setwd("C:/Work Files/PSY 8170/Class Exercises")SEM Start To Finish
Packages and Libraries:
library(haven) #reading data from spss, sas, stata
library(tidyverse) #Data management── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.0.4
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tidySEM) #Tools for working with SEM outputLoading required package: OpenMx
Registered S3 method overwritten by 'tidySEM':
method from
predict.MxModel OpenMxlibrary (corrplot) #correlation tablescorrplot 0.95 loadedlibrary(psych) ## basic psychometrics and statistics
Attaching package: 'psych'
The following object is masked from 'package:OpenMx':
tr
The following objects are masked from 'package:ggplot2':
%+%, alphalibrary(finalfit) #Testing Assumptions
library (performance) #Testing Assumptions
library(MVN) #Assessing multivariate normality
library(lavaan) #SEM packageThis is lavaan 0.6-19
lavaan is FREE software! Please report any bugs.
Attaching package: 'lavaan'
The following object is masked from 'package:psych':
cor2covlibrary(knitr) #Making Tables
library(kableExtra) #Making Tables
Attaching package: 'kableExtra'
The following object is masked from 'package:dplyr':
group_rowslibrary(apaTables) #Generating APA formated tables
library (sjPlot) #SEM figures and plotsLearn more about sjPlot with 'browseVignettes("sjPlot")'.library (semPlot) #Sem figures and plots
library (report) #Template Results
Attaching package: 'report'
The following object is masked from 'package:tidySEM':
reportlibrary (codebookr) #Generating a code book
library(see) # Data visualizationClass_SEM_Data <- read.csv("HBAT example.csv")names(Class_SEM_Data) [1] "id" "JS1" "OC1" "OC2" "EP1" "OC3" "OC4" "EP2" "EP3" "AC1" "EP4" "JS2"
[13] "JS3" "AC2" "SI1" "JS4" "SI2" "JS5" "AC3" "SI3" "AC4" "SI4" "C1" "C2"
[25] "C3" "AGE" "EXP" "JP" "JS" "OC" "SI" "EP" "AC" str(Class_SEM_Data)'data.frame': 400 obs. of 33 variables:
$ id : int 1 2 3 4 5 6 7 8 9 10 ...
$ JS1: int 5 3 4 4 5 6 2 2 4 5 ...
$ OC1: int 3 0 6 7 2 5 6 4 9 5 ...
$ OC2: int 5 5 10 7 10 8 10 9 10 9 ...
$ EP1: int 10 10 10 10 10 8 9 10 8 10 ...
$ OC3: int 10 3 10 10 9 7 10 9 10 9 ...
$ OC4: int 10 7 10 7 9 7 9 7 10 10 ...
$ EP2: int 10 10 10 10 9 10 9 10 6 10 ...
$ EP3: int 5 10 10 9 10 7 9 10 8 8 ...
$ AC1: int 1 2 1 2 1 1 2 1 3 2 ...
$ EP4: int NA 7 7 7 6 7 6 7 3 7 ...
$ JS2: int 4 4 2 5 4 6 3 2 5 3 ...
$ JS3: int 3 3 2 4 3 5 6 1 1 2 ...
$ AC2: int 2 1 4 1 1 2 4 1 4 4 ...
$ SI1: int 4 5 5 5 5 5 5 3 3 4 ...
$ JS4: int 3 2 3 2 2 3 4 1 1 2 ...
$ SI2: int 4 4 5 4 5 4 5 4 3 4 ...
$ JS5: int 23 43 60 33 58 62 11 21 80 33 ...
$ AC3: int 1 1 1 1 2 1 3 1 3 1 ...
$ SI3: int 3 4 5 3 3 3 5 4 2 3 ...
$ AC4: int 1 1 2 1 2 1 3 3 1 4 ...
$ SI4: int 3 4 5 4 4 3 4 2 2 3 ...
$ C1 : int 1 1 1 1 1 1 1 1 1 1 ...
$ C2 : int 0 1 1 0 1 0 1 1 0 0 ...
$ C3 : int 1 1 1 1 1 1 1 1 1 1 ...
$ AGE: int 42 32 43 26 37 35 44 50 27 47 ...
$ EXP: num 6 5.8 1 3 14 8 12.5 0.2 0.3 5.3 ...
$ JP : int 5 4 5 5 5 4 5 5 4 5 ...
$ JS : num -0.22499 -0.55478 -0.51594 -0.13101 0.00117 ...
$ OC : num -0.364 -1.661 0.781 -0.33 0.321 ...
$ SI : num -0.37 0.388 1.281 0.222 0.641 ...
$ EP : num NA 0.868 0.868 0.659 0.435 ...
$ AC : num -1.295 -1.257 -0.827 -1.257 -1.086 ...Class_SEM_Data_Final <- Class_SEM_Data[,c(1:27)]
Class_SEM_Data_Final <- Class_SEM_Data_Final %>%
rename(
Sex = C1,
Employee_Type = C2,
Work_Location = C3
)
Class_SEM_Data_Final$Sex<-factor(Class_SEM_Data_Final$Sex,
levels = c(0,1),
labels = c("male","female"))
Class_SEM_Data_Final$Employee_Type <-factor (Class_SEM_Data_Final$Employee_Type,
levels = c(0,1),
labels = c("Part-Time", "Full-Time"))
Class_SEM_Data_Final$Work_Location <-factor (Class_SEM_Data_Final$Work_Location,
levels = c(0,1),
labels = c("U.S.", "Non-U.S."))
Class_SEM_Data_Final[, c(1:22)] <- lapply(Class_SEM_Data_Final[, c(1:22)], as.numeric)
names(Class_SEM_Data_Final) [1] "id" "JS1" "OC1" "OC2"
[5] "EP1" "OC3" "OC4" "EP2"
[9] "EP3" "AC1" "EP4" "JS2"
[13] "JS3" "AC2" "SI1" "JS4"
[17] "SI2" "JS5" "AC3" "SI3"
[21] "AC4" "SI4" "Sex" "Employee_Type"
[25] "Work_Location" "AGE" "EXP" str(Class_SEM_Data_Final)'data.frame': 400 obs. of 27 variables:
$ id : num 1 2 3 4 5 6 7 8 9 10 ...
$ JS1 : num 5 3 4 4 5 6 2 2 4 5 ...
$ OC1 : num 3 0 6 7 2 5 6 4 9 5 ...
$ OC2 : num 5 5 10 7 10 8 10 9 10 9 ...
$ EP1 : num 10 10 10 10 10 8 9 10 8 10 ...
$ OC3 : num 10 3 10 10 9 7 10 9 10 9 ...
$ OC4 : num 10 7 10 7 9 7 9 7 10 10 ...
$ EP2 : num 10 10 10 10 9 10 9 10 6 10 ...
$ EP3 : num 5 10 10 9 10 7 9 10 8 8 ...
$ AC1 : num 1 2 1 2 1 1 2 1 3 2 ...
$ EP4 : num NA 7 7 7 6 7 6 7 3 7 ...
$ JS2 : num 4 4 2 5 4 6 3 2 5 3 ...
$ JS3 : num 3 3 2 4 3 5 6 1 1 2 ...
$ AC2 : num 2 1 4 1 1 2 4 1 4 4 ...
$ SI1 : num 4 5 5 5 5 5 5 3 3 4 ...
$ JS4 : num 3 2 3 2 2 3 4 1 1 2 ...
$ SI2 : num 4 4 5 4 5 4 5 4 3 4 ...
$ JS5 : num 23 43 60 33 58 62 11 21 80 33 ...
$ AC3 : num 1 1 1 1 2 1 3 1 3 1 ...
$ SI3 : num 3 4 5 3 3 3 5 4 2 3 ...
$ AC4 : num 1 1 2 1 2 1 3 3 1 4 ...
$ SI4 : num 3 4 5 4 4 3 4 2 2 3 ...
$ Sex : Factor w/ 2 levels "male","female": 2 2 2 2 2 2 2 2 2 2 ...
$ Employee_Type: Factor w/ 2 levels "Part-Time","Full-Time": 1 2 2 1 2 1 2 2 1 1 ...
$ Work_Location: Factor w/ 2 levels "U.S.","Non-U.S.": 2 2 2 2 2 2 2 2 2 2 ...
$ AGE : int 42 32 43 26 37 35 44 50 27 47 ...
$ EXP : num 6 5.8 1 3 14 8 12.5 0.2 0.3 5.3 ...describe(Class_SEM_Data_Final[,-1])%>%
knitr::kable(digits = 3, format="html", booktabs=TRUE, caption="Table 1. Descriptives")%>%
kable_classic(full_width = F, html_font = "Cambria")| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| JS1 | 1 | 400 | 4.197 | 1.339 | 4.0 | 4.216 | 1.483 | 1.0 | 7 | 6.0 | -0.087 | -0.215 | 0.067 |
| OC1 | 2 | 400 | 4.888 | 2.525 | 5.0 | 4.922 | 2.965 | 0.0 | 10 | 10.0 | -0.130 | -0.723 | 0.126 |
| OC2 | 3 | 400 | 8.418 | 2.186 | 9.0 | 8.887 | 1.483 | 0.0 | 10 | 10.0 | -2.067 | 4.519 | 0.109 |
| EP1 | 4 | 400 | 8.528 | 1.831 | 9.0 | 8.853 | 1.483 | 0.0 | 10 | 10.0 | -1.983 | 5.354 | 0.092 |
| OC3 | 5 | 400 | 8.655 | 1.755 | 9.0 | 8.991 | 1.483 | 0.0 | 10 | 10.0 | -2.031 | 5.239 | 0.088 |
| OC4 | 6 | 400 | 8.405 | 2.053 | 9.0 | 8.828 | 1.483 | 0.0 | 10 | 10.0 | -1.973 | 4.294 | 0.103 |
| EP2 | 7 | 400 | 8.848 | 1.628 | 9.0 | 9.169 | 1.483 | 0.0 | 10 | 10.0 | -2.111 | 5.706 | 0.081 |
| EP3 | 8 | 400 | 8.928 | 1.335 | 9.0 | 9.159 | 1.483 | 0.0 | 10 | 10.0 | -1.810 | 5.391 | 0.067 |
| AC1 | 9 | 400 | 2.760 | 1.394 | 3.0 | 2.700 | 1.483 | 1.0 | 5 | 4.0 | 0.216 | -1.221 | 0.070 |
| EP4 | 10 | 399 | 5.830 | 1.395 | 6.0 | 6.056 | 1.483 | 1.0 | 7 | 6.0 | -1.280 | 1.301 | 0.070 |
| JS2 | 11 | 400 | 4.202 | 1.370 | 4.0 | 4.219 | 1.483 | 1.0 | 7 | 6.0 | -0.034 | -0.190 | 0.068 |
| JS3 | 12 | 400 | 3.215 | 1.316 | 3.0 | 3.216 | 1.483 | 1.0 | 6 | 5.0 | 0.107 | -0.692 | 0.066 |
| AC2 | 13 | 400 | 3.553 | 1.726 | 4.0 | 3.566 | 1.483 | 1.0 | 6 | 5.0 | -0.070 | -1.324 | 0.086 |
| SI1 | 14 | 400 | 4.202 | 0.871 | 4.0 | 4.334 | 1.483 | 1.0 | 5 | 4.0 | -1.310 | 2.095 | 0.044 |
| JS4 | 15 | 400 | 2.668 | 1.281 | 3.0 | 2.584 | 1.483 | 1.0 | 5 | 4.0 | 0.301 | -0.965 | 0.064 |
| SI2 | 16 | 400 | 4.205 | 0.877 | 4.0 | 4.331 | 1.483 | 1.0 | 5 | 4.0 | -1.252 | 1.839 | 0.044 |
| JS5 | 17 | 400 | 54.818 | 20.594 | 55.5 | 54.834 | 22.980 | 0.0 | 100 | 100.0 | -0.023 | -0.658 | 1.030 |
| AC3 | 18 | 400 | 2.775 | 1.419 | 3.0 | 2.719 | 1.483 | 1.0 | 5 | 4.0 | 0.206 | -1.249 | 0.071 |
| SI3 | 19 | 400 | 3.473 | 1.016 | 3.0 | 3.497 | 1.483 | 1.0 | 5 | 4.0 | 0.003 | -0.550 | 0.051 |
| AC4 | 20 | 399 | 3.211 | 1.612 | 3.0 | 3.140 | 1.483 | 1.0 | 6 | 5.0 | 0.171 | -1.113 | 0.081 |
| SI4 | 21 | 400 | 3.480 | 0.968 | 4.0 | 3.522 | 1.483 | 1.0 | 5 | 4.0 | -0.481 | 0.005 | 0.048 |
| Sex* | 22 | 400 | 1.500 | 0.501 | 1.5 | 1.500 | 0.741 | 1.0 | 2 | 1.0 | 0.000 | -2.005 | 0.025 |
| Employee_Type* | 23 | 400 | 1.522 | 0.500 | 2.0 | 1.528 | 0.000 | 1.0 | 2 | 1.0 | -0.090 | -1.997 | 0.025 |
| Work_Location* | 24 | 400 | 1.500 | 0.501 | 1.5 | 1.500 | 0.741 | 1.0 | 2 | 1.0 | 0.000 | -2.005 | 0.025 |
| AGE | 25 | 399 | 43.383 | 7.138 | 43.0 | 43.364 | 7.413 | 24.0 | 62 | 38.0 | 0.019 | -0.256 | 0.357 |
| EXP | 26 | 399 | 8.639 | 3.620 | 8.6 | 8.683 | 3.558 | 0.1 | 25 | 24.9 | 0.097 | 0.856 | 0.181 |
Class_SEM_Data_Final_No_NA <-na.omit(Class_SEM_Data_Final)
apa.cor.table(Class_SEM_Data_Final_No_NA[,c(2:22)])
Means, standard deviations, and correlations with confidence intervals
Variable M SD 1 2 3 4
1. JS1 4.20 1.34
2. OC1 4.89 2.53 .06
[-.03, .16]
3. OC2 8.42 2.18 .14** .52**
[.05, .24] [.45, .59]
4. EP1 8.52 1.83 .06 .11* .26**
[-.03, .16] [.01, .21] [.16, .35]
5. OC3 8.65 1.76 .16** .45** .56** .23**
[.06, .25] [.36, .52] [.49, .63] [.14, .33]
6. OC4 8.40 2.06 .16** .49** .74** .28**
[.06, .26] [.41, .56] [.70, .78] [.18, .37]
7. EP2 8.84 1.63 .12* .23** .38** .60**
[.02, .22] [.14, .32] [.29, .46] [.53, .66]
8. EP3 8.94 1.32 .11* .12* .34** .52**
[.01, .21] [.02, .21] [.25, .43] [.44, .58]
9. AC1 2.76 1.39 .04 .05 .26** .14**
[-.06, .14] [-.04, .15] [.17, .35] [.05, .24]
10. EP4 5.83 1.40 .09 .23** .33** .57**
[-.00, .19] [.13, .32] [.24, .42] [.50, .63]
11. JS2 4.20 1.37 .56** .05 .09 .17**
[.49, .62] [-.04, .15] [-.01, .19] [.07, .26]
12. JS3 3.22 1.32 .51** .07 .13** .15**
[.44, .58] [-.02, .17] [.03, .23] [.05, .24]
13. AC2 3.56 1.73 .00 .10* .21** .16**
[-.09, .10] [.00, .20] [.11, .30] [.06, .26]
14. SI1 4.20 0.87 .11* .18** .43** .34**
[.02, .21] [.09, .28] [.35, .51] [.25, .43]
15. JS4 2.67 1.28 .51** .06 .10* .11*
[.43, .58] [-.04, .16] [.00, .20] [.02, .21]
16. SI2 4.21 0.88 .11* .19** .47** .37**
[.02, .21] [.09, .28] [.39, .55] [.28, .45]
17. JS5 54.84 20.55 .55** .08 .18** .16**
[.48, .62] [-.02, .18] [.09, .28] [.07, .26]
18. AC3 2.78 1.42 -.02 .09 .27** .16**
[-.12, .08] [-.01, .19] [.17, .36] [.06, .25]
19. SI3 3.47 1.02 .05 .14** .36** .33**
[-.05, .14] [.04, .23] [.27, .44] [.24, .42]
20. AC4 3.22 1.61 .02 .13* .28** .16**
[-.08, .12] [.03, .22] [.19, .37] [.06, .25]
21. SI4 3.48 0.97 .14** .18** .46** .38**
[.04, .24] [.08, .27] [.38, .53] [.29, .46]
5 6 7 8 9 10
.57**
[.49, .63]
.37** .38**
[.28, .45] [.29, .46]
.32** .30** .63**
[.23, .41] [.21, .39] [.56, .68]
.14** .15** .18** .18**
[.04, .23] [.06, .25] [.08, .27] [.08, .27]
.37** .33** .65** .67** .14**
[.28, .45] [.24, .41] [.59, .70] [.62, .72] [.04, .23]
.11* .09 .17** .16** -.00 .16**
[.01, .21] [-.01, .19] [.07, .26] [.06, .25] [-.10, .09] [.07, .26]
.08 .11* .12* .15** .04 .15**
[-.01, .18] [.01, .21] [.02, .22] [.05, .24] [-.06, .14] [.05, .24]
.09 .17** .18** .13* .67** .16**
[-.01, .19] [.07, .27] [.08, .27] [.03, .22] [.61, .72] [.06, .25]
.23** .38** .35** .29** .23** .40**
[.13, .32] [.29, .46] [.26, .43] [.20, .38] [.13, .32] [.31, .48]
.13** .09 .11* .12* .05 .12*
[.03, .22] [-.00, .19] [.02, .21] [.02, .21] [-.05, .15] [.03, .22]
.28** .39** .39** .32** .20** .37**
[.19, .37] [.30, .47] [.30, .47] [.23, .41] [.10, .29] [.28, .45]
.14** .18** .14** .20** .11* .15**
[.05, .24] [.08, .27] [.05, .24] [.10, .29] [.01, .20] [.06, .25]
.12* .21** .17** .14** .69** .13*
[.03, .22] [.11, .30] [.07, .26] [.05, .24] [.63, .74] [.03, .22]
.20** .31** .35** .30** .20** .36**
[.11, .30] [.21, .39] [.26, .43] [.21, .39] [.11, .29] [.27, .44]
.16** .25** .23** .18** .67** .20**
[.07, .26] [.15, .34] [.13, .32] [.08, .27] [.61, .72] [.11, .29]
.26** .42** .45** .36** .20** .39**
[.17, .35] [.33, .49] [.37, .53] [.27, .45] [.11, .29] [.31, .47]
11 12 13 14 15 16
.50**
[.42, .57]
-.02 .01
[-.11, .08] [-.09, .11]
.12* .15** .20**
[.03, .22] [.05, .24] [.10, .29]
.52** .51** .01 .12*
[.45, .59] [.43, .58] [-.08, .11] [.02, .21]
.13* .10* .19** .73** .09
[.03, .22] [.01, .20] [.09, .28] [.68, .78] [-.01, .19]
.56** .47** .07 .18** .52** .17**
[.49, .62] [.39, .55] [-.02, .17] [.08, .27] [.44, .58] [.07, .26]
-.03 -.01 .69** .21** .05 .21**
[-.13, .07] [-.11, .09] [.63, .74] [.11, .30] [-.05, .15] [.11, .30]
.10* .16** .16** .58** .15** .62**
[.01, .20] [.06, .26] [.07, .26] [.51, .64] [.05, .24] [.56, .68]
.01 .07 .67** .21** .05 .22**
[-.09, .11] [-.03, .17] [.61, .72] [.11, .30] [-.05, .15] [.13, .32]
.15** .18** .20** .67** .17** .73**
[.05, .25] [.08, .27] [.11, .30] [.61, .72] [.07, .26] [.68, .77]
17 18 19 20
.04
[-.05, .14]
.13* .20**
[.03, .22] [.10, .29]
.11* .67** .19**
[.01, .21] [.62, .72] [.10, .29]
.21** .24** .67** .25**
[.12, .30] [.15, .34] [.61, .72] [.16, .34]
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.
res <- cor(Class_SEM_Data_Final_No_NA[,c(2:22)])
round(res, 2) JS1 OC1 OC2 EP1 OC3 OC4 EP2 EP3 AC1 EP4 JS2 JS3 AC2 SI1
JS1 1.00 0.06 0.14 0.06 0.16 0.16 0.12 0.11 0.04 0.09 0.56 0.51 0.00 0.11
OC1 0.06 1.00 0.52 0.11 0.45 0.49 0.23 0.12 0.05 0.23 0.05 0.07 0.10 0.18
OC2 0.14 0.52 1.00 0.26 0.56 0.74 0.38 0.34 0.26 0.33 0.09 0.13 0.21 0.43
EP1 0.06 0.11 0.26 1.00 0.23 0.28 0.60 0.52 0.14 0.57 0.17 0.15 0.16 0.34
OC3 0.16 0.45 0.56 0.23 1.00 0.57 0.37 0.32 0.14 0.37 0.11 0.08 0.09 0.23
OC4 0.16 0.49 0.74 0.28 0.57 1.00 0.38 0.30 0.15 0.33 0.09 0.11 0.17 0.38
EP2 0.12 0.23 0.38 0.60 0.37 0.38 1.00 0.63 0.18 0.65 0.17 0.12 0.18 0.35
EP3 0.11 0.12 0.34 0.52 0.32 0.30 0.63 1.00 0.18 0.67 0.16 0.15 0.13 0.29
AC1 0.04 0.05 0.26 0.14 0.14 0.15 0.18 0.18 1.00 0.14 0.00 0.04 0.67 0.23
EP4 0.09 0.23 0.33 0.57 0.37 0.33 0.65 0.67 0.14 1.00 0.16 0.15 0.16 0.40
JS2 0.56 0.05 0.09 0.17 0.11 0.09 0.17 0.16 0.00 0.16 1.00 0.50 -0.02 0.12
JS3 0.51 0.07 0.13 0.15 0.08 0.11 0.12 0.15 0.04 0.15 0.50 1.00 0.01 0.15
AC2 0.00 0.10 0.21 0.16 0.09 0.17 0.18 0.13 0.67 0.16 -0.02 0.01 1.00 0.20
SI1 0.11 0.18 0.43 0.34 0.23 0.38 0.35 0.29 0.23 0.40 0.12 0.15 0.20 1.00
JS4 0.51 0.06 0.10 0.11 0.13 0.09 0.11 0.12 0.05 0.12 0.52 0.51 0.01 0.12
SI2 0.11 0.19 0.47 0.37 0.28 0.39 0.39 0.32 0.20 0.37 0.13 0.10 0.19 0.73
JS5 0.55 0.08 0.18 0.16 0.14 0.18 0.14 0.20 0.11 0.15 0.56 0.47 0.07 0.18
AC3 -0.02 0.09 0.27 0.16 0.12 0.21 0.17 0.14 0.69 0.13 -0.03 -0.01 0.69 0.21
SI3 0.05 0.14 0.36 0.33 0.20 0.31 0.35 0.30 0.20 0.36 0.10 0.16 0.16 0.58
AC4 0.02 0.13 0.28 0.16 0.16 0.25 0.23 0.18 0.67 0.20 0.01 0.07 0.67 0.21
SI4 0.14 0.18 0.46 0.38 0.26 0.42 0.45 0.36 0.20 0.39 0.15 0.18 0.20 0.67
JS4 SI2 JS5 AC3 SI3 AC4 SI4
JS1 0.51 0.11 0.55 -0.02 0.05 0.02 0.14
OC1 0.06 0.19 0.08 0.09 0.14 0.13 0.18
OC2 0.10 0.47 0.18 0.27 0.36 0.28 0.46
EP1 0.11 0.37 0.16 0.16 0.33 0.16 0.38
OC3 0.13 0.28 0.14 0.12 0.20 0.16 0.26
OC4 0.09 0.39 0.18 0.21 0.31 0.25 0.42
EP2 0.11 0.39 0.14 0.17 0.35 0.23 0.45
EP3 0.12 0.32 0.20 0.14 0.30 0.18 0.36
AC1 0.05 0.20 0.11 0.69 0.20 0.67 0.20
EP4 0.12 0.37 0.15 0.13 0.36 0.20 0.39
JS2 0.52 0.13 0.56 -0.03 0.10 0.01 0.15
JS3 0.51 0.10 0.47 -0.01 0.16 0.07 0.18
AC2 0.01 0.19 0.07 0.69 0.16 0.67 0.20
SI1 0.12 0.73 0.18 0.21 0.58 0.21 0.67
JS4 1.00 0.09 0.52 0.05 0.15 0.05 0.17
SI2 0.09 1.00 0.17 0.21 0.62 0.22 0.73
JS5 0.52 0.17 1.00 0.04 0.13 0.11 0.21
AC3 0.05 0.21 0.04 1.00 0.20 0.67 0.24
SI3 0.15 0.62 0.13 0.20 1.00 0.19 0.67
AC4 0.05 0.22 0.11 0.67 0.19 1.00 0.25
SI4 0.17 0.73 0.21 0.24 0.67 0.25 1.00corrplot(res, type = "lower", order = "hclust",
tl.col = "black", tl.srt = 45)
random <- rnorm(nrow(Class_SEM_Data_Final[,-1]), 7)
#The command above generates a random variable with the same number of rows (values)as the dataset
hist(random)#just to check the distribtuion of this new variable
fakereg <-lm(random ~., data = Class_SEM_Data_Final[,-1])
##runs a regression with the new random variable as the dv and all the variables in the dataset as IVs
##This generates a set of residuals in order to check the assumptions
##The following set of code just scales the residuals
standardized <- rstudent(fakereg)
fitted <- scale(fakereg$fitted.values)
hist(fitted)
check_model (fakereg) 
M_model <- '
Env_Supp =~ EP1 + EP2 + EP3 + EP4
Att_Cowrk =~ AC1 + AC2 + AC3 + AC4
Org_Comm =~ OC1 + OC2 + OC3 + OC4
Job_Sat =~ JS1 + JS2 + JS3 + JS4
Stay_Int =~ SI1 + SI2 + SI3 + SI4
'
M_MDL_fit <- sem(M_model, estimator= "ML", data=Class_SEM_Data_Final, mimic = "Mplus", missing = "FIML")
summary(M_MDL_fit, fit.measures = TRUE, standardized=TRUE)lavaan 0.6-19 ended normally after 86 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 70
Number of observations 400
Number of missing patterns 3
Model Test User Model:
Test statistic 217.464
Degrees of freedom 160
P-value (Chi-square) 0.002
Model Test Baseline Model:
Test statistic 4203.038
Degrees of freedom 190
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.986
Tucker-Lewis Index (TLI) 0.983
Robust Comparative Fit Index (CFI) 0.986
Robust Tucker-Lewis Index (TLI) 0.983
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -12246.237
Loglikelihood unrestricted model (H1) -12137.505
Akaike (AIC) 24632.475
Bayesian (BIC) 24911.877
Sample-size adjusted Bayesian (SABIC) 24689.763
Root Mean Square Error of Approximation:
RMSEA 0.030
90 Percent confidence interval - lower 0.019
90 Percent confidence interval - upper 0.040
P-value H_0: RMSEA <= 0.050 1.000
P-value H_0: RMSEA >= 0.080 0.000
Robust RMSEA 0.030
90 Percent confidence interval - lower 0.019
90 Percent confidence interval - upper 0.040
P-value H_0: Robust RMSEA <= 0.050 1.000
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.033
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp =~
EP1 1.000 1.274 0.697
EP2 1.034 0.071 14.477 0.000 1.317 0.810
EP3 0.805 0.060 13.455 0.000 1.026 0.770
EP4 0.901 0.063 14.289 0.000 1.147 0.824
Att_Cowrk =~
AC1 1.000 1.144 0.822
AC2 1.236 0.067 18.375 0.000 1.414 0.820
AC3 1.037 0.055 18.898 0.000 1.187 0.837
AC4 1.146 0.063 18.227 0.000 1.311 0.815
Org_Comm =~
OC1 1.000 1.471 0.583
OC2 1.313 0.108 12.203 0.000 1.932 0.885
OC3 0.783 0.075 10.388 0.000 1.152 0.657
OC4 1.166 0.098 11.938 0.000 1.716 0.837
Job_Sat =~
JS1 1.000 0.986 0.737
JS2 1.023 0.080 12.720 0.000 1.008 0.737
JS3 0.929 0.077 12.074 0.000 0.916 0.697
JS4 0.920 0.076 12.137 0.000 0.907 0.709
Stay_Int =~
SI1 1.000 0.706 0.811
SI2 1.073 0.054 19.955 0.000 0.757 0.864
SI3 1.065 0.067 15.855 0.000 0.752 0.741
SI4 1.167 0.062 18.815 0.000 0.823 0.852
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp ~~
Att_Cowrk 0.371 0.088 4.210 0.000 0.254 0.254
Org_Comm 0.932 0.143 6.514 0.000 0.497 0.497
Job_Sat 0.287 0.079 3.653 0.000 0.229 0.229
Stay_Int 0.507 0.066 7.727 0.000 0.563 0.563
Att_Cowrk ~~
Org_Comm 0.516 0.106 4.855 0.000 0.307 0.307
Job_Sat 0.029 0.066 0.434 0.664 0.026 0.026
Stay_Int 0.249 0.048 5.160 0.000 0.308 0.308
Org_Comm ~~
Job_Sat 0.275 0.090 3.042 0.002 0.190 0.190
Stay_Int 0.574 0.079 7.258 0.000 0.553 0.553
Job_Sat ~~
Stay_Int 0.149 0.042 3.520 0.000 0.214 0.214
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 8.528 0.091 93.262 0.000 8.528 4.663
.EP2 8.848 0.081 108.818 0.000 8.848 5.441
.EP3 8.928 0.067 133.933 0.000 8.928 6.697
.EP4 5.828 0.070 83.654 0.000 5.828 4.185
.AC1 2.760 0.070 39.658 0.000 2.760 1.983
.AC2 3.553 0.086 41.212 0.000 3.553 2.061
.AC3 2.775 0.071 39.153 0.000 2.775 1.958
.AC4 3.212 0.081 39.901 0.000 3.212 1.996
.OC1 4.887 0.126 38.761 0.000 4.887 1.938
.OC2 8.417 0.109 77.097 0.000 8.417 3.855
.OC3 8.655 0.088 98.778 0.000 8.655 4.939
.OC4 8.405 0.103 81.966 0.000 8.405 4.098
.JS1 4.197 0.067 62.774 0.000 4.197 3.139
.JS2 4.202 0.068 61.439 0.000 4.202 3.072
.JS3 3.215 0.066 48.904 0.000 3.215 2.445
.JS4 2.668 0.064 41.698 0.000 2.668 2.085
.SI1 4.203 0.043 96.635 0.000 4.203 4.832
.SI2 4.205 0.044 95.967 0.000 4.205 4.798
.SI3 3.473 0.051 68.456 0.000 3.473 3.423
.SI4 3.480 0.048 71.994 0.000 3.480 3.600
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 1.721 0.142 12.111 0.000 1.721 0.515
.EP2 0.909 0.093 9.807 0.000 0.909 0.344
.EP3 0.725 0.066 10.959 0.000 0.725 0.408
.EP4 0.623 0.066 9.464 0.000 0.623 0.321
.AC1 0.628 0.059 10.569 0.000 0.628 0.324
.AC2 0.972 0.092 10.620 0.000 0.972 0.327
.AC3 0.601 0.060 10.097 0.000 0.601 0.299
.AC4 0.870 0.081 10.709 0.000 0.870 0.336
.OC1 4.195 0.319 13.166 0.000 4.195 0.660
.OC2 1.036 0.153 6.757 0.000 1.036 0.217
.OC3 1.744 0.139 12.528 0.000 1.744 0.568
.OC4 1.262 0.140 9.042 0.000 1.262 0.300
.JS1 0.817 0.081 10.122 0.000 0.817 0.457
.JS2 0.855 0.085 10.115 0.000 0.855 0.457
.JS3 0.889 0.081 10.951 0.000 0.889 0.514
.JS4 0.815 0.076 10.736 0.000 0.815 0.498
.SI1 0.258 0.023 11.029 0.000 0.258 0.342
.SI2 0.195 0.021 9.320 0.000 0.195 0.253
.SI3 0.464 0.038 12.191 0.000 0.464 0.451
.SI4 0.257 0.026 9.687 0.000 0.257 0.275
Env_Supp 1.623 0.216 7.526 0.000 1.000 1.000
Att_Cowrk 1.309 0.135 9.664 0.000 1.000 1.000
Org_Comm 2.165 0.357 6.057 0.000 1.000 1.000
Job_Sat 0.972 0.126 7.723 0.000 1.000 1.000
Stay_Int 0.498 0.052 9.510 0.000 1.000 1.000modindices(M_MDL_fit) lhs op rhs mi epc sepc.lv sepc.all sepc.nox
81 Env_Supp =~ AC1 0.064 -0.010 -0.013 -0.009 -0.009
82 Env_Supp =~ AC2 0.228 -0.024 -0.030 -0.018 -0.018
83 Env_Supp =~ AC3 0.885 -0.038 -0.048 -0.034 -0.034
84 Env_Supp =~ AC4 3.019 0.081 0.104 0.065 0.065
85 Env_Supp =~ OC1 3.024 -0.188 -0.240 -0.095 -0.095
86 Env_Supp =~ OC2 0.458 -0.053 -0.067 -0.031 -0.031
87 Env_Supp =~ OC3 7.553 0.196 0.250 0.142 0.142
88 Env_Supp =~ OC4 0.029 -0.013 -0.016 -0.008 -0.008
89 Env_Supp =~ JS1 1.786 -0.062 -0.079 -0.059 -0.059
90 Env_Supp =~ JS2 1.360 0.056 0.071 0.052 0.052
91 Env_Supp =~ JS3 0.339 0.027 0.035 0.026 0.026
92 Env_Supp =~ JS4 0.151 -0.018 -0.022 -0.017 -0.017
93 Env_Supp =~ SI1 0.293 -0.017 -0.022 -0.025 -0.025
94 Env_Supp =~ SI2 1.681 -0.039 -0.050 -0.057 -0.057
95 Env_Supp =~ SI3 0.105 0.013 0.016 0.016 0.016
96 Env_Supp =~ SI4 2.481 0.053 0.067 0.070 0.070
97 Att_Cowrk =~ EP1 0.047 0.015 0.017 0.009 0.009
98 Att_Cowrk =~ EP2 0.514 0.040 0.045 0.028 0.028
99 Att_Cowrk =~ EP3 0.001 0.001 0.001 0.001 0.001
100 Att_Cowrk =~ EP4 0.787 -0.042 -0.048 -0.034 -0.034
101 Att_Cowrk =~ OC1 2.752 -0.174 -0.199 -0.079 -0.079
102 Att_Cowrk =~ OC2 7.177 0.193 0.221 0.101 0.101
103 Att_Cowrk =~ OC3 1.875 -0.094 -0.108 -0.062 -0.062
104 Att_Cowrk =~ OC4 1.024 -0.069 -0.079 -0.039 -0.039
105 Att_Cowrk =~ JS1 0.064 -0.012 -0.014 -0.011 -0.011
106 Att_Cowrk =~ JS2 0.963 -0.049 -0.056 -0.041 -0.041
107 Att_Cowrk =~ JS3 0.197 0.022 0.025 0.019 0.019
108 Att_Cowrk =~ JS4 0.755 0.041 0.047 0.037 0.037
109 Att_Cowrk =~ SI1 0.020 0.004 0.005 0.005 0.005
110 Att_Cowrk =~ SI2 0.594 -0.021 -0.024 -0.027 -0.027
111 Att_Cowrk =~ SI3 0.004 0.002 0.003 0.003 0.003
112 Att_Cowrk =~ SI4 0.381 0.019 0.021 0.022 0.022
113 Org_Comm =~ EP1 1.819 -0.084 -0.124 -0.068 -0.068
114 Org_Comm =~ EP2 3.571 0.096 0.142 0.087 0.087
115 Org_Comm =~ EP3 0.199 -0.019 -0.028 -0.021 -0.021
116 Org_Comm =~ EP4 0.165 -0.018 -0.026 -0.019 -0.019
117 Org_Comm =~ AC1 0.377 -0.022 -0.032 -0.023 -0.023
118 Org_Comm =~ AC2 2.457 -0.069 -0.101 -0.059 -0.059
119 Org_Comm =~ AC3 0.075 0.010 0.014 0.010 0.010
120 Org_Comm =~ AC4 3.712 0.079 0.117 0.073 0.073
121 Org_Comm =~ JS1 0.953 0.039 0.057 0.042 0.042
122 Org_Comm =~ JS2 0.795 -0.036 -0.053 -0.039 -0.039
123 Org_Comm =~ JS3 0.089 0.012 0.017 0.013 0.013
124 Org_Comm =~ JS4 0.146 -0.015 -0.022 -0.017 -0.017
125 Org_Comm =~ SI1 0.000 0.000 0.000 0.000 0.000
126 Org_Comm =~ SI2 0.083 0.007 0.011 0.012 0.012
127 Org_Comm =~ SI3 1.860 -0.046 -0.068 -0.067 -0.067
128 Org_Comm =~ SI4 0.554 0.021 0.031 0.032 0.032
129 Job_Sat =~ EP1 0.073 0.022 0.022 0.012 0.012
130 Job_Sat =~ EP2 0.002 -0.003 -0.003 -0.002 -0.002
131 Job_Sat =~ EP3 0.011 0.006 0.006 0.004 0.004
132 Job_Sat =~ EP4 0.067 -0.015 -0.014 -0.010 -0.010
133 Job_Sat =~ AC1 0.570 0.039 0.038 0.027 0.027
134 Job_Sat =~ AC2 0.482 -0.044 -0.044 -0.025 -0.025
135 Job_Sat =~ AC3 1.108 -0.054 -0.053 -0.038 -0.038
136 Job_Sat =~ AC4 1.101 0.063 0.062 0.039 0.039
137 Job_Sat =~ OC1 0.462 -0.083 -0.082 -0.033 -0.033
138 Job_Sat =~ OC2 0.177 -0.035 -0.035 -0.016 -0.016
139 Job_Sat =~ OC3 1.387 0.095 0.093 0.053 0.053
140 Job_Sat =~ OC4 0.002 0.004 0.004 0.002 0.002
141 Job_Sat =~ SI1 0.000 0.001 0.001 0.001 0.001
142 Job_Sat =~ SI2 2.200 -0.047 -0.046 -0.053 -0.053
143 Job_Sat =~ SI3 0.012 -0.005 -0.005 -0.005 -0.005
144 Job_Sat =~ SI4 2.549 0.056 0.056 0.057 0.057
145 Stay_Int =~ EP1 1.805 0.185 0.131 0.071 0.071
146 Stay_Int =~ EP2 0.839 0.103 0.073 0.045 0.045
147 Stay_Int =~ EP3 3.169 -0.169 -0.119 -0.089 -0.089
148 Stay_Int =~ EP4 0.106 -0.031 -0.022 -0.016 -0.016
149 Stay_Int =~ AC1 0.021 -0.011 -0.007 -0.005 -0.005
150 Stay_Int =~ AC2 0.840 -0.083 -0.058 -0.034 -0.034
151 Stay_Int =~ AC3 0.018 0.010 0.007 0.005 0.005
152 Stay_Int =~ AC4 0.873 0.079 0.056 0.035 0.035
153 Stay_Int =~ OC1 10.917 -0.666 -0.470 -0.186 -0.186
154 Stay_Int =~ OC2 10.888 0.492 0.347 0.159 0.159
155 Stay_Int =~ OC3 3.159 -0.236 -0.167 -0.095 -0.095
156 Stay_Int =~ OC4 0.069 -0.037 -0.026 -0.013 -0.013
157 Stay_Int =~ JS1 0.589 -0.063 -0.044 -0.033 -0.033
158 Stay_Int =~ JS2 0.002 -0.004 -0.003 -0.002 -0.002
159 Stay_Int =~ JS3 0.674 0.068 0.048 0.036 0.036
160 Stay_Int =~ JS4 0.003 0.005 0.003 0.003 0.003
161 EP1 ~~ EP2 4.754 0.196 0.196 0.157 0.157
162 EP1 ~~ EP3 3.478 -0.138 -0.138 -0.123 -0.123
163 EP1 ~~ EP4 0.420 -0.050 -0.050 -0.048 -0.048
164 EP1 ~~ AC1 0.173 -0.026 -0.026 -0.025 -0.025
165 EP1 ~~ AC2 0.833 0.071 0.071 0.055 0.055
166 EP1 ~~ AC3 1.056 0.064 0.064 0.063 0.063
167 EP1 ~~ AC4 1.932 -0.102 -0.102 -0.083 -0.083
168 EP1 ~~ OC1 1.640 -0.189 -0.189 -0.070 -0.070
169 EP1 ~~ OC2 2.462 -0.146 -0.146 -0.109 -0.109
170 EP1 ~~ OC3 0.775 -0.085 -0.085 -0.049 -0.049
171 EP1 ~~ OC4 1.601 0.116 0.116 0.079 0.079
172 EP1 ~~ JS1 2.964 -0.123 -0.123 -0.104 -0.104
173 EP1 ~~ JS2 1.310 0.084 0.084 0.069 0.069
174 EP1 ~~ JS3 0.610 0.057 0.057 0.046 0.046
175 EP1 ~~ JS4 0.013 0.008 0.008 0.007 0.007
176 EP1 ~~ SI1 0.041 0.008 0.008 0.012 0.012
177 EP1 ~~ SI2 1.357 0.043 0.043 0.073 0.073
178 EP1 ~~ SI3 0.188 0.022 0.022 0.025 0.025
179 EP1 ~~ SI4 0.015 -0.005 -0.005 -0.008 -0.008
180 EP2 ~~ EP3 0.686 -0.056 -0.056 -0.069 -0.069
181 EP2 ~~ EP4 5.595 -0.176 -0.176 -0.234 -0.234
182 EP2 ~~ AC1 0.168 -0.020 -0.020 -0.027 -0.027
183 EP2 ~~ AC2 0.021 0.009 0.009 0.009 0.009
184 EP2 ~~ AC3 0.245 -0.024 -0.024 -0.033 -0.033
185 EP2 ~~ AC4 1.068 0.059 0.059 0.067 0.067
186 EP2 ~~ OC1 0.595 0.089 0.089 0.046 0.046
187 EP2 ~~ OC2 0.727 -0.062 -0.062 -0.064 -0.064
188 EP2 ~~ OC3 1.320 0.087 0.087 0.069 0.069
189 EP2 ~~ OC4 1.239 0.080 0.080 0.075 0.075
190 EP2 ~~ JS1 0.961 0.055 0.055 0.064 0.064
191 EP2 ~~ JS2 0.243 0.028 0.028 0.032 0.032
192 EP2 ~~ JS3 1.854 -0.077 -0.077 -0.086 -0.086
193 EP2 ~~ JS4 0.191 -0.024 -0.024 -0.028 -0.028
194 EP2 ~~ SI1 3.884 -0.061 -0.061 -0.126 -0.126
195 EP2 ~~ SI2 0.067 -0.007 -0.007 -0.018 -0.018
196 EP2 ~~ SI3 0.333 -0.023 -0.023 -0.035 -0.035
197 EP2 ~~ SI4 6.714 0.083 0.083 0.173 0.173
198 EP3 ~~ EP4 11.824 0.201 0.201 0.300 0.300
199 EP3 ~~ AC1 3.924 0.084 0.084 0.124 0.124
200 EP3 ~~ AC2 2.360 -0.081 -0.081 -0.096 -0.096
201 EP3 ~~ AC3 0.021 0.006 0.006 0.009 0.009
202 EP3 ~~ AC4 0.108 -0.016 -0.016 -0.020 -0.020
203 EP3 ~~ OC1 5.828 -0.240 -0.240 -0.138 -0.138
204 EP3 ~~ OC2 5.436 0.147 0.147 0.169 0.169
205 EP3 ~~ OC3 0.298 0.036 0.036 0.032 0.032
206 EP3 ~~ OC4 2.284 -0.094 -0.094 -0.098 -0.098
207 EP3 ~~ JS1 0.011 -0.005 -0.005 -0.007 -0.007
208 EP3 ~~ JS2 0.024 0.008 0.008 0.010 0.010
209 EP3 ~~ JS3 0.284 0.026 0.026 0.032 0.032
210 EP3 ~~ JS4 0.073 -0.013 -0.013 -0.017 -0.017
211 EP3 ~~ SI1 2.073 -0.038 -0.038 -0.088 -0.088
212 EP3 ~~ SI2 0.244 -0.012 -0.012 -0.032 -0.032
213 EP3 ~~ SI3 0.005 -0.002 -0.002 -0.004 -0.004
214 EP3 ~~ SI4 0.161 0.011 0.011 0.026 0.026
215 EP4 ~~ AC1 1.373 -0.048 -0.048 -0.077 -0.077
216 EP4 ~~ AC2 0.824 0.047 0.047 0.060 0.060
217 EP4 ~~ AC3 2.194 -0.061 -0.061 -0.100 -0.100
218 EP4 ~~ AC4 1.485 0.059 0.059 0.080 0.080
219 EP4 ~~ OC1 3.991 0.195 0.195 0.120 0.120
220 EP4 ~~ OC2 3.335 -0.113 -0.113 -0.140 -0.140
221 EP4 ~~ OC3 5.653 0.152 0.152 0.146 0.146
222 EP4 ~~ OC4 0.481 -0.042 -0.042 -0.048 -0.048
223 EP4 ~~ JS1 1.011 -0.048 -0.048 -0.067 -0.067
224 EP4 ~~ JS2 0.036 0.009 0.009 0.013 0.013
225 EP4 ~~ JS3 0.205 0.022 0.022 0.029 0.029
226 EP4 ~~ JS4 0.027 0.008 0.008 0.011 0.011
227 EP4 ~~ SI1 6.336 0.066 0.066 0.163 0.163
228 EP4 ~~ SI2 1.387 -0.028 -0.028 -0.082 -0.082
229 EP4 ~~ SI3 0.857 0.031 0.031 0.058 0.058
230 EP4 ~~ SI4 3.047 -0.047 -0.047 -0.119 -0.119
231 AC1 ~~ AC2 0.017 -0.009 -0.009 -0.011 -0.011
232 AC1 ~~ AC3 0.105 0.018 0.018 0.029 0.029
233 AC1 ~~ AC4 0.000 0.001 0.001 0.001 0.001
234 AC1 ~~ OC1 3.197 -0.169 -0.169 -0.104 -0.104
235 AC1 ~~ OC2 5.637 0.142 0.142 0.176 0.176
236 AC1 ~~ OC3 0.498 0.044 0.044 0.042 0.042
237 AC1 ~~ OC4 7.303 -0.159 -0.159 -0.179 -0.179
238 AC1 ~~ JS1 1.444 0.055 0.055 0.077 0.077
239 AC1 ~~ JS2 0.379 -0.029 -0.029 -0.039 -0.039
240 AC1 ~~ JS3 0.130 0.017 0.017 0.022 0.022
241 AC1 ~~ JS4 0.006 -0.003 -0.003 -0.005 -0.005
242 AC1 ~~ SI1 3.345 0.046 0.046 0.115 0.115
243 AC1 ~~ SI2 0.195 -0.010 -0.010 -0.030 -0.030
244 AC1 ~~ SI3 0.952 0.032 0.032 0.059 0.059
245 AC1 ~~ SI4 3.225 -0.047 -0.047 -0.118 -0.118
246 AC2 ~~ AC3 0.152 0.026 0.026 0.034 0.034
247 AC2 ~~ AC4 0.035 0.014 0.014 0.015 0.015
248 AC2 ~~ OC1 1.513 0.145 0.145 0.072 0.072
249 AC2 ~~ OC2 1.986 -0.104 -0.104 -0.104 -0.104
250 AC2 ~~ OC3 0.945 -0.075 -0.075 -0.057 -0.057
251 AC2 ~~ OC4 0.157 0.029 0.029 0.026 0.026
252 AC2 ~~ JS1 0.080 0.016 0.016 0.018 0.018
253 AC2 ~~ JS2 0.051 0.013 0.013 0.014 0.014
254 AC2 ~~ JS3 0.065 -0.015 -0.015 -0.016 -0.016
255 AC2 ~~ JS4 0.519 -0.040 -0.040 -0.045 -0.045
256 AC2 ~~ SI1 0.087 0.009 0.009 0.018 0.018
257 AC2 ~~ SI2 0.025 0.005 0.005 0.011 0.011
258 AC2 ~~ SI3 0.296 -0.022 -0.022 -0.033 -0.033
259 AC2 ~~ SI4 0.055 -0.008 -0.008 -0.015 -0.015
260 AC3 ~~ AC4 0.620 -0.049 -0.049 -0.068 -0.068
261 AC3 ~~ OC1 0.271 -0.049 -0.049 -0.031 -0.031
262 AC3 ~~ OC2 0.806 0.053 0.053 0.068 0.068
263 AC3 ~~ OC3 0.716 -0.052 -0.052 -0.051 -0.051
264 AC3 ~~ OC4 0.133 0.021 0.021 0.025 0.025
265 AC3 ~~ JS1 1.136 -0.049 -0.049 -0.070 -0.070
266 AC3 ~~ JS2 0.033 -0.009 -0.009 -0.012 -0.012
267 AC3 ~~ JS3 1.827 -0.063 -0.063 -0.086 -0.086
268 AC3 ~~ JS4 2.707 0.074 0.074 0.105 0.105
269 AC3 ~~ SI1 0.549 -0.019 -0.019 -0.047 -0.047
270 AC3 ~~ SI2 0.135 -0.009 -0.009 -0.025 -0.025
271 AC3 ~~ SI3 0.079 0.009 0.009 0.017 0.017
272 AC3 ~~ SI4 1.771 0.035 0.035 0.089 0.089
273 AC4 ~~ OC1 0.061 0.027 0.027 0.014 0.014
274 AC4 ~~ OC2 0.085 -0.020 -0.020 -0.021 -0.021
275 AC4 ~~ OC3 0.035 0.013 0.013 0.011 0.011
276 AC4 ~~ OC4 1.505 0.084 0.084 0.081 0.081
277 AC4 ~~ JS1 0.293 -0.029 -0.029 -0.034 -0.034
278 AC4 ~~ JS2 0.027 -0.009 -0.009 -0.010 -0.010
279 AC4 ~~ JS3 2.255 0.082 0.082 0.093 0.093
280 AC4 ~~ JS4 0.018 -0.007 -0.007 -0.008 -0.008
281 AC4 ~~ SI1 1.532 -0.037 -0.037 -0.077 -0.077
282 AC4 ~~ SI2 0.001 0.001 0.001 0.002 0.002
283 AC4 ~~ SI3 0.301 -0.021 -0.021 -0.033 -0.033
284 AC4 ~~ SI4 1.116 0.033 0.033 0.069 0.069
285 OC1 ~~ OC2 0.443 0.115 0.115 0.055 0.055
286 OC1 ~~ OC3 4.787 0.329 0.329 0.122 0.122
287 OC1 ~~ OC4 0.052 -0.037 -0.037 -0.016 -0.016
288 OC1 ~~ JS1 0.631 -0.086 -0.086 -0.046 -0.046
289 OC1 ~~ JS2 0.049 0.024 0.024 0.013 0.013
290 OC1 ~~ JS3 0.074 0.030 0.030 0.015 0.015
291 OC1 ~~ JS4 0.005 0.007 0.007 0.004 0.004
292 OC1 ~~ SI1 0.002 -0.002 -0.002 -0.002 -0.002
293 OC1 ~~ SI2 0.254 -0.028 -0.028 -0.031 -0.031
294 OC1 ~~ SI3 0.314 -0.043 -0.043 -0.031 -0.031
295 OC1 ~~ SI4 1.557 -0.077 -0.077 -0.075 -0.075
296 OC2 ~~ OC3 6.159 -0.311 -0.311 -0.232 -0.232
297 OC2 ~~ OC4 0.280 -0.105 -0.105 -0.092 -0.092
298 OC2 ~~ JS1 0.005 -0.005 -0.005 -0.005 -0.005
299 OC2 ~~ JS2 0.691 -0.058 -0.058 -0.062 -0.062
300 OC2 ~~ JS3 0.917 0.066 0.066 0.069 0.069
301 OC2 ~~ JS4 0.157 -0.026 -0.026 -0.029 -0.029
302 OC2 ~~ SI1 0.284 0.020 0.020 0.039 0.039
303 OC2 ~~ SI2 3.577 0.066 0.066 0.147 0.147
304 OC2 ~~ SI3 0.001 0.001 0.001 0.002 0.002
305 OC2 ~~ SI4 0.010 0.004 0.004 0.007 0.007
306 OC3 ~~ OC4 1.437 0.136 0.136 0.092 0.092
307 OC3 ~~ JS1 1.060 0.073 0.073 0.061 0.061
308 OC3 ~~ JS2 0.099 0.023 0.023 0.019 0.019
309 OC3 ~~ JS3 2.252 -0.108 -0.108 -0.086 -0.086
310 OC3 ~~ JS4 0.921 0.066 0.066 0.056 0.056
311 OC3 ~~ SI1 2.567 -0.062 -0.062 -0.093 -0.093
312 OC3 ~~ SI2 0.084 0.010 0.010 0.018 0.018
313 OC3 ~~ SI3 0.294 -0.027 -0.027 -0.030 -0.030
314 OC3 ~~ SI4 0.911 -0.039 -0.039 -0.058 -0.058
315 OC4 ~~ JS1 2.513 0.107 0.107 0.105 0.105
316 OC4 ~~ JS2 0.253 -0.035 -0.035 -0.033 -0.033
317 OC4 ~~ JS3 0.220 -0.032 -0.032 -0.030 -0.030
318 OC4 ~~ JS4 0.451 -0.044 -0.044 -0.044 -0.044
319 OC4 ~~ SI1 0.308 0.021 0.021 0.036 0.036
320 OC4 ~~ SI2 1.722 -0.045 -0.045 -0.091 -0.091
321 OC4 ~~ SI3 0.448 -0.032 -0.032 -0.042 -0.042
322 OC4 ~~ SI4 1.715 0.051 0.051 0.089 0.089
323 JS1 ~~ JS2 0.912 0.075 0.075 0.089 0.089
324 JS1 ~~ JS3 0.006 0.005 0.005 0.006 0.006
325 JS1 ~~ JS4 0.592 -0.054 -0.054 -0.066 -0.066
326 JS1 ~~ SI1 0.020 0.004 0.004 0.009 0.009
327 JS1 ~~ SI2 1.520 0.033 0.033 0.083 0.083
328 JS1 ~~ SI3 7.715 -0.103 -0.103 -0.168 -0.168
329 JS1 ~~ SI4 0.000 0.000 0.000 0.000 0.000
330 JS2 ~~ JS3 1.500 -0.089 -0.089 -0.102 -0.102
331 JS2 ~~ JS4 0.001 0.002 0.002 0.003 0.003
332 JS2 ~~ SI1 0.062 -0.007 -0.007 -0.016 -0.016
333 JS2 ~~ SI2 0.678 0.023 0.023 0.055 0.055
334 JS2 ~~ SI3 0.259 -0.019 -0.019 -0.031 -0.031
335 JS2 ~~ SI4 0.092 -0.009 -0.009 -0.020 -0.020
336 JS3 ~~ JS4 0.881 0.062 0.062 0.073 0.073
337 JS3 ~~ SI1 0.304 0.016 0.016 0.034 0.034
338 JS3 ~~ SI2 3.509 -0.051 -0.051 -0.122 -0.122
339 JS3 ~~ SI3 3.054 0.066 0.066 0.102 0.102
340 JS3 ~~ SI4 0.461 0.021 0.021 0.043 0.043
341 JS4 ~~ SI1 0.111 -0.009 -0.009 -0.020 -0.020
342 JS4 ~~ SI2 3.091 -0.046 -0.046 -0.115 -0.115
343 JS4 ~~ SI3 3.003 0.063 0.063 0.102 0.102
344 JS4 ~~ SI4 1.153 0.032 0.032 0.069 0.069
345 SI1 ~~ SI2 14.548 0.076 0.076 0.339 0.339
346 SI1 ~~ SI3 1.999 -0.032 -0.032 -0.093 -0.093
347 SI1 ~~ SI4 5.758 -0.052 -0.052 -0.203 -0.203
348 SI2 ~~ SI3 1.860 -0.031 -0.031 -0.102 -0.102
349 SI2 ~~ SI4 3.058 -0.040 -0.040 -0.181 -0.181
350 SI3 ~~ SI4 9.433 0.076 0.076 0.222 0.222Metric <- cfa(model = M_model, data = Class_SEM_Data_Final,
missing = "fiml", group = "Sex",
group.equal = c("loadings"))
summary(Metric, standardized = TRUE, fit.measures = TRUE)lavaan 0.6-19 ended normally after 119 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 140
Number of equality constraints 15
Number of observations per group:
female 200
male 200
Number of missing patterns per group:
female 2
male 2
Model Test User Model:
Test statistic 460.808
Degrees of freedom 335
P-value (Chi-square) 0.000
Test statistic for each group:
female 240.032
male 220.776
Model Test Baseline Model:
Test statistic 3882.375
Degrees of freedom 380
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.964
Tucker-Lewis Index (TLI) 0.959
Robust Comparative Fit Index (CFI) 0.964
Robust Tucker-Lewis Index (TLI) 0.959
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -11963.295
Loglikelihood unrestricted model (H1) -11732.891
Akaike (AIC) 24176.589
Bayesian (BIC) 24675.522
Sample-size adjusted Bayesian (SABIC) 24278.889
Root Mean Square Error of Approximation:
RMSEA 0.043
90 Percent confidence interval - lower 0.033
90 Percent confidence interval - upper 0.053
P-value H_0: RMSEA <= 0.050 0.874
P-value H_0: RMSEA >= 0.080 0.000
Robust RMSEA 0.043
90 Percent confidence interval - lower 0.033
90 Percent confidence interval - upper 0.053
P-value H_0: Robust RMSEA <= 0.050 0.875
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.060
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Group 1 [female]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp =~
EP1 1.000 1.391 0.642
EP2 (.p2.) 1.024 0.074 13.749 0.000 1.424 0.768
EP3 (.p3.) 0.840 0.062 13.542 0.000 1.169 0.877
EP4 (.p4.) 0.948 0.066 14.322 0.000 1.319 0.860
Att_Cowrk =~
AC1 1.000 0.709 0.691
AC2 (.p6.) 0.998 0.104 9.597 0.000 0.708 0.609
AC3 (.p7.) 1.181 0.103 11.452 0.000 0.838 0.771
AC4 (.p8.) 1.205 0.112 10.761 0.000 0.855 0.680
Org_Comm =~
OC1 1.000 1.491 0.591
OC2 (.10.) 1.291 0.107 12.122 0.000 1.925 0.869
OC3 (.11.) 0.774 0.077 10.094 0.000 1.154 0.688
OC4 (.12.) 1.162 0.098 11.850 0.000 1.733 0.826
Job_Sat =~
JS1 1.000 0.938 0.718
JS2 (.14.) 1.015 0.080 12.732 0.000 0.952 0.721
JS3 (.15.) 0.921 0.077 11.996 0.000 0.863 0.673
JS4 (.16.) 0.917 0.075 12.177 0.000 0.860 0.691
Stay_Int =~
SI1 1.000 0.768 0.803
SI2 (.18.) 1.101 0.059 18.771 0.000 0.846 0.893
SI3 (.19.) 1.087 0.073 14.987 0.000 0.835 0.775
SI4 (.20.) 1.198 0.068 17.673 0.000 0.920 0.885
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp ~~
Att_Cowrk 0.213 0.086 2.472 0.013 0.215 0.215
Org_Comm 1.060 0.207 5.123 0.000 0.511 0.511
Job_Sat 0.359 0.115 3.110 0.002 0.275 0.275
Stay_Int 0.652 0.106 6.128 0.000 0.610 0.610
Att_Cowrk ~~
Org_Comm 0.489 0.107 4.568 0.000 0.462 0.462
Job_Sat 0.195 0.063 3.080 0.002 0.293 0.293
Stay_Int 0.161 0.048 3.354 0.001 0.296 0.296
Org_Comm ~~
Job_Sat 0.371 0.126 2.942 0.003 0.266 0.266
Stay_Int 0.718 0.119 6.016 0.000 0.626 0.626
Job_Sat ~~
Stay_Int 0.166 0.062 2.693 0.007 0.231 0.231
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 8.095 0.153 52.851 0.000 8.095 3.737
.EP2 8.575 0.131 65.383 0.000 8.575 4.623
.EP3 8.780 0.094 93.233 0.000 8.780 6.593
.EP4 5.610 0.109 51.679 0.000 5.610 3.658
.AC1 1.905 0.073 26.228 0.000 1.905 1.855
.AC2 2.290 0.082 27.821 0.000 2.290 1.967
.AC3 1.970 0.077 25.643 0.000 1.970 1.813
.AC4 2.255 0.089 25.364 0.000 2.255 1.793
.OC1 4.875 0.178 27.318 0.000 4.875 1.932
.OC2 8.195 0.157 52.309 0.000 8.195 3.699
.OC3 8.665 0.119 73.021 0.000 8.665 5.163
.OC4 8.295 0.148 55.941 0.000 8.295 3.956
.JS1 4.210 0.092 45.560 0.000 4.210 3.222
.JS2 4.200 0.093 44.953 0.000 4.200 3.179
.JS3 3.230 0.091 35.622 0.000 3.230 2.519
.JS4 2.665 0.088 30.293 0.000 2.665 2.142
.SI1 3.995 0.068 59.030 0.000 3.995 4.174
.SI2 4.035 0.067 60.267 0.000 4.035 4.262
.SI3 3.280 0.076 43.052 0.000 3.280 3.044
.SI4 3.305 0.074 44.951 0.000 3.305 3.179
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 2.757 0.301 9.163 0.000 2.757 0.588
.EP2 1.413 0.176 8.049 0.000 1.413 0.411
.EP3 0.408 0.068 6.029 0.000 0.408 0.230
.EP4 0.614 0.090 6.799 0.000 0.614 0.261
.AC1 0.552 0.069 8.038 0.000 0.552 0.523
.AC2 0.853 0.104 8.191 0.000 0.853 0.630
.AC3 0.479 0.073 6.576 0.000 0.479 0.406
.AC4 0.850 0.107 7.911 0.000 0.850 0.538
.OC1 4.146 0.451 9.193 0.000 4.146 0.651
.OC2 1.202 0.213 5.652 0.000 1.202 0.245
.OC3 1.484 0.169 8.776 0.000 1.484 0.527
.OC4 1.395 0.194 7.185 0.000 1.395 0.317
.JS1 0.828 0.108 7.636 0.000 0.828 0.485
.JS2 0.839 0.112 7.495 0.000 0.839 0.481
.JS3 0.899 0.114 7.900 0.000 0.899 0.547
.JS4 0.808 0.102 7.890 0.000 0.808 0.522
.SI1 0.326 0.039 8.268 0.000 0.326 0.355
.SI2 0.181 0.028 6.390 0.000 0.181 0.202
.SI3 0.463 0.054 8.622 0.000 0.463 0.399
.SI4 0.234 0.035 6.678 0.000 0.234 0.217
Env_Supp 1.935 0.312 6.203 0.000 1.000 1.000
Att_Cowrk 0.503 0.084 5.996 0.000 1.000 1.000
Org_Comm 2.223 0.414 5.370 0.000 1.000 1.000
Job_Sat 0.879 0.137 6.414 0.000 1.000 1.000
Stay_Int 0.590 0.081 7.332 0.000 1.000 1.000
Group 2 [male]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp =~
EP1 1.000 0.982 0.747
EP2 (.p2.) 1.024 0.074 13.749 0.000 1.005 0.808
EP3 (.p3.) 0.840 0.062 13.542 0.000 0.825 0.637
EP4 (.p4.) 0.948 0.066 14.322 0.000 0.931 0.774
Att_Cowrk =~
AC1 1.000 0.768 0.669
AC2 (.p6.) 0.998 0.104 9.597 0.000 0.767 0.651
AC3 (.p7.) 1.181 0.103 11.452 0.000 0.907 0.734
AC4 (.p8.) 1.205 0.112 10.761 0.000 0.926 0.696
Org_Comm =~
OC1 1.000 1.454 0.576
OC2 (.10.) 1.291 0.107 12.122 0.000 1.877 0.889
OC3 (.11.) 0.774 0.077 10.094 0.000 1.125 0.631
OC4 (.12.) 1.162 0.098 11.850 0.000 1.689 0.845
Job_Sat =~
JS1 1.000 1.041 0.761
JS2 (.14.) 1.015 0.080 12.732 0.000 1.057 0.748
JS3 (.15.) 0.921 0.077 11.996 0.000 0.959 0.712
JS4 (.16.) 0.917 0.075 12.177 0.000 0.955 0.727
Stay_Int =~
SI1 1.000 0.552 0.789
SI2 (.18.) 1.101 0.059 18.771 0.000 0.608 0.799
SI3 (.19.) 1.087 0.073 14.987 0.000 0.601 0.662
SI4 (.20.) 1.198 0.068 17.673 0.000 0.662 0.779
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp ~~
Att_Cowrk 0.063 0.066 0.949 0.343 0.083 0.083
Org_Comm 0.766 0.145 5.263 0.000 0.536 0.536
Job_Sat 0.232 0.090 2.577 0.010 0.227 0.227
Stay_Int 0.214 0.050 4.332 0.000 0.395 0.395
Att_Cowrk ~~
Org_Comm 0.327 0.103 3.176 0.001 0.293 0.293
Job_Sat -0.134 0.072 -1.873 0.061 -0.168 -0.168
Stay_Int 0.030 0.037 0.812 0.417 0.071 0.071
Org_Comm ~~
Job_Sat 0.186 0.127 1.459 0.145 0.123 0.123
Stay_Int 0.365 0.076 4.805 0.000 0.455 0.455
Job_Sat ~~
Stay_Int 0.122 0.050 2.462 0.014 0.213 0.213
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 8.960 0.093 96.394 0.000 8.960 6.816
.EP2 9.120 0.088 103.644 0.000 9.120 7.329
.EP3 9.075 0.092 99.115 0.000 9.075 7.008
.EP4 6.040 0.085 70.970 0.000 6.040 5.018
.AC1 3.615 0.081 44.508 0.000 3.615 3.147
.AC2 4.815 0.083 57.773 0.000 4.815 4.085
.AC3 3.580 0.087 40.964 0.000 3.580 2.897
.AC4 4.170 0.094 44.272 0.000 4.170 3.136
.OC1 4.900 0.178 27.476 0.000 4.900 1.943
.OC2 8.640 0.149 57.891 0.000 8.640 4.094
.OC3 8.645 0.126 68.585 0.000 8.645 4.850
.OC4 8.515 0.141 60.217 0.000 8.515 4.258
.JS1 4.185 0.097 43.242 0.000 4.185 3.058
.JS2 4.205 0.100 42.083 0.000 4.205 2.976
.JS3 3.200 0.095 33.619 0.000 3.200 2.377
.JS4 2.670 0.093 28.742 0.000 2.670 2.032
.SI1 4.410 0.049 89.129 0.000 4.410 6.302
.SI2 4.375 0.054 81.269 0.000 4.375 5.747
.SI3 3.665 0.064 57.117 0.000 3.665 4.039
.SI4 3.655 0.060 60.887 0.000 3.655 4.305
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 0.763 0.099 7.677 0.000 0.763 0.442
.EP2 0.538 0.081 6.663 0.000 0.538 0.347
.EP3 0.996 0.113 8.822 0.000 0.996 0.594
.EP4 0.582 0.080 7.295 0.000 0.582 0.402
.AC1 0.729 0.096 7.610 0.000 0.729 0.552
.AC2 0.800 0.096 8.326 0.000 0.800 0.576
.AC3 0.704 0.101 6.989 0.000 0.704 0.461
.AC4 0.912 0.119 7.672 0.000 0.912 0.515
.OC1 4.248 0.451 9.430 0.000 4.248 0.668
.OC2 0.932 0.187 4.991 0.000 0.932 0.209
.OC3 1.911 0.222 8.614 0.000 1.911 0.602
.OC4 1.146 0.183 6.269 0.000 1.146 0.287
.JS1 0.789 0.111 7.104 0.000 0.789 0.421
.JS2 0.879 0.118 7.459 0.000 0.879 0.440
.JS3 0.893 0.111 8.074 0.000 0.893 0.493
.JS4 0.814 0.105 7.768 0.000 0.814 0.472
.SI1 0.185 0.025 7.430 0.000 0.185 0.377
.SI2 0.210 0.029 7.196 0.000 0.210 0.362
.SI3 0.463 0.053 8.797 0.000 0.463 0.562
.SI4 0.283 0.038 7.406 0.000 0.283 0.393
Env_Supp 0.965 0.149 6.465 0.000 1.000 1.000
Att_Cowrk 0.591 0.109 5.401 0.000 1.000 1.000
Org_Comm 2.113 0.390 5.412 0.000 1.000 1.000
Job_Sat 1.084 0.170 6.391 0.000 1.000 1.000
Stay_Int 0.305 0.041 7.399 0.000 1.000 1.000##Strong (Scalar) Invariance
Strong <- cfa(model = M_model, data = Class_SEM_Data_Final,
missing = "fiml", mimic = "Mplus", group = "Sex",
group.equal = c("loadings", "intercepts"))
summary(Strong, standardized = TRUE, fit.measures = TRUE)lavaan 0.6-19 ended normally after 124 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 145
Number of equality constraints 35
Number of observations per group:
female 200
male 200
Number of missing patterns per group:
female 2
male 2
Model Test User Model:
Test statistic 514.882
Degrees of freedom 350
P-value (Chi-square) 0.000
Test statistic for each group:
female 249.589
male 265.293
Model Test Baseline Model:
Test statistic 3882.375
Degrees of freedom 380
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.953
Tucker-Lewis Index (TLI) 0.949
Robust Comparative Fit Index (CFI) 0.953
Robust Tucker-Lewis Index (TLI) 0.949
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -11990.331
Loglikelihood unrestricted model (H1) -11732.891
Akaike (AIC) 24200.663
Bayesian (BIC) 24639.724
Sample-size adjusted Bayesian (SABIC) 24290.687
Root Mean Square Error of Approximation:
RMSEA 0.049
90 Percent confidence interval - lower 0.039
90 Percent confidence interval - upper 0.057
P-value H_0: RMSEA <= 0.050 0.599
P-value H_0: RMSEA >= 0.080 0.000
Robust RMSEA 0.049
90 Percent confidence interval - lower 0.039
90 Percent confidence interval - upper 0.057
P-value H_0: Robust RMSEA <= 0.050 0.600
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.083
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Group 1 [female]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp =~
EP1 1.000 1.442 0.651
EP2 (.p2.) 0.997 0.070 14.327 0.000 1.438 0.772
EP3 (.p3.) 0.801 0.058 13.929 0.000 1.155 0.873
EP4 (.p4.) 0.912 0.062 14.789 0.000 1.316 0.859
Att_Cowrk =~
AC1 1.000 0.700 0.683
AC2 (.p6.) 1.342 0.071 18.881 0.000 0.940 0.741
AC3 (.p7.) 1.024 0.056 18.119 0.000 0.717 0.695
AC4 (.p8.) 1.158 0.065 17.823 0.000 0.811 0.655
Org_Comm =~
OC1 1.000 1.483 0.588
OC2 (.10.) 1.302 0.108 12.074 0.000 1.931 0.869
OC3 (.11.) 0.772 0.077 10.042 0.000 1.145 0.684
OC4 (.12.) 1.168 0.099 11.823 0.000 1.733 0.826
Job_Sat =~
JS1 1.000 0.938 0.718
JS2 (.14.) 1.015 0.080 12.730 0.000 0.952 0.720
JS3 (.15.) 0.922 0.077 12.001 0.000 0.864 0.674
JS4 (.16.) 0.917 0.075 12.179 0.000 0.860 0.691
Stay_Int =~
SI1 1.000 0.783 0.808
SI2 (.18.) 1.077 0.055 19.584 0.000 0.843 0.893
SI3 (.19.) 1.072 0.068 15.672 0.000 0.840 0.777
SI4 (.20.) 1.166 0.063 18.441 0.000 0.913 0.882
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp ~~
Att_Cowrk 0.216 0.087 2.464 0.014 0.213 0.213
Org_Comm 1.093 0.213 5.135 0.000 0.511 0.511
Job_Sat 0.371 0.119 3.111 0.002 0.275 0.275
Stay_Int 0.690 0.111 6.191 0.000 0.611 0.611
Att_Cowrk ~~
Org_Comm 0.495 0.103 4.787 0.000 0.477 0.477
Job_Sat 0.197 0.062 3.190 0.001 0.299 0.299
Stay_Int 0.163 0.048 3.410 0.001 0.297 0.297
Org_Comm ~~
Job_Sat 0.370 0.126 2.942 0.003 0.266 0.266
Stay_Int 0.728 0.121 6.034 0.000 0.627 0.627
Job_Sat ~~
Stay_Int 0.170 0.063 2.698 0.007 0.231 0.231
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 (.56.) 8.364 0.125 66.756 0.000 8.364 3.777
.EP2 (.57.) 8.591 0.118 72.846 0.000 8.591 4.614
.EP3 (.58.) 8.744 0.091 95.792 0.000 8.744 6.604
.EP4 (.59.) 5.585 0.104 53.768 0.000 5.585 3.646
.AC1 (.60.) 1.897 0.071 26.854 0.000 1.897 1.849
.AC2 (.61.) 2.390 0.090 26.480 0.000 2.390 1.884
.AC3 (.62.) 1.908 0.071 26.873 0.000 1.908 1.850
.AC4 (.63.) 2.214 0.084 26.224 0.000 2.214 1.789
.OC1 (.64.) 4.775 0.150 31.900 0.000 4.775 1.893
.OC2 (.65.) 8.278 0.152 54.394 0.000 8.278 3.725
.OC3 (.66.) 8.580 0.106 81.053 0.000 8.580 5.127
.OC4 (.67.) 8.270 0.140 59.259 0.000 8.270 3.943
.JS1 (.68.) 4.203 0.084 50.196 0.000 4.203 3.217
.JS2 (.69.) 4.208 0.085 49.472 0.000 4.208 3.184
.JS3 (.70.) 3.220 0.080 40.078 0.000 3.220 2.511
.JS4 (.71.) 2.673 0.079 33.942 0.000 2.673 2.148
.SI1 (.72.) 4.043 0.063 64.148 0.000 4.043 4.175
.SI2 (.73.) 4.023 0.065 62.058 0.000 4.023 4.260
.SI3 (.74.) 3.290 0.070 46.923 0.000 3.290 3.045
.SI4 (.75.) 3.284 0.071 46.424 0.000 3.284 3.174
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 2.824 0.313 9.029 0.000 2.824 0.576
.EP2 1.399 0.175 8.004 0.000 1.399 0.403
.EP3 0.418 0.068 6.153 0.000 0.418 0.239
.EP4 0.616 0.090 6.811 0.000 0.616 0.262
.AC1 0.562 0.069 8.149 0.000 0.562 0.534
.AC2 0.726 0.102 7.133 0.000 0.726 0.451
.AC3 0.549 0.070 7.898 0.000 0.549 0.517
.AC4 0.874 0.104 8.372 0.000 0.874 0.571
.OC1 4.162 0.453 9.192 0.000 4.162 0.654
.OC2 1.209 0.215 5.618 0.000 1.209 0.245
.OC3 1.490 0.170 8.775 0.000 1.490 0.532
.OC4 1.396 0.194 7.184 0.000 1.396 0.317
.JS1 0.828 0.108 7.638 0.000 0.828 0.485
.JS2 0.841 0.112 7.503 0.000 0.841 0.481
.JS3 0.897 0.114 7.892 0.000 0.897 0.546
.JS4 0.809 0.102 7.893 0.000 0.809 0.522
.SI1 0.325 0.040 8.189 0.000 0.325 0.346
.SI2 0.181 0.028 6.447 0.000 0.181 0.203
.SI3 0.463 0.054 8.614 0.000 0.463 0.396
.SI4 0.237 0.035 6.778 0.000 0.237 0.221
Env_Supp 2.079 0.327 6.355 0.000 1.000 1.000
Att_Cowrk 0.490 0.070 6.979 0.000 1.000 1.000
Org_Comm 2.200 0.411 5.355 0.000 1.000 1.000
Job_Sat 0.879 0.137 6.414 0.000 1.000 1.000
Stay_Int 0.613 0.082 7.487 0.000 1.000 1.000
Group 2 [male]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp =~
EP1 1.000 1.007 0.756
EP2 (.p2.) 0.997 0.070 14.327 0.000 1.004 0.808
EP3 (.p3.) 0.801 0.058 13.929 0.000 0.807 0.625
EP4 (.p4.) 0.912 0.062 14.789 0.000 0.919 0.768
Att_Cowrk =~
AC1 1.000 0.703 0.622
AC2 (.p6.) 1.342 0.071 18.881 0.000 0.943 0.733
AC3 (.p7.) 1.024 0.056 18.119 0.000 0.719 0.613
AC4 (.p8.) 1.158 0.065 17.823 0.000 0.813 0.634
Org_Comm =~
OC1 1.000 1.444 0.574
OC2 (.10.) 1.302 0.108 12.074 0.000 1.881 0.888
OC3 (.11.) 0.772 0.077 10.042 0.000 1.114 0.625
OC4 (.12.) 1.168 0.099 11.823 0.000 1.687 0.845
Job_Sat =~
JS1 1.000 1.041 0.760
JS2 (.14.) 1.015 0.080 12.730 0.000 1.056 0.748
JS3 (.15.) 0.922 0.077 12.001 0.000 0.959 0.713
JS4 (.16.) 0.917 0.075 12.179 0.000 0.955 0.727
Stay_Int =~
SI1 1.000 0.560 0.795
SI2 (.18.) 1.077 0.055 19.584 0.000 0.604 0.796
SI3 (.19.) 1.072 0.068 15.672 0.000 0.601 0.662
SI4 (.20.) 1.166 0.063 18.441 0.000 0.654 0.773
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp ~~
Att_Cowrk 0.054 0.063 0.857 0.391 0.077 0.077
Org_Comm 0.782 0.148 5.286 0.000 0.538 0.538
Job_Sat 0.240 0.092 2.600 0.009 0.229 0.229
Stay_Int 0.223 0.051 4.349 0.000 0.395 0.395
Att_Cowrk ~~
Org_Comm 0.278 0.095 2.935 0.003 0.274 0.274
Job_Sat -0.135 0.067 -2.014 0.044 -0.184 -0.184
Stay_Int 0.016 0.035 0.462 0.644 0.041 0.041
Org_Comm ~~
Job_Sat 0.185 0.127 1.458 0.145 0.123 0.123
Stay_Int 0.368 0.077 4.804 0.000 0.455 0.455
Job_Sat ~~
Stay_Int 0.124 0.050 2.455 0.014 0.212 0.212
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 (.56.) 8.364 0.125 66.756 0.000 8.364 6.282
.EP2 (.57.) 8.591 0.118 72.846 0.000 8.591 6.910
.EP3 (.58.) 8.744 0.091 95.792 0.000 8.744 6.776
.EP4 (.59.) 5.585 0.104 53.768 0.000 5.585 4.668
.AC1 (.60.) 1.897 0.071 26.854 0.000 1.897 1.679
.AC2 (.61.) 2.390 0.090 26.480 0.000 2.390 1.858
.AC3 (.62.) 1.908 0.071 26.873 0.000 1.908 1.625
.AC4 (.63.) 2.214 0.084 26.224 0.000 2.214 1.725
.OC1 (.64.) 4.775 0.150 31.900 0.000 4.775 1.896
.OC2 (.65.) 8.278 0.152 54.394 0.000 8.278 3.911
.OC3 (.66.) 8.580 0.106 81.053 0.000 8.580 4.814
.OC4 (.67.) 8.270 0.140 59.259 0.000 8.270 4.141
.JS1 (.68.) 4.203 0.084 50.196 0.000 4.203 3.071
.JS2 (.69.) 4.208 0.085 49.472 0.000 4.208 2.978
.JS3 (.70.) 3.220 0.080 40.078 0.000 3.220 2.392
.JS4 (.71.) 2.673 0.079 33.942 0.000 2.673 2.035
.SI1 (.72.) 4.043 0.063 64.148 0.000 4.043 5.732
.SI2 (.73.) 4.023 0.065 62.058 0.000 4.023 5.304
.SI3 (.74.) 3.290 0.070 46.923 0.000 3.290 3.624
.SI4 (.75.) 3.284 0.071 46.424 0.000 3.284 3.885
Env_Spp 0.524 0.138 3.795 0.000 0.521 0.521
Att_Cwr 1.729 0.100 17.248 0.000 2.461 2.461
Org_Cmm 0.228 0.158 1.446 0.148 0.158 0.158
Job_Sat -0.011 0.110 -0.103 0.918 -0.011 -0.011
Sty_Int 0.340 0.073 4.623 0.000 0.606 0.606
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 0.759 0.101 7.519 0.000 0.759 0.428
.EP2 0.538 0.081 6.669 0.000 0.538 0.348
.EP3 1.014 0.114 8.874 0.000 1.014 0.609
.EP4 0.588 0.080 7.390 0.000 0.588 0.411
.AC1 0.782 0.098 7.943 0.000 0.782 0.613
.AC2 0.765 0.117 6.544 0.000 0.765 0.462
.AC3 0.861 0.106 8.147 0.000 0.861 0.625
.AC4 0.986 0.123 8.000 0.000 0.986 0.598
.OC1 4.256 0.452 9.421 0.000 4.256 0.671
.OC2 0.945 0.190 4.962 0.000 0.945 0.211
.OC3 1.934 0.225 8.609 0.000 1.934 0.609
.OC4 1.143 0.184 6.221 0.000 1.143 0.286
.JS1 0.790 0.111 7.112 0.000 0.790 0.422
.JS2 0.880 0.118 7.466 0.000 0.880 0.441
.JS3 0.892 0.111 8.068 0.000 0.892 0.492
.JS4 0.814 0.105 7.766 0.000 0.814 0.472
.SI1 0.183 0.025 7.302 0.000 0.183 0.369
.SI2 0.211 0.029 7.263 0.000 0.211 0.367
.SI3 0.463 0.053 8.787 0.000 0.463 0.562
.SI4 0.287 0.038 7.510 0.000 0.287 0.402
Env_Supp 1.014 0.153 6.636 0.000 1.000 1.000
Att_Cowrk 0.494 0.082 6.042 0.000 1.000 1.000
Org_Comm 2.086 0.387 5.393 0.000 1.000 1.000
Job_Sat 1.083 0.170 6.389 0.000 1.000 1.000
Stay_Int 0.314 0.042 7.518 0.000 1.000 1.000##Strict Invariance
Strict <- cfa(model = M_model, data = Class_SEM_Data_Final,
missing = "fiml", mimic = "Mplus", group = "Sex",
group.equal = c("loadings", "intercepts","residuals"))
summary(Strict, standardized = TRUE, fit.measures = TRUE)lavaan 0.6-19 ended normally after 108 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 145
Number of equality constraints 55
Number of observations per group:
female 200
male 200
Number of missing patterns per group:
female 2
male 2
Model Test User Model:
Test statistic 644.554
Degrees of freedom 370
P-value (Chi-square) 0.000
Test statistic for each group:
female 306.798
male 337.755
Model Test Baseline Model:
Test statistic 3882.375
Degrees of freedom 380
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.922
Tucker-Lewis Index (TLI) 0.919
Robust Comparative Fit Index (CFI) 0.921
Robust Tucker-Lewis Index (TLI) 0.919
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -12055.167
Loglikelihood unrestricted model (H1) -11732.891
Akaike (AIC) 24290.335
Bayesian (BIC) 24649.566
Sample-size adjusted Bayesian (SABIC) 24363.990
Root Mean Square Error of Approximation:
RMSEA 0.061
90 Percent confidence interval - lower 0.053
90 Percent confidence interval - upper 0.069
P-value H_0: RMSEA <= 0.050 0.013
P-value H_0: RMSEA >= 0.080 0.000
Robust RMSEA 0.061
90 Percent confidence interval - lower 0.053
90 Percent confidence interval - upper 0.069
P-value H_0: Robust RMSEA <= 0.050 0.011
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.141
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Group 1 [female]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp =~
EP1 1.000 1.480 0.753
EP2 (.p2.) 1.043 0.070 14.813 0.000 1.544 0.862
EP3 (.p3.) 0.774 0.058 13.309 0.000 1.146 0.793
EP4 (.p4.) 0.874 0.061 14.289 0.000 1.293 0.846
Att_Cowrk =~
AC1 1.000 0.697 0.654
AC2 (.p6.) 1.305 0.068 19.330 0.000 0.910 0.718
AC3 (.p7.) 1.011 0.055 18.491 0.000 0.705 0.646
AC4 (.p8.) 1.140 0.063 18.214 0.000 0.795 0.639
Org_Comm =~
OC1 1.000 1.487 0.587
OC2 (.10.) 1.314 0.108 12.185 0.000 1.954 0.884
OC3 (.11.) 0.794 0.076 10.391 0.000 1.180 0.669
OC4 (.12.) 1.173 0.099 11.899 0.000 1.745 0.842
Job_Sat =~
JS1 1.000 0.937 0.722
JS2 (.14.) 1.015 0.079 12.771 0.000 0.951 0.716
JS3 (.15.) 0.922 0.076 12.093 0.000 0.864 0.674
JS4 (.16.) 0.917 0.075 12.203 0.000 0.859 0.690
Stay_Int =~
SI1 1.000 0.791 0.844
SI2 (.18.) 1.066 0.053 20.094 0.000 0.844 0.886
SI3 (.19.) 1.059 0.066 15.945 0.000 0.838 0.776
SI4 (.20.) 1.155 0.061 18.882 0.000 0.914 0.872
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp ~~
Att_Cowrk 0.209 0.090 2.312 0.021 0.202 0.202
Org_Comm 1.059 0.212 4.995 0.000 0.481 0.481
Job_Sat 0.364 0.121 3.000 0.003 0.263 0.263
Stay_Int 0.719 0.114 6.327 0.000 0.614 0.614
Att_Cowrk ~~
Org_Comm 0.505 0.105 4.830 0.000 0.487 0.487
Job_Sat 0.201 0.062 3.221 0.001 0.307 0.307
Stay_Int 0.166 0.049 3.394 0.001 0.300 0.300
Org_Comm ~~
Job_Sat 0.368 0.125 2.939 0.003 0.264 0.264
Stay_Int 0.737 0.121 6.076 0.000 0.626 0.626
Job_Sat ~~
Stay_Int 0.174 0.064 2.733 0.006 0.234 0.234
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 (.56.) 8.260 0.127 65.199 0.000 8.260 4.202
.EP2 (.57.) 8.568 0.122 70.438 0.000 8.568 4.783
.EP3 (.58.) 8.720 0.095 92.087 0.000 8.720 6.034
.EP4 (.59.) 5.594 0.103 54.358 0.000 5.594 3.661
.AC1 (.60.) 1.884 0.073 25.926 0.000 1.884 1.767
.AC2 (.61.) 2.409 0.089 26.967 0.000 2.409 1.900
.AC3 (.62.) 1.889 0.074 25.572 0.000 1.889 1.731
.AC4 (.63.) 2.214 0.085 26.192 0.000 2.214 1.778
.OC1 (.64.) 4.774 0.150 31.886 0.000 4.774 1.884
.OC2 (.65.) 8.269 0.152 54.365 0.000 8.269 3.743
.OC3 (.66.) 8.565 0.108 78.968 0.000 8.565 4.855
.OC4 (.67.) 8.272 0.139 59.366 0.000 8.272 3.991
.JS1 (.68.) 4.203 0.084 50.316 0.000 4.203 3.237
.JS2 (.69.) 4.208 0.085 49.382 0.000 4.208 3.167
.JS3 (.70.) 3.220 0.080 40.088 0.000 3.220 2.513
.JS4 (.71.) 2.673 0.079 33.945 0.000 2.673 2.146
.SI1 (.72.) 4.031 0.063 64.151 0.000 4.031 4.301
.SI2 (.73.) 4.022 0.065 61.820 0.000 4.022 4.224
.SI3 (.74.) 3.291 0.070 47.003 0.000 3.291 3.048
.SI4 (.75.) 3.282 0.071 46.177 0.000 3.282 3.133
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 (.21.) 1.674 0.140 11.955 0.000 1.674 0.433
.EP2 (.22.) 0.827 0.093 8.929 0.000 0.827 0.258
.EP3 (.23.) 0.776 0.070 11.115 0.000 0.776 0.371
.EP4 (.24.) 0.663 0.069 9.626 0.000 0.663 0.284
.AC1 (.25.) 0.651 0.058 11.156 0.000 0.651 0.572
.AC2 (.26.) 0.780 0.080 9.787 0.000 0.780 0.485
.AC3 (.27.) 0.693 0.061 11.333 0.000 0.693 0.582
.AC4 (.28.) 0.918 0.080 11.504 0.000 0.918 0.592
.OC1 (.29.) 4.214 0.320 13.189 0.000 4.214 0.656
.OC2 (.30.) 1.064 0.150 7.114 0.000 1.064 0.218
.OC3 (.31.) 1.719 0.138 12.449 0.000 1.719 0.552
.OC4 (.32.) 1.252 0.136 9.215 0.000 1.252 0.291
.JS1 (.33.) 0.808 0.080 10.073 0.000 0.808 0.479
.JS2 (.34.) 0.861 0.084 10.219 0.000 0.861 0.487
.JS3 (.35.) 0.895 0.081 11.025 0.000 0.895 0.545
.JS4 (.36.) 0.813 0.076 10.760 0.000 0.813 0.524
.SI1 (.37.) 0.253 0.023 10.906 0.000 0.253 0.287
.SI2 (.38.) 0.195 0.021 9.461 0.000 0.195 0.215
.SI3 (.39.) 0.464 0.038 12.204 0.000 0.464 0.398
.SI4 (.40.) 0.263 0.027 9.897 0.000 0.263 0.239
Env_Spp 2.190 0.333 6.581 0.000 1.000 1.000
Att_Cwr 0.486 0.071 6.858 0.000 1.000 1.000
Org_Cmm 2.211 0.410 5.397 0.000 1.000 1.000
Job_Sat 0.878 0.137 6.436 0.000 1.000 1.000
Sty_Int 0.626 0.081 7.716 0.000 1.000 1.000
Group 2 [male]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp =~
EP1 1.000 1.004 0.613
EP2 (.p2.) 1.043 0.070 14.813 0.000 1.047 0.755
EP3 (.p3.) 0.774 0.058 13.309 0.000 0.777 0.662
EP4 (.p4.) 0.874 0.061 14.289 0.000 0.877 0.733
Att_Cowrk =~
AC1 1.000 0.744 0.678
AC2 (.p6.) 1.305 0.068 19.330 0.000 0.971 0.740
AC3 (.p7.) 1.011 0.055 18.491 0.000 0.752 0.670
AC4 (.p8.) 1.140 0.063 18.214 0.000 0.848 0.663
Org_Comm =~
OC1 1.000 1.434 0.573
OC2 (.10.) 1.314 0.108 12.185 0.000 1.884 0.877
OC3 (.11.) 0.794 0.076 10.391 0.000 1.138 0.656
OC4 (.12.) 1.173 0.099 11.899 0.000 1.682 0.833
Job_Sat =~
JS1 1.000 1.041 0.757
JS2 (.14.) 1.015 0.079 12.771 0.000 1.056 0.751
JS3 (.15.) 0.922 0.076 12.093 0.000 0.959 0.712
JS4 (.16.) 0.917 0.075 12.203 0.000 0.954 0.727
Stay_Int =~
SI1 1.000 0.569 0.749
SI2 (.18.) 1.066 0.053 20.094 0.000 0.606 0.808
SI3 (.19.) 1.059 0.066 15.945 0.000 0.602 0.662
SI4 (.20.) 1.155 0.061 18.882 0.000 0.657 0.788
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp ~~
Att_Cowrk 0.069 0.067 1.031 0.303 0.093 0.093
Org_Comm 0.775 0.150 5.148 0.000 0.538 0.538
Job_Sat 0.224 0.094 2.383 0.017 0.214 0.214
Stay_Int 0.226 0.054 4.225 0.000 0.396 0.396
Att_Cowrk ~~
Org_Comm 0.296 0.097 3.051 0.002 0.277 0.277
Job_Sat -0.136 0.069 -1.966 0.049 -0.176 -0.176
Stay_Int 0.026 0.037 0.710 0.478 0.062 0.062
Org_Comm ~~
Job_Sat 0.186 0.126 1.478 0.139 0.125 0.125
Stay_Int 0.372 0.077 4.809 0.000 0.457 0.457
Job_Sat ~~
Stay_Int 0.128 0.051 2.500 0.012 0.217 0.217
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 (.56.) 8.260 0.127 65.199 0.000 8.260 5.044
.EP2 (.57.) 8.568 0.122 70.438 0.000 8.568 6.179
.EP3 (.58.) 8.720 0.095 92.087 0.000 8.720 7.424
.EP4 (.59.) 5.594 0.103 54.358 0.000 5.594 4.674
.AC1 (.60.) 1.884 0.073 25.926 0.000 1.884 1.717
.AC2 (.61.) 2.409 0.089 26.967 0.000 2.409 1.836
.AC3 (.62.) 1.889 0.074 25.572 0.000 1.889 1.684
.AC4 (.63.) 2.214 0.085 26.192 0.000 2.214 1.731
.OC1 (.64.) 4.774 0.150 31.886 0.000 4.774 1.907
.OC2 (.65.) 8.269 0.152 54.365 0.000 8.269 3.850
.OC3 (.66.) 8.565 0.108 78.968 0.000 8.565 4.934
.OC4 (.67.) 8.272 0.139 59.366 0.000 8.272 4.095
.JS1 (.68.) 4.203 0.084 50.316 0.000 4.203 3.057
.JS2 (.69.) 4.208 0.085 49.382 0.000 4.208 2.993
.JS3 (.70.) 3.220 0.080 40.088 0.000 3.220 2.390
.JS4 (.71.) 2.673 0.079 33.945 0.000 2.673 2.036
.SI1 (.72.) 4.031 0.063 64.151 0.000 4.031 5.312
.SI2 (.73.) 4.022 0.065 61.820 0.000 4.022 5.363
.SI3 (.74.) 3.291 0.070 47.003 0.000 3.291 3.620
.SI4 (.75.) 3.282 0.071 46.177 0.000 3.282 3.941
Env_Spp 0.536 0.140 3.823 0.000 0.534 0.534
Att_Cwr 1.751 0.100 17.433 0.000 2.355 2.355
Org_Cmm 0.226 0.157 1.441 0.150 0.158 0.158
Job_Sat -0.011 0.110 -0.104 0.917 -0.011 -0.011
Sty_Int 0.342 0.074 4.619 0.000 0.602 0.602
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 (.21.) 1.674 0.140 11.955 0.000 1.674 0.624
.EP2 (.22.) 0.827 0.093 8.929 0.000 0.827 0.430
.EP3 (.23.) 0.776 0.070 11.115 0.000 0.776 0.562
.EP4 (.24.) 0.663 0.069 9.626 0.000 0.663 0.463
.AC1 (.25.) 0.651 0.058 11.156 0.000 0.651 0.541
.AC2 (.26.) 0.780 0.080 9.787 0.000 0.780 0.453
.AC3 (.27.) 0.693 0.061 11.333 0.000 0.693 0.551
.AC4 (.28.) 0.918 0.080 11.504 0.000 0.918 0.561
.OC1 (.29.) 4.214 0.320 13.189 0.000 4.214 0.672
.OC2 (.30.) 1.064 0.150 7.114 0.000 1.064 0.231
.OC3 (.31.) 1.719 0.138 12.449 0.000 1.719 0.570
.OC4 (.32.) 1.252 0.136 9.215 0.000 1.252 0.307
.JS1 (.33.) 0.808 0.080 10.073 0.000 0.808 0.427
.JS2 (.34.) 0.861 0.084 10.219 0.000 0.861 0.436
.JS3 (.35.) 0.895 0.081 11.025 0.000 0.895 0.493
.JS4 (.36.) 0.813 0.076 10.760 0.000 0.813 0.472
.SI1 (.37.) 0.253 0.023 10.906 0.000 0.253 0.439
.SI2 (.38.) 0.195 0.021 9.461 0.000 0.195 0.347
.SI3 (.39.) 0.464 0.038 12.204 0.000 0.464 0.561
.SI4 (.40.) 0.263 0.027 9.897 0.000 0.263 0.379
Env_Spp 1.008 0.162 6.207 0.000 1.000 1.000
Att_Cwr 0.553 0.085 6.533 0.000 1.000 1.000
Org_Cmm 2.055 0.379 5.418 0.000 1.000 1.000
Job_Sat 1.083 0.168 6.433 0.000 1.000 1.000
Sty_Int 0.323 0.043 7.438 0.000 1.000 1.000M1 <- '
Env_Supp =~ EP1 + EP2 + EP3 + EP4
Att_Cowrk =~ AC1 + AC2 + AC3 + AC4
Org_Comm =~ OC1 + OC2 + OC3 + OC4
Job_Sat =~ JS1 + JS2 + JS3 + JS4
Stay_Int =~ SI1 + SI2 + SI3 + SI4
Org_Comm ~ a*Env_Supp + b*Att_Cowrk
Job_Sat ~ c*Org_Comm
Stay_Int ~ d*Job_Sat + e*Org_Comm
'
M1_fit <- sem(model = M1, data = Class_SEM_Data_Final,
missing = "fiml", mimic = "Mplus")
summary(M1_fit, standardized = TRUE, fit.measures = TRUE)lavaan 0.6-19 ended normally after 83 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 66
Number of observations 400
Number of missing patterns 3
Model Test User Model:
Test statistic 272.407
Degrees of freedom 164
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 4203.038
Degrees of freedom 190
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.973
Tucker-Lewis Index (TLI) 0.969
Robust Comparative Fit Index (CFI) 0.973
Robust Tucker-Lewis Index (TLI) 0.969
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -12273.709
Loglikelihood unrestricted model (H1) -12137.505
Akaike (AIC) 24679.417
Bayesian (BIC) 24942.854
Sample-size adjusted Bayesian (SABIC) 24733.432
Root Mean Square Error of Approximation:
RMSEA 0.041
90 Percent confidence interval - lower 0.032
90 Percent confidence interval - upper 0.049
P-value H_0: RMSEA <= 0.050 0.967
P-value H_0: RMSEA >= 0.080 0.000
Robust RMSEA 0.041
90 Percent confidence interval - lower 0.032
90 Percent confidence interval - upper 0.049
P-value H_0: Robust RMSEA <= 0.050 0.967
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.063
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp =~
EP1 1.000 1.261 0.690
EP2 1.043 0.073 14.275 0.000 1.316 0.809
EP3 0.819 0.061 13.336 0.000 1.033 0.775
EP4 0.911 0.065 14.086 0.000 1.149 0.825
Att_Cowrk =~
AC1 1.000 1.144 0.822
AC2 1.236 0.067 18.369 0.000 1.414 0.820
AC3 1.036 0.055 18.880 0.000 1.186 0.836
AC4 1.147 0.063 18.234 0.000 1.312 0.816
Org_Comm =~
OC1 1.000 1.453 0.576
OC2 1.326 0.109 12.111 0.000 1.926 0.882
OC3 0.793 0.077 10.284 0.000 1.152 0.657
OC4 1.175 0.100 11.787 0.000 1.707 0.832
Job_Sat =~
JS1 1.000 0.989 0.740
JS2 1.014 0.080 12.715 0.000 1.003 0.733
JS3 0.926 0.077 12.074 0.000 0.916 0.697
JS4 0.918 0.076 12.137 0.000 0.908 0.710
Stay_Int =~
SI1 1.000 0.707 0.813
SI2 1.076 0.054 20.052 0.000 0.761 0.869
SI3 1.058 0.067 15.779 0.000 0.749 0.738
SI4 1.158 0.062 18.726 0.000 0.819 0.847
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Org_Comm ~
Env_Supp (a) 0.551 0.079 7.012 0.000 0.478 0.478
Att_Cowrk (b) 0.252 0.068 3.700 0.000 0.198 0.198
Job_Sat ~
Org_Comm (c) 0.136 0.042 3.263 0.001 0.200 0.200
Stay_Int ~
Job_Sat (d) 0.072 0.037 1.939 0.053 0.101 0.101
Org_Comm (e) 0.273 0.033 8.204 0.000 0.560 0.560
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp ~~
Att_Cowrk 0.367 0.087 4.204 0.000 0.254 0.254
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 8.527 0.091 93.262 0.000 8.527 4.663
.EP2 8.847 0.081 108.818 0.000 8.847 5.441
.EP3 8.928 0.067 133.933 0.000 8.928 6.697
.EP4 5.828 0.070 83.647 0.000 5.828 4.185
.AC1 2.760 0.070 39.658 0.000 2.760 1.983
.AC2 3.553 0.086 41.212 0.000 3.553 2.061
.AC3 2.775 0.071 39.153 0.000 2.775 1.958
.AC4 3.212 0.081 39.901 0.000 3.212 1.996
.OC1 4.887 0.126 38.761 0.000 4.887 1.938
.OC2 8.417 0.109 77.097 0.000 8.417 3.855
.OC3 8.655 0.088 98.778 0.000 8.655 4.939
.OC4 8.405 0.103 81.966 0.000 8.405 4.098
.JS1 4.197 0.067 62.774 0.000 4.197 3.139
.JS2 4.202 0.068 61.439 0.000 4.202 3.072
.JS3 3.215 0.066 48.904 0.000 3.215 2.445
.JS4 2.667 0.064 41.698 0.000 2.667 2.085
.SI1 4.202 0.043 96.635 0.000 4.202 4.832
.SI2 4.205 0.044 95.967 0.000 4.205 4.798
.SI3 3.472 0.051 68.456 0.000 3.472 3.423
.SI4 3.480 0.048 71.994 0.000 3.480 3.600
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 1.753 0.144 12.138 0.000 1.753 0.524
.EP2 0.913 0.095 9.645 0.000 0.913 0.345
.EP3 0.710 0.066 10.744 0.000 0.710 0.400
.EP4 0.620 0.067 9.290 0.000 0.620 0.320
.AC1 0.628 0.059 10.554 0.000 0.628 0.324
.AC2 0.972 0.092 10.610 0.000 0.972 0.327
.AC3 0.603 0.060 10.114 0.000 0.603 0.300
.AC4 0.867 0.081 10.680 0.000 0.867 0.335
.OC1 4.249 0.321 13.222 0.000 4.249 0.668
.OC2 1.059 0.144 7.331 0.000 1.059 0.222
.OC3 1.745 0.138 12.606 0.000 1.745 0.568
.OC4 1.293 0.135 9.605 0.000 1.293 0.307
.JS1 0.810 0.081 10.043 0.000 0.810 0.453
.JS2 0.866 0.085 10.216 0.000 0.866 0.462
.JS3 0.889 0.081 10.942 0.000 0.889 0.514
.JS4 0.812 0.076 10.692 0.000 0.812 0.496
.SI1 0.256 0.023 10.953 0.000 0.256 0.339
.SI2 0.188 0.021 9.018 0.000 0.188 0.245
.SI3 0.469 0.039 12.166 0.000 0.469 0.455
.SI4 0.264 0.027 9.725 0.000 0.264 0.282
Env_Supp 1.591 0.215 7.415 0.000 1.000 1.000
Att_Cowrk 1.310 0.136 9.665 0.000 1.000 1.000
.Org_Comm 1.444 0.250 5.771 0.000 0.684 0.684
.Job_Sat 0.939 0.122 7.725 0.000 0.960 0.960
.Stay_Int 0.327 0.037 8.898 0.000 0.654 0.654M2 <- '
Env_Supp =~ EP1 + EP2 + EP3 + EP4
Att_Cowrk =~ AC1 + AC2 + AC3 + AC4
Org_Comm =~ OC1 + OC2 + OC3 + OC4
Job_Sat =~ JS1 + JS2 + JS3 + JS4
Stay_Int =~ SI1 + SI2 + SI3 + SI4
Job_Sat ~ a*Env_Supp + b*Att_Cowrk + c*Org_Comm
Stay_Int ~ d*Job_Sat + e*Org_Comm
Env_Supp ~~ Att_Cowrk
Env_Supp ~~ Org_Comm
Att_Cowrk ~~ Org_Comm
'
M2_fit <- sem(model = M2, data = Class_SEM_Data_Final,
missing = "fiml", mimic = "Mplus")
summary(M2_fit, standardized = TRUE, fit.measures = TRUE)lavaan 0.6-19 ended normally after 87 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 68
Number of observations 400
Number of missing patterns 3
Model Test User Model:
Test statistic 264.748
Degrees of freedom 162
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 4203.038
Degrees of freedom 190
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.974
Tucker-Lewis Index (TLI) 0.970
Robust Comparative Fit Index (CFI) 0.974
Robust Tucker-Lewis Index (TLI) 0.970
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -12269.879
Loglikelihood unrestricted model (H1) -12137.505
Akaike (AIC) 24675.758
Bayesian (BIC) 24947.178
Sample-size adjusted Bayesian (SABIC) 24731.410
Root Mean Square Error of Approximation:
RMSEA 0.040
90 Percent confidence interval - lower 0.031
90 Percent confidence interval - upper 0.048
P-value H_0: RMSEA <= 0.050 0.976
P-value H_0: RMSEA >= 0.080 0.000
Robust RMSEA 0.040
90 Percent confidence interval - lower 0.031
90 Percent confidence interval - upper 0.048
P-value H_0: Robust RMSEA <= 0.050 0.976
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.058
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp =~
EP1 1.000 1.263 0.690
EP2 1.042 0.073 14.294 0.000 1.316 0.809
EP3 0.818 0.061 13.358 0.000 1.033 0.775
EP4 0.909 0.064 14.110 0.000 1.148 0.825
Att_Cowrk =~
AC1 1.000 1.144 0.822
AC2 1.236 0.067 18.367 0.000 1.414 0.820
AC3 1.037 0.055 18.880 0.000 1.186 0.837
AC4 1.147 0.063 18.227 0.000 1.312 0.815
Org_Comm =~
OC1 1.000 1.454 0.577
OC2 1.326 0.109 12.124 0.000 1.929 0.884
OC3 0.790 0.077 10.284 0.000 1.150 0.656
OC4 1.173 0.099 11.801 0.000 1.707 0.832
Job_Sat =~
JS1 1.000 0.983 0.735
JS2 1.027 0.081 12.711 0.000 1.009 0.738
JS3 0.933 0.077 12.065 0.000 0.917 0.697
JS4 0.923 0.076 12.131 0.000 0.907 0.709
Stay_Int =~
SI1 1.000 0.707 0.813
SI2 1.076 0.054 20.049 0.000 0.761 0.869
SI3 1.059 0.067 15.780 0.000 0.749 0.738
SI4 1.158 0.062 18.727 0.000 0.819 0.848
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Job_Sat ~
Env_Supp (a) 0.154 0.057 2.686 0.007 0.198 0.198
Att_Cowrk (b) -0.046 0.053 -0.857 0.391 -0.053 -0.053
Org_Comm (c) 0.069 0.051 1.365 0.172 0.102 0.102
Stay_Int ~
Job_Sat (d) 0.084 0.038 2.215 0.027 0.116 0.116
Org_Comm (e) 0.271 0.033 8.192 0.000 0.556 0.556
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Env_Supp ~~
Att_Cowrk 0.367 0.087 4.205 0.000 0.254 0.254
Org_Comm 0.963 0.145 6.657 0.000 0.524 0.524
Att_Cowrk ~~
Org_Comm 0.534 0.106 5.031 0.000 0.321 0.321
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 8.527 0.091 93.262 0.000 8.527 4.663
.EP2 8.847 0.081 108.818 0.000 8.847 5.441
.EP3 8.927 0.067 133.933 0.000 8.927 6.697
.EP4 5.828 0.070 83.648 0.000 5.828 4.185
.AC1 2.760 0.070 39.658 0.000 2.760 1.983
.AC2 3.553 0.086 41.212 0.000 3.553 2.061
.AC3 2.775 0.071 39.153 0.000 2.775 1.958
.AC4 3.212 0.081 39.901 0.000 3.212 1.996
.OC1 4.887 0.126 38.761 0.000 4.887 1.938
.OC2 8.417 0.109 77.097 0.000 8.417 3.855
.OC3 8.655 0.088 98.778 0.000 8.655 4.939
.OC4 8.405 0.103 81.966 0.000 8.405 4.098
.JS1 4.197 0.067 62.774 0.000 4.197 3.139
.JS2 4.202 0.068 61.439 0.000 4.202 3.072
.JS3 3.215 0.066 48.904 0.000 3.215 2.445
.JS4 2.667 0.064 41.698 0.000 2.667 2.085
.SI1 4.202 0.043 96.635 0.000 4.202 4.832
.SI2 4.205 0.044 95.967 0.000 4.205 4.798
.SI3 3.472 0.051 68.456 0.000 3.472 3.423
.SI4 3.480 0.048 71.994 0.000 3.480 3.600
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EP1 1.750 0.144 12.139 0.000 1.750 0.523
.EP2 0.913 0.094 9.686 0.000 0.913 0.345
.EP3 0.710 0.066 10.756 0.000 0.710 0.399
.EP4 0.621 0.067 9.333 0.000 0.621 0.320
.AC1 0.628 0.060 10.562 0.000 0.628 0.324
.AC2 0.972 0.092 10.608 0.000 0.972 0.327
.AC3 0.603 0.060 10.104 0.000 0.603 0.300
.AC4 0.868 0.081 10.685 0.000 0.868 0.335
.OC1 4.244 0.321 13.220 0.000 4.244 0.667
.OC2 1.046 0.145 7.212 0.000 1.046 0.219
.OC3 1.749 0.139 12.611 0.000 1.749 0.570
.OC4 1.293 0.135 9.559 0.000 1.293 0.307
.JS1 0.823 0.081 10.181 0.000 0.823 0.460
.JS2 0.853 0.084 10.098 0.000 0.853 0.456
.JS3 0.889 0.081 10.947 0.000 0.889 0.514
.JS4 0.814 0.076 10.743 0.000 0.814 0.498
.SI1 0.256 0.023 10.953 0.000 0.256 0.339
.SI2 0.189 0.021 9.025 0.000 0.189 0.246
.SI3 0.469 0.039 12.166 0.000 0.469 0.455
.SI4 0.263 0.027 9.721 0.000 0.263 0.282
Env_Supp 1.594 0.215 7.426 0.000 1.000 1.000
Att_Cowrk 1.309 0.135 9.660 0.000 1.000 1.000
Org_Comm 2.115 0.354 5.979 0.000 1.000 1.000
.Job_Sat 0.903 0.119 7.599 0.000 0.935 0.935
.Stay_Int 0.327 0.037 8.898 0.000 0.653 0.653