Data Assignment #2 Answer Key

Author

Ty Partridge

Packages and Libraries:

library(haven) #reading data from spss, sas, stata
library(tidyverse) #Data management
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.0     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   4.0.2     ✔ tibble    3.3.0
✔ 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 errors
library(tidySEM) #Tools for working with SEM output
Warning: package 'tidySEM' was built under R version 4.5.3
library (corrplot) #correlation tables
corrplot 0.95 loaded
library(psych) ## basic psychometrics and statistics

Attaching package: 'psych'

The following objects are masked from 'package:ggplot2':

    %+%, alpha
library(finalfit) #Testing Assumptions
library (performance) #Testing Assumptions
library(MVN) #Assessing multivariate normality

Attaching package: 'MVN'

The following object is masked from 'package:psych':

    mardia

The following object is masked from 'package:tidySEM':

    descriptives
library(lavaan) #SEM package
Warning: package 'lavaan' was built under R version 4.5.3
This is lavaan 0.6-21
lavaan is FREE software! Please report any bugs.

Attaching package: 'lavaan'

The following object is masked from 'package:psych':

    cor2cov
library(knitr) #Making Tables
library(kableExtra) #Making Tables

Attaching package: 'kableExtra'

The following object is masked from 'package:dplyr':

    group_rows
library(apaTables) #Generating APA formated tables
library(semTools)
 
###############################################################################
This is semTools 0.5-7
All users of R (or SEM) are invited to submit functions or ideas for functions.
###############################################################################

Attaching package: 'semTools'

The following objects are masked from 'package:psych':

    reliability, skew

The following object is masked from 'package:readr':

    clipboard
Class_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")
Table 1. Descriptives
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 1.00
corrplot(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 0.6-21 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    1.000
#modindices(M_MDL_fit)
Conf <- cfa(model = M_model, data = Class_SEM_Data_Final,
missing = "fiml", group = "Sex")

summary(Conf, standardized = TRUE, fit.measures = TRUE)
lavaan 0.6-21 ended normally after 140 iterations

  Estimator                                         ML
  Optimization method                           NLMINB
  Number of model parameters                       140

  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                               402.329
  Degrees of freedom                               320
  P-value (Chi-square)                           0.001
  Test statistic for each group:
    female                                     212.384
    male                                       189.945

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)                    0.976
  Tucker-Lewis Index (TLI)                       0.972
                                                      
  Robust Comparative Fit Index (CFI)             0.977
  Robust Tucker-Lewis Index (TLI)                0.972

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)             -11934.055
  Loglikelihood unrestricted model (H1)     -11732.891
                                                      
  Akaike (AIC)                               24148.110
  Bayesian (BIC)                             24706.915
  Sample-size adjusted Bayesian (SABIC)      24262.686

Root Mean Square Error of Approximation:

  RMSEA                                          0.036
  90 Percent confidence interval - lower         0.023
  90 Percent confidence interval - upper         0.046
  P-value H_0: RMSEA <= 0.050                    0.988
  P-value H_0: RMSEA >= 0.080                    0.000
                                                      
  Robust RMSEA                                   0.036
  90 Percent confidence interval - lower         0.023
  90 Percent confidence interval - upper         0.046
  P-value H_0: Robust RMSEA <= 0.050             0.988
  P-value H_0: Robust RMSEA >= 0.080             0.000

Standardized Root Mean Square Residual:

  SRMR                                           0.047

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                               1.342    0.629
    EP2               1.139    0.123    9.243    0.000    1.528    0.796
    EP3               0.852    0.091    9.364    0.000    1.143    0.868
    EP4               0.977    0.104    9.362    0.000    1.311    0.857
  Att_Cowrk =~                                                          
    AC1               1.000                               0.589    0.609
    AC2               1.551    0.210    7.372    0.000    0.914    0.733
    AC3               1.371    0.178    7.694    0.000    0.808    0.754
    AC4               1.504    0.206    7.291    0.000    0.886    0.696
  Org_Comm =~                                                           
    OC1               1.000                               1.651    0.632
    OC2               1.247    0.129    9.673    0.000    2.059    0.900
    OC3               0.541    0.074    7.267    0.000    0.893    0.589
    OC4               1.033    0.112    9.254    0.000    1.706    0.818
  Job_Sat =~                                                            
    JS1               1.000                               0.864    0.680
    JS2               1.091    0.134    8.132    0.000    0.943    0.715
    JS3               1.124    0.139    8.072    0.000    0.971    0.731
    JS4               0.985    0.128    7.725    0.000    0.851    0.688
  Stay_Int =~                                                           
    SI1               1.000                               0.805    0.820
    SI2               1.041    0.067   15.430    0.000    0.838    0.892
    SI3               1.054    0.085   12.378    0.000    0.848    0.780
    SI4               1.119    0.077   14.548    0.000    0.901    0.878

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  Env_Supp ~~                                                           
    Att_Cowrk         0.170    0.071    2.401    0.016    0.215    0.215
    Org_Comm          1.108    0.238    4.647    0.000    0.500    0.500
    Job_Sat           0.318    0.107    2.979    0.003    0.274    0.274
    Stay_Int          0.661    0.119    5.574    0.000    0.612    0.612
  Att_Cowrk ~~                                                          
    Org_Comm          0.459    0.107    4.274    0.000    0.472    0.472
    Job_Sat           0.146    0.051    2.881    0.004    0.288    0.288
    Stay_Int          0.139    0.043    3.194    0.001    0.292    0.292
  Org_Comm ~~                                                           
    Job_Sat           0.373    0.132    2.828    0.005    0.262    0.262
    Stay_Int          0.828    0.145    5.706    0.000    0.623    0.623
  Job_Sat ~~                                                            
    Stay_Int          0.164    0.061    2.702    0.007    0.236    0.236

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .EP1               8.095    0.151   53.693    0.000    8.095    3.797
   .EP2               8.575    0.136   63.178    0.000    8.575    4.467
   .EP3               8.780    0.093   94.360    0.000    8.780    6.672
   .EP4               5.611    0.108   51.821    0.000    5.611    3.668
   .AC1               1.905    0.068   27.847    0.000    1.905    1.969
   .AC2               2.290    0.088   25.963    0.000    2.290    1.836
   .AC3               1.970    0.076   25.990    0.000    1.970    1.838
   .AC4               2.255    0.090   25.056    0.000    2.255    1.772
   .OC1               4.875    0.185   26.401    0.000    4.875    1.867
   .OC2               8.195    0.162   50.644    0.000    8.195    3.581
   .OC3               8.665    0.107   80.753    0.000    8.665    5.710
   .OC4               8.295    0.147   56.258    0.000    8.295    3.978
   .JS1               4.210    0.090   46.837    0.000    4.210    3.312
   .JS2               4.200    0.093   45.029    0.000    4.200    3.184
   .JS3               3.230    0.094   34.363    0.000    3.230    2.430
   .JS4               2.665    0.088   30.442    0.000    2.665    2.153
   .SI1               3.995    0.069   57.514    0.000    3.995    4.067
   .SI2               4.035    0.066   60.700    0.000    4.035    4.292
   .SI3               3.280    0.077   42.673    0.000    3.280    3.017
   .SI4               3.305    0.073   45.571    0.000    3.305    3.222

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .EP1               2.745    0.301    9.125    0.000    2.745    0.604
   .EP2               1.350    0.176    7.654    0.000    1.350    0.366
   .EP3               0.426    0.070    6.107    0.000    0.426    0.246
   .EP4               0.620    0.093    6.703    0.000    0.620    0.265
   .AC1               0.589    0.069    8.559    0.000    0.589    0.629
   .AC2               0.721    0.101    7.158    0.000    0.721    0.463
   .AC3               0.497    0.073    6.776    0.000    0.497    0.432
   .AC4               0.835    0.109    7.658    0.000    0.835    0.516
   .OC1               4.093    0.448    9.140    0.000    4.093    0.600
   .OC2               0.998    0.218    4.578    0.000    0.998    0.191
   .OC3               1.505    0.163    9.255    0.000    1.505    0.654
   .OC4               1.439    0.199    7.218    0.000    1.439    0.331
   .JS1               0.869    0.112    7.788    0.000    0.869    0.538
   .JS2               0.851    0.117    7.278    0.000    0.851    0.489
   .JS3               0.824    0.117    7.027    0.000    0.824    0.466
   .JS4               0.808    0.105    7.707    0.000    0.808    0.527
   .SI1               0.317    0.039    8.103    0.000    0.317    0.328
   .SI2               0.181    0.028    6.377    0.000    0.181    0.205
   .SI3               0.462    0.054    8.554    0.000    0.462    0.391
   .SI4               0.240    0.036    6.762    0.000    0.240    0.228
    Env_Supp          1.801    0.383    4.706    0.000    1.000    1.000
    Att_Cowrk         0.347    0.081    4.284    0.000    1.000    1.000
    Org_Comm          2.726    0.575    4.742    0.000    1.000    1.000
    Job_Sat           0.747    0.153    4.875    0.000    1.000    1.000
    Stay_Int          0.648    0.094    6.896    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                               1.012    0.761
    EP2               0.929    0.092   10.055    0.000    0.940    0.779
    EP3               0.891    0.099    8.961    0.000    0.901    0.676
    EP4               0.935    0.088   10.644    0.000    0.947    0.784
  Att_Cowrk =~                                                          
    AC1               1.000                               0.922    0.759
    AC2               0.654    0.095    6.883    0.000    0.603    0.550
    AC3               1.017    0.118    8.621    0.000    0.938    0.748
    AC4               0.970    0.116    8.347    0.000    0.894    0.681
  Org_Comm =~                                                           
    OC1               1.000                               1.280    0.527
    OC2               1.374    0.180    7.627    0.000    1.758    0.858
    OC3               1.162    0.161    7.228    0.000    1.487    0.759
    OC4               1.348    0.178    7.588    0.000    1.725    0.858
  Job_Sat =~                                                            
    JS1               1.000                               1.102    0.787
    JS2               0.964    0.098    9.865    0.000    1.063    0.751
    JS3               0.793    0.089    8.945    0.000    0.874    0.673
    JS4               0.873    0.092    9.506    0.000    0.962    0.729
  Stay_Int =~                                                           
    SI1               1.000                               0.519    0.763
    SI2               1.197    0.107   11.143    0.000    0.621    0.805
    SI3               1.124    0.130    8.634    0.000    0.583    0.650
    SI4               1.347    0.128   10.487    0.000    0.699    0.804

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  Env_Supp ~~                                                           
    Att_Cowrk         0.083    0.081    1.017    0.309    0.089    0.089
    Org_Comm          0.698    0.149    4.691    0.000    0.539    0.539
    Job_Sat           0.248    0.099    2.513    0.012    0.222    0.222
    Stay_Int          0.208    0.049    4.207    0.000    0.396    0.396
  Att_Cowrk ~~                                                          
    Org_Comm          0.349    0.112    3.111    0.002    0.295    0.295
    Job_Sat          -0.161    0.090   -1.784    0.074   -0.158   -0.158
    Stay_Int          0.041    0.041    0.989    0.323    0.086    0.086
  Org_Comm ~~                                                           
    Job_Sat           0.188    0.120    1.559    0.119    0.133    0.133
    Stay_Int          0.296    0.070    4.240    0.000    0.446    0.446
  Job_Sat ~~                                                            
    Stay_Int          0.123    0.050    2.474    0.013    0.215    0.215

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .EP1               8.960    0.094   95.287    0.000    8.960    6.738
   .EP2               9.120    0.085  106.903    0.000    9.120    7.559
   .EP3               9.075    0.094   96.212    0.000    9.075    6.803
   .EP4               6.040    0.085   70.732    0.000    6.040    5.001
   .AC1               3.615    0.086   42.069    0.000    3.615    2.975
   .AC2               4.815    0.077   62.141    0.000    4.815    4.394
   .AC3               3.580    0.089   40.360    0.000    3.580    2.854
   .AC4               4.170    0.093   44.807    0.000    4.170    3.174
   .OC1               4.900    0.172   28.529    0.000    4.900    2.017
   .OC2               8.640    0.145   59.619    0.000    8.640    4.216
   .OC3               8.645    0.139   62.398    0.000    8.645    4.412
   .OC4               8.515    0.142   59.913    0.000    8.515    4.236
   .JS1               4.185    0.099   42.267    0.000    4.185    2.989
   .JS2               4.205    0.100   42.019    0.000    4.205    2.971
   .JS3               3.200    0.092   34.811    0.000    3.200    2.462
   .JS4               2.670    0.093   28.616    0.000    2.670    2.023
   .SI1               4.410    0.048   91.766    0.000    4.410    6.489
   .SI2               4.375    0.055   80.253    0.000    4.375    5.675
   .SI3               3.665    0.063   57.848    0.000    3.665    4.091
   .SI4               3.655    0.061   59.450    0.000    3.655    4.204

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .EP1               0.744    0.100    7.445    0.000    0.744    0.421
   .EP2               0.571    0.083    6.897    0.000    0.571    0.393
   .EP3               0.967    0.114    8.489    0.000    0.967    0.543
   .EP4               0.562    0.081    6.967    0.000    0.562    0.386
   .AC1               0.627    0.098    6.368    0.000    0.627    0.424
   .AC2               0.837    0.094    8.885    0.000    0.837    0.697
   .AC3               0.694    0.106    6.551    0.000    0.694    0.441
   .AC4               0.927    0.120    7.738    0.000    0.927    0.537
   .OC1               4.262    0.449    9.499    0.000    4.262    0.722
   .OC2               1.109    0.185    6.007    0.000    1.109    0.264
   .OC3               1.627    0.201    8.096    0.000    1.627    0.424
   .OC4               1.064    0.174    6.115    0.000    1.064    0.263
   .JS1               0.746    0.115    6.502    0.000    0.746    0.380
   .JS2               0.874    0.121    7.247    0.000    0.874    0.436
   .JS3               0.925    0.112    8.266    0.000    0.925    0.548
   .JS4               0.816    0.108    7.580    0.000    0.816    0.469
   .SI1               0.193    0.026    7.523    0.000    0.193    0.418
   .SI2               0.209    0.032    6.629    0.000    0.209    0.352
   .SI3               0.463    0.053    8.731    0.000    0.463    0.577
   .SI4               0.268    0.041    6.557    0.000    0.268    0.354
    Env_Supp          1.025    0.174    5.896    0.000    1.000    1.000
    Att_Cowrk         0.850    0.154    5.531    0.000    1.000    1.000
    Org_Comm          1.638    0.431    3.797    0.000    1.000    1.000
    Job_Sat           1.215    0.201    6.039    0.000    1.000    1.000
    Stay_Int          0.269    0.045    5.943    0.000    1.000    1.000
Metric <- 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 0.6-21 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 0.6-21 ended normally after 121 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   53.769    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    7.504    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   53.769    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 0.6-21 ended normally after 97 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   12.770    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   65.198    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   33.944    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   12.770    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   65.198    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   33.944    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    1.000

Formal Tests of Invariance

anova(Conf, Metric, Strong, Strict)

Chi-Squared Difference Test

        Df   AIC   BIC  Chisq Chisq diff Df diff Pr(>Chisq)    
Conf   320 24148 24707 402.33                                  
Metric 335 24177 24676 460.81     58.479      15  4.597e-07 ***
Strong 350 24201 24640 514.88     54.074      15  2.553e-06 ***
Strict 370 24290 24650 644.55    129.672      20  < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit_comp <- compareFit(Conf, Metric, Strong, Strict)
Warning: lavaan->FUN():  
   Unknown argument 'pool.robust' for 'fitMeasures'
Warning: lavaan->FUN():  
   Unknown argument 'pool.method' for 'fitMeasures'
Warning: lavaan->FUN():  
   Unknown argument 'pool.robust' for 'fitMeasures'
Warning: lavaan->FUN():  
   Unknown argument 'pool.method' for 'fitMeasures'
Warning: lavaan->FUN():  
   Unknown argument 'pool.robust' for 'fitMeasures'
Warning: lavaan->FUN():  
   Unknown argument 'pool.method' for 'fitMeasures'
Warning: lavaan->FUN():  
   Unknown argument 'pool.robust' for 'fitMeasures'
Warning: lavaan->FUN():  
   Unknown argument 'pool.method' for 'fitMeasures'
summary(fit_comp)
################### Nested Model Comparison #########################

Chi-Squared Difference Test

        Df   AIC   BIC  Chisq Chisq diff Df diff Pr(>Chisq)    
Conf   320 24148 24707 402.33                                  
Metric 335 24177 24676 460.81     58.479      15  4.597e-07 ***
Strong 350 24201 24640 514.88     54.074      15  2.553e-06 ***
Strict 370 24290 24650 644.55    129.672      20  < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

####################### Model Fit Indices ###########################
          chisq  df pvalue rmsea   cfi   tli  srmr        aic        bic
Conf   402.329† 320   .001 .036† .976† .972† .047† 24148.110† 24706.915 
Metric 460.808  335   .000 .043  .964  .959  .060  24176.589  24675.522 
Strong 514.882  350   .000 .049  .953  .949  .083  24200.663  24639.724†
Strict 644.554  370   .000 .061  .922  .919  .141  24290.335  24649.566 

################## Differences in Fit Indices #######################
                df rmsea    cfi    tli  srmr    aic     bic
Metric - Conf   15 0.007 -0.012 -0.013 0.013 28.479 -31.393
Strong - Metric 15 0.005 -0.011 -0.010 0.023 24.074 -35.798
Strict - Strong 20 0.012 -0.031 -0.029 0.058 89.672   9.842