KFA u R-u
Konfirmatorna faktorska analiza u R-u (lavaan paket)
lavaan tutorial: http://lavaan.ugent.be/tutorial/index.html
Učitavanje paketa
# install.packages("foreign") # za učitavanje fajla sa podacima
# install.packages("lavaan", dependencies=TRUE) # za testiranje modela
library(foreign)
library(lavaan)## This is lavaan 0.6-9
## lavaan is FREE software! Please report any bugs.Učitavanje podataka
# KFA_ds = read.spss(file.choose(), use.value.labels=FALSE, to.data.frame=TRUE)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
KFA_ds <- read.spss("KFA_podaci.sav", use.value.labels=FALSE, to.data.frame=TRUE)## re-encoding from CP1252Definisanje modela
za dodatne informacije o sintaksnom definisanju modela
?lavaan::model.syntax
KFA.3Fmodel <- ' intrinsic =~ jss1 + jss6 + jss8 + jss9 + jss10 + jss15 + jss16
organizational =~ jss2 + jss7 + jss12 + jss13 + jss14
salary_promotion =~ jss4 + jss5 'Procena modela
za više informacija o proceni modela
?lavaan::cfa
fit.3F <- cfa(KFA.3Fmodel, data=KFA_ds, missing="ML")Ispis analize
za više informacija o ispisu analize
?lavaan::lavaan-class
summary(fit.3F, header=TRUE, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-9 ended normally after 69 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 45
##
## Number of observations 139
## Number of missing patterns 16
##
## Model Test User Model:
##
## Test statistic 223.483
## Degrees of freedom 74
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 966.747
## Degrees of freedom 91
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.829
## Tucker-Lewis Index (TLI) 0.790
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3331.241
## Loglikelihood unrestricted model (H1) -3219.500
##
## Akaike (AIC) 6752.483
## Bayesian (BIC) 6884.534
## Sample-size adjusted Bayesian (BIC) 6742.165
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.121
## 90 Percent confidence interval - lower 0.103
## 90 Percent confidence interval - upper 0.139
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.078
##
## 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
## intrinsic =~
## jss1 1.000 0.625 0.590
## jss6 1.889 0.300 6.295 0.000 1.180 0.715
## jss8 1.330 0.290 4.583 0.000 0.831 0.468
## jss9 1.786 0.289 6.189 0.000 1.115 0.722
## jss10 1.994 0.299 6.675 0.000 1.246 0.827
## jss15 2.183 0.326 6.695 0.000 1.364 0.829
## jss16 1.535 0.259 5.920 0.000 0.959 0.651
## organizational =~
## jss2 1.000 1.406 0.738
## jss7 1.185 0.143 8.316 0.000 1.667 0.748
## jss12 0.737 0.119 6.203 0.000 1.037 0.564
## jss13 1.019 0.112 9.078 0.000 1.433 0.804
## jss14 1.175 0.135 8.715 0.000 1.653 0.788
## salary_promotion =~
## jss4 1.000 0.998 0.543
## jss5 1.762 0.506 3.479 0.001 1.757 0.937
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intrinsic ~~
## organizational 0.578 0.130 4.432 0.000 0.657 0.657
## salary_promotn 0.243 0.097 2.496 0.013 0.390 0.390
## organizational ~~
## salary_promotn 0.690 0.244 2.824 0.005 0.492 0.492
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .jss1 5.892 0.091 64.772 0.000 5.892 5.568
## .jss6 5.249 0.141 37.349 0.000 5.249 3.181
## .jss8 4.439 0.151 29.474 0.000 4.439 2.500
## .jss9 5.446 0.132 41.259 0.000 5.446 3.522
## .jss10 5.495 0.128 42.822 0.000 5.495 3.646
## .jss15 5.285 0.146 36.288 0.000 5.285 3.211
## .jss16 5.419 0.126 42.989 0.000 5.419 3.681
## .jss2 5.101 0.162 31.563 0.000 5.101 2.677
## .jss7 4.282 0.189 22.600 0.000 4.282 1.921
## .jss12 4.271 0.157 27.227 0.000 4.271 2.322
## .jss13 4.777 0.151 31.580 0.000 4.777 2.679
## .jss14 4.302 0.178 24.142 0.000 4.302 2.051
## .jss4 4.743 0.156 30.365 0.000 4.743 2.582
## .jss5 2.861 0.160 17.855 0.000 2.861 1.525
## intrinsic 0.000 0.000 0.000
## organizational 0.000 0.000 0.000
## salary_promotn 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .jss1 0.730 0.096 7.612 0.000 0.730 0.652
## .jss6 1.331 0.190 7.007 0.000 1.331 0.489
## .jss8 2.462 0.308 7.985 0.000 2.462 0.781
## .jss9 1.146 0.179 6.393 0.000 1.146 0.479
## .jss10 0.720 0.138 5.203 0.000 0.720 0.317
## .jss15 0.849 0.161 5.267 0.000 0.849 0.313
## .jss16 1.248 0.177 7.049 0.000 1.248 0.576
## .jss2 1.652 0.240 6.891 0.000 1.652 0.455
## .jss7 2.193 0.330 6.641 0.000 2.193 0.441
## .jss12 2.309 0.299 7.719 0.000 2.309 0.682
## .jss13 1.126 0.189 5.971 0.000 1.126 0.354
## .jss14 1.666 0.267 6.228 0.000 1.666 0.379
## .jss4 2.378 0.384 6.189 0.000 2.378 0.705
## .jss5 0.431 0.801 0.538 0.590 0.431 0.122
## intrinsic 0.390 0.112 3.483 0.000 1.000 1.000
## organizational 1.978 0.411 4.818 0.000 1.000 1.000
## salary_promotn 0.995 0.385 2.583 0.010 1.000 1.000Hi-kvadrat nije isti kao u AMOS-u. AKo želimo da dobijemo istu verdnost, treba da uključimo “wishart” opciju. Vrednosti AIC i BIC se takođe razlikuju zbog drugačije aproksimacije u AMOS-u i lavaan-u, ne mogu se podesiti da budu identični, ali su konzistentni (daju isti redosled modela po visini indeksa).
fit.3F <- cfa(KFA.3Fmodel, data=KFA_ds, missing="ML", likelihood = "wishart")
summary(fit.3F, header=TRUE, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-9 ended normally after 68 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 45
##
## Number of observations 139
## Number of missing patterns 16
##
## Model Test User Model:
##
## Test statistic 221.875
## Degrees of freedom 74
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 959.792
## Degrees of freedom 91
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.830
## Tucker-Lewis Index (TLI) 0.791
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3331.241
## Loglikelihood unrestricted model (H1) -3219.500
##
## Akaike (AIC) 6752.483
## Bayesian (BIC) 6884.534
## Sample-size adjusted Bayesian (BIC) 6742.165
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.120
## 90 Percent confidence interval - lower 0.102
## 90 Percent confidence interval - upper 0.139
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.078
##
## 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
## intrinsic =~
## jss1 1.000 0.625 0.590
## jss6 1.889 0.301 6.273 0.000 1.180 0.715
## jss8 1.330 0.291 4.567 0.000 0.831 0.468
## jss9 1.786 0.290 6.166 0.000 1.115 0.722
## jss10 1.994 0.300 6.651 0.000 1.246 0.827
## jss15 2.183 0.327 6.671 0.000 1.364 0.829
## jss16 1.535 0.260 5.898 0.000 0.959 0.651
## organizational =~
## jss2 1.000 1.406 0.738
## jss7 1.185 0.143 8.286 0.000 1.667 0.748
## jss12 0.737 0.119 6.181 0.000 1.037 0.564
## jss13 1.019 0.113 9.045 0.000 1.433 0.804
## jss14 1.175 0.135 8.683 0.000 1.653 0.788
## salary_promotion =~
## jss4 1.000 0.998 0.543
## jss5 1.762 0.508 3.467 0.001 1.757 0.937
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intrinsic ~~
## organizational 0.578 0.131 4.416 0.000 0.657 0.657
## salary_promotn 0.243 0.098 2.487 0.013 0.390 0.390
## organizational ~~
## salary_promotn 0.690 0.245 2.814 0.005 0.492 0.492
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .jss1 5.892 0.091 64.538 0.000 5.892 5.568
## .jss6 5.249 0.141 37.214 0.000 5.249 3.181
## .jss8 4.439 0.151 29.367 0.000 4.439 2.500
## .jss9 5.446 0.132 41.110 0.000 5.446 3.522
## .jss10 5.495 0.129 42.668 0.000 5.495 3.646
## .jss15 5.285 0.146 36.157 0.000 5.285 3.211
## .jss16 5.419 0.127 42.834 0.000 5.419 3.681
## .jss2 5.101 0.162 31.449 0.000 5.101 2.677
## .jss7 4.282 0.190 22.519 0.000 4.282 1.921
## .jss12 4.271 0.157 27.129 0.000 4.271 2.322
## .jss13 4.777 0.152 31.466 0.000 4.777 2.679
## .jss14 4.302 0.179 24.055 0.000 4.302 2.051
## .jss4 4.743 0.157 30.256 0.000 4.743 2.582
## .jss5 2.861 0.161 17.790 0.000 2.861 1.525
## intrinsic 0.000 0.000 0.000
## organizational 0.000 0.000 0.000
## salary_promotn 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .jss1 0.730 0.096 7.584 0.000 0.730 0.652
## .jss6 1.331 0.191 6.982 0.000 1.331 0.489
## .jss8 2.462 0.309 7.957 0.000 2.462 0.781
## .jss9 1.146 0.180 6.370 0.000 1.146 0.479
## .jss10 0.720 0.139 5.185 0.000 0.720 0.317
## .jss15 0.849 0.162 5.248 0.000 0.849 0.313
## .jss16 1.248 0.178 7.024 0.000 1.248 0.576
## .jss2 1.652 0.241 6.867 0.000 1.652 0.455
## .jss7 2.193 0.331 6.617 0.000 2.193 0.441
## .jss12 2.309 0.300 7.691 0.000 2.309 0.682
## .jss13 1.126 0.189 5.950 0.000 1.126 0.354
## .jss14 1.666 0.268 6.205 0.000 1.666 0.379
## .jss4 2.378 0.386 6.166 0.000 2.378 0.705
## .jss5 0.431 0.803 0.536 0.592 0.431 0.122
## intrinsic 0.390 0.112 3.470 0.001 1.000 1.000
## organizational 1.978 0.412 4.800 0.000 1.000 1.000
## salary_promotn 0.995 0.387 2.574 0.010 1.000 1.000Izdvajanje željenih delova ispisa:
Samo procene parametara
parameterEstimates(fit.3F)## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 intrinsic =~ jss1 1.000 0.000 NA NA 1.000 1.000
## 2 intrinsic =~ jss6 1.889 0.301 6.273 0.000 1.299 2.479
## 3 intrinsic =~ jss8 1.330 0.291 4.567 0.000 0.759 1.901
## 4 intrinsic =~ jss9 1.786 0.290 6.166 0.000 1.218 2.353
## 5 intrinsic =~ jss10 1.994 0.300 6.651 0.000 1.407 2.582
## 6 intrinsic =~ jss15 2.183 0.327 6.671 0.000 1.542 2.825
## 7 intrinsic =~ jss16 1.535 0.260 5.898 0.000 1.025 2.045
## 8 organizational =~ jss2 1.000 0.000 NA NA 1.000 1.000
## 9 organizational =~ jss7 1.185 0.143 8.286 0.000 0.905 1.466
## 10 organizational =~ jss12 0.737 0.119 6.181 0.000 0.504 0.971
## 11 organizational =~ jss13 1.019 0.113 9.045 0.000 0.798 1.240
## 12 organizational =~ jss14 1.175 0.135 8.683 0.000 0.910 1.441
## 13 salary_promotion =~ jss4 1.000 0.000 NA NA 1.000 1.000
## 14 salary_promotion =~ jss5 1.762 0.508 3.467 0.001 0.766 2.758
## 15 jss1 ~~ jss1 0.730 0.096 7.584 0.000 0.541 0.918
## 16 jss6 ~~ jss6 1.331 0.191 6.982 0.000 0.957 1.704
## 17 jss8 ~~ jss8 2.462 0.309 7.957 0.000 1.856 3.069
## 18 jss9 ~~ jss9 1.146 0.180 6.370 0.000 0.793 1.498
## 19 jss10 ~~ jss10 0.720 0.139 5.185 0.000 0.448 0.992
## 20 jss15 ~~ jss15 0.849 0.162 5.248 0.000 0.532 1.166
## 21 jss16 ~~ jss16 1.248 0.178 7.024 0.000 0.899 1.596
## 22 jss2 ~~ jss2 1.652 0.241 6.867 0.000 1.181 2.124
## 23 jss7 ~~ jss7 2.193 0.331 6.617 0.000 1.544 2.843
## 24 jss12 ~~ jss12 2.309 0.300 7.691 0.000 1.720 2.897
## 25 jss13 ~~ jss13 1.126 0.189 5.950 0.000 0.755 1.497
## 26 jss14 ~~ jss14 1.666 0.268 6.205 0.000 1.140 2.192
## 27 jss4 ~~ jss4 2.378 0.386 6.166 0.000 1.622 3.134
## 28 jss5 ~~ jss5 0.431 0.803 0.536 0.592 -1.144 2.006
## 29 intrinsic ~~ intrinsic 0.390 0.112 3.470 0.001 0.170 0.611
## 30 organizational ~~ organizational 1.978 0.412 4.800 0.000 1.170 2.785
## 31 salary_promotion ~~ salary_promotion 0.995 0.387 2.574 0.010 0.237 1.753
## 32 intrinsic ~~ organizational 0.578 0.131 4.416 0.000 0.321 0.834
## 33 intrinsic ~~ salary_promotion 0.243 0.098 2.487 0.013 0.052 0.435
## 34 organizational ~~ salary_promotion 0.690 0.245 2.814 0.005 0.209 1.170
## 35 jss1 ~1 5.892 0.091 64.538 0.000 5.713 6.071
## 36 jss6 ~1 5.249 0.141 37.214 0.000 4.972 5.525
## 37 jss8 ~1 4.439 0.151 29.367 0.000 4.143 4.735
## 38 jss9 ~1 5.446 0.132 41.110 0.000 5.186 5.705
## 39 jss10 ~1 5.495 0.129 42.668 0.000 5.243 5.747
## 40 jss15 ~1 5.285 0.146 36.157 0.000 4.999 5.572
## 41 jss16 ~1 5.419 0.127 42.834 0.000 5.171 5.667
## 42 jss2 ~1 5.101 0.162 31.449 0.000 4.783 5.419
## 43 jss7 ~1 4.282 0.190 22.519 0.000 3.910 4.655
## 44 jss12 ~1 4.271 0.157 27.129 0.000 3.962 4.579
## 45 jss13 ~1 4.777 0.152 31.466 0.000 4.479 5.075
## 46 jss14 ~1 4.302 0.179 24.055 0.000 3.952 4.653
## 47 jss4 ~1 4.743 0.157 30.256 0.000 4.436 5.050
## 48 jss5 ~1 2.861 0.161 17.790 0.000 2.546 3.177
## 49 intrinsic ~1 0.000 0.000 NA NA 0.000 0.000
## 50 organizational ~1 0.000 0.000 NA NA 0.000 0.000
## 51 salary_promotion ~1 0.000 0.000 NA NA 0.000 0.000Samo standardizovane procene parametara
standardizedSolution(fit.3F) ## lhs op rhs est.std se z pvalue ci.lower ci.upper
## 1 intrinsic =~ jss1 0.590 0.064 9.269 0.000 0.465 0.715
## 2 intrinsic =~ jss6 0.715 0.049 14.533 0.000 0.619 0.812
## 3 intrinsic =~ jss8 0.468 0.073 6.411 0.000 0.325 0.611
## 4 intrinsic =~ jss9 0.722 0.054 13.452 0.000 0.616 0.827
## 5 intrinsic =~ jss10 0.827 0.040 20.534 0.000 0.748 0.905
## 6 intrinsic =~ jss15 0.829 0.040 20.924 0.000 0.751 0.906
## 7 intrinsic =~ jss16 0.651 0.060 10.915 0.000 0.534 0.768
## 8 organizational =~ jss2 0.738 0.046 15.957 0.000 0.647 0.829
## 9 organizational =~ jss7 0.748 0.046 16.193 0.000 0.657 0.838
## 10 organizational =~ jss12 0.564 0.065 8.659 0.000 0.436 0.691
## 11 organizational =~ jss13 0.804 0.040 20.255 0.000 0.726 0.882
## 12 organizational =~ jss14 0.788 0.041 19.092 0.000 0.707 0.869
## 13 salary_promotion =~ jss4 0.543 0.092 5.875 0.000 0.362 0.724
## 14 salary_promotion =~ jss5 0.937 0.122 7.669 0.000 0.697 1.176
## 15 jss1 ~~ jss1 0.652 0.075 8.667 0.000 0.504 0.799
## 16 jss6 ~~ jss6 0.489 0.070 6.944 0.000 0.351 0.627
## 17 jss8 ~~ jss8 0.781 0.068 11.433 0.000 0.647 0.915
## 18 jss9 ~~ jss9 0.479 0.077 6.193 0.000 0.328 0.631
## 19 jss10 ~~ jss10 0.317 0.067 4.762 0.000 0.186 0.447
## 20 jss15 ~~ jss15 0.313 0.066 4.774 0.000 0.185 0.442
## 21 jss16 ~~ jss16 0.576 0.078 7.404 0.000 0.423 0.728
## 22 jss2 ~~ jss2 0.455 0.068 6.665 0.000 0.321 0.589
## 23 jss7 ~~ jss7 0.441 0.069 6.391 0.000 0.306 0.576
## 24 jss12 ~~ jss12 0.682 0.073 9.295 0.000 0.538 0.826
## 25 jss13 ~~ jss13 0.354 0.064 5.549 0.000 0.229 0.479
## 26 jss14 ~~ jss14 0.379 0.065 5.819 0.000 0.251 0.506
## 27 jss4 ~~ jss4 0.705 0.100 7.018 0.000 0.508 0.902
## 28 jss5 ~~ jss5 0.122 0.229 0.535 0.593 -0.326 0.571
## 29 intrinsic ~~ intrinsic 1.000 0.000 NA NA 1.000 1.000
## 30 organizational ~~ organizational 1.000 0.000 NA NA 1.000 1.000
## 31 salary_promotion ~~ salary_promotion 1.000 0.000 NA NA 1.000 1.000
## 32 intrinsic ~~ organizational 0.657 0.062 10.532 0.000 0.535 0.780
## 33 intrinsic ~~ salary_promotion 0.390 0.097 4.019 0.000 0.200 0.581
## 34 organizational ~~ salary_promotion 0.492 0.096 5.136 0.000 0.304 0.679
## 35 jss1 ~1 5.568 0.357 15.613 0.000 4.869 6.267
## 36 jss6 ~1 3.181 0.211 15.098 0.000 2.768 3.594
## 37 jss8 ~1 2.500 0.173 14.460 0.000 2.161 2.839
## 38 jss9 ~1 3.522 0.235 15.017 0.000 3.063 3.982
## 39 jss10 ~1 3.646 0.243 15.023 0.000 3.170 4.122
## 40 jss15 ~1 3.211 0.232 13.854 0.000 2.757 3.666
## 41 jss16 ~1 3.681 0.241 15.256 0.000 3.208 4.154
## 42 jss2 ~1 2.677 0.182 14.690 0.000 2.320 3.034
## 43 jss7 ~1 1.921 0.144 13.369 0.000 1.639 2.202
## 44 jss12 ~1 2.322 0.165 14.091 0.000 1.999 2.644
## 45 jss13 ~1 2.679 0.182 14.691 0.000 2.321 3.036
## 46 jss14 ~1 2.051 0.151 13.588 0.000 1.755 2.347
## 47 jss4 ~1 2.582 0.178 14.524 0.000 2.234 2.931
## 48 jss5 ~1 1.525 0.127 12.016 0.000 1.276 1.774
## 49 intrinsic ~1 0.000 0.000 NA NA 0.000 0.000
## 50 organizational ~1 0.000 0.000 NA NA 0.000 0.000
## 51 salary_promotion ~1 0.000 0.000 NA NA 0.000 0.000Samo pokazatelji fita modela
fitMeasures(fit.3F)## npar fmin chisq df pvalue
## 45.000 0.804 221.875 74.000 0.000
## baseline.chisq baseline.df baseline.pvalue cfi tli
## 959.792 91.000 0.000 0.830 0.791
## nnfi rfi nfi pnfi ifi
## 0.791 0.716 0.769 0.625 0.833
## rni logl unrestricted.logl aic bic
## 0.830 -3331.241 -3219.500 6752.483 6884.534
## ntotal bic2 rmsea rmsea.ci.lower rmsea.ci.upper
## 139.000 6742.165 0.120 0.102 0.139
## rmsea.pvalue rmr rmr_nomean srmr srmr_bentler
## 0.000 0.235 0.250 0.078 0.078
## srmr_bentler_nomean crmr crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.082 0.083 0.088 0.078 0.082
## cn_05 cn_01 gfi agfi pgfi
## 60.138 66.433 0.965 0.943 0.600
## mfi ecvi
## 0.585 2.260Samo konkretni pokazatelj fita modela
fitMeasures(fit.3F, "cfi")## cfi
## 0.83Vektor izabranih pokazatelja
fitMeasures(fit.3F, c("cfi","rmsea","srmr")) ## cfi rmsea srmr
## 0.830 0.120 0.078Modification indices
modindices(fit.3F) ## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 52 intrinsic =~ jss2 0.629 0.244 0.152 0.080 0.080
## 53 intrinsic =~ jss7 0.223 -0.169 -0.106 -0.047 -0.047
## 54 intrinsic =~ jss12 0.141 0.126 0.079 0.043 0.043
## 55 intrinsic =~ jss13 0.153 0.108 0.067 0.038 0.038
## 56 intrinsic =~ jss14 0.841 -0.301 -0.188 -0.090 -0.090
## 57 intrinsic =~ jss4 0.007 0.034 0.021 0.012 0.012
## 58 intrinsic =~ jss5 0.007 -0.060 -0.037 -0.020 -0.020
## 59 organizational =~ jss1 2.733 -0.141 -0.198 -0.187 -0.187
## 60 organizational =~ jss6 0.438 -0.079 -0.111 -0.067 -0.067
## 61 organizational =~ jss8 18.853 0.654 0.920 0.518 0.518
## 62 organizational =~ jss9 2.882 -0.189 -0.266 -0.172 -0.172
## 63 organizational =~ jss10 0.446 -0.066 -0.092 -0.061 -0.061
## 64 organizational =~ jss15 4.470 0.240 0.338 0.205 0.205
## 65 organizational =~ jss16 0.378 -0.069 -0.098 -0.066 -0.066
## 66 organizational =~ jss4 0.007 -0.053 -0.074 -0.040 -0.040
## 67 organizational =~ jss5 0.007 0.093 0.131 0.070 0.070
## 68 salary_promotion =~ jss1 1.152 -0.098 -0.098 -0.092 -0.092
## 69 salary_promotion =~ jss6 4.973 0.284 0.283 0.172 0.172
## 70 salary_promotion =~ jss8 12.931 0.582 0.580 0.327 0.327
## 71 salary_promotion =~ jss9 8.158 -0.339 -0.338 -0.219 -0.219
## 72 salary_promotion =~ jss10 3.015 -0.179 -0.179 -0.119 -0.119
## 73 salary_promotion =~ jss15 0.248 0.060 0.060 0.036 0.036
## 74 salary_promotion =~ jss16 1.695 0.157 0.157 0.107 0.107
## 75 salary_promotion =~ jss2 1.040 -0.160 -0.160 -0.084 -0.084
## 76 salary_promotion =~ jss7 1.747 -0.242 -0.241 -0.108 -0.108
## 77 salary_promotion =~ jss12 2.238 -0.260 -0.259 -0.141 -0.141
## 78 salary_promotion =~ jss13 3.854 0.273 0.273 0.153 0.153
## 79 salary_promotion =~ jss14 0.994 0.166 0.165 0.079 0.079
## 80 jss1 ~~ jss6 0.042 -0.020 -0.020 -0.020 -0.020
## 81 jss1 ~~ jss8 1.196 -0.134 -0.134 -0.100 -0.100
## 82 jss1 ~~ jss9 2.025 0.128 0.128 0.140 0.140
## 83 jss1 ~~ jss10 0.660 -0.064 -0.064 -0.088 -0.088
## 84 jss1 ~~ jss15 0.110 -0.030 -0.030 -0.038 -0.038
## 85 jss1 ~~ jss16 6.610 0.235 0.235 0.246 0.246
## 86 jss1 ~~ jss2 0.074 0.029 0.029 0.026 0.026
## 87 jss1 ~~ jss7 2.466 -0.193 -0.193 -0.153 -0.153
## 88 jss1 ~~ jss12 1.560 0.150 0.150 0.115 0.115
## 89 jss1 ~~ jss13 1.077 -0.095 -0.095 -0.105 -0.105
## 90 jss1 ~~ jss14 0.020 0.016 0.016 0.014 0.014
## 91 jss1 ~~ jss4 4.268 0.249 0.249 0.189 0.189
## 92 jss1 ~~ jss5 2.750 -0.192 -0.192 -0.342 -0.342
## 93 jss6 ~~ jss8 5.860 0.413 0.413 0.228 0.228
## 94 jss6 ~~ jss9 0.175 -0.054 -0.054 -0.043 -0.043
## 95 jss6 ~~ jss10 2.101 -0.167 -0.167 -0.171 -0.171
## 96 jss6 ~~ jss15 0.434 -0.087 -0.087 -0.082 -0.082
## 97 jss6 ~~ jss16 5.290 0.296 0.296 0.230 0.230
## 98 jss6 ~~ jss2 0.353 0.087 0.087 0.059 0.059
## 99 jss6 ~~ jss7 0.788 -0.151 -0.151 -0.088 -0.088
## 100 jss6 ~~ jss12 17.617 -0.694 -0.694 -0.396 -0.396
## 101 jss6 ~~ jss13 1.616 0.161 0.161 0.132 0.132
## 102 jss6 ~~ jss14 0.056 -0.036 -0.036 -0.024 -0.024
## 103 jss6 ~~ jss4 1.100 -0.175 -0.175 -0.098 -0.098
## 104 jss6 ~~ jss5 8.464 0.467 0.467 0.617 0.617
## 105 jss8 ~~ jss9 3.427 -0.294 -0.294 -0.175 -0.175
## 106 jss8 ~~ jss10 2.102 -0.200 -0.200 -0.150 -0.150
## 107 jss8 ~~ jss15 0.481 -0.111 -0.111 -0.077 -0.077
## 108 jss8 ~~ jss16 0.742 -0.140 -0.140 -0.080 -0.080
## 109 jss8 ~~ jss2 2.408 -0.292 -0.292 -0.145 -0.145
## 110 jss8 ~~ jss7 0.139 0.082 0.082 0.035 0.035
## 111 jss8 ~~ jss12 0.384 -0.132 -0.132 -0.055 -0.055
## 112 jss8 ~~ jss13 10.578 0.530 0.530 0.318 0.318
## 113 jss8 ~~ jss14 1.901 0.270 0.270 0.133 0.133
## 114 jss8 ~~ jss4 2.287 -0.325 -0.325 -0.134 -0.134
## 115 jss8 ~~ jss5 8.812 0.609 0.609 0.592 0.592
## 116 jss9 ~~ jss10 51.670 0.774 0.774 0.853 0.853
## 117 jss9 ~~ jss15 11.192 -0.412 -0.412 -0.418 -0.418
## 118 jss9 ~~ jss16 11.296 -0.404 -0.404 -0.338 -0.338
## 119 jss9 ~~ jss2 0.005 -0.010 -0.010 -0.007 -0.007
## 120 jss9 ~~ jss7 0.878 0.148 0.148 0.094 0.094
## 121 jss9 ~~ jss12 5.435 0.359 0.359 0.221 0.221
## 122 jss9 ~~ jss13 5.799 -0.284 -0.284 -0.250 -0.250
## 123 jss9 ~~ jss14 0.107 -0.046 -0.046 -0.034 -0.034
## 124 jss9 ~~ jss4 2.580 -0.250 -0.250 -0.151 -0.151
## 125 jss9 ~~ jss5 1.830 -0.203 -0.203 -0.288 -0.288
## 126 jss10 ~~ jss15 0.080 -0.033 -0.033 -0.042 -0.042
## 127 jss10 ~~ jss16 13.516 -0.390 -0.390 -0.412 -0.412
## 128 jss10 ~~ jss2 0.032 0.021 0.021 0.019 0.019
## 129 jss10 ~~ jss7 1.189 0.147 0.147 0.117 0.117
## 130 jss10 ~~ jss12 2.058 0.187 0.187 0.145 0.145
## 131 jss10 ~~ jss13 0.038 -0.020 -0.020 -0.022 -0.022
## 132 jss10 ~~ jss14 3.231 -0.217 -0.217 -0.198 -0.198
## 133 jss10 ~~ jss4 3.441 -0.245 -0.245 -0.187 -0.187
## 134 jss10 ~~ jss5 0.182 -0.055 -0.055 -0.099 -0.099
## 135 jss15 ~~ jss16 14.458 0.463 0.463 0.450 0.450
## 136 jss15 ~~ jss2 0.452 0.091 0.091 0.077 0.077
## 137 jss15 ~~ jss7 0.324 -0.090 -0.090 -0.066 -0.066
## 138 jss15 ~~ jss12 0.000 0.002 0.002 0.001 0.001
## 139 jss15 ~~ jss13 2.854 0.199 0.199 0.204 0.204
## 140 jss15 ~~ jss14 0.043 0.029 0.029 0.025 0.025
## 141 jss15 ~~ jss4 3.266 0.279 0.279 0.196 0.196
## 142 jss15 ~~ jss5 1.308 -0.171 -0.171 -0.283 -0.283
## 143 jss16 ~~ jss2 0.012 0.015 0.015 0.011 0.011
## 144 jss16 ~~ jss7 0.078 -0.045 -0.045 -0.027 -0.027
## 145 jss16 ~~ jss12 0.002 -0.006 -0.006 -0.004 -0.004
## 146 jss16 ~~ jss13 3.095 -0.213 -0.213 -0.180 -0.180
## 147 jss16 ~~ jss14 0.664 0.119 0.119 0.082 0.082
## 148 jss16 ~~ jss4 7.321 0.432 0.432 0.251 0.251
## 149 jss16 ~~ jss5 0.001 -0.005 -0.005 -0.007 -0.007
## 150 jss2 ~~ jss7 0.041 0.043 0.043 0.023 0.023
## 151 jss2 ~~ jss12 1.037 -0.197 -0.197 -0.101 -0.101
## 152 jss2 ~~ jss13 1.148 0.179 0.179 0.131 0.131
## 153 jss2 ~~ jss14 0.556 -0.147 -0.147 -0.089 -0.089
## 154 jss2 ~~ jss4 1.697 0.247 0.247 0.125 0.125
## 155 jss2 ~~ jss5 2.862 -0.311 -0.311 -0.368 -0.368
## 156 jss7 ~~ jss12 0.000 -0.005 -0.005 -0.002 -0.002
## 157 jss7 ~~ jss13 4.299 -0.407 -0.407 -0.259 -0.259
## 158 jss7 ~~ jss14 7.992 0.654 0.654 0.342 0.342
## 159 jss7 ~~ jss4 1.842 -0.299 -0.299 -0.131 -0.131
## 160 jss7 ~~ jss5 0.151 -0.083 -0.083 -0.085 -0.085
## 161 jss12 ~~ jss13 3.154 0.306 0.306 0.190 0.190
## 162 jss12 ~~ jss14 0.304 -0.113 -0.113 -0.058 -0.058
## 163 jss12 ~~ jss4 0.820 0.193 0.193 0.082 0.082
## 164 jss12 ~~ jss5 3.480 -0.381 -0.381 -0.382 -0.382
## 165 jss13 ~~ jss14 1.918 -0.257 -0.257 -0.187 -0.187
## 166 jss13 ~~ jss4 1.767 -0.220 -0.220 -0.134 -0.134
## 167 jss13 ~~ jss5 5.959 0.395 0.395 0.567 0.567
## 168 jss14 ~~ jss4 0.743 0.171 0.171 0.086 0.086
## 169 jss14 ~~ jss5 0.234 0.094 0.094 0.111 0.111Možemo ih sortirati po veličini i ograničiti na neki manji broj
modindices(fit.3F, sort. = TRUE, maximum.number = 10) ## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 116 jss9 ~~ jss10 51.670 0.774 0.774 0.853 0.853
## 61 organizational =~ jss8 18.853 0.654 0.920 0.518 0.518
## 100 jss6 ~~ jss12 17.617 -0.694 -0.694 -0.396 -0.396
## 135 jss15 ~~ jss16 14.458 0.463 0.463 0.450 0.450
## 127 jss10 ~~ jss16 13.516 -0.390 -0.390 -0.412 -0.412
## 70 salary_promotion =~ jss8 12.931 0.582 0.580 0.327 0.327
## 118 jss9 ~~ jss16 11.296 -0.404 -0.404 -0.338 -0.338
## 117 jss9 ~~ jss15 11.192 -0.412 -0.412 -0.418 -0.418
## 112 jss8 ~~ jss13 10.578 0.530 0.530 0.318 0.318
## 115 jss8 ~~ jss5 8.812 0.609 0.609 0.592 0.592Mogu se uključiti i kao deo “običnog” ispisa
summary(fit.3F, header=TRUE, fit.measures=TRUE, standardized=TRUE, modindices=TRUE)## lavaan 0.6-9 ended normally after 68 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 45
##
## Number of observations 139
## Number of missing patterns 16
##
## Model Test User Model:
##
## Test statistic 221.875
## Degrees of freedom 74
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 959.792
## Degrees of freedom 91
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.830
## Tucker-Lewis Index (TLI) 0.791
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3331.241
## Loglikelihood unrestricted model (H1) -3219.500
##
## Akaike (AIC) 6752.483
## Bayesian (BIC) 6884.534
## Sample-size adjusted Bayesian (BIC) 6742.165
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.120
## 90 Percent confidence interval - lower 0.102
## 90 Percent confidence interval - upper 0.139
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.078
##
## 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
## intrinsic =~
## jss1 1.000 0.625 0.590
## jss6 1.889 0.301 6.273 0.000 1.180 0.715
## jss8 1.330 0.291 4.567 0.000 0.831 0.468
## jss9 1.786 0.290 6.166 0.000 1.115 0.722
## jss10 1.994 0.300 6.651 0.000 1.246 0.827
## jss15 2.183 0.327 6.671 0.000 1.364 0.829
## jss16 1.535 0.260 5.898 0.000 0.959 0.651
## organizational =~
## jss2 1.000 1.406 0.738
## jss7 1.185 0.143 8.286 0.000 1.667 0.748
## jss12 0.737 0.119 6.181 0.000 1.037 0.564
## jss13 1.019 0.113 9.045 0.000 1.433 0.804
## jss14 1.175 0.135 8.683 0.000 1.653 0.788
## salary_promotion =~
## jss4 1.000 0.998 0.543
## jss5 1.762 0.508 3.467 0.001 1.757 0.937
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intrinsic ~~
## organizational 0.578 0.131 4.416 0.000 0.657 0.657
## salary_promotn 0.243 0.098 2.487 0.013 0.390 0.390
## organizational ~~
## salary_promotn 0.690 0.245 2.814 0.005 0.492 0.492
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .jss1 5.892 0.091 64.538 0.000 5.892 5.568
## .jss6 5.249 0.141 37.214 0.000 5.249 3.181
## .jss8 4.439 0.151 29.367 0.000 4.439 2.500
## .jss9 5.446 0.132 41.110 0.000 5.446 3.522
## .jss10 5.495 0.129 42.668 0.000 5.495 3.646
## .jss15 5.285 0.146 36.157 0.000 5.285 3.211
## .jss16 5.419 0.127 42.834 0.000 5.419 3.681
## .jss2 5.101 0.162 31.449 0.000 5.101 2.677
## .jss7 4.282 0.190 22.519 0.000 4.282 1.921
## .jss12 4.271 0.157 27.129 0.000 4.271 2.322
## .jss13 4.777 0.152 31.466 0.000 4.777 2.679
## .jss14 4.302 0.179 24.055 0.000 4.302 2.051
## .jss4 4.743 0.157 30.256 0.000 4.743 2.582
## .jss5 2.861 0.161 17.790 0.000 2.861 1.525
## intrinsic 0.000 0.000 0.000
## organizational 0.000 0.000 0.000
## salary_promotn 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .jss1 0.730 0.096 7.584 0.000 0.730 0.652
## .jss6 1.331 0.191 6.982 0.000 1.331 0.489
## .jss8 2.462 0.309 7.957 0.000 2.462 0.781
## .jss9 1.146 0.180 6.370 0.000 1.146 0.479
## .jss10 0.720 0.139 5.185 0.000 0.720 0.317
## .jss15 0.849 0.162 5.248 0.000 0.849 0.313
## .jss16 1.248 0.178 7.024 0.000 1.248 0.576
## .jss2 1.652 0.241 6.867 0.000 1.652 0.455
## .jss7 2.193 0.331 6.617 0.000 2.193 0.441
## .jss12 2.309 0.300 7.691 0.000 2.309 0.682
## .jss13 1.126 0.189 5.950 0.000 1.126 0.354
## .jss14 1.666 0.268 6.205 0.000 1.666 0.379
## .jss4 2.378 0.386 6.166 0.000 2.378 0.705
## .jss5 0.431 0.803 0.536 0.592 0.431 0.122
## intrinsic 0.390 0.112 3.470 0.001 1.000 1.000
## organizational 1.978 0.412 4.800 0.000 1.000 1.000
## salary_promotn 0.995 0.387 2.574 0.010 1.000 1.000
##
## Modification Indices:
##
## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 52 intrinsic =~ jss2 0.629 0.244 0.152 0.080 0.080
## 53 intrinsic =~ jss7 0.223 -0.169 -0.106 -0.047 -0.047
## 54 intrinsic =~ jss12 0.141 0.126 0.079 0.043 0.043
## 55 intrinsic =~ jss13 0.153 0.108 0.067 0.038 0.038
## 56 intrinsic =~ jss14 0.841 -0.301 -0.188 -0.090 -0.090
## 57 intrinsic =~ jss4 0.007 0.034 0.021 0.012 0.012
## 58 intrinsic =~ jss5 0.007 -0.060 -0.037 -0.020 -0.020
## 59 organizational =~ jss1 2.733 -0.141 -0.198 -0.187 -0.187
## 60 organizational =~ jss6 0.438 -0.079 -0.111 -0.067 -0.067
## 61 organizational =~ jss8 18.853 0.654 0.920 0.518 0.518
## 62 organizational =~ jss9 2.882 -0.189 -0.266 -0.172 -0.172
## 63 organizational =~ jss10 0.446 -0.066 -0.092 -0.061 -0.061
## 64 organizational =~ jss15 4.470 0.240 0.338 0.205 0.205
## 65 organizational =~ jss16 0.378 -0.069 -0.098 -0.066 -0.066
## 66 organizational =~ jss4 0.007 -0.053 -0.074 -0.040 -0.040
## 67 organizational =~ jss5 0.007 0.093 0.131 0.070 0.070
## 68 salary_promotion =~ jss1 1.152 -0.098 -0.098 -0.092 -0.092
## 69 salary_promotion =~ jss6 4.973 0.284 0.283 0.172 0.172
## 70 salary_promotion =~ jss8 12.931 0.582 0.580 0.327 0.327
## 71 salary_promotion =~ jss9 8.158 -0.339 -0.338 -0.219 -0.219
## 72 salary_promotion =~ jss10 3.015 -0.179 -0.179 -0.119 -0.119
## 73 salary_promotion =~ jss15 0.248 0.060 0.060 0.036 0.036
## 74 salary_promotion =~ jss16 1.695 0.157 0.157 0.107 0.107
## 75 salary_promotion =~ jss2 1.040 -0.160 -0.160 -0.084 -0.084
## 76 salary_promotion =~ jss7 1.747 -0.242 -0.241 -0.108 -0.108
## 77 salary_promotion =~ jss12 2.238 -0.260 -0.259 -0.141 -0.141
## 78 salary_promotion =~ jss13 3.854 0.273 0.273 0.153 0.153
## 79 salary_promotion =~ jss14 0.994 0.166 0.165 0.079 0.079
## 80 jss1 ~~ jss6 0.042 -0.020 -0.020 -0.020 -0.020
## 81 jss1 ~~ jss8 1.196 -0.134 -0.134 -0.100 -0.100
## 82 jss1 ~~ jss9 2.025 0.128 0.128 0.140 0.140
## 83 jss1 ~~ jss10 0.660 -0.064 -0.064 -0.088 -0.088
## 84 jss1 ~~ jss15 0.110 -0.030 -0.030 -0.038 -0.038
## 85 jss1 ~~ jss16 6.610 0.235 0.235 0.246 0.246
## 86 jss1 ~~ jss2 0.074 0.029 0.029 0.026 0.026
## 87 jss1 ~~ jss7 2.466 -0.193 -0.193 -0.153 -0.153
## 88 jss1 ~~ jss12 1.560 0.150 0.150 0.115 0.115
## 89 jss1 ~~ jss13 1.077 -0.095 -0.095 -0.105 -0.105
## 90 jss1 ~~ jss14 0.020 0.016 0.016 0.014 0.014
## 91 jss1 ~~ jss4 4.268 0.249 0.249 0.189 0.189
## 92 jss1 ~~ jss5 2.750 -0.192 -0.192 -0.342 -0.342
## 93 jss6 ~~ jss8 5.860 0.413 0.413 0.228 0.228
## 94 jss6 ~~ jss9 0.175 -0.054 -0.054 -0.043 -0.043
## 95 jss6 ~~ jss10 2.101 -0.167 -0.167 -0.171 -0.171
## 96 jss6 ~~ jss15 0.434 -0.087 -0.087 -0.082 -0.082
## 97 jss6 ~~ jss16 5.290 0.296 0.296 0.230 0.230
## 98 jss6 ~~ jss2 0.353 0.087 0.087 0.059 0.059
## 99 jss6 ~~ jss7 0.788 -0.151 -0.151 -0.088 -0.088
## 100 jss6 ~~ jss12 17.617 -0.694 -0.694 -0.396 -0.396
## 101 jss6 ~~ jss13 1.616 0.161 0.161 0.132 0.132
## 102 jss6 ~~ jss14 0.056 -0.036 -0.036 -0.024 -0.024
## 103 jss6 ~~ jss4 1.100 -0.175 -0.175 -0.098 -0.098
## 104 jss6 ~~ jss5 8.464 0.467 0.467 0.617 0.617
## 105 jss8 ~~ jss9 3.427 -0.294 -0.294 -0.175 -0.175
## 106 jss8 ~~ jss10 2.102 -0.200 -0.200 -0.150 -0.150
## 107 jss8 ~~ jss15 0.481 -0.111 -0.111 -0.077 -0.077
## 108 jss8 ~~ jss16 0.742 -0.140 -0.140 -0.080 -0.080
## 109 jss8 ~~ jss2 2.408 -0.292 -0.292 -0.145 -0.145
## 110 jss8 ~~ jss7 0.139 0.082 0.082 0.035 0.035
## 111 jss8 ~~ jss12 0.384 -0.132 -0.132 -0.055 -0.055
## 112 jss8 ~~ jss13 10.578 0.530 0.530 0.318 0.318
## 113 jss8 ~~ jss14 1.901 0.270 0.270 0.133 0.133
## 114 jss8 ~~ jss4 2.287 -0.325 -0.325 -0.134 -0.134
## 115 jss8 ~~ jss5 8.812 0.609 0.609 0.592 0.592
## 116 jss9 ~~ jss10 51.670 0.774 0.774 0.853 0.853
## 117 jss9 ~~ jss15 11.192 -0.412 -0.412 -0.418 -0.418
## 118 jss9 ~~ jss16 11.296 -0.404 -0.404 -0.338 -0.338
## 119 jss9 ~~ jss2 0.005 -0.010 -0.010 -0.007 -0.007
## 120 jss9 ~~ jss7 0.878 0.148 0.148 0.094 0.094
## 121 jss9 ~~ jss12 5.435 0.359 0.359 0.221 0.221
## 122 jss9 ~~ jss13 5.799 -0.284 -0.284 -0.250 -0.250
## 123 jss9 ~~ jss14 0.107 -0.046 -0.046 -0.034 -0.034
## 124 jss9 ~~ jss4 2.580 -0.250 -0.250 -0.151 -0.151
## 125 jss9 ~~ jss5 1.830 -0.203 -0.203 -0.288 -0.288
## 126 jss10 ~~ jss15 0.080 -0.033 -0.033 -0.042 -0.042
## 127 jss10 ~~ jss16 13.516 -0.390 -0.390 -0.412 -0.412
## 128 jss10 ~~ jss2 0.032 0.021 0.021 0.019 0.019
## 129 jss10 ~~ jss7 1.189 0.147 0.147 0.117 0.117
## 130 jss10 ~~ jss12 2.058 0.187 0.187 0.145 0.145
## 131 jss10 ~~ jss13 0.038 -0.020 -0.020 -0.022 -0.022
## 132 jss10 ~~ jss14 3.231 -0.217 -0.217 -0.198 -0.198
## 133 jss10 ~~ jss4 3.441 -0.245 -0.245 -0.187 -0.187
## 134 jss10 ~~ jss5 0.182 -0.055 -0.055 -0.099 -0.099
## 135 jss15 ~~ jss16 14.458 0.463 0.463 0.450 0.450
## 136 jss15 ~~ jss2 0.452 0.091 0.091 0.077 0.077
## 137 jss15 ~~ jss7 0.324 -0.090 -0.090 -0.066 -0.066
## 138 jss15 ~~ jss12 0.000 0.002 0.002 0.001 0.001
## 139 jss15 ~~ jss13 2.854 0.199 0.199 0.204 0.204
## 140 jss15 ~~ jss14 0.043 0.029 0.029 0.025 0.025
## 141 jss15 ~~ jss4 3.266 0.279 0.279 0.196 0.196
## 142 jss15 ~~ jss5 1.308 -0.171 -0.171 -0.283 -0.283
## 143 jss16 ~~ jss2 0.012 0.015 0.015 0.011 0.011
## 144 jss16 ~~ jss7 0.078 -0.045 -0.045 -0.027 -0.027
## 145 jss16 ~~ jss12 0.002 -0.006 -0.006 -0.004 -0.004
## 146 jss16 ~~ jss13 3.095 -0.213 -0.213 -0.180 -0.180
## 147 jss16 ~~ jss14 0.664 0.119 0.119 0.082 0.082
## 148 jss16 ~~ jss4 7.321 0.432 0.432 0.251 0.251
## 149 jss16 ~~ jss5 0.001 -0.005 -0.005 -0.007 -0.007
## 150 jss2 ~~ jss7 0.041 0.043 0.043 0.023 0.023
## 151 jss2 ~~ jss12 1.037 -0.197 -0.197 -0.101 -0.101
## 152 jss2 ~~ jss13 1.148 0.179 0.179 0.131 0.131
## 153 jss2 ~~ jss14 0.556 -0.147 -0.147 -0.089 -0.089
## 154 jss2 ~~ jss4 1.697 0.247 0.247 0.125 0.125
## 155 jss2 ~~ jss5 2.862 -0.311 -0.311 -0.368 -0.368
## 156 jss7 ~~ jss12 0.000 -0.005 -0.005 -0.002 -0.002
## 157 jss7 ~~ jss13 4.299 -0.407 -0.407 -0.259 -0.259
## 158 jss7 ~~ jss14 7.992 0.654 0.654 0.342 0.342
## 159 jss7 ~~ jss4 1.842 -0.299 -0.299 -0.131 -0.131
## 160 jss7 ~~ jss5 0.151 -0.083 -0.083 -0.085 -0.085
## 161 jss12 ~~ jss13 3.154 0.306 0.306 0.190 0.190
## 162 jss12 ~~ jss14 0.304 -0.113 -0.113 -0.058 -0.058
## 163 jss12 ~~ jss4 0.820 0.193 0.193 0.082 0.082
## 164 jss12 ~~ jss5 3.480 -0.381 -0.381 -0.382 -0.382
## 165 jss13 ~~ jss14 1.918 -0.257 -0.257 -0.187 -0.187
## 166 jss13 ~~ jss4 1.767 -0.220 -0.220 -0.134 -0.134
## 167 jss13 ~~ jss5 5.959 0.395 0.395 0.567 0.567
## 168 jss14 ~~ jss4 0.743 0.171 0.171 0.086 0.086
## 169 jss14 ~~ jss5 0.234 0.094 0.094 0.111 0.111Alternativni dvofaktorski model
KFA.2Fmodel <- ' intrinsic =~ jss1 + jss6 + jss8 + jss9 + jss10 + jss15 + jss16
organizational =~ jss2 + jss7 + jss12 + jss13 + jss14 + jss4 + jss5 '
fit.2F <- cfa(KFA.2Fmodel, data=KFA_ds, missing="ML", likelihood = "wishart")
summary(fit.2F, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-9 ended normally after 58 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 43
##
## Number of observations 139
## Number of missing patterns 16
##
## Model Test User Model:
##
## Test statistic 251.522
## Degrees of freedom 76
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 959.792
## Degrees of freedom 91
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.798
## Tucker-Lewis Index (TLI) 0.758
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3346.172
## Loglikelihood unrestricted model (H1) -3219.500
##
## Akaike (AIC) 6778.345
## Bayesian (BIC) 6904.527
## Sample-size adjusted Bayesian (BIC) 6768.485
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.129
## 90 Percent confidence interval - lower 0.112
## 90 Percent confidence interval - upper 0.147
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.086
##
## 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
## intrinsic =~
## jss1 1.000 0.626 0.591
## jss6 1.877 0.299 6.275 0.000 1.175 0.712
## jss8 1.319 0.290 4.556 0.000 0.826 0.465
## jss9 1.789 0.289 6.192 0.000 1.120 0.724
## jss10 1.996 0.299 6.672 0.000 1.249 0.829
## jss15 2.180 0.326 6.685 0.000 1.365 0.829
## jss16 1.528 0.259 5.904 0.000 0.956 0.650
## organizational =~
## jss2 1.000 1.407 0.739
## jss7 1.170 0.142 8.239 0.000 1.646 0.738
## jss12 0.733 0.119 6.160 0.000 1.031 0.561
## jss13 1.018 0.112 9.077 0.000 1.432 0.803
## jss14 1.175 0.135 8.713 0.000 1.653 0.788
## jss4 0.397 0.119 3.335 0.001 0.559 0.304
## jss5 0.640 0.122 5.226 0.000 0.900 0.482
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intrinsic ~~
## organizational 0.585 0.132 4.442 0.000 0.664 0.664
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .jss1 5.892 0.091 64.524 0.000 5.892 5.566
## .jss6 5.249 0.141 37.210 0.000 5.249 3.180
## .jss8 4.439 0.151 29.367 0.000 4.439 2.500
## .jss9 5.445 0.133 41.094 0.000 5.445 3.521
## .jss10 5.495 0.129 42.645 0.000 5.495 3.644
## .jss15 5.286 0.146 36.162 0.000 5.286 3.211
## .jss16 5.419 0.127 42.838 0.000 5.419 3.682
## .jss2 5.101 0.162 31.449 0.000 5.101 2.677
## .jss7 4.282 0.190 22.517 0.000 4.282 1.920
## .jss12 4.270 0.157 27.123 0.000 4.270 2.321
## .jss13 4.777 0.152 31.466 0.000 4.777 2.679
## .jss14 4.301 0.179 24.033 0.000 4.301 2.049
## .jss4 4.743 0.157 30.217 0.000 4.743 2.581
## .jss5 2.867 0.160 17.876 0.000 2.867 1.535
## intrinsic 0.000 0.000 0.000
## organizational 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .jss1 0.728 0.096 7.582 0.000 0.728 0.650
## .jss6 1.343 0.191 7.035 0.000 1.343 0.493
## .jss8 2.471 0.310 7.968 0.000 2.471 0.784
## .jss9 1.137 0.178 6.370 0.000 1.137 0.475
## .jss10 0.712 0.137 5.182 0.000 0.712 0.313
## .jss15 0.848 0.162 5.224 0.000 0.848 0.313
## .jss16 1.252 0.178 7.043 0.000 1.252 0.578
## .jss2 1.650 0.239 6.894 0.000 1.650 0.454
## .jss7 2.262 0.335 6.760 0.000 2.262 0.455
## .jss12 2.321 0.301 7.710 0.000 2.321 0.686
## .jss13 1.129 0.186 6.059 0.000 1.129 0.355
## .jss14 1.671 0.267 6.262 0.000 1.671 0.379
## .jss4 3.065 0.376 8.150 0.000 3.065 0.908
## .jss5 2.677 0.341 7.847 0.000 2.677 0.768
## intrinsic 0.392 0.113 3.478 0.001 1.000 1.000
## organizational 1.981 0.412 4.813 0.000 1.000 1.000Poređenje modela - anova() funkcija
anova(fit.2F,fit.3F)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.3F 74 6752.5 6884.5 221.88
## fit.2F 76 6778.3 6904.5 251.52 29.647 2 3.649e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Grafički prikaz modela
# install.packages("semPlot")
library(semPlot)
semPaths(fit.3F, what = "path", whatLabels = "std", layout = "tree2")
semPaths(fit.2F, what = "path", whatLabels = "std", layout = "tree2")
semPaths(fit.2F, what = "path", whatLabels = "std", layout = "spring")