 0.06495788
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.07738085 0.10010378
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.0798676 0.1025814
RMSEA.CI(T=Td[4],df=dfd[4],N=N,G=2)
## [1] NA 0.02696979
RMSEA.CI(T=Td[5],df=dfd[5],N=N,G=2)
## [1] NA NA
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.895 0.248 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 1.000 0.897 0.052
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 1.000 0.948 0.102
round(pvals(T=Td[4],df=dfd[4],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.548 0.406 0.000 0.000 0.000 0.000
round(pvals(T=Td[5],df=dfd[5],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.003 0.002 0.000 0.000 0.000 0.000
tests<-lavTestLRT(configural, metric, scalar2, strict)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 134.4341 172.5852 206.8025
dfd=tests[2:4,"Df diff"]
dfd
## [1] 11 6 12
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-3503+ 3590 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.05625777 0.08849196 0.06766559
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.04796441 0.06495788
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.07738085 0.10010378
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.05973751 0.07590982
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.895 0.248 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 1.000 0.897 0.052
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 0.944 0.007 0.000
tests<-lavTestLRT(configural, metric, scalar)
Td=tests[2:3,"Chisq diff"]
Td
## [1] 134.4341 720.4718
dfd=tests[2:3,"Df diff"]
dfd
## [1] 11 8
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-3503+ 3590 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.05625777 0.15848936
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.04796441 0.06495788
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.1487983 0.1683781
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.895 0.248 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
# ONE FACTOR, just for checking if gap direction aligns with HOF
fmodel<-'
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
configural<-cfa(fmodel, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 8038.541 108.000 0.000 0.883 0.144 0.059
## aic bic
## 176860.294 177354.708
Mc(configural)
## [1] 0.5717126
metric<-cfa(fmodel, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 8423.576 119.000 0.000 0.877 0.140 0.075
## aic bic
## 177223.329 177642.207
Mc(metric)
## [1] 0.5568335
scalar<-cfa(fmodel, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 12334.367 130.000 0.000 0.819 0.163 0.094
## aic bic
## 181112.120 181455.463
Mc(scalar)
## [1] 0.4229793
summary(scalar, standardized=T, ci=T) # 0.056
## lavaan 0.6-18 ended normally after 41 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 74
## Number of equality constraints 24
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 12334.367 9391.033
## Degrees of freedom 130 130
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.313
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 4666.956 3553.286
## 0 7667.412 5837.747
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g =~
## ssgs (.p1.) 0.795 0.011 70.741 0.000 0.773
## ssar (.p2.) 0.776 0.012 65.097 0.000 0.753
## sswk (.p3.) 0.787 0.012 66.391 0.000 0.764
## sspc (.p4.) 0.779 0.011 68.833 0.000 0.756
## ssno (.p5.) 0.566 0.013 43.269 0.000 0.540
## sscs (.p6.) 0.531 0.012 42.996 0.000 0.507
## ssai (.p7.) 0.516 0.012 44.119 0.000 0.493
## sssi (.p8.) 0.546 0.012 46.026 0.000 0.523
## ssmk (.p9.) 0.777 0.012 64.492 0.000 0.753
## ssmc (.10.) 0.723 0.011 65.536 0.000 0.701
## ssei (.11.) 0.710 0.011 61.942 0.000 0.687
## ssao (.12.) 0.648 0.011 58.082 0.000 0.626
## ci.upper Std.lv Std.all
##
## 0.817 0.795 0.867
## 0.800 0.776 0.855
## 0.811 0.787 0.859
## 0.801 0.779 0.847
## 0.591 0.566 0.596
## 0.555 0.531 0.563
## 0.539 0.516 0.620
## 0.569 0.546 0.636
## 0.800 0.777 0.833
## 0.744 0.723 0.798
## 0.732 0.710 0.802
## 0.670 0.648 0.691
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.26.) 0.170 0.016 10.412 0.000 0.138
## .ssar (.27.) 0.146 0.016 8.939 0.000 0.114
## .sswk (.28.) 0.156 0.017 9.338 0.000 0.123
## .sspc (.29.) 0.147 0.017 8.485 0.000 0.113
## .ssno (.30.) 0.076 0.016 4.800 0.000 0.045
## .sscs (.31.) 0.089 0.016 5.622 0.000 0.058
## .ssai (.32.) 0.048 0.014 3.466 0.001 0.021
## .sssi (.33.) 0.075 0.016 4.819 0.000 0.045
## .ssmk (.34.) 0.131 0.017 7.598 0.000 0.097
## .ssmc (.35.) 0.146 0.015 9.610 0.000 0.116
## .ssei (.36.) 0.104 0.015 6.869 0.000 0.074
## .ssao (.37.) 0.124 0.016 7.735 0.000 0.092
## ci.upper Std.lv Std.all
## 0.202 0.170 0.185
## 0.178 0.146 0.161
## 0.189 0.156 0.170
## 0.181 0.147 0.160
## 0.107 0.076 0.080
## 0.120 0.089 0.094
## 0.075 0.048 0.058
## 0.106 0.075 0.088
## 0.165 0.131 0.140
## 0.176 0.146 0.161
## 0.134 0.104 0.117
## 0.155 0.124 0.132
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.209 0.007 29.493 0.000 0.195
## .ssar 0.221 0.007 30.793 0.000 0.207
## .sswk 0.220 0.008 28.839 0.000 0.205
## .sspc 0.240 0.009 26.066 0.000 0.222
## .ssno 0.581 0.023 25.518 0.000 0.537
## .sscs 0.607 0.020 29.897 0.000 0.567
## .ssai 0.427 0.014 31.558 0.000 0.400
## .sssi 0.439 0.015 29.990 0.000 0.410
## .ssmk 0.267 0.008 31.509 0.000 0.250
## .ssmc 0.297 0.011 28.229 0.000 0.277
## .ssei 0.280 0.010 29.436 0.000 0.262
## .ssao 0.460 0.014 33.755 0.000 0.433
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.223 0.209 0.249
## 0.235 0.221 0.268
## 0.235 0.220 0.262
## 0.258 0.240 0.283
## 0.626 0.581 0.645
## 0.646 0.607 0.683
## 0.453 0.427 0.616
## 0.468 0.439 0.595
## 0.284 0.267 0.307
## 0.318 0.297 0.363
## 0.299 0.280 0.357
## 0.487 0.460 0.523
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g =~
## ssgs (.p1.) 0.795 0.011 70.741 0.000 0.773
## ssar (.p2.) 0.776 0.012 65.097 0.000 0.753
## sswk (.p3.) 0.787 0.012 66.391 0.000 0.764
## sspc (.p4.) 0.779 0.011 68.833 0.000 0.756
## ssno (.p5.) 0.566 0.013 43.269 0.000 0.540
## sscs (.p6.) 0.531 0.012 42.996 0.000 0.507
## ssai (.p7.) 0.516 0.012 44.119 0.000 0.493
## sssi (.p8.) 0.546 0.012 46.026 0.000 0.523
## ssmk (.p9.) 0.777 0.012 64.492 0.000 0.753
## ssmc (.10.) 0.723 0.011 65.536 0.000 0.701
## ssei (.11.) 0.710 0.011 61.942 0.000 0.687
## ssao (.12.) 0.648 0.011 58.082 0.000 0.626
## ci.upper Std.lv Std.all
##
## 0.817 0.915 0.883
## 0.800 0.894 0.870
## 0.811 0.906 0.876
## 0.801 0.896 0.859
## 0.591 0.651 0.613
## 0.555 0.611 0.589
## 0.539 0.594 0.527
## 0.569 0.629 0.570
## 0.800 0.894 0.860
## 0.744 0.832 0.806
## 0.732 0.817 0.762
## 0.670 0.746 0.710
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.26.) 0.170 0.016 10.412 0.000 0.138
## .ssar (.27.) 0.146 0.016 8.939 0.000 0.114
## .sswk (.28.) 0.156 0.017 9.338 0.000 0.123
## .sspc (.29.) 0.147 0.017 8.485 0.000 0.113
## .ssno (.30.) 0.076 0.016 4.800 0.000 0.045
## .sscs (.31.) 0.089 0.016 5.622 0.000 0.058
## .ssai (.32.) 0.048 0.014 3.466 0.001 0.021
## .sssi (.33.) 0.075 0.016 4.819 0.000 0.045
## .ssmk (.34.) 0.131 0.017 7.598 0.000 0.097
## .ssmc (.35.) 0.146 0.015 9.610 0.000 0.116
## .ssei (.36.) 0.104 0.015 6.869 0.000 0.074
## .ssao (.37.) 0.124 0.016 7.735 0.000 0.092
## g 0.065 0.030 2.155 0.031 0.006
## ci.upper Std.lv Std.all
## 0.202 0.170 0.164
## 0.178 0.146 0.142
## 0.189 0.156 0.151
## 0.181 0.147 0.141
## 0.107 0.076 0.071
## 0.120 0.089 0.085
## 0.075 0.048 0.043
## 0.106 0.075 0.068
## 0.165 0.131 0.126
## 0.176 0.146 0.141
## 0.134 0.104 0.097
## 0.155 0.124 0.118
## 0.124 0.056 0.056
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.238 0.008 29.927 0.000 0.222
## .ssar 0.258 0.009 30.189 0.000 0.241
## .sswk 0.250 0.008 29.518 0.000 0.233
## .sspc 0.285 0.011 26.907 0.000 0.264
## .ssno 0.706 0.026 27.310 0.000 0.656
## .sscs 0.705 0.025 28.223 0.000 0.656
## .ssai 0.918 0.035 26.113 0.000 0.850
## .sssi 0.821 0.029 28.405 0.000 0.764
## .ssmk 0.281 0.010 29.043 0.000 0.262
## .ssmc 0.373 0.012 30.072 0.000 0.349
## .ssei 0.483 0.018 26.448 0.000 0.447
## .ssao 0.549 0.015 36.039 0.000 0.519
## g 1.326 0.046 29.079 0.000 1.236
## ci.upper Std.lv Std.all
## 0.253 0.238 0.221
## 0.275 0.258 0.244
## 0.267 0.250 0.233
## 0.305 0.285 0.262
## 0.757 0.706 0.625
## 0.754 0.705 0.654
## 0.987 0.918 0.722
## 0.877 0.821 0.675
## 0.300 0.281 0.260
## 0.397 0.373 0.350
## 0.519 0.483 0.420
## 0.579 0.549 0.496
## 1.415 1.000 1.000
# HIGH ORDER FACTOR, FREEING GS INSTEAD OF NO IS ALMOST SIMILAR BUT NO HAS POOR G-LOADING
hof.model<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
'
hof.lv<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
math~~1*math
speed~~1*speed
'
hof.weak<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
math~~1*math
speed~~1*speed
verbal~0*1
'
hof.weak2<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
math~~1*math
speed~~1*speed
verbal~0*1
math~0*1
g~0*1
'
baseline<-cfa(hof.model, data=dgroup, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2852.943 47.000 0.000 0.959 0.092 0.040
## aic bic
## 175005.017 175300.293
Mc(baseline)
## [1] 0.8205138
configural<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2415.150 94.000 0.000 0.966 0.083 0.033
## aic bic
## 171264.902 171855.453
Mc(configural)
## [1] 0.8490428
summary(configural, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 122 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 86
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2415.150 1844.965
## Degrees of freedom 94 94
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.309
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 891.801 681.259
## 0 1523.349 1163.706
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.125 0.032 3.921 0.000 0.062
## sswk 0.131 0.033 3.906 0.000 0.065
## sspc 0.125 0.032 3.918 0.000 0.062
## ssei 0.065 0.018 3.627 0.000 0.030
## math =~
## ssar 0.333 0.015 22.422 0.000 0.304
## ssmk 0.256 0.014 18.379 0.000 0.229
## ssmc 0.171 0.013 13.103 0.000 0.145
## ssao 0.288 0.014 21.304 0.000 0.261
## electronic =~
## ssai 0.269 0.014 18.648 0.000 0.241
## sssi 0.295 0.016 18.603 0.000 0.264
## ssmc 0.170 0.014 12.081 0.000 0.142
## ssei 0.127 0.017 7.355 0.000 0.093
## speed =~
## ssno 0.554 0.021 26.396 0.000 0.513
## sscs 0.492 0.018 27.013 0.000 0.457
## ssmk 0.214 0.013 16.014 0.000 0.188
## g =~
## verbal 6.409 1.674 3.830 0.000 3.129
## math 2.258 0.121 18.727 0.000 2.022
## electronic 1.696 0.097 17.575 0.000 1.507
## speed 1.004 0.052 19.121 0.000 0.901
## ci.upper Std.lv Std.all
##
## 0.187 0.809 0.887
## 0.196 0.847 0.897
## 0.187 0.809 0.872
## 0.099 0.419 0.516
##
## 0.362 0.822 0.902
## 0.284 0.633 0.660
## 0.197 0.422 0.480
## 0.314 0.711 0.745
##
## 0.298 0.530 0.673
## 0.327 0.582 0.723
## 0.197 0.334 0.380
## 0.161 0.251 0.309
##
## 0.595 0.785 0.829
## 0.528 0.697 0.749
## 0.241 0.304 0.317
##
## 9.689 0.988 0.988
## 2.494 0.914 0.914
## 1.885 0.861 0.861
## 1.106 0.708 0.708
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.120 0.017 7.097 0.000 0.087
## .sswk 0.181 0.017 10.369 0.000 0.147
## .sspc 0.284 0.017 16.543 0.000 0.251
## .ssei -0.010 0.015 -0.667 0.505 -0.040
## .ssar 0.148 0.017 8.728 0.000 0.115
## .ssmk 0.224 0.018 12.435 0.000 0.189
## .ssmc 0.039 0.016 2.369 0.018 0.007
## .ssao 0.198 0.018 11.088 0.000 0.163
## .ssai -0.097 0.015 -6.622 0.000 -0.126
## .sssi -0.131 0.015 -8.757 0.000 -0.160
## .ssno 0.173 0.018 9.602 0.000 0.138
## .sscs 0.271 0.018 15.206 0.000 0.236
## ci.upper Std.lv Std.all
## 0.153 0.120 0.131
## 0.216 0.181 0.192
## 0.318 0.284 0.306
## 0.020 -0.010 -0.013
## 0.181 0.148 0.162
## 0.260 0.224 0.234
## 0.070 0.039 0.044
## 0.232 0.198 0.207
## -0.069 -0.097 -0.124
## -0.102 -0.131 -0.163
## 0.209 0.173 0.183
## 0.306 0.271 0.291
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.178 0.007 26.981 0.000 0.165
## .sswk 0.173 0.007 25.937 0.000 0.160
## .sspc 0.207 0.009 23.554 0.000 0.189
## .ssei 0.243 0.008 28.691 0.000 0.226
## .ssar 0.155 0.007 21.577 0.000 0.141
## .ssmk 0.177 0.007 27.022 0.000 0.164
## .ssmc 0.260 0.009 27.620 0.000 0.241
## .ssao 0.406 0.013 30.402 0.000 0.380
## .ssai 0.340 0.013 27.008 0.000 0.316
## .sssi 0.309 0.012 25.431 0.000 0.285
## .ssno 0.280 0.015 18.301 0.000 0.250
## .sscs 0.382 0.017 22.590 0.000 0.349
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.191 0.178 0.214
## 0.186 0.173 0.195
## 0.224 0.207 0.240
## 0.259 0.243 0.368
## 0.169 0.155 0.187
## 0.190 0.177 0.193
## 0.278 0.260 0.337
## 0.432 0.406 0.445
## 0.365 0.340 0.548
## 0.333 0.309 0.478
## 0.310 0.280 0.312
## 0.415 0.382 0.440
## 1.000 0.024 0.024
## 1.000 0.164 0.164
## 1.000 0.258 0.258
## 1.000 0.498 0.498
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.213 0.031 6.918 0.000 0.153
## sswk 0.205 0.030 6.876 0.000 0.147
## sspc 0.203 0.029 6.882 0.000 0.145
## ssei 0.127 0.019 6.501 0.000 0.089
## math =~
## ssar 0.293 0.025 11.904 0.000 0.245
## ssmk 0.202 0.019 10.814 0.000 0.166
## ssmc 0.171 0.014 12.003 0.000 0.143
## ssao 0.237 0.020 11.748 0.000 0.197
## electronic =~
## ssai 0.604 0.019 31.507 0.000 0.567
## sssi 0.617 0.018 34.067 0.000 0.582
## ssmc 0.296 0.014 20.882 0.000 0.268
## ssei 0.346 0.018 19.428 0.000 0.311
## speed =~
## ssno 0.599 0.022 27.230 0.000 0.556
## sscs 0.522 0.018 28.548 0.000 0.487
## ssmk 0.229 0.014 16.578 0.000 0.202
## g =~
## verbal 4.277 0.654 6.536 0.000 2.994
## math 2.987 0.280 10.663 0.000 2.438
## electronic 1.055 0.044 23.736 0.000 0.968
## speed 1.086 0.056 19.290 0.000 0.975
## ci.upper Std.lv Std.all
##
## 0.273 0.936 0.904
## 0.264 0.901 0.894
## 0.260 0.890 0.873
## 0.165 0.557 0.493
##
## 0.342 0.924 0.902
## 0.239 0.637 0.636
## 0.199 0.540 0.518
## 0.276 0.745 0.723
##
## 0.642 0.878 0.781
## 0.653 0.897 0.842
## 0.324 0.430 0.413
## 0.380 0.502 0.445
##
## 0.642 0.884 0.835
## 0.558 0.771 0.757
## 0.256 0.338 0.338
##
## 5.560 0.974 0.974
## 3.536 0.948 0.948
## 1.142 0.726 0.726
## 1.196 0.736 0.736
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.276 0.019 14.542 0.000 0.239
## .sswk 0.179 0.018 9.735 0.000 0.143
## .sspc 0.041 0.019 2.207 0.027 0.005
## .ssei 0.339 0.021 16.134 0.000 0.298
## .ssar 0.194 0.019 10.391 0.000 0.157
## .ssmk 0.087 0.019 4.675 0.000 0.050
## .ssmc 0.322 0.019 17.179 0.000 0.286
## .ssao 0.081 0.019 4.256 0.000 0.044
## .ssai 0.382 0.021 18.202 0.000 0.341
## .sssi 0.482 0.020 24.659 0.000 0.443
## .ssno -0.002 0.020 -0.083 0.934 -0.040
## .sscs -0.080 0.019 -4.255 0.000 -0.117
## ci.upper Std.lv Std.all
## 0.313 0.276 0.267
## 0.215 0.179 0.178
## 0.078 0.041 0.041
## 0.380 0.339 0.300
## 0.230 0.194 0.189
## 0.123 0.087 0.087
## 0.359 0.322 0.309
## 0.119 0.081 0.079
## 0.423 0.382 0.340
## 0.520 0.482 0.452
## 0.037 -0.002 -0.002
## -0.043 -0.080 -0.079
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.195 0.007 27.088 0.000 0.181
## .sswk 0.205 0.008 25.411 0.000 0.189
## .sspc 0.247 0.009 26.004 0.000 0.228
## .ssei 0.317 0.012 26.821 0.000 0.294
## .ssar 0.196 0.009 21.975 0.000 0.179
## .ssmk 0.182 0.007 25.943 0.000 0.168
## .ssmc 0.290 0.010 27.650 0.000 0.270
## .ssao 0.508 0.015 34.488 0.000 0.479
## .ssai 0.493 0.019 25.313 0.000 0.455
## .sssi 0.332 0.016 20.431 0.000 0.300
## .ssno 0.340 0.019 17.586 0.000 0.302
## .sscs 0.443 0.020 22.592 0.000 0.404
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.209 0.195 0.182
## 0.221 0.205 0.201
## 0.266 0.247 0.238
## 0.340 0.317 0.249
## 0.214 0.196 0.187
## 0.196 0.182 0.182
## 0.311 0.290 0.267
## 0.537 0.508 0.478
## 0.531 0.493 0.390
## 0.364 0.332 0.292
## 0.378 0.340 0.303
## 0.481 0.443 0.427
## 1.000 0.052 0.052
## 1.000 0.101 0.101
## 1.000 0.473 0.473
## 1.000 0.459 0.459
## 1.000 1.000 1.000
#modificationIndices(configural, sort=T, maximum.number=30)
metric<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2673.316 108.000 0.000 0.962 0.082 0.050
## aic bic
## 171495.069 171989.483
Mc(metric)
## [1] 0.8345523
summary(metric, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 124 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 91
## Number of equality constraints 19
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2673.316 2038.806
## Degrees of freedom 108 108
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.311
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1013.814 773.185
## 0 1659.503 1265.621
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.151 0.023 6.531 0.000 0.106
## sswk (.p2.) 0.152 0.023 6.503 0.000 0.106
## sspc (.p3.) 0.148 0.023 6.520 0.000 0.103
## ssei (.p4.) 0.078 0.012 6.246 0.000 0.053
## math =~
## ssar (.p5.) 0.328 0.014 23.826 0.000 0.301
## ssmk (.p6.) 0.239 0.012 20.700 0.000 0.217
## ssmc (.p7.) 0.186 0.009 21.255 0.000 0.169
## ssao (.p8.) 0.275 0.012 23.011 0.000 0.251
## electronic =~
## ssai (.p9.) 0.268 0.013 20.167 0.000 0.242
## sssi (.10.) 0.280 0.014 19.909 0.000 0.253
## ssmc (.11.) 0.140 0.008 16.543 0.000 0.123
## ssei (.12.) 0.158 0.009 17.710 0.000 0.141
## speed =~
## ssno (.13.) 0.556 0.019 29.778 0.000 0.519
## sscs (.14.) 0.489 0.016 30.122 0.000 0.458
## ssmk (.15.) 0.211 0.010 21.379 0.000 0.192
## g =~
## verbal (.16.) 5.270 0.834 6.316 0.000 3.635
## math (.17.) 2.319 0.113 20.562 0.000 2.098
## elctrnc (.18.) 1.826 0.095 19.147 0.000 1.639
## speed (.19.) 1.018 0.044 23.064 0.000 0.932
## ci.upper Std.lv Std.all
##
## 0.197 0.813 0.888
## 0.197 0.814 0.888
## 0.192 0.792 0.866
## 0.102 0.417 0.479
##
## 0.355 0.829 0.904
## 0.262 0.604 0.645
## 0.203 0.470 0.529
## 0.298 0.693 0.736
##
## 0.294 0.557 0.692
## 0.308 0.583 0.720
## 0.156 0.291 0.327
## 0.176 0.330 0.379
##
## 0.592 0.793 0.833
## 0.521 0.699 0.749
## 0.231 0.302 0.322
##
## 6.906 0.982 0.982
## 2.540 0.918 0.918
## 2.013 0.877 0.877
## 1.105 0.713 0.713
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.120 0.017 7.097 0.000 0.087
## .sswk 0.181 0.017 10.369 0.000 0.147
## .sspc 0.284 0.017 16.543 0.000 0.251
## .ssei -0.010 0.015 -0.667 0.505 -0.040
## .ssar 0.148 0.017 8.728 0.000 0.115
## .ssmk 0.224 0.018 12.435 0.000 0.189
## .ssmc 0.039 0.016 2.369 0.018 0.007
## .ssao 0.198 0.018 11.088 0.000 0.163
## .ssai -0.097 0.015 -6.622 0.000 -0.126
## .sssi -0.131 0.015 -8.757 0.000 -0.160
## .ssno 0.173 0.018 9.602 0.000 0.138
## .sscs 0.271 0.018 15.206 0.000 0.236
## ci.upper Std.lv Std.all
## 0.153 0.120 0.131
## 0.216 0.181 0.198
## 0.318 0.284 0.311
## 0.020 -0.010 -0.012
## 0.181 0.148 0.161
## 0.260 0.224 0.239
## 0.070 0.039 0.043
## 0.232 0.198 0.210
## -0.069 -0.097 -0.121
## -0.102 -0.131 -0.162
## 0.209 0.173 0.182
## 0.306 0.271 0.290
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.176 0.007 27.016 0.000 0.163
## .sswk 0.178 0.007 26.351 0.000 0.164
## .sspc 0.209 0.009 24.406 0.000 0.192
## .ssei 0.238 0.008 28.386 0.000 0.222
## .ssar 0.154 0.007 21.847 0.000 0.140
## .ssmk 0.183 0.006 28.290 0.000 0.170
## .ssmc 0.263 0.009 27.859 0.000 0.245
## .ssao 0.406 0.013 31.240 0.000 0.381
## .ssai 0.339 0.012 28.224 0.000 0.315
## .sssi 0.317 0.012 26.894 0.000 0.294
## .ssno 0.277 0.014 19.223 0.000 0.249
## .sscs 0.382 0.016 23.994 0.000 0.351
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.189 0.176 0.211
## 0.191 0.178 0.212
## 0.226 0.209 0.250
## 0.255 0.238 0.315
## 0.168 0.154 0.183
## 0.196 0.183 0.209
## 0.282 0.263 0.333
## 0.432 0.406 0.458
## 0.362 0.339 0.522
## 0.340 0.317 0.482
## 0.305 0.277 0.306
## 0.413 0.382 0.439
## 1.000 0.035 0.035
## 1.000 0.157 0.157
## 1.000 0.231 0.231
## 1.000 0.491 0.491
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.151 0.023 6.531 0.000 0.106
## sswk (.p2.) 0.152 0.023 6.503 0.000 0.106
## sspc (.p3.) 0.148 0.023 6.520 0.000 0.103
## ssei (.p4.) 0.078 0.012 6.246 0.000 0.053
## math =~
## ssar (.p5.) 0.328 0.014 23.826 0.000 0.301
## ssmk (.p6.) 0.239 0.012 20.700 0.000 0.217
## ssmc (.p7.) 0.186 0.009 21.255 0.000 0.169
## ssao (.p8.) 0.275 0.012 23.011 0.000 0.251
## electronic =~
## ssai (.p9.) 0.268 0.013 20.167 0.000 0.242
## sssi (.10.) 0.280 0.014 19.909 0.000 0.253
## ssmc (.11.) 0.140 0.008 16.543 0.000 0.123
## ssei (.12.) 0.158 0.009 17.710 0.000 0.141
## speed =~
## ssno (.13.) 0.556 0.019 29.778 0.000 0.519
## sscs (.14.) 0.489 0.016 30.122 0.000 0.458
## ssmk (.15.) 0.211 0.010 21.379 0.000 0.192
## g =~
## verbal (.16.) 5.270 0.834 6.316 0.000 3.635
## math (.17.) 2.319 0.113 20.562 0.000 2.098
## elctrnc (.18.) 1.826 0.095 19.147 0.000 1.639
## speed (.19.) 1.018 0.044 23.064 0.000 0.932
## ci.upper Std.lv Std.all
##
## 0.197 0.933 0.902
## 0.197 0.934 0.901
## 0.192 0.909 0.879
## 0.102 0.479 0.456
##
## 0.355 0.913 0.899
## 0.262 0.665 0.652
## 0.203 0.518 0.511
## 0.298 0.764 0.731
##
## 0.294 0.824 0.759
## 0.308 0.862 0.834
## 0.156 0.430 0.424
## 0.176 0.487 0.464
##
## 0.592 0.875 0.831
## 0.521 0.771 0.757
## 0.231 0.333 0.326
##
## 6.906 0.975 0.975
## 2.540 0.950 0.950
## 2.013 0.676 0.676
## 1.105 0.736 0.736
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.276 0.019 14.542 0.000 0.239
## .sswk 0.179 0.018 9.735 0.000 0.143
## .sspc 0.041 0.019 2.207 0.027 0.005
## .ssei 0.339 0.021 16.134 0.000 0.298
## .ssar 0.194 0.019 10.391 0.000 0.157
## .ssmk 0.087 0.019 4.675 0.000 0.050
## .ssmc 0.322 0.019 17.179 0.000 0.286
## .ssao 0.081 0.019 4.256 0.000 0.044
## .ssai 0.382 0.021 18.202 0.000 0.341
## .sssi 0.482 0.020 24.659 0.000 0.443
## .ssno -0.002 0.020 -0.083 0.934 -0.040
## .sscs -0.080 0.019 -4.255 0.000 -0.117
## ci.upper Std.lv Std.all
## 0.313 0.276 0.267
## 0.215 0.179 0.173
## 0.078 0.041 0.040
## 0.380 0.339 0.323
## 0.230 0.194 0.191
## 0.123 0.087 0.085
## 0.359 0.322 0.318
## 0.119 0.081 0.078
## 0.423 0.382 0.352
## 0.520 0.482 0.466
## 0.037 -0.002 -0.002
## -0.043 -0.080 -0.079
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.199 0.007 27.935 0.000 0.185
## .sswk 0.203 0.008 25.207 0.000 0.187
## .sspc 0.243 0.009 26.103 0.000 0.225
## .ssei 0.330 0.012 26.516 0.000 0.305
## .ssar 0.199 0.009 22.870 0.000 0.182
## .ssmk 0.179 0.007 25.921 0.000 0.166
## .ssmc 0.290 0.010 27.827 0.000 0.269
## .ssao 0.508 0.015 34.871 0.000 0.479
## .ssai 0.500 0.019 25.941 0.000 0.462
## .sssi 0.326 0.016 20.710 0.000 0.296
## .ssno 0.344 0.018 18.675 0.000 0.308
## .sscs 0.441 0.019 22.886 0.000 0.404
## .verbal 1.906 0.612 3.115 0.002 0.706
## .math 0.760 0.111 6.830 0.000 0.542
## .electronic 5.149 0.555 9.273 0.000 4.061
## .speed 1.135 0.094 12.113 0.000 0.951
## g 1.297 0.047 27.857 0.000 1.206
## ci.upper Std.lv Std.all
## 0.213 0.199 0.186
## 0.219 0.203 0.189
## 0.261 0.243 0.227
## 0.354 0.330 0.299
## 0.216 0.199 0.192
## 0.193 0.179 0.172
## 0.310 0.290 0.282
## 0.536 0.508 0.465
## 0.537 0.500 0.424
## 0.357 0.326 0.305
## 0.380 0.344 0.310
## 0.479 0.441 0.426
## 3.105 0.050 0.050
## 0.978 0.098 0.098
## 6.237 0.543 0.543
## 1.319 0.458 0.458
## 1.388 1.000 1.000
lavTestScore(metric, release = 1:19)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 257.778 19 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p54. 0.877 1 0.349
## 2 .p2. == .p55. 47.056 1 0.000
## 3 .p3. == .p56. 8.852 1 0.003
## 4 .p4. == .p57. 132.580 1 0.000
## 5 .p5. == .p58. 9.034 1 0.003
## 6 .p6. == .p59. 26.258 1 0.000
## 7 .p7. == .p60. 3.073 1 0.080
## 8 .p8. == .p61. 4.492 1 0.034
## 9 .p9. == .p62. 4.799 1 0.028
## 10 .p10. == .p63. 0.639 1 0.424
## 11 .p11. == .p64. 0.031 1 0.859
## 12 .p12. == .p65. 133.532 1 0.000
## 13 .p13. == .p66. 4.135 1 0.042
## 14 .p14. == .p67. 0.168 1 0.682
## 15 .p15. == .p68. 17.307 1 0.000
## 16 .p16. == .p69. 15.192 1 0.000
## 17 .p17. == .p70. 2.726 1 0.099
## 18 .p18. == .p71. 87.898 1 0.000
## 19 .p19. == .p72. 0.070 1 0.791
metric2<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"), group.partial=c("electronic=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2538.265 107.000 0.000 0.964 0.080 0.042
## aic bic
## 171362.018 171863.299
Mc(metric2)
## [1] 0.8424769
summary(metric2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 134 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 91
## Number of equality constraints 18
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2538.265 1935.375
## Degrees of freedom 107 107
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.312
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 952.055 725.923
## 0 1586.210 1209.452
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.155 0.023 6.710 0.000 0.109
## sswk (.p2.) 0.155 0.023 6.682 0.000 0.109
## sspc (.p3.) 0.151 0.022 6.695 0.000 0.107
## ssei (.p4.) 0.091 0.014 6.496 0.000 0.063
## math =~
## ssar (.p5.) 0.326 0.014 23.126 0.000 0.299
## ssmk (.p6.) 0.238 0.012 20.153 0.000 0.215
## ssmc (.p7.) 0.185 0.009 20.589 0.000 0.167
## ssao (.p8.) 0.273 0.012 22.370 0.000 0.249
## electronic =~
## ssai (.p9.) 0.284 0.013 21.483 0.000 0.258
## sssi (.10.) 0.299 0.014 21.267 0.000 0.271
## ssmc (.11.) 0.149 0.009 17.042 0.000 0.132
## ssei 0.094 0.011 8.405 0.000 0.072
## speed =~
## ssno (.13.) 0.555 0.019 29.677 0.000 0.519
## sscs (.14.) 0.489 0.016 30.022 0.000 0.457
## ssmk (.15.) 0.212 0.010 21.464 0.000 0.192
## g =~
## verbal (.16.) 5.210 0.805 6.476 0.000 3.633
## math (.17.) 2.348 0.116 20.154 0.000 2.119
## elctrnc (.18.) 1.757 0.087 20.166 0.000 1.587
## speed (.19.) 1.024 0.044 23.069 0.000 0.937
## ci.upper Std.lv Std.all
##
## 0.200 0.820 0.890
## 0.200 0.821 0.890
## 0.195 0.799 0.868
## 0.118 0.481 0.588
##
## 0.354 0.833 0.904
## 0.261 0.607 0.645
## 0.202 0.471 0.526
## 0.297 0.697 0.738
##
## 0.310 0.574 0.706
## 0.326 0.604 0.738
## 0.166 0.302 0.337
## 0.116 0.190 0.233
##
## 0.592 0.795 0.834
## 0.521 0.700 0.750
## 0.231 0.303 0.322
##
## 6.787 0.982 0.982
## 2.576 0.920 0.920
## 1.928 0.869 0.869
## 1.111 0.715 0.715
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.120 0.017 7.097 0.000 0.087
## .sswk 0.181 0.017 10.369 0.000 0.147
## .sspc 0.284 0.017 16.543 0.000 0.251
## .ssei -0.010 0.015 -0.667 0.505 -0.040
## .ssar 0.148 0.017 8.728 0.000 0.115
## .ssmk 0.224 0.018 12.435 0.000 0.189
## .ssmc 0.039 0.016 2.369 0.018 0.007
## .ssao 0.198 0.018 11.088 0.000 0.163
## .ssai -0.097 0.015 -6.622 0.000 -0.126
## .sssi -0.131 0.015 -8.757 0.000 -0.160
## .ssno 0.173 0.018 9.602 0.000 0.138
## .sscs 0.271 0.018 15.206 0.000 0.236
## ci.upper Std.lv Std.all
## 0.153 0.120 0.130
## 0.216 0.181 0.197
## 0.318 0.284 0.309
## 0.020 -0.010 -0.012
## 0.181 0.148 0.161
## 0.260 0.224 0.238
## 0.070 0.039 0.043
## 0.232 0.198 0.209
## -0.069 -0.097 -0.120
## -0.102 -0.131 -0.160
## 0.209 0.173 0.182
## 0.306 0.271 0.290
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.176 0.006 27.046 0.000 0.163
## .sswk 0.177 0.007 26.469 0.000 0.164
## .sspc 0.209 0.009 24.343 0.000 0.192
## .ssei 0.246 0.008 29.710 0.000 0.229
## .ssar 0.154 0.007 21.889 0.000 0.140
## .ssmk 0.183 0.006 28.278 0.000 0.170
## .ssmc 0.261 0.009 27.639 0.000 0.243
## .ssao 0.406 0.013 31.243 0.000 0.381
## .ssai 0.333 0.012 27.506 0.000 0.309
## .sssi 0.306 0.012 26.186 0.000 0.283
## .ssno 0.277 0.014 19.247 0.000 0.249
## .sscs 0.382 0.016 24.000 0.000 0.351
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.188 0.176 0.207
## 0.190 0.177 0.208
## 0.225 0.209 0.246
## 0.262 0.246 0.367
## 0.168 0.154 0.182
## 0.196 0.183 0.207
## 0.280 0.261 0.326
## 0.432 0.406 0.456
## 0.356 0.333 0.502
## 0.329 0.306 0.456
## 0.306 0.277 0.305
## 0.413 0.382 0.438
## 1.000 0.036 0.036
## 1.000 0.154 0.154
## 1.000 0.245 0.245
## 1.000 0.488 0.488
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.155 0.023 6.710 0.000 0.109
## sswk (.p2.) 0.155 0.023 6.682 0.000 0.109
## sspc (.p3.) 0.151 0.022 6.695 0.000 0.107
## ssei (.p4.) 0.091 0.014 6.496 0.000 0.063
## math =~
## ssar (.p5.) 0.326 0.014 23.126 0.000 0.299
## ssmk (.p6.) 0.238 0.012 20.153 0.000 0.215
## ssmc (.p7.) 0.185 0.009 20.589 0.000 0.167
## ssao (.p8.) 0.273 0.012 22.370 0.000 0.249
## electronic =~
## ssai (.p9.) 0.284 0.013 21.483 0.000 0.258
## sssi (.10.) 0.299 0.014 21.267 0.000 0.271
## ssmc (.11.) 0.149 0.009 17.042 0.000 0.132
## ssei 0.174 0.011 16.003 0.000 0.152
## speed =~
## ssno (.13.) 0.555 0.019 29.677 0.000 0.519
## sscs (.14.) 0.489 0.016 30.022 0.000 0.457
## ssmk (.15.) 0.212 0.010 21.464 0.000 0.192
## g =~
## verbal (.16.) 5.210 0.805 6.476 0.000 3.633
## math (.17.) 2.348 0.116 20.154 0.000 2.119
## elctrnc (.18.) 1.757 0.087 20.166 0.000 1.587
## speed (.19.) 1.024 0.044 23.069 0.000 0.937
## ci.upper Std.lv Std.all
##
## 0.200 0.926 0.900
## 0.200 0.927 0.899
## 0.195 0.902 0.877
## 0.118 0.543 0.490
##
## 0.354 0.910 0.898
## 0.261 0.663 0.652
## 0.202 0.515 0.508
## 0.297 0.761 0.730
##
## 0.310 0.818 0.755
## 0.326 0.861 0.832
## 0.166 0.430 0.424
## 0.195 0.500 0.452
##
## 0.592 0.873 0.830
## 0.521 0.769 0.757
## 0.231 0.333 0.327
##
## 6.787 0.977 0.977
## 2.576 0.946 0.946
## 1.928 0.685 0.685
## 1.111 0.732 0.732
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.276 0.019 14.542 0.000 0.239
## .sswk 0.179 0.018 9.735 0.000 0.143
## .sspc 0.041 0.019 2.207 0.027 0.005
## .ssei 0.339 0.021 16.134 0.000 0.298
## .ssar 0.194 0.019 10.391 0.000 0.157
## .ssmk 0.087 0.019 4.675 0.000 0.050
## .ssmc 0.322 0.019 17.179 0.000 0.286
## .ssao 0.081 0.019 4.256 0.000 0.044
## .ssai 0.382 0.021 18.202 0.000 0.341
## .sssi 0.482 0.020 24.659 0.000 0.443
## .ssno -0.002 0.020 -0.083 0.934 -0.040
## .sscs -0.080 0.019 -4.255 0.000 -0.117
## ci.upper Std.lv Std.all
## 0.313 0.276 0.268
## 0.215 0.179 0.174
## 0.078 0.041 0.040
## 0.380 0.339 0.306
## 0.230 0.194 0.191
## 0.123 0.087 0.085
## 0.359 0.322 0.318
## 0.119 0.081 0.078
## 0.423 0.382 0.353
## 0.520 0.482 0.465
## 0.037 -0.002 -0.002
## -0.043 -0.080 -0.079
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.200 0.007 28.134 0.000 0.186
## .sswk 0.204 0.008 25.323 0.000 0.188
## .sspc 0.244 0.009 26.260 0.000 0.226
## .ssei 0.315 0.012 26.668 0.000 0.292
## .ssar 0.199 0.009 22.785 0.000 0.182
## .ssmk 0.179 0.007 25.917 0.000 0.165
## .ssmc 0.290 0.010 27.846 0.000 0.270
## .ssao 0.507 0.015 34.845 0.000 0.479
## .ssai 0.504 0.019 26.040 0.000 0.466
## .sssi 0.331 0.016 20.836 0.000 0.300
## .ssno 0.344 0.018 18.604 0.000 0.307
## .sscs 0.441 0.019 22.842 0.000 0.403
## .verbal 1.612 0.526 3.067 0.002 0.582
## .math 0.823 0.114 7.236 0.000 0.600
## .electronic 4.401 0.462 9.535 0.000 3.496
## .speed 1.149 0.094 12.167 0.000 0.964
## g 1.263 0.045 28.120 0.000 1.175
## ci.upper Std.lv Std.all
## 0.214 0.200 0.189
## 0.219 0.204 0.192
## 0.262 0.244 0.231
## 0.339 0.315 0.258
## 0.216 0.199 0.193
## 0.192 0.179 0.173
## 0.311 0.290 0.282
## 0.536 0.507 0.467
## 0.542 0.504 0.429
## 0.362 0.331 0.308
## 0.380 0.344 0.311
## 0.479 0.441 0.427
## 2.642 0.045 0.045
## 1.045 0.106 0.106
## 5.305 0.530 0.530
## 1.334 0.465 0.465
## 1.351 1.000 1.000
lavTestScore(metric2, release = 1:18)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 124.816 18 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p54. 6.824 1 0.009
## 2 .p2. == .p55. 27.925 1 0.000
## 3 .p3. == .p56. 2.437 1 0.118
## 4 .p4. == .p57. 6.175 1 0.013
## 5 .p5. == .p58. 10.503 1 0.001
## 6 .p6. == .p59. 23.282 1 0.000
## 7 .p7. == .p60. 8.816 1 0.003
## 8 .p8. == .p61. 4.019 1 0.045
## 9 .p9. == .p62. 21.275 1 0.000
## 10 .p10. == .p63. 2.828 1 0.093
## 11 .p11. == .p64. 2.791 1 0.095
## 12 .p13. == .p66. 4.682 1 0.030
## 13 .p14. == .p67. 0.217 1 0.641
## 14 .p15. == .p68. 14.999 1 0.000
## 15 .p16. == .p69. 17.110 1 0.000
## 16 .p17. == .p70. 0.069 1 0.793
## 17 .p18. == .p71. 52.570 1 0.000
## 18 .p19. == .p72. 0.020 1 0.887
scalar<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 7.033718e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 3409.614 114.000 0.000 0.951 0.090 0.046
## aic bic
## 172219.367 172672.580
Mc(scalar)
## [1] 0.7926708
summary(scalar, standardized=T, ci=T) # -.066
## lavaan 0.6-18 ended normally after 150 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 30
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3409.614 2586.348
## Degrees of freedom 114 114
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.318
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1351.031 1024.819
## 0 2058.583 1561.530
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.151 0.024 6.337 0.000 0.104
## sswk (.p2.) 0.151 0.024 6.311 0.000 0.104
## sspc (.p3.) 0.147 0.023 6.329 0.000 0.101
## ssei (.p4.) 0.087 0.014 6.175 0.000 0.060
## math =~
## ssar (.p5.) 0.328 0.014 23.265 0.000 0.300
## ssmk (.p6.) 0.235 0.012 20.055 0.000 0.212
## ssmc (.p7.) 0.185 0.009 21.203 0.000 0.168
## ssao (.p8.) 0.274 0.012 22.447 0.000 0.250
## electronic =~
## ssai (.p9.) 0.278 0.013 21.489 0.000 0.253
## sssi (.10.) 0.302 0.014 21.381 0.000 0.275
## ssmc (.11.) 0.150 0.008 18.304 0.000 0.134
## ssei 0.097 0.010 9.612 0.000 0.078
## speed =~
## ssno (.13.) 0.540 0.018 29.656 0.000 0.505
## sscs (.14.) 0.494 0.017 29.682 0.000 0.462
## ssmk (.15.) 0.218 0.010 22.580 0.000 0.199
## g =~
## verbal (.16.) 5.335 0.870 6.130 0.000 3.629
## math (.17.) 2.336 0.115 20.269 0.000 2.111
## elctrnc (.18.) 1.763 0.088 20.104 0.000 1.591
## speed (.19.) 1.037 0.045 23.018 0.000 0.949
## ci.upper Std.lv Std.all
##
## 0.197 0.818 0.885
## 0.198 0.822 0.891
## 0.192 0.797 0.860
## 0.115 0.474 0.579
##
## 0.355 0.833 0.904
## 0.258 0.598 0.635
## 0.202 0.470 0.525
## 0.298 0.697 0.737
##
## 0.303 0.563 0.697
## 0.330 0.612 0.743
## 0.166 0.304 0.339
## 0.117 0.197 0.242
##
## 0.576 0.779 0.821
## 0.527 0.712 0.756
## 0.236 0.314 0.333
##
## 7.040 0.983 0.983
## 2.562 0.919 0.919
## 1.934 0.870 0.870
## 1.126 0.720 0.720
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.197 0.016 11.996 0.000 0.165
## .sswk (.38.) 0.183 0.017 10.887 0.000 0.150
## .sspc (.39.) 0.173 0.017 10.123 0.000 0.140
## .ssei (.40.) -0.007 0.015 -0.460 0.645 -0.036
## .ssar (.41.) 0.169 0.017 10.168 0.000 0.136
## .ssmk (.42.) 0.209 0.017 11.998 0.000 0.175
## .ssmc (.43.) 0.036 0.015 2.322 0.020 0.006
## .ssao (.44.) 0.145 0.016 8.914 0.000 0.113
## .ssai (.45.) -0.122 0.014 -9.034 0.000 -0.149
## .sssi (.46.) -0.118 0.014 -8.406 0.000 -0.146
## .ssno (.47.) 0.215 0.017 12.479 0.000 0.181
## .sscs (.48.) 0.220 0.017 12.806 0.000 0.186
## ci.upper Std.lv Std.all
## 0.229 0.197 0.213
## 0.216 0.183 0.199
## 0.207 0.173 0.187
## 0.022 -0.007 -0.008
## 0.202 0.169 0.183
## 0.244 0.209 0.222
## 0.066 0.036 0.040
## 0.177 0.145 0.154
## -0.096 -0.122 -0.151
## -0.091 -0.118 -0.143
## 0.248 0.215 0.226
## 0.253 0.220 0.233
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.184 0.007 26.524 0.000 0.171
## .sswk 0.176 0.007 26.091 0.000 0.163
## .sspc 0.225 0.009 23.903 0.000 0.206
## .ssei 0.245 0.008 29.597 0.000 0.229
## .ssar 0.155 0.007 21.796 0.000 0.141
## .ssmk 0.183 0.007 28.042 0.000 0.170
## .ssmc 0.261 0.009 27.695 0.000 0.242
## .ssao 0.409 0.013 31.445 0.000 0.384
## .ssai 0.336 0.012 27.906 0.000 0.312
## .sssi 0.304 0.012 25.779 0.000 0.281
## .ssno 0.293 0.015 20.178 0.000 0.265
## .sscs 0.379 0.016 23.436 0.000 0.348
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.198 0.184 0.216
## 0.189 0.176 0.206
## 0.243 0.225 0.261
## 0.261 0.245 0.367
## 0.169 0.155 0.183
## 0.196 0.183 0.206
## 0.279 0.261 0.325
## 0.435 0.409 0.458
## 0.359 0.336 0.514
## 0.327 0.304 0.448
## 0.322 0.293 0.326
## 0.411 0.379 0.428
## 1.000 0.034 0.034
## 1.000 0.155 0.155
## 1.000 0.244 0.244
## 1.000 0.482 0.482
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.151 0.024 6.337 0.000 0.104
## sswk (.p2.) 0.151 0.024 6.311 0.000 0.104
## sspc (.p3.) 0.147 0.023 6.329 0.000 0.101
## ssei (.p4.) 0.087 0.014 6.175 0.000 0.060
## math =~
## ssar (.p5.) 0.328 0.014 23.265 0.000 0.300
## ssmk (.p6.) 0.235 0.012 20.055 0.000 0.212
## ssmc (.p7.) 0.185 0.009 21.203 0.000 0.168
## ssao (.p8.) 0.274 0.012 22.447 0.000 0.250
## electronic =~
## ssai (.p9.) 0.278 0.013 21.489 0.000 0.253
## sssi (.10.) 0.302 0.014 21.381 0.000 0.275
## ssmc (.11.) 0.150 0.008 18.304 0.000 0.134
## ssei 0.178 0.010 17.715 0.000 0.158
## speed =~
## ssno (.13.) 0.540 0.018 29.656 0.000 0.505
## sscs (.14.) 0.494 0.017 29.682 0.000 0.462
## ssmk (.15.) 0.218 0.010 22.580 0.000 0.199
## g =~
## verbal (.16.) 5.335 0.870 6.130 0.000 3.629
## math (.17.) 2.336 0.115 20.269 0.000 2.111
## elctrnc (.18.) 1.763 0.088 20.104 0.000 1.591
## speed (.19.) 1.037 0.045 23.018 0.000 0.949
## ci.upper Std.lv Std.all
##
## 0.197 0.923 0.895
## 0.198 0.927 0.900
## 0.192 0.899 0.868
## 0.115 0.534 0.482
##
## 0.355 0.910 0.897
## 0.258 0.653 0.642
## 0.202 0.514 0.507
## 0.298 0.761 0.729
##
## 0.303 0.800 0.744
## 0.330 0.869 0.835
## 0.166 0.431 0.425
## 0.198 0.512 0.462
##
## 0.576 0.855 0.817
## 0.527 0.782 0.763
## 0.236 0.344 0.338
##
## 7.040 0.978 0.978
## 2.562 0.945 0.945
## 1.934 0.688 0.688
## 1.126 0.736 0.736
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.197 0.016 11.996 0.000 0.165
## .sswk (.38.) 0.183 0.017 10.887 0.000 0.150
## .sspc (.39.) 0.173 0.017 10.123 0.000 0.140
## .ssei (.40.) -0.007 0.015 -0.460 0.645 -0.036
## .ssar (.41.) 0.169 0.017 10.168 0.000 0.136
## .ssmk (.42.) 0.209 0.017 11.998 0.000 0.175
## .ssmc (.43.) 0.036 0.015 2.322 0.020 0.006
## .ssao (.44.) 0.145 0.016 8.914 0.000 0.113
## .ssai (.45.) -0.122 0.014 -9.034 0.000 -0.149
## .sssi (.46.) -0.118 0.014 -8.406 0.000 -0.146
## .ssno (.47.) 0.215 0.017 12.479 0.000 0.181
## .sscs (.48.) 0.220 0.017 12.806 0.000 0.186
## .verbal -0.454 0.038 -12.105 0.000 -0.528
## .math -0.187 0.047 -3.935 0.000 -0.279
## .elctrnc 1.802 0.097 18.659 0.000 1.613
## .speed -0.572 0.044 -12.905 0.000 -0.659
## g 0.074 0.030 2.437 0.015 0.014
## ci.upper Std.lv Std.all
## 0.229 0.197 0.191
## 0.216 0.183 0.178
## 0.207 0.173 0.167
## 0.022 -0.007 -0.006
## 0.202 0.169 0.167
## 0.244 0.209 0.206
## 0.066 0.036 0.035
## 0.177 0.145 0.139
## -0.096 -0.122 -0.114
## -0.091 -0.118 -0.114
## 0.248 0.215 0.205
## 0.253 0.220 0.214
## -0.381 -0.074 -0.074
## -0.094 -0.067 -0.067
## 1.992 0.627 0.627
## -0.485 -0.362 -0.362
## 0.134 0.066 0.066
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.211 0.008 27.096 0.000 0.196
## .sswk 0.202 0.008 24.918 0.000 0.186
## .sspc 0.265 0.011 24.750 0.000 0.244
## .ssei 0.314 0.012 26.958 0.000 0.291
## .ssar 0.200 0.009 22.817 0.000 0.183
## .ssmk 0.178 0.007 25.745 0.000 0.165
## .ssmc 0.290 0.010 27.816 0.000 0.270
## .ssao 0.512 0.015 34.471 0.000 0.483
## .ssai 0.515 0.019 27.306 0.000 0.478
## .sssi 0.327 0.015 21.474 0.000 0.298
## .ssno 0.364 0.019 19.579 0.000 0.327
## .sscs 0.439 0.020 22.285 0.000 0.400
## .verbal 1.595 0.552 2.888 0.004 0.512
## .math 0.832 0.114 7.310 0.000 0.609
## .electronic 4.351 0.457 9.516 0.000 3.455
## .speed 1.147 0.095 12.092 0.000 0.961
## g 1.261 0.045 28.085 0.000 1.173
## ci.upper Std.lv Std.all
## 0.226 0.211 0.198
## 0.218 0.202 0.190
## 0.286 0.265 0.247
## 0.337 0.314 0.255
## 0.217 0.200 0.195
## 0.192 0.178 0.172
## 0.311 0.290 0.282
## 0.541 0.512 0.469
## 0.552 0.515 0.446
## 0.357 0.327 0.302
## 0.400 0.364 0.332
## 0.477 0.439 0.418
## 2.677 0.043 0.043
## 1.055 0.108 0.108
## 5.247 0.526 0.526
## 1.333 0.458 0.458
## 1.349 1.000 1.000
lavTestScore(scalar, release = 19:30)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 847.929 12 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p37. == .p90. 382.450 1 0.000
## 2 .p38. == .p91. 1.267 1 0.260
## 3 .p39. == .p92. 526.717 1 0.000
## 4 .p40. == .p93. 2.080 1 0.149
## 5 .p41. == .p94. 64.339 1 0.000
## 6 .p42. == .p95. 9.655 1 0.002
## 7 .p43. == .p96. 0.008 1 0.928
## 8 .p44. == .p97. 55.425 1 0.000
## 9 .p45. == .p98. 23.012 1 0.000
## 10 .p46. == .p99. 12.890 1 0.000
## 11 .p47. == .p100. 108.290 1 0.000
## 12 .p48. == .p101. 77.183 1 0.000
scalar2<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 7.251107e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2752.628 112.000 0.000 0.961 0.082 0.043
## aic bic
## 171566.380 172033.327
Mc(scalar2)
## [1] 0.8301329
summary(scalar2, standardized=T, ci=T) # g -.112 Std.all
## lavaan 0.6-18 ended normally after 154 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 28
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2752.628 2084.690
## Degrees of freedom 112 112
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.320
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1051.422 796.290
## 0 1701.206 1288.401
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.152 0.024 6.456 0.000 0.106
## sswk (.p2.) 0.152 0.024 6.429 0.000 0.105
## sspc (.p3.) 0.148 0.023 6.439 0.000 0.103
## ssei (.p4.) 0.091 0.014 6.316 0.000 0.063
## math =~
## ssar (.p5.) 0.325 0.014 23.000 0.000 0.298
## ssmk (.p6.) 0.238 0.012 20.397 0.000 0.215
## ssmc (.p7.) 0.185 0.009 21.043 0.000 0.168
## ssao (.p8.) 0.272 0.012 22.198 0.000 0.248
## electronic =~
## ssai (.p9.) 0.280 0.013 21.602 0.000 0.255
## sssi (.10.) 0.305 0.014 21.489 0.000 0.277
## ssmc (.11.) 0.149 0.008 18.195 0.000 0.133
## ssei 0.090 0.010 8.616 0.000 0.069
## speed =~
## ssno (.13.) 0.556 0.019 29.786 0.000 0.519
## sscs (.14.) 0.490 0.016 30.488 0.000 0.459
## ssmk (.15.) 0.210 0.009 22.811 0.000 0.192
## g =~
## verbal (.16.) 5.308 0.851 6.238 0.000 3.641
## math (.17.) 2.357 0.117 20.074 0.000 2.126
## elctrnc (.18.) 1.748 0.087 20.189 0.000 1.578
## speed (.19.) 1.023 0.044 23.130 0.000 0.936
## ci.upper Std.lv Std.all
##
## 0.198 0.821 0.889
## 0.198 0.818 0.888
## 0.193 0.799 0.868
## 0.119 0.490 0.599
##
## 0.353 0.833 0.904
## 0.261 0.609 0.647
## 0.202 0.473 0.529
## 0.296 0.696 0.736
##
## 0.306 0.564 0.698
## 0.332 0.613 0.744
## 0.165 0.300 0.335
## 0.110 0.181 0.221
##
## 0.592 0.795 0.834
## 0.522 0.701 0.750
## 0.228 0.300 0.319
##
## 6.976 0.983 0.983
## 2.587 0.921 0.921
## 1.918 0.868 0.868
## 1.109 0.715 0.715
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.161 0.016 9.857 0.000 0.129
## .sswk (.38.) 0.147 0.017 8.805 0.000 0.115
## .sspc 0.287 0.017 16.758 0.000 0.253
## .ssei (.40.) -0.014 0.015 -0.918 0.359 -0.043
## .ssar (.41.) 0.167 0.017 10.040 0.000 0.134
## .ssmk (.42.) 0.229 0.017 13.136 0.000 0.195
## .ssmc (.43.) 0.041 0.015 2.650 0.008 0.011
## .ssao (.44.) 0.143 0.016 8.793 0.000 0.111
## .ssai (.45.) -0.114 0.013 -8.472 0.000 -0.141
## .sssi (.46.) -0.109 0.014 -7.728 0.000 -0.136
## .ssno 0.175 0.018 9.705 0.000 0.140
## .sscs (.48.) 0.270 0.017 15.630 0.000 0.236
## ci.upper Std.lv Std.all
## 0.193 0.161 0.174
## 0.180 0.147 0.160
## 0.320 0.287 0.312
## 0.016 -0.014 -0.017
## 0.199 0.167 0.181
## 0.263 0.229 0.244
## 0.071 0.041 0.045
## 0.175 0.143 0.152
## -0.088 -0.114 -0.141
## -0.081 -0.109 -0.132
## 0.211 0.175 0.184
## 0.304 0.270 0.289
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.178 0.007 26.939 0.000 0.165
## .sswk 0.180 0.007 26.275 0.000 0.166
## .sspc 0.208 0.009 24.342 0.000 0.191
## .ssei 0.246 0.008 29.691 0.000 0.230
## .ssar 0.155 0.007 22.005 0.000 0.141
## .ssmk 0.183 0.006 28.332 0.000 0.170
## .ssmc 0.261 0.009 27.706 0.000 0.242
## .ssao 0.410 0.013 31.531 0.000 0.385
## .ssai 0.335 0.012 27.821 0.000 0.311
## .sssi 0.303 0.012 25.680 0.000 0.280
## .ssno 0.277 0.014 19.185 0.000 0.249
## .sscs 0.382 0.016 23.929 0.000 0.350
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.191 0.178 0.209
## 0.193 0.180 0.212
## 0.225 0.208 0.246
## 0.262 0.246 0.367
## 0.169 0.155 0.183
## 0.196 0.183 0.207
## 0.279 0.261 0.326
## 0.436 0.410 0.459
## 0.359 0.335 0.513
## 0.326 0.303 0.446
## 0.305 0.277 0.305
## 0.413 0.382 0.437
## 1.000 0.034 0.034
## 1.000 0.153 0.153
## 1.000 0.247 0.247
## 1.000 0.489 0.489
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.152 0.024 6.456 0.000 0.106
## sswk (.p2.) 0.152 0.024 6.429 0.000 0.105
## sspc (.p3.) 0.148 0.023 6.439 0.000 0.103
## ssei (.p4.) 0.091 0.014 6.316 0.000 0.063
## math =~
## ssar (.p5.) 0.325 0.014 23.000 0.000 0.298
## ssmk (.p6.) 0.238 0.012 20.397 0.000 0.215
## ssmc (.p7.) 0.185 0.009 21.043 0.000 0.168
## ssao (.p8.) 0.272 0.012 22.198 0.000 0.248
## electronic =~
## ssai (.p9.) 0.280 0.013 21.602 0.000 0.255
## sssi (.10.) 0.305 0.014 21.489 0.000 0.277
## ssmc (.11.) 0.149 0.008 18.195 0.000 0.133
## ssei 0.169 0.010 16.998 0.000 0.150
## speed =~
## ssno (.13.) 0.556 0.019 29.786 0.000 0.519
## sscs (.14.) 0.490 0.016 30.488 0.000 0.459
## ssmk (.15.) 0.210 0.009 22.811 0.000 0.192
## g =~
## verbal (.16.) 5.308 0.851 6.238 0.000 3.641
## math (.17.) 2.357 0.117 20.074 0.000 2.126
## elctrnc (.18.) 1.748 0.087 20.189 0.000 1.578
## speed (.19.) 1.023 0.044 23.130 0.000 0.936
## ci.upper Std.lv Std.all
##
## 0.198 0.927 0.899
## 0.198 0.924 0.897
## 0.193 0.903 0.877
## 0.119 0.553 0.501
##
## 0.353 0.910 0.898
## 0.261 0.666 0.654
## 0.202 0.517 0.510
## 0.296 0.761 0.728
##
## 0.306 0.805 0.747
## 0.332 0.875 0.839
## 0.165 0.428 0.422
## 0.189 0.486 0.440
##
## 0.592 0.873 0.830
## 0.522 0.770 0.757
## 0.228 0.330 0.324
##
## 6.976 0.978 0.978
## 2.587 0.946 0.946
## 1.918 0.683 0.683
## 1.109 0.731 0.731
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.161 0.016 9.857 0.000 0.129
## .sswk (.38.) 0.147 0.017 8.805 0.000 0.115
## .sspc -0.029 0.019 -1.514 0.130 -0.067
## .ssei (.40.) -0.014 0.015 -0.918 0.359 -0.043
## .ssar (.41.) 0.167 0.017 10.040 0.000 0.134
## .ssmk (.42.) 0.229 0.017 13.136 0.000 0.195
## .ssmc (.43.) 0.041 0.015 2.650 0.008 0.011
## .ssao (.44.) 0.143 0.016 8.793 0.000 0.111
## .ssai (.45.) -0.114 0.013 -8.472 0.000 -0.141
## .sssi (.46.) -0.109 0.014 -7.728 0.000 -0.136
## .ssno 0.394 0.025 15.523 0.000 0.344
## .sscs (.48.) 0.270 0.017 15.630 0.000 0.236
## .verbal -0.192 0.033 -5.821 0.000 -0.257
## .math -0.277 0.045 -6.134 0.000 -0.365
## .elctrnc 1.651 0.090 18.319 0.000 1.474
## .speed -0.840 0.049 -17.091 0.000 -0.936
## g 0.126 0.029 4.410 0.000 0.070
## ci.upper Std.lv Std.all
## 0.193 0.161 0.156
## 0.180 0.147 0.143
## 0.009 -0.029 -0.028
## 0.016 -0.014 -0.012
## 0.199 0.167 0.164
## 0.263 0.229 0.225
## 0.071 0.041 0.040
## 0.175 0.143 0.137
## -0.088 -0.114 -0.106
## -0.081 -0.109 -0.104
## 0.443 0.394 0.374
## 0.304 0.270 0.266
## -0.127 -0.031 -0.031
## -0.188 -0.099 -0.099
## 1.828 0.574 0.574
## -0.744 -0.535 -0.535
## 0.182 0.112 0.112
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.203 0.007 27.927 0.000 0.189
## .sswk 0.207 0.008 25.467 0.000 0.191
## .sspc 0.243 0.009 26.215 0.000 0.225
## .ssei 0.318 0.012 27.355 0.000 0.295
## .ssar 0.200 0.009 22.981 0.000 0.183
## .ssmk 0.179 0.007 26.008 0.000 0.166
## .ssmc 0.290 0.010 27.836 0.000 0.270
## .ssao 0.513 0.015 34.440 0.000 0.483
## .ssai 0.512 0.019 27.068 0.000 0.475
## .sssi 0.321 0.015 20.913 0.000 0.291
## .ssno 0.343 0.018 18.597 0.000 0.307
## .sscs 0.441 0.019 23.031 0.000 0.403
## .verbal 1.639 0.550 2.982 0.003 0.562
## .math 0.821 0.114 7.203 0.000 0.597
## .electronic 4.401 0.460 9.577 0.000 3.501
## .speed 1.148 0.094 12.159 0.000 0.963
## g 1.263 0.045 28.147 0.000 1.175
## ci.upper Std.lv Std.all
## 0.217 0.203 0.191
## 0.223 0.207 0.195
## 0.262 0.243 0.230
## 0.341 0.318 0.261
## 0.217 0.200 0.194
## 0.193 0.179 0.173
## 0.311 0.290 0.283
## 0.542 0.513 0.470
## 0.549 0.512 0.441
## 0.351 0.321 0.295
## 0.380 0.343 0.310
## 0.478 0.441 0.426
## 2.717 0.044 0.044
## 1.044 0.105 0.105
## 5.302 0.533 0.533
## 1.333 0.465 0.465
## 1.351 1.000 1.000
lavTestScore(scalar2, release = 19:28, standardized=T, epc=T)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 213.25 10 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p37. == .p90. 116.499 1 0.000
## 2 .p38. == .p91. 102.271 1 0.000
## 3 .p40. == .p93. 2.090 1 0.148
## 4 .p41. == .p94. 33.330 1 0.000
## 5 .p42. == .p95. 0.402 1 0.526
## 6 .p43. == .p96. 0.014 1 0.907
## 7 .p44. == .p97. 69.963 1 0.000
## 8 .p45. == .p98. 16.097 1 0.000
## 9 .p46. == .p99. 22.504 1 0.000
## 10 .p48. == .p101. 0.402 1 0.526
##
## $epc
##
## expected parameter changes (epc) and expected parameter values (epv):
##
## lhs op rhs block group free label plabel est epc
## 1 verbal =~ ssgs 1 1 1 .p1. .p1. 0.152 0.000
## 2 verbal =~ sswk 1 1 2 .p2. .p2. 0.152 0.001
## 3 verbal =~ sspc 1 1 3 .p3. .p3. 0.148 0.001
## 4 verbal =~ ssei 1 1 4 .p4. .p4. 0.091 -0.001
## 5 math =~ ssar 1 1 5 .p5. .p5. 0.325 0.000
## 6 math =~ ssmk 1 1 6 .p6. .p6. 0.238 0.000
## 7 math =~ ssmc 1 1 7 .p7. .p7. 0.185 -0.001
## 8 math =~ ssao 1 1 8 .p8. .p8. 0.272 0.000
## 9 electronic =~ ssai 1 1 9 .p9. .p9. 0.280 0.004
## 10 electronic =~ sssi 1 1 10 .p10. .p10. 0.305 -0.006
## 11 electronic =~ ssmc 1 1 11 .p11. .p11. 0.149 0.001
## 12 electronic =~ ssei 1 1 12 .p12. 0.090 0.004
## 13 speed =~ ssno 1 1 13 .p13. .p13. 0.556 0.000
## 14 speed =~ sscs 1 1 14 .p14. .p14. 0.490 -0.001
## 15 speed =~ ssmk 1 1 15 .p15. .p15. 0.210 0.002
## 16 g =~ verbal 1 1 16 .p16. .p16. 5.308 -0.027
## 17 g =~ math 1 1 17 .p17. .p17. 2.357 -0.003
## 18 g =~ electronic 1 1 18 .p18. .p18. 1.748 0.011
## 19 g =~ speed 1 1 19 .p19. .p19. 1.023 0.001
## 20 ssgs ~~ ssgs 1 1 20 .p20. 0.178 0.000
## 21 sswk ~~ sswk 1 1 21 .p21. 0.180 0.000
## 22 sspc ~~ sspc 1 1 22 .p22. 0.208 0.000
## 23 ssei ~~ ssei 1 1 23 .p23. 0.246 0.000
## 24 ssar ~~ ssar 1 1 24 .p24. 0.155 0.000
## 25 ssmk ~~ ssmk 1 1 25 .p25. 0.183 0.000
## 26 ssmc ~~ ssmc 1 1 26 .p26. 0.261 0.000
## 27 ssao ~~ ssao 1 1 27 .p27. 0.410 0.000
## 28 ssai ~~ ssai 1 1 28 .p28. 0.335 -0.002
## 29 sssi ~~ sssi 1 1 29 .p29. 0.303 0.004
## 30 ssno ~~ ssno 1 1 30 .p30. 0.277 0.000
## 31 sscs ~~ sscs 1 1 31 .p31. 0.382 0.000
## 32 verbal ~~ verbal 1 1 0 .p32. 1.000 NA
## 33 math ~~ math 1 1 0 .p33. 1.000 NA
## 34 electronic ~~ electronic 1 1 0 .p34. 1.000 NA
## 35 speed ~~ speed 1 1 0 .p35. 1.000 NA
## 36 g ~~ g 1 1 0 .p36. 1.000 NA
## 37 ssgs ~1 1 1 32 .p37. .p37. 0.161 -0.041
## 38 sswk ~1 1 1 33 .p38. .p38. 0.147 0.034
## 39 sspc ~1 1 1 34 .p39. 0.287 -0.003
## 40 ssei ~1 1 1 35 .p40. .p40. -0.014 0.004
## 41 ssar ~1 1 1 36 .p41. .p41. 0.167 -0.019
## 42 ssmk ~1 1 1 37 .p42. .p42. 0.229 -0.005
## 43 ssmc ~1 1 1 38 .p43. .p43. 0.041 -0.002
## 44 ssao ~1 1 1 39 .p44. .p44. 0.143 0.054
## 45 ssai ~1 1 1 40 .p45. .p45. -0.114 0.017
## 46 sssi ~1 1 1 41 .p46. .p46. -0.109 -0.022
## 47 ssno ~1 1 1 42 .p47. 0.175 -0.002
## 48 sscs ~1 1 1 43 .p48. .p48. 0.270 0.000
## 49 verbal ~1 1 1 0 .p49. 0.000 NA
## 50 math ~1 1 1 0 .p50. 0.000 NA
## 51 electronic ~1 1 1 0 .p51. 0.000 NA
## 52 speed ~1 1 1 0 .p52. 0.000 NA
## 53 g ~1 1 1 0 .p53. 0.000 NA
## 54 verbal =~ ssgs 2 2 44 .p1. .p54. 0.152 0.000
## 55 verbal =~ sswk 2 2 45 .p2. .p55. 0.152 0.001
## 56 verbal =~ sspc 2 2 46 .p3. .p56. 0.148 0.001
## 57 verbal =~ ssei 2 2 47 .p4. .p57. 0.091 -0.001
## 58 math =~ ssar 2 2 48 .p5. .p58. 0.325 0.000
## 59 math =~ ssmk 2 2 49 .p6. .p59. 0.238 0.000
## 60 math =~ ssmc 2 2 50 .p7. .p60. 0.185 -0.001
## 61 math =~ ssao 2 2 51 .p8. .p61. 0.272 0.000
## 62 electronic =~ ssai 2 2 52 .p9. .p62. 0.280 0.004
## 63 electronic =~ sssi 2 2 53 .p10. .p63. 0.305 -0.006
## 64 electronic =~ ssmc 2 2 54 .p11. .p64. 0.149 0.001
## 65 electronic =~ ssei 2 2 55 .p65. 0.169 0.004
## 66 speed =~ ssno 2 2 56 .p13. .p66. 0.556 0.000
## 67 speed =~ sscs 2 2 57 .p14. .p67. 0.490 -0.001
## 68 speed =~ ssmk 2 2 58 .p15. .p68. 0.210 0.002
## 69 g =~ verbal 2 2 59 .p16. .p69. 5.308 -0.027
## 70 g =~ math 2 2 60 .p17. .p70. 2.357 -0.003
## 71 g =~ electronic 2 2 61 .p18. .p71. 1.748 0.011
## epv sepc.lv sepc.all sepc.nox
## 1 0.152 0.002 0.003 0.003
## 2 0.153 0.006 0.006 0.006
## 3 0.149 0.004 0.004 0.004
## 4 0.090 -0.006 -0.007 -0.007
## 5 0.326 0.001 0.001 0.001
## 6 0.237 -0.001 -0.001 -0.001
## 7 0.184 -0.002 -0.003 -0.003
## 8 0.272 0.001 0.001 0.001
## 9 0.284 0.007 0.009 0.009
## 10 0.298 -0.013 -0.015 -0.015
## 11 0.150 0.001 0.001 0.001
## 12 0.093 0.008 0.009 0.009
## 13 0.555 -0.001 -0.001 -0.001
## 14 0.489 -0.001 -0.001 -0.001
## 15 0.212 0.002 0.002 0.002
## 16 5.281 -0.005 -0.005 -0.005
## 17 2.354 -0.001 -0.001 -0.001
## 18 1.759 0.006 0.006 0.006
## 19 1.024 0.001 0.001 0.001
## 20 0.178 0.178 0.209 0.209
## 21 0.179 -0.180 -0.212 -0.212
## 22 0.208 -0.208 -0.246 -0.246
## 23 0.246 -0.246 -0.367 -0.367
## 24 0.155 -0.155 -0.183 -0.183
## 25 0.183 -0.183 -0.207 -0.207
## 26 0.261 -0.261 -0.326 -0.326
## 27 0.410 -0.410 -0.459 -0.459
## 28 0.333 -0.335 -0.513 -0.513
## 29 0.307 0.303 0.446 0.446
## 30 0.278 0.277 0.305 0.305
## 31 0.382 0.382 0.437 0.437
## 32 NA NA NA NA
## 33 NA NA NA NA
## 34 NA NA NA NA
## 35 NA NA NA NA
## 36 NA NA NA NA
## 37 0.120 -0.041 -0.045 -0.045
## 38 0.181 0.034 0.037 0.037
## 39 0.284 -0.003 -0.003 -0.003
## 40 -0.010 0.004 0.004 0.004
## 41 0.148 -0.019 -0.021 -0.021
## 42 0.224 -0.005 -0.005 -0.005
## 43 0.039 -0.002 -0.002 -0.002
## 44 0.198 0.054 0.057 0.057
## 45 -0.097 0.017 0.021 0.021
## 46 -0.131 -0.022 -0.027 -0.027
## 47 0.173 -0.002 -0.002 -0.002
## 48 0.271 0.000 0.000 0.000
## 49 NA NA NA NA
## 50 NA NA NA NA
## 51 NA NA NA NA
## 52 NA NA NA NA
## 53 NA NA NA NA
## 54 0.152 0.003 0.003 0.003
## 55 0.153 0.007 0.006 0.006
## 56 0.149 0.005 0.004 0.004
## 57 0.090 -0.007 -0.006 -0.006
## 58 0.326 0.001 0.001 0.001
## 59 0.237 -0.001 -0.001 -0.001
## 60 0.184 -0.003 -0.003 -0.003
## 61 0.272 0.001 0.001 0.001
## 62 0.284 0.011 0.010 0.010
## 63 0.298 -0.018 -0.017 -0.017
## 64 0.150 0.002 0.002 0.002
## 65 0.173 0.012 0.011 0.011
## 66 0.555 -0.001 -0.001 -0.001
## 67 0.489 -0.001 -0.001 -0.001
## 68 0.212 0.002 0.002 0.002
## 69 5.281 -0.005 -0.005 -0.005
## 70 2.354 -0.001 -0.001 -0.001
## 71 1.759 0.004 0.004 0.004
## [ reached 'max' / getOption("max.print") -- omitted 35 rows ]
strict<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 2.149336e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 3043.319 124.000 0.000 0.957 0.081 0.046
## aic bic
## 171833.072 172217.616
Mc(strict)
## [1] 0.8139814
summary(strict, standardized=T, ci=T) # g -.094 Std.all
## lavaan 0.6-18 ended normally after 142 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
## Number of equality constraints 40
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3043.319 2282.517
## Degrees of freedom 124 124
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.333
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1234.624 925.979
## 0 1808.695 1356.537
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.142 0.025 5.622 0.000 0.093
## sswk (.p2.) 0.141 0.025 5.603 0.000 0.092
## sspc (.p3.) 0.138 0.025 5.612 0.000 0.090
## ssei (.p4.) 0.085 0.015 5.531 0.000 0.055
## math =~
## ssar (.p5.) 0.310 0.015 20.877 0.000 0.281
## ssmk (.p6.) 0.226 0.012 18.612 0.000 0.202
## ssmc (.p7.) 0.177 0.009 19.568 0.000 0.159
## ssao (.p8.) 0.258 0.013 20.266 0.000 0.233
## electronic =~
## ssai (.p9.) 0.257 0.014 17.707 0.000 0.228
## sssi (.10.) 0.272 0.016 17.188 0.000 0.241
## ssmc (.11.) 0.133 0.009 15.362 0.000 0.116
## ssei 0.078 0.010 7.792 0.000 0.059
## speed =~
## ssno (.13.) 0.544 0.019 28.617 0.000 0.506
## sscs (.14.) 0.478 0.016 29.278 0.000 0.446
## ssmk (.15.) 0.204 0.010 21.496 0.000 0.186
## g =~
## verbal (.16.) 5.685 1.041 5.464 0.000 3.646
## math (.17.) 2.483 0.134 18.502 0.000 2.220
## elctrnc (.18.) 1.938 0.116 16.665 0.000 1.710
## speed (.19.) 1.048 0.047 22.449 0.000 0.957
## ci.upper Std.lv Std.all
##
## 0.192 0.820 0.883
## 0.191 0.815 0.880
## 0.186 0.797 0.859
## 0.115 0.491 0.588
##
## 0.339 0.829 0.891
## 0.250 0.606 0.647
## 0.194 0.473 0.525
## 0.282 0.690 0.712
##
## 0.285 0.560 0.656
## 0.303 0.594 0.722
## 0.150 0.290 0.322
## 0.098 0.171 0.204
##
## 0.581 0.788 0.817
## 0.510 0.693 0.734
## 0.223 0.296 0.316
##
## 7.724 0.985 0.985
## 2.746 0.928 0.928
## 2.166 0.889 0.889
## 1.140 0.724 0.724
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.164 0.016 10.022 0.000 0.132
## .sswk (.38.) 0.145 0.017 8.687 0.000 0.112
## .sspc 0.287 0.017 16.794 0.000 0.253
## .ssei (.40.) -0.014 0.015 -0.965 0.334 -0.044
## .ssar (.41.) 0.169 0.017 10.130 0.000 0.136
## .ssmk (.42.) 0.229 0.017 13.133 0.000 0.195
## .ssmc (.43.) 0.042 0.015 2.762 0.006 0.012
## .ssao (.44.) 0.136 0.016 8.437 0.000 0.105
## .ssai (.45.) -0.123 0.014 -9.069 0.000 -0.149
## .sssi (.46.) -0.105 0.014 -7.486 0.000 -0.132
## .ssno 0.175 0.018 9.715 0.000 0.140
## .sscs (.48.) 0.271 0.017 15.653 0.000 0.237
## ci.upper Std.lv Std.all
## 0.195 0.164 0.176
## 0.177 0.145 0.156
## 0.320 0.287 0.309
## 0.015 -0.014 -0.017
## 0.202 0.169 0.182
## 0.263 0.229 0.244
## 0.072 0.042 0.047
## 0.168 0.136 0.141
## -0.096 -0.123 -0.144
## -0.077 -0.105 -0.127
## 0.211 0.175 0.182
## 0.305 0.271 0.287
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.20.) 0.190 0.005 38.197 0.000 0.180
## .sswk (.21.) 0.194 0.005 36.056 0.000 0.184
## .sspc (.22.) 0.226 0.006 35.104 0.000 0.214
## .ssei (.23.) 0.280 0.007 39.333 0.000 0.266
## .ssar (.24.) 0.178 0.006 31.327 0.000 0.167
## .ssmk (.25.) 0.182 0.005 37.671 0.000 0.172
## .ssmc (.26.) 0.277 0.007 39.114 0.000 0.263
## .ssao (.27.) 0.463 0.010 46.201 0.000 0.443
## .ssai (.28.) 0.414 0.011 36.919 0.000 0.392
## .sssi (.29.) 0.324 0.010 32.841 0.000 0.304
## .ssno (.30.) 0.310 0.012 24.912 0.000 0.285
## .sscs (.31.) 0.412 0.013 32.139 0.000 0.387
## .verbal 1.000 1.000
## .math 1.000 1.000
## .elctrnc 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.200 0.190 0.221
## 0.205 0.194 0.226
## 0.239 0.226 0.263
## 0.294 0.280 0.401
## 0.189 0.178 0.206
## 0.191 0.182 0.207
## 0.291 0.277 0.342
## 0.483 0.463 0.493
## 0.436 0.414 0.569
## 0.343 0.324 0.478
## 0.334 0.310 0.333
## 0.437 0.412 0.462
## 1.000 0.030 0.030
## 1.000 0.140 0.140
## 1.000 0.210 0.210
## 1.000 0.476 0.476
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.142 0.025 5.622 0.000 0.093
## sswk (.p2.) 0.141 0.025 5.603 0.000 0.092
## sspc (.p3.) 0.138 0.025 5.612 0.000 0.090
## ssei (.p4.) 0.085 0.015 5.531 0.000 0.055
## math =~
## ssar (.p5.) 0.310 0.015 20.877 0.000 0.281
## ssmk (.p6.) 0.226 0.012 18.612 0.000 0.202
## ssmc (.p7.) 0.177 0.009 19.568 0.000 0.159
## ssao (.p8.) 0.258 0.013 20.266 0.000 0.233
## electronic =~
## ssai (.p9.) 0.257 0.014 17.707 0.000 0.228
## sssi (.10.) 0.272 0.016 17.188 0.000 0.241
## ssmc (.11.) 0.133 0.009 15.362 0.000 0.116
## ssei 0.153 0.010 14.738 0.000 0.132
## speed =~
## ssno (.13.) 0.544 0.019 28.617 0.000 0.506
## sscs (.14.) 0.478 0.016 29.278 0.000 0.446
## ssmk (.15.) 0.204 0.010 21.496 0.000 0.186
## g =~
## verbal (.16.) 5.685 1.041 5.464 0.000 3.646
## math (.17.) 2.483 0.134 18.502 0.000 2.220
## elctrnc (.18.) 1.938 0.116 16.665 0.000 1.710
## speed (.19.) 1.048 0.047 22.449 0.000 0.957
## ci.upper Std.lv Std.all
##
## 0.192 0.930 0.905
## 0.191 0.924 0.903
## 0.186 0.904 0.885
## 0.115 0.557 0.510
##
## 0.339 0.916 0.908
## 0.250 0.670 0.655
## 0.194 0.523 0.518
## 0.282 0.762 0.746
##
## 0.285 0.826 0.789
## 0.303 0.876 0.839
## 0.150 0.428 0.424
## 0.173 0.491 0.450
##
## 0.581 0.883 0.846
## 0.510 0.777 0.771
## 0.223 0.332 0.325
##
## 7.724 0.975 0.975
## 2.746 0.943 0.943
## 2.166 0.677 0.677
## 1.140 0.725 0.725
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.164 0.016 10.022 0.000 0.132
## .sswk (.38.) 0.145 0.017 8.687 0.000 0.112
## .sspc -0.029 0.019 -1.529 0.126 -0.067
## .ssei (.40.) -0.014 0.015 -0.965 0.334 -0.044
## .ssar (.41.) 0.169 0.017 10.130 0.000 0.136
## .ssmk (.42.) 0.229 0.017 13.133 0.000 0.195
## .ssmc (.43.) 0.042 0.015 2.762 0.006 0.012
## .ssao (.44.) 0.136 0.016 8.437 0.000 0.105
## .ssai (.45.) -0.123 0.014 -9.069 0.000 -0.149
## .sssi (.46.) -0.105 0.014 -7.486 0.000 -0.132
## .ssno 0.395 0.025 15.530 0.000 0.345
## .sscs (.48.) 0.271 0.017 15.653 0.000 0.237
## .verbal -0.087 0.039 -2.269 0.023 -0.163
## .math -0.239 0.047 -5.101 0.000 -0.331
## .elctrnc 1.865 0.117 15.883 0.000 1.635
## .speed -0.841 0.052 -16.293 0.000 -0.942
## g 0.106 0.028 3.720 0.000 0.050
## ci.upper Std.lv Std.all
## 0.195 0.164 0.159
## 0.177 0.145 0.141
## 0.008 -0.029 -0.029
## 0.015 -0.014 -0.013
## 0.202 0.169 0.167
## 0.263 0.229 0.224
## 0.072 0.042 0.042
## 0.168 0.136 0.133
## -0.096 -0.123 -0.117
## -0.077 -0.105 -0.100
## 0.445 0.395 0.379
## 0.305 0.271 0.269
## -0.012 -0.013 -0.013
## -0.147 -0.081 -0.081
## 2.095 0.580 0.580
## -0.740 -0.518 -0.518
## 0.161 0.094 0.094
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.20.) 0.190 0.005 38.197 0.000 0.180
## .sswk (.21.) 0.194 0.005 36.056 0.000 0.184
## .sspc (.22.) 0.226 0.006 35.104 0.000 0.214
## .ssei (.23.) 0.280 0.007 39.333 0.000 0.266
## .ssar (.24.) 0.178 0.006 31.327 0.000 0.167
## .ssmk (.25.) 0.182 0.005 37.671 0.000 0.172
## .ssmc (.26.) 0.277 0.007 39.114 0.000 0.263
## .ssao (.27.) 0.463 0.010 46.201 0.000 0.443
## .ssai (.28.) 0.414 0.011 36.919 0.000 0.392
## .sssi (.29.) 0.324 0.010 32.841 0.000 0.304
## .ssno (.30.) 0.310 0.012 24.912 0.000 0.285
## .sscs (.31.) 0.412 0.013 32.139 0.000 0.387
## .verbal 2.105 0.728 2.891 0.004 0.678
## .math 0.976 0.127 7.673 0.000 0.727
## .elctrnc 5.607 0.690 8.120 0.000 4.253
## .speed 1.249 0.104 12.043 0.000 1.045
## g 1.262 0.045 28.209 0.000 1.174
## ci.upper Std.lv Std.all
## 0.200 0.190 0.180
## 0.205 0.194 0.185
## 0.239 0.226 0.217
## 0.294 0.280 0.234
## 0.189 0.178 0.175
## 0.191 0.182 0.174
## 0.291 0.277 0.272
## 0.483 0.463 0.444
## 0.436 0.414 0.378
## 0.343 0.324 0.297
## 0.334 0.310 0.284
## 0.437 0.412 0.406
## 3.532 0.049 0.049
## 1.226 0.111 0.111
## 6.960 0.542 0.542
## 1.452 0.474 0.474
## 1.350 1.000 1.000
latent<-cfa(hof.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 1.451686e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 3251.881 117.000 0.000 0.954 0.087 0.099
## aic bic
## 172055.634 172488.246
Mc(latent)
## [1] 0.8017044
summary(latent, standardized=T, ci=T) # g -.056 Std.all
## lavaan 0.6-18 ended normally after 88 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 91
## Number of equality constraints 28
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3251.881 2457.265
## Degrees of freedom 117 117
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.323
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1336.844 1010.179
## 0 1915.037 1447.087
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.154 0.024 6.517 0.000 0.108
## sswk (.p2.) 0.154 0.024 6.488 0.000 0.107
## sspc (.p3.) 0.150 0.023 6.499 0.000 0.105
## ssei (.p4.) 0.089 0.014 6.345 0.000 0.062
## math =~
## ssar (.p5.) 0.322 0.013 24.665 0.000 0.297
## ssmk (.p6.) 0.235 0.011 21.360 0.000 0.214
## ssmc (.p7.) 0.179 0.008 22.151 0.000 0.163
## ssao (.p8.) 0.269 0.011 23.801 0.000 0.247
## electronic =~
## ssai (.p9.) 0.433 0.012 36.507 0.000 0.409
## sssi (.10.) 0.477 0.012 38.856 0.000 0.453
## ssmc (.11.) 0.241 0.009 27.513 0.000 0.224
## ssei 0.141 0.012 11.346 0.000 0.116
## speed =~
## ssno (.13.) 0.580 0.015 38.426 0.000 0.550
## sscs (.14.) 0.511 0.013 40.483 0.000 0.486
## ssmk (.15.) 0.221 0.009 25.227 0.000 0.204
## g =~
## verbal (.16.) 5.590 0.886 6.309 0.000 3.853
## math (.17.) 2.521 0.119 21.173 0.000 2.287
## elctrnc (.18.) 1.236 0.039 32.048 0.000 1.161
## speed (.19.) 1.036 0.038 27.526 0.000 0.962
## ci.upper Std.lv Std.all
##
## 0.201 0.876 0.900
## 0.200 0.874 0.901
## 0.195 0.852 0.883
## 0.117 0.506 0.594
##
## 0.348 0.874 0.913
## 0.257 0.638 0.651
## 0.195 0.486 0.510
## 0.291 0.729 0.750
##
## 0.456 0.688 0.770
## 0.502 0.759 0.826
## 0.259 0.384 0.402
## 0.165 0.224 0.263
##
## 0.609 0.835 0.849
## 0.536 0.736 0.766
## 0.238 0.318 0.324
##
## 7.327 0.984 0.984
## 2.754 0.930 0.930
## 1.312 0.778 0.778
## 1.110 0.720 0.720
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.170 0.016 10.367 0.000 0.138
## .sswk (.38.) 0.156 0.017 9.287 0.000 0.123
## .sspc 0.293 0.017 17.057 0.000 0.259
## .ssei (.40.) -0.016 0.015 -1.074 0.283 -0.045
## .ssar (.41.) 0.173 0.017 10.386 0.000 0.140
## .ssmk (.42.) 0.236 0.018 13.476 0.000 0.202
## .ssmc (.43.) 0.045 0.015 2.935 0.003 0.015
## .ssao (.44.) 0.149 0.016 9.089 0.000 0.116
## .ssai (.45.) -0.100 0.014 -7.424 0.000 -0.127
## .sssi (.46.) -0.100 0.014 -7.117 0.000 -0.128
## .ssno 0.180 0.018 9.920 0.000 0.144
## .sscs (.48.) 0.274 0.017 15.793 0.000 0.240
## ci.upper Std.lv Std.all
## 0.202 0.170 0.175
## 0.189 0.156 0.161
## 0.327 0.293 0.304
## 0.013 -0.016 -0.019
## 0.206 0.173 0.181
## 0.270 0.236 0.241
## 0.076 0.045 0.048
## 0.181 0.149 0.153
## -0.074 -0.100 -0.112
## -0.073 -0.100 -0.109
## 0.215 0.180 0.183
## 0.308 0.274 0.285
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.180 0.007 26.853 0.000 0.167
## .sswk 0.178 0.007 26.219 0.000 0.164
## .sspc 0.205 0.008 24.136 0.000 0.188
## .ssei 0.246 0.008 29.705 0.000 0.230
## .ssar 0.153 0.007 22.137 0.000 0.139
## .ssmk 0.181 0.006 28.070 0.000 0.168
## .ssmc 0.257 0.009 27.618 0.000 0.239
## .ssao 0.413 0.013 31.551 0.000 0.387
## .ssai 0.325 0.013 25.410 0.000 0.300
## .sssi 0.268 0.012 22.493 0.000 0.245
## .ssno 0.269 0.015 18.394 0.000 0.240
## .sscs 0.382 0.016 23.749 0.000 0.350
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.193 0.180 0.190
## 0.191 0.178 0.189
## 0.221 0.205 0.220
## 0.262 0.246 0.339
## 0.166 0.153 0.167
## 0.194 0.181 0.188
## 0.275 0.257 0.282
## 0.438 0.413 0.437
## 0.350 0.325 0.407
## 0.292 0.268 0.318
## 0.297 0.269 0.278
## 0.413 0.382 0.413
## 1.000 0.031 0.031
## 1.000 0.136 0.136
## 1.000 0.395 0.395
## 1.000 0.482 0.482
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.154 0.024 6.517 0.000 0.108
## sswk (.p2.) 0.154 0.024 6.488 0.000 0.107
## sspc (.p3.) 0.150 0.023 6.499 0.000 0.105
## ssei (.p4.) 0.089 0.014 6.345 0.000 0.062
## math =~
## ssar (.p5.) 0.322 0.013 24.665 0.000 0.297
## ssmk (.p6.) 0.235 0.011 21.360 0.000 0.214
## ssmc (.p7.) 0.179 0.008 22.151 0.000 0.163
## ssao (.p8.) 0.269 0.011 23.801 0.000 0.247
## electronic =~
## ssai (.p9.) 0.433 0.012 36.507 0.000 0.409
## sssi (.10.) 0.477 0.012 38.856 0.000 0.453
## ssmc (.11.) 0.241 0.009 27.513 0.000 0.224
## ssei 0.290 0.012 25.076 0.000 0.267
## speed =~
## ssno (.13.) 0.580 0.015 38.426 0.000 0.550
## sscs (.14.) 0.511 0.013 40.483 0.000 0.486
## ssmk (.15.) 0.221 0.009 25.227 0.000 0.204
## g =~
## verbal (.16.) 5.590 0.886 6.309 0.000 3.853
## math (.17.) 2.521 0.119 21.173 0.000 2.287
## elctrnc (.18.) 1.236 0.039 32.048 0.000 1.161
## speed (.19.) 1.036 0.038 27.526 0.000 0.962
## ci.upper Std.lv Std.all
##
## 0.201 0.876 0.890
## 0.200 0.874 0.887
## 0.195 0.852 0.863
## 0.117 0.506 0.474
##
## 0.348 0.874 0.890
## 0.257 0.638 0.651
## 0.195 0.486 0.500
## 0.291 0.729 0.714
##
## 0.456 0.688 0.679
## 0.502 0.759 0.778
## 0.259 0.384 0.395
## 0.312 0.461 0.432
##
## 0.609 0.835 0.815
## 0.536 0.736 0.742
## 0.238 0.318 0.324
##
## 7.327 0.984 0.984
## 2.754 0.930 0.930
## 1.312 0.778 0.778
## 1.110 0.720 0.720
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.170 0.016 10.367 0.000 0.138
## .sswk (.38.) 0.156 0.017 9.287 0.000 0.123
## .sspc -0.019 0.019 -0.987 0.324 -0.057
## .ssei (.40.) -0.016 0.015 -1.074 0.283 -0.045
## .ssar (.41.) 0.173 0.017 10.386 0.000 0.140
## .ssmk (.42.) 0.236 0.018 13.476 0.000 0.202
## .ssmc (.43.) 0.045 0.015 2.935 0.003 0.015
## .ssao (.44.) 0.149 0.016 9.089 0.000 0.116
## .ssai (.45.) -0.100 0.014 -7.424 0.000 -0.127
## .sssi (.46.) -0.100 0.014 -7.117 0.000 -0.128
## .ssno 0.397 0.025 15.653 0.000 0.347
## .sscs (.48.) 0.274 0.017 15.793 0.000 0.240
## .verbal 0.089 0.022 4.048 0.000 0.046
## .math -0.142 0.040 -3.526 0.000 -0.221
## .elctrnc 1.092 0.038 28.688 0.000 1.018
## .speed -0.746 0.041 -17.981 0.000 -0.827
## g 0.056 0.026 2.138 0.033 0.005
## ci.upper Std.lv Std.all
## 0.202 0.170 0.173
## 0.189 0.156 0.158
## 0.019 -0.019 -0.019
## 0.013 -0.016 -0.015
## 0.206 0.173 0.176
## 0.270 0.236 0.241
## 0.076 0.045 0.047
## 0.181 0.149 0.145
## -0.074 -0.100 -0.099
## -0.073 -0.100 -0.103
## 0.446 0.397 0.387
## 0.308 0.274 0.276
## 0.131 0.016 0.016
## -0.063 -0.052 -0.052
## 1.167 0.687 0.687
## -0.664 -0.518 -0.518
## 0.108 0.056 0.056
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.200 0.007 28.285 0.000 0.186
## .sswk 0.207 0.008 26.267 0.000 0.191
## .sspc 0.249 0.009 26.609 0.000 0.231
## .ssei 0.315 0.012 27.137 0.000 0.292
## .ssar 0.200 0.009 23.254 0.000 0.183
## .ssmk 0.181 0.007 26.026 0.000 0.167
## .ssmc 0.293 0.011 27.811 0.000 0.272
## .ssao 0.511 0.015 34.397 0.000 0.482
## .ssai 0.553 0.020 27.849 0.000 0.514
## .sssi 0.376 0.016 23.871 0.000 0.345
## .ssno 0.353 0.019 18.927 0.000 0.316
## .sscs 0.442 0.019 23.109 0.000 0.404
## .verbal 1.000 1.000
## .math 1.000 1.000
## .electronic 1.000 1.000
## .speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.214 0.200 0.207
## 0.222 0.207 0.213
## 0.268 0.249 0.256
## 0.337 0.315 0.276
## 0.217 0.200 0.208
## 0.194 0.181 0.188
## 0.313 0.293 0.309
## 0.541 0.511 0.490
## 0.592 0.553 0.539
## 0.407 0.376 0.395
## 0.389 0.353 0.336
## 0.479 0.442 0.449
## 1.000 0.031 0.031
## 1.000 0.136 0.136
## 1.000 0.395 0.395
## 1.000 0.482 0.482
## 1.000 1.000 1.000
latent2<-cfa(hof.lv, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 5.053686e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2762.109 114.000 0.000 0.961 0.081 0.043
## aic bic
## 171571.862 172025.075
Mc(latent2)
## [1] 0.8296951
summary(latent2, standardized=T, ci=T) # -.086
## lavaan 0.6-18 ended normally after 114 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 94
## Number of equality constraints 28
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2762.109 2084.559
## Degrees of freedom 114 114
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.325
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1056.021 796.977
## 0 1706.089 1287.582
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.156 0.023 6.760 0.000 0.111
## sswk (.p2.) 0.155 0.023 6.730 0.000 0.110
## sspc (.p3.) 0.152 0.022 6.742 0.000 0.108
## ssei (.p4.) 0.093 0.014 6.596 0.000 0.065
## math =~
## ssar (.p5.) 0.314 0.013 24.095 0.000 0.288
## ssmk (.p6.) 0.229 0.011 21.002 0.000 0.208
## ssmc (.p7.) 0.178 0.008 22.117 0.000 0.162
## ssao (.p8.) 0.262 0.011 23.230 0.000 0.240
## electronic =~
## ssai (.p9.) 0.279 0.013 21.458 0.000 0.254
## sssi (.10.) 0.304 0.014 21.346 0.000 0.276
## ssmc (.11.) 0.149 0.008 18.165 0.000 0.133
## ssei 0.089 0.010 8.555 0.000 0.069
## speed =~
## ssno (.13.) 0.575 0.015 37.905 0.000 0.546
## sscs (.14.) 0.508 0.013 39.999 0.000 0.483
## ssmk (.15.) 0.218 0.009 24.828 0.000 0.201
## g =~
## verbal (.16.) 5.181 0.794 6.525 0.000 3.625
## math (.17.) 2.438 0.120 20.339 0.000 2.203
## elctrnc (.18.) 1.753 0.087 20.097 0.000 1.582
## speed (.19.) 0.986 0.038 26.290 0.000 0.913
## ci.upper Std.lv Std.all
##
## 0.201 0.822 0.890
## 0.201 0.820 0.888
## 0.196 0.800 0.869
## 0.121 0.491 0.600
##
## 0.340 0.827 0.902
## 0.251 0.605 0.643
## 0.194 0.469 0.525
## 0.284 0.691 0.733
##
## 0.305 0.564 0.698
## 0.332 0.613 0.744
## 0.165 0.301 0.337
## 0.109 0.180 0.220
##
## 0.605 0.808 0.841
## 0.533 0.713 0.756
## 0.235 0.306 0.325
##
## 6.737 0.982 0.982
## 2.673 0.925 0.925
## 1.924 0.869 0.869
## 1.060 0.702 0.702
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.161 0.016 9.859 0.000 0.129
## .sswk (.38.) 0.147 0.017 8.806 0.000 0.115
## .sspc 0.287 0.017 16.760 0.000 0.253
## .ssei (.40.) -0.014 0.015 -0.918 0.359 -0.043
## .ssar (.41.) 0.167 0.017 10.059 0.000 0.135
## .ssmk (.42.) 0.229 0.017 13.143 0.000 0.195
## .ssmc (.43.) 0.040 0.015 2.629 0.009 0.010
## .ssao (.44.) 0.143 0.016 8.791 0.000 0.111
## .ssai (.45.) -0.114 0.013 -8.466 0.000 -0.141
## .sssi (.46.) -0.109 0.014 -7.722 0.000 -0.136
## .ssno 0.175 0.018 9.705 0.000 0.140
## .sscs (.48.) 0.270 0.017 15.631 0.000 0.236
## ci.upper Std.lv Std.all
## 0.193 0.161 0.174
## 0.180 0.147 0.160
## 0.320 0.287 0.311
## 0.016 -0.014 -0.017
## 0.200 0.167 0.182
## 0.263 0.229 0.244
## 0.070 0.040 0.045
## 0.175 0.143 0.152
## -0.088 -0.114 -0.141
## -0.081 -0.109 -0.132
## 0.211 0.175 0.182
## 0.304 0.270 0.287
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .math 1.000 1.000
## .speed 1.000 1.000
## .ssgs 0.178 0.007 26.920 0.000 0.165
## .sswk 0.180 0.007 26.304 0.000 0.166
## .sspc 0.208 0.009 24.341 0.000 0.191
## .ssei 0.246 0.008 29.717 0.000 0.230
## .ssar 0.157 0.007 22.437 0.000 0.143
## .ssmk 0.184 0.006 28.311 0.000 0.171
## .ssmc 0.261 0.009 27.718 0.000 0.242
## .ssao 0.411 0.013 31.634 0.000 0.386
## .ssai 0.335 0.012 27.833 0.000 0.312
## .sssi 0.303 0.012 25.693 0.000 0.280
## .ssno 0.271 0.015 18.488 0.000 0.242
## .sscs 0.381 0.016 23.754 0.000 0.350
## .verbal 1.000 1.000
## .electronic 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.144 0.144
## 1.000 0.507 0.507
## 0.191 0.178 0.209
## 0.193 0.180 0.211
## 0.225 0.208 0.245
## 0.262 0.246 0.368
## 0.171 0.157 0.187
## 0.196 0.184 0.208
## 0.279 0.261 0.327
## 0.436 0.411 0.462
## 0.359 0.335 0.513
## 0.326 0.303 0.447
## 0.299 0.271 0.293
## 0.412 0.381 0.428
## 1.000 0.036 0.036
## 1.000 0.245 0.245
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.156 0.023 6.760 0.000 0.111
## sswk (.p2.) 0.155 0.023 6.730 0.000 0.110
## sspc (.p3.) 0.152 0.022 6.742 0.000 0.108
## ssei (.p4.) 0.093 0.014 6.596 0.000 0.065
## math =~
## ssar (.p5.) 0.314 0.013 24.095 0.000 0.288
## ssmk (.p6.) 0.229 0.011 21.002 0.000 0.208
## ssmc (.p7.) 0.178 0.008 22.117 0.000 0.162
## ssao (.p8.) 0.262 0.011 23.230 0.000 0.240
## electronic =~
## ssai (.p9.) 0.279 0.013 21.458 0.000 0.254
## sssi (.10.) 0.304 0.014 21.346 0.000 0.276
## ssmc (.11.) 0.149 0.008 18.165 0.000 0.133
## ssei 0.169 0.010 16.911 0.000 0.149
## speed =~
## ssno (.13.) 0.575 0.015 37.905 0.000 0.546
## sscs (.14.) 0.508 0.013 39.999 0.000 0.483
## ssmk (.15.) 0.218 0.009 24.828 0.000 0.201
## g =~
## verbal (.16.) 5.181 0.794 6.525 0.000 3.625
## math (.17.) 2.438 0.120 20.339 0.000 2.203
## elctrnc (.18.) 1.753 0.087 20.097 0.000 1.582
## speed (.19.) 0.986 0.038 26.290 0.000 0.913
## ci.upper Std.lv Std.all
##
## 0.201 0.925 0.899
## 0.201 0.923 0.897
## 0.196 0.901 0.877
## 0.121 0.552 0.500
##
## 0.340 0.916 0.900
## 0.251 0.669 0.658
## 0.194 0.520 0.512
## 0.284 0.765 0.730
##
## 0.305 0.805 0.747
## 0.332 0.875 0.839
## 0.165 0.429 0.423
## 0.188 0.486 0.440
##
## 0.605 0.859 0.823
## 0.533 0.758 0.752
## 0.235 0.325 0.319
##
## 6.737 0.980 0.980
## 2.673 0.939 0.939
## 1.924 0.684 0.684
## 1.060 0.743 0.743
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs (.37.) 0.161 0.016 9.859 0.000 0.129
## .sswk (.38.) 0.147 0.017 8.806 0.000 0.115
## .sspc -0.029 0.019 -1.510 0.131 -0.067
## .ssei (.40.) -0.014 0.015 -0.918 0.359 -0.043
## .ssar (.41.) 0.167 0.017 10.059 0.000 0.135
## .ssmk (.42.) 0.229 0.017 13.143 0.000 0.195
## .ssmc (.43.) 0.040 0.015 2.629 0.009 0.010
## .ssao (.44.) 0.143 0.016 8.791 0.000 0.111
## .ssai (.45.) -0.114 0.013 -8.466 0.000 -0.141
## .sssi (.46.) -0.109 0.014 -7.722 0.000 -0.136
## .ssno 0.393 0.025 15.522 0.000 0.343
## .sscs (.48.) 0.270 0.017 15.631 0.000 0.236
## .verbal -0.035 0.032 -1.075 0.282 -0.097
## .math -0.214 0.043 -4.961 0.000 -0.299
## .elctrnc 1.706 0.092 18.541 0.000 1.526
## .speed -0.781 0.043 -18.065 0.000 -0.866
## g 0.096 0.028 3.408 0.001 0.041
## ci.upper Std.lv Std.all
## 0.193 0.161 0.156
## 0.180 0.147 0.143
## 0.009 -0.029 -0.028
## 0.016 -0.014 -0.012
## 0.200 0.167 0.164
## 0.263 0.229 0.225
## 0.070 0.040 0.040
## 0.175 0.143 0.137
## -0.088 -0.114 -0.106
## -0.081 -0.109 -0.104
## 0.443 0.393 0.377
## 0.304 0.270 0.268
## 0.028 -0.006 -0.006
## -0.130 -0.073 -0.073
## 1.887 0.592 0.592
## -0.697 -0.523 -0.523
## 0.152 0.086 0.086
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .math 1.000 1.000
## .speed 1.000 1.000
## .ssgs 0.203 0.007 27.932 0.000 0.189
## .sswk 0.206 0.008 25.445 0.000 0.191
## .sspc 0.244 0.009 26.279 0.000 0.225
## .ssei 0.318 0.012 27.342 0.000 0.295
## .ssar 0.197 0.009 22.953 0.000 0.180
## .ssmk 0.179 0.007 25.978 0.000 0.165
## .ssmc 0.290 0.010 27.822 0.000 0.270
## .ssao 0.512 0.015 34.414 0.000 0.483
## .ssai 0.512 0.019 27.077 0.000 0.475
## .sssi 0.321 0.015 20.920 0.000 0.291
## .ssno 0.352 0.019 18.976 0.000 0.316
## .sscs 0.442 0.019 23.170 0.000 0.405
## .verbal 1.378 0.412 3.342 0.001 0.570
## .electronic 4.415 0.463 9.533 0.000 3.507
## g 1.264 0.045 28.131 0.000 1.176
## ci.upper Std.lv Std.all
## 1.000 0.118 0.118
## 1.000 0.449 0.449
## 0.217 0.203 0.192
## 0.222 0.206 0.195
## 0.262 0.244 0.231
## 0.340 0.318 0.260
## 0.214 0.197 0.190
## 0.192 0.179 0.172
## 0.311 0.290 0.282
## 0.541 0.512 0.467
## 0.549 0.512 0.441
## 0.351 0.321 0.295
## 0.388 0.352 0.323
## 0.479 0.442 0.435
## 2.186 0.039 0.039
## 5.323 0.532 0.532
## 1.352 1.000 1.000
weak<-cfa(hof.weak, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
fitMeasures(weak, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2762.109 115.000 0.000 0.961 0.081 0.043
## aic bic
## 171569.862 172016.208
Mc(weak)
## [1] 0.8297536
summary(weak, standardized=T, ci=T) # -.080
## lavaan 0.6-18 ended normally after 117 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 93
## Number of equality constraints 28
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2762.109 2102.844
## Degrees of freedom 115 115
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.314
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1056.021 803.968
## 0 1706.088 1298.876
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.156 0.023 6.761 0.000 0.111
## sswk (.p2.) 0.155 0.023 6.730 0.000 0.110
## sspc (.p3.) 0.152 0.022 6.743 0.000 0.108
## ssei (.p4.) 0.093 0.014 6.596 0.000 0.065
## math =~
## ssar (.p5.) 0.314 0.013 24.095 0.000 0.288
## ssmk (.p6.) 0.229 0.011 21.002 0.000 0.208
## ssmc (.p7.) 0.178 0.008 22.117 0.000 0.162
## ssao (.p8.) 0.262 0.011 23.231 0.000 0.240
## electronic =~
## ssai (.p9.) 0.279 0.013 21.458 0.000 0.254
## sssi (.10.) 0.304 0.014 21.346 0.000 0.276
## ssmc (.11.) 0.149 0.008 18.165 0.000 0.133
## ssei 0.089 0.010 8.555 0.000 0.069
## speed =~
## ssno (.13.) 0.575 0.015 37.905 0.000 0.546
## sscs (.14.) 0.508 0.013 39.999 0.000 0.483
## ssmk (.15.) 0.218 0.009 24.828 0.000 0.201
## g =~
## verbal (.16.) 5.181 0.794 6.525 0.000 3.625
## math (.17.) 2.438 0.120 20.339 0.000 2.203
## elctrnc (.18.) 1.753 0.087 20.097 0.000 1.582
## speed (.19.) 0.986 0.038 26.290 0.000 0.913
## ci.upper Std.lv Std.all
##
## 0.201 0.822 0.890
## 0.201 0.820 0.888
## 0.196 0.800 0.869
## 0.121 0.491 0.600
##
## 0.340 0.827 0.902
## 0.251 0.605 0.643
## 0.194 0.469 0.525
## 0.284 0.691 0.733
##
## 0.305 0.564 0.698
## 0.332 0.613 0.744
## 0.165 0.301 0.337
## 0.109 0.180 0.220
##
## 0.605 0.808 0.841
## 0.533 0.713 0.756
## 0.235 0.306 0.325
##
## 6.737 0.982 0.982
## 2.673 0.925 0.925
## 1.924 0.869 0.869
## 1.060 0.702 0.702
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.38.) 0.161 0.016 9.859 0.000 0.129
## .sswk (.39.) 0.147 0.017 8.806 0.000 0.115
## .sspc 0.287 0.017 16.760 0.000 0.253
## .ssei (.41.) -0.014 0.015 -0.918 0.359 -0.043
## .ssar (.42.) 0.167 0.017 10.059 0.000 0.135
## .ssmk (.43.) 0.229 0.017 13.143 0.000 0.195
## .ssmc (.44.) 0.040 0.015 2.629 0.009 0.010
## .ssao (.45.) 0.143 0.016 8.791 0.000 0.111
## .ssai (.46.) -0.114 0.013 -8.466 0.000 -0.141
## .sssi (.47.) -0.109 0.014 -7.722 0.000 -0.136
## .ssno 0.175 0.018 9.705 0.000 0.140
## .sscs (.49.) 0.270 0.017 15.631 0.000 0.236
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.193 0.161 0.174
## 0.180 0.147 0.160
## 0.320 0.287 0.311
## 0.016 -0.014 -0.017
## 0.200 0.167 0.182
## 0.263 0.229 0.244
## 0.070 0.040 0.045
## 0.175 0.143 0.152
## -0.088 -0.114 -0.141
## -0.081 -0.109 -0.132
## 0.211 0.175 0.182
## 0.304 0.270 0.287
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .math 1.000 1.000
## .speed 1.000 1.000
## .ssgs 0.178 0.007 26.920 0.000 0.165
## .sswk 0.180 0.007 26.304 0.000 0.166
## .sspc 0.208 0.009 24.341 0.000 0.191
## .ssei 0.246 0.008 29.717 0.000 0.230
## .ssar 0.157 0.007 22.436 0.000 0.143
## .ssmk 0.184 0.006 28.311 0.000 0.171
## .ssmc 0.261 0.009 27.718 0.000 0.242
## .ssao 0.411 0.013 31.634 0.000 0.386
## .ssai 0.335 0.012 27.833 0.000 0.312
## .sssi 0.303 0.012 25.693 0.000 0.280
## .ssno 0.271 0.015 18.488 0.000 0.242
## .sscs 0.381 0.016 23.754 0.000 0.350
## .verbal 1.000 1.000
## .electronic 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.144 0.144
## 1.000 0.507 0.507
## 0.191 0.178 0.209
## 0.193 0.180 0.211
## 0.225 0.208 0.245
## 0.262 0.246 0.368
## 0.171 0.157 0.187
## 0.196 0.184 0.208
## 0.279 0.261 0.327
## 0.436 0.411 0.462
## 0.359 0.335 0.513
## 0.326 0.303 0.447
## 0.299 0.271 0.293
## 0.412 0.381 0.428
## 1.000 0.036 0.036
## 1.000 0.245 0.245
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.156 0.023 6.761 0.000 0.111
## sswk (.p2.) 0.155 0.023 6.730 0.000 0.110
## sspc (.p3.) 0.152 0.022 6.743 0.000 0.108
## ssei (.p4.) 0.093 0.014 6.596 0.000 0.065
## math =~
## ssar (.p5.) 0.314 0.013 24.095 0.000 0.288
## ssmk (.p6.) 0.229 0.011 21.002 0.000 0.208
## ssmc (.p7.) 0.178 0.008 22.117 0.000 0.162
## ssao (.p8.) 0.262 0.011 23.231 0.000 0.240
## electronic =~
## ssai (.p9.) 0.279 0.013 21.458 0.000 0.254
## sssi (.10.) 0.304 0.014 21.346 0.000 0.276
## ssmc (.11.) 0.149 0.008 18.165 0.000 0.133
## ssei 0.169 0.010 16.911 0.000 0.149
## speed =~
## ssno (.13.) 0.575 0.015 37.905 0.000 0.546
## sscs (.14.) 0.508 0.013 39.999 0.000 0.483
## ssmk (.15.) 0.218 0.009 24.828 0.000 0.201
## g =~
## verbal (.16.) 5.181 0.794 6.525 0.000 3.625
## math (.17.) 2.438 0.120 20.339 0.000 2.203
## elctrnc (.18.) 1.753 0.087 20.097 0.000 1.582
## speed (.19.) 0.986 0.038 26.290 0.000 0.913
## ci.upper Std.lv Std.all
##
## 0.201 0.925 0.899
## 0.201 0.923 0.897
## 0.196 0.901 0.877
## 0.121 0.552 0.500
##
## 0.340 0.916 0.900
## 0.251 0.669 0.658
## 0.194 0.520 0.512
## 0.284 0.765 0.730
##
## 0.305 0.805 0.747
## 0.332 0.875 0.839
## 0.165 0.429 0.423
## 0.188 0.486 0.440
##
## 0.605 0.859 0.823
## 0.533 0.758 0.752
## 0.235 0.325 0.319
##
## 6.737 0.980 0.980
## 2.673 0.939 0.939
## 1.924 0.684 0.684
## 1.060 0.743 0.743
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.38.) 0.161 0.016 9.859 0.000 0.129
## .sswk (.39.) 0.147 0.017 8.806 0.000 0.115
## .sspc -0.029 0.019 -1.510 0.131 -0.067
## .ssei (.41.) -0.014 0.015 -0.918 0.359 -0.043
## .ssar (.42.) 0.167 0.017 10.059 0.000 0.135
## .ssmk (.43.) 0.229 0.017 13.143 0.000 0.195
## .ssmc (.44.) 0.040 0.015 2.629 0.009 0.010
## .ssao (.45.) 0.143 0.016 8.791 0.000 0.111
## .ssai (.46.) -0.114 0.013 -8.466 0.000 -0.141
## .sssi (.47.) -0.109 0.014 -7.722 0.000 -0.136
## .ssno 0.393 0.025 15.522 0.000 0.343
## .sscs (.49.) 0.270 0.017 15.631 0.000 0.236
## .math -0.198 0.051 -3.904 0.000 -0.297
## .elctrnc 1.718 0.100 17.099 0.000 1.521
## .speed -0.775 0.044 -17.473 0.000 -0.862
## g 0.090 0.030 3.032 0.002 0.032
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.193 0.161 0.156
## 0.180 0.147 0.143
## 0.009 -0.029 -0.028
## 0.016 -0.014 -0.012
## 0.200 0.167 0.164
## 0.263 0.229 0.225
## 0.070 0.040 0.040
## 0.175 0.143 0.137
## -0.088 -0.114 -0.106
## -0.081 -0.109 -0.104
## 0.443 0.393 0.377
## 0.304 0.270 0.268
## -0.099 -0.068 -0.068
## 1.915 0.596 0.596
## -0.688 -0.519 -0.519
## 0.148 0.080 0.080
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .math 1.000 1.000
## .speed 1.000 1.000
## .ssgs 0.203 0.007 27.932 0.000 0.189
## .sswk 0.206 0.008 25.445 0.000 0.191
## .sspc 0.244 0.009 26.279 0.000 0.225
## .ssei 0.318 0.012 27.342 0.000 0.295
## .ssar 0.197 0.009 22.953 0.000 0.180
## .ssmk 0.179 0.007 25.978 0.000 0.165
## .ssmc 0.290 0.010 27.822 0.000 0.270
## .ssao 0.512 0.015 34.414 0.000 0.483
## .ssai 0.512 0.019 27.077 0.000 0.475
## .sssi 0.321 0.015 20.920 0.000 0.291
## .ssno 0.352 0.019 18.976 0.000 0.316
## .sscs 0.442 0.019 23.170 0.000 0.405
## .verbal 1.378 0.412 3.342 0.001 0.570
## .electronic 4.415 0.463 9.533 0.000 3.507
## g 1.264 0.045 28.131 0.000 1.176
## ci.upper Std.lv Std.all
## 1.000 0.118 0.118
## 1.000 0.449 0.449
## 0.217 0.203 0.192
## 0.222 0.206 0.195
## 0.262 0.244 0.231
## 0.340 0.318 0.260
## 0.214 0.197 0.190
## 0.192 0.179 0.172
## 0.311 0.290 0.282
## 0.541 0.512 0.467
## 0.549 0.512 0.441
## 0.351 0.321 0.295
## 0.388 0.352 0.323
## 0.479 0.442 0.435
## 2.186 0.039 0.039
## 5.323 0.532 0.532
## 1.352 1.000 1.000
standardizedSolution(weak) # get the correct SEs for standardized solution
## lhs op rhs group label est.std se z pvalue
## 1 verbal =~ ssgs 1 .p1. 0.890 0.004 201.870 0.000
## 2 verbal =~ sswk 1 .p2. 0.888 0.005 191.389 0.000
## 3 verbal =~ sspc 1 .p3. 0.869 0.006 151.790 0.000
## 4 verbal =~ ssei 1 .p4. 0.600 0.019 31.542 0.000
## 5 math =~ ssar 1 .p5. 0.902 0.004 201.748 0.000
## 6 math =~ ssmk 1 .p6. 0.643 0.012 51.686 0.000
## 7 math =~ ssmc 1 .p7. 0.525 0.013 40.909 0.000
## 8 math =~ ssao 1 .p8. 0.733 0.008 86.573 0.000
## 9 electronic =~ ssai 1 .p9. 0.698 0.011 65.152 0.000
## 10 electronic =~ sssi 1 .p10. 0.744 0.010 74.011 0.000
## 11 electronic =~ ssmc 1 .p11. 0.337 0.012 26.941 0.000
## 12 electronic =~ ssei 1 0.220 0.022 9.841 0.000
## 13 speed =~ ssno 1 .p13. 0.841 0.008 98.995 0.000
## 14 speed =~ sscs 1 .p14. 0.756 0.010 77.041 0.000
## 15 speed =~ ssmk 1 .p15. 0.325 0.013 24.536 0.000
## 16 g =~ verbal 1 .p16. 0.982 0.005 181.665 0.000
## 17 g =~ math 1 .p17. 0.925 0.007 141.204 0.000
## 18 g =~ electronic 1 .p18. 0.869 0.011 81.883 0.000
## 19 g =~ speed 1 .p19. 0.702 0.014 51.863 0.000
## 20 math ~~ math 1 0.144 0.012 11.881 0.000
## 21 speed ~~ speed 1 0.507 0.019 26.659 0.000
## 22 verbal ~1 1 0.000 0.000 NA NA
## 23 ssgs ~~ ssgs 1 0.209 0.008 26.613 0.000
## 24 sswk ~~ sswk 1 0.211 0.008 25.623 0.000
## 25 sspc ~~ sspc 1 0.245 0.010 24.660 0.000
## 26 ssei ~~ ssei 1 0.368 0.012 31.425 0.000
## 27 ssar ~~ ssar 1 0.187 0.008 23.152 0.000
## 28 ssmk ~~ ssmk 1 0.208 0.008 26.960 0.000
## 29 ssmc ~~ ssmc 1 0.327 0.011 30.949 0.000
## 30 ssao ~~ ssao 1 0.462 0.012 37.245 0.000
## 31 ssai ~~ ssai 1 0.513 0.015 34.358 0.000
## 32 sssi ~~ sssi 1 0.447 0.015 29.856 0.000
## 33 ssno ~~ ssno 1 0.293 0.014 20.518 0.000
## 34 sscs ~~ sscs 1 0.428 0.015 28.860 0.000
## 35 verbal ~~ verbal 1 0.036 0.011 3.384 0.001
## 36 electronic ~~ electronic 1 0.245 0.018 13.317 0.000
## 37 g ~~ g 1 1.000 0.000 NA NA
## 38 ssgs ~1 1 .p38. 0.174 0.018 9.818 0.000
## 39 sswk ~1 1 .p39. 0.160 0.018 8.766 0.000
## 40 sspc ~1 1 0.311 0.019 16.123 0.000
## 41 ssei ~1 1 .p41. -0.017 0.018 -0.916 0.360
## 42 ssar ~1 1 .p42. 0.182 0.019 9.795 0.000
## 43 ssmk ~1 1 .p43. 0.244 0.019 12.874 0.000
## 44 ssmc ~1 1 .p44. 0.045 0.017 2.609 0.009
## 45 ssao ~1 1 .p45. 0.152 0.017 8.718 0.000
## 46 ssai ~1 1 .p46. -0.141 0.017 -8.443 0.000
## 47 sssi ~1 1 .p47. -0.132 0.017 -7.612 0.000
## 48 ssno ~1 1 0.182 0.019 9.516 0.000
## 49 sscs ~1 1 .p49. 0.287 0.019 15.360 0.000
## 50 math ~1 1 0.000 0.000 NA NA
## 51 electronic ~1 1 0.000 0.000 NA NA
## 52 speed ~1 1 0.000 0.000 NA NA
## 53 g ~1 1 0.000 0.000 NA NA
## 54 verbal =~ ssgs 2 .p1. 0.899 0.004 218.079 0.000
## 55 verbal =~ sswk 2 .p2. 0.897 0.004 208.103 0.000
## 56 verbal =~ sspc 2 .p3. 0.877 0.005 177.429 0.000
## 57 verbal =~ ssei 2 .p4. 0.500 0.014 34.781 0.000
## 58 math =~ ssar 2 .p5. 0.900 0.005 198.410 0.000
## 59 math =~ ssmk 2 .p6. 0.658 0.013 51.693 0.000
## 60 math =~ ssmc 2 .p7. 0.512 0.013 39.745 0.000
## 61 math =~ ssao 2 .p8. 0.730 0.008 91.905 0.000
## 62 electronic =~ ssai 2 .p9. 0.747 0.009 79.833 0.000
## 63 electronic =~ sssi 2 .p10. 0.839 0.008 104.106 0.000
## 64 electronic =~ ssmc 2 .p11. 0.423 0.014 31.250 0.000
## 65 electronic =~ ssei 2 0.440 0.015 29.091 0.000
## 66 speed =~ ssno 2 .p13. 0.823 0.009 89.137 0.000
## 67 speed =~ sscs 2 .p14. 0.752 0.010 77.565 0.000
## 68 speed =~ ssmk 2 .p15. 0.319 0.013 24.400 0.000
## 69 g =~ verbal 2 .p16. 0.980 0.006 174.237 0.000
## 70 g =~ math 2 .p17. 0.939 0.005 175.805 0.000
## 71 g =~ electronic 2 .p18. 0.684 0.013 51.669 0.000
## 72 g =~ speed 2 .p19. 0.743 0.012 59.598 0.000
## 73 math ~~ math 2 0.118 0.010 11.705 0.000
## 74 speed ~~ speed 2 0.449 0.019 24.242 0.000
## 75 verbal ~1 2 0.000 0.000 NA NA
## 76 ssgs ~~ ssgs 2 0.192 0.007 25.869 0.000
## 77 sswk ~~ sswk 2 0.195 0.008 25.226 0.000
## 78 sspc ~~ sspc 2 0.231 0.009 26.624 0.000
## 79 ssei ~~ ssei 2 0.260 0.009 27.615 0.000
## 80 ssar ~~ ssar 2 0.190 0.008 23.273 0.000
## 81 ssmk ~~ ssmk 2 0.172 0.007 25.684 0.000
## 82 ssmc ~~ ssmc 2 0.282 0.010 29.506 0.000
## 83 ssao ~~ ssao 2 0.467 0.012 40.186 0.000
## 84 ssai ~~ ssai 2 0.441 0.014 31.545 0.000
## 85 sssi ~~ sssi 2 0.295 0.014 21.828 0.000
## 86 ssno ~~ ssno 2 0.323 0.015 21.269 0.000
## 87 sscs ~~ sscs 2 0.435 0.015 29.831 0.000
## 88 verbal ~~ verbal 2 0.039 0.011 3.539 0.000
## 89 electronic ~~ electronic 2 0.532 0.018 29.360 0.000
## 90 g ~~ g 2 1.000 0.000 NA NA
## ci.lower ci.upper
## 1 0.881 0.898
## 2 0.879 0.897
## 3 0.858 0.880
## 4 0.562 0.637
## 5 0.893 0.911
## 6 0.619 0.668
## 7 0.500 0.551
## 8 0.717 0.750
## 9 0.677 0.719
## 10 0.724 0.764
## 11 0.312 0.361
## 12 0.176 0.263
## 13 0.824 0.857
## 14 0.737 0.775
## 15 0.299 0.352
## 16 0.971 0.992
## 17 0.912 0.938
## 18 0.848 0.889
## 19 0.676 0.729
## 20 0.120 0.168
## 21 0.470 0.544
## 22 0.000 0.000
## 23 0.193 0.224
## 24 0.195 0.227
## 25 0.226 0.265
## 26 0.345 0.390
## 27 0.171 0.202
## 28 0.193 0.223
## 29 0.306 0.347
## 30 0.438 0.487
## 31 0.484 0.543
## 32 0.417 0.476
## 33 0.265 0.321
## 34 0.399 0.457
## 35 0.015 0.057
## 36 0.209 0.282
## 37 1.000 1.000
## 38 0.140 0.209
## 39 0.124 0.195
## 40 0.273 0.349
## 41 -0.052 0.019
## 42 0.146 0.219
## 43 0.207 0.281
## 44 0.011 0.079
## 45 0.118 0.186
## 46 -0.174 -0.109
## 47 -0.166 -0.098
## 48 0.145 0.220
## 49 0.250 0.323
## 50 0.000 0.000
## 51 0.000 0.000
## 52 0.000 0.000
## 53 0.000 0.000
## 54 0.891 0.907
## 55 0.889 0.906
## 56 0.867 0.887
## 57 0.472 0.528
## 58 0.891 0.909
## 59 0.633 0.683
## 60 0.486 0.537
## 61 0.715 0.746
## 62 0.729 0.766
## 63 0.824 0.855
## 64 0.396 0.449
## 65 0.411 0.470
## 66 0.805 0.841
## 67 0.733 0.771
## 68 0.294 0.345
## 69 0.969 0.991
## 70 0.929 0.950
## 71 0.658 0.710
## 72 0.718 0.767
## 73 0.098 0.137
## 74 0.412 0.485
## 75 0.000 0.000
## 76 0.177 0.206
## 77 0.180 0.210
## 78 0.214 0.248
## 79 0.242 0.279
## 80 0.174 0.206
## 81 0.159 0.185
## 82 0.263 0.300
## 83 0.444 0.489
## 84 0.414 0.469
## 85 0.269 0.322
## 86 0.293 0.353
## 87 0.406 0.463
## 88 0.017 0.061
## 89 0.496 0.567
## 90 1.000 1.000
## [ reached 'max' / getOption("max.print") -- omitted 16 rows ]
weak2<-cfa(hof.weak2, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
fitMeasures(weak2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2789.755 117.000 0.000 0.960 0.080 0.044
## aic bic
## 171593.507 172026.120
Mc(weak2)
## [1] 0.8282548
tests<-lavTestLRT(configural, metric2, scalar2, latent2, weak)
Td=tests[2:5,"Chisq diff"]
Td
## [1] 9.261594e+01 1.419000e+02 5.984379e+00 1.517153e-04
dfd=tests[2:5,"Df diff"]
dfd
## [1] 13 5 2 1
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-3503+ 3590 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
## Warning in sqrt((ld) * G/(N - G)): NaNs produced
RMSEAD
## [1] 0.04156134 0.08787756 0.02370427 NaN
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.03382688 0.04972355
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.07573808 0.10062511
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] NA 0.04664614
RMSEA.CI(T=Td[4],df=dfd[4],N=N,G=2)
## [1] NA NA
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.044 0.000 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 1.000 0.861 0.059
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.950 0.892 0.027 0.003 0.000 0.000
round(pvals(T=Td[4],df=dfd[4],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.010 0.008 0.000 0.000 0.000 0.000
tests<-lavTestLRT(configural, metric2, scalar2, latent)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 92.61594 141.90002 359.18499
dfd=tests[2:4,"Df diff"]
dfd
## [1] 13 5 5
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-3503+ 3590 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.04156134 0.08787756 0.14134854
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.07573808 0.10062511
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.1291476 0.1539235
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 1.000 0.861 0.059
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
tests<-lavTestLRT(configural, metric2, scalar2, strict)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 92.61594 141.90002 199.94352
dfd=tests[2:4,"Df diff"]
dfd
## [1] 13 5 12
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-3503+ 3590 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.04156134 0.08787756 0.06646367
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.03382688 0.04972355
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.07573808 0.10062511
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.05853411 0.07471461
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.044 0.000 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 1.000 0.861 0.059
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 0.911 0.003 0.000
tests<-lavTestLRT(configural, metric2, scalar)
Td=tests[2:3,"Chisq diff"]
Td
## [1] 92.61594 612.64546
dfd=tests[2:3,"Df diff"]
dfd
## [1] 13 7
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-3503+ 3590 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.04156134 0.15621454
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.03382688 0.04972355
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.1458650 0.1667971
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.044 0.000 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
tests<-lavTestLRT(configural, metric)
Td=tests[2,"Chisq diff"]
Td
## [1] 194.7295
dfd=tests[2,"Df diff"]
dfd
## [1] 14
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-3503+ 3590 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.0603409
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.05297571 0.06800495
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.989 0.544 0.000 0.000
hof.age<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
math~~1*math
speed~~1*speed
verbal~0*1
g ~agec
'
hof.ageq<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
math~~1*math
speed~~1*speed
verbal~0*1
g ~ c(a,a)*agec
'
hof.age2<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
math~~1*math
speed~~1*speed
verbal~0*1
g ~agec + agec2
'
hof.age2q<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
math~~1*math
speed~~1*speed
verbal~0*1
g ~c(a,a)*agec + c(b,b)*agec2
'
sem.age<-sem(hof.age, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
fitMeasures(sem.age, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 3851.654 137.000 0.000 0.947 0.087 0.047
## ecvi aic bic
## 0.562 170705.414 171165.494
Mc(sem.age)
## [1] 0.7695953
summary(sem.age, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 113 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 95
## Number of equality constraints 28
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3851.654 2939.393
## Degrees of freedom 137 137
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.310
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1503.731 1147.573
## 0 2347.923 1791.820
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.166 0.020 8.332 0.000 0.127
## sswk (.p2.) 0.166 0.020 8.288 0.000 0.126
## sspc (.p3.) 0.161 0.019 8.303 0.000 0.123
## ssei (.p4.) 0.099 0.012 8.111 0.000 0.075
## math =~
## ssar (.p5.) 0.312 0.012 26.511 0.000 0.289
## ssmk (.p6.) 0.230 0.010 23.264 0.000 0.210
## ssmc (.p7.) 0.177 0.008 23.466 0.000 0.162
## ssao (.p8.) 0.261 0.010 25.416 0.000 0.241
## electronic =~
## ssai (.p9.) 0.278 0.013 21.311 0.000 0.252
## sssi (.10.) 0.302 0.014 21.170 0.000 0.274
## ssmc (.11.) 0.148 0.008 18.030 0.000 0.132
## ssei 0.088 0.010 8.560 0.000 0.068
## speed =~
## ssno (.13.) 0.569 0.015 37.671 0.000 0.540
## sscs (.14.) 0.503 0.013 39.835 0.000 0.479
## ssmk (.15.) 0.213 0.009 24.495 0.000 0.196
## g =~
## verbal (.16.) 4.543 0.569 7.990 0.000 3.429
## math (.17.) 2.296 0.104 22.154 0.000 2.093
## elctrnc (.18.) 1.658 0.083 19.934 0.000 1.495
## speed (.19.) 0.940 0.035 26.571 0.000 0.871
## ci.upper Std.lv Std.all
##
## 0.205 0.821 0.889
## 0.205 0.820 0.889
## 0.199 0.799 0.867
## 0.123 0.492 0.601
##
## 0.335 0.827 0.901
## 0.249 0.608 0.647
## 0.192 0.468 0.524
## 0.281 0.691 0.733
##
## 0.304 0.565 0.699
## 0.330 0.614 0.744
## 0.164 0.301 0.337
## 0.108 0.179 0.219
##
## 0.599 0.807 0.840
## 0.528 0.713 0.756
## 0.230 0.302 0.322
##
## 5.658 0.979 0.979
## 2.500 0.926 0.926
## 1.821 0.871 0.871
## 1.009 0.708 0.708
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.259 0.014 18.480 0.000 0.232
## ci.upper Std.lv Std.all
##
## 0.287 0.243 0.349
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.40.) 0.168 0.015 10.885 0.000 0.138
## .sswk (.41.) 0.155 0.016 9.833 0.000 0.124
## .sspc 0.294 0.016 17.843 0.000 0.261
## .ssei (.43.) -0.007 0.014 -0.522 0.602 -0.035
## .ssar (.44.) 0.174 0.016 10.874 0.000 0.143
## .ssmk (.45.) 0.236 0.016 14.547 0.000 0.204
## .ssmc (.46.) 0.046 0.015 3.134 0.002 0.017
## .ssao (.47.) 0.149 0.016 9.373 0.000 0.118
## .ssai (.48.) -0.110 0.013 -8.547 0.000 -0.136
## .sssi (.49.) -0.104 0.014 -7.647 0.000 -0.131
## .ssno 0.180 0.017 10.385 0.000 0.146
## .sscs (.51.) 0.275 0.016 16.713 0.000 0.243
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.199 0.168 0.182
## 0.185 0.155 0.167
## 0.326 0.294 0.319
## 0.020 -0.007 -0.009
## 0.206 0.174 0.190
## 0.267 0.236 0.251
## 0.075 0.046 0.052
## 0.180 0.149 0.158
## -0.085 -0.110 -0.136
## -0.077 -0.104 -0.126
## 0.214 0.180 0.188
## 0.308 0.275 0.292
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .math 1.000 1.000
## .speed 1.000 1.000
## .ssgs 0.179 0.007 27.112 0.000 0.166
## .sswk 0.178 0.007 26.150 0.000 0.165
## .sspc 0.210 0.009 24.377 0.000 0.193
## .ssei 0.245 0.008 29.741 0.000 0.229
## .ssar 0.159 0.007 22.637 0.000 0.145
## .ssmk 0.181 0.006 28.165 0.000 0.169
## .ssmc 0.262 0.009 27.715 0.000 0.243
## .ssao 0.411 0.013 31.596 0.000 0.386
## .ssai 0.334 0.012 27.760 0.000 0.311
## .sssi 0.304 0.012 25.731 0.000 0.281
## .ssno 0.273 0.015 18.641 0.000 0.244
## .sscs 0.380 0.016 23.756 0.000 0.349
## .verbal 1.000 1.000
## .electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.143 0.143
## 1.000 0.498 0.498
## 0.191 0.179 0.209
## 0.191 0.178 0.209
## 0.227 0.210 0.248
## 0.261 0.245 0.366
## 0.172 0.159 0.189
## 0.194 0.181 0.205
## 0.280 0.262 0.328
## 0.437 0.411 0.463
## 0.358 0.334 0.511
## 0.328 0.304 0.447
## 0.301 0.273 0.295
## 0.412 0.380 0.428
## 1.000 0.041 0.041
## 1.000 0.242 0.242
## 1.000 0.878 0.878
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.166 0.020 8.332 0.000 0.127
## sswk (.p2.) 0.166 0.020 8.288 0.000 0.126
## sspc (.p3.) 0.161 0.019 8.303 0.000 0.123
## ssei (.p4.) 0.099 0.012 8.111 0.000 0.075
## math =~
## ssar (.p5.) 0.312 0.012 26.511 0.000 0.289
## ssmk (.p6.) 0.230 0.010 23.264 0.000 0.210
## ssmc (.p7.) 0.177 0.008 23.466 0.000 0.162
## ssao (.p8.) 0.261 0.010 25.416 0.000 0.241
## electronic =~
## ssai (.p9.) 0.278 0.013 21.311 0.000 0.252
## sssi (.10.) 0.302 0.014 21.170 0.000 0.274
## ssmc (.11.) 0.148 0.008 18.030 0.000 0.132
## ssei 0.168 0.010 16.842 0.000 0.148
## speed =~
## ssno (.13.) 0.569 0.015 37.671 0.000 0.540
## sscs (.14.) 0.503 0.013 39.835 0.000 0.479
## ssmk (.15.) 0.213 0.009 24.495 0.000 0.196
## g =~
## verbal (.16.) 4.543 0.569 7.990 0.000 3.429
## math (.17.) 2.296 0.104 22.154 0.000 2.093
## elctrnc (.18.) 1.658 0.083 19.934 0.000 1.495
## speed (.19.) 0.940 0.035 26.571 0.000 0.871
## ci.upper Std.lv Std.all
##
## 0.205 0.926 0.899
## 0.205 0.925 0.899
## 0.199 0.900 0.876
## 0.123 0.554 0.502
##
## 0.335 0.915 0.899
## 0.249 0.673 0.661
## 0.192 0.518 0.510
## 0.281 0.765 0.730
##
## 0.304 0.804 0.747
## 0.330 0.873 0.838
## 0.164 0.429 0.422
## 0.187 0.485 0.439
##
## 0.599 0.858 0.822
## 0.528 0.759 0.752
## 0.230 0.322 0.316
##
## 5.658 0.976 0.976
## 2.500 0.940 0.940
## 1.821 0.688 0.688
## 1.009 0.748 0.748
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.297 0.016 18.921 0.000 0.266
## ci.upper Std.lv Std.all
##
## 0.327 0.247 0.355
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.40.) 0.168 0.015 10.885 0.000 0.138
## .sswk (.41.) 0.155 0.016 9.833 0.000 0.124
## .sspc -0.022 0.018 -1.176 0.240 -0.058
## .ssei (.43.) -0.007 0.014 -0.522 0.602 -0.035
## .ssar (.44.) 0.174 0.016 10.874 0.000 0.143
## .ssmk (.45.) 0.236 0.016 14.547 0.000 0.204
## .ssmc (.46.) 0.046 0.015 3.134 0.002 0.017
## .ssao (.47.) 0.149 0.016 9.373 0.000 0.118
## .ssai (.48.) -0.110 0.013 -8.547 0.000 -0.136
## .sssi (.49.) -0.104 0.014 -7.647 0.000 -0.131
## .ssno 0.399 0.025 16.253 0.000 0.350
## .sscs (.51.) 0.275 0.016 16.713 0.000 0.243
## .math -0.201 0.051 -3.953 0.000 -0.301
## .elctrnc 1.729 0.102 17.019 0.000 1.530
## .speed -0.784 0.045 -17.487 0.000 -0.872
## .g 0.106 0.030 3.556 0.000 0.048
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.199 0.168 0.163
## 0.185 0.155 0.150
## 0.014 -0.022 -0.021
## 0.020 -0.007 -0.007
## 0.206 0.174 0.171
## 0.267 0.236 0.231
## 0.075 0.046 0.046
## 0.180 0.149 0.142
## -0.085 -0.110 -0.102
## -0.077 -0.104 -0.100
## 0.447 0.399 0.382
## 0.308 0.275 0.273
## -0.101 -0.069 -0.069
## 1.928 0.598 0.598
## -0.696 -0.520 -0.520
## 0.165 0.088 0.088
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .math 1.000 1.000
## .speed 1.000 1.000
## .ssgs 0.203 0.007 28.054 0.000 0.189
## .sswk 0.204 0.008 25.414 0.000 0.188
## .sspc 0.246 0.009 26.268 0.000 0.228
## .ssei 0.317 0.012 27.364 0.000 0.294
## .ssar 0.199 0.009 23.166 0.000 0.182
## .ssmk 0.176 0.007 25.698 0.000 0.162
## .ssmc 0.292 0.010 27.862 0.000 0.271
## .ssao 0.512 0.015 34.348 0.000 0.483
## .ssai 0.511 0.019 27.045 0.000 0.474
## .sssi 0.322 0.015 20.984 0.000 0.292
## .ssno 0.354 0.019 19.089 0.000 0.318
## .sscs 0.442 0.019 23.203 0.000 0.404
## .verbal 1.450 0.373 3.889 0.000 0.719
## .electronic 4.412 0.466 9.475 0.000 3.499
## .g 1.257 0.047 26.958 0.000 1.166
## ci.upper Std.lv Std.all
## 1.000 0.116 0.116
## 1.000 0.440 0.440
## 0.218 0.203 0.192
## 0.220 0.204 0.193
## 0.264 0.246 0.233
## 0.340 0.317 0.260
## 0.215 0.199 0.192
## 0.189 0.176 0.169
## 0.312 0.292 0.283
## 0.541 0.512 0.467
## 0.548 0.511 0.442
## 0.352 0.322 0.297
## 0.390 0.354 0.325
## 0.479 0.442 0.434
## 2.180 0.047 0.047
## 5.324 0.527 0.527
## 1.349 0.874 0.874
sem.ageq<-sem(hof.ageq, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
fitMeasures(sem.ageq, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 3855.846 138.000 0.000 0.946 0.087 0.050
## ecvi aic bic
## 0.562 170707.606 171160.819
Mc(sem.ageq)
## [1] 0.7694222
summary(sem.ageq, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 114 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 95
## Number of equality constraints 29
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3855.846 2944.471
## Degrees of freedom 138 138
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.310
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1505.307 1149.510
## 0 2350.539 1794.961
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.166 0.020 8.335 0.000 0.127
## sswk (.p2.) 0.166 0.020 8.290 0.000 0.127
## sspc (.p3.) 0.161 0.019 8.305 0.000 0.123
## ssei (.p4.) 0.099 0.012 8.114 0.000 0.075
## math =~
## ssar (.p5.) 0.312 0.012 26.485 0.000 0.289
## ssmk (.p6.) 0.230 0.010 23.242 0.000 0.210
## ssmc (.p7.) 0.177 0.008 23.444 0.000 0.162
## ssao (.p8.) 0.261 0.010 25.386 0.000 0.241
## electronic =~
## ssai (.p9.) 0.278 0.013 21.312 0.000 0.252
## sssi (.10.) 0.302 0.014 21.170 0.000 0.274
## ssmc (.11.) 0.148 0.008 18.027 0.000 0.132
## ssei 0.088 0.010 8.545 0.000 0.068
## speed =~
## ssno (.13.) 0.569 0.015 37.650 0.000 0.540
## sscs (.14.) 0.503 0.013 39.817 0.000 0.479
## ssmk (.15.) 0.213 0.009 24.496 0.000 0.196
## g =~
## verbal (.16.) 4.545 0.569 7.993 0.000 3.431
## math (.17.) 2.296 0.104 22.117 0.000 2.092
## elctrnc (.18.) 1.658 0.083 19.930 0.000 1.495
## speed (.19.) 0.940 0.035 26.535 0.000 0.871
## ci.upper Std.lv Std.all
##
## 0.205 0.828 0.891
## 0.205 0.827 0.891
## 0.199 0.805 0.869
## 0.123 0.496 0.603
##
## 0.336 0.833 0.902
## 0.249 0.613 0.648
## 0.192 0.471 0.525
## 0.281 0.696 0.735
##
## 0.304 0.569 0.701
## 0.330 0.617 0.746
## 0.164 0.303 0.337
## 0.108 0.180 0.219
##
## 0.599 0.810 0.841
## 0.528 0.716 0.758
## 0.231 0.304 0.321
##
## 5.660 0.980 0.980
## 2.499 0.927 0.927
## 1.821 0.872 0.872
## 1.010 0.711 0.711
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.276 0.011 25.405 0.000 0.255
## ci.upper Std.lv Std.all
##
## 0.297 0.256 0.369
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.40.) 0.169 0.015 10.929 0.000 0.139
## .sswk (.41.) 0.155 0.016 9.885 0.000 0.124
## .sspc 0.294 0.016 17.862 0.000 0.262
## .ssei (.43.) -0.007 0.014 -0.495 0.621 -0.034
## .ssar (.44.) 0.175 0.016 10.901 0.000 0.143
## .ssmk (.45.) 0.236 0.016 14.621 0.000 0.205
## .ssmc (.46.) 0.047 0.015 3.162 0.002 0.018
## .ssao (.47.) 0.150 0.016 9.396 0.000 0.118
## .ssai (.48.) -0.110 0.013 -8.534 0.000 -0.135
## .sssi (.49.) -0.104 0.014 -7.625 0.000 -0.130
## .ssno 0.181 0.017 10.421 0.000 0.147
## .sscs (.51.) 0.276 0.016 16.775 0.000 0.243
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.199 0.169 0.182
## 0.186 0.155 0.167
## 0.326 0.294 0.318
## 0.021 -0.007 -0.008
## 0.206 0.175 0.189
## 0.268 0.236 0.250
## 0.076 0.047 0.052
## 0.181 0.150 0.158
## -0.085 -0.110 -0.136
## -0.077 -0.104 -0.125
## 0.215 0.181 0.188
## 0.308 0.276 0.292
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .math 1.000 1.000
## .speed 1.000 1.000
## .ssgs 0.179 0.007 27.127 0.000 0.166
## .sswk 0.178 0.007 26.150 0.000 0.165
## .sspc 0.210 0.009 24.370 0.000 0.193
## .ssei 0.245 0.008 29.745 0.000 0.229
## .ssar 0.159 0.007 22.646 0.000 0.145
## .ssmk 0.181 0.006 28.173 0.000 0.169
## .ssmc 0.262 0.009 27.715 0.000 0.243
## .ssao 0.411 0.013 31.591 0.000 0.386
## .ssai 0.334 0.012 27.756 0.000 0.311
## .sssi 0.304 0.012 25.732 0.000 0.281
## .ssno 0.273 0.015 18.645 0.000 0.244
## .sscs 0.380 0.016 23.755 0.000 0.349
## .verbal 1.000 1.000
## .electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.141 0.141
## 1.000 0.494 0.494
## 0.191 0.179 0.207
## 0.191 0.178 0.206
## 0.227 0.210 0.245
## 0.261 0.245 0.363
## 0.173 0.159 0.186
## 0.194 0.181 0.203
## 0.280 0.262 0.325
## 0.437 0.411 0.459
## 0.358 0.334 0.508
## 0.328 0.304 0.444
## 0.301 0.273 0.293
## 0.412 0.380 0.426
## 1.000 0.040 0.040
## 1.000 0.239 0.239
## 1.000 0.864 0.864
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.166 0.020 8.335 0.000 0.127
## sswk (.p2.) 0.166 0.020 8.290 0.000 0.127
## sspc (.p3.) 0.161 0.019 8.305 0.000 0.123
## ssei (.p4.) 0.099 0.012 8.114 0.000 0.075
## math =~
## ssar (.p5.) 0.312 0.012 26.485 0.000 0.289
## ssmk (.p6.) 0.230 0.010 23.242 0.000 0.210
## ssmc (.p7.) 0.177 0.008 23.444 0.000 0.162
## ssao (.p8.) 0.261 0.010 25.386 0.000 0.241
## electronic =~
## ssai (.p9.) 0.278 0.013 21.312 0.000 0.252
## sssi (.10.) 0.302 0.014 21.170 0.000 0.274
## ssmc (.11.) 0.148 0.008 18.027 0.000 0.132
## ssei 0.168 0.010 16.844 0.000 0.148
## speed =~
## ssno (.13.) 0.569 0.015 37.650 0.000 0.540
## sscs (.14.) 0.503 0.013 39.817 0.000 0.479
## ssmk (.15.) 0.213 0.009 24.496 0.000 0.196
## g =~
## verbal (.16.) 4.545 0.569 7.993 0.000 3.431
## math (.17.) 2.296 0.104 22.117 0.000 2.092
## elctrnc (.18.) 1.658 0.083 19.930 0.000 1.495
## speed (.19.) 0.940 0.035 26.535 0.000 0.871
## ci.upper Std.lv Std.all
##
## 0.205 0.918 0.898
## 0.205 0.917 0.897
## 0.199 0.893 0.874
## 0.123 0.550 0.501
##
## 0.336 0.909 0.898
## 0.249 0.669 0.660
## 0.192 0.514 0.509
## 0.281 0.759 0.728
##
## 0.304 0.801 0.746
## 0.330 0.870 0.837
## 0.164 0.427 0.423
## 0.187 0.483 0.439
##
## 0.599 0.854 0.821
## 0.528 0.755 0.751
## 0.231 0.320 0.316
##
## 5.660 0.976 0.976
## 2.499 0.939 0.939
## 1.821 0.684 0.684
## 1.010 0.745 0.745
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.276 0.011 25.405 0.000 0.255
## ci.upper Std.lv Std.all
##
## 0.297 0.232 0.333
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.40.) 0.169 0.015 10.929 0.000 0.139
## .sswk (.41.) 0.155 0.016 9.885 0.000 0.124
## .sspc -0.021 0.018 -1.149 0.251 -0.057
## .ssei (.43.) -0.007 0.014 -0.495 0.621 -0.034
## .ssar (.44.) 0.175 0.016 10.901 0.000 0.143
## .ssmk (.45.) 0.236 0.016 14.621 0.000 0.205
## .ssmc (.46.) 0.047 0.015 3.162 0.002 0.018
## .ssao (.47.) 0.150 0.016 9.396 0.000 0.118
## .ssai (.48.) -0.110 0.013 -8.534 0.000 -0.135
## .sssi (.49.) -0.104 0.014 -7.625 0.000 -0.130
## .ssno 0.399 0.024 16.300 0.000 0.351
## .sscs (.51.) 0.276 0.016 16.775 0.000 0.243
## .math -0.201 0.051 -3.950 0.000 -0.301
## .elctrnc 1.729 0.102 17.019 0.000 1.530
## .speed -0.784 0.045 -17.477 0.000 -0.872
## .g 0.104 0.030 3.501 0.000 0.046
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.199 0.169 0.165
## 0.186 0.155 0.152
## 0.015 -0.021 -0.021
## 0.021 -0.007 -0.006
## 0.206 0.175 0.173
## 0.268 0.236 0.233
## 0.076 0.047 0.046
## 0.181 0.150 0.143
## -0.085 -0.110 -0.102
## -0.077 -0.104 -0.100
## 0.447 0.399 0.383
## 0.308 0.276 0.274
## -0.101 -0.069 -0.069
## 1.929 0.600 0.600
## -0.696 -0.522 -0.522
## 0.162 0.087 0.087
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .math 1.000 1.000
## .speed 1.000 1.000
## .ssgs 0.203 0.007 28.047 0.000 0.189
## .sswk 0.204 0.008 25.413 0.000 0.188
## .sspc 0.246 0.009 26.256 0.000 0.228
## .ssei 0.317 0.012 27.359 0.000 0.294
## .ssar 0.198 0.009 23.149 0.000 0.182
## .ssmk 0.176 0.007 25.731 0.000 0.162
## .ssmc 0.292 0.010 27.864 0.000 0.271
## .ssao 0.512 0.015 34.353 0.000 0.483
## .ssai 0.511 0.019 27.047 0.000 0.474
## .sssi 0.322 0.015 20.983 0.000 0.292
## .ssno 0.354 0.019 19.087 0.000 0.318
## .sscs 0.442 0.019 23.200 0.000 0.404
## .verbal 1.435 0.371 3.873 0.000 0.709
## .electronic 4.418 0.466 9.474 0.000 3.504
## .g 1.258 0.047 26.953 0.000 1.166
## ci.upper Std.lv Std.all
## 1.000 0.118 0.118
## 1.000 0.444 0.444
## 0.218 0.203 0.194
## 0.220 0.204 0.195
## 0.264 0.246 0.236
## 0.340 0.317 0.262
## 0.215 0.198 0.194
## 0.189 0.176 0.172
## 0.312 0.292 0.286
## 0.541 0.512 0.470
## 0.548 0.511 0.443
## 0.352 0.322 0.299
## 0.390 0.354 0.327
## 0.479 0.442 0.437
## 2.161 0.047 0.047
## 5.331 0.532 0.532
## 1.349 0.889 0.889
sem.age2<-sem(hof.age2, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
fitMeasures(sem.age2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 3963.875 159.000 0.000 0.945 0.082 0.045
## ecvi aic bic
## 0.578 170682.731 171156.544
Mc(sem.age2)
## [1] 0.7647157
summary(sem.age2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 118 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 97
## Number of equality constraints 28
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3963.875 3033.688
## Degrees of freedom 159 159
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.307
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1578.656 1208.199
## 0 2385.219 1825.489
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.168 0.020 8.576 0.000 0.130
## sswk (.p2.) 0.168 0.020 8.528 0.000 0.129
## sspc (.p3.) 0.163 0.019 8.543 0.000 0.126
## ssei (.p4.) 0.100 0.012 8.325 0.000 0.076
## math =~
## ssar (.p5.) 0.311 0.012 26.339 0.000 0.288
## ssmk (.p6.) 0.229 0.010 23.175 0.000 0.210
## ssmc (.p7.) 0.176 0.008 23.386 0.000 0.161
## ssao (.p8.) 0.260 0.010 25.276 0.000 0.240
## electronic =~
## ssai (.p9.) 0.278 0.013 21.341 0.000 0.253
## sssi (.10.) 0.302 0.014 21.201 0.000 0.274
## ssmc (.11.) 0.148 0.008 18.061 0.000 0.132
## ssei 0.090 0.010 8.871 0.000 0.070
## speed =~
## ssno (.13.) 0.569 0.015 37.640 0.000 0.539
## sscs (.14.) 0.503 0.013 39.768 0.000 0.478
## ssmk (.15.) 0.213 0.009 24.447 0.000 0.196
## g =~
## verbal (.16.) 4.465 0.544 8.213 0.000 3.400
## math (.17.) 2.300 0.104 22.056 0.000 2.095
## elctrnc (.18.) 1.651 0.083 19.943 0.000 1.488
## speed (.19.) 0.939 0.035 26.588 0.000 0.869
## ci.upper Std.lv Std.all
##
## 0.207 0.821 0.889
## 0.206 0.820 0.889
## 0.201 0.799 0.867
## 0.123 0.488 0.597
##
## 0.334 0.826 0.901
## 0.248 0.609 0.647
## 0.191 0.468 0.523
## 0.280 0.691 0.733
##
## 0.304 0.565 0.699
## 0.330 0.614 0.744
## 0.164 0.301 0.337
## 0.110 0.183 0.224
##
## 0.598 0.807 0.839
## 0.528 0.713 0.756
## 0.230 0.302 0.321
##
## 5.531 0.979 0.979
## 2.504 0.927 0.927
## 1.813 0.870 0.870
## 1.008 0.709 0.709
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.256 0.014 18.028 0.000 0.228
## agec2 -0.044 0.010 -4.268 0.000 -0.065
## ci.upper Std.lv Std.all
##
## 0.283 0.239 0.344
## -0.024 -0.041 -0.077
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.43.) 0.235 0.022 10.777 0.000 0.193
## .sswk (.44.) 0.221 0.022 10.020 0.000 0.178
## .sspc 0.359 0.022 16.077 0.000 0.316
## .ssei (.46.) 0.049 0.019 2.554 0.011 0.011
## .ssar (.47.) 0.239 0.022 11.043 0.000 0.196
## .ssmk (.48.) 0.301 0.022 13.513 0.000 0.257
## .ssmc (.49.) 0.104 0.020 5.270 0.000 0.066
## .ssao (.50.) 0.203 0.020 9.967 0.000 0.163
## .ssai (.51.) -0.070 0.016 -4.382 0.000 -0.101
## .sssi (.52.) -0.060 0.017 -3.558 0.000 -0.093
## .ssno 0.228 0.021 11.070 0.000 0.188
## .sscs (.54.) 0.318 0.019 16.494 0.000 0.280
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.278 0.235 0.255
## 0.265 0.221 0.240
## 0.403 0.359 0.390
## 0.086 0.049 0.060
## 0.281 0.239 0.260
## 0.345 0.301 0.320
## 0.143 0.104 0.117
## 0.243 0.203 0.216
## -0.039 -0.070 -0.086
## -0.027 -0.060 -0.073
## 0.269 0.228 0.238
## 0.356 0.318 0.337
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .math 1.000 1.000
## .speed 1.000 1.000
## .ssgs 0.178 0.007 27.096 0.000 0.166
## .sswk 0.178 0.007 26.149 0.000 0.165
## .sspc 0.210 0.009 24.411 0.000 0.193
## .ssei 0.245 0.008 29.740 0.000 0.229
## .ssar 0.159 0.007 22.681 0.000 0.145
## .ssmk 0.181 0.006 28.139 0.000 0.169
## .ssmc 0.262 0.009 27.708 0.000 0.243
## .ssao 0.411 0.013 31.601 0.000 0.386
## .ssai 0.334 0.012 27.750 0.000 0.311
## .sssi 0.304 0.012 25.724 0.000 0.281
## .ssno 0.273 0.015 18.650 0.000 0.244
## .sscs 0.380 0.016 23.766 0.000 0.349
## .verbal 1.000 1.000
## .electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.142 0.142
## 1.000 0.497 0.497
## 0.191 0.178 0.209
## 0.191 0.178 0.209
## 0.227 0.210 0.248
## 0.261 0.245 0.366
## 0.173 0.159 0.189
## 0.194 0.181 0.205
## 0.280 0.262 0.328
## 0.437 0.411 0.463
## 0.358 0.334 0.512
## 0.327 0.304 0.447
## 0.301 0.273 0.295
## 0.412 0.380 0.428
## 1.000 0.042 0.042
## 1.000 0.242 0.242
## 1.000 0.872 0.872
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.168 0.020 8.576 0.000 0.130
## sswk (.p2.) 0.168 0.020 8.528 0.000 0.129
## sspc (.p3.) 0.163 0.019 8.543 0.000 0.126
## ssei (.p4.) 0.100 0.012 8.325 0.000 0.076
## math =~
## ssar (.p5.) 0.311 0.012 26.339 0.000 0.288
## ssmk (.p6.) 0.229 0.010 23.175 0.000 0.210
## ssmc (.p7.) 0.176 0.008 23.386 0.000 0.161
## ssao (.p8.) 0.260 0.010 25.276 0.000 0.240
## electronic =~
## ssai (.p9.) 0.278 0.013 21.341 0.000 0.253
## sssi (.10.) 0.302 0.014 21.201 0.000 0.274
## ssmc (.11.) 0.148 0.008 18.061 0.000 0.132
## ssei 0.170 0.010 16.920 0.000 0.150
## speed =~
## ssno (.13.) 0.569 0.015 37.640 0.000 0.539
## sscs (.14.) 0.503 0.013 39.768 0.000 0.478
## ssmk (.15.) 0.213 0.009 24.447 0.000 0.196
## g =~
## verbal (.16.) 4.465 0.544 8.213 0.000 3.400
## math (.17.) 2.300 0.104 22.056 0.000 2.095
## elctrnc (.18.) 1.651 0.083 19.943 0.000 1.488
## speed (.19.) 0.939 0.035 26.588 0.000 0.869
## ci.upper Std.lv Std.all
##
## 0.207 0.926 0.899
## 0.206 0.925 0.898
## 0.201 0.900 0.876
## 0.123 0.550 0.498
##
## 0.334 0.915 0.899
## 0.248 0.674 0.661
## 0.191 0.518 0.510
## 0.280 0.765 0.730
##
## 0.304 0.803 0.747
## 0.330 0.872 0.838
## 0.164 0.428 0.422
## 0.190 0.491 0.444
##
## 0.598 0.858 0.822
## 0.528 0.759 0.752
## 0.230 0.321 0.315
##
## 5.531 0.976 0.976
## 2.504 0.940 0.940
## 1.813 0.688 0.688
## 1.008 0.749 0.749
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.295 0.016 18.510 0.000 0.264
## agec2 -0.022 0.012 -1.903 0.057 -0.045
## ci.upper Std.lv Std.all
##
## 0.326 0.245 0.353
## 0.001 -0.018 -0.034
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.43.) 0.235 0.022 10.777 0.000 0.193
## .sswk (.44.) 0.221 0.022 10.020 0.000 0.178
## .sspc 0.043 0.024 1.812 0.070 -0.004
## .ssei (.46.) 0.049 0.019 2.554 0.011 0.011
## .ssar (.47.) 0.239 0.022 11.043 0.000 0.196
## .ssmk (.48.) 0.301 0.022 13.513 0.000 0.257
## .ssmc (.49.) 0.104 0.020 5.270 0.000 0.066
## .ssao (.50.) 0.203 0.020 9.967 0.000 0.163
## .ssai (.51.) -0.070 0.016 -4.382 0.000 -0.101
## .sssi (.52.) -0.060 0.017 -3.558 0.000 -0.093
## .ssno 0.447 0.027 16.373 0.000 0.393
## .sscs (.54.) 0.318 0.019 16.494 0.000 0.280
## .math -0.207 0.051 -4.042 0.000 -0.307
## .elctrnc 1.732 0.101 17.070 0.000 1.533
## .speed -0.786 0.045 -17.497 0.000 -0.874
## .g 0.064 0.043 1.492 0.136 -0.020
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.278 0.235 0.229
## 0.265 0.221 0.215
## 0.089 0.043 0.042
## 0.086 0.049 0.044
## 0.281 0.239 0.235
## 0.345 0.301 0.296
## 0.143 0.104 0.103
## 0.243 0.203 0.194
## -0.039 -0.070 -0.065
## -0.027 -0.060 -0.058
## 0.500 0.447 0.428
## 0.356 0.318 0.315
## -0.107 -0.070 -0.070
## 1.931 0.600 0.600
## -0.698 -0.521 -0.521
## 0.147 0.053 0.053
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .math 1.000 1.000
## .speed 1.000 1.000
## .ssgs 0.203 0.007 28.061 0.000 0.189
## .sswk 0.204 0.008 25.400 0.000 0.188
## .sspc 0.246 0.009 26.269 0.000 0.228
## .ssei 0.316 0.012 27.248 0.000 0.293
## .ssar 0.199 0.009 23.179 0.000 0.182
## .ssmk 0.176 0.007 25.686 0.000 0.162
## .ssmc 0.292 0.010 27.872 0.000 0.271
## .ssao 0.512 0.015 34.350 0.000 0.483
## .ssai 0.512 0.019 27.087 0.000 0.475
## .sssi 0.323 0.015 21.053 0.000 0.293
## .ssno 0.354 0.019 19.096 0.000 0.318
## .sscs 0.442 0.019 23.211 0.000 0.405
## .verbal 1.440 0.362 3.980 0.000 0.731
## .electronic 4.389 0.463 9.481 0.000 3.482
## .g 1.264 0.047 26.889 0.000 1.172
## ci.upper Std.lv Std.all
## 1.000 0.115 0.115
## 1.000 0.439 0.439
## 0.218 0.203 0.192
## 0.220 0.204 0.193
## 0.264 0.246 0.233
## 0.339 0.316 0.259
## 0.216 0.199 0.192
## 0.189 0.176 0.169
## 0.312 0.292 0.283
## 0.541 0.512 0.467
## 0.549 0.512 0.442
## 0.354 0.323 0.298
## 0.390 0.354 0.325
## 0.479 0.442 0.434
## 2.150 0.048 0.048
## 5.296 0.527 0.527
## 1.356 0.872 0.872
sem.age2q<-sem(hof.age2q, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
fitMeasures(sem.age2q, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 3970.492 161.000 0.000 0.945 0.082 0.047
## ecvi aic bic
## 0.579 170685.347 171145.427
Mc(sem.age2q)
## [1] 0.7644668
summary(sem.age2q, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 114 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 97
## Number of equality constraints 30
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3970.492 3041.491
## Degrees of freedom 161 161
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.305
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1581.385 1211.378
## 0 2389.107 1830.112
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.168 0.020 8.569 0.000 0.130
## sswk (.p2.) 0.168 0.020 8.522 0.000 0.129
## sspc (.p3.) 0.163 0.019 8.536 0.000 0.126
## ssei (.p4.) 0.100 0.012 8.323 0.000 0.076
## math =~
## ssar (.p5.) 0.311 0.012 26.321 0.000 0.288
## ssmk (.p6.) 0.229 0.010 23.159 0.000 0.210
## ssmc (.p7.) 0.176 0.008 23.366 0.000 0.161
## ssao (.p8.) 0.260 0.010 25.256 0.000 0.240
## electronic =~
## ssai (.p9.) 0.278 0.013 21.335 0.000 0.253
## sssi (.10.) 0.302 0.014 21.192 0.000 0.274
## ssmc (.11.) 0.148 0.008 18.051 0.000 0.132
## ssei 0.089 0.010 8.783 0.000 0.070
## speed =~
## ssno (.13.) 0.569 0.015 37.624 0.000 0.539
## sscs (.14.) 0.503 0.013 39.754 0.000 0.478
## ssmk (.15.) 0.213 0.009 24.450 0.000 0.196
## g =~
## verbal (.16.) 4.471 0.545 8.208 0.000 3.403
## math (.17.) 2.300 0.104 22.029 0.000 2.095
## elctrnc (.18.) 1.651 0.083 19.933 0.000 1.489
## speed (.19.) 0.939 0.035 26.558 0.000 0.870
## ci.upper Std.lv Std.all
##
## 0.206 0.827 0.891
## 0.206 0.826 0.891
## 0.201 0.804 0.869
## 0.124 0.492 0.600
##
## 0.334 0.832 0.902
## 0.248 0.612 0.648
## 0.191 0.471 0.525
## 0.280 0.695 0.735
##
## 0.304 0.568 0.701
## 0.330 0.617 0.745
## 0.164 0.303 0.337
## 0.109 0.183 0.223
##
## 0.599 0.810 0.840
## 0.528 0.716 0.757
## 0.230 0.303 0.321
##
## 5.539 0.979 0.979
## 2.505 0.927 0.927
## 1.813 0.872 0.872
## 1.008 0.711 0.711
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.273 0.011 24.896 0.000 0.252
## agec2 (b) -0.034 0.008 -4.442 0.000 -0.050
## ci.upper Std.lv Std.all
##
## 0.295 0.253 0.365
## -0.019 -0.032 -0.060
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.43.) 0.221 0.019 11.454 0.000 0.183
## .sswk (.44.) 0.207 0.020 10.606 0.000 0.169
## .sspc 0.345 0.020 17.276 0.000 0.306
## .ssei (.46.) 0.037 0.017 2.152 0.031 0.003
## .ssar (.47.) 0.225 0.019 11.622 0.000 0.187
## .ssmk (.48.) 0.287 0.020 14.493 0.000 0.248
## .ssmc (.49.) 0.092 0.018 5.175 0.000 0.057
## .ssao (.50.) 0.192 0.019 10.317 0.000 0.155
## .ssai (.51.) -0.078 0.015 -5.352 0.000 -0.107
## .sssi (.52.) -0.070 0.016 -4.479 0.000 -0.100
## .ssno 0.218 0.019 11.315 0.000 0.180
## .sscs (.54.) 0.309 0.018 17.068 0.000 0.273
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.259 0.221 0.238
## 0.245 0.207 0.223
## 0.385 0.345 0.373
## 0.070 0.037 0.045
## 0.263 0.225 0.244
## 0.326 0.287 0.304
## 0.127 0.092 0.103
## 0.228 0.192 0.203
## -0.050 -0.078 -0.097
## -0.039 -0.070 -0.084
## 0.256 0.218 0.226
## 0.344 0.309 0.327
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .math 1.000 1.000
## .speed 1.000 1.000
## .ssgs 0.178 0.007 27.115 0.000 0.166
## .sswk 0.178 0.007 26.148 0.000 0.165
## .sspc 0.210 0.009 24.397 0.000 0.193
## .ssei 0.245 0.008 29.744 0.000 0.229
## .ssar 0.159 0.007 22.686 0.000 0.145
## .ssmk 0.181 0.006 28.157 0.000 0.168
## .ssmc 0.262 0.009 27.709 0.000 0.243
## .ssao 0.411 0.013 31.597 0.000 0.386
## .ssai 0.334 0.012 27.748 0.000 0.311
## .sssi 0.304 0.012 25.726 0.000 0.281
## .ssno 0.273 0.015 18.654 0.000 0.244
## .sscs 0.380 0.016 23.764 0.000 0.349
## .verbal 1.000 1.000
## .electronic 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 0.140 0.140
## 1.000 0.494 0.494
## 0.191 0.178 0.207
## 0.191 0.178 0.207
## 0.227 0.210 0.245
## 0.261 0.245 0.363
## 0.173 0.159 0.187
## 0.194 0.181 0.203
## 0.280 0.262 0.325
## 0.437 0.411 0.460
## 0.358 0.334 0.509
## 0.328 0.304 0.444
## 0.301 0.273 0.294
## 0.412 0.380 0.426
## 1.000 0.041 0.041
## 1.000 0.240 0.240
## 1.000 0.860 0.860
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs (.p1.) 0.168 0.020 8.569 0.000 0.130
## sswk (.p2.) 0.168 0.020 8.522 0.000 0.129
## sspc (.p3.) 0.163 0.019 8.536 0.000 0.126
## ssei (.p4.) 0.100 0.012 8.323 0.000 0.076
## math =~
## ssar (.p5.) 0.311 0.012 26.321 0.000 0.288
## ssmk (.p6.) 0.229 0.010 23.159 0.000 0.210
## ssmc (.p7.) 0.176 0.008 23.366 0.000 0.161
## ssao (.p8.) 0.260 0.010 25.256 0.000 0.240
## electronic =~
## ssai (.p9.) 0.278 0.013 21.335 0.000 0.253
## sssi (.10.) 0.302 0.014 21.192 0.000 0.274
## ssmc (.11.) 0.148 0.008 18.051 0.000 0.132
## ssei 0.170 0.010 16.913 0.000 0.150
## speed =~
## ssno (.13.) 0.569 0.015 37.624 0.000 0.539
## sscs (.14.) 0.503 0.013 39.754 0.000 0.478
## ssmk (.15.) 0.213 0.009 24.450 0.000 0.196
## g =~
## verbal (.16.) 4.471 0.545 8.208 0.000 3.403
## math (.17.) 2.300 0.104 22.029 0.000 2.095
## elctrnc (.18.) 1.651 0.083 19.933 0.000 1.489
## speed (.19.) 0.939 0.035 26.558 0.000 0.870
## ci.upper Std.lv Std.all
##
## 0.206 0.919 0.898
## 0.206 0.918 0.897
## 0.201 0.894 0.875
## 0.124 0.547 0.497
##
## 0.334 0.909 0.898
## 0.248 0.670 0.661
## 0.191 0.515 0.509
## 0.280 0.760 0.728
##
## 0.304 0.801 0.746
## 0.330 0.870 0.837
## 0.164 0.427 0.422
## 0.189 0.488 0.443
##
## 0.599 0.855 0.821
## 0.528 0.756 0.751
## 0.230 0.320 0.316
##
## 5.539 0.976 0.976
## 2.505 0.940 0.940
## 1.813 0.685 0.685
## 1.008 0.746 0.746
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.273 0.011 24.896 0.000 0.252
## agec2 (b) -0.034 0.008 -4.442 0.000 -0.050
## ci.upper Std.lv Std.all
##
## 0.295 0.229 0.329
## -0.019 -0.029 -0.054
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .verbal 0.000 0.000
## .ssgs (.43.) 0.221 0.019 11.454 0.000 0.183
## .sswk (.44.) 0.207 0.020 10.606 0.000 0.169
## .sspc 0.029 0.022 1.350 0.177 -0.013
## .ssei (.46.) 0.037 0.017 2.152 0.031 0.003
## .ssar (.47.) 0.225 0.019 11.622 0.000 0.187
## .ssmk (.48.) 0.287 0.020 14.493 0.000 0.248
## .ssmc (.49.) 0.092 0.018 5.175 0.000 0.057
## .ssao (.50.) 0.192 0.019 10.317 0.000 0.155
## .ssai (.51.) -0.078 0.015 -5.352 0.000 -0.107
## .sssi (.52.) -0.070 0.016 -4.479 0.000 -0.100
## .ssno 0.436 0.026 16.643 0.000 0.385
## .sscs (.54.) 0.309 0.018 17.068 0.000 0.273
## .math -0.206 0.051 -4.021 0.000 -0.306
## .elctrnc 1.732 0.102 17.060 0.000 1.533
## .speed -0.786 0.045 -17.485 0.000 -0.874
## .g 0.107 0.030 3.576 0.000 0.048
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.259 0.221 0.216
## 0.245 0.207 0.202
## 0.071 0.029 0.028
## 0.070 0.037 0.033
## 0.263 0.225 0.222
## 0.326 0.287 0.283
## 0.127 0.092 0.091
## 0.228 0.192 0.184
## -0.050 -0.078 -0.073
## -0.039 -0.070 -0.067
## 0.488 0.436 0.419
## 0.344 0.309 0.307
## -0.105 -0.070 -0.070
## 1.931 0.601 0.601
## -0.698 -0.523 -0.523
## 0.165 0.089 0.089
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .math 1.000 1.000
## .speed 1.000 1.000
## .ssgs 0.203 0.007 28.059 0.000 0.189
## .sswk 0.204 0.008 25.401 0.000 0.188
## .sspc 0.246 0.009 26.263 0.000 0.228
## .ssei 0.316 0.012 27.265 0.000 0.294
## .ssar 0.199 0.009 23.160 0.000 0.182
## .ssmk 0.176 0.007 25.716 0.000 0.162
## .ssmc 0.292 0.010 27.872 0.000 0.271
## .ssao 0.512 0.015 34.354 0.000 0.483
## .ssai 0.512 0.019 27.080 0.000 0.475
## .sssi 0.323 0.015 21.048 0.000 0.293
## .ssno 0.354 0.019 19.094 0.000 0.318
## .sscs 0.442 0.019 23.210 0.000 0.405
## .verbal 1.434 0.361 3.966 0.000 0.725
## .electronic 4.401 0.464 9.479 0.000 3.491
## .g 1.264 0.047 26.877 0.000 1.172
## ci.upper Std.lv Std.all
## 1.000 0.117 0.117
## 1.000 0.443 0.443
## 0.218 0.203 0.194
## 0.220 0.204 0.195
## 0.264 0.246 0.235
## 0.339 0.316 0.261
## 0.215 0.199 0.194
## 0.189 0.176 0.171
## 0.312 0.292 0.285
## 0.541 0.512 0.470
## 0.549 0.512 0.444
## 0.353 0.323 0.299
## 0.390 0.354 0.326
## 0.479 0.442 0.436
## 2.142 0.048 0.048
## 5.311 0.531 0.531
## 1.356 0.886 0.886
# BIFACTOR MODEL (verbal ill defined because only wk has high loading, and removing the nonsignificant ei worsens the outcome, causing wk to be out of bound, then gs has negative loading)
bf.notworking<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
baseline<-cfa(bf.notworking, data=dgroup, meanstructure=T, sampling.weights="sweight", std.lv=T, orthogonal=T)
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2233.877 38.000 0.000 0.968 0.090 0.039
## aic bic
## 174403.951 174761.028
Mc(baseline)
## [1] 0.8565748
summary(baseline, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 50 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 52
##
## Number of observations 7093
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2233.877 2342.239
## Degrees of freedom 38 38
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.954
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal =~
## ssgs 0.134 0.128 1.044 0.297 -0.118
## sswk 0.362 0.099 3.650 0.000 0.168
## sspc 0.176 0.112 1.574 0.116 -0.043
## ssei 0.067 0.124 0.538 0.591 -0.177
## math =~
## ssar 0.268 0.078 3.436 0.001 0.115
## sspc 0.220 0.034 6.554 0.000 0.154
## ssmk 0.250 0.062 4.041 0.000 0.129
## ssmc 0.233 0.020 11.674 0.000 0.194
## ssao 0.412 0.025 16.465 0.000 0.363
## electronic =~
## ssai 0.510 0.021 24.585 0.000 0.469
## sssi 0.573 0.018 32.261 0.000 0.539
## ssmc 0.297 0.016 18.369 0.000 0.265
## ssei 0.311 0.027 11.671 0.000 0.259
## speed =~
## ssno 0.704 0.026 27.011 0.000 0.653
## sscs 0.446 0.028 16.078 0.000 0.392
## ssmk 0.231 0.018 12.834 0.000 0.195
## g =~
## ssgs 0.868 0.027 32.641 0.000 0.816
## ssar 0.816 0.029 28.114 0.000 0.759
## sswk 0.847 0.023 36.281 0.000 0.801
## sspc 0.803 0.018 45.270 0.000 0.769
## ssno 0.582 0.025 22.902 0.000 0.532
## sscs 0.542 0.020 26.488 0.000 0.502
## ssai 0.565 0.016 34.793 0.000 0.533
## sssi 0.589 0.018 33.440 0.000 0.555
## ssmk 0.810 0.027 29.724 0.000 0.757
## ssmc 0.745 0.011 67.495 0.000 0.724
## ssei 0.784 0.030 26.589 0.000 0.727
## ssao 0.647 0.017 37.323 0.000 0.613
## ci.upper Std.lv Std.all
##
## 0.386 0.134 0.137
## 0.557 0.362 0.371
## 0.396 0.176 0.179
## 0.310 0.067 0.067
##
## 0.421 0.268 0.276
## 0.286 0.220 0.223
## 0.371 0.250 0.255
## 0.272 0.233 0.238
## 0.461 0.412 0.414
##
## 0.550 0.510 0.508
## 0.608 0.573 0.576
## 0.328 0.297 0.303
## 0.363 0.311 0.310
##
## 0.755 0.704 0.698
## 0.501 0.446 0.450
## 0.266 0.231 0.235
##
## 0.920 0.868 0.886
## 0.872 0.816 0.840
## 0.893 0.847 0.867
## 0.838 0.803 0.816
## 0.632 0.582 0.577
## 0.582 0.542 0.546
## 0.597 0.565 0.563
## 0.624 0.589 0.592
## 0.864 0.810 0.826
## 0.767 0.745 0.762
## 0.842 0.784 0.782
## 0.681 0.647 0.649
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## verbal ~~
## math 0.000 0.000
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.199 0.013 15.604 0.000 0.174
## .sswk 0.180 0.013 14.181 0.000 0.155
## .sspc 0.160 0.013 12.482 0.000 0.135
## .ssei 0.168 0.013 12.604 0.000 0.142
## .ssar 0.171 0.013 13.560 0.000 0.147
## .ssmk 0.154 0.013 11.881 0.000 0.129
## .ssmc 0.183 0.013 14.520 0.000 0.159
## .ssao 0.138 0.013 10.543 0.000 0.113
## .ssai 0.147 0.013 11.020 0.000 0.121
## .sssi 0.181 0.013 13.829 0.000 0.156
## .ssno 0.084 0.013 6.248 0.000 0.058
## .sscs 0.092 0.013 6.963 0.000 0.066
## ci.upper Std.lv Std.all
## 0.224 0.199 0.203
## 0.205 0.180 0.184
## 0.186 0.160 0.163
## 0.194 0.168 0.167
## 0.196 0.171 0.176
## 0.180 0.154 0.157
## 0.208 0.183 0.187
## 0.164 0.138 0.139
## 0.173 0.147 0.147
## 0.207 0.181 0.182
## 0.110 0.084 0.083
## 0.118 0.092 0.092
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssgs 0.189 0.012 16.024 0.000 0.166
## .sswk 0.106 0.097 1.090 0.276 -0.085
## .sspc 0.244 0.026 9.325 0.000 0.193
## .ssei 0.289 0.008 34.156 0.000 0.272
## .ssar 0.206 0.008 24.690 0.000 0.189
## .ssmk 0.190 0.008 23.117 0.000 0.174
## .ssmc 0.259 0.011 24.047 0.000 0.238
## .ssao 0.404 0.026 15.259 0.000 0.352
## .ssai 0.428 0.013 33.483 0.000 0.403
## .sssi 0.315 0.012 25.326 0.000 0.291
## .ssno 0.184 0.026 6.960 0.000 0.132
## .sscs 0.492 0.015 32.485 0.000 0.463
## verbal 1.000 1.000
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.212 0.189 0.197
## 0.297 0.106 0.111
## 0.295 0.244 0.252
## 0.305 0.289 0.287
## 0.222 0.206 0.218
## 0.206 0.190 0.197
## 0.281 0.259 0.271
## 0.456 0.404 0.407
## 0.453 0.428 0.425
## 0.339 0.315 0.318
## 0.236 0.184 0.181
## 0.522 0.492 0.500
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
bf.model<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
bf.lv<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
'
bf.reduced<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
g~0*1
'
baseline<-cfa(bf.model, data=dgroup, meanstructure=T, sampling.weights="sweight", std.lv=T, orthogonal=T)
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2403.374 42.000 0.000 0.965 0.089 0.040
## aic bic
## 174565.448 174895.058
Mc(baseline)
## [1] 0.8466384
summary(baseline, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 41 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 48
##
## Number of observations 7093
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2403.374 1846.367
## Degrees of freedom 42 42
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.302
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar 0.328 0.015 22.151 0.000 0.299
## sspc 0.188 0.012 15.529 0.000 0.164
## ssmk 0.296 0.014 21.325 0.000 0.269
## ssmc 0.248 0.015 16.265 0.000 0.218
## ssao 0.425 0.020 21.721 0.000 0.387
## electronic =~
## ssai 0.512 0.017 29.327 0.000 0.478
## sssi 0.574 0.014 39.869 0.000 0.545
## ssmc 0.300 0.011 27.584 0.000 0.278
## ssei 0.311 0.013 24.669 0.000 0.286
## speed =~
## ssno 0.712 0.021 33.475 0.000 0.671
## sscs 0.461 0.017 27.568 0.000 0.428
## ssmk 0.242 0.010 23.910 0.000 0.222
## g =~
## ssgs 0.885 0.010 92.567 0.000 0.866
## ssar 0.791 0.011 73.418 0.000 0.770
## sswk 0.882 0.009 93.026 0.000 0.863
## sspc 0.825 0.009 91.645 0.000 0.807
## ssno 0.567 0.013 42.818 0.000 0.541
## sscs 0.531 0.012 43.762 0.000 0.508
## ssai 0.563 0.012 45.435 0.000 0.539
## sssi 0.588 0.012 49.007 0.000 0.565
## ssmk 0.789 0.010 77.373 0.000 0.769
## ssmc 0.736 0.011 69.302 0.000 0.715
## ssei 0.791 0.012 68.435 0.000 0.768
## ssao 0.630 0.011 58.983 0.000 0.609
## ci.upper Std.lv Std.all
##
## 0.357 0.328 0.338
## 0.212 0.188 0.191
## 0.323 0.296 0.303
## 0.278 0.248 0.254
## 0.464 0.425 0.427
##
## 0.546 0.512 0.510
## 0.602 0.574 0.576
## 0.321 0.300 0.307
## 0.335 0.311 0.310
##
## 0.754 0.712 0.706
## 0.493 0.461 0.464
## 0.261 0.242 0.247
##
## 0.904 0.885 0.903
## 0.813 0.791 0.815
## 0.901 0.882 0.902
## 0.842 0.825 0.839
## 0.593 0.567 0.562
## 0.555 0.531 0.535
## 0.587 0.563 0.561
## 0.612 0.588 0.591
## 0.809 0.789 0.808
## 0.756 0.736 0.753
## 0.813 0.791 0.788
## 0.651 0.630 0.633
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.171 0.013 13.560 0.000 0.147
## .sspc 0.160 0.013 12.482 0.000 0.135
## .ssmk 0.154 0.013 11.881 0.000 0.129
## .ssmc 0.183 0.013 14.520 0.000 0.159
## .ssao 0.138 0.013 10.543 0.000 0.113
## .ssai 0.147 0.013 11.020 0.000 0.121
## .sssi 0.181 0.013 13.829 0.000 0.156
## .ssei 0.168 0.013 12.604 0.000 0.142
## .ssno 0.084 0.013 6.248 0.000 0.058
## .sscs 0.092 0.013 6.963 0.000 0.066
## .ssgs 0.199 0.013 15.604 0.000 0.174
## .sswk 0.180 0.013 14.181 0.000 0.155
## ci.upper Std.lv Std.all
## 0.196 0.171 0.176
## 0.186 0.160 0.163
## 0.180 0.154 0.158
## 0.208 0.183 0.188
## 0.164 0.138 0.139
## 0.173 0.147 0.147
## 0.207 0.181 0.182
## 0.194 0.168 0.167
## 0.110 0.084 0.083
## 0.118 0.092 0.092
## 0.224 0.199 0.203
## 0.205 0.180 0.184
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.209 0.007 28.992 0.000 0.195
## .sspc 0.252 0.006 39.381 0.000 0.239
## .ssmk 0.185 0.006 29.548 0.000 0.173
## .ssmc 0.261 0.008 31.862 0.000 0.245
## .ssao 0.414 0.014 29.035 0.000 0.386
## .ssai 0.428 0.013 33.544 0.000 0.403
## .sssi 0.316 0.012 26.279 0.000 0.292
## .ssei 0.286 0.008 37.243 0.000 0.271
## .ssno 0.190 0.024 7.737 0.000 0.142
## .sscs 0.491 0.015 32.650 0.000 0.461
## .ssgs 0.177 0.005 34.831 0.000 0.167
## .sswk 0.177 0.005 33.913 0.000 0.167
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.223 0.209 0.222
## 0.264 0.252 0.260
## 0.198 0.185 0.194
## 0.277 0.261 0.274
## 0.442 0.414 0.417
## 0.453 0.428 0.425
## 0.339 0.316 0.319
## 0.301 0.286 0.284
## 0.238 0.190 0.186
## 0.520 0.491 0.498
## 0.187 0.177 0.185
## 0.188 0.177 0.186
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
configural<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T)
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2014.783 84.000 0.000 0.971 0.081 0.032
## aic bic
## 170884.535 171543.754
Mc(configural)
## [1] 0.8727344
summary(configural, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2014.783 1580.281
## Degrees of freedom 84 84
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.275
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 708.459 555.675
## 0 1306.324 1024.606
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar 0.333 0.019 17.238 0.000 0.296
## sspc 0.139 0.015 9.186 0.000 0.109
## ssmk 0.290 0.017 16.610 0.000 0.255
## ssmc 0.245 0.021 11.924 0.000 0.205
## ssao 0.407 0.026 15.630 0.000 0.356
## electronic =~
## ssai 0.223 0.025 8.996 0.000 0.174
## sssi 0.330 0.029 11.377 0.000 0.273
## ssmc 0.194 0.021 9.411 0.000 0.154
## ssei 0.118 0.019 6.069 0.000 0.080
## speed =~
## ssno 0.688 0.033 21.160 0.000 0.625
## sscs 0.403 0.024 16.975 0.000 0.356
## ssmk 0.215 0.015 14.616 0.000 0.186
## g =~
## ssgs 0.814 0.012 65.165 0.000 0.790
## ssar 0.735 0.014 50.862 0.000 0.706
## sswk 0.852 0.013 64.811 0.000 0.827
## sspc 0.794 0.013 62.638 0.000 0.769
## ssno 0.529 0.017 30.676 0.000 0.495
## sscs 0.509 0.016 31.458 0.000 0.477
## ssai 0.464 0.014 33.741 0.000 0.437
## sssi 0.499 0.014 35.513 0.000 0.472
## ssmk 0.773 0.014 55.194 0.000 0.746
## ssmc 0.656 0.013 49.289 0.000 0.629
## ssei 0.637 0.013 49.866 0.000 0.612
## ssao 0.614 0.015 42.039 0.000 0.585
## ci.upper Std.lv Std.all
##
## 0.371 0.333 0.366
## 0.168 0.139 0.149
## 0.324 0.290 0.303
## 0.285 0.245 0.279
## 0.458 0.407 0.426
##
## 0.272 0.223 0.283
## 0.387 0.330 0.410
## 0.234 0.194 0.221
## 0.156 0.118 0.145
##
## 0.752 0.688 0.728
## 0.449 0.403 0.432
## 0.243 0.215 0.225
##
## 0.839 0.814 0.892
## 0.763 0.735 0.806
## 0.878 0.852 0.903
## 0.819 0.794 0.855
## 0.563 0.529 0.559
## 0.541 0.509 0.546
## 0.491 0.464 0.589
## 0.527 0.499 0.621
## 0.801 0.773 0.809
## 0.682 0.656 0.747
## 0.662 0.637 0.783
## 0.643 0.614 0.643
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.148 0.017 8.728 0.000 0.115
## .sspc 0.284 0.017 16.543 0.000 0.251
## .ssmk 0.224 0.018 12.435 0.000 0.189
## .ssmc 0.039 0.016 2.369 0.018 0.007
## .ssao 0.198 0.018 11.088 0.000 0.163
## .ssai -0.097 0.015 -6.622 0.000 -0.126
## .sssi -0.131 0.015 -8.757 0.000 -0.160
## .ssei -0.010 0.015 -0.667 0.505 -0.040
## .ssno 0.173 0.018 9.602 0.000 0.138
## .sscs 0.271 0.018 15.206 0.000 0.236
## .ssgs 0.120 0.017 7.097 0.000 0.087
## .sswk 0.181 0.017 10.369 0.000 0.147
## ci.upper Std.lv Std.all
## 0.181 0.148 0.162
## 0.318 0.284 0.306
## 0.260 0.224 0.235
## 0.070 0.039 0.044
## 0.232 0.198 0.207
## -0.069 -0.097 -0.124
## -0.102 -0.131 -0.163
## 0.020 -0.010 -0.013
## 0.209 0.173 0.183
## 0.306 0.271 0.291
## 0.153 0.120 0.131
## 0.216 0.181 0.192
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.179 0.010 18.410 0.000 0.160
## .sspc 0.212 0.008 25.654 0.000 0.196
## .ssmk 0.186 0.008 22.746 0.000 0.170
## .ssmc 0.243 0.012 20.764 0.000 0.220
## .ssao 0.369 0.018 20.245 0.000 0.333
## .ssai 0.356 0.013 26.424 0.000 0.330
## .sssi 0.289 0.018 16.024 0.000 0.254
## .ssei 0.241 0.008 28.393 0.000 0.225
## .ssno 0.141 0.036 3.941 0.000 0.071
## .sscs 0.447 0.019 23.807 0.000 0.410
## .ssgs 0.170 0.006 26.224 0.000 0.157
## .sswk 0.164 0.007 24.880 0.000 0.151
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.199 0.179 0.216
## 0.228 0.212 0.246
## 0.202 0.186 0.204
## 0.266 0.243 0.316
## 0.405 0.369 0.405
## 0.383 0.356 0.573
## 0.325 0.289 0.447
## 0.258 0.241 0.365
## 0.211 0.141 0.158
## 0.484 0.447 0.515
## 0.182 0.170 0.204
## 0.177 0.164 0.184
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar 0.339 0.025 13.313 0.000 0.289
## sspc 0.184 0.018 10.400 0.000 0.150
## ssmk 0.295 0.022 13.352 0.000 0.251
## ssmc 0.246 0.027 9.112 0.000 0.193
## ssao 0.404 0.034 11.784 0.000 0.337
## electronic =~
## ssai 0.590 0.025 23.392 0.000 0.540
## sssi 0.601 0.020 30.104 0.000 0.562
## ssmc 0.296 0.015 19.528 0.000 0.267
## ssei 0.325 0.018 18.118 0.000 0.290
## speed =~
## ssno 0.752 0.032 23.284 0.000 0.689
## sscs 0.448 0.024 19.040 0.000 0.402
## ssmk 0.244 0.015 16.687 0.000 0.215
## g =~
## ssgs 0.945 0.014 68.645 0.000 0.918
## ssar 0.841 0.016 53.736 0.000 0.810
## sswk 0.908 0.014 66.997 0.000 0.882
## sspc 0.870 0.012 72.502 0.000 0.846
## ssno 0.604 0.019 31.379 0.000 0.566
## sscs 0.567 0.017 32.942 0.000 0.533
## ssai 0.651 0.019 35.060 0.000 0.614
## sssi 0.666 0.017 38.875 0.000 0.633
## ssmk 0.810 0.014 56.318 0.000 0.782
## ssmc 0.810 0.015 52.392 0.000 0.780
## ssei 0.927 0.017 54.303 0.000 0.893
## ssao 0.659 0.015 42.938 0.000 0.629
## ci.upper Std.lv Std.all
##
## 0.389 0.339 0.331
## 0.219 0.184 0.181
## 0.338 0.295 0.296
## 0.299 0.246 0.236
## 0.471 0.404 0.392
##
## 0.639 0.590 0.524
## 0.641 0.601 0.564
## 0.326 0.296 0.285
## 0.361 0.325 0.288
##
## 0.816 0.752 0.710
## 0.494 0.448 0.440
## 0.273 0.244 0.245
##
## 0.972 0.945 0.913
## 0.872 0.841 0.821
## 0.935 0.908 0.900
## 0.893 0.870 0.853
## 0.642 0.604 0.570
## 0.600 0.567 0.556
## 0.687 0.651 0.579
## 0.700 0.666 0.625
## 0.838 0.810 0.815
## 0.841 0.810 0.778
## 0.960 0.927 0.819
## 0.689 0.659 0.639
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.194 0.019 10.391 0.000 0.157
## .sspc 0.041 0.019 2.207 0.027 0.005
## .ssmk 0.087 0.019 4.675 0.000 0.050
## .ssmc 0.322 0.019 17.179 0.000 0.286
## .ssao 0.081 0.019 4.256 0.000 0.044
## .ssai 0.382 0.021 18.202 0.000 0.341
## .sssi 0.482 0.020 24.659 0.000 0.443
## .ssei 0.339 0.021 16.134 0.000 0.298
## .ssno -0.002 0.020 -0.083 0.934 -0.040
## .sscs -0.080 0.019 -4.255 0.000 -0.117
## .ssgs 0.276 0.019 14.542 0.000 0.239
## .sswk 0.179 0.018 9.735 0.000 0.143
## ci.upper Std.lv Std.all
## 0.230 0.194 0.189
## 0.078 0.041 0.041
## 0.123 0.087 0.087
## 0.359 0.322 0.310
## 0.119 0.081 0.079
## 0.423 0.382 0.340
## 0.520 0.482 0.452
## 0.380 0.339 0.299
## 0.037 -0.002 -0.002
## -0.043 -0.080 -0.079
## 0.313 0.276 0.267
## 0.215 0.179 0.178
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.228 0.013 17.407 0.000 0.202
## .sspc 0.249 0.009 28.666 0.000 0.232
## .ssmk 0.186 0.010 18.479 0.000 0.166
## .ssmc 0.279 0.013 21.613 0.000 0.254
## .ssao 0.466 0.023 19.950 0.000 0.420
## .ssai 0.494 0.022 22.721 0.000 0.451
## .sssi 0.332 0.018 18.525 0.000 0.297
## .ssei 0.316 0.012 26.741 0.000 0.293
## .ssno 0.191 0.042 4.600 0.000 0.110
## .sscs 0.516 0.022 23.480 0.000 0.473
## .ssgs 0.179 0.007 25.260 0.000 0.165
## .sswk 0.193 0.008 24.843 0.000 0.178
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.253 0.228 0.217
## 0.267 0.249 0.240
## 0.205 0.186 0.188
## 0.304 0.279 0.257
## 0.511 0.466 0.438
## 0.536 0.494 0.390
## 0.367 0.332 0.292
## 0.340 0.316 0.247
## 0.273 0.191 0.171
## 0.559 0.516 0.497
## 0.193 0.179 0.167
## 0.208 0.193 0.190
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
#modificationIndices(configural, sort=T, maximum.number=30)
metric<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2295.700 104.000 0.000 0.968 0.077 0.049
## aic bic
## 171125.453 171647.335
Mc(metric)
## [1] 0.856827
summary(metric, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 70 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 100
## Number of equality constraints 24
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2295.700 1769.021
## Degrees of freedom 104 104
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.298
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 845.570 651.579
## 0 1450.130 1117.442
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.332 0.017 19.791 0.000 0.299
## sspc (.p2.) 0.158 0.011 13.765 0.000 0.136
## ssmk (.p3.) 0.292 0.015 19.131 0.000 0.262
## ssmc (.p4.) 0.243 0.017 14.313 0.000 0.210
## ssao (.p5.) 0.407 0.023 18.009 0.000 0.363
## electronic =~
## ssai (.p6.) 0.267 0.014 18.742 0.000 0.239
## sssi (.p7.) 0.276 0.016 17.633 0.000 0.245
## ssmc (.p8.) 0.142 0.009 15.064 0.000 0.123
## ssei (.p9.) 0.154 0.009 17.050 0.000 0.136
## speed =~
## ssno (.10.) 0.687 0.025 26.962 0.000 0.637
## sscs (.11.) 0.405 0.018 22.434 0.000 0.370
## ssmk (.12.) 0.219 0.010 21.009 0.000 0.198
## g =~
## ssgs (.13.) 0.819 0.011 71.977 0.000 0.797
## ssar (.14.) 0.734 0.012 60.246 0.000 0.710
## sswk (.15.) 0.819 0.012 67.740 0.000 0.795
## sspc (.16.) 0.775 0.011 67.859 0.000 0.753
## ssno (.17.) 0.529 0.013 40.428 0.000 0.503
## sscs (.18.) 0.502 0.012 41.327 0.000 0.478
## ssai (.19.) 0.492 0.011 44.645 0.000 0.471
## sssi (.20.) 0.518 0.011 47.286 0.000 0.497
## ssmk (.21.) 0.735 0.012 60.873 0.000 0.712
## ssmc (.22.) 0.671 0.011 61.379 0.000 0.650
## ssei (.23.) 0.708 0.011 64.513 0.000 0.686
## ssao (.24.) 0.593 0.012 51.205 0.000 0.570
## ci.upper Std.lv Std.all
##
## 0.365 0.332 0.365
## 0.181 0.158 0.172
## 0.322 0.292 0.314
## 0.276 0.243 0.274
## 0.451 0.407 0.432
##
## 0.295 0.267 0.331
## 0.307 0.276 0.340
## 0.160 0.142 0.160
## 0.172 0.154 0.177
##
## 0.737 0.687 0.726
## 0.440 0.405 0.436
## 0.239 0.219 0.236
##
## 0.842 0.819 0.895
## 0.758 0.734 0.806
## 0.842 0.819 0.893
## 0.798 0.775 0.846
## 0.555 0.529 0.559
## 0.526 0.502 0.541
## 0.514 0.492 0.610
## 0.540 0.518 0.637
## 0.759 0.735 0.792
## 0.692 0.671 0.758
## 0.729 0.708 0.812
## 0.615 0.593 0.630
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.148 0.017 8.728 0.000 0.115
## .sspc 0.284 0.017 16.543 0.000 0.251
## .ssmk 0.224 0.018 12.435 0.000 0.189
## .ssmc 0.039 0.016 2.369 0.018 0.007
## .ssao 0.198 0.018 11.088 0.000 0.163
## .ssai -0.097 0.015 -6.622 0.000 -0.126
## .sssi -0.131 0.015 -8.757 0.000 -0.160
## .ssei -0.010 0.015 -0.667 0.505 -0.040
## .ssno 0.173 0.018 9.602 0.000 0.138
## .sscs 0.271 0.018 15.206 0.000 0.236
## .ssgs 0.120 0.017 7.097 0.000 0.087
## .sswk 0.181 0.017 10.369 0.000 0.147
## ci.upper Std.lv Std.all
## 0.181 0.148 0.162
## 0.318 0.284 0.310
## 0.260 0.224 0.242
## 0.070 0.039 0.044
## 0.232 0.198 0.210
## -0.069 -0.097 -0.121
## -0.102 -0.131 -0.161
## 0.020 -0.010 -0.012
## 0.209 0.173 0.183
## 0.306 0.271 0.292
## 0.153 0.120 0.131
## 0.216 0.181 0.198
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.181 0.009 20.239 0.000 0.163
## .sspc 0.214 0.008 26.458 0.000 0.198
## .ssmk 0.188 0.008 24.519 0.000 0.173
## .ssmc 0.255 0.010 24.938 0.000 0.235
## .ssao 0.369 0.017 21.935 0.000 0.336
## .ssai 0.338 0.012 28.028 0.000 0.315
## .sssi 0.317 0.012 26.418 0.000 0.293
## .ssei 0.235 0.008 28.049 0.000 0.219
## .ssno 0.144 0.026 5.485 0.000 0.093
## .sscs 0.446 0.017 26.414 0.000 0.413
## .ssgs 0.166 0.006 26.459 0.000 0.154
## .sswk 0.171 0.007 25.633 0.000 0.158
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.198 0.181 0.218
## 0.230 0.214 0.255
## 0.203 0.188 0.218
## 0.275 0.255 0.325
## 0.402 0.369 0.416
## 0.362 0.338 0.519
## 0.340 0.317 0.479
## 0.252 0.235 0.310
## 0.196 0.144 0.161
## 0.479 0.446 0.517
## 0.179 0.166 0.199
## 0.184 0.171 0.203
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.332 0.017 19.791 0.000 0.299
## sspc (.p2.) 0.158 0.011 13.765 0.000 0.136
## ssmk (.p3.) 0.292 0.015 19.131 0.000 0.262
## ssmc (.p4.) 0.243 0.017 14.313 0.000 0.210
## ssao (.p5.) 0.407 0.023 18.009 0.000 0.363
## electronic =~
## ssai (.p6.) 0.267 0.014 18.742 0.000 0.239
## sssi (.p7.) 0.276 0.016 17.633 0.000 0.245
## ssmc (.p8.) 0.142 0.009 15.064 0.000 0.123
## ssei (.p9.) 0.154 0.009 17.050 0.000 0.136
## speed =~
## ssno (.10.) 0.687 0.025 26.962 0.000 0.637
## sscs (.11.) 0.405 0.018 22.434 0.000 0.370
## ssmk (.12.) 0.219 0.010 21.009 0.000 0.198
## g =~
## ssgs (.13.) 0.819 0.011 71.977 0.000 0.797
## ssar (.14.) 0.734 0.012 60.246 0.000 0.710
## sswk (.15.) 0.819 0.012 67.740 0.000 0.795
## sspc (.16.) 0.775 0.011 67.859 0.000 0.753
## ssno (.17.) 0.529 0.013 40.428 0.000 0.503
## sscs (.18.) 0.502 0.012 41.327 0.000 0.478
## ssai (.19.) 0.492 0.011 44.645 0.000 0.471
## sssi (.20.) 0.518 0.011 47.286 0.000 0.497
## ssmk (.21.) 0.735 0.012 60.873 0.000 0.712
## ssmc (.22.) 0.671 0.011 61.379 0.000 0.650
## ssei (.23.) 0.708 0.011 64.513 0.000 0.686
## ssao (.24.) 0.593 0.012 51.205 0.000 0.570
## ci.upper Std.lv Std.all
##
## 0.365 0.334 0.326
## 0.181 0.159 0.154
## 0.322 0.293 0.288
## 0.276 0.244 0.241
## 0.451 0.409 0.391
##
## 0.295 0.597 0.551
## 0.307 0.617 0.599
## 0.160 0.317 0.312
## 0.172 0.344 0.327
##
## 0.737 0.751 0.710
## 0.440 0.443 0.433
## 0.239 0.239 0.235
##
## 0.842 0.939 0.910
## 0.758 0.841 0.822
## 0.842 0.939 0.907
## 0.798 0.889 0.862
## 0.555 0.607 0.573
## 0.526 0.576 0.564
## 0.514 0.564 0.521
## 0.540 0.594 0.576
## 0.759 0.843 0.827
## 0.692 0.769 0.759
## 0.729 0.811 0.772
## 0.615 0.679 0.650
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.194 0.019 10.391 0.000 0.157
## .sspc 0.041 0.019 2.207 0.027 0.005
## .ssmk 0.087 0.019 4.675 0.000 0.050
## .ssmc 0.322 0.019 17.179 0.000 0.286
## .ssao 0.081 0.019 4.256 0.000 0.044
## .ssai 0.382 0.021 18.202 0.000 0.341
## .sssi 0.482 0.020 24.659 0.000 0.443
## .ssei 0.339 0.021 16.134 0.000 0.298
## .ssno -0.002 0.020 -0.083 0.934 -0.040
## .sscs -0.080 0.019 -4.255 0.000 -0.117
## .ssgs 0.276 0.019 14.542 0.000 0.239
## .sswk 0.179 0.018 9.735 0.000 0.143
## ci.upper Std.lv Std.all
## 0.230 0.194 0.189
## 0.078 0.041 0.040
## 0.123 0.087 0.085
## 0.359 0.322 0.318
## 0.119 0.081 0.078
## 0.423 0.382 0.353
## 0.520 0.482 0.467
## 0.380 0.339 0.322
## 0.037 -0.002 -0.002
## -0.043 -0.080 -0.079
## 0.313 0.276 0.267
## 0.215 0.179 0.173
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.228 0.010 22.303 0.000 0.208
## .sspc 0.248 0.009 28.591 0.000 0.231
## .ssmk 0.185 0.008 22.364 0.000 0.169
## .ssmc 0.277 0.011 24.407 0.000 0.255
## .ssao 0.463 0.018 25.323 0.000 0.427
## .ssai 0.498 0.021 23.423 0.000 0.456
## .sssi 0.329 0.017 18.905 0.000 0.295
## .ssei 0.329 0.012 26.407 0.000 0.305
## .ssno 0.188 0.034 5.601 0.000 0.122
## .sscs 0.516 0.021 25.063 0.000 0.476
## .ssgs 0.184 0.007 26.549 0.000 0.170
## .sswk 0.189 0.008 24.466 0.000 0.174
## math 1.010 0.083 12.161 0.000 0.847
## electronic 4.997 0.550 9.090 0.000 3.919
## speed 1.195 0.085 14.023 0.000 1.028
## g 1.315 0.047 28.131 0.000 1.223
## ci.upper Std.lv Std.all
## 0.248 0.228 0.218
## 0.265 0.248 0.233
## 0.201 0.185 0.178
## 0.299 0.277 0.269
## 0.499 0.463 0.424
## 0.539 0.498 0.425
## 0.363 0.329 0.310
## 0.353 0.329 0.298
## 0.254 0.188 0.168
## 0.557 0.516 0.495
## 0.198 0.184 0.173
## 0.204 0.189 0.177
## 1.173 1.000 1.000
## 6.074 1.000 1.000
## 1.362 1.000 1.000
## 1.406 1.000 1.000
lavTestScore(metric, release = 1:24)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 272.038 24 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p1. == .p63. 0.228 1 0.633
## 2 .p2. == .p64. 3.901 1 0.048
## 3 .p3. == .p65. 0.338 1 0.561
## 4 .p4. == .p66. 0.240 1 0.624
## 5 .p5. == .p67. 0.324 1 0.569
## 6 .p6. == .p68. 2.792 1 0.095
## 7 .p7. == .p69. 2.933 1 0.087
## 8 .p8. == .p70. 11.056 1 0.001
## 9 .p9. == .p71. 9.171 1 0.002
## 10 .p10. == .p72. 0.000 1 0.997
## 11 .p11. == .p73. 0.003 1 0.953
## 12 .p12. == .p74. 0.003 1 0.953
## 13 .p13. == .p75. 0.460 1 0.498
## 14 .p14. == .p76. 4.615 1 0.032
## 15 .p15. == .p77. 51.712 1 0.000
## 16 .p16. == .p78. 4.611 1 0.032
## 17 .p17. == .p79. 4.231 1 0.040
## 18 .p18. == .p80. 0.150 1 0.698
## 19 .p19. == .p81. 4.733 1 0.030
## 20 .p20. == .p82. 0.008 1 0.928
## 21 .p21. == .p83. 26.981 1 0.000
## 22 .p22. == .p84. 3.900 1 0.048
## 23 .p23. == .p85. 133.472 1 0.000
## 24 .p24. == .p86. 1.804 1 0.179
metric2<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"), group.partial=c("g=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2159.783 103.000 0.000 0.970 0.075 0.041
## aic bic
## 170991.536 171520.284
Mc(metric2)
## [1] 0.865016
scalar<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 3135.666 112.000 0.000 0.955 0.087 0.053
## aic bic
## 171949.419 172416.366
Mc(scalar)
## [1] 0.8080152
summary(scalar, standardized=T, ci=T) # -.044
## lavaan 0.6-18 ended normally after 87 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 104
## Number of equality constraints 36
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3135.666 2435.316
## Degrees of freedom 112 112
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.288
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1246.913 968.415
## 0 1888.753 1466.901
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.306 0.018 17.195 0.000 0.271
## sspc (.p2.) 0.194 0.013 15.138 0.000 0.168
## ssmk (.p3.) 0.278 0.017 16.309 0.000 0.245
## ssmc (.p4.) 0.254 0.016 16.353 0.000 0.223
## ssao (.p5.) 0.425 0.021 20.711 0.000 0.385
## electronic =~
## ssai (.p6.) 0.249 0.013 19.614 0.000 0.225
## sssi (.p7.) 0.288 0.015 19.631 0.000 0.259
## ssmc (.p8.) 0.153 0.009 16.663 0.000 0.135
## ssei (.p9.) 0.155 0.008 18.641 0.000 0.139
## speed =~
## ssno (.10.) 0.619 0.024 26.201 0.000 0.573
## sscs (.11.) 0.460 0.019 24.479 0.000 0.423
## ssmk (.12.) 0.233 0.010 23.873 0.000 0.213
## g =~
## ssgs (.13.) 0.820 0.011 71.478 0.000 0.797
## ssar (.14.) 0.736 0.012 59.888 0.000 0.712
## sswk (.15.) 0.818 0.012 67.296 0.000 0.794
## sspc (.16.) 0.767 0.012 66.090 0.000 0.744
## ssno (.17.) 0.531 0.013 40.393 0.000 0.505
## sscs (.18.) 0.497 0.012 40.850 0.000 0.473
## ssai (.19.) 0.495 0.011 44.971 0.000 0.473
## sssi (.20.) 0.518 0.011 47.279 0.000 0.496
## ssmk (.21.) 0.736 0.012 60.378 0.000 0.712
## ssmc (.22.) 0.668 0.011 61.152 0.000 0.647
## ssei (.23.) 0.708 0.011 64.514 0.000 0.686
## ssao (.24.) 0.589 0.011 51.227 0.000 0.566
## ci.upper Std.lv Std.all
##
## 0.341 0.306 0.336
## 0.219 0.194 0.209
## 0.312 0.278 0.300
## 0.284 0.254 0.287
## 0.466 0.425 0.452
##
## 0.274 0.249 0.309
## 0.317 0.288 0.354
## 0.171 0.153 0.173
## 0.172 0.155 0.178
##
## 0.665 0.619 0.656
## 0.497 0.460 0.490
## 0.252 0.233 0.251
##
## 0.842 0.820 0.893
## 0.760 0.736 0.809
## 0.842 0.818 0.892
## 0.790 0.767 0.830
## 0.556 0.531 0.562
## 0.521 0.497 0.529
## 0.516 0.495 0.612
## 0.539 0.518 0.637
## 0.760 0.736 0.794
## 0.689 0.668 0.755
## 0.729 0.708 0.812
## 0.611 0.589 0.626
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.195 0.016 11.978 0.000 0.163
## .sspc (.48.) 0.179 0.017 10.574 0.000 0.146
## .ssmk (.49.) 0.231 0.018 13.094 0.000 0.196
## .ssmc (.50.) 0.050 0.016 3.177 0.001 0.019
## .ssao (.51.) 0.191 0.018 10.806 0.000 0.157
## .ssai (.52.) -0.115 0.014 -8.301 0.000 -0.143
## .sssi (.53.) -0.125 0.015 -8.612 0.000 -0.154
## .ssei (.54.) -0.009 0.015 -0.606 0.545 -0.038
## .ssno (.55.) 0.204 0.018 11.393 0.000 0.169
## .sscs (.56.) 0.187 0.018 10.173 0.000 0.151
## .ssgs (.57.) 0.175 0.017 10.424 0.000 0.142
## .sswk (.58.) 0.160 0.017 9.335 0.000 0.126
## ci.upper Std.lv Std.all
## 0.227 0.195 0.214
## 0.213 0.179 0.194
## 0.265 0.231 0.249
## 0.081 0.050 0.056
## 0.226 0.191 0.203
## -0.088 -0.115 -0.143
## -0.097 -0.125 -0.154
## 0.020 -0.009 -0.010
## 0.239 0.204 0.216
## 0.223 0.187 0.199
## 0.208 0.175 0.191
## 0.194 0.160 0.175
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.193 0.009 22.019 0.000 0.176
## .sspc 0.228 0.009 26.139 0.000 0.211
## .ssmk 0.187 0.008 24.232 0.000 0.172
## .ssmc 0.249 0.010 24.401 0.000 0.229
## .ssao 0.357 0.017 21.122 0.000 0.324
## .ssai 0.346 0.012 29.149 0.000 0.323
## .sssi 0.311 0.012 25.859 0.000 0.287
## .ssei 0.235 0.008 27.943 0.000 0.218
## .ssno 0.226 0.021 10.682 0.000 0.185
## .sscs 0.423 0.018 22.880 0.000 0.387
## .ssgs 0.170 0.007 25.874 0.000 0.157
## .sswk 0.171 0.007 25.166 0.000 0.158
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.210 0.193 0.233
## 0.246 0.228 0.267
## 0.202 0.187 0.217
## 0.268 0.249 0.318
## 0.391 0.357 0.404
## 0.369 0.346 0.530
## 0.334 0.311 0.470
## 0.251 0.235 0.309
## 0.268 0.226 0.254
## 0.459 0.423 0.480
## 0.183 0.170 0.202
## 0.185 0.171 0.204
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.306 0.018 17.195 0.000 0.271
## sspc (.p2.) 0.194 0.013 15.138 0.000 0.168
## ssmk (.p3.) 0.278 0.017 16.309 0.000 0.245
## ssmc (.p4.) 0.254 0.016 16.353 0.000 0.223
## ssao (.p5.) 0.425 0.021 20.711 0.000 0.385
## electronic =~
## ssai (.p6.) 0.249 0.013 19.614 0.000 0.225
## sssi (.p7.) 0.288 0.015 19.631 0.000 0.259
## ssmc (.p8.) 0.153 0.009 16.663 0.000 0.135
## ssei (.p9.) 0.155 0.008 18.641 0.000 0.139
## speed =~
## ssno (.10.) 0.619 0.024 26.201 0.000 0.573
## sscs (.11.) 0.460 0.019 24.479 0.000 0.423
## ssmk (.12.) 0.233 0.010 23.873 0.000 0.213
## g =~
## ssgs (.13.) 0.820 0.011 71.478 0.000 0.797
## ssar (.14.) 0.736 0.012 59.888 0.000 0.712
## sswk (.15.) 0.818 0.012 67.296 0.000 0.794
## sspc (.16.) 0.767 0.012 66.090 0.000 0.744
## ssno (.17.) 0.531 0.013 40.393 0.000 0.505
## sscs (.18.) 0.497 0.012 40.850 0.000 0.473
## ssai (.19.) 0.495 0.011 44.971 0.000 0.473
## sssi (.20.) 0.518 0.011 47.279 0.000 0.496
## ssmk (.21.) 0.736 0.012 60.378 0.000 0.712
## ssmc (.22.) 0.668 0.011 61.152 0.000 0.647
## ssei (.23.) 0.708 0.011 64.514 0.000 0.686
## ssao (.24.) 0.589 0.011 51.227 0.000 0.566
## ci.upper Std.lv Std.all
##
## 0.341 0.310 0.303
## 0.219 0.196 0.189
## 0.312 0.282 0.276
## 0.284 0.257 0.252
## 0.466 0.431 0.412
##
## 0.274 0.549 0.512
## 0.317 0.633 0.612
## 0.171 0.337 0.331
## 0.172 0.342 0.325
##
## 0.665 0.678 0.642
## 0.497 0.504 0.487
## 0.252 0.255 0.250
##
## 0.842 0.941 0.908
## 0.760 0.844 0.824
## 0.842 0.939 0.908
## 0.790 0.880 0.848
## 0.556 0.609 0.577
## 0.521 0.570 0.551
## 0.516 0.568 0.530
## 0.539 0.594 0.575
## 0.760 0.844 0.828
## 0.689 0.767 0.753
## 0.729 0.812 0.772
## 0.611 0.676 0.646
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.195 0.016 11.978 0.000 0.163
## .sspc (.48.) 0.179 0.017 10.574 0.000 0.146
## .ssmk (.49.) 0.231 0.018 13.094 0.000 0.196
## .ssmc (.50.) 0.050 0.016 3.177 0.001 0.019
## .ssao (.51.) 0.191 0.018 10.806 0.000 0.157
## .ssai (.52.) -0.115 0.014 -8.301 0.000 -0.143
## .sssi (.53.) -0.125 0.015 -8.612 0.000 -0.154
## .ssei (.54.) -0.009 0.015 -0.606 0.545 -0.038
## .ssno (.55.) 0.204 0.018 11.393 0.000 0.169
## .sscs (.56.) 0.187 0.018 10.173 0.000 0.151
## .ssgs (.57.) 0.175 0.017 10.424 0.000 0.142
## .sswk (.58.) 0.160 0.017 9.335 0.000 0.126
## math -0.311 0.054 -5.776 0.000 -0.417
## elctrnc 1.998 0.116 17.282 0.000 1.772
## speed -0.434 0.046 -9.491 0.000 -0.523
## g 0.051 0.031 1.656 0.098 -0.009
## ci.upper Std.lv Std.all
## 0.227 0.195 0.191
## 0.213 0.179 0.173
## 0.265 0.231 0.226
## 0.081 0.050 0.049
## 0.226 0.191 0.183
## -0.088 -0.115 -0.108
## -0.097 -0.125 -0.121
## 0.020 -0.009 -0.009
## 0.239 0.204 0.193
## 0.223 0.187 0.181
## 0.208 0.175 0.169
## 0.194 0.160 0.155
## -0.206 -0.307 -0.307
## 2.225 0.909 0.909
## -0.344 -0.396 -0.396
## 0.111 0.044 0.044
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.240 0.010 24.193 0.000 0.221
## .sspc 0.265 0.010 27.450 0.000 0.246
## .ssmk 0.183 0.008 22.457 0.000 0.167
## .ssmc 0.269 0.011 23.614 0.000 0.247
## .ssao 0.451 0.018 24.753 0.000 0.416
## .ssai 0.525 0.019 27.751 0.000 0.488
## .sssi 0.315 0.015 20.626 0.000 0.285
## .ssei 0.331 0.012 27.134 0.000 0.307
## .ssno 0.283 0.028 10.255 0.000 0.229
## .sscs 0.492 0.022 22.488 0.000 0.449
## .ssgs 0.188 0.007 25.639 0.000 0.173
## .sswk 0.188 0.008 24.235 0.000 0.173
## math 1.024 0.085 11.993 0.000 0.857
## electronic 4.838 0.525 9.221 0.000 3.809
## speed 1.199 0.088 13.593 0.000 1.026
## g 1.317 0.047 27.924 0.000 1.225
## ci.upper Std.lv Std.all
## 0.259 0.240 0.229
## 0.284 0.265 0.246
## 0.199 0.183 0.176
## 0.291 0.269 0.260
## 0.487 0.451 0.413
## 0.562 0.525 0.457
## 0.345 0.315 0.294
## 0.355 0.331 0.299
## 0.337 0.283 0.254
## 0.535 0.492 0.459
## 0.202 0.188 0.175
## 0.203 0.188 0.176
## 1.192 1.000 1.000
## 5.866 1.000 1.000
## 1.372 1.000 1.000
## 1.410 1.000 1.000
lavTestScore(scalar, release = 25:36)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 822.754 12 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p47. == .p109. 134.408 1 0.000
## 2 .p48. == .p110. 446.050 1 0.000
## 3 .p49. == .p111. 2.624 1 0.105
## 4 .p50. == .p112. 8.316 1 0.004
## 5 .p51. == .p113. 1.299 1 0.254
## 6 .p52. == .p114. 20.354 1 0.000
## 7 .p53. == .p115. 2.875 1 0.090
## 8 .p54. == .p116. 0.099 1 0.753
## 9 .p55. == .p117. 125.706 1 0.000
## 10 .p56. == .p118. 182.620 1 0.000
## 11 .p57. == .p119. 229.924 1 0.000
## 12 .p58. == .p120. 33.331 1 0.000
scalar2<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "sswk~1")) # RMSEAD bad unless sswk freed
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2369.324 109.000 0.000 0.967 0.076 0.050
## aic bic
## 171189.077 171676.624
Mc(scalar2)
## [1] 0.8526916
summary(scalar2, standardized=T, ci=T) # -.167 but -.090 if sswk is not freed
## lavaan 0.6-18 ended normally after 91 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 104
## Number of equality constraints 33
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2369.324 1826.952
## Degrees of freedom 109 109
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.297
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 869.314 670.315
## 0 1500.011 1156.637
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.320 0.018 18.261 0.000 0.286
## sspc (.p2.) 0.158 0.011 13.866 0.000 0.136
## ssmk (.p3.) 0.280 0.017 16.853 0.000 0.248
## ssmc (.p4.) 0.248 0.016 15.765 0.000 0.218
## ssao (.p5.) 0.431 0.022 19.883 0.000 0.388
## electronic =~
## ssai (.p6.) 0.256 0.013 19.733 0.000 0.231
## sssi (.p7.) 0.297 0.015 19.487 0.000 0.267
## ssmc (.p8.) 0.146 0.009 16.191 0.000 0.128
## ssei (.p9.) 0.147 0.008 17.790 0.000 0.131
## speed =~
## ssno (.10.) 0.687 0.026 26.580 0.000 0.636
## sscs (.11.) 0.415 0.017 23.771 0.000 0.381
## ssmk (.12.) 0.210 0.010 21.216 0.000 0.191
## g =~
## ssgs (.13.) 0.819 0.011 72.004 0.000 0.797
## ssar (.14.) 0.736 0.012 60.103 0.000 0.712
## sswk (.15.) 0.818 0.012 67.762 0.000 0.795
## sspc (.16.) 0.775 0.011 67.784 0.000 0.753
## ssno (.17.) 0.530 0.013 40.411 0.000 0.504
## sscs (.18.) 0.500 0.012 41.326 0.000 0.476
## ssai (.19.) 0.494 0.011 44.775 0.000 0.472
## sssi (.20.) 0.518 0.011 47.354 0.000 0.497
## ssmk (.21.) 0.738 0.012 60.620 0.000 0.714
## ssmc (.22.) 0.671 0.011 61.569 0.000 0.650
## ssei (.23.) 0.707 0.011 64.466 0.000 0.686
## ssao (.24.) 0.587 0.011 51.481 0.000 0.565
## ci.upper Std.lv Std.all
##
## 0.355 0.320 0.352
## 0.180 0.158 0.173
## 0.313 0.280 0.302
## 0.279 0.248 0.280
## 0.473 0.431 0.457
##
## 0.282 0.256 0.317
## 0.327 0.297 0.365
## 0.164 0.146 0.165
## 0.163 0.147 0.169
##
## 0.737 0.687 0.725
## 0.449 0.415 0.446
## 0.229 0.210 0.226
##
## 0.841 0.819 0.895
## 0.760 0.736 0.808
## 0.842 0.818 0.892
## 0.798 0.775 0.846
## 0.555 0.530 0.560
## 0.524 0.500 0.537
## 0.515 0.494 0.611
## 0.540 0.518 0.636
## 0.762 0.738 0.796
## 0.692 0.671 0.757
## 0.729 0.707 0.812
## 0.610 0.587 0.623
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.157 0.017 9.478 0.000 0.124
## .sspc 0.284 0.017 16.543 0.000 0.251
## .ssmk (.49.) 0.239 0.017 13.681 0.000 0.205
## .ssmc (.50.) 0.042 0.016 2.680 0.007 0.011
## .ssao (.51.) 0.164 0.018 9.254 0.000 0.129
## .ssai (.52.) -0.111 0.014 -8.013 0.000 -0.138
## .sssi (.53.) -0.116 0.015 -7.894 0.000 -0.144
## .ssei (.54.) -0.021 0.015 -1.434 0.152 -0.050
## .ssno 0.173 0.018 9.602 0.000 0.138
## .sscs (.56.) 0.253 0.017 14.543 0.000 0.219
## .ssgs (.57.) 0.120 0.017 7.179 0.000 0.087
## .sswk 0.181 0.017 10.369 0.000 0.147
## ci.upper Std.lv Std.all
## 0.189 0.157 0.172
## 0.318 0.284 0.310
## 0.273 0.239 0.258
## 0.072 0.042 0.047
## 0.199 0.164 0.174
## -0.084 -0.111 -0.137
## -0.087 -0.116 -0.142
## 0.008 -0.021 -0.024
## 0.209 0.173 0.183
## 0.288 0.253 0.272
## 0.152 0.120 0.131
## 0.216 0.181 0.198
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.185 0.009 20.784 0.000 0.168
## .sspc 0.214 0.008 26.443 0.000 0.198
## .ssmk 0.192 0.008 24.924 0.000 0.177
## .ssmc 0.252 0.010 24.650 0.000 0.232
## .ssao 0.358 0.018 19.941 0.000 0.323
## .ssai 0.343 0.012 28.839 0.000 0.320
## .sssi 0.307 0.012 25.101 0.000 0.283
## .ssei 0.237 0.008 28.175 0.000 0.220
## .ssno 0.144 0.027 5.410 0.000 0.092
## .sscs 0.445 0.017 25.811 0.000 0.411
## .ssgs 0.167 0.006 26.488 0.000 0.155
## .sswk 0.171 0.007 25.677 0.000 0.158
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.203 0.185 0.223
## 0.230 0.214 0.255
## 0.208 0.192 0.224
## 0.272 0.252 0.320
## 0.393 0.358 0.403
## 0.367 0.343 0.526
## 0.331 0.307 0.463
## 0.253 0.237 0.312
## 0.196 0.144 0.161
## 0.479 0.445 0.513
## 0.179 0.167 0.199
## 0.184 0.171 0.204
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.320 0.018 18.261 0.000 0.286
## sspc (.p2.) 0.158 0.011 13.866 0.000 0.136
## ssmk (.p3.) 0.280 0.017 16.853 0.000 0.248
## ssmc (.p4.) 0.248 0.016 15.765 0.000 0.218
## ssao (.p5.) 0.431 0.022 19.883 0.000 0.388
## electronic =~
## ssai (.p6.) 0.256 0.013 19.733 0.000 0.231
## sssi (.p7.) 0.297 0.015 19.487 0.000 0.267
## ssmc (.p8.) 0.146 0.009 16.191 0.000 0.128
## ssei (.p9.) 0.147 0.008 17.790 0.000 0.131
## speed =~
## ssno (.10.) 0.687 0.026 26.580 0.000 0.636
## sscs (.11.) 0.415 0.017 23.771 0.000 0.381
## ssmk (.12.) 0.210 0.010 21.216 0.000 0.191
## g =~
## ssgs (.13.) 0.819 0.011 72.004 0.000 0.797
## ssar (.14.) 0.736 0.012 60.103 0.000 0.712
## sswk (.15.) 0.818 0.012 67.762 0.000 0.795
## sspc (.16.) 0.775 0.011 67.784 0.000 0.753
## ssno (.17.) 0.530 0.013 40.411 0.000 0.504
## sscs (.18.) 0.500 0.012 41.326 0.000 0.476
## ssai (.19.) 0.494 0.011 44.775 0.000 0.472
## sssi (.20.) 0.518 0.011 47.354 0.000 0.497
## ssmk (.21.) 0.738 0.012 60.620 0.000 0.714
## ssmc (.22.) 0.671 0.011 61.569 0.000 0.650
## ssei (.23.) 0.707 0.011 64.466 0.000 0.686
## ssao (.24.) 0.587 0.011 51.481 0.000 0.565
## ci.upper Std.lv Std.all
##
## 0.355 0.323 0.315
## 0.180 0.159 0.155
## 0.313 0.283 0.277
## 0.279 0.250 0.247
## 0.473 0.434 0.415
##
## 0.282 0.561 0.522
## 0.327 0.650 0.626
## 0.164 0.320 0.315
## 0.163 0.322 0.307
##
## 0.737 0.752 0.710
## 0.449 0.455 0.444
## 0.229 0.230 0.226
##
## 0.841 0.939 0.909
## 0.760 0.844 0.824
## 0.842 0.939 0.907
## 0.798 0.889 0.862
## 0.555 0.608 0.574
## 0.524 0.574 0.560
## 0.515 0.566 0.527
## 0.540 0.594 0.573
## 0.762 0.847 0.830
## 0.692 0.770 0.758
## 0.729 0.811 0.774
## 0.610 0.674 0.644
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.157 0.017 9.478 0.000 0.124
## .sspc -0.050 0.019 -2.646 0.008 -0.087
## .ssmk (.49.) 0.239 0.017 13.681 0.000 0.205
## .ssmc (.50.) 0.042 0.016 2.680 0.007 0.011
## .ssao (.51.) 0.164 0.018 9.254 0.000 0.129
## .ssai (.52.) -0.111 0.014 -8.013 0.000 -0.138
## .sssi (.53.) -0.116 0.015 -7.894 0.000 -0.144
## .ssei (.54.) -0.021 0.015 -1.434 0.152 -0.050
## .ssno 0.575 0.047 12.322 0.000 0.484
## .sscs (.56.) 0.253 0.017 14.543 0.000 0.219
## .ssgs (.57.) 0.120 0.017 7.179 0.000 0.087
## .sswk 0.023 0.020 1.155 0.248 -0.016
## math -0.357 0.045 -7.916 0.000 -0.446
## elctrnc 1.628 0.100 16.232 0.000 1.432
## speed -0.988 0.062 -16.041 0.000 -1.108
## g 0.191 0.031 6.237 0.000 0.131
## ci.upper Std.lv Std.all
## 0.189 0.157 0.153
## -0.013 -0.050 -0.049
## 0.273 0.239 0.235
## 0.072 0.042 0.041
## 0.199 0.164 0.157
## -0.084 -0.111 -0.103
## -0.087 -0.116 -0.111
## 0.008 -0.021 -0.020
## 0.667 0.575 0.543
## 0.288 0.253 0.247
## 0.152 0.120 0.116
## 0.061 0.023 0.022
## -0.269 -0.355 -0.355
## 1.825 0.744 0.744
## -0.867 -0.902 -0.902
## 0.251 0.167 0.167
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.232 0.010 23.062 0.000 0.212
## .sspc 0.248 0.009 28.610 0.000 0.231
## .ssmk 0.190 0.008 22.887 0.000 0.174
## .ssmc 0.274 0.011 24.128 0.000 0.252
## .ssao 0.452 0.019 23.445 0.000 0.414
## .ssai 0.521 0.019 26.835 0.000 0.483
## .sssi 0.301 0.016 18.569 0.000 0.269
## .ssei 0.336 0.012 27.519 0.000 0.312
## .ssno 0.186 0.034 5.459 0.000 0.119
## .sscs 0.515 0.021 24.691 0.000 0.474
## .ssgs 0.184 0.007 26.525 0.000 0.171
## .sswk 0.189 0.008 24.525 0.000 0.174
## math 1.016 0.083 12.181 0.000 0.853
## electronic 4.785 0.513 9.320 0.000 3.779
## speed 1.200 0.086 13.921 0.000 1.031
## g 1.315 0.047 28.117 0.000 1.224
## ci.upper Std.lv Std.all
## 0.252 0.232 0.221
## 0.265 0.248 0.233
## 0.206 0.190 0.183
## 0.297 0.274 0.266
## 0.490 0.452 0.413
## 0.559 0.521 0.451
## 0.333 0.301 0.280
## 0.360 0.336 0.306
## 0.253 0.186 0.166
## 0.556 0.515 0.490
## 0.198 0.184 0.173
## 0.205 0.189 0.177
## 1.180 1.000 1.000
## 5.791 1.000 1.000
## 1.369 1.000 1.000
## 1.407 1.000 1.000
lavTestScore(scalar2, release = 25:33)
## Warning: lavaan->lavTestScore():
## se is not `standard'; not implemented yet; falling back to
## ordinary score test
## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 73.549 9 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p47. == .p109. 6.349 1 0.012
## 2 .p49. == .p111. 21.444 1 0.000
## 3 .p50. == .p112. 0.570 1 0.450
## 4 .p51. == .p113. 43.004 1 0.000
## 5 .p52. == .p114. 9.737 1 0.002
## 6 .p53. == .p115. 18.720 1 0.000
## 7 .p54. == .p116. 7.830 1 0.005
## 8 .p56. == .p118. 21.444 1 0.000
## 9 .p57. == .p119. 0.010 1 0.920
strict<-cfa(bf.model, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("sspc~1", "ssno~1", "sswk~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2668.434 121.000 0.000 0.962 0.077 0.053
## aic bic
## 171464.186 171869.331
Mc(strict)
## [1] 0.8356051
summary(strict, standardized=T, ci=T) # -.165
## lavaan 0.6-18 ended normally after 92 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 104
## Number of equality constraints 45
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2668.434 2045.448
## Degrees of freedom 121 121
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.305
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1062.451 814.406
## 0 1605.983 1231.042
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.308 0.019 16.574 0.000 0.271
## sspc (.p2.) 0.155 0.011 13.868 0.000 0.133
## ssmk (.p3.) 0.269 0.018 15.304 0.000 0.235
## ssmc (.p4.) 0.238 0.016 15.355 0.000 0.208
## ssao (.p5.) 0.415 0.021 19.333 0.000 0.373
## electronic =~
## ssai (.p6.) 0.243 0.014 16.951 0.000 0.214
## sssi (.p7.) 0.258 0.017 15.259 0.000 0.225
## ssmc (.p8.) 0.130 0.010 13.614 0.000 0.111
## ssei (.p9.) 0.136 0.009 15.362 0.000 0.119
## speed =~
## ssno (.10.) 0.682 0.026 26.150 0.000 0.631
## sscs (.11.) 0.404 0.017 23.780 0.000 0.371
## ssmk (.12.) 0.205 0.010 20.653 0.000 0.185
## g =~
## ssgs (.13.) 0.818 0.011 71.875 0.000 0.796
## ssar (.14.) 0.736 0.012 60.181 0.000 0.712
## sswk (.15.) 0.816 0.012 67.501 0.000 0.792
## sspc (.16.) 0.773 0.011 67.615 0.000 0.751
## ssno (.17.) 0.529 0.013 40.432 0.000 0.504
## sscs (.18.) 0.499 0.012 41.228 0.000 0.475
## ssai (.19.) 0.491 0.011 44.118 0.000 0.469
## sssi (.20.) 0.518 0.011 47.394 0.000 0.497
## ssmk (.21.) 0.738 0.012 60.440 0.000 0.714
## ssmc (.22.) 0.671 0.011 61.362 0.000 0.649
## ssei (.23.) 0.716 0.011 65.264 0.000 0.694
## ssao (.24.) 0.587 0.011 51.301 0.000 0.564
## ci.upper Std.lv Std.all
##
## 0.344 0.308 0.334
## 0.177 0.155 0.168
## 0.304 0.269 0.292
## 0.269 0.238 0.268
## 0.457 0.415 0.432
##
## 0.271 0.243 0.287
## 0.291 0.258 0.318
## 0.149 0.130 0.147
## 0.154 0.136 0.151
##
## 0.733 0.682 0.718
## 0.438 0.404 0.427
## 0.224 0.205 0.222
##
## 0.841 0.818 0.890
## 0.760 0.736 0.800
## 0.839 0.816 0.887
## 0.796 0.773 0.837
## 0.555 0.529 0.558
## 0.523 0.499 0.527
## 0.513 0.491 0.581
## 0.539 0.518 0.637
## 0.762 0.738 0.799
## 0.692 0.671 0.756
## 0.737 0.716 0.794
## 0.609 0.587 0.611
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.158 0.017 9.487 0.000 0.125
## .sspc 0.284 0.017 16.543 0.000 0.251
## .ssmk (.49.) 0.239 0.018 13.639 0.000 0.205
## .ssmc (.50.) 0.044 0.016 2.806 0.005 0.013
## .ssao (.51.) 0.159 0.018 8.888 0.000 0.124
## .ssai (.52.) -0.123 0.014 -8.879 0.000 -0.150
## .sssi (.53.) -0.105 0.015 -7.177 0.000 -0.134
## .ssei (.54.) -0.026 0.015 -1.776 0.076 -0.056
## .ssno 0.173 0.018 9.602 0.000 0.138
## .sscs (.56.) 0.252 0.017 14.490 0.000 0.218
## .ssgs (.57.) 0.120 0.017 7.232 0.000 0.088
## .sswk 0.181 0.017 10.369 0.000 0.147
## ci.upper Std.lv Std.all
## 0.190 0.158 0.171
## 0.318 0.284 0.308
## 0.273 0.239 0.259
## 0.074 0.044 0.049
## 0.194 0.159 0.166
## -0.096 -0.123 -0.145
## -0.077 -0.105 -0.130
## 0.003 -0.026 -0.029
## 0.209 0.173 0.183
## 0.286 0.252 0.266
## 0.153 0.120 0.131
## 0.216 0.181 0.197
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.25.) 0.210 0.008 25.633 0.000 0.194
## .sspc (.26.) 0.231 0.006 38.680 0.000 0.219
## .ssmk (.27.) 0.193 0.007 28.822 0.000 0.180
## .ssmc (.28.) 0.264 0.008 31.131 0.000 0.248
## .ssao (.29.) 0.405 0.017 24.114 0.000 0.372
## .ssai (.30.) 0.414 0.012 35.172 0.000 0.391
## .sssi (.31.) 0.326 0.011 30.830 0.000 0.305
## .ssei (.32.) 0.283 0.007 38.509 0.000 0.268
## .ssno (.33.) 0.155 0.029 5.339 0.000 0.098
## .sscs (.34.) 0.484 0.015 32.615 0.000 0.455
## .ssgs (.35.) 0.175 0.005 36.143 0.000 0.166
## .sswk (.36.) 0.181 0.005 34.790 0.000 0.171
## math 1.000 1.000
## elctrnc 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.226 0.210 0.248
## 0.243 0.231 0.271
## 0.206 0.193 0.226
## 0.281 0.264 0.336
## 0.438 0.405 0.440
## 0.437 0.414 0.580
## 0.347 0.326 0.493
## 0.297 0.283 0.348
## 0.212 0.155 0.173
## 0.513 0.484 0.540
## 0.185 0.175 0.207
## 0.191 0.181 0.214
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.308 0.019 16.574 0.000 0.271
## sspc (.p2.) 0.155 0.011 13.868 0.000 0.133
## ssmk (.p3.) 0.269 0.018 15.304 0.000 0.235
## ssmc (.p4.) 0.238 0.016 15.355 0.000 0.208
## ssao (.p5.) 0.415 0.021 19.333 0.000 0.373
## electronic =~
## ssai (.p6.) 0.243 0.014 16.951 0.000 0.214
## sssi (.p7.) 0.258 0.017 15.259 0.000 0.225
## ssmc (.p8.) 0.130 0.010 13.614 0.000 0.111
## ssei (.p9.) 0.136 0.009 15.362 0.000 0.119
## speed =~
## ssno (.10.) 0.682 0.026 26.150 0.000 0.631
## sscs (.11.) 0.404 0.017 23.780 0.000 0.371
## ssmk (.12.) 0.205 0.010 20.653 0.000 0.185
## g =~
## ssgs (.13.) 0.818 0.011 71.875 0.000 0.796
## ssar (.14.) 0.736 0.012 60.181 0.000 0.712
## sswk (.15.) 0.816 0.012 67.501 0.000 0.792
## sspc (.16.) 0.773 0.011 67.615 0.000 0.751
## ssno (.17.) 0.529 0.013 40.432 0.000 0.504
## sscs (.18.) 0.499 0.012 41.228 0.000 0.475
## ssai (.19.) 0.491 0.011 44.118 0.000 0.469
## sssi (.20.) 0.518 0.011 47.394 0.000 0.497
## ssmk (.21.) 0.738 0.012 60.440 0.000 0.714
## ssmc (.22.) 0.671 0.011 61.362 0.000 0.649
## ssei (.23.) 0.716 0.011 65.264 0.000 0.694
## ssao (.24.) 0.587 0.011 51.301 0.000 0.564
## ci.upper Std.lv Std.all
##
## 0.344 0.333 0.327
## 0.177 0.168 0.164
## 0.304 0.291 0.284
## 0.269 0.258 0.254
## 0.457 0.449 0.436
##
## 0.271 0.600 0.574
## 0.291 0.638 0.612
## 0.149 0.322 0.317
## 0.154 0.337 0.325
##
## 0.733 0.770 0.728
## 0.438 0.457 0.452
## 0.224 0.231 0.226
##
## 0.841 0.941 0.914
## 0.760 0.847 0.831
## 0.839 0.938 0.911
## 0.796 0.889 0.868
## 0.555 0.609 0.575
## 0.523 0.574 0.568
## 0.513 0.565 0.540
## 0.539 0.596 0.571
## 0.762 0.848 0.827
## 0.692 0.771 0.760
## 0.737 0.823 0.794
## 0.609 0.675 0.655
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.158 0.017 9.487 0.000 0.125
## .sspc -0.048 0.019 -2.556 0.011 -0.085
## .ssmk (.49.) 0.239 0.018 13.639 0.000 0.205
## .ssmc (.50.) 0.044 0.016 2.806 0.005 0.013
## .ssao (.51.) 0.159 0.018 8.888 0.000 0.124
## .ssai (.52.) -0.123 0.014 -8.879 0.000 -0.150
## .sssi (.53.) -0.105 0.015 -7.177 0.000 -0.134
## .ssei (.54.) -0.026 0.015 -1.776 0.076 -0.056
## .ssno 0.588 0.049 12.041 0.000 0.492
## .sscs (.56.) 0.252 0.017 14.490 0.000 0.218
## .ssgs (.57.) 0.120 0.017 7.232 0.000 0.088
## .sswk 0.025 0.020 1.253 0.210 -0.014
## math -0.366 0.048 -7.687 0.000 -0.459
## elctrnc 1.799 0.129 13.899 0.000 1.545
## speed -1.012 0.064 -15.727 0.000 -1.138
## g 0.190 0.031 6.201 0.000 0.130
## ci.upper Std.lv Std.all
## 0.190 0.158 0.155
## -0.011 -0.048 -0.047
## 0.273 0.239 0.233
## 0.074 0.044 0.043
## 0.194 0.159 0.154
## -0.096 -0.123 -0.118
## -0.077 -0.105 -0.101
## 0.003 -0.026 -0.026
## 0.684 0.588 0.556
## 0.286 0.252 0.250
## 0.153 0.120 0.117
## 0.063 0.025 0.024
## -0.273 -0.338 -0.338
## 2.052 0.728 0.728
## -0.886 -0.896 -0.896
## 0.249 0.165 0.165
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.25.) 0.210 0.008 25.633 0.000 0.194
## .sspc (.26.) 0.231 0.006 38.680 0.000 0.219
## .ssmk (.27.) 0.193 0.007 28.822 0.000 0.180
## .ssmc (.28.) 0.264 0.008 31.131 0.000 0.248
## .ssao (.29.) 0.405 0.017 24.114 0.000 0.372
## .ssai (.30.) 0.414 0.012 35.172 0.000 0.391
## .sssi (.31.) 0.326 0.011 30.830 0.000 0.305
## .ssei (.32.) 0.283 0.007 38.509 0.000 0.268
## .ssno (.33.) 0.155 0.029 5.339 0.000 0.098
## .sscs (.34.) 0.484 0.015 32.615 0.000 0.455
## .ssgs (.35.) 0.175 0.005 36.143 0.000 0.166
## .sswk (.36.) 0.181 0.005 34.790 0.000 0.171
## math 1.170 0.096 12.219 0.000 0.982
## elctrnc 6.112 0.798 7.661 0.000 4.548
## speed 1.276 0.083 15.392 0.000 1.113
## g 1.322 0.047 28.136 0.000 1.230
## ci.upper Std.lv Std.all
## 0.226 0.210 0.202
## 0.243 0.231 0.220
## 0.206 0.193 0.183
## 0.281 0.264 0.257
## 0.438 0.405 0.382
## 0.437 0.414 0.379
## 0.347 0.326 0.299
## 0.297 0.283 0.263
## 0.212 0.155 0.139
## 0.513 0.484 0.474
## 0.185 0.175 0.165
## 0.191 0.181 0.171
## 1.358 1.000 1.000
## 7.676 1.000 1.000
## 1.438 1.000 1.000
## 1.414 1.000 1.000
latent<-cfa(bf.lv, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "sswk~1"))
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2369.380 110.000 0.000 0.967 0.076 0.050
## aic bic
## 171187.133 171667.813
Mc(latent)
## [1] 0.8527484
summary(latent, standardized=T, ci=T) # -.167
## lavaan 0.6-18 ended normally after 82 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 103
## Number of equality constraints 33
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2369.380 1825.375
## Degrees of freedom 110 110
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.298
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 869.326 669.730
## 0 1500.055 1155.645
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.322 0.016 19.584 0.000 0.289
## sspc (.p2.) 0.159 0.011 13.792 0.000 0.136
## ssmk (.p3.) 0.281 0.015 18.165 0.000 0.251
## ssmc (.p4.) 0.249 0.015 16.160 0.000 0.219
## ssao (.p5.) 0.432 0.020 21.393 0.000 0.393
## electronic =~
## ssai (.p6.) 0.256 0.013 19.733 0.000 0.231
## sssi (.p7.) 0.297 0.015 19.488 0.000 0.267
## ssmc (.p8.) 0.146 0.009 16.186 0.000 0.128
## ssei (.p9.) 0.147 0.008 17.792 0.000 0.131
## speed =~
## ssno (.10.) 0.687 0.026 26.572 0.000 0.636
## sscs (.11.) 0.415 0.017 23.783 0.000 0.381
## ssmk (.12.) 0.210 0.010 21.235 0.000 0.191
## g =~
## ssgs (.13.) 0.819 0.011 72.067 0.000 0.797
## ssar (.14.) 0.736 0.012 60.161 0.000 0.712
## sswk (.15.) 0.818 0.012 67.806 0.000 0.795
## sspc (.16.) 0.775 0.011 67.805 0.000 0.753
## ssno (.17.) 0.530 0.013 40.407 0.000 0.504
## sscs (.18.) 0.500 0.012 41.317 0.000 0.476
## ssai (.19.) 0.494 0.011 44.780 0.000 0.472
## sssi (.20.) 0.518 0.011 47.376 0.000 0.497
## ssmk (.21.) 0.738 0.012 60.664 0.000 0.714
## ssmc (.22.) 0.671 0.011 61.593 0.000 0.650
## ssei (.23.) 0.707 0.011 64.484 0.000 0.686
## ssao (.24.) 0.587 0.011 51.530 0.000 0.565
## ci.upper Std.lv Std.all
##
## 0.354 0.322 0.353
## 0.181 0.159 0.173
## 0.312 0.281 0.303
## 0.280 0.249 0.281
## 0.472 0.432 0.458
##
## 0.282 0.256 0.317
## 0.327 0.297 0.365
## 0.164 0.146 0.165
## 0.163 0.147 0.169
##
## 0.737 0.687 0.725
## 0.449 0.415 0.446
## 0.229 0.210 0.226
##
## 0.841 0.819 0.895
## 0.760 0.736 0.808
## 0.842 0.818 0.892
## 0.798 0.775 0.846
## 0.555 0.530 0.560
## 0.524 0.500 0.537
## 0.515 0.494 0.611
## 0.539 0.518 0.636
## 0.762 0.738 0.796
## 0.692 0.671 0.757
## 0.729 0.707 0.812
## 0.610 0.587 0.623
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.157 0.017 9.477 0.000 0.124
## .sspc 0.284 0.017 16.543 0.000 0.251
## .ssmk (.49.) 0.239 0.017 13.681 0.000 0.205
## .ssmc (.50.) 0.042 0.016 2.681 0.007 0.011
## .ssao (.51.) 0.164 0.018 9.254 0.000 0.129
## .ssai (.52.) -0.111 0.014 -8.013 0.000 -0.138
## .sssi (.53.) -0.116 0.015 -7.894 0.000 -0.144
## .ssei (.54.) -0.021 0.015 -1.434 0.152 -0.050
## .ssno 0.173 0.018 9.602 0.000 0.138
## .sscs (.56.) 0.253 0.017 14.541 0.000 0.219
## .ssgs (.57.) 0.120 0.017 7.179 0.000 0.087
## .sswk 0.181 0.017 10.369 0.000 0.147
## ci.upper Std.lv Std.all
## 0.189 0.157 0.172
## 0.318 0.284 0.310
## 0.273 0.239 0.258
## 0.072 0.042 0.047
## 0.199 0.164 0.174
## -0.084 -0.111 -0.137
## -0.087 -0.116 -0.142
## 0.008 -0.021 -0.024
## 0.209 0.173 0.183
## 0.288 0.253 0.272
## 0.152 0.120 0.131
## 0.216 0.181 0.198
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.185 0.009 20.801 0.000 0.168
## .sspc 0.214 0.008 26.447 0.000 0.198
## .ssmk 0.192 0.008 24.973 0.000 0.177
## .ssmc 0.252 0.010 24.636 0.000 0.231
## .ssao 0.358 0.018 19.949 0.000 0.323
## .ssai 0.343 0.012 28.836 0.000 0.320
## .sssi 0.307 0.012 25.100 0.000 0.283
## .ssei 0.237 0.008 28.164 0.000 0.220
## .ssno 0.144 0.027 5.411 0.000 0.092
## .sscs 0.445 0.017 25.811 0.000 0.411
## .ssgs 0.167 0.006 26.468 0.000 0.155
## .sswk 0.171 0.007 25.665 0.000 0.158
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.202 0.185 0.223
## 0.230 0.214 0.255
## 0.207 0.192 0.223
## 0.272 0.252 0.320
## 0.393 0.358 0.402
## 0.367 0.343 0.526
## 0.331 0.307 0.463
## 0.253 0.237 0.312
## 0.197 0.144 0.161
## 0.479 0.445 0.513
## 0.179 0.167 0.199
## 0.184 0.171 0.203
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.322 0.016 19.584 0.000 0.289
## sspc (.p2.) 0.159 0.011 13.792 0.000 0.136
## ssmk (.p3.) 0.281 0.015 18.165 0.000 0.251
## ssmc (.p4.) 0.249 0.015 16.160 0.000 0.219
## ssao (.p5.) 0.432 0.020 21.393 0.000 0.393
## electronic =~
## ssai (.p6.) 0.256 0.013 19.733 0.000 0.231
## sssi (.p7.) 0.297 0.015 19.488 0.000 0.267
## ssmc (.p8.) 0.146 0.009 16.186 0.000 0.128
## ssei (.p9.) 0.147 0.008 17.792 0.000 0.131
## speed =~
## ssno (.10.) 0.687 0.026 26.572 0.000 0.636
## sscs (.11.) 0.415 0.017 23.783 0.000 0.381
## ssmk (.12.) 0.210 0.010 21.235 0.000 0.191
## g =~
## ssgs (.13.) 0.819 0.011 72.067 0.000 0.797
## ssar (.14.) 0.736 0.012 60.161 0.000 0.712
## sswk (.15.) 0.818 0.012 67.806 0.000 0.795
## sspc (.16.) 0.775 0.011 67.805 0.000 0.753
## ssno (.17.) 0.530 0.013 40.407 0.000 0.504
## sscs (.18.) 0.500 0.012 41.317 0.000 0.476
## ssai (.19.) 0.494 0.011 44.780 0.000 0.472
## sssi (.20.) 0.518 0.011 47.376 0.000 0.497
## ssmk (.21.) 0.738 0.012 60.664 0.000 0.714
## ssmc (.22.) 0.671 0.011 61.593 0.000 0.650
## ssei (.23.) 0.707 0.011 64.484 0.000 0.686
## ssao (.24.) 0.587 0.011 51.530 0.000 0.565
## ci.upper Std.lv Std.all
##
## 0.354 0.322 0.314
## 0.181 0.159 0.154
## 0.312 0.281 0.276
## 0.280 0.249 0.246
## 0.472 0.432 0.413
##
## 0.282 0.561 0.522
## 0.327 0.650 0.626
## 0.164 0.320 0.315
## 0.163 0.322 0.307
##
## 0.737 0.752 0.710
## 0.449 0.455 0.444
## 0.229 0.230 0.226
##
## 0.841 0.939 0.909
## 0.760 0.844 0.825
## 0.842 0.939 0.907
## 0.798 0.889 0.862
## 0.555 0.608 0.574
## 0.524 0.574 0.560
## 0.515 0.566 0.527
## 0.539 0.594 0.573
## 0.762 0.847 0.831
## 0.692 0.770 0.758
## 0.729 0.811 0.774
## 0.610 0.674 0.644
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.47.) 0.157 0.017 9.477 0.000 0.124
## .sspc -0.050 0.019 -2.649 0.008 -0.087
## .ssmk (.49.) 0.239 0.017 13.681 0.000 0.205
## .ssmc (.50.) 0.042 0.016 2.681 0.007 0.011
## .ssao (.51.) 0.164 0.018 9.254 0.000 0.129
## .ssai (.52.) -0.111 0.014 -8.013 0.000 -0.138
## .sssi (.53.) -0.116 0.015 -7.894 0.000 -0.144
## .ssei (.54.) -0.021 0.015 -1.434 0.152 -0.050
## .ssno 0.575 0.047 12.320 0.000 0.484
## .sscs (.56.) 0.253 0.017 14.541 0.000 0.219
## .ssgs (.57.) 0.120 0.017 7.179 0.000 0.087
## .sswk 0.023 0.020 1.156 0.248 -0.016
## math -0.356 0.044 -8.104 0.000 -0.442
## elctrnc 1.628 0.100 16.232 0.000 1.432
## speed -0.988 0.062 -16.048 0.000 -1.108
## g 0.191 0.031 6.237 0.000 0.131
## ci.upper Std.lv Std.all
## 0.189 0.157 0.153
## -0.013 -0.050 -0.049
## 0.273 0.239 0.235
## 0.072 0.042 0.041
## 0.199 0.164 0.157
## -0.084 -0.111 -0.103
## -0.087 -0.116 -0.111
## 0.008 -0.021 -0.020
## 0.667 0.575 0.543
## 0.288 0.253 0.247
## 0.152 0.120 0.116
## 0.061 0.023 0.022
## -0.270 -0.356 -0.356
## 1.825 0.744 0.744
## -0.867 -0.902 -0.902
## 0.251 0.167 0.167
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.232 0.010 23.098 0.000 0.212
## .sspc 0.248 0.009 28.610 0.000 0.231
## .ssmk 0.190 0.008 22.877 0.000 0.174
## .ssmc 0.274 0.011 24.161 0.000 0.252
## .ssao 0.452 0.019 23.669 0.000 0.415
## .ssai 0.521 0.019 26.834 0.000 0.483
## .sssi 0.301 0.016 18.568 0.000 0.269
## .ssei 0.336 0.012 27.522 0.000 0.312
## .ssno 0.186 0.034 5.464 0.000 0.119
## .sscs 0.515 0.021 24.716 0.000 0.474
## .ssgs 0.185 0.007 26.598 0.000 0.171
## .sswk 0.190 0.008 24.731 0.000 0.175
## electronic 4.785 0.513 9.320 0.000 3.779
## speed 1.200 0.086 13.954 0.000 1.031
## g 1.315 0.047 28.156 0.000 1.224
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.252 0.232 0.221
## 0.265 0.248 0.233
## 0.206 0.190 0.183
## 0.297 0.274 0.266
## 0.490 0.452 0.414
## 0.559 0.521 0.451
## 0.333 0.301 0.280
## 0.360 0.336 0.306
## 0.253 0.186 0.166
## 0.556 0.515 0.490
## 0.198 0.185 0.173
## 0.205 0.190 0.177
## 5.791 1.000 1.000
## 1.368 1.000 1.000
## 1.407 1.000 1.000
standardizedSolution(latent) # get the correct SEs for standardized solution
## lhs op rhs group label est.std se z pvalue
## 1 math =~ ssar 1 .p1. 0.353 0.018 19.414 0.000
## 2 math =~ sspc 1 .p2. 0.173 0.013 13.757 0.000
## 3 math =~ ssmk 1 .p3. 0.303 0.017 17.976 0.000
## 4 math =~ ssmc 1 .p4. 0.281 0.017 16.270 0.000
## 5 math =~ ssao 1 .p5. 0.458 0.021 21.747 0.000
## 6 electronic =~ ssai 1 .p6. 0.317 0.015 20.477 0.000
## 7 electronic =~ sssi 1 .p7. 0.365 0.018 20.295 0.000
## 8 electronic =~ ssmc 1 .p8. 0.165 0.010 16.248 0.000
## 9 electronic =~ ssei 1 .p9. 0.169 0.010 17.682 0.000
## 10 speed =~ ssno 1 .p10. 0.725 0.024 30.705 0.000
## 11 speed =~ sscs 1 .p11. 0.446 0.017 25.553 0.000
## 12 speed =~ ssmk 1 .p12. 0.226 0.011 20.980 0.000
## 13 g =~ ssgs 1 .p13. 0.895 0.004 206.645 0.000
## 14 g =~ ssar 1 .p14. 0.808 0.007 121.918 0.000
## 15 g =~ sswk 1 .p15. 0.892 0.005 192.795 0.000
## 16 g =~ sspc 1 .p16. 0.846 0.006 132.616 0.000
## 17 g =~ ssno 1 .p17. 0.560 0.013 42.764 0.000
## 18 g =~ sscs 1 .p18. 0.537 0.011 46.763 0.000
## 19 g =~ ssai 1 .p19. 0.611 0.011 58.105 0.000
## 20 g =~ sssi 1 .p20. 0.636 0.010 63.977 0.000
## 21 g =~ ssmk 1 .p21. 0.796 0.007 116.463 0.000
## 22 g =~ ssmc 1 .p22. 0.757 0.007 101.129 0.000
## 23 g =~ ssei 1 .p23. 0.812 0.006 141.246 0.000
## 24 g =~ ssao 1 .p24. 0.623 0.010 62.431 0.000
## 25 math ~~ math 1 1.000 0.000 NA NA
## 26 ssar ~~ ssar 1 0.223 0.011 21.134 0.000
## 27 sspc ~~ sspc 1 0.255 0.010 26.625 0.000
## 28 ssmk ~~ ssmk 1 0.223 0.009 24.425 0.000
## 29 ssmc ~~ ssmc 1 0.320 0.012 26.597 0.000
## 30 ssao ~~ ssao 1 0.402 0.019 21.114 0.000
## 31 ssai ~~ ssai 1 0.526 0.014 36.760 0.000
## 32 sssi ~~ sssi 1 0.463 0.016 29.429 0.000
## 33 ssei ~~ ssei 1 0.312 0.009 34.289 0.000
## 34 ssno ~~ ssno 1 0.161 0.030 5.379 0.000
## 35 sscs ~~ sscs 1 0.513 0.016 31.437 0.000
## 36 ssgs ~~ ssgs 1 0.199 0.008 25.717 0.000
## 37 sswk ~~ sswk 1 0.203 0.008 24.628 0.000
## 38 electronic ~~ electronic 1 1.000 0.000 NA NA
## 39 speed ~~ speed 1 1.000 0.000 NA NA
## 40 g ~~ g 1 1.000 0.000 NA NA
## 41 math ~~ electronic 1 0.000 0.000 NA NA
## 42 math ~~ speed 1 0.000 0.000 NA NA
## 43 math ~~ g 1 0.000 0.000 NA NA
## 44 electronic ~~ speed 1 0.000 0.000 NA NA
## 45 electronic ~~ g 1 0.000 0.000 NA NA
## 46 speed ~~ g 1 0.000 0.000 NA NA
## 47 ssar ~1 1 .p47. 0.172 0.019 9.257 0.000
## 48 sspc ~1 1 0.310 0.019 15.935 0.000
## 49 ssmk ~1 1 .p49. 0.258 0.019 13.396 0.000
## 50 ssmc ~1 1 .p50. 0.047 0.018 2.661 0.008
## 51 ssao ~1 1 .p51. 0.174 0.019 9.188 0.000
## 52 ssai ~1 1 .p52. -0.137 0.017 -7.975 0.000
## 53 sssi ~1 1 .p53. -0.142 0.018 -7.755 0.000
## 54 ssei ~1 1 .p54. -0.024 0.017 -1.432 0.152
## 55 ssno ~1 1 0.183 0.020 9.366 0.000
## 56 sscs ~1 1 .p56. 0.272 0.019 14.290 0.000
## 57 ssgs ~1 1 .p57. 0.131 0.018 7.173 0.000
## 58 sswk ~1 1 0.198 0.019 10.293 0.000
## 59 math ~1 1 0.000 0.000 NA NA
## 60 electronic ~1 1 0.000 0.000 NA NA
## 61 speed ~1 1 0.000 0.000 NA NA
## 62 g ~1 1 0.000 0.000 NA NA
## 63 math =~ ssar 2 .p1. 0.314 0.016 19.418 0.000
## 64 math =~ sspc 2 .p2. 0.154 0.011 13.726 0.000
## 65 math =~ ssmk 2 .p3. 0.276 0.015 17.853 0.000
## 66 math =~ ssmc 2 .p4. 0.246 0.015 16.185 0.000
## 67 math =~ ssao 2 .p5. 0.413 0.019 21.401 0.000
## 68 electronic =~ ssai 2 .p6. 0.522 0.017 30.250 0.000
## 69 electronic =~ sssi 2 .p7. 0.626 0.014 45.388 0.000
## 70 electronic =~ ssmc 2 .p8. 0.315 0.013 24.983 0.000
## 71 electronic =~ ssei 2 .p9. 0.307 0.015 20.956 0.000
## 72 speed =~ ssno 2 .p10. 0.710 0.023 30.317 0.000
## 73 speed =~ sscs 2 .p11. 0.444 0.016 27.019 0.000
## 74 speed =~ ssmk 2 .p12. 0.226 0.011 20.809 0.000
## 75 g =~ ssgs 2 .p13. 0.909 0.004 233.953 0.000
## 76 g =~ ssar 2 .p14. 0.825 0.006 136.566 0.000
## 77 g =~ sswk 2 .p15. 0.907 0.004 228.966 0.000
## 78 g =~ sspc 2 .p16. 0.862 0.005 165.678 0.000
## 79 g =~ ssno 2 .p17. 0.574 0.012 46.192 0.000
## 80 g =~ sscs 2 .p18. 0.560 0.011 50.540 0.000
## 81 g =~ ssai 2 .p19. 0.527 0.012 44.235 0.000
## 82 g =~ sssi 2 .p20. 0.573 0.011 51.882 0.000
## 83 g =~ ssmk 2 .p21. 0.831 0.006 149.095 0.000
## 84 g =~ ssmc 2 .p22. 0.758 0.008 98.282 0.000
## 85 g =~ ssei 2 .p23. 0.774 0.008 94.353 0.000
## 86 g =~ ssao 2 .p24. 0.644 0.009 68.942 0.000
## 87 math ~~ math 2 1.000 0.000 NA NA
## 88 ssar ~~ ssar 2 0.221 0.010 23.242 0.000
## 89 sspc ~~ sspc 2 0.233 0.008 28.749 0.000
## 90 ssmk ~~ ssmk 2 0.183 0.008 22.897 0.000
## ci.lower ci.upper
## 1 0.317 0.389
## 2 0.148 0.198
## 3 0.270 0.336
## 4 0.248 0.315
## 5 0.417 0.499
## 6 0.287 0.348
## 7 0.329 0.400
## 8 0.145 0.185
## 9 0.150 0.188
## 10 0.679 0.771
## 11 0.412 0.480
## 12 0.205 0.248
## 13 0.886 0.903
## 14 0.795 0.821
## 15 0.883 0.902
## 16 0.833 0.858
## 17 0.534 0.585
## 18 0.515 0.560
## 19 0.590 0.632
## 20 0.616 0.655
## 21 0.782 0.809
## 22 0.743 0.772
## 23 0.801 0.823
## 24 0.603 0.642
## 25 1.000 1.000
## 26 0.202 0.244
## 27 0.236 0.273
## 28 0.206 0.241
## 29 0.297 0.344
## 30 0.365 0.440
## 31 0.498 0.554
## 32 0.432 0.494
## 33 0.294 0.330
## 34 0.102 0.220
## 35 0.481 0.545
## 36 0.184 0.214
## 37 0.187 0.220
## 38 1.000 1.000
## 39 1.000 1.000
## 40 1.000 1.000
## 41 0.000 0.000
## 42 0.000 0.000
## 43 0.000 0.000
## 44 0.000 0.000
## 45 0.000 0.000
## 46 0.000 0.000
## 47 0.136 0.209
## 48 0.272 0.348
## 49 0.220 0.296
## 50 0.012 0.082
## 51 0.137 0.211
## 52 -0.171 -0.103
## 53 -0.178 -0.106
## 54 -0.057 0.009
## 55 0.145 0.221
## 56 0.235 0.309
## 57 0.095 0.166
## 58 0.160 0.235
## 59 0.000 0.000
## 60 0.000 0.000
## 61 0.000 0.000
## 62 0.000 0.000
## 63 0.282 0.346
## 64 0.132 0.176
## 65 0.246 0.306
## 66 0.216 0.275
## 67 0.375 0.451
## 68 0.488 0.555
## 69 0.599 0.654
## 70 0.290 0.339
## 71 0.279 0.336
## 72 0.664 0.756
## 73 0.411 0.476
## 74 0.204 0.247
## 75 0.902 0.917
## 76 0.813 0.836
## 77 0.899 0.915
## 78 0.852 0.872
## 79 0.550 0.598
## 80 0.538 0.581
## 81 0.503 0.550
## 82 0.551 0.594
## 83 0.820 0.841
## 84 0.743 0.773
## 85 0.758 0.790
## 86 0.626 0.663
## 87 1.000 1.000
## 88 0.203 0.240
## 89 0.217 0.249
## 90 0.167 0.199
## [ reached 'max' / getOption("max.print") -- omitted 34 rows ]
reduced<-cfa(bf.reduced, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "sswk~1"))
fitMeasures(reduced, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 2416.081 110.000 0.000 0.966 0.077 0.054
## aic bic
## 171233.833 171714.514
Mc(reduced)
## [1] 0.8499453
summary(reduced, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 88 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 103
## Number of equality constraints 33
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2416.081 1865.567
## Degrees of freedom 110 110
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.295
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 892.070 688.809
## 0 1524.010 1176.759
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.321 0.018 17.874 0.000 0.286
## sspc (.p2.) 0.158 0.011 13.851 0.000 0.136
## ssmk (.p3.) 0.282 0.017 16.726 0.000 0.249
## ssmc (.p4.) 0.248 0.016 15.367 0.000 0.217
## ssao (.p5.) 0.428 0.022 19.177 0.000 0.385
## electronic =~
## ssai (.p6.) 0.256 0.013 19.720 0.000 0.230
## sssi (.p7.) 0.296 0.015 19.514 0.000 0.266
## ssmc (.p8.) 0.146 0.009 16.220 0.000 0.128
## ssei (.p9.) 0.149 0.008 18.019 0.000 0.133
## speed =~
## ssno (.10.) 0.687 0.026 26.592 0.000 0.636
## sscs (.11.) 0.415 0.017 23.709 0.000 0.380
## ssmk (.12.) 0.210 0.010 21.221 0.000 0.191
## g =~
## ssgs (.13.) 0.822 0.011 71.750 0.000 0.799
## ssar (.14.) 0.738 0.012 59.830 0.000 0.714
## sswk (.15.) 0.820 0.012 67.670 0.000 0.797
## sspc (.16.) 0.777 0.012 67.553 0.000 0.755
## ssno (.17.) 0.531 0.013 40.360 0.000 0.505
## sscs (.18.) 0.502 0.012 41.246 0.000 0.478
## ssai (.19.) 0.495 0.011 44.769 0.000 0.473
## sssi (.20.) 0.519 0.011 47.270 0.000 0.497
## ssmk (.21.) 0.740 0.012 60.278 0.000 0.716
## ssmc (.22.) 0.673 0.011 61.329 0.000 0.651
## ssei (.23.) 0.709 0.011 64.326 0.000 0.688
## ssao (.24.) 0.589 0.011 51.351 0.000 0.567
## ci.upper Std.lv Std.all
##
## 0.356 0.321 0.352
## 0.180 0.158 0.172
## 0.315 0.282 0.303
## 0.280 0.248 0.280
## 0.472 0.428 0.454
##
## 0.281 0.256 0.316
## 0.326 0.296 0.363
## 0.163 0.146 0.164
## 0.166 0.149 0.171
##
## 0.737 0.687 0.725
## 0.449 0.415 0.445
## 0.230 0.210 0.226
##
## 0.844 0.822 0.895
## 0.762 0.738 0.809
## 0.844 0.820 0.893
## 0.800 0.777 0.847
## 0.557 0.531 0.561
## 0.526 0.502 0.538
## 0.517 0.495 0.612
## 0.540 0.519 0.636
## 0.764 0.740 0.796
## 0.694 0.673 0.758
## 0.731 0.709 0.813
## 0.612 0.589 0.625
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g 0.000 0.000
## .ssar (.48.) 0.211 0.014 14.933 0.000 0.183
## .sspc 0.339 0.015 22.754 0.000 0.310
## .ssmk (.50.) 0.291 0.015 19.021 0.000 0.261
## .ssmc (.51.) 0.088 0.014 6.343 0.000 0.061
## .ssao (.52.) 0.202 0.017 12.063 0.000 0.169
## .ssai (.53.) -0.076 0.013 -5.927 0.000 -0.101
## .sssi (.54.) -0.080 0.014 -5.861 0.000 -0.107
## .ssei (.55.) 0.032 0.013 2.556 0.011 0.008
## .ssno 0.211 0.017 12.237 0.000 0.177
## .sscs (.57.) 0.289 0.017 17.339 0.000 0.256
## .ssgs (.58.) 0.190 0.013 14.960 0.000 0.165
## .sswk 0.239 0.015 16.208 0.000 0.210
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.239 0.211 0.231
## 0.368 0.339 0.369
## 0.321 0.291 0.314
## 0.115 0.088 0.099
## 0.235 0.202 0.214
## -0.051 -0.076 -0.094
## -0.053 -0.080 -0.099
## 0.057 0.032 0.037
## 0.245 0.211 0.222
## 0.321 0.289 0.310
## 0.215 0.190 0.207
## 0.268 0.239 0.260
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.185 0.009 20.491 0.000 0.167
## .sspc 0.214 0.008 26.441 0.000 0.198
## .ssmk 0.192 0.008 24.625 0.000 0.177
## .ssmc 0.252 0.010 24.525 0.000 0.232
## .ssao 0.360 0.018 19.766 0.000 0.324
## .ssai 0.344 0.012 28.875 0.000 0.320
## .sssi 0.308 0.012 25.204 0.000 0.284
## .ssei 0.236 0.008 28.133 0.000 0.220
## .ssno 0.144 0.027 5.400 0.000 0.092
## .sscs 0.445 0.017 25.826 0.000 0.411
## .ssgs 0.167 0.006 26.450 0.000 0.155
## .sswk 0.171 0.007 25.672 0.000 0.158
## math 1.000 1.000
## electronic 1.000 1.000
## speed 1.000 1.000
## g 1.000 1.000
## ci.upper Std.lv Std.all
## 0.203 0.185 0.222
## 0.230 0.214 0.254
## 0.207 0.192 0.223
## 0.272 0.252 0.320
## 0.395 0.360 0.404
## 0.367 0.344 0.525
## 0.332 0.308 0.463
## 0.253 0.236 0.310
## 0.196 0.144 0.160
## 0.479 0.445 0.512
## 0.179 0.167 0.198
## 0.184 0.171 0.203
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.321 0.018 17.874 0.000 0.286
## sspc (.p2.) 0.158 0.011 13.851 0.000 0.136
## ssmk (.p3.) 0.282 0.017 16.726 0.000 0.249
## ssmc (.p4.) 0.248 0.016 15.367 0.000 0.217
## ssao (.p5.) 0.428 0.022 19.177 0.000 0.385
## electronic =~
## ssai (.p6.) 0.256 0.013 19.720 0.000 0.230
## sssi (.p7.) 0.296 0.015 19.514 0.000 0.266
## ssmc (.p8.) 0.146 0.009 16.220 0.000 0.128
## ssei (.p9.) 0.149 0.008 18.019 0.000 0.133
## speed =~
## ssno (.10.) 0.687 0.026 26.592 0.000 0.636
## sscs (.11.) 0.415 0.017 23.709 0.000 0.380
## ssmk (.12.) 0.210 0.010 21.221 0.000 0.191
## g =~
## ssgs (.13.) 0.822 0.011 71.750 0.000 0.799
## ssar (.14.) 0.738 0.012 59.830 0.000 0.714
## sswk (.15.) 0.820 0.012 67.670 0.000 0.797
## sspc (.16.) 0.777 0.012 67.553 0.000 0.755
## ssno (.17.) 0.531 0.013 40.360 0.000 0.505
## sscs (.18.) 0.502 0.012 41.246 0.000 0.478
## ssai (.19.) 0.495 0.011 44.769 0.000 0.473
## sssi (.20.) 0.519 0.011 47.270 0.000 0.497
## ssmk (.21.) 0.740 0.012 60.278 0.000 0.716
## ssmc (.22.) 0.673 0.011 61.329 0.000 0.651
## ssei (.23.) 0.709 0.011 64.326 0.000 0.688
## ssao (.24.) 0.589 0.011 51.351 0.000 0.567
## ci.upper Std.lv Std.all
##
## 0.356 0.323 0.315
## 0.180 0.159 0.154
## 0.315 0.284 0.278
## 0.280 0.250 0.246
## 0.472 0.432 0.412
##
## 0.281 0.560 0.521
## 0.326 0.649 0.625
## 0.163 0.319 0.314
## 0.166 0.327 0.311
##
## 0.737 0.752 0.710
## 0.449 0.454 0.443
## 0.230 0.230 0.225
##
## 0.844 0.943 0.910
## 0.762 0.847 0.825
## 0.844 0.941 0.908
## 0.800 0.892 0.863
## 0.557 0.609 0.575
## 0.526 0.576 0.561
## 0.517 0.568 0.528
## 0.540 0.595 0.574
## 0.764 0.849 0.831
## 0.694 0.772 0.759
## 0.731 0.814 0.775
## 0.612 0.676 0.646
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## g 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## g 0.000 0.000
## speed ~~
## g 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g 0.000 0.000
## .ssar (.48.) 0.211 0.014 14.933 0.000 0.183
## .sspc 0.020 0.015 1.308 0.191 -0.010
## .ssmk (.50.) 0.291 0.015 19.021 0.000 0.261
## .ssmc (.51.) 0.088 0.014 6.343 0.000 0.061
## .ssao (.52.) 0.202 0.017 12.063 0.000 0.169
## .ssai (.53.) -0.076 0.013 -5.927 0.000 -0.101
## .sssi (.54.) -0.080 0.014 -5.861 0.000 -0.107
## .ssei (.55.) 0.032 0.013 2.556 0.011 0.008
## .ssno 0.604 0.046 13.260 0.000 0.515
## .sscs (.57.) 0.289 0.017 17.339 0.000 0.256
## .ssgs (.58.) 0.190 0.013 14.960 0.000 0.165
## .sswk 0.106 0.015 7.309 0.000 0.078
## math -0.301 0.044 -6.767 0.000 -0.388
## elctrnc 1.697 0.103 16.418 0.000 1.495
## speed -0.951 0.060 -15.835 0.000 -1.068
## ci.upper Std.lv Std.all
## 0.000 0.000 0.000
## 0.239 0.211 0.206
## 0.050 0.020 0.019
## 0.321 0.291 0.285
## 0.115 0.088 0.087
## 0.235 0.202 0.193
## -0.051 -0.076 -0.071
## -0.053 -0.080 -0.077
## 0.057 0.032 0.031
## 0.693 0.604 0.570
## 0.321 0.289 0.281
## 0.215 0.190 0.183
## 0.135 0.106 0.102
## -0.214 -0.299 -0.299
## 1.900 0.775 0.775
## -0.833 -0.868 -0.868
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar 0.232 0.010 22.741 0.000 0.212
## .sspc 0.248 0.009 28.604 0.000 0.231
## .ssmk 0.190 0.008 22.554 0.000 0.173
## .ssmc 0.275 0.011 24.009 0.000 0.252
## .ssao 0.454 0.020 23.138 0.000 0.415
## .ssai 0.521 0.019 26.989 0.000 0.483
## .sssi 0.302 0.016 18.879 0.000 0.271
## .ssei 0.335 0.012 27.459 0.000 0.311
## .ssno 0.186 0.034 5.453 0.000 0.119
## .sscs 0.515 0.021 24.691 0.000 0.474
## .ssgs 0.185 0.007 26.492 0.000 0.171
## .sswk 0.189 0.008 24.514 0.000 0.174
## math 1.016 0.083 12.173 0.000 0.852
## electronic 4.803 0.516 9.308 0.000 3.792
## speed 1.200 0.086 13.923 0.000 1.031
## g 1.317 0.047 27.961 0.000 1.224
## ci.upper Std.lv Std.all
## 0.252 0.232 0.220
## 0.265 0.248 0.232
## 0.206 0.190 0.182
## 0.297 0.275 0.265
## 0.492 0.454 0.413
## 0.559 0.521 0.450
## 0.334 0.302 0.281
## 0.359 0.335 0.303
## 0.253 0.186 0.166
## 0.556 0.515 0.489
## 0.198 0.185 0.172
## 0.204 0.189 0.176
## 1.179 1.000 1.000
## 5.814 1.000 1.000
## 1.368 1.000 1.000
## 1.409 1.000 1.000
tests<-lavTestLRT(configural, metric, scalar2, reduced)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 201.61105 57.55581 42.46979
dfd=tests[2:4,"Df diff"]
dfd
## [1] 20 5 1
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-3503+ 3590 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.05060779 0.05444859 0.10815016
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.04439061 0.05706437
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.04232668 0.06750731
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.08181647 0.13706082
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.577 0.008 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.741 0.255 0.001 0.000
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 0.998 0.960 0.713
tests<-lavTestLRT(configural, metric, scalar2, latent)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 201.61105319 57.55580921 0.03923921
dfd=tests[2:4,"Df diff"]
dfd
## [1] 20 5 1
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-3503+ 3590 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
## Warning in sqrt((ld) * G/(N - G)): NaNs produced
RMSEAD
## [1] 0.05060779 0.05444859 NaN
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.04232668 0.06750731
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] NA 0.02557208
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.741 0.255 0.001 0.000
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 0.157 0.132 0.002 0.000 0.000 0.000
tests<-lavTestLRT(configural, metric, scalar2, strict)
Td=tests[2:4,"Chisq diff"]
Td
## [1] 201.61105 57.55581 217.61233
dfd=tests[2:4,"Df diff"]
dfd
## [1] 20 5 12
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-3503+ 3590 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.05060779 0.05444859 0.06951767
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.04439061 0.05706437
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.04232668 0.06750731
RMSEA.CI(T=Td[3],df=dfd[3],N=N,G=2)
## [1] 0.06159157 0.07775209
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.577 0.008 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.741 0.255 0.001 0.000
round(pvals(T=Td[3],df=dfd[3],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 1.000 0.976 0.018 0.000
tests<-lavTestLRT(configural, metric, scalar)
Td=tests[2:3,"Chisq diff"]
Td
## [1] 201.6111 726.7885
dfd=tests[2:3,"Df diff"]
dfd
## [1] 20 8
lambda<-Td-dfd
ld<-lambda/dfd
G<-2 # number of groups
N<-3503+ 3590 # sample size
RMSEAD<-sqrt((ld)*G/(N-G))
RMSEAD
## [1] 0.05060779 0.15919039
RMSEA.CI(T=Td[1],df=dfd[1],N=N,G=2)
## [1] 0.04439061 0.05706437
RMSEA.CI(T=Td[2],df=dfd[2],N=N,G=2)
## [1] 0.1494990 0.1690784
round(pvals(T=Td[1],df=dfd[1],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1.000 1.000 0.577 0.008 0.000 0.000
round(pvals(T=Td[2],df=dfd[2],N=N,G=2),3)
## RMSEA>0 RMSEA>.01 RMSEA>.05 RMSEA>.06 RMSEA>.08 RMSEA>.10
## 1 1 1 1 1 1
bf.age<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
g ~ agec
'
bf.ageq<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
g ~ c(a,a)*agec
'
bf.age2<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
g ~ agec+agec2
'
bf.age2q<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
g ~ c(a,a)*agec+c(b,b)*agec2
'
sem.age<-sem(bf.age, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "sswk~1"))
fitMeasures(sem.age, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 3457.618 132.000 0.000 0.952 0.084 0.053
## ecvi aic bic
## 0.508 170321.378 170815.792
Mc(sem.age)
## [1] 0.7909958
summary(sem.age, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 81 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 105
## Number of equality constraints 33
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3457.618 2665.997
## Degrees of freedom 132 132
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.297
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1309.684 1009.832
## 0 2147.933 1656.164
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.322 0.016 19.747 0.000 0.290
## sspc (.p2.) 0.162 0.012 14.114 0.000 0.140
## ssmk (.p3.) 0.276 0.015 18.246 0.000 0.247
## ssmc (.p4.) 0.255 0.015 16.899 0.000 0.226
## ssao (.p5.) 0.436 0.020 22.233 0.000 0.398
## electronic =~
## ssai (.p6.) 0.254 0.013 19.736 0.000 0.229
## sssi (.p7.) 0.297 0.015 19.436 0.000 0.267
## ssmc (.p8.) 0.147 0.009 16.144 0.000 0.130
## ssei (.p9.) 0.146 0.008 17.713 0.000 0.130
## speed =~
## ssno (.10.) 0.687 0.026 26.194 0.000 0.635
## sscs (.11.) 0.411 0.018 23.466 0.000 0.376
## ssmk (.12.) 0.207 0.010 21.058 0.000 0.188
## g =~
## ssgs (.13.) 0.765 0.011 68.875 0.000 0.743
## ssar (.14.) 0.688 0.012 57.886 0.000 0.665
## sswk (.15.) 0.767 0.012 65.864 0.000 0.744
## sspc (.16.) 0.724 0.011 64.192 0.000 0.702
## ssno (.17.) 0.498 0.012 40.216 0.000 0.474
## sscs (.18.) 0.471 0.011 41.518 0.000 0.448
## ssai (.19.) 0.465 0.010 45.445 0.000 0.445
## sssi (.20.) 0.486 0.010 46.584 0.000 0.465
## ssmk (.21.) 0.694 0.011 60.397 0.000 0.671
## ssmc (.22.) 0.627 0.011 58.807 0.000 0.606
## ssei (.23.) 0.664 0.011 62.777 0.000 0.643
## ssao (.24.) 0.549 0.011 49.840 0.000 0.527
## ci.upper Std.lv Std.all
##
## 0.354 0.322 0.354
## 0.185 0.162 0.177
## 0.306 0.276 0.298
## 0.285 0.255 0.288
## 0.475 0.436 0.463
##
## 0.280 0.254 0.314
## 0.327 0.297 0.365
## 0.165 0.147 0.166
## 0.162 0.146 0.167
##
## 0.738 0.687 0.726
## 0.445 0.411 0.441
## 0.227 0.207 0.223
##
## 0.787 0.817 0.893
## 0.711 0.735 0.806
## 0.789 0.819 0.893
## 0.747 0.774 0.844
## 0.522 0.532 0.562
## 0.493 0.502 0.540
## 0.485 0.497 0.614
## 0.506 0.519 0.636
## 0.716 0.741 0.798
## 0.648 0.669 0.755
## 0.684 0.709 0.813
## 0.571 0.586 0.622
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.260 0.014 18.686 0.000 0.233
## ci.upper Std.lv Std.all
##
## 0.288 0.244 0.351
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.164 0.016 10.309 0.000 0.133
## .sspc 0.292 0.017 17.640 0.000 0.259
## .ssmk (.48.) 0.246 0.016 15.140 0.000 0.214
## .ssmc (.49.) 0.048 0.015 3.190 0.001 0.019
## .ssao (.50.) 0.171 0.017 9.811 0.000 0.137
## .ssai (.51.) -0.105 0.013 -7.970 0.000 -0.131
## .sssi (.52.) -0.112 0.014 -7.841 0.000 -0.139
## .ssei (.53.) -0.014 0.014 -1.003 0.316 -0.041
## .ssno 0.179 0.017 10.268 0.000 0.144
## .sscs (.55.) 0.259 0.017 15.623 0.000 0.227
## .ssgs (.56.) 0.127 0.016 8.019 0.000 0.096
## .sswk 0.189 0.016 11.534 0.000 0.157
## ci.upper Std.lv Std.all
## 0.196 0.164 0.180
## 0.324 0.292 0.318
## 0.277 0.246 0.265
## 0.078 0.048 0.054
## 0.205 0.171 0.181
## -0.079 -0.105 -0.130
## -0.084 -0.112 -0.137
## 0.013 -0.014 -0.016
## 0.213 0.179 0.189
## 0.292 0.259 0.278
## 0.158 0.127 0.139
## 0.221 0.189 0.206
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.186 0.009 20.947 0.000 0.169
## .sspc 0.215 0.008 26.514 0.000 0.199
## .ssmk 0.193 0.008 25.531 0.000 0.178
## .ssmc 0.251 0.010 24.542 0.000 0.231
## .ssao 0.356 0.018 20.092 0.000 0.321
## .ssai 0.343 0.012 28.861 0.000 0.319
## .sssi 0.307 0.012 25.058 0.000 0.283
## .ssei 0.236 0.008 28.198 0.000 0.219
## .ssno 0.141 0.027 5.207 0.000 0.088
## .sscs 0.446 0.017 25.874 0.000 0.412
## .ssgs 0.169 0.006 26.895 0.000 0.157
## .sswk 0.170 0.007 25.561 0.000 0.157
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.204 0.186 0.224
## 0.231 0.215 0.256
## 0.208 0.193 0.224
## 0.271 0.251 0.319
## 0.390 0.356 0.400
## 0.366 0.343 0.524
## 0.331 0.307 0.462
## 0.252 0.236 0.310
## 0.195 0.141 0.158
## 0.479 0.446 0.514
## 0.181 0.169 0.202
## 0.183 0.170 0.203
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.877 0.877
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.322 0.016 19.747 0.000 0.290
## sspc (.p2.) 0.162 0.012 14.114 0.000 0.140
## ssmk (.p3.) 0.276 0.015 18.246 0.000 0.247
## ssmc (.p4.) 0.255 0.015 16.899 0.000 0.226
## ssao (.p5.) 0.436 0.020 22.233 0.000 0.398
## electronic =~
## ssai (.p6.) 0.254 0.013 19.736 0.000 0.229
## sssi (.p7.) 0.297 0.015 19.436 0.000 0.267
## ssmc (.p8.) 0.147 0.009 16.144 0.000 0.130
## ssei (.p9.) 0.146 0.008 17.713 0.000 0.130
## speed =~
## ssno (.10.) 0.687 0.026 26.194 0.000 0.635
## sscs (.11.) 0.411 0.018 23.466 0.000 0.376
## ssmk (.12.) 0.207 0.010 21.058 0.000 0.188
## g =~
## ssgs (.13.) 0.765 0.011 68.875 0.000 0.743
## ssar (.14.) 0.688 0.012 57.886 0.000 0.665
## sswk (.15.) 0.767 0.012 65.864 0.000 0.744
## sspc (.16.) 0.724 0.011 64.192 0.000 0.702
## ssno (.17.) 0.498 0.012 40.216 0.000 0.474
## sscs (.18.) 0.471 0.011 41.518 0.000 0.448
## ssai (.19.) 0.465 0.010 45.445 0.000 0.445
## sssi (.20.) 0.486 0.010 46.584 0.000 0.465
## ssmk (.21.) 0.694 0.011 60.397 0.000 0.671
## ssmc (.22.) 0.627 0.011 58.807 0.000 0.606
## ssei (.23.) 0.664 0.011 62.777 0.000 0.643
## ssao (.24.) 0.549 0.011 49.840 0.000 0.527
## ci.upper Std.lv Std.all
##
## 0.354 0.322 0.315
## 0.185 0.162 0.158
## 0.306 0.276 0.271
## 0.285 0.255 0.251
## 0.475 0.436 0.417
##
## 0.280 0.554 0.516
## 0.327 0.648 0.625
## 0.165 0.321 0.316
## 0.162 0.318 0.303
##
## 0.738 0.753 0.711
## 0.445 0.450 0.439
## 0.227 0.227 0.223
##
## 0.787 0.938 0.908
## 0.711 0.843 0.823
## 0.789 0.939 0.908
## 0.747 0.888 0.861
## 0.522 0.610 0.576
## 0.493 0.576 0.562
## 0.485 0.570 0.531
## 0.506 0.595 0.574
## 0.716 0.850 0.833
## 0.648 0.768 0.756
## 0.684 0.813 0.776
## 0.571 0.673 0.643
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.299 0.016 18.744 0.000 0.267
## ci.upper Std.lv Std.all
##
## 0.330 0.244 0.350
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.164 0.016 10.309 0.000 0.133
## .sspc -0.041 0.018 -2.258 0.024 -0.077
## .ssmk (.48.) 0.246 0.016 15.140 0.000 0.214
## .ssmc (.49.) 0.048 0.015 3.190 0.001 0.019
## .ssao (.50.) 0.171 0.017 9.811 0.000 0.137
## .ssai (.51.) -0.105 0.013 -7.970 0.000 -0.131
## .sssi (.52.) -0.112 0.014 -7.841 0.000 -0.139
## .ssei (.53.) -0.014 0.014 -1.003 0.316 -0.041
## .ssno 0.592 0.047 12.465 0.000 0.499
## .sscs (.55.) 0.259 0.017 15.623 0.000 0.227
## .ssgs (.56.) 0.127 0.016 8.019 0.000 0.096
## .sswk 0.030 0.019 1.565 0.118 -0.007
## math -0.359 0.043 -8.271 0.000 -0.445
## elctrnc 1.631 0.101 16.220 0.000 1.434
## speed -1.005 0.063 -15.966 0.000 -1.128
## .g 0.215 0.031 6.908 0.000 0.154
## ci.upper Std.lv Std.all
## 0.196 0.164 0.161
## -0.005 -0.041 -0.040
## 0.277 0.246 0.241
## 0.078 0.048 0.047
## 0.205 0.171 0.163
## -0.079 -0.105 -0.098
## -0.084 -0.112 -0.108
## 0.013 -0.014 -0.013
## 0.685 0.592 0.559
## 0.292 0.259 0.253
## 0.158 0.127 0.123
## 0.067 0.030 0.029
## -0.274 -0.359 -0.359
## 1.828 0.749 0.749
## -0.882 -0.916 -0.916
## 0.276 0.175 0.175
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.233 0.010 23.296 0.000 0.214
## .sspc 0.249 0.009 28.660 0.000 0.232
## .ssmk 0.191 0.008 23.461 0.000 0.175
## .ssmc 0.273 0.011 23.983 0.000 0.251
## .ssao 0.450 0.019 23.867 0.000 0.413
## .ssai 0.522 0.019 27.023 0.000 0.484
## .sssi 0.300 0.016 18.537 0.000 0.269
## .ssei 0.336 0.012 27.561 0.000 0.312
## .ssno 0.182 0.035 5.245 0.000 0.114
## .sscs 0.516 0.021 24.789 0.000 0.476
## .ssgs 0.188 0.007 26.995 0.000 0.174
## .sswk 0.189 0.008 24.853 0.000 0.174
## electronic 4.744 0.510 9.295 0.000 3.743
## speed 1.203 0.087 13.847 0.000 1.033
## .g 1.317 0.048 27.178 0.000 1.222
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.253 0.233 0.223
## 0.266 0.249 0.234
## 0.207 0.191 0.183
## 0.295 0.273 0.265
## 0.487 0.450 0.412
## 0.560 0.522 0.453
## 0.332 0.300 0.280
## 0.360 0.336 0.306
## 0.250 0.182 0.162
## 0.557 0.516 0.491
## 0.201 0.188 0.176
## 0.204 0.189 0.176
## 5.744 1.000 1.000
## 1.373 1.000 1.000
## 1.412 0.877 0.877
sem.ageq<-sem(bf.ageq, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "sswk~1"))
fitMeasures(sem.ageq, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 3461.936 133.000 0.000 0.952 0.084 0.055
## ecvi aic bic
## 0.508 170323.696 170811.243
Mc(sem.ageq)
## [1] 0.7908108
summary(sem.ageq, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 84 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 105
## Number of equality constraints 34
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3461.936 2670.778
## Degrees of freedom 133 133
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.296
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1311.035 1011.423
## 0 2150.901 1659.355
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.322 0.016 19.743 0.000 0.290
## sspc (.p2.) 0.162 0.012 14.111 0.000 0.140
## ssmk (.p3.) 0.276 0.015 18.242 0.000 0.247
## ssmc (.p4.) 0.255 0.015 16.905 0.000 0.226
## ssao (.p5.) 0.436 0.020 22.237 0.000 0.398
## electronic =~
## ssai (.p6.) 0.254 0.013 19.742 0.000 0.229
## sssi (.p7.) 0.297 0.015 19.430 0.000 0.267
## ssmc (.p8.) 0.147 0.009 16.143 0.000 0.130
## ssei (.p9.) 0.146 0.008 17.723 0.000 0.130
## speed =~
## ssno (.10.) 0.687 0.026 26.175 0.000 0.635
## sscs (.11.) 0.411 0.017 23.467 0.000 0.376
## ssmk (.12.) 0.207 0.010 21.063 0.000 0.188
## g =~
## ssgs (.13.) 0.766 0.011 68.792 0.000 0.744
## ssar (.14.) 0.688 0.012 57.836 0.000 0.665
## sswk (.15.) 0.767 0.012 65.823 0.000 0.744
## sspc (.16.) 0.725 0.011 64.156 0.000 0.703
## ssno (.17.) 0.498 0.012 40.196 0.000 0.474
## sscs (.18.) 0.471 0.011 41.501 0.000 0.448
## ssai (.19.) 0.465 0.010 45.404 0.000 0.445
## sssi (.20.) 0.486 0.010 46.537 0.000 0.465
## ssmk (.21.) 0.694 0.011 60.340 0.000 0.671
## ssmc (.22.) 0.627 0.011 58.734 0.000 0.606
## ssei (.23.) 0.664 0.011 62.670 0.000 0.643
## ssao (.24.) 0.549 0.011 49.810 0.000 0.528
## ci.upper Std.lv Std.all
##
## 0.354 0.322 0.352
## 0.185 0.162 0.176
## 0.306 0.276 0.296
## 0.285 0.255 0.287
## 0.475 0.436 0.461
##
## 0.280 0.254 0.313
## 0.327 0.297 0.363
## 0.165 0.147 0.166
## 0.162 0.146 0.167
##
## 0.738 0.687 0.724
## 0.445 0.411 0.440
## 0.227 0.207 0.222
##
## 0.787 0.824 0.895
## 0.712 0.741 0.809
## 0.790 0.826 0.895
## 0.747 0.780 0.846
## 0.522 0.536 0.565
## 0.493 0.507 0.543
## 0.485 0.501 0.617
## 0.506 0.523 0.639
## 0.716 0.747 0.801
## 0.648 0.675 0.758
## 0.684 0.715 0.816
## 0.571 0.591 0.625
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.277 0.011 25.480 0.000 0.256
## ci.upper Std.lv Std.all
##
## 0.299 0.257 0.371
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.165 0.016 10.337 0.000 0.134
## .sspc 0.292 0.017 17.657 0.000 0.260
## .ssmk (.48.) 0.246 0.016 15.214 0.000 0.214
## .ssmc (.49.) 0.049 0.015 3.218 0.001 0.019
## .ssao (.50.) 0.171 0.017 9.828 0.000 0.137
## .ssai (.51.) -0.105 0.013 -7.960 0.000 -0.131
## .sssi (.52.) -0.111 0.014 -7.816 0.000 -0.139
## .ssei (.53.) -0.013 0.014 -0.975 0.329 -0.041
## .ssno 0.179 0.017 10.303 0.000 0.145
## .sscs (.55.) 0.259 0.017 15.682 0.000 0.227
## .ssgs (.56.) 0.128 0.016 8.056 0.000 0.097
## .sswk 0.190 0.016 11.579 0.000 0.158
## ci.upper Std.lv Std.all
## 0.196 0.165 0.180
## 0.325 0.292 0.317
## 0.278 0.246 0.264
## 0.078 0.049 0.055
## 0.205 0.171 0.181
## -0.079 -0.105 -0.129
## -0.083 -0.111 -0.136
## 0.014 -0.013 -0.015
## 0.213 0.179 0.188
## 0.292 0.259 0.278
## 0.159 0.128 0.139
## 0.222 0.190 0.206
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.186 0.009 20.949 0.000 0.169
## .sspc 0.215 0.008 26.511 0.000 0.199
## .ssmk 0.193 0.008 25.543 0.000 0.178
## .ssmc 0.251 0.010 24.542 0.000 0.231
## .ssao 0.356 0.018 20.090 0.000 0.321
## .ssai 0.343 0.012 28.859 0.000 0.319
## .sssi 0.308 0.012 25.065 0.000 0.283
## .ssei 0.236 0.008 28.200 0.000 0.219
## .ssno 0.142 0.027 5.215 0.000 0.088
## .sscs 0.445 0.017 25.879 0.000 0.412
## .ssgs 0.169 0.006 26.926 0.000 0.157
## .sswk 0.170 0.007 25.562 0.000 0.157
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.204 0.186 0.222
## 0.231 0.215 0.253
## 0.207 0.193 0.221
## 0.271 0.251 0.316
## 0.390 0.356 0.397
## 0.366 0.343 0.521
## 0.332 0.308 0.459
## 0.252 0.236 0.307
## 0.195 0.142 0.157
## 0.479 0.445 0.512
## 0.182 0.169 0.199
## 0.183 0.170 0.200
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.863 0.863
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.322 0.016 19.743 0.000 0.290
## sspc (.p2.) 0.162 0.012 14.111 0.000 0.140
## ssmk (.p3.) 0.276 0.015 18.242 0.000 0.247
## ssmc (.p4.) 0.255 0.015 16.905 0.000 0.226
## ssao (.p5.) 0.436 0.020 22.237 0.000 0.398
## electronic =~
## ssai (.p6.) 0.254 0.013 19.742 0.000 0.229
## sssi (.p7.) 0.297 0.015 19.430 0.000 0.267
## ssmc (.p8.) 0.147 0.009 16.143 0.000 0.130
## ssei (.p9.) 0.146 0.008 17.723 0.000 0.130
## speed =~
## ssno (.10.) 0.687 0.026 26.175 0.000 0.635
## sscs (.11.) 0.411 0.017 23.467 0.000 0.376
## ssmk (.12.) 0.207 0.010 21.063 0.000 0.188
## g =~
## ssgs (.13.) 0.766 0.011 68.792 0.000 0.744
## ssar (.14.) 0.688 0.012 57.836 0.000 0.665
## sswk (.15.) 0.767 0.012 65.823 0.000 0.744
## sspc (.16.) 0.725 0.011 64.156 0.000 0.703
## ssno (.17.) 0.498 0.012 40.196 0.000 0.474
## sscs (.18.) 0.471 0.011 41.501 0.000 0.448
## ssai (.19.) 0.465 0.010 45.404 0.000 0.445
## sssi (.20.) 0.486 0.010 46.537 0.000 0.465
## ssmk (.21.) 0.694 0.011 60.340 0.000 0.671
## ssmc (.22.) 0.627 0.011 58.734 0.000 0.606
## ssei (.23.) 0.664 0.011 62.670 0.000 0.643
## ssao (.24.) 0.549 0.011 49.810 0.000 0.528
## ci.upper Std.lv Std.all
##
## 0.354 0.322 0.317
## 0.185 0.162 0.158
## 0.306 0.276 0.272
## 0.285 0.255 0.253
## 0.475 0.436 0.419
##
## 0.280 0.554 0.517
## 0.327 0.648 0.627
## 0.165 0.321 0.318
## 0.162 0.318 0.305
##
## 0.738 0.754 0.713
## 0.445 0.451 0.440
## 0.227 0.227 0.224
##
## 0.787 0.930 0.907
## 0.712 0.836 0.821
## 0.790 0.931 0.906
## 0.747 0.880 0.859
## 0.522 0.605 0.573
## 0.493 0.572 0.559
## 0.485 0.565 0.527
## 0.506 0.590 0.571
## 0.716 0.843 0.831
## 0.648 0.762 0.754
## 0.684 0.806 0.773
## 0.571 0.667 0.640
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.277 0.011 25.480 0.000 0.256
## ci.upper Std.lv Std.all
##
## 0.299 0.228 0.328
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.46.) 0.165 0.016 10.337 0.000 0.134
## .sspc -0.041 0.018 -2.232 0.026 -0.077
## .ssmk (.48.) 0.246 0.016 15.214 0.000 0.214
## .ssmc (.49.) 0.049 0.015 3.218 0.001 0.019
## .ssao (.50.) 0.171 0.017 9.828 0.000 0.137
## .ssai (.51.) -0.105 0.013 -7.960 0.000 -0.131
## .sssi (.52.) -0.111 0.014 -7.816 0.000 -0.139
## .ssei (.53.) -0.013 0.014 -0.975 0.329 -0.041
## .ssno 0.592 0.047 12.480 0.000 0.499
## .sscs (.55.) 0.259 0.017 15.682 0.000 0.227
## .ssgs (.56.) 0.128 0.016 8.056 0.000 0.097
## .sswk 0.030 0.019 1.595 0.111 -0.007
## math -0.359 0.043 -8.270 0.000 -0.445
## elctrnc 1.631 0.101 16.221 0.000 1.434
## speed -1.005 0.063 -15.965 0.000 -1.129
## .g 0.213 0.031 6.870 0.000 0.152
## ci.upper Std.lv Std.all
## 0.196 0.165 0.162
## -0.005 -0.041 -0.040
## 0.278 0.246 0.243
## 0.078 0.049 0.048
## 0.205 0.171 0.164
## -0.079 -0.105 -0.098
## -0.083 -0.111 -0.108
## 0.014 -0.013 -0.013
## 0.685 0.592 0.561
## 0.292 0.259 0.254
## 0.159 0.128 0.124
## 0.067 0.030 0.029
## -0.274 -0.359 -0.359
## 1.828 0.748 0.748
## -0.882 -0.916 -0.916
## 0.273 0.175 0.175
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.233 0.010 23.293 0.000 0.214
## .sspc 0.249 0.009 28.647 0.000 0.232
## .ssmk 0.191 0.008 23.472 0.000 0.175
## .ssmc 0.273 0.011 23.991 0.000 0.251
## .ssao 0.450 0.019 23.861 0.000 0.413
## .ssai 0.522 0.019 27.013 0.000 0.484
## .sssi 0.300 0.016 18.540 0.000 0.269
## .ssei 0.336 0.012 27.556 0.000 0.312
## .ssno 0.182 0.035 5.240 0.000 0.114
## .sscs 0.516 0.021 24.785 0.000 0.476
## .ssgs 0.187 0.007 26.973 0.000 0.174
## .sswk 0.189 0.008 24.838 0.000 0.174
## electronic 4.751 0.511 9.292 0.000 3.749
## speed 1.204 0.087 13.848 0.000 1.034
## .g 1.317 0.048 27.168 0.000 1.222
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.253 0.233 0.225
## 0.266 0.249 0.237
## 0.207 0.191 0.185
## 0.295 0.273 0.267
## 0.487 0.450 0.414
## 0.560 0.522 0.455
## 0.332 0.300 0.281
## 0.360 0.336 0.309
## 0.249 0.182 0.163
## 0.557 0.516 0.494
## 0.201 0.187 0.178
## 0.204 0.189 0.179
## 5.753 1.000 1.000
## 1.375 1.000 1.000
## 1.412 0.892 0.892
sem.age2<-sem(bf.age2, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "sswk~1"))
fitMeasures(sem.age2, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 3574.176 154.000 0.000 0.951 0.079 0.050
## ecvi aic bic
## 0.525 170303.032 170811.180
Mc(sem.age2)
## [1] 0.7857401
summary(sem.age2, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 87 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 107
## Number of equality constraints 33
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3574.176 2760.485
## Degrees of freedom 154 154
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.295
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1387.460 1071.593
## 0 2186.716 1688.892
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.322 0.016 19.681 0.000 0.290
## sspc (.p2.) 0.162 0.012 14.105 0.000 0.140
## ssmk (.p3.) 0.276 0.015 18.188 0.000 0.246
## ssmc (.p4.) 0.255 0.015 16.894 0.000 0.226
## ssao (.p5.) 0.436 0.020 22.207 0.000 0.398
## electronic =~
## ssai (.p6.) 0.255 0.013 19.742 0.000 0.229
## sssi (.p7.) 0.298 0.015 19.450 0.000 0.268
## ssmc (.p8.) 0.148 0.009 16.152 0.000 0.130
## ssei (.p9.) 0.146 0.008 17.716 0.000 0.130
## speed =~
## ssno (.10.) 0.687 0.026 26.170 0.000 0.635
## sscs (.11.) 0.410 0.018 23.449 0.000 0.376
## ssmk (.12.) 0.207 0.010 21.053 0.000 0.188
## g =~
## ssgs (.13.) 0.763 0.011 68.471 0.000 0.741
## ssar (.14.) 0.686 0.012 57.730 0.000 0.663
## sswk (.15.) 0.764 0.012 65.497 0.000 0.741
## sspc (.16.) 0.722 0.011 64.070 0.000 0.700
## ssno (.17.) 0.497 0.012 40.263 0.000 0.472
## sscs (.18.) 0.469 0.011 41.437 0.000 0.447
## ssai (.19.) 0.464 0.010 45.244 0.000 0.444
## sssi (.20.) 0.484 0.010 46.509 0.000 0.464
## ssmk (.21.) 0.692 0.011 60.348 0.000 0.669
## ssmc (.22.) 0.625 0.011 58.647 0.000 0.604
## ssei (.23.) 0.662 0.011 62.500 0.000 0.641
## ssao (.24.) 0.548 0.011 49.714 0.000 0.526
## ci.upper Std.lv Std.all
##
## 0.354 0.322 0.354
## 0.185 0.162 0.177
## 0.305 0.276 0.297
## 0.285 0.255 0.288
## 0.475 0.436 0.462
##
## 0.280 0.255 0.315
## 0.327 0.298 0.365
## 0.166 0.148 0.167
## 0.162 0.146 0.168
##
## 0.738 0.687 0.725
## 0.445 0.410 0.441
## 0.226 0.207 0.223
##
## 0.785 0.817 0.893
## 0.709 0.735 0.806
## 0.787 0.819 0.893
## 0.744 0.774 0.844
## 0.521 0.532 0.562
## 0.491 0.503 0.540
## 0.484 0.497 0.614
## 0.505 0.519 0.636
## 0.714 0.741 0.799
## 0.646 0.669 0.755
## 0.682 0.709 0.813
## 0.569 0.586 0.622
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.257 0.014 18.257 0.000 0.230
## agec2 -0.041 0.010 -3.942 0.000 -0.061
## ci.upper Std.lv Std.all
##
## 0.285 0.240 0.346
## -0.021 -0.038 -0.072
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.49.) 0.223 0.021 10.445 0.000 0.181
## .sspc 0.353 0.023 15.681 0.000 0.309
## .ssmk (.51.) 0.304 0.022 13.857 0.000 0.261
## .ssmc (.52.) 0.101 0.020 5.068 0.000 0.062
## .ssao (.53.) 0.217 0.021 10.279 0.000 0.176
## .ssai (.54.) -0.066 0.016 -3.993 0.000 -0.098
## .sssi (.55.) -0.070 0.018 -3.985 0.000 -0.105
## .ssei (.56.) 0.042 0.020 2.151 0.031 0.004
## .ssno 0.221 0.020 10.871 0.000 0.181
## .sscs (.58.) 0.299 0.019 15.462 0.000 0.261
## .ssgs (.59.) 0.192 0.023 8.492 0.000 0.148
## .sswk 0.254 0.023 10.995 0.000 0.209
## ci.upper Std.lv Std.all
## 0.264 0.223 0.244
## 0.397 0.353 0.385
## 0.347 0.304 0.328
## 0.140 0.101 0.114
## 0.259 0.217 0.230
## -0.034 -0.066 -0.081
## -0.036 -0.070 -0.086
## 0.081 0.042 0.049
## 0.260 0.221 0.233
## 0.337 0.299 0.321
## 0.236 0.192 0.210
## 0.299 0.254 0.277
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.186 0.009 20.935 0.000 0.169
## .sspc 0.215 0.008 26.555 0.000 0.199
## .ssmk 0.193 0.008 25.551 0.000 0.178
## .ssmc 0.251 0.010 24.530 0.000 0.231
## .ssao 0.356 0.018 20.059 0.000 0.321
## .ssai 0.343 0.012 28.848 0.000 0.320
## .sssi 0.307 0.012 25.058 0.000 0.283
## .ssei 0.236 0.008 28.211 0.000 0.219
## .ssno 0.141 0.027 5.209 0.000 0.088
## .sscs 0.446 0.017 25.877 0.000 0.412
## .ssgs 0.169 0.006 26.897 0.000 0.157
## .sswk 0.170 0.007 25.595 0.000 0.157
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.204 0.186 0.225
## 0.231 0.215 0.256
## 0.208 0.193 0.224
## 0.271 0.251 0.319
## 0.390 0.356 0.400
## 0.366 0.343 0.524
## 0.331 0.307 0.462
## 0.252 0.236 0.310
## 0.195 0.141 0.158
## 0.479 0.446 0.514
## 0.181 0.169 0.202
## 0.183 0.170 0.203
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.872 0.872
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.322 0.016 19.681 0.000 0.290
## sspc (.p2.) 0.162 0.012 14.105 0.000 0.140
## ssmk (.p3.) 0.276 0.015 18.188 0.000 0.246
## ssmc (.p4.) 0.255 0.015 16.894 0.000 0.226
## ssao (.p5.) 0.436 0.020 22.207 0.000 0.398
## electronic =~
## ssai (.p6.) 0.255 0.013 19.742 0.000 0.229
## sssi (.p7.) 0.298 0.015 19.450 0.000 0.268
## ssmc (.p8.) 0.148 0.009 16.152 0.000 0.130
## ssei (.p9.) 0.146 0.008 17.716 0.000 0.130
## speed =~
## ssno (.10.) 0.687 0.026 26.170 0.000 0.635
## sscs (.11.) 0.410 0.018 23.449 0.000 0.376
## ssmk (.12.) 0.207 0.010 21.053 0.000 0.188
## g =~
## ssgs (.13.) 0.763 0.011 68.471 0.000 0.741
## ssar (.14.) 0.686 0.012 57.730 0.000 0.663
## sswk (.15.) 0.764 0.012 65.497 0.000 0.741
## sspc (.16.) 0.722 0.011 64.070 0.000 0.700
## ssno (.17.) 0.497 0.012 40.263 0.000 0.472
## sscs (.18.) 0.469 0.011 41.437 0.000 0.447
## ssai (.19.) 0.464 0.010 45.244 0.000 0.444
## sssi (.20.) 0.484 0.010 46.509 0.000 0.464
## ssmk (.21.) 0.692 0.011 60.348 0.000 0.669
## ssmc (.22.) 0.625 0.011 58.647 0.000 0.604
## ssei (.23.) 0.662 0.011 62.500 0.000 0.641
## ssao (.24.) 0.548 0.011 49.714 0.000 0.526
## ci.upper Std.lv Std.all
##
## 0.354 0.322 0.315
## 0.185 0.162 0.157
## 0.305 0.276 0.270
## 0.285 0.255 0.251
## 0.475 0.436 0.417
##
## 0.280 0.554 0.516
## 0.327 0.648 0.625
## 0.166 0.321 0.316
## 0.162 0.318 0.303
##
## 0.738 0.753 0.711
## 0.445 0.450 0.439
## 0.226 0.227 0.223
##
## 0.785 0.937 0.908
## 0.709 0.843 0.823
## 0.787 0.939 0.907
## 0.744 0.888 0.861
## 0.521 0.610 0.576
## 0.491 0.577 0.562
## 0.484 0.570 0.531
## 0.505 0.595 0.574
## 0.714 0.850 0.833
## 0.646 0.768 0.756
## 0.682 0.813 0.776
## 0.569 0.673 0.644
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec 0.297 0.016 18.348 0.000 0.266
## agec2 -0.019 0.012 -1.652 0.099 -0.043
## ci.upper Std.lv Std.all
##
## 0.329 0.242 0.348
## 0.004 -0.016 -0.030
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.49.) 0.223 0.021 10.445 0.000 0.181
## .sspc 0.020 0.024 0.840 0.401 -0.027
## .ssmk (.51.) 0.304 0.022 13.857 0.000 0.261
## .ssmc (.52.) 0.101 0.020 5.068 0.000 0.062
## .ssao (.53.) 0.217 0.021 10.279 0.000 0.176
## .ssai (.54.) -0.066 0.016 -3.993 0.000 -0.098
## .sssi (.55.) -0.070 0.018 -3.985 0.000 -0.105
## .ssei (.56.) 0.042 0.020 2.151 0.031 0.004
## .ssno 0.634 0.049 12.997 0.000 0.539
## .sscs (.58.) 0.299 0.019 15.462 0.000 0.261
## .ssgs (.59.) 0.192 0.023 8.492 0.000 0.148
## .sswk 0.095 0.025 3.811 0.000 0.046
## math -0.360 0.043 -8.277 0.000 -0.445
## elctrnc 1.630 0.100 16.226 0.000 1.433
## speed -1.006 0.063 -15.964 0.000 -1.130
## .g 0.171 0.044 3.880 0.000 0.085
## ci.upper Std.lv Std.all
## 0.264 0.223 0.217
## 0.067 0.020 0.019
## 0.347 0.304 0.298
## 0.140 0.101 0.100
## 0.259 0.217 0.208
## -0.034 -0.066 -0.061
## -0.036 -0.070 -0.068
## 0.081 0.042 0.040
## 0.730 0.634 0.599
## 0.337 0.299 0.292
## 0.236 0.192 0.186
## 0.143 0.095 0.091
## -0.275 -0.360 -0.360
## 1.827 0.749 0.749
## -0.883 -0.917 -0.917
## 0.257 0.139 0.139
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.233 0.010 23.280 0.000 0.214
## .sspc 0.249 0.009 28.666 0.000 0.232
## .ssmk 0.191 0.008 23.476 0.000 0.175
## .ssmc 0.273 0.011 23.970 0.000 0.251
## .ssao 0.450 0.019 23.836 0.000 0.413
## .ssai 0.522 0.019 27.019 0.000 0.484
## .sssi 0.301 0.016 18.545 0.000 0.269
## .ssei 0.336 0.012 27.562 0.000 0.312
## .ssno 0.182 0.035 5.239 0.000 0.114
## .sscs 0.516 0.021 24.789 0.000 0.476
## .ssgs 0.188 0.007 27.016 0.000 0.174
## .sswk 0.189 0.008 24.865 0.000 0.174
## electronic 4.738 0.509 9.299 0.000 3.739
## speed 1.204 0.087 13.841 0.000 1.033
## .g 1.323 0.049 27.098 0.000 1.227
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.253 0.233 0.223
## 0.266 0.249 0.234
## 0.207 0.191 0.183
## 0.295 0.273 0.265
## 0.487 0.450 0.412
## 0.560 0.522 0.453
## 0.332 0.301 0.280
## 0.360 0.336 0.306
## 0.250 0.182 0.162
## 0.557 0.516 0.491
## 0.201 0.188 0.176
## 0.204 0.189 0.176
## 5.736 1.000 1.000
## 1.374 1.000 1.000
## 1.419 0.876 0.876
sem.age2q<-sem(bf.age2q, data=dgroup, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "sswk~1"))
fitMeasures(sem.age2q, c("chisq", "df", "pvalue", "cfi", "rmsea", "srmr", "ecvi", "aic", "bic"))
## chisq df pvalue cfi rmsea srmr
## 3580.732 156.000 0.000 0.951 0.079 0.052
## ecvi aic bic
## 0.525 170305.588 170800.002
Mc(sem.age2q)
## [1] 0.7854878
summary(sem.age2q, standardized=T, ci=T)
## lavaan 0.6-18 ended normally after 85 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 107
## Number of equality constraints 35
##
## Number of observations per group:
## 1 3503
## 0 3590
## Sampling weights variable sweight
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3580.732 2767.620
## Degrees of freedom 156 156
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.294
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 1 1389.827 1074.225
## 0 2190.906 1693.395
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.322 0.016 19.684 0.000 0.290
## sspc (.p2.) 0.162 0.012 14.108 0.000 0.140
## ssmk (.p3.) 0.276 0.015 18.189 0.000 0.246
## ssmc (.p4.) 0.255 0.015 16.899 0.000 0.226
## ssao (.p5.) 0.436 0.020 22.213 0.000 0.398
## electronic =~
## ssai (.p6.) 0.254 0.013 19.746 0.000 0.229
## sssi (.p7.) 0.297 0.015 19.437 0.000 0.267
## ssmc (.p8.) 0.148 0.009 16.148 0.000 0.130
## ssei (.p9.) 0.146 0.008 17.724 0.000 0.130
## speed =~
## ssno (.10.) 0.686 0.026 26.155 0.000 0.635
## sscs (.11.) 0.410 0.017 23.455 0.000 0.376
## ssmk (.12.) 0.207 0.010 21.059 0.000 0.188
## g =~
## ssgs (.13.) 0.763 0.011 68.419 0.000 0.742
## ssar (.14.) 0.686 0.012 57.704 0.000 0.663
## sswk (.15.) 0.765 0.012 65.501 0.000 0.742
## sspc (.16.) 0.723 0.011 64.060 0.000 0.701
## ssno (.17.) 0.497 0.012 40.246 0.000 0.473
## sscs (.18.) 0.469 0.011 41.422 0.000 0.447
## ssai (.19.) 0.464 0.010 45.214 0.000 0.444
## sssi (.20.) 0.484 0.010 46.489 0.000 0.464
## ssmk (.21.) 0.692 0.011 60.297 0.000 0.670
## ssmc (.22.) 0.625 0.011 58.610 0.000 0.604
## ssei (.23.) 0.662 0.011 62.404 0.000 0.641
## ssao (.24.) 0.548 0.011 49.698 0.000 0.526
## ci.upper Std.lv Std.all
##
## 0.354 0.322 0.352
## 0.185 0.162 0.176
## 0.305 0.276 0.296
## 0.285 0.255 0.287
## 0.475 0.436 0.461
##
## 0.280 0.254 0.314
## 0.327 0.297 0.364
## 0.165 0.148 0.166
## 0.162 0.146 0.167
##
## 0.738 0.686 0.724
## 0.445 0.410 0.440
## 0.226 0.207 0.222
##
## 0.785 0.823 0.895
## 0.710 0.740 0.809
## 0.788 0.825 0.894
## 0.745 0.780 0.846
## 0.521 0.536 0.565
## 0.492 0.506 0.543
## 0.484 0.500 0.617
## 0.505 0.522 0.639
## 0.715 0.747 0.801
## 0.646 0.674 0.758
## 0.683 0.714 0.815
## 0.569 0.591 0.625
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.275 0.011 24.991 0.000 0.253
## agec2 (b) -0.032 0.008 -4.036 0.000 -0.047
## ci.upper Std.lv Std.all
##
## 0.296 0.255 0.367
## -0.016 -0.029 -0.055
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.49.) 0.210 0.019 10.936 0.000 0.172
## .sspc 0.339 0.020 16.827 0.000 0.300
## .ssmk (.51.) 0.291 0.020 14.823 0.000 0.253
## .ssmc (.52.) 0.090 0.018 4.958 0.000 0.054
## .ssao (.53.) 0.207 0.020 10.521 0.000 0.168
## .ssai (.54.) -0.075 0.015 -4.927 0.000 -0.104
## .sssi (.55.) -0.079 0.016 -4.872 0.000 -0.111
## .ssei (.56.) 0.030 0.017 1.714 0.086 -0.004
## .ssno 0.211 0.019 11.062 0.000 0.174
## .sscs (.58.) 0.290 0.018 15.952 0.000 0.255
## .ssgs (.59.) 0.178 0.020 8.898 0.000 0.138
## .sswk 0.240 0.021 11.697 0.000 0.200
## ci.upper Std.lv Std.all
## 0.247 0.210 0.229
## 0.379 0.339 0.368
## 0.330 0.291 0.313
## 0.125 0.090 0.101
## 0.246 0.207 0.219
## -0.045 -0.075 -0.092
## -0.047 -0.079 -0.097
## 0.064 0.030 0.034
## 0.249 0.211 0.223
## 0.326 0.290 0.311
## 0.217 0.178 0.193
## 0.280 0.240 0.260
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.186 0.009 20.937 0.000 0.169
## .sspc 0.215 0.008 26.542 0.000 0.199
## .ssmk 0.193 0.008 25.562 0.000 0.178
## .ssmc 0.251 0.010 24.530 0.000 0.231
## .ssao 0.356 0.018 20.061 0.000 0.321
## .ssai 0.343 0.012 28.849 0.000 0.319
## .sssi 0.307 0.012 25.064 0.000 0.283
## .ssei 0.236 0.008 28.209 0.000 0.219
## .ssno 0.142 0.027 5.218 0.000 0.088
## .sscs 0.445 0.017 25.880 0.000 0.412
## .ssgs 0.169 0.006 26.929 0.000 0.157
## .sswk 0.170 0.007 25.590 0.000 0.157
## electronic 1.000 1.000
## speed 1.000 1.000
## .g 1.000 1.000
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.204 0.186 0.222
## 0.231 0.215 0.253
## 0.207 0.193 0.222
## 0.271 0.251 0.316
## 0.390 0.356 0.397
## 0.366 0.343 0.521
## 0.332 0.307 0.460
## 0.252 0.236 0.307
## 0.195 0.142 0.157
## 0.479 0.445 0.512
## 0.182 0.169 0.200
## 0.183 0.170 0.200
## 1.000 1.000 1.000
## 1.000 1.000 1.000
## 1.000 0.860 0.860
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math =~
## ssar (.p1.) 0.322 0.016 19.684 0.000 0.290
## sspc (.p2.) 0.162 0.012 14.108 0.000 0.140
## ssmk (.p3.) 0.276 0.015 18.189 0.000 0.246
## ssmc (.p4.) 0.255 0.015 16.899 0.000 0.226
## ssao (.p5.) 0.436 0.020 22.213 0.000 0.398
## electronic =~
## ssai (.p6.) 0.254 0.013 19.746 0.000 0.229
## sssi (.p7.) 0.297 0.015 19.437 0.000 0.267
## ssmc (.p8.) 0.148 0.009 16.148 0.000 0.130
## ssei (.p9.) 0.146 0.008 17.724 0.000 0.130
## speed =~
## ssno (.10.) 0.686 0.026 26.155 0.000 0.635
## sscs (.11.) 0.410 0.017 23.455 0.000 0.376
## ssmk (.12.) 0.207 0.010 21.059 0.000 0.188
## g =~
## ssgs (.13.) 0.763 0.011 68.419 0.000 0.742
## ssar (.14.) 0.686 0.012 57.704 0.000 0.663
## sswk (.15.) 0.765 0.012 65.501 0.000 0.742
## sspc (.16.) 0.723 0.011 64.060 0.000 0.701
## ssno (.17.) 0.497 0.012 40.246 0.000 0.473
## sscs (.18.) 0.469 0.011 41.422 0.000 0.447
## ssai (.19.) 0.464 0.010 45.214 0.000 0.444
## sssi (.20.) 0.484 0.010 46.489 0.000 0.464
## ssmk (.21.) 0.692 0.011 60.297 0.000 0.670
## ssmc (.22.) 0.625 0.011 58.610 0.000 0.604
## ssei (.23.) 0.662 0.011 62.404 0.000 0.641
## ssao (.24.) 0.548 0.011 49.698 0.000 0.526
## ci.upper Std.lv Std.all
##
## 0.354 0.322 0.316
## 0.185 0.162 0.158
## 0.305 0.276 0.272
## 0.285 0.255 0.252
## 0.475 0.436 0.419
##
## 0.280 0.554 0.517
## 0.327 0.648 0.627
## 0.165 0.321 0.318
## 0.162 0.318 0.305
##
## 0.738 0.753 0.713
## 0.445 0.450 0.440
## 0.226 0.227 0.224
##
## 0.785 0.931 0.907
## 0.710 0.837 0.822
## 0.788 0.932 0.906
## 0.745 0.881 0.859
## 0.521 0.606 0.573
## 0.492 0.572 0.559
## 0.484 0.566 0.528
## 0.505 0.591 0.571
## 0.715 0.844 0.831
## 0.646 0.762 0.754
## 0.683 0.807 0.773
## 0.569 0.668 0.641
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower
## g ~
## agec (a) 0.275 0.011 24.991 0.000 0.253
## agec2 (b) -0.032 0.008 -4.036 0.000 -0.047
## ci.upper Std.lv Std.all
##
## 0.296 0.225 0.324
## -0.016 -0.026 -0.048
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math ~~
## electronic 0.000 0.000
## speed 0.000 0.000
## electronic ~~
## speed 0.000 0.000
## ci.upper Std.lv Std.all
##
## 0.000 0.000 0.000
## 0.000 0.000 0.000
##
## 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower
## .ssar (.49.) 0.210 0.019 10.936 0.000 0.172
## .sspc 0.006 0.022 0.297 0.767 -0.036
## .ssmk (.51.) 0.291 0.020 14.823 0.000 0.253
## .ssmc (.52.) 0.090 0.018 4.958 0.000 0.054
## .ssao (.53.) 0.207 0.020 10.521 0.000 0.168
## .ssai (.54.) -0.075 0.015 -4.927 0.000 -0.104
## .sssi (.55.) -0.079 0.016 -4.872 0.000 -0.111
## .ssei (.56.) 0.030 0.017 1.714 0.086 -0.004
## .ssno 0.625 0.048 12.977 0.000 0.530
## .sscs (.58.) 0.290 0.018 15.952 0.000 0.255
## .ssgs (.59.) 0.178 0.020 8.898 0.000 0.138
## .sswk 0.080 0.022 3.589 0.000 0.036
## math -0.360 0.043 -8.275 0.000 -0.445
## elctrnc 1.630 0.100 16.225 0.000 1.433
## speed -1.006 0.063 -15.964 0.000 -1.130
## .g 0.213 0.031 6.872 0.000 0.152
## ci.upper Std.lv Std.all
## 0.247 0.210 0.206
## 0.049 0.006 0.006
## 0.330 0.291 0.287
## 0.125 0.090 0.089
## 0.246 0.207 0.199
## -0.045 -0.075 -0.070
## -0.047 -0.079 -0.077
## 0.064 0.030 0.029
## 0.719 0.625 0.591
## 0.326 0.290 0.284
## 0.217 0.178 0.173
## 0.124 0.080 0.078
## -0.275 -0.360 -0.360
## 1.827 0.748 0.748
## -0.883 -0.917 -0.917
## 0.274 0.175 0.175
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower
## math 1.000 1.000
## .ssar 0.233 0.010 23.279 0.000 0.214
## .sspc 0.249 0.009 28.655 0.000 0.232
## .ssmk 0.191 0.008 23.487 0.000 0.175
## .ssmc 0.273 0.011 23.978 0.000 0.251
## .ssao 0.450 0.019 23.833 0.000 0.413
## .ssai 0.522 0.019 27.008 0.000 0.484
## .sssi 0.301 0.016 18.548 0.000 0.269
## .ssei 0.336 0.012 27.554 0.000 0.312
## .ssno 0.181 0.035 5.237 0.000 0.114
## .sscs 0.516 0.021 24.785 0.000 0.476
## .ssgs 0.187 0.007 27.008 0.000 0.174
## .sswk 0.189 0.008 24.854 0.000 0.174
## electronic 4.747 0.511 9.294 0.000 3.746
## speed 1.205 0.087 13.843 0.000 1.034
## .g 1.323 0.049 27.077 0.000 1.227
## ci.upper Std.lv Std.all
## 1.000 1.000 1.000
## 0.253 0.233 0.225
## 0.266 0.249 0.237
## 0.207 0.191 0.185
## 0.295 0.273 0.267
## 0.487 0.450 0.414
## 0.560 0.522 0.454
## 0.332 0.301 0.281
## 0.360 0.336 0.309
## 0.249 0.181 0.163
## 0.557 0.516 0.493
## 0.201 0.187 0.178
## 0.204 0.189 0.179
## 5.747 1.000 1.000
## 1.375 1.000 1.000
## 1.419 0.890 0.890
# CROSS VALIDATION
set.seed(123) # For reproducibility, set seed if needed
split_indices <- sample(1:nrow(dgroup), size = nrow(dgroup) / 2)
dhalf1 <- dgroup[split_indices, ]
dhalf2 <- dgroup[-split_indices, ]
# ALL RACE GROUP
# CORRELATED FACTOR MODEL
cf.model<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
'
cf.lv<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
verbal~~1*verbal
math~~1*math
speed~~1*speed
'
cf.reduced<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
verbal~~1*verbal
math~~1*math
speed~~1*speed
math~0*1
'
baseline<-cfa(cf.model, data=dhalf1, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1027.175 45.000 0.000 0.971 0.871 0.078 0.027
## aic bic
## 87169.392 87447.203
configural<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 933.086 90.000 0.000 0.975 0.888 0.073 0.025
## aic bic
## 85306.914 85862.536
metric<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1075.373 101.000 0.000 0.971 0.872 0.074 0.039
## aic bic
## 85427.201 85914.914
scalar<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1481.455 109.000 0.000 0.959 0.824 0.084 0.042
## aic bic
## 85817.283 86255.607
scalar2<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1162.143 107.000 0.000 0.968 0.862 0.075 0.040
## aic bic
## 85501.971 85952.642
strict<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1312.685 119.000 0.000 0.964 0.845 0.075 0.044
## aic bic
## 85628.513 86005.102
cf.cov<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(cf.cov, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1283.196 113.000 0.000 0.965 0.848 0.076 0.103
## aic bic
## 85611.024 86024.654
cf.vcov<-cfa(cf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances", "lv.variances"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(cf.vcov, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1564.473 117.000 0.000 0.957 0.815 0.084 0.123
## aic bic
## 85884.301 86273.236
cf.cov2<-cfa(cf.lv, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(cf.cov2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1287.709 116.000 0.000 0.965 0.848 0.075 0.102
## aic bic
## 85609.537 86004.646
reduced<-cfa(cf.reduced, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(reduced, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1291.103 117.000 0.000 0.965 0.847 0.075 0.102
## aic bic
## 85610.931 85999.866
baseline<-cfa(cf.model, data=dhalf2, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1054.900 45.000 0.000 0.971 0.867 0.080 0.028
## aic bic
## 87030.381 87308.204
configural<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 918.421 90.000 0.000 0.976 0.890 0.072 0.025
## aic bic
## 85310.932 85866.579
metric<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 985.639 101.000 0.000 0.974 0.883 0.070 0.033
## aic bic
## 85356.151 85843.885
scalar<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1477.093 109.000 0.000 0.960 0.825 0.084 0.038
## aic bic
## 85831.604 86269.948
scalar2<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1128.410 107.000 0.000 0.970 0.866 0.073 0.035
## aic bic
## 85486.921 85937.613
strict<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1289.034 119.000 0.000 0.966 0.848 0.074 0.037
## aic bic
## 85623.546 86000.151
cf.cov<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(cf.cov, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1243.560 113.000 0.000 0.967 0.853 0.075 0.088
## aic bic
## 85590.071 86003.720
cf.vcov<-cfa(cf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances", "lv.variances"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(cf.vcov, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1438.294 117.000 0.000 0.961 0.830 0.080 0.107
## aic bic
## 85776.806 86165.759
cf.cov2<-cfa(cf.lv, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(cf.cov2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1245.224 116.000 0.000 0.967 0.853 0.074 0.088
## aic bic
## 85585.735 85980.862
reduced<-cfa(cf.reduced, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.covariances"), group.partial=c("sspc~1", "ssno~1"))
fitMeasures(reduced, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1248.661 117.000 0.000 0.967 0.853 0.074 0.088
## aic bic
## 85587.173 85976.126
# HIGH ORDER FACTOR
hof.model<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
'
hof.lv<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
math~~1*math
speed~~1*speed
'
hof.weak<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
math~~1*math
speed~~1*speed
verbal~0*1
'
hof.weak2<-'
verbal =~ ssgs + sswk + sspc + ssei
math =~ ssar + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ verbal + math + electronic + speed
math~~1*math
speed~~1*speed
verbal~0*1
math~0*1
g~0*1
'
baseline<-cfa(hof.model, data=dhalf1, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1474.262 47.000 0.000 0.957 0.818 0.093 0.042
## aic bic
## 87612.479 87877.943
configural<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1290.926 94.000 0.000 0.964 0.845 0.085 0.035
## aic bic
## 85656.754 86187.682
metric<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1478.106 108.000 0.000 0.959 0.824 0.085 0.054
## aic bic
## 85815.934 86260.432
metric2<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"), group.partial=c("electronic=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1389.940 107.000 0.000 0.962 0.835 0.082 0.045
## aic bic
## 85729.768 86180.439
scalar<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 5.480728e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1784.858 114.000 0.000 0.950 0.790 0.091 0.048
## aic bic
## 86110.686 86518.142
scalar2<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 9.706880e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1469.347 112.000 0.000 0.959 0.826 0.083 0.045
## aic bic
## 85799.176 86218.979
strict<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 8.773901e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1615.348 124.000 0.000 0.955 0.810 0.082 0.049
## aic bic
## 85921.177 86266.897
latent<-cfa(hof.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 7.060389e-14) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1761.709 117.000 0.000 0.951 0.793 0.089 0.105
## aic bic
## 86081.537 86470.473
latent2<-cfa(hof.lv, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 5.307590e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1474.120 114.000 0.000 0.959 0.825 0.082 0.045
## aic bic
## 85799.949 86207.405
weak<-cfa(hof.weak, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
fitMeasures(weak, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1474.120 115.000 0.000 0.959 0.826 0.082 0.045
## aic bic
## 85797.949 86199.231
weak2<-cfa(hof.weak2, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
fitMeasures(weak2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1487.261 117.000 0.000 0.959 0.824 0.081 0.046
## aic bic
## 85807.090 86196.025
baseline<-cfa(hof.model, data=dhalf2, meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1456.378 47.000 0.000 0.959 0.820 0.092 0.039
## aic bic
## 87427.859 87693.335
configural<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight")
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1255.041 94.000 0.000 0.966 0.849 0.083 0.033
## aic bic
## 85639.553 86170.505
metric<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1363.387 108.000 0.000 0.963 0.838 0.081 0.048
## aic bic
## 85719.899 86164.417
metric2<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings"), group.partial=c("electronic=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1310.888 107.000 0.000 0.965 0.844 0.080 0.042
## aic bic
## 85669.400 86120.091
scalar<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 7.183670e-13) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1801.412 114.000 0.000 0.951 0.788 0.091 0.047
## aic bic
## 86145.923 86553.398
scalar2<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1459.623 112.000 0.000 0.961 0.827 0.082 0.044
## aic bic
## 85808.135 86227.957
strict<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1619.020 124.000 0.000 0.956 0.810 0.082 0.046
## aic bic
## 85943.532 86289.268
latent<-cfa(hof.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts", "lv.variances"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 8.915235e-14) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1672.659 117.000 0.000 0.955 0.803 0.087 0.094
## aic bic
## 86011.171 86400.124
latent2<-cfa(hof.lv, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
## Warning: lavaan->lav_model_vcov():
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 1.423960e-12) is close to zero. This may be a symptom that the
## model is not identified.
fitMeasures(latent2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1465.043 114.000 0.000 0.961 0.827 0.082 0.044
## aic bic
## 85809.555 86217.029
weak<-cfa(hof.weak, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
fitMeasures(weak, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1465.043 115.000 0.000 0.961 0.827 0.081 0.044
## aic bic
## 85807.555 86208.856
weak2<-cfa(hof.weak2, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", group.equal=c("loadings", "intercepts"), group.partial=c("electronic=~ssei", "sspc~1", "ssno~1"))
fitMeasures(weak2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1486.102 117.000 0.000 0.960 0.824 0.081 0.047
## aic bic
## 85824.613 86213.566
# BIFACTOR MODEL
bf.model<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
'
bf.lv<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
math~~1*math
'
bf.reduced<-'
math =~ ssar + sspc + ssmk + ssmc + ssao
electronic =~ ssai + sssi + ssmc + ssei
speed =~ ssno + sscs + ssmk
g =~ ssgs + ssar + sswk + sspc + ssno + sscs + ssai + sssi + ssmk + ssmc + ssei + ssao
g~0*1
'
baseline<-cfa(bf.model, data=dhalf1, meanstructure=T, sampling.weights="sweight", std.lv=T, orthogonal=T)
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1242.533 42.000 0.000 0.964 0.844 0.090 0.042
## aic bic
## 87390.751 87687.082
configural<-cfa(bf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T)
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1086.074 84.000 0.000 0.970 0.868 0.082 0.034
## aic bic
## 85471.902 86064.565
metric<-cfa(bf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1299.194 104.000 0.000 0.964 0.845 0.081 0.054
## aic bic
## 85645.023 86114.214
metric2<-cfa(bf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"), group.partial=c("g=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1205.084 103.000 0.000 0.967 0.856 0.078 0.045
## aic bic
## 85552.912 86028.277
scalar<-cfa(bf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1662.522 112.000 0.000 0.953 0.804 0.088 0.057
## aic bic
## 85992.350 86412.154
scalar2<-cfa(bf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "sswk~1")) # RMSEAD bad unless sswk freed
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1318.098 109.000 0.000 0.964 0.843 0.079 0.054
## aic bic
## 85653.926 86092.250
strict<-cfa(bf.model, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("sspc~1", "ssno~1", "sswk~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1466.056 121.000 0.000 0.960 0.827 0.079 0.058
## aic bic
## 85777.884 86142.125
latent<-cfa(bf.lv, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "sswk~1"))
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1318.098 110.000 0.000 0.964 0.843 0.079 0.054
## aic bic
## 85651.927 86084.077
reduced<-cfa(bf.reduced, data=dhalf1, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "sswk~1"))
fitMeasures(reduced, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1326.174 110.000 0.000 0.964 0.842 0.079 0.055
## aic bic
## 85660.002 86092.153
baseline<-cfa(bf.model, data=dhalf2, meanstructure=T, sampling.weights="sweight", std.lv=T, orthogonal=T)
fitMeasures(baseline, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1226.098 42.000 0.000 0.965 0.846 0.089 0.038
## aic bic
## 87207.579 87503.924
configural<-cfa(bf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T)
fitMeasures(configural, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1031.511 84.000 0.000 0.972 0.875 0.080 0.031
## aic bic
## 85436.023 86028.713
metric<-cfa(bf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"))
fitMeasures(metric, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1152.408 104.000 0.000 0.969 0.863 0.075 0.048
## aic bic
## 85516.920 85986.133
metric2<-cfa(bf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings"), group.partial=c("g=~ssei"))
fitMeasures(metric2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1104.876 103.000 0.000 0.971 0.868 0.074 0.042
## aic bic
## 85471.388 85946.775
scalar<-cfa(bf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"))
fitMeasures(scalar, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1641.330 112.000 0.000 0.955 0.806 0.088 0.052
## aic bic
## 85989.841 86409.664
scalar2<-cfa(bf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "sswk~1")) # RMSEAD bad unless sswk freed
fitMeasures(scalar2, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1218.747 109.000 0.000 0.968 0.855 0.076 0.049
## aic bic
## 85573.258 86011.602
strict<-cfa(bf.model, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts", "residuals"), group.partial=c("sspc~1", "ssno~1", "sswk~1"))
fitMeasures(strict, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1380.272 121.000 0.000 0.963 0.837 0.077 0.052
## aic bic
## 85710.784 86075.041
latent<-cfa(bf.lv, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "sswk~1"))
fitMeasures(latent, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1218.835 110.000 0.000 0.968 0.855 0.075 0.049
## aic bic
## 85571.347 86003.517
reduced<-cfa(bf.reduced, data=dhalf2, group="sex", meanstructure=T, std.lv=T, sampling.weights="sweight", orthogonal=T, group.equal=c("loadings", "intercepts"), group.partial=c("sspc~1", "ssno~1", "sswk~1"))
fitMeasures(reduced, c("chisq", "df", "pvalue", "cfi", "mfi", "rmsea", "srmr", "aic", "bic"))
## chisq df pvalue cfi mfi rmsea srmr
## 1265.257 110.000 0.000 0.966 0.850 0.077 0.056
## aic bic
## 85617.768 86049.939