DT::datatable(psych::describe(Sample_1_Nielsen))DT::datatable(psych::describe(UofA_Sample))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
IND =~ IND1 + IND2
'## 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 115 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 192
##
## Number of observations per group:
## UofA 2445
## Nielsen 2009
##
## Model Test User Model:
##
## Test statistic 2841.424
## Degrees of freedom 508
## P-value (Chi-square) 0.000
## Test statistic for each group:
## UofA 1478.213
## Nielsen 1363.210
##
## Model Test Baseline Model:
##
## Test statistic 60554.335
## Degrees of freedom 600
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.961
## Tucker-Lewis Index (TLI) 0.954
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -149205.136
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 298794.273
## Bayesian (BIC) 300023.372
## Sample-size adjusted Bayesian (BIC) 299413.271
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.045
## 90 Percent confidence interval - lower 0.044
## 90 Percent confidence interval - upper 0.047
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.038
##
## 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 45.557 0.000 0.849 0.804
## DISC3 1.036 0.022 46.854 0.000 0.861 0.820
## DISC4 1.085 0.022 48.923 0.000 0.901 0.845
## DISC5 1.129 0.022 52.464 0.000 0.938 0.887
## DISC6 0.959 0.023 41.211 0.000 0.797 0.747
## JOB =~
## JOB1 1.000 1.180 0.872
## JOB2 1.038 0.019 55.804 0.000 1.225 0.852
## JOB3 0.946 0.016 59.152 0.000 1.116 0.880
## JOB4 0.913 0.016 58.744 0.000 1.078 0.877
## JOB5 2.766 0.091 30.358 0.000 3.265 0.566
## CARE =~
## CARE1 1.000 0.985 0.957
## CARE2 0.997 0.009 111.654 0.000 0.981 0.964
## CARE3 0.976 0.011 90.359 0.000 0.961 0.918
## CARE4 2.674 0.110 24.364 0.000 2.633 0.452
## RISK =~
## RISK1 1.000 0.811 0.820
## RISK2 1.029 0.031 33.218 0.000 0.835 0.747
## RISK3 0.980 0.030 33.021 0.000 0.796 0.739
## SOC =~
## SOC1 1.000 1.013 0.829
## SOC2 0.889 0.086 10.283 0.000 0.900 0.705
## EMO =~
## EMO1 1.000 0.604 0.597
## EMO2 1.333 0.069 19.454 0.000 0.805 0.851
## EMO3 0.859 0.047 18.106 0.000 0.519 0.460
## IND =~
## IND1 1.000 0.760 0.926
## IND2 0.695 0.115 6.054 0.000 0.528 0.625
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.330 0.023 14.260 0.000 0.336 0.336
## CARE 0.260 0.019 13.966 0.000 0.318 0.318
## RISK 0.129 0.016 8.046 0.000 0.192 0.192
## SOC 0.004 0.020 0.205 0.838 0.005 0.005
## EMO 0.154 0.014 10.974 0.000 0.307 0.307
## IND -0.016 0.014 -1.145 0.252 -0.026 -0.026
## JOB ~~
## CARE 0.218 0.025 8.618 0.000 0.188 0.188
## RISK 0.217 0.023 9.451 0.000 0.227 0.227
## SOC 0.088 0.029 3.049 0.002 0.074 0.074
## EMO 0.191 0.019 9.934 0.000 0.268 0.268
## IND 0.032 0.020 1.582 0.114 0.036 0.036
## CARE ~~
## RISK 0.134 0.018 7.278 0.000 0.168 0.168
## SOC 0.085 0.024 3.576 0.000 0.085 0.085
## EMO 0.125 0.015 8.302 0.000 0.210 0.210
## IND 0.013 0.017 0.786 0.432 0.017 0.017
## RISK ~~
## SOC 0.172 0.022 7.769 0.000 0.209 0.209
## EMO 0.119 0.014 8.721 0.000 0.244 0.244
## IND 0.101 0.015 6.681 0.000 0.163 0.163
## SOC ~~
## EMO 0.122 0.017 6.998 0.000 0.200 0.200
## IND 0.002 0.019 0.093 0.926 0.002 0.002
## EMO ~~
## IND 0.000 0.011 0.009 0.993 0.000 0.000
##
## 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 0.020 140.894 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
## .IND1 4.061 0.017 244.589 0.000 4.061 4.946
## .IND2 4.207 0.017 246.214 0.000 4.207 4.979
## 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
## .DISC1 0.360 0.012 29.915 0.000 0.360 0.343
## .DISC2 0.395 0.013 30.159 0.000 0.395 0.354
## .DISC3 0.361 0.012 29.550 0.000 0.361 0.327
## .DISC4 0.325 0.011 28.342 0.000 0.325 0.285
## .DISC5 0.238 0.009 25.166 0.000 0.238 0.213
## .DISC6 0.503 0.016 31.663 0.000 0.503 0.442
## .JOB1 0.440 0.017 26.249 0.000 0.440 0.240
## .JOB2 0.565 0.020 27.695 0.000 0.565 0.273
## .JOB3 0.364 0.014 25.524 0.000 0.364 0.226
## .JOB4 0.350 0.014 25.834 0.000 0.350 0.232
## .JOB5 22.603 0.671 33.710 0.000 22.603 0.680
## .CARE1 0.089 0.005 19.709 0.000 0.089 0.084
## .CARE2 0.073 0.004 17.139 0.000 0.073 0.070
## .CARE3 0.173 0.006 28.260 0.000 0.173 0.158
## .CARE4 26.924 0.777 34.662 0.000 26.924 0.795
## .RISK1 0.322 0.018 18.224 0.000 0.322 0.328
## .RISK2 0.552 0.023 24.370 0.000 0.552 0.442
## .RISK3 0.526 0.021 24.947 0.000 0.526 0.454
## .SOC1 0.469 0.099 4.757 0.000 0.469 0.313
## .SOC2 0.818 0.081 10.156 0.000 0.818 0.502
## .EMO1 0.659 0.025 26.181 0.000 0.659 0.644
## .EMO2 0.247 0.029 8.387 0.000 0.247 0.276
## .EMO3 1.001 0.032 31.559 0.000 1.001 0.788
## .IND1 0.096 0.094 1.016 0.310 0.096 0.142
## .IND2 0.435 0.047 9.195 0.000 0.435 0.609
## DISC 0.690 0.029 23.888 0.000 1.000 1.000
## JOB 1.393 0.052 26.753 0.000 1.000 1.000
## CARE 0.969 0.030 31.901 0.000 1.000 1.000
## RISK 0.658 0.030 21.605 0.000 1.000 1.000
## SOC 1.027 0.106 9.708 0.000 1.000 1.000
## EMO 0.365 0.028 13.056 0.000 1.000 1.000
## IND 0.578 0.096 6.000 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 35.440 0.000 0.656 0.707
## DISC3 0.878 0.022 40.403 0.000 0.708 0.776
## DISC4 0.841 0.023 36.824 0.000 0.679 0.727
## DISC5 1.008 0.022 45.823 0.000 0.814 0.846
## DISC6 0.751 0.024 31.538 0.000 0.607 0.648
## JOB =~
## JOB1 1.000 1.173 0.864
## JOB2 0.972 0.022 43.758 0.000 1.141 0.821
## JOB3 0.808 0.019 41.618 0.000 0.948 0.792
## JOB4 0.776 0.019 40.330 0.000 0.910 0.775
## JOB5 1.486 0.062 24.001 0.000 1.743 0.520
## CARE =~
## CARE1 1.000 1.015 0.953
## CARE2 0.968 0.011 86.085 0.000 0.982 0.941
## CARE3 1.082 0.014 76.331 0.000 1.098 0.910
## CARE4 1.743 0.049 35.438 0.000 1.768 0.643
## RISK =~
## RISK1 1.000 0.787 0.837
## RISK2 0.952 0.040 24.054 0.000 0.749 0.672
## RISK3 0.914 0.039 23.646 0.000 0.719 0.649
## SOC =~
## SOC1 1.000 1.065 0.896
## SOC2 0.696 0.067 10.369 0.000 0.741 0.610
## EMO =~
## EMO1 1.000 0.485 0.513
## EMO2 1.401 0.087 16.172 0.000 0.680 0.745
## EMO3 1.406 0.087 16.142 0.000 0.682 0.593
## IND =~
## IND1 1.000 0.697 0.758
## IND2 1.070 0.093 11.508 0.000 0.746 0.793
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.089 0.024 3.787 0.000 0.094 0.094
## CARE 0.107 0.020 5.376 0.000 0.130 0.130
## RISK 0.031 0.017 1.880 0.060 0.050 0.050
## SOC 0.012 0.022 0.529 0.597 0.014 0.014
## EMO 0.082 0.012 6.827 0.000 0.208 0.208
## IND 0.017 0.015 1.146 0.252 0.031 0.031
## JOB ~~
## CARE 0.124 0.029 4.314 0.000 0.105 0.105
## RISK 0.202 0.025 8.061 0.000 0.219 0.219
## SOC 0.028 0.032 0.851 0.395 0.022 0.022
## EMO 0.074 0.017 4.436 0.000 0.130 0.130
## IND 0.123 0.023 5.318 0.000 0.151 0.151
## CARE ~~
## RISK 0.034 0.020 1.675 0.094 0.043 0.043
## SOC 0.094 0.027 3.457 0.001 0.087 0.087
## EMO 0.066 0.014 4.733 0.000 0.135 0.135
## IND 0.050 0.019 2.688 0.007 0.071 0.071
## RISK ~~
## SOC 0.104 0.023 4.445 0.000 0.124 0.124
## EMO 0.092 0.013 7.283 0.000 0.240 0.240
## IND 0.140 0.018 7.975 0.000 0.256 0.256
## SOC ~~
## EMO 0.190 0.018 10.346 0.000 0.368 0.368
## IND -0.049 0.021 -2.294 0.022 -0.065 -0.065
## EMO ~~
## IND -0.033 0.011 -3.097 0.002 -0.099 -0.099
##
## 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 0.021 152.961 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
## .IND1 4.053 0.020 197.770 0.000 4.053 4.412
## .IND2 3.810 0.021 181.709 0.000 3.810 4.054
## 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
## .DISC1 0.250 0.011 22.729 0.000 0.250 0.277
## .DISC2 0.430 0.015 28.315 0.000 0.430 0.500
## .DISC3 0.331 0.012 26.535 0.000 0.331 0.397
## .DISC4 0.411 0.015 27.898 0.000 0.411 0.471
## .DISC5 0.263 0.011 23.031 0.000 0.263 0.284
## .DISC6 0.508 0.017 29.262 0.000 0.508 0.580
## .JOB1 0.469 0.023 20.426 0.000 0.469 0.254
## .JOB2 0.629 0.027 23.718 0.000 0.629 0.326
## .JOB3 0.532 0.021 25.238 0.000 0.532 0.372
## .JOB4 0.550 0.021 25.957 0.000 0.550 0.399
## .JOB5 8.203 0.271 30.323 0.000 8.203 0.730
## .CARE1 0.104 0.006 16.674 0.000 0.104 0.092
## .CARE2 0.124 0.006 19.510 0.000 0.124 0.114
## .CARE3 0.251 0.010 24.596 0.000 0.251 0.172
## .CARE4 4.427 0.144 30.720 0.000 4.427 0.586
## .RISK1 0.264 0.022 11.822 0.000 0.264 0.299
## .RISK2 0.683 0.029 23.562 0.000 0.683 0.549
## .RISK3 0.712 0.029 24.707 0.000 0.712 0.579
## .SOC1 0.279 0.105 2.647 0.008 0.279 0.197
## .SOC2 0.925 0.059 15.804 0.000 0.925 0.628
## .EMO1 0.661 0.025 26.512 0.000 0.661 0.737
## .EMO2 0.371 0.026 14.137 0.000 0.371 0.445
## .EMO3 0.858 0.037 23.289 0.000 0.858 0.648
## .IND1 0.359 0.042 8.451 0.000 0.359 0.425
## .IND2 0.328 0.048 6.827 0.000 0.328 0.371
## DISC 0.652 0.028 22.961 0.000 1.000 1.000
## JOB 1.376 0.059 23.327 0.000 1.000 1.000
## CARE 1.030 0.036 28.587 0.000 1.000 1.000
## RISK 0.619 0.034 18.354 0.000 1.000 1.000
## SOC 1.134 0.114 9.985 0.000 1.000 1.000
## EMO 0.236 0.024 10.027 0.000 1.000 1.000
## IND 0.485 0.047 10.220 0.000 1.000 1.000summary(metric, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 78 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 192
## Number of equality constraints 18
##
## Number of observations per group:
## UofA 2445
## Nielsen 2009
##
## Model Test User Model:
##
## Test statistic 3241.884
## Degrees of freedom 526
## P-value (Chi-square) 0.000
## Test statistic for each group:
## UofA 1683.535
## Nielsen 1558.349
##
## Model Test Baseline Model:
##
## Test statistic 60554.335
## Degrees of freedom 600
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.955
## Tucker-Lewis Index (TLI) 0.948
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -149405.366
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 299158.733
## Bayesian (BIC) 300272.604
## Sample-size adjusted Bayesian (BIC) 299719.701
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.048
## 90 Percent confidence interval - lower 0.047
## 90 Percent confidence interval - upper 0.050
## P-value RMSEA <= 0.05 0.972
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.046
##
## 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 0.829
## DISC2 (.p2.) 0.937 0.016 58.359 0.000 0.828 0.795
## DISC3 (.p3.) 0.969 0.016 61.957 0.000 0.856 0.818
## DISC4 (.p4.) 0.989 0.016 62.300 0.000 0.873 0.835
## DISC5 (.p5.) 1.073 0.015 69.543 0.000 0.948 0.890
## DISC6 (.p6.) 0.873 0.017 52.337 0.000 0.771 0.735
## JOB =~
## JOB1 1.000 1.225 0.883
## JOB2 (.p8.) 1.016 0.014 70.553 0.000 1.244 0.858
## JOB3 (.p9.) 0.901 0.012 72.595 0.000 1.104 0.876
## JOB4 (.10.) 0.869 0.012 71.550 0.000 1.064 0.871
## JOB5 (.11.) 1.978 0.054 36.670 0.000 2.422 0.446
## CARE =~
## CARE1 1.000 0.983 0.957
## CARE2 (.13.) 0.987 0.007 140.887 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 42.216 0.000 1.853 0.333
## RISK =~
## RISK1 1.000 0.823 0.827
## RISK2 (.17.) 1.002 0.024 41.078 0.000 0.825 0.741
## RISK3 (.18.) 0.957 0.024 40.682 0.000 0.788 0.734
## SOC =~
## SOC1 1.000 1.096 0.895
## SOC2 (.20.) 0.758 0.053 14.359 0.000 0.830 0.651
## EMO =~
## EMO1 1.000 0.579 0.580
## EMO2 (.22.) 1.348 0.053 25.555 0.000 0.780 0.828
## EMO3 (.23.) 1.042 0.042 24.713 0.000 0.603 0.521
## IND =~
## IND1 1.000 0.642 0.783
## IND2 (.25.) 0.976 0.073 13.370 0.000 0.627 0.740
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.361 0.025 14.386 0.000 0.334 0.334
## CARE 0.276 0.020 14.101 0.000 0.318 0.318
## RISK 0.139 0.017 8.057 0.000 0.191 0.191
## SOC -0.012 0.022 -0.521 0.603 -0.012 -0.012
## EMO 0.157 0.014 11.447 0.000 0.307 0.307
## IND -0.022 0.014 -1.567 0.117 -0.038 -0.038
## JOB ~~
## CARE 0.224 0.026 8.568 0.000 0.186 0.186
## RISK 0.227 0.024 9.434 0.000 0.225 0.225
## SOC 0.079 0.031 2.538 0.011 0.059 0.059
## EMO 0.189 0.019 10.186 0.000 0.266 0.266
## IND 0.033 0.019 1.728 0.084 0.042 0.042
## CARE ~~
## RISK 0.136 0.019 7.304 0.000 0.168 0.168
## SOC 0.086 0.024 3.518 0.000 0.080 0.080
## EMO 0.121 0.014 8.535 0.000 0.213 0.213
## IND 0.003 0.015 0.193 0.847 0.005 0.005
## RISK ~~
## SOC 0.169 0.023 7.504 0.000 0.188 0.188
## EMO 0.122 0.013 9.294 0.000 0.256 0.256
## IND 0.087 0.014 6.149 0.000 0.165 0.165
## SOC ~~
## EMO 0.131 0.017 7.684 0.000 0.206 0.206
## IND 0.006 0.018 0.347 0.728 0.009 0.009
## EMO ~~
## IND 0.008 0.010 0.767 0.443 0.021 0.021
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 1.923 0.022 89.283 0.000 1.923 1.806
## .DISC2 1.780 0.021 84.557 0.000 1.780 1.710
## .DISC3 1.719 0.021 81.191 0.000 1.719 1.642
## .DISC4 1.680 0.021 79.374 0.000 1.680 1.605
## .DISC5 1.854 0.022 86.089 0.000 1.854 1.741
## .DISC6 1.663 0.021 78.320 0.000 1.663 1.584
## .JOB1 2.315 0.028 82.519 0.000 2.315 1.669
## .JOB2 2.445 0.029 83.398 0.000 2.445 1.687
## .JOB3 2.120 0.025 83.156 0.000 2.120 1.682
## .JOB4 2.045 0.025 82.856 0.000 2.045 1.676
## .JOB5 5.818 0.110 52.990 0.000 5.818 1.072
## .CARE1 1.508 0.021 72.588 0.000 1.508 1.468
## .CARE2 1.506 0.020 73.890 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 0.020 132.642 0.000 2.670 2.683
## .RISK2 2.609 0.023 115.911 0.000 2.609 2.344
## .RISK3 2.881 0.022 132.734 0.000 2.881 2.684
## .SOC1 3.380 0.025 136.548 0.000 3.380 2.762
## .SOC2 2.975 0.026 115.438 0.000 2.975 2.335
## .EMO1 2.883 0.020 142.798 0.000 2.883 2.888
## .EMO2 2.451 0.019 128.663 0.000 2.451 2.602
## .EMO3 2.652 0.023 113.357 0.000 2.652 2.292
## .IND1 4.061 0.017 245.046 0.000 4.061 4.956
## .IND2 4.207 0.017 245.676 0.000 4.207 4.968
## 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
## .DISC1 0.354 0.012 29.295 0.000 0.354 0.312
## .DISC2 0.398 0.013 30.539 0.000 0.398 0.367
## .DISC3 0.363 0.012 29.774 0.000 0.363 0.331
## .DISC4 0.332 0.011 29.023 0.000 0.332 0.303
## .DISC5 0.235 0.009 25.049 0.000 0.235 0.207
## .DISC6 0.508 0.016 31.973 0.000 0.508 0.460
## .JOB1 0.425 0.017 25.431 0.000 0.425 0.221
## .JOB2 0.555 0.020 27.483 0.000 0.555 0.264
## .JOB3 0.371 0.014 26.100 0.000 0.371 0.233
## .JOB4 0.358 0.014 26.443 0.000 0.358 0.241
## .JOB5 23.611 0.688 34.333 0.000 23.611 0.801
## .CARE1 0.088 0.004 20.043 0.000 0.088 0.083
## .CARE2 0.075 0.004 18.352 0.000 0.075 0.074
## .CARE3 0.171 0.006 27.660 0.000 0.171 0.147
## .CARE4 27.631 0.794 34.821 0.000 27.631 0.889
## .RISK1 0.312 0.017 18.375 0.000 0.312 0.315
## .RISK2 0.559 0.022 25.492 0.000 0.559 0.451
## .RISK3 0.531 0.021 25.913 0.000 0.531 0.461
## .SOC1 0.297 0.086 3.465 0.001 0.297 0.198
## .SOC2 0.935 0.056 16.764 0.000 0.935 0.576
## .EMO1 0.662 0.023 28.526 0.000 0.662 0.664
## .EMO2 0.278 0.024 11.696 0.000 0.278 0.314
## .EMO3 0.974 0.032 30.560 0.000 0.974 0.728
## .IND1 0.260 0.033 7.876 0.000 0.260 0.387
## .IND2 0.324 0.032 10.141 0.000 0.324 0.452
## DISC 0.780 0.029 27.093 0.000 1.000 1.000
## JOB 1.499 0.052 28.691 0.000 1.000 1.000
## CARE 0.967 0.030 32.613 0.000 1.000 1.000
## RISK 0.678 0.029 23.553 0.000 1.000 1.000
## SOC 1.201 0.093 12.856 0.000 1.000 1.000
## EMO 0.335 0.022 15.217 0.000 1.000 1.000
## IND 0.412 0.035 11.802 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 58.359 0.000 0.694 0.729
## DISC3 (.p3.) 0.969 0.016 61.957 0.000 0.717 0.783
## DISC4 (.p4.) 0.989 0.016 62.300 0.000 0.732 0.757
## DISC5 (.p5.) 1.073 0.015 69.543 0.000 0.794 0.835
## DISC6 (.p6.) 0.873 0.017 52.337 0.000 0.646 0.674
## JOB =~
## JOB1 1.000 1.087 0.832
## JOB2 (.p8.) 1.016 0.014 70.553 0.000 1.104 0.806
## JOB3 (.p9.) 0.901 0.012 72.595 0.000 0.980 0.810
## JOB4 (.10.) 0.869 0.012 71.550 0.000 0.945 0.793
## JOB5 (.11.) 1.978 0.054 36.670 0.000 2.151 0.600
## CARE =~
## CARE1 1.000 1.017 0.953
## CARE2 (.13.) 0.987 0.007 140.887 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 42.216 0.000 1.917 0.674
## RISK =~
## RISK1 1.000 0.767 0.821
## RISK2 (.17.) 1.002 0.024 41.078 0.000 0.768 0.685
## RISK3 (.18.) 0.957 0.024 40.682 0.000 0.734 0.659
## SOC =~
## SOC1 1.000 1.022 0.861
## SOC2 (.20.) 0.758 0.053 14.359 0.000 0.775 0.637
## EMO =~
## EMO1 1.000 0.531 0.551
## EMO2 (.22.) 1.348 0.053 25.555 0.000 0.715 0.778
## EMO3 (.23.) 1.042 0.042 24.713 0.000 0.553 0.496
## IND =~
## IND1 1.000 0.729 0.792
## IND2 (.25.) 0.976 0.073 13.370 0.000 0.712 0.759
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.081 0.020 4.003 0.000 0.100 0.100
## CARE 0.101 0.018 5.516 0.000 0.134 0.134
## RISK 0.030 0.015 1.976 0.048 0.052 0.052
## SOC 0.010 0.020 0.512 0.609 0.014 0.014
## EMO 0.086 0.012 7.424 0.000 0.218 0.218
## IND 0.016 0.015 1.119 0.263 0.030 0.030
## JOB ~~
## CARE 0.113 0.027 4.221 0.000 0.102 0.102
## RISK 0.185 0.023 8.135 0.000 0.221 0.221
## SOC 0.022 0.030 0.739 0.460 0.020 0.020
## EMO 0.073 0.017 4.429 0.000 0.127 0.127
## IND 0.121 0.022 5.471 0.000 0.153 0.153
## CARE ~~
## RISK 0.034 0.020 1.679 0.093 0.043 0.043
## SOC 0.096 0.027 3.564 0.000 0.092 0.092
## EMO 0.071 0.015 4.750 0.000 0.132 0.132
## IND 0.050 0.020 2.542 0.011 0.067 0.067
## RISK ~~
## SOC 0.103 0.022 4.565 0.000 0.131 0.131
## EMO 0.091 0.013 7.143 0.000 0.223 0.223
## IND 0.143 0.017 8.285 0.000 0.255 0.255
## SOC ~~
## EMO 0.195 0.018 10.764 0.000 0.359 0.359
## IND -0.055 0.022 -2.540 0.011 -0.074 -0.074
## EMO ~~
## IND -0.039 0.012 -3.257 0.001 -0.102 -0.102
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 1.967 0.020 97.197 0.000 1.967 2.169
## .DISC2 1.591 0.021 74.967 0.000 1.591 1.673
## .DISC3 1.636 0.020 80.060 0.000 1.636 1.786
## .DISC4 1.528 0.022 70.876 0.000 1.528 1.581
## .DISC5 1.897 0.021 89.427 0.000 1.897 1.995
## .DISC6 1.577 0.021 73.719 0.000 1.577 1.645
## .JOB1 2.791 0.029 95.734 0.000 2.791 2.136
## .JOB2 2.837 0.031 92.745 0.000 2.837 2.069
## .JOB3 2.296 0.027 85.032 0.000 2.296 1.897
## .JOB4 2.180 0.027 82.055 0.000 2.180 1.831
## .JOB5 6.732 0.080 84.185 0.000 6.732 1.878
## .CARE1 1.595 0.024 66.978 0.000 1.595 1.494
## .CARE2 1.577 0.024 66.628 0.000 1.577 1.487
## .CARE3 1.670 0.026 64.933 0.000 1.670 1.449
## .CARE4 1.666 0.063 26.241 0.000 1.666 0.585
## .RISK1 2.742 0.021 131.606 0.000 2.742 2.936
## .RISK2 2.642 0.025 105.545 0.000 2.642 2.355
## .RISK3 3.046 0.025 122.586 0.000 3.046 2.735
## .SOC1 3.562 0.026 134.434 0.000 3.562 2.999
## .SOC2 2.959 0.027 109.035 0.000 2.959 2.433
## .EMO1 3.232 0.022 150.278 0.000 3.232 3.353
## .EMO2 2.600 0.021 126.707 0.000 2.600 2.827
## .EMO3 2.636 0.025 105.957 0.000 2.636 2.364
## .IND1 4.053 0.021 197.336 0.000 4.053 4.403
## .IND2 3.810 0.021 182.161 0.000 3.810 4.064
## 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
## .DISC1 0.275 0.011 25.125 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 27.324 0.000 0.398 0.427
## .DISC5 0.273 0.011 24.127 0.000 0.273 0.302
## .DISC6 0.501 0.017 28.992 0.000 0.501 0.545
## .JOB1 0.526 0.022 23.489 0.000 0.526 0.308
## .JOB2 0.660 0.026 24.965 0.000 0.660 0.351
## .JOB3 0.504 0.020 24.768 0.000 0.504 0.344
## .JOB4 0.526 0.021 25.554 0.000 0.526 0.371
## .JOB5 8.222 0.278 29.620 0.000 8.222 0.640
## .CARE1 0.105 0.006 16.908 0.000 0.105 0.092
## .CARE2 0.119 0.006 18.769 0.000 0.119 0.106
## .CARE3 0.266 0.010 26.057 0.000 0.266 0.200
## .CARE4 4.422 0.145 30.551 0.000 4.422 0.546
## .RISK1 0.284 0.018 15.513 0.000 0.284 0.325
## .RISK2 0.669 0.027 24.543 0.000 0.669 0.531
## .RISK3 0.702 0.027 25.621 0.000 0.702 0.566
## .SOC1 0.365 0.075 4.897 0.000 0.365 0.259
## .SOC2 0.880 0.051 17.402 0.000 0.880 0.595
## .EMO1 0.647 0.025 26.402 0.000 0.647 0.697
## .EMO2 0.334 0.024 13.666 0.000 0.334 0.395
## .EMO3 0.938 0.034 27.903 0.000 0.938 0.754
## .IND1 0.316 0.041 7.789 0.000 0.316 0.373
## .IND2 0.372 0.039 9.479 0.000 0.372 0.424
## DISC 0.548 0.022 25.041 0.000 1.000 1.000
## JOB 1.182 0.046 25.625 0.000 1.000 1.000
## CARE 1.034 0.035 29.608 0.000 1.000 1.000
## RISK 0.588 0.027 21.528 0.000 1.000 1.000
## SOC 1.045 0.083 12.532 0.000 1.000 1.000
## EMO 0.282 0.020 14.232 0.000 1.000 1.000
## IND 0.531 0.046 11.652 0.000 1.000 1.000summary(scalar, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 118 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 192
## Number of equality constraints 43
##
## Number of observations per group:
## UofA 2445
## Nielsen 2009
##
## Model Test User Model:
##
## Test statistic 4462.604
## Degrees of freedom 551
## P-value (Chi-square) 0.000
## Test statistic for each group:
## UofA 2313.140
## Nielsen 2149.464
##
## Model Test Baseline Model:
##
## Test statistic 60554.335
## 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) -150015.726
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 300329.453
## Bayesian (BIC) 301283.285
## Sample-size adjusted Bayesian (BIC) 300809.822
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.056
## 90 Percent confidence interval - lower 0.055
## 90 Percent confidence interval - upper 0.058
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.053
##
## 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 58.029 0.000 0.829 0.794
## DISC3 (.p3.) 0.973 0.016 61.814 0.000 0.857 0.818
## DISC4 (.p4.) 0.993 0.016 62.030 0.000 0.875 0.834
## DISC5 (.p5.) 1.073 0.016 69.063 0.000 0.946 0.889
## DISC6 (.p6.) 0.877 0.017 52.273 0.000 0.773 0.735
## JOB =~
## JOB1 1.000 1.245 0.885
## JOB2 (.p8.) 1.012 0.014 71.446 0.000 1.261 0.862
## JOB3 (.p9.) 0.887 0.012 72.793 0.000 1.105 0.874
## JOB4 (.10.) 0.853 0.012 71.576 0.000 1.063 0.869
## JOB5 (.11.) 1.979 0.053 37.275 0.000 2.465 0.452
## CARE =~
## CARE1 1.000 0.984 0.957
## CARE2 (.13.) 0.986 0.007 140.966 0.000 0.970 0.962
## CARE3 (.14.) 1.015 0.009 117.348 0.000 0.998 0.923
## CARE4 (.15.) 1.851 0.045 40.933 0.000 1.822 0.314
## RISK =~
## RISK1 1.000 0.824 0.828
## RISK2 (.17.) 0.999 0.024 41.093 0.000 0.824 0.740
## RISK3 (.18.) 0.959 0.024 40.742 0.000 0.791 0.735
## SOC =~
## SOC1 1.000 1.129 0.921
## SOC2 (.20.) 0.713 0.051 14.035 0.000 0.805 0.632
## EMO =~
## EMO1 1.000 0.593 0.586
## EMO2 (.22.) 1.322 0.051 25.867 0.000 0.785 0.831
## EMO3 (.23.) 1.003 0.041 24.708 0.000 0.595 0.515
## IND =~
## IND1 1.000 0.656 0.800
## IND2 (.25.) 0.937 0.075 12.549 0.000 0.614 0.713
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.361 0.025 14.198 0.000 0.329 0.329
## CARE 0.275 0.020 14.070 0.000 0.317 0.317
## RISK 0.138 0.017 8.026 0.000 0.190 0.190
## SOC -0.018 0.022 -0.794 0.427 -0.018 -0.018
## EMO 0.159 0.014 11.408 0.000 0.305 0.305
## IND -0.021 0.014 -1.452 0.146 -0.036 -0.036
## JOB ~~
## CARE 0.232 0.027 8.710 0.000 0.189 0.189
## RISK 0.236 0.024 9.635 0.000 0.230 0.230
## SOC 0.085 0.032 2.666 0.008 0.060 0.060
## EMO 0.205 0.019 10.573 0.000 0.277 0.277
## IND 0.024 0.020 1.206 0.228 0.029 0.029
## CARE ~~
## RISK 0.138 0.019 7.387 0.000 0.170 0.170
## SOC 0.089 0.025 3.588 0.000 0.080 0.080
## EMO 0.126 0.015 8.676 0.000 0.216 0.216
## IND 0.002 0.015 0.139 0.890 0.003 0.003
## RISK ~~
## SOC 0.169 0.023 7.430 0.000 0.182 0.182
## EMO 0.127 0.013 9.416 0.000 0.259 0.259
## IND 0.088 0.014 6.093 0.000 0.163 0.163
## SOC ~~
## EMO 0.138 0.018 7.876 0.000 0.206 0.206
## IND 0.008 0.019 0.406 0.685 0.010 0.010
## EMO ~~
## IND 0.003 0.011 0.265 0.791 0.007 0.007
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 (.79.) 1.963 0.015 133.986 0.000 1.963 1.844
## .DISC2 (.80.) 1.711 0.015 115.169 0.000 1.711 1.639
## .DISC3 (.81.) 1.695 0.015 116.064 0.000 1.695 1.618
## .DISC4 (.82.) 1.631 0.015 109.125 0.000 1.631 1.556
## .DISC5 (.83.) 1.886 0.015 126.246 0.000 1.886 1.772
## .DISC6 (.84.) 1.637 0.015 109.422 0.000 1.637 1.558
## .JOB1 (.85.) 2.538 0.020 125.046 0.000 2.538 1.804
## .JOB2 (.86.) 2.636 0.021 124.659 0.000 2.636 1.801
## .JOB3 (.87.) 2.222 0.018 121.010 0.000 2.222 1.758
## .JOB4 (.88.) 2.133 0.018 119.354 0.000 2.133 1.745
## .JOB5 (.89.) 6.352 0.064 99.331 0.000 6.352 1.166
## .CARE1 (.90.) 1.550 0.016 99.769 0.000 1.550 1.509
## .CARE2 (.91.) 1.543 0.015 100.733 0.000 1.543 1.530
## .CARE3 (.92.) 1.575 0.017 95.336 0.000 1.575 1.457
## .CARE4 (.93.) 1.884 0.052 36.400 0.000 1.884 0.324
## .RISK1 (.94.) 2.708 0.014 187.623 0.000 2.708 2.719
## .RISK2 (.95.) 2.632 0.017 157.946 0.000 2.632 2.364
## .RISK3 (.96.) 2.955 0.016 181.357 0.000 2.955 2.746
## .SOC1 (.97.) 3.457 0.018 191.146 0.000 3.457 2.820
## .SOC2 (.98.) 2.965 0.019 158.877 0.000 2.965 2.326
## .EMO1 (.99.) 3.044 0.015 204.170 0.000 3.044 3.008
## .EMO2 (.100) 2.522 0.014 181.127 0.000 2.522 2.669
## .EMO3 (.101) 2.646 0.017 155.617 0.000 2.646 2.290
## .IND1 (.102) 4.060 0.013 315.644 0.000 4.060 4.957
## .IND2 (.103) 4.050 0.014 299.372 0.000 4.050 4.699
## 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
## .DISC1 0.356 0.012 29.324 0.000 0.356 0.315
## .DISC2 0.402 0.013 30.542 0.000 0.402 0.369
## .DISC3 0.363 0.012 29.722 0.000 0.363 0.331
## .DISC4 0.334 0.012 28.997 0.000 0.334 0.304
## .DISC5 0.237 0.009 25.121 0.000 0.237 0.210
## .DISC6 0.508 0.016 31.943 0.000 0.508 0.460
## .JOB1 0.429 0.017 25.257 0.000 0.429 0.216
## .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 26.672 0.000 0.365 0.244
## .JOB5 23.608 0.688 34.314 0.000 23.608 0.795
## .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 30.408 0.873 34.839 0.000 30.408 0.902
## .RISK1 0.312 0.017 18.359 0.000 0.312 0.315
## .RISK2 0.560 0.022 25.558 0.000 0.560 0.452
## .RISK3 0.532 0.021 25.869 0.000 0.532 0.460
## .SOC1 0.229 0.093 2.468 0.014 0.229 0.152
## .SOC2 0.977 0.055 17.870 0.000 0.977 0.601
## .EMO1 0.672 0.024 28.341 0.000 0.672 0.656
## .EMO2 0.277 0.024 11.639 0.000 0.277 0.310
## .EMO3 0.981 0.032 30.798 0.000 0.981 0.735
## .IND1 0.241 0.036 6.660 0.000 0.241 0.360
## .IND2 0.365 0.033 11.083 0.000 0.365 0.492
## DISC 0.777 0.029 27.026 0.000 1.000 1.000
## JOB 1.551 0.054 28.789 0.000 1.000 1.000
## CARE 0.968 0.030 32.618 0.000 1.000 1.000
## RISK 0.680 0.029 23.571 0.000 1.000 1.000
## SOC 1.275 0.100 12.692 0.000 1.000 1.000
## EMO 0.352 0.023 15.400 0.000 1.000 1.000
## IND 0.430 0.038 11.231 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 58.029 0.000 0.696 0.727
## DISC3 (.p3.) 0.973 0.016 61.814 0.000 0.719 0.784
## DISC4 (.p4.) 0.993 0.016 62.030 0.000 0.734 0.756
## DISC5 (.p5.) 1.073 0.016 69.063 0.000 0.794 0.833
## DISC6 (.p6.) 0.877 0.017 52.273 0.000 0.648 0.675
## JOB =~
## JOB1 1.000 1.107 0.834
## JOB2 (.p8.) 1.012 0.014 71.446 0.000 1.120 0.810
## JOB3 (.p9.) 0.887 0.012 72.793 0.000 0.982 0.809
## JOB4 (.10.) 0.853 0.012 71.576 0.000 0.944 0.790
## JOB5 (.11.) 1.979 0.053 37.275 0.000 2.190 0.607
## CARE =~
## CARE1 1.000 1.019 0.953
## CARE2 (.13.) 0.986 0.007 140.966 0.000 1.004 0.946
## CARE3 (.14.) 1.015 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 0.768 0.822
## RISK2 (.17.) 0.999 0.024 41.093 0.000 0.768 0.684
## RISK3 (.18.) 0.959 0.024 40.742 0.000 0.737 0.660
## SOC =~
## SOC1 1.000 1.054 0.883
## SOC2 (.20.) 0.713 0.051 14.035 0.000 0.751 0.618
## EMO =~
## EMO1 1.000 0.545 0.555
## EMO2 (.22.) 1.322 0.051 25.867 0.000 0.720 0.780
## EMO3 (.23.) 1.003 0.041 24.708 0.000 0.546 0.489
## IND =~
## IND1 1.000 0.744 0.808
## IND2 (.25.) 0.937 0.075 12.549 0.000 0.698 0.722
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.073 0.020 3.584 0.000 0.090 0.090
## CARE 0.099 0.018 5.403 0.000 0.131 0.131
## RISK 0.028 0.015 1.855 0.064 0.049 0.049
## SOC 0.006 0.020 0.274 0.784 0.007 0.007
## EMO 0.084 0.012 7.118 0.000 0.208 0.208
## IND 0.021 0.015 1.385 0.166 0.038 0.038
## JOB ~~
## CARE 0.121 0.027 4.419 0.000 0.107 0.107
## RISK 0.192 0.023 8.322 0.000 0.226 0.226
## SOC 0.035 0.031 1.139 0.255 0.030 0.030
## EMO 0.087 0.017 5.010 0.000 0.144 0.144
## IND 0.110 0.023 4.820 0.000 0.134 0.134
## CARE ~~
## RISK 0.036 0.020 1.769 0.077 0.045 0.045
## SOC 0.099 0.027 3.633 0.000 0.093 0.093
## EMO 0.076 0.015 4.913 0.000 0.136 0.136
## IND 0.044 0.020 2.171 0.030 0.057 0.057
## RISK ~~
## SOC 0.105 0.023 4.625 0.000 0.130 0.130
## EMO 0.095 0.013 7.279 0.000 0.228 0.228
## IND 0.140 0.017 8.044 0.000 0.245 0.245
## SOC ~~
## EMO 0.208 0.019 11.064 0.000 0.362 0.362
## IND -0.061 0.023 -2.683 0.007 -0.077 -0.077
## EMO ~~
## IND -0.048 0.013 -3.742 0.000 -0.117 -0.117
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 (.79.) 1.963 0.015 133.986 0.000 1.963 2.162
## .DISC2 (.80.) 1.711 0.015 115.169 0.000 1.711 1.788
## .DISC3 (.81.) 1.695 0.015 116.064 0.000 1.695 1.848
## .DISC4 (.82.) 1.631 0.015 109.125 0.000 1.631 1.681
## .DISC5 (.83.) 1.886 0.015 126.246 0.000 1.886 1.979
## .DISC6 (.84.) 1.637 0.015 109.422 0.000 1.637 1.705
## .JOB1 (.85.) 2.538 0.020 125.046 0.000 2.538 1.912
## .JOB2 (.86.) 2.636 0.021 124.659 0.000 2.636 1.905
## .JOB3 (.87.) 2.222 0.018 121.010 0.000 2.222 1.830
## .JOB4 (.88.) 2.133 0.018 119.354 0.000 2.133 1.785
## .JOB5 (.89.) 6.352 0.064 99.331 0.000 6.352 1.760
## .CARE1 (.90.) 1.550 0.016 99.769 0.000 1.550 1.451
## .CARE2 (.91.) 1.543 0.015 100.733 0.000 1.543 1.453
## .CARE3 (.92.) 1.575 0.017 95.336 0.000 1.575 1.362
## .CARE4 (.93.) 1.884 0.052 36.400 0.000 1.884 0.663
## .RISK1 (.94.) 2.708 0.014 187.623 0.000 2.708 2.898
## .RISK2 (.95.) 2.632 0.017 157.946 0.000 2.632 2.345
## .RISK3 (.96.) 2.955 0.016 181.357 0.000 2.955 2.645
## .SOC1 (.97.) 3.457 0.018 191.146 0.000 3.457 2.899
## .SOC2 (.98.) 2.965 0.019 158.877 0.000 2.965 2.438
## .EMO1 (.99.) 3.044 0.015 204.170 0.000 3.044 3.101
## .EMO2 (.100) 2.522 0.014 181.127 0.000 2.522 2.732
## .EMO3 (.101) 2.646 0.017 155.617 0.000 2.646 2.370
## .IND1 (.102) 4.060 0.013 315.644 0.000 4.060 4.409
## .IND2 (.103) 4.050 0.014 299.372 0.000 4.050 4.190
## 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
## .DISC1 0.278 0.011 25.155 0.000 0.278 0.337
## .DISC2 0.432 0.015 28.024 0.000 0.432 0.472
## .DISC3 0.324 0.012 26.421 0.000 0.324 0.385
## .DISC4 0.403 0.015 27.318 0.000 0.403 0.428
## .DISC5 0.278 0.011 24.203 0.000 0.278 0.306
## .DISC6 0.502 0.017 28.956 0.000 0.502 0.544
## .JOB1 0.537 0.023 23.434 0.000 0.537 0.305
## .JOB2 0.659 0.027 24.803 0.000 0.659 0.344
## .JOB3 0.510 0.021 24.874 0.000 0.510 0.346
## .JOB4 0.536 0.021 25.714 0.000 0.536 0.376
## .JOB5 8.233 0.279 29.559 0.000 8.233 0.632
## .CARE1 0.104 0.006 16.771 0.000 0.104 0.091
## .CARE2 0.120 0.006 18.725 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 0.284 0.018 15.481 0.000 0.284 0.325
## .RISK2 0.670 0.027 24.579 0.000 0.670 0.532
## .RISK3 0.705 0.028 25.609 0.000 0.705 0.565
## .SOC1 0.312 0.080 3.893 0.000 0.312 0.219
## .SOC2 0.915 0.050 18.407 0.000 0.915 0.618
## .EMO1 0.667 0.025 26.334 0.000 0.667 0.692
## .EMO2 0.333 0.024 13.595 0.000 0.333 0.391
## .EMO3 0.948 0.034 28.099 0.000 0.948 0.761
## .IND1 0.294 0.045 6.572 0.000 0.294 0.347
## .IND2 0.447 0.041 10.918 0.000 0.447 0.479
## DISC 0.547 0.022 24.984 0.000 1.000 1.000
## JOB 1.224 0.048 25.683 0.000 1.000 1.000
## CARE 1.037 0.035 29.616 0.000 1.000 1.000
## RISK 0.590 0.027 21.542 0.000 1.000 1.000
## SOC 1.110 0.089 12.431 0.000 1.000 1.000
## EMO 0.297 0.021 14.363 0.000 1.000 1.000
## IND 0.554 0.050 11.164 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 508 298794 300023 2841.4
## metric 526 299159 300273 3241.9 400.46 18 < 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 526 299159 300273 3241.9
## scalar 551 300329 301283 4462.6 1220.7 25 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1