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 114 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 0.023 115.411 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
## .IND1 4.061 0.017 244.589 0.000 4.061 4.946
## .IND2 4.207 0.017 246.215 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 43.757 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 0.982 0.941
## CARE2 1.033 0.012 86.085 0.000 1.015 0.953
## CARE3 1.118 0.015 73.896 0.000 1.098 0.910
## CARE4 1.800 0.051 35.166 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 0.746 0.793
## IND2 0.934 0.081 11.508 0.000 0.697 0.758
##
## 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 0.103 0.019 5.375 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 0.019 0.016 1.146 0.252 0.031 0.031
## JOB ~~
## CARE 0.120 0.028 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 4.437 0.000 0.130 0.130
## IND 0.132 0.025 5.383 0.000 0.151 0.151
## CARE ~~
## RISK 0.033 0.020 1.675 0.094 0.043 0.043
## SOC 0.091 0.026 3.457 0.001 0.087 0.087
## EMO 0.064 0.014 4.732 0.000 0.135 0.135
## IND 0.052 0.019 2.697 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 0.150 0.018 8.200 0.000 0.256 0.256
## SOC ~~
## EMO 0.190 0.018 10.346 0.000 0.368 0.368
## IND -0.052 0.023 -2.299 0.022 -0.065 -0.065
## EMO ~~
## IND -0.036 0.011 -3.110 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 1.577 0.023 67.745 0.000 1.577 1.511
## .CARE2 1.595 0.024 67.127 0.000 1.595 1.498
## .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 0.021 130.781 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
## .IND1 3.810 0.021 181.709 0.000 3.810 4.054
## .IND2 4.053 0.020 197.770 0.000 4.053 4.412
## 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 0.124 0.006 19.510 0.000 0.124 0.114
## .CARE2 0.104 0.006 16.674 0.000 0.104 0.092
## .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 0.328 0.048 6.827 0.000 0.328 0.371
## .IND2 0.359 0.042 8.451 0.000 0.359 0.425
## 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 0.965 0.035 27.967 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 0.556 0.054 10.383 0.000 1.000 1.000summary(metric, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 79 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 3240.443
## Degrees of freedom 526
## P-value (Chi-square) 0.000
## Test statistic for each group:
## UofA 1682.479
## Nielsen 1557.964
##
## 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) -149404.646
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 299157.292
## Bayesian (BIC) 300271.163
## Sample-size adjusted Bayesian (BIC) 299718.259
##
## 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 58.360 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 62.299 0.000 0.873 0.835
## DISC5 (.p5.) 1.073 0.015 69.544 0.000 0.948 0.890
## DISC6 (.p6.) 0.873 0.017 52.336 0.000 0.771 0.735
## JOB =~
## JOB1 1.000 1.224 0.883
## JOB2 (.p8.) 1.016 0.014 70.549 0.000 1.244 0.858
## JOB3 (.p9.) 0.901 0.012 72.591 0.000 1.104 0.876
## JOB4 (.10.) 0.869 0.012 71.551 0.000 1.064 0.871
## JOB5 (.11.) 1.978 0.054 36.665 0.000 2.422 0.446
## CARE =~
## CARE1 1.000 0.970 0.955
## CARE2 (.13.) 1.011 0.007 140.631 0.000 0.981 0.965
## CARE3 (.14.) 1.027 0.009 115.941 0.000 0.996 0.924
## CARE4 (.15.) 1.909 0.045 42.122 0.000 1.852 0.332
## RISK =~
## RISK1 1.000 0.823 0.827
## RISK2 (.17.) 1.002 0.024 41.089 0.000 0.825 0.741
## RISK3 (.18.) 0.957 0.024 40.694 0.000 0.788 0.734
## SOC =~
## SOC1 1.000 1.100 0.899
## SOC2 (.20.) 0.752 0.053 14.288 0.000 0.827 0.649
## EMO =~
## EMO1 1.000 0.579 0.580
## EMO2 (.22.) 1.349 0.053 25.556 0.000 0.781 0.829
## EMO3 (.23.) 1.042 0.042 24.713 0.000 0.603 0.521
## IND =~
## IND1 1.000 0.678 0.827
## IND2 (.25.) 0.874 0.066 13.232 0.000 0.593 0.701
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.361 0.025 14.387 0.000 0.334 0.334
## CARE 0.272 0.019 14.082 0.000 0.317 0.317
## RISK 0.139 0.017 8.058 0.000 0.191 0.191
## SOC -0.012 0.022 -0.553 0.580 -0.013 -0.013
## EMO 0.157 0.014 11.447 0.000 0.307 0.307
## IND -0.021 0.014 -1.435 0.151 -0.035 -0.035
## JOB ~~
## CARE 0.221 0.026 8.560 0.000 0.186 0.186
## RISK 0.226 0.024 9.434 0.000 0.225 0.225
## SOC 0.078 0.031 2.512 0.012 0.058 0.058
## EMO 0.189 0.019 10.185 0.000 0.266 0.266
## IND 0.034 0.020 1.704 0.088 0.041 0.041
## CARE ~~
## RISK 0.134 0.018 7.295 0.000 0.168 0.168
## SOC 0.085 0.024 3.511 0.000 0.079 0.079
## EMO 0.119 0.014 8.512 0.000 0.212 0.212
## IND 0.006 0.016 0.411 0.681 0.010 0.010
## RISK ~~
## SOC 0.169 0.023 7.481 0.000 0.187 0.187
## EMO 0.122 0.013 9.290 0.000 0.256 0.256
## IND 0.094 0.015 6.425 0.000 0.169 0.169
## SOC ~~
## EMO 0.131 0.017 7.684 0.000 0.205 0.205
## IND 0.009 0.019 0.491 0.624 0.013 0.013
## EMO ~~
## IND 0.006 0.011 0.581 0.561 0.016 0.016
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 1.923 0.022 89.284 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 82.520 0.000 2.315 1.669
## .JOB2 2.445 0.029 83.397 0.000 2.445 1.687
## .JOB3 2.120 0.025 83.156 0.000 2.120 1.682
## .JOB4 2.045 0.025 82.854 0.000 2.045 1.676
## .JOB5 5.818 0.110 52.991 0.000 5.818 1.072
## .CARE1 1.508 0.021 73.333 0.000 1.508 1.483
## .CARE2 1.506 0.021 73.256 0.000 1.506 1.482
## .CARE3 1.506 0.022 69.052 0.000 1.506 1.396
## .CARE4 3.452 0.113 30.624 0.000 3.452 0.619
## .RISK1 2.670 0.020 132.651 0.000 2.670 2.683
## .RISK2 2.609 0.023 115.906 0.000 2.609 2.344
## .RISK3 2.881 0.022 132.727 0.000 2.881 2.684
## .SOC1 3.380 0.025 136.554 0.000 3.380 2.762
## .SOC2 2.975 0.026 115.428 0.000 2.975 2.334
## .EMO1 2.883 0.020 142.786 0.000 2.883 2.888
## .EMO2 2.451 0.019 128.670 0.000 2.451 2.602
## .EMO3 2.652 0.023 113.351 0.000 2.652 2.292
## .IND1 4.061 0.017 244.848 0.000 4.061 4.952
## .IND2 4.207 0.017 245.794 0.000 4.207 4.971
## 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 29.294 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 25.048 0.000 0.235 0.207
## .DISC6 0.508 0.016 31.973 0.000 0.508 0.460
## .JOB1 0.425 0.017 25.433 0.000 0.425 0.221
## .JOB2 0.555 0.020 27.484 0.000 0.555 0.264
## .JOB3 0.371 0.014 26.100 0.000 0.371 0.233
## .JOB4 0.358 0.014 26.441 0.000 0.358 0.241
## .JOB5 23.611 0.688 34.333 0.000 23.611 0.801
## .CARE1 0.092 0.004 20.924 0.000 0.092 0.089
## .CARE2 0.072 0.004 17.382 0.000 0.072 0.069
## .CARE3 0.171 0.006 27.665 0.000 0.171 0.147
## .CARE4 27.634 0.794 34.822 0.000 27.634 0.890
## .RISK1 0.312 0.017 18.394 0.000 0.312 0.316
## .RISK2 0.559 0.022 25.492 0.000 0.559 0.451
## .RISK3 0.531 0.021 25.911 0.000 0.531 0.461
## .SOC1 0.288 0.087 3.322 0.001 0.288 0.192
## .SOC2 0.940 0.056 16.867 0.000 0.940 0.579
## .EMO1 0.662 0.023 28.539 0.000 0.662 0.664
## .EMO2 0.278 0.024 11.660 0.000 0.278 0.313
## .EMO3 0.975 0.032 30.566 0.000 0.975 0.728
## .IND1 0.212 0.037 5.780 0.000 0.212 0.316
## .IND2 0.364 0.030 12.321 0.000 0.364 0.509
## DISC 0.780 0.029 27.093 0.000 1.000 1.000
## JOB 1.499 0.052 28.690 0.000 1.000 1.000
## CARE 0.942 0.029 32.496 0.000 1.000 1.000
## RISK 0.678 0.029 23.552 0.000 1.000 1.000
## SOC 1.210 0.094 12.815 0.000 1.000 1.000
## EMO 0.335 0.022 15.215 0.000 1.000 1.000
## IND 0.460 0.039 11.775 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 58.360 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 62.299 0.000 0.732 0.757
## DISC5 (.p5.) 1.073 0.015 69.544 0.000 0.794 0.835
## DISC6 (.p6.) 0.873 0.017 52.336 0.000 0.646 0.674
## JOB =~
## JOB1 1.000 1.087 0.832
## JOB2 (.p8.) 1.016 0.014 70.549 0.000 1.104 0.806
## JOB3 (.p9.) 0.901 0.012 72.591 0.000 0.980 0.810
## JOB4 (.10.) 0.869 0.012 71.551 0.000 0.945 0.793
## JOB5 (.11.) 1.978 0.054 36.665 0.000 2.151 0.600
## CARE =~
## CARE1 1.000 1.005 0.946
## CARE2 (.13.) 1.011 0.007 140.631 0.000 1.016 0.953
## CARE3 (.14.) 1.027 0.009 115.941 0.000 1.031 0.894
## CARE4 (.15.) 1.909 0.045 42.122 0.000 1.917 0.674
## RISK =~
## RISK1 1.000 0.767 0.821
## RISK2 (.17.) 1.002 0.024 41.089 0.000 0.768 0.685
## RISK3 (.18.) 0.957 0.024 40.694 0.000 0.734 0.659
## SOC =~
## SOC1 1.000 1.026 0.864
## SOC2 (.20.) 0.752 0.053 14.288 0.000 0.772 0.635
## EMO =~
## EMO1 1.000 0.531 0.550
## EMO2 (.22.) 1.349 0.053 25.556 0.000 0.716 0.778
## EMO3 (.23.) 1.042 0.042 24.713 0.000 0.553 0.496
## IND =~
## IND1 1.000 0.770 0.818
## IND2 (.25.) 0.874 0.066 13.232 0.000 0.673 0.734
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.081 0.020 4.004 0.000 0.100 0.100
## CARE 0.099 0.018 5.514 0.000 0.134 0.134
## RISK 0.030 0.015 1.976 0.048 0.052 0.052
## SOC 0.010 0.020 0.507 0.612 0.013 0.013
## EMO 0.086 0.012 7.423 0.000 0.218 0.218
## IND 0.017 0.015 1.125 0.260 0.030 0.030
## JOB ~~
## CARE 0.112 0.026 4.221 0.000 0.102 0.102
## RISK 0.185 0.023 8.137 0.000 0.221 0.221
## SOC 0.022 0.030 0.732 0.464 0.020 0.020
## EMO 0.073 0.017 4.429 0.000 0.127 0.127
## IND 0.123 0.023 5.308 0.000 0.146 0.146
## CARE ~~
## RISK 0.033 0.020 1.679 0.093 0.043 0.043
## SOC 0.095 0.027 3.558 0.000 0.092 0.092
## EMO 0.070 0.015 4.748 0.000 0.132 0.132
## IND 0.057 0.020 2.799 0.005 0.073 0.073
## RISK ~~
## SOC 0.103 0.023 4.554 0.000 0.130 0.130
## EMO 0.091 0.013 7.141 0.000 0.223 0.223
## IND 0.150 0.018 8.378 0.000 0.254 0.254
## SOC ~~
## EMO 0.195 0.018 10.777 0.000 0.359 0.359
## IND -0.058 0.023 -2.551 0.011 -0.074 -0.074
## EMO ~~
## IND -0.042 0.013 -3.345 0.001 -0.104 -0.104
##
## 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 70.877 0.000 1.528 1.581
## .DISC5 1.897 0.021 89.426 0.000 1.897 1.995
## .DISC6 1.577 0.021 73.720 0.000 1.577 1.645
## .JOB1 2.791 0.029 95.732 0.000 2.791 2.136
## .JOB2 2.837 0.031 92.746 0.000 2.837 2.069
## .JOB3 2.296 0.027 85.031 0.000 2.296 1.897
## .JOB4 2.180 0.027 82.058 0.000 2.180 1.831
## .JOB5 6.732 0.080 84.183 0.000 6.732 1.878
## .CARE1 1.577 0.024 66.559 0.000 1.577 1.485
## .CARE2 1.595 0.024 67.052 0.000 1.595 1.496
## .CARE3 1.670 0.026 64.907 0.000 1.670 1.448
## .CARE4 1.666 0.063 26.240 0.000 1.666 0.585
## .RISK1 2.742 0.021 131.596 0.000 2.742 2.936
## .RISK2 2.642 0.025 105.553 0.000 2.642 2.355
## .RISK3 3.046 0.025 122.596 0.000 3.046 2.735
## .SOC1 3.562 0.026 134.425 0.000 3.562 2.999
## .SOC2 2.959 0.027 109.047 0.000 2.959 2.433
## .EMO1 3.232 0.022 150.294 0.000 3.232 3.353
## .EMO2 2.600 0.021 126.696 0.000 2.600 2.827
## .EMO3 2.636 0.025 105.962 0.000 2.636 2.364
## .IND1 3.810 0.021 181.464 0.000 3.810 4.049
## .IND2 4.053 0.020 198.142 0.000 4.053 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
## .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 0.546
## .JOB1 0.526 0.022 23.491 0.000 0.526 0.308
## .JOB2 0.660 0.026 24.964 0.000 0.660 0.351
## .JOB3 0.504 0.020 24.767 0.000 0.504 0.344
## .JOB4 0.526 0.021 25.550 0.000 0.526 0.371
## .JOB5 8.224 0.278 29.621 0.000 8.224 0.640
## .CARE1 0.119 0.006 18.702 0.000 0.119 0.106
## .CARE2 0.105 0.006 17.004 0.000 0.105 0.093
## .CARE3 0.266 0.010 26.045 0.000 0.266 0.200
## .CARE4 4.422 0.145 30.550 0.000 4.422 0.546
## .RISK1 0.284 0.018 15.539 0.000 0.284 0.326
## .RISK2 0.668 0.027 24.537 0.000 0.668 0.531
## .RISK3 0.701 0.027 25.615 0.000 0.701 0.566
## .SOC1 0.358 0.075 4.750 0.000 0.358 0.254
## .SOC2 0.884 0.051 17.497 0.000 0.884 0.597
## .EMO1 0.648 0.025 26.413 0.000 0.648 0.697
## .EMO2 0.334 0.024 13.657 0.000 0.334 0.395
## .EMO3 0.938 0.034 27.908 0.000 0.938 0.754
## .IND1 0.293 0.045 6.464 0.000 0.293 0.330
## .IND2 0.387 0.036 10.755 0.000 0.387 0.461
## DISC 0.548 0.022 25.040 0.000 1.000 1.000
## JOB 1.182 0.046 25.625 0.000 1.000 1.000
## CARE 1.009 0.034 29.447 0.000 1.000 1.000
## RISK 0.588 0.027 21.524 0.000 1.000 1.000
## SOC 1.053 0.084 12.501 0.000 1.000 1.000
## EMO 0.281 0.020 14.229 0.000 1.000 1.000
## IND 0.593 0.051 11.688 0.000 1.000 1.000summary(scalar, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6-7 ended normally after 123 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 4238.009
## Degrees of freedom 551
## P-value (Chi-square) 0.000
## Test statistic for each group:
## UofA 2222.815
## Nielsen 2015.193
##
## 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) 0.939
## Tucker-Lewis Index (TLI) 0.933
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -149903.429
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) 300104.858
## Bayesian (BIC) 301058.690
## Sample-size adjusted Bayesian (BIC) 300585.227
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.055
## 90 Percent confidence interval - lower 0.053
## 90 Percent confidence interval - upper 0.056
## 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 58.034 0.000 0.829 0.794
## DISC3 (.p3.) 0.973 0.016 61.816 0.000 0.857 0.818
## DISC4 (.p4.) 0.993 0.016 62.032 0.000 0.875 0.834
## DISC5 (.p5.) 1.073 0.016 69.066 0.000 0.946 0.889
## DISC6 (.p6.) 0.877 0.017 52.274 0.000 0.773 0.735
## JOB =~
## JOB1 1.000 1.246 0.885
## JOB2 (.p8.) 1.012 0.014 71.439 0.000 1.261 0.862
## JOB3 (.p9.) 0.887 0.012 72.791 0.000 1.105 0.874
## JOB4 (.10.) 0.853 0.012 71.580 0.000 1.063 0.869
## JOB5 (.11.) 1.979 0.053 37.263 0.000 2.465 0.452
## CARE =~
## CARE1 1.000 0.971 0.954
## CARE2 (.13.) 1.011 0.007 140.698 0.000 0.982 0.965
## CARE3 (.14.) 1.028 0.009 115.772 0.000 0.998 0.923
## CARE4 (.15.) 1.875 0.046 40.840 0.000 1.820 0.313
## RISK =~
## RISK1 1.000 0.824 0.828
## RISK2 (.17.) 1.000 0.024 41.106 0.000 0.824 0.740
## RISK3 (.18.) 0.960 0.024 40.750 0.000 0.791 0.735
## SOC =~
## SOC1 1.000 1.131 0.923
## SOC2 (.20.) 0.710 0.051 14.004 0.000 0.803 0.630
## EMO =~
## EMO1 1.000 0.593 0.586
## EMO2 (.22.) 1.323 0.051 25.886 0.000 0.785 0.831
## EMO3 (.23.) 1.002 0.041 24.708 0.000 0.595 0.515
## IND =~
## IND1 1.000 0.689 0.834
## IND2 (.25.) 0.861 0.067 12.921 0.000 0.593 0.699
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.361 0.025 14.205 0.000 0.329 0.329
## CARE 0.271 0.019 14.051 0.000 0.317 0.317
## RISK 0.138 0.017 8.028 0.000 0.190 0.190
## SOC -0.018 0.022 -0.808 0.419 -0.018 -0.018
## EMO 0.159 0.014 11.417 0.000 0.305 0.305
## IND -0.020 0.015 -1.366 0.172 -0.033 -0.033
## JOB ~~
## CARE 0.229 0.026 8.712 0.000 0.189 0.189
## RISK 0.236 0.024 9.643 0.000 0.230 0.230
## SOC 0.084 0.032 2.646 0.008 0.060 0.060
## EMO 0.205 0.019 10.569 0.000 0.277 0.277
## IND 0.021 0.021 1.001 0.317 0.024 0.024
## CARE ~~
## RISK 0.136 0.018 7.384 0.000 0.170 0.170
## SOC 0.087 0.024 3.584 0.000 0.079 0.079
## EMO 0.124 0.014 8.658 0.000 0.216 0.216
## IND 0.003 0.016 0.192 0.847 0.005 0.005
## RISK ~~
## SOC 0.169 0.023 7.417 0.000 0.181 0.181
## EMO 0.127 0.013 9.416 0.000 0.259 0.259
## IND 0.091 0.015 6.148 0.000 0.160 0.160
## SOC ~~
## EMO 0.138 0.018 7.868 0.000 0.206 0.206
## IND 0.008 0.019 0.403 0.687 0.010 0.010
## EMO ~~
## IND 0.000 0.011 0.035 0.972 0.001 0.001
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 (.79.) 1.964 0.015 134.079 0.000 1.964 1.845
## .DISC2 (.80.) 1.712 0.015 115.255 0.000 1.712 1.640
## .DISC3 (.81.) 1.696 0.015 116.154 0.000 1.696 1.619
## .DISC4 (.82.) 1.633 0.015 109.215 0.000 1.633 1.557
## .DISC5 (.83.) 1.887 0.015 126.343 0.000 1.887 1.774
## .DISC6 (.84.) 1.638 0.015 109.501 0.000 1.638 1.559
## .JOB1 (.85.) 2.539 0.020 125.086 0.000 2.539 1.805
## .JOB2 (.86.) 2.637 0.021 124.698 0.000 2.637 1.802
## .JOB3 (.87.) 2.223 0.018 121.050 0.000 2.223 1.759
## .JOB4 (.88.) 2.134 0.018 119.394 0.000 2.134 1.745
## .JOB5 (.89.) 6.355 0.064 99.360 0.000 6.355 1.166
## .CARE1 (.90.) 1.545 0.015 100.255 0.000 1.545 1.519
## .CARE2 (.91.) 1.551 0.015 100.486 0.000 1.551 1.524
## .CARE3 (.92.) 1.577 0.017 95.433 0.000 1.577 1.459
## .CARE4 (.93.) 1.887 0.052 36.456 0.000 1.887 0.325
## .RISK1 (.94.) 2.710 0.014 187.724 0.000 2.710 2.721
## .RISK2 (.95.) 2.633 0.017 158.025 0.000 2.633 2.365
## .RISK3 (.96.) 2.956 0.016 181.432 0.000 2.956 2.747
## .SOC1 (.97.) 3.456 0.018 191.071 0.000 3.456 2.819
## .SOC2 (.98.) 2.964 0.019 158.819 0.000 2.964 2.325
## .EMO1 (.99.) 3.043 0.015 204.097 0.000 3.043 3.007
## .EMO2 (.100) 2.520 0.014 181.032 0.000 2.520 2.668
## .EMO3 (.101) 2.645 0.017 155.549 0.000 2.645 2.289
## .IND1 (.102) 3.967 0.013 302.749 0.000 3.967 4.805
## .IND2 (.103) 4.148 0.013 315.748 0.000 4.148 4.889
## 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 29.323 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 25.120 0.000 0.237 0.210
## .DISC6 0.508 0.016 31.943 0.000 0.508 0.460
## .JOB1 0.429 0.017 25.261 0.000 0.429 0.216
## .JOB2 0.552 0.020 27.282 0.000 0.552 0.258
## .JOB3 0.376 0.014 26.265 0.000 0.376 0.235
## .JOB4 0.365 0.014 26.668 0.000 0.365 0.244
## .JOB5 23.609 0.688 34.315 0.000 23.609 0.795
## .CARE1 0.092 0.004 20.924 0.000 0.092 0.089
## .CARE2 0.072 0.004 17.311 0.000 0.072 0.069
## .CARE3 0.172 0.006 27.687 0.000 0.172 0.147
## .CARE4 30.413 0.873 34.840 0.000 30.413 0.902
## .RISK1 0.312 0.017 18.376 0.000 0.312 0.315
## .RISK2 0.560 0.022 25.549 0.000 0.560 0.452
## .RISK3 0.532 0.021 25.868 0.000 0.532 0.460
## .SOC1 0.223 0.093 2.391 0.017 0.223 0.148
## .SOC2 0.980 0.055 17.939 0.000 0.980 0.603
## .EMO1 0.672 0.024 28.349 0.000 0.672 0.656
## .EMO2 0.276 0.024 11.623 0.000 0.276 0.310
## .EMO3 0.982 0.032 30.809 0.000 0.982 0.735
## .IND1 0.207 0.039 5.369 0.000 0.207 0.304
## .IND2 0.369 0.030 12.218 0.000 0.369 0.512
## DISC 0.777 0.029 27.027 0.000 1.000 1.000
## JOB 1.551 0.054 28.787 0.000 1.000 1.000
## CARE 0.942 0.029 32.494 0.000 1.000 1.000
## RISK 0.679 0.029 23.570 0.000 1.000 1.000
## SOC 1.280 0.101 12.673 0.000 1.000 1.000
## EMO 0.352 0.023 15.405 0.000 1.000 1.000
## IND 0.474 0.041 11.564 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 58.034 0.000 0.696 0.727
## DISC3 (.p3.) 0.973 0.016 61.816 0.000 0.719 0.784
## DISC4 (.p4.) 0.993 0.016 62.032 0.000 0.734 0.756
## DISC5 (.p5.) 1.073 0.016 69.066 0.000 0.794 0.833
## DISC6 (.p6.) 0.877 0.017 52.274 0.000 0.648 0.675
## JOB =~
## JOB1 1.000 1.106 0.834
## JOB2 (.p8.) 1.012 0.014 71.439 0.000 1.120 0.810
## JOB3 (.p9.) 0.887 0.012 72.791 0.000 0.982 0.809
## JOB4 (.10.) 0.853 0.012 71.580 0.000 0.944 0.790
## JOB5 (.11.) 1.979 0.053 37.263 0.000 2.189 0.607
## CARE =~
## CARE1 1.000 1.006 0.946
## CARE2 (.13.) 1.011 0.007 140.698 0.000 1.017 0.953
## CARE3 (.14.) 1.028 0.009 115.772 0.000 1.034 0.894
## CARE4 (.15.) 1.875 0.046 40.840 0.000 1.886 0.664
## RISK =~
## RISK1 1.000 0.768 0.821
## RISK2 (.17.) 1.000 0.024 41.106 0.000 0.768 0.684
## RISK3 (.18.) 0.960 0.024 40.750 0.000 0.737 0.660
## SOC =~
## SOC1 1.000 1.056 0.885
## SOC2 (.20.) 0.710 0.051 14.004 0.000 0.750 0.616
## EMO =~
## EMO1 1.000 0.545 0.555
## EMO2 (.22.) 1.323 0.051 25.886 0.000 0.721 0.781
## EMO3 (.23.) 1.002 0.041 24.708 0.000 0.546 0.489
## IND =~
## IND1 1.000 0.787 0.825
## IND2 (.25.) 0.861 0.067 12.921 0.000 0.677 0.735
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## JOB 0.073 0.020 3.578 0.000 0.089 0.089
## CARE 0.097 0.018 5.401 0.000 0.131 0.131
## RISK 0.028 0.015 1.854 0.064 0.049 0.049
## SOC 0.005 0.020 0.261 0.794 0.007 0.007
## EMO 0.084 0.012 7.103 0.000 0.207 0.207
## IND 0.024 0.016 1.514 0.130 0.041 0.041
## JOB ~~
## CARE 0.119 0.027 4.411 0.000 0.107 0.107
## RISK 0.192 0.023 8.315 0.000 0.226 0.226
## SOC 0.035 0.031 1.141 0.254 0.030 0.030
## EMO 0.087 0.017 5.015 0.000 0.144 0.144
## IND 0.102 0.024 4.304 0.000 0.117 0.117
## CARE ~~
## RISK 0.035 0.020 1.764 0.078 0.045 0.045
## SOC 0.098 0.027 3.628 0.000 0.092 0.092
## EMO 0.075 0.015 4.908 0.000 0.136 0.136
## IND 0.051 0.021 2.491 0.013 0.065 0.065
## RISK ~~
## SOC 0.105 0.023 4.616 0.000 0.130 0.130
## EMO 0.095 0.013 7.272 0.000 0.227 0.227
## IND 0.145 0.018 7.998 0.000 0.240 0.240
## SOC ~~
## EMO 0.208 0.019 11.078 0.000 0.362 0.362
## IND -0.068 0.024 -2.884 0.004 -0.082 -0.082
## EMO ~~
## IND -0.054 0.013 -4.085 0.000 -0.127 -0.127
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DISC1 (.79.) 1.964 0.015 134.079 0.000 1.964 2.163
## .DISC2 (.80.) 1.712 0.015 115.255 0.000 1.712 1.789
## .DISC3 (.81.) 1.696 0.015 116.154 0.000 1.696 1.849
## .DISC4 (.82.) 1.633 0.015 109.215 0.000 1.633 1.682
## .DISC5 (.83.) 1.887 0.015 126.343 0.000 1.887 1.981
## .DISC6 (.84.) 1.638 0.015 109.501 0.000 1.638 1.707
## .JOB1 (.85.) 2.539 0.020 125.086 0.000 2.539 1.913
## .JOB2 (.86.) 2.637 0.021 124.698 0.000 2.637 1.907
## .JOB3 (.87.) 2.223 0.018 121.050 0.000 2.223 1.831
## .JOB4 (.88.) 2.134 0.018 119.394 0.000 2.134 1.786
## .JOB5 (.89.) 6.355 0.064 99.360 0.000 6.355 1.761
## .CARE1 (.90.) 1.545 0.015 100.255 0.000 1.545 1.453
## .CARE2 (.91.) 1.551 0.015 100.486 0.000 1.551 1.453
## .CARE3 (.92.) 1.577 0.017 95.433 0.000 1.577 1.363
## .CARE4 (.93.) 1.887 0.052 36.456 0.000 1.887 0.664
## .RISK1 (.94.) 2.710 0.014 187.724 0.000 2.710 2.899
## .RISK2 (.95.) 2.633 0.017 158.025 0.000 2.633 2.347
## .RISK3 (.96.) 2.956 0.016 181.432 0.000 2.956 2.646
## .SOC1 (.97.) 3.456 0.018 191.071 0.000 3.456 2.897
## .SOC2 (.98.) 2.964 0.019 158.819 0.000 2.964 2.437
## .EMO1 (.99.) 3.043 0.015 204.097 0.000 3.043 3.100
## .EMO2 (.100) 2.520 0.014 181.032 0.000 2.520 2.730
## .EMO3 (.101) 2.645 0.017 155.549 0.000 2.645 2.369
## .IND1 (.102) 3.967 0.013 302.749 0.000 3.967 4.158
## .IND2 (.103) 4.148 0.013 315.748 0.000 4.148 4.500
## 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 0.471
## .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 24.204 0.000 0.278 0.306
## .DISC6 0.502 0.017 28.956 0.000 0.502 0.544
## .JOB1 0.538 0.023 23.439 0.000 0.538 0.305
## .JOB2 0.659 0.027 24.801 0.000 0.659 0.344
## .JOB3 0.510 0.021 24.870 0.000 0.510 0.346
## .JOB4 0.536 0.021 25.708 0.000 0.536 0.375
## .JOB5 8.235 0.279 29.560 0.000 8.235 0.632
## .CARE1 0.119 0.006 18.664 0.000 0.119 0.106
## .CARE2 0.105 0.006 16.864 0.000 0.105 0.092
## .CARE3 0.269 0.010 26.087 0.000 0.269 0.201
## .CARE4 4.513 0.147 30.611 0.000 4.513 0.559
## .RISK1 0.284 0.018 15.517 0.000 0.284 0.325
## .RISK2 0.670 0.027 24.568 0.000 0.670 0.532
## .RISK3 0.705 0.028 25.606 0.000 0.705 0.565
## .SOC1 0.307 0.081 3.812 0.000 0.307 0.216
## .SOC2 0.917 0.050 18.471 0.000 0.917 0.620
## .EMO1 0.667 0.025 26.345 0.000 0.667 0.692
## .EMO2 0.333 0.024 13.594 0.000 0.333 0.390
## .EMO3 0.948 0.034 28.111 0.000 0.948 0.761
## .IND1 0.291 0.048 6.024 0.000 0.291 0.319
## .IND2 0.391 0.037 10.508 0.000 0.391 0.460
## DISC 0.547 0.022 24.985 0.000 1.000 1.000
## JOB 1.224 0.048 25.680 0.000 1.000 1.000
## CARE 1.011 0.034 29.447 0.000 1.000 1.000
## RISK 0.589 0.027 21.535 0.000 1.000 1.000
## SOC 1.115 0.090 12.422 0.000 1.000 1.000
## EMO 0.297 0.021 14.367 0.000 1.000 1.000
## IND 0.620 0.054 11.507 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 299157 300271 3240.4 399.02 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 299157 300271 3240.4
## scalar 551 300105 301059 4238.0 997.57 25 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Transform the data into long format
# Put all variables in the same column except `sample_group`, the grouping variable\
combined <- combined[,-1]
combined.long <- combined %>%
pivot_longer(-sample_group, names_to = "variables", values_to = "value")stat.test <- combined.long%>%
group_by(variables) %>%
t_test(value ~ sample_group) %>%
adjust_pvalue(method = "fdr") %>%
add_significance()
DT::datatable(stat.test)# # Create the plot
# myplot <- ggboxplot(
# combined.long, x = "sample_group", y = "value",
# fill = "sample_group", palette = "npg", legend = "none",
# ggtheme = theme_pubr(border = TRUE)
# ) +
# facet_wrap(~variables)
# # Add statistical test p-values
# stat.test <- stat.test %>% add_xy_position(x = "sample_group")
# myplot + stat_pvalue_manual(stat.test, label = "p.adj.signif")#z-scores for all items
Sample_1_Nielsen_scaled <- as.data.frame(scale(Sample_1_Nielsen[2:26]))
UofA_Sample_scaled <- as.data.frame(scale(UofA_Sample[2:26]))
Sample_1_Nielsen_scaled$sample_group = "Nielsen"
UofA_Sample_scaled$sample_group = "UofA"
combined_scaled <- rbind(UofA_Sample_scaled, Sample_1_Nielsen_scaled)#Create subscale scores
combined_scaled$DISC <- rowMeans(combined_scaled[1:6])
combined_scaled$JOB <- rowMeans(combined_scaled[7:11])
combined_scaled$CARE <- rowMeans(combined_scaled[12:15])
combined_scaled$RISK <- rowMeans(combined_scaled[16:18])
combined_scaled$SOC <- rowMeans(combined_scaled[19:20])
combined_scaled$EMO <- rowMeans(combined_scaled[21:23])
combined_scaled$IND <- rowMeans(combined_scaled[24:25])combined_scaled$CASE <- combined$CASE## Warning: Unknown or uninitialised column: `CASE`.