Department of Industrial Psychology
Stellenbosch University
South Africa
The data should be stored in a .csv file. Each column in the .csv file should have a heading.
library(lavaan)
library(psych)
library(semTools)
mydata <- read.csv(file.choose())
#describe(mydata)
### Choose an estimator from "ML", "MLM", "MLR"
estimator <- "MLM"
### Type the name of the grouping variable
group <- "Country_habitat"mymodel <- '
Stress =~ GWS1 + GWS2 + GWS3 + GWS4 + GWS5 + GWS6 + GWS7 + GWS8 + GWS9
'# Estimate and inspect the item parameters
fit.mymodel <- cfa(model = mymodel,
data = mydata,
estimator = estimator)summary(fit.mymodel, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6.15 ended normally after 22 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 18
##
## Number of observations 1377
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 808.572 576.336
## Degrees of freedom 27 27
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.403
## Satorra-Bentler correction
##
## Model Test Baseline Model:
##
## Test statistic 6506.495 4273.114
## Degrees of freedom 36 36
## P-value 0.000 0.000
## Scaling correction factor 1.523
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.879 0.870
## Tucker-Lewis Index (TLI) 0.839 0.827
##
## Robust Comparative Fit Index (CFI) 0.881
## Robust Tucker-Lewis Index (TLI) 0.841
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -14826.170 -14826.170
## Loglikelihood unrestricted model (H1) -14421.884 -14421.884
##
## Akaike (AIC) 29688.341 29688.341
## Bayesian (BIC) 29782.439 29782.439
## Sample-size adjusted Bayesian (SABIC) 29725.260 29725.260
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.145 0.122
## 90 Percent confidence interval - lower 0.136 0.114
## 90 Percent confidence interval - upper 0.154 0.129
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA 0.144
## 90 Percent confidence interval - lower 0.134
## 90 Percent confidence interval - upper 0.154
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.057 0.057
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Stress =~
## GWS1 1.000 0.810 0.762
## GWS2 1.114 0.025 44.563 0.000 0.902 0.814
## GWS3 0.963 0.033 29.149 0.000 0.780 0.737
## GWS4 0.895 0.039 23.233 0.000 0.725 0.668
## GWS5 0.763 0.031 24.228 0.000 0.618 0.665
## GWS6 0.831 0.029 28.291 0.000 0.673 0.748
## GWS7 0.813 0.034 24.209 0.000 0.659 0.627
## GWS8 0.837 0.033 25.583 0.000 0.678 0.723
## GWS9 0.771 0.034 22.972 0.000 0.624 0.651
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GWS1 0.474 0.021 22.512 0.000 0.474 0.419
## .GWS2 0.414 0.023 18.140 0.000 0.414 0.337
## .GWS3 0.511 0.030 16.999 0.000 0.511 0.457
## .GWS4 0.651 0.030 22.004 0.000 0.651 0.553
## .GWS5 0.481 0.022 22.205 0.000 0.481 0.557
## .GWS6 0.356 0.018 19.805 0.000 0.356 0.440
## .GWS7 0.671 0.029 23.433 0.000 0.671 0.607
## .GWS8 0.420 0.023 18.098 0.000 0.420 0.478
## .GWS9 0.530 0.026 20.110 0.000 0.530 0.576
## Stress 0.656 0.039 16.682 0.000 1.000 1.000measurement.invariance <- function(fit.config, fit.weak, fit.strong, fit.strict, fit.means) {
config.fit <- fitmeasures(fit.config, c("cfi.robust", "tli.robust", "rmsea.robust", "srmr", "BIC", "chisq.scaled", "df.scaled", "pvalue.scaled"))
weak.fit <- fitmeasures(fit.weak, c("cfi.robust", "tli.robust", "rmsea.robust", "srmr", "BIC", "chisq.scaled", "df.scaled", "pvalue.scaled"))
strong.fit <- fitmeasures(fit.strong, c("cfi.robust", "tli.robust", "rmsea.robust", "srmr", "BIC", "chisq.scaled", "df.scaled", "pvalue.scaled"))
strict.fit <- fitmeasures(fit.strict, c("cfi.robust", "tli.robust", "rmsea.robust", "srmr", "BIC", "chisq.scaled", "df.scaled", "pvalue.scaled"))
means.fit <- fitmeasures(fit.means, c("cfi.robust", "tli.robust", "rmsea.robust", "srmr", "BIC", "chisq.scaled", "df.scaled", "pvalue.scaled"))
fit.models <- rbind(config.fit, weak.fit, strong.fit, strict.fit, means.fit)
delta.weak <- config.fit - weak.fit
delta.strong <- weak.fit - strong.fit
delta.strict <- strong.fit - strict.fit
delta.means <- strict.fit - means.fit
delta.models <- rbind(delta.weak, delta.strong, delta.strict, delta.means)
fit.models
delta.models
anova.models <- anova(fit.config, fit.weak, fit.strong, fit.strict, fit.means)
compare.models <- list(anova = anova.models, model.fit = fit.models, model.delta = delta.models[ , c(1, 3, 5)])
compare.models
}##### Configural invariance
fit.config <- cfa(mymodel, data = mydata, group = group, estimator = estimator)
#summary(fit.config)
####### Weak invariance
fit.weak <- cfa(mymodel, data = mydata, group = group, estimator = estimator, group.equal ="loadings")
#summary(fit.weak)
####### Strong invariance
fit.strong <- cfa(mymodel, data = mydata, group = group, estimator = estimator,
group.equal = c("loadings", "intercepts"))
#summary(fit.strong)
####### Strict invariance
fit.strict <- cfa(mymodel, data = mydata, group = group, estimator = estimator,
group.equal = c("loadings", "intercepts",
"residuals"))
#summary(fit.strict)
####### Equal factor means
fit.means <- cfa(mymodel, data = mydata, group = group, estimator = estimator,
group.equal = c("loadings", "intercepts",
"residuals", "means"))
#summary(fit.means)
###################################################################
# Run the measurement invariance function in the MeasInvar.R file #
###################################################################
measurement.invariance(fit.config, fit.weak, fit.strong, fit.strict, fit.means)## $anova
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.config 54 29471 29754 842.56
## fit.weak 62 29464 29704 850.86 8.292 8 0.4055
## fit.strong 70 29618 29817 1021.49 260.932 8 < 2.2e-16 ***
## fit.strict 79 29664 29816 1085.56 58.199 9 2.976e-09 ***
## fit.means 80 29687 29833 1109.87 25.893 1 3.608e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $model.fit
## cfi.robust tli.robust rmsea.robust srmr bic chisq.scaled
## config.fit 0.8807417 0.8409889 0.1435070 0.05366521 29753.55 591.7743
## weak.fit 0.8806962 0.8614536 0.1339543 0.05570739 29704.03 621.4061
## strong.fit 0.8549340 0.8507893 0.1390142 0.06820978 29816.84 793.3926
## strict.fit 0.8464985 0.8600999 0.1346072 0.07333484 29815.86 857.3154
## means.fit 0.8428583 0.8585725 0.1353400 0.08323715 29832.94 879.3561
## df.scaled pvalue.scaled
## config.fit 54 0
## weak.fit 62 0
## strong.fit 70 0
## strict.fit 79 0
## means.fit 80 0
##
## $model.delta
## cfi.robust rmsea.robust bic
## delta.weak 4.546861e-05 0.0095526256 49.5202773
## delta.strong 2.576218e-02 -0.0050598926 -112.8109903
## delta.strict 8.435499e-03 0.0044070506 0.9834126
## delta.means 3.640199e-03 -0.0007328253 -17.0823393Type here the name of the fitted model that you want to inspect. This will typically be the best fitting model that was identified in the previous step.
summary(fit.strong, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6.15 ended normally after 38 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 55
## Number of equality constraints 17
##
## Number of observations per group:
## 2 793
## 1 584
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1021.491 793.393
## Degrees of freedom 70 70
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.287
## Satorra-Bentler correction
## Test statistic for each group:
## 2 494.400 384.001
## 1 527.091 409.392
##
## Model Test Baseline Model:
##
## Test statistic 6523.400 4555.388
## Degrees of freedom 72 72
## P-value 0.000 0.000
## Scaling correction factor 1.432
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.853 0.839
## Tucker-Lewis Index (TLI) 0.848 0.834
##
## Robust Comparative Fit Index (CFI) 0.855
## Robust Tucker-Lewis Index (TLI) 0.851
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -14771.097 -14771.097
## Loglikelihood unrestricted model (H1) -14260.351 -14260.351
##
## Akaike (AIC) 29618.193 29618.193
## Bayesian (BIC) 29816.844 29816.844
## Sample-size adjusted Bayesian (SABIC) 29696.133 29696.133
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.141 0.123
## 90 Percent confidence interval - lower 0.133 0.116
## 90 Percent confidence interval - upper 0.148 0.129
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA 0.139
## 90 Percent confidence interval - lower 0.130
## 90 Percent confidence interval - upper 0.148
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.068 0.068
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Stress =~
## GWS1 1.000 0.865 0.791
## GWS2 (.p2.) 1.115 0.025 44.382 0.000 0.965 0.834
## GWS3 (.p3.) 0.969 0.033 29.531 0.000 0.838 0.750
## GWS4 (.p4.) 0.899 0.039 23.294 0.000 0.778 0.686
## GWS5 (.p5.) 0.765 0.031 24.549 0.000 0.662 0.667
## GWS6 (.p6.) 0.833 0.030 28.159 0.000 0.721 0.767
## GWS7 (.p7.) 0.822 0.033 25.075 0.000 0.711 0.688
## GWS8 (.p8.) 0.836 0.033 25.409 0.000 0.723 0.740
## GWS9 (.p9.) 0.770 0.033 23.073 0.000 0.666 0.648
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GWS1 (.20.) 2.699 0.036 75.742 0.000 2.699 2.467
## .GWS2 (.21.) 2.252 0.039 57.787 0.000 2.252 1.945
## .GWS3 (.22.) 2.313 0.036 65.071 0.000 2.313 2.069
## .GWS4 (.23.) 2.329 0.036 64.392 0.000 2.329 2.055
## .GWS5 (.24.) 2.099 0.031 68.180 0.000 2.099 2.115
## .GWS6 (.25.) 2.158 0.031 69.325 0.000 2.158 2.298
## .GWS7 (.26.) 2.707 0.034 80.274 0.000 2.707 2.620
## .GWS8 (.27.) 2.052 0.032 63.127 0.000 2.052 2.098
## .GWS9 (.28.) 2.060 0.032 64.571 0.000 2.060 2.005
## Stress 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GWS1 0.448 0.026 17.091 0.000 0.448 0.374
## .GWS2 0.408 0.029 14.291 0.000 0.408 0.305
## .GWS3 0.547 0.045 12.226 0.000 0.547 0.438
## .GWS4 0.679 0.040 16.852 0.000 0.679 0.529
## .GWS5 0.547 0.031 17.617 0.000 0.547 0.555
## .GWS6 0.363 0.024 15.178 0.000 0.363 0.411
## .GWS7 0.562 0.032 17.390 0.000 0.562 0.526
## .GWS8 0.433 0.030 14.509 0.000 0.433 0.453
## .GWS9 0.612 0.038 16.026 0.000 0.612 0.580
## Stress 0.749 0.050 14.892 0.000 1.000 1.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Stress =~
## GWS1 1.000 0.704 0.702
## GWS2 (.p2.) 1.115 0.025 44.382 0.000 0.785 0.769
## GWS3 (.p3.) 0.969 0.033 29.531 0.000 0.682 0.708
## GWS4 (.p4.) 0.899 0.039 23.294 0.000 0.632 0.630
## GWS5 (.p5.) 0.765 0.031 24.549 0.000 0.538 0.654
## GWS6 (.p6.) 0.833 0.030 28.159 0.000 0.586 0.706
## GWS7 (.p7.) 0.822 0.033 25.075 0.000 0.578 0.537
## GWS8 (.p8.) 0.836 0.033 25.409 0.000 0.588 0.677
## GWS9 (.p9.) 0.770 0.033 23.073 0.000 0.542 0.641
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GWS1 (.20.) 2.699 0.036 75.742 0.000 2.699 2.691
## .GWS2 (.21.) 2.252 0.039 57.787 0.000 2.252 2.208
## .GWS3 (.22.) 2.313 0.036 65.071 0.000 2.313 2.403
## .GWS4 (.23.) 2.329 0.036 64.392 0.000 2.329 2.321
## .GWS5 (.24.) 2.099 0.031 68.180 0.000 2.099 2.551
## .GWS6 (.25.) 2.158 0.031 69.325 0.000 2.158 2.599
## .GWS7 (.26.) 2.707 0.034 80.274 0.000 2.707 2.516
## .GWS8 (.27.) 2.052 0.032 63.127 0.000 2.052 2.364
## .GWS9 (.28.) 2.060 0.032 64.571 0.000 2.060 2.439
## Stress -0.223 0.044 -5.049 0.000 -0.317 -0.317
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GWS1 0.511 0.033 15.287 0.000 0.511 0.508
## .GWS2 0.424 0.036 11.903 0.000 0.424 0.408
## .GWS3 0.462 0.034 13.759 0.000 0.462 0.499
## .GWS4 0.607 0.040 15.349 0.000 0.607 0.603
## .GWS5 0.388 0.026 15.011 0.000 0.388 0.572
## .GWS6 0.346 0.026 13.571 0.000 0.346 0.502
## .GWS7 0.824 0.044 18.583 0.000 0.824 0.711
## .GWS8 0.408 0.033 12.455 0.000 0.408 0.541
## .GWS9 0.420 0.033 12.795 0.000 0.420 0.589
## Stress 0.495 0.042 11.743 0.000 1.000 1.000lavTestScore(fit.strong)## Warning in lavTestScore(fit.strong): lavaan WARNING: se is not `standard'; not
## implemented yet; falling back to ordinary score test## $test
##
## total score test:
##
## test X2 df p.value
## 1 score 173.413 17 0
##
## $uni
##
## univariate score tests:
##
## lhs op rhs X2 df p.value
## 1 .p2. == .p31. 0.417 1 0.519
## 2 .p3. == .p32. 7.943 1 0.005
## 3 .p4. == .p33. 0.537 1 0.464
## 4 .p5. == .p34. 0.030 1 0.863
## 5 .p6. == .p35. 0.379 1 0.538
## 6 .p7. == .p36. 7.786 1 0.005
## 7 .p8. == .p37. 4.583 1 0.032
## 8 .p9. == .p38. 0.058 1 0.809
## 9 .p20. == .p49. 6.564 1 0.010
## 10 .p21. == .p50. 13.114 1 0.000
## 11 .p22. == .p51. 7.656 1 0.006
## 12 .p23. == .p52. 0.727 1 0.394
## 13 .p24. == .p53. 22.339 1 0.000
## 14 .p25. == .p54. 3.098 1 0.078
## 15 .p26. == .p55. 88.060 1 0.000
## 16 .p27. == .p56. 37.233 1 0.000
## 17 .p28. == .p57. 2.999 1 0.083parTable(fit.strong)## id lhs op rhs user block group free ustart exo label plabel start
## 1 1 Stress =~ GWS1 1 1 1 0 1 0 .p1. 1.000
## 2 2 Stress =~ GWS2 1 1 1 1 NA 0 .p2. .p2. 1.163
## 3 3 Stress =~ GWS3 1 1 1 2 NA 0 .p3. .p3. 0.889
## 4 4 Stress =~ GWS4 1 1 1 3 NA 0 .p4. .p4. 0.841
## 5 5 Stress =~ GWS5 1 1 1 4 NA 0 .p5. .p5. 0.667
## 6 6 Stress =~ GWS6 1 1 1 5 NA 0 .p6. .p6. 0.757
## 7 7 Stress =~ GWS7 1 1 1 6 NA 0 .p7. .p7. 0.756
## 8 8 Stress =~ GWS8 1 1 1 7 NA 0 .p8. .p8. 0.804
## 9 9 Stress =~ GWS9 1 1 1 8 NA 0 .p9. .p9. 0.725
## 10 10 GWS1 ~~ GWS1 0 1 1 9 NA 0 .p10. 0.581
## 11 11 GWS2 ~~ GWS2 0 1 1 10 NA 0 .p11. 0.662
## 12 12 GWS3 ~~ GWS3 0 1 1 11 NA 0 .p12. 0.591
## 13 13 GWS4 ~~ GWS4 0 1 1 12 NA 0 .p13. 0.651
## 14 14 GWS5 ~~ GWS5 0 1 1 13 NA 0 .p14. 0.487
## 15 15 GWS6 ~~ GWS6 0 1 1 14 NA 0 .p15. 0.445
## 16 16 GWS7 ~~ GWS7 0 1 1 15 NA 0 .p16. 0.549
## 17 17 GWS8 ~~ GWS8 0 1 1 16 NA 0 .p17. 0.493
## 18 18 GWS9 ~~ GWS9 0 1 1 17 NA 0 .p18. 0.530
## 19 19 Stress ~~ Stress 0 1 1 18 NA 0 .p19. 0.050
## 20 20 GWS1 ~1 0 1 1 19 NA 0 .p20. .p20. 2.734
## 21 21 GWS2 ~1 0 1 1 20 NA 0 .p21. .p21. 2.299
## 22 22 GWS3 ~1 0 1 1 21 NA 0 .p22. .p22. 2.359
## 23 23 GWS4 ~1 0 1 1 22 NA 0 .p23. .p23. 2.313
## 24 24 GWS5 ~1 0 1 1 23 NA 0 .p24. .p24. 2.183
## 25 25 GWS6 ~1 0 1 1 24 NA 0 .p25. .p25. 2.135
## 26 26 GWS7 ~1 0 1 1 25 NA 0 .p26. .p26. 2.569
## 27 27 GWS8 ~1 0 1 1 26 NA 0 .p27. .p27. 1.963
## 28 28 GWS9 ~1 0 1 1 27 NA 0 .p28. .p28. 2.093
## 29 29 Stress ~1 0 1 1 0 0 0 .p29. 0.000
## 30 30 Stress =~ GWS1 1 2 2 0 1 0 .p30. 1.000
## 31 31 Stress =~ GWS2 1 2 2 28 NA 0 .p2. .p31. 1.064
## 32 32 Stress =~ GWS3 1 2 2 29 NA 0 .p3. .p32. 0.812
## 33 33 Stress =~ GWS4 1 2 2 30 NA 0 .p4. .p33. 0.553
## 34 34 Stress =~ GWS5 1 2 2 31 NA 0 .p5. .p34. 0.459
## 35 35 Stress =~ GWS6 1 2 2 32 NA 0 .p6. .p35. 0.555
## 36 36 Stress =~ GWS7 1 2 2 33 NA 0 .p7. .p36. 0.490
## 37 37 Stress =~ GWS8 1 2 2 34 NA 0 .p8. .p37. 0.566
## 38 38 Stress =~ GWS9 1 2 2 35 NA 0 .p9. .p38. 0.529
## 39 39 GWS1 ~~ GWS1 0 2 2 36 NA 0 .p39. 0.516
## 40 40 GWS2 ~~ GWS2 0 2 2 37 NA 0 .p40. 0.511
## 41 41 GWS3 ~~ GWS3 0 2 2 38 NA 0 .p41. 0.489
## 42 42 GWS4 ~~ GWS4 0 2 2 39 NA 0 .p42. 0.496
## 43 43 GWS5 ~~ GWS5 0 2 2 40 NA 0 .p43. 0.323
## 44 44 GWS6 ~~ GWS6 0 2 2 41 NA 0 .p44. 0.343
## 45 45 GWS7 ~~ GWS7 0 2 2 42 NA 0 .p45. 0.542
## 46 46 GWS8 ~~ GWS8 0 2 2 43 NA 0 .p46. 0.367
## 47 47 GWS9 ~~ GWS9 0 2 2 44 NA 0 .p47. 0.349
## 48 48 Stress ~~ Stress 0 2 2 45 NA 0 .p48. 0.050
## 49 49 GWS1 ~1 0 2 2 46 NA 0 .p20. .p49. 2.421
## 50 50 GWS2 ~1 0 2 2 47 NA 0 .p21. .p50. 1.937
## 51 51 GWS3 ~1 0 2 2 48 NA 0 .p22. .p51. 2.045
## 52 52 GWS4 ~1 0 2 2 49 NA 0 .p23. .p52. 2.147
## 53 53 GWS5 ~1 0 2 2 50 NA 0 .p24. .p53. 1.848
## 54 54 GWS6 ~1 0 2 2 51 NA 0 .p25. .p54. 2.002
## 55 55 GWS7 ~1 0 2 2 52 NA 0 .p26. .p55. 2.800
## 56 56 GWS8 ~1 0 2 2 53 NA 0 .p27. .p56. 1.978
## 57 57 GWS9 ~1 0 2 2 54 NA 0 .p28. .p57. 1.858
## 58 58 Stress ~1 0 2 2 55 NA 0 .p58. 0.000
## 59 59 .p2. == .p31. 2 0 0 0 NA 0 0.000
## 60 60 .p3. == .p32. 2 0 0 0 NA 0 0.000
## 61 61 .p4. == .p33. 2 0 0 0 NA 0 0.000
## 62 62 .p5. == .p34. 2 0 0 0 NA 0 0.000
## 63 63 .p6. == .p35. 2 0 0 0 NA 0 0.000
## 64 64 .p7. == .p36. 2 0 0 0 NA 0 0.000
## 65 65 .p8. == .p37. 2 0 0 0 NA 0 0.000
## 66 66 .p9. == .p38. 2 0 0 0 NA 0 0.000
## 67 67 .p20. == .p49. 2 0 0 0 NA 0 0.000
## 68 68 .p21. == .p50. 2 0 0 0 NA 0 0.000
## 69 69 .p22. == .p51. 2 0 0 0 NA 0 0.000
## 70 70 .p23. == .p52. 2 0 0 0 NA 0 0.000
## 71 71 .p24. == .p53. 2 0 0 0 NA 0 0.000
## 72 72 .p25. == .p54. 2 0 0 0 NA 0 0.000
## 73 73 .p26. == .p55. 2 0 0 0 NA 0 0.000
## 74 74 .p27. == .p56. 2 0 0 0 NA 0 0.000
## 75 75 .p28. == .p57. 2 0 0 0 NA 0 0.000
## est se
## 1 1.000 0.000
## 2 1.115 0.025
## 3 0.969 0.033
## 4 0.899 0.039
## 5 0.765 0.031
## 6 0.833 0.030
## 7 0.822 0.033
## 8 0.836 0.033
## 9 0.770 0.033
## 10 0.448 0.026
## 11 0.408 0.029
## 12 0.547 0.045
## 13 0.679 0.040
## 14 0.547 0.031
## 15 0.363 0.024
## 16 0.562 0.032
## 17 0.433 0.030
## 18 0.612 0.038
## 19 0.749 0.050
## 20 2.699 0.036
## 21 2.252 0.039
## 22 2.313 0.036
## 23 2.329 0.036
## 24 2.099 0.031
## 25 2.158 0.031
## 26 2.707 0.034
## 27 2.052 0.032
## 28 2.060 0.032
## 29 0.000 0.000
## 30 1.000 0.000
## 31 1.115 0.025
## 32 0.969 0.033
## 33 0.899 0.039
## 34 0.765 0.031
## 35 0.833 0.030
## 36 0.822 0.033
## 37 0.836 0.033
## 38 0.770 0.033
## 39 0.511 0.033
## 40 0.424 0.036
## 41 0.462 0.034
## 42 0.607 0.040
## 43 0.388 0.026
## 44 0.346 0.026
## 45 0.824 0.044
## 46 0.408 0.033
## 47 0.420 0.033
## 48 0.495 0.042
## 49 2.699 0.036
## 50 2.252 0.039
## 51 2.313 0.036
## 52 2.329 0.036
## 53 2.099 0.031
## 54 2.158 0.031
## 55 2.707 0.034
## 56 2.052 0.032
## 57 2.060 0.032
## 58 -0.223 0.044
## 59 0.000 0.000
## 60 0.000 0.000
## 61 0.000 0.000
## 62 0.000 0.000
## 63 0.000 0.000
## 64 0.000 0.000
## 65 0.000 0.000
## 66 0.000 0.000
## 67 0.000 0.000
## 68 0.000 0.000
## 69 0.000 0.000
## 70 0.000 0.000
## 71 0.000 0.000
## 72 0.000 0.000
## 73 0.000 0.000
## 74 0.000 0.000
## 75 0.000 0.000de Bruin, G. P. & Taylor, N. (2005). Development of the Sources of Work Stress Inventory. South African Journal of Psychology, 35, 748-765.
de Bruin, G. P. (2006). Dimensionality of the General Work Stress Scale. SA Journal of Industrial Psychology, 32(4), 68-75.