library('lavaan')
## This is lavaan 0.6-16
## lavaan is FREE software! Please report any bugs.
spasd_spins_yj_z_df <- read.csv('spasd_spins_cfa_input.csv')
# model 1: scog vars across all groups
CFA_scog_model1 <- 'simulation =~ er40_total + rmet_total + mean_ea + tasit3_lies
mentalizing =~ tasit2_ssar + tasit2_psar + tasit3_sar'
CFA_scog_model1_fit <- cfa(CFA_scog_model1, data = spasd_spins_yj_z_df[,c(4:26)], std.lv=TRUE, estimator = "MLR",
missing = "ml")
cfa_results <- summary(CFA_scog_model1_fit, fit.measures = TRUE, modindices = TRUE, standardized = TRUE, rsquare =
TRUE)
# manual testing for measurement invariance
# configural - is factor structure model equal across groups (same as look at model fit in each group)
CFA_scog_model1_grp_fit <- cfa(CFA_scog_model1, data = spasd_spins_yj_z_df, group = 'group', std.lv=TRUE,
estimator = "MLR", missing = "ml")
summary(CFA_scog_model1_grp_fit, fit.measures = TRUE, modindices = TRUE, standardized = TRUE, rsquare = TRUE)
## lavaan 0.6.16 ended normally after 64 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 66
##
## Number of observations per group:
## ASD 100
## Control 209
## SSD 276
## Number of missing patterns per group:
## ASD 4
## Control 6
## SSD 20
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 68.870 70.585
## Degrees of freedom 39 39
## P-value (Chi-square) 0.002 0.001
## Scaling correction factor 0.976
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## ASD 22.175 22.727
## Control 16.782 17.200
## SSD 29.913 30.657
##
## Model Test Baseline Model:
##
## Test statistic 973.724 901.247
## Degrees of freedom 63 63
## P-value 0.000 0.000
## Scaling correction factor 1.080
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.967 0.962
## Tucker-Lewis Index (TLI) 0.947 0.939
##
## Robust Comparative Fit Index (CFI) 0.957
## Robust Tucker-Lewis Index (TLI) 0.930
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4307.123 -4307.123
## Scaling correction factor 1.243
## for the MLR correction
## Loglikelihood unrestricted model (H1) -4272.688 -4272.688
## Scaling correction factor 1.143
## for the MLR correction
##
## Akaike (AIC) 8746.247 8746.247
## Bayesian (BIC) 9034.773 9034.773
## Sample-size adjusted Bayesian (SABIC) 8825.247 8825.247
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.063 0.064
## 90 Percent confidence interval - lower 0.037 0.039
## 90 Percent confidence interval - upper 0.087 0.088
## P-value H_0: RMSEA <= 0.050 0.186 0.157
## P-value H_0: RMSEA >= 0.080 0.123 0.152
##
## Robust RMSEA 0.076
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.130
## P-value H_0: Robust RMSEA <= 0.050 0.242
## P-value H_0: Robust RMSEA >= 0.080 0.479
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.064 0.064
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [ASD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_total 0.425 0.163 2.615 0.009 0.425 0.431
## rmet_total 0.773 0.139 5.561 0.000 0.773 0.829
## mean_ea -0.106 0.194 -0.546 0.585 -0.106 -0.113
## tasit3_lies 0.363 0.114 3.198 0.001 0.363 0.345
## mentalizing =~
## tasit2_ssar 0.690 0.123 5.627 0.000 0.690 0.775
## tasit2_psar 0.765 0.110 6.958 0.000 0.765 0.830
## tasit3_sar 0.598 0.095 6.319 0.000 0.598 0.751
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.809 0.149 5.414 0.000 0.809 0.809
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total -0.066 0.101 -0.650 0.516 -0.066 -0.067
## .rmet_total 0.149 0.093 1.597 0.110 0.149 0.160
## .mean_ea 0.073 0.230 0.318 0.750 0.073 0.079
## .tasit3_lies -0.328 0.105 -3.115 0.002 -0.328 -0.311
## .tasit2_ssar 0.141 0.091 1.555 0.120 0.141 0.158
## .tasit2_psar 0.111 0.092 1.209 0.227 0.111 0.121
## .tasit3_sar 0.290 0.080 3.642 0.000 0.290 0.364
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.794 0.133 5.990 0.000 0.794 0.815
## .rmet_total 0.271 0.182 1.491 0.136 0.271 0.313
## .mean_ea 0.855 0.461 1.852 0.064 0.855 0.987
## .tasit3_lies 0.977 0.155 6.295 0.000 0.977 0.881
## .tasit2_ssar 0.316 0.086 3.682 0.000 0.316 0.399
## .tasit2_psar 0.263 0.100 2.640 0.008 0.263 0.310
## .tasit3_sar 0.277 0.055 5.008 0.000 0.277 0.436
## simulation 1.000 1.000 1.000
## mentalizing 1.000 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.185
## rmet_total 0.687
## mean_ea 0.013
## tasit3_lies 0.119
## tasit2_ssar 0.601
## tasit2_psar 0.690
## tasit3_sar 0.564
##
##
## Group 2 [Control]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_total 0.311 0.094 3.308 0.001 0.311 0.359
## rmet_total 0.630 0.109 5.799 0.000 0.630 0.764
## mean_ea 0.274 0.205 1.338 0.181 0.274 0.235
## tasit3_lies 0.290 0.087 3.340 0.001 0.290 0.336
## mentalizing =~
## tasit2_ssar 0.263 0.062 4.239 0.000 0.263 0.543
## tasit2_psar 0.470 0.068 6.899 0.000 0.470 0.724
## tasit3_sar 0.463 0.074 6.243 0.000 0.463 0.602
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.606 0.111 5.445 0.000 0.606 0.606
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.255 0.060 4.226 0.000 0.255 0.294
## .rmet_total 0.333 0.057 5.841 0.000 0.333 0.405
## .mean_ea 0.684 0.197 3.473 0.001 0.684 0.586
## .tasit3_lies 0.373 0.060 6.217 0.000 0.373 0.432
## .tasit2_ssar 0.468 0.033 13.971 0.000 0.468 0.966
## .tasit2_psar 0.424 0.045 9.443 0.000 0.424 0.653
## .tasit3_sar 0.419 0.053 7.875 0.000 0.419 0.545
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.657 0.077 8.584 0.000 0.657 0.871
## .rmet_total 0.282 0.114 2.471 0.013 0.282 0.416
## .mean_ea 1.291 0.288 4.485 0.000 1.291 0.945
## .tasit3_lies 0.662 0.109 6.077 0.000 0.662 0.887
## .tasit2_ssar 0.165 0.021 8.051 0.000 0.165 0.705
## .tasit2_psar 0.200 0.051 3.899 0.000 0.200 0.475
## .tasit3_sar 0.377 0.101 3.729 0.000 0.377 0.637
## simulation 1.000 1.000 1.000
## mentalizing 1.000 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.129
## rmet_total 0.584
## mean_ea 0.055
## tasit3_lies 0.113
## tasit2_ssar 0.295
## tasit2_psar 0.525
## tasit3_sar 0.363
##
##
## Group 3 [SSD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_total 0.638 0.075 8.494 0.000 0.638 0.601
## rmet_total 0.924 0.064 14.324 0.000 0.924 0.867
## mean_ea 0.218 0.082 2.668 0.008 0.218 0.269
## tasit3_lies 0.525 0.062 8.506 0.000 0.525 0.531
## mentalizing =~
## tasit2_ssar 0.947 0.059 15.975 0.000 0.947 0.820
## tasit2_psar 0.863 0.061 14.186 0.000 0.863 0.762
## tasit3_sar 0.900 0.051 17.777 0.000 0.900 0.850
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.686 0.048 14.447 0.000 0.686 0.686
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total -0.200 0.065 -3.054 0.002 -0.200 -0.188
## .rmet_total -0.337 0.065 -5.198 0.000 -0.337 -0.316
## .mean_ea -0.198 0.085 -2.341 0.019 -0.198 -0.245
## .tasit3_lies -0.170 0.060 -2.836 0.005 -0.170 -0.172
## .tasit2_ssar -0.447 0.070 -6.339 0.000 -0.447 -0.387
## .tasit2_psar -0.424 0.070 -6.088 0.000 -0.424 -0.375
## .tasit3_sar -0.462 0.064 -7.185 0.000 -0.462 -0.436
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.721 0.100 7.241 0.000 0.721 0.639
## .rmet_total 0.282 0.078 3.638 0.000 0.282 0.248
## .mean_ea 0.609 0.125 4.863 0.000 0.609 0.928
## .tasit3_lies 0.700 0.060 11.594 0.000 0.700 0.718
## .tasit2_ssar 0.437 0.057 7.651 0.000 0.437 0.328
## .tasit2_psar 0.537 0.063 8.493 0.000 0.537 0.419
## .tasit3_sar 0.311 0.047 6.604 0.000 0.311 0.277
## simulation 1.000 1.000 1.000
## mentalizing 1.000 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.361
## rmet_total 0.752
## mean_ea 0.072
## tasit3_lies 0.282
## tasit2_ssar 0.672
## tasit2_psar 0.581
## tasit3_sar 0.723
##
## Modification Indices:
##
## lhs op rhs block group level mi epc sepc.lv sepc.all
## 1 simulation =~ tasit2_ssar 1 1 1 2.324 -0.316 -0.316 -0.355
## 2 simulation =~ tasit2_psar 1 1 1 0.077 0.063 0.063 0.068
## 3 simulation =~ tasit3_sar 1 1 1 1.592 0.230 0.230 0.289
## 4 mentalizing =~ er40_total 1 1 1 2.584 0.590 0.590 0.598
## 5 mentalizing =~ rmet_total 1 1 1 0.721 -0.932 -0.932 -1.000
## 6 mentalizing =~ mean_ea 1 1 1 1.515 -0.719 -0.719 -0.772
## 7 mentalizing =~ tasit3_lies 1 1 1 1.702 -0.416 -0.416 -0.395
## 8 er40_total ~~ rmet_total 1 1 1 1.385 -0.145 -0.145 -0.312
## 9 er40_total ~~ mean_ea 1 1 1 3.319 0.376 0.376 0.457
## 10 er40_total ~~ tasit3_lies 1 1 1 0.247 -0.047 -0.047 -0.054
## 11 er40_total ~~ tasit2_ssar 1 1 1 0.000 0.000 0.000 0.001
## 12 er40_total ~~ tasit2_psar 1 1 1 0.373 -0.038 -0.038 -0.082
## 13 er40_total ~~ tasit3_sar 1 1 1 3.537 0.105 0.105 0.224
## 14 rmet_total ~~ mean_ea 1 1 1 0.023 -0.027 -0.027 -0.057
## 15 rmet_total ~~ tasit3_lies 1 1 1 2.748 0.172 0.172 0.335
## 16 rmet_total ~~ tasit2_ssar 1 1 1 1.415 -0.063 -0.063 -0.216
## 17 rmet_total ~~ tasit2_psar 1 1 1 0.009 0.005 0.005 0.020
## 18 rmet_total ~~ tasit3_sar 1 1 1 0.891 0.045 0.045 0.162
## 19 mean_ea ~~ tasit3_lies 1 1 1 1.278 0.256 0.256 0.280
## 20 mean_ea ~~ tasit2_ssar 1 1 1 0.526 0.107 0.107 0.206
## 21 mean_ea ~~ tasit2_psar 1 1 1 1.292 -0.161 -0.161 -0.339
## 22 mean_ea ~~ tasit3_sar 1 1 1 0.544 -0.097 -0.097 -0.200
## 23 tasit3_lies ~~ tasit2_ssar 1 1 1 0.393 -0.041 -0.041 -0.074
## 24 tasit3_lies ~~ tasit2_psar 1 1 1 1.027 0.066 0.066 0.130
## 25 tasit3_lies ~~ tasit3_sar 1 1 1 2.929 -0.102 -0.102 -0.197
## 26 tasit2_ssar ~~ tasit2_psar 1 1 1 1.592 0.087 0.087 0.302
## 27 tasit2_ssar ~~ tasit3_sar 1 1 1 0.077 0.014 0.014 0.049
## 28 tasit2_psar ~~ tasit3_sar 1 1 1 2.324 -0.090 -0.090 -0.332
## 29 simulation =~ tasit2_ssar 2 2 1 0.442 -0.042 -0.042 -0.088
## 30 simulation =~ tasit2_psar 2 2 1 0.109 -0.035 -0.035 -0.054
## 31 simulation =~ tasit3_sar 2 2 1 0.945 0.104 0.104 0.135
## 32 mentalizing =~ er40_total 2 2 1 1.333 -0.157 -0.157 -0.181
## 33 mentalizing =~ rmet_total 2 2 1 3.914 0.652 0.652 0.792
## 34 mentalizing =~ mean_ea 2 2 1 0.042 -0.070 -0.070 -0.060
## 35 mentalizing =~ tasit3_lies 2 2 1 0.190 -0.056 -0.056 -0.065
## 36 er40_total ~~ rmet_total 2 2 1 0.447 -0.060 -0.060 -0.140
## 37 er40_total ~~ mean_ea 2 2 1 0.814 -0.144 -0.144 -0.156
## 38 er40_total ~~ tasit3_lies 2 2 1 6.641 0.134 0.134 0.204
## 39 er40_total ~~ tasit2_ssar 2 2 1 0.704 -0.022 -0.022 -0.065
## 40 er40_total ~~ tasit2_psar 2 2 1 4.507 -0.072 -0.072 -0.199
## 41 er40_total ~~ tasit3_sar 2 2 1 4.538 0.086 0.086 0.172
## 42 rmet_total ~~ mean_ea 2 2 1 1.158 0.199 0.199 0.329
## 43 rmet_total ~~ tasit3_lies 2 2 1 2.066 -0.119 -0.119 -0.276
## 44 rmet_total ~~ tasit2_ssar 2 2 1 0.117 -0.008 -0.008 -0.039
## 45 rmet_total ~~ tasit2_psar 2 2 1 0.671 0.031 0.031 0.130
## 46 rmet_total ~~ tasit3_sar 2 2 1 0.064 0.010 0.010 0.031
## 47 mean_ea ~~ tasit3_lies 2 2 1 0.527 -0.114 -0.114 -0.123
## 48 mean_ea ~~ tasit2_ssar 2 2 1 0.104 0.026 0.026 0.057
## 49 mean_ea ~~ tasit2_psar 2 2 1 0.226 -0.049 -0.049 -0.096
## 50 mean_ea ~~ tasit3_sar 2 2 1 0.003 0.007 0.007 0.009
## 51 tasit3_lies ~~ tasit2_ssar 2 2 1 0.016 0.003 0.003 0.010
## 52 tasit3_lies ~~ tasit2_psar 2 2 1 0.057 0.008 0.008 0.022
## 53 tasit3_lies ~~ tasit3_sar 2 2 1 0.738 -0.034 -0.034 -0.069
## 54 tasit2_ssar ~~ tasit2_psar 2 2 1 0.945 0.029 0.029 0.159
## 55 tasit2_ssar ~~ tasit3_sar 2 2 1 0.109 -0.009 -0.009 -0.038
## 56 tasit2_psar ~~ tasit3_sar 2 2 1 0.442 -0.037 -0.037 -0.134
## 57 simulation =~ tasit2_ssar 3 3 1 0.299 -0.054 -0.054 -0.046
## 58 simulation =~ tasit2_psar 3 3 1 6.064 0.235 0.235 0.208
## 59 simulation =~ tasit3_sar 3 3 1 2.964 -0.157 -0.157 -0.148
## 60 mentalizing =~ er40_total 3 3 1 0.297 -0.066 -0.066 -0.062
## 61 mentalizing =~ rmet_total 3 3 1 1.524 0.232 0.232 0.218
## 62 mentalizing =~ mean_ea 3 3 1 0.003 -0.007 -0.007 -0.009
## 63 mentalizing =~ tasit3_lies 3 3 1 0.386 -0.065 -0.065 -0.066
## 64 er40_total ~~ rmet_total 3 3 1 0.948 -0.086 -0.086 -0.191
## 65 er40_total ~~ mean_ea 3 3 1 1.618 0.096 0.096 0.145
## 66 er40_total ~~ tasit3_lies 3 3 1 1.625 0.069 0.069 0.097
## 67 er40_total ~~ tasit2_ssar 3 3 1 1.637 0.057 0.057 0.101
## 68 er40_total ~~ tasit2_psar 3 3 1 0.231 -0.022 -0.022 -0.036
## 69 er40_total ~~ tasit3_sar 3 3 1 1.411 -0.047 -0.047 -0.100
## 70 rmet_total ~~ mean_ea 3 3 1 0.001 0.003 0.003 0.007
## 71 rmet_total ~~ tasit3_lies 3 3 1 0.047 -0.015 -0.015 -0.035
## 72 rmet_total ~~ tasit2_ssar 3 3 1 0.771 -0.036 -0.036 -0.102
## 73 rmet_total ~~ tasit2_psar 3 3 1 0.391 0.026 0.026 0.066
## 74 rmet_total ~~ tasit3_sar 3 3 1 0.666 0.030 0.030 0.103
## 75 mean_ea ~~ tasit3_lies 3 3 1 2.056 -0.101 -0.101 -0.154
## 76 mean_ea ~~ tasit2_ssar 3 3 1 3.355 -0.113 -0.113 -0.219
## 77 mean_ea ~~ tasit2_psar 3 3 1 3.184 0.117 0.117 0.205
## 78 mean_ea ~~ tasit3_sar 3 3 1 0.059 0.014 0.014 0.031
## 79 tasit3_lies ~~ tasit2_ssar 3 3 1 0.044 -0.009 -0.009 -0.016
## 80 tasit3_lies ~~ tasit2_psar 3 3 1 8.841 0.131 0.131 0.213
## 81 tasit3_lies ~~ tasit3_sar 3 3 1 7.315 -0.102 -0.102 -0.217
## 82 tasit2_ssar ~~ tasit2_psar 3 3 1 2.964 -0.110 -0.110 -0.227
## 83 tasit2_ssar ~~ tasit3_sar 3 3 1 6.065 0.179 0.179 0.485
## 84 tasit2_psar ~~ tasit3_sar 3 3 1 0.299 -0.034 -0.034 -0.083
## sepc.nox
## 1 -0.355
## 2 0.068
## 3 0.289
## 4 0.598
## 5 -1.000
## 6 -0.772
## 7 -0.395
## 8 -0.312
## 9 0.457
## 10 -0.054
## 11 0.001
## 12 -0.082
## 13 0.224
## 14 -0.057
## 15 0.335
## 16 -0.216
## 17 0.020
## 18 0.162
## 19 0.280
## 20 0.206
## 21 -0.339
## 22 -0.200
## 23 -0.074
## 24 0.130
## 25 -0.197
## 26 0.302
## 27 0.049
## 28 -0.332
## 29 -0.088
## 30 -0.054
## 31 0.135
## 32 -0.181
## 33 0.792
## 34 -0.060
## 35 -0.065
## 36 -0.140
## 37 -0.156
## 38 0.204
## 39 -0.065
## 40 -0.199
## 41 0.172
## 42 0.329
## 43 -0.276
## 44 -0.039
## 45 0.130
## 46 0.031
## 47 -0.123
## 48 0.057
## 49 -0.096
## 50 0.009
## 51 0.010
## 52 0.022
## 53 -0.069
## 54 0.159
## 55 -0.038
## 56 -0.134
## 57 -0.046
## 58 0.208
## 59 -0.148
## 60 -0.062
## 61 0.218
## 62 -0.009
## 63 -0.066
## 64 -0.191
## 65 0.145
## 66 0.097
## 67 0.101
## 68 -0.036
## 69 -0.100
## 70 0.007
## 71 -0.035
## 72 -0.102
## 73 0.066
## 74 0.103
## 75 -0.154
## 76 -0.219
## 77 0.205
## 78 0.031
## 79 -0.016
## 80 0.213
## 81 -0.217
## 82 -0.227
## 83 0.485
## 84 -0.083
# metric - are factor loadings equal across groups
CFA_sc_model1_grp_fit2 <- cfa(model= CFA_scog_model1,data = spasd_spins_yj_z_df,group = "group",
group.equal=c("loadings"),estimator = "MLR", missing = "ml")
summary(CFA_sc_model1_grp_fit2, fit.measures = TRUE, modindices = TRUE, standardized = TRUE, rsquare = TRUE)
## lavaan 0.6.16 ended normally after 63 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 66
## Number of equality constraints 10
##
## Number of observations per group:
## ASD 100
## Control 209
## SSD 276
## Number of missing patterns per group:
## ASD 4
## Control 6
## SSD 20
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 90.697 88.980
## Degrees of freedom 49 49
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.019
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## ASD 26.555 26.052
## Control 31.038 30.450
## SSD 33.105 32.478
##
## Model Test Baseline Model:
##
## Test statistic 973.724 901.247
## Degrees of freedom 63 63
## P-value 0.000 0.000
## Scaling correction factor 1.080
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.954 0.952
## Tucker-Lewis Index (TLI) 0.941 0.939
##
## Robust Comparative Fit Index (CFI) 0.948
## Robust Tucker-Lewis Index (TLI) 0.933
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4318.037 -4318.037
## Scaling correction factor 1.062
## for the MLR correction
## Loglikelihood unrestricted model (H1) -4272.688 -4272.688
## Scaling correction factor 1.143
## for the MLR correction
##
## Akaike (AIC) 8748.074 8748.074
## Bayesian (BIC) 8992.884 8992.884
## Sample-size adjusted Bayesian (SABIC) 8815.104 8815.104
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.066 0.065
## 90 Percent confidence interval - lower 0.044 0.043
## 90 Percent confidence interval - upper 0.087 0.086
## P-value H_0: RMSEA <= 0.050 0.105 0.124
## P-value H_0: RMSEA >= 0.080 0.145 0.120
##
## Robust RMSEA 0.074
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.119
## P-value H_0: Robust RMSEA <= 0.050 0.217
## P-value H_0: Robust RMSEA >= 0.080 0.440
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.076 0.076
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [ASD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.472 0.474
## rmt_ttl (.p2.) 1.546 0.194 7.972 0.000 0.730 0.793
## mean_ea (.p3.) 0.315 0.135 2.324 0.020 0.149 0.152
## tst3_ls (.p4.) 0.844 0.102 8.284 0.000 0.399 0.374
## mentalizing =~
## tst2_ss 1.000 0.667 0.760
## tst2_ps (.p6.) 1.044 0.076 13.733 0.000 0.696 0.784
## tst3_sr (.p7.) 1.001 0.067 14.851 0.000 0.668 0.800
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.262 0.088 2.981 0.003 0.832 0.832
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total -0.067 0.101 -0.669 0.504 -0.067 -0.068
## .rmet_total 0.149 0.093 1.597 0.110 0.149 0.162
## .mean_ea 0.093 0.235 0.397 0.691 0.093 0.095
## .tasit3_lies -0.328 0.105 -3.115 0.002 -0.328 -0.307
## .tasit2_ssar 0.142 0.090 1.581 0.114 0.142 0.162
## .tasit2_psar 0.111 0.092 1.209 0.226 0.111 0.125
## .tasit3_sar 0.290 0.080 3.643 0.000 0.290 0.348
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.770 0.125 6.157 0.000 0.770 0.775
## .rmet_total 0.314 0.099 3.180 0.001 0.314 0.371
## .mean_ea 0.936 0.437 2.144 0.032 0.936 0.977
## .tasit3_lies 0.979 0.153 6.385 0.000 0.979 0.860
## .tasit2_ssar 0.325 0.085 3.847 0.000 0.325 0.422
## .tasit2_psar 0.305 0.098 3.117 0.002 0.305 0.386
## .tasit3_sar 0.251 0.053 4.692 0.000 0.251 0.360
## simulation 0.223 0.069 3.214 0.001 1.000 1.000
## mentalizing 0.445 0.136 3.279 0.001 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.225
## rmet_total 0.629
## mean_ea 0.023
## tasit3_lies 0.140
## tasit2_ssar 0.578
## tasit2_psar 0.614
## tasit3_sar 0.640
##
##
## Group 2 [Control]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.371 0.422
## rmt_ttl (.p2.) 1.546 0.194 7.972 0.000 0.573 0.698
## mean_ea (.p3.) 0.315 0.135 2.324 0.020 0.117 0.100
## tst3_ls (.p4.) 0.844 0.102 8.284 0.000 0.313 0.363
## mentalizing =~
## tst2_ss 1.000 0.358 0.699
## tst2_ps (.p6.) 1.044 0.076 13.733 0.000 0.374 0.598
## tst3_sr (.p7.) 1.001 0.067 14.851 0.000 0.359 0.483
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.079 0.026 3.079 0.002 0.597 0.597
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.255 0.060 4.220 0.000 0.255 0.290
## .rmet_total 0.333 0.057 5.842 0.000 0.333 0.406
## .mean_ea 0.651 0.189 3.453 0.001 0.651 0.561
## .tasit3_lies 0.373 0.060 6.227 0.000 0.373 0.433
## .tasit2_ssar 0.468 0.033 13.971 0.000 0.468 0.912
## .tasit2_psar 0.424 0.045 9.443 0.000 0.424 0.677
## .tasit3_sar 0.419 0.053 7.875 0.000 0.419 0.564
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.633 0.073 8.650 0.000 0.633 0.822
## .rmet_total 0.345 0.069 4.965 0.000 0.345 0.512
## .mean_ea 1.333 0.288 4.631 0.000 1.333 0.990
## .tasit3_lies 0.645 0.103 6.278 0.000 0.645 0.868
## .tasit2_ssar 0.134 0.021 6.501 0.000 0.134 0.511
## .tasit2_psar 0.251 0.049 5.142 0.000 0.251 0.642
## .tasit3_sar 0.422 0.087 4.843 0.000 0.422 0.766
## simulation 0.137 0.039 3.540 0.000 1.000 1.000
## mentalizing 0.129 0.036 3.618 0.000 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.178
## rmet_total 0.488
## mean_ea 0.010
## tasit3_lies 0.132
## tasit2_ssar 0.489
## tasit2_psar 0.358
## tasit3_sar 0.234
##
##
## Group 3 [SSD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.610 0.580
## rmt_ttl (.p2.) 1.546 0.194 7.972 0.000 0.944 0.881
## mean_ea (.p3.) 0.315 0.135 2.324 0.020 0.192 0.238
## tst3_ls (.p4.) 0.844 0.102 8.284 0.000 0.515 0.523
## mentalizing =~
## tst2_ss 1.000 0.893 0.794
## tst2_ps (.p6.) 1.044 0.076 13.733 0.000 0.932 0.794
## tst3_sr (.p7.) 1.001 0.067 14.851 0.000 0.894 0.846
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.374 0.063 5.938 0.000 0.687 0.687
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total -0.199 0.065 -3.048 0.002 -0.199 -0.189
## .rmet_total -0.338 0.065 -5.203 0.000 -0.338 -0.315
## .mean_ea -0.205 0.084 -2.451 0.014 -0.205 -0.254
## .tasit3_lies -0.170 0.060 -2.839 0.005 -0.170 -0.172
## .tasit2_ssar -0.445 0.070 -6.326 0.000 -0.445 -0.396
## .tasit2_psar -0.429 0.070 -6.138 0.000 -0.429 -0.365
## .tasit3_sar -0.462 0.064 -7.188 0.000 -0.462 -0.438
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.735 0.097 7.606 0.000 0.735 0.664
## .rmet_total 0.256 0.076 3.361 0.001 0.256 0.223
## .mean_ea 0.611 0.126 4.872 0.000 0.611 0.943
## .tasit3_lies 0.705 0.059 12.021 0.000 0.705 0.727
## .tasit2_ssar 0.468 0.058 8.098 0.000 0.468 0.370
## .tasit2_psar 0.510 0.062 8.205 0.000 0.510 0.370
## .tasit3_sar 0.317 0.047 6.743 0.000 0.317 0.284
## simulation 0.372 0.086 4.349 0.000 1.000 1.000
## mentalizing 0.797 0.111 7.183 0.000 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.336
## rmet_total 0.777
## mean_ea 0.057
## tasit3_lies 0.273
## tasit2_ssar 0.630
## tasit2_psar 0.630
## tasit3_sar 0.716
##
## Modification Indices:
##
## lhs op rhs block group level mi epc sepc.lv sepc.all
## 1 simulation =~ er40_total 1 1 1 0.063 -0.068 -0.032 -0.032
## 2 mentalizing =~ tasit2_ssar 1 1 1 0.134 0.049 0.033 0.037
## 3 simulation =~ er40_total 2 2 1 0.483 -0.191 -0.071 -0.081
## 4 mentalizing =~ tasit2_ssar 2 2 1 16.318 -0.732 -0.263 -0.512
## 5 simulation =~ er40_total 3 3 1 0.537 0.156 0.095 0.090
## 6 mentalizing =~ tasit2_ssar 3 3 1 5.296 0.271 0.242 0.215
## 7 simulation =~ tasit2_ssar 1 1 1 0.047 -0.040 -0.019 -0.022
## 8 simulation =~ tasit2_psar 1 1 1 1.621 0.237 0.112 0.126
## 9 simulation =~ tasit3_sar 1 1 1 1.075 -0.180 -0.085 -0.102
## 10 mentalizing =~ er40_total 1 1 1 0.024 0.026 0.018 0.018
## 11 mentalizing =~ rmet_total 1 1 1 0.590 0.170 0.113 0.123
## 12 mentalizing =~ mean_ea 1 1 1 4.369 -0.823 -0.549 -0.560
## 13 mentalizing =~ tasit3_lies 1 1 1 0.955 -0.175 -0.116 -0.109
## 14 er40_total ~~ rmet_total 1 1 1 1.479 -0.126 -0.126 -0.256
## 15 er40_total ~~ mean_ea 1 1 1 2.823 0.359 0.359 0.423
## 16 er40_total ~~ tasit3_lies 1 1 1 0.389 -0.059 -0.059 -0.067
## 17 er40_total ~~ tasit2_ssar 1 1 1 0.070 -0.016 -0.016 -0.032
## 18 er40_total ~~ tasit2_psar 1 1 1 0.434 -0.040 -0.040 -0.082
## 19 er40_total ~~ tasit3_sar 1 1 1 2.865 0.094 0.094 0.215
## 20 rmet_total ~~ mean_ea 1 1 1 0.557 -0.126 -0.126 -0.232
## 21 rmet_total ~~ tasit3_lies 1 1 1 2.314 0.135 0.135 0.243
## 22 rmet_total ~~ tasit2_ssar 1 1 1 0.884 -0.047 -0.047 -0.148
## 23 rmet_total ~~ tasit2_psar 1 1 1 0.499 0.036 0.036 0.115
## 24 rmet_total ~~ tasit3_sar 1 1 1 0.277 0.025 0.025 0.089
## 25 mean_ea ~~ tasit3_lies 1 1 1 0.806 0.213 0.213 0.223
## 26 mean_ea ~~ tasit2_ssar 1 1 1 0.090 0.047 0.047 0.085
## 27 mean_ea ~~ tasit2_psar 1 1 1 1.659 -0.193 -0.193 -0.360
## 28 mean_ea ~~ tasit3_sar 1 1 1 1.036 -0.141 -0.141 -0.290
## 29 tasit3_lies ~~ tasit2_ssar 1 1 1 0.384 -0.041 -0.041 -0.072
## 30 tasit3_lies ~~ tasit2_psar 1 1 1 0.857 0.060 0.060 0.110
## 31 tasit3_lies ~~ tasit3_sar 1 1 1 3.288 -0.109 -0.109 -0.220
## 32 tasit2_ssar ~~ tasit2_psar 1 1 1 3.787 0.100 0.100 0.317
## 33 tasit2_ssar ~~ tasit3_sar 1 1 1 0.218 -0.023 -0.023 -0.080
## 34 tasit2_psar ~~ tasit3_sar 1 1 1 2.172 -0.075 -0.075 -0.273
## 35 simulation =~ tasit2_ssar 2 2 1 12.037 -0.469 -0.174 -0.339
## 36 simulation =~ tasit2_psar 2 2 1 3.382 0.279 0.103 0.165
## 37 simulation =~ tasit3_sar 2 2 1 5.207 0.401 0.148 0.200
## 38 mentalizing =~ er40_total 2 2 1 1.966 -0.308 -0.110 -0.126
## 39 mentalizing =~ rmet_total 2 2 1 1.961 0.395 0.142 0.173
## 40 mentalizing =~ mean_ea 2 2 1 0.186 0.279 0.100 0.086
## 41 mentalizing =~ tasit3_lies 2 2 1 0.143 -0.081 -0.029 -0.034
## 42 er40_total ~~ rmet_total 2 2 1 0.604 -0.050 -0.050 -0.108
## 43 er40_total ~~ mean_ea 2 2 1 0.724 -0.135 -0.135 -0.147
## 44 er40_total ~~ tasit3_lies 2 2 1 4.483 0.104 0.104 0.163
## 45 er40_total ~~ tasit2_ssar 2 2 1 1.664 -0.034 -0.034 -0.117
## 46 er40_total ~~ tasit2_psar 2 2 1 3.043 -0.057 -0.057 -0.143
## 47 er40_total ~~ tasit3_sar 2 2 1 3.223 0.072 0.072 0.139
## 48 rmet_total ~~ mean_ea 2 2 1 1.947 0.195 0.195 0.287
## 49 rmet_total ~~ tasit3_lies 2 2 1 0.697 -0.046 -0.046 -0.098
## 50 rmet_total ~~ tasit2_ssar 2 2 1 1.745 -0.034 -0.034 -0.159
## 51 rmet_total ~~ tasit2_psar 2 2 1 4.257 0.063 0.063 0.214
## 52 rmet_total ~~ tasit3_sar 2 2 1 1.377 0.043 0.043 0.112
## 53 mean_ea ~~ tasit3_lies 2 2 1 0.442 -0.103 -0.103 -0.111
## 54 mean_ea ~~ tasit2_ssar 2 2 1 0.048 0.018 0.018 0.043
## 55 mean_ea ~~ tasit2_psar 2 2 1 0.074 -0.028 -0.028 -0.049
## 56 mean_ea ~~ tasit3_sar 2 2 1 0.028 0.021 0.021 0.029
## 57 tasit3_lies ~~ tasit2_ssar 2 2 1 0.062 -0.006 -0.006 -0.022
## 58 tasit3_lies ~~ tasit2_psar 2 2 1 0.037 0.006 0.006 0.015
## 59 tasit3_lies ~~ tasit3_sar 2 2 1 0.730 -0.034 -0.034 -0.065
## 60 tasit2_ssar ~~ tasit2_psar 2 2 1 0.560 -0.019 -0.019 -0.105
## 61 tasit2_ssar ~~ tasit3_sar 2 2 1 2.258 -0.038 -0.038 -0.158
## 62 tasit2_psar ~~ tasit3_sar 2 2 1 6.092 0.071 0.071 0.216
## 63 simulation =~ tasit2_ssar 3 3 1 0.814 0.110 0.067 0.060
## 64 simulation =~ tasit2_psar 3 3 1 0.150 0.049 0.030 0.026
## 65 simulation =~ tasit3_sar 3 3 1 1.489 -0.141 -0.086 -0.081
## 66 mentalizing =~ er40_total 3 3 1 0.016 0.012 0.011 0.011
## 67 mentalizing =~ rmet_total 3 3 1 0.008 -0.012 -0.010 -0.010
## 68 mentalizing =~ mean_ea 3 3 1 0.187 0.061 0.055 0.068
## 69 mentalizing =~ tasit3_lies 3 3 1 0.018 -0.012 -0.011 -0.011
## 70 er40_total ~~ rmet_total 3 3 1 0.541 -0.059 -0.059 -0.137
## 71 er40_total ~~ mean_ea 3 3 1 1.697 0.098 0.098 0.146
## 72 er40_total ~~ tasit3_lies 3 3 1 2.067 0.075 0.075 0.105
## 73 er40_total ~~ tasit2_ssar 3 3 1 1.887 0.061 0.061 0.104
## 74 er40_total ~~ tasit2_psar 3 3 1 0.199 -0.021 -0.021 -0.034
## 75 er40_total ~~ tasit3_sar 3 3 1 1.083 -0.041 -0.041 -0.086
## 76 rmet_total ~~ mean_ea 3 3 1 0.002 0.003 0.003 0.008
## 77 rmet_total ~~ tasit3_lies 3 3 1 0.246 -0.033 -0.033 -0.078
## 78 rmet_total ~~ tasit2_ssar 3 3 1 0.486 -0.028 -0.028 -0.081
## 79 rmet_total ~~ tasit2_psar 3 3 1 0.053 0.010 0.010 0.027
## 80 rmet_total ~~ tasit3_sar 3 3 1 0.444 0.025 0.025 0.087
## 81 mean_ea ~~ tasit3_lies 3 3 1 1.776 -0.093 -0.093 -0.142
## 82 mean_ea ~~ tasit2_ssar 3 3 1 3.084 -0.109 -0.109 -0.203
## 83 mean_ea ~~ tasit2_psar 3 3 1 3.295 0.119 0.119 0.214
## 84 mean_ea ~~ tasit3_sar 3 3 1 0.017 0.007 0.007 0.017
## 85 tasit3_lies ~~ tasit2_ssar 3 3 1 0.044 -0.009 -0.009 -0.015
## 86 tasit3_lies ~~ tasit2_psar 3 3 1 9.243 0.134 0.134 0.224
## 87 tasit3_lies ~~ tasit3_sar 3 3 1 7.589 -0.103 -0.103 -0.219
## 88 tasit2_ssar ~~ tasit2_psar 3 3 1 3.005 -0.101 -0.101 -0.207
## 89 tasit2_ssar ~~ tasit3_sar 3 3 1 11.516 0.193 0.193 0.500
## 90 tasit2_psar ~~ tasit3_sar 3 3 1 2.675 -0.097 -0.097 -0.242
## sepc.nox
## 1 -0.032
## 2 0.037
## 3 -0.081
## 4 -0.512
## 5 0.090
## 6 0.215
## 7 -0.022
## 8 0.126
## 9 -0.102
## 10 0.018
## 11 0.123
## 12 -0.560
## 13 -0.109
## 14 -0.256
## 15 0.423
## 16 -0.067
## 17 -0.032
## 18 -0.082
## 19 0.215
## 20 -0.232
## 21 0.243
## 22 -0.148
## 23 0.115
## 24 0.089
## 25 0.223
## 26 0.085
## 27 -0.360
## 28 -0.290
## 29 -0.072
## 30 0.110
## 31 -0.220
## 32 0.317
## 33 -0.080
## 34 -0.273
## 35 -0.339
## 36 0.165
## 37 0.200
## 38 -0.126
## 39 0.173
## 40 0.086
## 41 -0.034
## 42 -0.108
## 43 -0.147
## 44 0.163
## 45 -0.117
## 46 -0.143
## 47 0.139
## 48 0.287
## 49 -0.098
## 50 -0.159
## 51 0.214
## 52 0.112
## 53 -0.111
## 54 0.043
## 55 -0.049
## 56 0.029
## 57 -0.022
## 58 0.015
## 59 -0.065
## 60 -0.105
## 61 -0.158
## 62 0.216
## 63 0.060
## 64 0.026
## 65 -0.081
## 66 0.011
## 67 -0.010
## 68 0.068
## 69 -0.011
## 70 -0.137
## 71 0.146
## 72 0.105
## 73 0.104
## 74 -0.034
## 75 -0.086
## 76 0.008
## 77 -0.078
## 78 -0.081
## 79 0.027
## 80 0.087
## 81 -0.142
## 82 -0.203
## 83 0.214
## 84 0.017
## 85 -0.015
## 86 0.224
## 87 -0.219
## 88 -0.207
## 89 0.500
## 90 -0.242
anova(CFA_scog_model1_grp_fit,CFA_sc_model1_grp_fit2 )
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## CFA_scog_model1_grp_fit 39 8746.2 9034.8 68.870
## CFA_sc_model1_grp_fit2 49 8748.1 8992.9 90.697 18.353 10 0.0493 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
modindices(CFA_sc_model1_grp_fit2, sort = TRUE, maximum.number = 10)
## lhs op rhs block group level mi epc sepc.lv sepc.all
## 31 mentalizing =~ tasit2_ssar 2 2 1 16.318 -0.732 -0.263 -0.512
## 117 simulation =~ tasit2_ssar 2 2 1 12.037 -0.469 -0.174 -0.339
## 171 tasit2_ssar ~~ tasit3_sar 3 3 1 11.516 0.193 0.193 0.500
## 168 tasit3_lies ~~ tasit2_psar 3 3 1 9.243 0.134 0.134 0.224
## 169 tasit3_lies ~~ tasit3_sar 3 3 1 7.589 -0.103 -0.103 -0.219
## 144 tasit2_psar ~~ tasit3_sar 2 2 1 6.092 0.071 0.071 0.216
## 57 mentalizing =~ tasit2_ssar 3 3 1 5.296 0.271 0.242 0.215
## 119 simulation =~ tasit3_sar 2 2 1 5.207 0.401 0.148 0.200
## 126 er40_total ~~ tasit3_lies 2 2 1 4.483 0.104 0.104 0.163
## 94 mentalizing =~ mean_ea 1 1 1 4.369 -0.823 -0.549 -0.560
## sepc.nox
## 31 -0.512
## 117 -0.339
## 171 0.500
## 168 0.224
## 169 -0.219
## 144 0.216
## 57 0.215
## 119 0.200
## 126 0.163
## 94 -0.560
# metric - partial isolating mean_ea
CFA_sc_model1_grp_fit2_part <- cfa(model= CFA_scog_model1,data = spasd_spins_yj_z_df, group =
"group",group.equal=c("loadings"),estimator = "MLR", missing = "ml",
group.partial = c("simulation=~mean_ea"))
summary(CFA_sc_model1_grp_fit2_part, fit.measures = TRUE, modindices = TRUE, standardized = TRUE, rsquare = TRUE)
## lavaan 0.6.16 ended normally after 84 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 66
## Number of equality constraints 8
##
## Number of observations per group:
## ASD 100
## Control 209
## SSD 276
## Number of missing patterns per group:
## ASD 4
## Control 6
## SSD 20
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 88.629 87.935
## Degrees of freedom 47 47
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.008
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## ASD 24.951 24.756
## Control 30.590 30.350
## SSD 33.088 32.829
##
## Model Test Baseline Model:
##
## Test statistic 973.724 901.247
## Degrees of freedom 63 63
## P-value 0.000 0.000
## Scaling correction factor 1.080
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.954 0.951
## Tucker-Lewis Index (TLI) 0.939 0.935
##
## Robust Comparative Fit Index (CFI) 0.942
## Robust Tucker-Lewis Index (TLI) 0.922
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4317.003 -4317.003
## Scaling correction factor 1.101
## for the MLR correction
## Loglikelihood unrestricted model (H1) -4272.688 -4272.688
## Scaling correction factor 1.143
## for the MLR correction
##
## Akaike (AIC) 8750.006 8750.006
## Bayesian (BIC) 9003.559 9003.559
## Sample-size adjusted Bayesian (SABIC) 8819.430 8819.430
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.067 0.067
## 90 Percent confidence interval - lower 0.045 0.045
## 90 Percent confidence interval - upper 0.089 0.088
## P-value H_0: RMSEA <= 0.050 0.091 0.098
## P-value H_0: RMSEA >= 0.080 0.175 0.163
##
## Robust RMSEA 0.080
## 90 Percent confidence interval - lower 0.016
## 90 Percent confidence interval - upper 0.124
## P-value H_0: Robust RMSEA <= 0.050 0.160
## P-value H_0: Robust RMSEA >= 0.080 0.523
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.072 0.072
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [ASD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.472 0.473
## rmt_ttl (.p2.) 1.551 0.196 7.915 0.000 0.732 0.794
## mean_ea -0.220 0.416 -0.528 0.597 -0.104 -0.111
## tst3_ls (.p4.) 0.843 0.102 8.233 0.000 0.398 0.373
## mentalizing =~
## tst2_ss 1.000 0.666 0.759
## tst2_ps (.p6.) 1.045 0.076 13.721 0.000 0.696 0.784
## tst3_sr (.p7.) 1.002 0.067 14.854 0.000 0.668 0.800
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.264 0.088 2.995 0.003 0.839 0.839
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total -0.067 0.101 -0.668 0.504 -0.067 -0.067
## .rmet_total 0.149 0.093 1.597 0.110 0.149 0.161
## .mean_ea 0.075 0.229 0.325 0.745 0.075 0.080
## .tasit3_lies -0.328 0.105 -3.115 0.002 -0.328 -0.307
## .tasit2_ssar 0.142 0.090 1.582 0.114 0.142 0.162
## .tasit2_psar 0.111 0.092 1.209 0.227 0.111 0.125
## .tasit3_sar 0.290 0.080 3.643 0.000 0.290 0.348
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.775 0.126 6.138 0.000 0.775 0.776
## .rmet_total 0.315 0.104 3.031 0.002 0.315 0.370
## .mean_ea 0.855 0.461 1.853 0.064 0.855 0.988
## .tasit3_lies 0.983 0.154 6.399 0.000 0.983 0.861
## .tasit2_ssar 0.327 0.085 3.856 0.000 0.327 0.424
## .tasit2_psar 0.304 0.098 3.115 0.002 0.304 0.386
## .tasit3_sar 0.250 0.053 4.678 0.000 0.250 0.360
## simulation 0.223 0.070 3.176 0.001 1.000 1.000
## mentalizing 0.444 0.135 3.281 0.001 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.224
## rmet_total 0.630
## mean_ea 0.012
## tasit3_lies 0.139
## tasit2_ssar 0.576
## tasit2_psar 0.614
## tasit3_sar 0.640
##
##
## Group 2 [Control]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.370 0.421
## rmt_ttl (.p2.) 1.551 0.196 7.915 0.000 0.573 0.699
## mean_ea 0.720 0.637 1.130 0.258 0.266 0.227
## tst3_ls (.p4.) 0.843 0.102 8.233 0.000 0.312 0.361
## mentalizing =~
## tst2_ss 1.000 0.358 0.699
## tst2_ps (.p6.) 1.045 0.076 13.721 0.000 0.374 0.598
## tst3_sr (.p7.) 1.002 0.067 14.854 0.000 0.359 0.484
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.079 0.026 3.076 0.002 0.597 0.597
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.255 0.060 4.221 0.000 0.255 0.290
## .rmet_total 0.333 0.057 5.842 0.000 0.333 0.407
## .mean_ea 0.681 0.197 3.466 0.001 0.681 0.582
## .tasit3_lies 0.373 0.060 6.227 0.000 0.373 0.433
## .tasit2_ssar 0.468 0.033 13.971 0.000 0.468 0.912
## .tasit2_psar 0.424 0.045 9.443 0.000 0.424 0.677
## .tasit3_sar 0.419 0.053 7.875 0.000 0.419 0.564
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.634 0.073 8.671 0.000 0.634 0.823
## .rmet_total 0.343 0.069 4.970 0.000 0.343 0.511
## .mean_ea 1.297 0.299 4.340 0.000 1.297 0.948
## .tasit3_lies 0.646 0.103 6.270 0.000 0.646 0.869
## .tasit2_ssar 0.134 0.021 6.510 0.000 0.134 0.511
## .tasit2_psar 0.251 0.049 5.141 0.000 0.251 0.642
## .tasit3_sar 0.422 0.087 4.841 0.000 0.422 0.766
## simulation 0.137 0.039 3.522 0.000 1.000 1.000
## mentalizing 0.128 0.035 3.618 0.000 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.177
## rmet_total 0.489
## mean_ea 0.052
## tasit3_lies 0.131
## tasit2_ssar 0.489
## tasit2_psar 0.358
## tasit3_sar 0.234
##
##
## Group 3 [SSD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.608 0.579
## rmt_ttl (.p2.) 1.551 0.196 7.915 0.000 0.943 0.881
## mean_ea 0.358 0.138 2.583 0.010 0.218 0.269
## tst3_ls (.p4.) 0.843 0.102 8.233 0.000 0.513 0.521
## mentalizing =~
## tst2_ss 1.000 0.892 0.794
## tst2_ps (.p6.) 1.045 0.076 13.721 0.000 0.932 0.794
## tst3_sr (.p7.) 1.002 0.067 14.854 0.000 0.894 0.846
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.373 0.063 5.916 0.000 0.687 0.687
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total -0.199 0.065 -3.048 0.002 -0.199 -0.189
## .rmet_total -0.338 0.065 -5.202 0.000 -0.338 -0.315
## .mean_ea -0.198 0.085 -2.335 0.020 -0.198 -0.244
## .tasit3_lies -0.170 0.060 -2.839 0.005 -0.170 -0.172
## .tasit2_ssar -0.445 0.070 -6.326 0.000 -0.445 -0.396
## .tasit2_psar -0.429 0.070 -6.138 0.000 -0.429 -0.365
## .tasit3_sar -0.462 0.064 -7.189 0.000 -0.462 -0.438
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.736 0.097 7.611 0.000 0.736 0.665
## .rmet_total 0.256 0.076 3.358 0.001 0.256 0.223
## .mean_ea 0.609 0.125 4.870 0.000 0.609 0.928
## .tasit3_lies 0.706 0.059 12.047 0.000 0.706 0.728
## .tasit2_ssar 0.468 0.058 8.103 0.000 0.468 0.370
## .tasit2_psar 0.510 0.062 8.197 0.000 0.510 0.370
## .tasit3_sar 0.317 0.047 6.733 0.000 0.317 0.284
## simulation 0.370 0.086 4.316 0.000 1.000 1.000
## mentalizing 0.796 0.111 7.176 0.000 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.335
## rmet_total 0.777
## mean_ea 0.072
## tasit3_lies 0.272
## tasit2_ssar 0.630
## tasit2_psar 0.630
## tasit3_sar 0.716
##
## Modification Indices:
##
## lhs op rhs block group level mi epc sepc.lv sepc.all
## 1 simulation =~ er40_total 1 1 1 0.101 -0.086 -0.041 -0.041
## 2 mentalizing =~ tasit2_ssar 1 1 1 0.118 0.046 0.031 0.035
## 3 simulation =~ er40_total 2 2 1 0.517 -0.198 -0.073 -0.083
## 4 mentalizing =~ tasit2_ssar 2 2 1 16.216 -0.729 -0.261 -0.510
## 5 simulation =~ er40_total 3 3 1 0.645 0.171 0.104 0.099
## 6 mentalizing =~ tasit2_ssar 3 3 1 5.362 0.273 0.243 0.216
## 7 simulation =~ tasit2_ssar 1 1 1 0.056 -0.044 -0.021 -0.023
## 8 simulation =~ tasit2_psar 1 1 1 1.673 0.240 0.114 0.128
## 9 simulation =~ tasit3_sar 1 1 1 1.078 -0.180 -0.085 -0.102
## 10 mentalizing =~ er40_total 1 1 1 0.010 0.017 0.012 0.012
## 11 mentalizing =~ rmet_total 1 1 1 0.303 0.123 0.082 0.089
## 12 mentalizing =~ mean_ea 1 1 1 1.893 -1.439 -0.959 -1.031
## 13 mentalizing =~ tasit3_lies 1 1 1 1.002 -0.179 -0.120 -0.112
## 14 er40_total ~~ rmet_total 1 1 1 1.146 -0.111 -0.111 -0.226
## 15 er40_total ~~ mean_ea 1 1 1 3.540 0.388 0.388 0.477
## 16 er40_total ~~ tasit3_lies 1 1 1 0.333 -0.054 -0.054 -0.062
## 17 er40_total ~~ tasit2_ssar 1 1 1 0.059 -0.015 -0.015 -0.029
## 18 er40_total ~~ tasit2_psar 1 1 1 0.431 -0.039 -0.039 -0.081
## 19 er40_total ~~ tasit3_sar 1 1 1 2.890 0.095 0.095 0.216
## 20 rmet_total ~~ mean_ea 1 1 1 0.028 -0.029 -0.029 -0.056
## 21 rmet_total ~~ tasit3_lies 1 1 1 2.585 0.142 0.142 0.256
## 22 rmet_total ~~ tasit2_ssar 1 1 1 0.933 -0.049 -0.049 -0.151
## 23 rmet_total ~~ tasit2_psar 1 1 1 0.357 0.030 0.030 0.098
## 24 rmet_total ~~ tasit3_sar 1 1 1 0.194 0.021 0.021 0.074
## 25 mean_ea ~~ tasit3_lies 1 1 1 1.299 0.260 0.260 0.283
## 26 mean_ea ~~ tasit2_ssar 1 1 1 0.374 0.091 0.091 0.173
## 27 mean_ea ~~ tasit2_psar 1 1 1 1.246 -0.160 -0.160 -0.314
## 28 mean_ea ~~ tasit3_sar 1 1 1 0.634 -0.106 -0.106 -0.229
## 29 tasit3_lies ~~ tasit2_ssar 1 1 1 0.380 -0.041 -0.041 -0.072
## 30 tasit3_lies ~~ tasit2_psar 1 1 1 0.832 0.059 0.059 0.108
## 31 tasit3_lies ~~ tasit3_sar 1 1 1 3.317 -0.110 -0.110 -0.221
## 32 tasit2_ssar ~~ tasit2_psar 1 1 1 3.838 0.100 0.100 0.317
## 33 tasit2_ssar ~~ tasit3_sar 1 1 1 0.193 -0.021 -0.021 -0.075
## 34 tasit2_psar ~~ tasit3_sar 1 1 1 2.290 -0.077 -0.077 -0.279
## 35 simulation =~ tasit2_ssar 2 2 1 11.923 -0.467 -0.173 -0.337
## 36 simulation =~ tasit2_psar 2 2 1 3.325 0.277 0.102 0.164
## 37 simulation =~ tasit3_sar 2 2 1 5.191 0.401 0.148 0.199
## 38 mentalizing =~ er40_total 2 2 1 1.942 -0.306 -0.110 -0.125
## 39 mentalizing =~ rmet_total 2 2 1 2.038 0.403 0.144 0.176
## 40 mentalizing =~ mean_ea 2 2 1 0.002 -0.041 -0.015 -0.013
## 41 mentalizing =~ tasit3_lies 2 2 1 0.139 -0.080 -0.029 -0.033
## 42 er40_total ~~ rmet_total 2 2 1 0.545 -0.048 -0.048 -0.102
## 43 er40_total ~~ mean_ea 2 2 1 0.859 -0.150 -0.150 -0.165
## 44 er40_total ~~ tasit3_lies 2 2 1 4.583 0.106 0.106 0.165
## 45 er40_total ~~ tasit2_ssar 2 2 1 1.637 -0.034 -0.034 -0.116
## 46 er40_total ~~ tasit2_psar 2 2 1 2.983 -0.056 -0.056 -0.141
## 47 er40_total ~~ tasit3_sar 2 2 1 3.249 0.072 0.072 0.139
## 48 rmet_total ~~ mean_ea 2 2 1 1.302 0.197 0.197 0.295
## 49 rmet_total ~~ tasit3_lies 2 2 1 0.643 -0.044 -0.044 -0.093
## 50 rmet_total ~~ tasit2_ssar 2 2 1 1.807 -0.035 -0.035 -0.162
## 51 rmet_total ~~ tasit2_psar 2 2 1 4.264 0.063 0.063 0.214
## 52 rmet_total ~~ tasit3_sar 2 2 1 1.361 0.042 0.042 0.111
## 53 mean_ea ~~ tasit3_lies 2 2 1 0.632 -0.125 -0.125 -0.137
## 54 mean_ea ~~ tasit2_ssar 2 2 1 0.066 0.022 0.022 0.052
## 55 mean_ea ~~ tasit2_psar 2 2 1 0.163 -0.042 -0.042 -0.074
## 56 mean_ea ~~ tasit3_sar 2 2 1 0.003 0.008 0.008 0.010
## 57 tasit3_lies ~~ tasit2_ssar 2 2 1 0.059 -0.006 -0.006 -0.021
## 58 tasit3_lies ~~ tasit2_psar 2 2 1 0.042 0.007 0.007 0.016
## 59 tasit3_lies ~~ tasit3_sar 2 2 1 0.716 -0.033 -0.033 -0.064
## 60 tasit2_ssar ~~ tasit2_psar 2 2 1 0.556 -0.019 -0.019 -0.104
## 61 tasit2_ssar ~~ tasit3_sar 2 2 1 2.261 -0.038 -0.038 -0.158
## 62 tasit2_psar ~~ tasit3_sar 2 2 1 6.079 0.070 0.070 0.216
## 63 simulation =~ tasit2_ssar 3 3 1 0.813 0.110 0.067 0.060
## 64 simulation =~ tasit2_psar 3 3 1 0.153 0.050 0.030 0.026
## 65 simulation =~ tasit3_sar 3 3 1 1.495 -0.141 -0.086 -0.081
## 66 mentalizing =~ er40_total 3 3 1 0.028 0.016 0.015 0.014
## 67 mentalizing =~ rmet_total 3 3 1 0.008 -0.012 -0.011 -0.010
## 68 mentalizing =~ mean_ea 3 3 1 0.008 0.013 0.012 0.015
## 69 mentalizing =~ tasit3_lies 3 3 1 0.009 -0.009 -0.008 -0.008
## 70 er40_total ~~ rmet_total 3 3 1 0.541 -0.059 -0.059 -0.136
## 71 er40_total ~~ mean_ea 3 3 1 1.638 0.096 0.096 0.144
## 72 er40_total ~~ tasit3_lies 3 3 1 2.107 0.076 0.076 0.106
## 73 er40_total ~~ tasit2_ssar 3 3 1 1.913 0.061 0.061 0.104
## 74 er40_total ~~ tasit2_psar 3 3 1 0.200 -0.021 -0.021 -0.034
## 75 er40_total ~~ tasit3_sar 3 3 1 1.082 -0.041 -0.041 -0.086
## 76 rmet_total ~~ mean_ea 3 3 1 0.005 -0.005 -0.005 -0.013
## 77 rmet_total ~~ tasit3_lies 3 3 1 0.195 -0.029 -0.029 -0.069
## 78 rmet_total ~~ tasit2_ssar 3 3 1 0.460 -0.027 -0.027 -0.079
## 79 rmet_total ~~ tasit2_psar 3 3 1 0.045 0.009 0.009 0.025
## 80 rmet_total ~~ tasit3_sar 3 3 1 0.438 0.025 0.025 0.086
## 81 mean_ea ~~ tasit3_lies 3 3 1 1.989 -0.099 -0.099 -0.151
## 82 mean_ea ~~ tasit2_ssar 3 3 1 3.239 -0.111 -0.111 -0.209
## 83 mean_ea ~~ tasit2_psar 3 3 1 3.280 0.119 0.119 0.214
## 84 mean_ea ~~ tasit3_sar 3 3 1 0.009 0.005 0.005 0.012
## 85 tasit3_lies ~~ tasit2_ssar 3 3 1 0.039 -0.008 -0.008 -0.014
## 86 tasit3_lies ~~ tasit2_psar 3 3 1 9.240 0.134 0.134 0.223
## 87 tasit3_lies ~~ tasit3_sar 3 3 1 7.565 -0.103 -0.103 -0.218
## 88 tasit2_ssar ~~ tasit2_psar 3 3 1 2.978 -0.101 -0.101 -0.206
## 89 tasit2_ssar ~~ tasit3_sar 3 3 1 11.575 0.193 0.193 0.501
## 90 tasit2_psar ~~ tasit3_sar 3 3 1 2.733 -0.098 -0.098 -0.244
## sepc.nox
## 1 -0.041
## 2 0.035
## 3 -0.083
## 4 -0.510
## 5 0.099
## 6 0.216
## 7 -0.023
## 8 0.128
## 9 -0.102
## 10 0.012
## 11 0.089
## 12 -1.031
## 13 -0.112
## 14 -0.226
## 15 0.477
## 16 -0.062
## 17 -0.029
## 18 -0.081
## 19 0.216
## 20 -0.056
## 21 0.256
## 22 -0.151
## 23 0.098
## 24 0.074
## 25 0.283
## 26 0.173
## 27 -0.314
## 28 -0.229
## 29 -0.072
## 30 0.108
## 31 -0.221
## 32 0.317
## 33 -0.075
## 34 -0.279
## 35 -0.337
## 36 0.164
## 37 0.199
## 38 -0.125
## 39 0.176
## 40 -0.013
## 41 -0.033
## 42 -0.102
## 43 -0.165
## 44 0.165
## 45 -0.116
## 46 -0.141
## 47 0.139
## 48 0.295
## 49 -0.093
## 50 -0.162
## 51 0.214
## 52 0.111
## 53 -0.137
## 54 0.052
## 55 -0.074
## 56 0.010
## 57 -0.021
## 58 0.016
## 59 -0.064
## 60 -0.104
## 61 -0.158
## 62 0.216
## 63 0.060
## 64 0.026
## 65 -0.081
## 66 0.014
## 67 -0.010
## 68 0.015
## 69 -0.008
## 70 -0.136
## 71 0.144
## 72 0.106
## 73 0.104
## 74 -0.034
## 75 -0.086
## 76 -0.013
## 77 -0.069
## 78 -0.079
## 79 0.025
## 80 0.086
## 81 -0.151
## 82 -0.209
## 83 0.214
## 84 0.012
## 85 -0.014
## 86 0.223
## 87 -0.218
## 88 -0.206
## 89 0.501
## 90 -0.244
anova(CFA_scog_model1_grp_fit,CFA_sc_model1_grp_fit2_part) # sig
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff
## CFA_scog_model1_grp_fit 39 8746.2 9034.8 68.870
## CFA_sc_model1_grp_fit2_part 47 8750.0 9003.6 88.629 16.963 8
## Pr(>Chisq)
## CFA_scog_model1_grp_fit
## CFA_sc_model1_grp_fit2_part 0.0305 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# metric - partial isolating mean ea and tasit2_ssar (according to mod indices from above)
CFA_sc_model1_grp_fit2_part2 <- cfa(model= CFA_scog_model1,data = spasd_spins_yj_z_df,group = "group",
group.equal=c("loadings"),estimator = "MLR", missing = "ml",
group.partial = c("simulation=~mean_ea","mentalizing=~tasit2_ssar"))
summary(CFA_sc_model1_grp_fit2_part2, fit.measures = TRUE, modindices = TRUE, standardized = TRUE, rsquare = TRUE)
## lavaan 0.6.16 ended normally after 84 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 66
## Number of equality constraints 8
##
## Number of observations per group:
## ASD 100
## Control 209
## SSD 276
## Number of missing patterns per group:
## ASD 4
## Control 6
## SSD 20
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 88.629 87.935
## Degrees of freedom 47 47
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.008
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## ASD 24.951 24.756
## Control 30.590 30.350
## SSD 33.088 32.829
##
## Model Test Baseline Model:
##
## Test statistic 973.724 901.247
## Degrees of freedom 63 63
## P-value 0.000 0.000
## Scaling correction factor 1.080
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.954 0.951
## Tucker-Lewis Index (TLI) 0.939 0.935
##
## Robust Comparative Fit Index (CFI) 0.942
## Robust Tucker-Lewis Index (TLI) 0.922
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4317.003 -4317.003
## Scaling correction factor 1.101
## for the MLR correction
## Loglikelihood unrestricted model (H1) -4272.688 -4272.688
## Scaling correction factor 1.143
## for the MLR correction
##
## Akaike (AIC) 8750.006 8750.006
## Bayesian (BIC) 9003.559 9003.559
## Sample-size adjusted Bayesian (SABIC) 8819.430 8819.430
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.067 0.067
## 90 Percent confidence interval - lower 0.045 0.045
## 90 Percent confidence interval - upper 0.089 0.088
## P-value H_0: RMSEA <= 0.050 0.091 0.098
## P-value H_0: RMSEA >= 0.080 0.175 0.163
##
## Robust RMSEA 0.080
## 90 Percent confidence interval - lower 0.016
## 90 Percent confidence interval - upper 0.124
## P-value H_0: Robust RMSEA <= 0.050 0.160
## P-value H_0: Robust RMSEA >= 0.080 0.523
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.072 0.072
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [ASD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.472 0.473
## rmt_ttl (.p2.) 1.551 0.196 7.915 0.000 0.732 0.794
## mean_ea -0.220 0.416 -0.528 0.597 -0.104 -0.111
## tst3_ls (.p4.) 0.843 0.102 8.233 0.000 0.398 0.373
## mentalizing =~
## tst2_ss 1.000 0.666 0.759
## tst2_ps (.p6.) 1.045 0.076 13.721 0.000 0.696 0.784
## tst3_sr (.p7.) 1.002 0.067 14.854 0.000 0.668 0.800
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.264 0.088 2.995 0.003 0.839 0.839
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total -0.067 0.101 -0.668 0.504 -0.067 -0.067
## .rmet_total 0.149 0.093 1.597 0.110 0.149 0.161
## .mean_ea 0.075 0.229 0.325 0.745 0.075 0.080
## .tasit3_lies -0.328 0.105 -3.115 0.002 -0.328 -0.307
## .tasit2_ssar 0.142 0.090 1.582 0.114 0.142 0.162
## .tasit2_psar 0.111 0.092 1.209 0.227 0.111 0.125
## .tasit3_sar 0.290 0.080 3.643 0.000 0.290 0.348
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.775 0.126 6.138 0.000 0.775 0.776
## .rmet_total 0.315 0.104 3.031 0.002 0.315 0.370
## .mean_ea 0.855 0.461 1.853 0.064 0.855 0.988
## .tasit3_lies 0.983 0.154 6.399 0.000 0.983 0.861
## .tasit2_ssar 0.327 0.085 3.856 0.000 0.327 0.424
## .tasit2_psar 0.304 0.098 3.115 0.002 0.304 0.386
## .tasit3_sar 0.250 0.053 4.678 0.000 0.250 0.360
## simulation 0.223 0.070 3.176 0.001 1.000 1.000
## mentalizing 0.444 0.135 3.281 0.001 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.224
## rmet_total 0.630
## mean_ea 0.012
## tasit3_lies 0.139
## tasit2_ssar 0.576
## tasit2_psar 0.614
## tasit3_sar 0.640
##
##
## Group 2 [Control]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.370 0.421
## rmt_ttl (.p2.) 1.551 0.196 7.915 0.000 0.573 0.699
## mean_ea 0.720 0.637 1.130 0.258 0.266 0.227
## tst3_ls (.p4.) 0.843 0.102 8.233 0.000 0.312 0.361
## mentalizing =~
## tst2_ss 1.000 0.358 0.699
## tst2_ps (.p6.) 1.045 0.076 13.721 0.000 0.374 0.598
## tst3_sr (.p7.) 1.002 0.067 14.854 0.000 0.359 0.484
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.079 0.026 3.076 0.002 0.597 0.597
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.255 0.060 4.221 0.000 0.255 0.290
## .rmet_total 0.333 0.057 5.842 0.000 0.333 0.407
## .mean_ea 0.681 0.197 3.466 0.001 0.681 0.582
## .tasit3_lies 0.373 0.060 6.227 0.000 0.373 0.433
## .tasit2_ssar 0.468 0.033 13.971 0.000 0.468 0.912
## .tasit2_psar 0.424 0.045 9.443 0.000 0.424 0.677
## .tasit3_sar 0.419 0.053 7.875 0.000 0.419 0.564
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.634 0.073 8.671 0.000 0.634 0.823
## .rmet_total 0.343 0.069 4.970 0.000 0.343 0.511
## .mean_ea 1.297 0.299 4.340 0.000 1.297 0.948
## .tasit3_lies 0.646 0.103 6.270 0.000 0.646 0.869
## .tasit2_ssar 0.134 0.021 6.510 0.000 0.134 0.511
## .tasit2_psar 0.251 0.049 5.141 0.000 0.251 0.642
## .tasit3_sar 0.422 0.087 4.841 0.000 0.422 0.766
## simulation 0.137 0.039 3.522 0.000 1.000 1.000
## mentalizing 0.128 0.035 3.618 0.000 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.177
## rmet_total 0.489
## mean_ea 0.052
## tasit3_lies 0.131
## tasit2_ssar 0.489
## tasit2_psar 0.358
## tasit3_sar 0.234
##
##
## Group 3 [SSD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.608 0.579
## rmt_ttl (.p2.) 1.551 0.196 7.915 0.000 0.943 0.881
## mean_ea 0.358 0.138 2.583 0.010 0.218 0.269
## tst3_ls (.p4.) 0.843 0.102 8.233 0.000 0.513 0.521
## mentalizing =~
## tst2_ss 1.000 0.892 0.794
## tst2_ps (.p6.) 1.045 0.076 13.721 0.000 0.932 0.794
## tst3_sr (.p7.) 1.002 0.067 14.854 0.000 0.894 0.846
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.373 0.063 5.916 0.000 0.687 0.687
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total -0.199 0.065 -3.048 0.002 -0.199 -0.189
## .rmet_total -0.338 0.065 -5.202 0.000 -0.338 -0.315
## .mean_ea -0.198 0.085 -2.335 0.020 -0.198 -0.244
## .tasit3_lies -0.170 0.060 -2.839 0.005 -0.170 -0.172
## .tasit2_ssar -0.445 0.070 -6.326 0.000 -0.445 -0.396
## .tasit2_psar -0.429 0.070 -6.138 0.000 -0.429 -0.365
## .tasit3_sar -0.462 0.064 -7.189 0.000 -0.462 -0.438
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.736 0.097 7.611 0.000 0.736 0.665
## .rmet_total 0.256 0.076 3.358 0.001 0.256 0.223
## .mean_ea 0.609 0.125 4.870 0.000 0.609 0.928
## .tasit3_lies 0.706 0.059 12.047 0.000 0.706 0.728
## .tasit2_ssar 0.468 0.058 8.103 0.000 0.468 0.370
## .tasit2_psar 0.510 0.062 8.197 0.000 0.510 0.370
## .tasit3_sar 0.317 0.047 6.733 0.000 0.317 0.284
## simulation 0.370 0.086 4.316 0.000 1.000 1.000
## mentalizing 0.796 0.111 7.176 0.000 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.335
## rmet_total 0.777
## mean_ea 0.072
## tasit3_lies 0.272
## tasit2_ssar 0.630
## tasit2_psar 0.630
## tasit3_sar 0.716
##
## Modification Indices:
##
## lhs op rhs block group level mi epc sepc.lv sepc.all
## 1 simulation =~ er40_total 1 1 1 0.101 -0.086 -0.041 -0.041
## 2 mentalizing =~ tasit2_ssar 1 1 1 0.118 0.046 0.031 0.035
## 3 simulation =~ er40_total 2 2 1 0.517 -0.198 -0.073 -0.083
## 4 mentalizing =~ tasit2_ssar 2 2 1 16.216 -0.729 -0.261 -0.510
## 5 simulation =~ er40_total 3 3 1 0.645 0.171 0.104 0.099
## 6 mentalizing =~ tasit2_ssar 3 3 1 5.362 0.273 0.243 0.216
## 7 simulation =~ tasit2_ssar 1 1 1 0.056 -0.044 -0.021 -0.023
## 8 simulation =~ tasit2_psar 1 1 1 1.673 0.240 0.114 0.128
## 9 simulation =~ tasit3_sar 1 1 1 1.078 -0.180 -0.085 -0.102
## 10 mentalizing =~ er40_total 1 1 1 0.010 0.017 0.012 0.012
## 11 mentalizing =~ rmet_total 1 1 1 0.303 0.123 0.082 0.089
## 12 mentalizing =~ mean_ea 1 1 1 1.893 -1.439 -0.959 -1.031
## 13 mentalizing =~ tasit3_lies 1 1 1 1.002 -0.179 -0.120 -0.112
## 14 er40_total ~~ rmet_total 1 1 1 1.146 -0.111 -0.111 -0.226
## 15 er40_total ~~ mean_ea 1 1 1 3.540 0.388 0.388 0.477
## 16 er40_total ~~ tasit3_lies 1 1 1 0.333 -0.054 -0.054 -0.062
## 17 er40_total ~~ tasit2_ssar 1 1 1 0.059 -0.015 -0.015 -0.029
## 18 er40_total ~~ tasit2_psar 1 1 1 0.431 -0.039 -0.039 -0.081
## 19 er40_total ~~ tasit3_sar 1 1 1 2.890 0.095 0.095 0.216
## 20 rmet_total ~~ mean_ea 1 1 1 0.028 -0.029 -0.029 -0.056
## 21 rmet_total ~~ tasit3_lies 1 1 1 2.585 0.142 0.142 0.256
## 22 rmet_total ~~ tasit2_ssar 1 1 1 0.933 -0.049 -0.049 -0.151
## 23 rmet_total ~~ tasit2_psar 1 1 1 0.357 0.030 0.030 0.098
## 24 rmet_total ~~ tasit3_sar 1 1 1 0.194 0.021 0.021 0.074
## 25 mean_ea ~~ tasit3_lies 1 1 1 1.299 0.260 0.260 0.283
## 26 mean_ea ~~ tasit2_ssar 1 1 1 0.374 0.091 0.091 0.173
## 27 mean_ea ~~ tasit2_psar 1 1 1 1.246 -0.160 -0.160 -0.314
## 28 mean_ea ~~ tasit3_sar 1 1 1 0.634 -0.106 -0.106 -0.229
## 29 tasit3_lies ~~ tasit2_ssar 1 1 1 0.380 -0.041 -0.041 -0.072
## 30 tasit3_lies ~~ tasit2_psar 1 1 1 0.832 0.059 0.059 0.108
## 31 tasit3_lies ~~ tasit3_sar 1 1 1 3.317 -0.110 -0.110 -0.221
## 32 tasit2_ssar ~~ tasit2_psar 1 1 1 3.838 0.100 0.100 0.317
## 33 tasit2_ssar ~~ tasit3_sar 1 1 1 0.193 -0.021 -0.021 -0.075
## 34 tasit2_psar ~~ tasit3_sar 1 1 1 2.290 -0.077 -0.077 -0.279
## 35 simulation =~ tasit2_ssar 2 2 1 11.923 -0.467 -0.173 -0.337
## 36 simulation =~ tasit2_psar 2 2 1 3.325 0.277 0.102 0.164
## 37 simulation =~ tasit3_sar 2 2 1 5.191 0.401 0.148 0.199
## 38 mentalizing =~ er40_total 2 2 1 1.942 -0.306 -0.110 -0.125
## 39 mentalizing =~ rmet_total 2 2 1 2.038 0.403 0.144 0.176
## 40 mentalizing =~ mean_ea 2 2 1 0.002 -0.041 -0.015 -0.013
## 41 mentalizing =~ tasit3_lies 2 2 1 0.139 -0.080 -0.029 -0.033
## 42 er40_total ~~ rmet_total 2 2 1 0.545 -0.048 -0.048 -0.102
## 43 er40_total ~~ mean_ea 2 2 1 0.859 -0.150 -0.150 -0.165
## 44 er40_total ~~ tasit3_lies 2 2 1 4.583 0.106 0.106 0.165
## 45 er40_total ~~ tasit2_ssar 2 2 1 1.637 -0.034 -0.034 -0.116
## 46 er40_total ~~ tasit2_psar 2 2 1 2.983 -0.056 -0.056 -0.141
## 47 er40_total ~~ tasit3_sar 2 2 1 3.249 0.072 0.072 0.139
## 48 rmet_total ~~ mean_ea 2 2 1 1.302 0.197 0.197 0.295
## 49 rmet_total ~~ tasit3_lies 2 2 1 0.643 -0.044 -0.044 -0.093
## 50 rmet_total ~~ tasit2_ssar 2 2 1 1.807 -0.035 -0.035 -0.162
## 51 rmet_total ~~ tasit2_psar 2 2 1 4.264 0.063 0.063 0.214
## 52 rmet_total ~~ tasit3_sar 2 2 1 1.361 0.042 0.042 0.111
## 53 mean_ea ~~ tasit3_lies 2 2 1 0.632 -0.125 -0.125 -0.137
## 54 mean_ea ~~ tasit2_ssar 2 2 1 0.066 0.022 0.022 0.052
## 55 mean_ea ~~ tasit2_psar 2 2 1 0.163 -0.042 -0.042 -0.074
## 56 mean_ea ~~ tasit3_sar 2 2 1 0.003 0.008 0.008 0.010
## 57 tasit3_lies ~~ tasit2_ssar 2 2 1 0.059 -0.006 -0.006 -0.021
## 58 tasit3_lies ~~ tasit2_psar 2 2 1 0.042 0.007 0.007 0.016
## 59 tasit3_lies ~~ tasit3_sar 2 2 1 0.716 -0.033 -0.033 -0.064
## 60 tasit2_ssar ~~ tasit2_psar 2 2 1 0.556 -0.019 -0.019 -0.104
## 61 tasit2_ssar ~~ tasit3_sar 2 2 1 2.261 -0.038 -0.038 -0.158
## 62 tasit2_psar ~~ tasit3_sar 2 2 1 6.079 0.070 0.070 0.216
## 63 simulation =~ tasit2_ssar 3 3 1 0.813 0.110 0.067 0.060
## 64 simulation =~ tasit2_psar 3 3 1 0.153 0.050 0.030 0.026
## 65 simulation =~ tasit3_sar 3 3 1 1.495 -0.141 -0.086 -0.081
## 66 mentalizing =~ er40_total 3 3 1 0.028 0.016 0.015 0.014
## 67 mentalizing =~ rmet_total 3 3 1 0.008 -0.012 -0.011 -0.010
## 68 mentalizing =~ mean_ea 3 3 1 0.008 0.013 0.012 0.015
## 69 mentalizing =~ tasit3_lies 3 3 1 0.009 -0.009 -0.008 -0.008
## 70 er40_total ~~ rmet_total 3 3 1 0.541 -0.059 -0.059 -0.136
## 71 er40_total ~~ mean_ea 3 3 1 1.638 0.096 0.096 0.144
## 72 er40_total ~~ tasit3_lies 3 3 1 2.107 0.076 0.076 0.106
## 73 er40_total ~~ tasit2_ssar 3 3 1 1.913 0.061 0.061 0.104
## 74 er40_total ~~ tasit2_psar 3 3 1 0.200 -0.021 -0.021 -0.034
## 75 er40_total ~~ tasit3_sar 3 3 1 1.082 -0.041 -0.041 -0.086
## 76 rmet_total ~~ mean_ea 3 3 1 0.005 -0.005 -0.005 -0.013
## 77 rmet_total ~~ tasit3_lies 3 3 1 0.195 -0.029 -0.029 -0.069
## 78 rmet_total ~~ tasit2_ssar 3 3 1 0.460 -0.027 -0.027 -0.079
## 79 rmet_total ~~ tasit2_psar 3 3 1 0.045 0.009 0.009 0.025
## 80 rmet_total ~~ tasit3_sar 3 3 1 0.438 0.025 0.025 0.086
## 81 mean_ea ~~ tasit3_lies 3 3 1 1.989 -0.099 -0.099 -0.151
## 82 mean_ea ~~ tasit2_ssar 3 3 1 3.239 -0.111 -0.111 -0.209
## 83 mean_ea ~~ tasit2_psar 3 3 1 3.280 0.119 0.119 0.214
## 84 mean_ea ~~ tasit3_sar 3 3 1 0.009 0.005 0.005 0.012
## 85 tasit3_lies ~~ tasit2_ssar 3 3 1 0.039 -0.008 -0.008 -0.014
## 86 tasit3_lies ~~ tasit2_psar 3 3 1 9.240 0.134 0.134 0.223
## 87 tasit3_lies ~~ tasit3_sar 3 3 1 7.565 -0.103 -0.103 -0.218
## 88 tasit2_ssar ~~ tasit2_psar 3 3 1 2.978 -0.101 -0.101 -0.206
## 89 tasit2_ssar ~~ tasit3_sar 3 3 1 11.575 0.193 0.193 0.501
## 90 tasit2_psar ~~ tasit3_sar 3 3 1 2.733 -0.098 -0.098 -0.244
## sepc.nox
## 1 -0.041
## 2 0.035
## 3 -0.083
## 4 -0.510
## 5 0.099
## 6 0.216
## 7 -0.023
## 8 0.128
## 9 -0.102
## 10 0.012
## 11 0.089
## 12 -1.031
## 13 -0.112
## 14 -0.226
## 15 0.477
## 16 -0.062
## 17 -0.029
## 18 -0.081
## 19 0.216
## 20 -0.056
## 21 0.256
## 22 -0.151
## 23 0.098
## 24 0.074
## 25 0.283
## 26 0.173
## 27 -0.314
## 28 -0.229
## 29 -0.072
## 30 0.108
## 31 -0.221
## 32 0.317
## 33 -0.075
## 34 -0.279
## 35 -0.337
## 36 0.164
## 37 0.199
## 38 -0.125
## 39 0.176
## 40 -0.013
## 41 -0.033
## 42 -0.102
## 43 -0.165
## 44 0.165
## 45 -0.116
## 46 -0.141
## 47 0.139
## 48 0.295
## 49 -0.093
## 50 -0.162
## 51 0.214
## 52 0.111
## 53 -0.137
## 54 0.052
## 55 -0.074
## 56 0.010
## 57 -0.021
## 58 0.016
## 59 -0.064
## 60 -0.104
## 61 -0.158
## 62 0.216
## 63 0.060
## 64 0.026
## 65 -0.081
## 66 0.014
## 67 -0.010
## 68 0.015
## 69 -0.008
## 70 -0.136
## 71 0.144
## 72 0.106
## 73 0.104
## 74 -0.034
## 75 -0.086
## 76 -0.013
## 77 -0.069
## 78 -0.079
## 79 0.025
## 80 0.086
## 81 -0.151
## 82 -0.209
## 83 0.214
## 84 0.012
## 85 -0.014
## 86 0.223
## 87 -0.218
## 88 -0.206
## 89 0.501
## 90 -0.244
anova(CFA_scog_model1_grp_fit,CFA_sc_model1_grp_fit2_part2)
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff
## CFA_scog_model1_grp_fit 39 8746.2 9034.8 68.870
## CFA_sc_model1_grp_fit2_part2 47 8750.0 9003.6 88.629 16.963 8
## Pr(>Chisq)
## CFA_scog_model1_grp_fit
## CFA_sc_model1_grp_fit2_part2 0.0305 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# metric - partial isolating mean ea, tasit2_ssar, tasit3_psar (according to mod indices from above)
CFA_sc_model1_grp_fit2_part3 <- cfa(model= CFA_scog_model1,data = spasd_spins_yj_z_df, group ="group",
group.equal=c("loadings"),estimator = "MLR", missing = "ml",
group.partial=
c("simulation=~mean_ea","mentalizing=~tasit2_ssar","mentalizing=~tasit3_psar"))
summary(CFA_sc_model1_grp_fit2_part3, fit.measures = TRUE, modindices = TRUE, standardized = TRUE, rsquare = TRUE)
## lavaan 0.6.16 ended normally after 84 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 66
## Number of equality constraints 8
##
## Number of observations per group:
## ASD 100
## Control 209
## SSD 276
## Number of missing patterns per group:
## ASD 4
## Control 6
## SSD 20
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 88.629 87.935
## Degrees of freedom 47 47
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.008
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## ASD 24.951 24.756
## Control 30.590 30.350
## SSD 33.088 32.829
##
## Model Test Baseline Model:
##
## Test statistic 973.724 901.247
## Degrees of freedom 63 63
## P-value 0.000 0.000
## Scaling correction factor 1.080
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.954 0.951
## Tucker-Lewis Index (TLI) 0.939 0.935
##
## Robust Comparative Fit Index (CFI) 0.942
## Robust Tucker-Lewis Index (TLI) 0.922
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4317.003 -4317.003
## Scaling correction factor 1.101
## for the MLR correction
## Loglikelihood unrestricted model (H1) -4272.688 -4272.688
## Scaling correction factor 1.143
## for the MLR correction
##
## Akaike (AIC) 8750.006 8750.006
## Bayesian (BIC) 9003.559 9003.559
## Sample-size adjusted Bayesian (SABIC) 8819.430 8819.430
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.067 0.067
## 90 Percent confidence interval - lower 0.045 0.045
## 90 Percent confidence interval - upper 0.089 0.088
## P-value H_0: RMSEA <= 0.050 0.091 0.098
## P-value H_0: RMSEA >= 0.080 0.175 0.163
##
## Robust RMSEA 0.080
## 90 Percent confidence interval - lower 0.016
## 90 Percent confidence interval - upper 0.124
## P-value H_0: Robust RMSEA <= 0.050 0.160
## P-value H_0: Robust RMSEA >= 0.080 0.523
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.072 0.072
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [ASD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.472 0.473
## rmt_ttl (.p2.) 1.551 0.196 7.915 0.000 0.732 0.794
## mean_ea -0.220 0.416 -0.528 0.597 -0.104 -0.111
## tst3_ls (.p4.) 0.843 0.102 8.233 0.000 0.398 0.373
## mentalizing =~
## tst2_ss 1.000 0.666 0.759
## tst2_ps (.p6.) 1.045 0.076 13.721 0.000 0.696 0.784
## tst3_sr (.p7.) 1.002 0.067 14.854 0.000 0.668 0.800
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.264 0.088 2.995 0.003 0.839 0.839
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total -0.067 0.101 -0.668 0.504 -0.067 -0.067
## .rmet_total 0.149 0.093 1.597 0.110 0.149 0.161
## .mean_ea 0.075 0.229 0.325 0.745 0.075 0.080
## .tasit3_lies -0.328 0.105 -3.115 0.002 -0.328 -0.307
## .tasit2_ssar 0.142 0.090 1.582 0.114 0.142 0.162
## .tasit2_psar 0.111 0.092 1.209 0.227 0.111 0.125
## .tasit3_sar 0.290 0.080 3.643 0.000 0.290 0.348
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.775 0.126 6.138 0.000 0.775 0.776
## .rmet_total 0.315 0.104 3.031 0.002 0.315 0.370
## .mean_ea 0.855 0.461 1.853 0.064 0.855 0.988
## .tasit3_lies 0.983 0.154 6.399 0.000 0.983 0.861
## .tasit2_ssar 0.327 0.085 3.856 0.000 0.327 0.424
## .tasit2_psar 0.304 0.098 3.115 0.002 0.304 0.386
## .tasit3_sar 0.250 0.053 4.678 0.000 0.250 0.360
## simulation 0.223 0.070 3.176 0.001 1.000 1.000
## mentalizing 0.444 0.135 3.281 0.001 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.224
## rmet_total 0.630
## mean_ea 0.012
## tasit3_lies 0.139
## tasit2_ssar 0.576
## tasit2_psar 0.614
## tasit3_sar 0.640
##
##
## Group 2 [Control]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.370 0.421
## rmt_ttl (.p2.) 1.551 0.196 7.915 0.000 0.573 0.699
## mean_ea 0.720 0.637 1.130 0.258 0.266 0.227
## tst3_ls (.p4.) 0.843 0.102 8.233 0.000 0.312 0.361
## mentalizing =~
## tst2_ss 1.000 0.358 0.699
## tst2_ps (.p6.) 1.045 0.076 13.721 0.000 0.374 0.598
## tst3_sr (.p7.) 1.002 0.067 14.854 0.000 0.359 0.484
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.079 0.026 3.076 0.002 0.597 0.597
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.255 0.060 4.221 0.000 0.255 0.290
## .rmet_total 0.333 0.057 5.842 0.000 0.333 0.407
## .mean_ea 0.681 0.197 3.466 0.001 0.681 0.582
## .tasit3_lies 0.373 0.060 6.227 0.000 0.373 0.433
## .tasit2_ssar 0.468 0.033 13.971 0.000 0.468 0.912
## .tasit2_psar 0.424 0.045 9.443 0.000 0.424 0.677
## .tasit3_sar 0.419 0.053 7.875 0.000 0.419 0.564
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.634 0.073 8.671 0.000 0.634 0.823
## .rmet_total 0.343 0.069 4.970 0.000 0.343 0.511
## .mean_ea 1.297 0.299 4.340 0.000 1.297 0.948
## .tasit3_lies 0.646 0.103 6.270 0.000 0.646 0.869
## .tasit2_ssar 0.134 0.021 6.510 0.000 0.134 0.511
## .tasit2_psar 0.251 0.049 5.141 0.000 0.251 0.642
## .tasit3_sar 0.422 0.087 4.841 0.000 0.422 0.766
## simulation 0.137 0.039 3.522 0.000 1.000 1.000
## mentalizing 0.128 0.035 3.618 0.000 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.177
## rmet_total 0.489
## mean_ea 0.052
## tasit3_lies 0.131
## tasit2_ssar 0.489
## tasit2_psar 0.358
## tasit3_sar 0.234
##
##
## Group 3 [SSD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.608 0.579
## rmt_ttl (.p2.) 1.551 0.196 7.915 0.000 0.943 0.881
## mean_ea 0.358 0.138 2.583 0.010 0.218 0.269
## tst3_ls (.p4.) 0.843 0.102 8.233 0.000 0.513 0.521
## mentalizing =~
## tst2_ss 1.000 0.892 0.794
## tst2_ps (.p6.) 1.045 0.076 13.721 0.000 0.932 0.794
## tst3_sr (.p7.) 1.002 0.067 14.854 0.000 0.894 0.846
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.373 0.063 5.916 0.000 0.687 0.687
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total -0.199 0.065 -3.048 0.002 -0.199 -0.189
## .rmet_total -0.338 0.065 -5.202 0.000 -0.338 -0.315
## .mean_ea -0.198 0.085 -2.335 0.020 -0.198 -0.244
## .tasit3_lies -0.170 0.060 -2.839 0.005 -0.170 -0.172
## .tasit2_ssar -0.445 0.070 -6.326 0.000 -0.445 -0.396
## .tasit2_psar -0.429 0.070 -6.138 0.000 -0.429 -0.365
## .tasit3_sar -0.462 0.064 -7.189 0.000 -0.462 -0.438
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.736 0.097 7.611 0.000 0.736 0.665
## .rmet_total 0.256 0.076 3.358 0.001 0.256 0.223
## .mean_ea 0.609 0.125 4.870 0.000 0.609 0.928
## .tasit3_lies 0.706 0.059 12.047 0.000 0.706 0.728
## .tasit2_ssar 0.468 0.058 8.103 0.000 0.468 0.370
## .tasit2_psar 0.510 0.062 8.197 0.000 0.510 0.370
## .tasit3_sar 0.317 0.047 6.733 0.000 0.317 0.284
## simulation 0.370 0.086 4.316 0.000 1.000 1.000
## mentalizing 0.796 0.111 7.176 0.000 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.335
## rmet_total 0.777
## mean_ea 0.072
## tasit3_lies 0.272
## tasit2_ssar 0.630
## tasit2_psar 0.630
## tasit3_sar 0.716
##
## Modification Indices:
##
## lhs op rhs block group level mi epc sepc.lv sepc.all
## 1 simulation =~ er40_total 1 1 1 0.101 -0.086 -0.041 -0.041
## 2 mentalizing =~ tasit2_ssar 1 1 1 0.118 0.046 0.031 0.035
## 3 simulation =~ er40_total 2 2 1 0.517 -0.198 -0.073 -0.083
## 4 mentalizing =~ tasit2_ssar 2 2 1 16.216 -0.729 -0.261 -0.510
## 5 simulation =~ er40_total 3 3 1 0.645 0.171 0.104 0.099
## 6 mentalizing =~ tasit2_ssar 3 3 1 5.362 0.273 0.243 0.216
## 7 simulation =~ tasit2_ssar 1 1 1 0.056 -0.044 -0.021 -0.023
## 8 simulation =~ tasit2_psar 1 1 1 1.673 0.240 0.114 0.128
## 9 simulation =~ tasit3_sar 1 1 1 1.078 -0.180 -0.085 -0.102
## 10 mentalizing =~ er40_total 1 1 1 0.010 0.017 0.012 0.012
## 11 mentalizing =~ rmet_total 1 1 1 0.303 0.123 0.082 0.089
## 12 mentalizing =~ mean_ea 1 1 1 1.893 -1.439 -0.959 -1.031
## 13 mentalizing =~ tasit3_lies 1 1 1 1.002 -0.179 -0.120 -0.112
## 14 er40_total ~~ rmet_total 1 1 1 1.146 -0.111 -0.111 -0.226
## 15 er40_total ~~ mean_ea 1 1 1 3.540 0.388 0.388 0.477
## 16 er40_total ~~ tasit3_lies 1 1 1 0.333 -0.054 -0.054 -0.062
## 17 er40_total ~~ tasit2_ssar 1 1 1 0.059 -0.015 -0.015 -0.029
## 18 er40_total ~~ tasit2_psar 1 1 1 0.431 -0.039 -0.039 -0.081
## 19 er40_total ~~ tasit3_sar 1 1 1 2.890 0.095 0.095 0.216
## 20 rmet_total ~~ mean_ea 1 1 1 0.028 -0.029 -0.029 -0.056
## 21 rmet_total ~~ tasit3_lies 1 1 1 2.585 0.142 0.142 0.256
## 22 rmet_total ~~ tasit2_ssar 1 1 1 0.933 -0.049 -0.049 -0.151
## 23 rmet_total ~~ tasit2_psar 1 1 1 0.357 0.030 0.030 0.098
## 24 rmet_total ~~ tasit3_sar 1 1 1 0.194 0.021 0.021 0.074
## 25 mean_ea ~~ tasit3_lies 1 1 1 1.299 0.260 0.260 0.283
## 26 mean_ea ~~ tasit2_ssar 1 1 1 0.374 0.091 0.091 0.173
## 27 mean_ea ~~ tasit2_psar 1 1 1 1.246 -0.160 -0.160 -0.314
## 28 mean_ea ~~ tasit3_sar 1 1 1 0.634 -0.106 -0.106 -0.229
## 29 tasit3_lies ~~ tasit2_ssar 1 1 1 0.380 -0.041 -0.041 -0.072
## 30 tasit3_lies ~~ tasit2_psar 1 1 1 0.832 0.059 0.059 0.108
## 31 tasit3_lies ~~ tasit3_sar 1 1 1 3.317 -0.110 -0.110 -0.221
## 32 tasit2_ssar ~~ tasit2_psar 1 1 1 3.838 0.100 0.100 0.317
## 33 tasit2_ssar ~~ tasit3_sar 1 1 1 0.193 -0.021 -0.021 -0.075
## 34 tasit2_psar ~~ tasit3_sar 1 1 1 2.290 -0.077 -0.077 -0.279
## 35 simulation =~ tasit2_ssar 2 2 1 11.923 -0.467 -0.173 -0.337
## 36 simulation =~ tasit2_psar 2 2 1 3.325 0.277 0.102 0.164
## 37 simulation =~ tasit3_sar 2 2 1 5.191 0.401 0.148 0.199
## 38 mentalizing =~ er40_total 2 2 1 1.942 -0.306 -0.110 -0.125
## 39 mentalizing =~ rmet_total 2 2 1 2.038 0.403 0.144 0.176
## 40 mentalizing =~ mean_ea 2 2 1 0.002 -0.041 -0.015 -0.013
## 41 mentalizing =~ tasit3_lies 2 2 1 0.139 -0.080 -0.029 -0.033
## 42 er40_total ~~ rmet_total 2 2 1 0.545 -0.048 -0.048 -0.102
## 43 er40_total ~~ mean_ea 2 2 1 0.859 -0.150 -0.150 -0.165
## 44 er40_total ~~ tasit3_lies 2 2 1 4.583 0.106 0.106 0.165
## 45 er40_total ~~ tasit2_ssar 2 2 1 1.637 -0.034 -0.034 -0.116
## 46 er40_total ~~ tasit2_psar 2 2 1 2.983 -0.056 -0.056 -0.141
## 47 er40_total ~~ tasit3_sar 2 2 1 3.249 0.072 0.072 0.139
## 48 rmet_total ~~ mean_ea 2 2 1 1.302 0.197 0.197 0.295
## 49 rmet_total ~~ tasit3_lies 2 2 1 0.643 -0.044 -0.044 -0.093
## 50 rmet_total ~~ tasit2_ssar 2 2 1 1.807 -0.035 -0.035 -0.162
## 51 rmet_total ~~ tasit2_psar 2 2 1 4.264 0.063 0.063 0.214
## 52 rmet_total ~~ tasit3_sar 2 2 1 1.361 0.042 0.042 0.111
## 53 mean_ea ~~ tasit3_lies 2 2 1 0.632 -0.125 -0.125 -0.137
## 54 mean_ea ~~ tasit2_ssar 2 2 1 0.066 0.022 0.022 0.052
## 55 mean_ea ~~ tasit2_psar 2 2 1 0.163 -0.042 -0.042 -0.074
## 56 mean_ea ~~ tasit3_sar 2 2 1 0.003 0.008 0.008 0.010
## 57 tasit3_lies ~~ tasit2_ssar 2 2 1 0.059 -0.006 -0.006 -0.021
## 58 tasit3_lies ~~ tasit2_psar 2 2 1 0.042 0.007 0.007 0.016
## 59 tasit3_lies ~~ tasit3_sar 2 2 1 0.716 -0.033 -0.033 -0.064
## 60 tasit2_ssar ~~ tasit2_psar 2 2 1 0.556 -0.019 -0.019 -0.104
## 61 tasit2_ssar ~~ tasit3_sar 2 2 1 2.261 -0.038 -0.038 -0.158
## 62 tasit2_psar ~~ tasit3_sar 2 2 1 6.079 0.070 0.070 0.216
## 63 simulation =~ tasit2_ssar 3 3 1 0.813 0.110 0.067 0.060
## 64 simulation =~ tasit2_psar 3 3 1 0.153 0.050 0.030 0.026
## 65 simulation =~ tasit3_sar 3 3 1 1.495 -0.141 -0.086 -0.081
## 66 mentalizing =~ er40_total 3 3 1 0.028 0.016 0.015 0.014
## 67 mentalizing =~ rmet_total 3 3 1 0.008 -0.012 -0.011 -0.010
## 68 mentalizing =~ mean_ea 3 3 1 0.008 0.013 0.012 0.015
## 69 mentalizing =~ tasit3_lies 3 3 1 0.009 -0.009 -0.008 -0.008
## 70 er40_total ~~ rmet_total 3 3 1 0.541 -0.059 -0.059 -0.136
## 71 er40_total ~~ mean_ea 3 3 1 1.638 0.096 0.096 0.144
## 72 er40_total ~~ tasit3_lies 3 3 1 2.107 0.076 0.076 0.106
## 73 er40_total ~~ tasit2_ssar 3 3 1 1.913 0.061 0.061 0.104
## 74 er40_total ~~ tasit2_psar 3 3 1 0.200 -0.021 -0.021 -0.034
## 75 er40_total ~~ tasit3_sar 3 3 1 1.082 -0.041 -0.041 -0.086
## 76 rmet_total ~~ mean_ea 3 3 1 0.005 -0.005 -0.005 -0.013
## 77 rmet_total ~~ tasit3_lies 3 3 1 0.195 -0.029 -0.029 -0.069
## 78 rmet_total ~~ tasit2_ssar 3 3 1 0.460 -0.027 -0.027 -0.079
## 79 rmet_total ~~ tasit2_psar 3 3 1 0.045 0.009 0.009 0.025
## 80 rmet_total ~~ tasit3_sar 3 3 1 0.438 0.025 0.025 0.086
## 81 mean_ea ~~ tasit3_lies 3 3 1 1.989 -0.099 -0.099 -0.151
## 82 mean_ea ~~ tasit2_ssar 3 3 1 3.239 -0.111 -0.111 -0.209
## 83 mean_ea ~~ tasit2_psar 3 3 1 3.280 0.119 0.119 0.214
## 84 mean_ea ~~ tasit3_sar 3 3 1 0.009 0.005 0.005 0.012
## 85 tasit3_lies ~~ tasit2_ssar 3 3 1 0.039 -0.008 -0.008 -0.014
## 86 tasit3_lies ~~ tasit2_psar 3 3 1 9.240 0.134 0.134 0.223
## 87 tasit3_lies ~~ tasit3_sar 3 3 1 7.565 -0.103 -0.103 -0.218
## 88 tasit2_ssar ~~ tasit2_psar 3 3 1 2.978 -0.101 -0.101 -0.206
## 89 tasit2_ssar ~~ tasit3_sar 3 3 1 11.575 0.193 0.193 0.501
## 90 tasit2_psar ~~ tasit3_sar 3 3 1 2.733 -0.098 -0.098 -0.244
## sepc.nox
## 1 -0.041
## 2 0.035
## 3 -0.083
## 4 -0.510
## 5 0.099
## 6 0.216
## 7 -0.023
## 8 0.128
## 9 -0.102
## 10 0.012
## 11 0.089
## 12 -1.031
## 13 -0.112
## 14 -0.226
## 15 0.477
## 16 -0.062
## 17 -0.029
## 18 -0.081
## 19 0.216
## 20 -0.056
## 21 0.256
## 22 -0.151
## 23 0.098
## 24 0.074
## 25 0.283
## 26 0.173
## 27 -0.314
## 28 -0.229
## 29 -0.072
## 30 0.108
## 31 -0.221
## 32 0.317
## 33 -0.075
## 34 -0.279
## 35 -0.337
## 36 0.164
## 37 0.199
## 38 -0.125
## 39 0.176
## 40 -0.013
## 41 -0.033
## 42 -0.102
## 43 -0.165
## 44 0.165
## 45 -0.116
## 46 -0.141
## 47 0.139
## 48 0.295
## 49 -0.093
## 50 -0.162
## 51 0.214
## 52 0.111
## 53 -0.137
## 54 0.052
## 55 -0.074
## 56 0.010
## 57 -0.021
## 58 0.016
## 59 -0.064
## 60 -0.104
## 61 -0.158
## 62 0.216
## 63 0.060
## 64 0.026
## 65 -0.081
## 66 0.014
## 67 -0.010
## 68 0.015
## 69 -0.008
## 70 -0.136
## 71 0.144
## 72 0.106
## 73 0.104
## 74 -0.034
## 75 -0.086
## 76 -0.013
## 77 -0.069
## 78 -0.079
## 79 0.025
## 80 0.086
## 81 -0.151
## 82 -0.209
## 83 0.214
## 84 0.012
## 85 -0.014
## 86 0.223
## 87 -0.218
## 88 -0.206
## 89 0.501
## 90 -0.244
anova(CFA_scog_model1_grp_fit,CFA_sc_model1_grp_fit2_part3)
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff
## CFA_scog_model1_grp_fit 39 8746.2 9034.8 68.870
## CFA_sc_model1_grp_fit2_part3 47 8750.0 9003.6 88.629 16.963 8
## Pr(>Chisq)
## CFA_scog_model1_grp_fit
## CFA_sc_model1_grp_fit2_part3 0.0305 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#! metric - partial isolating mean ea and tasit2_psar (according to mod indices from above)
CFA_sc_model1_grp_fit2_part2a <- cfa(model= CFA_scog_model1,data = spasd_spins_yj_z_df, group = "group",
group.equal=c("loadings"),estimator = "MLR", missing = "ml",
group.partial = c("simulation=~mean_ea","mentalizing=~tasit2_psar"))
summary(CFA_sc_model1_grp_fit2_part2a, fit.measures = TRUE, modindices = TRUE, standardized = TRUE, rsquare =
TRUE)
## lavaan 0.6.16 ended normally after 92 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 66
## Number of equality constraints 6
##
## Number of observations per group:
## ASD 100
## Control 209
## SSD 276
## Number of missing patterns per group:
## ASD 4
## Control 6
## SSD 20
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 80.391 79.328
## Degrees of freedom 45 45
## P-value (Chi-square) 0.001 0.001
## Scaling correction factor 1.013
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## ASD 23.345 23.036
## Control 26.434 26.085
## SSD 30.612 30.207
##
## Model Test Baseline Model:
##
## Test statistic 973.724 901.247
## Degrees of freedom 63 63
## P-value 0.000 0.000
## Scaling correction factor 1.080
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.961 0.959
## Tucker-Lewis Index (TLI) 0.946 0.943
##
## Robust Comparative Fit Index (CFI) 0.950
## Robust Tucker-Lewis Index (TLI) 0.929
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4312.884 -4312.884
## Scaling correction factor 1.128
## for the MLR correction
## Loglikelihood unrestricted model (H1) -4272.688 -4272.688
## Scaling correction factor 1.143
## for the MLR correction
##
## Akaike (AIC) 8745.767 8745.767
## Bayesian (BIC) 9008.064 9008.064
## Sample-size adjusted Bayesian (SABIC) 8817.586 8817.586
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.064 0.063
## 90 Percent confidence interval - lower 0.040 0.039
## 90 Percent confidence interval - upper 0.086 0.085
## P-value H_0: RMSEA <= 0.050 0.156 0.172
## P-value H_0: RMSEA >= 0.080 0.116 0.101
##
## Robust RMSEA 0.076
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.123
## P-value H_0: Robust RMSEA <= 0.050 0.207
## P-value H_0: Robust RMSEA >= 0.080 0.474
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.071 0.071
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [ASD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.468 0.469
## rmt_ttl (.p2.) 1.571 0.202 7.783 0.000 0.736 0.797
## mean_ea -0.227 0.416 -0.547 0.584 -0.107 -0.115
## tst3_ls (.p4.) 0.847 0.103 8.215 0.000 0.397 0.372
## mentalizing =~
## tst2_ss 1.000 0.642 0.744
## tst2_ps 1.184 0.149 7.953 0.000 0.760 0.825
## tst3_sr (.p7.) 0.987 0.062 15.878 0.000 0.634 0.776
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.251 0.087 2.888 0.004 0.836 0.836
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total -0.067 0.101 -0.667 0.505 -0.067 -0.067
## .rmet_total 0.149 0.093 1.597 0.110 0.149 0.161
## .mean_ea 0.073 0.230 0.319 0.750 0.073 0.079
## .tasit3_lies -0.328 0.105 -3.115 0.002 -0.328 -0.308
## .tasit2_ssar 0.142 0.090 1.588 0.112 0.142 0.165
## .tasit2_psar 0.111 0.092 1.209 0.227 0.111 0.121
## .tasit3_sar 0.290 0.080 3.642 0.000 0.290 0.355
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.779 0.127 6.152 0.000 0.779 0.780
## .rmet_total 0.311 0.105 2.956 0.003 0.311 0.365
## .mean_ea 0.854 0.461 1.852 0.064 0.854 0.987
## .tasit3_lies 0.979 0.153 6.395 0.000 0.979 0.861
## .tasit2_ssar 0.333 0.084 3.982 0.000 0.333 0.447
## .tasit2_psar 0.270 0.098 2.762 0.006 0.270 0.319
## .tasit3_sar 0.265 0.053 4.967 0.000 0.265 0.398
## simulation 0.219 0.069 3.158 0.002 1.000 1.000
## mentalizing 0.412 0.134 3.082 0.002 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.220
## rmet_total 0.635
## mean_ea 0.013
## tasit3_lies 0.139
## tasit2_ssar 0.553
## tasit2_psar 0.681
## tasit3_sar 0.602
##
##
## Group 2 [Control]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.368 0.418
## rmt_ttl (.p2.) 1.571 0.202 7.783 0.000 0.578 0.706
## mean_ea 0.708 0.618 1.147 0.252 0.261 0.223
## tst3_ls (.p4.) 0.847 0.103 8.215 0.000 0.312 0.361
## mentalizing =~
## tst2_ss 1.000 0.319 0.634
## tst2_ps 1.478 0.255 5.788 0.000 0.472 0.727
## tst3_sr (.p7.) 0.987 0.062 15.878 0.000 0.315 0.431
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.070 0.025 2.862 0.004 0.598 0.598
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.255 0.060 4.222 0.000 0.255 0.290
## .rmet_total 0.333 0.057 5.838 0.000 0.333 0.407
## .mean_ea 0.682 0.197 3.469 0.001 0.682 0.583
## .tasit3_lies 0.373 0.060 6.224 0.000 0.373 0.433
## .tasit2_ssar 0.468 0.033 13.971 0.000 0.468 0.928
## .tasit2_psar 0.424 0.045 9.443 0.000 0.424 0.653
## .tasit3_sar 0.419 0.053 7.875 0.000 0.419 0.573
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.639 0.074 8.653 0.000 0.639 0.825
## .rmet_total 0.336 0.069 4.855 0.000 0.336 0.501
## .mean_ea 1.300 0.296 4.388 0.000 1.300 0.950
## .tasit3_lies 0.647 0.103 6.265 0.000 0.647 0.869
## .tasit2_ssar 0.152 0.022 7.007 0.000 0.152 0.598
## .tasit2_psar 0.198 0.054 3.707 0.000 0.198 0.472
## .tasit3_sar 0.436 0.088 4.984 0.000 0.436 0.815
## simulation 0.135 0.038 3.521 0.000 1.000 1.000
## mentalizing 0.102 0.036 2.828 0.005 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.175
## rmet_total 0.499
## mean_ea 0.050
## tasit3_lies 0.131
## tasit2_ssar 0.402
## tasit2_psar 0.528
## tasit3_sar 0.185
##
##
## Group 3 [SSD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.603 0.575
## rmt_ttl (.p2.) 1.571 0.202 7.783 0.000 0.948 0.885
## mean_ea 0.358 0.140 2.559 0.010 0.216 0.267
## tst3_ls (.p4.) 0.847 0.103 8.215 0.000 0.511 0.519
## mentalizing =~
## tst2_ss 1.000 0.928 0.811
## tst2_ps 0.929 0.079 11.772 0.000 0.862 0.761
## tst3_sr (.p7.) 0.987 0.062 15.878 0.000 0.916 0.858
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.380 0.064 5.944 0.000 0.679 0.679
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total -0.199 0.065 -3.047 0.002 -0.199 -0.189
## .rmet_total -0.338 0.065 -5.203 0.000 -0.338 -0.315
## .mean_ea -0.198 0.085 -2.342 0.019 -0.198 -0.245
## .tasit3_lies -0.170 0.060 -2.838 0.005 -0.170 -0.172
## .tasit2_ssar -0.446 0.070 -6.336 0.000 -0.446 -0.390
## .tasit2_psar -0.424 0.070 -6.088 0.000 -0.424 -0.375
## .tasit3_sar -0.463 0.064 -7.192 0.000 -0.463 -0.434
## simulation 0.000 0.000 0.000
## mentalizing 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.738 0.097 7.601 0.000 0.738 0.670
## .rmet_total 0.248 0.077 3.220 0.001 0.248 0.217
## .mean_ea 0.610 0.125 4.872 0.000 0.610 0.929
## .tasit3_lies 0.708 0.059 12.087 0.000 0.708 0.730
## .tasit2_ssar 0.448 0.057 7.884 0.000 0.448 0.342
## .tasit2_psar 0.539 0.064 8.464 0.000 0.539 0.420
## .tasit3_sar 0.301 0.047 6.427 0.000 0.301 0.264
## simulation 0.364 0.086 4.255 0.000 1.000 1.000
## mentalizing 0.861 0.112 7.697 0.000 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.330
## rmet_total 0.783
## mean_ea 0.071
## tasit3_lies 0.270
## tasit2_ssar 0.658
## tasit2_psar 0.580
## tasit3_sar 0.736
##
## Modification Indices:
##
## lhs op rhs block group level mi epc sepc.lv sepc.all
## 1 simulation =~ er40_total 1 1 1 0.110 -0.091 -0.043 -0.043
## 2 mentalizing =~ tasit2_ssar 1 1 1 0.909 0.148 0.095 0.110
## 3 simulation =~ er40_total 2 2 1 0.734 -0.235 -0.086 -0.098
## 4 mentalizing =~ tasit2_ssar 2 2 1 9.255 -0.689 -0.220 -0.436
## 5 simulation =~ er40_total 3 3 1 0.856 0.197 0.119 0.113
## 6 mentalizing =~ tasit2_ssar 3 3 1 1.006 0.138 0.128 0.112
## 7 simulation =~ tasit2_ssar 1 1 1 0.060 0.051 0.024 0.028
## 8 simulation =~ tasit2_psar 1 1 1 0.054 0.134 0.063 0.068
## 9 simulation =~ tasit3_sar 1 1 1 0.116 -0.068 -0.032 -0.039
## 10 mentalizing =~ er40_total 1 1 1 0.005 0.012 0.008 0.008
## 11 mentalizing =~ rmet_total 1 1 1 0.256 0.118 0.076 0.082
## 12 mentalizing =~ mean_ea 1 1 1 1.957 -1.484 -0.953 -1.024
## 13 mentalizing =~ tasit3_lies 1 1 1 0.806 -0.166 -0.107 -0.100
## 14 er40_total ~~ rmet_total 1 1 1 0.956 -0.101 -0.101 -0.206
## 15 er40_total ~~ mean_ea 1 1 1 3.540 0.388 0.388 0.476
## 16 er40_total ~~ tasit3_lies 1 1 1 0.332 -0.054 -0.054 -0.062
## 17 er40_total ~~ tasit2_ssar 1 1 1 0.017 -0.008 -0.008 -0.015
## 18 er40_total ~~ tasit2_psar 1 1 1 0.592 -0.047 -0.047 -0.102
## 19 er40_total ~~ tasit3_sar 1 1 1 3.153 0.099 0.099 0.217
## 20 rmet_total ~~ mean_ea 1 1 1 0.018 -0.024 -0.024 -0.046
## 21 rmet_total ~~ tasit3_lies 1 1 1 2.300 0.135 0.135 0.244
## 22 rmet_total ~~ tasit2_ssar 1 1 1 0.706 -0.042 -0.042 -0.130
## 23 rmet_total ~~ tasit2_psar 1 1 1 0.029 0.009 0.009 0.032
## 24 rmet_total ~~ tasit3_sar 1 1 1 0.531 0.034 0.034 0.120
## 25 mean_ea ~~ tasit3_lies 1 1 1 1.326 0.262 0.262 0.286
## 26 mean_ea ~~ tasit2_ssar 1 1 1 0.373 0.091 0.091 0.170
## 27 mean_ea ~~ tasit2_psar 1 1 1 1.256 -0.160 -0.160 -0.333
## 28 mean_ea ~~ tasit3_sar 1 1 1 0.540 -0.097 -0.097 -0.204
## 29 tasit3_lies ~~ tasit2_ssar 1 1 1 0.508 -0.047 -0.047 -0.082
## 30 tasit3_lies ~~ tasit2_psar 1 1 1 0.873 0.061 0.061 0.118
## 31 tasit3_lies ~~ tasit3_sar 1 1 1 3.271 -0.108 -0.108 -0.212
## 32 tasit2_ssar ~~ tasit2_psar 1 1 1 2.483 0.086 0.086 0.287
## 33 tasit2_ssar ~~ tasit3_sar 1 1 1 0.054 0.012 0.012 0.041
## 34 tasit2_psar ~~ tasit3_sar 1 1 1 3.201 -0.097 -0.097 -0.363
## 35 simulation =~ tasit2_ssar 2 2 1 6.152 -0.390 -0.144 -0.285
## 36 simulation =~ tasit2_psar 2 2 1 0.009 -0.030 -0.011 -0.017
## 37 simulation =~ tasit3_sar 2 2 1 9.196 0.561 0.206 0.282
## 38 mentalizing =~ er40_total 2 2 1 2.631 -0.394 -0.126 -0.143
## 39 mentalizing =~ rmet_total 2 2 1 2.548 0.501 0.160 0.196
## 40 mentalizing =~ mean_ea 2 2 1 0.018 -0.147 -0.047 -0.040
## 41 mentalizing =~ tasit3_lies 2 2 1 0.114 -0.080 -0.025 -0.029
## 42 er40_total ~~ rmet_total 2 2 1 0.449 -0.043 -0.043 -0.093
## 43 er40_total ~~ mean_ea 2 2 1 0.832 -0.148 -0.148 -0.162
## 44 er40_total ~~ tasit3_lies 2 2 1 4.738 0.107 0.107 0.167
## 45 er40_total ~~ tasit2_ssar 2 2 1 0.692 -0.022 -0.022 -0.070
## 46 er40_total ~~ tasit2_psar 2 2 1 4.123 -0.068 -0.068 -0.190
## 47 er40_total ~~ tasit3_sar 2 2 1 3.269 0.072 0.072 0.137
## 48 rmet_total ~~ mean_ea 2 2 1 1.403 0.203 0.203 0.307
## 49 rmet_total ~~ tasit3_lies 2 2 1 0.803 -0.049 -0.049 -0.106
## 50 rmet_total ~~ tasit2_ssar 2 2 1 0.490 -0.018 -0.018 -0.081
## 51 rmet_total ~~ tasit2_psar 2 2 1 1.920 0.049 0.049 0.192
## 52 rmet_total ~~ tasit3_sar 2 2 1 1.425 0.043 0.043 0.112
## 53 mean_ea ~~ tasit3_lies 2 2 1 0.618 -0.124 -0.124 -0.135
## 54 mean_ea ~~ tasit2_ssar 2 2 1 0.108 0.027 0.027 0.061
## 55 mean_ea ~~ tasit2_psar 2 2 1 0.215 -0.048 -0.048 -0.095
## 56 mean_ea ~~ tasit3_sar 2 2 1 0.008 0.011 0.011 0.015
## 57 tasit3_lies ~~ tasit2_ssar 2 2 1 0.010 -0.003 -0.003 -0.008
## 58 tasit3_lies ~~ tasit2_psar 2 2 1 0.000 0.000 0.000 0.000
## 59 tasit3_lies ~~ tasit3_sar 2 2 1 0.663 -0.032 -0.032 -0.061
## 60 tasit2_ssar ~~ tasit2_psar 2 2 1 2.641 -0.051 -0.051 -0.292
## 61 tasit2_ssar ~~ tasit3_sar 2 2 1 0.009 -0.003 -0.003 -0.010
## 62 tasit2_psar ~~ tasit3_sar 2 2 1 2.876 0.052 0.052 0.177
## 63 simulation =~ tasit2_ssar 3 3 1 0.008 0.011 0.007 0.006
## 64 simulation =~ tasit2_psar 3 3 1 5.919 0.374 0.226 0.199
## 65 simulation =~ tasit3_sar 3 3 1 3.723 -0.239 -0.144 -0.135
## 66 mentalizing =~ er40_total 3 3 1 0.095 0.029 0.027 0.025
## 67 mentalizing =~ rmet_total 3 3 1 0.016 -0.016 -0.015 -0.014
## 68 mentalizing =~ mean_ea 3 3 1 0.000 0.002 0.002 0.003
## 69 mentalizing =~ tasit3_lies 3 3 1 0.035 -0.016 -0.015 -0.015
## 70 er40_total ~~ rmet_total 3 3 1 0.665 -0.066 -0.066 -0.154
## 71 er40_total ~~ mean_ea 3 3 1 1.660 0.097 0.097 0.145
## 72 er40_total ~~ tasit3_lies 3 3 1 2.282 0.079 0.079 0.110
## 73 er40_total ~~ tasit2_ssar 3 3 1 1.919 0.061 0.061 0.106
## 74 er40_total ~~ tasit2_psar 3 3 1 0.103 -0.015 -0.015 -0.024
## 75 er40_total ~~ tasit3_sar 3 3 1 1.231 -0.044 -0.044 -0.093
## 76 rmet_total ~~ mean_ea 3 3 1 0.001 -0.002 -0.002 -0.006
## 77 rmet_total ~~ tasit3_lies 3 3 1 0.184 -0.029 -0.029 -0.069
## 78 rmet_total ~~ tasit2_ssar 3 3 1 0.713 -0.034 -0.034 -0.102
## 79 rmet_total ~~ tasit2_psar 3 3 1 0.260 0.021 0.021 0.058
## 80 rmet_total ~~ tasit3_sar 3 3 1 0.333 0.022 0.022 0.079
## 81 mean_ea ~~ tasit3_lies 3 3 1 1.919 -0.097 -0.097 -0.148
## 82 mean_ea ~~ tasit2_ssar 3 3 1 3.156 -0.110 -0.110 -0.209
## 83 mean_ea ~~ tasit2_psar 3 3 1 3.079 0.115 0.115 0.201
## 84 mean_ea ~~ tasit3_sar 3 3 1 0.057 0.013 0.013 0.031
## 85 tasit3_lies ~~ tasit2_ssar 3 3 1 0.004 -0.003 -0.003 -0.004
## 86 tasit3_lies ~~ tasit2_psar 3 3 1 9.264 0.134 0.134 0.216
## 87 tasit3_lies ~~ tasit3_sar 3 3 1 7.189 -0.101 -0.101 -0.218
## 88 tasit2_ssar ~~ tasit2_psar 3 3 1 1.410 -0.069 -0.069 -0.141
## 89 tasit2_ssar ~~ tasit3_sar 3 3 1 5.919 0.176 0.176 0.480
## 90 tasit2_psar ~~ tasit3_sar 3 3 1 0.824 -0.053 -0.053 -0.132
## sepc.nox
## 1 -0.043
## 2 0.110
## 3 -0.098
## 4 -0.436
## 5 0.113
## 6 0.112
## 7 0.028
## 8 0.068
## 9 -0.039
## 10 0.008
## 11 0.082
## 12 -1.024
## 13 -0.100
## 14 -0.206
## 15 0.476
## 16 -0.062
## 17 -0.015
## 18 -0.102
## 19 0.217
## 20 -0.046
## 21 0.244
## 22 -0.130
## 23 0.032
## 24 0.120
## 25 0.286
## 26 0.170
## 27 -0.333
## 28 -0.204
## 29 -0.082
## 30 0.118
## 31 -0.212
## 32 0.287
## 33 0.041
## 34 -0.363
## 35 -0.285
## 36 -0.017
## 37 0.282
## 38 -0.143
## 39 0.196
## 40 -0.040
## 41 -0.029
## 42 -0.093
## 43 -0.162
## 44 0.167
## 45 -0.070
## 46 -0.190
## 47 0.137
## 48 0.307
## 49 -0.106
## 50 -0.081
## 51 0.192
## 52 0.112
## 53 -0.135
## 54 0.061
## 55 -0.095
## 56 0.015
## 57 -0.008
## 58 0.000
## 59 -0.061
## 60 -0.292
## 61 -0.010
## 62 0.177
## 63 0.006
## 64 0.199
## 65 -0.135
## 66 0.025
## 67 -0.014
## 68 0.003
## 69 -0.015
## 70 -0.154
## 71 0.145
## 72 0.110
## 73 0.106
## 74 -0.024
## 75 -0.093
## 76 -0.006
## 77 -0.069
## 78 -0.102
## 79 0.058
## 80 0.079
## 81 -0.148
## 82 -0.209
## 83 0.201
## 84 0.031
## 85 -0.004
## 86 0.216
## 87 -0.218
## 88 -0.141
## 89 0.480
## 90 -0.132
# compare configural & metric models
anova(CFA_scog_model1_grp_fit,CFA_sc_model1_grp_fit2_part2a) # non sig diff in models
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff
## CFA_scog_model1_grp_fit 39 8746.2 9034.8 68.870
## CFA_sc_model1_grp_fit2_part2a 45 8745.8 9008.1 80.391 9.1555 6
## Pr(>Chisq)
## CFA_scog_model1_grp_fit
## CFA_sc_model1_grp_fit2_part2a 0.165
# scalar invariance - are item intercepts equal across groups
# scalar invariance - partial isolating mean ea & tasit2_psar
CFA_sc_model1_grp_fit3_part3a <- cfa(model=CFA_scog_model1,data = spasd_spins_yj_z_df,group = "group",
group.equal=c("loadings","intercepts"), estimator = "MLR", missing = "ml",
group.partial = c("simulation=~mean_ea","mentalizing=~tasit2_psar"))
summary(CFA_sc_model1_grp_fit3_part3a, fit.measures = TRUE, modindices = TRUE, standardized = TRUE, rsquare =
TRUE)
## lavaan 0.6.16 ended normally after 98 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 70
## Number of equality constraints 20
##
## Number of observations per group:
## ASD 100
## Control 209
## SSD 276
## Number of missing patterns per group:
## ASD 4
## Control 6
## SSD 20
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 118.659 120.493
## Degrees of freedom 55 55
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.985
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## ASD 46.701 47.423
## Control 38.381 38.975
## SSD 33.576 34.095
##
## Model Test Baseline Model:
##
## Test statistic 973.724 901.247
## Degrees of freedom 63 63
## P-value 0.000 0.000
## Scaling correction factor 1.080
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.930 0.922
## Tucker-Lewis Index (TLI) 0.920 0.911
##
## Robust Comparative Fit Index (CFI) 0.902
## Robust Tucker-Lewis Index (TLI) 0.888
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4332.018 -4332.018
## Scaling correction factor 0.941
## for the MLR correction
## Loglikelihood unrestricted model (H1) -4272.688 -4272.688
## Scaling correction factor 1.143
## for the MLR correction
##
## Akaike (AIC) 8764.035 8764.035
## Bayesian (BIC) 8982.616 8982.616
## Sample-size adjusted Bayesian (SABIC) 8823.884 8823.884
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.077 0.078
## 90 Percent confidence interval - lower 0.058 0.059
## 90 Percent confidence interval - upper 0.096 0.097
## P-value H_0: RMSEA <= 0.050 0.012 0.009
## P-value H_0: RMSEA >= 0.080 0.417 0.455
##
## Robust RMSEA 0.096
## 90 Percent confidence interval - lower 0.055
## 90 Percent confidence interval - upper 0.132
## P-value H_0: Robust RMSEA <= 0.050 0.035
## P-value H_0: Robust RMSEA >= 0.080 0.762
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.092 0.092
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [ASD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.455 0.457
## rmt_ttl (.p2.) 1.496 0.152 9.844 0.000 0.681 0.734
## mean_ea -0.281 0.417 -0.674 0.500 -0.128 -0.137
## tst3_ls (.p4.) 0.897 0.094 9.548 0.000 0.408 0.357
## mentalizing =~
## tst2_ss 1.000 0.636 0.735
## tst2_ps 1.197 0.150 7.974 0.000 0.762 0.827
## tst3_sr (.p7.) 0.991 0.053 18.789 0.000 0.631 0.770
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.257 0.087 2.966 0.003 0.887 0.887
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_tt (.18.) 0.010 0.072 0.143 0.886 0.010 0.010
## .rmt_ttl (.19.) 0.020 0.100 0.199 0.842 0.020 0.021
## .mean_ea (.20.) -0.009 0.114 -0.079 0.937 -0.009 -0.010
## .tst3_ls (.21.) 0.046 0.068 0.666 0.505 0.046 0.040
## .tst2_ss (.22.) 0.232 0.076 3.063 0.002 0.232 0.268
## .tst2_ps (.23.) 0.138 0.077 1.795 0.073 0.138 0.150
## .tst3_sr (.24.) 0.225 0.076 2.955 0.003 0.225 0.275
## simultn 0.000 0.000 0.000
## mntlzng 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.782 0.131 5.970 0.000 0.782 0.791
## .rmet_total 0.397 0.108 3.671 0.000 0.397 0.462
## .mean_ea 0.854 0.488 1.750 0.080 0.854 0.981
## .tasit3_lies 1.142 0.212 5.398 0.000 1.142 0.873
## .tasit2_ssar 0.345 0.089 3.894 0.000 0.345 0.460
## .tasit2_psar 0.269 0.100 2.691 0.007 0.269 0.317
## .tasit3_sar 0.273 0.052 5.229 0.000 0.273 0.407
## simulation 0.207 0.070 2.959 0.003 1.000 1.000
## mentalizing 0.405 0.129 3.137 0.002 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.209
## rmet_total 0.538
## mean_ea 0.019
## tasit3_lies 0.127
## tasit2_ssar 0.540
## tasit2_psar 0.683
## tasit3_sar 0.593
##
##
## Group 2 [Control]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.357 0.409
## rmt_ttl (.p2.) 1.496 0.152 9.844 0.000 0.534 0.656
## mean_ea 1.540 0.821 1.876 0.061 0.550 0.422
## tst3_ls (.p4.) 0.897 0.094 9.548 0.000 0.320 0.368
## mentalizing =~
## tst2_ss 1.000 0.331 0.653
## tst2_ps 1.353 0.190 7.120 0.000 0.448 0.696
## tst3_sr (.p7.) 0.991 0.053 18.789 0.000 0.328 0.447
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.073 0.024 2.977 0.003 0.613 0.613
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_tt (.18.) 0.010 0.072 0.143 0.886 0.010 0.012
## .rmt_ttl (.19.) 0.020 0.100 0.199 0.842 0.020 0.024
## .mean_ea (.20.) -0.009 0.114 -0.079 0.937 -0.009 -0.007
## .tst3_ls (.21.) 0.046 0.068 0.666 0.505 0.046 0.052
## .tst2_ss (.22.) 0.232 0.076 3.063 0.002 0.232 0.457
## .tst2_ps (.23.) 0.138 0.077 1.795 0.073 0.138 0.215
## .tst3_sr (.24.) 0.225 0.076 2.955 0.003 0.225 0.306
## simultn 0.246 0.084 2.922 0.003 0.688 0.688
## mntlzng 0.219 0.079 2.771 0.006 0.660 0.660
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.637 0.075 8.504 0.000 0.637 0.833
## .rmet_total 0.378 0.070 5.417 0.000 0.378 0.570
## .mean_ea 1.398 0.420 3.328 0.001 1.398 0.822
## .tasit3_lies 0.656 0.106 6.170 0.000 0.656 0.865
## .tasit2_ssar 0.147 0.022 6.760 0.000 0.147 0.573
## .tasit2_psar 0.214 0.055 3.874 0.000 0.214 0.516
## .tasit3_sar 0.431 0.088 4.922 0.000 0.431 0.800
## simulation 0.128 0.040 3.176 0.001 1.000 1.000
## mentalizing 0.110 0.033 3.315 0.001 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.167
## rmet_total 0.430
## mean_ea 0.178
## tasit3_lies 0.135
## tasit2_ssar 0.427
## tasit2_psar 0.484
## tasit3_sar 0.200
##
##
## Group 3 [SSD]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation =~
## er40_tt 1.000 0.615 0.585
## rmt_ttl (.p2.) 1.496 0.152 9.844 0.000 0.919 0.865
## mean_ea 0.449 0.142 3.154 0.002 0.276 0.333
## tst3_ls (.p4.) 0.897 0.094 9.548 0.000 0.551 0.551
## mentalizing =~
## tst2_ss 1.000 0.937 0.815
## tst2_ps 0.883 0.071 12.467 0.000 0.827 0.743
## tst3_sr (.p7.) 0.991 0.053 18.789 0.000 0.928 0.863
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## simulation ~~
## mentalizing 0.393 0.058 6.729 0.000 0.683 0.683
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_tt (.18.) 0.010 0.072 0.143 0.886 0.010 0.010
## .rmt_ttl (.19.) 0.020 0.100 0.199 0.842 0.020 0.019
## .mean_ea (.20.) -0.009 0.114 -0.079 0.937 -0.009 -0.011
## .tst3_ls (.21.) 0.046 0.068 0.666 0.505 0.046 0.046
## .tst2_ss (.22.) 0.232 0.076 3.063 0.002 0.232 0.202
## .tst2_ps (.23.) 0.138 0.077 1.795 0.073 0.138 0.124
## .tst3_sr (.24.) 0.225 0.076 2.955 0.003 0.225 0.209
## simultn -0.237 0.077 -3.053 0.002 -0.385 -0.385
## mntlzng -0.677 0.099 -6.822 0.000 -0.723 -0.723
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .er40_total 0.727 0.091 8.032 0.000 0.727 0.658
## .rmet_total 0.284 0.071 3.986 0.000 0.284 0.251
## .mean_ea 0.611 0.126 4.847 0.000 0.611 0.889
## .tasit3_lies 0.697 0.058 12.115 0.000 0.697 0.696
## .tasit2_ssar 0.443 0.055 8.106 0.000 0.443 0.336
## .tasit2_psar 0.555 0.060 9.199 0.000 0.555 0.448
## .tasit3_sar 0.294 0.045 6.556 0.000 0.294 0.255
## simulation 0.378 0.075 5.043 0.000 1.000 1.000
## mentalizing 0.877 0.107 8.192 0.000 1.000 1.000
##
## R-Square:
## Estimate
## er40_total 0.342
## rmet_total 0.749
## mean_ea 0.111
## tasit3_lies 0.304
## tasit2_ssar 0.664
## tasit2_psar 0.552
## tasit3_sar 0.745
##
## Modification Indices:
##
## lhs op rhs block group level mi epc sepc.lv sepc.all
## 1 simulation =~ er40_total 1 1 1 0.014 -0.032 -0.015 -0.015
## 2 mentalizing =~ tasit2_ssar 1 1 1 0.803 0.136 0.087 0.100
## 3 simulation ~1 1 1 1 3.310 0.367 0.806 0.806
## 4 mentalizing ~1 1 1 1 2.177 -0.304 -0.479 -0.479
## 5 simulation =~ er40_total 2 2 1 0.135 -0.088 -0.032 -0.036
## 6 mentalizing =~ tasit2_ssar 2 2 1 4.403 -0.418 -0.138 -0.273
## 7 simulation =~ er40_total 3 3 1 0.161 0.082 0.050 0.048
## 8 mentalizing =~ tasit2_ssar 3 3 1 0.387 0.083 0.078 0.068
## 9 simulation =~ tasit2_ssar 1 1 1 0.194 0.094 0.043 0.050
## 10 simulation =~ tasit2_psar 1 1 1 0.010 0.089 0.040 0.044
## 11 simulation =~ tasit3_sar 1 1 1 0.215 -0.098 -0.044 -0.054
## 12 mentalizing =~ er40_total 1 1 1 0.000 0.001 0.001 0.001
## 13 mentalizing =~ rmet_total 1 1 1 0.468 0.156 0.099 0.107
## 14 mentalizing =~ mean_ea 1 1 1 2.449 -2.511 -1.597 -1.712
## 15 mentalizing =~ tasit3_lies 1 1 1 1.149 -0.214 -0.136 -0.119
## 16 er40_total ~~ rmet_total 1 1 1 0.209 -0.048 -0.048 -0.086
## 17 er40_total ~~ mean_ea 1 1 1 3.498 0.387 0.387 0.473
## 18 er40_total ~~ tasit3_lies 1 1 1 0.021 -0.015 -0.015 -0.016
## 19 er40_total ~~ tasit2_ssar 1 1 1 0.035 -0.012 -0.012 -0.022
## 20 er40_total ~~ tasit2_psar 1 1 1 0.847 -0.057 -0.057 -0.124
## 21 er40_total ~~ tasit3_sar 1 1 1 2.470 0.089 0.089 0.192
## 22 rmet_total ~~ mean_ea 1 1 1 0.023 0.026 0.026 0.045
## 23 rmet_total ~~ tasit3_lies 1 1 1 0.713 0.081 0.081 0.121
## 24 rmet_total ~~ tasit2_ssar 1 1 1 1.223 -0.058 -0.058 -0.156
## 25 rmet_total ~~ tasit2_psar 1 1 1 0.009 0.005 0.005 0.016
## 26 rmet_total ~~ tasit3_sar 1 1 1 1.489 0.060 0.060 0.182
## 27 mean_ea ~~ tasit3_lies 1 1 1 1.049 0.251 0.251 0.254
## 28 mean_ea ~~ tasit2_ssar 1 1 1 0.224 0.072 0.072 0.132
## 29 mean_ea ~~ tasit2_psar 1 1 1 1.716 -0.190 -0.190 -0.396
## 30 mean_ea ~~ tasit3_sar 1 1 1 0.248 -0.067 -0.067 -0.139
## 31 tasit3_lies ~~ tasit2_ssar 1 1 1 0.043 -0.015 -0.015 -0.024
## 32 tasit3_lies ~~ tasit2_psar 1 1 1 0.951 0.069 0.069 0.124
## 33 tasit3_lies ~~ tasit3_sar 1 1 1 4.904 -0.145 -0.145 -0.259
## 34 tasit2_ssar ~~ tasit2_psar 1 1 1 2.866 0.093 0.093 0.305
## 35 tasit2_ssar ~~ tasit3_sar 1 1 1 0.010 0.005 0.005 0.017
## 36 tasit2_psar ~~ tasit3_sar 1 1 1 3.187 -0.097 -0.097 -0.359
## 37 simulation =~ tasit2_ssar 2 2 1 3.213 -0.274 -0.098 -0.193
## 38 simulation =~ tasit2_psar 2 2 1 0.026 -0.054 -0.019 -0.030
## 39 simulation =~ tasit3_sar 2 2 1 4.682 0.375 0.134 0.182
## 40 mentalizing =~ er40_total 2 2 1 1.737 -0.286 -0.095 -0.108
## 41 mentalizing =~ rmet_total 2 2 1 0.016 0.034 0.011 0.014
## 42 mentalizing =~ mean_ea 2 2 1 0.200 0.538 0.178 0.137
## 43 mentalizing =~ tasit3_lies 2 2 1 1.311 0.245 0.081 0.093
## 44 er40_total ~~ rmet_total 2 2 1 0.000 -0.001 -0.001 -0.002
## 45 er40_total ~~ mean_ea 2 2 1 0.489 -0.121 -0.121 -0.128
## 46 er40_total ~~ tasit3_lies 2 2 1 4.575 0.107 0.107 0.165
## 47 er40_total ~~ tasit2_ssar 2 2 1 1.091 -0.027 -0.027 -0.089
## 48 er40_total ~~ tasit2_psar 2 2 1 3.878 -0.065 -0.065 -0.177
## 49 er40_total ~~ tasit3_sar 2 2 1 3.232 0.072 0.072 0.137
## 50 rmet_total ~~ mean_ea 2 2 1 0.278 -0.089 -0.089 -0.123
## 51 rmet_total ~~ tasit3_lies 2 2 1 0.361 -0.032 -0.032 -0.065
## 52 rmet_total ~~ tasit2_ssar 2 2 1 0.769 -0.022 -0.022 -0.094
## 53 rmet_total ~~ tasit2_psar 2 2 1 3.107 0.058 0.058 0.205
## 54 rmet_total ~~ tasit3_sar 2 2 1 1.731 0.047 0.047 0.117
## 55 mean_ea ~~ tasit3_lies 2 2 1 1.405 -0.202 -0.202 -0.211
## 56 mean_ea ~~ tasit2_ssar 2 2 1 0.067 0.023 0.023 0.051
## 57 mean_ea ~~ tasit2_psar 2 2 1 0.835 -0.102 -0.102 -0.187
## 58 mean_ea ~~ tasit3_sar 2 2 1 0.150 0.053 0.053 0.068
## 59 tasit3_lies ~~ tasit2_ssar 2 2 1 0.012 -0.003 -0.003 -0.009
## 60 tasit3_lies ~~ tasit2_psar 2 2 1 0.001 -0.001 -0.001 -0.002
## 61 tasit3_lies ~~ tasit3_sar 2 2 1 0.937 -0.039 -0.039 -0.073
## 62 tasit2_ssar ~~ tasit2_psar 2 2 1 1.956 -0.042 -0.042 -0.234
## 63 tasit2_ssar ~~ tasit3_sar 2 2 1 0.327 -0.015 -0.015 -0.060
## 64 tasit2_psar ~~ tasit3_sar 2 2 1 3.603 0.057 0.057 0.187
## 65 simulation =~ tasit2_ssar 3 3 1 0.001 -0.003 -0.002 -0.002
## 66 simulation =~ tasit2_psar 3 3 1 7.928 0.419 0.258 0.232
## 67 simulation =~ tasit3_sar 3 3 1 4.038 -0.243 -0.149 -0.139
## 68 mentalizing =~ er40_total 3 3 1 0.266 -0.046 -0.043 -0.041
## 69 mentalizing =~ rmet_total 3 3 1 1.354 0.135 0.127 0.119
## 70 mentalizing =~ mean_ea 3 3 1 0.336 0.082 0.076 0.092
## 71 mentalizing =~ tasit3_lies 3 3 1 1.113 -0.091 -0.085 -0.085
## 72 er40_total ~~ rmet_total 3 3 1 0.159 -0.029 -0.029 -0.064
## 73 er40_total ~~ mean_ea 3 3 1 1.404 0.090 0.090 0.135
## 74 er40_total ~~ tasit3_lies 3 3 1 1.434 0.062 0.062 0.087
## 75 er40_total ~~ tasit2_ssar 3 3 1 1.708 0.057 0.057 0.101
## 76 er40_total ~~ tasit2_psar 3 3 1 0.121 -0.016 -0.016 -0.025
## 77 er40_total ~~ tasit3_sar 3 3 1 1.524 -0.049 -0.049 -0.106
## 78 rmet_total ~~ mean_ea 3 3 1 0.000 -0.001 -0.001 -0.003
## 79 rmet_total ~~ tasit3_lies 3 3 1 0.227 -0.031 -0.031 -0.069
## 80 rmet_total ~~ tasit2_ssar 3 3 1 0.495 -0.028 -0.028 -0.079
## 81 rmet_total ~~ tasit2_psar 3 3 1 0.451 0.027 0.027 0.069
## 82 rmet_total ~~ tasit3_sar 3 3 1 0.453 0.025 0.025 0.086
## 83 mean_ea ~~ tasit3_lies 3 3 1 2.709 -0.117 -0.117 -0.179
## 84 mean_ea ~~ tasit2_ssar 3 3 1 2.971 -0.107 -0.107 -0.205
## 85 mean_ea ~~ tasit2_psar 3 3 1 2.940 0.114 0.114 0.195
## 86 mean_ea ~~ tasit3_sar 3 3 1 0.044 0.012 0.012 0.028
## 87 tasit3_lies ~~ tasit2_ssar 3 3 1 0.009 -0.004 -0.004 -0.007
## 88 tasit3_lies ~~ tasit2_psar 3 3 1 8.444 0.128 0.128 0.206
## 89 tasit3_lies ~~ tasit3_sar 3 3 1 7.400 -0.102 -0.102 -0.226
## 90 tasit2_ssar ~~ tasit2_psar 3 3 1 0.605 -0.041 -0.041 -0.083
## 91 tasit2_ssar ~~ tasit3_sar 3 3 1 2.443 0.107 0.107 0.295
## 92 tasit2_psar ~~ tasit3_sar 3 3 1 0.350 -0.031 -0.031 -0.077
## sepc.nox
## 1 -0.015
## 2 0.100
## 3 0.806
## 4 -0.479
## 5 -0.036
## 6 -0.273
## 7 0.048
## 8 0.068
## 9 0.050
## 10 0.044
## 11 -0.054
## 12 0.001
## 13 0.107
## 14 -1.712
## 15 -0.119
## 16 -0.086
## 17 0.473
## 18 -0.016
## 19 -0.022
## 20 -0.124
## 21 0.192
## 22 0.045
## 23 0.121
## 24 -0.156
## 25 0.016
## 26 0.182
## 27 0.254
## 28 0.132
## 29 -0.396
## 30 -0.139
## 31 -0.024
## 32 0.124
## 33 -0.259
## 34 0.305
## 35 0.017
## 36 -0.359
## 37 -0.193
## 38 -0.030
## 39 0.182
## 40 -0.108
## 41 0.014
## 42 0.137
## 43 0.093
## 44 -0.002
## 45 -0.128
## 46 0.165
## 47 -0.089
## 48 -0.177
## 49 0.137
## 50 -0.123
## 51 -0.065
## 52 -0.094
## 53 0.205
## 54 0.117
## 55 -0.211
## 56 0.051
## 57 -0.187
## 58 0.068
## 59 -0.009
## 60 -0.002
## 61 -0.073
## 62 -0.234
## 63 -0.060
## 64 0.187
## 65 -0.002
## 66 0.232
## 67 -0.139
## 68 -0.041
## 69 0.119
## 70 0.092
## 71 -0.085
## 72 -0.064
## 73 0.135
## 74 0.087
## 75 0.101
## 76 -0.025
## 77 -0.106
## 78 -0.003
## 79 -0.069
## 80 -0.079
## 81 0.069
## 82 0.086
## 83 -0.179
## 84 -0.205
## 85 0.195
## 86 0.028
## 87 -0.007
## 88 0.206
## 89 -0.226
## 90 -0.083
## 91 0.295
## 92 -0.077
# compare metric & scalar models
anova(CFA_sc_model1_grp_fit2_part2a, CFA_sc_model1_grp_fit3_part3a) # sig diff - did not pass scalar invariance
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff
## CFA_sc_model1_grp_fit2_part2a 45 8745.8 9008.1 80.391
## CFA_sc_model1_grp_fit3_part3a 55 8764.0 8982.6 118.659 44.705 10
## Pr(>Chisq)
## CFA_sc_model1_grp_fit2_part2a
## CFA_sc_model1_grp_fit3_part3a 2.458e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
modindices(CFA_sc_model1_grp_fit3_part3a , sort = TRUE, maximum.number = 10)
## lhs op rhs block group level mi epc sepc.lv sepc.all
## 178 tasit3_lies ~~ tasit2_psar 3 3 1 8.444 0.128 0.128 0.206
## 156 simulation =~ tasit2_psar 3 3 1 7.928 0.419 0.258 0.232
## 179 tasit3_lies ~~ tasit3_sar 3 3 1 7.400 -0.102 -0.102 -0.226
## 123 tasit3_lies ~~ tasit3_sar 1 1 1 4.904 -0.145 -0.145 -0.259
## 129 simulation =~ tasit3_sar 2 2 1 4.682 0.375 0.134 0.182
## 136 er40_total ~~ tasit3_lies 2 2 1 4.575 0.107 0.107 0.165
## 31 mentalizing =~ tasit2_ssar 2 2 1 4.403 -0.418 -0.138 -0.273
## 157 simulation =~ tasit3_sar 3 3 1 4.038 -0.243 -0.149 -0.139
## 138 er40_total ~~ tasit2_psar 2 2 1 3.878 -0.065 -0.065 -0.177
## 154 tasit2_psar ~~ tasit3_sar 2 2 1 3.603 0.057 0.057 0.187
## sepc.nox
## 178 0.206
## 156 0.232
## 179 -0.226
## 123 -0.259
## 129 0.182
## 136 0.165
## 31 -0.273
## 157 -0.139
## 138 -0.177
## 154 0.187