Sample_1_Nielsen$sample_group = "Nielsen"
UofA_Sample$sample_group = "UofA"
combined <- rbind(UofA_Sample, Sample_1_Nielsen)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
'## configural invariance
config <- cfa(model, data = combined, group = "sample_group")
## Metric invariance
metric <- cfa(model, data = combined, group = "sample_group",
#set factor loadings to be equal between groups
group.equal="loadings")
## Scalar invarance
scalar <- cfa(model, 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 89 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 168
##
## Number of observations per group:
## UofA 2445
## Nielsen 2009
##
## Model Test User Model:
##
## Test statistic 2624.956
## Degrees of freedom 430
## P-value (Chi-square) 0.000
## Test statistic for each group:
## UofA 1370.994
## Nielsen 1253.961
##
## Model Test Baseline Model:
##
## Test statistic 58246.320
## Degrees of freedom 506
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.962
## Tucker-Lewis Index (TLI) 0.955
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -138800.539
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 277937.079
## Bayesian (BIC) 279012.540
## Sample-size adjusted Bayesian (BIC) 278478.703
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.048
## 90 Percent confidence interval - lower 0.046
## 90 Percent confidence interval - upper 0.050
## P-value RMSEA <= 0.05 0.976
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.040
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [UofA]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.831 0.811
## DISC2 1.022 0.022 45.568 0.000 0.850 0.804
## DISC3 1.036 0.022 46.855 0.000 0.861 0.820
## DISC4 1.085 0.022 48.930 0.000 0.901 0.845
## DISC5 1.129 0.022 52.468 0.000 0.938 0.887
## DISC6 0.959 0.023 41.210 0.000 0.797 0.747
## JOB =~
## JOB1 1.000 1.180 0.872
## JOB2 1.038 0.019 55.800 0.000 1.225 0.852
## JOB3 0.946 0.016 59.143 0.000 1.116 0.880
## JOB4 0.913 0.016 58.744 0.000 1.078 0.877
## JOB5 2.766 0.091 30.360 0.000 3.265 0.566
## CARE =~
## CARE1 1.000 0.985 0.957
## CARE2 0.997 0.009 111.658 0.000 0.981 0.964
## CARE3 0.976 0.011 90.360 0.000 0.961 0.918
## CARE4 2.674 0.110 24.364 0.000 2.633 0.452
## RISK =~
## RISK1 1.000 0.812 0.820
## RISK2 1.030 0.031 33.068 0.000 0.836 0.748
## RISK3 0.979 0.030 32.850 0.000 0.795 0.738
## SOC =~
## SOC1 1.000 1.054 0.862
## SOC2 0.821 0.083 9.909 0.000 0.866 0.678
## EMO =~
## EMO1 1.000 0.608 0.600
## EMO2 1.316 0.067 19.583 0.000 0.800 0.845
## EMO3 0.856 0.047 18.115 0.000 0.520 0.462
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.330 0.023 14.261 0.000 0.336 0.336
## CARE 0.260 0.019 13.967 0.000 0.318 0.318
## RISK 0.129 0.016 8.044 0.000 0.192 0.192
## SOC -0.004 0.021 -0.174 0.862 -0.004 -0.004
## EMO 0.156 0.014 11.012 0.000 0.308 0.308
## JOB ~~
## CARE 0.218 0.025 8.619 0.000 0.188 0.188
## RISK 0.217 0.023 9.445 0.000 0.226 0.226
## SOC 0.082 0.030 2.776 0.006 0.066 0.066
## EMO 0.193 0.019 9.968 0.000 0.269 0.269
## CARE ~~
## RISK 0.134 0.018 7.277 0.000 0.168 0.168
## SOC 0.086 0.024 3.566 0.000 0.083 0.083
## EMO 0.126 0.015 8.336 0.000 0.211 0.211
## RISK ~~
## SOC 0.170 0.022 7.650 0.000 0.199 0.199
## EMO 0.121 0.014 8.785 0.000 0.246 0.246
## SOC ~~
## EMO 0.128 0.018 7.190 0.000 0.200 0.200
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 1.923 0.021 92.760 0.000 1.923 1.876
## .DISC2 1.780 0.021 83.271 0.000 1.780 1.684
## .DISC3 1.719 0.021 80.977 0.000 1.719 1.638
## .DISC4 1.680 0.022 77.890 0.000 1.680 1.575
## .DISC5 1.854 0.021 86.649 0.000 1.854 1.752
## .DISC6 1.663 0.022 77.080 0.000 1.663 1.559
## .JOB1 2.315 0.027 84.548 0.000 2.315 1.710
## .JOB2 2.445 0.029 84.107 0.000 2.445 1.701
## .JOB3 2.120 0.026 82.623 0.000 2.120 1.671
## .JOB4 2.045 0.025 82.239 0.000 2.045 1.663
## .JOB5 5.818 0.117 49.883 0.000 5.818 1.009
## .CARE1 1.508 0.021 72.468 0.000 1.508 1.466
## .CARE2 1.506 0.021 73.205 0.000 1.506 1.480
## .CARE3 1.506 0.021 71.128 0.000 1.506 1.438
## .CARE4 3.452 0.118 29.334 0.000 3.452 0.593
## .RISK1 2.670 0.020 133.316 0.000 2.670 2.696
## .RISK2 2.609 0.023 115.410 0.000 2.609 2.334
## .RISK3 2.881 0.022 132.346 0.000 2.881 2.677
## .SOC1 3.380 0.025 136.625 0.000 3.380 2.763
## .SOC2 2.975 0.026 115.250 0.000 2.975 2.331
## .EMO1 2.883 0.020 140.893 0.000 2.883 2.849
## .EMO2 2.451 0.019 128.080 0.000 2.451 2.590
## .EMO3 2.652 0.023 116.366 0.000 2.652 2.353
## 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
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 0.360 0.012 29.912 0.000 0.360 0.343
## .DISC2 0.395 0.013 30.156 0.000 0.395 0.354
## .DISC3 0.361 0.012 29.551 0.000 0.361 0.328
## .DISC4 0.325 0.011 28.340 0.000 0.325 0.285
## .DISC5 0.238 0.009 25.167 0.000 0.238 0.213
## .DISC6 0.504 0.016 31.664 0.000 0.504 0.442
## .JOB1 0.440 0.017 26.251 0.000 0.440 0.240
## .JOB2 0.565 0.020 27.693 0.000 0.565 0.273
## .JOB3 0.364 0.014 25.526 0.000 0.364 0.226
## .JOB4 0.350 0.014 25.829 0.000 0.350 0.232
## .JOB5 22.602 0.670 33.710 0.000 22.602 0.679
## .CARE1 0.089 0.005 19.706 0.000 0.089 0.084
## .CARE2 0.073 0.004 17.141 0.000 0.073 0.070
## .CARE3 0.173 0.006 28.261 0.000 0.173 0.158
## .CARE4 26.924 0.777 34.662 0.000 26.924 0.795
## .RISK1 0.322 0.018 18.050 0.000 0.322 0.328
## .RISK2 0.551 0.023 24.230 0.000 0.551 0.441
## .RISK3 0.527 0.021 24.908 0.000 0.527 0.455
## .SOC1 0.385 0.110 3.495 0.000 0.385 0.257
## .SOC2 0.880 0.078 11.266 0.000 0.880 0.540
## .EMO1 0.655 0.025 26.022 0.000 0.655 0.639
## .EMO2 0.256 0.029 8.801 0.000 0.256 0.285
## .EMO3 0.999 0.032 31.515 0.000 0.999 0.787
## DISC 0.691 0.029 23.891 0.000 1.000 1.000
## JOB 1.393 0.052 26.751 0.000 1.000 1.000
## CARE 0.969 0.030 31.902 0.000 1.000 1.000
## RISK 0.659 0.031 21.554 0.000 1.000 1.000
## SOC 1.111 0.117 9.485 0.000 1.000 1.000
## EMO 0.369 0.028 13.141 0.000 1.000 1.000
##
##
## Group 2 [Nielsen]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.807 0.850
## DISC2 0.813 0.023 35.430 0.000 0.656 0.707
## DISC3 0.878 0.022 40.399 0.000 0.708 0.776
## DISC4 0.842 0.023 36.825 0.000 0.679 0.727
## DISC5 1.008 0.022 45.813 0.000 0.814 0.846
## DISC6 0.752 0.024 31.538 0.000 0.607 0.648
## JOB =~
## JOB1 1.000 1.172 0.863
## JOB2 0.973 0.022 43.686 0.000 1.141 0.821
## JOB3 0.810 0.019 41.627 0.000 0.949 0.793
## JOB4 0.777 0.019 40.342 0.000 0.911 0.776
## JOB5 1.485 0.062 23.935 0.000 1.740 0.519
## CARE =~
## CARE1 1.000 1.015 0.953
## CARE2 0.968 0.011 86.089 0.000 0.982 0.941
## CARE3 1.082 0.014 76.332 0.000 1.098 0.910
## CARE4 1.742 0.049 35.433 0.000 1.768 0.643
## RISK =~
## RISK1 1.000 0.780 0.830
## RISK2 0.966 0.041 23.676 0.000 0.753 0.676
## RISK3 0.927 0.040 23.326 0.000 0.723 0.652
## SOC =~
## SOC1 1.000 1.116 0.939
## SOC2 0.633 0.066 9.570 0.000 0.707 0.582
## EMO =~
## EMO1 1.000 0.496 0.524
## EMO2 1.341 0.082 16.265 0.000 0.665 0.729
## EMO3 1.385 0.085 16.218 0.000 0.688 0.598
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.090 0.024 3.796 0.000 0.095 0.095
## CARE 0.107 0.020 5.377 0.000 0.130 0.130
## RISK 0.031 0.017 1.861 0.063 0.049 0.049
## SOC 0.011 0.022 0.471 0.638 0.012 0.012
## EMO 0.084 0.012 6.872 0.000 0.210 0.210
## JOB ~~
## CARE 0.124 0.029 4.319 0.000 0.105 0.105
## RISK 0.202 0.025 8.103 0.000 0.221 0.221
## SOC 0.025 0.033 0.760 0.447 0.019 0.019
## EMO 0.077 0.017 4.469 0.000 0.132 0.132
## CARE ~~
## RISK 0.034 0.020 1.680 0.093 0.043 0.043
## SOC 0.092 0.027 3.346 0.001 0.081 0.081
## EMO 0.069 0.014 4.801 0.000 0.137 0.137
## RISK ~~
## SOC 0.100 0.023 4.286 0.000 0.115 0.115
## EMO 0.095 0.013 7.412 0.000 0.246 0.246
## SOC ~~
## EMO 0.200 0.019 10.580 0.000 0.361 0.361
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 1.967 0.021 92.849 0.000 1.967 2.072
## .DISC2 1.591 0.021 76.847 0.000 1.591 1.714
## .DISC3 1.636 0.020 80.362 0.000 1.636 1.793
## .DISC4 1.528 0.021 73.331 0.000 1.528 1.636
## .DISC5 1.897 0.021 88.421 0.000 1.897 1.973
## .DISC6 1.577 0.021 75.506 0.000 1.577 1.685
## .JOB1 2.791 0.030 92.100 0.000 2.791 2.055
## .JOB2 2.837 0.031 91.510 0.000 2.837 2.042
## .JOB3 2.296 0.027 86.036 0.000 2.296 1.920
## .JOB4 2.180 0.026 83.240 0.000 2.180 1.857
## .JOB5 6.732 0.075 89.993 0.000 6.732 2.008
## .CARE1 1.595 0.024 67.127 0.000 1.595 1.498
## .CARE2 1.577 0.023 67.745 0.000 1.577 1.511
## .CARE3 1.670 0.027 62.021 0.000 1.670 1.384
## .CARE4 1.666 0.061 27.168 0.000 1.666 0.606
## .RISK1 2.742 0.021 130.780 0.000 2.742 2.918
## .RISK2 2.642 0.025 106.209 0.000 2.642 2.370
## .RISK3 3.046 0.025 123.126 0.000 3.046 2.747
## .SOC1 3.562 0.027 134.315 0.000 3.562 2.997
## .SOC2 2.959 0.027 109.259 0.000 2.959 2.438
## .EMO1 3.232 0.021 152.960 0.000 3.232 3.413
## .EMO2 2.600 0.020 127.603 0.000 2.600 2.847
## .EMO3 2.636 0.026 102.704 0.000 2.636 2.291
## 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
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 0.250 0.011 22.739 0.000 0.250 0.277
## .DISC2 0.430 0.015 28.316 0.000 0.430 0.500
## .DISC3 0.331 0.012 26.532 0.000 0.331 0.397
## .DISC4 0.411 0.015 27.895 0.000 0.411 0.471
## .DISC5 0.263 0.011 23.027 0.000 0.263 0.284
## .DISC6 0.508 0.017 29.261 0.000 0.508 0.580
## .JOB1 0.472 0.023 20.480 0.000 0.472 0.256
## .JOB2 0.630 0.027 23.715 0.000 0.630 0.326
## .JOB3 0.530 0.021 25.190 0.000 0.530 0.371
## .JOB4 0.548 0.021 25.916 0.000 0.548 0.398
## .JOB5 8.215 0.271 30.327 0.000 8.215 0.731
## .CARE1 0.104 0.006 16.661 0.000 0.104 0.092
## .CARE2 0.124 0.006 19.510 0.000 0.124 0.114
## .CARE3 0.251 0.010 24.598 0.000 0.251 0.172
## .CARE4 4.428 0.144 30.720 0.000 4.428 0.586
## .RISK1 0.274 0.023 12.014 0.000 0.274 0.311
## .RISK2 0.676 0.029 23.074 0.000 0.676 0.543
## .RISK3 0.706 0.029 24.321 0.000 0.706 0.574
## .SOC1 0.167 0.125 1.336 0.182 0.167 0.118
## .SOC2 0.974 0.059 16.602 0.000 0.974 0.661
## .EMO1 0.651 0.025 26.030 0.000 0.651 0.725
## .EMO2 0.391 0.026 15.092 0.000 0.391 0.469
## .EMO3 0.851 0.037 22.940 0.000 0.851 0.643
## DISC 0.651 0.028 22.955 0.000 1.000 1.000
## JOB 1.374 0.059 23.292 0.000 1.000 1.000
## CARE 1.030 0.036 28.589 0.000 1.000 1.000
## RISK 0.609 0.034 17.970 0.000 1.000 1.000
## SOC 1.246 0.132 9.418 0.000 1.000 1.000
## EMO 0.246 0.024 10.204 0.000 1.000 1.000summary(metric, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 72 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 168
## Number of equality constraints 17
##
## Number of observations per group:
## UofA 2445
## Nielsen 2009
##
## Model Test User Model:
##
## Test statistic 3020.014
## Degrees of freedom 447
## P-value (Chi-square) 0.000
## Test statistic for each group:
## UofA 1572.409
## Nielsen 1447.605
##
## Model Test Baseline Model:
##
## Test statistic 58246.320
## Degrees of freedom 506
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.955
## Tucker-Lewis Index (TLI) 0.950
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -138998.069
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 278298.137
## Bayesian (BIC) 279264.772
## Sample-size adjusted Bayesian (BIC) 278784.954
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.051
## 90 Percent confidence interval - lower 0.049
## 90 Percent confidence interval - upper 0.053
## P-value RMSEA <= 0.05 0.208
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.049
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [UofA]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.883 0.830
## DISC2 (.p2.) 0.937 0.016 58.364 0.000 0.828 0.796
## DISC3 (.p3.) 0.969 0.016 61.952 0.000 0.856 0.818
## DISC4 (.p4.) 0.989 0.016 62.299 0.000 0.873 0.835
## DISC5 (.p5.) 1.073 0.015 69.542 0.000 0.948 0.890
## DISC6 (.p6.) 0.873 0.017 52.330 0.000 0.771 0.735
## JOB =~
## JOB1 1.000 1.224 0.883
## JOB2 (.p8.) 1.016 0.014 70.496 0.000 1.244 0.858
## JOB3 (.p9.) 0.902 0.012 72.579 0.000 1.104 0.876
## JOB4 (.10.) 0.869 0.012 71.551 0.000 1.064 0.872
## JOB5 (.11.) 1.977 0.054 36.617 0.000 2.420 0.446
## CARE =~
## CARE1 1.000 0.983 0.957
## CARE2 (.13.) 0.987 0.007 140.891 0.000 0.970 0.962
## CARE3 (.14.) 1.014 0.009 117.450 0.000 0.996 0.924
## CARE4 (.15.) 1.885 0.045 42.212 0.000 1.853 0.332
## RISK =~
## RISK1 1.000 0.821 0.826
## RISK2 (.17.) 1.007 0.025 40.736 0.000 0.827 0.742
## RISK3 (.18.) 0.961 0.024 40.363 0.000 0.789 0.734
## SOC =~
## SOC1 1.000 1.150 0.940
## SOC2 (.20.) 0.687 0.052 13.324 0.000 0.790 0.620
## EMO =~
## EMO1 1.000 0.588 0.588
## EMO2 (.22.) 1.312 0.051 25.721 0.000 0.771 0.820
## EMO3 (.23.) 1.031 0.042 24.737 0.000 0.606 0.524
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.361 0.025 14.389 0.000 0.334 0.334
## CARE 0.276 0.020 14.101 0.000 0.318 0.318
## RISK 0.138 0.017 8.060 0.000 0.191 0.191
## SOC -0.021 0.023 -0.934 0.350 -0.021 -0.021
## EMO 0.160 0.014 11.472 0.000 0.308 0.308
## JOB ~~
## CARE 0.224 0.026 8.570 0.000 0.186 0.186
## RISK 0.226 0.024 9.432 0.000 0.225 0.225
## SOC 0.069 0.031 2.198 0.028 0.049 0.049
## EMO 0.193 0.019 10.210 0.000 0.268 0.268
## CARE ~~
## RISK 0.136 0.019 7.300 0.000 0.168 0.168
## SOC 0.085 0.025 3.456 0.001 0.075 0.075
## EMO 0.124 0.014 8.571 0.000 0.214 0.214
## RISK ~~
## SOC 0.163 0.023 7.186 0.000 0.173 0.173
## EMO 0.125 0.013 9.393 0.000 0.259 0.259
## SOC ~~
## EMO 0.136 0.017 7.804 0.000 0.202 0.202
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 1.923 0.022 89.291 0.000 1.923 1.806
## .DISC2 1.780 0.021 84.561 0.000 1.780 1.710
## .DISC3 1.719 0.021 81.188 0.000 1.719 1.642
## .DISC4 1.680 0.021 79.372 0.000 1.680 1.605
## .DISC5 1.854 0.022 86.088 0.000 1.854 1.741
## .DISC6 1.663 0.021 78.317 0.000 1.663 1.584
## .JOB1 2.315 0.028 82.536 0.000 2.315 1.669
## .JOB2 2.445 0.029 83.401 0.000 2.445 1.687
## .JOB3 2.120 0.026 83.141 0.000 2.120 1.681
## .JOB4 2.045 0.025 82.843 0.000 2.045 1.675
## .JOB5 5.818 0.110 52.996 0.000 5.818 1.072
## .CARE1 1.508 0.021 72.587 0.000 1.508 1.468
## .CARE2 1.506 0.020 73.891 0.000 1.506 1.494
## .CARE3 1.506 0.022 69.040 0.000 1.506 1.396
## .CARE4 3.452 0.113 30.622 0.000 3.452 0.619
## .RISK1 2.670 0.020 132.782 0.000 2.670 2.685
## .RISK2 2.609 0.023 115.826 0.000 2.609 2.342
## .RISK3 2.881 0.022 132.624 0.000 2.881 2.682
## .SOC1 3.380 0.025 136.577 0.000 3.380 2.762
## .SOC2 2.975 0.026 115.466 0.000 2.975 2.335
## .EMO1 2.883 0.020 142.490 0.000 2.883 2.882
## .EMO2 2.451 0.019 128.849 0.000 2.451 2.606
## .EMO3 2.652 0.023 113.305 0.000 2.652 2.291
## 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
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 0.354 0.012 29.292 0.000 0.354 0.312
## .DISC2 0.398 0.013 30.536 0.000 0.398 0.367
## .DISC3 0.363 0.012 29.775 0.000 0.363 0.331
## .DISC4 0.332 0.011 29.022 0.000 0.332 0.303
## .DISC5 0.235 0.009 25.047 0.000 0.235 0.207
## .DISC6 0.508 0.016 31.974 0.000 0.508 0.461
## .JOB1 0.425 0.017 25.449 0.000 0.425 0.221
## .JOB2 0.555 0.020 27.488 0.000 0.555 0.264
## .JOB3 0.371 0.014 26.091 0.000 0.371 0.233
## .JOB4 0.358 0.014 26.425 0.000 0.358 0.240
## .JOB5 23.615 0.688 34.334 0.000 23.615 0.801
## .CARE1 0.088 0.004 20.043 0.000 0.088 0.083
## .CARE2 0.075 0.004 18.351 0.000 0.075 0.074
## .CARE3 0.171 0.006 27.661 0.000 0.171 0.147
## .CARE4 27.632 0.794 34.821 0.000 27.632 0.889
## .RISK1 0.314 0.017 18.376 0.000 0.314 0.318
## .RISK2 0.557 0.022 25.313 0.000 0.557 0.449
## .RISK3 0.531 0.021 25.813 0.000 0.531 0.461
## .SOC1 0.174 0.101 1.732 0.083 0.174 0.117
## .SOC2 0.999 0.055 18.036 0.000 0.999 0.616
## .EMO1 0.656 0.023 28.195 0.000 0.656 0.655
## .EMO2 0.290 0.023 12.412 0.000 0.290 0.328
## .EMO3 0.972 0.032 30.465 0.000 0.972 0.726
## DISC 0.780 0.029 27.093 0.000 1.000 1.000
## JOB 1.498 0.052 28.682 0.000 1.000 1.000
## CARE 0.967 0.030 32.613 0.000 1.000 1.000
## RISK 0.674 0.029 23.435 0.000 1.000 1.000
## SOC 1.323 0.108 12.214 0.000 1.000 1.000
## EMO 0.346 0.023 15.343 0.000 1.000 1.000
##
##
## Group 2 [Nielsen]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.740 0.816
## DISC2 (.p2.) 0.937 0.016 58.364 0.000 0.694 0.729
## DISC3 (.p3.) 0.969 0.016 61.952 0.000 0.717 0.783
## DISC4 (.p4.) 0.989 0.016 62.299 0.000 0.732 0.757
## DISC5 (.p5.) 1.073 0.015 69.542 0.000 0.794 0.835
## DISC6 (.p6.) 0.873 0.017 52.330 0.000 0.646 0.674
## JOB =~
## JOB1 1.000 1.087 0.831
## JOB2 (.p8.) 1.016 0.014 70.496 0.000 1.104 0.805
## JOB3 (.p9.) 0.902 0.012 72.579 0.000 0.981 0.810
## JOB4 (.10.) 0.869 0.012 71.551 0.000 0.945 0.794
## JOB5 (.11.) 1.977 0.054 36.617 0.000 2.149 0.599
## CARE =~
## CARE1 1.000 1.017 0.953
## CARE2 (.13.) 0.987 0.007 140.891 0.000 1.003 0.945
## CARE3 (.14.) 1.014 0.009 117.450 0.000 1.031 0.894
## CARE4 (.15.) 1.885 0.045 42.212 0.000 1.917 0.674
## RISK =~
## RISK1 1.000 0.764 0.817
## RISK2 (.17.) 1.007 0.025 40.736 0.000 0.770 0.687
## RISK3 (.18.) 0.961 0.024 40.363 0.000 0.734 0.660
## SOC =~
## SOC1 1.000 1.075 0.905
## SOC2 (.20.) 0.687 0.052 13.324 0.000 0.738 0.607
## EMO =~
## EMO1 1.000 0.537 0.559
## EMO2 (.22.) 1.312 0.051 25.721 0.000 0.705 0.764
## EMO3 (.23.) 1.031 0.042 24.737 0.000 0.554 0.497
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.081 0.020 4.011 0.000 0.100 0.100
## CARE 0.101 0.018 5.517 0.000 0.134 0.134
## RISK 0.029 0.015 1.965 0.049 0.052 0.052
## SOC 0.009 0.020 0.440 0.660 0.011 0.011
## EMO 0.088 0.012 7.491 0.000 0.221 0.221
## JOB ~~
## CARE 0.113 0.027 4.225 0.000 0.102 0.102
## RISK 0.185 0.023 8.161 0.000 0.222 0.222
## SOC 0.019 0.030 0.640 0.522 0.017 0.017
## EMO 0.075 0.017 4.464 0.000 0.129 0.129
## CARE ~~
## RISK 0.034 0.020 1.681 0.093 0.043 0.043
## SOC 0.095 0.027 3.458 0.001 0.086 0.086
## EMO 0.074 0.015 4.821 0.000 0.135 0.135
## RISK ~~
## SOC 0.100 0.023 4.407 0.000 0.122 0.122
## EMO 0.094 0.013 7.251 0.000 0.229 0.229
## SOC ~~
## EMO 0.206 0.019 11.031 0.000 0.356 0.356
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 1.967 0.020 97.191 0.000 1.967 2.168
## .DISC2 1.591 0.021 74.962 0.000 1.591 1.672
## .DISC3 1.636 0.020 80.064 0.000 1.636 1.786
## .DISC4 1.528 0.022 70.881 0.000 1.528 1.581
## .DISC5 1.897 0.021 89.428 0.000 1.897 1.995
## .DISC6 1.577 0.021 73.725 0.000 1.577 1.645
## .JOB1 2.791 0.029 95.709 0.000 2.791 2.135
## .JOB2 2.837 0.031 92.740 0.000 2.837 2.069
## .JOB3 2.296 0.027 85.061 0.000 2.296 1.898
## .JOB4 2.180 0.027 82.080 0.000 2.180 1.831
## .JOB5 6.732 0.080 84.168 0.000 6.732 1.878
## .CARE1 1.595 0.024 66.979 0.000 1.595 1.494
## .CARE2 1.577 0.024 66.627 0.000 1.577 1.486
## .CARE3 1.670 0.026 64.932 0.000 1.670 1.449
## .CARE4 1.666 0.063 26.241 0.000 1.666 0.585
## .RISK1 2.742 0.021 131.444 0.000 2.742 2.933
## .RISK2 2.642 0.025 105.659 0.000 2.642 2.357
## .RISK3 3.046 0.025 122.740 0.000 3.046 2.738
## .SOC1 3.562 0.027 134.404 0.000 3.562 2.999
## .SOC2 2.959 0.027 109.003 0.000 2.959 2.432
## .EMO1 3.232 0.021 150.716 0.000 3.232 3.363
## .EMO2 2.600 0.021 126.410 0.000 2.600 2.820
## .EMO3 2.636 0.025 106.018 0.000 2.636 2.365
## 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
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 0.275 0.011 25.129 0.000 0.275 0.334
## .DISC2 0.424 0.015 28.005 0.000 0.424 0.468
## .DISC3 0.324 0.012 26.496 0.000 0.324 0.387
## .DISC4 0.398 0.015 27.322 0.000 0.398 0.426
## .DISC5 0.273 0.011 24.126 0.000 0.273 0.302
## .DISC6 0.501 0.017 28.992 0.000 0.501 0.546
## .JOB1 0.528 0.022 23.517 0.000 0.528 0.309
## .JOB2 0.660 0.026 24.965 0.000 0.660 0.351
## .JOB3 0.503 0.020 24.733 0.000 0.503 0.343
## .JOB4 0.524 0.021 25.522 0.000 0.524 0.370
## .JOB5 8.236 0.278 29.625 0.000 8.236 0.641
## .CARE1 0.105 0.006 16.897 0.000 0.105 0.092
## .CARE2 0.119 0.006 18.770 0.000 0.119 0.106
## .CARE3 0.266 0.010 26.058 0.000 0.266 0.200
## .CARE4 4.423 0.145 30.551 0.000 4.423 0.546
## .RISK1 0.290 0.019 15.578 0.000 0.290 0.332
## .RISK2 0.664 0.027 24.269 0.000 0.664 0.529
## .RISK3 0.698 0.027 25.428 0.000 0.698 0.564
## .SOC1 0.256 0.087 2.932 0.003 0.256 0.181
## .SOC2 0.936 0.051 18.517 0.000 0.936 0.632
## .EMO1 0.635 0.024 26.018 0.000 0.635 0.688
## .EMO2 0.354 0.024 14.514 0.000 0.354 0.416
## .EMO3 0.935 0.034 27.784 0.000 0.935 0.753
## DISC 0.548 0.022 25.039 0.000 1.000 1.000
## JOB 1.181 0.046 25.615 0.000 1.000 1.000
## CARE 1.034 0.035 29.609 0.000 1.000 1.000
## RISK 0.584 0.027 21.371 0.000 1.000 1.000
## SOC 1.155 0.096 12.019 0.000 1.000 1.000
## EMO 0.288 0.020 14.330 0.000 1.000 1.000summary(scalar, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 107 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 168
## Number of equality constraints 40
##
## Number of observations per group:
## UofA 2445
## Nielsen 2009
##
## Model Test User Model:
##
## Test statistic 3918.762
## Degrees of freedom 470
## P-value (Chi-square) 0.000
## Test statistic for each group:
## UofA 2076.586
## Nielsen 1842.176
##
## Model Test Baseline Model:
##
## Test statistic 58246.320
## Degrees of freedom 506
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.940
## Tucker-Lewis Index (TLI) 0.936
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -139447.442
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 279150.885
## Bayesian (BIC) 279970.284
## Sample-size adjusted Bayesian (BIC) 279563.551
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.057
## 90 Percent confidence interval - lower 0.056
## 90 Percent confidence interval - upper 0.059
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.055
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [UofA]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.881 0.828
## DISC2 (.p2.) 0.941 0.016 58.022 0.000 0.829 0.794
## DISC3 (.p3.) 0.973 0.016 61.799 0.000 0.857 0.818
## DISC4 (.p4.) 0.993 0.016 62.021 0.000 0.875 0.834
## DISC5 (.p5.) 1.073 0.016 69.044 0.000 0.946 0.889
## DISC6 (.p6.) 0.877 0.017 52.260 0.000 0.773 0.735
## JOB =~
## JOB1 1.000 1.244 0.885
## JOB2 (.p8.) 1.013 0.014 71.415 0.000 1.260 0.861
## JOB3 (.p9.) 0.888 0.012 72.773 0.000 1.105 0.874
## JOB4 (.10.) 0.854 0.012 71.564 0.000 1.062 0.869
## JOB5 (.11.) 1.978 0.053 37.242 0.000 2.461 0.452
## CARE =~
## CARE1 1.000 0.984 0.957
## CARE2 (.13.) 0.986 0.007 140.963 0.000 0.970 0.962
## CARE3 (.14.) 1.014 0.009 117.348 0.000 0.998 0.923
## CARE4 (.15.) 1.851 0.045 40.933 0.000 1.821 0.314
## RISK =~
## RISK1 1.000 0.822 0.826
## RISK2 (.17.) 1.005 0.025 40.758 0.000 0.826 0.741
## RISK3 (.18.) 0.964 0.024 40.439 0.000 0.792 0.736
## SOC =~
## SOC1 1.000 1.177 0.960
## SOC2 (.20.) 0.655 0.050 13.078 0.000 0.771 0.605
## EMO =~
## EMO1 1.000 0.603 0.595
## EMO2 (.22.) 1.285 0.049 26.005 0.000 0.775 0.822
## EMO3 (.23.) 0.993 0.040 24.725 0.000 0.599 0.518
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.360 0.025 14.182 0.000 0.328 0.328
## CARE 0.275 0.020 14.065 0.000 0.317 0.317
## RISK 0.137 0.017 8.022 0.000 0.190 0.190
## SOC -0.026 0.023 -1.151 0.250 -0.025 -0.025
## EMO 0.162 0.014 11.406 0.000 0.305 0.305
## JOB ~~
## CARE 0.231 0.027 8.687 0.000 0.189 0.189
## RISK 0.234 0.024 9.607 0.000 0.229 0.229
## SOC 0.077 0.032 2.411 0.016 0.053 0.053
## EMO 0.209 0.020 10.610 0.000 0.279 0.279
## CARE ~~
## RISK 0.137 0.019 7.370 0.000 0.170 0.170
## SOC 0.088 0.025 3.529 0.000 0.076 0.076
## EMO 0.129 0.015 8.707 0.000 0.218 0.218
## RISK ~~
## SOC 0.163 0.023 7.145 0.000 0.168 0.168
## EMO 0.130 0.014 9.511 0.000 0.262 0.262
## SOC ~~
## EMO 0.144 0.018 8.010 0.000 0.203 0.203
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 (.68.) 1.958 0.015 133.591 0.000 1.958 1.840
## .DISC2 (.69.) 1.707 0.015 114.820 0.000 1.707 1.635
## .DISC3 (.70.) 1.691 0.015 115.692 0.000 1.691 1.613
## .DISC4 (.71.) 1.627 0.015 108.758 0.000 1.627 1.551
## .DISC5 (.72.) 1.881 0.015 125.829 0.000 1.881 1.768
## .DISC6 (.73.) 1.633 0.015 109.106 0.000 1.633 1.554
## .JOB1 (.74.) 2.532 0.020 124.635 0.000 2.532 1.801
## .JOB2 (.75.) 2.630 0.021 124.263 0.000 2.630 1.798
## .JOB3 (.76.) 2.217 0.018 120.614 0.000 2.217 1.755
## .JOB4 (.77.) 2.127 0.018 118.967 0.000 2.127 1.741
## .JOB5 (.78.) 6.340 0.064 99.080 0.000 6.340 1.164
## .CARE1 (.79.) 1.547 0.016 99.486 0.000 1.547 1.505
## .CARE2 (.80.) 1.540 0.015 100.450 0.000 1.540 1.527
## .CARE3 (.81.) 1.572 0.017 95.071 0.000 1.572 1.454
## .CARE4 (.82.) 1.877 0.052 36.261 0.000 1.877 0.323
## .RISK1 (.83.) 2.704 0.014 187.137 0.000 2.704 2.718
## .RISK2 (.84.) 2.628 0.017 157.621 0.000 2.628 2.359
## .RISK3 (.85.) 2.951 0.016 181.047 0.000 2.951 2.741
## .SOC1 (.86.) 3.461 0.018 191.262 0.000 3.461 2.822
## .SOC2 (.87.) 2.967 0.019 158.951 0.000 2.967 2.329
## .EMO1 (.88.) 3.047 0.015 204.388 0.000 3.047 3.004
## .EMO2 (.89.) 2.525 0.014 181.216 0.000 2.525 2.676
## .EMO3 (.90.) 2.649 0.017 155.759 0.000 2.649 2.291
## 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
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 0.356 0.012 29.322 0.000 0.356 0.314
## .DISC2 0.402 0.013 30.539 0.000 0.402 0.369
## .DISC3 0.363 0.012 29.722 0.000 0.363 0.331
## .DISC4 0.334 0.012 28.995 0.000 0.334 0.304
## .DISC5 0.237 0.009 25.120 0.000 0.237 0.210
## .DISC6 0.508 0.016 31.943 0.000 0.508 0.460
## .JOB1 0.429 0.017 25.262 0.000 0.429 0.217
## .JOB2 0.552 0.020 27.281 0.000 0.552 0.258
## .JOB3 0.376 0.014 26.267 0.000 0.376 0.236
## .JOB4 0.365 0.014 26.666 0.000 0.365 0.244
## .JOB5 23.611 0.688 34.316 0.000 23.611 0.796
## .CARE1 0.088 0.004 19.985 0.000 0.088 0.083
## .CARE2 0.075 0.004 18.345 0.000 0.075 0.074
## .CARE3 0.172 0.006 27.682 0.000 0.172 0.147
## .CARE4 30.409 0.873 34.839 0.000 30.409 0.902
## .RISK1 0.314 0.017 18.370 0.000 0.314 0.317
## .RISK2 0.559 0.022 25.396 0.000 0.559 0.450
## .RISK3 0.532 0.021 25.752 0.000 0.532 0.459
## .SOC1 0.117 0.107 1.092 0.275 0.117 0.078
## .SOC2 1.029 0.055 18.850 0.000 1.029 0.634
## .EMO1 0.665 0.024 27.979 0.000 0.665 0.647
## .EMO2 0.290 0.023 12.394 0.000 0.290 0.325
## .EMO3 0.978 0.032 30.688 0.000 0.978 0.732
## DISC 0.777 0.029 27.024 0.000 1.000 1.000
## JOB 1.549 0.054 28.782 0.000 1.000 1.000
## CARE 0.968 0.030 32.618 0.000 1.000 1.000
## RISK 0.676 0.029 23.452 0.000 1.000 1.000
## SOC 1.386 0.115 12.072 0.000 1.000 1.000
## EMO 0.364 0.023 15.522 0.000 1.000 1.000
##
##
## Group 2 [Nielsen]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## DISC1 1.000 0.739 0.814
## DISC2 (.p2.) 0.941 0.016 58.022 0.000 0.696 0.727
## DISC3 (.p3.) 0.973 0.016 61.799 0.000 0.719 0.784
## DISC4 (.p4.) 0.993 0.016 62.021 0.000 0.734 0.756
## DISC5 (.p5.) 1.073 0.016 69.044 0.000 0.793 0.833
## DISC6 (.p6.) 0.877 0.017 52.260 0.000 0.648 0.675
## JOB =~
## JOB1 1.000 1.107 0.834
## JOB2 (.p8.) 1.013 0.014 71.415 0.000 1.121 0.810
## JOB3 (.p9.) 0.888 0.012 72.773 0.000 0.983 0.809
## JOB4 (.10.) 0.854 0.012 71.564 0.000 0.945 0.791
## JOB5 (.11.) 1.978 0.053 37.242 0.000 2.190 0.606
## CARE =~
## CARE1 1.000 1.019 0.953
## CARE2 (.13.) 0.986 0.007 140.963 0.000 1.005 0.946
## CARE3 (.14.) 1.014 0.009 117.348 0.000 1.033 0.894
## CARE4 (.15.) 1.851 0.045 40.933 0.000 1.886 0.664
## RISK =~
## RISK1 1.000 0.765 0.818
## RISK2 (.17.) 1.005 0.025 40.758 0.000 0.769 0.686
## RISK3 (.18.) 0.964 0.024 40.439 0.000 0.738 0.661
## SOC =~
## SOC1 1.000 1.100 0.923
## SOC2 (.20.) 0.655 0.050 13.078 0.000 0.720 0.592
## EMO =~
## EMO1 1.000 0.551 0.563
## EMO2 (.22.) 1.285 0.049 26.005 0.000 0.708 0.766
## EMO3 (.23.) 0.993 0.040 24.725 0.000 0.547 0.490
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.074 0.020 3.611 0.000 0.090 0.090
## CARE 0.099 0.018 5.407 0.000 0.131 0.131
## RISK 0.028 0.015 1.845 0.065 0.049 0.049
## SOC 0.005 0.021 0.226 0.821 0.006 0.006
## EMO 0.087 0.012 7.222 0.000 0.212 0.212
## JOB ~~
## CARE 0.122 0.027 4.449 0.000 0.108 0.108
## RISK 0.194 0.023 8.379 0.000 0.228 0.228
## SOC 0.034 0.031 1.086 0.277 0.028 0.028
## EMO 0.089 0.018 5.056 0.000 0.147 0.147
## CARE ~~
## RISK 0.036 0.020 1.788 0.074 0.046 0.046
## SOC 0.098 0.028 3.550 0.000 0.087 0.087
## EMO 0.078 0.016 5.000 0.000 0.140 0.140
## RISK ~~
## SOC 0.103 0.023 4.501 0.000 0.123 0.123
## EMO 0.099 0.013 7.410 0.000 0.234 0.234
## SOC ~~
## EMO 0.217 0.019 11.281 0.000 0.358 0.358
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 (.68.) 1.958 0.015 133.591 0.000 1.958 2.157
## .DISC2 (.69.) 1.707 0.015 114.820 0.000 1.707 1.783
## .DISC3 (.70.) 1.691 0.015 115.692 0.000 1.691 1.844
## .DISC4 (.71.) 1.627 0.015 108.758 0.000 1.627 1.677
## .DISC5 (.72.) 1.881 0.015 125.829 0.000 1.881 1.975
## .DISC6 (.73.) 1.633 0.015 109.106 0.000 1.633 1.702
## .JOB1 (.74.) 2.532 0.020 124.635 0.000 2.532 1.906
## .JOB2 (.75.) 2.630 0.021 124.263 0.000 2.630 1.900
## .JOB3 (.76.) 2.217 0.018 120.614 0.000 2.217 1.825
## .JOB4 (.77.) 2.127 0.018 118.967 0.000 2.127 1.780
## .JOB5 (.78.) 6.340 0.064 99.080 0.000 6.340 1.756
## .CARE1 (.79.) 1.547 0.016 99.486 0.000 1.547 1.447
## .CARE2 (.80.) 1.540 0.015 100.450 0.000 1.540 1.449
## .CARE3 (.81.) 1.572 0.017 95.071 0.000 1.572 1.359
## .CARE4 (.82.) 1.877 0.052 36.261 0.000 1.877 0.661
## .RISK1 (.83.) 2.704 0.014 187.137 0.000 2.704 2.889
## .RISK2 (.84.) 2.628 0.017 157.621 0.000 2.628 2.344
## .RISK3 (.85.) 2.951 0.016 181.047 0.000 2.951 2.645
## .SOC1 (.86.) 3.461 0.018 191.262 0.000 3.461 2.902
## .SOC2 (.87.) 2.967 0.019 158.951 0.000 2.967 2.439
## .EMO1 (.88.) 3.047 0.015 204.388 0.000 3.047 3.115
## .EMO2 (.89.) 2.525 0.014 181.216 0.000 2.525 2.731
## .EMO3 (.90.) 2.649 0.017 155.759 0.000 2.649 2.375
## 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
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 0.278 0.011 25.164 0.000 0.278 0.337
## .DISC2 0.432 0.015 28.025 0.000 0.432 0.472
## .DISC3 0.324 0.012 26.419 0.000 0.324 0.385
## .DISC4 0.403 0.015 27.315 0.000 0.403 0.428
## .DISC5 0.278 0.011 24.202 0.000 0.278 0.306
## .DISC6 0.501 0.017 28.955 0.000 0.501 0.544
## .JOB1 0.539 0.023 23.448 0.000 0.539 0.305
## .JOB2 0.659 0.027 24.798 0.000 0.659 0.344
## .JOB3 0.509 0.021 24.851 0.000 0.509 0.345
## .JOB4 0.535 0.021 25.694 0.000 0.535 0.375
## .JOB5 8.244 0.279 29.563 0.000 8.244 0.632
## .CARE1 0.104 0.006 16.761 0.000 0.104 0.091
## .CARE2 0.120 0.006 18.729 0.000 0.120 0.106
## .CARE3 0.270 0.010 26.101 0.000 0.270 0.202
## .CARE4 4.514 0.147 30.612 0.000 4.514 0.559
## .RISK1 0.290 0.019 15.561 0.000 0.290 0.331
## .RISK2 0.666 0.027 24.329 0.000 0.666 0.530
## .RISK3 0.701 0.028 25.405 0.000 0.701 0.563
## .SOC1 0.212 0.093 2.284 0.022 0.212 0.149
## .SOC2 0.961 0.050 19.253 0.000 0.961 0.649
## .EMO1 0.653 0.025 25.921 0.000 0.653 0.683
## .EMO2 0.353 0.024 14.473 0.000 0.353 0.413
## .EMO3 0.945 0.034 27.971 0.000 0.945 0.760
## DISC 0.546 0.022 24.979 0.000 1.000 1.000
## JOB 1.226 0.048 25.681 0.000 1.000 1.000
## CARE 1.038 0.035 29.618 0.000 1.000 1.000
## RISK 0.586 0.027 21.390 0.000 1.000 1.000
## SOC 1.210 0.102 11.918 0.000 1.000 1.000
## EMO 0.304 0.021 14.454 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 430 277937 279013 2625
## metric 447 278298 279265 3020 395.06 17 < 2.2e-16 ***
## ---
## 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 447 278298 279265 3020.0
## scalar 470 279151 279970 3918.8 898.75 23 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1