DT::datatable(psych::describe(Sample_2_Nielsen))DT::datatable(psych::describe(Sample_3_Nielsen_org))## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -InfSample_2_Nielsen$sample_group = "sample2"
Sample_3_Nielsen_org$sample_group = "sample3"
Sample_3_Nielsen_cfa$sample_group = "sample3"
combined <- rbind(Sample_2_Nielsen, Sample_3_Nielsen_org)
combined_alt <- rbind(Sample_2_Nielsen, Sample_3_Nielsen_cfa)Refer to the Lavaan model syntax for information on model set-up and constraints
model <-'
DISC =~ DISC1 + DISC2 + DISC3 + DISC4 + DISC5 + DISC6
JOB =~ JOB1 + JOB2 +JOB3 + JOB4 + JOB5
CARE =~ CARE1 + CARE2 + CARE3 + CARE4
RISK =~ RISK1 + RISK2 + RISK3
SOC =~ SOC1 + SOC2
EMO =~ EMO1 + EMO2 + EMO3
IND =~ IND1 + IND2
# Constrain SOC2/socsupchores error variance (replicating original paper)
# error variance was constrained to be within the range from 0-1
SOC2 ~~ 1 * SOC2
'#model with missing DISC3 -- replicating Nielsen et al 2021's table 3, S16 and S17 results
model_missing <-'
DISC =~ DISC1 + DISC2 + DISC4 + DISC5 + DISC6
JOB =~ JOB1 + JOB2 +JOB3 + JOB4 + JOB5
CARE =~ CARE1 + CARE2 + CARE3 + CARE4
RISK =~ RISK1 + RISK2 + RISK3
SOC =~ SOC1 + SOC2
EMO =~ EMO1 + EMO2 + EMO3
IND =~ IND1 + IND2
# Constrain SOC2/socsupchores error variance (replicating original paper)
# error variance was constrained to be within the range from 0-1
SOC2 ~~ 1 * SOC2
'cfa_sample2 <- cfa(model, data = Sample_2_Nielsen)
summary(cfa_sample2, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 63 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 70
##
## Number of observations 2054
##
## Model Test User Model:
##
## Test statistic 1441.402
## Degrees of freedom 255
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 24374.721
## Degrees of freedom 300
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.951
## Tucker-Lewis Index (TLI) 0.942
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -66320.325
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 132780.651
## Bayesian (BIC) 133174.579
## Sample-size adjusted Bayesian (BIC) 132952.183
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.048
## 90 Percent confidence interval - lower 0.045
## 90 Percent confidence interval - upper 0.050
## P-value RMSEA <= 0.05 0.950
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.040
##
## 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
## DISC =~
## DISC1 1.000 0.746 0.840
## DISC2 0.866 0.024 36.589 0.000 0.646 0.726
## DISC3 0.809 0.021 37.959 0.000 0.604 0.746
## DISC4 0.722 0.021 34.390 0.000 0.538 0.693
## DISC5 1.024 0.022 46.262 0.000 0.764 0.860
## DISC6 0.709 0.024 30.112 0.000 0.528 0.625
## JOB =~
## JOB1 1.000 1.139 0.850
## JOB2 0.928 0.023 40.071 0.000 1.056 0.789
## JOB3 0.795 0.020 40.263 0.000 0.905 0.791
## JOB4 0.755 0.020 38.163 0.000 0.859 0.760
## JOB5 1.448 0.065 22.412 0.000 1.649 0.492
## CARE =~
## CARE1 1.000 1.012 0.971
## CARE2 0.960 0.010 97.817 0.000 0.972 0.947
## CARE3 1.033 0.013 76.630 0.000 1.045 0.892
## CARE4 1.617 0.047 34.351 0.000 1.636 0.620
## RISK =~
## RISK1 1.000 0.770 0.842
## RISK2 0.966 0.042 23.267 0.000 0.744 0.679
## RISK3 0.893 0.040 22.391 0.000 0.688 0.620
## SOC =~
## SOC1 1.000 1.124 0.973
## SOC2 0.637 0.039 16.268 0.000 0.715 0.582
## EMO =~
## EMO1 1.000 0.490 0.526
## EMO2 1.361 0.084 16.239 0.000 0.667 0.760
## EMO3 1.350 0.083 16.312 0.000 0.662 0.566
## IND =~
## IND1 1.000 0.615 0.717
## IND2 1.202 0.115 10.448 0.000 0.739 0.837
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.092 0.021 4.338 0.000 0.108 0.108
## CARE 0.100 0.018 5.538 0.000 0.132 0.132
## RISK 0.001 0.015 0.041 0.968 0.001 0.001
## SOC -0.047 0.020 -2.343 0.019 -0.056 -0.056
## EMO 0.067 0.011 6.165 0.000 0.182 0.182
## IND -0.040 0.012 -3.217 0.001 -0.087 -0.087
## JOB ~~
## CARE 0.117 0.028 4.226 0.000 0.101 0.101
## RISK 0.117 0.023 5.014 0.000 0.133 0.133
## SOC 0.027 0.031 0.869 0.385 0.021 0.021
## EMO 0.044 0.016 2.740 0.006 0.078 0.078
## IND 0.073 0.019 3.772 0.000 0.104 0.104
## CARE ~~
## RISK 0.025 0.020 1.258 0.208 0.032 0.032
## SOC 0.037 0.026 1.403 0.160 0.032 0.032
## EMO 0.028 0.013 2.053 0.040 0.056 0.056
## IND 0.036 0.016 2.219 0.027 0.057 0.057
## RISK ~~
## SOC 0.107 0.022 4.821 0.000 0.123 0.123
## EMO 0.070 0.012 5.913 0.000 0.185 0.185
## IND 0.081 0.015 5.605 0.000 0.172 0.172
## SOC ~~
## EMO 0.173 0.017 9.903 0.000 0.313 0.313
## IND -0.028 0.018 -1.574 0.116 -0.041 -0.041
## EMO ~~
## IND -0.058 0.010 -5.594 0.000 -0.191 -0.191
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.662
## .DISC1 0.232 0.010 23.394 0.000 0.232 0.295
## .DISC2 0.375 0.013 28.104 0.000 0.375 0.473
## .DISC3 0.291 0.011 27.600 0.000 0.291 0.444
## .DISC4 0.313 0.011 28.788 0.000 0.313 0.520
## .DISC5 0.204 0.009 21.785 0.000 0.204 0.260
## .DISC6 0.436 0.015 29.801 0.000 0.436 0.610
## .JOB1 0.498 0.024 20.639 0.000 0.498 0.278
## .JOB2 0.678 0.027 24.842 0.000 0.678 0.378
## .JOB3 0.489 0.020 24.692 0.000 0.489 0.374
## .JOB4 0.541 0.021 26.115 0.000 0.541 0.423
## .JOB5 8.497 0.277 30.695 0.000 8.497 0.758
## .CARE1 0.063 0.005 12.182 0.000 0.063 0.058
## .CARE2 0.108 0.006 19.371 0.000 0.108 0.103
## .CARE3 0.279 0.010 27.293 0.000 0.279 0.204
## .CARE4 4.293 0.137 31.358 0.000 4.293 0.616
## .RISK1 0.244 0.023 10.807 0.000 0.244 0.292
## .RISK2 0.648 0.029 22.698 0.000 0.648 0.539
## .RISK3 0.757 0.029 25.761 0.000 0.757 0.615
## .SOC1 0.071 0.068 1.034 0.301 0.071 0.053
## .EMO1 0.630 0.024 26.239 0.000 0.630 0.724
## .EMO2 0.325 0.025 12.870 0.000 0.325 0.422
## .EMO3 0.928 0.038 24.569 0.000 0.928 0.679
## .IND1 0.356 0.037 9.644 0.000 0.356 0.485
## .IND2 0.234 0.051 4.557 0.000 0.234 0.300
## DISC 0.556 0.024 22.731 0.000 1.000 1.000
## JOB 1.296 0.057 22.789 0.000 1.000 1.000
## CARE 1.024 0.034 29.939 0.000 1.000 1.000
## RISK 0.593 0.033 18.067 0.000 1.000 1.000
## SOC 1.263 0.080 15.782 0.000 1.000 1.000
## EMO 0.240 0.023 10.335 0.000 1.000 1.000
## IND 0.378 0.040 9.325 0.000 1.000 1.000cfa_sample3 <- cfa(model_missing, data = Sample_3_Nielsen_org)
summary(cfa_sample3, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 59 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 68
##
## Number of observations 449
##
## Model Test User Model:
##
## Test statistic 497.563
## Degrees of freedom 232
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 5063.220
## Degrees of freedom 276
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.945
## Tucker-Lewis Index (TLI) 0.934
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -14502.968
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 29141.937
## Bayesian (BIC) 29421.214
## Sample-size adjusted Bayesian (BIC) 29205.409
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.050
## 90 Percent confidence interval - lower 0.044
## 90 Percent confidence interval - upper 0.057
## P-value RMSEA <= 0.05 0.438
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.051
##
## 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
## DISC =~
## DISC1 1.000 0.789 0.860
## DISC2 0.555 0.048 11.444 0.000 0.438 0.537
## DISC4 0.648 0.049 13.239 0.000 0.511 0.608
## DISC5 1.018 0.053 19.073 0.000 0.803 0.845
## DISC6 0.619 0.050 12.448 0.000 0.489 0.577
## JOB =~
## JOB1 1.000 1.279 0.905
## JOB2 0.880 0.033 26.268 0.000 1.125 0.865
## JOB3 0.807 0.030 26.741 0.000 1.032 0.872
## JOB4 0.732 0.031 23.903 0.000 0.936 0.825
## JOB5 2.220 0.148 14.957 0.000 2.839 0.619
## CARE =~
## CARE1 1.000 0.956 0.942
## CARE2 0.946 0.026 36.908 0.000 0.905 0.933
## CARE3 1.092 0.032 33.661 0.000 1.044 0.905
## CARE4 1.907 0.153 12.439 0.000 1.823 0.526
## RISK =~
## RISK1 1.000 0.764 0.820
## RISK2 0.824 0.116 7.113 0.000 0.630 0.575
## RISK3 0.716 0.104 6.866 0.000 0.547 0.501
## SOC =~
## SOC1 1.000 1.050 0.909
## SOC2 0.727 0.088 8.298 0.000 0.764 0.607
## EMO =~
## EMO1 1.000 0.493 0.521
## EMO2 1.383 0.186 7.446 0.000 0.681 0.775
## EMO3 1.222 0.164 7.461 0.000 0.602 0.541
## IND =~
## IND1 1.000 0.541 0.656
## IND2 1.392 0.304 4.574 0.000 0.754 0.768
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.069 0.053 1.296 0.195 0.068 0.068
## CARE 0.166 0.040 4.139 0.000 0.220 0.220
## RISK -0.011 0.035 -0.312 0.755 -0.018 -0.018
## SOC 0.035 0.046 0.769 0.442 0.042 0.042
## EMO 0.046 0.024 1.908 0.056 0.119 0.119
## IND 0.005 0.026 0.199 0.842 0.012 0.012
## JOB ~~
## CARE 0.131 0.062 2.131 0.033 0.108 0.108
## RISK -0.026 0.056 -0.477 0.634 -0.027 -0.027
## SOC -0.044 0.072 -0.615 0.538 -0.033 -0.033
## EMO 0.041 0.037 1.109 0.267 0.066 0.066
## IND 0.062 0.042 1.477 0.140 0.089 0.089
## CARE ~~
## RISK -0.035 0.041 -0.843 0.399 -0.047 -0.047
## SOC 0.029 0.053 0.540 0.589 0.028 0.028
## EMO 0.026 0.027 0.956 0.339 0.056 0.056
## IND -0.039 0.031 -1.274 0.203 -0.076 -0.076
## RISK ~~
## SOC 0.000 0.048 0.010 0.992 0.001 0.001
## EMO 0.074 0.026 2.816 0.005 0.196 0.196
## IND 0.043 0.028 1.526 0.127 0.104 0.104
## SOC ~~
## EMO 0.178 0.038 4.715 0.000 0.344 0.344
## IND -0.115 0.039 -2.931 0.003 -0.203 -0.203
## EMO ~~
## IND -0.069 0.022 -3.128 0.002 -0.260 -0.260
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.632
## .DISC1 0.219 0.027 8.056 0.000 0.219 0.260
## .DISC2 0.474 0.034 14.088 0.000 0.474 0.712
## .DISC4 0.446 0.033 13.677 0.000 0.446 0.631
## .DISC5 0.258 0.029 8.738 0.000 0.258 0.285
## .DISC6 0.479 0.034 13.874 0.000 0.479 0.667
## .JOB1 0.362 0.037 9.708 0.000 0.362 0.181
## .JOB2 0.427 0.037 11.524 0.000 0.427 0.252
## .JOB3 0.335 0.030 11.263 0.000 0.335 0.239
## .JOB4 0.410 0.033 12.523 0.000 0.410 0.319
## .JOB5 12.982 0.909 14.285 0.000 12.982 0.617
## .CARE1 0.115 0.014 8.235 0.000 0.115 0.112
## .CARE2 0.121 0.013 9.151 0.000 0.121 0.129
## .CARE3 0.242 0.022 11.262 0.000 0.242 0.182
## .CARE4 8.687 0.591 14.690 0.000 8.687 0.723
## .RISK1 0.285 0.076 3.731 0.000 0.285 0.328
## .RISK2 0.804 0.074 10.858 0.000 0.804 0.670
## .RISK3 0.893 0.071 12.523 0.000 0.893 0.749
## .SOC1 0.232 0.123 1.888 0.059 0.232 0.174
## .EMO1 0.653 0.053 12.306 0.000 0.653 0.729
## .EMO2 0.309 0.056 5.476 0.000 0.309 0.399
## .EMO3 0.876 0.073 11.932 0.000 0.876 0.707
## .IND1 0.389 0.068 5.752 0.000 0.389 0.570
## .IND2 0.394 0.124 3.183 0.001 0.394 0.410
## DISC 0.623 0.059 10.574 0.000 1.000 1.000
## JOB 1.636 0.134 12.191 0.000 1.000 1.000
## CARE 0.914 0.069 13.199 0.000 1.000 1.000
## RISK 0.583 0.092 6.336 0.000 1.000 1.000
## SOC 1.102 0.150 7.345 0.000 1.000 1.000
## EMO 0.243 0.051 4.770 0.000 1.000 1.000
## IND 0.293 0.073 4.028 0.000 1.000 1.000Notes: 1. Configural invariance (unconstrained model in AMOS): the factor structure is the same across groups. 2. Metric invariance (measurement weight model in AMOS): the factor loadings are similar across groups. 3. scalar invariance (measurement intercept model in AMOS): The intercepts/means are equivalent across groups.
## configural invariance
config <- cfa(model_missing, data = combined, group = "sample_group")
## Metric invariance
metric <- cfa(model_missing, data = combined, group = "sample_group",
#set factor loadings to be equal between groups
group.equal="loadings")
## Scalar invarance
scalar <- cfa(model_missing, data = combined, group = "sample_group",
#set factor loadings and intercepts (means) to be equal between groups
group.equal=c("loadings", "intercepts", "means"))Notes: “Std.lv” standardizes to the latent factors, while the “std.all” uses all path information to determine the standardized estimates for paths
summary(config, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 111 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 184
##
## Number of observations per group:
## sample2 2054
## sample3 449
##
## Model Test User Model:
##
## Test statistic 1828.961
## Degrees of freedom 464
## P-value (Chi-square) 0.000
## Test statistic for each group:
## sample2 1331.398
## sample3 497.563
##
## Model Test Baseline Model:
##
## Test statistic 27935.276
## Degrees of freedom 552
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.950
## Tucker-Lewis Index (TLI) 0.941
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -79039.904
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 158447.808
## Bayesian (BIC) 159519.654
## Sample-size adjusted Bayesian (BIC) 158935.039
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.048
## 90 Percent confidence interval - lower 0.046
## 90 Percent confidence interval - upper 0.051
## P-value RMSEA <= 0.05 0.855
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.041
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [sample2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.761 0.858
## DISC2 0.846 0.023 36.458 0.000 0.644 0.724
## DISC4 0.684 0.021 32.962 0.000 0.521 0.671
## DISC5 0.999 0.022 45.445 0.000 0.761 0.857
## DISC6 0.693 0.023 30.050 0.000 0.528 0.624
## JOB =~
## JOB1 1.000 1.139 0.850
## JOB2 0.928 0.023 40.075 0.000 1.056 0.789
## JOB3 0.795 0.020 40.263 0.000 0.905 0.791
## JOB4 0.754 0.020 38.164 0.000 0.859 0.760
## JOB5 1.448 0.065 22.410 0.000 1.649 0.492
## CARE =~
## CARE1 1.000 1.012 0.971
## CARE2 0.960 0.010 97.819 0.000 0.972 0.947
## CARE3 1.033 0.013 76.630 0.000 1.045 0.892
## CARE4 1.617 0.047 34.352 0.000 1.636 0.620
## RISK =~
## RISK1 1.000 0.770 0.841
## RISK2 0.966 0.042 23.276 0.000 0.744 0.679
## RISK3 0.893 0.040 22.393 0.000 0.688 0.620
## SOC =~
## SOC1 1.000 1.123 0.973
## SOC2 0.637 0.039 16.293 0.000 0.716 0.582
## EMO =~
## EMO1 1.000 0.492 0.527
## EMO2 1.353 0.083 16.282 0.000 0.666 0.758
## EMO3 1.346 0.082 16.329 0.000 0.662 0.567
## IND =~
## IND1 1.000 0.614 0.717
## IND2 1.205 0.116 10.355 0.000 0.739 0.837
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.095 0.022 4.378 0.000 0.110 0.110
## CARE 0.105 0.019 5.679 0.000 0.136 0.136
## RISK -0.004 0.015 -0.247 0.805 -0.006 -0.006
## SOC -0.051 0.021 -2.460 0.014 -0.059 -0.059
## EMO 0.069 0.011 6.170 0.000 0.184 0.184
## IND -0.038 0.013 -3.007 0.003 -0.082 -0.082
## JOB ~~
## CARE 0.117 0.028 4.226 0.000 0.101 0.101
## RISK 0.117 0.023 5.014 0.000 0.133 0.133
## SOC 0.027 0.031 0.870 0.384 0.021 0.021
## EMO 0.044 0.016 2.750 0.006 0.079 0.079
## IND 0.073 0.019 3.769 0.000 0.104 0.104
## CARE ~~
## RISK 0.025 0.020 1.258 0.208 0.032 0.032
## SOC 0.037 0.026 1.404 0.160 0.032 0.032
## EMO 0.028 0.014 2.059 0.040 0.056 0.056
## IND 0.036 0.016 2.217 0.027 0.057 0.057
## RISK ~~
## SOC 0.107 0.022 4.823 0.000 0.124 0.124
## EMO 0.070 0.012 5.930 0.000 0.186 0.186
## IND 0.081 0.015 5.593 0.000 0.172 0.172
## SOC ~~
## EMO 0.174 0.017 9.930 0.000 0.314 0.314
## IND -0.028 0.018 -1.572 0.116 -0.041 -0.041
## EMO ~~
## IND -0.057 0.010 -5.566 0.000 -0.190 -0.190
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 1.813 0.020 92.532 0.000 1.813 2.042
## .DISC2 1.601 0.020 81.554 0.000 1.601 1.799
## .DISC4 1.373 0.017 80.157 0.000 1.373 1.769
## .DISC5 1.709 0.020 87.263 0.000 1.709 1.925
## .DISC6 1.466 0.019 78.581 0.000 1.466 1.734
## .JOB1 2.813 0.030 95.167 0.000 2.813 2.100
## .JOB2 2.828 0.030 95.702 0.000 2.828 2.112
## .JOB3 2.263 0.025 89.650 0.000 2.263 1.978
## .JOB4 2.129 0.025 85.323 0.000 2.129 1.883
## .JOB5 7.145 0.074 96.688 0.000 7.145 2.133
## .CARE1 1.596 0.023 69.399 0.000 1.596 1.531
## .CARE2 1.575 0.023 69.575 0.000 1.575 1.535
## .CARE3 1.667 0.026 64.498 0.000 1.667 1.423
## .CARE4 1.825 0.058 31.325 0.000 1.825 0.691
## .RISK1 2.755 0.020 136.413 0.000 2.755 3.010
## .RISK2 2.699 0.024 111.611 0.000 2.699 2.463
## .RISK3 2.979 0.024 121.698 0.000 2.979 2.685
## .SOC1 3.614 0.025 141.839 0.000 3.614 3.130
## .SOC2 2.892 0.027 106.582 0.000 2.892 2.352
## .EMO1 3.229 0.021 156.909 0.000 3.229 3.462
## .EMO2 2.544 0.019 131.413 0.000 2.544 2.900
## .EMO3 2.747 0.026 106.531 0.000 2.747 2.351
## .IND1 4.167 0.019 220.449 0.000 4.167 4.864
## .IND2 3.935 0.019 201.971 0.000 3.935 4.456
## DISC 0.000 0.000 0.000
## JOB 0.000 0.000 0.000
## CARE 0.000 0.000 0.000
## RISK 0.000 0.000 0.000
## SOC 0.000 0.000 0.000
## EMO 0.000 0.000 0.000
## IND 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.661
## .DISC1 0.208 0.010 20.328 0.000 0.208 0.264
## .DISC2 0.377 0.014 27.594 0.000 0.377 0.476
## .DISC4 0.332 0.012 28.762 0.000 0.332 0.550
## .DISC5 0.209 0.010 20.352 0.000 0.209 0.265
## .DISC6 0.437 0.015 29.500 0.000 0.437 0.610
## .JOB1 0.498 0.024 20.637 0.000 0.498 0.278
## .JOB2 0.678 0.027 24.841 0.000 0.678 0.378
## .JOB3 0.489 0.020 24.694 0.000 0.489 0.374
## .JOB4 0.541 0.021 26.116 0.000 0.541 0.423
## .JOB5 8.497 0.277 30.695 0.000 8.497 0.758
## .CARE1 0.063 0.005 12.180 0.000 0.063 0.058
## .CARE2 0.108 0.006 19.374 0.000 0.108 0.103
## .CARE3 0.279 0.010 27.294 0.000 0.279 0.204
## .CARE4 4.292 0.137 31.358 0.000 4.292 0.616
## .RISK1 0.245 0.023 10.819 0.000 0.245 0.292
## .RISK2 0.647 0.029 22.685 0.000 0.647 0.539
## .RISK3 0.758 0.029 25.770 0.000 0.758 0.616
## .SOC1 0.072 0.068 1.056 0.291 0.072 0.054
## .EMO1 0.628 0.024 26.177 0.000 0.628 0.722
## .EMO2 0.327 0.025 13.024 0.000 0.327 0.425
## .EMO3 0.927 0.038 24.563 0.000 0.927 0.679
## .IND1 0.357 0.037 9.604 0.000 0.357 0.486
## .IND2 0.233 0.052 4.480 0.000 0.233 0.299
## DISC 0.580 0.025 23.181 0.000 1.000 1.000
## JOB 1.296 0.057 22.791 0.000 1.000 1.000
## CARE 1.024 0.034 29.940 0.000 1.000 1.000
## RISK 0.593 0.033 18.067 0.000 1.000 1.000
## SOC 1.261 0.080 15.795 0.000 1.000 1.000
## EMO 0.242 0.023 10.369 0.000 1.000 1.000
## IND 0.377 0.041 9.256 0.000 1.000 1.000
##
##
## Group 2 [sample3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.789 0.860
## DISC2 0.555 0.048 11.444 0.000 0.438 0.537
## DISC4 0.648 0.049 13.239 0.000 0.511 0.608
## DISC5 1.018 0.053 19.073 0.000 0.803 0.845
## DISC6 0.619 0.050 12.448 0.000 0.489 0.577
## JOB =~
## JOB1 1.000 1.279 0.905
## JOB2 0.880 0.033 26.268 0.000 1.125 0.865
## JOB3 0.807 0.030 26.741 0.000 1.032 0.872
## JOB4 0.732 0.031 23.903 0.000 0.936 0.825
## JOB5 2.220 0.148 14.957 0.000 2.839 0.619
## CARE =~
## CARE1 1.000 0.956 0.942
## CARE2 0.946 0.026 36.908 0.000 0.905 0.933
## CARE3 1.092 0.032 33.661 0.000 1.044 0.905
## CARE4 1.907 0.153 12.439 0.000 1.823 0.526
## RISK =~
## RISK1 1.000 0.764 0.820
## RISK2 0.824 0.116 7.113 0.000 0.630 0.575
## RISK3 0.716 0.104 6.867 0.000 0.547 0.501
## SOC =~
## SOC1 1.000 1.050 0.909
## SOC2 0.727 0.088 8.298 0.000 0.764 0.607
## EMO =~
## EMO1 1.000 0.493 0.521
## EMO2 1.383 0.186 7.446 0.000 0.681 0.775
## EMO3 1.222 0.164 7.461 0.000 0.602 0.541
## IND =~
## IND1 1.000 0.541 0.656
## IND2 1.392 0.304 4.574 0.000 0.754 0.768
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.069 0.053 1.296 0.195 0.068 0.068
## CARE 0.166 0.040 4.139 0.000 0.220 0.220
## RISK -0.011 0.035 -0.312 0.755 -0.018 -0.018
## SOC 0.035 0.046 0.769 0.442 0.042 0.042
## EMO 0.046 0.024 1.908 0.056 0.119 0.119
## IND 0.005 0.026 0.199 0.842 0.012 0.012
## JOB ~~
## CARE 0.131 0.062 2.131 0.033 0.108 0.108
## RISK -0.026 0.056 -0.477 0.634 -0.027 -0.027
## SOC -0.044 0.072 -0.615 0.538 -0.033 -0.033
## EMO 0.041 0.037 1.109 0.267 0.066 0.066
## IND 0.062 0.042 1.477 0.140 0.089 0.089
## CARE ~~
## RISK -0.035 0.041 -0.843 0.399 -0.047 -0.047
## SOC 0.029 0.053 0.540 0.590 0.028 0.028
## EMO 0.026 0.027 0.956 0.339 0.056 0.056
## IND -0.039 0.031 -1.274 0.203 -0.076 -0.076
## RISK ~~
## SOC 0.000 0.048 0.010 0.992 0.001 0.001
## EMO 0.074 0.026 2.816 0.005 0.196 0.196
## IND 0.043 0.028 1.526 0.127 0.104 0.104
## SOC ~~
## EMO 0.178 0.038 4.715 0.000 0.344 0.344
## IND -0.115 0.039 -2.931 0.003 -0.203 -0.203
## EMO ~~
## IND -0.069 0.022 -3.128 0.002 -0.260 -0.260
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 2.042 0.043 47.153 0.000 2.042 2.225
## .DISC2 1.448 0.039 37.589 0.000 1.448 1.774
## .DISC4 1.470 0.040 37.020 0.000 1.470 1.747
## .DISC5 1.880 0.045 41.913 0.000 1.880 1.978
## .DISC6 1.486 0.040 37.162 0.000 1.486 1.754
## .JOB1 2.414 0.067 36.195 0.000 2.414 1.708
## .JOB2 2.247 0.061 36.611 0.000 2.247 1.728
## .JOB3 2.049 0.056 36.685 0.000 2.049 1.731
## .JOB4 1.891 0.054 35.324 0.000 1.891 1.667
## .JOB5 5.977 0.216 27.606 0.000 5.977 1.303
## .CARE1 1.570 0.048 32.798 0.000 1.570 1.548
## .CARE2 1.526 0.046 33.347 0.000 1.526 1.574
## .CARE3 1.635 0.054 30.013 0.000 1.635 1.416
## .CARE4 1.876 0.164 11.469 0.000 1.876 0.541
## .RISK1 2.996 0.044 68.108 0.000 2.996 3.214
## .RISK2 2.764 0.052 53.455 0.000 2.764 2.523
## .RISK3 3.102 0.052 60.209 0.000 3.102 2.841
## .SOC1 3.755 0.055 68.885 0.000 3.755 3.251
## .SOC2 3.018 0.059 50.819 0.000 3.018 2.398
## .EMO1 3.488 0.045 78.088 0.000 3.488 3.685
## .EMO2 2.817 0.041 67.906 0.000 2.817 3.205
## .EMO3 2.895 0.053 55.128 0.000 2.895 2.602
## .IND1 4.187 0.039 107.422 0.000 4.187 5.070
## .IND2 3.710 0.046 80.122 0.000 3.710 3.781
## DISC 0.000 0.000 0.000
## JOB 0.000 0.000 0.000
## CARE 0.000 0.000 0.000
## RISK 0.000 0.000 0.000
## SOC 0.000 0.000 0.000
## EMO 0.000 0.000 0.000
## IND 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.632
## .DISC1 0.219 0.027 8.056 0.000 0.219 0.260
## .DISC2 0.474 0.034 14.088 0.000 0.474 0.712
## .DISC4 0.446 0.033 13.677 0.000 0.446 0.631
## .DISC5 0.258 0.029 8.738 0.000 0.258 0.285
## .DISC6 0.479 0.034 13.874 0.000 0.479 0.667
## .JOB1 0.362 0.037 9.708 0.000 0.362 0.181
## .JOB2 0.427 0.037 11.524 0.000 0.427 0.252
## .JOB3 0.335 0.030 11.263 0.000 0.335 0.239
## .JOB4 0.410 0.033 12.523 0.000 0.410 0.319
## .JOB5 12.982 0.909 14.285 0.000 12.982 0.617
## .CARE1 0.115 0.014 8.235 0.000 0.115 0.112
## .CARE2 0.121 0.013 9.151 0.000 0.121 0.129
## .CARE3 0.242 0.022 11.262 0.000 0.242 0.182
## .CARE4 8.687 0.591 14.690 0.000 8.687 0.723
## .RISK1 0.285 0.076 3.731 0.000 0.285 0.328
## .RISK2 0.804 0.074 10.858 0.000 0.804 0.670
## .RISK3 0.893 0.071 12.523 0.000 0.893 0.749
## .SOC1 0.232 0.123 1.888 0.059 0.232 0.174
## .EMO1 0.653 0.053 12.306 0.000 0.653 0.729
## .EMO2 0.309 0.056 5.476 0.000 0.309 0.399
## .EMO3 0.876 0.073 11.932 0.000 0.876 0.707
## .IND1 0.389 0.068 5.752 0.000 0.389 0.570
## .IND2 0.394 0.124 3.183 0.001 0.394 0.410
## DISC 0.623 0.059 10.574 0.000 1.000 1.000
## JOB 1.636 0.134 12.191 0.000 1.000 1.000
## CARE 0.914 0.069 13.199 0.000 1.000 1.000
## RISK 0.583 0.092 6.336 0.000 1.000 1.000
## SOC 1.102 0.150 7.345 0.000 1.000 1.000
## EMO 0.243 0.051 4.770 0.000 1.000 1.000
## IND 0.293 0.073 4.028 0.000 1.000 1.000summary(metric, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 85 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 184
## Number of equality constraints 17
##
## Number of observations per group:
## sample2 2054
## sample3 449
##
## Model Test User Model:
##
## Test statistic 1901.368
## Degrees of freedom 481
## P-value (Chi-square) 0.000
## Test statistic for each group:
## sample2 1342.208
## sample3 559.160
##
## Model Test Baseline Model:
##
## Test statistic 27935.276
## Degrees of freedom 552
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.948
## Tucker-Lewis Index (TLI) 0.940
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -79076.108
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 158486.216
## Bayesian (BIC) 159459.032
## Sample-size adjusted Bayesian (BIC) 158928.430
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.049
## 90 Percent confidence interval - lower 0.046
## 90 Percent confidence interval - upper 0.051
## P-value RMSEA <= 0.05 0.844
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.043
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [sample2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.768 0.860
## DISC2 (.p2.) 0.801 0.021 38.060 0.000 0.615 0.705
## DISC4 (.p3.) 0.679 0.019 35.533 0.000 0.522 0.671
## DISC5 (.p4.) 1.001 0.020 49.242 0.000 0.769 0.862
## DISC6 (.p5.) 0.682 0.021 32.511 0.000 0.524 0.621
## JOB =~
## JOB1 1.000 1.138 0.850
## JOB2 (.p7.) 0.913 0.019 47.747 0.000 1.038 0.782
## JOB3 (.p8.) 0.800 0.017 48.268 0.000 0.911 0.794
## JOB4 (.p9.) 0.750 0.017 45.053 0.000 0.853 0.757
## JOB5 (.10.) 1.564 0.060 26.259 0.000 1.779 0.522
## CARE =~
## CARE1 1.000 1.011 0.970
## CARE2 (.12.) 0.958 0.009 104.559 0.000 0.968 0.947
## CARE3 (.13.) 1.042 0.012 83.924 0.000 1.054 0.894
## CARE4 (.14.) 1.642 0.045 36.482 0.000 1.660 0.625
## RISK =~
## RISK1 1.000 0.777 0.847
## RISK2 (.16.) 0.952 0.039 24.334 0.000 0.740 0.675
## RISK3 (.17.) 0.874 0.037 23.401 0.000 0.679 0.614
## SOC =~
## SOC1 1.000 1.113 0.964
## SOC2 (.19.) 0.653 0.036 18.269 0.000 0.726 0.588
## EMO =~
## EMO1 1.000 0.493 0.529
## EMO2 (.21.) 1.358 0.076 17.898 0.000 0.670 0.763
## EMO3 (.22.) 1.323 0.074 17.948 0.000 0.653 0.560
## IND =~
## IND1 1.000 0.608 0.710
## IND2 (.24.) 1.229 0.109 11.306 0.000 0.747 0.845
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.096 0.022 4.375 0.000 0.110 0.110
## CARE 0.106 0.019 5.670 0.000 0.136 0.136
## RISK -0.004 0.016 -0.270 0.787 -0.007 -0.007
## SOC -0.050 0.021 -2.428 0.015 -0.059 -0.059
## EMO 0.070 0.011 6.255 0.000 0.185 0.185
## IND -0.038 0.013 -3.038 0.002 -0.082 -0.082
## JOB ~~
## CARE 0.116 0.028 4.199 0.000 0.101 0.101
## RISK 0.118 0.023 5.024 0.000 0.133 0.133
## SOC 0.027 0.031 0.884 0.377 0.021 0.021
## EMO 0.044 0.016 2.739 0.006 0.078 0.078
## IND 0.072 0.019 3.799 0.000 0.105 0.105
## CARE ~~
## RISK 0.025 0.020 1.258 0.208 0.032 0.032
## SOC 0.037 0.026 1.406 0.160 0.033 0.033
## EMO 0.028 0.014 2.070 0.038 0.056 0.056
## IND 0.035 0.016 2.215 0.027 0.057 0.057
## RISK ~~
## SOC 0.107 0.022 4.805 0.000 0.124 0.124
## EMO 0.071 0.012 5.942 0.000 0.184 0.184
## IND 0.080 0.014 5.631 0.000 0.170 0.170
## SOC ~~
## EMO 0.173 0.017 10.053 0.000 0.314 0.314
## IND -0.028 0.018 -1.584 0.113 -0.041 -0.041
## EMO ~~
## IND -0.057 0.010 -5.684 0.000 -0.191 -0.191
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 1.813 0.020 91.986 0.000 1.813 2.030
## .DISC2 1.601 0.019 83.127 0.000 1.601 1.834
## .DISC4 1.373 0.017 80.108 0.000 1.373 1.768
## .DISC5 1.709 0.020 86.819 0.000 1.709 1.916
## .DISC6 1.466 0.019 78.805 0.000 1.466 1.739
## .JOB1 2.813 0.030 95.198 0.000 2.813 2.101
## .JOB2 2.828 0.029 96.523 0.000 2.828 2.130
## .JOB3 2.263 0.025 89.394 0.000 2.263 1.972
## .JOB4 2.129 0.025 85.628 0.000 2.129 1.889
## .JOB5 7.145 0.075 94.981 0.000 7.145 2.096
## .CARE1 1.596 0.023 69.449 0.000 1.596 1.532
## .CARE2 1.575 0.023 69.777 0.000 1.575 1.540
## .CARE3 1.667 0.026 64.093 0.000 1.667 1.414
## .CARE4 1.825 0.059 31.153 0.000 1.825 0.687
## .RISK1 2.755 0.020 136.182 0.000 2.755 3.005
## .RISK2 2.699 0.024 111.700 0.000 2.699 2.465
## .RISK3 2.979 0.024 122.042 0.000 2.979 2.693
## .SOC1 3.614 0.025 141.870 0.000 3.614 3.130
## .SOC2 2.892 0.027 106.052 0.000 2.892 2.340
## .EMO1 3.229 0.021 156.773 0.000 3.229 3.459
## .EMO2 2.544 0.019 131.299 0.000 2.544 2.897
## .EMO3 2.747 0.026 106.771 0.000 2.747 2.356
## .IND1 4.167 0.019 220.565 0.000 4.167 4.867
## .IND2 3.935 0.019 201.909 0.000 3.935 4.455
## DISC 0.000 0.000 0.000
## JOB 0.000 0.000 0.000
## CARE 0.000 0.000 0.000
## RISK 0.000 0.000 0.000
## SOC 0.000 0.000 0.000
## EMO 0.000 0.000 0.000
## IND 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.655
## .DISC1 0.208 0.010 20.283 0.000 0.208 0.261
## .DISC2 0.384 0.014 28.144 0.000 0.384 0.503
## .DISC4 0.332 0.012 28.817 0.000 0.332 0.549
## .DISC5 0.205 0.010 20.066 0.000 0.205 0.257
## .DISC6 0.437 0.015 29.596 0.000 0.437 0.614
## .JOB1 0.499 0.024 21.059 0.000 0.499 0.278
## .JOB2 0.685 0.027 25.419 0.000 0.685 0.389
## .JOB3 0.487 0.020 24.835 0.000 0.487 0.370
## .JOB4 0.542 0.021 26.403 0.000 0.542 0.427
## .JOB5 8.457 0.277 30.488 0.000 8.457 0.728
## .CARE1 0.063 0.005 12.385 0.000 0.063 0.058
## .CARE2 0.109 0.006 19.669 0.000 0.109 0.104
## .CARE3 0.278 0.010 27.222 0.000 0.278 0.201
## .CARE4 4.290 0.137 31.338 0.000 4.290 0.609
## .RISK1 0.237 0.022 10.549 0.000 0.237 0.282
## .RISK2 0.652 0.028 23.075 0.000 0.652 0.544
## .RISK3 0.763 0.029 26.170 0.000 0.763 0.624
## .SOC1 0.094 0.062 1.527 0.127 0.094 0.071
## .EMO1 0.628 0.024 26.473 0.000 0.628 0.721
## .EMO2 0.322 0.024 13.266 0.000 0.322 0.418
## .EMO3 0.934 0.037 25.294 0.000 0.934 0.687
## .IND1 0.364 0.034 10.686 0.000 0.364 0.496
## .IND2 0.223 0.049 4.547 0.000 0.223 0.285
## DISC 0.589 0.025 23.814 0.000 1.000 1.000
## JOB 1.294 0.054 24.049 0.000 1.000 1.000
## CARE 1.022 0.034 30.060 0.000 1.000 1.000
## RISK 0.603 0.033 18.548 0.000 1.000 1.000
## SOC 1.238 0.074 16.692 0.000 1.000 1.000
## EMO 0.243 0.022 11.141 0.000 1.000 1.000
## IND 0.369 0.037 9.932 0.000 1.000 1.000
##
##
## Group 2 [sample3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.751 0.843
## DISC2 (.p2.) 0.801 0.021 38.060 0.000 0.602 0.660
## DISC4 (.p3.) 0.679 0.019 35.533 0.000 0.511 0.609
## DISC5 (.p4.) 1.001 0.020 49.242 0.000 0.752 0.815
## DISC6 (.p5.) 0.682 0.021 32.511 0.000 0.513 0.597
## JOB =~
## JOB1 1.000 1.276 0.902
## JOB2 (.p7.) 0.913 0.019 47.747 0.000 1.165 0.874
## JOB3 (.p8.) 0.800 0.017 48.268 0.000 1.022 0.871
## JOB4 (.p9.) 0.750 0.017 45.053 0.000 0.957 0.834
## JOB5 (.10.) 1.564 0.060 26.259 0.000 1.996 0.471
## CARE =~
## CARE1 1.000 0.964 0.944
## CARE2 (.12.) 0.958 0.009 104.559 0.000 0.923 0.937
## CARE3 (.13.) 1.042 0.012 83.924 0.000 1.004 0.895
## CARE4 (.14.) 1.642 0.045 36.482 0.000 1.582 0.471
## RISK =~
## RISK1 1.000 0.699 0.758
## RISK2 (.16.) 0.952 0.039 24.334 0.000 0.665 0.604
## RISK3 (.17.) 0.874 0.037 23.401 0.000 0.610 0.551
## SOC =~
## SOC1 1.000 1.094 0.946
## SOC2 (.19.) 0.653 0.036 18.269 0.000 0.714 0.581
## EMO =~
## EMO1 1.000 0.486 0.515
## EMO2 (.21.) 1.358 0.076 17.898 0.000 0.660 0.754
## EMO3 (.22.) 1.323 0.074 17.948 0.000 0.643 0.572
## IND =~
## IND1 1.000 0.577 0.696
## IND2 (.24.) 1.229 0.109 11.306 0.000 0.709 0.724
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.066 0.051 1.296 0.195 0.068 0.068
## CARE 0.164 0.039 4.246 0.000 0.226 0.226
## RISK -0.006 0.032 -0.194 0.846 -0.012 -0.012
## SOC 0.031 0.044 0.693 0.488 0.037 0.037
## EMO 0.040 0.023 1.762 0.078 0.109 0.109
## IND 0.010 0.027 0.379 0.705 0.024 0.024
## JOB ~~
## CARE 0.135 0.062 2.173 0.030 0.110 0.110
## RISK -0.034 0.052 -0.643 0.520 -0.038 -0.038
## SOC -0.052 0.072 -0.728 0.467 -0.038 -0.038
## EMO 0.039 0.037 1.056 0.291 0.063 0.063
## IND 0.066 0.044 1.485 0.138 0.089 0.089
## CARE ~~
## RISK -0.039 0.039 -1.000 0.317 -0.058 -0.058
## SOC 0.027 0.054 0.493 0.622 0.025 0.025
## EMO 0.027 0.027 0.998 0.318 0.059 0.059
## IND -0.040 0.033 -1.203 0.229 -0.071 -0.071
## RISK ~~
## SOC 0.005 0.045 0.115 0.909 0.007 0.007
## EMO 0.072 0.024 3.056 0.002 0.212 0.212
## IND 0.045 0.028 1.617 0.106 0.112 0.112
## SOC ~~
## EMO 0.180 0.034 5.312 0.000 0.339 0.339
## IND -0.123 0.039 -3.136 0.002 -0.195 -0.195
## EMO ~~
## IND -0.073 0.020 -3.580 0.000 -0.260 -0.260
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 2.042 0.042 48.540 0.000 2.042 2.291
## .DISC2 1.448 0.043 33.628 0.000 1.448 1.587
## .DISC4 1.470 0.040 37.136 0.000 1.470 1.753
## .DISC5 1.880 0.044 43.161 0.000 1.880 2.037
## .DISC6 1.486 0.041 36.648 0.000 1.486 1.730
## .JOB1 2.414 0.067 36.159 0.000 2.414 1.706
## .JOB2 2.247 0.063 35.722 0.000 2.247 1.686
## .JOB3 2.049 0.055 36.997 0.000 2.049 1.746
## .JOB4 1.891 0.054 34.908 0.000 1.891 1.647
## .JOB5 5.977 0.200 29.877 0.000 5.977 1.410
## .CARE1 1.570 0.048 32.598 0.000 1.570 1.538
## .CARE2 1.526 0.046 32.822 0.000 1.526 1.549
## .CARE3 1.635 0.053 30.858 0.000 1.635 1.456
## .CARE4 1.876 0.159 11.832 0.000 1.876 0.558
## .RISK1 2.996 0.043 68.898 0.000 2.996 3.251
## .RISK2 2.764 0.052 53.227 0.000 2.764 2.512
## .RISK3 3.102 0.052 59.330 0.000 3.102 2.800
## .SOC1 3.755 0.055 68.783 0.000 3.755 3.246
## .SOC2 3.018 0.058 52.039 0.000 3.018 2.456
## .EMO1 3.488 0.044 78.402 0.000 3.488 3.700
## .EMO2 2.817 0.041 68.185 0.000 2.817 3.218
## .EMO3 2.895 0.053 54.568 0.000 2.895 2.575
## .IND1 4.187 0.039 107.153 0.000 4.187 5.057
## .IND2 3.710 0.046 80.307 0.000 3.710 3.790
## DISC 0.000 0.000 0.000
## JOB 0.000 0.000 0.000
## CARE 0.000 0.000 0.000
## RISK 0.000 0.000 0.000
## SOC 0.000 0.000 0.000
## EMO 0.000 0.000 0.000
## IND 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.662
## .DISC1 0.230 0.024 9.658 0.000 0.230 0.290
## .DISC2 0.469 0.035 13.352 0.000 0.469 0.564
## .DISC4 0.443 0.032 13.749 0.000 0.443 0.629
## .DISC5 0.285 0.027 10.646 0.000 0.285 0.335
## .DISC6 0.475 0.034 13.820 0.000 0.475 0.644
## .JOB1 0.373 0.037 10.101 0.000 0.373 0.186
## .JOB2 0.420 0.037 11.372 0.000 0.420 0.236
## .JOB3 0.334 0.029 11.493 0.000 0.334 0.242
## .JOB4 0.401 0.032 12.468 0.000 0.401 0.305
## .JOB5 13.983 0.952 14.681 0.000 13.983 0.778
## .CARE1 0.113 0.014 8.374 0.000 0.113 0.109
## .CARE2 0.118 0.013 9.076 0.000 0.118 0.122
## .CARE3 0.252 0.021 11.872 0.000 0.252 0.200
## .CARE4 8.783 0.594 14.776 0.000 8.783 0.778
## .RISK1 0.361 0.044 8.113 0.000 0.361 0.425
## .RISK2 0.768 0.064 12.095 0.000 0.768 0.635
## .RISK3 0.855 0.067 12.854 0.000 0.855 0.696
## .SOC1 0.140 0.092 1.534 0.125 0.140 0.105
## .EMO1 0.653 0.050 13.155 0.000 0.653 0.734
## .EMO2 0.331 0.042 7.911 0.000 0.331 0.432
## .EMO3 0.851 0.068 12.447 0.000 0.851 0.673
## .IND1 0.353 0.044 8.011 0.000 0.353 0.515
## .IND2 0.456 0.064 7.136 0.000 0.456 0.476
## DISC 0.565 0.046 12.401 0.000 1.000 1.000
## JOB 1.629 0.122 13.306 0.000 1.000 1.000
## CARE 0.928 0.066 14.106 0.000 1.000 1.000
## RISK 0.488 0.052 9.387 0.000 1.000 1.000
## SOC 1.198 0.123 9.703 0.000 1.000 1.000
## EMO 0.236 0.030 7.766 0.000 1.000 1.000
## IND 0.333 0.046 7.268 0.000 1.000 1.000summary(scalar, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 121 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 184
## Number of equality constraints 41
##
## Number of observations per group:
## sample2 2054
## sample3 449
##
## Model Test User Model:
##
## Test statistic 2168.784
## Degrees of freedom 505
## P-value (Chi-square) 0.000
## Test statistic for each group:
## sample2 1385.669
## sample3 783.115
##
## Model Test Baseline Model:
##
## Test statistic 27935.276
## Degrees of freedom 552
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.939
## Tucker-Lewis Index (TLI) 0.934
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -79209.816
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 158705.631
## Bayesian (BIC) 159538.641
## Sample-size adjusted Bayesian (BIC) 159084.294
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.051
## 90 Percent confidence interval - lower 0.049
## 90 Percent confidence interval - upper 0.054
## P-value RMSEA <= 0.05 0.164
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.047
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [sample2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.768 0.859
## DISC2 (.p2.) 0.794 0.021 37.572 0.000 0.610 0.701
## DISC4 (.p3.) 0.680 0.019 35.588 0.000 0.522 0.672
## DISC5 (.p4.) 1.003 0.020 49.246 0.000 0.771 0.863
## DISC6 (.p5.) 0.680 0.021 32.472 0.000 0.523 0.620
## JOB =~
## JOB1 1.000 1.139 0.851
## JOB2 (.p7.) 0.924 0.019 48.419 0.000 1.052 0.787
## JOB3 (.p8.) 0.792 0.016 48.582 0.000 0.903 0.790
## JOB4 (.p9.) 0.745 0.016 45.455 0.000 0.848 0.754
## JOB5 (.10.) 1.581 0.059 26.707 0.000 1.801 0.526
## CARE =~
## CARE1 1.000 1.011 0.970
## CARE2 (.12.) 0.958 0.009 104.551 0.000 0.968 0.947
## CARE3 (.13.) 1.042 0.012 83.934 0.000 1.054 0.894
## CARE4 (.14.) 1.642 0.045 36.474 0.000 1.660 0.625
## RISK =~
## RISK1 1.000 0.779 0.849
## RISK2 (.16.) 0.945 0.039 24.351 0.000 0.736 0.673
## RISK3 (.17.) 0.871 0.037 23.442 0.000 0.679 0.613
## SOC =~
## SOC1 1.000 1.112 0.963
## SOC2 (.19.) 0.654 0.036 18.383 0.000 0.727 0.588
## EMO =~
## EMO1 1.000 0.498 0.533
## EMO2 (.21.) 1.350 0.074 18.267 0.000 0.673 0.765
## EMO3 (.22.) 1.298 0.071 18.213 0.000 0.647 0.555
## IND =~
## IND1 1.000 0.591 0.691
## IND2 (.24.) 1.295 0.116 11.186 0.000 0.766 0.866
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.094 0.022 4.287 0.000 0.107 0.107
## CARE 0.106 0.019 5.657 0.000 0.136 0.136
## RISK -0.004 0.016 -0.231 0.817 -0.006 -0.006
## SOC -0.050 0.021 -2.400 0.016 -0.058 -0.058
## EMO 0.072 0.011 6.338 0.000 0.187 0.187
## IND -0.038 0.012 -3.091 0.002 -0.083 -0.083
## JOB ~~
## CARE 0.115 0.028 4.176 0.000 0.100 0.100
## RISK 0.117 0.024 4.981 0.000 0.132 0.132
## SOC 0.027 0.031 0.878 0.380 0.021 0.021
## EMO 0.043 0.016 2.651 0.008 0.075 0.075
## IND 0.070 0.018 3.821 0.000 0.105 0.105
## CARE ~~
## RISK 0.025 0.020 1.252 0.211 0.031 0.031
## SOC 0.037 0.026 1.402 0.161 0.033 0.033
## EMO 0.028 0.014 2.069 0.039 0.056 0.056
## IND 0.033 0.015 2.194 0.028 0.056 0.056
## RISK ~~
## SOC 0.107 0.022 4.812 0.000 0.124 0.124
## EMO 0.072 0.012 5.990 0.000 0.185 0.185
## IND 0.077 0.014 5.505 0.000 0.166 0.166
## SOC ~~
## EMO 0.174 0.017 10.090 0.000 0.314 0.314
## IND -0.026 0.017 -1.527 0.127 -0.039 -0.039
## EMO ~~
## IND -0.056 0.010 -5.671 0.000 -0.191 -0.191
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 (.77.) 1.850 0.018 103.451 0.000 1.850 2.069
## .DISC2 (.78.) 1.583 0.018 89.930 0.000 1.583 1.817
## .DISC4 (.79.) 1.390 0.016 88.446 0.000 1.390 1.788
## .DISC5 (.80.) 1.736 0.018 96.850 0.000 1.736 1.944
## .DISC6 (.81.) 1.470 0.017 87.015 0.000 1.470 1.744
## .JOB1 (.82.) 2.768 0.027 102.484 0.000 2.768 2.067
## .JOB2 (.83.) 2.734 0.027 102.233 0.000 2.734 2.044
## .JOB3 (.84.) 2.252 0.023 98.419 0.000 2.252 1.970
## .JOB4 (.85.) 2.109 0.022 93.747 0.000 2.109 1.875
## .JOB5 (.86.) 7.014 0.071 99.273 0.000 7.014 2.051
## .CARE1 (.87.) 1.591 0.021 76.921 0.000 1.591 1.527
## .CARE2 (.88.) 1.566 0.020 77.276 0.000 1.566 1.531
## .CARE3 (.89.) 1.661 0.023 71.264 0.000 1.661 1.409
## .CARE4 (.90.) 1.825 0.054 33.501 0.000 1.825 0.688
## .RISK1 (.91.) 2.785 0.018 152.015 0.000 2.785 3.035
## .RISK2 (.92.) 2.706 0.022 123.901 0.000 2.706 2.474
## .RISK3 (.93.) 2.995 0.022 135.676 0.000 2.995 2.707
## .SOC1 (.94.) 3.632 0.023 157.613 0.000 3.632 3.145
## .SOC2 (.95.) 2.909 0.025 117.930 0.000 2.909 2.353
## .EMO1 (.96.) 3.271 0.019 174.120 0.000 3.271 3.496
## .EMO2 (.97.) 2.588 0.018 146.585 0.000 2.588 2.942
## .EMO3 (.98.) 2.768 0.023 119.488 0.000 2.768 2.377
## .IND1 (.99.) 4.172 0.017 245.861 0.000 4.172 4.879
## .IND2 (.100) 3.908 0.018 218.290 0.000 3.908 4.421
## DISC 0.000 0.000 0.000
## JOB 0.000 0.000 0.000
## CARE 0.000 0.000 0.000
## RISK 0.000 0.000 0.000
## SOC 0.000 0.000 0.000
## EMO 0.000 0.000 0.000
## IND 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.654
## .DISC1 0.209 0.010 20.294 0.000 0.209 0.262
## .DISC2 0.387 0.014 28.235 0.000 0.387 0.509
## .DISC4 0.331 0.012 28.806 0.000 0.331 0.549
## .DISC5 0.204 0.010 19.926 0.000 0.204 0.255
## .DISC6 0.437 0.015 29.607 0.000 0.437 0.615
## .JOB1 0.495 0.024 20.983 0.000 0.495 0.276
## .JOB2 0.683 0.027 25.215 0.000 0.683 0.381
## .JOB3 0.492 0.020 25.070 0.000 0.492 0.377
## .JOB4 0.546 0.021 26.526 0.000 0.546 0.431
## .JOB5 8.454 0.278 30.454 0.000 8.454 0.723
## .CARE1 0.063 0.005 12.385 0.000 0.063 0.058
## .CARE2 0.109 0.006 19.668 0.000 0.109 0.104
## .CARE3 0.278 0.010 27.221 0.000 0.278 0.201
## .CARE4 4.290 0.137 31.338 0.000 4.290 0.609
## .RISK1 0.235 0.023 10.405 0.000 0.235 0.279
## .RISK2 0.655 0.028 23.211 0.000 0.655 0.547
## .RISK3 0.764 0.029 26.175 0.000 0.764 0.624
## .SOC1 0.096 0.061 1.560 0.119 0.096 0.072
## .EMO1 0.627 0.024 26.399 0.000 0.627 0.716
## .EMO2 0.321 0.024 13.280 0.000 0.321 0.415
## .EMO3 0.938 0.037 25.565 0.000 0.938 0.691
## .IND1 0.382 0.033 11.586 0.000 0.382 0.522
## .IND2 0.195 0.052 3.762 0.000 0.195 0.250
## DISC 0.590 0.025 23.788 0.000 1.000 1.000
## JOB 1.298 0.054 24.162 0.000 1.000 1.000
## CARE 1.022 0.034 30.060 0.000 1.000 1.000
## RISK 0.607 0.033 18.597 0.000 1.000 1.000
## SOC 1.237 0.074 16.741 0.000 1.000 1.000
## EMO 0.248 0.022 11.337 0.000 1.000 1.000
## IND 0.349 0.036 9.790 0.000 1.000 1.000
##
##
## Group 2 [sample3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.761 0.837
## DISC2 (.p2.) 0.794 0.021 37.572 0.000 0.604 0.640
## DISC4 (.p3.) 0.680 0.019 35.588 0.000 0.517 0.614
## DISC5 (.p4.) 1.003 0.020 49.246 0.000 0.763 0.821
## DISC6 (.p5.) 0.680 0.021 32.472 0.000 0.517 0.600
## JOB =~
## JOB1 1.000 1.329 0.909
## JOB2 (.p7.) 0.924 0.019 48.419 0.000 1.227 0.875
## JOB3 (.p8.) 0.792 0.016 48.582 0.000 1.053 0.875
## JOB4 (.p9.) 0.745 0.016 45.455 0.000 0.989 0.842
## JOB5 (.10.) 1.581 0.059 26.707 0.000 2.100 0.487
## CARE =~
## CARE1 1.000 0.964 0.944
## CARE2 (.12.) 0.958 0.009 104.551 0.000 0.923 0.937
## CARE3 (.13.) 1.042 0.012 83.934 0.000 1.005 0.895
## CARE4 (.14.) 1.642 0.045 36.474 0.000 1.582 0.471
## RISK =~
## RISK1 1.000 0.714 0.758
## RISK2 (.16.) 0.945 0.039 24.351 0.000 0.674 0.608
## RISK3 (.17.) 0.871 0.037 23.442 0.000 0.622 0.559
## SOC =~
## SOC1 1.000 1.101 0.946
## SOC2 (.19.) 0.654 0.036 18.383 0.000 0.720 0.584
## EMO =~
## EMO1 1.000 0.515 0.535
## EMO2 (.21.) 1.350 0.074 18.267 0.000 0.695 0.768
## EMO3 (.22.) 1.298 0.071 18.213 0.000 0.668 0.587
## IND =~
## IND1 1.000 0.561 0.673
## IND2 (.24.) 1.295 0.116 11.186 0.000 0.726 0.731
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.025 0.053 0.461 0.644 0.024 0.024
## CARE 0.161 0.039 4.125 0.000 0.219 0.219
## RISK 0.010 0.033 0.303 0.762 0.018 0.018
## SOC 0.046 0.045 1.021 0.307 0.055 0.055
## EMO 0.059 0.024 2.453 0.014 0.150 0.150
## IND -0.002 0.027 -0.062 0.950 -0.004 -0.004
## JOB ~~
## CARE 0.145 0.065 2.255 0.024 0.114 0.114
## RISK -0.091 0.055 -1.644 0.100 -0.096 -0.096
## SOC -0.097 0.075 -1.291 0.197 -0.067 -0.067
## EMO -0.020 0.040 -0.508 0.611 -0.030 -0.030
## IND 0.094 0.045 2.087 0.037 0.127 0.127
## CARE ~~
## RISK -0.044 0.040 -1.107 0.268 -0.064 -0.064
## SOC 0.023 0.054 0.424 0.671 0.022 0.022
## EMO 0.023 0.029 0.803 0.422 0.046 0.046
## IND -0.037 0.032 -1.154 0.249 -0.069 -0.069
## RISK ~~
## SOC 0.026 0.047 0.553 0.580 0.033 0.033
## EMO 0.098 0.025 3.866 0.000 0.268 0.268
## IND 0.031 0.028 1.120 0.263 0.078 0.078
## SOC ~~
## EMO 0.202 0.036 5.628 0.000 0.357 0.357
## IND -0.130 0.039 -3.354 0.001 -0.211 -0.211
## EMO ~~
## IND -0.085 0.021 -4.015 0.000 -0.293 -0.293
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 (.77.) 1.850 0.018 103.451 0.000 1.850 2.037
## .DISC2 (.78.) 1.583 0.018 89.930 0.000 1.583 1.678
## .DISC4 (.79.) 1.390 0.016 88.446 0.000 1.390 1.651
## .DISC5 (.80.) 1.736 0.018 96.850 0.000 1.736 1.869
## .DISC6 (.81.) 1.470 0.017 87.015 0.000 1.470 1.705
## .JOB1 (.82.) 2.768 0.027 102.484 0.000 2.768 1.895
## .JOB2 (.83.) 2.734 0.027 102.233 0.000 2.734 1.949
## .JOB3 (.84.) 2.252 0.023 98.419 0.000 2.252 1.871
## .JOB4 (.85.) 2.109 0.022 93.747 0.000 2.109 1.795
## .JOB5 (.86.) 7.014 0.071 99.273 0.000 7.014 1.627
## .CARE1 (.87.) 1.591 0.021 76.921 0.000 1.591 1.558
## .CARE2 (.88.) 1.566 0.020 77.276 0.000 1.566 1.589
## .CARE3 (.89.) 1.661 0.023 71.264 0.000 1.661 1.479
## .CARE4 (.90.) 1.825 0.054 33.501 0.000 1.825 0.543
## .RISK1 (.91.) 2.785 0.018 152.015 0.000 2.785 2.959
## .RISK2 (.92.) 2.706 0.022 123.901 0.000 2.706 2.441
## .RISK3 (.93.) 2.995 0.022 135.676 0.000 2.995 2.693
## .SOC1 (.94.) 3.632 0.023 157.613 0.000 3.632 3.121
## .SOC2 (.95.) 2.909 0.025 117.930 0.000 2.909 2.361
## .EMO1 (.96.) 3.271 0.019 174.120 0.000 3.271 3.402
## .EMO2 (.97.) 2.588 0.018 146.585 0.000 2.588 2.860
## .EMO3 (.98.) 2.768 0.023 119.488 0.000 2.768 2.431
## .IND1 (.99.) 4.172 0.017 245.861 0.000 4.172 5.010
## .IND2 (.100) 3.908 0.018 218.290 0.000 3.908 3.931
## DISC 0.000 0.000 0.000
## JOB 0.000 0.000 0.000
## CARE 0.000 0.000 0.000
## RISK 0.000 0.000 0.000
## SOC 0.000 0.000 0.000
## EMO 0.000 0.000 0.000
## IND 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.659
## .DISC1 0.247 0.025 9.838 0.000 0.247 0.299
## .DISC2 0.526 0.039 13.508 0.000 0.526 0.590
## .DISC4 0.441 0.032 13.698 0.000 0.441 0.623
## .DISC5 0.281 0.027 10.412 0.000 0.281 0.326
## .DISC6 0.475 0.034 13.787 0.000 0.475 0.640
## .JOB1 0.369 0.037 9.904 0.000 0.369 0.173
## .JOB2 0.463 0.040 11.529 0.000 0.463 0.235
## .JOB3 0.340 0.030 11.527 0.000 0.340 0.235
## .JOB4 0.402 0.032 12.430 0.000 0.402 0.291
## .JOB5 14.165 0.966 14.669 0.000 14.165 0.763
## .CARE1 0.113 0.014 8.365 0.000 0.113 0.109
## .CARE2 0.119 0.013 9.094 0.000 0.119 0.122
## .CARE3 0.252 0.021 11.864 0.000 0.252 0.200
## .CARE4 8.794 0.595 14.776 0.000 8.794 0.778
## .RISK1 0.376 0.046 8.224 0.000 0.376 0.425
## .RISK2 0.774 0.064 12.092 0.000 0.774 0.630
## .RISK3 0.851 0.066 12.800 0.000 0.851 0.688
## .SOC1 0.141 0.091 1.548 0.122 0.141 0.104
## .EMO1 0.659 0.050 13.089 0.000 0.659 0.713
## .EMO2 0.336 0.043 7.827 0.000 0.336 0.410
## .EMO3 0.850 0.068 12.429 0.000 0.850 0.656
## .IND1 0.379 0.044 8.682 0.000 0.379 0.546
## .IND2 0.461 0.067 6.867 0.000 0.461 0.466
## DISC 0.578 0.047 12.365 0.000 1.000 1.000
## JOB 1.765 0.132 13.386 0.000 1.000 1.000
## CARE 0.929 0.066 14.106 0.000 1.000 1.000
## RISK 0.510 0.054 9.436 0.000 1.000 1.000
## SOC 1.213 0.124 9.780 0.000 1.000 1.000
## EMO 0.265 0.033 7.987 0.000 1.000 1.000
## IND 0.315 0.044 7.126 0.000 1.000 1.000Notes on interpretation: If p-value > 0.05, the constrained model is equivalent to the unconstrained/free model (the coefficients and intercepts do not vary by group). Thus, it would be fair to analyse the pooled data in a single global model.
If p< 0.05, the free model is significantly different from the constrained model, implying differences in coefficients/intercepts between the two groups.
*Difference between config and metric
anova(config, metric)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## config 464 158448 159520 1829.0
## metric 481 158486 159459 1901.4 72.408 17 8.271e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1*Difference between metric and scalar
anova(metric, scalar)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## metric 481 158486 159459 1901.4
## scalar 505 158706 159539 2168.8 267.42 24 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## configural invariance
config <- cfa(model, data = combined_alt, group = "sample_group")
## Metric invariance
metric <- cfa(model, data = combined_alt, group = "sample_group",
#set factor loadings to be equal between groups
group.equal="loadings")
## Scalar invarance
scalar <- cfa(model, data = combined_alt, group = "sample_group",
#set factor loadings and intercepts (means) to be equal between groups
group.equal=c("loadings", "intercepts", "means"))Notes: “Std.lv” standardizes to the latent factors, while the “std.all” uses all path information to determine the standardized estimates for paths
summary(config, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 130 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 190
##
## Number of observations per group:
## sample2 2054
## sample3 348
##
## Model Test User Model:
##
## Test statistic 2016.296
## Degrees of freedom 510
## P-value (Chi-square) 0.000
## Test statistic for each group:
## sample2 1441.402
## sample3 574.894
##
## Model Test Baseline Model:
##
## Test statistic 28605.147
## Degrees of freedom 600
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.946
## Tucker-Lewis Index (TLI) 0.937
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -77750.148
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 155880.296
## Bayesian (BIC) 156979.267
## Sample-size adjusted Bayesian (BIC) 156375.595
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.050
## 90 Percent confidence interval - lower 0.047
## 90 Percent confidence interval - upper 0.052
## P-value RMSEA <= 0.05 0.612
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.042
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [sample2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.746 0.840
## DISC2 0.866 0.024 36.589 0.000 0.646 0.726
## DISC3 0.809 0.021 37.959 0.000 0.604 0.746
## DISC4 0.722 0.021 34.390 0.000 0.538 0.693
## DISC5 1.024 0.022 46.262 0.000 0.764 0.860
## DISC6 0.709 0.024 30.112 0.000 0.528 0.625
## JOB =~
## JOB1 1.000 1.139 0.850
## JOB2 0.928 0.023 40.071 0.000 1.056 0.789
## JOB3 0.795 0.020 40.263 0.000 0.905 0.791
## JOB4 0.755 0.020 38.163 0.000 0.859 0.760
## JOB5 1.448 0.065 22.412 0.000 1.649 0.492
## CARE =~
## CARE1 1.000 1.012 0.971
## CARE2 0.960 0.010 97.817 0.000 0.972 0.947
## CARE3 1.033 0.013 76.630 0.000 1.045 0.892
## CARE4 1.617 0.047 34.351 0.000 1.636 0.620
## RISK =~
## RISK1 1.000 0.770 0.842
## RISK2 0.966 0.042 23.268 0.000 0.744 0.679
## RISK3 0.893 0.040 22.391 0.000 0.688 0.620
## SOC =~
## SOC1 1.000 1.124 0.973
## SOC2 0.637 0.039 16.268 0.000 0.715 0.582
## EMO =~
## EMO1 1.000 0.490 0.526
## EMO2 1.361 0.084 16.239 0.000 0.667 0.760
## EMO3 1.350 0.083 16.312 0.000 0.662 0.566
## IND =~
## IND1 1.000 0.615 0.717
## IND2 1.202 0.115 10.448 0.000 0.739 0.837
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.092 0.021 4.338 0.000 0.108 0.108
## CARE 0.100 0.018 5.538 0.000 0.132 0.132
## RISK 0.001 0.015 0.041 0.968 0.001 0.001
## SOC -0.047 0.020 -2.343 0.019 -0.056 -0.056
## EMO 0.067 0.011 6.165 0.000 0.182 0.182
## IND -0.040 0.012 -3.217 0.001 -0.087 -0.087
## JOB ~~
## CARE 0.117 0.028 4.226 0.000 0.101 0.101
## RISK 0.117 0.023 5.014 0.000 0.133 0.133
## SOC 0.027 0.031 0.869 0.385 0.021 0.021
## EMO 0.044 0.016 2.740 0.006 0.078 0.078
## IND 0.073 0.019 3.772 0.000 0.104 0.104
## CARE ~~
## RISK 0.025 0.020 1.258 0.208 0.032 0.032
## SOC 0.037 0.026 1.403 0.160 0.032 0.032
## EMO 0.028 0.013 2.053 0.040 0.056 0.056
## IND 0.036 0.016 2.219 0.027 0.057 0.057
## RISK ~~
## SOC 0.107 0.022 4.821 0.000 0.123 0.123
## EMO 0.070 0.012 5.913 0.000 0.185 0.185
## IND 0.081 0.015 5.605 0.000 0.172 0.172
## SOC ~~
## EMO 0.173 0.017 9.903 0.000 0.313 0.313
## IND -0.028 0.018 -1.574 0.116 -0.041 -0.041
## EMO ~~
## IND -0.058 0.010 -5.594 0.000 -0.191 -0.191
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 1.813 0.020 92.532 0.000 1.813 2.042
## .DISC2 1.601 0.020 81.554 0.000 1.601 1.799
## .DISC3 1.485 0.018 83.190 0.000 1.485 1.836
## .DISC4 1.373 0.017 80.157 0.000 1.373 1.769
## .DISC5 1.709 0.020 87.263 0.000 1.709 1.925
## .DISC6 1.466 0.019 78.581 0.000 1.466 1.734
## .JOB1 2.813 0.030 95.167 0.000 2.813 2.100
## .JOB2 2.828 0.030 95.702 0.000 2.828 2.112
## .JOB3 2.263 0.025 89.650 0.000 2.263 1.978
## .JOB4 2.129 0.025 85.323 0.000 2.129 1.883
## .JOB5 7.145 0.074 96.688 0.000 7.145 2.133
## .CARE1 1.596 0.023 69.399 0.000 1.596 1.531
## .CARE2 1.575 0.023 69.575 0.000 1.575 1.535
## .CARE3 1.667 0.026 64.498 0.000 1.667 1.423
## .CARE4 1.825 0.058 31.325 0.000 1.825 0.691
## .RISK1 2.755 0.020 136.413 0.000 2.755 3.010
## .RISK2 2.699 0.024 111.611 0.000 2.699 2.463
## .RISK3 2.979 0.024 121.698 0.000 2.979 2.685
## .SOC1 3.614 0.025 141.839 0.000 3.614 3.130
## .SOC2 2.892 0.027 106.601 0.000 2.892 2.352
## .EMO1 3.229 0.021 156.909 0.000 3.229 3.462
## .EMO2 2.544 0.019 131.413 0.000 2.544 2.900
## .EMO3 2.747 0.026 106.531 0.000 2.747 2.351
## .IND1 4.167 0.019 220.449 0.000 4.167 4.864
## .IND2 3.935 0.019 201.971 0.000 3.935 4.456
## DISC 0.000 0.000 0.000
## JOB 0.000 0.000 0.000
## CARE 0.000 0.000 0.000
## RISK 0.000 0.000 0.000
## SOC 0.000 0.000 0.000
## EMO 0.000 0.000 0.000
## IND 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.662
## .DISC1 0.232 0.010 23.394 0.000 0.232 0.295
## .DISC2 0.375 0.013 28.104 0.000 0.375 0.473
## .DISC3 0.291 0.011 27.600 0.000 0.291 0.444
## .DISC4 0.313 0.011 28.788 0.000 0.313 0.520
## .DISC5 0.204 0.009 21.785 0.000 0.204 0.260
## .DISC6 0.436 0.015 29.801 0.000 0.436 0.610
## .JOB1 0.498 0.024 20.639 0.000 0.498 0.278
## .JOB2 0.678 0.027 24.842 0.000 0.678 0.378
## .JOB3 0.489 0.020 24.692 0.000 0.489 0.374
## .JOB4 0.541 0.021 26.115 0.000 0.541 0.423
## .JOB5 8.497 0.277 30.695 0.000 8.497 0.758
## .CARE1 0.063 0.005 12.182 0.000 0.063 0.058
## .CARE2 0.108 0.006 19.371 0.000 0.108 0.103
## .CARE3 0.279 0.010 27.293 0.000 0.279 0.204
## .CARE4 4.293 0.137 31.358 0.000 4.293 0.616
## .RISK1 0.244 0.023 10.807 0.000 0.244 0.292
## .RISK2 0.648 0.029 22.698 0.000 0.648 0.539
## .RISK3 0.757 0.029 25.761 0.000 0.757 0.615
## .SOC1 0.071 0.068 1.034 0.301 0.071 0.053
## .EMO1 0.630 0.024 26.239 0.000 0.630 0.724
## .EMO2 0.325 0.025 12.870 0.000 0.325 0.422
## .EMO3 0.928 0.038 24.570 0.000 0.928 0.679
## .IND1 0.356 0.037 9.644 0.000 0.356 0.485
## .IND2 0.234 0.051 4.556 0.000 0.234 0.300
## DISC 0.556 0.024 22.731 0.000 1.000 1.000
## JOB 1.296 0.057 22.789 0.000 1.000 1.000
## CARE 1.024 0.034 29.939 0.000 1.000 1.000
## RISK 0.593 0.033 18.067 0.000 1.000 1.000
## SOC 1.263 0.080 15.782 0.000 1.000 1.000
## EMO 0.240 0.023 10.335 0.000 1.000 1.000
## IND 0.378 0.040 9.325 0.000 1.000 1.000
##
##
## Group 2 [sample3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.851 0.912
## DISC2 0.424 0.041 10.434 0.000 0.361 0.527
## DISC3 0.442 0.041 10.873 0.000 0.376 0.544
## DISC4 0.569 0.045 12.560 0.000 0.485 0.609
## DISC5 0.986 0.046 21.373 0.000 0.840 0.875
## DISC6 0.521 0.047 11.186 0.000 0.444 0.557
## JOB =~
## JOB1 1.000 1.259 0.898
## JOB2 0.867 0.042 20.872 0.000 1.092 0.833
## JOB3 0.811 0.036 22.514 0.000 1.021 0.867
## JOB4 0.735 0.036 20.290 0.000 0.926 0.820
## JOB5 1.958 0.167 11.709 0.000 2.466 0.572
## CARE =~
## CARE1 1.000 0.957 0.938
## CARE2 0.941 0.029 32.960 0.000 0.901 0.942
## CARE3 1.093 0.038 28.660 0.000 1.046 0.897
## CARE4 2.023 0.166 12.198 0.000 1.936 0.571
## RISK =~
## RISK1 1.000 0.795 0.843
## RISK2 0.748 0.137 5.473 0.000 0.595 0.546
## RISK3 0.614 0.118 5.202 0.000 0.488 0.448
## SOC =~
## SOC1 1.000 1.076 0.934
## SOC2 0.708 0.095 7.485 0.000 0.762 0.606
## EMO =~
## EMO1 1.000 0.479 0.506
## EMO2 1.414 0.223 6.348 0.000 0.677 0.769
## EMO3 1.285 0.201 6.398 0.000 0.616 0.542
## IND =~
## IND1 1.000 0.570 0.696
## IND2 1.205 0.269 4.483 0.000 0.687 0.706
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.245 0.065 3.791 0.000 0.228 0.228
## CARE 0.176 0.048 3.667 0.000 0.217 0.217
## RISK -0.016 0.044 -0.371 0.710 -0.024 -0.024
## SOC 0.123 0.055 2.215 0.027 0.134 0.134
## EMO 0.074 0.029 2.520 0.012 0.181 0.181
## IND 0.024 0.034 0.701 0.483 0.049 0.049
## JOB ~~
## CARE 0.138 0.069 1.992 0.046 0.115 0.115
## RISK 0.028 0.064 0.435 0.663 0.028 0.028
## SOC -0.052 0.081 -0.651 0.515 -0.039 -0.039
## EMO 0.053 0.041 1.283 0.199 0.088 0.088
## IND 0.099 0.051 1.950 0.051 0.138 0.138
## CARE ~~
## RISK -0.004 0.048 -0.090 0.928 -0.006 -0.006
## SOC 0.059 0.060 0.982 0.326 0.058 0.058
## EMO 0.032 0.031 1.037 0.300 0.069 0.069
## IND -0.054 0.038 -1.435 0.151 -0.099 -0.099
## RISK ~~
## SOC -0.060 0.056 -1.074 0.283 -0.071 -0.071
## EMO 0.057 0.029 1.957 0.050 0.151 0.151
## IND 0.065 0.035 1.832 0.067 0.143 0.143
## SOC ~~
## EMO 0.173 0.042 4.098 0.000 0.337 0.337
## IND -0.131 0.046 -2.839 0.005 -0.214 -0.214
## EMO ~~
## IND -0.068 0.025 -2.758 0.006 -0.251 -0.251
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 1.894 0.050 37.855 0.000 1.894 2.029
## .DISC2 1.290 0.037 35.095 0.000 1.290 1.881
## .DISC3 1.307 0.037 35.304 0.000 1.307 1.893
## .DISC4 1.391 0.043 32.568 0.000 1.391 1.746
## .DISC5 1.776 0.051 34.519 0.000 1.776 1.850
## .DISC6 1.397 0.043 32.672 0.000 1.397 1.751
## .JOB1 2.598 0.075 34.573 0.000 2.598 1.853
## .JOB2 2.425 0.070 34.483 0.000 2.425 1.848
## .JOB3 2.187 0.063 34.632 0.000 2.187 1.856
## .JOB4 1.977 0.061 32.658 0.000 1.977 1.751
## .JOB5 7.053 0.231 30.509 0.000 7.053 1.635
## .CARE1 1.592 0.055 29.115 0.000 1.592 1.561
## .CARE2 1.537 0.051 29.977 0.000 1.537 1.607
## .CARE3 1.664 0.063 26.604 0.000 1.664 1.426
## .CARE4 1.828 0.182 10.053 0.000 1.828 0.539
## .RISK1 2.991 0.051 59.223 0.000 2.991 3.175
## .RISK2 2.716 0.058 46.510 0.000 2.716 2.493
## .RISK3 3.101 0.058 53.063 0.000 3.101 2.844
## .SOC1 3.779 0.062 61.182 0.000 3.779 3.280
## .SOC2 3.034 0.067 45.021 0.000 3.034 2.413
## .EMO1 3.445 0.051 67.884 0.000 3.445 3.639
## .EMO2 2.807 0.047 59.447 0.000 2.807 3.187
## .EMO3 2.879 0.061 47.291 0.000 2.879 2.535
## .IND1 4.161 0.044 94.864 0.000 4.161 5.085
## .IND2 3.713 0.052 71.202 0.000 3.713 3.817
## DISC 0.000 0.000 0.000
## JOB 0.000 0.000 0.000
## CARE 0.000 0.000 0.000
## RISK 0.000 0.000 0.000
## SOC 0.000 0.000 0.000
## EMO 0.000 0.000 0.000
## IND 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.633
## .DISC1 0.146 0.024 6.044 0.000 0.146 0.168
## .DISC2 0.340 0.027 12.686 0.000 0.340 0.723
## .DISC3 0.336 0.027 12.636 0.000 0.336 0.704
## .DISC4 0.400 0.032 12.410 0.000 0.400 0.630
## .DISC5 0.216 0.027 8.021 0.000 0.216 0.234
## .DISC6 0.439 0.035 12.598 0.000 0.439 0.690
## .JOB1 0.379 0.045 8.390 0.000 0.379 0.193
## .JOB2 0.528 0.050 10.621 0.000 0.528 0.307
## .JOB3 0.346 0.036 9.715 0.000 0.346 0.249
## .JOB4 0.418 0.038 10.865 0.000 0.418 0.328
## .JOB5 12.520 0.989 12.656 0.000 12.520 0.673
## .CARE1 0.124 0.016 7.622 0.000 0.124 0.120
## .CARE2 0.104 0.014 7.327 0.000 0.104 0.113
## .CARE3 0.266 0.026 10.252 0.000 0.266 0.195
## .CARE4 7.763 0.603 12.868 0.000 7.763 0.674
## .RISK1 0.256 0.107 2.393 0.017 0.256 0.289
## .RISK2 0.833 0.087 9.579 0.000 0.833 0.702
## .RISK3 0.950 0.083 11.449 0.000 0.950 0.800
## .SOC1 0.170 0.139 1.223 0.221 0.170 0.128
## .EMO1 0.667 0.061 10.963 0.000 0.667 0.744
## .EMO2 0.317 0.065 4.859 0.000 0.317 0.409
## .EMO3 0.911 0.088 10.389 0.000 0.911 0.706
## .IND1 0.345 0.075 4.604 0.000 0.345 0.515
## .IND2 0.475 0.108 4.386 0.000 0.475 0.502
## DISC 0.725 0.069 10.575 0.000 1.000 1.000
## JOB 1.586 0.150 10.556 0.000 1.000 1.000
## CARE 0.916 0.079 11.532 0.000 1.000 1.000
## RISK 0.632 0.123 5.114 0.000 1.000 1.000
## SOC 1.158 0.170 6.797 0.000 1.000 1.000
## EMO 0.229 0.057 4.051 0.000 1.000 1.000
## IND 0.324 0.083 3.927 0.000 1.000 1.000summary(metric, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 86 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 190
## Number of equality constraints 18
##
## Number of observations per group:
## sample2 2054
## sample3 348
##
## Model Test User Model:
##
## Test statistic 2151.514
## Degrees of freedom 528
## P-value (Chi-square) 0.000
## Test statistic for each group:
## sample2 1455.032
## sample3 696.482
##
## Model Test Baseline Model:
##
## Test statistic 28605.147
## Degrees of freedom 600
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.942
## Tucker-Lewis Index (TLI) 0.934
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -77817.757
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 155979.515
## Bayesian (BIC) 156974.373
## Sample-size adjusted Bayesian (BIC) 156427.890
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.051
## 90 Percent confidence interval - lower 0.048
## 90 Percent confidence interval - upper 0.053
## P-value RMSEA <= 0.05 0.325
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.044
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [sample2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.763 0.847
## DISC2 (.p2.) 0.805 0.021 38.068 0.000 0.614 0.705
## DISC3 (.p3.) 0.765 0.019 39.690 0.000 0.584 0.732
## DISC4 (.p4.) 0.703 0.019 36.420 0.000 0.536 0.691
## DISC5 (.p5.) 1.017 0.020 49.897 0.000 0.776 0.867
## DISC6 (.p6.) 0.686 0.021 32.053 0.000 0.524 0.621
## JOB =~
## JOB1 1.000 1.139 0.850
## JOB2 (.p8.) 0.914 0.020 45.122 0.000 1.042 0.783
## JOB3 (.p9.) 0.800 0.017 46.100 0.000 0.911 0.794
## JOB4 (.10.) 0.751 0.017 43.230 0.000 0.856 0.758
## JOB5 (.11.) 1.511 0.060 25.028 0.000 1.721 0.509
## CARE =~
## CARE1 1.000 1.011 0.971
## CARE2 (.13.) 0.957 0.009 103.194 0.000 0.968 0.947
## CARE3 (.14.) 1.040 0.013 81.978 0.000 1.052 0.894
## CARE4 (.15.) 1.647 0.045 36.360 0.000 1.666 0.627
## RISK =~
## RISK1 1.000 0.777 0.848
## RISK2 (.17.) 0.952 0.040 23.909 0.000 0.740 0.675
## RISK3 (.18.) 0.872 0.038 22.970 0.000 0.678 0.613
## SOC =~
## SOC1 1.000 1.116 0.967
## SOC2 (.20.) 0.648 0.036 17.893 0.000 0.723 0.586
## EMO =~
## EMO1 1.000 0.490 0.525
## EMO2 (.22.) 1.370 0.079 17.413 0.000 0.671 0.764
## EMO3 (.23.) 1.340 0.077 17.514 0.000 0.656 0.562
## IND =~
## IND1 1.000 0.614 0.717
## IND2 (.25.) 1.202 0.106 11.389 0.000 0.739 0.837
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.094 0.022 4.348 0.000 0.108 0.108
## CARE 0.102 0.018 5.536 0.000 0.132 0.132
## RISK -0.000 0.015 -0.009 0.993 -0.000 -0.000
## SOC -0.047 0.020 -2.314 0.021 -0.056 -0.056
## EMO 0.068 0.011 6.234 0.000 0.183 0.183
## IND -0.041 0.013 -3.226 0.001 -0.087 -0.087
## JOB ~~
## CARE 0.117 0.028 4.216 0.000 0.101 0.101
## RISK 0.118 0.023 5.017 0.000 0.133 0.133
## SOC 0.027 0.031 0.876 0.381 0.021 0.021
## EMO 0.043 0.016 2.731 0.006 0.078 0.078
## IND 0.073 0.019 3.798 0.000 0.105 0.105
## CARE ~~
## RISK 0.025 0.020 1.258 0.208 0.032 0.032
## SOC 0.037 0.026 1.406 0.160 0.033 0.033
## EMO 0.028 0.013 2.055 0.040 0.056 0.056
## IND 0.036 0.016 2.223 0.026 0.057 0.057
## RISK ~~
## SOC 0.107 0.022 4.796 0.000 0.123 0.123
## EMO 0.070 0.012 5.923 0.000 0.184 0.184
## IND 0.082 0.014 5.674 0.000 0.172 0.172
## SOC ~~
## EMO 0.171 0.017 9.994 0.000 0.313 0.313
## IND -0.029 0.018 -1.602 0.109 -0.042 -0.042
## EMO ~~
## IND -0.058 0.010 -5.711 0.000 -0.192 -0.192
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 1.813 0.020 91.245 0.000 1.813 2.013
## .DISC2 1.601 0.019 83.351 0.000 1.601 1.839
## .DISC3 1.485 0.018 84.429 0.000 1.485 1.863
## .DISC4 1.373 0.017 80.213 0.000 1.373 1.770
## .DISC5 1.709 0.020 86.512 0.000 1.709 1.909
## .DISC6 1.466 0.019 78.839 0.000 1.466 1.740
## .JOB1 2.813 0.030 95.115 0.000 2.813 2.099
## .JOB2 2.828 0.029 96.367 0.000 2.828 2.126
## .JOB3 2.263 0.025 89.374 0.000 2.263 1.972
## .JOB4 2.129 0.025 85.506 0.000 2.129 1.887
## .JOB5 7.145 0.075 95.764 0.000 7.145 2.113
## .CARE1 1.596 0.023 69.427 0.000 1.596 1.532
## .CARE2 1.575 0.023 69.785 0.000 1.575 1.540
## .CARE3 1.667 0.026 64.175 0.000 1.667 1.416
## .CARE4 1.825 0.059 31.111 0.000 1.825 0.686
## .RISK1 2.755 0.020 136.176 0.000 2.755 3.005
## .RISK2 2.699 0.024 111.672 0.000 2.699 2.464
## .RISK3 2.979 0.024 122.089 0.000 2.979 2.694
## .SOC1 3.614 0.025 141.859 0.000 3.614 3.130
## .SOC2 2.892 0.027 106.214 0.000 2.892 2.344
## .EMO1 3.229 0.021 156.900 0.000 3.229 3.462
## .EMO2 2.544 0.019 131.318 0.000 2.544 2.898
## .EMO3 2.747 0.026 106.663 0.000 2.747 2.354
## .IND1 4.167 0.019 220.456 0.000 4.167 4.864
## .IND2 3.935 0.019 201.966 0.000 3.935 4.456
## DISC 0.000 0.000 0.000
## JOB 0.000 0.000 0.000
## CARE 0.000 0.000 0.000
## RISK 0.000 0.000 0.000
## SOC 0.000 0.000 0.000
## EMO 0.000 0.000 0.000
## IND 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.657
## .DISC1 0.229 0.010 22.955 0.000 0.229 0.282
## .DISC2 0.381 0.013 28.616 0.000 0.381 0.502
## .DISC3 0.295 0.011 28.026 0.000 0.295 0.464
## .DISC4 0.315 0.011 28.880 0.000 0.315 0.523
## .DISC5 0.200 0.009 21.327 0.000 0.200 0.249
## .DISC6 0.436 0.015 29.884 0.000 0.436 0.614
## .JOB1 0.499 0.024 20.939 0.000 0.499 0.278
## .JOB2 0.684 0.027 25.294 0.000 0.684 0.387
## .JOB3 0.486 0.020 24.760 0.000 0.486 0.369
## .JOB4 0.541 0.021 26.313 0.000 0.541 0.425
## .JOB5 8.471 0.277 30.589 0.000 8.471 0.741
## .CARE1 0.063 0.005 12.326 0.000 0.063 0.058
## .CARE2 0.109 0.006 19.650 0.000 0.109 0.104
## .CARE3 0.279 0.010 27.233 0.000 0.279 0.201
## .CARE4 4.290 0.137 31.333 0.000 4.290 0.607
## .RISK1 0.237 0.023 10.429 0.000 0.237 0.281
## .RISK2 0.652 0.028 22.976 0.000 0.652 0.544
## .RISK3 0.763 0.029 26.130 0.000 0.763 0.624
## .SOC1 0.087 0.063 1.384 0.166 0.087 0.065
## .EMO1 0.630 0.024 26.515 0.000 0.630 0.724
## .EMO2 0.321 0.025 13.037 0.000 0.321 0.416
## .EMO3 0.932 0.037 25.080 0.000 0.932 0.684
## .IND1 0.356 0.034 10.357 0.000 0.356 0.486
## .IND2 0.234 0.048 4.910 0.000 0.234 0.300
## DISC 0.582 0.025 23.515 0.000 1.000 1.000
## JOB 1.298 0.055 23.740 0.000 1.000 1.000
## CARE 1.023 0.034 30.037 0.000 1.000 1.000
## RISK 0.604 0.033 18.407 0.000 1.000 1.000
## SOC 1.246 0.075 16.539 0.000 1.000 1.000
## EMO 0.240 0.022 10.890 0.000 1.000 1.000
## IND 0.377 0.038 10.014 0.000 1.000 1.000
##
##
## Group 2 [sample3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.701 0.819
## DISC2 (.p2.) 0.805 0.021 38.068 0.000 0.564 0.707
## DISC3 (.p3.) 0.765 0.019 39.690 0.000 0.536 0.696
## DISC4 (.p4.) 0.703 0.019 36.420 0.000 0.493 0.615
## DISC5 (.p5.) 1.017 0.020 49.897 0.000 0.713 0.796
## DISC6 (.p6.) 0.686 0.021 32.053 0.000 0.481 0.591
## JOB =~
## JOB1 1.000 1.253 0.896
## JOB2 (.p8.) 0.914 0.020 45.122 0.000 1.146 0.847
## JOB3 (.p9.) 0.800 0.017 46.100 0.000 1.003 0.862
## JOB4 (.10.) 0.751 0.017 43.230 0.000 0.942 0.826
## JOB5 (.11.) 1.511 0.060 25.028 0.000 1.894 0.464
## CARE =~
## CARE1 1.000 0.963 0.939
## CARE2 (.13.) 0.957 0.009 103.194 0.000 0.922 0.946
## CARE3 (.14.) 1.040 0.013 81.978 0.000 1.001 0.885
## CARE4 (.15.) 1.647 0.045 36.360 0.000 1.586 0.490
## RISK =~
## RISK1 1.000 0.682 0.736
## RISK2 (.17.) 0.952 0.040 23.909 0.000 0.649 0.594
## RISK3 (.18.) 0.872 0.038 22.970 0.000 0.595 0.534
## SOC =~
## SOC1 1.000 1.111 0.964
## SOC2 (.20.) 0.648 0.036 17.893 0.000 0.720 0.584
## EMO =~
## EMO1 1.000 0.481 0.508
## EMO2 (.22.) 1.370 0.079 17.413 0.000 0.659 0.751
## EMO3 (.23.) 1.340 0.077 17.514 0.000 0.645 0.563
## IND =~
## IND1 1.000 0.570 0.697
## IND2 (.25.) 1.202 0.106 11.389 0.000 0.686 0.705
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.232 0.054 4.286 0.000 0.264 0.264
## CARE 0.152 0.041 3.743 0.000 0.225 0.225
## RISK 0.001 0.033 0.033 0.973 0.002 0.002
## SOC 0.096 0.047 2.056 0.040 0.123 0.123
## EMO 0.056 0.024 2.354 0.019 0.166 0.166
## IND 0.019 0.028 0.683 0.495 0.048 0.048
## JOB ~~
## CARE 0.141 0.069 2.024 0.043 0.116 0.116
## RISK 0.022 0.058 0.384 0.701 0.026 0.026
## SOC -0.059 0.081 -0.738 0.461 -0.043 -0.043
## EMO 0.052 0.041 1.266 0.206 0.086 0.086
## IND 0.099 0.050 1.987 0.047 0.138 0.138
## CARE ~~
## RISK -0.018 0.044 -0.414 0.679 -0.028 -0.028
## SOC 0.059 0.061 0.963 0.336 0.055 0.055
## EMO 0.034 0.031 1.085 0.278 0.073 0.073
## IND -0.055 0.037 -1.467 0.142 -0.100 -0.100
## RISK ~~
## SOC -0.044 0.051 -0.852 0.394 -0.057 -0.057
## EMO 0.051 0.026 1.963 0.050 0.156 0.156
## IND 0.063 0.031 2.001 0.045 0.162 0.162
## SOC ~~
## EMO 0.177 0.038 4.650 0.000 0.331 0.331
## IND -0.132 0.044 -2.986 0.003 -0.209 -0.209
## EMO ~~
## IND -0.068 0.023 -3.006 0.003 -0.249 -0.249
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 1.894 0.046 41.293 0.000 1.894 2.214
## .DISC2 1.290 0.043 30.151 0.000 1.290 1.616
## .DISC3 1.307 0.041 31.638 0.000 1.307 1.696
## .DISC4 1.391 0.043 32.408 0.000 1.391 1.737
## .DISC5 1.776 0.048 36.998 0.000 1.776 1.983
## .DISC6 1.397 0.044 31.992 0.000 1.397 1.715
## .JOB1 2.598 0.075 34.653 0.000 2.598 1.858
## .JOB2 2.425 0.073 33.428 0.000 2.425 1.792
## .JOB3 2.187 0.062 35.065 0.000 2.187 1.880
## .JOB4 1.977 0.061 32.345 0.000 1.977 1.734
## .JOB5 7.053 0.219 32.272 0.000 7.053 1.730
## .CARE1 1.592 0.055 28.975 0.000 1.592 1.553
## .CARE2 1.537 0.052 29.429 0.000 1.537 1.578
## .CARE3 1.664 0.061 27.436 0.000 1.664 1.471
## .CARE4 1.828 0.173 10.547 0.000 1.828 0.565
## .RISK1 2.991 0.050 60.203 0.000 2.991 3.227
## .RISK2 2.716 0.059 46.331 0.000 2.716 2.484
## .RISK3 3.101 0.060 51.895 0.000 3.101 2.782
## .SOC1 3.779 0.062 61.126 0.000 3.779 3.277
## .SOC2 3.034 0.066 45.944 0.000 3.034 2.463
## .EMO1 3.445 0.051 67.907 0.000 3.445 3.640
## .EMO2 2.807 0.047 59.712 0.000 2.807 3.201
## .EMO3 2.879 0.061 46.944 0.000 2.879 2.516
## .IND1 4.161 0.044 94.844 0.000 4.161 5.084
## .IND2 3.713 0.052 71.216 0.000 3.713 3.818
## DISC 0.000 0.000 0.000
## JOB 0.000 0.000 0.000
## CARE 0.000 0.000 0.000
## RISK 0.000 0.000 0.000
## SOC 0.000 0.000 0.000
## EMO 0.000 0.000 0.000
## IND 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.659
## .DISC1 0.241 0.025 9.679 0.000 0.241 0.329
## .DISC2 0.319 0.028 11.483 0.000 0.319 0.500
## .DISC3 0.307 0.026 11.594 0.000 0.307 0.516
## .DISC4 0.398 0.033 12.162 0.000 0.398 0.622
## .DISC5 0.294 0.029 10.222 0.000 0.294 0.366
## .DISC6 0.432 0.035 12.283 0.000 0.432 0.651
## .JOB1 0.385 0.044 8.796 0.000 0.385 0.197
## .JOB2 0.519 0.050 10.476 0.000 0.519 0.283
## .JOB3 0.348 0.035 10.080 0.000 0.348 0.257
## .JOB4 0.413 0.038 10.905 0.000 0.413 0.318
## .JOB5 13.036 1.010 12.908 0.000 13.036 0.784
## .CARE1 0.123 0.016 7.775 0.000 0.123 0.117
## .CARE2 0.100 0.014 7.171 0.000 0.100 0.105
## .CARE3 0.277 0.026 10.763 0.000 0.277 0.216
## .CARE4 7.941 0.611 12.990 0.000 7.941 0.759
## .RISK1 0.394 0.052 7.556 0.000 0.394 0.458
## .RISK2 0.774 0.073 10.655 0.000 0.774 0.647
## .RISK3 0.888 0.078 11.381 0.000 0.888 0.715
## .SOC1 0.095 0.099 0.957 0.338 0.095 0.072
## .EMO1 0.665 0.057 11.639 0.000 0.665 0.742
## .EMO2 0.335 0.048 7.007 0.000 0.335 0.435
## .EMO3 0.894 0.081 11.059 0.000 0.894 0.683
## .IND1 0.345 0.047 7.353 0.000 0.345 0.514
## .IND2 0.476 0.067 7.117 0.000 0.476 0.503
## DISC 0.491 0.044 11.067 0.000 1.000 1.000
## JOB 1.571 0.134 11.716 0.000 1.000 1.000
## CARE 0.927 0.075 12.440 0.000 1.000 1.000
## RISK 0.465 0.057 8.103 0.000 1.000 1.000
## SOC 1.235 0.138 8.936 0.000 1.000 1.000
## EMO 0.231 0.033 7.016 0.000 1.000 1.000
## IND 0.325 0.049 6.663 0.000 1.000 1.000summary(scalar, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 120 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 190
## Number of equality constraints 43
##
## Number of observations per group:
## sample2 2054
## sample3 348
##
## Model Test User Model:
##
## Test statistic 2382.134
## Degrees of freedom 553
## P-value (Chi-square) 0.000
## Test statistic for each group:
## sample2 1482.145
## sample3 899.988
##
## Model Test Baseline Model:
##
## Test statistic 28605.147
## Degrees of freedom 600
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.935
## Tucker-Lewis Index (TLI) 0.929
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -77933.067
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 156160.134
## Bayesian (BIC) 157010.391
## Sample-size adjusted Bayesian (BIC) 156543.339
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.052
## 90 Percent confidence interval - lower 0.050
## 90 Percent confidence interval - upper 0.055
## P-value RMSEA <= 0.05 0.029
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.046
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [sample2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.761 0.846
## DISC2 (.p2.) 0.813 0.021 37.824 0.000 0.618 0.707
## DISC3 (.p3.) 0.768 0.019 39.521 0.000 0.584 0.732
## DISC4 (.p4.) 0.704 0.019 36.364 0.000 0.536 0.691
## DISC5 (.p5.) 1.019 0.020 49.708 0.000 0.775 0.866
## DISC6 (.p6.) 0.689 0.021 32.051 0.000 0.524 0.621
## JOB =~
## JOB1 1.000 1.140 0.850
## JOB2 (.p8.) 0.919 0.020 45.175 0.000 1.047 0.785
## JOB3 (.p9.) 0.796 0.017 46.071 0.000 0.908 0.792
## JOB4 (.10.) 0.751 0.017 43.359 0.000 0.855 0.758
## JOB5 (.11.) 1.507 0.060 25.025 0.000 1.717 0.508
## CARE =~
## CARE1 1.000 1.011 0.971
## CARE2 (.13.) 0.957 0.009 103.170 0.000 0.968 0.947
## CARE3 (.14.) 1.040 0.013 81.973 0.000 1.052 0.894
## CARE4 (.15.) 1.647 0.045 36.359 0.000 1.666 0.627
## RISK =~
## RISK1 1.000 0.779 0.849
## RISK2 (.17.) 0.946 0.040 23.881 0.000 0.737 0.673
## RISK3 (.18.) 0.871 0.038 22.975 0.000 0.679 0.613
## SOC =~
## SOC1 1.000 1.116 0.966
## SOC2 (.20.) 0.649 0.036 18.007 0.000 0.724 0.586
## EMO =~
## EMO1 1.000 0.492 0.527
## EMO2 (.22.) 1.366 0.077 17.641 0.000 0.673 0.765
## EMO3 (.23.) 1.327 0.075 17.683 0.000 0.653 0.560
## IND =~
## IND1 1.000 0.605 0.707
## IND2 (.25.) 1.238 0.109 11.373 0.000 0.749 0.848
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.094 0.022 4.340 0.000 0.108 0.108
## CARE 0.102 0.018 5.537 0.000 0.132 0.132
## RISK -0.000 0.015 -0.004 0.997 -0.000 -0.000
## SOC -0.047 0.020 -2.319 0.020 -0.056 -0.056
## EMO 0.069 0.011 6.240 0.000 0.183 0.183
## IND -0.040 0.012 -3.236 0.001 -0.087 -0.087
## JOB ~~
## CARE 0.116 0.028 4.210 0.000 0.101 0.101
## RISK 0.118 0.024 4.997 0.000 0.132 0.132
## SOC 0.027 0.031 0.868 0.386 0.021 0.021
## EMO 0.043 0.016 2.700 0.007 0.077 0.077
## IND 0.072 0.019 3.802 0.000 0.105 0.105
## CARE ~~
## RISK 0.025 0.020 1.256 0.209 0.032 0.032
## SOC 0.037 0.026 1.404 0.160 0.033 0.033
## EMO 0.028 0.013 2.057 0.040 0.056 0.056
## IND 0.035 0.016 2.212 0.027 0.057 0.057
## RISK ~~
## SOC 0.107 0.022 4.812 0.000 0.124 0.124
## EMO 0.071 0.012 5.962 0.000 0.185 0.185
## IND 0.080 0.014 5.607 0.000 0.169 0.169
## SOC ~~
## EMO 0.172 0.017 10.028 0.000 0.314 0.314
## IND -0.028 0.018 -1.579 0.114 -0.041 -0.041
## EMO ~~
## IND -0.057 0.010 -5.713 0.000 -0.192 -0.192
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 (.79.) 1.826 0.018 100.583 0.000 1.826 2.031
## .DISC2 (.80.) 1.559 0.018 88.091 0.000 1.559 1.784
## .DISC3 (.81.) 1.464 0.016 90.378 0.000 1.464 1.834
## .DISC4 (.82.) 1.377 0.016 86.940 0.000 1.377 1.774
## .DISC5 (.83.) 1.717 0.018 94.547 0.000 1.717 1.918
## .DISC6 (.84.) 1.458 0.017 85.382 0.000 1.458 1.729
## .JOB1 (.85.) 2.793 0.027 101.733 0.000 2.793 2.084
## .JOB2 (.86.) 2.775 0.027 101.712 0.000 2.775 2.080
## .JOB3 (.87.) 2.265 0.023 96.995 0.000 2.265 1.977
## .JOB4 (.88.) 2.116 0.023 92.040 0.000 2.116 1.875
## .JOB5 (.89.) 7.139 0.071 101.210 0.000 7.139 2.112
## .CARE1 (.90.) 1.595 0.021 75.418 0.000 1.595 1.531
## .CARE2 (.91.) 1.569 0.021 75.868 0.000 1.569 1.534
## .CARE3 (.92.) 1.666 0.024 69.907 0.000 1.666 1.416
## .CARE4 (.93.) 1.823 0.055 33.017 0.000 1.823 0.686
## .RISK1 (.94.) 2.784 0.019 148.744 0.000 2.784 3.034
## .RISK2 (.95.) 2.704 0.022 121.362 0.000 2.704 2.472
## .RISK3 (.96.) 2.998 0.023 132.915 0.000 2.998 2.709
## .SOC1 (.97.) 3.637 0.023 154.916 0.000 3.637 3.150
## .SOC2 (.98.) 2.912 0.025 115.775 0.000 2.912 2.359
## .EMO1 (.99.) 3.258 0.019 170.329 0.000 3.258 3.489
## .EMO2 (.100) 2.579 0.018 143.265 0.000 2.579 2.935
## .EMO3 (.101) 2.764 0.024 116.319 0.000 2.764 2.369
## .IND1 (.102) 4.169 0.017 240.912 0.000 4.169 4.870
## .IND2 (.103) 3.915 0.018 215.359 0.000 3.915 4.432
## DISC 0.000 0.000 0.000
## JOB 0.000 0.000 0.000
## CARE 0.000 0.000 0.000
## RISK 0.000 0.000 0.000
## SOC 0.000 0.000 0.000
## EMO 0.000 0.000 0.000
## IND 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.656
## .DISC1 0.229 0.010 23.011 0.000 0.229 0.284
## .DISC2 0.382 0.013 28.566 0.000 0.382 0.500
## .DISC3 0.295 0.011 28.011 0.000 0.295 0.464
## .DISC4 0.315 0.011 28.876 0.000 0.315 0.523
## .DISC5 0.200 0.009 21.346 0.000 0.200 0.250
## .DISC6 0.436 0.015 29.876 0.000 0.436 0.614
## .JOB1 0.498 0.024 20.904 0.000 0.498 0.277
## .JOB2 0.683 0.027 25.205 0.000 0.683 0.384
## .JOB3 0.489 0.020 24.856 0.000 0.489 0.372
## .JOB4 0.542 0.021 26.333 0.000 0.542 0.425
## .JOB5 8.472 0.277 30.596 0.000 8.472 0.742
## .CARE1 0.063 0.005 12.325 0.000 0.063 0.058
## .CARE2 0.109 0.006 19.650 0.000 0.109 0.104
## .CARE3 0.279 0.010 27.232 0.000 0.279 0.201
## .CARE4 4.290 0.137 31.333 0.000 4.290 0.607
## .RISK1 0.235 0.023 10.327 0.000 0.235 0.279
## .RISK2 0.654 0.028 23.086 0.000 0.654 0.546
## .RISK3 0.764 0.029 26.108 0.000 0.764 0.624
## .SOC1 0.089 0.063 1.414 0.157 0.089 0.067
## .EMO1 0.630 0.024 26.483 0.000 0.630 0.722
## .EMO2 0.320 0.025 13.058 0.000 0.320 0.414
## .EMO3 0.934 0.037 25.239 0.000 0.934 0.686
## .IND1 0.366 0.034 10.890 0.000 0.366 0.500
## .IND2 0.219 0.049 4.473 0.000 0.219 0.281
## DISC 0.579 0.025 23.457 0.000 1.000 1.000
## JOB 1.299 0.055 23.766 0.000 1.000 1.000
## CARE 1.023 0.034 30.037 0.000 1.000 1.000
## RISK 0.607 0.033 18.430 0.000 1.000 1.000
## SOC 1.245 0.075 16.593 0.000 1.000 1.000
## EMO 0.242 0.022 11.009 0.000 1.000 1.000
## IND 0.366 0.037 9.969 0.000 1.000 1.000
##
##
## Group 2 [sample3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.704 0.815
## DISC2 (.p2.) 0.813 0.021 37.824 0.000 0.572 0.674
## DISC3 (.p3.) 0.768 0.019 39.521 0.000 0.540 0.683
## DISC4 (.p4.) 0.704 0.019 36.364 0.000 0.496 0.618
## DISC5 (.p5.) 1.019 0.020 49.708 0.000 0.717 0.797
## DISC6 (.p6.) 0.689 0.021 32.051 0.000 0.485 0.595
## JOB =~
## JOB1 1.000 1.270 0.899
## JOB2 (.p8.) 0.919 0.020 45.175 0.000 1.167 0.841
## JOB3 (.p9.) 0.796 0.017 46.071 0.000 1.011 0.862
## JOB4 (.10.) 0.751 0.017 43.359 0.000 0.953 0.830
## JOB5 (.11.) 1.507 0.060 25.025 0.000 1.914 0.468
## CARE =~
## CARE1 1.000 0.963 0.939
## CARE2 (.13.) 0.957 0.009 103.170 0.000 0.922 0.945
## CARE3 (.14.) 1.040 0.013 81.973 0.000 1.002 0.885
## CARE4 (.15.) 1.647 0.045 36.359 0.000 1.586 0.490
## RISK =~
## RISK1 1.000 0.691 0.730
## RISK2 (.17.) 0.946 0.040 23.881 0.000 0.654 0.594
## RISK3 (.18.) 0.871 0.038 22.975 0.000 0.602 0.539
## SOC =~
## SOC1 1.000 1.121 0.964
## SOC2 (.20.) 0.649 0.036 18.007 0.000 0.727 0.588
## EMO =~
## EMO1 1.000 0.504 0.525
## EMO2 (.22.) 1.366 0.077 17.641 0.000 0.689 0.760
## EMO3 (.23.) 1.327 0.075 17.683 0.000 0.669 0.579
## IND =~
## IND1 1.000 0.563 0.685
## IND2 (.25.) 1.238 0.109 11.373 0.000 0.698 0.705
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.236 0.055 4.285 0.000 0.264 0.264
## CARE 0.154 0.041 3.758 0.000 0.226 0.226
## RISK -0.006 0.034 -0.181 0.856 -0.013 -0.013
## SOC 0.093 0.047 1.958 0.050 0.117 0.117
## EMO 0.051 0.025 2.076 0.038 0.145 0.145
## IND 0.022 0.028 0.767 0.443 0.055 0.055
## JOB ~~
## CARE 0.144 0.070 2.045 0.041 0.118 0.118
## RISK -0.006 0.060 -0.099 0.921 -0.007 -0.007
## SOC -0.088 0.082 -1.066 0.287 -0.062 -0.062
## EMO 0.020 0.043 0.474 0.636 0.032 0.032
## IND 0.115 0.050 2.303 0.021 0.161 0.161
## CARE ~~
## RISK -0.021 0.044 -0.478 0.633 -0.032 -0.032
## SOC 0.057 0.061 0.919 0.358 0.052 0.052
## EMO 0.032 0.032 0.985 0.324 0.065 0.065
## IND -0.053 0.037 -1.429 0.153 -0.098 -0.098
## RISK ~~
## SOC -0.022 0.052 -0.428 0.669 -0.029 -0.029
## EMO 0.074 0.028 2.652 0.008 0.211 0.211
## IND 0.050 0.032 1.569 0.117 0.127 0.127
## SOC ~~
## EMO 0.199 0.040 4.977 0.000 0.353 0.353
## IND -0.143 0.045 -3.216 0.001 -0.227 -0.227
## EMO ~~
## IND -0.081 0.024 -3.423 0.001 -0.285 -0.285
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 (.79.) 1.826 0.018 100.583 0.000 1.826 2.114
## .DISC2 (.80.) 1.559 0.018 88.091 0.000 1.559 1.836
## .DISC3 (.81.) 1.464 0.016 90.378 0.000 1.464 1.851
## .DISC4 (.82.) 1.377 0.016 86.940 0.000 1.377 1.716
## .DISC5 (.83.) 1.717 0.018 94.547 0.000 1.717 1.908
## .DISC6 (.84.) 1.458 0.017 85.382 0.000 1.458 1.788
## .JOB1 (.85.) 2.793 0.027 101.733 0.000 2.793 1.977
## .JOB2 (.86.) 2.775 0.027 101.712 0.000 2.775 2.000
## .JOB3 (.87.) 2.265 0.023 96.995 0.000 2.265 1.929
## .JOB4 (.88.) 2.116 0.023 92.040 0.000 2.116 1.842
## .JOB5 (.89.) 7.139 0.071 101.210 0.000 7.139 1.745
## .CARE1 (.90.) 1.595 0.021 75.418 0.000 1.595 1.556
## .CARE2 (.91.) 1.569 0.021 75.868 0.000 1.569 1.610
## .CARE3 (.92.) 1.666 0.024 69.907 0.000 1.666 1.473
## .CARE4 (.93.) 1.823 0.055 33.017 0.000 1.823 0.564
## .RISK1 (.94.) 2.784 0.019 148.744 0.000 2.784 2.942
## .RISK2 (.95.) 2.704 0.022 121.362 0.000 2.704 2.457
## .RISK3 (.96.) 2.998 0.023 132.915 0.000 2.998 2.685
## .SOC1 (.97.) 3.637 0.023 154.916 0.000 3.637 3.130
## .SOC2 (.98.) 2.912 0.025 115.775 0.000 2.912 2.355
## .EMO1 (.99.) 3.258 0.019 170.329 0.000 3.258 3.392
## .EMO2 (.100) 2.579 0.018 143.265 0.000 2.579 2.848
## .EMO3 (.101) 2.764 0.024 116.319 0.000 2.764 2.393
## .IND1 (.102) 4.169 0.017 240.912 0.000 4.169 5.072
## .IND2 (.103) 3.915 0.018 215.359 0.000 3.915 3.957
## DISC 0.000 0.000 0.000
## JOB 0.000 0.000 0.000
## CARE 0.000 0.000 0.000
## RISK 0.000 0.000 0.000
## SOC 0.000 0.000 0.000
## EMO 0.000 0.000 0.000
## IND 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SOC2 1.000 1.000 0.654
## .DISC1 0.251 0.026 9.669 0.000 0.251 0.336
## .DISC2 0.394 0.034 11.729 0.000 0.394 0.546
## .DISC3 0.334 0.029 11.649 0.000 0.334 0.533
## .DISC4 0.398 0.033 12.110 0.000 0.398 0.618
## .DISC5 0.296 0.029 10.100 0.000 0.296 0.365
## .DISC6 0.430 0.035 12.232 0.000 0.430 0.646
## .JOB1 0.383 0.044 8.682 0.000 0.383 0.192
## .JOB2 0.563 0.053 10.620 0.000 0.563 0.293
## .JOB3 0.355 0.035 10.105 0.000 0.355 0.258
## .JOB4 0.411 0.038 10.844 0.000 0.411 0.311
## .JOB5 13.071 1.013 12.905 0.000 13.071 0.781
## .CARE1 0.123 0.016 7.763 0.000 0.123 0.117
## .CARE2 0.101 0.014 7.201 0.000 0.101 0.106
## .CARE3 0.277 0.026 10.756 0.000 0.277 0.216
## .CARE4 7.943 0.612 12.990 0.000 7.943 0.759
## .RISK1 0.417 0.054 7.710 0.000 0.417 0.466
## .RISK2 0.784 0.074 10.650 0.000 0.784 0.647
## .RISK3 0.884 0.078 11.318 0.000 0.884 0.709
## .SOC1 0.094 0.099 0.952 0.341 0.094 0.070
## .EMO1 0.669 0.058 11.582 0.000 0.669 0.724
## .EMO2 0.346 0.049 7.028 0.000 0.346 0.422
## .EMO3 0.887 0.081 10.994 0.000 0.887 0.665
## .IND1 0.358 0.047 7.661 0.000 0.358 0.530
## .IND2 0.492 0.069 7.107 0.000 0.492 0.503
## DISC 0.496 0.045 11.012 0.000 1.000 1.000
## JOB 1.613 0.137 11.734 0.000 1.000 1.000
## CARE 0.927 0.075 12.439 0.000 1.000 1.000
## RISK 0.478 0.059 8.080 0.000 1.000 1.000
## SOC 1.256 0.139 9.022 0.000 1.000 1.000
## EMO 0.254 0.035 7.176 0.000 1.000 1.000
## IND 0.317 0.048 6.591 0.000 1.000 1.000Notes on interpretation: If p-value > 0.05, the constrained model is equivalent to the unconstrained/free model (the coefficients and intercepts do not vary by group). Thus, it would be fair to analyse the pooled data in a single global model.
If p< 0.05, the free model is significantly different from the constrained model, implying differences in coefficients/intercepts between the two groups.
anova(config, metric)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## config 510 155880 156979 2016.3
## metric 528 155980 156974 2151.5 135.22 18 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(metric, scalar)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## metric 528 155980 156974 2151.5
## scalar 553 156160 157010 2382.1 230.62 25 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Running full model comparisons
measurementInvariance(model = model, data=combined_alt, group="sample_group")## 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 510 155880 156979 2016.3
## fit.loadings 528 155980 156974 2151.5 135.218 18 < 2.2e-16 ***
## fit.intercepts 546 156125 157016 2332.8 181.308 18 < 2.2e-16 ***
## fit.means 553 156160 157010 2382.1 49.311 7 1.971e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Fit measures:
##
## cfi rmsea cfi.delta rmsea.delta
## fit.configural 0.946 0.050 NA NA
## fit.loadings 0.942 0.051 0.004 0.001
## fit.intercepts 0.936 0.052 0.006 0.002
## fit.means 0.935 0.052 0.002 0.000Similar to the previous comment, our chi-squared estimates were slightly different from Nielsen et al’s. However, factor loadings for sample 2 were the same (as compared to table S18 and S19) but the results for sample 3 were slightly different.
Comments:
The (standardlized) factor loadings values from configural invariance model matched Nielsen et al’s reports (table 3).
Chi-squares statistics for sample 2 and 3 were slightly different from Nielsen’s et al reports (current results: Chi-squared sample 2 = 1441.402, Chi-squared sample 3 = 497.563; Nielsen’s results: Chi-squared sample 2 = 1440.7, Chi-squared sample 3 = 496.5). Results from configural factor loadings for sample 2 here were different from table S15 (but this information from the supp doc may be incorrect since the loadings were highly different from factor loadings when sample 2 was analyzed separately).
Similarly, chi-squared from metric invariance (chisq= 1901.4) and scalar (chisq= 2168.8) invariance models were different from original report (metric chisq =1899.849, scalar chisq = 2167.051). However, current factor loadings were the same as reported by Nielsen et al.
The current report showed that the unconstrained (configural) model significantly fit the data better than those with equality contraints between sample groups. This was different than the reports of metric and scalar invariance from Nielsen et al (2021). Additionally, we could not directly make comparisons between the current result and theirs because they did not report results from Chi-square difference tests. Their claim of metric and scalar invariances came from reasonable model fit even after adding constraints.