library(tidyverse)
library(psych)
library(lavaan)
library(semPlot)
library(semPower)
library(knitr)
library(apaTables)
library(stats)
library(dplyr)
library(rstatix)
teacher2<- read.csv("teacher2.csv")
teacher2<-
separate(data = teacher2, col = Q5, into = c("teach1", "Teach2"), sep = ",")%>%
mutate(Level = case_when(teach1 %in% c("10th Grade", "11th Grade", "12th Grade", "10th Grade", "9th Grade") ~ "High_School",
teach1 %in% c("6th Grade", "7th Grade", "8th Grade") ~ "Middle_School",
teach1 %in% c("Kindergarten", "1st Grade", "2nd Grade", "3rd Grade", "4th Grade", "5th Grade") ~ "Elementary",
teach1 == "Post Birth or Pre-K" ~ "Pre_K",
teach1 == "Other (please explain)" ~ "Other"))
teacher2<- teacher2%>%
mutate(gender = case_when(Q4 == "Female" ~ "Female",
Q4 == "Male" ~ "Male"))
teacher2<- teacher2%>%
mutate(Teach = Q17_1 + Q17_3 + Q17_5)%>%
mutate(anx = Q19_1 + Q19_2 + Q19_3 + Q19_4 + Q19_5)%>%
mutate(Dep = Q20_2 + Q20_3 + Q20_5 + Q20_7 + Q20_8)%>%
mutate(Dep2 = Q20_1 + Q20_2 + Q20_3 + Q20_4 + Q20_5 + Q20_6 + Q20_7 + Q20_8)%>%
mutate(PROS = Q23_3 + Q23_8 + Q23_9)%>%
mutate(GOV = Q23_5 + Q23_6 + Q23_7) %>%
mutate(COM = Q23_1 + Q23_2 + Q23_4) %>%
mutate(ASSM = Q41_1 + Q41_2 + Q41_4)%>%
mutate(TEFF = Q44_1 + Q44_2 + Q44_3 + Q44_4) %>%
mutate(Leave = Q15_4 + Q21 + Q25 + Q29 + Q41_3)
teacher2<- teacher2%>%
mutate(locale = case_when(Q10 %in% c("Rural community", "Small community, non-rural") ~ "Small Rural",
Q10 %in% c("Other", "Suburban Area", "Traditional public school", "Urban Area", "Small community, non-rural") ~ "Larger Community"))
library(dplyr)
descriptives<- dplyr:: select(teacher2, Teach, anx, Dep, PROS, GOV, COM, ASSM, TEFF, Leave)
descriptivetable<- psych::describe(descriptives)
write.csv(descriptivetable, "descriptives.csv")
descriptivetable
## vars n mean sd median trimmed mad min max range skew kurtosis se
## Teach 1 621 9.30 1.76 9 9.37 1.48 3 12 9 -0.43 0.15 0.07
## anx 2 619 14.13 3.72 14 14.32 4.45 5 20 15 -0.40 -0.39 0.15
## Dep 3 619 12.12 3.42 12 12.13 2.97 5 20 15 -0.01 -0.02 0.14
## PROS 4 602 7.72 2.10 8 7.69 1.48 3 12 9 -0.01 -0.58 0.09
## GOV 5 591 6.16 2.12 6 6.10 1.48 3 12 9 0.30 -0.36 0.09
## COM 6 601 7.63 2.00 8 7.71 1.48 3 12 9 -0.32 -0.12 0.08
## ASSM 7 545 9.70 1.73 10 9.84 1.48 4 12 8 -0.53 -0.27 0.07
## TEFF 8 521 11.88 2.27 12 11.91 1.48 4 16 12 -0.25 0.64 0.10
## Leave 9 483 13.63 3.35 14 13.82 2.97 5 20 15 -0.52 -0.28 0.15
gendertable<-with(teacher2, table(gender))
genderprop<- prop.table(gendertable)
teachertable<- with(teacher2, table(Cont1))
teacherprop<- prop.table(teachertable)
gradetable<- with(teacher2, table(Level))
gradeprop<- prop.table(gradetable)
gendertable
## gender
## Female Male
## 502 156
genderprop
## gender
## Female Male
## 0.7629179 0.2370821
teachertable
## Cont1
## Elective English Language Arts General Elementary
## 87 70 195
## Math Other Science
## 83 46 42
## Social Studies Special Education
## 53 89
teacherprop
## Cont1
## Elective English Language Arts General Elementary
## 0.13082707 0.10526316 0.29323308
## Math Other Science
## 0.12481203 0.06917293 0.06315789
## Social Studies Special Education
## 0.07969925 0.13383459
gradetable
## Level
## Elementary High_School Middle_School Other Pre_K
## 277 166 172 34 25
gradeprop
## Level
## Elementary High_School Middle_School Other Pre_K
## 0.41097923 0.24629080 0.25519288 0.05044510 0.03709199
#Teach and Gender
teachgender<-lm(Teach ~ gender, data = teacher2)
apa.aov.table(teachgender,"teachgenderanova.doc")
##
##
## ANOVA results using Teach as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2
## (Intercept) 39328.77 1 39328.77 12734.92 .000
## gender 11.51 1 11.51 3.73 .054 .01
## Error 1871.49 606 3.09
## CI_90_partial_eta2
##
## [.00, .02]
##
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
cohens_d(teacher2, Teach~gender)
## # A tibble: 1 x 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 Teach Female Male -0.184 502 156 negligible
genderTeach<- na.omit(dplyr::select(teacher2, ResponseId, Teach, gender))
genderTeach<- genderTeach %>%
group_by(gender) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(Teach),
SD = sd(Teach, .75))
#Teach and Grade Level
teachgrade<-lm(Teach ~ Level, data = teacher2)
apa.aov.table(teachgrade,"teachlevelanova.doc")
##
##
## ANOVA results using Teach as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 22153.85 1 22153.85 7090.84 .000
## Level 8.76 4 2.19 0.70 .591 .00 [.00, .01]
## Error 1921.44 615 3.12
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
cohens_d(teacher2, Teach~Level)
## # A tibble: 10 x 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 Teach Elementary High_School -0.0753 277 166 negligible
## 2 Teach Elementary Middle_School -0.0785 277 172 negligible
## 3 Teach Elementary Other -0.186 277 34 negligible
## 4 Teach Elementary Pre_K 0.203 277 25 small
## 5 Teach High_School Middle_School -0.00522 166 172 negligible
## 6 Teach High_School Other -0.107 166 34 negligible
## 7 Teach High_School Pre_K 0.269 166 25 small
## 8 Teach Middle_School Other -0.0982 172 34 negligible
## 9 Teach Middle_School Pre_K 0.268 172 25 small
## 10 Teach Other Pre_K 0.377 34 25 small
teachLevel<- na.omit(dplyr::select(teacher2, ResponseId, Teach, Level))
teachLevel<- teachLevel %>%
group_by(Level) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(Teach),
SD = sd(Teach, .75))
teachLevel
## # A tibble: 5 x 4
## Level n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Elementary 260 9.23 1.68
## 2 High_School 155 9.36 1.78
## 3 Middle_School 151 9.37 1.88
## 4 Other 31 9.55 1.73
## 5 Pre_K 23 8.87 1.87
#Anxiety and Gender
anxgender<-lm(anx ~ gender, data = teacher2)
apa.aov.table(anxgender,"anxgenderanova.doc")
##
##
## ANOVA results using anx as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 97370.49 1 97370.49 7343.53 .000
## gender 354.52 1 354.52 26.74 .000 .04 [.02, .07]
## Error 7995.39 603 13.26
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
cohens_d(teacher2, anx~gender)
## # A tibble: 1 x 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 anx Female Male 0.480 502 156 small
GenAnx<- na.omit(dplyr::select(teacher2, ResponseId, anx, gender))
GenAnx<- GenAnx %>%
group_by(gender) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(anx),
SD = sd(anx, .75))
#Anxiety and Grade Level
anxgrade<-lm(anx ~ Level, data = teacher2)
apa.aov.table(anxgrade,"anxlevelanova.doc")
##
##
## ANOVA results using anx as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 54484.68 1 54484.68 3989.30 .000
## Level 190.42 4 47.60 3.49 .008 .02 [.00, .04]
## Error 8372.16 613 13.66
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
cohens_d(teacher2, anx~Level)
## # A tibble: 10 x 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 anx Elementary High_School 0.365 277 166 small
## 2 anx Elementary Middle_School 0.0839 277 172 negligible
## 3 anx Elementary Other -0.0418 277 34 negligible
## 4 anx Elementary Pre_K 0.102 277 25 negligible
## 5 anx High_School Middle_School -0.271 166 172 small
## 6 anx High_School Other -0.420 166 34 small
## 7 anx High_School Pre_K -0.298 166 25 small
## 8 anx Middle_School Other -0.127 172 34 negligible
## 9 anx Middle_School Pre_K 0.00694 172 25 negligible
## 10 anx Other Pre_K 0.152 34 25 negligible
ANXLevel<- na.omit(dplyr::select(teacher2, ResponseId, anx, Level))
ANXLevel<- ANXLevel %>%
group_by(Level) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(anx),
SD = sd(anx, .75))
GenAnx
## # A tibble: 2 x 4
## gender n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Female 458 14.6 3.56
## 2 Male 147 12.8 3.87
ANXLevel
## # A tibble: 5 x 4
## Level n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Elementary 257 14.6 3.69
## 2 High_School 158 13.2 3.63
## 3 Middle_School 149 14.2 3.90
## 4 Other 31 14.7 3.45
## 5 Pre_K 23 14.2 3.03
#Depression and Gender
Depgender<-lm(Dep ~ gender, data = teacher2)
apa.aov.table(Depgender,"Depgenderanova.doc")
##
##
## ANOVA results using Dep as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 70238.96 1 70238.96 6109.00 .000
## gender 110.88 1 110.88 9.64 .002 .02 [.00, .04]
## Error 6933.06 603 11.50
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
cohens_d(teacher2, Dep~gender)
## # A tibble: 1 x 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 Dep Female Male 0.293 502 156 small
GenDep<- na.omit(dplyr::select(teacher2, ResponseId, Dep, gender))
GenDep<- GenDep %>%
group_by(gender) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(Dep),
SD = sd(Dep, .75))
#Depression and Grade Level
Depgrade<-lm(Dep ~ Level, data = teacher2)
apa.aov.table(Depgrade,"Deplevelanova.doc")
##
##
## ANOVA results using Dep as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 39466.98 1 39466.98 3387.87 .000
## Level 73.51 4 18.38 1.58 .179 .01 [.00, .02]
## Error 7141.14 613 11.65
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
cohens_d(teacher2, Dep~Level)
## # A tibble: 10 x 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 Dep Elementary High_School 0.241 277 166 small
## 2 Dep Elementary Middle_School 0.0216 277 172 negligible
## 3 Dep Elementary Other 0.0236 277 34 negligible
## 4 Dep Elementary Pre_K 0.131 277 25 negligible
## 5 Dep High_School Middle_School -0.214 166 172 small
## 6 Dep High_School Other -0.224 166 34 small
## 7 Dep High_School Pre_K -0.130 166 25 negligible
## 8 Dep Middle_School Other 0.000890 172 34 negligible
## 9 Dep Middle_School Pre_K 0.105 172 25 negligible
## 10 Dep Other Pre_K 0.110 34 25 negligible
DEPLevel<- na.omit(dplyr::select(teacher2, ResponseId, Dep, Level))
DEPLevel<- DEPLevel %>%
group_by(Level) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(Dep),
SD = sd(Dep, .75))
GenDep
## # A tibble: 2 x 4
## gender n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Female 459 12.4 3.36
## 2 Male 146 11.4 3.47
DEPLevel
## # A tibble: 5 x 4
## Level n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Elementary 258 12.4 3.39
## 2 High_School 156 11.5 3.43
## 3 Middle_School 150 12.3 3.55
## 4 Other 31 12.3 3.21
## 5 Pre_K 23 12.0 2.85
counselingdata<- dplyr::select(teacher2, Q11, Q14_1, Q14_2, Q14_3, Q14_4, Q14_5, Q15_1, Q15_2, Q15_3, Q15_4, Q17_1, Q17_2, Q17_3, Q17_4, Q17_5, Q19_1, Q19_2, Q19_3, Q19_4, Q19_5, Q20_1, Q20_2, Q20_3, Q20_4, Q20_5, Q20_6, Q20_7, Q20_8, Q20_9, Q20_10, Q21, Q23_1, Q23_2, Q23_3, Q23_4, Q23_5, Q23_6, Q23_7, Q23_8, Q23_9, Q23_10, Q24_1, Q24_2, Q24_3, Q24_4, Q24_5, Q24_6, Q24_7, Q24_8, Q24_9, Q24_10, Q25, Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7, Q27_8, Q28_1, Q28_2, Q28_3, Q28_4, Q28_5, Q28_6, Q28_7, Q28_8, Q29, Q30, Q32_1, Q32_2, Q32_3, Q32_4, Q32_5, Q32_6, Q32_7, Q37, Q38, Q39, Q41_1, Q41_2, Q41_3, Q41_4, Q41_5, Q41_6, Q41_7, Q41_8, Q41_9, Q41_10, Q44_1, Q44_2, Q44_3, Q44_4, Q45_1, Q45_2, Q45_3, Q45_4, Level, gender, Teach, anx, Dep, Dep2, PROS, ASSM, TEFF, Leave, Q16, Q22, Q26, Q33, Q40, Q42, Q43, Q46, Q50, Q53, Q56, Q58, Q60, Q62, Q63)
write.csv(counselingdata, "counselingdata.csv")
#Depression and Gender
Depgender2<-lm(Dep2 ~ gender, data = teacher2)
apa.aov.table(Depgender2,"Depgenderanovas.doc")
##
##
## ANOVA results using Dep2 as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2
## (Intercept) 182683.02 1 182683.02 6661.70 .000
## gender 223.10 1 223.10 8.14 .004 .01
## Error 16316.62 595 27.42
## CI_90_partial_eta2
##
## [.00, .03]
##
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
cohens_d(teacher2, Dep2~gender)
## # A tibble: 1 x 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 Dep2 Female Male 0.271 502 156 small
GenDep2<- na.omit(dplyr::select(teacher2, ResponseId, Dep2, gender))
GenDep2<- GenDep2 %>%
dplyr::group_by(gender) %>%
dplyr::summarize(
n = n_distinct(ResponseId),
Avg = mean(Dep2),
SD = sd(Dep2, .75))
#Depression and Grade Level
Depgrade2<-lm(Dep2 ~ Level, data = teacher2)
apa.aov.table(Depgrade2,"Deplevelanovas.doc")
##
##
## ANOVA results using Dep2 as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2
## (Intercept) 103807.94 1 103807.94 3746.13 .000
## Level 243.22 4 60.80 2.19 .068 .01
## Error 16764.96 605 27.71
## CI_90_partial_eta2
##
## [.00, .03]
##
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
cohens_d(teacher2, Dep2~Level)
## # A tibble: 10 x 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 Dep2 Elementary High_School 0.286 277 166 small
## 2 Dep2 Elementary Middle_School 0.0336 277 172 negligible
## 3 Dep2 Elementary Other 0.0716 277 34 negligible
## 4 Dep2 Elementary Pre_K 0.0356 277 25 negligible
## 5 Dep2 High_School Middle_School -0.253 166 172 small
## 6 Dep2 High_School Other -0.220 166 34 small
## 7 Dep2 High_School Pre_K -0.267 166 25 small
## 8 Dep2 Middle_School Other 0.0374 172 34 negligible
## 9 Dep2 Middle_School Pre_K 0 172 25 negligible
## 10 Dep2 Other Pre_K -0.0397 34 25 negligible
DEPLevel2<- na.omit(dplyr::select(teacher2, ResponseId, Dep2, Level))
DEPLevel2<- DEPLevel2 %>%
group_by(Level) %>%
dplyr::summarize(
n = n_distinct(ResponseId),
Avg = mean(Dep2),
SD = sd(Dep2, .75))
GenDep2
## # A tibble: 2 x 4
## gender n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Female 453 20.1 5.20
## 2 Male 144 18.7 5.34
DEPLevel2
## # A tibble: 5 x 4
## Level n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Elementary 255 20.2 5.24
## 2 High_School 154 18.6 5.42
## 3 Middle_School 147 20 5.26
## 4 Other 31 19.8 5.09
## 5 Pre_K 23 20 4.65
#Prof Supp and Gender
PROSgender<-lm(PROS ~ gender, data = teacher2)
apa.aov.table(PROSgender,"PROSgenderanova.doc")
##
##
## ANOVA results using PROS as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 26113.74 1 26113.74 5924.75 .000
## gender 24.16 1 24.16 5.48 .020 .01 [.00, .03]
## Error 2596.06 589 4.41
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
cohens_d(teacher2, PROS~gender)
## # A tibble: 1 x 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 PROS Female Male -0.232 502 156 small
PSGend<- na.omit(dplyr::select(teacher2, ResponseId, PROS, gender))
PSGend<- PSGend %>%
group_by(gender) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(PROS),
SD = sd(PROS, .75))
#Prof Supp and Grade Level
PROSgrade<-lm(PROS ~ Level, data = teacher2)
apa.aov.table(PROSgrade,"PROSlevelanova.doc")
##
##
## ANOVA results using PROS as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 15574.01 1 15574.01 3541.07 .000
## Level 26.20 4 6.55 1.49 .204 .01 [.00, .02]
## Error 2621.28 596 4.40
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
cohens_d(teacher2, PROS~Level)
## # A tibble: 10 x 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 PROS Elementary High_School 0.0381 277 166 negligible
## 2 PROS Elementary Middle_School 0.0913 277 172 negligible
## 3 PROS Elementary Other 0.207 277 34 small
## 4 PROS Elementary Pre_K 0.486 277 25 small
## 5 PROS High_School Middle_School 0.0574 166 172 negligible
## 6 PROS High_School Other 0.176 166 34 negligible
## 7 PROS High_School Pre_K 0.466 166 25 small
## 8 PROS Middle_School Other 0.110 172 34 negligible
## 9 PROS Middle_School Pre_K 0.383 172 25 small
## 10 PROS Other Pre_K 0.284 34 25 small
PROLevel<- na.omit(dplyr::select(teacher2, ResponseId, PROS, Level))
PROLevel<- PROLevel %>%
group_by(Level) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(PROS),
SD = sd(PROS, .75))
PSGend
## # A tibble: 2 x 4
## gender n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Female 450 7.62 2.14
## 2 Male 141 8.09 1.95
PROLevel
## # A tibble: 5 x 4
## Level n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Elementary 253 7.85 2.11
## 2 High_School 151 7.77 1.96
## 3 Middle_School 145 7.65 2.21
## 4 Other 29 7.41 2.06
## 5 Pre_K 23 6.83 2.08
#Assessment and Gender
ASSMgender<-lm(ASSM ~ gender, data = teacher2)
apa.aov.table(ASSMgender,"ASSMgenderanova.doc")
##
##
## ANOVA results using ASSM as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2
## (Intercept) 39024.39 1 39024.39 12887.60 .000
## gender 4.85 1 4.85 1.60 .206 .00
## Error 1616.98 534 3.03
## CI_90_partial_eta2
##
## [.00, .02]
##
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
cohens_d(teacher2, ASSM~gender)
## # A tibble: 1 x 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 ASSM Female Male 0.124 502 156 negligible
ASSMGen<- na.omit(dplyr::select(teacher2, ResponseId, ASSM, gender))
ASSMGen<- ASSMGen %>%
group_by(gender) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(ASSM),
SD = sd(ASSM, .75))
#Assessment and Grade Level
ASSMgrade<-lm(ASSM ~ Level, data = teacher2)
apa.aov.table(ASSMgrade,"ASSMlevelanova.doc")
##
##
## ANOVA results using ASSM as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 22660.21 1 22660.21 7762.27 .000
## Level 57.86 4 14.46 4.96 .001 .04 [.01, .06]
## Error 1573.49 539 2.92
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
asmd<-cohens_d(teacher2, ASSM~Level)
ASSMLevel<- na.omit(dplyr:: select(teacher2, ResponseId, ASSM, Level))
ASSMLevel<-ASSMLevel %>%
group_by(Level) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(ASSM),
SD = sd(ASSM, .75))
ASSMGen
## # A tibble: 2 x 4
## gender n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Female 410 9.76 1.68
## 2 Male 126 9.53 1.93
ASSMLevel
## # A tibble: 5 x 4
## Level n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Elementary 228 9.97 1.65
## 2 High_School 137 9.20 1.76
## 3 Middle_School 131 9.72 1.79
## 4 Other 27 9.56 1.42
## 5 Pre_K 21 10.2 1.84
write.csv(asmd, "asmd.csv")
#Efficacy and Gender
TEFFgender<-lm(TEFF ~ gender, data = teacher2)
apa.aov.table(TEFFgender,"TEFFgenderanova.doc")
##
##
## ANOVA results using TEFF as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2
## (Intercept) 55513.87 1 55513.87 10736.84 .000
## gender 5.09 1 5.09 0.98 .322 .00
## Error 2636.91 510 5.17
## CI_90_partial_eta2
##
## [.00, .01]
##
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
cohens_d(teacher2, TEFF~gender)
## # A tibble: 1 x 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 TEFF Female Male 0.106 502 156 negligible
TEFFGen<- na.omit(dplyr::select(teacher2, ResponseId, TEFF, gender))
TEFFGen<- TEFFGen %>%
group_by(gender) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(TEFF),
SD = sd(TEFF, .75))
#Efficacy and Grade Level
TEFFgrade<-lm(TEFF ~ Level, data = teacher2)
apa.aov.table(TEFFgrade,"TEFFlevelanova.doc")
##
##
## ANOVA results using TEFF as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 33049.24 1 33049.24 6605.79 .000
## Level 99.04 4 24.76 4.95 .001 .04 [.01, .06]
## Error 2576.58 515 5.00
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
teffd<-cohens_d(teacher2, TEFF~Level)
TEFFLevel<- na.omit(dplyr::select(teacher2, ResponseId, TEFF, Level))
TEFFLevel<- TEFFLevel %>%
group_by(Level) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(TEFF),
SD = sd(TEFF, .75))
TEFFGen
## # A tibble: 2 x 4
## gender n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Female 390 11.9 2.32
## 2 Male 122 11.7 2.10
TEFFLevel
## # A tibble: 5 x 4
## Level n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Elementary 217 12.3 2.38
## 2 High_School 129 11.5 2.15
## 3 Middle_School 129 11.4 2.11
## 4 Other 25 12.2 2.31
## 5 Pre_K 20 12.0 1.79
write.csv(teffd, "teffd.csv")
#leave and Gender
LVgender<-lm(Leave ~ gender, data = teacher2)
apa.aov.table(LVgender,"Leavegenderanova.doc")
##
##
## ANOVA results using Leave as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 68337.78 1 68337.78 6072.17 .000
## gender 36.52 1 36.52 3.25 .072 .01 [.00, .02]
## Error 5323.27 473 11.25
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
cohens_d(teacher2, Leave~gender)
## # A tibble: 1 x 7
## .y. group1 group2 effsize n1 n2 magnitude
## * <chr> <chr> <chr> <dbl> <int> <int> <ord>
## 1 Leave Female Male 0.193 502 156 negligible
LVGen<- na.omit(dplyr::select(teacher2, ResponseId, Leave, gender))
LVGen<- LVGen %>%
group_by(gender) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(Leave),
SD = sd(Leave, .75))
#leave and Grade Level
LVgrade<-lm(Leave ~ Level, data = teacher2)
apa.aov.table(LVgrade,"Leavelevelanova.doc")
##
##
## ANOVA results using Leave as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 39761.10 1 39761.10 3585.66 .000
## Level 121.61 4 30.40 2.74 .028 .02 [.00, .04]
## Error 5289.42 477 11.09
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
LVD<-cohens_d(teacher2, Leave~Level)
LVLevel<- na.omit(dplyr::select(teacher2, ResponseId, Leave, Level))
LVLevel<- LVLevel %>%
group_by(Level) %>%
summarize(
n = n_distinct(ResponseId),
Avg = mean(Leave),
SD = sd(Leave, .75))
LVGen
## # A tibble: 2 x 4
## gender n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Female 360 13.8 3.36
## 2 Male 115 13.1 3.35
LVLevel
## # A tibble: 5 x 4
## Level n Avg SD
## <chr> <int> <dbl> <dbl>
## 1 Elementary 205 13.9 3.33
## 2 High_School 120 12.9 3.27
## 3 Middle_School 117 13.8 3.47
## 4 Other 21 12.8 3.32
## 5 Pre_K 19 14.8 2.72
write.csv(LVD, "LeaveD.csv")
teacher2$Q15_4b<- as.character(teacher2$Q15_4)
## Anxiety
TukeyHSD(aov(anx ~ Level, data = teacher2))
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = anx ~ Level, data = teacher2)
##
## $Level
## diff lwr upr p adj
## High_School-Elementary -1.33879230 -2.3609551 -0.3166294 0.0033563
## Middle_School-Elementary -0.31870055 -1.3598054 0.7224043 0.9188549
## Other-Elementary 0.14936614 -1.7730172 2.0717494 0.9995456
## Pre_K-Elementary -0.34291998 -2.5435128 1.8576728 0.9931008
## Middle_School-High_School 1.02009175 -0.1345271 2.1747106 0.1120739
## Other-High_School 1.48815843 -0.4979944 3.4743113 0.2435892
## Pre_K-High_School 0.99587232 -1.2606414 3.2523860 0.7470906
## Other-Middle_School 0.46806668 -1.5279006 2.4640340 0.9681609
## Pre_K-Middle_School -0.02421943 -2.2893765 2.2409376 0.9999998
## Pre_K-Other -0.49228612 -3.2748380 2.2902657 0.9888124
### Assessment
TukeyHSD(aov(ASSM ~ Level, data = teacher2))
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ASSM ~ Level, data = teacher2)
##
## $Level
## diff lwr upr p adj
## High_School-Elementary -0.7722180 -1.27773590 -0.2667000 0.0003259
## Middle_School-Elementary -0.2517410 -0.76443945 0.2609575 0.6638017
## Other-Elementary -0.4137427 -1.36552721 0.5380418 0.7572898
## Pre_K-Elementary 0.2687970 -0.79765290 1.3352469 0.9586540
## Middle_School-High_School 0.5204770 -0.05098762 1.0919415 0.0937340
## Other-High_School 0.3584753 -0.62621039 1.3431609 0.8569605
## Pre_K-High_School 1.0410149 -0.05489903 2.1369289 0.0717706
## Other-Middle_School -0.1620017 -1.15039289 0.8263895 0.9916150
## Pre_K-Middle_School 0.5205380 -0.57870664 1.6197826 0.6937557
## Pre_K-Other 0.6825397 -0.67811193 2.0431913 0.6453002
### Teaching Efficacy
TukeyHSD(aov(TEFF ~ Level, data = teacher2))
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = TEFF ~ Level, data = teacher2)
##
## $Level
## diff lwr upr p adj
## High_School-Elementary -0.8448898 -1.5256289 -0.1641507 0.0065500
## Middle_School-Elementary -0.9534169 -1.6341560 -0.2726778 0.0013306
## Other-Elementary -0.1410138 -1.4342421 1.1522144 0.9982683
## Pre_K-Elementary -0.3910138 -1.8218723 1.0398447 0.9449479
## Middle_School-High_School -0.1085271 -0.8709353 0.6538810 0.9951154
## Other-High_School 0.7038760 -0.6341449 2.0418968 0.6019920
## Pre_K-High_School 0.4538760 -1.0175915 1.9253434 0.9165693
## Other-Middle_School 0.8124031 -0.5256177 2.1504239 0.4584861
## Pre_K-Middle_School 0.5624031 -0.9090643 2.0338705 0.8336089
## Pre_K-Other -0.2500000 -2.0869132 1.5869132 0.9958935
### Leave Efficacy
TukeyHSD(aov(Leave ~ Level, data = teacher2))
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Leave ~ Level, data = teacher2)
##
## $Level
## diff lwr upr p adj
## High_School-Elementary -1.0018293 -2.0498999 0.04624135 0.0688013
## Middle_School-Elementary -0.1490515 -1.2055636 0.90746065 0.9952749
## Other-Elementary -1.1649245 -3.2541440 0.92429494 0.5454984
## Pre_K-Elementary 0.8626444 -1.3240430 3.04933179 0.8167078
## Middle_School-High_School 0.8527778 -0.3319185 2.03747409 0.2817981
## Other-High_School -0.1630952 -2.3199739 1.99378338 0.9995902
## Pre_K-High_School 1.8644737 -0.3869457 4.11589304 0.1573041
## Other-Middle_School -1.0158730 -3.1768661 1.14512010 0.6992407
## Pre_K-Middle_School 1.0116959 -1.2436655 3.26705729 0.7348478
## Pre_K-Other 2.0275689 -0.8595187 4.91465655 0.3064501
matrix<- dplyr::select(teacher2, Teach, PROS, GOV, COM, ASSM, TEFF,)
matrix2<-na.omit(matrix)
apa.cor.table(matrix, filename = "correlation.doc")
##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3 4
## 1. Teach 9.30 1.76
##
## 2. PROS 7.72 2.10 .39**
## [.32, .46]
##
## 3. GOV 6.16 2.12 .34** .39**
## [.27, .41] [.32, .46]
##
## 4. COM 7.63 2.00 .48** .39** .50**
## [.41, .54] [.32, .46] [.43, .55]
##
## 5. ASSM 9.70 1.73 -.01 -.17** -.17** -.08
## [-.10, .07] [-.25, -.09] [-.25, -.09] [-.16, .01]
##
## 6. TEFF 11.88 2.27 .39** .14** .20** .46**
## [.31, .46] [.05, .22] [.11, .28] [.39, .53]
##
## 5
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .05
## [-.04, .13]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
##
#Build data set for Writing Apprehension EFA
EFAData<- read.csv("EFAData.csv")
IPSEFA<-fa(r = EFAData, nfactors = 3, rotate= 'oblimin')
loadings <- unclass(IPSEFA$loadings)
h2 <- IPSEFA$communalities
#There is also factors_data$communality which has same values
u2 <- IPSEFA$uniquenesses
com <- IPSEFA$complexity
EFA <- cbind(loadings, h2, u2, com)
EFA
## MR1 MR2 MR3 h2 u2 com
## X 0.13701206 -0.10258100 -0.027125974 0.01617835 0.9838288 1.945041
## Q23_1 -0.02229822 -0.09148562 0.818594701 0.60596262 0.3940362 1.026479
## Q23_2 -0.01145255 0.09420182 0.779050016 0.66375329 0.3362476 1.029675
## Q23_3 -0.11255183 0.44289848 0.032870330 0.17306547 0.8269328 1.140298
## Q23_4 0.19667523 0.14922216 0.522969724 0.51549346 0.4845068 1.456567
## Q23_5 0.86338828 -0.09811495 0.100926082 0.76125758 0.2387438 1.053491
## Q23_6 0.86376750 0.08294811 -0.076558917 0.75771363 0.2422852 1.034295
## Q23_7 0.48050141 0.18671265 0.080493493 0.39982869 0.6001707 1.358140
## Q23_8 -0.12948205 0.77413116 0.029820277 0.54132613 0.4586734 1.058957
## Q23_9 0.11461797 0.81838406 0.006064608 0.77162996 0.2283703 1.039327
## Q23_10 0.35983605 0.45204979 0.118099642 0.57414507 0.4258545 2.059821
IPSEFA
## Factor Analysis using method = minres
## Call: fa(r = EFAData, nfactors = 3, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## X 0.14 -0.10 -0.03 0.016 0.98 1.9
## Q23_1 -0.02 -0.09 0.82 0.606 0.39 1.0
## Q23_2 -0.01 0.09 0.78 0.664 0.34 1.0
## Q23_3 -0.11 0.44 0.03 0.173 0.83 1.1
## Q23_4 0.20 0.15 0.52 0.515 0.48 1.5
## Q23_5 0.86 -0.10 0.10 0.761 0.24 1.1
## Q23_6 0.86 0.08 -0.08 0.758 0.24 1.0
## Q23_7 0.48 0.19 0.08 0.400 0.60 1.4
## Q23_8 -0.13 0.77 0.03 0.541 0.46 1.1
## Q23_9 0.11 0.82 0.01 0.772 0.23 1.0
## Q23_10 0.36 0.45 0.12 0.574 0.43 2.1
##
## MR1 MR2 MR3
## SS loadings 2.12 1.93 1.73
## Proportion Var 0.19 0.18 0.16
## Cumulative Var 0.19 0.37 0.53
## Proportion Explained 0.37 0.33 0.30
## Cumulative Proportion 0.37 0.70 1.00
##
## With factor correlations of
## MR1 MR2 MR3
## MR1 1.00 0.45 0.46
## MR2 0.45 1.00 0.39
## MR3 0.46 0.39 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 55 and the objective function was 4.48 with Chi Square of 870.64
## The degrees of freedom for the model are 25 and the objective function was 0.24
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.05
##
## The harmonic number of observations is 200 with the empirical chi square 21.69 with prob < 0.65
## The total number of observations was 200 with Likelihood Chi Square = 47 with prob < 0.0049
##
## Tucker Lewis Index of factoring reliability = 0.94
## RMSEA index = 0.066 and the 90 % confidence intervals are 0.036 0.095
## BIC = -85.45
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of (regression) scores with factors 0.94 0.92 0.90
## Multiple R square of scores with factors 0.88 0.85 0.82
## Minimum correlation of possible factor scores 0.76 0.70 0.64
write.csv(EFA, "EFA.csv")
LVModel<-
'
LV=~ Q15_4 + Q25 + Q29 + Q41_3
'
fitlv<- cfa(LVModel, data=teacher2)
summary(fitlv, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-12 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 8
##
## Used Total
## Number of observations 487 679
##
## Model Test User Model:
##
## Test statistic 8.624
## Degrees of freedom 2
## P-value (Chi-square) 0.013
##
## Model Test Baseline Model:
##
## Test statistic 342.394
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.980
## Tucker-Lewis Index (TLI) 0.941
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2448.454
## Loglikelihood unrestricted model (H1) -2444.142
##
## Akaike (AIC) 4912.908
## Bayesian (BIC) 4946.414
## Sample-size adjusted Bayesian (BIC) 4921.022
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.082
## 90 Percent confidence interval - lower 0.032
## 90 Percent confidence interval - upper 0.142
## P-value RMSEA <= 0.05 0.128
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.026
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LV =~
## Q15_4 1.000 0.741 0.675
## Q25 0.943 0.095 9.931 0.000 0.699 0.811
## Q29 0.567 0.064 8.825 0.000 0.420 0.494
## Q41_3 0.499 0.067 7.412 0.000 0.370 0.405
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q15_4 0.656 0.065 10.132 0.000 0.656 0.544
## .Q25 0.255 0.046 5.556 0.000 0.255 0.343
## .Q29 0.548 0.039 13.884 0.000 0.548 0.756
## .Q41_3 0.698 0.048 14.607 0.000 0.698 0.836
## LV 0.550 0.081 6.753 0.000 1.000 1.000
parameterEstimates(fitlv, standardized=TRUE) %>%
filter(op == "=~") %>%
dplyr:: select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'=pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")
Factor Loadings
| LV |
Q15_4 |
1.000 |
0.000 |
NA |
NA |
0.675 |
| LV |
Q25 |
0.943 |
0.095 |
9.931 |
0 |
0.811 |
| LV |
Q29 |
0.567 |
0.064 |
8.825 |
0 |
0.494 |
| LV |
Q41_3 |
0.499 |
0.067 |
7.412 |
0 |
0.405 |
residuals(fitlv, type = "cor")
## $type
## [1] "cor.bollen"
##
## $cov
## Q15_4 Q25 Q29 Q41_3
## Q15_4 0.000
## Q25 0.003 0.000
## Q29 -0.047 0.019 0.000
## Q41_3 0.049 -0.033 0.026 0.000
semPaths(fitlv, "par", edge.label.cex = 1.2, fade = FALSE)
Support<-
'
SUP=~ Q23_5 + Q23_6 + Q23_7 + Q23_3 + Q23_8 + Q23_9 + Q23_10 + Q23_1 + Q23_2 + Q23_4
'
fitC<- cfa(Support, data=teacher2)
summary(fitC, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-12 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 20
##
## Used Total
## Number of observations 583 679
##
## Model Test User Model:
##
## Test statistic 879.500
## Degrees of freedom 35
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2679.393
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.679
## Tucker-Lewis Index (TLI) 0.588
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -6314.829
## Loglikelihood unrestricted model (H1) -5875.080
##
## Akaike (AIC) 12669.659
## Bayesian (BIC) 12757.023
## Sample-size adjusted Bayesian (BIC) 12693.530
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.203
## 90 Percent confidence interval - lower 0.192
## 90 Percent confidence interval - upper 0.215
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.105
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SUP =~
## Q23_5 1.000 0.541 0.684
## Q23_6 1.086 0.071 15.205 0.000 0.587 0.710
## Q23_7 0.963 0.073 13.171 0.000 0.521 0.606
## Q23_3 0.400 0.058 6.873 0.000 0.216 0.307
## Q23_8 0.972 0.083 11.730 0.000 0.526 0.535
## Q23_9 1.226 0.080 15.240 0.000 0.663 0.712
## Q23_10 1.286 0.080 16.135 0.000 0.695 0.761
## Q23_1 0.775 0.067 11.508 0.000 0.419 0.524
## Q23_2 0.912 0.068 13.478 0.000 0.493 0.621
## Q23_4 0.952 0.066 14.355 0.000 0.515 0.666
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q23_5 0.333 0.022 14.964 0.000 0.333 0.532
## .Q23_6 0.339 0.023 14.630 0.000 0.339 0.496
## .Q23_7 0.468 0.030 15.688 0.000 0.468 0.633
## .Q23_3 0.449 0.027 16.826 0.000 0.449 0.906
## .Q23_8 0.690 0.043 16.118 0.000 0.690 0.714
## .Q23_9 0.428 0.029 14.604 0.000 0.428 0.493
## .Q23_10 0.352 0.026 13.770 0.000 0.352 0.421
## .Q23_1 0.465 0.029 16.170 0.000 0.465 0.726
## .Q23_2 0.388 0.025 15.571 0.000 0.388 0.614
## .Q23_4 0.333 0.022 15.166 0.000 0.333 0.557
## SUP 0.292 0.033 8.917 0.000 1.000 1.000
parameterEstimates(fitC, standardized=TRUE) %>%
filter(op == "=~") %>%
select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'=pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")
Factor Loadings
| SUP |
Q23_5 |
1.000 |
0.000 |
NA |
NA |
0.684 |
| SUP |
Q23_6 |
1.086 |
0.071 |
15.205 |
0 |
0.710 |
| SUP |
Q23_7 |
0.963 |
0.073 |
13.171 |
0 |
0.606 |
| SUP |
Q23_3 |
0.400 |
0.058 |
6.873 |
0 |
0.307 |
| SUP |
Q23_8 |
0.972 |
0.083 |
11.730 |
0 |
0.535 |
| SUP |
Q23_9 |
1.226 |
0.080 |
15.240 |
0 |
0.712 |
| SUP |
Q23_10 |
1.286 |
0.080 |
16.135 |
0 |
0.761 |
| SUP |
Q23_1 |
0.775 |
0.067 |
11.508 |
0 |
0.524 |
| SUP |
Q23_2 |
0.912 |
0.068 |
13.478 |
0 |
0.621 |
| SUP |
Q23_4 |
0.952 |
0.066 |
14.355 |
0 |
0.666 |
residuals(fitC, type = "cor")
## $type
## [1] "cor.bollen"
##
## $cov
## Q23_5 Q23_6 Q23_7 Q23_3 Q23_8 Q23_9 Q23_10 Q23_1 Q23_2 Q23_4
## Q23_5 0.000
## Q23_6 0.248 0.000
## Q23_7 0.071 0.142 0.000
## Q23_3 -0.133 -0.084 -0.010 0.000
## Q23_8 -0.181 -0.094 -0.047 0.221 0.000
## Q23_9 -0.127 -0.080 -0.032 0.070 0.270 0.000
## Q23_10 -0.033 -0.036 -0.038 -0.027 0.090 0.185 0.000
## Q23_1 -0.027 -0.081 -0.065 0.003 -0.087 -0.099 -0.056 0.000
## Q23_2 -0.023 -0.057 -0.063 0.023 -0.064 -0.084 -0.078 0.327 0.000
## Q23_4 0.038 -0.050 -0.025 0.048 -0.074 -0.080 -0.063 0.159 0.199 0.000
semPaths(fitC, "par", edge.label.cex = 1.2, fade = FALSE)
OnSupport2F<-
'
GOVs=~ Q23_5 + Q23_6 + Q23_7
PRO=~ Q23_3 + Q23_8 + Q23_9 + Q23_10
COMs=~ Q23_1 + Q23_2 + Q23_4
'
fitd<- cfa(OnSupport2F, data=teacher2)
summary(fitd, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-12 ended normally after 41 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 23
##
## Used Total
## Number of observations 583 679
##
## Model Test User Model:
##
## Test statistic 211.674
## Degrees of freedom 32
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2679.393
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.932
## Tucker-Lewis Index (TLI) 0.904
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5980.916
## Loglikelihood unrestricted model (H1) -5875.080
##
## Akaike (AIC) 12007.833
## Bayesian (BIC) 12108.301
## Sample-size adjusted Bayesian (BIC) 12035.285
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.098
## 90 Percent confidence interval - lower 0.086
## 90 Percent confidence interval - upper 0.111
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.063
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## GOVs =~
## Q23_5 1.000 0.645 0.816
## Q23_6 1.141 0.055 20.884 0.000 0.736 0.890
## Q23_7 0.851 0.054 15.677 0.000 0.549 0.639
## PRO =~
## Q23_3 1.000 0.236 0.335
## Q23_8 2.870 0.380 7.552 0.000 0.678 0.689
## Q23_9 3.560 0.452 7.872 0.000 0.841 0.903
## Q23_10 3.114 0.400 7.787 0.000 0.735 0.805
## COMs =~
## Q23_1 1.000 0.584 0.729
## Q23_2 1.159 0.067 17.253 0.000 0.677 0.852
## Q23_4 0.976 0.061 16.047 0.000 0.570 0.737
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## GOVs ~~
## PRO 0.087 0.014 6.349 0.000 0.570 0.570
## COMs 0.214 0.023 9.228 0.000 0.568 0.568
## PRO ~~
## COMs 0.072 0.012 6.065 0.000 0.521 0.521
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q23_5 0.209 0.019 10.982 0.000 0.209 0.334
## .Q23_6 0.142 0.020 6.998 0.000 0.142 0.208
## .Q23_7 0.438 0.029 15.301 0.000 0.438 0.592
## .Q23_3 0.440 0.026 16.792 0.000 0.440 0.888
## .Q23_8 0.507 0.034 14.863 0.000 0.507 0.525
## .Q23_9 0.160 0.024 6.682 0.000 0.160 0.185
## .Q23_10 0.294 0.025 11.956 0.000 0.294 0.352
## .Q23_1 0.300 0.023 13.145 0.000 0.300 0.468
## .Q23_2 0.173 0.021 8.307 0.000 0.173 0.274
## .Q23_4 0.274 0.021 12.945 0.000 0.274 0.457
## GOVs 0.416 0.037 11.111 0.000 1.000 1.000
## PRO 0.056 0.014 3.929 0.000 1.000 1.000
## COMs 0.341 0.036 9.411 0.000 1.000 1.000
parameterEstimates(fitd, standardized=TRUE) %>%
filter(op == "=~") %>%
select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'=pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")
Factor Loadings
| GOVs |
Q23_5 |
1.000 |
0.000 |
NA |
NA |
0.816 |
| GOVs |
Q23_6 |
1.141 |
0.055 |
20.884 |
0 |
0.890 |
| GOVs |
Q23_7 |
0.851 |
0.054 |
15.677 |
0 |
0.639 |
| PRO |
Q23_3 |
1.000 |
0.000 |
NA |
NA |
0.335 |
| PRO |
Q23_8 |
2.870 |
0.380 |
7.552 |
0 |
0.689 |
| PRO |
Q23_9 |
3.560 |
0.452 |
7.872 |
0 |
0.903 |
| PRO |
Q23_10 |
3.114 |
0.400 |
7.787 |
0 |
0.805 |
| COMs |
Q23_1 |
1.000 |
0.000 |
NA |
NA |
0.729 |
| COMs |
Q23_2 |
1.159 |
0.067 |
17.253 |
0 |
0.852 |
| COMs |
Q23_4 |
0.976 |
0.061 |
16.047 |
0 |
0.737 |
residuals(fitd, type = "cor")
## $type
## [1] "cor.bollen"
##
## $cov
## Q23_5 Q23_6 Q23_7 Q23_3 Q23_8 Q23_9 Q23_10 Q23_1 Q23_2 Q23_4
## Q23_5 0.000
## Q23_6 0.008 0.000
## Q23_7 -0.036 0.004 0.000
## Q23_3 -0.079 -0.036 0.053 0.000
## Q23_8 -0.136 -0.064 0.026 0.154 0.000
## Q23_9 -0.060 -0.032 0.070 -0.014 0.029 0.000
## Q23_10 0.113 0.096 0.130 -0.063 -0.058 0.000 0.000
## Q23_1 -0.007 -0.078 -0.012 0.036 -0.069 -0.069 0.037 0.000
## Q23_2 0.006 -0.047 0.004 0.064 -0.039 -0.043 0.037 0.032 0.000
## Q23_4 0.151 0.050 0.111 0.123 0.017 0.047 0.134 -0.030 -0.016 0.000
semPaths(fitd, "par", edge.label.cex = 1.2, fade = FALSE)
AnxModel<-
'
ANXs=~ Q19_1 + Q19_2 + Q19_3 + Q19_4 + Q19_5
'
fitAnx<- cfa(AnxModel, data=teacher2)
summary(fitAnx, fit.measures=TRUE, standardized=TRUE)
## lavaan 0.6-12 ended normally after 21 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Used Total
## Number of observations 619 679
##
## Model Test User Model:
##
## Test statistic 19.833
## Degrees of freedom 5
## P-value (Chi-square) 0.001
##
## Model Test Baseline Model:
##
## Test statistic 1699.553
## Degrees of freedom 10
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.991
## Tucker-Lewis Index (TLI) 0.982
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3202.234
## Loglikelihood unrestricted model (H1) -3192.318
##
## Akaike (AIC) 6424.469
## Bayesian (BIC) 6468.750
## Sample-size adjusted Bayesian (BIC) 6437.002
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.069
## 90 Percent confidence interval - lower 0.039
## 90 Percent confidence interval - upper 0.102
## P-value RMSEA <= 0.05 0.135
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.017
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ANXs =~
## Q19_1 1.000 0.743 0.811
## Q19_2 0.899 0.043 21.085 0.000 0.668 0.775
## Q19_3 1.036 0.042 24.470 0.000 0.770 0.873
## Q19_4 0.934 0.043 21.597 0.000 0.694 0.789
## Q19_5 0.855 0.047 18.011 0.000 0.635 0.684
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q19_1 0.288 0.021 13.738 0.000 0.288 0.342
## .Q19_2 0.297 0.020 14.609 0.000 0.297 0.400
## .Q19_3 0.185 0.017 11.177 0.000 0.185 0.238
## .Q19_4 0.292 0.020 14.299 0.000 0.292 0.377
## .Q19_5 0.459 0.029 15.871 0.000 0.459 0.532
## ANXs 0.552 0.047 11.821 0.000 1.000 1.000
parameterEstimates(fitAnx, standardized=TRUE) %>%
filter(op == "=~") %>%
select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'=pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")
Factor Loadings
| ANXs |
Q19_1 |
1.000 |
0.000 |
NA |
NA |
0.811 |
| ANXs |
Q19_2 |
0.899 |
0.043 |
21.085 |
0 |
0.775 |
| ANXs |
Q19_3 |
1.036 |
0.042 |
24.470 |
0 |
0.873 |
| ANXs |
Q19_4 |
0.934 |
0.043 |
21.597 |
0 |
0.789 |
| ANXs |
Q19_5 |
0.855 |
0.047 |
18.011 |
0 |
0.684 |
residuals(fitAnx, type = "cor")
## $type
## [1] "cor.bollen"
##
## $cov
## Q19_1 Q19_2 Q19_3 Q19_4 Q19_5
## Q19_1 0.000
## Q19_2 0.018 0.000
## Q19_3 -0.019 0.013 0.000
## Q19_4 -0.008 -0.028 0.018 0.000
## Q19_5 0.040 -0.026 -0.015 0.004 0.000
semPaths(fitAnx, "par", edge.label.cex = 1.2, fade = FALSE)
DepModel<-
'DEPs=~ Q20_1 + Q20_2 + Q20_3 + Q20_4 + Q20_5 + Q20_6 + Q20_7 + Q20_8'
fitdep<- cfa(DepModel, data=teacher2)
summary(fitdep, fit.measures=TRUE, standardized=TRUE)
## lavaan 0.6-12 ended normally after 21 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 16
##
## Used Total
## Number of observations 611 679
##
## Model Test User Model:
##
## Test statistic 151.742
## Degrees of freedom 20
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2426.622
## Degrees of freedom 28
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.945
## Tucker-Lewis Index (TLI) 0.923
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5093.503
## Loglikelihood unrestricted model (H1) -5017.632
##
## Akaike (AIC) 10219.006
## Bayesian (BIC) 10289.648
## Sample-size adjusted Bayesian (BIC) 10238.851
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.104
## 90 Percent confidence interval - lower 0.089
## 90 Percent confidence interval - upper 0.120
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.041
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DEPs =~
## Q20_1 1.000 0.677 0.795
## Q20_2 1.064 0.048 22.379 0.000 0.720 0.829
## Q20_3 0.956 0.053 18.188 0.000 0.647 0.701
## Q20_4 0.801 0.046 17.554 0.000 0.542 0.681
## Q20_5 0.876 0.052 16.960 0.000 0.593 0.661
## Q20_6 0.978 0.052 18.927 0.000 0.662 0.724
## Q20_7 1.006 0.050 20.096 0.000 0.681 0.760
## Q20_8 0.694 0.046 15.010 0.000 0.470 0.595
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q20_1 0.266 0.019 14.270 0.000 0.266 0.367
## .Q20_2 0.237 0.018 13.393 0.000 0.237 0.313
## .Q20_3 0.434 0.028 15.692 0.000 0.434 0.509
## .Q20_4 0.341 0.021 15.884 0.000 0.341 0.537
## .Q20_5 0.453 0.028 16.045 0.000 0.453 0.563
## .Q20_6 0.397 0.026 15.435 0.000 0.397 0.475
## .Q20_7 0.339 0.023 14.934 0.000 0.339 0.422
## .Q20_8 0.402 0.024 16.467 0.000 0.402 0.646
## DEPs 0.458 0.040 11.452 0.000 1.000 1.000
parameterEstimates(fitdep, standardized=TRUE) %>%
filter(op == "=~") %>%
select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'=pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")
Factor Loadings
| DEPs |
Q20_1 |
1.000 |
0.000 |
NA |
NA |
0.795 |
| DEPs |
Q20_2 |
1.064 |
0.048 |
22.379 |
0 |
0.829 |
| DEPs |
Q20_3 |
0.956 |
0.053 |
18.188 |
0 |
0.701 |
| DEPs |
Q20_4 |
0.801 |
0.046 |
17.554 |
0 |
0.681 |
| DEPs |
Q20_5 |
0.876 |
0.052 |
16.960 |
0 |
0.661 |
| DEPs |
Q20_6 |
0.978 |
0.052 |
18.927 |
0 |
0.724 |
| DEPs |
Q20_7 |
1.006 |
0.050 |
20.096 |
0 |
0.760 |
| DEPs |
Q20_8 |
0.694 |
0.046 |
15.010 |
0 |
0.595 |
residuals(fitdep, type = "cor")
## $type
## [1] "cor.bollen"
##
## $cov
## Q20_1 Q20_2 Q20_3 Q20_4 Q20_5 Q20_6 Q20_7 Q20_8
## Q20_1 0.000
## Q20_2 0.064 0.000
## Q20_3 -0.034 -0.022 0.000
## Q20_4 0.005 -0.039 0.090 0.000
## Q20_5 -0.006 -0.059 0.058 0.113 0.000
## Q20_6 -0.039 0.032 -0.022 -0.053 -0.003 0.000
## Q20_7 -0.030 -0.020 0.004 0.001 -0.021 0.040 0.000
## Q20_8 -0.008 -0.004 -0.027 -0.094 -0.011 0.032 0.081 0.000
semPaths(fitdep, "par", edge.label.cex = 1.2, fade = FALSE)
DepModel2<-
'DEPs=~ Q20_1 + Q20_2 + Q20_3 + Q20_5 + Q20_6 + Q20_7 + Q20_8'
fitdep2<- cfa(DepModel2, data=teacher2)
summary(fitdep2, fit.measures=TRUE, standardized=TRUE)
## lavaan 0.6-12 ended normally after 21 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Used Total
## Number of observations 616 679
##
## Model Test User Model:
##
## Test statistic 79.787
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2073.338
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.968
## Tucker-Lewis Index (TLI) 0.952
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4567.307
## Loglikelihood unrestricted model (H1) -4527.413
##
## Akaike (AIC) 9162.614
## Bayesian (BIC) 9224.539
## Sample-size adjusted Bayesian (BIC) 9180.092
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.087
## 90 Percent confidence interval - lower 0.069
## 90 Percent confidence interval - upper 0.106
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.030
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DEPs =~
## Q20_1 1.000 0.680 0.796
## Q20_2 1.081 0.047 22.778 0.000 0.736 0.843
## Q20_3 0.933 0.053 17.692 0.000 0.635 0.685
## Q20_5 0.842 0.052 16.259 0.000 0.573 0.638
## Q20_6 0.994 0.051 19.330 0.000 0.676 0.737
## Q20_7 1.007 0.050 20.117 0.000 0.685 0.762
## Q20_8 0.721 0.046 15.600 0.000 0.491 0.616
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q20_1 0.267 0.019 14.072 0.000 0.267 0.366
## .Q20_2 0.220 0.017 12.604 0.000 0.220 0.289
## .Q20_3 0.455 0.029 15.792 0.000 0.455 0.530
## .Q20_5 0.478 0.030 16.191 0.000 0.478 0.593
## .Q20_6 0.383 0.025 15.174 0.000 0.383 0.456
## .Q20_7 0.339 0.023 14.785 0.000 0.339 0.420
## .Q20_8 0.394 0.024 16.343 0.000 0.394 0.621
## DEPs 0.463 0.040 11.474 0.000 1.000 1.000
parameterEstimates(fitdep2, standardized=TRUE) %>%
filter(op == "=~") %>%
select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'=pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")
Factor Loadings
| DEPs |
Q20_1 |
1.000 |
0.000 |
NA |
NA |
0.796 |
| DEPs |
Q20_2 |
1.081 |
0.047 |
22.778 |
0 |
0.843 |
| DEPs |
Q20_3 |
0.933 |
0.053 |
17.692 |
0 |
0.685 |
| DEPs |
Q20_5 |
0.842 |
0.052 |
16.259 |
0 |
0.638 |
| DEPs |
Q20_6 |
0.994 |
0.051 |
19.330 |
0 |
0.737 |
| DEPs |
Q20_7 |
1.007 |
0.050 |
20.117 |
0 |
0.762 |
| DEPs |
Q20_8 |
0.721 |
0.046 |
15.600 |
0 |
0.616 |
residuals(fitdep2, type = "cor")
## $type
## [1] "cor.bollen"
##
## $cov
## Q20_1 Q20_2 Q20_3 Q20_5 Q20_6 Q20_7 Q20_8
## Q20_1 0.000
## Q20_2 0.054 0.000
## Q20_3 -0.019 -0.014 0.000
## Q20_5 0.012 -0.047 0.087 0.000
## Q20_6 -0.044 0.015 -0.016 0.006 0.000
## Q20_7 -0.030 -0.028 0.020 -0.002 0.030 0.000
## Q20_8 -0.022 -0.022 -0.023 -0.007 0.012 0.072 0.000
semPaths(fitdep2, "par", edge.label.cex = 1.2, fade = FALSE)
TeachModel<-
'
T=~ Q17_1 + Q17_2 + Q17_3 + Q17_4 + Q17_5
'
fitA<- cfa(TeachModel, data=teacher2)
summary(fitA, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-12 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Used Total
## Number of observations 618 679
##
## Model Test User Model:
##
## Test statistic 23.308
## Degrees of freedom 5
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 520.307
## Degrees of freedom 10
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.964
## Tucker-Lewis Index (TLI) 0.928
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3339.952
## Loglikelihood unrestricted model (H1) -3328.298
##
## Akaike (AIC) 6699.903
## Bayesian (BIC) 6744.168
## Sample-size adjusted Bayesian (BIC) 6712.420
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.077
## 90 Percent confidence interval - lower 0.047
## 90 Percent confidence interval - upper 0.110
## P-value RMSEA <= 0.05 0.066
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.037
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## T =~
## Q17_1 1.000 0.358 0.506
## Q17_2 1.606 0.171 9.370 0.000 0.574 0.585
## Q17_3 1.618 0.159 10.166 0.000 0.579 0.750
## Q17_4 0.418 0.090 4.634 0.000 0.150 0.227
## Q17_5 1.473 0.148 9.978 0.000 0.527 0.674
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q17_1 0.372 0.024 15.518 0.000 0.372 0.744
## .Q17_2 0.636 0.044 14.396 0.000 0.636 0.658
## .Q17_3 0.260 0.027 9.598 0.000 0.260 0.437
## .Q17_4 0.411 0.024 17.262 0.000 0.411 0.948
## .Q17_5 0.333 0.027 12.247 0.000 0.333 0.546
## T 0.128 0.022 5.787 0.000 1.000 1.000
parameterEstimates(fitA, standardized=TRUE) %>%
filter(op == "=~") %>%
dplyr::select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'=pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")
Factor Loadings
| T |
Q17_1 |
1.000 |
0.000 |
NA |
NA |
0.506 |
| T |
Q17_2 |
1.606 |
0.171 |
9.370 |
0 |
0.585 |
| T |
Q17_3 |
1.618 |
0.159 |
10.166 |
0 |
0.750 |
| T |
Q17_4 |
0.418 |
0.090 |
4.634 |
0 |
0.227 |
| T |
Q17_5 |
1.473 |
0.148 |
9.978 |
0 |
0.674 |
residuals(fitA, type = "cor")
## $type
## [1] "cor.bollen"
##
## $cov
## Q17_1 Q17_2 Q17_3 Q17_4 Q17_5
## Q17_1 0.000
## Q17_2 0.003 0.000
## Q17_3 0.010 0.010 0.000
## Q17_4 -0.038 -0.094 -0.008 0.000
## Q17_5 -0.009 0.002 -0.011 0.100 0.000
semPaths(fitA, "par", edge.label.cex = 1.2, fade = FALSE)
QualityModel<-
'
T=~ Q17_1 + Q17_3 + Q17_5
'
fitA2<- cfa(QualityModel, data=teacher2)
summary(fitA2, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-12 ended normally after 16 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 6
##
## Used Total
## Number of observations 621 679
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 299.475
## Degrees of freedom 3
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1966.063
## Loglikelihood unrestricted model (H1) -1966.063
##
## Akaike (AIC) 3944.126
## Bayesian (BIC) 3970.713
## Sample-size adjusted Bayesian (BIC) 3951.664
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## T =~
## Q17_1 1.000 0.364 0.514
## Q17_3 1.616 0.186 8.676 0.000 0.589 0.764
## Q17_5 1.396 0.149 9.351 0.000 0.509 0.651
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q17_1 0.369 0.025 14.703 0.000 0.369 0.735
## .Q17_3 0.248 0.039 6.397 0.000 0.248 0.417
## .Q17_5 0.352 0.034 10.489 0.000 0.352 0.576
## T 0.133 0.024 5.591 0.000 1.000 1.000
parameterEstimates(fitA2, standardized=TRUE) %>%
filter(op == "=~") %>%
dplyr::select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'=pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")
Factor Loadings
| T |
Q17_1 |
1.000 |
0.000 |
NA |
NA |
0.514 |
| T |
Q17_3 |
1.616 |
0.186 |
8.676 |
0 |
0.764 |
| T |
Q17_5 |
1.396 |
0.149 |
9.351 |
0 |
0.651 |
residuals(fitA2, type = "cor")
## $type
## [1] "cor.bollen"
##
## $cov
## Q17_1 Q17_3 Q17_5
## Q17_1 0
## Q17_3 0 0
## Q17_5 0 0 0
semPaths(fitA2, "par", edge.label.cex = 1.2, fade = FALSE)
EffModel<-
'
TEFFs=~ Q44_1 + Q44_2 + Q44_3 + Q44_4
'
fitj<- cfa(EffModel, data=teacher2)
summary(fitj, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-12 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 8
##
## Used Total
## Number of observations 521 679
##
## Model Test User Model:
##
## Test statistic 3.169
## Degrees of freedom 2
## P-value (Chi-square) 0.205
##
## Model Test Baseline Model:
##
## Test statistic 1168.182
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.999
## Tucker-Lewis Index (TLI) 0.997
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1502.573
## Loglikelihood unrestricted model (H1) -1500.988
##
## Akaike (AIC) 3021.146
## Bayesian (BIC) 3055.192
## Sample-size adjusted Bayesian (BIC) 3029.798
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.033
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.099
## P-value RMSEA <= 0.05 0.560
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.008
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TEFFs =~
## Q44_1 1.000 0.545 0.782
## Q44_2 0.941 0.044 21.152 0.000 0.513 0.870
## Q44_3 1.079 0.050 21.403 0.000 0.588 0.882
## Q44_4 0.887 0.053 16.668 0.000 0.484 0.708
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q44_1 0.189 0.014 13.236 0.000 0.189 0.389
## .Q44_2 0.085 0.008 10.099 0.000 0.085 0.243
## .Q44_3 0.099 0.011 9.423 0.000 0.099 0.222
## .Q44_4 0.233 0.016 14.319 0.000 0.233 0.499
## TEFFs 0.297 0.029 10.270 0.000 1.000 1.000
parameterEstimates(fitj, standardized=TRUE) %>%
filter(op == "=~") %>%
dplyr::select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'=pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")
Factor Loadings
| TEFFs |
Q44_1 |
1.000 |
0.000 |
NA |
NA |
0.782 |
| TEFFs |
Q44_2 |
0.941 |
0.044 |
21.152 |
0 |
0.870 |
| TEFFs |
Q44_3 |
1.079 |
0.050 |
21.403 |
0 |
0.882 |
| TEFFs |
Q44_4 |
0.887 |
0.053 |
16.668 |
0 |
0.708 |
residuals(fitj, type = "cor")
## $type
## [1] "cor.bollen"
##
## $cov
## Q44_1 Q44_2 Q44_3 Q44_4
## Q44_1 0.000
## Q44_2 0.011 0.000
## Q44_3 -0.010 0.000 0.000
## Q44_4 -0.001 -0.015 0.014 0.000
semPaths(fitj, "par", edge.label.cex = 1.2, fade = FALSE)
Assessmentmodel<-
'
ASSMs=~ Q41_1 + Q41_2 + Q41_4
'
fitasm<- cfa(Assessmentmodel, data=teacher2)
summary(fitasm, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-12 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 6
##
## Used Total
## Number of observations 545 679
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 277.610
## Degrees of freedom 3
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1694.809
## Loglikelihood unrestricted model (H1) -1694.809
##
## Akaike (AIC) 3401.619
## Bayesian (BIC) 3427.424
## Sample-size adjusted Bayesian (BIC) 3408.377
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ASSMs =~
## Q41_1 1.000 0.549 0.683
## Q41_2 1.160 0.160 7.245 0.000 0.637 0.829
## Q41_4 0.475 0.063 7.517 0.000 0.260 0.393
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q41_1 0.345 0.045 7.749 0.000 0.345 0.534
## .Q41_2 0.185 0.054 3.414 0.001 0.185 0.313
## .Q41_4 0.372 0.024 15.360 0.000 0.372 0.846
## ASSMs 0.301 0.051 5.857 0.000 1.000 1.000
parameterEstimates(fitasm, standardized=TRUE) %>%
filter(op == "=~") %>%
dplyr::select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'=pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")
Factor Loadings
| ASSMs |
Q41_1 |
1.000 |
0.000 |
NA |
NA |
0.683 |
| ASSMs |
Q41_2 |
1.160 |
0.160 |
7.245 |
0 |
0.829 |
| ASSMs |
Q41_4 |
0.475 |
0.063 |
7.517 |
0 |
0.393 |
residuals(fitasm, type = "cor")
## $type
## [1] "cor.bollen"
##
## $cov
## Q41_1 Q41_2 Q41_4
## Q41_1 0
## Q41_2 0 0
## Q41_4 0 0 0
semPaths(fitasm, "par", edge.label.cex = 1.2, fade = FALSE)
TeachModel2<-
'
Teachs=~Q17_1 + Q17_3 + Q17_5
ANXs=~ Q19_1 + Q19_2 + Q19_3 + Q19_4 + Q19_5
DEPs=~ Q20_1 + Q20_2 + Q20_3 + Q20_5 + Q20_6 + Q20_7 + Q20_8
GOVt=~ Q23_5 + Q23_6 + Q23_7
PRO=~ Q23_3 + Q23_8 + Q23_9 + Q23_10
COMt=~ Q23_1 + Q23_2 + Q23_4
ASSMs=~ Q41_1 + Q41_2 + Q41_4
TEFFs=~ Q44_1 + Q44_2 + Q44_3 + Q44_4
LV=~ Q15_4 + Q21 + Q25 + Q29 + Q41_3
'
fitE<- cfa(TeachModel2, data=teacher2)
summary(fitE, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-12 ended normally after 117 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 110
##
## Used Total
## Number of observations 433 679
##
## Model Test User Model:
##
## Test statistic 1386.876
## Degrees of freedom 593
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 8851.338
## Degrees of freedom 666
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.903
## Tucker-Lewis Index (TLI) 0.891
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -15857.149
## Loglikelihood unrestricted model (H1) -15163.711
##
## Akaike (AIC) 31934.298
## Bayesian (BIC) 32382.079
## Sample-size adjusted Bayesian (BIC) 32033.000
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.056
## 90 Percent confidence interval - lower 0.052
## 90 Percent confidence interval - upper 0.059
## P-value RMSEA <= 0.05 0.008
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.060
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Teachs =~
## Q17_1 1.000 0.352 0.555
## Q17_3 1.382 0.148 9.332 0.000 0.487 0.642
## Q17_5 1.657 0.164 10.129 0.000 0.583 0.776
## ANXs =~
## Q19_1 1.000 0.767 0.836
## Q19_2 0.850 0.047 17.982 0.000 0.652 0.755
## Q19_3 0.995 0.046 21.804 0.000 0.763 0.861
## Q19_4 0.933 0.048 19.383 0.000 0.715 0.796
## Q19_5 0.903 0.052 17.291 0.000 0.693 0.734
## DEPs =~
## Q20_1 1.000 0.660 0.769
## Q20_2 1.118 0.061 18.353 0.000 0.738 0.840
## Q20_3 0.949 0.067 14.250 0.000 0.627 0.674
## Q20_5 0.849 0.064 13.316 0.000 0.560 0.635
## Q20_6 0.995 0.065 15.367 0.000 0.657 0.720
## Q20_7 1.042 0.063 16.640 0.000 0.688 0.772
## Q20_8 0.730 0.058 12.478 0.000 0.482 0.598
## GOVt =~
## Q23_5 1.000 0.630 0.805
## Q23_6 1.178 0.066 17.746 0.000 0.743 0.887
## Q23_7 0.901 0.064 14.000 0.000 0.568 0.665
## PRO =~
## Q23_3 1.000 0.249 0.352
## Q23_8 2.743 0.395 6.940 0.000 0.683 0.711
## Q23_9 3.417 0.472 7.244 0.000 0.851 0.920
## Q23_10 2.853 0.403 7.079 0.000 0.711 0.777
## COMt =~
## Q23_1 1.000 0.568 0.718
## Q23_2 1.074 0.075 14.379 0.000 0.610 0.806
## Q23_4 1.013 0.074 13.762 0.000 0.575 0.754
## ASSMs =~
## Q41_1 1.000 0.540 0.671
## Q41_2 1.219 0.128 9.552 0.000 0.658 0.870
## Q41_4 0.496 0.067 7.423 0.000 0.268 0.411
## TEFFs =~
## Q44_1 1.000 0.547 0.780
## Q44_2 0.967 0.048 19.958 0.000 0.529 0.889
## Q44_3 1.064 0.054 19.752 0.000 0.583 0.879
## Q44_4 0.874 0.058 15.146 0.000 0.478 0.702
## LV =~
## Q15_4 1.000 0.684 0.621
## Q21 1.056 0.084 12.576 0.000 0.722 0.760
## Q25 1.057 0.079 13.320 0.000 0.723 0.831
## Q29 0.596 0.070 8.546 0.000 0.408 0.468
## Q41_3 0.591 0.073 8.057 0.000 0.404 0.438
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Teachs ~~
## ANXs -0.108 0.019 -5.649 0.000 -0.398 -0.398
## DEPs -0.091 0.017 -5.496 0.000 -0.391 -0.391
## GOVt 0.091 0.016 5.625 0.000 0.410 0.410
## PRO 0.045 0.009 4.997 0.000 0.510 0.510
## COMt 0.122 0.018 6.874 0.000 0.608 0.608
## ASSMs 0.006 0.012 0.470 0.639 0.030 0.030
## TEFFs 0.095 0.015 6.423 0.000 0.492 0.492
## LV -0.163 0.023 -6.984 0.000 -0.676 -0.676
## ANXs ~~
## DEPs 0.405 0.039 10.449 0.000 0.799 0.799
## GOVt -0.123 0.028 -4.447 0.000 -0.254 -0.254
## PRO -0.074 0.015 -4.965 0.000 -0.385 -0.385
## COMt -0.162 0.027 -5.964 0.000 -0.373 -0.373
## ASSMs 0.109 0.026 4.253 0.000 0.264 0.264
## TEFFs -0.064 0.023 -2.813 0.005 -0.152 -0.152
## LV 0.355 0.042 8.547 0.000 0.676 0.676
## DEPs ~~
## GOVt -0.101 0.024 -4.238 0.000 -0.244 -0.244
## PRO -0.055 0.012 -4.553 0.000 -0.331 -0.331
## COMt -0.130 0.023 -5.534 0.000 -0.346 -0.346
## ASSMs 0.096 0.022 4.288 0.000 0.270 0.270
## TEFFs -0.075 0.020 -3.750 0.000 -0.208 -0.208
## LV 0.284 0.035 8.036 0.000 0.629 0.629
## GOVt ~~
## PRO 0.089 0.016 5.674 0.000 0.568 0.568
## COMt 0.191 0.025 7.550 0.000 0.534 0.534
## ASSMs -0.078 0.021 -3.702 0.000 -0.230 -0.230
## TEFFs 0.076 0.020 3.886 0.000 0.220 0.220
## LV -0.201 0.030 -6.686 0.000 -0.466 -0.466
## PRO ~~
## COMt 0.075 0.014 5.424 0.000 0.530 0.530
## ASSMs -0.020 0.008 -2.443 0.015 -0.151 -0.151
## TEFFs 0.030 0.009 3.546 0.000 0.222 0.222
## LV -0.116 0.020 -5.690 0.000 -0.680 -0.680
## COMt ~~
## ASSMs -0.031 0.019 -1.694 0.090 -0.102 -0.102
## TEFFs 0.152 0.021 7.231 0.000 0.489 0.489
## LV -0.211 0.030 -7.072 0.000 -0.543 -0.543
## ASSMs ~~
## TEFFs 0.023 0.017 1.364 0.173 0.077 0.077
## LV 0.133 0.026 5.023 0.000 0.359 0.359
## TEFFs ~~
## LV -0.109 0.023 -4.767 0.000 -0.291 -0.291
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q17_1 0.279 0.021 13.002 0.000 0.279 0.692
## .Q17_3 0.338 0.028 11.963 0.000 0.338 0.588
## .Q17_5 0.225 0.026 8.579 0.000 0.225 0.398
## .Q19_1 0.254 0.022 11.588 0.000 0.254 0.302
## .Q19_2 0.320 0.025 12.936 0.000 0.320 0.429
## .Q19_3 0.203 0.019 10.832 0.000 0.203 0.258
## .Q19_4 0.296 0.024 12.392 0.000 0.296 0.367
## .Q19_5 0.409 0.031 13.149 0.000 0.409 0.461
## .Q20_1 0.301 0.024 12.556 0.000 0.301 0.409
## .Q20_2 0.228 0.020 11.143 0.000 0.228 0.295
## .Q20_3 0.471 0.035 13.480 0.000 0.471 0.546
## .Q20_5 0.466 0.034 13.716 0.000 0.466 0.597
## .Q20_6 0.400 0.031 13.113 0.000 0.400 0.481
## .Q20_7 0.322 0.026 12.520 0.000 0.322 0.405
## .Q20_8 0.416 0.030 13.889 0.000 0.416 0.642
## .Q23_5 0.215 0.022 9.849 0.000 0.215 0.351
## .Q23_6 0.150 0.024 6.202 0.000 0.150 0.213
## .Q23_7 0.408 0.032 12.898 0.000 0.408 0.558
## .Q23_3 0.439 0.030 14.484 0.000 0.439 0.876
## .Q23_8 0.457 0.036 12.846 0.000 0.457 0.495
## .Q23_9 0.131 0.024 5.441 0.000 0.131 0.153
## .Q23_10 0.332 0.028 11.727 0.000 0.332 0.397
## .Q23_1 0.303 0.026 11.590 0.000 0.303 0.484
## .Q23_2 0.200 0.022 9.214 0.000 0.200 0.350
## .Q23_4 0.251 0.023 10.802 0.000 0.251 0.431
## .Q41_1 0.357 0.036 9.875 0.000 0.357 0.550
## .Q41_2 0.139 0.040 3.471 0.001 0.139 0.244
## .Q41_4 0.353 0.025 13.897 0.000 0.353 0.831
## .Q44_1 0.192 0.016 12.344 0.000 0.192 0.391
## .Q44_2 0.074 0.009 8.721 0.000 0.074 0.209
## .Q44_3 0.099 0.011 9.243 0.000 0.099 0.227
## .Q44_4 0.235 0.018 13.258 0.000 0.235 0.507
## .Q15_4 0.747 0.056 13.454 0.000 0.747 0.615
## .Q21 0.381 0.032 11.890 0.000 0.381 0.422
## .Q25 0.233 0.023 9.960 0.000 0.233 0.309
## .Q29 0.593 0.042 14.156 0.000 0.593 0.781
## .Q41_3 0.689 0.048 14.243 0.000 0.689 0.808
## Teachs 0.124 0.022 5.596 0.000 1.000 1.000
## ANXs 0.588 0.056 10.459 0.000 1.000 1.000
## DEPs 0.436 0.047 9.197 0.000 1.000 1.000
## GOVt 0.397 0.042 9.413 0.000 1.000 1.000
## PRO 0.062 0.017 3.593 0.000 1.000 1.000
## COMt 0.323 0.041 7.958 0.000 1.000 1.000
## ASSMs 0.292 0.046 6.407 0.000 1.000 1.000
## TEFFs 0.300 0.032 9.386 0.000 1.000 1.000
## LV 0.468 0.069 6.793 0.000 1.000 1.000
parameterEstimates(fitE, standardized=TRUE) %>%
filter(op == "=~") %>%
dplyr::select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'=pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")
Factor Loadings
| Teachs |
Q17_1 |
1.000 |
0.000 |
NA |
NA |
0.555 |
| Teachs |
Q17_3 |
1.382 |
0.148 |
9.332 |
0 |
0.642 |
| Teachs |
Q17_5 |
1.657 |
0.164 |
10.129 |
0 |
0.776 |
| ANXs |
Q19_1 |
1.000 |
0.000 |
NA |
NA |
0.836 |
| ANXs |
Q19_2 |
0.850 |
0.047 |
17.982 |
0 |
0.755 |
| ANXs |
Q19_3 |
0.995 |
0.046 |
21.804 |
0 |
0.861 |
| ANXs |
Q19_4 |
0.933 |
0.048 |
19.383 |
0 |
0.796 |
| ANXs |
Q19_5 |
0.903 |
0.052 |
17.291 |
0 |
0.734 |
| DEPs |
Q20_1 |
1.000 |
0.000 |
NA |
NA |
0.769 |
| DEPs |
Q20_2 |
1.118 |
0.061 |
18.353 |
0 |
0.840 |
| DEPs |
Q20_3 |
0.949 |
0.067 |
14.250 |
0 |
0.674 |
| DEPs |
Q20_5 |
0.849 |
0.064 |
13.316 |
0 |
0.635 |
| DEPs |
Q20_6 |
0.995 |
0.065 |
15.367 |
0 |
0.720 |
| DEPs |
Q20_7 |
1.042 |
0.063 |
16.640 |
0 |
0.772 |
| DEPs |
Q20_8 |
0.730 |
0.058 |
12.478 |
0 |
0.598 |
| GOVt |
Q23_5 |
1.000 |
0.000 |
NA |
NA |
0.805 |
| GOVt |
Q23_6 |
1.178 |
0.066 |
17.746 |
0 |
0.887 |
| GOVt |
Q23_7 |
0.901 |
0.064 |
14.000 |
0 |
0.665 |
| PRO |
Q23_3 |
1.000 |
0.000 |
NA |
NA |
0.352 |
| PRO |
Q23_8 |
2.743 |
0.395 |
6.940 |
0 |
0.711 |
| PRO |
Q23_9 |
3.417 |
0.472 |
7.244 |
0 |
0.920 |
| PRO |
Q23_10 |
2.853 |
0.403 |
7.079 |
0 |
0.777 |
| COMt |
Q23_1 |
1.000 |
0.000 |
NA |
NA |
0.718 |
| COMt |
Q23_2 |
1.074 |
0.075 |
14.379 |
0 |
0.806 |
| COMt |
Q23_4 |
1.013 |
0.074 |
13.762 |
0 |
0.754 |
| ASSMs |
Q41_1 |
1.000 |
0.000 |
NA |
NA |
0.671 |
| ASSMs |
Q41_2 |
1.219 |
0.128 |
9.552 |
0 |
0.870 |
| ASSMs |
Q41_4 |
0.496 |
0.067 |
7.423 |
0 |
0.411 |
| TEFFs |
Q44_1 |
1.000 |
0.000 |
NA |
NA |
0.780 |
| TEFFs |
Q44_2 |
0.967 |
0.048 |
19.958 |
0 |
0.889 |
| TEFFs |
Q44_3 |
1.064 |
0.054 |
19.752 |
0 |
0.879 |
| TEFFs |
Q44_4 |
0.874 |
0.058 |
15.146 |
0 |
0.702 |
| LV |
Q15_4 |
1.000 |
0.000 |
NA |
NA |
0.621 |
| LV |
Q21 |
1.056 |
0.084 |
12.576 |
0 |
0.760 |
| LV |
Q25 |
1.057 |
0.079 |
13.320 |
0 |
0.831 |
| LV |
Q29 |
0.596 |
0.070 |
8.546 |
0 |
0.468 |
| LV |
Q41_3 |
0.591 |
0.073 |
8.057 |
0 |
0.438 |
CFAStand<-standardizedsolution(fitE, type = "std.all")
write.csv(CFAStand, "CFAStand.csv")
CFAStand
## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 Teachs =~ Q17_1 0.555 0.040 13.826 0.000 0.476 0.634
## 2 Teachs =~ Q17_3 0.642 0.036 17.825 0.000 0.571 0.712
## 3 Teachs =~ Q17_5 0.776 0.031 25.110 0.000 0.715 0.837
## 4 ANXs =~ Q19_1 0.836 0.017 48.025 0.000 0.801 0.870
## 5 ANXs =~ Q19_2 0.755 0.023 32.642 0.000 0.710 0.801
## 6 ANXs =~ Q19_3 0.861 0.016 55.355 0.000 0.831 0.892
## 7 ANXs =~ Q19_4 0.796 0.020 39.249 0.000 0.756 0.836
## 8 ANXs =~ Q19_5 0.734 0.025 29.879 0.000 0.686 0.783
## 9 DEPs =~ Q20_1 0.769 0.023 34.150 0.000 0.725 0.813
## 10 DEPs =~ Q20_2 0.840 0.018 47.886 0.000 0.805 0.874
## 11 DEPs =~ Q20_3 0.674 0.029 23.363 0.000 0.618 0.731
## 12 DEPs =~ Q20_5 0.635 0.031 20.292 0.000 0.573 0.696
## 13 DEPs =~ Q20_6 0.720 0.026 27.854 0.000 0.670 0.771
## 14 DEPs =~ Q20_7 0.772 0.022 34.532 0.000 0.728 0.815
## 15 DEPs =~ Q20_8 0.598 0.033 17.931 0.000 0.533 0.664
## 16 GOVt =~ Q23_5 0.805 0.024 34.186 0.000 0.759 0.852
## 17 GOVt =~ Q23_6 0.887 0.020 43.563 0.000 0.847 0.927
## 18 GOVt =~ Q23_7 0.665 0.031 21.391 0.000 0.604 0.726
## 19 PRO =~ Q23_3 0.352 0.045 7.861 0.000 0.264 0.439
## 20 PRO =~ Q23_8 0.711 0.027 26.134 0.000 0.658 0.764
## 21 PRO =~ Q23_9 0.920 0.016 57.422 0.000 0.889 0.952
## 22 PRO =~ Q23_10 0.777 0.023 33.540 0.000 0.731 0.822
## 23 COMt =~ Q23_1 0.718 0.029 24.442 0.000 0.661 0.776
## 24 COMt =~ Q23_2 0.806 0.025 32.421 0.000 0.758 0.855
## 25 COMt =~ Q23_4 0.754 0.027 27.482 0.000 0.700 0.808
## 26 ASSMs =~ Q41_1 0.671 0.041 16.518 0.000 0.591 0.750
## 27 ASSMs =~ Q41_2 0.870 0.041 21.256 0.000 0.790 0.950
## 28 ASSMs =~ Q41_4 0.411 0.047 8.843 0.000 0.320 0.503
## 29 TEFFs =~ Q44_1 0.780 0.022 35.800 0.000 0.738 0.823
## 30 TEFFs =~ Q44_2 0.889 0.015 60.052 0.000 0.860 0.918
## 31 TEFFs =~ Q44_3 0.879 0.015 57.251 0.000 0.849 0.910
## 32 TEFFs =~ Q44_4 0.702 0.027 25.951 0.000 0.649 0.755
## 33 LV =~ Q15_4 0.621 0.033 18.756 0.000 0.556 0.686
## 34 LV =~ Q21 0.760 0.025 30.997 0.000 0.712 0.808
## 35 LV =~ Q25 0.831 0.020 41.147 0.000 0.792 0.871
## 36 LV =~ Q29 0.468 0.041 11.473 0.000 0.388 0.548
## 37 LV =~ Q41_3 0.438 0.042 10.399 0.000 0.355 0.520
## 38 Q17_1 ~~ Q17_1 0.692 0.045 15.540 0.000 0.605 0.779
## 39 Q17_3 ~~ Q17_3 0.588 0.046 12.727 0.000 0.498 0.679
## 40 Q17_5 ~~ Q17_5 0.398 0.048 8.296 0.000 0.304 0.492
## 41 Q19_1 ~~ Q19_1 0.302 0.029 10.382 0.000 0.245 0.359
## 42 Q19_2 ~~ Q19_2 0.429 0.035 12.283 0.000 0.361 0.498
## 43 Q19_3 ~~ Q19_3 0.258 0.027 9.642 0.000 0.206 0.311
## 44 Q19_4 ~~ Q19_4 0.367 0.032 11.365 0.000 0.303 0.430
## 45 Q19_5 ~~ Q19_5 0.461 0.036 12.753 0.000 0.390 0.531
## 46 Q20_1 ~~ Q20_1 0.409 0.035 11.795 0.000 0.341 0.476
## 47 Q20_2 ~~ Q20_2 0.295 0.029 10.027 0.000 0.237 0.353
## 48 Q20_3 ~~ Q20_3 0.546 0.039 14.028 0.000 0.469 0.622
## 49 Q20_5 ~~ Q20_5 0.597 0.040 15.047 0.000 0.519 0.675
## 50 Q20_6 ~~ Q20_6 0.481 0.037 12.922 0.000 0.408 0.554
## 51 Q20_7 ~~ Q20_7 0.405 0.034 11.737 0.000 0.337 0.472
## 52 Q20_8 ~~ Q20_8 0.642 0.040 16.068 0.000 0.564 0.720
## 53 Q23_5 ~~ Q23_5 0.351 0.038 9.252 0.000 0.277 0.426
## 54 Q23_6 ~~ Q23_6 0.213 0.036 5.908 0.000 0.143 0.284
## 55 Q23_7 ~~ Q23_7 0.558 0.041 13.517 0.000 0.477 0.639
## 56 Q23_3 ~~ Q23_3 0.876 0.031 27.837 0.000 0.815 0.938
## 57 Q23_8 ~~ Q23_8 0.495 0.039 12.795 0.000 0.419 0.571
## 58 Q23_9 ~~ Q23_9 0.153 0.029 5.200 0.000 0.096 0.211
## 59 Q23_10 ~~ Q23_10 0.397 0.036 11.026 0.000 0.326 0.467
## 60 Q23_1 ~~ Q23_1 0.484 0.042 11.479 0.000 0.402 0.567
## 61 Q23_2 ~~ Q23_2 0.350 0.040 8.715 0.000 0.271 0.428
## 62 Q23_4 ~~ Q23_4 0.431 0.041 10.423 0.000 0.350 0.512
## 63 Q41_1 ~~ Q41_1 0.550 0.054 10.107 0.000 0.444 0.657
## 64 Q41_2 ~~ Q41_2 0.244 0.071 3.422 0.001 0.104 0.383
## 65 Q41_4 ~~ Q41_4 0.831 0.038 21.704 0.000 0.756 0.906
## 66 Q44_1 ~~ Q44_1 0.391 0.034 11.493 0.000 0.324 0.458
## 67 Q44_2 ~~ Q44_2 0.209 0.026 7.947 0.000 0.158 0.261
## 68 Q44_3 ~~ Q44_3 0.227 0.027 8.383 0.000 0.174 0.279
## 69 Q44_4 ~~ Q44_4 0.507 0.038 13.332 0.000 0.432 0.581
## 70 Q15_4 ~~ Q15_4 0.615 0.041 14.968 0.000 0.534 0.695
## 71 Q21 ~~ Q21 0.422 0.037 11.329 0.000 0.349 0.495
## 72 Q25 ~~ Q25 0.309 0.034 9.187 0.000 0.243 0.375
## 73 Q29 ~~ Q29 0.781 0.038 20.425 0.000 0.706 0.856
## 74 Q41_3 ~~ Q41_3 0.808 0.037 21.916 0.000 0.736 0.881
## 75 Teachs ~~ Teachs 1.000 0.000 NA NA 1.000 1.000
## 76 ANXs ~~ ANXs 1.000 0.000 NA NA 1.000 1.000
## 77 DEPs ~~ DEPs 1.000 0.000 NA NA 1.000 1.000
## 78 GOVt ~~ GOVt 1.000 0.000 NA NA 1.000 1.000
## 79 PRO ~~ PRO 1.000 0.000 NA NA 1.000 1.000
## 80 COMt ~~ COMt 1.000 0.000 NA NA 1.000 1.000
## 81 ASSMs ~~ ASSMs 1.000 0.000 NA NA 1.000 1.000
## 82 TEFFs ~~ TEFFs 1.000 0.000 NA NA 1.000 1.000
## 83 LV ~~ LV 1.000 0.000 NA NA 1.000 1.000
## 84 Teachs ~~ ANXs -0.398 0.052 -7.641 0.000 -0.501 -0.296
## 85 Teachs ~~ DEPs -0.391 0.053 -7.410 0.000 -0.494 -0.287
## 86 Teachs ~~ GOVt 0.410 0.053 7.715 0.000 0.306 0.515
## 87 Teachs ~~ PRO 0.510 0.048 10.591 0.000 0.416 0.605
## 88 Teachs ~~ COMt 0.608 0.047 13.047 0.000 0.517 0.699
## 89 Teachs ~~ ASSMs 0.030 0.063 0.471 0.638 -0.094 0.153
## 90 Teachs ~~ TEFFs 0.492 0.048 10.180 0.000 0.397 0.587
## 91 Teachs ~~ LV -0.676 0.042 -15.983 0.000 -0.759 -0.593
## 92 ANXs ~~ DEPs 0.799 0.023 34.346 0.000 0.754 0.845
## 93 ANXs ~~ GOVt -0.254 0.051 -4.939 0.000 -0.354 -0.153
## 94 ANXs ~~ PRO -0.385 0.047 -8.269 0.000 -0.476 -0.294
## 95 ANXs ~~ COMt -0.373 0.050 -7.504 0.000 -0.470 -0.275
## 96 ANXs ~~ ASSMs 0.264 0.053 4.942 0.000 0.159 0.369
## 97 ANXs ~~ TEFFs -0.152 0.052 -2.927 0.003 -0.254 -0.050
## 98 ANXs ~~ LV 0.676 0.034 19.754 0.000 0.609 0.744
## 99 DEPs ~~ GOVt -0.244 0.052 -4.693 0.000 -0.345 -0.142
## 100 DEPs ~~ PRO -0.331 0.049 -6.814 0.000 -0.427 -0.236
## 101 DEPs ~~ COMt -0.346 0.051 -6.807 0.000 -0.446 -0.247
## 102 DEPs ~~ ASSMs 0.270 0.054 5.038 0.000 0.165 0.375
## 103 DEPs ~~ TEFFs -0.208 0.051 -4.054 0.000 -0.309 -0.107
## 104 DEPs ~~ LV 0.629 0.037 16.788 0.000 0.555 0.702
## 105 GOVt ~~ PRO 0.568 0.040 14.264 0.000 0.490 0.646
## 106 GOVt ~~ COMt 0.534 0.044 12.028 0.000 0.447 0.621
## 107 GOVt ~~ ASSMs -0.230 0.055 -4.153 0.000 -0.339 -0.122
## 108 GOVt ~~ TEFFs 0.220 0.052 4.225 0.000 0.118 0.322
## 109 GOVt ~~ LV -0.466 0.047 -9.959 0.000 -0.558 -0.374
## 110 PRO ~~ COMt 0.530 0.044 12.133 0.000 0.444 0.615
## 111 PRO ~~ ASSMs -0.151 0.056 -2.711 0.007 -0.261 -0.042
## 112 PRO ~~ TEFFs 0.222 0.051 4.351 0.000 0.122 0.323
## 113 PRO ~~ LV -0.680 0.035 -19.575 0.000 -0.748 -0.612
## 114 COMt ~~ ASSMs -0.102 0.059 -1.734 0.083 -0.218 0.013
## 115 COMt ~~ TEFFs 0.489 0.045 10.892 0.000 0.401 0.577
## 116 COMt ~~ LV -0.543 0.045 -12.011 0.000 -0.632 -0.454
## 117 ASSMs ~~ TEFFs 0.077 0.056 1.384 0.166 -0.032 0.187
## 118 ASSMs ~~ LV 0.359 0.054 6.685 0.000 0.254 0.464
## 119 TEFFs ~~ LV -0.291 0.052 -5.638 0.000 -0.392 -0.190
residuals(fitE, type = "cor")
## $type
## [1] "cor.bollen"
##
## $cov
## Q17_1 Q17_3 Q17_5 Q19_1 Q19_2 Q19_3 Q19_4 Q19_5 Q20_1 Q20_2
## Q17_1 0.000
## Q17_3 0.054 0.000
## Q17_5 -0.023 -0.005 0.000
## Q19_1 -0.003 -0.046 0.025 0.000
## Q19_2 0.004 -0.067 0.033 0.014 0.000
## Q19_3 0.051 -0.043 0.061 -0.011 0.045 0.000
## Q19_4 -0.050 -0.092 -0.022 -0.027 -0.029 0.026 0.000
## Q19_5 -0.062 -0.061 0.073 0.013 -0.025 -0.015 -0.017 0.000
## Q20_1 -0.058 -0.030 -0.029 -0.061 -0.139 -0.110 0.000 -0.005 0.000
## Q20_2 -0.012 0.025 0.039 0.028 -0.084 -0.038 0.038 0.086 0.071 0.000
## Q20_3 0.042 -0.028 0.033 0.059 0.066 0.069 0.048 0.064 -0.013 -0.034
## Q20_5 -0.007 -0.004 0.021 0.007 -0.012 0.010 0.065 0.018 0.030 -0.060
## Q20_6 -0.109 -0.061 -0.061 0.052 -0.064 -0.054 -0.019 0.057 -0.028 0.027
## Q20_7 -0.001 -0.017 0.032 0.030 0.008 0.028 0.039 0.043 -0.027 -0.031
## Q20_8 0.052 0.036 0.065 0.025 -0.091 -0.045 -0.021 0.055 -0.003 -0.013
## Q23_5 0.023 0.046 -0.036 -0.008 -0.047 0.001 -0.065 -0.014 -0.031 0.004
## Q23_6 -0.009 0.078 -0.059 0.006 -0.033 0.034 0.010 0.033 -0.021 0.010
## Q23_7 0.002 0.068 0.031 0.001 -0.039 0.053 -0.013 -0.051 -0.050 -0.033
## Q23_3 0.077 0.081 0.083 -0.099 0.000 -0.020 -0.058 -0.087 -0.099 -0.175
## Q23_8 -0.019 0.015 -0.050 -0.054 -0.044 0.011 -0.034 -0.030 -0.021 -0.015
## Q23_9 -0.047 0.009 0.000 -0.028 -0.015 0.035 0.010 0.005 -0.003 -0.003
## Q23_10 -0.008 0.050 0.021 0.017 -0.018 0.041 0.010 0.023 -0.054 0.014
## Q23_1 0.017 -0.025 0.053 0.092 0.030 0.036 -0.012 0.080 -0.002 0.052
## Q23_2 -0.031 -0.076 -0.038 0.004 -0.049 0.027 -0.020 -0.026 -0.005 -0.012
## Q23_4 0.048 0.029 0.048 -0.027 -0.053 0.010 -0.074 -0.041 -0.094 -0.073
## Q41_1 0.032 -0.034 -0.049 -0.046 -0.046 -0.045 0.070 -0.076 0.043 0.003
## Q41_2 0.060 0.009 0.009 0.010 0.029 -0.018 0.049 -0.015 -0.029 -0.044
## Q41_4 0.025 -0.060 -0.123 0.005 0.013 -0.002 0.012 0.016 0.012 0.075
## Q44_1 -0.028 -0.028 0.104 0.026 0.033 0.043 -0.021 0.028 0.009 0.023
## Q44_2 -0.027 -0.089 0.018 -0.013 0.028 -0.021 -0.044 -0.025 -0.054 -0.028
## Q44_3 0.002 -0.057 0.005 -0.001 0.063 0.028 -0.001 0.019 -0.073 -0.044
## Q44_4 -0.012 0.013 0.090 -0.056 -0.021 -0.025 -0.042 -0.054 -0.065 -0.077
## Q15_4 -0.026 0.003 -0.062 -0.033 -0.036 -0.089 -0.058 -0.040 0.016 -0.043
## Q21 0.007 -0.025 0.006 0.133 0.091 0.054 0.062 0.080 0.067 0.088
## Q25 0.053 0.005 0.016 -0.039 -0.025 -0.075 -0.017 -0.052 -0.035 -0.033
## Q29 -0.028 -0.037 -0.150 -0.033 0.018 -0.001 0.051 -0.028 -0.023 -0.033
## Q41_3 0.096 0.089 0.061 0.053 0.024 0.045 0.079 0.037 -0.026 0.037
## Q20_3 Q20_5 Q20_6 Q20_7 Q20_8 Q23_5 Q23_6 Q23_7 Q23_3 Q23_8
## Q17_1
## Q17_3
## Q17_5
## Q19_1
## Q19_2
## Q19_3
## Q19_4
## Q19_5
## Q20_1
## Q20_2
## Q20_3 0.000
## Q20_5 0.073 0.000
## Q20_6 -0.035 0.001 0.000
## Q20_7 0.001 0.006 0.006 0.000
## Q20_8 -0.020 -0.036 0.003 0.086 0.000
## Q23_5 0.018 -0.070 -0.029 -0.004 0.038 0.000
## Q23_6 0.033 -0.010 -0.015 0.041 0.071 0.009 0.000
## Q23_7 0.010 -0.048 -0.014 0.000 -0.030 -0.034 0.001 0.000
## Q23_3 -0.013 -0.047 -0.164 -0.008 -0.058 -0.123 -0.083 -0.005 0.000
## Q23_8 -0.008 -0.061 0.025 0.064 0.050 -0.117 -0.046 0.016 0.155 0.000
## Q23_9 0.005 -0.060 -0.003 0.045 0.013 -0.060 -0.030 0.070 -0.021 0.011
## Q23_10 0.033 -0.067 0.040 0.060 0.041 0.152 0.119 0.131 -0.070 -0.050
## Q23_1 0.116 -0.006 0.014 0.081 0.129 -0.010 -0.093 -0.042 -0.011 -0.087
## Q23_2 0.025 -0.030 0.024 0.036 0.024 0.020 -0.032 0.012 0.047 -0.045
## Q23_4 -0.031 -0.091 -0.081 0.005 0.036 0.118 0.030 0.079 0.100 0.007
## Q41_1 -0.015 0.064 -0.021 -0.058 -0.039 -0.017 -0.029 -0.011 -0.065 -0.034
## Q41_2 0.034 0.051 0.008 0.028 -0.018 0.008 0.033 -0.017 -0.028 -0.021
## Q41_4 0.054 -0.010 0.033 0.010 0.005 -0.107 -0.050 -0.080 -0.046 -0.026
## Q44_1 0.060 0.009 0.042 0.065 0.075 -0.013 -0.055 0.007 -0.066 -0.097
## Q44_2 0.024 -0.004 -0.011 0.056 0.034 0.015 -0.045 0.047 0.009 -0.013
## Q44_3 0.034 -0.007 -0.013 0.059 0.063 0.014 0.000 0.041 -0.013 -0.018
## Q44_4 0.024 0.000 -0.036 0.022 0.042 0.033 0.061 0.086 0.057 0.052
## Q15_4 -0.011 0.022 0.008 -0.040 -0.029 -0.005 -0.028 -0.058 -0.077 -0.028
## Q21 0.059 0.076 0.077 0.055 0.010 0.095 0.107 0.031 -0.042 0.068
## Q25 -0.043 -0.018 -0.019 -0.057 -0.056 -0.042 -0.037 -0.139 -0.115 -0.122
## Q29 -0.058 0.007 0.008 -0.041 -0.053 0.008 0.020 -0.059 0.066 0.120
## Q41_3 0.014 0.029 0.075 0.007 0.033 -0.001 0.030 -0.025 -0.043 0.056
## Q23_9 Q23_10 Q23_1 Q23_2 Q23_4 Q41_1 Q41_2 Q41_4 Q44_1 Q44_2
## Q17_1
## Q17_3
## Q17_5
## Q19_1
## Q19_2
## Q19_3
## Q19_4
## Q19_5
## Q20_1
## Q20_2
## Q20_3
## Q20_5
## Q20_6
## Q20_7
## Q20_8
## Q23_5
## Q23_6
## Q23_7
## Q23_3
## Q23_8
## Q23_9 0.000
## Q23_10 0.005 0.000
## Q23_1 -0.062 0.035 0.000
## Q23_2 -0.030 0.036 0.045 0.000
## Q23_4 0.047 0.126 -0.055 -0.003 0.000
## Q41_1 -0.032 -0.013 0.027 -0.016 -0.090 0.000
## Q41_2 0.023 0.049 0.094 0.004 -0.032 0.001 0.000
## Q41_4 -0.068 -0.111 -0.007 -0.053 -0.091 -0.008 0.000 0.000
## Q44_1 -0.081 0.016 0.106 -0.054 -0.022 -0.009 0.105 -0.022 0.000
## Q44_2 -0.004 0.075 0.080 -0.056 -0.011 -0.081 0.021 -0.039 0.012 0.000
## Q44_3 -0.023 0.040 0.071 -0.047 -0.025 -0.105 0.026 -0.077 -0.017 0.005
## Q44_4 0.084 0.110 0.046 0.067 0.005 -0.061 -0.009 -0.124 -0.009 -0.018
## Q15_4 0.048 0.046 0.089 0.067 -0.037 0.039 -0.025 0.058 0.052 0.034
## Q21 0.048 0.092 0.112 0.122 -0.019 0.010 -0.038 0.035 0.035 0.040
## Q25 -0.095 -0.041 0.009 0.006 -0.089 0.006 -0.078 0.039 0.055 0.010
## Q29 0.072 0.091 -0.304 -0.224 -0.170 -0.003 -0.098 0.005 -0.195 -0.212
## Q41_3 0.114 0.145 0.072 0.044 0.024 0.314 0.426 0.221 0.127 0.095
## Q44_3 Q44_4 Q15_4 Q21 Q25 Q29 Q41_3
## Q17_1
## Q17_3
## Q17_5
## Q19_1
## Q19_2
## Q19_3
## Q19_4
## Q19_5
## Q20_1
## Q20_2
## Q20_3
## Q20_5
## Q20_6
## Q20_7
## Q20_8
## Q23_5
## Q23_6
## Q23_7
## Q23_3
## Q23_8
## Q23_9
## Q23_10
## Q23_1
## Q23_2
## Q23_4
## Q41_1
## Q41_2
## Q41_4
## Q44_1
## Q44_2
## Q44_3 0.000
## Q44_4 0.015 0.000
## Q15_4 0.034 -0.050 0.000
## Q21 0.025 -0.071 0.006 0.000
## Q25 0.023 -0.107 0.018 0.002 0.000
## Q29 -0.240 -0.268 -0.007 -0.056 0.034 0.000
## Q41_3 0.086 0.041 0.053 -0.009 -0.063 0.035 0.000
semPaths(fitE, "par", edge.label.cex = 1.2, fade = FALSE, exoCov = FALSE)
TeachModel3<-
'
Teachs=~ Q17_1 + Q17_3 + Q17_5
ANXs=~ Q19_1 + Q19_2 + Q19_3 + Q19_4 + Q19_5
DEPs=~ Q20_2 + Q20_3 + Q20_5 + Q20_6 + Q20_7 + Q20_8
PRO=~ Q23_3 + Q23_8 + Q23_9
ASSMs=~ Q41_1 + Q41_2 + Q41_4
TEFFs=~ Q44_1 + Q44_2 + Q44_3 + Q44_4
LV=~ Q15_4 + Q21 + Q25 + Q29 + Q41_3
'
fitE2<- cfa(TeachModel3, data=teacher2)
summary(fitE2, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-12 ended normally after 91 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 79
##
## Used Total
## Number of observations 443 679
##
## Model Test User Model:
##
## Test statistic 853.511
## Degrees of freedom 356
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 6462.376
## Degrees of freedom 406
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.918
## Tucker-Lewis Index (TLI) 0.906
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -12895.672
## Loglikelihood unrestricted model (H1) -12468.917
##
## Akaike (AIC) 25949.344
## Bayesian (BIC) 26272.736
## Sample-size adjusted Bayesian (BIC) 26022.026
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.056
## 90 Percent confidence interval - lower 0.051
## 90 Percent confidence interval - upper 0.061
## P-value RMSEA <= 0.05 0.018
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.059
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Teachs =~
## Q17_1 1.000 0.358 0.557
## Q17_3 1.370 0.146 9.400 0.000 0.490 0.647
## Q17_5 1.603 0.159 10.071 0.000 0.574 0.767
## ANXs =~
## Q19_1 1.000 0.762 0.834
## Q19_2 0.854 0.047 18.181 0.000 0.650 0.756
## Q19_3 1.000 0.046 21.938 0.000 0.761 0.860
## Q19_4 0.930 0.048 19.394 0.000 0.708 0.791
## Q19_5 0.901 0.052 17.187 0.000 0.686 0.726
## DEPs =~
## Q20_2 1.000 0.701 0.800
## Q20_3 0.897 0.061 14.802 0.000 0.628 0.680
## Q20_5 0.786 0.058 13.446 0.000 0.550 0.626
## Q20_6 0.930 0.059 15.824 0.000 0.652 0.719
## Q20_7 1.000 0.057 17.616 0.000 0.700 0.785
## Q20_8 0.687 0.054 12.740 0.000 0.481 0.598
## PRO =~
## Q23_3 1.000 0.296 0.420
## Q23_8 2.520 0.310 8.139 0.000 0.746 0.771
## Q23_9 2.667 0.325 8.203 0.000 0.789 0.849
## ASSMs =~
## Q41_1 1.000 0.540 0.674
## Q41_2 1.216 0.129 9.459 0.000 0.657 0.869
## Q41_4 0.489 0.066 7.416 0.000 0.264 0.406
## TEFFs =~
## Q44_1 1.000 0.546 0.782
## Q44_2 0.967 0.048 20.181 0.000 0.528 0.890
## Q44_3 1.061 0.053 19.979 0.000 0.579 0.880
## Q44_4 0.871 0.058 15.129 0.000 0.475 0.695
## LV =~
## Q15_4 1.000 0.689 0.627
## Q21 1.042 0.081 12.819 0.000 0.718 0.757
## Q25 1.048 0.077 13.640 0.000 0.721 0.833
## Q29 0.577 0.068 8.547 0.000 0.398 0.460
## Q41_3 0.599 0.072 8.329 0.000 0.412 0.447
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Teachs ~~
## ANXs -0.110 0.019 -5.745 0.000 -0.403 -0.403
## DEPs -0.095 0.018 -5.390 0.000 -0.379 -0.379
## PRO 0.053 0.010 5.271 0.000 0.501 0.501
## ASSMs 0.007 0.012 0.565 0.572 0.035 0.035
## TEFFs 0.095 0.015 6.415 0.000 0.484 0.484
## LV -0.167 0.024 -7.076 0.000 -0.676 -0.676
## ANXs ~~
## DEPs 0.442 0.041 10.895 0.000 0.829 0.829
## PRO -0.094 0.017 -5.483 0.000 -0.418 -0.418
## ASSMs 0.110 0.025 4.317 0.000 0.266 0.266
## TEFFs -0.062 0.022 -2.771 0.006 -0.148 -0.148
## LV 0.353 0.041 8.659 0.000 0.673 0.673
## DEPs ~~
## PRO -0.072 0.015 -4.869 0.000 -0.349 -0.349
## ASSMs 0.107 0.024 4.434 0.000 0.282 0.282
## TEFFs -0.069 0.021 -3.277 0.001 -0.180 -0.180
## LV 0.302 0.037 8.183 0.000 0.626 0.626
## PRO ~~
## ASSMs -0.028 0.010 -2.766 0.006 -0.178 -0.178
## TEFFs 0.030 0.010 3.094 0.002 0.187 0.187
## LV -0.145 0.023 -6.333 0.000 -0.711 -0.711
## ASSMs ~~
## TEFFs 0.025 0.017 1.509 0.131 0.085 0.085
## LV 0.135 0.026 5.101 0.000 0.362 0.362
## TEFFs ~~
## LV -0.104 0.022 -4.651 0.000 -0.278 -0.278
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q17_1 0.285 0.022 13.018 0.000 0.285 0.690
## .Q17_3 0.333 0.028 11.821 0.000 0.333 0.581
## .Q17_5 0.230 0.026 8.712 0.000 0.230 0.411
## .Q19_1 0.254 0.022 11.760 0.000 0.254 0.305
## .Q19_2 0.316 0.024 13.071 0.000 0.316 0.428
## .Q19_3 0.204 0.019 10.992 0.000 0.204 0.260
## .Q19_4 0.299 0.024 12.604 0.000 0.299 0.374
## .Q19_5 0.421 0.032 13.373 0.000 0.421 0.472
## .Q20_2 0.277 0.024 11.645 0.000 0.277 0.361
## .Q20_3 0.460 0.034 13.328 0.000 0.460 0.538
## .Q20_5 0.469 0.034 13.718 0.000 0.469 0.608
## .Q20_6 0.397 0.031 12.944 0.000 0.397 0.484
## .Q20_7 0.306 0.026 11.953 0.000 0.306 0.384
## .Q20_8 0.416 0.030 13.881 0.000 0.416 0.643
## .Q23_3 0.408 0.029 14.232 0.000 0.408 0.823
## .Q23_8 0.379 0.040 9.482 0.000 0.379 0.405
## .Q23_9 0.241 0.038 6.396 0.000 0.241 0.279
## .Q41_1 0.351 0.036 9.736 0.000 0.351 0.546
## .Q41_2 0.141 0.041 3.444 0.001 0.141 0.246
## .Q41_4 0.353 0.025 14.069 0.000 0.353 0.835
## .Q44_1 0.190 0.015 12.421 0.000 0.190 0.389
## .Q44_2 0.074 0.008 8.666 0.000 0.074 0.209
## .Q44_3 0.098 0.011 9.207 0.000 0.098 0.226
## .Q44_4 0.242 0.018 13.449 0.000 0.242 0.517
## .Q15_4 0.731 0.054 13.550 0.000 0.731 0.607
## .Q21 0.383 0.032 12.054 0.000 0.383 0.427
## .Q25 0.230 0.023 9.996 0.000 0.230 0.307
## .Q29 0.589 0.041 14.338 0.000 0.589 0.788
## .Q41_3 0.682 0.047 14.377 0.000 0.682 0.800
## Teachs 0.128 0.023 5.638 0.000 1.000 1.000
## ANXs 0.580 0.055 10.546 0.000 1.000 1.000
## DEPs 0.491 0.050 9.749 0.000 1.000 1.000
## PRO 0.088 0.021 4.234 0.000 1.000 1.000
## ASSMs 0.292 0.045 6.440 0.000 1.000 1.000
## TEFFs 0.298 0.031 9.507 0.000 1.000 1.000
## LV 0.474 0.068 6.960 0.000 1.000 1.000
parameterEstimates(fitE2, standardized=TRUE) %>%
filter(op == "=~") %>%
dplyr::select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'=pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")
Factor Loadings
| Teachs |
Q17_1 |
1.000 |
0.000 |
NA |
NA |
0.557 |
| Teachs |
Q17_3 |
1.370 |
0.146 |
9.400 |
0 |
0.647 |
| Teachs |
Q17_5 |
1.603 |
0.159 |
10.071 |
0 |
0.767 |
| ANXs |
Q19_1 |
1.000 |
0.000 |
NA |
NA |
0.834 |
| ANXs |
Q19_2 |
0.854 |
0.047 |
18.181 |
0 |
0.756 |
| ANXs |
Q19_3 |
1.000 |
0.046 |
21.938 |
0 |
0.860 |
| ANXs |
Q19_4 |
0.930 |
0.048 |
19.394 |
0 |
0.791 |
| ANXs |
Q19_5 |
0.901 |
0.052 |
17.187 |
0 |
0.726 |
| DEPs |
Q20_2 |
1.000 |
0.000 |
NA |
NA |
0.800 |
| DEPs |
Q20_3 |
0.897 |
0.061 |
14.802 |
0 |
0.680 |
| DEPs |
Q20_5 |
0.786 |
0.058 |
13.446 |
0 |
0.626 |
| DEPs |
Q20_6 |
0.930 |
0.059 |
15.824 |
0 |
0.719 |
| DEPs |
Q20_7 |
1.000 |
0.057 |
17.616 |
0 |
0.785 |
| DEPs |
Q20_8 |
0.687 |
0.054 |
12.740 |
0 |
0.598 |
| PRO |
Q23_3 |
1.000 |
0.000 |
NA |
NA |
0.420 |
| PRO |
Q23_8 |
2.520 |
0.310 |
8.139 |
0 |
0.771 |
| PRO |
Q23_9 |
2.667 |
0.325 |
8.203 |
0 |
0.849 |
| ASSMs |
Q41_1 |
1.000 |
0.000 |
NA |
NA |
0.674 |
| ASSMs |
Q41_2 |
1.216 |
0.129 |
9.459 |
0 |
0.869 |
| ASSMs |
Q41_4 |
0.489 |
0.066 |
7.416 |
0 |
0.406 |
| TEFFs |
Q44_1 |
1.000 |
0.000 |
NA |
NA |
0.782 |
| TEFFs |
Q44_2 |
0.967 |
0.048 |
20.181 |
0 |
0.890 |
| TEFFs |
Q44_3 |
1.061 |
0.053 |
19.979 |
0 |
0.880 |
| TEFFs |
Q44_4 |
0.871 |
0.058 |
15.129 |
0 |
0.695 |
| LV |
Q15_4 |
1.000 |
0.000 |
NA |
NA |
0.627 |
| LV |
Q21 |
1.042 |
0.081 |
12.819 |
0 |
0.757 |
| LV |
Q25 |
1.048 |
0.077 |
13.640 |
0 |
0.833 |
| LV |
Q29 |
0.577 |
0.068 |
8.547 |
0 |
0.460 |
| LV |
Q41_3 |
0.599 |
0.072 |
8.329 |
0 |
0.447 |
CFAStand<-standardizedsolution(fitE2, type = "std.all")
write.csv(CFAStand, "CFAStand.csv")
CFAStand
## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 Teachs =~ Q17_1 0.557 0.040 13.896 0.000 0.478 0.635
## 2 Teachs =~ Q17_3 0.647 0.036 17.996 0.000 0.577 0.718
## 3 Teachs =~ Q17_5 0.767 0.032 24.199 0.000 0.705 0.829
## 4 ANXs =~ Q19_1 0.834 0.017 48.144 0.000 0.800 0.868
## 5 ANXs =~ Q19_2 0.756 0.023 33.160 0.000 0.712 0.801
## 6 ANXs =~ Q19_3 0.860 0.015 55.612 0.000 0.830 0.890
## 7 ANXs =~ Q19_4 0.791 0.020 38.857 0.000 0.751 0.831
## 8 ANXs =~ Q19_5 0.726 0.025 29.226 0.000 0.678 0.775
## 9 DEPs =~ Q20_2 0.800 0.021 38.138 0.000 0.758 0.841
## 10 DEPs =~ Q20_3 0.680 0.029 23.627 0.000 0.623 0.736
## 11 DEPs =~ Q20_5 0.626 0.032 19.595 0.000 0.564 0.689
## 12 DEPs =~ Q20_6 0.719 0.026 27.315 0.000 0.667 0.770
## 13 DEPs =~ Q20_7 0.785 0.022 35.778 0.000 0.742 0.828
## 14 DEPs =~ Q20_8 0.598 0.034 17.798 0.000 0.532 0.664
## 15 PRO =~ Q23_3 0.420 0.044 9.552 0.000 0.334 0.506
## 16 PRO =~ Q23_8 0.771 0.029 26.822 0.000 0.715 0.828
## 17 PRO =~ Q23_9 0.849 0.027 31.825 0.000 0.797 0.902
## 18 ASSMs =~ Q41_1 0.674 0.041 16.513 0.000 0.594 0.754
## 19 ASSMs =~ Q41_2 0.869 0.042 20.875 0.000 0.787 0.950
## 20 ASSMs =~ Q41_4 0.406 0.046 8.778 0.000 0.316 0.497
## 21 TEFFs =~ Q44_1 0.782 0.022 36.294 0.000 0.739 0.824
## 22 TEFFs =~ Q44_2 0.890 0.015 60.024 0.000 0.860 0.919
## 23 TEFFs =~ Q44_3 0.880 0.015 57.289 0.000 0.850 0.910
## 24 TEFFs =~ Q44_4 0.695 0.027 25.454 0.000 0.641 0.748
## 25 LV =~ Q15_4 0.627 0.032 19.376 0.000 0.564 0.691
## 26 LV =~ Q21 0.757 0.024 30.948 0.000 0.709 0.805
## 27 LV =~ Q25 0.833 0.020 41.687 0.000 0.793 0.872
## 28 LV =~ Q29 0.460 0.041 11.291 0.000 0.380 0.540
## 29 LV =~ Q41_3 0.447 0.041 10.819 0.000 0.366 0.528
## 30 Q17_1 ~~ Q17_1 0.690 0.045 15.455 0.000 0.602 0.777
## 31 Q17_3 ~~ Q17_3 0.581 0.047 12.476 0.000 0.490 0.672
## 32 Q17_5 ~~ Q17_5 0.411 0.049 8.459 0.000 0.316 0.507
## 33 Q19_1 ~~ Q19_1 0.305 0.029 10.543 0.000 0.248 0.361
## 34 Q19_2 ~~ Q19_2 0.428 0.035 12.399 0.000 0.360 0.495
## 35 Q19_3 ~~ Q19_3 0.260 0.027 9.787 0.000 0.208 0.312
## 36 Q19_4 ~~ Q19_4 0.374 0.032 11.598 0.000 0.311 0.437
## 37 Q19_5 ~~ Q19_5 0.472 0.036 13.085 0.000 0.402 0.543
## 38 Q20_2 ~~ Q20_2 0.361 0.034 10.764 0.000 0.295 0.426
## 39 Q20_3 ~~ Q20_3 0.538 0.039 13.754 0.000 0.461 0.615
## 40 Q20_5 ~~ Q20_5 0.608 0.040 15.177 0.000 0.529 0.686
## 41 Q20_6 ~~ Q20_6 0.484 0.038 12.786 0.000 0.409 0.558
## 42 Q20_7 ~~ Q20_7 0.384 0.034 11.147 0.000 0.316 0.451
## 43 Q20_8 ~~ Q20_8 0.643 0.040 16.012 0.000 0.564 0.721
## 44 Q23_3 ~~ Q23_3 0.823 0.037 22.268 0.000 0.751 0.896
## 45 Q23_8 ~~ Q23_8 0.405 0.044 9.137 0.000 0.318 0.492
## 46 Q23_9 ~~ Q23_9 0.279 0.045 6.149 0.000 0.190 0.368
## 47 Q41_1 ~~ Q41_1 0.546 0.055 9.934 0.000 0.438 0.654
## 48 Q41_2 ~~ Q41_2 0.246 0.072 3.398 0.001 0.104 0.387
## 49 Q41_4 ~~ Q41_4 0.835 0.038 22.211 0.000 0.761 0.909
## 50 Q44_1 ~~ Q44_1 0.389 0.034 11.560 0.000 0.323 0.455
## 51 Q44_2 ~~ Q44_2 0.209 0.026 7.919 0.000 0.157 0.260
## 52 Q44_3 ~~ Q44_3 0.226 0.027 8.371 0.000 0.173 0.279
## 53 Q44_4 ~~ Q44_4 0.517 0.038 13.644 0.000 0.443 0.592
## 54 Q15_4 ~~ Q15_4 0.607 0.041 14.933 0.000 0.527 0.686
## 55 Q21 ~~ Q21 0.427 0.037 11.513 0.000 0.354 0.499
## 56 Q25 ~~ Q25 0.307 0.033 9.224 0.000 0.242 0.372
## 57 Q29 ~~ Q29 0.788 0.037 21.035 0.000 0.715 0.862
## 58 Q41_3 ~~ Q41_3 0.800 0.037 21.690 0.000 0.728 0.873
## 59 Teachs ~~ Teachs 1.000 0.000 NA NA 1.000 1.000
## 60 ANXs ~~ ANXs 1.000 0.000 NA NA 1.000 1.000
## 61 DEPs ~~ DEPs 1.000 0.000 NA NA 1.000 1.000
## 62 PRO ~~ PRO 1.000 0.000 NA NA 1.000 1.000
## 63 ASSMs ~~ ASSMs 1.000 0.000 NA NA 1.000 1.000
## 64 TEFFs ~~ TEFFs 1.000 0.000 NA NA 1.000 1.000
## 65 LV ~~ LV 1.000 0.000 NA NA 1.000 1.000
## 66 Teachs ~~ ANXs -0.403 0.052 -7.820 0.000 -0.505 -0.302
## 67 Teachs ~~ DEPs -0.379 0.054 -7.056 0.000 -0.484 -0.274
## 68 Teachs ~~ PRO 0.501 0.051 9.761 0.000 0.401 0.602
## 69 Teachs ~~ ASSMs 0.035 0.062 0.567 0.571 -0.087 0.157
## 70 Teachs ~~ TEFFs 0.484 0.048 10.012 0.000 0.389 0.579
## 71 Teachs ~~ LV -0.676 0.042 -16.136 0.000 -0.759 -0.594
## 72 ANXs ~~ DEPs 0.829 0.022 37.867 0.000 0.786 0.872
## 73 ANXs ~~ PRO -0.418 0.048 -8.784 0.000 -0.511 -0.325
## 74 ANXs ~~ ASSMs 0.266 0.053 5.035 0.000 0.163 0.370
## 75 ANXs ~~ TEFFs -0.148 0.051 -2.877 0.004 -0.249 -0.047
## 76 ANXs ~~ LV 0.673 0.034 19.726 0.000 0.606 0.740
## 77 DEPs ~~ PRO -0.349 0.051 -6.832 0.000 -0.450 -0.249
## 78 DEPs ~~ ASSMs 0.282 0.054 5.246 0.000 0.176 0.387
## 79 DEPs ~~ TEFFs -0.180 0.052 -3.463 0.001 -0.282 -0.078
## 80 DEPs ~~ LV 0.626 0.038 16.457 0.000 0.552 0.701
## 81 PRO ~~ ASSMs -0.178 0.058 -3.091 0.002 -0.291 -0.065
## 82 PRO ~~ TEFFs 0.187 0.054 3.465 0.001 0.081 0.292
## 83 PRO ~~ LV -0.711 0.036 -19.885 0.000 -0.781 -0.641
## 84 ASSMs ~~ TEFFs 0.085 0.055 1.535 0.125 -0.023 0.193
## 85 ASSMs ~~ LV 0.362 0.053 6.818 0.000 0.258 0.466
## 86 TEFFs ~~ LV -0.278 0.051 -5.416 0.000 -0.378 -0.177
residuals(fitE2, type = "cor")
## $type
## [1] "cor.bollen"
##
## $cov
## Q17_1 Q17_3 Q17_5 Q19_1 Q19_2 Q19_3 Q19_4 Q19_5 Q20_2 Q20_3
## Q17_1 0.000
## Q17_3 0.062 0.000
## Q17_5 -0.026 -0.008 0.000
## Q19_1 -0.003 -0.038 0.029 0.000
## Q19_2 -0.003 -0.065 0.035 0.011 0.000
## Q19_3 0.044 -0.037 0.068 -0.012 0.044 0.000
## Q19_4 -0.060 -0.094 -0.026 -0.026 -0.024 0.025 0.000
## Q19_5 -0.065 -0.065 0.075 0.007 -0.025 -0.012 -0.010 0.000
## Q20_2 -0.025 0.008 0.020 0.027 -0.079 -0.039 0.045 0.098 0.000
## Q20_3 0.040 -0.024 0.032 0.034 0.045 0.051 0.025 0.048 -0.017 0.000
## Q20_5 0.001 0.002 0.018 -0.004 -0.032 -0.006 0.048 0.000 -0.039 0.072
## Q20_6 -0.110 -0.061 -0.073 0.035 -0.079 -0.075 -0.032 0.034 0.049 -0.043
## Q20_7 -0.013 -0.018 0.039 0.005 -0.019 0.000 0.009 0.016 -0.016 -0.016
## Q20_8 0.034 0.027 0.066 0.017 -0.106 -0.057 -0.037 0.040 0.001 -0.027
## Q23_3 0.058 0.061 0.064 -0.064 0.033 0.017 -0.030 -0.063 -0.151 0.011
## Q23_8 -0.036 -0.017 -0.075 -0.024 -0.007 0.049 0.006 0.020 0.007 0.015
## Q23_9 -0.016 0.031 0.030 -0.030 -0.020 0.020 0.005 -0.002 -0.018 -0.008
## Q41_1 0.029 -0.032 -0.050 -0.042 -0.044 -0.044 0.067 -0.085 -0.001 -0.024
## Q41_2 0.060 0.010 0.010 0.007 0.033 -0.016 0.045 -0.016 -0.037 0.027
## Q41_4 0.006 -0.064 -0.125 0.010 0.019 0.008 0.018 0.013 0.069 0.056
## Q44_1 -0.030 -0.026 0.107 0.025 0.032 0.045 -0.021 0.024 -0.001 0.052
## Q44_2 -0.032 -0.085 0.019 -0.016 0.028 -0.017 -0.043 -0.030 -0.056 0.016
## Q44_3 -0.002 -0.054 0.012 -0.002 0.064 0.030 -0.001 0.014 -0.067 0.025
## Q44_4 -0.013 0.011 0.086 -0.063 -0.026 -0.029 -0.041 -0.044 -0.084 0.015
## Q15_4 -0.030 0.010 -0.059 -0.034 -0.033 -0.083 -0.056 -0.045 -0.036 -0.012
## Q21 0.007 -0.020 0.000 0.132 0.094 0.047 0.066 0.067 0.098 0.047
## Q25 0.044 0.008 0.015 -0.034 -0.024 -0.073 -0.012 -0.058 -0.019 -0.049
## Q29 -0.033 -0.041 -0.157 -0.030 0.022 -0.003 0.058 -0.029 -0.022 -0.061
## Q41_3 0.095 0.098 0.069 0.053 0.028 0.049 0.075 0.025 0.041 0.014
## Q20_5 Q20_6 Q20_7 Q20_8 Q23_3 Q23_8 Q23_9 Q41_1 Q41_2 Q41_4
## Q17_1
## Q17_3
## Q17_5
## Q19_1
## Q19_2
## Q19_3
## Q19_4
## Q19_5
## Q20_2
## Q20_3
## Q20_5 0.000
## Q20_6 0.006 0.000
## Q20_7 0.001 -0.009 0.000
## Q20_8 -0.034 -0.003 0.081 0.000
## Q23_3 -0.028 -0.143 0.024 -0.039 0.000
## Q23_8 -0.052 0.040 0.078 0.060 0.069 0.000
## Q23_9 -0.067 -0.013 0.033 0.004 -0.055 0.003 0.000
## Q41_1 0.062 -0.022 -0.062 -0.045 -0.045 -0.022 -0.027 0.000
## Q41_2 0.046 0.004 0.022 -0.032 -0.001 0.002 0.029 0.001 0.000
## Q41_4 -0.009 0.036 0.012 0.006 -0.036 -0.023 -0.078 -0.004 -0.001 0.000
## Q44_1 0.004 0.034 0.058 0.058 -0.066 -0.096 -0.058 -0.007 0.105 -0.015
## Q44_2 -0.015 -0.022 0.043 0.014 0.011 -0.010 0.024 -0.080 0.017 -0.029
## Q44_3 -0.016 -0.024 0.052 0.047 -0.010 -0.020 0.006 -0.103 0.027 -0.070
## Q44_4 -0.012 -0.045 0.008 0.019 0.053 0.064 0.108 -0.064 -0.008 -0.123
## Q15_4 0.019 0.008 -0.044 -0.031 -0.032 0.016 0.032 0.041 -0.026 0.070
## Q21 0.073 0.082 0.043 0.005 0.008 0.119 0.042 0.014 -0.038 0.040
## Q25 -0.017 -0.014 -0.059 -0.050 -0.061 -0.066 -0.107 0.005 -0.084 0.049
## Q29 0.008 0.013 -0.045 -0.051 0.093 0.149 0.066 -0.005 -0.102 0.008
## Q41_3 0.030 0.077 0.009 0.030 -0.005 0.077 0.099 0.315 0.424 0.226
## Q44_1 Q44_2 Q44_3 Q44_4 Q15_4 Q21 Q25 Q29 Q41_3
## Q17_1
## Q17_3
## Q17_5
## Q19_1
## Q19_2
## Q19_3
## Q19_4
## Q19_5
## Q20_2
## Q20_3
## Q20_5
## Q20_6
## Q20_7
## Q20_8
## Q23_3
## Q23_8
## Q23_9
## Q41_1
## Q41_2
## Q41_4
## Q44_1 0.000
## Q44_2 0.011 0.000
## Q44_3 -0.016 0.004 0.000
## Q44_4 -0.007 -0.017 0.014 0.000
## Q15_4 0.056 0.040 0.037 -0.051 0.000
## Q21 0.031 0.036 0.020 -0.080 0.002 0.000
## Q25 0.054 0.009 0.021 -0.110 0.014 0.003 0.000
## Q29 -0.200 -0.215 -0.244 -0.272 -0.007 -0.044 0.041 0.000
## Q41_3 0.134 0.100 0.095 0.037 0.052 -0.012 -0.066 0.031 0.000
semPaths(fitE2, "par", edge.label.cex = 1.2, fade = FALSE, exoCov = FALSE)
library(bannerCommenter)
TeachAlpha<-dplyr:: select(teacher2, Q17_1, Q17_3, Q17_5)
GOVSAlpha<- dplyr:: select(teacher2, Q23_5, Q23_6, Q23_7)
PROSAlpha<- dplyr::select(teacher2, Q23_3, Q23_8, Q23_9, Q23_10)
COMSAlpha<- dplyr::select(teacher2, Q23_1, Q23_2, Q23_4)
ASAlpha<- dplyr::select(teacher2, Q41_1, Q41_2, Q41_4)
TEFFAlpha<- dplyr::select(teacher2, Q44_1, Q44_2, Q44_3, Q44_4)
TotalAlpha<-dplyr:: select(teacher2, Q17_1, Q17_3, Q17_5,
Q23_5, Q23_6, Q23_7,
Q23_3, Q23_8, Q23_9,
Q23_1, Q23_2, Q23_4,
Q41_1, Q41_2, Q41_4,
Q44_1, Q44_2, Q44_3, Q44_4)
banner("Teacher Identity")
##
## ##################################################################
## ## Teacher Identity ##
## ##################################################################
omega(TeachAlpha)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.67
## G.6: 0.59
## Omega Hierarchical: 0.06
## Omega H asymptotic: 0.09
## Omega Total 0.69
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## Q17_1 0.49 0.29 0.71 0.12
## Q17_3 0.22 0.71 0.56 0.44 0.09
## Q17_5 0.65 0.46 0.54 0.06
##
## With eigenvalues of:
## g F1* F2* F3*
## 0.11 1.17 0.03 0.00
##
## general/max 0.09 max/min = Inf
## mean percent general = 0.09 with sd = 0.03 and cv of 0.33
## Explained Common Variance of the general factor = 0.08
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 679 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 0 and the fit is 0.4
## The number of observations was 679 with Chi Square = 271.32 with prob < NA
## The root mean square of the residuals is 0.38
## The df corrected root mean square of the residuals is NA
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.25 0.81 0.20 0
## Multiple R square of scores with factors 0.06 0.65 0.04 0
## Minimum correlation of factor score estimates -0.88 0.31 -0.92 -1
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.69 0.69 NA NA
## Omega general for total scores and subscales 0.06 0.06 NA NA
## Omega group for total scores and subscales 0.63 0.63 NA NA
banner("Government Support")
##
## ##################################################################
## ## Government Support ##
## ##################################################################
omega(GOVSAlpha)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.82
## G.6: 0.77
## Omega Hierarchical: 0.02
## Omega H asymptotic: 0.02
## Omega Total 0.84
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## Q23_5 0.80 0.67 0.33 0.02
## Q23_6 0.90 0.84 0.16 0.03
## Q23_7 0.61 0.41 0.59 0.03
##
## With eigenvalues of:
## g F1* F2* F3*
## 0.05 1.83 0.03 0.00
##
## general/max 0.03 max/min = Inf
## mean percent general = 0.03 with sd = 0.01 and cv of 0.22
## Explained Common Variance of the general factor = 0.02
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 679 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 0 and the fit is 1.14
## The number of observations was 679 with Chi Square = 769.39 with prob < NA
## The root mean square of the residuals is 0.59
## The df corrected root mean square of the residuals is NA
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.15 0.93 0.27 0
## Multiple R square of scores with factors 0.02 0.86 0.07 0
## Minimum correlation of factor score estimates -0.95 0.73 -0.85 -1
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.84 0.83 NA NA
## Omega general for total scores and subscales 0.02 0.02 NA NA
## Omega group for total scores and subscales 0.81 0.81 NA NA
banner("Professional Support")
##
## ##################################################################
## ## Professional Support ##
## ##################################################################
omega(PROSAlpha)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.77
## G.6: 0.76
## Omega Hierarchical: 0.66
## Omega H asymptotic: 0.79
## Omega Total 0.83
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## Q23_3 0.36 0.39 0.29 0.71 0.45
## Q23_8 0.70 0.34 0.64 0.36 0.77
## Q23_9 0.79 0.45 0.84 0.16 0.75
## Q23_10 0.64 0.51 0.67 0.33 0.61
##
## With eigenvalues of:
## g F1* F2* F3*
## 1.66 0.47 0.27 0.03
##
## general/max 3.52 max/min = 18.01
## mean percent general = 0.64 with sd = 0.15 and cv of 0.23
## Explained Common Variance of the general factor = 0.68
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 679 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 2 and the fit is 0.22
## The number of observations was 679 with Chi Square = 145.88 with prob < 2.1e-32
## The root mean square of the residuals is 0.11
## The df corrected root mean square of the residuals is 0.19
##
## RMSEA index = 0.326 and the 10 % confidence intervals are 0.282 0.372
## BIC = 132.84
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.84 0.58 0.51 0.25
## Multiple R square of scores with factors 0.71 0.33 0.26 0.06
## Minimum correlation of factor score estimates 0.41 -0.33 -0.47 -0.88
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.83 0.86 0.61 NA
## Omega general for total scores and subscales 0.66 0.60 0.41 NA
## Omega group for total scores and subscales 0.15 0.26 0.20 NA
banner("Community Support")
##
## #################################################################
## ## Community Support ##
## #################################################################
omega(COMSAlpha)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.81
## G.6: 0.75
## Omega Hierarchical: 0.02
## Omega H asymptotic: 0.02
## Omega Total 0.82
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## Q23_1 0.74 0.57 0.43 0.01
## Q23_2 0.87 0.76 0.24 0.02
## Q23_4 0.68 0.49 0.51 0.03
##
## With eigenvalues of:
## g F1* F2* F3*
## 0.04 1.76 0.03 0.00
##
## general/max 0.02 max/min = Inf
## mean percent general = 0.02 with sd = 0.01 and cv of 0.5
## Explained Common Variance of the general factor = 0.02
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 679 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 0 and the fit is 1
## The number of observations was 679 with Chi Square = 673.48 with prob < NA
## The root mean square of the residuals is 0.58
## The df corrected root mean square of the residuals is NA
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.13 0.91 0.25 0
## Multiple R square of scores with factors 0.02 0.83 0.06 0
## Minimum correlation of factor score estimates -0.96 0.66 -0.88 -1
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.82 0.82 NA NA
## Omega general for total scores and subscales 0.02 0.02 NA NA
## Omega group for total scores and subscales 0.80 0.80 NA NA
banner("Assessment")
##
## ##################################################################
## ## Assessment ##
## ##################################################################
omega(ASAlpha)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.65
## G.6: 0.58
## Omega Hierarchical: 0.03
## Omega H asymptotic: 0.04
## Omega Total 0.69
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## Q41_1 0.71 0.53 0.47 0.02
## Q41_2 0.79 0.65 0.35 0.04
## Q41_4 0.38 0.18 0.82 0.06
##
## With eigenvalues of:
## g F1* F2* F3*
## 0.05 1.26 0.05 0.00
##
## general/max 0.04 max/min = Inf
## mean percent general = 0.04 with sd = 0.02 and cv of 0.5
## Explained Common Variance of the general factor = 0.04
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 679 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 0 and the fit is 0.47
## The number of observations was 679 with Chi Square = 317.93 with prob < NA
## The root mean square of the residuals is 0.39
## The df corrected root mean square of the residuals is NA
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.17 0.85 0.27 0
## Multiple R square of scores with factors 0.03 0.73 0.07 0
## Minimum correlation of factor score estimates -0.94 0.46 -0.86 -1
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.69 0.69 NA NA
## Omega general for total scores and subscales 0.03 0.03 NA NA
## Omega group for total scores and subscales 0.66 0.66 NA NA
banner("Teaching Efficacy")
##
## #################################################################
## ## Teaching Efficacy ##
## #################################################################
banner("Total")
##
## #################################################################
## ## Total ##
## #################################################################
omega(TotalAlpha)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.84
## G.6: 0.89
## Omega Hierarchical: 0.43
## Omega H asymptotic: 0.48
## Omega Total 0.88
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## Q17_1 0.24 0.35 0.19 0.81 0.31
## Q17_3 0.27 0.42 0.25 0.75 0.30
## Q17_5 0.47 0.40 0.38 0.62 0.59
## Q23_5 0.64 0.44 0.56 0.05
## Q23_6 0.70 0.51 0.49 0.03
## Q23_7 0.59 0.37 0.63 0.05
## Q23_3 0.31 0.12 0.88 0.09
## Q23_8 0.50 0.27 0.73 0.02
## Q23_9 0.66 0.46 0.54 0.03
## Q23_1 0.48 0.43 0.43 0.57 0.55
## Q23_2 0.39 0.55 0.45 0.55 0.34
## Q23_4 0.37 0.60 0.50 0.50 0.28
## Q41_1- -0.67 0.45 0.55 0.00
## Q41_2 0.84 0.70 0.30 0.01
## Q41_4- -0.36 0.18 0.82 0.00
## Q44_1 0.80 0.65 0.35 0.99
## Q44_2 0.85 0.71 0.29 1.01
## Q44_3 0.86 0.74 0.26 1.01
## Q44_4 0.70 0.50 0.50 0.98
##
## With eigenvalues of:
## g F1* F2* F3*
## 3.6 3.4 0.0 1.3
##
## general/max 1.06 max/min = Inf
## mean percent general = 0.35 with sd = 0.39 and cv of 1.11
## Explained Common Variance of the general factor = 0.43
##
## The degrees of freedom are 117 and the fit is 1.55
## The number of observations was 679 with Chi Square = 1037.41 with prob < 2.5e-147
## The root mean square of the residuals is 0.06
## The df corrected root mean square of the residuals is 0.07
## RMSEA index = 0.108 and the 10 % confidence intervals are 0.102 0.114
## BIC = 274.5
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 152 and the fit is 4.55
## The number of observations was 679 with Chi Square = 3050.99 with prob < 0
## The root mean square of the residuals is 0.19
## The df corrected root mean square of the residuals is 0.21
##
## RMSEA index = 0.168 and the 10 % confidence intervals are 0.163 0.173
## BIC = 2059.86
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.95 0.93 0 0.89
## Multiple R square of scores with factors 0.90 0.86 0 0.79
## Minimum correlation of factor score estimates 0.80 0.73 -1 0.59
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.88 0.87 NA 0.61
## Omega general for total scores and subscales 0.43 0.29 NA 0.60
## Omega group for total scores and subscales 0.42 0.57 NA 0.01
itemdesc<-
teacher2%>%
dplyr::select(Q17_1, Q17_3, Q17_5,
Q19_1, Q19_2, Q19_3, Q19_4, Q19_5,
Q20_2, Q20_3, Q20_5, Q20_7, Q20_8,
Q23_3, Q23_8, Q23_9,
Q41_1, Q41_2, Q41_3, Q41_4,
Q44_1, Q44_2, Q44_3, Q44_4,
Q15_4, Q21, Q25, Q29)
itemsdescr<- describe(itemdesc)
write.csv(itemsdescr, "discrimination.csv")
itemsdescr
## vars n mean sd median trimmed mad min max range skew kurtosis se
## Q17_1 1 623 3.45 0.71 4 3.57 0.00 1 4 3 -1.28 1.53 0.03
## Q17_3 2 624 2.98 0.77 3 3.03 0.00 1 4 3 -0.53 0.08 0.03
## Q17_5 3 624 2.88 0.78 3 2.90 1.48 1 4 3 -0.30 -0.35 0.03
## Q19_1 4 623 2.83 0.92 3 2.90 1.48 1 4 3 -0.36 -0.72 0.04
## Q19_2 5 625 3.12 0.86 3 3.22 1.48 1 4 3 -0.73 -0.20 0.03
## Q19_3 6 622 2.91 0.88 3 2.99 1.48 1 4 3 -0.48 -0.49 0.04
## Q19_4 7 623 2.84 0.88 3 2.89 1.48 1 4 3 -0.32 -0.65 0.04
## Q19_5 8 624 2.43 0.93 2 2.41 1.48 1 4 3 0.15 -0.85 0.04
## Q20_2 9 623 2.30 0.87 2 2.26 1.48 1 4 3 0.16 -0.70 0.03
## Q20_3 10 622 2.77 0.93 3 2.84 1.48 1 4 3 -0.34 -0.73 0.04
## Q20_5 11 622 2.67 0.90 3 2.71 1.48 1 4 3 -0.20 -0.73 0.04
## Q20_7 12 622 2.53 0.90 3 2.53 1.48 1 4 3 -0.06 -0.77 0.04
## Q20_8 13 621 1.87 0.80 2 1.78 1.48 1 4 3 0.79 0.37 0.03
## Q23_3 14 605 3.06 0.70 3 3.11 0.00 1 4 3 -0.60 0.69 0.03
## Q23_8 15 604 2.55 0.99 3 2.57 1.48 1 4 3 -0.22 -1.00 0.04
## Q23_9 16 604 2.10 0.93 2 2.05 1.48 1 4 3 0.27 -1.01 0.04
## Q41_1 17 551 3.33 0.81 4 3.46 0.00 1 4 3 -1.10 0.59 0.03
## Q41_2 18 547 3.12 0.77 3 3.17 1.48 1 4 3 -0.44 -0.51 0.03
## Q41_3 19 548 2.59 0.91 3 2.62 1.48 1 4 3 0.08 -0.86 0.04
## Q41_4 20 552 3.25 0.66 3 3.32 0.00 1 4 3 -0.44 -0.29 0.03
## Q44_1 21 530 2.94 0.70 3 2.97 0.00 1 4 3 -0.55 0.63 0.03
## Q44_2 22 529 3.09 0.59 3 3.12 0.00 1 4 3 -0.19 0.45 0.03
## Q44_3 23 530 2.99 0.67 3 3.01 0.00 1 4 3 -0.33 0.23 0.03
## Q44_4 24 522 2.86 0.68 3 2.87 0.00 1 4 3 -0.47 0.51 0.03
## Q15_4 25 594 2.84 1.10 3 2.93 1.48 1 4 3 -0.49 -1.10 0.05
## Q21 26 620 2.70 0.96 3 2.75 1.48 1 4 3 -0.33 -0.84 0.04
## Q25 27 597 2.95 0.87 3 3.01 1.48 1 4 3 -0.50 -0.43 0.04
## Q29 28 574 2.56 0.87 3 2.57 1.48 1 4 3 -0.09 -0.66 0.04
mentalSEM<-
'
ANXs=~ Q19_1 + Q19_2 + Q19_3 + Q19_4 + Q19_5
DEPs=~ Q20_1 + Q20_2 + Q20_3 + Q20_4 + Q20_5 + Q20_6 + Q20_7 + Q20_8
LV=~ Q15_4 + Q21 + Q25 + Q29 + Q41_3
LV ~ ANXs
LV ~ DEPs
'
fithealth<- sem(mentalSEM, data=teacher2)
summary(fithealth, fit.measures=TRUE, standardized = TRUE, rsquare = T)
## lavaan 0.6-12 ended normally after 39 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 39
##
## Used Total
## Number of observations 475 679
##
## Model Test User Model:
##
## Test statistic 374.897
## Degrees of freedom 132
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 4570.783
## Degrees of freedom 153
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.945
## Tucker-Lewis Index (TLI) 0.936
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9037.860
## Loglikelihood unrestricted model (H1) -8850.412
##
## Akaike (AIC) 18153.721
## Bayesian (BIC) 18316.090
## Sample-size adjusted Bayesian (BIC) 18192.310
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.062
## 90 Percent confidence interval - lower 0.055
## 90 Percent confidence interval - upper 0.070
## P-value RMSEA <= 0.05 0.003
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.042
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ANXs =~
## Q19_1 1.000 0.765 0.832
## Q19_2 0.851 0.045 18.933 0.000 0.651 0.762
## Q19_3 0.993 0.044 22.490 0.000 0.759 0.858
## Q19_4 0.935 0.046 20.371 0.000 0.715 0.802
## Q19_5 0.875 0.051 17.223 0.000 0.669 0.711
## DEPs =~
## Q20_1 1.000 0.648 0.769
## Q20_2 1.097 0.058 18.800 0.000 0.711 0.820
## Q20_3 0.985 0.064 15.435 0.000 0.639 0.692
## Q20_4 0.828 0.055 15.158 0.000 0.537 0.681
## Q20_5 0.890 0.061 14.519 0.000 0.577 0.656
## Q20_6 0.985 0.062 15.856 0.000 0.639 0.709
## Q20_7 1.049 0.060 17.367 0.000 0.681 0.766
## Q20_8 0.705 0.056 12.599 0.000 0.457 0.577
## LV =~
## Q15_4 1.000 0.696 0.634
## Q21 1.084 0.081 13.371 0.000 0.755 0.798
## Q25 1.000 0.075 13.421 0.000 0.696 0.803
## Q29 0.569 0.065 8.724 0.000 0.396 0.462
## Q41_3 0.559 0.069 8.099 0.000 0.389 0.425
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LV ~
## ANXs 0.440 0.081 5.426 0.000 0.483 0.483
## DEPs 0.282 0.092 3.080 0.002 0.263 0.263
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ANXs ~~
## DEPs 0.402 0.037 11.006 0.000 0.810 0.810
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q19_1 0.261 0.021 12.214 0.000 0.261 0.309
## .Q19_2 0.307 0.023 13.457 0.000 0.307 0.420
## .Q19_3 0.208 0.018 11.442 0.000 0.208 0.265
## .Q19_4 0.284 0.022 12.855 0.000 0.284 0.357
## .Q19_5 0.439 0.031 13.972 0.000 0.439 0.495
## .Q20_1 0.291 0.022 13.287 0.000 0.291 0.409
## .Q20_2 0.247 0.020 12.387 0.000 0.247 0.328
## .Q20_3 0.444 0.032 14.065 0.000 0.444 0.521
## .Q20_4 0.333 0.024 14.144 0.000 0.333 0.536
## .Q20_5 0.442 0.031 14.308 0.000 0.442 0.570
## .Q20_6 0.405 0.029 13.933 0.000 0.405 0.498
## .Q20_7 0.325 0.024 13.318 0.000 0.325 0.413
## .Q20_8 0.418 0.028 14.682 0.000 0.418 0.667
## .Q15_4 0.722 0.053 13.532 0.000 0.722 0.598
## .Q21 0.326 0.031 10.353 0.000 0.326 0.364
## .Q25 0.267 0.026 10.160 0.000 0.267 0.355
## .Q29 0.579 0.039 14.663 0.000 0.579 0.787
## .Q41_3 0.687 0.046 14.804 0.000 0.687 0.819
## ANXs 0.585 0.054 10.869 0.000 1.000 1.000
## DEPs 0.421 0.044 9.652 0.000 1.000 1.000
## .LV 0.239 0.036 6.619 0.000 0.492 0.492
##
## R-Square:
## Estimate
## Q19_1 0.691
## Q19_2 0.580
## Q19_3 0.735
## Q19_4 0.643
## Q19_5 0.505
## Q20_1 0.591
## Q20_2 0.672
## Q20_3 0.479
## Q20_4 0.464
## Q20_5 0.430
## Q20_6 0.502
## Q20_7 0.587
## Q20_8 0.333
## Q15_4 0.402
## Q21 0.636
## Q25 0.645
## Q29 0.213
## Q41_3 0.181
## LV 0.508
semfit<- standardizedsolution(fithealth, type = "std.all")
write.csv(semfit, "anxSEMModel.csv")
semfit
## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 ANXs =~ Q19_1 0.832 0.017 49.141 0.000 0.798 0.865
## 2 ANXs =~ Q19_2 0.762 0.022 35.087 0.000 0.719 0.804
## 3 ANXs =~ Q19_3 0.858 0.015 56.650 0.000 0.828 0.887
## 4 ANXs =~ Q19_4 0.802 0.019 42.229 0.000 0.764 0.839
## 5 ANXs =~ Q19_5 0.711 0.025 28.413 0.000 0.662 0.760
## 6 DEPs =~ Q20_1 0.769 0.021 36.037 0.000 0.727 0.811
## 7 DEPs =~ Q20_2 0.820 0.018 45.983 0.000 0.785 0.855
## 8 DEPs =~ Q20_3 0.692 0.026 26.324 0.000 0.641 0.744
## 9 DEPs =~ Q20_4 0.681 0.027 25.264 0.000 0.628 0.734
## 10 DEPs =~ Q20_5 0.656 0.028 23.018 0.000 0.600 0.712
## 11 DEPs =~ Q20_6 0.709 0.025 28.048 0.000 0.659 0.758
## 12 DEPs =~ Q20_7 0.766 0.021 35.667 0.000 0.724 0.809
## 13 DEPs =~ Q20_8 0.577 0.033 17.566 0.000 0.513 0.642
## 14 LV =~ Q15_4 0.634 0.032 19.729 0.000 0.571 0.697
## 15 LV =~ Q21 0.798 0.023 34.170 0.000 0.752 0.843
## 16 LV =~ Q25 0.803 0.023 34.823 0.000 0.758 0.848
## 17 LV =~ Q29 0.462 0.040 11.474 0.000 0.383 0.541
## 18 LV =~ Q41_3 0.425 0.042 10.189 0.000 0.343 0.507
## 19 LV ~ ANXs 0.483 0.081 5.975 0.000 0.325 0.642
## 20 LV ~ DEPs 0.263 0.083 3.178 0.001 0.101 0.425
## 21 Q19_1 ~~ Q19_1 0.309 0.028 10.966 0.000 0.253 0.364
## 22 Q19_2 ~~ Q19_2 0.420 0.033 12.705 0.000 0.355 0.485
## 23 Q19_3 ~~ Q19_3 0.265 0.026 10.196 0.000 0.214 0.316
## 24 Q19_4 ~~ Q19_4 0.357 0.030 11.743 0.000 0.298 0.417
## 25 Q19_5 ~~ Q19_5 0.495 0.036 13.913 0.000 0.425 0.564
## 26 Q20_1 ~~ Q20_1 0.409 0.033 12.470 0.000 0.345 0.473
## 27 Q20_2 ~~ Q20_2 0.328 0.029 11.202 0.000 0.270 0.385
## 28 Q20_3 ~~ Q20_3 0.521 0.036 14.318 0.000 0.450 0.592
## 29 Q20_4 ~~ Q20_4 0.536 0.037 14.594 0.000 0.464 0.608
## 30 Q20_5 ~~ Q20_5 0.570 0.037 15.258 0.000 0.497 0.643
## 31 Q20_6 ~~ Q20_6 0.498 0.036 13.910 0.000 0.428 0.568
## 32 Q20_7 ~~ Q20_7 0.413 0.033 12.526 0.000 0.348 0.477
## 33 Q20_8 ~~ Q20_8 0.667 0.038 17.586 0.000 0.593 0.741
## 34 Q15_4 ~~ Q15_4 0.598 0.041 14.691 0.000 0.518 0.678
## 35 Q21 ~~ Q21 0.364 0.037 9.770 0.000 0.291 0.437
## 36 Q25 ~~ Q25 0.355 0.037 9.574 0.000 0.282 0.427
## 37 Q29 ~~ Q29 0.787 0.037 21.137 0.000 0.714 0.859
## 38 Q41_3 ~~ Q41_3 0.819 0.035 23.103 0.000 0.750 0.889
## 39 ANXs ~~ ANXs 1.000 0.000 NA NA 1.000 1.000
## 40 DEPs ~~ DEPs 1.000 0.000 NA NA 1.000 1.000
## 41 LV ~~ LV 0.492 0.042 11.745 0.000 0.410 0.574
## 42 ANXs ~~ DEPs 0.810 0.021 38.034 0.000 0.768 0.852
semPaths(fithealth, whatLabels = "std.all", structural = FALSE, edge.label.cex = .8, node.label.cex = .8,
label.prop=0.9, edge.label.color = "black", rotation = 4,
equalizeManifests = TRUE, optimizeLatRes = TRUE, node.width = 1.5,
edge.width = 0.5, shapeMan = "rectangle", shapeLat = "ellipse",
shapeInt = "triangle", sizeMan = 4, sizeInt = 2, sizeLat = 4,
curve=2, unCol = "#070b8c", title = FALSE, intercepts = FALSE, residuals = TRUE)
semPaths(fithealth,
whatLabels = "std.all", structural = TRUE, edge.label.cex = 1, node.label.cex = 1.5,
label.prop=0.9, edge.label.color = "black", rotation = 2,
equalizeManifests = FALSE, optimizeLatRes = TRUE, node.width = 1.5,
edge.width = 0.5, shapeMan = "rectangle", shapeLat = "ellipse",
shapeInt = "triangle", sizeMan = 4, sizeInt = 2, sizeLat = 4,
curve=2, unCol = "#070b8c", title = FALSE, intercepts = FALSE, residuals = FALSE)
ANXAlpha<- dplyr:: select(teacher2, Q19_1, Q19_2, Q19_3, Q19_4, Q19_5)
DEPAlpha2<- dplyr:: select(teacher2, Q20_1, Q20_2, Q20_3, Q20_4, Q20_5, Q20_7, Q20_8)
LeaveAlpha<-dplyr::select(teacher2, Q15_4, Q21, Q25, Q29, Q41_3)
totalAlpha2<- dplyr:: select(teacher2, Q19_1, Q19_2, Q19_3, Q19_4, Q19_5,
Q20_1, Q20_2, Q20_3, Q20_4, Q20_5, Q20_7, Q20_8,
Q15_4, Q21, Q25, Q29, Q41_3)
bannerCommenter::banner("Anxiety Alpha and Omega")
##
## #################################################################
## ## Anxiety Alpha and Omega ##
## #################################################################
omega(ANXAlpha)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.89
## G.6: 0.87
## Omega Hierarchical: 0.87
## Omega H asymptotic: 0.96
## Omega Total 0.91
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## Q19_1 0.85 0.74 0.26 0.98
## Q19_2 0.79 0.20 0.66 0.34 0.96
## Q19_3 0.83 0.35 0.82 0.18 0.84
## Q19_4 0.74 0.30 0.64 0.36 0.85
## Q19_5 0.67 0.51 0.49 0.90
##
## With eigenvalues of:
## g F1* F2* F3*
## 3.03 0.00 0.23 0.10
##
## general/max 13.12 max/min = 208.65
## mean percent general = 0.91 with sd = 0.06 and cv of 0.07
## Explained Common Variance of the general factor = 0.9
##
## The degrees of freedom are -2 and the fit is 0
## The number of observations was 679 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.07
## The number of observations was 679 with Chi Square = 47.39 with prob < 4.7e-09
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.06
##
## RMSEA index = 0.112 and the 10 % confidence intervals are 0.084 0.142
## BIC = 14.79
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.94 0.04 0.56 0.46
## Multiple R square of scores with factors 0.88 0.00 0.31 0.21
## Minimum correlation of factor score estimates 0.77 -1.00 -0.38 -0.58
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.91 NA 0.84 0.82
## Omega general for total scores and subscales 0.87 NA 0.72 0.82
## Omega group for total scores and subscales 0.02 NA 0.12 0.00
bannerCommenter::banner("Depression Alpha and Omega")
##
## ##################################################################
## ## Depression Alpha and Omega ##
## ##################################################################
omega(DEPAlpha2)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.88
## G.6: 0.88
## Omega Hierarchical: 0.76
## Omega H asymptotic: 0.83
## Omega Total 0.92
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## Q20_1 0.72 0.21 0.21 0.62 0.38 0.85
## Q20_2 0.86 0.50 1.00 0.00 0.75
## Q20_3 0.63 0.36 0.54 0.46 0.74
## Q20_4 0.62 0.52 0.65 0.35 0.58
## Q20_5 0.58 0.40 0.51 0.49 0.67
## Q20_7 0.68 0.22 0.25 0.58 0.42 0.79
## Q20_8 0.58 0.60 0.70 0.30 0.48
##
## With eigenvalues of:
## g F1* F2* F3*
## 3.18 0.66 0.30 0.45
##
## general/max 4.84 max/min = 2.19
## mean percent general = 0.69 with sd = 0.13 and cv of 0.18
## Explained Common Variance of the general factor = 0.69
##
## The degrees of freedom are 3 and the fit is 0.01
## The number of observations was 679 with Chi Square = 4 with prob < 0.26
## The root mean square of the residuals is 0.01
## The df corrected root mean square of the residuals is 0.02
## RMSEA index = 0.022 and the 10 % confidence intervals are 0 0.072
## BIC = -15.56
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 14 and the fit is 0.33
## The number of observations was 679 with Chi Square = 219.32 with prob < 6.1e-39
## The root mean square of the residuals is 0.1
## The df corrected root mean square of the residuals is 0.12
##
## RMSEA index = 0.147 and the 10 % confidence intervals are 0.13 0.165
## BIC = 128.03
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.91 0.70 0.68 0.72
## Multiple R square of scores with factors 0.83 0.49 0.46 0.52
## Minimum correlation of factor score estimates 0.65 -0.01 -0.07 0.04
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.92 0.83 1.00 0.75
## Omega general for total scores and subscales 0.76 0.62 0.75 0.51
## Omega group for total scores and subscales 0.11 0.21 0.25 0.24
bannerCommenter::banner("Quitting")
##
## ##################################################################
## ## Quitting ##
## ##################################################################
omega(LeaveAlpha)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.76
## G.6: 0.73
## Omega Hierarchical: 0.74
## Omega H asymptotic: 0.94
## Omega Total 0.79
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## Q15_4 0.66 0.20 0.49 0.51 0.90
## Q21 0.72 0.53 0.47 0.98
## Q25 0.87 0.77 0.23 0.99
## Q29 0.45 0.34 0.32 0.68 0.64
## Q41_3 0.36 0.35 0.28 0.72 0.46
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.05 0.00 0.20 0.15
##
## general/max 10.44 max/min = Inf
## mean percent general = 0.79 with sd = 0.23 and cv of 0.3
## Explained Common Variance of the general factor = 0.85
##
## The degrees of freedom are -2 and the fit is 0
## The number of observations was 679 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.03
## The number of observations was 679 with Chi Square = 23.01 with prob < 0.00034
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.05
##
## RMSEA index = 0.073 and the 10 % confidence intervals are 0.044 0.104
## BIC = -9.59
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.92 0 0.52 0.41
## Multiple R square of scores with factors 0.85 0 0.27 0.17
## Minimum correlation of factor score estimates 0.70 -1 -0.46 -0.67
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.79 NA 0.78 0.32
## Omega general for total scores and subscales 0.74 NA 0.75 0.20
## Omega group for total scores and subscales 0.03 NA 0.03 0.12
bannerCommenter::banner("Total")
##
## #################################################################
## ## Total ##
## #################################################################
omega(totalAlpha2)
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.92
## G.6: 0.93
## Omega Hierarchical: 0.78
## Omega H asymptotic: 0.83
## Omega Total 0.94
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## Q19_1 0.72 0.36 0.67 0.33 0.78
## Q19_2 0.65 0.44 0.63 0.37 0.68
## Q19_3 0.74 0.48 0.78 0.22 0.71
## Q19_4 0.71 0.33 0.62 0.38 0.80
## Q19_5 0.63 0.26 0.48 0.52 0.82
## Q20_1 0.65 0.49 0.68 0.32 0.63
## Q20_2 0.71 0.41 0.68 0.32 0.75
## Q20_3 0.64 0.30 0.53 0.47 0.77
## Q20_4 0.62 0.30 0.48 0.52 0.79
## Q20_5 0.57 0.33 0.44 0.56 0.75
## Q20_7 0.67 0.36 0.58 0.42 0.76
## Q20_8 0.50 0.33 0.35 0.65 0.69
## Q15_4 0.44 0.52 0.46 0.54 0.41
## Q21 0.62 0.44 0.59 0.41 0.65
## Q25 0.54 0.60 0.66 0.34 0.45
## Q29 0.30 0.37 0.24 0.76 0.39
## Q41_3 0.33 0.25 0.18 0.82 0.61
##
## With eigenvalues of:
## g F1* F2* F3*
## 6.20 0.99 0.81 1.04
##
## general/max 5.94 max/min = 1.29
## mean percent general = 0.67 with sd = 0.14 and cv of 0.2
## Explained Common Variance of the general factor = 0.68
##
## The degrees of freedom are 88 and the fit is 0.42
## The number of observations was 679 with Chi Square = 278.26 with prob < 1.3e-21
## The root mean square of the residuals is 0.03
## The df corrected root mean square of the residuals is 0.03
## RMSEA index = 0.056 and the 10 % confidence intervals are 0.049 0.064
## BIC = -295.56
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 119 and the fit is 1.77
## The number of observations was 679 with Chi Square = 1188.47 with prob < 9.8e-176
## The root mean square of the residuals is 0.1
## The df corrected root mean square of the residuals is 0.1
##
## RMSEA index = 0.115 and the 10 % confidence intervals are 0.109 0.121
## BIC = 412.52
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.89 0.68 0.67 0.78
## Multiple R square of scores with factors 0.80 0.46 0.44 0.61
## Minimum correlation of factor score estimates 0.60 -0.08 -0.11 0.21
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.94 0.88 0.89 0.77
## Omega general for total scores and subscales 0.78 0.66 0.68 0.39
## Omega group for total scores and subscales 0.11 0.22 0.20 0.37
matrix<- dplyr::select(teacher2, anx, Dep2, Leave)
matrix2<-na.omit(matrix)
apa.cor.table(matrix, filename = "correlation2.doc")
##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2
## 1. anx 14.13 3.72
##
## 2. Dep2 19.72 5.28 .74**
## [.71, .78]
##
## 3. Leave 13.63 3.35 .58** .53**
## [.51, .63] [.47, .59]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
##
TeachModel2a<-
'
Teachs=~Q17_1 + Q17_3 + Q17_5
GOVa=~ Q23_5 + Q23_6 + Q23_7
PROa=~ Q23_3 + Q23_8 + Q23_9 + Q23_10
COMa=~ Q23_1 + Q23_2 + Q23_4
ASSMs=~ Q41_1 + Q41_2 + Q41_4
TEFFs=~ Q44_1 + Q44_2 + Q44_3 + Q44_4
'
fitE<- cfa(TeachModel2a, data=teacher2)
summary(fitE, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-12 ended normally after 84 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 55
##
## Used Total
## Number of observations 494 679
##
## Model Test User Model:
##
## Test statistic 399.146
## Degrees of freedom 155
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 4406.553
## Degrees of freedom 190
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.942
## Tucker-Lewis Index (TLI) 0.929
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9425.359
## Loglikelihood unrestricted model (H1) -9225.786
##
## Akaike (AIC) 18960.718
## Bayesian (BIC) 19191.857
## Sample-size adjusted Bayesian (BIC) 19017.286
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.056
## 90 Percent confidence interval - lower 0.050
## 90 Percent confidence interval - upper 0.063
## P-value RMSEA <= 0.05 0.058
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.055
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Teachs =~
## Q17_1 1.000 0.354 0.524
## Q17_3 1.333 0.146 9.155 0.000 0.472 0.621
## Q17_5 1.737 0.176 9.848 0.000 0.615 0.787
## GOVa =~
## Q23_5 1.000 0.632 0.802
## Q23_6 1.180 0.062 18.946 0.000 0.746 0.897
## Q23_7 0.884 0.061 14.504 0.000 0.559 0.646
## PROa =~
## Q23_3 1.000 0.248 0.346
## Q23_8 2.762 0.384 7.202 0.000 0.686 0.704
## Q23_9 3.375 0.451 7.491 0.000 0.838 0.903
## Q23_10 2.899 0.393 7.370 0.000 0.720 0.785
## COMa =~
## Q23_1 1.000 0.597 0.746
## Q23_2 1.071 0.065 16.370 0.000 0.639 0.813
## Q23_4 0.982 0.063 15.502 0.000 0.586 0.756
## ASSMs =~
## Q41_1 1.000 0.556 0.683
## Q41_2 1.175 0.142 8.300 0.000 0.654 0.853
## Q41_4 0.493 0.063 7.804 0.000 0.274 0.414
## TEFFs =~
## Q44_1 1.000 0.554 0.784
## Q44_2 0.928 0.045 20.650 0.000 0.514 0.864
## Q44_3 1.070 0.051 21.004 0.000 0.593 0.879
## Q44_4 0.885 0.053 16.581 0.000 0.490 0.718
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Teachs ~~
## GOVa 0.090 0.016 5.772 0.000 0.402 0.402
## PROa 0.044 0.009 5.119 0.000 0.501 0.501
## COMa 0.134 0.018 7.282 0.000 0.633 0.633
## ASSMs 0.004 0.012 0.311 0.756 0.018 0.018
## TEFFs 0.104 0.015 6.879 0.000 0.528 0.528
## GOVa ~~
## PROa 0.088 0.015 5.930 0.000 0.563 0.563
## COMa 0.209 0.025 8.383 0.000 0.553 0.553
## ASSMs -0.062 0.020 -3.050 0.002 -0.175 -0.175
## TEFFs 0.076 0.019 4.076 0.000 0.216 0.216
## PROa ~~
## COMa 0.076 0.013 5.670 0.000 0.510 0.510
## ASSMs -0.021 0.008 -2.572 0.010 -0.153 -0.153
## TEFFs 0.029 0.008 3.575 0.000 0.210 0.210
## COMa ~~
## ASSMs -0.020 0.019 -1.077 0.281 -0.061 -0.061
## TEFFs 0.173 0.021 8.192 0.000 0.522 0.522
## ASSMs ~~
## TEFFs 0.031 0.017 1.873 0.061 0.102 0.102
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q17_1 0.331 0.024 13.947 0.000 0.331 0.725
## .Q17_3 0.354 0.028 12.673 0.000 0.354 0.614
## .Q17_5 0.232 0.029 7.909 0.000 0.232 0.380
## .Q23_5 0.221 0.021 10.614 0.000 0.221 0.356
## .Q23_6 0.134 0.022 5.981 0.000 0.134 0.194
## .Q23_7 0.437 0.031 14.018 0.000 0.437 0.583
## .Q23_3 0.453 0.029 15.436 0.000 0.453 0.880
## .Q23_8 0.478 0.036 13.460 0.000 0.478 0.504
## .Q23_9 0.159 0.026 6.183 0.000 0.159 0.184
## .Q23_10 0.323 0.028 11.692 0.000 0.323 0.384
## .Q23_1 0.283 0.024 12.008 0.000 0.283 0.443
## .Q23_2 0.210 0.021 9.969 0.000 0.210 0.340
## .Q23_4 0.258 0.022 11.778 0.000 0.258 0.429
## .Q41_1 0.353 0.041 8.596 0.000 0.353 0.533
## .Q41_2 0.160 0.048 3.298 0.001 0.160 0.272
## .Q41_4 0.364 0.025 14.644 0.000 0.364 0.829
## .Q44_1 0.192 0.015 12.940 0.000 0.192 0.385
## .Q44_2 0.090 0.009 10.418 0.000 0.090 0.254
## .Q44_3 0.104 0.011 9.659 0.000 0.104 0.227
## .Q44_4 0.226 0.016 13.896 0.000 0.226 0.485
## Teachs 0.125 0.023 5.499 0.000 1.000 1.000
## GOVa 0.400 0.040 9.997 0.000 1.000 1.000
## PROa 0.062 0.016 3.738 0.000 1.000 1.000
## COMa 0.356 0.040 9.015 0.000 1.000 1.000
## ASSMs 0.310 0.050 6.243 0.000 1.000 1.000
## TEFFs 0.307 0.031 10.062 0.000 1.000 1.000
parameterEstimates(fitE, standardized=TRUE) %>%
filter(op == "=~") %>%
dplyr::select('Latent Factor'=lhs, Indicator=rhs, B=est, SE=se, Z=z, 'p-value'=pvalue, Beta=std.all) %>%
kable(digits = 3, format="pandoc", caption="Factor Loadings")
Factor Loadings
| Teachs |
Q17_1 |
1.000 |
0.000 |
NA |
NA |
0.524 |
| Teachs |
Q17_3 |
1.333 |
0.146 |
9.155 |
0 |
0.621 |
| Teachs |
Q17_5 |
1.737 |
0.176 |
9.848 |
0 |
0.787 |
| GOVa |
Q23_5 |
1.000 |
0.000 |
NA |
NA |
0.802 |
| GOVa |
Q23_6 |
1.180 |
0.062 |
18.946 |
0 |
0.897 |
| GOVa |
Q23_7 |
0.884 |
0.061 |
14.504 |
0 |
0.646 |
| PROa |
Q23_3 |
1.000 |
0.000 |
NA |
NA |
0.346 |
| PROa |
Q23_8 |
2.762 |
0.384 |
7.202 |
0 |
0.704 |
| PROa |
Q23_9 |
3.375 |
0.451 |
7.491 |
0 |
0.903 |
| PROa |
Q23_10 |
2.899 |
0.393 |
7.370 |
0 |
0.785 |
| COMa |
Q23_1 |
1.000 |
0.000 |
NA |
NA |
0.746 |
| COMa |
Q23_2 |
1.071 |
0.065 |
16.370 |
0 |
0.813 |
| COMa |
Q23_4 |
0.982 |
0.063 |
15.502 |
0 |
0.756 |
| ASSMs |
Q41_1 |
1.000 |
0.000 |
NA |
NA |
0.683 |
| ASSMs |
Q41_2 |
1.175 |
0.142 |
8.300 |
0 |
0.853 |
| ASSMs |
Q41_4 |
0.493 |
0.063 |
7.804 |
0 |
0.414 |
| TEFFs |
Q44_1 |
1.000 |
0.000 |
NA |
NA |
0.784 |
| TEFFs |
Q44_2 |
0.928 |
0.045 |
20.650 |
0 |
0.864 |
| TEFFs |
Q44_3 |
1.070 |
0.051 |
21.004 |
0 |
0.879 |
| TEFFs |
Q44_4 |
0.885 |
0.053 |
16.581 |
0 |
0.718 |
CFAStand<-standardizedsolution(fitE, type = "std.all")
write.csv(CFAStand, "CFAStand.csv")
CFAStand
## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 Teachs =~ Q17_1 0.524 0.040 13.146 0.000 0.446 0.602
## 2 Teachs =~ Q17_3 0.621 0.036 17.264 0.000 0.551 0.692
## 3 Teachs =~ Q17_5 0.787 0.031 25.048 0.000 0.726 0.849
## 4 GOVa =~ Q23_5 0.802 0.022 36.076 0.000 0.759 0.846
## 5 GOVa =~ Q23_6 0.897 0.019 47.486 0.000 0.860 0.935
## 6 GOVa =~ Q23_7 0.646 0.030 21.503 0.000 0.587 0.704
## 7 PROa =~ Q23_3 0.346 0.043 8.139 0.000 0.263 0.429
## 8 PROa =~ Q23_8 0.704 0.027 26.559 0.000 0.652 0.756
## 9 PROa =~ Q23_9 0.903 0.017 52.202 0.000 0.869 0.937
## 10 PROa =~ Q23_10 0.785 0.022 35.310 0.000 0.741 0.828
## 11 COMa =~ Q23_1 0.746 0.026 29.267 0.000 0.697 0.796
## 12 COMa =~ Q23_2 0.813 0.022 36.501 0.000 0.769 0.856
## 13 COMa =~ Q23_4 0.756 0.025 30.213 0.000 0.707 0.805
## 14 ASSMs =~ Q41_1 0.683 0.045 15.211 0.000 0.595 0.771
## 15 ASSMs =~ Q41_2 0.853 0.049 17.491 0.000 0.758 0.949
## 16 ASSMs =~ Q41_4 0.414 0.045 9.232 0.000 0.326 0.501
## 17 TEFFs =~ Q44_1 0.784 0.020 38.420 0.000 0.744 0.824
## 18 TEFFs =~ Q44_2 0.864 0.016 55.614 0.000 0.834 0.894
## 19 TEFFs =~ Q44_3 0.879 0.015 59.751 0.000 0.850 0.908
## 20 TEFFs =~ Q44_4 0.718 0.025 29.189 0.000 0.670 0.766
## 21 Q17_1 ~~ Q17_1 0.725 0.042 17.367 0.000 0.644 0.807
## 22 Q17_3 ~~ Q17_3 0.614 0.045 13.732 0.000 0.526 0.702
## 23 Q17_5 ~~ Q17_5 0.380 0.049 7.679 0.000 0.283 0.477
## 24 Q23_5 ~~ Q23_5 0.356 0.036 9.980 0.000 0.286 0.426
## 25 Q23_6 ~~ Q23_6 0.194 0.034 5.733 0.000 0.128 0.261
## 26 Q23_7 ~~ Q23_7 0.583 0.039 15.051 0.000 0.507 0.659
## 27 Q23_3 ~~ Q23_3 0.880 0.029 29.906 0.000 0.823 0.938
## 28 Q23_8 ~~ Q23_8 0.504 0.037 13.501 0.000 0.431 0.577
## 29 Q23_9 ~~ Q23_9 0.184 0.031 5.902 0.000 0.123 0.246
## 30 Q23_10 ~~ Q23_10 0.384 0.035 11.006 0.000 0.316 0.452
## 31 Q23_1 ~~ Q23_1 0.443 0.038 11.626 0.000 0.368 0.517
## 32 Q23_2 ~~ Q23_2 0.340 0.036 9.382 0.000 0.269 0.410
## 33 Q23_4 ~~ Q23_4 0.429 0.038 11.333 0.000 0.355 0.503
## 34 Q41_1 ~~ Q41_1 0.533 0.061 8.682 0.000 0.413 0.653
## 35 Q41_2 ~~ Q41_2 0.272 0.083 3.265 0.001 0.109 0.435
## 36 Q41_4 ~~ Q41_4 0.829 0.037 22.359 0.000 0.756 0.902
## 37 Q44_1 ~~ Q44_1 0.385 0.032 12.037 0.000 0.323 0.448
## 38 Q44_2 ~~ Q44_2 0.254 0.027 9.445 0.000 0.201 0.306
## 39 Q44_3 ~~ Q44_3 0.227 0.026 8.796 0.000 0.177 0.278
## 40 Q44_4 ~~ Q44_4 0.485 0.035 13.734 0.000 0.416 0.554
## 41 Teachs ~~ Teachs 1.000 0.000 NA NA 1.000 1.000
## 42 GOVa ~~ GOVa 1.000 0.000 NA NA 1.000 1.000
## 43 PROa ~~ PROa 1.000 0.000 NA NA 1.000 1.000
## 44 COMa ~~ COMa 1.000 0.000 NA NA 1.000 1.000
## 45 ASSMs ~~ ASSMs 1.000 0.000 NA NA 1.000 1.000
## 46 TEFFs ~~ TEFFs 1.000 0.000 NA NA 1.000 1.000
## 47 Teachs ~~ GOVa 0.402 0.050 8.021 0.000 0.303 0.500
## 48 Teachs ~~ PROa 0.501 0.046 10.826 0.000 0.410 0.591
## 49 Teachs ~~ COMa 0.633 0.042 14.919 0.000 0.550 0.716
## 50 Teachs ~~ ASSMs 0.018 0.059 0.311 0.756 -0.098 0.135
## 51 Teachs ~~ TEFFs 0.528 0.044 11.922 0.000 0.441 0.615
## 52 GOVa ~~ PROa 0.563 0.038 14.865 0.000 0.488 0.637
## 53 GOVa ~~ COMa 0.553 0.040 13.785 0.000 0.474 0.632
## 54 GOVa ~~ ASSMs -0.175 0.053 -3.291 0.001 -0.279 -0.071
## 55 GOVa ~~ TEFFs 0.216 0.049 4.419 0.000 0.120 0.311
## 56 PROa ~~ COMa 0.510 0.042 12.217 0.000 0.429 0.592
## 57 PROa ~~ ASSMs -0.153 0.053 -2.879 0.004 -0.257 -0.049
## 58 PROa ~~ TEFFs 0.210 0.049 4.306 0.000 0.114 0.305
## 59 COMa ~~ ASSMs -0.061 0.056 -1.087 0.277 -0.170 0.049
## 60 COMa ~~ TEFFs 0.522 0.040 12.913 0.000 0.443 0.601
## 61 ASSMs ~~ TEFFs 0.102 0.053 1.925 0.054 -0.002 0.206
residuals(fitE, type = "cor")
## $type
## [1] "cor.bollen"
##
## $cov
## Q17_1 Q17_3 Q17_5 Q23_5 Q23_6 Q23_7 Q23_3 Q23_8 Q23_9 Q23_10
## Q17_1 0.000
## Q17_3 0.063 0.000
## Q17_5 -0.032 0.001 0.000
## Q23_5 0.047 0.020 -0.036 0.000
## Q23_6 0.025 0.068 -0.056 0.008 0.000
## Q23_7 0.038 0.067 0.032 -0.038 0.003 0.000
## Q23_3 0.092 0.112 0.102 -0.085 -0.044 0.039 0.000
## Q23_8 -0.011 0.040 -0.026 -0.125 -0.052 0.030 0.147 0.000
## Q23_9 -0.022 -0.004 -0.017 -0.057 -0.032 0.069 -0.024 0.023 0.000
## Q23_10 0.005 0.044 0.011 0.121 0.100 0.134 -0.070 -0.060 0.003 0.000
## Q23_1 0.033 -0.023 0.053 -0.022 -0.092 -0.028 0.029 -0.079 -0.085 0.025
## Q23_2 0.011 -0.084 -0.048 0.013 -0.023 0.020 0.058 -0.028 -0.031 0.052
## Q23_4 0.060 0.013 0.037 0.133 0.035 0.089 0.120 0.011 0.039 0.132
## Q41_1 0.027 -0.044 -0.047 -0.024 -0.030 -0.007 -0.094 -0.015 -0.029 -0.007
## Q41_2 0.074 0.016 0.013 0.012 0.040 -0.004 -0.043 -0.014 0.025 0.063
## Q41_4 -0.011 -0.038 -0.117 -0.138 -0.083 -0.103 -0.056 -0.028 -0.104 -0.103
## Q44_1 -0.055 -0.058 0.088 -0.006 -0.055 -0.010 -0.063 -0.105 -0.098 0.033
## Q44_2 -0.029 -0.090 0.024 0.009 -0.037 0.052 0.025 0.007 -0.021 0.067
## Q44_3 -0.033 -0.063 0.021 0.003 0.004 0.038 0.000 -0.001 -0.021 0.057
## Q44_4 -0.028 0.005 0.075 0.028 0.057 0.087 0.070 0.054 0.063 0.117
## Q23_1 Q23_2 Q23_4 Q41_1 Q41_2 Q41_4 Q44_1 Q44_2 Q44_3 Q44_4
## Q17_1
## Q17_3
## Q17_5
## Q23_5
## Q23_6
## Q23_7
## Q23_3
## Q23_8
## Q23_9
## Q23_10
## Q23_1 0.000
## Q23_2 0.038 0.000
## Q23_4 -0.052 0.000 0.000
## Q41_1 0.009 -0.035 -0.085 0.000
## Q41_2 0.079 0.027 -0.021 0.002 0.000
## Q41_4 -0.020 -0.083 -0.121 -0.005 -0.004 0.000
## Q44_1 0.092 -0.051 -0.009 -0.017 0.094 0.002 0.000
## Q44_2 0.069 -0.066 -0.015 -0.077 0.022 -0.040 0.011 0.000
## Q44_3 0.066 -0.043 -0.024 -0.094 0.033 -0.059 -0.013 0.005 0.000
## Q44_4 0.047 0.048 0.012 -0.056 -0.007 -0.095 -0.012 -0.017 0.014 0.000
semPaths(fitE, "par", edge.label.cex = 1.2, fade = FALSE, exoCov = FALSE)
supportSEM<-
'
TI=~ Q17_1 + Q17_3 + Q17_5
GS=~ Q23_5 + Q23_6 + Q23_7
PS=~ Q23_3 + Q23_8 + Q23_9 + Q23_10
CS=~ Q23_1 + Q23_2 + Q23_4
AS=~ Q41_1 + Q41_2 + Q41_4
TE=~ Q44_1 + Q44_2 + Q44_3 + Q44_4
LV=~ Q15_4
LV ~ TI
LV ~ GS
LV ~ PS
LV ~ CS
LV ~ AS
LV ~ TE
'
fit<- sem(supportSEM, data=teacher2, ordered = "LV")
summary(fit, fit.measures=TRUE, standardized = TRUE, rsquare = T)
## lavaan 0.6-12 ended normally after 113 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 83
##
## Used Total
## Number of observations 461 679
##
## Model Test User Model:
## Standard Robust
## Test Statistic 209.052 345.562
## Degrees of freedom 169 169
## P-value (Chi-square) 0.020 0.000
## Scaling correction factor 0.760
## Shift parameter 70.449
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 6324.372 2296.079
## Degrees of freedom 210 210
## P-value 0.000 0.000
## Scaling correction factor 2.931
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.993 0.915
## Tucker-Lewis Index (TLI) 0.992 0.895
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.023 0.048
## 90 Percent confidence interval - lower 0.010 0.040
## 90 Percent confidence interval - upper 0.032 0.055
## P-value RMSEA <= 0.05 1.000 0.696
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.046 0.046
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TI =~
## Q17_1 1.000 0.336 0.523
## Q17_3 1.415 0.169 8.357 0.000 0.475 0.633
## Q17_5 1.768 0.208 8.493 0.000 0.593 0.787
## GS =~
## Q23_5 1.000 0.618 0.784
## Q23_6 1.145 0.064 17.990 0.000 0.708 0.845
## Q23_7 0.986 0.075 13.111 0.000 0.610 0.705
## PS =~
## Q23_3 1.000 0.274 0.388
## Q23_8 2.282 0.326 6.992 0.000 0.625 0.645
## Q23_9 2.889 0.431 6.708 0.000 0.791 0.851
## Q23_10 2.868 0.438 6.554 0.000 0.785 0.851
## CS =~
## Q23_1 1.000 0.562 0.703
## Q23_2 1.033 0.072 14.274 0.000 0.580 0.754
## Q23_4 1.132 0.091 12.382 0.000 0.636 0.826
## AS =~
## Q41_1 1.000 0.550 0.681
## Q41_2 1.001 0.130 7.706 0.000 0.551 0.727
## Q41_4 0.612 0.109 5.614 0.000 0.337 0.509
## TE =~
## Q44_1 1.000 0.530 0.748
## Q44_2 0.932 0.075 12.397 0.000 0.494 0.836
## Q44_3 1.098 0.083 13.171 0.000 0.582 0.863
## Q44_4 1.017 0.085 11.938 0.000 0.539 0.787
## LV =~
## Q15_4 1.000 1.091 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LV ~
## TI -1.450 0.336 -4.311 0.000 -0.446 -0.446
## GS -0.097 0.126 -0.771 0.441 -0.055 -0.055
## PS -0.627 0.312 -2.008 0.045 -0.157 -0.157
## CS 0.212 0.166 1.279 0.201 0.109 0.109
## AS 0.416 0.121 3.434 0.001 0.210 0.210
## TE 0.078 0.147 0.534 0.593 0.038 0.038
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TI ~~
## GS 0.091 0.016 5.719 0.000 0.440 0.440
## PS 0.048 0.011 4.466 0.000 0.520 0.520
## CS 0.116 0.018 6.339 0.000 0.617 0.617
## AS -0.001 0.014 -0.053 0.958 -0.004 -0.004
## TE 0.088 0.019 4.742 0.000 0.494 0.494
## GS ~~
## PS 0.101 0.019 5.282 0.000 0.598 0.598
## CS 0.196 0.027 7.323 0.000 0.564 0.564
## AS -0.089 0.022 -4.118 0.000 -0.261 -0.261
## TE 0.076 0.020 3.833 0.000 0.231 0.231
## PS ~~
## CS 0.085 0.017 4.896 0.000 0.553 0.553
## AS -0.032 0.011 -2.847 0.004 -0.213 -0.213
## TE 0.034 0.010 3.400 0.001 0.235 0.235
## CS ~~
## AS -0.036 0.020 -1.793 0.073 -0.118 -0.118
## TE 0.154 0.026 6.012 0.000 0.518 0.518
## AS ~~
## TE 0.014 0.018 0.743 0.457 0.047 0.047
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q17_1 3.499 0.030 117.124 0.000 3.499 5.455
## .Q17_3 3.002 0.035 85.905 0.000 3.002 4.001
## .Q17_5 2.879 0.035 82.019 0.000 2.879 3.820
## .Q23_5 1.751 0.037 47.641 0.000 1.751 2.219
## .Q23_6 2.052 0.039 52.600 0.000 2.052 2.450
## .Q23_7 2.377 0.040 59.000 0.000 2.377 2.748
## .Q23_3 3.063 0.033 93.171 0.000 3.063 4.339
## .Q23_8 2.570 0.045 57.020 0.000 2.570 2.656
## .Q23_9 2.126 0.043 49.150 0.000 2.126 2.289
## .Q23_10 2.089 0.043 48.626 0.000 2.089 2.265
## .Q23_1 2.777 0.037 74.572 0.000 2.777 3.473
## .Q23_2 2.534 0.036 70.649 0.000 2.534 3.290
## .Q23_4 2.401 0.036 66.956 0.000 2.401 3.118
## .Q41_1 3.343 0.038 88.896 0.000 3.343 4.140
## .Q41_2 3.113 0.035 88.200 0.000 3.113 4.108
## .Q41_4 3.215 0.031 104.227 0.000 3.215 4.854
## .Q44_1 2.928 0.033 88.797 0.000 2.928 4.136
## .Q44_2 3.098 0.028 112.557 0.000 3.098 5.242
## .Q44_3 2.993 0.031 95.357 0.000 2.993 4.441
## .Q44_4 2.876 0.032 90.135 0.000 2.876 4.198
## .Q15_4 2.883 0.051 56.727 0.000 2.883 2.642
## TI 0.000 0.000 0.000
## GS 0.000 0.000 0.000
## PS 0.000 0.000 0.000
## CS 0.000 0.000 0.000
## AS 0.000 0.000 0.000
## TE 0.000 0.000 0.000
## .LV 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q17_1 0.299 0.030 10.086 0.000 0.299 0.726
## .Q17_3 0.337 0.032 10.645 0.000 0.337 0.599
## .Q17_5 0.216 0.034 6.368 0.000 0.216 0.380
## .Q23_5 0.240 0.026 9.209 0.000 0.240 0.386
## .Q23_6 0.200 0.033 6.005 0.000 0.200 0.286
## .Q23_7 0.376 0.040 9.332 0.000 0.376 0.503
## .Q23_3 0.423 0.036 11.698 0.000 0.423 0.850
## .Q23_8 0.547 0.048 11.293 0.000 0.547 0.584
## .Q23_9 0.237 0.038 6.200 0.000 0.237 0.275
## .Q23_10 0.234 0.040 5.910 0.000 0.234 0.276
## .Q23_1 0.324 0.031 10.530 0.000 0.324 0.506
## .Q23_2 0.256 0.027 9.595 0.000 0.256 0.432
## .Q23_4 0.188 0.028 6.663 0.000 0.188 0.318
## .Q41_1 0.349 0.072 4.822 0.000 0.349 0.536
## .Q41_2 0.271 0.047 5.764 0.000 0.271 0.472
## .Q41_4 0.325 0.035 9.176 0.000 0.325 0.741
## .Q44_1 0.221 0.026 8.355 0.000 0.221 0.440
## .Q44_2 0.105 0.018 5.840 0.000 0.105 0.301
## .Q44_3 0.116 0.023 5.139 0.000 0.116 0.255
## .Q44_4 0.179 0.027 6.562 0.000 0.179 0.381
## .Q15_4 0.000 0.000 0.000
## TI 0.113 0.025 4.572 0.000 1.000 1.000
## GS 0.382 0.043 8.890 0.000 1.000 1.000
## PS 0.075 0.022 3.424 0.001 1.000 1.000
## CS 0.316 0.043 7.273 0.000 1.000 1.000
## AS 0.303 0.055 5.536 0.000 1.000 1.000
## TE 0.281 0.041 6.845 0.000 1.000 1.000
## .LV 0.830 0.060 13.889 0.000 0.697 0.697
##
## R-Square:
## Estimate
## Q17_1 0.274
## Q17_3 0.401
## Q17_5 0.620
## Q23_5 0.614
## Q23_6 0.714
## Q23_7 0.497
## Q23_3 0.150
## Q23_8 0.416
## Q23_9 0.725
## Q23_10 0.724
## Q23_1 0.494
## Q23_2 0.568
## Q23_4 0.682
## Q41_1 0.464
## Q41_2 0.528
## Q41_4 0.259
## Q44_1 0.560
## Q44_2 0.699
## Q44_3 0.745
## Q44_4 0.619
## Q15_4 1.000
## LV 0.303
semfit<- standardizedsolution(fit, type = "std.all")
write.csv(semfit, "SEMModel.csv")
semfit
## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 TI =~ Q17_1 0.523 0.050 10.458 0.000 0.425 0.621
## 2 TI =~ Q17_3 0.633 0.038 16.641 0.000 0.558 0.708
## 3 TI =~ Q17_5 0.787 0.039 20.411 0.000 0.712 0.863
## 4 GS =~ Q23_5 0.784 0.029 27.053 0.000 0.727 0.841
## 5 GS =~ Q23_6 0.845 0.029 29.359 0.000 0.789 0.902
## 6 GS =~ Q23_7 0.705 0.037 19.030 0.000 0.633 0.778
## 7 PS =~ Q23_3 0.388 0.053 7.308 0.000 0.284 0.492
## 8 PS =~ Q23_8 0.645 0.039 16.750 0.000 0.570 0.721
## 9 PS =~ Q23_9 0.851 0.026 32.583 0.000 0.800 0.903
## 10 PS =~ Q23_10 0.851 0.028 30.289 0.000 0.796 0.906
## 11 CS =~ Q23_1 0.703 0.036 19.794 0.000 0.633 0.772
## 12 CS =~ Q23_2 0.754 0.030 24.734 0.000 0.694 0.813
## 13 CS =~ Q23_4 0.826 0.030 27.934 0.000 0.768 0.884
## 14 AS =~ Q41_1 0.681 0.066 10.332 0.000 0.552 0.811
## 15 AS =~ Q41_2 0.727 0.056 12.890 0.000 0.616 0.837
## 16 AS =~ Q41_4 0.509 0.066 7.671 0.000 0.379 0.638
## 17 TE =~ Q44_1 0.748 0.037 20.082 0.000 0.675 0.821
## 18 TE =~ Q44_2 0.836 0.032 26.135 0.000 0.773 0.899
## 19 TE =~ Q44_3 0.863 0.030 28.891 0.000 0.805 0.922
## 20 TE =~ Q44_4 0.787 0.037 20.980 0.000 0.713 0.860
## 21 LV =~ Q15_4 1.000 0.000 NA NA 1.000 1.000
## 22 LV ~ TI -0.446 0.085 -5.231 0.000 -0.613 -0.279
## 23 LV ~ GS -0.055 0.072 -0.771 0.441 -0.196 0.085
## 24 LV ~ PS -0.157 0.076 -2.080 0.038 -0.306 -0.009
## 25 LV ~ CS 0.109 0.085 1.290 0.197 -0.057 0.275
## 26 LV ~ AS 0.210 0.058 3.621 0.000 0.096 0.323
## 27 LV ~ TE 0.038 0.071 0.534 0.594 -0.102 0.178
## 28 Q17_1 ~~ Q17_1 0.726 0.052 13.872 0.000 0.624 0.829
## 29 Q17_3 ~~ Q17_3 0.599 0.048 12.446 0.000 0.505 0.694
## 30 Q17_5 ~~ Q17_5 0.380 0.061 6.261 0.000 0.261 0.499
## 31 Q23_5 ~~ Q23_5 0.386 0.045 8.487 0.000 0.297 0.475
## 32 Q23_6 ~~ Q23_6 0.286 0.049 5.872 0.000 0.190 0.381
## 33 Q23_7 ~~ Q23_7 0.503 0.052 9.622 0.000 0.400 0.605
## 34 Q23_3 ~~ Q23_3 0.850 0.041 20.642 0.000 0.769 0.930
## 35 Q23_8 ~~ Q23_8 0.584 0.050 11.741 0.000 0.486 0.681
## 36 Q23_9 ~~ Q23_9 0.275 0.045 6.178 0.000 0.188 0.362
## 37 Q23_10 ~~ Q23_10 0.276 0.048 5.761 0.000 0.182 0.369
## 38 Q23_1 ~~ Q23_1 0.506 0.050 10.146 0.000 0.408 0.604
## 39 Q23_2 ~~ Q23_2 0.432 0.046 9.412 0.000 0.342 0.522
## 40 Q23_4 ~~ Q23_4 0.318 0.049 6.498 0.000 0.222 0.413
## 41 Q41_1 ~~ Q41_1 0.536 0.090 5.964 0.000 0.360 0.712
## 42 Q41_2 ~~ Q41_2 0.472 0.082 5.758 0.000 0.311 0.632
## 43 Q41_4 ~~ Q41_4 0.741 0.067 10.998 0.000 0.609 0.874
## 44 Q44_1 ~~ Q44_1 0.440 0.056 7.891 0.000 0.331 0.549
## 45 Q44_2 ~~ Q44_2 0.301 0.053 5.626 0.000 0.196 0.406
## 46 Q44_3 ~~ Q44_3 0.255 0.052 4.933 0.000 0.153 0.356
## 47 Q44_4 ~~ Q44_4 0.381 0.059 6.462 0.000 0.266 0.497
## 48 Q15_4 ~~ Q15_4 0.000 0.000 NA NA 0.000 0.000
## 49 TI ~~ TI 1.000 0.000 NA NA 1.000 1.000
## 50 GS ~~ GS 1.000 0.000 NA NA 1.000 1.000
## 51 PS ~~ PS 1.000 0.000 NA NA 1.000 1.000
## 52 CS ~~ CS 1.000 0.000 NA NA 1.000 1.000
## 53 AS ~~ AS 1.000 0.000 NA NA 1.000 1.000
## 54 TE ~~ TE 1.000 0.000 NA NA 1.000 1.000
## 55 LV ~~ LV 0.697 0.046 15.206 0.000 0.607 0.787
## 56 TI ~~ GS 0.440 0.052 8.550 0.000 0.339 0.541
## 57 TI ~~ PS 0.520 0.053 9.805 0.000 0.416 0.624
## 58 TI ~~ CS 0.617 0.048 12.846 0.000 0.523 0.711
## 59 TI ~~ AS -0.004 0.075 -0.053 0.958 -0.150 0.143
## 60 TI ~~ TE 0.494 0.057 8.733 0.000 0.383 0.605
## 61 GS ~~ PS 0.598 0.046 13.037 0.000 0.508 0.688
## 62 GS ~~ CS 0.564 0.048 11.667 0.000 0.469 0.659
## 63 GS ~~ AS -0.261 0.060 -4.381 0.000 -0.378 -0.144
## 64 GS ~~ TE 0.231 0.053 4.362 0.000 0.127 0.335
## 65 PS ~~ CS 0.553 0.046 11.912 0.000 0.462 0.644
## 66 PS ~~ AS -0.213 0.063 -3.395 0.001 -0.336 -0.090
## 67 PS ~~ TE 0.235 0.053 4.436 0.000 0.131 0.339
## 68 CS ~~ AS -0.118 0.064 -1.825 0.068 -0.244 0.009
## 69 CS ~~ TE 0.518 0.048 10.858 0.000 0.424 0.611
## 70 AS ~~ TE 0.047 0.063 0.746 0.455 -0.077 0.171
## 71 Q17_1 ~1 5.455 0.252 21.687 0.000 4.962 5.948
## 72 Q17_3 ~1 4.001 0.157 25.446 0.000 3.693 4.309
## 73 Q17_5 ~1 3.820 0.139 27.554 0.000 3.548 4.092
## 74 Q23_5 ~1 2.219 0.057 39.260 0.000 2.108 2.330
## 75 Q23_6 ~1 2.450 0.068 35.922 0.000 2.316 2.583
## 76 Q23_7 ~1 2.748 0.089 30.835 0.000 2.573 2.923
## 77 Q23_3 ~1 4.339 0.187 23.157 0.000 3.972 4.707
## 78 Q23_8 ~1 2.656 0.089 29.972 0.000 2.482 2.829
## 79 Q23_9 ~1 2.289 0.062 37.022 0.000 2.168 2.410
## 80 Q23_10 ~1 2.265 0.060 37.656 0.000 2.147 2.383
## 81 Q23_1 ~1 3.473 0.130 26.615 0.000 3.217 3.729
## 82 Q23_2 ~1 3.290 0.122 26.866 0.000 3.050 3.531
## 83 Q23_4 ~1 3.118 0.106 29.452 0.000 2.911 3.326
## 84 Q41_1 ~1 4.140 0.192 21.586 0.000 3.764 4.516
## 85 Q41_2 ~1 4.108 0.138 29.866 0.000 3.838 4.377
## 86 Q41_4 ~1 4.854 0.172 28.271 0.000 4.518 5.191
## 87 Q44_1 ~1 4.136 0.177 23.385 0.000 3.789 4.482
## 88 Q44_2 ~1 5.242 0.207 25.327 0.000 4.837 5.648
## 89 Q44_3 ~1 4.441 0.170 26.114 0.000 4.108 4.775
## 90 Q44_4 ~1 4.198 0.176 23.862 0.000 3.853 4.543
## 91 Q15_4 ~1 2.642 0.096 27.621 0.000 2.455 2.830
## 92 TI ~1 0.000 0.000 NA NA 0.000 0.000
## 93 GS ~1 0.000 0.000 NA NA 0.000 0.000
## 94 PS ~1 0.000 0.000 NA NA 0.000 0.000
## 95 CS ~1 0.000 0.000 NA NA 0.000 0.000
## 96 AS ~1 0.000 0.000 NA NA 0.000 0.000
## 97 TE ~1 0.000 0.000 NA NA 0.000 0.000
## 98 LV ~1 0.000 0.000 NA NA 0.000 0.000
semPaths(fit, whatLabels = "std.all", structural = FALSE, edge.label.cex = .8, node.label.cex = .8,
label.prop=0.9, edge.label.color = "black", rotation = 4,
equalizeManifests = TRUE, optimizeLatRes = TRUE, node.width = 1.5,
edge.width = 0.5, shapeMan = "rectangle", shapeLat = "ellipse",
shapeInt = "triangle", sizeMan = 4, sizeInt = 2, sizeLat = 4,
curve=2, unCol = "#070b8c", title = FALSE, intercepts = FALSE, residuals = FALSE, exoCov = FALSE)
semPaths(fit,
whatLabels = "std.all", structural = TRUE, edge.label.cex = 1, node.label.cex = 1.5,
label.prop=0.9, edge.label.color = "black", rotation = 2,
equalizeManifests = FALSE, optimizeLatRes = TRUE, node.width = 1.5,
edge.width = 0.5, shapeMan = "rectangle", shapeLat = "ellipse",
shapeInt = "triangle", sizeMan = 4, sizeInt = 2, sizeLat = 4,
curve=2, unCol = "#070b8c", title = FALSE, intercepts = FALSE, residuals = FALSE, exoCov = FALSE)
