Foothill EFA/CFA
Heather Perkins
7/14/2020
Skewness & Kurtosis
Output
# check skewness and kurtosis
library(psych)
sk1 <- describe(efadf[2:28], na.rm = TRUE, skew = TRUE, ranges = TRUE, check = TRUE)
sk2 <- subset(sk1, select=-c(trimmed,mad,min,max,range))
sk_hist_items <- sk1$vars[sk1$kurtosis > 2]
library(DT)
datatable(sk2, options = list(pageLength = 10), rownames = T, class = "cell-border stripe") %>%
formatRound(c("mean","sd","skew","kurtosis","se"), 2) %>%
formatStyle("skew", target="row", backgroundColor = styleInterval(-2, c('yellow','none'))) %>%
formatStyle("kurtosis", target="row", backgroundColor = styleInterval(2, c('none','yellow')))Plots
sk_hist <- subset(df, select=c(sk_hist_items))
sk_hist_n <- colnames(sk_hist)
plots <- mapply(hist, sk_hist, main=sk_hist_n)
Correlations
Matrix
# correlation plots
idcors <- cor(efadf[2:14], use = "pairwise.complete.obs")
salgcors <- cor(efadf[15:37], use = "pairwise.complete.obs")
idcors2 <- round(idcors, digits = 2)
colnames(idcors2) <- abbreviate(colnames(idcors2))
rownames(idcors2) <- abbreviate(rownames(idcors2))
salgcors2 <- round(salgcors, digits = 2)
colnames(salgcors2) <- abbreviate(colnames(salgcors2))
rownames(salgcors2) <- abbreviate(rownames(salgcors2))
datatable(idcors2, options = list(pageLength = 10), rownames = T, class = "cell-border stripe")datatable(salgcors2, options = list(pageLength = 10), rownames = T, class = "cell-border stripe")Plots
library(corrplot)## corrplot 0.84 loadedcorrplot(idcors, order = "hclust")
corrplot(salgcors, order = "hclust")
Items
items <- read.csv(file="items.csv",header=F)
datatable(items, options = list(pageLength = 50), rownames = T, class = "cell-border stripe")EFAs
PCIR
issue caused by two-item factor (interest), wants to split across rec/pc factors. good loadings when 3-factor solution imposed
library(nFactors)## Loading required package: lattice##
## Attaching package: 'nFactors'## The following object is masked from 'package:lattice':
##
## paralleld <- na.omit(efadf[2:13])
ev <- eigen(cor(d)) # get eigenvalues
ap <- parallel(subject=nrow(d),var=ncol(d),rep=100,cent=.05)
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea)
plotnScree(nS)
EFA3 <- factanal(d, factors = 3, rotation = "promax", cutoff = 0.3)
print(EFA3, digits=3, cutoff=.3, sort=TRUE)##
## Call:
## factanal(x = d, factors = 3, rotation = "promax", cutoff = 0.3)
##
## Uniquenesses:
## pre_FeltLikeSciencePerson pre_SeeMyselfSciencePerson
## 0.396 0.277
## pre_FamilySeeSciencePerson pre_InstructorSeeSciencePerson
## 0.416 0.379
## pre_PeerSeeSciencePerson pre_EnjoyScience
## 0.313 0.152
## pre_InterestedScience pre_UnderstandPreviousScience
## 0.170 0.434
## pre_UnderstandNewScience pre_OvercomeSetbacks
## 0.291 0.410
## pre_ConfidentOutsideClass pre_ConfidentExams
## 0.224 0.450
##
## Loadings:
## Factor1 Factor2 Factor3
## pre_FeltLikeSciencePerson 0.718
## pre_SeeMyselfSciencePerson 0.785
## pre_FamilySeeSciencePerson 0.788
## pre_InstructorSeeSciencePerson 0.805
## pre_PeerSeeSciencePerson 0.865
## pre_UnderstandPreviousScience 0.582
## pre_UnderstandNewScience 0.795
## pre_OvercomeSetbacks 0.801
## pre_ConfidentOutsideClass 0.880
## pre_ConfidentExams 0.779
## pre_EnjoyScience 0.876
## pre_InterestedScience 0.830
##
## Factor1 Factor2 Factor3
## SS loadings 3.172 3.023 1.523
## Proportion Var 0.264 0.252 0.127
## Cumulative Var 0.264 0.516 0.643
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.000 0.556 0.676
## Factor2 0.556 1.000 0.637
## Factor3 0.676 0.637 1.000
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 108.75 on 33 degrees of freedom.
## The p-value is 5.13e-10SALG
four-factor solution is best. group of most recently dropped items (enjoy, interest, real world, connected, applying) seem to be spinning off into their own factor or two, but it’s not clear what it is/they are
# efas
library(nFactors)
d <- na.omit(efadf[15:37])
names(d)## [1] "pre_OutsideClassInSubject1" "pre_OutsideClassInSubject2"
## [3] "pre_RealWorldIssues" "pre_FindArticles"
## [5] "pre_CriticallyRead" "pre_IdentifyPatterns"
## [7] "pre_RecognizeArgument" "pre_DevelopArgument"
## [9] "pre_WriteDocuments" "pre_WorkWithOthers"
## [11] "pre_OralPresentation" "pre_Enthusiastic"
## [13] "pre_DiscussWithFriends" "pre_PlanningAdditionalClasses"
## [15] "pre_PursuringCareer" "pre_UnderstandSubject"
## [17] "pre_SucceedSubject" "pre_ComplexIdeas"
## [19] "pre_AskingForHelp" "pre_ConnectIdeas"
## [21] "pre_ApplyingOutsideClass" "pre_SystematicReasoning"
## [23] "pre_AnalyzingData"ev <- eigen(cor(d)) # get eigenvalues
ap <- parallel(subject=nrow(d),var=ncol(d),rep=100,cent=.05)
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea)
plotnScree(nS)
EFA4 <- factanal(d, factors = 4, rotation = "promax", cutoff = 0.3)
print(EFA4, digits=3, cutoff=.29, sort=TRUE) ##
## Call:
## factanal(x = d, factors = 4, rotation = "promax", cutoff = 0.3)
##
## Uniquenesses:
## pre_OutsideClassInSubject1 pre_OutsideClassInSubject2
## 0.423 0.267
## pre_RealWorldIssues pre_FindArticles
## 0.640 0.522
## pre_CriticallyRead pre_IdentifyPatterns
## 0.457 0.491
## pre_RecognizeArgument pre_DevelopArgument
## 0.275 0.485
## pre_WriteDocuments pre_WorkWithOthers
## 0.474 0.722
## pre_OralPresentation pre_Enthusiastic
## 0.733 0.462
## pre_DiscussWithFriends pre_PlanningAdditionalClasses
## 0.534 0.436
## pre_PursuringCareer pre_UnderstandSubject
## 0.568 0.357
## pre_SucceedSubject pre_ComplexIdeas
## 0.376 0.408
## pre_AskingForHelp pre_ConnectIdeas
## 0.828 0.380
## pre_ApplyingOutsideClass pre_SystematicReasoning
## 0.347 0.236
## pre_AnalyzingData
## 0.263
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## pre_Enthusiastic 0.708
## pre_DiscussWithFriends 0.644
## pre_PlanningAdditionalClasses 0.748
## pre_PursuringCareer 0.783
## pre_UnderstandSubject 0.802
## pre_SucceedSubject 0.825
## pre_ComplexIdeas 0.700
## pre_FindArticles 0.701
## pre_CriticallyRead 0.741
## pre_IdentifyPatterns 0.698
## pre_RecognizeArgument 0.800
## pre_DevelopArgument 0.688
## pre_WriteDocuments 0.731
## pre_ConnectIdeas 0.305 0.570
## pre_ApplyingOutsideClass 0.675
## pre_SystematicReasoning 0.863
## pre_AnalyzingData 0.809
## pre_OutsideClassInSubject1 0.719
## pre_OutsideClassInSubject2 0.923
## pre_RealWorldIssues 0.574
## pre_WorkWithOthers 0.303
## pre_OralPresentation 0.439
## pre_AskingForHelp
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 4.113 3.560 2.344 1.863
## Proportion Var 0.179 0.155 0.102 0.081
## Cumulative Var 0.179 0.334 0.436 0.517
##
## Factor Correlations:
## Factor1 Factor2 Factor3 Factor4
## Factor1 1.000 0.499 0.661 0.612
## Factor2 0.499 1.000 0.523 0.483
## Factor3 0.661 0.523 1.000 0.675
## Factor4 0.612 0.483 0.675 1.000
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 554.82 on 167 degrees of freedom.
## The p-value is 3.95e-43d <- subset(d, select=-c(10,11,16,17,18,19))
ev <- eigen(cor(d)) # get eigenvalues
ap <- parallel(subject=nrow(d),var=ncol(d),rep=100,cent=.05)
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea)
plotnScree(nS)
EFA4 <- factanal(d, factors = 4, rotation = "promax", cutoff = 0.3)
print(EFA4, digits=3, cutoff=.29, sort=TRUE) ##
## Call:
## factanal(x = d, factors = 4, rotation = "promax", cutoff = 0.3)
##
## Uniquenesses:
## pre_OutsideClassInSubject1 pre_OutsideClassInSubject2
## 0.490 0.137
## pre_RealWorldIssues pre_FindArticles
## 0.660 0.508
## pre_CriticallyRead pre_IdentifyPatterns
## 0.432 0.480
## pre_RecognizeArgument pre_DevelopArgument
## 0.274 0.496
## pre_WriteDocuments pre_Enthusiastic
## 0.495 0.433
## pre_DiscussWithFriends pre_PlanningAdditionalClasses
## 0.364 0.318
## pre_PursuringCareer pre_ConnectIdeas
## 0.596 0.405
## pre_ApplyingOutsideClass pre_SystematicReasoning
## 0.353 0.224
## pre_AnalyzingData
## 0.260
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## pre_FindArticles 0.728
## pre_CriticallyRead 0.763
## pre_IdentifyPatterns 0.714
## pre_RecognizeArgument 0.796
## pre_DevelopArgument 0.659
## pre_WriteDocuments 0.701
## pre_Enthusiastic 0.708
## pre_DiscussWithFriends 0.844
## pre_PlanningAdditionalClasses 0.853
## pre_PursuringCareer 0.693
## pre_ConnectIdeas 0.603
## pre_ApplyingOutsideClass 0.685
## pre_SystematicReasoning 0.906
## pre_AnalyzingData 0.828
## pre_OutsideClassInSubject1 0.612
## pre_OutsideClassInSubject2 0.994
## pre_RealWorldIssues 0.483
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 3.232 2.587 2.436 1.637
## Proportion Var 0.190 0.152 0.143 0.096
## Cumulative Var 0.190 0.342 0.486 0.582
##
## Factor Correlations:
## Factor1 Factor2 Factor3 Factor4
## Factor1 1.000 -0.524 0.599 -0.636
## Factor2 -0.524 1.000 -0.422 0.449
## Factor3 0.599 -0.422 1.000 -0.618
## Factor4 -0.636 0.449 -0.618 1.000
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 236.64 on 74 degrees of freedom.
## The p-value is 6.75e-19CFAs
PCIR
df <- read.csv(file="fa_data.csv", header=T)
names(df)## [1] "ID" "pre_FeltLikeSciencePerson"
## [3] "pre_SeeMyselfSciencePerson" "pre_FamilySeeSciencePerson"
## [5] "pre_InstructorSeeSciencePerson" "pre_PeerSeeSciencePerson"
## [7] "pre_EnjoyScience" "pre_InterestedScience"
## [9] "pre_UnderstandPreviousScience" "pre_UnderstandNewScience"
## [11] "pre_OvercomeSetbacks" "pre_ConfidentOutsideClass"
## [13] "pre_ConfidentExams" "pre_OthersAskHelp"
## [15] "pre_OutsideClassInSubject1" "pre_OutsideClassInSubject2"
## [17] "pre_RealWorldIssues" "pre_FindArticles"
## [19] "pre_CriticallyRead" "pre_IdentifyPatterns"
## [21] "pre_RecognizeArgument" "pre_DevelopArgument"
## [23] "pre_WriteDocuments" "pre_WorkWithOthers"
## [25] "pre_OralPresentation" "pre_Enthusiastic"
## [27] "pre_DiscussWithFriends" "pre_PlanningAdditionalClasses"
## [29] "pre_PursuringCareer" "pre_UnderstandSubject"
## [31] "pre_SucceedSubject" "pre_ComplexIdeas"
## [33] "pre_AskingForHelp" "pre_ConnectIdeas"
## [35] "pre_ApplyingOutsideClass" "pre_SystematicReasoning"
## [37] "pre_AnalyzingData" "pre_Course"
## [39] "pre_Ethnicity" "freq"df3 <- subset(df, select=c(2:13,15:23,26:29,34:37))
names(df3)## [1] "pre_FeltLikeSciencePerson" "pre_SeeMyselfSciencePerson"
## [3] "pre_FamilySeeSciencePerson" "pre_InstructorSeeSciencePerson"
## [5] "pre_PeerSeeSciencePerson" "pre_EnjoyScience"
## [7] "pre_InterestedScience" "pre_UnderstandPreviousScience"
## [9] "pre_UnderstandNewScience" "pre_OvercomeSetbacks"
## [11] "pre_ConfidentOutsideClass" "pre_ConfidentExams"
## [13] "pre_OutsideClassInSubject1" "pre_OutsideClassInSubject2"
## [15] "pre_RealWorldIssues" "pre_FindArticles"
## [17] "pre_CriticallyRead" "pre_IdentifyPatterns"
## [19] "pre_RecognizeArgument" "pre_DevelopArgument"
## [21] "pre_WriteDocuments" "pre_Enthusiastic"
## [23] "pre_DiscussWithFriends" "pre_PlanningAdditionalClasses"
## [25] "pre_PursuringCareer" "pre_ConnectIdeas"
## [27] "pre_ApplyingOutsideClass" "pre_SystematicReasoning"
## [29] "pre_AnalyzingData"set.seed(7)
ss <- sample(1:2,size=nrow(df3),replace=TRUE,prob=c(0.5,0.5))
d <- df3[ss==2,]
rename_vars <- c(
paste("rec",1:5,sep=""),
paste("gen_int",1:2,sep=""),
paste("subj_pc",1:5,sep=""),
paste("rw_int",1:3,sep=""),
paste("verb_pc",1:6,sep=""),
paste("car_int",1:4,sep=""),
paste("sci",1:4,sep="")
)
colnames(d) <- rename_vars
library(lavaan)## This is lavaan 0.6-6## lavaan is BETA software! Please report any bugs.##
## Attaching package: 'lavaan'## The following object is masked from 'package:psych':
##
## cor2covpcir3 <- 'recognition =~ rec1 + rec2 + rec3 + rec4 + rec5
subject_pc =~ subj_pc1 + subj_pc3 + subj_pc5'
fit3 <- cfa(pcir3, data=d, std.lv=T,missing="fiml")
summary(fit3, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-6 ended normally after 40 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 25
##
## Number of observations 430
## Number of missing patterns 5
##
## Model Test User Model:
##
## Test statistic 83.660
## Degrees of freedom 19
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1923.416
## Degrees of freedom 28
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.966
## Tucker-Lewis Index (TLI) 0.950
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3997.265
## Loglikelihood unrestricted model (H1) -3955.434
##
## Akaike (AIC) 8044.529
## Bayesian (BIC) 8146.124
## Sample-size adjusted Bayesian (BIC) 8066.789
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.089
## 90 Percent confidence interval - lower 0.070
## 90 Percent confidence interval - upper 0.109
## P-value RMSEA <= 0.05 0.001
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.039
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## recognition =~
## rec1 0.803 0.047 17.027 0.000 0.803 0.729
## rec2 0.977 0.043 22.579 0.000 0.977 0.882
## rec3 0.980 0.051 19.156 0.000 0.980 0.792
## rec4 0.768 0.041 18.518 0.000 0.768 0.774
## rec5 0.949 0.044 21.696 0.000 0.949 0.862
## subject_pc =~
## subj_pc1 0.675 0.040 16.937 0.000 0.675 0.787
## subj_pc3 0.537 0.040 13.534 0.000 0.537 0.645
## subj_pc5 0.772 0.044 17.514 0.000 0.772 0.807
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## recognition ~~
## subject_pc 0.619 0.039 15.760 0.000 0.619 0.619
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .rec1 3.447 0.053 64.849 0.000 3.447 3.127
## .rec2 3.502 0.053 65.525 0.000 3.502 3.161
## .rec3 3.297 0.060 55.149 0.000 3.297 2.662
## .rec4 3.230 0.048 67.411 0.000 3.230 3.256
## .rec5 3.289 0.053 61.852 0.000 3.289 2.989
## .subj_pc1 3.779 0.041 91.412 0.000 3.779 4.408
## .subj_pc3 3.940 0.040 98.060 0.000 3.940 4.729
## .subj_pc5 3.560 0.046 77.138 0.000 3.560 3.720
## recognition 0.000 0.000 0.000
## subject_pc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .rec1 0.569 0.044 13.058 0.000 0.569 0.469
## .rec2 0.273 0.029 9.501 0.000 0.273 0.223
## .rec3 0.573 0.046 12.359 0.000 0.573 0.373
## .rec4 0.394 0.032 12.427 0.000 0.394 0.400
## .rec5 0.311 0.030 10.228 0.000 0.311 0.257
## .subj_pc1 0.280 0.033 8.418 0.000 0.280 0.381
## .subj_pc3 0.405 0.033 12.183 0.000 0.405 0.584
## .subj_pc5 0.320 0.041 7.797 0.000 0.320 0.349
## recognition 1.000 1.000 1.000
## subject_pc 1.000 1.000 1.000parameterEstimates(fit3, standardized=T)## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 recognition =~ rec1 0.803 0.047 17.027 0 0.711 0.896
## 2 recognition =~ rec2 0.977 0.043 22.579 0 0.892 1.061
## 3 recognition =~ rec3 0.980 0.051 19.156 0 0.880 1.081
## 4 recognition =~ rec4 0.768 0.041 18.518 0 0.687 0.849
## 5 recognition =~ rec5 0.949 0.044 21.696 0 0.863 1.034
## 6 subject_pc =~ subj_pc1 0.675 0.040 16.937 0 0.597 0.753
## 7 subject_pc =~ subj_pc3 0.537 0.040 13.534 0 0.459 0.615
## 8 subject_pc =~ subj_pc5 0.772 0.044 17.514 0 0.686 0.859
## 9 rec1 ~~ rec1 0.569 0.044 13.058 0 0.484 0.655
## 10 rec2 ~~ rec2 0.273 0.029 9.501 0 0.217 0.330
## 11 rec3 ~~ rec3 0.573 0.046 12.359 0 0.482 0.664
## 12 rec4 ~~ rec4 0.394 0.032 12.427 0 0.332 0.456
## 13 rec5 ~~ rec5 0.311 0.030 10.228 0 0.251 0.370
## 14 subj_pc1 ~~ subj_pc1 0.280 0.033 8.418 0 0.215 0.345
## 15 subj_pc3 ~~ subj_pc3 0.405 0.033 12.183 0 0.340 0.471
## 16 subj_pc5 ~~ subj_pc5 0.320 0.041 7.797 0 0.240 0.400
## 17 recognition ~~ recognition 1.000 0.000 NA NA 1.000 1.000
## 18 subject_pc ~~ subject_pc 1.000 0.000 NA NA 1.000 1.000
## 19 recognition ~~ subject_pc 0.619 0.039 15.760 0 0.542 0.696
## 20 rec1 ~1 3.447 0.053 64.849 0 3.342 3.551
## 21 rec2 ~1 3.502 0.053 65.525 0 3.397 3.607
## 22 rec3 ~1 3.297 0.060 55.149 0 3.180 3.414
## 23 rec4 ~1 3.230 0.048 67.411 0 3.136 3.324
## 24 rec5 ~1 3.289 0.053 61.852 0 3.184 3.393
## 25 subj_pc1 ~1 3.779 0.041 91.412 0 3.698 3.860
## 26 subj_pc3 ~1 3.940 0.040 98.060 0 3.861 4.018
## 27 subj_pc5 ~1 3.560 0.046 77.138 0 3.470 3.651
## 28 recognition ~1 0.000 0.000 NA NA 0.000 0.000
## 29 subject_pc ~1 0.000 0.000 NA NA 0.000 0.000
## std.lv std.all std.nox
## 1 0.803 0.729 0.729
## 2 0.977 0.882 0.882
## 3 0.980 0.792 0.792
## 4 0.768 0.774 0.774
## 5 0.949 0.862 0.862
## 6 0.675 0.787 0.787
## 7 0.537 0.645 0.645
## 8 0.772 0.807 0.807
## 9 0.569 0.469 0.469
## 10 0.273 0.223 0.223
## 11 0.573 0.373 0.373
## 12 0.394 0.400 0.400
## 13 0.311 0.257 0.257
## 14 0.280 0.381 0.381
## 15 0.405 0.584 0.584
## 16 0.320 0.349 0.349
## 17 1.000 1.000 1.000
## 18 1.000 1.000 1.000
## 19 0.619 0.619 0.619
## 20 3.447 3.127 3.127
## 21 3.502 3.161 3.161
## 22 3.297 2.662 2.662
## 23 3.230 3.256 3.256
## 24 3.289 2.989 2.989
## 25 3.779 4.408 4.408
## 26 3.940 4.729 4.729
## 27 3.560 3.720 3.720
## 28 0.000 0.000 0.000
## 29 0.000 0.000 0.000round(residuals(fit3, type="cor")$cov, digits = 2)## rec1 rec2 rec3 rec4 rec5 sbj_p1 sbj_p3 sbj_p5
## rec1 0.00
## rec2 0.04 0.00
## rec3 -0.03 0.02 0.00
## rec4 -0.03 -0.02 -0.03 0.00
## rec5 -0.03 -0.02 0.02 0.06 0.00
## subj_pc1 0.10 0.06 0.04 0.05 0.05 0.00
## subj_pc3 -0.03 -0.02 -0.14 -0.03 -0.07 -0.03 0.00
## subj_pc5 0.00 -0.03 -0.04 0.01 -0.04 -0.01 0.05 0.00SALG
salg2 <- 'verb_pc =~ verb_pc1 + verb_pc2 + verb_pc3 + verb_pc4 + verb_pc5 + verb_pc6
careerint =~ car_int1 + car_int2 + car_int3 + car_int4
sciapp =~ sci1 + sci2 + sci3 + sci4
rw_int =~ rw_int1 + rw_int2 + rw_int3'
fit2 <- cfa(salg2, data=d, std.lv=T,missing="fiml")
summary(fit2, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-6 ended normally after 68 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 57
##
## Number of observations 430
## Number of missing patterns 10
##
## Model Test User Model:
##
## Test statistic 379.994
## Degrees of freedom 113
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 4246.876
## Degrees of freedom 136
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.935
## Tucker-Lewis Index (TLI) 0.922
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7716.723
## Loglikelihood unrestricted model (H1) -7526.726
##
## Akaike (AIC) 15547.445
## Bayesian (BIC) 15779.081
## Sample-size adjusted Bayesian (BIC) 15598.196
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.074
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.082
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.045
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## verb_pc =~
## verb_pc1 0.618 0.047 13.194 0.000 0.618 0.611
## verb_pc2 0.705 0.044 16.074 0.000 0.705 0.714
## verb_pc3 0.679 0.041 16.562 0.000 0.679 0.723
## verb_pc4 0.733 0.036 20.149 0.000 0.733 0.831
## verb_pc5 0.668 0.037 18.296 0.000 0.668 0.779
## verb_pc6 0.648 0.043 15.084 0.000 0.648 0.674
## careerint =~
## car_int1 0.765 0.033 22.995 0.000 0.765 0.896
## car_int2 0.811 0.038 21.098 0.000 0.811 0.846
## car_int3 0.869 0.044 19.889 0.000 0.869 0.818
## car_int4 0.768 0.049 15.723 0.000 0.768 0.690
## sciapp =~
## sci1 0.668 0.033 20.039 0.000 0.668 0.830
## sci2 0.668 0.034 19.881 0.000 0.668 0.825
## sci3 0.682 0.037 18.563 0.000 0.682 0.791
## sci4 0.636 0.038 16.606 0.000 0.636 0.733
## rw_int =~
## rw_int1 0.707 0.034 21.070 0.000 0.707 0.869
## rw_int2 0.771 0.037 20.694 0.000 0.771 0.858
## rw_int3 0.558 0.037 14.983 0.000 0.558 0.675
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## verb_pc ~~
## careerint 0.326 0.049 6.641 0.000 0.326 0.326
## sciapp 0.608 0.038 15.984 0.000 0.608 0.608
## rw_int 0.491 0.044 11.227 0.000 0.491 0.491
## careerint ~~
## sciapp 0.518 0.042 12.432 0.000 0.518 0.518
## rw_int 0.460 0.044 10.355 0.000 0.460 0.460
## sciapp ~~
## rw_int 0.539 0.042 12.954 0.000 0.539 0.539
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .verb_pc1 3.849 0.049 78.973 0.000 3.849 3.808
## .verb_pc2 3.721 0.048 78.211 0.000 3.721 3.772
## .verb_pc3 3.728 0.045 82.323 0.000 3.728 3.970
## .verb_pc4 3.854 0.043 90.521 0.000 3.854 4.370
## .verb_pc5 3.804 0.041 91.977 0.000 3.804 4.438
## .verb_pc6 3.588 0.046 77.330 0.000 3.588 3.729
## .car_int1 4.209 0.041 102.281 0.000 4.209 4.932
## .car_int2 4.006 0.046 86.602 0.000 4.006 4.178
## .car_int3 3.929 0.051 76.618 0.000 3.929 3.697
## .car_int4 4.129 0.054 76.855 0.000 4.129 3.711
## .sci1 4.119 0.039 106.036 0.000 4.119 5.114
## .sci2 4.153 0.039 106.379 0.000 4.153 5.130
## .sci3 3.995 0.042 96.041 0.000 3.995 4.631
## .sci4 4.009 0.042 95.783 0.000 4.009 4.619
## .rw_int1 4.154 0.039 105.837 0.000 4.154 5.108
## .rw_int2 3.980 0.043 91.670 0.000 3.980 4.429
## .rw_int3 4.359 0.040 108.980 0.000 4.359 5.272
## verb_pc 0.000 0.000 0.000
## careerint 0.000 0.000 0.000
## sciapp 0.000 0.000 0.000
## rw_int 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .verb_pc1 0.640 0.048 13.342 0.000 0.640 0.627
## .verb_pc2 0.477 0.039 12.213 0.000 0.477 0.490
## .verb_pc3 0.420 0.034 12.498 0.000 0.420 0.477
## .verb_pc4 0.240 0.024 10.064 0.000 0.240 0.309
## .verb_pc5 0.288 0.025 11.349 0.000 0.288 0.392
## .verb_pc6 0.505 0.039 13.062 0.000 0.505 0.546
## .car_int1 0.144 0.018 8.153 0.000 0.144 0.197
## .car_int2 0.261 0.024 10.669 0.000 0.261 0.284
## .car_int3 0.374 0.033 11.194 0.000 0.374 0.331
## .car_int4 0.648 0.049 13.211 0.000 0.648 0.523
## .sci1 0.202 0.020 10.002 0.000 0.202 0.312
## .sci2 0.209 0.021 10.179 0.000 0.209 0.319
## .sci3 0.278 0.026 10.809 0.000 0.278 0.374
## .sci4 0.349 0.029 11.839 0.000 0.349 0.463
## .rw_int1 0.162 0.021 7.562 0.000 0.162 0.244
## .rw_int2 0.213 0.026 8.145 0.000 0.213 0.264
## .rw_int3 0.372 0.029 12.782 0.000 0.372 0.544
## verb_pc 1.000 1.000 1.000
## careerint 1.000 1.000 1.000
## sciapp 1.000 1.000 1.000
## rw_int 1.000 1.000 1.000parameterEstimates(fit2, standardized=T)## lhs op rhs est se z pvalue ci.lower ci.upper std.lv
## 1 verb_pc =~ verb_pc1 0.618 0.047 13.194 0 0.526 0.709 0.618
## 2 verb_pc =~ verb_pc2 0.705 0.044 16.074 0 0.619 0.790 0.705
## 3 verb_pc =~ verb_pc3 0.679 0.041 16.562 0 0.599 0.760 0.679
## 4 verb_pc =~ verb_pc4 0.733 0.036 20.149 0 0.662 0.804 0.733
## 5 verb_pc =~ verb_pc5 0.668 0.037 18.296 0 0.597 0.740 0.668
## 6 verb_pc =~ verb_pc6 0.648 0.043 15.084 0 0.564 0.733 0.648
## 7 careerint =~ car_int1 0.765 0.033 22.995 0 0.699 0.830 0.765
## 8 careerint =~ car_int2 0.811 0.038 21.098 0 0.736 0.887 0.811
## 9 careerint =~ car_int3 0.869 0.044 19.889 0 0.784 0.955 0.869
## 10 careerint =~ car_int4 0.768 0.049 15.723 0 0.672 0.864 0.768
## 11 sciapp =~ sci1 0.668 0.033 20.039 0 0.603 0.734 0.668
## 12 sciapp =~ sci2 0.668 0.034 19.881 0 0.602 0.734 0.668
## 13 sciapp =~ sci3 0.682 0.037 18.563 0 0.610 0.755 0.682
## 14 sciapp =~ sci4 0.636 0.038 16.606 0 0.561 0.711 0.636
## 15 rw_int =~ rw_int1 0.707 0.034 21.070 0 0.641 0.773 0.707
## 16 rw_int =~ rw_int2 0.771 0.037 20.694 0 0.698 0.844 0.771
## 17 rw_int =~ rw_int3 0.558 0.037 14.983 0 0.485 0.631 0.558
## 18 verb_pc1 ~~ verb_pc1 0.640 0.048 13.342 0 0.546 0.734 0.640
## 19 verb_pc2 ~~ verb_pc2 0.477 0.039 12.213 0 0.400 0.553 0.477
## 20 verb_pc3 ~~ verb_pc3 0.420 0.034 12.498 0 0.354 0.486 0.420
## 21 verb_pc4 ~~ verb_pc4 0.240 0.024 10.064 0 0.194 0.287 0.240
## 22 verb_pc5 ~~ verb_pc5 0.288 0.025 11.349 0 0.239 0.338 0.288
## 23 verb_pc6 ~~ verb_pc6 0.505 0.039 13.062 0 0.430 0.581 0.505
## 24 car_int1 ~~ car_int1 0.144 0.018 8.153 0 0.109 0.178 0.144
## 25 car_int2 ~~ car_int2 0.261 0.024 10.669 0 0.213 0.309 0.261
## 26 car_int3 ~~ car_int3 0.374 0.033 11.194 0 0.308 0.439 0.374
## 27 car_int4 ~~ car_int4 0.648 0.049 13.211 0 0.552 0.744 0.648
## 28 sci1 ~~ sci1 0.202 0.020 10.002 0 0.163 0.242 0.202
## 29 sci2 ~~ sci2 0.209 0.021 10.179 0 0.169 0.250 0.209
## 30 sci3 ~~ sci3 0.278 0.026 10.809 0 0.228 0.329 0.278
## 31 sci4 ~~ sci4 0.349 0.029 11.839 0 0.291 0.406 0.349
## 32 rw_int1 ~~ rw_int1 0.162 0.021 7.562 0 0.120 0.204 0.162
## 33 rw_int2 ~~ rw_int2 0.213 0.026 8.145 0 0.162 0.265 0.213
## 34 rw_int3 ~~ rw_int3 0.372 0.029 12.782 0 0.315 0.429 0.372
## 35 verb_pc ~~ verb_pc 1.000 0.000 NA NA 1.000 1.000 1.000
## 36 careerint ~~ careerint 1.000 0.000 NA NA 1.000 1.000 1.000
## 37 sciapp ~~ sciapp 1.000 0.000 NA NA 1.000 1.000 1.000
## 38 rw_int ~~ rw_int 1.000 0.000 NA NA 1.000 1.000 1.000
## 39 verb_pc ~~ careerint 0.326 0.049 6.641 0 0.230 0.422 0.326
## 40 verb_pc ~~ sciapp 0.608 0.038 15.984 0 0.533 0.682 0.608
## 41 verb_pc ~~ rw_int 0.491 0.044 11.227 0 0.406 0.577 0.491
## 42 careerint ~~ sciapp 0.518 0.042 12.432 0 0.436 0.599 0.518
## 43 careerint ~~ rw_int 0.460 0.044 10.355 0 0.373 0.547 0.460
## 44 sciapp ~~ rw_int 0.539 0.042 12.954 0 0.458 0.621 0.539
## 45 verb_pc1 ~1 3.849 0.049 78.973 0 3.753 3.944 3.849
## 46 verb_pc2 ~1 3.721 0.048 78.211 0 3.628 3.814 3.721
## 47 verb_pc3 ~1 3.728 0.045 82.323 0 3.639 3.817 3.728
## 48 verb_pc4 ~1 3.854 0.043 90.521 0 3.770 3.937 3.854
## 49 verb_pc5 ~1 3.804 0.041 91.977 0 3.723 3.885 3.804
## 50 verb_pc6 ~1 3.588 0.046 77.330 0 3.497 3.679 3.588
## 51 car_int1 ~1 4.209 0.041 102.281 0 4.129 4.290 4.209
## 52 car_int2 ~1 4.006 0.046 86.602 0 3.916 4.097 4.006
## 53 car_int3 ~1 3.929 0.051 76.618 0 3.828 4.029 3.929
## 54 car_int4 ~1 4.129 0.054 76.855 0 4.024 4.235 4.129
## 55 sci1 ~1 4.119 0.039 106.036 0 4.042 4.195 4.119
## 56 sci2 ~1 4.153 0.039 106.379 0 4.077 4.230 4.153
## 57 sci3 ~1 3.995 0.042 96.041 0 3.914 4.077 3.995
## 58 sci4 ~1 4.009 0.042 95.783 0 3.927 4.091 4.009
## 59 rw_int1 ~1 4.154 0.039 105.837 0 4.077 4.231 4.154
## 60 rw_int2 ~1 3.980 0.043 91.670 0 3.895 4.065 3.980
## 61 rw_int3 ~1 4.359 0.040 108.980 0 4.281 4.438 4.359
## 62 verb_pc ~1 0.000 0.000 NA NA 0.000 0.000 0.000
## 63 careerint ~1 0.000 0.000 NA NA 0.000 0.000 0.000
## 64 sciapp ~1 0.000 0.000 NA NA 0.000 0.000 0.000
## 65 rw_int ~1 0.000 0.000 NA NA 0.000 0.000 0.000
## std.all std.nox
## 1 0.611 0.611
## 2 0.714 0.714
## 3 0.723 0.723
## 4 0.831 0.831
## 5 0.779 0.779
## 6 0.674 0.674
## 7 0.896 0.896
## 8 0.846 0.846
## 9 0.818 0.818
## 10 0.690 0.690
## 11 0.830 0.830
## 12 0.825 0.825
## 13 0.791 0.791
## 14 0.733 0.733
## 15 0.869 0.869
## 16 0.858 0.858
## 17 0.675 0.675
## 18 0.627 0.627
## 19 0.490 0.490
## 20 0.477 0.477
## 21 0.309 0.309
## 22 0.392 0.392
## 23 0.546 0.546
## 24 0.197 0.197
## 25 0.284 0.284
## 26 0.331 0.331
## 27 0.523 0.523
## 28 0.312 0.312
## 29 0.319 0.319
## 30 0.374 0.374
## 31 0.463 0.463
## 32 0.244 0.244
## 33 0.264 0.264
## 34 0.544 0.544
## 35 1.000 1.000
## 36 1.000 1.000
## 37 1.000 1.000
## 38 1.000 1.000
## 39 0.326 0.326
## 40 0.608 0.608
## 41 0.491 0.491
## 42 0.518 0.518
## 43 0.460 0.460
## 44 0.539 0.539
## 45 3.808 3.808
## 46 3.772 3.772
## 47 3.970 3.970
## 48 4.370 4.370
## 49 4.438 4.438
## 50 3.729 3.729
## 51 4.932 4.932
## 52 4.178 4.178
## 53 3.697 3.697
## 54 3.711 3.711
## 55 5.114 5.114
## 56 5.130 5.130
## 57 4.631 4.631
## 58 4.619 4.619
## 59 5.108 5.108
## 60 4.429 4.429
## 61 5.272 5.272
## 62 0.000 0.000
## 63 0.000 0.000
## 64 0.000 0.000
## 65 0.000 0.000round(residuals(fit2, type="cor")$cov, digits = 2)## vrb_p1 vrb_p2 vrb_p3 vrb_p4 vrb_p5 vrb_p6 cr_nt1 cr_nt2 cr_nt3 cr_nt4
## verb_pc1 0.00
## verb_pc2 0.17 0.00
## verb_pc3 0.00 0.06 0.00
## verb_pc4 -0.05 -0.06 0.01 0.00
## verb_pc5 -0.07 -0.08 -0.05 0.07 0.00
## verb_pc6 -0.01 -0.01 -0.02 -0.01 0.07 0.00
## car_int1 0.00 0.07 0.02 -0.04 -0.04 -0.04 0.00
## car_int2 0.04 0.05 -0.01 -0.03 -0.03 -0.03 0.01 0.00
## car_int3 0.01 0.09 0.06 0.01 0.00 -0.04 -0.02 0.01 0.00
## car_int4 0.07 0.04 0.02 0.02 -0.03 0.03 0.01 -0.04 0.04 0.00
## sci1 0.01 0.06 -0.01 -0.05 -0.06 -0.10 0.05 0.06 0.04 0.01
## sci2 0.00 0.01 -0.07 -0.05 -0.05 -0.10 0.05 0.06 0.01 0.02
## sci3 0.03 0.08 0.09 0.01 0.05 -0.01 -0.06 -0.09 -0.02 -0.08
## sci4 0.02 0.12 0.07 0.04 0.11 0.06 -0.08 -0.06 -0.02 -0.09
## rw_int1 0.05 0.02 -0.02 0.04 -0.01 -0.05 0.01 -0.04 -0.04 0.07
## rw_int2 0.04 -0.01 -0.02 0.01 -0.04 -0.01 0.00 -0.04 -0.04 0.03
## rw_int3 0.09 0.05 -0.02 0.00 -0.05 -0.04 0.10 0.04 0.02 0.03
## sci1 sci2 sci3 sci4 rw_nt1 rw_nt2 rw_nt3
## verb_pc1
## verb_pc2
## verb_pc3
## verb_pc4
## verb_pc5
## verb_pc6
## car_int1
## car_int2
## car_int3
## car_int4
## sci1 0.00
## sci2 0.05 0.00
## sci3 -0.02 -0.04 0.00
## sci4 -0.06 -0.03 0.11 0.00
## rw_int1 0.03 0.02 -0.03 0.01 0.00
## rw_int2 0.00 0.00 -0.06 -0.05 0.00 0.00
## rw_int3 0.06 0.08 -0.01 -0.02 -0.01 0.00 0.00salg2 <- 'verb_pc =~ verb_pc2 + verb_pc3 + verb_pc4 + verb_pc5 + verb_pc6
careerint =~ car_int1 + car_int2 + car_int3 + car_int4
sciapp =~ sci1 + sci3 + sci4
rw_int =~ rw_int1 + rw_int2 + rw_int3'
fit2 <- cfa(salg2, data=d, std.lv=T,missing="fiml")
summary(fit2, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-6 ended normally after 65 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 51
##
## Number of observations 430
## Number of missing patterns 10
##
## Model Test User Model:
##
## Test statistic 231.204
## Degrees of freedom 84
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3579.516
## Degrees of freedom 105
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.958
## Tucker-Lewis Index (TLI) 0.947
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -6841.985
## Loglikelihood unrestricted model (H1) -6726.383
##
## Akaike (AIC) 13785.970
## Bayesian (BIC) 13993.223
## Sample-size adjusted Bayesian (BIC) 13831.379
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.064
## 90 Percent confidence interval - lower 0.054
## 90 Percent confidence interval - upper 0.074
## P-value RMSEA <= 0.05 0.010
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.045
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## verb_pc =~
## verb_pc2 0.666 0.044 14.977 0.000 0.666 0.675
## verb_pc3 0.675 0.041 16.321 0.000 0.675 0.718
## verb_pc4 0.746 0.036 20.706 0.000 0.746 0.846
## verb_pc5 0.687 0.036 19.105 0.000 0.687 0.802
## verb_pc6 0.650 0.043 15.094 0.000 0.650 0.676
## careerint =~
## car_int1 0.764 0.033 22.965 0.000 0.764 0.896
## car_int2 0.810 0.039 21.040 0.000 0.810 0.845
## car_int3 0.871 0.044 19.911 0.000 0.871 0.819
## car_int4 0.769 0.049 15.739 0.000 0.769 0.691
## sciapp =~
## sci1 0.595 0.036 16.651 0.000 0.595 0.739
## sci3 0.737 0.036 20.429 0.000 0.737 0.854
## sci4 0.685 0.037 18.371 0.000 0.685 0.789
## rw_int =~
## rw_int1 0.707 0.034 21.009 0.000 0.707 0.869
## rw_int2 0.772 0.037 20.660 0.000 0.772 0.859
## rw_int3 0.557 0.037 14.924 0.000 0.557 0.673
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## verb_pc ~~
## careerint 0.316 0.050 6.384 0.000 0.316 0.316
## sciapp 0.639 0.037 17.111 0.000 0.639 0.639
## rw_int 0.479 0.044 10.791 0.000 0.479 0.479
## careerint ~~
## sciapp 0.450 0.046 9.708 0.000 0.450 0.450
## rw_int 0.460 0.044 10.351 0.000 0.460 0.460
## sciapp ~~
## rw_int 0.494 0.045 10.864 0.000 0.494 0.494
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .verb_pc2 3.721 0.048 78.211 0.000 3.721 3.772
## .verb_pc3 3.728 0.045 82.323 0.000 3.728 3.970
## .verb_pc4 3.853 0.043 90.523 0.000 3.853 4.370
## .verb_pc5 3.804 0.041 91.979 0.000 3.804 4.438
## .verb_pc6 3.588 0.046 77.330 0.000 3.588 3.729
## .car_int1 4.209 0.041 102.281 0.000 4.209 4.932
## .car_int2 4.006 0.046 86.601 0.000 4.006 4.178
## .car_int3 3.929 0.051 76.614 0.000 3.929 3.696
## .car_int4 4.129 0.054 76.857 0.000 4.129 3.711
## .sci1 4.119 0.039 106.036 0.000 4.119 5.114
## .sci3 3.995 0.042 96.041 0.000 3.995 4.631
## .sci4 4.009 0.042 95.783 0.000 4.009 4.619
## .rw_int1 4.154 0.039 105.834 0.000 4.154 5.108
## .rw_int2 3.980 0.043 91.666 0.000 3.980 4.429
## .rw_int3 4.359 0.040 108.976 0.000 4.359 5.272
## verb_pc 0.000 0.000 0.000
## careerint 0.000 0.000 0.000
## sciapp 0.000 0.000 0.000
## rw_int 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .verb_pc2 0.529 0.041 12.782 0.000 0.529 0.544
## .verb_pc3 0.427 0.034 12.386 0.000 0.427 0.484
## .verb_pc4 0.221 0.023 9.595 0.000 0.221 0.285
## .verb_pc5 0.262 0.024 10.927 0.000 0.262 0.357
## .verb_pc6 0.503 0.039 12.970 0.000 0.503 0.543
## .car_int1 0.144 0.018 8.092 0.000 0.144 0.198
## .car_int2 0.263 0.025 10.697 0.000 0.263 0.286
## .car_int3 0.371 0.033 11.094 0.000 0.371 0.329
## .car_int4 0.646 0.049 13.192 0.000 0.646 0.522
## .sci1 0.294 0.026 11.465 0.000 0.294 0.454
## .sci3 0.201 0.025 8.034 0.000 0.201 0.270
## .sci4 0.284 0.027 10.584 0.000 0.284 0.377
## .rw_int1 0.161 0.022 7.430 0.000 0.161 0.244
## .rw_int2 0.212 0.027 7.978 0.000 0.212 0.262
## .rw_int3 0.374 0.029 12.803 0.000 0.374 0.547
## verb_pc 1.000 1.000 1.000
## careerint 1.000 1.000 1.000
## sciapp 1.000 1.000 1.000
## rw_int 1.000 1.000 1.000parameterEstimates(fit2, standardized=T)## lhs op rhs est se z pvalue ci.lower ci.upper std.lv
## 1 verb_pc =~ verb_pc2 0.666 0.044 14.977 0 0.579 0.753 0.666
## 2 verb_pc =~ verb_pc3 0.675 0.041 16.321 0 0.594 0.756 0.675
## 3 verb_pc =~ verb_pc4 0.746 0.036 20.706 0 0.675 0.817 0.746
## 4 verb_pc =~ verb_pc5 0.687 0.036 19.105 0 0.617 0.758 0.687
## 5 verb_pc =~ verb_pc6 0.650 0.043 15.094 0 0.566 0.735 0.650
## 6 careerint =~ car_int1 0.764 0.033 22.965 0 0.699 0.830 0.764
## 7 careerint =~ car_int2 0.810 0.039 21.040 0 0.735 0.886 0.810
## 8 careerint =~ car_int3 0.871 0.044 19.911 0 0.785 0.957 0.871
## 9 careerint =~ car_int4 0.769 0.049 15.739 0 0.673 0.865 0.769
## 10 sciapp =~ sci1 0.595 0.036 16.651 0 0.525 0.665 0.595
## 11 sciapp =~ sci3 0.737 0.036 20.429 0 0.666 0.808 0.737
## 12 sciapp =~ sci4 0.685 0.037 18.371 0 0.612 0.758 0.685
## 13 rw_int =~ rw_int1 0.707 0.034 21.009 0 0.641 0.773 0.707
## 14 rw_int =~ rw_int2 0.772 0.037 20.660 0 0.699 0.845 0.772
## 15 rw_int =~ rw_int3 0.557 0.037 14.924 0 0.483 0.630 0.557
## 16 verb_pc2 ~~ verb_pc2 0.529 0.041 12.782 0 0.448 0.610 0.529
## 17 verb_pc3 ~~ verb_pc3 0.427 0.034 12.386 0 0.359 0.494 0.427
## 18 verb_pc4 ~~ verb_pc4 0.221 0.023 9.595 0 0.176 0.267 0.221
## 19 verb_pc5 ~~ verb_pc5 0.262 0.024 10.927 0 0.215 0.309 0.262
## 20 verb_pc6 ~~ verb_pc6 0.503 0.039 12.970 0 0.427 0.579 0.503
## 21 car_int1 ~~ car_int1 0.144 0.018 8.092 0 0.109 0.179 0.144
## 22 car_int2 ~~ car_int2 0.263 0.025 10.697 0 0.215 0.311 0.263
## 23 car_int3 ~~ car_int3 0.371 0.033 11.094 0 0.306 0.437 0.371
## 24 car_int4 ~~ car_int4 0.646 0.049 13.192 0 0.550 0.743 0.646
## 25 sci1 ~~ sci1 0.294 0.026 11.465 0 0.244 0.345 0.294
## 26 sci3 ~~ sci3 0.201 0.025 8.034 0 0.152 0.250 0.201
## 27 sci4 ~~ sci4 0.284 0.027 10.584 0 0.232 0.337 0.284
## 28 rw_int1 ~~ rw_int1 0.161 0.022 7.430 0 0.119 0.204 0.161
## 29 rw_int2 ~~ rw_int2 0.212 0.027 7.978 0 0.160 0.264 0.212
## 30 rw_int3 ~~ rw_int3 0.374 0.029 12.803 0 0.317 0.431 0.374
## 31 verb_pc ~~ verb_pc 1.000 0.000 NA NA 1.000 1.000 1.000
## 32 careerint ~~ careerint 1.000 0.000 NA NA 1.000 1.000 1.000
## 33 sciapp ~~ sciapp 1.000 0.000 NA NA 1.000 1.000 1.000
## 34 rw_int ~~ rw_int 1.000 0.000 NA NA 1.000 1.000 1.000
## 35 verb_pc ~~ careerint 0.316 0.050 6.384 0 0.219 0.414 0.316
## 36 verb_pc ~~ sciapp 0.639 0.037 17.111 0 0.565 0.712 0.639
## 37 verb_pc ~~ rw_int 0.479 0.044 10.791 0 0.392 0.567 0.479
## 38 careerint ~~ sciapp 0.450 0.046 9.708 0 0.359 0.541 0.450
## 39 careerint ~~ rw_int 0.460 0.044 10.351 0 0.373 0.547 0.460
## 40 sciapp ~~ rw_int 0.494 0.045 10.864 0 0.405 0.583 0.494
## 41 verb_pc2 ~1 3.721 0.048 78.211 0 3.628 3.814 3.721
## 42 verb_pc3 ~1 3.728 0.045 82.323 0 3.639 3.817 3.728
## 43 verb_pc4 ~1 3.853 0.043 90.523 0 3.770 3.937 3.853
## 44 verb_pc5 ~1 3.804 0.041 91.979 0 3.723 3.885 3.804
## 45 verb_pc6 ~1 3.588 0.046 77.330 0 3.497 3.679 3.588
## 46 car_int1 ~1 4.209 0.041 102.281 0 4.129 4.290 4.209
## 47 car_int2 ~1 4.006 0.046 86.601 0 3.916 4.097 4.006
## 48 car_int3 ~1 3.929 0.051 76.614 0 3.828 4.029 3.929
## 49 car_int4 ~1 4.129 0.054 76.857 0 4.024 4.235 4.129
## 50 sci1 ~1 4.119 0.039 106.036 0 4.042 4.195 4.119
## 51 sci3 ~1 3.995 0.042 96.041 0 3.914 4.077 3.995
## 52 sci4 ~1 4.009 0.042 95.783 0 3.927 4.091 4.009
## 53 rw_int1 ~1 4.154 0.039 105.834 0 4.077 4.231 4.154
## 54 rw_int2 ~1 3.980 0.043 91.666 0 3.895 4.065 3.980
## 55 rw_int3 ~1 4.359 0.040 108.976 0 4.281 4.438 4.359
## 56 verb_pc ~1 0.000 0.000 NA NA 0.000 0.000 0.000
## 57 careerint ~1 0.000 0.000 NA NA 0.000 0.000 0.000
## 58 sciapp ~1 0.000 0.000 NA NA 0.000 0.000 0.000
## 59 rw_int ~1 0.000 0.000 NA NA 0.000 0.000 0.000
## std.all std.nox
## 1 0.675 0.675
## 2 0.718 0.718
## 3 0.846 0.846
## 4 0.802 0.802
## 5 0.676 0.676
## 6 0.896 0.896
## 7 0.845 0.845
## 8 0.819 0.819
## 9 0.691 0.691
## 10 0.739 0.739
## 11 0.854 0.854
## 12 0.789 0.789
## 13 0.869 0.869
## 14 0.859 0.859
## 15 0.673 0.673
## 16 0.544 0.544
## 17 0.484 0.484
## 18 0.285 0.285
## 19 0.357 0.357
## 20 0.543 0.543
## 21 0.198 0.198
## 22 0.286 0.286
## 23 0.329 0.329
## 24 0.522 0.522
## 25 0.454 0.454
## 26 0.270 0.270
## 27 0.377 0.377
## 28 0.244 0.244
## 29 0.262 0.262
## 30 0.547 0.547
## 31 1.000 1.000
## 32 1.000 1.000
## 33 1.000 1.000
## 34 1.000 1.000
## 35 0.316 0.316
## 36 0.639 0.639
## 37 0.479 0.479
## 38 0.450 0.450
## 39 0.460 0.460
## 40 0.494 0.494
## 41 3.772 3.772
## 42 3.970 3.970
## 43 4.370 4.370
## 44 4.438 4.438
## 45 3.729 3.729
## 46 4.932 4.932
## 47 4.178 4.178
## 48 3.696 3.696
## 49 3.711 3.711
## 50 5.114 5.114
## 51 4.631 4.631
## 52 4.619 4.619
## 53 5.108 5.108
## 54 4.429 4.429
## 55 5.272 5.272
## 56 0.000 0.000
## 57 0.000 0.000
## 58 0.000 0.000
## 59 0.000 0.000round(residuals(fit2, type="cor")$cov, digits = 2)## vrb_p2 vrb_p3 vrb_p4 vrb_p5 vrb_p6 cr_nt1 cr_nt2 cr_nt3 cr_nt4 sci1
## verb_pc2 0.00
## verb_pc3 0.10 0.00
## verb_pc4 -0.04 0.01 0.00
## verb_pc5 -0.06 -0.07 0.04 0.00
## verb_pc6 0.02 -0.01 -0.03 0.05 0.00
## car_int1 0.09 0.03 -0.04 -0.04 -0.04 0.00
## car_int2 0.07 -0.01 -0.03 -0.03 -0.02 0.01 0.00
## car_int3 0.10 0.07 0.02 0.00 -0.04 -0.02 0.01 0.00
## car_int4 0.06 0.02 0.02 -0.03 0.03 0.01 -0.04 0.03 0.00
## sci1 0.10 0.02 -0.03 -0.04 -0.08 0.14 0.14 0.12 0.08 0.00
## sci3 0.06 0.05 -0.05 -0.01 -0.05 -0.03 -0.07 0.00 -0.07 0.00
## sci4 0.09 0.03 -0.02 0.06 0.02 -0.05 -0.04 0.00 -0.08 -0.04
## rw_int1 0.04 -0.01 0.04 -0.01 -0.05 0.01 -0.04 -0.04 0.07 0.10
## rw_int2 0.01 -0.01 0.01 -0.04 -0.01 0.00 -0.04 -0.04 0.03 0.07
## rw_int3 0.07 -0.01 0.00 -0.05 -0.03 0.10 0.04 0.02 0.03 0.11
## sci3 sci4 rw_nt1 rw_nt2 rw_nt3
## verb_pc2
## verb_pc3
## verb_pc4
## verb_pc5
## verb_pc6
## car_int1
## car_int2
## car_int3
## car_int4
## sci1
## sci3 0.00
## sci4 0.02 0.00
## rw_int1 -0.03 0.02 0.00
## rw_int2 -0.06 -0.04 0.00 0.00
## rw_int3 -0.01 -0.01 -0.01 0.00 0.00write.csv(d, file="fa_finaldata.csv", row.names = F)