library("psych")
library("lavaan")
## Warning: package 'lavaan' was built under R version 3.6.3
## This is lavaan 0.6-7
## lavaan is BETA software! Please report any bugs.
##
## Attaching package: 'lavaan'
## The following object is masked from 'package:psych':
##
## cor2cov
library("semTools")
## Warning: package 'semTools' was built under R version 3.6.3
##
## ###############################################################################
## This is semTools 0.5-3
## All users of R (or SEM) are invited to submit functions or ideas for functions.
## ###############################################################################
##
## Attaching package: 'semTools'
## The following object is masked from 'package:psych':
##
## skew
library("lavaanPlot")
## Warning: package 'lavaanPlot' was built under R version 3.6.3
HW <- read.csv("C:/Users/Courtney/Desktop/HW.dat")
HW_model <- "jobauto =~ ja1 + ja2 + ja3 +ja4 + ja5 + ja6 + ja7 + ja8
jobstress =~ stress1 + stress2 + stress3 + stress4
jobsat =~ js1 + js2 + js3
org_commit =~ oc1 + oc2 + oc3 + oc4 + oc5 + oc6 + oc7 +oc8
turnover =~ ti1 + ti2 + ti3"
lavaanify(model=HW_model)
## id lhs op rhs user block group free ustart exo label
## 1 1 jobauto =~ ja1 1 1 1 1 NA 0
## 2 2 jobauto =~ ja2 1 1 1 2 NA 0
## 3 3 jobauto =~ ja3 1 1 1 3 NA 0
## 4 4 jobauto =~ ja4 1 1 1 4 NA 0
## 5 5 jobauto =~ ja5 1 1 1 5 NA 0
## 6 6 jobauto =~ ja6 1 1 1 6 NA 0
## 7 7 jobauto =~ ja7 1 1 1 7 NA 0
## 8 8 jobauto =~ ja8 1 1 1 8 NA 0
## 9 9 jobstress =~ stress1 1 1 1 9 NA 0
## 10 10 jobstress =~ stress2 1 1 1 10 NA 0
## 11 11 jobstress =~ stress3 1 1 1 11 NA 0
## 12 12 jobstress =~ stress4 1 1 1 12 NA 0
## 13 13 jobsat =~ js1 1 1 1 13 NA 0
## 14 14 jobsat =~ js2 1 1 1 14 NA 0
## 15 15 jobsat =~ js3 1 1 1 15 NA 0
## 16 16 org_commit =~ oc1 1 1 1 16 NA 0
## 17 17 org_commit =~ oc2 1 1 1 17 NA 0
## 18 18 org_commit =~ oc3 1 1 1 18 NA 0
## 19 19 org_commit =~ oc4 1 1 1 19 NA 0
## 20 20 org_commit =~ oc5 1 1 1 20 NA 0
## 21 21 org_commit =~ oc6 1 1 1 21 NA 0
## 22 22 org_commit =~ oc7 1 1 1 22 NA 0
## 23 23 org_commit =~ oc8 1 1 1 23 NA 0
## 24 24 turnover =~ ti1 1 1 1 24 NA 0
## 25 25 turnover =~ ti2 1 1 1 25 NA 0
## 26 26 turnover =~ ti3 1 1 1 26 NA 0
## 27 27 ja1 ~~ ja1 0 1 1 0 0 0
## 28 28 ja2 ~~ ja2 0 1 1 0 0 0
## 29 29 ja3 ~~ ja3 0 1 1 0 0 0
## 30 30 ja4 ~~ ja4 0 1 1 0 0 0
## 31 31 ja5 ~~ ja5 0 1 1 0 0 0
## 32 32 ja6 ~~ ja6 0 1 1 0 0 0
## 33 33 ja7 ~~ ja7 0 1 1 0 0 0
## 34 34 ja8 ~~ ja8 0 1 1 0 0 0
## 35 35 stress1 ~~ stress1 0 1 1 0 0 0
## 36 36 stress2 ~~ stress2 0 1 1 0 0 0
## 37 37 stress3 ~~ stress3 0 1 1 0 0 0
## 38 38 stress4 ~~ stress4 0 1 1 0 0 0
## 39 39 js1 ~~ js1 0 1 1 0 0 0
## 40 40 js2 ~~ js2 0 1 1 0 0 0
## 41 41 js3 ~~ js3 0 1 1 0 0 0
## 42 42 oc1 ~~ oc1 0 1 1 0 0 0
## 43 43 oc2 ~~ oc2 0 1 1 0 0 0
## 44 44 oc3 ~~ oc3 0 1 1 0 0 0
## 45 45 oc4 ~~ oc4 0 1 1 0 0 0
## 46 46 oc5 ~~ oc5 0 1 1 0 0 0
## 47 47 oc6 ~~ oc6 0 1 1 0 0 0
## 48 48 oc7 ~~ oc7 0 1 1 0 0 0
## 49 49 oc8 ~~ oc8 0 1 1 0 0 0
## 50 50 ti1 ~~ ti1 0 1 1 0 0 0
## 51 51 ti2 ~~ ti2 0 1 1 0 0 0
## 52 52 ti3 ~~ ti3 0 1 1 0 0 0
## 53 53 jobauto ~~ jobauto 0 1 1 0 0 0
## 54 54 jobstress ~~ jobstress 0 1 1 0 0 0
## 55 55 jobsat ~~ jobsat 0 1 1 0 0 0
## 56 56 org_commit ~~ org_commit 0 1 1 0 0 0
## 57 57 turnover ~~ turnover 0 1 1 0 0 0
## plabel
## 1 .p1.
## 2 .p2.
## 3 .p3.
## 4 .p4.
## 5 .p5.
## 6 .p6.
## 7 .p7.
## 8 .p8.
## 9 .p9.
## 10 .p10.
## 11 .p11.
## 12 .p12.
## 13 .p13.
## 14 .p14.
## 15 .p15.
## 16 .p16.
## 17 .p17.
## 18 .p18.
## 19 .p19.
## 20 .p20.
## 21 .p21.
## 22 .p22.
## 23 .p23.
## 24 .p24.
## 25 .p25.
## 26 .p26.
## 27 .p27.
## 28 .p28.
## 29 .p29.
## 30 .p30.
## 31 .p31.
## 32 .p32.
## 33 .p33.
## 34 .p34.
## 35 .p35.
## 36 .p36.
## 37 .p37.
## 38 .p38.
## 39 .p39.
## 40 .p40.
## 41 .p41.
## 42 .p42.
## 43 .p43.
## 44 .p44.
## 45 .p45.
## 46 .p46.
## 47 .p47.
## 48 .p48.
## 49 .p49.
## 50 .p50.
## 51 .p51.
## 52 .p52.
## 53 .p53.
## 54 .p54.
## 55 .p55.
## 56 .p56.
## 57 .p57.
HW.out <-cfa(HW_model, data=HW)
lavaanPlot(model = HW.out)
summary(HW.out, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-7 ended normally after 72 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 62
##
## Number of observations 400
##
## Model Test User Model:
##
## Test statistic 289.909
## Degrees of freedom 289
## P-value (Chi-square) 0.474
##
## Model Test Baseline Model:
##
## Test statistic 7360.262
## Degrees of freedom 325
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -16382.830
## Loglikelihood unrestricted model (H1) -16237.875
##
## Akaike (AIC) 32889.660
## Bayesian (BIC) 33137.131
## Sample-size adjusted Bayesian (BIC) 32940.401
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.003
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.020
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.035
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## jobauto =~
## ja1 1.000 1.044 0.726
## ja2 0.960 0.074 12.954 0.000 1.002 0.703
## ja3 0.993 0.073 13.554 0.000 1.037 0.738
## ja4 0.640 0.063 10.158 0.000 0.668 0.549
## ja5 0.729 0.065 11.211 0.000 0.761 0.606
## ja6 0.920 0.074 12.423 0.000 0.960 0.673
## ja7 0.513 0.059 8.635 0.000 0.536 0.466
## ja8 0.699 0.064 10.902 0.000 0.729 0.589
## jobstress =~
## stress1 1.000 1.057 0.722
## stress2 0.685 0.069 9.937 0.000 0.723 0.578
## stress3 0.887 0.075 11.890 0.000 0.937 0.722
## stress4 0.811 0.073 11.185 0.000 0.857 0.664
## jobsat =~
## js1 1.000 1.526 0.838
## js2 0.890 0.047 18.773 0.000 1.358 0.810
## js3 0.795 0.044 17.923 0.000 1.213 0.784
## org_commit =~
## oc1 1.000 2.277 0.914
## oc2 0.739 0.028 26.162 0.000 1.681 0.856
## oc3 0.602 0.026 23.406 0.000 1.372 0.815
## oc4 0.938 0.032 29.268 0.000 2.135 0.895
## oc5 0.787 0.029 27.602 0.000 1.792 0.875
## oc6 0.903 0.030 30.280 0.000 2.056 0.905
## oc7 0.523 0.025 21.059 0.000 1.191 0.774
## oc8 0.689 0.026 26.362 0.000 1.568 0.859
## turnover =~
## ti1 1.000 2.440 0.913
## ti2 0.866 0.030 29.098 0.000 2.114 0.907
## ti3 0.679 0.027 25.195 0.000 1.656 0.854
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## jobauto ~~
## jobstress -0.050 0.068 -0.733 0.464 -0.045 -0.045
## jobsat 0.746 0.108 6.879 0.000 0.468 0.468
## org_commit 0.930 0.147 6.328 0.000 0.392 0.392
## turnover 0.989 0.160 6.198 0.000 0.388 0.388
## jobstress ~~
## jobsat -0.681 0.111 -6.114 0.000 -0.423 -0.423
## org_commit -1.395 0.172 -8.102 0.000 -0.580 -0.580
## turnover -1.320 0.180 -7.352 0.000 -0.512 -0.512
## jobsat ~~
## org_commit 2.974 0.263 11.307 0.000 0.856 0.856
## turnover 3.251 0.286 11.362 0.000 0.873 0.873
## org_commit ~~
## turnover 5.001 0.410 12.205 0.000 0.900 0.900
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ja1 0.978 0.084 11.572 0.000 0.978 0.473
## .ja2 1.025 0.086 11.887 0.000 1.025 0.505
## .ja3 0.900 0.079 11.384 0.000 0.900 0.456
## .ja4 1.038 0.079 13.157 0.000 1.038 0.699
## .ja5 0.998 0.078 12.810 0.000 0.998 0.633
## .ja6 1.111 0.091 12.234 0.000 1.111 0.547
## .ja7 1.036 0.077 13.509 0.000 1.036 0.783
## .ja8 1.000 0.077 12.923 0.000 1.000 0.653
## .stress1 1.024 0.101 10.157 0.000 1.024 0.478
## .stress2 1.043 0.085 12.343 0.000 1.043 0.666
## .stress3 0.807 0.079 10.166 0.000 0.807 0.479
## .stress4 0.934 0.083 11.288 0.000 0.934 0.560
## .js1 0.990 0.096 10.356 0.000 0.990 0.298
## .js2 0.968 0.087 11.115 0.000 0.968 0.344
## .js3 0.924 0.079 11.638 0.000 0.924 0.386
## .oc1 1.018 0.087 11.730 0.000 1.018 0.164
## .oc2 1.028 0.080 12.845 0.000 1.028 0.267
## .oc3 0.950 0.072 13.210 0.000 0.950 0.335
## .oc4 1.137 0.093 12.248 0.000 1.137 0.200
## .oc5 0.983 0.078 12.599 0.000 0.983 0.234
## .oc6 0.929 0.077 11.988 0.000 0.929 0.180
## .oc7 0.951 0.071 13.442 0.000 0.951 0.402
## .oc8 0.873 0.068 12.814 0.000 0.873 0.262
## .ti1 1.189 0.123 9.701 0.000 1.189 0.166
## .ti2 0.961 0.096 10.024 0.000 0.961 0.177
## .ti3 1.014 0.086 11.798 0.000 1.014 0.270
## jobauto 1.089 0.138 7.916 0.000 1.000 1.000
## jobstress 1.117 0.150 7.431 0.000 1.000 1.000
## jobsat 2.328 0.233 9.982 0.000 1.000 1.000
## org_commit 5.184 0.435 11.909 0.000 1.000 1.000
## turnover 5.954 0.506 11.768 0.000 1.000 1.000
#multigroup by gender
HW.sex.out <-cfa(HW_model, data=HW, group = "sex")
lavaanPlot(model = HW.sex.out)
summary(HW.sex.out, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-7 ended normally after 132 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 176
##
## Number of observations per group:
## male 200
## female 200
##
## Model Test User Model:
##
## Test statistic 542.996
## Degrees of freedom 578
## P-value (Chi-square) 0.849
## Test statistic for each group:
## male 272.970
## female 270.026
##
## Model Test Baseline Model:
##
## Test statistic 7700.637
## Degrees of freedom 650
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.006
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -16281.780
## Loglikelihood unrestricted model (H1) -16010.282
##
## Akaike (AIC) 32915.560
## Bayesian (BIC) 33618.058
## Sample-size adjusted Bayesian (BIC) 33059.598
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.014
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.039
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [male]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## jobauto =~
## ja1 1.000 1.190 0.771
## ja2 0.683 0.078 8.779 0.000 0.813 0.634
## ja3 0.704 0.074 9.568 0.000 0.837 0.686
## ja4 0.754 0.080 9.443 0.000 0.897 0.678
## ja5 0.518 0.070 7.390 0.000 0.616 0.540
## ja6 0.659 0.082 8.017 0.000 0.784 0.583
## ja7 0.730 0.078 9.344 0.000 0.868 0.671
## ja8 0.828 0.086 9.658 0.000 0.985 0.692
## jobstress =~
## stress1 1.000 1.010 0.682
## stress2 0.641 0.100 6.398 0.000 0.647 0.542
## stress3 0.953 0.117 8.130 0.000 0.962 0.755
## stress4 0.787 0.108 7.252 0.000 0.794 0.630
## jobsat =~
## js1 1.000 1.673 0.869
## js2 0.902 0.059 15.394 0.000 1.509 0.842
## js3 0.808 0.056 14.514 0.000 1.352 0.813
## org_commit =~
## oc1 1.000 2.299 0.927
## oc2 0.776 0.040 19.595 0.000 1.784 0.865
## oc3 0.603 0.036 16.764 0.000 1.386 0.811
## oc4 0.950 0.043 22.133 0.000 2.183 0.903
## oc5 0.809 0.040 20.319 0.000 1.858 0.877
## oc6 0.922 0.041 22.326 0.000 2.118 0.905
## oc7 0.561 0.034 16.426 0.000 1.290 0.803
## oc8 0.701 0.036 19.366 0.000 1.611 0.861
## turnover =~
## ti1 1.000 2.557 0.929
## ti2 0.860 0.039 22.296 0.000 2.198 0.913
## ti3 0.676 0.036 18.935 0.000 1.728 0.860
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## jobauto ~~
## jobstress -0.084 0.105 -0.801 0.423 -0.070 -0.070
## jobsat 1.148 0.195 5.874 0.000 0.577 0.577
## org_commit 1.353 0.247 5.481 0.000 0.495 0.495
## turnover 1.419 0.273 5.192 0.000 0.466 0.466
## jobstress ~~
## jobsat -0.671 0.163 -4.105 0.000 -0.397 -0.397
## org_commit -1.333 0.240 -5.546 0.000 -0.574 -0.574
## turnover -1.168 0.250 -4.662 0.000 -0.452 -0.452
## jobsat ~~
## org_commit 3.317 0.401 8.261 0.000 0.862 0.862
## turnover 3.873 0.458 8.454 0.000 0.905 0.905
## org_commit ~~
## turnover 5.247 0.601 8.731 0.000 0.893 0.893
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ja1 0.007 0.109 0.061 0.952 0.007 0.004
## .ja2 -0.082 0.091 -0.909 0.363 -0.082 -0.064
## .ja3 0.006 0.086 0.067 0.946 0.006 0.005
## .ja4 0.067 0.094 0.720 0.472 0.067 0.051
## .ja5 0.019 0.081 0.236 0.814 0.019 0.017
## .ja6 -0.119 0.095 -1.248 0.212 -0.119 -0.088
## .ja7 0.010 0.092 0.111 0.911 0.010 0.008
## .ja8 0.014 0.101 0.140 0.889 0.014 0.010
## .stress1 -0.175 0.105 -1.671 0.095 -0.175 -0.118
## .stress2 0.032 0.084 0.374 0.708 0.032 0.026
## .stress3 -0.176 0.090 -1.955 0.051 -0.176 -0.138
## .stress4 -0.084 0.089 -0.941 0.347 -0.084 -0.067
## .js1 -0.006 0.136 -0.047 0.963 -0.006 -0.003
## .js2 -0.036 0.127 -0.288 0.773 -0.036 -0.020
## .js3 0.094 0.118 0.798 0.425 0.094 0.056
## .oc1 0.132 0.175 0.751 0.452 0.132 0.053
## .oc2 0.120 0.146 0.820 0.412 0.120 0.058
## .oc3 0.149 0.121 1.231 0.218 0.149 0.087
## .oc4 0.223 0.171 1.302 0.193 0.223 0.092
## .oc5 0.220 0.150 1.469 0.142 0.220 0.104
## .oc6 0.116 0.165 0.699 0.485 0.116 0.049
## .oc7 0.023 0.114 0.206 0.836 0.023 0.015
## .oc8 0.078 0.132 0.589 0.556 0.078 0.042
## .ti1 0.038 0.195 0.198 0.843 0.038 0.014
## .ti2 0.079 0.170 0.464 0.643 0.079 0.033
## .ti3 0.152 0.142 1.070 0.285 0.152 0.076
## jobauto 0.000 0.000 0.000
## jobstress 0.000 0.000 0.000
## jobsat 0.000 0.000 0.000
## org_commit 0.000 0.000 0.000
## turnover 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ja1 0.963 0.122 7.887 0.000 0.963 0.405
## .ja2 0.985 0.109 9.040 0.000 0.985 0.598
## .ja3 0.789 0.090 8.727 0.000 0.789 0.530
## .ja4 0.947 0.108 8.783 0.000 0.947 0.541
## .ja5 0.921 0.098 9.413 0.000 0.921 0.708
## .ja6 1.196 0.129 9.266 0.000 1.196 0.660
## .ja7 0.920 0.104 8.826 0.000 0.920 0.550
## .ja8 1.057 0.122 8.684 0.000 1.057 0.522
## .stress1 1.171 0.153 7.639 0.000 1.171 0.535
## .stress2 1.008 0.113 8.902 0.000 1.008 0.707
## .stress3 0.697 0.109 6.416 0.000 0.697 0.429
## .stress4 0.958 0.116 8.235 0.000 0.958 0.603
## .js1 0.906 0.124 7.296 0.000 0.906 0.245
## .js2 0.938 0.119 7.912 0.000 0.938 0.292
## .js3 0.937 0.112 8.340 0.000 0.937 0.339
## .oc1 0.862 0.107 8.084 0.000 0.862 0.140
## .oc2 1.073 0.118 9.077 0.000 1.073 0.252
## .oc3 1.004 0.107 9.406 0.000 1.004 0.343
## .oc4 1.084 0.126 8.628 0.000 1.084 0.185
## .oc5 1.041 0.116 8.967 0.000 1.041 0.232
## .oc6 0.990 0.115 8.585 0.000 0.990 0.181
## .oc7 0.917 0.097 9.437 0.000 0.917 0.355
## .oc8 0.906 0.099 9.109 0.000 0.906 0.259
## .ti1 1.036 0.159 6.515 0.000 1.036 0.137
## .ti2 0.960 0.133 7.222 0.000 0.960 0.166
## .ti3 1.049 0.124 8.468 0.000 1.049 0.260
## jobauto 1.415 0.230 6.152 0.000 1.000 1.000
## jobstress 1.019 0.210 4.858 0.000 1.000 1.000
## jobsat 2.799 0.369 7.582 0.000 1.000 1.000
## org_commit 5.283 0.612 8.637 0.000 1.000 1.000
## turnover 6.540 0.760 8.604 0.000 1.000 1.000
##
##
## Group 2 [female]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## jobauto =~
## ja1 1.000 0.894 0.675
## ja2 1.343 0.143 9.402 0.000 1.201 0.774
## ja3 1.389 0.145 9.573 0.000 1.243 0.792
## ja4 0.500 0.095 5.235 0.000 0.447 0.405
## ja5 1.013 0.122 8.286 0.000 0.906 0.666
## ja6 1.266 0.137 9.221 0.000 1.132 0.756
## ja7 0.237 0.084 2.827 0.005 0.212 0.215
## ja8 0.557 0.089 6.261 0.000 0.499 0.490
## jobstress =~
## stress1 1.000 1.104 0.765
## stress2 0.726 0.093 7.799 0.000 0.801 0.614
## stress3 0.831 0.095 8.726 0.000 0.917 0.695
## stress4 0.806 0.095 8.524 0.000 0.890 0.676
## jobsat =~
## js1 1.000 1.351 0.790
## js2 0.885 0.079 11.163 0.000 1.195 0.770
## js3 0.781 0.073 10.700 0.000 1.055 0.742
## org_commit =~
## oc1 1.000 2.257 0.903
## oc2 0.696 0.040 17.550 0.000 1.571 0.847
## oc3 0.600 0.037 16.346 0.000 1.355 0.819
## oc4 0.922 0.047 19.580 0.000 2.081 0.887
## oc5 0.758 0.040 18.798 0.000 1.711 0.872
## oc6 0.882 0.043 20.646 0.000 1.990 0.905
## oc7 0.481 0.036 13.494 0.000 1.086 0.739
## oc8 0.675 0.037 18.042 0.000 1.523 0.857
## turnover =~
## ti1 1.000 2.315 0.894
## ti2 0.877 0.046 19.114 0.000 2.031 0.902
## ti3 0.678 0.041 16.577 0.000 1.569 0.844
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## jobauto ~~
## jobstress -0.022 0.084 -0.267 0.789 -0.023 -0.023
## jobsat 0.418 0.113 3.715 0.000 0.346 0.346
## org_commit 0.579 0.169 3.432 0.001 0.287 0.287
## turnover 0.641 0.179 3.591 0.000 0.310 0.310
## jobstress ~~
## jobsat -0.714 0.151 -4.735 0.000 -0.479 -0.479
## org_commit -1.486 0.248 -5.995 0.000 -0.596 -0.596
## turnover -1.504 0.257 -5.856 0.000 -0.589 -0.589
## jobsat ~~
## org_commit 2.612 0.341 7.667 0.000 0.857 0.857
## turnover 2.609 0.349 7.482 0.000 0.835 0.835
## org_commit ~~
## turnover 4.745 0.557 8.525 0.000 0.908 0.908
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ja1 -0.035 0.094 -0.375 0.707 -0.035 -0.027
## .ja2 0.015 0.110 0.133 0.894 0.015 0.009
## .ja3 0.009 0.111 0.078 0.937 0.009 0.006
## .ja4 0.082 0.078 1.054 0.292 0.082 0.075
## .ja5 -0.110 0.096 -1.148 0.251 -0.110 -0.081
## .ja6 -0.239 0.106 -2.257 0.024 -0.239 -0.160
## .ja7 -0.073 0.070 -1.053 0.293 -0.073 -0.074
## .ja8 -0.043 0.072 -0.602 0.547 -0.043 -0.043
## .stress1 -0.044 0.102 -0.431 0.667 -0.044 -0.030
## .stress2 -0.056 0.092 -0.610 0.542 -0.056 -0.043
## .stress3 -0.057 0.093 -0.608 0.543 -0.057 -0.043
## .stress4 0.106 0.093 1.139 0.255 0.106 0.081
## .js1 -0.111 0.121 -0.918 0.359 -0.111 -0.065
## .js2 -0.029 0.110 -0.263 0.792 -0.029 -0.019
## .js3 0.006 0.101 0.061 0.952 0.006 0.004
## .oc1 -0.017 0.177 -0.097 0.923 -0.017 -0.007
## .oc2 -0.045 0.131 -0.344 0.731 -0.045 -0.024
## .oc3 0.113 0.117 0.965 0.335 0.113 0.068
## .oc4 -0.034 0.166 -0.203 0.839 -0.034 -0.014
## .oc5 -0.095 0.139 -0.685 0.494 -0.095 -0.048
## .oc6 -0.008 0.155 -0.052 0.959 -0.008 -0.004
## .oc7 -0.033 0.104 -0.322 0.748 -0.033 -0.023
## .oc8 -0.009 0.126 -0.069 0.945 -0.009 -0.005
## .ti1 -0.116 0.183 -0.633 0.526 -0.116 -0.045
## .ti2 0.002 0.159 0.010 0.992 0.002 0.001
## .ti3 -0.042 0.131 -0.317 0.751 -0.042 -0.022
## jobauto 0.000 0.000 0.000
## jobstress 0.000 0.000 0.000
## jobsat 0.000 0.000 0.000
## org_commit 0.000 0.000 0.000
## turnover 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ja1 0.954 0.109 8.761 0.000 0.954 0.544
## .ja2 0.964 0.124 7.765 0.000 0.964 0.401
## .ja3 0.915 0.122 7.480 0.000 0.915 0.372
## .ja4 1.017 0.105 9.713 0.000 1.017 0.836
## .ja5 1.027 0.116 8.819 0.000 1.027 0.556
## .ja6 0.963 0.120 8.014 0.000 0.963 0.429
## .ja7 0.922 0.093 9.929 0.000 0.922 0.954
## .ja8 0.788 0.083 9.538 0.000 0.788 0.760
## .stress1 0.863 0.129 6.677 0.000 0.863 0.415
## .stress2 1.060 0.123 8.593 0.000 1.060 0.623
## .stress3 0.900 0.115 7.799 0.000 0.900 0.517
## .stress4 0.938 0.117 8.021 0.000 0.938 0.542
## .js1 1.100 0.146 7.530 0.000 1.100 0.376
## .js2 0.978 0.125 7.844 0.000 0.978 0.407
## .js3 0.908 0.111 8.208 0.000 0.908 0.449
## .oc1 1.154 0.137 8.444 0.000 1.154 0.185
## .oc2 0.974 0.107 9.111 0.000 0.974 0.283
## .oc3 0.901 0.097 9.285 0.000 0.901 0.329
## .oc4 1.174 0.135 8.701 0.000 1.174 0.213
## .oc5 0.919 0.104 8.880 0.000 0.919 0.239
## .oc6 0.871 0.104 8.397 0.000 0.871 0.180
## .oc7 0.977 0.102 9.578 0.000 0.977 0.453
## .oc8 0.837 0.093 9.027 0.000 0.837 0.265
## .ti1 1.340 0.185 7.238 0.000 1.340 0.200
## .ti2 0.940 0.135 6.964 0.000 0.940 0.186
## .ti3 0.996 0.120 8.326 0.000 0.996 0.288
## jobauto 0.800 0.156 5.118 0.000 1.000 1.000
## jobstress 1.219 0.213 5.735 0.000 1.000 1.000
## jobsat 1.824 0.287 6.344 0.000 1.000 1.000
## org_commit 5.093 0.618 8.237 0.000 1.000 1.000
## turnover 5.358 0.668 8.015 0.000 1.000 1.000
#measurement invariance
measurementInvariance(model = HW_model, data=HW, group = "sex")
## Warning: The measurementInvariance function is deprecated, and it will
## cease to be included in future versions of semTools. See help('semTools-
## deprecated) for details.
## Warning in lavaan::lavTestLRT(...): lavaan WARNING: method = "satorra.bentler.2001"
## but no robust test statistics were used;
## switching to the standard chi-square difference test
##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.intercepts
## Model 4 : fit.means
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 578 32916 33618 543.00
## fit.loadings 599 33005 33624 674.83 131.829 21 <2e-16 ***
## fit.intercepts 620 32978 33513 689.55 14.729 21 0.8363
## fit.means 625 32969 33484 690.84 1.287 5 0.9363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Fit measures:
##
## cfi rmsea cfi.delta rmsea.delta
## fit.configural 1.000 0.000 NA NA
## fit.loadings 0.989 0.025 0.011 0.025
## fit.intercepts 0.990 0.024 0.001 0.001
## fit.means 0.991 0.023 0.001 0.001
# number 4
JA_model <- "jobauto =~ ja1 + ja2 + ja3 +ja4 + ja5 + ja6 + ja7 + ja8"
measurementInvariance(model = JA_model, data=HW, group = "sex")
## Warning: The measurementInvariance function is deprecated, and it will
## cease to be included in future versions of semTools. See help('semTools-
## deprecated) for details.
## Warning in lavaan::lavTestLRT(...): lavaan WARNING: method = "satorra.bentler.2001"
## but no robust test statistics were used;
## switching to the standard chi-square difference test
##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.intercepts
## Model 4 : fit.means
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 40 9837.5 10029 32.257
## fit.loadings 47 9944.4 10108 153.135 120.878 7 <2e-16 ***
## fit.intercepts 54 9934.7 10070 157.430 4.295 7 0.7453
## fit.means 55 9932.8 10064 157.558 0.129 1 0.7200
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Fit measures:
##
## cfi rmsea cfi.delta rmsea.delta
## fit.configural 1.000 0.000 NA NA
## fit.loadings 0.893 0.106 0.107 0.106
## fit.intercepts 0.895 0.098 0.003 0.008
## fit.means 0.896 0.097 0.001 0.001
JS_model <- "jobstress =~ stress1 + stress2 + stress3 + stress4"
measurementInvariance(model = JS_model, data=HW, group = "sex")
## Warning: The measurementInvariance function is deprecated, and it will cease to be included in future versions of semTools. See help('semTools-deprecated) for details.
## Warning: lavaan WARNING: method = "satorra.bentler.2001"
## but no robust test statistics were used;
## switching to the standard chi-square difference test
##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.intercepts
## Model 4 : fit.means
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 4 5091.9 5187.7 0.8334
## fit.loadings 7 5086.8 5170.6 1.7073 0.8739 3 0.8317
## fit.intercepts 10 5084.6 5156.4 5.5141 3.8069 3 0.2831
## fit.means 11 5083.5 5151.4 6.4592 0.9451 1 0.3310
##
##
## Fit measures:
##
## cfi rmsea cfi.delta rmsea.delta
## fit.configural 1 0 NA NA
## fit.loadings 1 0 0 0
## fit.intercepts 1 0 0 0
## fit.means 1 0 0 0
JbS_model <- "jobsat =~ js1 + js2 + js3"
measurementInvariance(model = JbS_model, data=HW, group = "sex")
## Warning: The measurementInvariance function is deprecated, and it will cease to be included in future versions of semTools. See help('semTools-deprecated) for details.
## Warning: lavaan WARNING: method = "satorra.bentler.2001"
## but no robust test statistics were used;
## switching to the standard chi-square difference test
##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.intercepts
## Model 4 : fit.means
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 0 4153.1 4225.0 0.0000
## fit.loadings 2 4149.7 4213.6 0.5829 0.58290 2 0.7472
## fit.intercepts 4 4146.5 4202.3 1.3144 0.73153 2 0.6937
## fit.means 5 4144.6 4196.5 1.4937 0.17922 1 0.6720
##
##
## Fit measures:
##
## cfi rmsea cfi.delta rmsea.delta
## fit.configural 1 0 NA NA
## fit.loadings 1 0 0 0
## fit.intercepts 1 0 0 0
## fit.means 1 0 0 0
OC_model <- "org_commit =~ oc1 + oc2 + oc3 + oc4 + oc5 + oc6 + oc7 +oc8"
measurementInvariance(model = OC_model, data=HW, group = "sex")
## Warning: The measurementInvariance function is deprecated, and it will cease to be included in future versions of semTools. See help('semTools-deprecated) for details.
## Warning: lavaan WARNING: method = "satorra.bentler.2001"
## but no robust test statistics were used;
## switching to the standard chi-square difference test
##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.intercepts
## Model 4 : fit.means
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 40 10416 10608 31.880
## fit.loadings 47 10406 10570 35.974 4.0936 7 0.7689
## fit.intercepts 54 10397 10533 40.827 4.8531 7 0.6779
## fit.means 55 10396 10528 41.531 0.7044 1 0.4013
##
##
## Fit measures:
##
## cfi rmsea cfi.delta rmsea.delta
## fit.configural 1 0 NA NA
## fit.loadings 1 0 0 0
## fit.intercepts 1 0 0 0
## fit.means 1 0 0 0
t_model <- "turnover =~ ti1 + ti2 + ti3"
measurementInvariance(model = t_model, data=HW, group = "sex")
## Warning: The measurementInvariance function is deprecated, and it will cease to be included in future versions of semTools. See help('semTools-deprecated) for details.
## Warning: lavaan WARNING: method = "satorra.bentler.2001"
## but no robust test statistics were used;
## switching to the standard chi-square difference test
##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.intercepts
## Model 4 : fit.means
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 0 4532.3 4604.1 0.0000
## fit.loadings 2 4528.3 4592.2 0.0570 0.05705 2 0.9719
## fit.intercepts 4 4525.4 4581.3 1.1552 1.09814 2 0.5775
## fit.means 5 4523.8 4575.7 1.5247 0.36949 1 0.5433
##
##
## Fit measures:
##
## cfi rmsea cfi.delta rmsea.delta
## fit.configural 1 0 NA NA
## fit.loadings 1 0 0 0
## fit.intercepts 1 0 0 0
## fit.means 1 0 0 0
# number 5
HW2_model <- "jobstress =~ stress1 + stress2 + stress3 + stress4
jobsat =~ js1 + js2 + js3
org_commit =~ oc1 + oc2 + oc3 + oc4 + oc5 + oc6 + oc7 +oc8
turnover =~ ti1 + ti2 + ti3"
# number 6
HW2.sex.out <-cfa(HW2_model, data=HW, group = "sex")
lavaanPlot(model = HW2.sex.out)
summary(HW2.sex.out, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-7 ended normally after 109 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 120
##
## Number of observations per group:
## male 200
## female 200
##
## Model Test User Model:
##
## Test statistic 231.792
## Degrees of freedom 258
## P-value (Chi-square) 0.878
## Test statistic for each group:
## male 103.800
## female 127.993
##
## Model Test Baseline Model:
##
## Test statistic 6284.179
## Degrees of freedom 306
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.005
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -11457.426
## Loglikelihood unrestricted model (H1) -11341.530
##
## Akaike (AIC) 23154.853
## Bayesian (BIC) 23633.829
## Sample-size adjusted Bayesian (BIC) 23253.061
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.015
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.031
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [male]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## jobstress =~
## stress1 1.000 1.021 0.690
## stress2 0.638 0.099 6.429 0.000 0.651 0.545
## stress3 0.929 0.115 8.047 0.000 0.948 0.745
## stress4 0.783 0.108 7.281 0.000 0.800 0.634
## jobsat =~
## js1 1.000 1.673 0.869
## js2 0.902 0.059 15.293 0.000 1.509 0.842
## js3 0.809 0.056 14.440 0.000 1.353 0.814
## org_commit =~
## oc1 1.000 2.298 0.927
## oc2 0.777 0.040 19.604 0.000 1.785 0.865
## oc3 0.604 0.036 16.787 0.000 1.387 0.811
## oc4 0.949 0.043 22.062 0.000 2.181 0.902
## oc5 0.809 0.040 20.309 0.000 1.858 0.877
## oc6 0.922 0.041 22.329 0.000 2.119 0.905
## oc7 0.561 0.034 16.405 0.000 1.290 0.803
## oc8 0.701 0.036 19.372 0.000 1.611 0.861
## turnover =~
## ti1 1.000 2.556 0.929
## ti2 0.860 0.039 22.237 0.000 2.197 0.913
## ti3 0.677 0.036 18.971 0.000 1.730 0.861
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## jobstress ~~
## jobsat -0.671 0.165 -4.076 0.000 -0.393 -0.393
## org_commit -1.342 0.242 -5.549 0.000 -0.572 -0.572
## turnover -1.173 0.252 -4.650 0.000 -0.450 -0.450
## jobsat ~~
## org_commit 3.315 0.402 8.256 0.000 0.862 0.862
## turnover 3.871 0.458 8.448 0.000 0.905 0.905
## org_commit ~~
## turnover 5.246 0.601 8.730 0.000 0.893 0.893
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .stress1 -0.175 0.105 -1.671 0.095 -0.175 -0.118
## .stress2 0.032 0.084 0.374 0.708 0.032 0.026
## .stress3 -0.176 0.090 -1.955 0.051 -0.176 -0.138
## .stress4 -0.084 0.089 -0.941 0.347 -0.084 -0.067
## .js1 -0.006 0.136 -0.047 0.963 -0.006 -0.003
## .js2 -0.036 0.127 -0.288 0.773 -0.036 -0.020
## .js3 0.094 0.118 0.798 0.425 0.094 0.056
## .oc1 0.132 0.175 0.751 0.452 0.132 0.053
## .oc2 0.120 0.146 0.820 0.412 0.120 0.058
## .oc3 0.149 0.121 1.231 0.218 0.149 0.087
## .oc4 0.223 0.171 1.302 0.193 0.223 0.092
## .oc5 0.220 0.150 1.469 0.142 0.220 0.104
## .oc6 0.116 0.165 0.699 0.485 0.116 0.049
## .oc7 0.023 0.114 0.206 0.836 0.023 0.015
## .oc8 0.078 0.132 0.589 0.556 0.078 0.042
## .ti1 0.038 0.195 0.198 0.843 0.038 0.014
## .ti2 0.079 0.170 0.464 0.643 0.079 0.033
## .ti3 0.152 0.142 1.070 0.285 0.152 0.076
## jobstress 0.000 0.000 0.000
## jobsat 0.000 0.000 0.000
## org_commit 0.000 0.000 0.000
## turnover 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .stress1 1.148 0.154 7.462 0.000 1.148 0.524
## .stress2 1.003 0.113 8.846 0.000 1.003 0.703
## .stress3 0.723 0.111 6.540 0.000 0.723 0.446
## .stress4 0.950 0.117 8.140 0.000 0.950 0.598
## .js1 0.908 0.126 7.189 0.000 0.908 0.245
## .js2 0.938 0.120 7.816 0.000 0.938 0.292
## .js3 0.935 0.113 8.258 0.000 0.935 0.338
## .oc1 0.864 0.107 8.076 0.000 0.864 0.141
## .oc2 1.070 0.118 9.068 0.000 1.070 0.251
## .oc3 1.000 0.106 9.399 0.000 1.000 0.342
## .oc4 1.091 0.126 8.631 0.000 1.091 0.187
## .oc5 1.041 0.116 8.960 0.000 1.041 0.232
## .oc6 0.987 0.115 8.571 0.000 0.987 0.180
## .oc7 0.918 0.097 9.435 0.000 0.918 0.356
## .oc8 0.904 0.099 9.101 0.000 0.904 0.258
## .ti1 1.040 0.159 6.521 0.000 1.040 0.137
## .ti2 0.965 0.133 7.233 0.000 0.965 0.167
## .ti3 1.042 0.123 8.450 0.000 1.042 0.258
## jobstress 1.042 0.213 4.898 0.000 1.000 1.000
## jobsat 2.798 0.370 7.564 0.000 1.000 1.000
## org_commit 5.281 0.612 8.634 0.000 1.000 1.000
## turnover 6.536 0.760 8.599 0.000 1.000 1.000
##
##
## Group 2 [female]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## jobstress =~
## stress1 1.000 1.102 0.764
## stress2 0.721 0.094 7.710 0.000 0.795 0.609
## stress3 0.831 0.096 8.679 0.000 0.917 0.695
## stress4 0.814 0.095 8.548 0.000 0.897 0.682
## jobsat =~
## js1 1.000 1.349 0.789
## js2 0.887 0.080 11.151 0.000 1.198 0.772
## js3 0.781 0.073 10.650 0.000 1.053 0.741
## org_commit =~
## oc1 1.000 2.257 0.903
## oc2 0.696 0.040 17.549 0.000 1.571 0.847
## oc3 0.600 0.037 16.346 0.000 1.355 0.819
## oc4 0.922 0.047 19.587 0.000 2.081 0.887
## oc5 0.758 0.040 18.804 0.000 1.711 0.872
## oc6 0.881 0.043 20.641 0.000 1.989 0.905
## oc7 0.481 0.036 13.489 0.000 1.085 0.739
## oc8 0.675 0.037 18.050 0.000 1.523 0.857
## turnover =~
## ti1 1.000 2.314 0.894
## ti2 0.879 0.046 19.137 0.000 2.033 0.904
## ti3 0.677 0.041 16.524 0.000 1.567 0.843
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## jobstress ~~
## jobsat -0.711 0.150 -4.724 0.000 -0.478 -0.478
## org_commit -1.484 0.248 -5.989 0.000 -0.597 -0.597
## turnover -1.502 0.257 -5.849 0.000 -0.589 -0.589
## jobsat ~~
## org_commit 2.611 0.341 7.661 0.000 0.857 0.857
## turnover 2.605 0.349 7.474 0.000 0.834 0.834
## org_commit ~~
## turnover 4.743 0.556 8.523 0.000 0.908 0.908
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .stress1 -0.044 0.102 -0.431 0.667 -0.044 -0.030
## .stress2 -0.056 0.092 -0.610 0.542 -0.056 -0.043
## .stress3 -0.057 0.093 -0.608 0.543 -0.057 -0.043
## .stress4 0.106 0.093 1.139 0.255 0.106 0.081
## .js1 -0.111 0.121 -0.918 0.359 -0.111 -0.065
## .js2 -0.029 0.110 -0.263 0.792 -0.029 -0.019
## .js3 0.006 0.101 0.061 0.952 0.006 0.004
## .oc1 -0.017 0.177 -0.097 0.923 -0.017 -0.007
## .oc2 -0.045 0.131 -0.344 0.731 -0.045 -0.024
## .oc3 0.113 0.117 0.965 0.335 0.113 0.068
## .oc4 -0.034 0.166 -0.203 0.839 -0.034 -0.014
## .oc5 -0.095 0.139 -0.685 0.494 -0.095 -0.048
## .oc6 -0.008 0.155 -0.052 0.959 -0.008 -0.004
## .oc7 -0.033 0.104 -0.322 0.748 -0.033 -0.023
## .oc8 -0.009 0.126 -0.069 0.945 -0.009 -0.005
## .ti1 -0.116 0.183 -0.633 0.526 -0.116 -0.045
## .ti2 0.002 0.159 0.010 0.992 0.002 0.001
## .ti3 -0.042 0.131 -0.317 0.751 -0.042 -0.022
## jobstress 0.000 0.000 0.000
## jobsat 0.000 0.000 0.000
## org_commit 0.000 0.000 0.000
## turnover 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .stress1 0.867 0.130 6.659 0.000 0.867 0.416
## .stress2 1.071 0.124 8.612 0.000 1.071 0.629
## .stress3 0.901 0.116 7.774 0.000 0.901 0.517
## .stress4 0.924 0.117 7.927 0.000 0.924 0.534
## .js1 1.103 0.147 7.511 0.000 1.103 0.377
## .js2 0.972 0.125 7.792 0.000 0.972 0.404
## .js3 0.911 0.111 8.196 0.000 0.911 0.451
## .oc1 1.153 0.137 8.443 0.000 1.153 0.185
## .oc2 0.974 0.107 9.111 0.000 0.974 0.283
## .oc3 0.901 0.097 9.286 0.000 0.901 0.329
## .oc4 1.173 0.135 8.700 0.000 1.173 0.213
## .oc5 0.919 0.103 8.879 0.000 0.919 0.239
## .oc6 0.872 0.104 8.400 0.000 0.872 0.181
## .oc7 0.977 0.102 9.578 0.000 0.977 0.453
## .oc8 0.836 0.093 9.025 0.000 0.836 0.265
## .ti1 1.344 0.186 7.238 0.000 1.344 0.201
## .ti2 0.930 0.135 6.911 0.000 0.930 0.184
## .ti3 1.001 0.120 8.333 0.000 1.001 0.290
## jobstress 1.215 0.213 5.710 0.000 1.000 1.000
## jobsat 1.821 0.288 6.331 0.000 1.000 1.000
## org_commit 5.093 0.618 8.238 0.000 1.000 1.000
## turnover 5.353 0.668 8.008 0.000 1.000 1.000
#measurement invariance
measurementInvariance(model = HW2_model, data=HW, group = "sex")
## Warning: The measurementInvariance function is deprecated, and it will
## cease to be included in future versions of semTools. See help('semTools-
## deprecated) for details.
## Warning in lavaan::lavTestLRT(...): lavaan WARNING: method = "satorra.bentler.2001"
## but no robust test statistics were used;
## switching to the standard chi-square difference test
##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.intercepts
## Model 4 : fit.means
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 258 23155 23634 231.79
## fit.loadings 272 23133 23556 237.72 5.9288 14 0.9683
## fit.intercepts 286 23115 23482 248.16 10.4398 14 0.7294
## fit.means 290 23109 23460 249.39 1.2334 4 0.8726
##
##
## Fit measures:
##
## cfi rmsea cfi.delta rmsea.delta
## fit.configural 1 0 NA NA
## fit.loadings 1 0 0 0
## fit.intercepts 1 0 0 0
## fit.means 1 0 0 0
# number 7
HW2_sem <- lavaan::sem(HW2_model, data=HW, group="sex")
#semPaths(HW2_sem)
#Number 9
partialm <- 'jobstress =~ stress1 + stress2 + stress3 + stress4
jobsat =~ js1 + js2 + js3
org_commit =~ oc1 + oc2 + oc3 + oc4 + oc5 + oc6 + oc7 + oc8
turnover =~ ti1 + ti2 + ti3
jobsat ~ jobstress + pay
org_commit ~ jobsat + jobstress + pay
turnover ~ jobsat + org_commit
'
partialm.out <- sem(partialm, data = HW)
summary(partialm.out)
## lavaan 0.6-7 ended normally after 48 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 43
##
## Number of observations 400
##
## Model Test User Model:
##
## Test statistic 123.406
## Degrees of freedom 146
## P-value (Chi-square) 0.913
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## jobstress =~
## stress1 1.000
## stress2 0.675 0.068 9.887 0.000
## stress3 0.883 0.074 11.969 0.000
## stress4 0.813 0.072 11.309 0.000
## jobsat =~
## js1 1.000
## js2 0.891 0.048 18.513 0.000
## js3 0.793 0.045 17.588 0.000
## org_commit =~
## oc1 1.000
## oc2 0.739 0.029 25.754 0.000
## oc3 0.604 0.026 23.070 0.000
## oc4 0.938 0.033 28.771 0.000
## oc5 0.788 0.029 27.134 0.000
## oc6 0.903 0.030 29.641 0.000
## oc7 0.523 0.025 20.652 0.000
## oc8 0.689 0.027 25.895 0.000
## turnover =~
## ti1 1.000
## ti2 0.867 0.030 28.663 0.000
## ti3 0.679 0.027 24.803 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## jobsat ~
## jobstress -0.561 0.082 -6.862 0.000
## pay 0.722 0.077 9.398 0.000
## org_commit ~
## jobsat 0.964 0.072 13.373 0.000
## jobstress -0.623 0.088 -7.105 0.000
## pay 0.458 0.082 5.549 0.000
## turnover ~
## jobsat 0.605 0.110 5.508 0.000
## org_commit 0.617 0.072 8.629 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .stress1 1.017 0.100 10.172 0.000
## .stress2 1.055 0.085 12.430 0.000
## .stress3 0.810 0.079 10.258 0.000
## .stress4 0.925 0.082 11.273 0.000
## .js1 0.986 0.096 10.234 0.000
## .js2 0.960 0.087 10.994 0.000
## .js3 0.930 0.080 11.594 0.000
## .oc1 1.022 0.087 11.781 0.000
## .oc2 1.023 0.080 12.857 0.000
## .oc3 0.944 0.071 13.216 0.000
## .oc4 1.134 0.092 12.271 0.000
## .oc5 0.981 0.078 12.619 0.000
## .oc6 0.938 0.078 12.048 0.000
## .oc7 0.954 0.071 13.456 0.000
## .oc8 0.872 0.068 12.835 0.000
## .ti1 1.193 0.123 9.705 0.000
## .ti2 0.957 0.096 9.988 0.000
## .ti3 1.015 0.086 11.792 0.000
## jobstress 1.124 0.150 7.480 0.000
## .jobsat 1.471 0.163 9.002 0.000
## .org_commit 0.956 0.125 7.658 0.000
## .turnover 0.901 0.121 7.413 0.000
#semPaths(partialm.out)
fullm <- 'jobstress =~ stress1 + stress2 + stress3 + stress4
jobsat =~ js1 + js2 + js3
org_commit =~ oc1 + oc2 + oc3 + oc4 + oc5 + oc6 + oc7 + oc8
turnover =~ ti1 + ti2 + ti3
jobsat ~ jobstress + pay
org_commit ~ jobsat + jobstress + pay
turnover ~ org_commit
'
fullm.out <- sem(fullm, data = HW)
summary(fullm.out)
## lavaan 0.6-7 ended normally after 46 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 42
##
## Number of observations 400
##
## Model Test User Model:
##
## Test statistic 155.043
## Degrees of freedom 147
## P-value (Chi-square) 0.309
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## jobstress =~
## stress1 1.000
## stress2 0.675 0.068 9.885 0.000
## stress3 0.882 0.074 11.966 0.000
## stress4 0.814 0.072 11.318 0.000
## jobsat =~
## js1 1.000
## js2 0.889 0.048 18.380 0.000
## js3 0.788 0.045 17.386 0.000
## org_commit =~
## oc1 1.000
## oc2 0.738 0.029 25.595 0.000
## oc3 0.604 0.026 23.051 0.000
## oc4 0.937 0.033 28.624 0.000
## oc5 0.787 0.029 27.043 0.000
## oc6 0.902 0.031 29.550 0.000
## oc7 0.523 0.025 20.622 0.000
## oc8 0.689 0.027 25.874 0.000
## turnover =~
## ti1 1.000
## ti2 0.866 0.030 28.565 0.000
## ti3 0.677 0.027 24.697 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## jobsat ~
## jobstress -0.555 0.082 -6.763 0.000
## pay 0.735 0.077 9.489 0.000
## org_commit ~
## jobsat 0.995 0.072 13.873 0.000
## jobstress -0.605 0.085 -7.095 0.000
## pay 0.424 0.081 5.230 0.000
## turnover ~
## org_commit 0.974 0.040 24.072 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .stress1 1.017 0.100 10.172 0.000
## .stress2 1.055 0.085 12.432 0.000
## .stress3 0.811 0.079 10.268 0.000
## .stress4 0.924 0.082 11.265 0.000
## .js1 0.976 0.098 9.975 0.000
## .js2 0.960 0.089 10.826 0.000
## .js3 0.942 0.082 11.512 0.000
## .oc1 1.029 0.087 11.885 0.000
## .oc2 1.036 0.080 12.926 0.000
## .oc3 0.945 0.071 13.251 0.000
## .oc4 1.149 0.093 12.369 0.000
## .oc5 0.988 0.078 12.688 0.000
## .oc6 0.945 0.078 12.144 0.000
## .oc7 0.956 0.071 13.483 0.000
## .oc8 0.873 0.068 12.883 0.000
## .ti1 1.178 0.124 9.480 0.000
## .ti2 0.960 0.097 9.852 0.000
## .ti3 1.022 0.087 11.733 0.000
## jobstress 1.124 0.150 7.481 0.000
## .jobsat 1.472 0.164 8.974 0.000
## .org_commit 0.875 0.119 7.362 0.000
## .turnover 1.055 0.130 8.110 0.000
#semPaths(fullm.out)
compareFit(partialm.out,fullm.out)
## ################### Nested Model Comparison #########################
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## partialm.out 146 22913 23084 123.41
## fullm.out 147 22942 23110 155.04 31.637 1 1.859e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue cfi tli aic bic rmsea
## partialm.out 123.406† 146 .913 1.000† 1.004† 22912.623† 23084.256† .000†
## fullm.out 155.043 147 .309 0.999 0.998 22942.260 23109.902 .012
## srmr
## partialm.out .033†
## fullm.out .036
##
## ################## Differences in Fit Indices #######################
## df cfi tli aic bic rmsea srmr
## fullm.out - partialm.out 1 -0.001 -0.006 29.637 25.646 0.012 0.003