This study aimed to identify the factor structure of the Defense Style Questionnaire (DSQ). Specifically, it compared three previously hypothesized factor structures: a one-factor solution (Trijsburg et al., 2000), a three-factor solution (Andrews et al., 1993), and a four-factor solution (Ruuttu et al., 2006).
# Specify CFA models
model_1f <- '
Factor1 =~ dsq_sublimation + dsq_humor + dsq_anticipation + dsq_suppression
+ dsq_pseudo_altruism + dsq_idealization + dsq_reaction_formation + dsq_undoing
+ dsq_rationalization + dsq_projection + dsq_passive_aggression + dsq_acting_out
+ dsq_isolation + dsq_autistic_fantasy + dsq_denial + dsq_displacement
+ dsq_dissociation + dsq_splitting + dsq_devaluation + dsq_somatization
'model_3f <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Immature =~ dsq_rationalization + dsq_isolation + dsq_dissociation + dsq_devaluation
+ dsq_splitting + dsq_denial + dsq_autistic_fantasy + dsq_displacement
+ dsq_passive_aggression + dsq_somatization + dsq_acting_out + dsq_projection
'model_4f <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation + dsq_rationalization
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Immature =~ dsq_autistic_fantasy + dsq_displacement + dsq_passive_aggression + dsq_somatization + dsq_acting_out + dsq_projection
Image_Distorting =~ dsq_isolation + dsq_dissociation + dsq_devaluation + dsq_splitting + dsq_denial
'model_1f_norat <- '
Factor1 =~ dsq_sublimation + dsq_humor + dsq_anticipation + dsq_suppression
+ dsq_pseudo_altruism + dsq_idealization + dsq_reaction_formation + dsq_undoing
+ dsq_projection + dsq_passive_aggression + dsq_acting_out
+ dsq_isolation + dsq_autistic_fantasy + dsq_denial + dsq_displacement
+ dsq_dissociation + dsq_splitting + dsq_devaluation + dsq_somatization
'model_3f_norat <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Immature =~ dsq_isolation + dsq_dissociation + dsq_devaluation
+ dsq_splitting + dsq_denial + dsq_autistic_fantasy + dsq_displacement
+ dsq_passive_aggression + dsq_somatization + dsq_acting_out + dsq_projection
'model_4f_norat <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Immature =~ dsq_autistic_fantasy + dsq_displacement + dsq_passive_aggression
+ dsq_somatization + dsq_acting_out + dsq_projection
Image_Distorting =~ dsq_isolation + dsq_dissociation + dsq_devaluation + dsq_splitting + dsq_denial
'# Set working directory and load data
#setwd("//cnas-wrkgrp.ru.nl/wrkgrp/FSW-BSI-EPT-PhD_Fritz_Wienicke/CFA Defense Mechanisms")
#setwd("C:/Users/fritz/OneDrive/Desktop/Defense mechanisms/Datasets")
DSQ_Data <- read.spss("Database_combined_v1.sav", to.data.frame = TRUE, use.value.labels = FALSE)
# Define DSQ item names
dsq_vars <- c(
"dsq_sublimation","dsq_humor","dsq_anticipation","dsq_suppression",
"dsq_pseudo_altruism","dsq_idealization","dsq_reaction_formation","dsq_undoing",
"dsq_rationalization","dsq_projection","dsq_passive_aggression","dsq_acting_out",
"dsq_isolation","dsq_autistic_fantasy","dsq_denial","dsq_displacement",
"dsq_dissociation","dsq_splitting","dsq_devaluation","dsq_somatization"
)
# Flag and drop cases with ALL DSQ items missing
rows_all_missing <- apply(DSQ_Data[ , dsq_vars], 1, function(x) all(is.na(x)))
DSQ_Data$all_dsq_missing <- rows_all_missing
DSQ_Data <- DSQ_Data[!DSQ_Data$all_dsq_missing, ]
DSQ_Data$all_dsq_missing <- NULL
# Convert key variables to factors with labels
DSQ_Data$StudyID <- factor(DSQ_Data$StudyID,
levels = c(1,3,4),
labels = c("Dekker_2008/Van_2009","Dos_Santos_2020","Knekt_2004"))
DSQ_Data$PatientID <- as.factor(DSQ_Data$PatientID)
DSQ_Data$gender <- factor(DSQ_Data$gender, levels = c(1,2), labels = c("male","female"))
DSQ_Data$marital_status <- factor(DSQ_Data$marital_status,
levels=c(1,2,3),labels=c("single","cohabiting or married","Separated, divorced or widowed"))
DSQ_Data$employment_status <- factor(DSQ_Data$employment_status,
levels=c(1,2,3),labels=c("Working","Studying","Not working or studying"))
DSQ_Data$education_level <- factor(DSQ_Data$education_level,
levels=c(1,2,3,4),labels=c("Basic education","Secondary education",
"Vocational education","Tertiary education"))
DSQ_Data$diagnosis <- factor(DSQ_Data$diagnosis,
levels=c(1,3),
labels=c("DSM-IV, mood disorder only","DSM-IV, comorbid mood and anxiety disorder"))
# Define subsets
dekker_df <- subset(DSQ_Data, StudyID == "Dekker_2008/Van_2009")
ds_df <- subset(DSQ_Data, StudyID == "Dos_Santos_2020")
kn_df <- subset(DSQ_Data, StudyID == "Knekt_2004")
DSQ_Data_mg <- filter(DSQ_Data, StudyID != "Knekt_2004")# Fit models
fit1_comb <- cfa(model_1f, data = DSQ_Data_mg, group = "StudyID", estimator = "mlr", missing = "fiml")
# Inspect 1-factor
summary(fit1_comb,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 176 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 120
##
## Number of observations per group:
## Dekker_2008/Van_2009 151
## Dos_Santos_2020 210
## Number of missing patterns per group:
## Dekker_2008/Van_2009 18
## Dos_Santos_2020 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 935.276 899.576
## Degrees of freedom 340 340
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.040
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## Dekker_2008/Van_2009 423.312 423.312
## Dos_Santos_2020 476.264 476.264
##
## Model Test Baseline Model:
##
## Test statistic 1390.926 1256.752
## Degrees of freedom 380 380
## P-value 0.000 0.000
## Scaling correction factor 1.107
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.411 0.362
## Tucker-Lewis Index (TLI) 0.342 0.287
##
## Robust Comparative Fit Index (CFI) 0.417
## Robust Tucker-Lewis Index (TLI) 0.349
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -18767.143 -18767.143
## Scaling correction factor 1.147
## for the MLR correction
## Loglikelihood unrestricted model (H1) -18299.505 -18299.505
## Scaling correction factor 1.068
## for the MLR correction
##
## Akaike (AIC) 37774.287 37774.287
## Bayesian (BIC) 38240.952 38240.952
## Sample-size adjusted Bayesian (SABIC) 37860.249 37860.249
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.098 0.095
## 90 Percent confidence interval - lower 0.091 0.088
## 90 Percent confidence interval - upper 0.106 0.103
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA 0.099
## 90 Percent confidence interval - lower 0.091
## 90 Percent confidence interval - upper 0.108
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.117 0.117
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [Dekker_2008/Van_2009]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Factor1 =~
## dsq_sublimatin 1.000 0.536 0.145
## dsq_humor -0.236 1.605 -0.147 0.883 -0.126 -0.030
## dsq_anticipatn 0.583 0.615 0.948 0.343 0.312 0.102
## dsq_suppressin 2.253 1.846 1.221 0.222 1.208 0.311
## dsq_psed_ltrsm 0.734 0.563 1.304 0.192 0.394 0.130
## dsq_idealizatn 2.333 2.323 1.004 0.315 1.251 0.327
## dsq_rctn_frmtn 1.256 1.323 0.949 0.342 0.674 0.179
## dsq_undoing 2.539 2.859 0.888 0.375 1.362 0.343
## dsq_rationlztn 0.677 0.611 1.110 0.267 0.363 0.122
## dsq_projection 3.272 4.148 0.789 0.430 1.755 0.464
## dsq_pssv_ggrss 3.311 3.543 0.934 0.350 1.776 0.555
## dsq_acting_out 3.287 3.533 0.930 0.352 1.763 0.418
## dsq_isolation 2.873 3.308 0.869 0.385 1.541 0.339
## dsq_tstc_fntsy 3.052 3.718 0.821 0.412 1.637 0.383
## dsq_denial 2.270 2.702 0.840 0.401 1.218 0.371
## dsq_displacmnt 2.115 2.578 0.820 0.412 1.134 0.277
## dsq_dissociatn 2.822 3.518 0.802 0.422 1.513 0.410
## dsq_splitting 4.228 4.706 0.898 0.369 2.268 0.596
## dsq_devaluatin 3.935 4.606 0.854 0.393 2.111 0.663
## dsq_somatizatn 2.309 2.595 0.890 0.374 1.238 0.350
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_sublimatin 10.016 0.308 32.524 0.000 10.016 2.709
## .dsq_humor 9.300 0.347 26.830 0.000 9.300 2.214
## .dsq_anticipatn 10.130 0.253 39.961 0.000 10.130 3.322
## .dsq_suppressin 8.575 0.321 26.749 0.000 8.575 2.206
## .dsq_psed_ltrsm 12.707 0.254 50.061 0.000 12.707 4.187
## .dsq_idealizatn 7.962 0.314 25.368 0.000 7.962 2.080
## .dsq_rctn_frmtn 9.898 0.310 31.967 0.000 9.898 2.629
## .dsq_undoing 11.142 0.333 33.503 0.000 11.142 2.806
## .dsq_rationlztn 8.830 0.245 36.025 0.000 8.830 2.971
## .dsq_projection 9.348 0.312 29.926 0.000 9.348 2.469
## .dsq_pssv_ggrss 7.757 0.269 28.885 0.000 7.757 2.426
## .dsq_acting_out 9.440 0.348 27.125 0.000 9.440 2.238
## .dsq_isolation 8.587 0.378 22.738 0.000 8.587 1.887
## .dsq_tstc_fntsy 9.194 0.352 26.093 0.000 9.194 2.153
## .dsq_denial 6.883 0.269 25.568 0.000 6.883 2.100
## .dsq_displacmnt 8.150 0.342 23.844 0.000 8.150 1.992
## .dsq_dissociatn 9.579 0.304 31.540 0.000 9.579 2.596
## .dsq_splitting 9.718 0.311 31.275 0.000 9.718 2.552
## .dsq_devaluatin 9.754 0.262 37.273 0.000 9.754 3.064
## .dsq_somatizatn 11.442 0.289 39.623 0.000 11.442 3.236
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_sublimatin 13.382 1.464 9.138 0.000 13.382 0.979
## .dsq_humor 17.622 1.497 11.768 0.000 17.622 0.999
## .dsq_anticipatn 9.201 1.006 9.144 0.000 9.201 0.989
## .dsq_suppressin 13.646 1.794 7.607 0.000 13.646 0.903
## .dsq_psed_ltrsm 9.057 1.162 7.795 0.000 9.057 0.983
## .dsq_idealizatn 13.083 1.611 8.119 0.000 13.083 0.893
## .dsq_rctn_frmtn 13.725 1.531 8.964 0.000 13.725 0.968
## .dsq_undoing 13.917 1.396 9.970 0.000 13.917 0.882
## .dsq_rationlztn 8.701 0.939 9.267 0.000 8.701 0.985
## .dsq_projection 11.253 1.445 7.787 0.000 11.253 0.785
## .dsq_pssv_ggrss 7.072 0.947 7.466 0.000 7.072 0.692
## .dsq_acting_out 14.678 1.801 8.148 0.000 14.678 0.825
## .dsq_isolation 18.341 1.798 10.199 0.000 18.341 0.885
## .dsq_tstc_fntsy 15.559 1.854 8.394 0.000 15.559 0.853
## .dsq_denial 9.265 0.987 9.391 0.000 9.265 0.862
## .dsq_displacmnt 15.453 1.670 9.255 0.000 15.453 0.923
## .dsq_dissociatn 11.325 1.583 7.153 0.000 11.325 0.832
## .dsq_splitting 9.358 1.671 5.601 0.000 9.358 0.645
## .dsq_devaluatin 5.682 0.945 6.014 0.000 5.682 0.561
## .dsq_somatizatn 10.965 1.087 10.084 0.000 10.965 0.877
## Factor1 0.288 0.634 0.454 0.650 1.000 1.000
##
## R-Square:
## Estimate
## dsq_sublimatin 0.021
## dsq_humor 0.001
## dsq_anticipatn 0.011
## dsq_suppressin 0.097
## dsq_psed_ltrsm 0.017
## dsq_idealizatn 0.107
## dsq_rctn_frmtn 0.032
## dsq_undoing 0.118
## dsq_rationlztn 0.015
## dsq_projection 0.215
## dsq_pssv_ggrss 0.308
## dsq_acting_out 0.175
## dsq_isolation 0.115
## dsq_tstc_fntsy 0.147
## dsq_denial 0.138
## dsq_displacmnt 0.077
## dsq_dissociatn 0.168
## dsq_splitting 0.355
## dsq_devaluatin 0.439
## dsq_somatizatn 0.123
##
##
## Group 2 [Dos_Santos_2020]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Factor1 =~
## dsq_sublimatin 1.000 1.627 0.409
## dsq_humor 1.409 0.658 2.141 0.032 2.293 0.510
## dsq_anticipatn 1.259 0.319 3.942 0.000 2.049 0.482
## dsq_suppressin 1.108 0.368 3.014 0.003 1.803 0.445
## dsq_psed_ltrsm 0.430 0.219 1.963 0.050 0.700 0.188
## dsq_idealizatn 1.058 0.334 3.163 0.002 1.721 0.424
## dsq_rctn_frmtn 1.019 0.457 2.227 0.026 1.657 0.412
## dsq_undoing 0.698 0.392 1.781 0.075 1.136 0.278
## dsq_rationlztn 1.675 0.725 2.311 0.021 2.726 0.667
## dsq_projection -0.127 0.890 -0.143 0.886 -0.207 -0.052
## dsq_pssv_ggrss -0.064 0.796 -0.081 0.935 -0.105 -0.027
## dsq_acting_out 0.048 0.842 0.057 0.954 0.078 0.016
## dsq_isolation 0.083 0.705 0.118 0.906 0.135 0.028
## dsq_tstc_fntsy 0.136 1.065 0.128 0.898 0.221 0.042
## dsq_denial 0.666 0.347 1.923 0.055 1.084 0.323
## dsq_displacmnt -0.110 0.551 -0.200 0.841 -0.180 -0.044
## dsq_dissociatn 1.018 0.274 3.718 0.000 1.657 0.530
## dsq_splitting 0.335 0.540 0.621 0.535 0.545 0.133
## dsq_devaluatin 0.142 0.529 0.268 0.789 0.231 0.071
## dsq_somatizatn -0.451 0.712 -0.634 0.526 -0.735 -0.154
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_sublimatin 9.796 0.283 34.584 0.000 9.796 2.462
## .dsq_humor 8.238 0.319 25.833 0.000 8.238 1.830
## .dsq_anticipatn 11.805 0.304 38.891 0.000 11.805 2.777
## .dsq_suppressin 6.755 0.289 23.402 0.000 6.755 1.667
## .dsq_psed_ltrsm 11.027 0.264 41.736 0.000 11.027 2.967
## .dsq_idealizatn 8.089 0.289 28.012 0.000 8.089 1.993
## .dsq_rctn_frmtn 8.948 0.286 31.249 0.000 8.948 2.223
## .dsq_undoing 9.445 0.291 32.462 0.000 9.445 2.309
## .dsq_rationlztn 8.428 0.290 29.028 0.000 8.428 2.064
## .dsq_projection 8.142 0.283 28.732 0.000 8.142 2.037
## .dsq_pssv_ggrss 8.743 0.277 31.579 0.000 8.743 2.245
## .dsq_acting_out 10.707 0.348 30.788 0.000 10.707 2.189
## .dsq_isolation 9.833 0.348 28.215 0.000 9.833 2.004
## .dsq_tstc_fntsy 10.292 0.377 27.295 0.000 10.292 1.940
## .dsq_denial 5.379 0.239 22.479 0.000 5.379 1.602
## .dsq_displacmnt 10.026 0.291 34.492 0.000 10.026 2.447
## .dsq_dissociatn 5.251 0.223 23.515 0.000 5.251 1.679
## .dsq_splitting 9.998 0.292 34.183 0.000 9.998 2.431
## .dsq_devaluatin 7.171 0.230 31.166 0.000 7.171 2.214
## .dsq_somatizatn 11.506 0.389 29.568 0.000 11.506 2.412
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_sublimatin 13.188 1.647 8.007 0.000 13.188 0.833
## .dsq_humor 14.998 2.721 5.513 0.000 14.998 0.740
## .dsq_anticipatn 13.875 1.674 8.290 0.000 13.875 0.768
## .dsq_suppressin 13.175 1.906 6.913 0.000 13.175 0.802
## .dsq_psed_ltrsm 13.327 1.320 10.097 0.000 13.327 0.965
## .dsq_idealizatn 13.509 2.406 5.615 0.000 13.509 0.820
## .dsq_rctn_frmtn 13.459 1.796 7.493 0.000 13.459 0.830
## .dsq_undoing 15.443 2.260 6.834 0.000 15.443 0.923
## .dsq_rationlztn 9.254 3.360 2.754 0.006 9.254 0.555
## .dsq_projection 15.937 1.390 11.466 0.000 15.937 0.997
## .dsq_pssv_ggrss 15.160 1.440 10.531 0.000 15.160 0.999
## .dsq_acting_out 23.918 1.747 13.695 0.000 23.918 1.000
## .dsq_isolation 24.050 1.813 13.262 0.000 24.050 0.999
## .dsq_tstc_fntsy 28.087 2.043 13.751 0.000 28.087 0.998
## .dsq_denial 10.102 1.422 7.102 0.000 10.102 0.896
## .dsq_displacmnt 16.750 1.485 11.281 0.000 16.750 0.998
## .dsq_dissociatn 7.031 1.131 6.214 0.000 7.031 0.719
## .dsq_splitting 16.622 1.819 9.140 0.000 16.622 0.982
## .dsq_devaluatin 10.432 1.083 9.637 0.000 10.432 0.995
## .dsq_somatizatn 22.225 2.313 9.610 0.000 22.225 0.976
## Factor1 2.648 1.383 1.915 0.055 1.000 1.000
##
## R-Square:
## Estimate
## dsq_sublimatin 0.167
## dsq_humor 0.260
## dsq_anticipatn 0.232
## dsq_suppressin 0.198
## dsq_psed_ltrsm 0.035
## dsq_idealizatn 0.180
## dsq_rctn_frmtn 0.170
## dsq_undoing 0.077
## dsq_rationlztn 0.445
## dsq_projection 0.003
## dsq_pssv_ggrss 0.001
## dsq_acting_out 0.000
## dsq_isolation 0.001
## dsq_tstc_fntsy 0.002
## dsq_denial 0.104
## dsq_displacmnt 0.002
## dsq_dissociatn 0.281
## dsq_splitting 0.018
## dsq_devaluatin 0.005
## dsq_somatizatn 0.024fit3_comb <- cfa(model_3f, data = DSQ_Data_mg, group = "StudyID", estimator = "mlr", missing = "fiml")
# Inspect 3-factor
summary(fit3_comb,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 452 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 126
##
## Number of observations per group:
## Dekker_2008/Van_2009 151
## Dos_Santos_2020 210
## Number of missing patterns per group:
## Dekker_2008/Van_2009 18
## Dos_Santos_2020 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 724.231 675.132
## Degrees of freedom 334 334
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.073
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## Dekker_2008/Van_2009 325.959 325.959
## Dos_Santos_2020 349.173 349.173
##
## Model Test Baseline Model:
##
## Test statistic 1390.926 1256.752
## Degrees of freedom 380 380
## P-value 0.000 0.000
## Scaling correction factor 1.107
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.614 0.611
## Tucker-Lewis Index (TLI) 0.561 0.557
##
## Robust Comparative Fit Index (CFI) 0.629
## Robust Tucker-Lewis Index (TLI) 0.578
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -18661.621 -18661.621
## Scaling correction factor 1.054
## for the MLR correction
## Loglikelihood unrestricted model (H1) -18299.505 -18299.505
## Scaling correction factor 1.068
## for the MLR correction
##
## Akaike (AIC) 37575.241 37575.241
## Bayesian (BIC) 38065.240 38065.240
## Sample-size adjusted Bayesian (SABIC) 37665.501 37665.501
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.080 0.075
## 90 Percent confidence interval - lower 0.072 0.067
## 90 Percent confidence interval - upper 0.088 0.083
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.545 0.162
##
## Robust RMSEA 0.080
## 90 Percent confidence interval - lower 0.071
## 90 Percent confidence interval - upper 0.089
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.501
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.101 0.101
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [Dekker_2008/Van_2009]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.754 0.654
## dsq_suppressin 0.566 0.223 2.543 0.011 1.559 0.401
## dsq_sublimatin 0.771 0.256 3.013 0.003 2.124 0.574
## dsq_anticipatn 0.703 0.162 4.334 0.000 1.936 0.634
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.359 0.360
## dsq_idealizatn 1.202 1.403 0.857 0.392 1.633 0.426
## dsq_psed_ltrsm 1.218 0.488 2.496 0.013 1.655 0.546
## dsq_undoing 1.128 0.413 2.734 0.006 1.533 0.385
## Immature =~
## dsq_rationlztn 1.000 0.201 0.068
## dsq_isolation 7.621 13.033 0.585 0.559 1.532 0.337
## dsq_dissociatn 7.967 14.397 0.553 0.580 1.602 0.434
## dsq_devaluatin 10.897 19.246 0.566 0.571 2.191 0.688
## dsq_splitting 10.947 18.910 0.579 0.563 2.201 0.578
## dsq_denial 6.006 10.181 0.590 0.555 1.207 0.368
## dsq_tstc_fntsy 8.495 15.376 0.552 0.581 1.708 0.400
## dsq_displacmnt 5.614 9.941 0.565 0.572 1.129 0.276
## dsq_pssv_ggrss 8.683 14.601 0.595 0.552 1.746 0.545
## dsq_somatizatn 5.966 10.879 0.548 0.583 1.199 0.339
## dsq_acting_out 8.505 14.522 0.586 0.558 1.710 0.406
## dsq_projection 9.677 17.666 0.548 0.584 1.945 0.514
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 2.475 0.976 2.536 0.011 0.662 0.662
## Immature 0.002 0.094 0.024 0.981 0.004 0.004
## Neurotic ~~
## Immature 0.106 0.189 0.563 0.574 0.390 0.390
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.295 0.347 26.805 0.000 9.295 2.208
## .dsq_suppressin 8.557 0.321 26.693 0.000 8.557 2.200
## .dsq_sublimatin 10.019 0.307 32.634 0.000 10.019 2.706
## .dsq_anticipatn 10.120 0.253 40.019 0.000 10.120 3.313
## .dsq_rctn_frmtn 9.887 0.311 31.838 0.000 9.887 2.623
## .dsq_idealizatn 7.962 0.314 25.351 0.000 7.962 2.080
## .dsq_psed_ltrsm 12.698 0.254 49.971 0.000 12.698 4.192
## .dsq_undoing 11.136 0.334 33.377 0.000 11.136 2.800
## .dsq_rationlztn 8.829 0.245 36.041 0.000 8.829 2.970
## .dsq_isolation 8.588 0.378 22.737 0.000 8.588 1.886
## .dsq_dissociatn 9.583 0.303 31.594 0.000 9.583 2.599
## .dsq_devaluatin 9.763 0.262 37.279 0.000 9.763 3.064
## .dsq_splitting 9.718 0.311 31.278 0.000 9.718 2.552
## .dsq_denial 6.886 0.269 25.555 0.000 6.886 2.100
## .dsq_tstc_fntsy 9.190 0.352 26.081 0.000 9.190 2.153
## .dsq_displacmnt 8.152 0.342 23.834 0.000 8.152 1.992
## .dsq_pssv_ggrss 7.752 0.269 28.835 0.000 7.752 2.421
## .dsq_somatizatn 11.442 0.289 39.627 0.000 11.442 3.236
## .dsq_acting_out 9.439 0.348 27.149 0.000 9.439 2.239
## .dsq_projection 9.350 0.312 29.933 0.000 9.350 2.469
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.138 2.431 4.170 0.000 10.138 0.572
## .dsq_suppressin 12.690 1.866 6.801 0.000 12.690 0.839
## .dsq_sublimatin 9.193 1.722 5.338 0.000 9.193 0.671
## .dsq_anticipatn 5.582 1.092 5.113 0.000 5.582 0.598
## .dsq_rctn_frmtn 12.361 2.543 4.862 0.000 12.361 0.870
## .dsq_idealizatn 11.991 3.017 3.974 0.000 11.991 0.818
## .dsq_psed_ltrsm 6.434 2.501 2.572 0.010 6.434 0.701
## .dsq_undoing 13.467 2.589 5.202 0.000 13.467 0.851
## .dsq_rationlztn 8.794 0.920 9.560 0.000 8.794 0.995
## .dsq_isolation 18.380 1.852 9.923 0.000 18.380 0.887
## .dsq_dissociatn 11.035 1.522 7.248 0.000 11.035 0.811
## .dsq_devaluatin 5.354 0.819 6.535 0.000 5.354 0.527
## .dsq_splitting 9.656 1.677 5.757 0.000 9.656 0.666
## .dsq_denial 9.292 1.029 9.033 0.000 9.292 0.864
## .dsq_tstc_fntsy 15.311 1.775 8.624 0.000 15.311 0.840
## .dsq_displacmnt 15.477 1.685 9.186 0.000 15.477 0.924
## .dsq_pssv_ggrss 7.205 0.973 7.402 0.000 7.205 0.703
## .dsq_somatizatn 11.060 1.075 10.292 0.000 11.060 0.885
## .dsq_acting_out 14.844 1.778 8.351 0.000 14.844 0.835
## .dsq_projection 10.552 1.281 8.240 0.000 10.552 0.736
## Mature 7.585 2.659 2.853 0.004 1.000 1.000
## Neurotic 1.846 2.345 0.787 0.431 1.000 1.000
## Immature 0.040 0.140 0.288 0.773 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.428
## dsq_suppressin 0.161
## dsq_sublimatin 0.329
## dsq_anticipatn 0.402
## dsq_rctn_frmtn 0.130
## dsq_idealizatn 0.182
## dsq_psed_ltrsm 0.299
## dsq_undoing 0.149
## dsq_rationlztn 0.005
## dsq_isolation 0.113
## dsq_dissociatn 0.189
## dsq_devaluatin 0.473
## dsq_splitting 0.334
## dsq_denial 0.136
## dsq_tstc_fntsy 0.160
## dsq_displacmnt 0.076
## dsq_pssv_ggrss 0.297
## dsq_somatizatn 0.115
## dsq_acting_out 0.165
## dsq_projection 0.264
##
##
## Group 2 [Dos_Santos_2020]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.003 0.445
## dsq_suppressin 0.912 0.256 3.559 0.000 1.826 0.451
## dsq_sublimatin 0.711 0.282 2.522 0.012 1.424 0.358
## dsq_anticipatn 1.169 0.313 3.736 0.000 2.343 0.551
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.492 0.371
## dsq_idealizatn 1.286 0.460 2.795 0.005 1.918 0.473
## dsq_psed_ltrsm 0.543 0.278 1.954 0.051 0.810 0.218
## dsq_undoing 1.115 0.602 1.851 0.064 1.663 0.407
## Immature =~
## dsq_rationlztn 1.000 0.483 0.118
## dsq_isolation -3.516 3.717 -0.946 0.344 -1.697 -0.346
## dsq_dissociatn -0.414 1.026 -0.403 0.687 -0.200 -0.064
## dsq_devaluatin -3.159 3.357 -0.941 0.347 -1.525 -0.471
## dsq_splitting -2.905 3.303 -0.879 0.379 -1.402 -0.341
## dsq_denial -1.658 2.104 -0.788 0.431 -0.800 -0.238
## dsq_tstc_fntsy -7.032 7.298 -0.964 0.335 -3.394 -0.640
## dsq_displacmnt -3.222 3.240 -0.995 0.320 -1.555 -0.380
## dsq_pssv_ggrss -5.151 5.233 -0.984 0.325 -2.486 -0.638
## dsq_somatizatn -3.368 3.384 -0.995 0.320 -1.626 -0.341
## dsq_acting_out -5.166 5.472 -0.944 0.345 -2.493 -0.510
## dsq_projection -5.162 5.225 -0.988 0.323 -2.491 -0.623
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 2.841 1.258 2.258 0.024 0.950 0.950
## Immature 0.009 0.167 0.054 0.957 0.009 0.009
## Neurotic ~~
## Immature -0.194 0.150 -1.299 0.194 -0.270 -0.270
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 8.247 0.319 25.813 0.000 8.247 1.832
## .dsq_suppressin 6.762 0.288 23.479 0.000 6.762 1.669
## .dsq_sublimatin 9.803 0.283 34.666 0.000 9.803 2.463
## .dsq_anticipatn 11.813 0.302 39.081 0.000 11.813 2.779
## .dsq_rctn_frmtn 8.958 0.286 31.312 0.000 8.958 2.225
## .dsq_idealizatn 8.101 0.288 28.128 0.000 8.101 1.996
## .dsq_psed_ltrsm 11.032 0.264 41.790 0.000 11.032 2.968
## .dsq_undoing 9.453 0.290 32.580 0.000 9.453 2.311
## .dsq_rationlztn 8.435 0.289 29.152 0.000 8.435 2.065
## .dsq_isolation 9.848 0.349 28.228 0.000 9.848 2.007
## .dsq_dissociatn 5.259 0.222 23.650 0.000 5.259 1.682
## .dsq_devaluatin 7.185 0.230 31.224 0.000 7.185 2.219
## .dsq_splitting 10.012 0.292 34.286 0.000 10.012 2.434
## .dsq_denial 5.391 0.239 22.516 0.000 5.391 1.605
## .dsq_tstc_fntsy 10.323 0.376 27.429 0.000 10.323 1.946
## .dsq_displacmnt 10.039 0.291 34.539 0.000 10.039 2.450
## .dsq_pssv_ggrss 8.764 0.277 31.694 0.000 8.764 2.249
## .dsq_somatizatn 11.555 0.387 29.840 0.000 11.555 2.423
## .dsq_acting_out 10.729 0.348 30.873 0.000 10.729 2.193
## .dsq_projection 8.163 0.283 28.859 0.000 8.163 2.042
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 16.243 1.886 8.613 0.000 16.243 0.802
## .dsq_suppressin 13.088 2.015 6.494 0.000 13.088 0.797
## .dsq_sublimatin 13.807 1.628 8.484 0.000 13.807 0.872
## .dsq_anticipatn 12.584 1.779 7.073 0.000 12.584 0.696
## .dsq_rctn_frmtn 13.979 2.052 6.813 0.000 13.979 0.863
## .dsq_idealizatn 12.791 1.754 7.294 0.000 12.791 0.777
## .dsq_psed_ltrsm 13.161 1.355 9.716 0.000 13.161 0.953
## .dsq_undoing 13.966 1.832 7.623 0.000 13.966 0.835
## .dsq_rationlztn 16.448 1.470 11.192 0.000 16.448 0.986
## .dsq_isolation 21.193 1.878 11.284 0.000 21.193 0.880
## .dsq_dissociatn 9.737 0.941 10.347 0.000 9.737 0.996
## .dsq_devaluatin 8.165 0.893 9.143 0.000 8.165 0.778
## .dsq_splitting 14.957 1.620 9.235 0.000 14.957 0.884
## .dsq_denial 10.638 0.971 10.955 0.000 10.638 0.943
## .dsq_tstc_fntsy 16.636 2.314 7.188 0.000 16.636 0.591
## .dsq_displacmnt 14.367 1.355 10.604 0.000 14.367 0.856
## .dsq_pssv_ggrss 9.000 1.362 6.605 0.000 9.000 0.593
## .dsq_somatizatn 20.110 2.234 9.002 0.000 20.110 0.884
## .dsq_acting_out 17.718 1.826 9.706 0.000 17.718 0.740
## .dsq_projection 9.783 1.345 7.274 0.000 9.783 0.612
## Mature 4.014 1.805 2.223 0.026 1.000 1.000
## Neurotic 2.226 1.761 1.264 0.206 1.000 1.000
## Immature 0.233 0.481 0.484 0.628 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.198
## dsq_suppressin 0.203
## dsq_sublimatin 0.128
## dsq_anticipatn 0.304
## dsq_rctn_frmtn 0.137
## dsq_idealizatn 0.223
## dsq_psed_ltrsm 0.047
## dsq_undoing 0.165
## dsq_rationlztn 0.014
## dsq_isolation 0.120
## dsq_dissociatn 0.004
## dsq_devaluatin 0.222
## dsq_splitting 0.116
## dsq_denial 0.057
## dsq_tstc_fntsy 0.409
## dsq_displacmnt 0.144
## dsq_pssv_ggrss 0.407
## dsq_somatizatn 0.116
## dsq_acting_out 0.260
## dsq_projection 0.388fit4_comb <- cfa(model_4f, data = DSQ_Data_mg, group = "StudyID", estimator = "mlr", missing = "fiml")## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite in group 1; use lavInspect(fit, "cov.lv") to
## investigate.## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite in group 2; use lavInspect(fit, "cov.lv") to
## investigate.# Inspect 4-factor
summary(fit4_comb,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 257 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 132
##
## Number of observations per group:
## Dekker_2008/Van_2009 151
## Dos_Santos_2020 210
## Number of missing patterns per group:
## Dekker_2008/Van_2009 18
## Dos_Santos_2020 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 608.104 572.937
## Degrees of freedom 328 328
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.061
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## Dekker_2008/Van_2009 301.554 301.554
## Dos_Santos_2020 271.382 271.382
##
## Model Test Baseline Model:
##
## Test statistic 1390.926 1256.752
## Degrees of freedom 380 380
## P-value 0.000 0.000
## Scaling correction factor 1.107
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.723 0.721
## Tucker-Lewis Index (TLI) 0.679 0.676
##
## Robust Comparative Fit Index (CFI) 0.741
## Robust Tucker-Lewis Index (TLI) 0.700
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -18603.557 -18603.557
## Scaling correction factor 1.083
## for the MLR correction
## Loglikelihood unrestricted model (H1) -18299.505 -18299.505
## Scaling correction factor 1.068
## for the MLR correction
##
## Akaike (AIC) 37471.115 37471.115
## Bayesian (BIC) 37984.447 37984.447
## Sample-size adjusted Bayesian (SABIC) 37565.673 37565.673
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.069 0.064
## 90 Percent confidence interval - lower 0.060 0.056
## 90 Percent confidence interval - upper 0.077 0.073
## P-value H_0: RMSEA <= 0.050 0.000 0.004
## P-value H_0: RMSEA >= 0.080 0.014 0.001
##
## Robust RMSEA 0.067
## 90 Percent confidence interval - lower 0.057
## 90 Percent confidence interval - upper 0.077
## P-value H_0: Robust RMSEA <= 0.050 0.003
## P-value H_0: Robust RMSEA >= 0.080 0.015
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.086 0.086
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [Dekker_2008/Van_2009]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.715 0.646
## dsq_suppressin 0.600 0.196 3.064 0.002 1.630 0.420
## dsq_sublimatin 0.769 0.221 3.487 0.000 2.088 0.565
## dsq_anticipatn 0.712 0.163 4.376 0.000 1.932 0.634
## dsq_rationlztn 0.559 0.128 4.364 0.000 1.516 0.510
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.487 0.394
## dsq_idealizatn 0.940 0.738 1.273 0.203 1.398 0.365
## dsq_psed_ltrsm 1.216 0.505 2.407 0.016 1.809 0.598
## dsq_undoing 1.072 0.374 2.869 0.004 1.594 0.401
## Immature =~
## dsq_tstc_fntsy 1.000 1.718 0.403
## dsq_displacmnt 0.649 0.293 2.214 0.027 1.115 0.272
## dsq_pssv_ggrss 0.936 0.306 3.056 0.002 1.609 0.502
## dsq_somatizatn 0.662 0.215 3.073 0.002 1.137 0.322
## dsq_acting_out 0.948 0.336 2.825 0.005 1.630 0.387
## dsq_projection 1.089 0.312 3.491 0.000 1.871 0.494
## Image_Distorting =~
## dsq_isolation 1.000 1.484 0.326
## dsq_dissociatn 1.052 0.358 2.942 0.003 1.562 0.423
## dsq_devaluatin 1.441 0.487 2.961 0.003 2.139 0.671
## dsq_splitting 1.477 0.600 2.459 0.014 2.191 0.575
## dsq_denial 0.752 0.243 3.091 0.002 1.116 0.340
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 2.485 0.780 3.187 0.001 0.615 0.615
## Immature -0.047 0.917 -0.051 0.959 -0.010 -0.010
## Image_Distrtng 0.154 0.598 0.258 0.797 0.038 0.038
## Neurotic ~~
## Immature 0.861 0.813 1.059 0.290 0.337 0.337
## Image_Distrtng 0.787 0.502 1.566 0.117 0.356 0.356
## Immature ~~
## Image_Distrtng 2.854 1.070 2.667 0.008 1.119 1.119
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.306 0.346 26.925 0.000 9.306 2.215
## .dsq_suppressin 8.568 0.320 26.748 0.000 8.568 2.206
## .dsq_sublimatin 10.026 0.308 32.571 0.000 10.026 2.711
## .dsq_anticipatn 10.123 0.253 40.053 0.000 10.123 3.320
## .dsq_rationlztn 8.847 0.245 36.084 0.000 8.847 2.976
## .dsq_rctn_frmtn 9.884 0.310 31.894 0.000 9.884 2.621
## .dsq_idealizatn 7.965 0.315 25.320 0.000 7.965 2.081
## .dsq_psed_ltrsm 12.696 0.254 50.029 0.000 12.696 4.195
## .dsq_undoing 11.139 0.333 33.474 0.000 11.139 2.802
## .dsq_tstc_fntsy 9.187 0.352 26.081 0.000 9.187 2.152
## .dsq_displacmnt 8.146 0.342 23.818 0.000 8.146 1.990
## .dsq_pssv_ggrss 7.737 0.270 28.699 0.000 7.737 2.416
## .dsq_somatizatn 11.442 0.289 39.622 0.000 11.442 3.236
## .dsq_acting_out 9.434 0.347 27.153 0.000 9.434 2.240
## .dsq_projection 9.348 0.313 29.891 0.000 9.348 2.469
## .dsq_isolation 8.592 0.377 22.759 0.000 8.592 1.887
## .dsq_dissociatn 9.582 0.303 31.595 0.000 9.582 2.598
## .dsq_devaluatin 9.761 0.262 37.288 0.000 9.761 3.063
## .dsq_splitting 9.718 0.311 31.272 0.000 9.718 2.552
## .dsq_denial 6.885 0.269 25.552 0.000 6.885 2.100
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.285 2.147 4.790 0.000 10.285 0.583
## .dsq_suppressin 12.433 1.791 6.943 0.000 12.433 0.824
## .dsq_sublimatin 9.315 1.620 5.748 0.000 9.315 0.681
## .dsq_anticipatn 5.561 1.054 5.276 0.000 5.561 0.598
## .dsq_rationlztn 6.540 0.926 7.060 0.000 6.540 0.740
## .dsq_rctn_frmtn 12.011 1.940 6.190 0.000 12.011 0.844
## .dsq_idealizatn 12.701 2.144 5.923 0.000 12.701 0.867
## .dsq_psed_ltrsm 5.888 2.203 2.672 0.008 5.888 0.643
## .dsq_undoing 13.260 2.032 6.524 0.000 13.260 0.839
## .dsq_tstc_fntsy 15.266 1.710 8.925 0.000 15.266 0.838
## .dsq_displacmnt 15.520 1.686 9.208 0.000 15.520 0.926
## .dsq_pssv_ggrss 7.668 1.061 7.227 0.000 7.668 0.748
## .dsq_somatizatn 11.205 1.089 10.293 0.000 11.205 0.896
## .dsq_acting_out 15.088 1.751 8.616 0.000 15.088 0.850
## .dsq_projection 10.833 1.463 7.404 0.000 10.833 0.756
## .dsq_isolation 18.522 1.913 9.681 0.000 18.522 0.894
## .dsq_dissociatn 11.162 1.459 7.648 0.000 11.162 0.821
## .dsq_devaluatin 5.580 0.793 7.040 0.000 5.580 0.550
## .dsq_splitting 9.699 1.624 5.973 0.000 9.699 0.669
## .dsq_denial 9.504 1.060 8.967 0.000 9.504 0.884
## Mature 7.371 2.356 3.129 0.002 1.000 1.000
## Neurotic 2.212 1.622 1.364 0.173 1.000 1.000
## Immature 2.953 1.281 2.305 0.021 1.000 1.000
## Image_Distrtng 2.202 1.431 1.539 0.124 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.417
## dsq_suppressin 0.176
## dsq_sublimatin 0.319
## dsq_anticipatn 0.402
## dsq_rationlztn 0.260
## dsq_rctn_frmtn 0.156
## dsq_idealizatn 0.133
## dsq_psed_ltrsm 0.357
## dsq_undoing 0.161
## dsq_tstc_fntsy 0.162
## dsq_displacmnt 0.074
## dsq_pssv_ggrss 0.252
## dsq_somatizatn 0.104
## dsq_acting_out 0.150
## dsq_projection 0.244
## dsq_isolation 0.106
## dsq_dissociatn 0.179
## dsq_devaluatin 0.450
## dsq_splitting 0.331
## dsq_denial 0.116
##
##
## Group 2 [Dos_Santos_2020]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.202 0.489
## dsq_suppressin 0.856 0.249 3.431 0.001 1.885 0.465
## dsq_sublimatin 0.721 0.212 3.408 0.001 1.588 0.399
## dsq_anticipatn 0.962 0.233 4.137 0.000 2.119 0.498
## dsq_rationlztn 1.274 0.240 5.306 0.000 2.806 0.687
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.599 0.397
## dsq_idealizatn 1.148 0.359 3.198 0.001 1.836 0.452
## dsq_psed_ltrsm 0.574 0.264 2.172 0.030 0.917 0.247
## dsq_undoing 1.012 0.474 2.136 0.033 1.617 0.395
## Immature =~
## dsq_tstc_fntsy 1.000 3.324 0.626
## dsq_displacmnt 0.493 0.118 4.194 0.000 1.639 0.400
## dsq_pssv_ggrss 0.744 0.123 6.071 0.000 2.474 0.635
## dsq_somatizatn 0.499 0.154 3.244 0.001 1.657 0.348
## dsq_acting_out 0.726 0.138 5.264 0.000 2.414 0.493
## dsq_projection 0.778 0.120 6.499 0.000 2.586 0.647
## Image_Distorting =~
## dsq_isolation 1.000 1.764 0.360
## dsq_dissociatn 0.648 0.633 1.024 0.306 1.143 0.366
## dsq_devaluatin 0.870 0.252 3.453 0.001 1.535 0.474
## dsq_splitting 0.763 0.330 2.313 0.021 1.346 0.327
## dsq_denial 0.865 0.545 1.587 0.113 1.527 0.455
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 3.088 1.139 2.712 0.007 0.877 0.877
## Immature -1.208 0.924 -1.307 0.191 -0.165 -0.165
## Image_Distrtng 1.628 0.877 1.857 0.063 0.419 0.419
## Neurotic ~~
## Immature 1.276 0.882 1.446 0.148 0.240 0.240
## Image_Distrtng 1.252 0.590 2.123 0.034 0.444 0.444
## Immature ~~
## Image_Distrtng 3.983 2.770 1.438 0.150 0.679 0.679
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 8.244 0.319 25.815 0.000 8.244 1.832
## .dsq_suppressin 6.760 0.288 23.488 0.000 6.760 1.668
## .dsq_sublimatin 9.801 0.283 34.646 0.000 9.801 2.463
## .dsq_anticipatn 11.810 0.302 39.076 0.000 11.810 2.778
## .dsq_rationlztn 8.435 0.290 29.133 0.000 8.435 2.065
## .dsq_rctn_frmtn 8.958 0.286 31.327 0.000 8.958 2.225
## .dsq_idealizatn 8.100 0.288 28.125 0.000 8.100 1.996
## .dsq_psed_ltrsm 11.032 0.264 41.786 0.000 11.032 2.968
## .dsq_undoing 9.453 0.290 32.580 0.000 9.453 2.311
## .dsq_tstc_fntsy 10.323 0.376 27.444 0.000 10.323 1.945
## .dsq_displacmnt 10.040 0.291 34.522 0.000 10.040 2.450
## .dsq_pssv_ggrss 8.765 0.276 31.700 0.000 8.765 2.250
## .dsq_somatizatn 11.561 0.388 29.778 0.000 11.561 2.425
## .dsq_acting_out 10.729 0.347 30.900 0.000 10.729 2.193
## .dsq_projection 8.165 0.283 28.834 0.000 8.165 2.042
## .dsq_isolation 9.844 0.349 28.227 0.000 9.844 2.006
## .dsq_dissociatn 5.265 0.223 23.615 0.000 5.265 1.684
## .dsq_devaluatin 7.181 0.230 31.232 0.000 7.181 2.217
## .dsq_splitting 10.008 0.292 34.261 0.000 10.008 2.433
## .dsq_denial 5.393 0.239 22.543 0.000 5.393 1.606
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 15.407 1.860 8.285 0.000 15.407 0.761
## .dsq_suppressin 12.872 1.983 6.491 0.000 12.872 0.784
## .dsq_sublimatin 13.313 1.510 8.815 0.000 13.313 0.841
## .dsq_anticipatn 13.583 1.510 8.996 0.000 13.583 0.752
## .dsq_rationlztn 8.805 1.386 6.355 0.000 8.805 0.528
## .dsq_rctn_frmtn 13.649 1.908 7.154 0.000 13.649 0.842
## .dsq_idealizatn 13.101 1.892 6.925 0.000 13.101 0.795
## .dsq_psed_ltrsm 12.976 1.307 9.927 0.000 12.976 0.939
## .dsq_undoing 14.116 1.818 7.764 0.000 14.116 0.844
## .dsq_tstc_fntsy 17.105 2.528 6.767 0.000 17.105 0.608
## .dsq_displacmnt 14.101 1.419 9.939 0.000 14.101 0.840
## .dsq_pssv_ggrss 9.062 1.373 6.601 0.000 9.062 0.597
## .dsq_somatizatn 19.989 2.221 9.000 0.000 19.989 0.879
## .dsq_acting_out 18.106 1.946 9.306 0.000 18.106 0.757
## .dsq_projection 9.302 1.373 6.776 0.000 9.302 0.582
## .dsq_isolation 20.958 2.302 9.106 0.000 20.958 0.871
## .dsq_dissociatn 8.471 1.967 4.307 0.000 8.471 0.866
## .dsq_devaluatin 8.132 1.424 5.710 0.000 8.132 0.775
## .dsq_splitting 15.109 1.745 8.657 0.000 15.109 0.893
## .dsq_denial 8.947 1.878 4.765 0.000 8.947 0.793
## Mature 4.850 1.887 2.570 0.010 1.000 1.000
## Neurotic 2.556 1.642 1.557 0.119 1.000 1.000
## Immature 11.047 2.577 4.287 0.000 1.000 1.000
## Image_Distrtng 3.112 2.101 1.481 0.139 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.239
## dsq_suppressin 0.216
## dsq_sublimatin 0.159
## dsq_anticipatn 0.248
## dsq_rationlztn 0.472
## dsq_rctn_frmtn 0.158
## dsq_idealizatn 0.205
## dsq_psed_ltrsm 0.061
## dsq_undoing 0.156
## dsq_tstc_fntsy 0.392
## dsq_displacmnt 0.160
## dsq_pssv_ggrss 0.403
## dsq_somatizatn 0.121
## dsq_acting_out 0.243
## dsq_projection 0.418
## dsq_isolation 0.129
## dsq_dissociatn 0.134
## dsq_devaluatin 0.225
## dsq_splitting 0.107
## dsq_denial 0.207cov2cor(lavInspect(fit4_comb, "cov.lv")[[1]]) # Group 1: Dekker## Mature Neurtc Immatr Img_Ds
## Mature 1.000
## Neurotic 0.615 1.000
## Immature -0.010 0.337 1.000
## Image_Distorting 0.038 0.356 1.119 1.000cov2cor(lavInspect(fit4_comb, "cov.lv")[[2]]) # Group 2: Dos Santos## Mature Neurtc Immatr Img_Ds
## Mature 1.000
## Neurotic 0.877 1.000
## Immature -0.165 0.240 1.000
## Image_Distorting 0.419 0.444 0.679 1.000eigen(lavInspect(fit4_comb, "cov.lv")[[1]])$values # For group 1## [1] 8.4624333 5.6195531 0.9606699 -0.3042989eigen(lavInspect(fit4_comb, "cov.lv")[[2]])$values # For group 2## [1] 12.9668323 7.6046000 1.1045038 -0.1094094# Fit models for Dekker & Van subset
fit1_dekker <- cfa(model_1f, data = dekker_df, estimator = "mlr", missing = "fiml")
# Inspect 1-factor
summary(fit1_dekker,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 124 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 60
##
## Number of observations 151
## Number of missing patterns 18
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 440.112 422.559
## Degrees of freedom 170 170
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.042
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 666.331 620.879
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.073
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.433 0.414
## Tucker-Lewis Index (TLI) 0.366 0.345
##
## Robust Comparative Fit Index (CFI) 0.434
## Robust Tucker-Lewis Index (TLI) 0.368
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7836.322 -7836.322
## Scaling correction factor 1.034
## for the MLR correction
## Loglikelihood unrestricted model (H1) -7616.266 -7616.266
## Scaling correction factor 1.040
## for the MLR correction
##
## Akaike (AIC) 15792.645 15792.645
## Bayesian (BIC) 15973.681 15973.681
## Sample-size adjusted Bayesian (SABIC) 15783.788 15783.788
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.103 0.099
## 90 Percent confidence interval - lower 0.091 0.088
## 90 Percent confidence interval - upper 0.114 0.111
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.999 0.996
##
## Robust RMSEA 0.104
## 90 Percent confidence interval - lower 0.091
## 90 Percent confidence interval - upper 0.117
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.999
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.112 0.112
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Factor1 =~
## dsq_sublimatin 1.000 0.536 0.145
## dsq_humor -0.236 1.605 -0.147 0.883 -0.126 -0.030
## dsq_anticipatn 0.583 0.615 0.948 0.343 0.313 0.102
## dsq_suppressin 2.253 1.845 1.221 0.222 1.208 0.311
## dsq_psed_ltrsm 0.734 0.563 1.304 0.192 0.394 0.130
## dsq_idealizatn 2.333 2.323 1.004 0.315 1.251 0.327
## dsq_rctn_frmtn 1.256 1.323 0.949 0.342 0.674 0.179
## dsq_undoing 2.539 2.859 0.888 0.375 1.362 0.343
## dsq_rationlztn 0.677 0.611 1.110 0.267 0.363 0.122
## dsq_projection 3.272 4.148 0.789 0.430 1.755 0.464
## dsq_pssv_ggrss 3.311 3.542 0.935 0.350 1.776 0.555
## dsq_acting_out 3.287 3.532 0.931 0.352 1.763 0.418
## dsq_isolation 2.873 3.306 0.869 0.385 1.541 0.339
## dsq_tstc_fntsy 3.052 3.717 0.821 0.412 1.637 0.383
## dsq_denial 2.270 2.702 0.840 0.401 1.218 0.371
## dsq_displacmnt 2.115 2.577 0.821 0.412 1.134 0.277
## dsq_dissociatn 2.821 3.517 0.802 0.422 1.513 0.410
## dsq_splitting 4.228 4.705 0.899 0.369 2.268 0.596
## dsq_devaluatin 3.935 4.604 0.855 0.393 2.111 0.663
## dsq_somatizatn 2.309 2.594 0.890 0.373 1.239 0.350
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_sublimatin 10.016 0.308 32.524 0.000 10.016 2.709
## .dsq_humor 9.300 0.347 26.830 0.000 9.300 2.214
## .dsq_anticipatn 10.130 0.253 39.961 0.000 10.130 3.322
## .dsq_suppressin 8.575 0.321 26.749 0.000 8.575 2.206
## .dsq_psed_ltrsm 12.707 0.254 50.061 0.000 12.707 4.187
## .dsq_idealizatn 7.962 0.314 25.368 0.000 7.962 2.080
## .dsq_rctn_frmtn 9.898 0.310 31.967 0.000 9.898 2.629
## .dsq_undoing 11.142 0.333 33.503 0.000 11.142 2.806
## .dsq_rationlztn 8.830 0.245 36.025 0.000 8.830 2.971
## .dsq_projection 9.348 0.312 29.926 0.000 9.348 2.469
## .dsq_pssv_ggrss 7.757 0.269 28.885 0.000 7.757 2.426
## .dsq_acting_out 9.440 0.348 27.125 0.000 9.440 2.238
## .dsq_isolation 8.587 0.378 22.738 0.000 8.587 1.887
## .dsq_tstc_fntsy 9.194 0.352 26.093 0.000 9.194 2.153
## .dsq_denial 6.883 0.269 25.568 0.000 6.883 2.100
## .dsq_displacmnt 8.150 0.342 23.844 0.000 8.150 1.992
## .dsq_dissociatn 9.579 0.304 31.540 0.000 9.579 2.596
## .dsq_splitting 9.718 0.311 31.275 0.000 9.718 2.552
## .dsq_devaluatin 9.754 0.262 37.273 0.000 9.754 3.064
## .dsq_somatizatn 11.442 0.289 39.623 0.000 11.442 3.236
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_sublimatin 13.382 1.465 9.138 0.000 13.382 0.979
## .dsq_humor 17.622 1.497 11.768 0.000 17.622 0.999
## .dsq_anticipatn 9.201 1.006 9.144 0.000 9.201 0.989
## .dsq_suppressin 13.646 1.794 7.607 0.000 13.646 0.903
## .dsq_psed_ltrsm 9.057 1.162 7.795 0.000 9.057 0.983
## .dsq_idealizatn 13.083 1.611 8.119 0.000 13.083 0.893
## .dsq_rctn_frmtn 13.725 1.531 8.964 0.000 13.725 0.968
## .dsq_undoing 13.917 1.396 9.970 0.000 13.917 0.882
## .dsq_rationlztn 8.701 0.939 9.267 0.000 8.701 0.985
## .dsq_projection 11.253 1.445 7.787 0.000 11.253 0.785
## .dsq_pssv_ggrss 7.072 0.947 7.466 0.000 7.072 0.692
## .dsq_acting_out 14.678 1.801 8.148 0.000 14.678 0.825
## .dsq_isolation 18.341 1.798 10.199 0.000 18.341 0.885
## .dsq_tstc_fntsy 15.559 1.854 8.394 0.000 15.559 0.853
## .dsq_denial 9.265 0.987 9.391 0.000 9.265 0.862
## .dsq_displacmnt 15.453 1.670 9.256 0.000 15.453 0.923
## .dsq_dissociatn 11.325 1.583 7.153 0.000 11.325 0.832
## .dsq_splitting 9.358 1.671 5.601 0.000 9.358 0.645
## .dsq_devaluatin 5.682 0.945 6.014 0.000 5.682 0.561
## .dsq_somatizatn 10.965 1.087 10.084 0.000 10.965 0.877
## Factor1 0.288 0.633 0.454 0.650 1.000 1.000
##
## R-Square:
## Estimate
## dsq_sublimatin 0.021
## dsq_humor 0.001
## dsq_anticipatn 0.011
## dsq_suppressin 0.097
## dsq_psed_ltrsm 0.017
## dsq_idealizatn 0.107
## dsq_rctn_frmtn 0.032
## dsq_undoing 0.118
## dsq_rationlztn 0.015
## dsq_projection 0.215
## dsq_pssv_ggrss 0.308
## dsq_acting_out 0.175
## dsq_isolation 0.115
## dsq_tstc_fntsy 0.147
## dsq_denial 0.138
## dsq_displacmnt 0.077
## dsq_dissociatn 0.168
## dsq_splitting 0.355
## dsq_devaluatin 0.439
## dsq_somatizatn 0.123fit3_dekker <- cfa(model_3f, data = dekker_df, estimator = "mlr", missing = "fiml")
# Inspect 3-factor
summary(fit3_dekker,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 201 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 63
##
## Number of observations 151
## Number of missing patterns 18
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 349.664 340.357
## Degrees of freedom 167 167
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.027
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 666.331 620.879
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.073
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.617 0.598
## Tucker-Lewis Index (TLI) 0.564 0.542
##
## Robust Comparative Fit Index (CFI) 0.619
## Robust Tucker-Lewis Index (TLI) 0.567
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7791.099 -7791.099
## Scaling correction factor 1.072
## for the MLR correction
## Loglikelihood unrestricted model (H1) -7616.266 -7616.266
## Scaling correction factor 1.040
## for the MLR correction
##
## Akaike (AIC) 15708.197 15708.197
## Bayesian (BIC) 15898.286 15898.286
## Sample-size adjusted Bayesian (SABIC) 15698.898 15698.898
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.085 0.083
## 90 Percent confidence interval - lower 0.073 0.070
## 90 Percent confidence interval - upper 0.098 0.095
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.757 0.660
##
## Robust RMSEA 0.086
## 90 Percent confidence interval - lower 0.073
## 90 Percent confidence interval - upper 0.100
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.781
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.098 0.098
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.754 0.654
## dsq_suppressin 0.566 0.223 2.543 0.011 1.559 0.401
## dsq_sublimatin 0.771 0.256 3.013 0.003 2.124 0.574
## dsq_anticipatn 0.703 0.162 4.334 0.000 1.936 0.634
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.359 0.360
## dsq_idealizatn 1.202 1.403 0.857 0.392 1.633 0.426
## dsq_psed_ltrsm 1.218 0.488 2.496 0.013 1.655 0.546
## dsq_undoing 1.129 0.413 2.734 0.006 1.533 0.385
## Immature =~
## dsq_rationlztn 1.000 0.201 0.068
## dsq_isolation 7.629 13.139 0.581 0.561 1.533 0.337
## dsq_dissociatn 7.975 14.505 0.550 0.582 1.602 0.434
## dsq_devaluatin 10.904 19.376 0.563 0.574 2.191 0.687
## dsq_splitting 10.954 19.035 0.575 0.565 2.201 0.578
## dsq_denial 6.010 10.255 0.586 0.558 1.207 0.368
## dsq_tstc_fntsy 8.501 15.480 0.549 0.583 1.708 0.400
## dsq_displacmnt 5.617 10.006 0.561 0.575 1.128 0.276
## dsq_pssv_ggrss 8.690 14.705 0.591 0.555 1.746 0.545
## dsq_somatizatn 5.970 10.949 0.545 0.586 1.199 0.339
## dsq_acting_out 8.509 14.618 0.582 0.560 1.709 0.406
## dsq_projection 9.684 17.787 0.544 0.586 1.946 0.514
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 2.475 0.976 2.536 0.011 0.662 0.662
## Immature 0.002 0.094 0.024 0.981 0.004 0.004
## Neurotic ~~
## Immature 0.106 0.190 0.559 0.576 0.390 0.390
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.295 0.347 26.805 0.000 9.295 2.208
## .dsq_suppressin 8.557 0.321 26.693 0.000 8.557 2.200
## .dsq_sublimatin 10.019 0.307 32.634 0.000 10.019 2.706
## .dsq_anticipatn 10.120 0.253 40.019 0.000 10.120 3.313
## .dsq_rctn_frmtn 9.887 0.311 31.838 0.000 9.887 2.623
## .dsq_idealizatn 7.962 0.314 25.351 0.000 7.962 2.080
## .dsq_psed_ltrsm 12.698 0.254 49.971 0.000 12.698 4.192
## .dsq_undoing 11.136 0.334 33.377 0.000 11.136 2.800
## .dsq_rationlztn 8.829 0.245 36.041 0.000 8.829 2.971
## .dsq_isolation 8.588 0.378 22.737 0.000 8.588 1.886
## .dsq_dissociatn 9.583 0.303 31.594 0.000 9.583 2.598
## .dsq_devaluatin 9.763 0.262 37.279 0.000 9.763 3.064
## .dsq_splitting 9.718 0.311 31.278 0.000 9.718 2.552
## .dsq_denial 6.886 0.269 25.555 0.000 6.886 2.100
## .dsq_tstc_fntsy 9.190 0.352 26.081 0.000 9.190 2.153
## .dsq_displacmnt 8.152 0.342 23.834 0.000 8.152 1.992
## .dsq_pssv_ggrss 7.752 0.269 28.835 0.000 7.752 2.421
## .dsq_somatizatn 11.442 0.289 39.627 0.000 11.442 3.236
## .dsq_acting_out 9.439 0.348 27.148 0.000 9.439 2.239
## .dsq_projection 9.350 0.312 29.933 0.000 9.350 2.469
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.137 2.431 4.170 0.000 10.137 0.572
## .dsq_suppressin 12.690 1.866 6.801 0.000 12.690 0.839
## .dsq_sublimatin 9.193 1.722 5.338 0.000 9.193 0.671
## .dsq_anticipatn 5.582 1.092 5.113 0.000 5.582 0.598
## .dsq_rctn_frmtn 12.361 2.542 4.862 0.000 12.361 0.870
## .dsq_idealizatn 11.992 3.017 3.974 0.000 11.992 0.818
## .dsq_psed_ltrsm 6.434 2.501 2.572 0.010 6.434 0.701
## .dsq_undoing 13.467 2.588 5.203 0.000 13.467 0.851
## .dsq_rationlztn 8.794 0.920 9.560 0.000 8.794 0.995
## .dsq_isolation 18.379 1.852 9.923 0.000 18.379 0.887
## .dsq_dissociatn 11.034 1.523 7.247 0.000 11.034 0.811
## .dsq_devaluatin 5.354 0.819 6.535 0.000 5.354 0.527
## .dsq_splitting 9.656 1.677 5.757 0.000 9.656 0.666
## .dsq_denial 9.292 1.029 9.033 0.000 9.292 0.864
## .dsq_tstc_fntsy 15.310 1.775 8.624 0.000 15.310 0.840
## .dsq_displacmnt 15.477 1.685 9.186 0.000 15.477 0.924
## .dsq_pssv_ggrss 7.205 0.973 7.401 0.000 7.205 0.703
## .dsq_somatizatn 11.060 1.075 10.292 0.000 11.060 0.885
## .dsq_acting_out 14.845 1.778 8.351 0.000 14.845 0.836
## .dsq_projection 10.552 1.281 8.238 0.000 10.552 0.736
## Mature 7.585 2.659 2.853 0.004 1.000 1.000
## Neurotic 1.846 2.345 0.787 0.431 1.000 1.000
## Immature 0.040 0.141 0.286 0.775 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.428
## dsq_suppressin 0.161
## dsq_sublimatin 0.329
## dsq_anticipatn 0.402
## dsq_rctn_frmtn 0.130
## dsq_idealizatn 0.182
## dsq_psed_ltrsm 0.299
## dsq_undoing 0.149
## dsq_rationlztn 0.005
## dsq_isolation 0.113
## dsq_dissociatn 0.189
## dsq_devaluatin 0.473
## dsq_splitting 0.334
## dsq_denial 0.136
## dsq_tstc_fntsy 0.160
## dsq_displacmnt 0.076
## dsq_pssv_ggrss 0.297
## dsq_somatizatn 0.115
## dsq_acting_out 0.164
## dsq_projection 0.264fit4_dekker <- cfa(model_4f, data = dekker_df, estimator = "mlr", missing = "fiml")## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use lavInspect(fit, "cov.lv") to investigate.# Inspect 4-factor for Dekker & Van
summary(fit4_dekker,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 141 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 66
##
## Number of observations 151
## Number of missing patterns 18
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 320.064 308.506
## Degrees of freedom 164 164
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.037
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 666.331 620.879
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.073
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.672 0.665
## Tucker-Lewis Index (TLI) 0.620 0.611
##
## Robust Comparative Fit Index (CFI) 0.680
## Robust Tucker-Lewis Index (TLI) 0.629
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7776.299 -7776.299
## Scaling correction factor 1.045
## for the MLR correction
## Loglikelihood unrestricted model (H1) -7616.266 -7616.266
## Scaling correction factor 1.040
## for the MLR correction
##
## Akaike (AIC) 15684.597 15684.597
## Bayesian (BIC) 15883.737 15883.737
## Sample-size adjusted Bayesian (SABIC) 15674.854 15674.854
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.079 0.076
## 90 Percent confidence interval - lower 0.066 0.063
## 90 Percent confidence interval - upper 0.092 0.089
## P-value H_0: RMSEA <= 0.050 0.000 0.001
## P-value H_0: RMSEA >= 0.080 0.480 0.331
##
## Robust RMSEA 0.080
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.094
## P-value H_0: Robust RMSEA <= 0.050 0.001
## P-value H_0: Robust RMSEA >= 0.080 0.500
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.089 0.089
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.715 0.646
## dsq_suppressin 0.600 0.196 3.064 0.002 1.630 0.420
## dsq_sublimatin 0.769 0.221 3.487 0.000 2.088 0.565
## dsq_anticipatn 0.712 0.163 4.376 0.000 1.932 0.634
## dsq_rationlztn 0.559 0.128 4.364 0.000 1.516 0.510
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.487 0.394
## dsq_idealizatn 0.940 0.739 1.273 0.203 1.398 0.365
## dsq_psed_ltrsm 1.217 0.506 2.407 0.016 1.809 0.598
## dsq_undoing 1.072 0.374 2.868 0.004 1.594 0.401
## Immature =~
## dsq_tstc_fntsy 1.000 1.719 0.403
## dsq_displacmnt 0.649 0.293 2.214 0.027 1.115 0.272
## dsq_pssv_ggrss 0.936 0.306 3.057 0.002 1.609 0.502
## dsq_somatizatn 0.662 0.215 3.074 0.002 1.137 0.322
## dsq_acting_out 0.948 0.336 2.826 0.005 1.630 0.387
## dsq_projection 1.088 0.312 3.492 0.000 1.871 0.494
## Image_Distorting =~
## dsq_isolation 1.000 1.485 0.326
## dsq_dissociatn 1.052 0.358 2.942 0.003 1.562 0.423
## dsq_devaluatin 1.441 0.486 2.962 0.003 2.139 0.671
## dsq_splitting 1.476 0.600 2.459 0.014 2.191 0.575
## dsq_denial 0.752 0.243 3.091 0.002 1.116 0.340
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 2.484 0.779 3.186 0.001 0.615 0.615
## Immature -0.047 0.918 -0.052 0.959 -0.010 -0.010
## Image_Distrtng 0.154 0.598 0.257 0.797 0.038 0.038
## Neurotic ~~
## Immature 0.861 0.813 1.059 0.290 0.337 0.337
## Image_Distrtng 0.787 0.502 1.566 0.117 0.356 0.356
## Immature ~~
## Image_Distrtng 2.856 1.071 2.667 0.008 1.119 1.119
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.306 0.346 26.925 0.000 9.306 2.215
## .dsq_suppressin 8.568 0.320 26.748 0.000 8.568 2.206
## .dsq_sublimatin 10.026 0.308 32.570 0.000 10.026 2.711
## .dsq_anticipatn 10.123 0.253 40.053 0.000 10.123 3.320
## .dsq_rationlztn 8.847 0.245 36.084 0.000 8.847 2.976
## .dsq_rctn_frmtn 9.884 0.310 31.894 0.000 9.884 2.621
## .dsq_idealizatn 7.965 0.315 25.320 0.000 7.965 2.081
## .dsq_psed_ltrsm 12.696 0.254 50.029 0.000 12.696 4.195
## .dsq_undoing 11.139 0.333 33.474 0.000 11.139 2.802
## .dsq_tstc_fntsy 9.187 0.352 26.081 0.000 9.187 2.152
## .dsq_displacmnt 8.146 0.342 23.818 0.000 8.146 1.990
## .dsq_pssv_ggrss 7.737 0.270 28.699 0.000 7.737 2.416
## .dsq_somatizatn 11.442 0.289 39.622 0.000 11.442 3.236
## .dsq_acting_out 9.434 0.347 27.153 0.000 9.434 2.240
## .dsq_projection 9.348 0.313 29.891 0.000 9.348 2.469
## .dsq_isolation 8.592 0.377 22.759 0.000 8.592 1.887
## .dsq_dissociatn 9.582 0.303 31.595 0.000 9.582 2.598
## .dsq_devaluatin 9.761 0.262 37.288 0.000 9.761 3.063
## .dsq_splitting 9.718 0.311 31.272 0.000 9.718 2.552
## .dsq_denial 6.885 0.269 25.552 0.000 6.885 2.100
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.285 2.147 4.790 0.000 10.285 0.583
## .dsq_suppressin 12.433 1.791 6.943 0.000 12.433 0.824
## .dsq_sublimatin 9.315 1.621 5.748 0.000 9.315 0.681
## .dsq_anticipatn 5.561 1.054 5.275 0.000 5.561 0.598
## .dsq_rationlztn 6.540 0.926 7.060 0.000 6.540 0.740
## .dsq_rctn_frmtn 12.011 1.940 6.191 0.000 12.011 0.845
## .dsq_idealizatn 12.701 2.145 5.921 0.000 12.701 0.867
## .dsq_psed_ltrsm 5.888 2.204 2.671 0.008 5.888 0.643
## .dsq_undoing 13.260 2.032 6.525 0.000 13.260 0.839
## .dsq_tstc_fntsy 15.265 1.710 8.925 0.000 15.265 0.838
## .dsq_displacmnt 15.520 1.685 9.208 0.000 15.520 0.926
## .dsq_pssv_ggrss 7.668 1.061 7.228 0.000 7.668 0.748
## .dsq_somatizatn 11.205 1.089 10.293 0.000 11.205 0.896
## .dsq_acting_out 15.088 1.751 8.616 0.000 15.088 0.850
## .dsq_projection 10.833 1.463 7.404 0.000 10.833 0.756
## .dsq_isolation 18.522 1.913 9.681 0.000 18.522 0.894
## .dsq_dissociatn 11.162 1.459 7.648 0.000 11.162 0.821
## .dsq_devaluatin 5.580 0.793 7.041 0.000 5.580 0.550
## .dsq_splitting 9.699 1.624 5.973 0.000 9.699 0.669
## .dsq_denial 9.504 1.060 8.967 0.000 9.504 0.884
## Mature 7.370 2.356 3.129 0.002 1.000 1.000
## Neurotic 2.211 1.622 1.363 0.173 1.000 1.000
## Immature 2.955 1.282 2.305 0.021 1.000 1.000
## Image_Distrtng 2.204 1.432 1.539 0.124 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.417
## dsq_suppressin 0.176
## dsq_sublimatin 0.319
## dsq_anticipatn 0.402
## dsq_rationlztn 0.260
## dsq_rctn_frmtn 0.155
## dsq_idealizatn 0.133
## dsq_psed_ltrsm 0.357
## dsq_undoing 0.161
## dsq_tstc_fntsy 0.162
## dsq_displacmnt 0.074
## dsq_pssv_ggrss 0.252
## dsq_somatizatn 0.104
## dsq_acting_out 0.150
## dsq_projection 0.244
## dsq_isolation 0.106
## dsq_dissociatn 0.179
## dsq_devaluatin 0.450
## dsq_splitting 0.331
## dsq_denial 0.116cov2cor(lavInspect(fit4_dekker, "cov.lv"))## Mature Neurtc Immatr Img_Ds
## Mature 1.000
## Neurotic 0.615 1.000
## Immature -0.010 0.337 1.000
## Image_Distorting 0.038 0.356 1.119 1.000# Likelihood Ratio Tests (Nested)
anova(fit1_dekker, fit3_dekker)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit3_dekker 167 15708 15898 349.66
## fit1_dekker 170 15793 15974 440.11 49.38 3 1.083e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit1_dekker, fit4_dekker)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit4_dekker 164 15685 15884 320.06
## fit1_dekker 170 15793 15974 440.11 104.13 6 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# BIC (Non-Nested)
fitMeasures(fit3_dekker, "bic")## bic
## 15898.29fitMeasures(fit4_dekker, "bic")## bic
## 15883.74modindices(fit4_dekker, sort. = TRUE, minimum.value = 3.84)## Warning: lavaan->lav_start_check_cov():
## starting values imply a correlation larger than 1; variables involved are: Immature Image_Distorting## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 113 Immature =~ dsq_pseudo_altruism 20.111 -1.053 -1.810 -0.598 -0.598
## 127 Image_Distorting =~ dsq_pseudo_altruism 18.915 -1.187 -1.762 -0.582 -0.582
## 148 dsq_humor ~~ dsq_projection 15.308 -4.067 -4.067 -0.385 -0.385
## 78 Mature =~ dsq_undoing 13.997 -0.885 -2.402 -0.604 -0.604
## 84 Mature =~ dsq_projection 13.003 -0.486 -1.321 -0.349 -0.349
## 121 Image_Distorting =~ dsq_suppression 11.268 0.811 1.205 0.310 0.310
## 77 Mature =~ dsq_pseudo_altruism 10.509 0.718 1.948 0.644 0.644
## 107 Immature =~ dsq_suppression 9.703 0.660 1.134 0.292 0.292
## 114 Immature =~ dsq_undoing 9.014 0.785 1.350 0.340 0.340
## 106 Immature =~ dsq_humor 8.735 -0.653 -1.123 -0.267 -0.267
## 283 dsq_displacement ~~ dsq_projection 8.495 -3.512 -3.512 -0.271 -0.271
## 282 dsq_displacement ~~ dsq_acting_out 8.383 3.970 3.970 0.259 0.259
## 128 Image_Distorting =~ dsq_undoing 8.343 0.877 1.301 0.327 0.327
## 176 dsq_sublimation ~~ dsq_pseudo_altruism 8.179 2.237 2.237 0.302 0.302
## 120 Image_Distorting =~ dsq_humor 7.498 -0.691 -1.026 -0.244 -0.244
## 318 dsq_isolation ~~ dsq_denial 7.289 3.131 3.131 0.236 0.236
## 227 dsq_reaction_formation ~~ dsq_acting_out 7.027 -3.177 -3.177 -0.236 -0.236
## 228 dsq_reaction_formation ~~ dsq_projection 6.303 2.623 2.623 0.230 0.230
## 268 dsq_undoing ~~ dsq_splitting 6.168 2.660 2.660 0.235 0.235
## 240 dsq_idealization ~~ dsq_acting_out 5.756 2.936 2.936 0.212 0.212
## 169 dsq_suppression ~~ dsq_devaluation 5.495 1.905 1.905 0.229 0.229
## 254 dsq_pseudo_altruism ~~ dsq_isolation 5.414 -2.344 -2.344 -0.224 -0.224
## 81 Mature =~ dsq_passive_aggression 5.265 0.266 0.722 0.225 0.225
## 126 Image_Distorting =~ dsq_idealization 5.145 0.650 0.965 0.252 0.252
## 112 Immature =~ dsq_idealization 4.942 0.549 0.944 0.247 0.247
## 222 dsq_reaction_formation ~~ dsq_undoing 4.733 2.702 2.702 0.214 0.214
## 212 dsq_rationalization ~~ dsq_somatization 4.445 -1.597 -1.597 -0.187 -0.187
## 201 dsq_anticipation ~~ dsq_dissociation 4.395 -1.587 -1.587 -0.201 -0.201
## 196 dsq_anticipation ~~ dsq_passive_aggression 4.382 1.360 1.360 0.208 0.208
## 224 dsq_reaction_formation ~~ dsq_displacement 4.308 -2.503 -2.503 -0.183 -0.183
## 259 dsq_undoing ~~ dsq_autistic_fantasy 4.305 2.666 2.666 0.187 0.187
## 90 Neurotic =~ dsq_humor 4.284 -0.897 -1.334 -0.317 -0.317
## 245 dsq_idealization ~~ dsq_splitting 4.234 2.104 2.104 0.190 0.190
## 321 dsq_dissociation ~~ dsq_denial 4.109 1.861 1.861 0.181 0.181
## 151 dsq_humor ~~ dsq_devaluation 3.924 -1.610 -1.610 -0.213 -0.213
## 142 dsq_humor ~~ dsq_undoing 3.853 -2.295 -2.295 -0.197 -0.197# New 3-factor model combining Immature + Image_Distorting
model_3f_combined <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation + dsq_rationalization
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Combined_Immature =~ dsq_autistic_fantasy + dsq_displacement + dsq_passive_aggression + dsq_somatization +
dsq_acting_out + dsq_projection + dsq_isolation + dsq_dissociation + dsq_devaluation +
dsq_splitting + dsq_denial
'
# Fit combined-factor model for Dekker
fit3_combined_dekker <- cfa(model_3f_combined, data = dekker_df, estimator = "mlr", missing = "fiml")
# Summary of results
summary(fit3_combined_dekker,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 116 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 63
##
## Number of observations 151
## Number of missing patterns 18
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 322.096 308.665
## Degrees of freedom 167 167
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.044
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 666.331 620.879
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.073
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.674 0.671
## Tucker-Lewis Index (TLI) 0.630 0.626
##
## Robust Comparative Fit Index (CFI) 0.682
## Robust Tucker-Lewis Index (TLI) 0.638
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7777.315 -7777.315
## Scaling correction factor 1.029
## for the MLR correction
## Loglikelihood unrestricted model (H1) -7616.266 -7616.266
## Scaling correction factor 1.040
## for the MLR correction
##
## Akaike (AIC) 15680.629 15680.629
## Bayesian (BIC) 15870.718 15870.718
## Sample-size adjusted Bayesian (SABIC) 15671.330 15671.330
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.078 0.075
## 90 Percent confidence interval - lower 0.065 0.062
## 90 Percent confidence interval - upper 0.091 0.088
## P-value H_0: RMSEA <= 0.050 0.000 0.001
## P-value H_0: RMSEA >= 0.080 0.431 0.265
##
## Robust RMSEA 0.079
## 90 Percent confidence interval - lower 0.065
## 90 Percent confidence interval - upper 0.093
## P-value H_0: Robust RMSEA <= 0.050 0.001
## P-value H_0: Robust RMSEA >= 0.080 0.452
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.089 0.089
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.705 0.644
## dsq_suppressin 0.599 0.196 3.053 0.002 1.619 0.417
## dsq_sublimatin 0.772 0.219 3.523 0.000 2.088 0.565
## dsq_anticipatn 0.720 0.149 4.843 0.000 1.948 0.639
## dsq_rationlztn 0.559 0.128 4.378 0.000 1.511 0.508
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.487 0.394
## dsq_idealizatn 0.941 0.737 1.278 0.201 1.400 0.366
## dsq_psed_ltrsm 1.217 0.498 2.447 0.014 1.810 0.598
## dsq_undoing 1.068 0.367 2.908 0.004 1.588 0.400
## Combined_Immature =~
## dsq_tstc_fntsy 1.000 1.708 0.400
## dsq_displacmnt 0.670 0.300 2.233 0.026 1.144 0.279
## dsq_pssv_ggrss 1.020 0.328 3.111 0.002 1.742 0.544
## dsq_somatizatn 0.713 0.223 3.196 0.001 1.217 0.344
## dsq_acting_out 0.998 0.368 2.713 0.007 1.704 0.404
## dsq_projection 1.142 0.322 3.542 0.000 1.950 0.515
## dsq_isolation 0.901 0.339 2.654 0.008 1.538 0.338
## dsq_dissociatn 0.942 0.279 3.371 0.001 1.609 0.436
## dsq_devaluatin 1.284 0.305 4.214 0.000 2.192 0.688
## dsq_splitting 1.282 0.343 3.742 0.000 2.189 0.575
## dsq_denial 0.702 0.273 2.573 0.010 1.198 0.366
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 2.479 0.767 3.231 0.001 0.616 0.616
## Combined_Immtr 0.095 0.647 0.147 0.883 0.021 0.021
## Neurotic ~~
## Combined_Immtr 0.854 0.693 1.232 0.218 0.336 0.336
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.307 0.346 26.920 0.000 9.307 2.215
## .dsq_suppressin 8.568 0.320 26.768 0.000 8.568 2.206
## .dsq_sublimatin 10.028 0.308 32.593 0.000 10.028 2.712
## .dsq_anticipatn 10.125 0.252 40.111 0.000 10.125 3.321
## .dsq_rationlztn 8.848 0.245 36.100 0.000 8.848 2.976
## .dsq_rctn_frmtn 9.884 0.310 31.876 0.000 9.884 2.621
## .dsq_idealizatn 7.966 0.314 25.332 0.000 7.966 2.081
## .dsq_psed_ltrsm 12.697 0.253 50.105 0.000 12.697 4.195
## .dsq_undoing 11.140 0.333 33.492 0.000 11.140 2.802
## .dsq_tstc_fntsy 9.190 0.352 26.071 0.000 9.190 2.152
## .dsq_displacmnt 8.152 0.342 23.836 0.000 8.152 1.992
## .dsq_pssv_ggrss 7.751 0.269 28.824 0.000 7.751 2.420
## .dsq_somatizatn 11.442 0.289 39.627 0.000 11.442 3.236
## .dsq_acting_out 9.438 0.348 27.149 0.000 9.438 2.239
## .dsq_projection 9.349 0.313 29.905 0.000 9.349 2.469
## .dsq_isolation 8.587 0.378 22.738 0.000 8.587 1.886
## .dsq_dissociatn 9.583 0.303 31.588 0.000 9.583 2.598
## .dsq_devaluatin 9.762 0.262 37.271 0.000 9.762 3.063
## .dsq_splitting 9.718 0.311 31.278 0.000 9.718 2.552
## .dsq_denial 6.886 0.269 25.555 0.000 6.886 2.100
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.341 2.088 4.953 0.000 10.341 0.586
## .dsq_suppressin 12.466 1.777 7.015 0.000 12.466 0.826
## .dsq_sublimatin 9.312 1.619 5.753 0.000 9.312 0.681
## .dsq_anticipatn 5.500 0.985 5.582 0.000 5.500 0.592
## .dsq_rationlztn 6.558 0.925 7.087 0.000 6.558 0.742
## .dsq_rctn_frmtn 12.011 1.930 6.224 0.000 12.011 0.845
## .dsq_idealizatn 12.696 2.130 5.960 0.000 12.696 0.866
## .dsq_psed_ltrsm 5.883 2.171 2.709 0.007 5.883 0.642
## .dsq_undoing 13.278 2.028 6.547 0.000 13.278 0.840
## .dsq_tstc_fntsy 15.314 1.773 8.636 0.000 15.314 0.840
## .dsq_displacmnt 15.442 1.680 9.190 0.000 15.442 0.922
## .dsq_pssv_ggrss 7.218 0.964 7.491 0.000 7.218 0.704
## .dsq_somatizatn 11.018 1.072 10.281 0.000 11.018 0.882
## .dsq_acting_out 14.864 1.766 8.418 0.000 14.864 0.837
## .dsq_projection 10.535 1.267 8.312 0.000 10.535 0.735
## .dsq_isolation 18.363 1.845 9.951 0.000 18.363 0.886
## .dsq_dissociatn 11.013 1.498 7.352 0.000 11.013 0.810
## .dsq_devaluatin 5.352 0.811 6.602 0.000 5.352 0.527
## .dsq_splitting 9.706 1.681 5.774 0.000 9.706 0.669
## .dsq_denial 9.314 1.031 9.034 0.000 9.314 0.866
## Mature 7.315 2.292 3.192 0.001 1.000 1.000
## Neurotic 2.211 1.616 1.369 0.171 1.000 1.000
## Combined_Immtr 2.916 1.384 2.106 0.035 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.414
## dsq_suppressin 0.174
## dsq_sublimatin 0.319
## dsq_anticipatn 0.408
## dsq_rationlztn 0.258
## dsq_rctn_frmtn 0.155
## dsq_idealizatn 0.134
## dsq_psed_ltrsm 0.358
## dsq_undoing 0.160
## dsq_tstc_fntsy 0.160
## dsq_displacmnt 0.078
## dsq_pssv_ggrss 0.296
## dsq_somatizatn 0.118
## dsq_acting_out 0.163
## dsq_projection 0.265
## dsq_isolation 0.114
## dsq_dissociatn 0.190
## dsq_devaluatin 0.473
## dsq_splitting 0.331
## dsq_denial 0.134model_3f_combined_adj_V <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation + dsq_rationalization
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Combined_Immature =~ dsq_autistic_fantasy + dsq_passive_aggression + dsq_somatization +
dsq_acting_out + dsq_projection + dsq_isolation + dsq_dissociation + dsq_devaluation +
dsq_splitting + dsq_denial
'
fit3f_combined_adj_V <- cfa(model_3f_combined_adj_V, data = dekker_df, estimator = "mlr", missing = "fiml")
summary(fit3f_combined_adj_V,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 115 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 60
##
## Number of observations 151
## Number of missing patterns 17
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 279.772 268.317
## Degrees of freedom 149 149
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.043
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 615.607 573.004
## Degrees of freedom 171 171
## P-value 0.000 0.000
## Scaling correction factor 1.074
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.706 0.703
## Tucker-Lewis Index (TLI) 0.662 0.659
##
## Robust Comparative Fit Index (CFI) 0.715
## Robust Tucker-Lewis Index (TLI) 0.673
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7377.131 -7377.131
## Scaling correction factor 1.033
## for the MLR correction
## Loglikelihood unrestricted model (H1) -7237.245 -7237.245
## Scaling correction factor 1.040
## for the MLR correction
##
## Akaike (AIC) 14874.262 14874.262
## Bayesian (BIC) 15055.299 15055.299
## Sample-size adjusted Bayesian (SABIC) 14865.405 14865.405
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.076 0.073
## 90 Percent confidence interval - lower 0.062 0.059
## 90 Percent confidence interval - upper 0.090 0.086
## P-value H_0: RMSEA <= 0.050 0.001 0.004
## P-value H_0: RMSEA >= 0.080 0.336 0.199
##
## Robust RMSEA 0.076
## 90 Percent confidence interval - lower 0.061
## 90 Percent confidence interval - upper 0.091
## P-value H_0: Robust RMSEA <= 0.050 0.003
## P-value H_0: Robust RMSEA >= 0.080 0.346
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.089 0.089
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.716 0.646
## dsq_suppressin 0.593 0.195 3.041 0.002 1.611 0.415
## dsq_sublimatin 0.767 0.216 3.560 0.000 2.084 0.563
## dsq_anticipatn 0.719 0.149 4.841 0.000 1.952 0.640
## dsq_rationlztn 0.554 0.127 4.351 0.000 1.505 0.506
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.497 0.397
## dsq_idealizatn 0.953 0.751 1.269 0.204 1.427 0.373
## dsq_psed_ltrsm 1.186 0.473 2.509 0.012 1.776 0.587
## dsq_undoing 1.062 0.352 3.015 0.003 1.589 0.400
## Combined_Immature =~
## dsq_tstc_fntsy 1.000 1.720 0.403
## dsq_pssv_ggrss 1.001 0.324 3.090 0.002 1.721 0.537
## dsq_somatizatn 0.716 0.225 3.182 0.001 1.231 0.348
## dsq_acting_out 0.933 0.350 2.664 0.008 1.605 0.381
## dsq_projection 1.190 0.332 3.581 0.000 2.046 0.540
## dsq_isolation 0.918 0.347 2.649 0.008 1.580 0.347
## dsq_dissociatn 0.929 0.281 3.312 0.001 1.598 0.433
## dsq_devaluatin 1.266 0.298 4.253 0.000 2.178 0.683
## dsq_splitting 1.248 0.329 3.792 0.000 2.146 0.564
## dsq_denial 0.713 0.279 2.553 0.011 1.226 0.374
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 2.505 0.776 3.230 0.001 0.616 0.616
## Combined_Immtr 0.045 0.674 0.067 0.947 0.010 0.010
## Neurotic ~~
## Combined_Immtr 0.931 0.675 1.379 0.168 0.361 0.361
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.307 0.346 26.921 0.000 9.307 2.215
## .dsq_suppressin 8.568 0.320 26.766 0.000 8.568 2.206
## .dsq_sublimatin 10.028 0.308 32.596 0.000 10.028 2.712
## .dsq_anticipatn 10.125 0.252 40.113 0.000 10.125 3.321
## .dsq_rationlztn 8.848 0.245 36.101 0.000 8.848 2.976
## .dsq_rctn_frmtn 9.884 0.310 31.870 0.000 9.884 2.621
## .dsq_idealizatn 7.965 0.314 25.331 0.000 7.965 2.081
## .dsq_psed_ltrsm 12.698 0.253 50.111 0.000 12.698 4.195
## .dsq_undoing 11.140 0.332 33.510 0.000 11.140 2.803
## .dsq_tstc_fntsy 9.191 0.352 26.081 0.000 9.191 2.153
## .dsq_pssv_ggrss 7.752 0.269 28.819 0.000 7.752 2.420
## .dsq_somatizatn 11.442 0.289 39.629 0.000 11.442 3.236
## .dsq_acting_out 9.441 0.348 27.143 0.000 9.441 2.240
## .dsq_projection 9.352 0.313 29.924 0.000 9.352 2.470
## .dsq_isolation 8.588 0.378 22.739 0.000 8.588 1.886
## .dsq_dissociatn 9.583 0.303 31.592 0.000 9.583 2.598
## .dsq_devaluatin 9.764 0.262 37.279 0.000 9.764 3.064
## .dsq_splitting 9.718 0.311 31.277 0.000 9.718 2.552
## .dsq_denial 6.886 0.269 25.556 0.000 6.886 2.100
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.282 2.085 4.931 0.000 10.282 0.582
## .dsq_suppressin 12.495 1.778 7.028 0.000 12.495 0.828
## .dsq_sublimatin 9.333 1.599 5.836 0.000 9.333 0.683
## .dsq_anticipatn 5.483 0.973 5.634 0.000 5.483 0.590
## .dsq_rationlztn 6.574 0.929 7.074 0.000 6.574 0.744
## .dsq_rctn_frmtn 11.980 1.980 6.051 0.000 11.980 0.842
## .dsq_idealizatn 12.619 2.131 5.922 0.000 12.619 0.861
## .dsq_psed_ltrsm 6.007 2.035 2.953 0.003 6.007 0.656
## .dsq_undoing 13.273 2.056 6.456 0.000 13.273 0.840
## .dsq_tstc_fntsy 15.270 1.746 8.745 0.000 15.270 0.838
## .dsq_pssv_ggrss 7.295 0.982 7.430 0.000 7.295 0.711
## .dsq_somatizatn 10.983 1.058 10.384 0.000 10.983 0.879
## .dsq_acting_out 15.194 1.759 8.639 0.000 15.194 0.855
## .dsq_projection 10.152 1.245 8.155 0.000 10.152 0.708
## .dsq_isolation 18.232 1.859 9.807 0.000 18.232 0.880
## .dsq_dissociatn 11.050 1.491 7.408 0.000 11.050 0.812
## .dsq_devaluatin 5.413 0.814 6.647 0.000 5.413 0.533
## .dsq_splitting 9.894 1.673 5.914 0.000 9.894 0.682
## .dsq_denial 9.246 1.039 8.894 0.000 9.246 0.860
## Mature 7.375 2.297 3.211 0.001 1.000 1.000
## Neurotic 2.242 1.686 1.330 0.184 1.000 1.000
## Combined_Immtr 2.958 1.397 2.117 0.034 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.418
## dsq_suppressin 0.172
## dsq_sublimatin 0.317
## dsq_anticipatn 0.410
## dsq_rationlztn 0.256
## dsq_rctn_frmtn 0.158
## dsq_idealizatn 0.139
## dsq_psed_ltrsm 0.344
## dsq_undoing 0.160
## dsq_tstc_fntsy 0.162
## dsq_pssv_ggrss 0.289
## dsq_somatizatn 0.121
## dsq_acting_out 0.145
## dsq_projection 0.292
## dsq_isolation 0.120
## dsq_dissociatn 0.188
## dsq_devaluatin 0.467
## dsq_splitting 0.318
## dsq_denial 0.140modindices(fit3f_combined_adj_V, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 117 dsq_humor ~~ dsq_projection 15.612 -3.948 -3.948 -0.386 -0.386
## 103 Combined_Immature =~ dsq_pseudo_altruism 15.001 -0.849 -1.461 -0.483 -0.483
## 70 Mature =~ dsq_undoing 13.408 -0.856 -2.324 -0.585 -0.585
## 75 Mature =~ dsq_projection 13.353 -0.447 -1.215 -0.321 -0.321
## 69 Mature =~ dsq_pseudo_altruism 10.358 0.685 1.860 0.615 0.615
## 97 Combined_Immature =~ dsq_suppression 9.988 0.642 1.105 0.284 0.284
## 144 dsq_sublimation ~~ dsq_pseudo_altruism 8.389 2.269 2.269 0.303 0.303
## 96 Combined_Immature =~ dsq_humor 7.624 -0.582 -1.001 -0.238 -0.238
## 104 Combined_Immature =~ dsq_undoing 7.262 0.682 1.174 0.295 0.295
## 269 dsq_isolation ~~ dsq_denial 7.236 3.064 3.064 0.236 0.236
## 191 dsq_reaction_formation ~~ dsq_acting_out 7.063 -3.162 -3.162 -0.234 -0.234
## 229 dsq_undoing ~~ dsq_splitting 6.248 2.651 2.651 0.231 0.231
## 192 dsq_reaction_formation ~~ dsq_projection 5.920 2.447 2.447 0.222 0.222
## 137 dsq_suppression ~~ dsq_devaluation 5.887 1.920 1.920 0.233 0.233
## 203 dsq_idealization ~~ dsq_acting_out 5.763 2.915 2.915 0.211 0.211
## 216 dsq_pseudo_altruism ~~ dsq_isolation 5.348 -2.310 -2.310 -0.221 -0.221
## 162 dsq_anticipation ~~ dsq_passive_aggression 4.914 1.387 1.387 0.219 0.219
## 177 dsq_rationalization ~~ dsq_somatization 4.851 -1.651 -1.651 -0.194 -0.194
## 90 Neurotic =~ dsq_projection 4.792 -0.592 -0.886 -0.234 -0.234
## 187 dsq_reaction_formation ~~ dsq_undoing 4.727 2.699 2.699 0.214 0.214
## 167 dsq_anticipation ~~ dsq_dissociation 4.588 -1.601 -1.601 -0.206 -0.206
## 221 dsq_undoing ~~ dsq_autistic_fantasy 4.535 2.710 2.710 0.190 0.190
## 102 Combined_Immature =~ dsq_idealization 4.487 0.508 0.874 0.228 0.228
## 272 dsq_dissociation ~~ dsq_denial 4.282 1.857 1.857 0.184 0.184
## 208 dsq_idealization ~~ dsq_splitting 4.232 2.081 2.081 0.186 0.186
## 254 dsq_somatization ~~ dsq_denial 4.213 -1.791 -1.791 -0.178 -0.178
## 81 Neurotic =~ dsq_humor 4.186 -0.875 -1.310 -0.312 -0.312
## 72 Mature =~ dsq_passive_aggression 4.165 0.215 0.585 0.183 0.183
## 112 dsq_humor ~~ dsq_undoing 3.921 -2.315 -2.315 -0.198 -0.198model_3f_combined_adj_VI <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation + dsq_rationalization
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Combined_Immature =~ dsq_autistic_fantasy + dsq_passive_aggression +
dsq_acting_out + dsq_projection + dsq_isolation + dsq_dissociation + dsq_devaluation +
dsq_splitting + dsq_denial
'
fit3f_combined_adj_VI <- cfa(model_3f_combined_adj_VI, data = dekker_df, estimator = "mlr", missing = "fiml")
summary(fit3f_combined_adj_VI,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 113 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 57
##
## Number of observations 151
## Number of missing patterns 17
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 256.577 245.258
## Degrees of freedom 132 132
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.046
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 578.118 534.354
## Degrees of freedom 153 153
## P-value 0.000 0.000
## Scaling correction factor 1.082
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.707 0.703
## Tucker-Lewis Index (TLI) 0.660 0.656
##
## Robust Comparative Fit Index (CFI) 0.715
## Robust Tucker-Lewis Index (TLI) 0.670
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -6982.004 -6982.004
## Scaling correction factor 1.042
## for the MLR correction
## Loglikelihood unrestricted model (H1) -6853.716 -6853.716
## Scaling correction factor 1.045
## for the MLR correction
##
## Akaike (AIC) 14078.009 14078.009
## Bayesian (BIC) 14249.994 14249.994
## Sample-size adjusted Bayesian (SABIC) 14069.595 14069.595
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.079 0.075
## 90 Percent confidence interval - lower 0.065 0.061
## 90 Percent confidence interval - upper 0.093 0.090
## P-value H_0: RMSEA <= 0.050 0.001 0.003
## P-value H_0: RMSEA >= 0.080 0.470 0.307
##
## Robust RMSEA 0.079
## 90 Percent confidence interval - lower 0.063
## 90 Percent confidence interval - upper 0.095
## P-value H_0: Robust RMSEA <= 0.050 0.002
## P-value H_0: Robust RMSEA >= 0.080 0.482
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.090 0.090
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.717 0.647
## dsq_suppressin 0.593 0.195 3.039 0.002 1.610 0.415
## dsq_sublimatin 0.767 0.218 3.524 0.000 2.085 0.564
## dsq_anticipatn 0.719 0.148 4.846 0.000 1.955 0.641
## dsq_rationlztn 0.552 0.130 4.253 0.000 1.501 0.505
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.487 0.394
## dsq_idealizatn 0.976 0.835 1.168 0.243 1.451 0.379
## dsq_psed_ltrsm 1.195 0.473 2.529 0.011 1.776 0.587
## dsq_undoing 1.043 0.344 3.034 0.002 1.551 0.390
## Combined_Immature =~
## dsq_tstc_fntsy 1.000 1.698 0.398
## dsq_pssv_ggrss 1.021 0.336 3.039 0.002 1.734 0.542
## dsq_acting_out 0.942 0.362 2.604 0.009 1.599 0.379
## dsq_projection 1.200 0.346 3.467 0.001 2.037 0.538
## dsq_isolation 0.920 0.350 2.630 0.009 1.562 0.343
## dsq_dissociatn 0.917 0.285 3.219 0.001 1.558 0.422
## dsq_devaluatin 1.292 0.311 4.149 0.000 2.193 0.688
## dsq_splitting 1.257 0.336 3.734 0.000 2.133 0.560
## dsq_denial 0.759 0.288 2.631 0.009 1.289 0.393
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 2.512 0.780 3.220 0.001 0.622 0.622
## Combined_Immtr 0.016 0.667 0.024 0.981 0.003 0.003
## Neurotic ~~
## Combined_Immtr 0.885 0.676 1.308 0.191 0.351 0.351
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.307 0.346 26.915 0.000 9.307 2.215
## .dsq_suppressin 8.568 0.320 26.766 0.000 8.568 2.206
## .dsq_sublimatin 10.029 0.308 32.597 0.000 10.029 2.712
## .dsq_anticipatn 10.125 0.252 40.114 0.000 10.125 3.321
## .dsq_rationlztn 8.848 0.245 36.102 0.000 8.848 2.976
## .dsq_rctn_frmtn 9.884 0.310 31.877 0.000 9.884 2.621
## .dsq_idealizatn 7.964 0.314 25.327 0.000 7.964 2.080
## .dsq_psed_ltrsm 12.698 0.254 50.088 0.000 12.698 4.195
## .dsq_undoing 11.140 0.333 33.492 0.000 11.140 2.803
## .dsq_tstc_fntsy 9.187 0.353 26.053 0.000 9.187 2.151
## .dsq_pssv_ggrss 7.747 0.269 28.849 0.000 7.747 2.420
## .dsq_acting_out 9.438 0.348 27.149 0.000 9.438 2.239
## .dsq_projection 9.347 0.313 29.904 0.000 9.347 2.469
## .dsq_isolation 8.587 0.378 22.739 0.000 8.587 1.886
## .dsq_dissociatn 9.581 0.304 31.544 0.000 9.581 2.597
## .dsq_devaluatin 9.763 0.262 37.267 0.000 9.763 3.063
## .dsq_splitting 9.718 0.311 31.284 0.000 9.718 2.552
## .dsq_denial 6.885 0.269 25.558 0.000 6.885 2.100
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.277 2.102 4.890 0.000 10.277 0.582
## .dsq_suppressin 12.496 1.779 7.022 0.000 12.496 0.828
## .dsq_sublimatin 9.328 1.607 5.805 0.000 9.328 0.682
## .dsq_anticipatn 5.475 0.984 5.563 0.000 5.475 0.589
## .dsq_rationlztn 6.588 0.940 7.007 0.000 6.588 0.745
## .dsq_rctn_frmtn 12.009 2.036 5.898 0.000 12.009 0.845
## .dsq_idealizatn 12.551 2.337 5.371 0.000 12.551 0.856
## .dsq_psed_ltrsm 6.005 2.224 2.700 0.007 6.005 0.656
## .dsq_undoing 13.394 2.062 6.494 0.000 13.394 0.848
## .dsq_tstc_fntsy 15.351 1.745 8.798 0.000 15.351 0.842
## .dsq_pssv_ggrss 7.242 0.974 7.438 0.000 7.242 0.707
## .dsq_acting_out 15.209 1.756 8.662 0.000 15.209 0.856
## .dsq_projection 10.185 1.273 8.003 0.000 10.185 0.710
## .dsq_isolation 18.284 1.833 9.973 0.000 18.284 0.882
## .dsq_dissociatn 11.181 1.493 7.489 0.000 11.181 0.822
## .dsq_devaluatin 5.347 0.826 6.471 0.000 5.347 0.526
## .dsq_splitting 9.948 1.626 6.117 0.000 9.948 0.686
## .dsq_denial 9.090 1.026 8.862 0.000 9.090 0.846
## Mature 7.381 2.315 3.188 0.001 1.000 1.000
## Neurotic 2.210 1.756 1.259 0.208 1.000 1.000
## Combined_Immtr 2.883 1.396 2.065 0.039 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.418
## dsq_suppressin 0.172
## dsq_sublimatin 0.318
## dsq_anticipatn 0.411
## dsq_rationlztn 0.255
## dsq_rctn_frmtn 0.155
## dsq_idealizatn 0.144
## dsq_psed_ltrsm 0.344
## dsq_undoing 0.152
## dsq_tstc_fntsy 0.158
## dsq_pssv_ggrss 0.293
## dsq_acting_out 0.144
## dsq_projection 0.290
## dsq_isolation 0.118
## dsq_dissociatn 0.178
## dsq_devaluatin 0.474
## dsq_splitting 0.314
## dsq_denial 0.154modindices(fit3f_combined_adj_VI, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 111 dsq_humor ~~ dsq_projection 15.983 -4.005 -4.005 -0.391 -0.391
## 98 Combined_Immature =~ dsq_pseudo_altruism 15.211 -0.865 -1.469 -0.485 -0.485
## 71 Mature =~ dsq_projection 12.913 -0.441 -1.198 -0.316 -0.316
## 67 Mature =~ dsq_undoing 12.706 -0.841 -2.285 -0.575 -0.575
## 66 Mature =~ dsq_pseudo_altruism 10.176 0.693 1.883 0.622 0.622
## 92 Combined_Immature =~ dsq_suppression 9.484 0.637 1.081 0.278 0.278
## 137 dsq_sublimation ~~ dsq_pseudo_altruism 8.379 2.270 2.270 0.303 0.303
## 91 Combined_Immature =~ dsq_humor 7.204 -0.576 -0.978 -0.233 -0.233
## 180 dsq_reaction_formation ~~ dsq_acting_out 7.104 -3.177 -3.177 -0.235 -0.235
## 246 dsq_isolation ~~ dsq_denial 6.988 3.007 3.007 0.233 0.233
## 215 dsq_undoing ~~ dsq_splitting 6.985 2.819 2.819 0.244 0.244
## 130 dsq_suppression ~~ dsq_devaluation 6.427 2.009 2.009 0.246 0.246
## 99 Combined_Immature =~ dsq_undoing 6.158 0.633 1.075 0.270 0.270
## 181 dsq_reaction_formation ~~ dsq_projection 5.863 2.443 2.443 0.221 0.221
## 97 Combined_Immature =~ dsq_idealization 5.427 0.565 0.960 0.251 0.251
## 154 dsq_anticipation ~~ dsq_passive_aggression 5.424 1.456 1.456 0.231 0.231
## 191 dsq_idealization ~~ dsq_acting_out 5.419 2.827 2.827 0.205 0.205
## 203 dsq_pseudo_altruism ~~ dsq_isolation 5.241 -2.291 -2.291 -0.219 -0.219
## 208 dsq_undoing ~~ dsq_autistic_fantasy 4.991 2.858 2.858 0.199 0.199
## 177 dsq_reaction_formation ~~ dsq_undoing 4.945 2.757 2.757 0.217 0.217
## 85 Neurotic =~ dsq_projection 4.488 -0.575 -0.855 -0.226 -0.226
## 69 Mature =~ dsq_passive_aggression 4.389 0.221 0.601 0.188 0.188
## 158 dsq_anticipation ~~ dsq_dissociation 4.296 -1.556 -1.556 -0.199 -0.199
## 249 dsq_dissociation ~~ dsq_denial 4.089 1.819 1.819 0.180 0.180
## 77 Neurotic =~ dsq_humor 4.002 -0.873 -1.298 -0.309 -0.309
## 107 dsq_humor ~~ dsq_undoing 3.970 -2.333 -2.333 -0.199 -0.199
## 196 dsq_idealization ~~ dsq_splitting 3.844 1.989 1.989 0.178 0.178model_3f_combined_adj_VII <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation + dsq_rationalization
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Combined_Immature =~ dsq_autistic_fantasy + dsq_passive_aggression +
dsq_acting_out + dsq_projection + dsq_dissociation + dsq_devaluation +
dsq_splitting + dsq_denial
'
fit3f_combined_adj_VII <- cfa(model_3f_combined_adj_VII, data = dekker_df, estimator = "mlr", missing = "fiml")
summary(fit3f_combined_adj_VII,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 115 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 54
##
## Number of observations 151
## Number of missing patterns 17
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 221.355 214.641
## Degrees of freedom 116 116
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.031
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 529.913 491.040
## Degrees of freedom 136 136
## P-value 0.000 0.000
## Scaling correction factor 1.079
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.733 0.722
## Tucker-Lewis Index (TLI) 0.686 0.674
##
## Robust Comparative Fit Index (CFI) 0.740
## Robust Tucker-Lewis Index (TLI) 0.695
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -6562.933 -6562.933
## Scaling correction factor 1.067
## for the MLR correction
## Loglikelihood unrestricted model (H1) -6452.255 -6452.255
## Scaling correction factor 1.043
## for the MLR correction
##
## Akaike (AIC) 13233.866 13233.866
## Bayesian (BIC) 13396.799 13396.799
## Sample-size adjusted Bayesian (SABIC) 13225.895 13225.895
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.078 0.075
## 90 Percent confidence interval - lower 0.062 0.059
## 90 Percent confidence interval - upper 0.093 0.090
## P-value H_0: RMSEA <= 0.050 0.003 0.005
## P-value H_0: RMSEA >= 0.080 0.410 0.309
##
## Robust RMSEA 0.078
## 90 Percent confidence interval - lower 0.061
## 90 Percent confidence interval - upper 0.094
## P-value H_0: Robust RMSEA <= 0.050 0.005
## P-value H_0: Robust RMSEA >= 0.080 0.427
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.088 0.088
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.729 0.649
## dsq_suppressin 0.586 0.193 3.042 0.002 1.600 0.412
## dsq_sublimatin 0.761 0.215 3.547 0.000 2.078 0.562
## dsq_anticipatn 0.719 0.148 4.845 0.000 1.963 0.644
## dsq_rationlztn 0.547 0.130 4.199 0.000 1.492 0.502
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.429 0.379
## dsq_idealizatn 1.076 1.080 0.997 0.319 1.538 0.402
## dsq_psed_ltrsm 1.204 0.450 2.677 0.007 1.721 0.569
## dsq_undoing 1.067 0.370 2.881 0.004 1.525 0.384
## Combined_Immature =~
## dsq_tstc_fntsy 1.000 1.706 0.400
## dsq_pssv_ggrss 1.009 0.339 2.976 0.003 1.721 0.538
## dsq_acting_out 0.974 0.378 2.574 0.010 1.662 0.394
## dsq_projection 1.157 0.341 3.398 0.001 1.975 0.522
## dsq_dissociatn 0.868 0.269 3.232 0.001 1.482 0.402
## dsq_devaluatin 1.309 0.329 3.973 0.000 2.233 0.700
## dsq_splitting 1.285 0.355 3.614 0.000 2.192 0.576
## dsq_denial 0.700 0.272 2.574 0.010 1.194 0.364
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 2.461 0.872 2.823 0.005 0.631 0.631
## Combined_Immtr -0.032 0.672 -0.048 0.962 -0.007 -0.007
## Neurotic ~~
## Combined_Immtr 0.968 0.605 1.601 0.109 0.397 0.397
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.308 0.346 26.913 0.000 9.308 2.215
## .dsq_suppressin 8.568 0.320 26.763 0.000 8.568 2.206
## .dsq_sublimatin 10.029 0.308 32.600 0.000 10.029 2.712
## .dsq_anticipatn 10.125 0.252 40.114 0.000 10.125 3.321
## .dsq_rationlztn 8.848 0.245 36.103 0.000 8.848 2.976
## .dsq_rctn_frmtn 9.885 0.310 31.868 0.000 9.885 2.622
## .dsq_idealizatn 7.963 0.314 25.325 0.000 7.963 2.080
## .dsq_psed_ltrsm 12.699 0.253 50.096 0.000 12.699 4.196
## .dsq_undoing 11.140 0.333 33.475 0.000 11.140 2.803
## .dsq_tstc_fntsy 9.184 0.352 26.056 0.000 9.184 2.151
## .dsq_pssv_ggrss 7.741 0.268 28.855 0.000 7.741 2.419
## .dsq_acting_out 9.436 0.347 27.160 0.000 9.436 2.239
## .dsq_projection 9.345 0.313 29.900 0.000 9.345 2.468
## .dsq_dissociatn 9.583 0.304 31.548 0.000 9.583 2.598
## .dsq_devaluatin 9.762 0.262 37.240 0.000 9.762 3.062
## .dsq_splitting 9.719 0.311 31.285 0.000 9.719 2.552
## .dsq_denial 6.884 0.269 25.560 0.000 6.884 2.100
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.211 2.101 4.859 0.000 10.211 0.578
## .dsq_suppressin 12.531 1.775 7.061 0.000 12.531 0.830
## .dsq_sublimatin 9.359 1.595 5.867 0.000 9.359 0.684
## .dsq_anticipatn 5.443 0.981 5.549 0.000 5.443 0.585
## .dsq_rationlztn 6.615 0.952 6.947 0.000 6.615 0.748
## .dsq_rctn_frmtn 12.170 2.281 5.337 0.000 12.170 0.856
## .dsq_idealizatn 12.290 2.644 4.648 0.000 12.290 0.839
## .dsq_psed_ltrsm 6.199 2.328 2.662 0.008 6.199 0.677
## .dsq_undoing 13.472 2.244 6.004 0.000 13.472 0.853
## .dsq_tstc_fntsy 15.326 1.767 8.675 0.000 15.326 0.840
## .dsq_pssv_ggrss 7.281 1.008 7.227 0.000 7.281 0.711
## .dsq_acting_out 15.002 1.781 8.424 0.000 15.002 0.845
## .dsq_projection 10.437 1.343 7.773 0.000 10.437 0.728
## .dsq_dissociatn 11.414 1.477 7.729 0.000 11.414 0.839
## .dsq_devaluatin 5.177 0.946 5.474 0.000 5.177 0.509
## .dsq_splitting 9.694 1.592 6.089 0.000 9.694 0.669
## .dsq_denial 9.322 1.032 9.035 0.000 9.322 0.867
## Mature 7.450 2.326 3.203 0.001 1.000 1.000
## Neurotic 2.042 2.045 0.998 0.318 1.000 1.000
## Combined_Immtr 2.911 1.431 2.035 0.042 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.422
## dsq_suppressin 0.170
## dsq_sublimatin 0.316
## dsq_anticipatn 0.415
## dsq_rationlztn 0.252
## dsq_rctn_frmtn 0.144
## dsq_idealizatn 0.161
## dsq_psed_ltrsm 0.323
## dsq_undoing 0.147
## dsq_tstc_fntsy 0.160
## dsq_pssv_ggrss 0.289
## dsq_acting_out 0.155
## dsq_projection 0.272
## dsq_dissociatn 0.161
## dsq_devaluatin 0.491
## dsq_splitting 0.331
## dsq_denial 0.133modindices(fit3f_combined_adj_VII, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 106 dsq_humor ~~ dsq_projection 16.190 -4.059 -4.059 -0.393 -0.393
## 93 Combined_Immature =~ dsq_pseudo_altruism 14.203 -0.852 -1.454 -0.480 -0.480
## 64 Mature =~ dsq_undoing 12.180 -0.810 -2.212 -0.556 -0.556
## 68 Mature =~ dsq_projection 12.158 -0.429 -1.171 -0.309 -0.309
## 63 Mature =~ dsq_pseudo_altruism 9.955 0.653 1.782 0.589 0.589
## 87 Combined_Immature =~ dsq_suppression 8.838 0.614 1.048 0.270 0.270
## 130 dsq_sublimation ~~ dsq_pseudo_altruism 8.283 2.262 2.262 0.297 0.297
## 123 dsq_suppression ~~ dsq_devaluation 7.458 2.160 2.160 0.268 0.268
## 170 dsq_reaction_formation ~~ dsq_acting_out 7.246 -3.205 -3.205 -0.237 -0.237
## 201 dsq_undoing ~~ dsq_splitting 6.601 2.732 2.732 0.239 0.239
## 86 Combined_Immature =~ dsq_humor 6.498 -0.546 -0.931 -0.222 -0.222
## 94 Combined_Immature =~ dsq_undoing 5.839 0.636 1.085 0.273 0.273
## 171 dsq_reaction_formation ~~ dsq_projection 5.748 2.448 2.448 0.217 0.217
## 146 dsq_anticipation ~~ dsq_passive_aggression 5.577 1.478 1.478 0.235 0.235
## 167 dsq_reaction_formation ~~ dsq_undoing 5.260 2.823 2.823 0.220 0.220
## 92 Combined_Immature =~ dsq_idealization 5.117 0.570 0.973 0.254 0.254
## 81 Neurotic =~ dsq_projection 5.107 -0.659 -0.941 -0.249 -0.249
## 227 dsq_dissociation ~~ dsq_denial 5.046 2.048 2.048 0.199 0.199
## 180 dsq_idealization ~~ dsq_acting_out 4.900 2.663 2.663 0.196 0.196
## 195 dsq_undoing ~~ dsq_autistic_fantasy 4.884 2.831 2.831 0.197 0.197
## 66 Mature =~ dsq_passive_aggression 4.483 0.223 0.609 0.190 0.190
## 73 Neurotic =~ dsq_humor 4.201 -0.918 -1.311 -0.312 -0.312
## 149 dsq_anticipation ~~ dsq_dissociation 4.164 -1.541 -1.541 -0.195 -0.195
## 102 dsq_humor ~~ dsq_undoing 3.995 -2.342 -2.342 -0.200 -0.200model_3f_combined_adj_VIII <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation + dsq_rationalization
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Combined_Immature =~ dsq_autistic_fantasy + dsq_passive_aggression +
dsq_acting_out + dsq_projection + dsq_dissociation + dsq_devaluation +
dsq_splitting
'
fit3f_combined_adj_VIII <- cfa(model_3f_combined_adj_VIII, data = dekker_df, estimator = "mlr", missing = "fiml")
summary(fit3f_combined_adj_VIII,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 113 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 51
##
## Number of observations 151
## Number of missing patterns 17
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 198.749 190.891
## Degrees of freedom 101 101
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.041
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 492.475 451.360
## Degrees of freedom 120 120
## P-value 0.000 0.000
## Scaling correction factor 1.091
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.738 0.729
## Tucker-Lewis Index (TLI) 0.688 0.678
##
## Robust Comparative Fit Index (CFI) 0.747
## Robust Tucker-Lewis Index (TLI) 0.699
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -6184.694 -6184.694
## Scaling correction factor 1.067
## for the MLR correction
## Loglikelihood unrestricted model (H1) -6085.319 -6085.319
## Scaling correction factor 1.050
## for the MLR correction
##
## Akaike (AIC) 12471.387 12471.387
## Bayesian (BIC) 12625.268 12625.268
## Sample-size adjusted Bayesian (SABIC) 12463.859 12463.859
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.080 0.077
## 90 Percent confidence interval - lower 0.064 0.060
## 90 Percent confidence interval - upper 0.096 0.093
## P-value H_0: RMSEA <= 0.050 0.002 0.005
## P-value H_0: RMSEA >= 0.080 0.517 0.385
##
## Robust RMSEA 0.080
## 90 Percent confidence interval - lower 0.062
## 90 Percent confidence interval - upper 0.098
## P-value H_0: Robust RMSEA <= 0.050 0.005
## P-value H_0: Robust RMSEA >= 0.080 0.515
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.089 0.089
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.740 0.652
## dsq_suppressin 0.580 0.192 3.021 0.003 1.588 0.409
## dsq_sublimatin 0.756 0.214 3.534 0.000 2.071 0.560
## dsq_anticipatn 0.718 0.149 4.819 0.000 1.967 0.645
## dsq_rationlztn 0.544 0.128 4.237 0.000 1.492 0.502
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.439 0.382
## dsq_idealizatn 1.053 0.966 1.090 0.276 1.515 0.396
## dsq_psed_ltrsm 1.206 0.450 2.683 0.007 1.735 0.573
## dsq_undoing 1.071 0.363 2.949 0.003 1.541 0.388
## Combined_Immature =~
## dsq_tstc_fntsy 1.000 1.771 0.415
## dsq_pssv_ggrss 0.949 0.311 3.054 0.002 1.680 0.525
## dsq_acting_out 0.901 0.357 2.522 0.012 1.595 0.379
## dsq_projection 1.142 0.334 3.422 0.001 2.022 0.534
## dsq_dissociatn 0.787 0.244 3.228 0.001 1.393 0.378
## dsq_devaluatin 1.285 0.332 3.868 0.000 2.276 0.714
## dsq_splitting 1.228 0.328 3.744 0.000 2.175 0.571
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 2.468 0.841 2.935 0.003 0.626 0.626
## Combined_Immtr -0.133 0.725 -0.183 0.855 -0.027 -0.027
## Neurotic ~~
## Combined_Immtr 1.016 0.614 1.656 0.098 0.399 0.399
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.308 0.346 26.917 0.000 9.308 2.215
## .dsq_suppressin 8.568 0.320 26.764 0.000 8.568 2.206
## .dsq_sublimatin 10.029 0.308 32.596 0.000 10.029 2.712
## .dsq_anticipatn 10.125 0.252 40.111 0.000 10.125 3.321
## .dsq_rationlztn 8.848 0.245 36.099 0.000 8.848 2.976
## .dsq_rctn_frmtn 9.886 0.310 31.864 0.000 9.886 2.622
## .dsq_idealizatn 7.964 0.314 25.335 0.000 7.964 2.081
## .dsq_psed_ltrsm 12.699 0.253 50.151 0.000 12.699 4.196
## .dsq_undoing 11.140 0.333 33.487 0.000 11.140 2.803
## .dsq_tstc_fntsy 9.185 0.352 26.068 0.000 9.185 2.151
## .dsq_pssv_ggrss 7.738 0.269 28.813 0.000 7.738 2.415
## .dsq_acting_out 9.437 0.348 27.153 0.000 9.437 2.239
## .dsq_projection 9.347 0.312 29.915 0.000 9.347 2.469
## .dsq_dissociatn 9.584 0.304 31.552 0.000 9.584 2.598
## .dsq_devaluatin 9.763 0.262 37.232 0.000 9.763 3.061
## .dsq_splitting 9.719 0.311 31.280 0.000 9.719 2.553
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.152 2.110 4.812 0.000 10.152 0.575
## .dsq_suppressin 12.567 1.775 7.081 0.000 12.567 0.833
## .dsq_sublimatin 9.392 1.596 5.883 0.000 9.392 0.687
## .dsq_anticipatn 5.428 0.968 5.605 0.000 5.428 0.584
## .dsq_rationlztn 6.616 0.944 7.007 0.000 6.616 0.748
## .dsq_rctn_frmtn 12.144 2.171 5.594 0.000 12.144 0.854
## .dsq_idealizatn 12.360 2.390 5.171 0.000 12.360 0.843
## .dsq_psed_ltrsm 6.146 2.102 2.923 0.003 6.146 0.671
## .dsq_undoing 13.421 2.169 6.187 0.000 13.421 0.850
## .dsq_tstc_fntsy 15.097 1.773 8.514 0.000 15.097 0.828
## .dsq_pssv_ggrss 7.439 1.051 7.075 0.000 7.439 0.725
## .dsq_acting_out 15.222 1.812 8.399 0.000 15.222 0.857
## .dsq_projection 10.247 1.378 7.438 0.000 10.247 0.715
## .dsq_dissociatn 11.667 1.476 7.906 0.000 11.667 0.857
## .dsq_devaluatin 4.988 0.996 5.007 0.000 4.988 0.491
## .dsq_splitting 9.764 1.588 6.150 0.000 9.764 0.674
## Mature 7.508 2.336 3.214 0.001 1.000 1.000
## Neurotic 2.070 1.916 1.080 0.280 1.000 1.000
## Combined_Immtr 3.136 1.485 2.112 0.035 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.425
## dsq_suppressin 0.167
## dsq_sublimatin 0.313
## dsq_anticipatn 0.416
## dsq_rationlztn 0.252
## dsq_rctn_frmtn 0.146
## dsq_idealizatn 0.157
## dsq_psed_ltrsm 0.329
## dsq_undoing 0.150
## dsq_tstc_fntsy 0.172
## dsq_pssv_ggrss 0.275
## dsq_acting_out 0.143
## dsq_projection 0.285
## dsq_dissociatn 0.143
## dsq_devaluatin 0.509
## dsq_splitting 0.326modindices(fit3f_combined_adj_VIII, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 101 dsq_humor ~~ dsq_projection 15.654 -3.971 -3.971 -0.389 -0.389
## 88 Combined_Immature =~ dsq_pseudo_altruism 13.119 -0.792 -1.403 -0.464 -0.464
## 61 Mature =~ dsq_undoing 12.234 -0.795 -2.177 -0.548 -0.548
## 65 Mature =~ dsq_projection 11.817 -0.421 -1.153 -0.305 -0.305
## 60 Mature =~ dsq_pseudo_altruism 9.567 0.625 1.714 0.566 0.566
## 82 Combined_Immature =~ dsq_suppression 8.665 0.589 1.043 0.268 0.268
## 117 dsq_suppression ~~ dsq_devaluation 8.168 2.261 2.261 0.286 0.286
## 123 dsq_sublimation ~~ dsq_pseudo_altruism 8.053 2.227 2.227 0.293 0.293
## 160 dsq_reaction_formation ~~ dsq_acting_out 7.103 -3.190 -3.190 -0.235 -0.235
## 81 Combined_Immature =~ dsq_humor 6.862 -0.543 -0.961 -0.229 -0.229
## 188 dsq_undoing ~~ dsq_splitting 6.327 2.684 2.684 0.234 0.234
## 89 Combined_Immature =~ dsq_undoing 6.225 0.634 1.124 0.283 0.283
## 138 dsq_anticipation ~~ dsq_passive_aggression 5.843 1.525 1.525 0.240 0.240
## 161 dsq_reaction_formation ~~ dsq_projection 5.722 2.434 2.434 0.218 0.218
## 169 dsq_idealization ~~ dsq_acting_out 5.375 2.808 2.808 0.205 0.205
## 77 Neurotic =~ dsq_projection 5.300 -0.665 -0.956 -0.253 -0.253
## 157 dsq_reaction_formation ~~ dsq_undoing 5.149 2.790 2.790 0.219 0.219
## 63 Mature =~ dsq_passive_aggression 4.696 0.229 0.628 0.196 0.196
## 182 dsq_undoing ~~ dsq_autistic_fantasy 4.602 2.735 2.735 0.192 0.192
## 69 Neurotic =~ dsq_humor 4.469 -0.922 -1.326 -0.316 -0.316
## 199 dsq_passive_aggression ~~ dsq_splitting 4.382 1.881 1.881 0.221 0.221
## 87 Combined_Immature =~ dsq_idealization 4.093 0.491 0.870 0.227 0.227
## 141 dsq_anticipation ~~ dsq_dissociation 3.981 -1.518 -1.518 -0.191 -0.191
## 97 dsq_humor ~~ dsq_undoing 3.948 -2.324 -2.324 -0.199 -0.199
## 164 dsq_reaction_formation ~~ dsq_splitting 3.883 -1.968 -1.968 -0.181 -0.181
## 134 dsq_anticipation ~~ dsq_idealization 3.853 1.597 1.597 0.195 0.195model_3f_combined_adj_IX <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation + dsq_rationalization
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Combined_Immature =~ dsq_autistic_fantasy + dsq_passive_aggression +
dsq_acting_out + dsq_projection + dsq_devaluation + dsq_splitting
'
fit3f_combined_adj_IX <- cfa(model_3f_combined_adj_IX, data = dekker_df, estimator = "mlr", missing = "fiml")
summary(fit3f_combined_adj_IX,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 98 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 48
##
## Number of observations 151
## Number of missing patterns 16
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 181.018 178.639
## Degrees of freedom 87 87
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.013
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 458.731 423.499
## Degrees of freedom 105 105
## P-value 0.000 0.000
## Scaling correction factor 1.083
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.734 0.712
## Tucker-Lewis Index (TLI) 0.679 0.653
##
## Robust Comparative Fit Index (CFI) 0.741
## Robust Tucker-Lewis Index (TLI) 0.688
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5789.446 -5789.446
## Scaling correction factor 1.094
## for the MLR correction
## Loglikelihood unrestricted model (H1) -5698.938 -5698.938
## Scaling correction factor 1.042
## for the MLR correction
##
## Akaike (AIC) 11674.893 11674.893
## Bayesian (BIC) 11819.722 11819.722
## Sample-size adjusted Bayesian (SABIC) 11667.807 11667.807
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.085 0.084
## 90 Percent confidence interval - lower 0.067 0.066
## 90 Percent confidence interval - upper 0.102 0.101
## P-value H_0: RMSEA <= 0.050 0.001 0.001
## P-value H_0: RMSEA >= 0.080 0.681 0.645
##
## Robust RMSEA 0.085
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.104
## P-value H_0: Robust RMSEA <= 0.050 0.002
## P-value H_0: Robust RMSEA >= 0.080 0.683
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.090 0.090
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.742 0.652
## dsq_suppressin 0.582 0.196 2.974 0.003 1.597 0.411
## dsq_sublimatin 0.755 0.220 3.436 0.001 2.070 0.560
## dsq_anticipatn 0.716 0.150 4.785 0.000 1.964 0.644
## dsq_rationlztn 0.543 0.134 4.062 0.000 1.490 0.501
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.397 0.371
## dsq_idealizatn 1.136 1.270 0.894 0.371 1.587 0.415
## dsq_psed_ltrsm 1.211 0.456 2.656 0.008 1.691 0.559
## dsq_undoing 1.070 0.384 2.785 0.005 1.494 0.376
## Combined_Immature =~
## dsq_tstc_fntsy 1.000 1.693 0.396
## dsq_pssv_ggrss 1.035 0.340 3.047 0.002 1.753 0.547
## dsq_acting_out 0.919 0.393 2.336 0.019 1.556 0.369
## dsq_projection 1.187 0.371 3.201 0.001 2.009 0.531
## dsq_devaluatin 1.316 0.368 3.571 0.000 2.228 0.699
## dsq_splitting 1.321 0.367 3.604 0.000 2.237 0.588
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 2.439 0.954 2.557 0.011 0.637 0.637
## Combined_Immtr -0.055 0.710 -0.077 0.938 -0.012 -0.012
## Neurotic ~~
## Combined_Immtr 0.994 0.595 1.669 0.095 0.420 0.420
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.308 0.346 26.908 0.000 9.308 2.215
## .dsq_suppressin 8.568 0.320 26.762 0.000 8.568 2.206
## .dsq_sublimatin 10.029 0.308 32.596 0.000 10.029 2.712
## .dsq_anticipatn 10.125 0.252 40.110 0.000 10.125 3.321
## .dsq_rationlztn 8.848 0.245 36.094 0.000 8.848 2.976
## .dsq_rctn_frmtn 9.888 0.310 31.888 0.000 9.888 2.623
## .dsq_idealizatn 7.965 0.314 25.350 0.000 7.965 2.081
## .dsq_psed_ltrsm 12.701 0.253 50.138 0.000 12.701 4.197
## .dsq_undoing 11.142 0.333 33.508 0.000 11.142 2.803
## .dsq_tstc_fntsy 9.191 0.353 26.072 0.000 9.191 2.152
## .dsq_pssv_ggrss 7.735 0.268 28.823 0.000 7.735 2.416
## .dsq_acting_out 9.445 0.348 27.160 0.000 9.445 2.240
## .dsq_projection 9.351 0.312 29.975 0.000 9.351 2.471
## .dsq_devaluatin 9.753 0.262 37.296 0.000 9.753 3.062
## .dsq_splitting 9.724 0.311 31.300 0.000 9.724 2.555
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.144 2.184 4.645 0.000 10.144 0.574
## .dsq_suppressin 12.540 1.781 7.041 0.000 12.540 0.831
## .dsq_sublimatin 9.395 1.616 5.815 0.000 9.395 0.687
## .dsq_anticipatn 5.439 0.984 5.525 0.000 5.439 0.585
## .dsq_rationlztn 6.622 0.964 6.869 0.000 6.622 0.749
## .dsq_rctn_frmtn 12.256 2.449 5.005 0.000 12.256 0.863
## .dsq_idealizatn 12.135 2.931 4.140 0.000 12.135 0.828
## .dsq_psed_ltrsm 6.298 2.506 2.513 0.012 6.298 0.688
## .dsq_undoing 13.562 2.389 5.677 0.000 13.562 0.859
## .dsq_tstc_fntsy 15.369 1.742 8.821 0.000 15.369 0.843
## .dsq_pssv_ggrss 7.181 1.067 6.732 0.000 7.181 0.700
## .dsq_acting_out 15.358 1.846 8.320 0.000 15.358 0.864
## .dsq_projection 10.282 1.447 7.105 0.000 10.282 0.718
## .dsq_devaluatin 5.182 1.038 4.990 0.000 5.182 0.511
## .dsq_splitting 9.487 1.644 5.772 0.000 9.487 0.655
## Mature 7.518 2.412 3.118 0.002 1.000 1.000
## Neurotic 1.952 2.260 0.863 0.388 1.000 1.000
## Combined_Immtr 2.866 1.449 1.978 0.048 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.426
## dsq_suppressin 0.169
## dsq_sublimatin 0.313
## dsq_anticipatn 0.415
## dsq_rationlztn 0.251
## dsq_rctn_frmtn 0.137
## dsq_idealizatn 0.172
## dsq_psed_ltrsm 0.312
## dsq_undoing 0.141
## dsq_tstc_fntsy 0.157
## dsq_pssv_ggrss 0.300
## dsq_acting_out 0.136
## dsq_projection 0.282
## dsq_devaluatin 0.489
## dsq_splitting 0.345modindices(fit3f_combined_adj_IX, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 96 dsq_humor ~~ dsq_projection 15.640 -3.984 -3.984 -0.390 -0.390
## 83 Combined_Immature =~ dsq_pseudo_altruism 13.846 -0.867 -1.468 -0.485 -0.485
## 62 Mature =~ dsq_projection 12.570 -0.437 -1.197 -0.316 -0.316
## 58 Mature =~ dsq_undoing 11.574 -0.784 -2.149 -0.541 -0.541
## 57 Mature =~ dsq_pseudo_altruism 9.814 0.636 1.744 0.576 0.576
## 110 dsq_suppression ~~ dsq_devaluation 8.531 2.340 2.340 0.290 0.290
## 77 Combined_Immature =~ dsq_suppression 8.072 0.600 1.015 0.261 0.261
## 116 dsq_sublimation ~~ dsq_pseudo_altruism 8.025 2.230 2.230 0.290 0.290
## 76 Combined_Immature =~ dsq_humor 7.403 -0.595 -1.007 -0.240 -0.240
## 150 dsq_reaction_formation ~~ dsq_acting_out 6.884 -3.161 -3.161 -0.230 -0.230
## 174 dsq_undoing ~~ dsq_splitting 6.711 2.767 2.767 0.244 0.244
## 73 Neurotic =~ dsq_projection 5.996 -0.744 -1.039 -0.275 -0.275
## 151 dsq_reaction_formation ~~ dsq_projection 5.769 2.461 2.461 0.219 0.219
## 147 dsq_reaction_formation ~~ dsq_undoing 5.526 2.884 2.884 0.224 0.224
## 84 Combined_Immature =~ dsq_undoing 5.460 0.637 1.079 0.271 0.271
## 130 dsq_anticipation ~~ dsq_passive_aggression 5.388 1.455 1.455 0.233 0.233
## 82 Combined_Immature =~ dsq_idealization 5.104 0.594 1.006 0.263 0.263
## 158 dsq_idealization ~~ dsq_acting_out 4.996 2.708 2.708 0.198 0.198
## 169 dsq_undoing ~~ dsq_autistic_fantasy 4.987 2.876 2.876 0.199 0.199
## 65 Neurotic =~ dsq_humor 4.793 -0.999 -1.395 -0.332 -0.332
## 60 Mature =~ dsq_passive_aggression 4.404 0.222 0.607 0.190 0.190
## 188 dsq_projection ~~ dsq_splitting 4.281 -2.225 -2.225 -0.225 -0.225
## 153 dsq_reaction_formation ~~ dsq_splitting 4.079 -2.018 -2.018 -0.187 -0.187
## 92 dsq_humor ~~ dsq_undoing 3.940 -2.327 -2.327 -0.198 -0.198model_3f_combined_adj_X <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation + dsq_rationalization
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Combined_Immature =~ dsq_autistic_fantasy + dsq_passive_aggression +
dsq_projection + dsq_devaluation + dsq_splitting
'
fit3f_combined_adj_X <- cfa(model_3f_combined_adj_X, data = dekker_df, estimator = "mlr", missing = "fiml")
summary(fit3f_combined_adj_X,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 114 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 45
##
## Number of observations 151
## Number of missing patterns 15
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 162.035 158.697
## Degrees of freedom 74 74
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.021
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 425.192 392.362
## Degrees of freedom 91 91
## P-value 0.000 0.000
## Scaling correction factor 1.084
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.737 0.719
## Tucker-Lewis Index (TLI) 0.676 0.654
##
## Robust Comparative Fit Index (CFI) 0.741
## Robust Tucker-Lewis Index (TLI) 0.682
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5376.701 -5376.701
## Scaling correction factor 1.076
## for the MLR correction
## Loglikelihood unrestricted model (H1) -5295.683 -5295.683
## Scaling correction factor 1.042
## for the MLR correction
##
## Akaike (AIC) 10843.401 10843.401
## Bayesian (BIC) 10979.179 10979.179
## Sample-size adjusted Bayesian (SABIC) 10836.759 10836.759
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.089 0.087
## 90 Percent confidence interval - lower 0.070 0.069
## 90 Percent confidence interval - upper 0.107 0.106
## P-value H_0: RMSEA <= 0.050 0.001 0.001
## P-value H_0: RMSEA >= 0.080 0.791 0.747
##
## Robust RMSEA 0.090
## 90 Percent confidence interval - lower 0.070
## 90 Percent confidence interval - upper 0.110
## P-value H_0: Robust RMSEA <= 0.050 0.001
## P-value H_0: Robust RMSEA >= 0.080 0.796
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.089 0.089
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.754 0.655
## dsq_suppressin 0.576 0.192 3.000 0.003 1.586 0.408
## dsq_sublimatin 0.752 0.214 3.506 0.000 2.070 0.560
## dsq_anticipatn 0.712 0.150 4.744 0.000 1.962 0.643
## dsq_rationlztn 0.541 0.129 4.195 0.000 1.489 0.501
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.470 0.390
## dsq_idealizatn 1.044 0.947 1.103 0.270 1.535 0.401
## dsq_psed_ltrsm 1.150 0.445 2.582 0.010 1.691 0.559
## dsq_undoing 1.055 0.346 3.046 0.002 1.550 0.390
## Combined_Immature =~
## dsq_tstc_fntsy 1.000 1.774 0.415
## dsq_pssv_ggrss 0.992 0.328 3.026 0.002 1.760 0.549
## dsq_projection 1.183 0.369 3.207 0.001 2.098 0.554
## dsq_devaluatin 1.218 0.329 3.707 0.000 2.161 0.678
## dsq_splitting 1.239 0.324 3.820 0.000 2.197 0.577
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 2.522 0.849 2.972 0.003 0.623 0.623
## Combined_Immtr -0.229 0.780 -0.293 0.769 -0.047 -0.047
## Neurotic ~~
## Combined_Immtr 1.070 0.661 1.620 0.105 0.411 0.411
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.309 0.346 26.909 0.000 9.309 2.215
## .dsq_suppressin 8.568 0.320 26.765 0.000 8.568 2.206
## .dsq_sublimatin 10.029 0.308 32.598 0.000 10.029 2.712
## .dsq_anticipatn 10.125 0.252 40.108 0.000 10.125 3.321
## .dsq_rationlztn 8.848 0.245 36.091 0.000 8.848 2.976
## .dsq_rctn_frmtn 9.887 0.310 31.887 0.000 9.887 2.623
## .dsq_idealizatn 7.965 0.314 25.340 0.000 7.965 2.081
## .dsq_psed_ltrsm 12.701 0.253 50.173 0.000 12.701 4.196
## .dsq_undoing 11.141 0.333 33.487 0.000 11.141 2.803
## .dsq_tstc_fntsy 9.188 0.353 26.059 0.000 9.188 2.151
## .dsq_pssv_ggrss 7.729 0.268 28.854 0.000 7.729 2.413
## .dsq_projection 9.347 0.312 29.949 0.000 9.347 2.471
## .dsq_devaluatin 9.753 0.262 37.275 0.000 9.753 3.061
## .dsq_splitting 9.724 0.311 31.299 0.000 9.724 2.554
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.080 2.162 4.662 0.000 10.080 0.571
## .dsq_suppressin 12.574 1.777 7.076 0.000 12.574 0.833
## .dsq_sublimatin 9.396 1.595 5.889 0.000 9.396 0.687
## .dsq_anticipatn 5.450 0.953 5.719 0.000 5.450 0.586
## .dsq_rationlztn 6.624 0.948 6.990 0.000 6.624 0.749
## .dsq_rctn_frmtn 12.051 2.208 5.458 0.000 12.051 0.848
## .dsq_idealizatn 12.299 2.414 5.096 0.000 12.299 0.839
## .dsq_psed_ltrsm 6.302 2.039 3.091 0.002 6.302 0.688
## .dsq_undoing 13.393 2.218 6.039 0.000 13.393 0.848
## .dsq_tstc_fntsy 15.091 1.707 8.840 0.000 15.091 0.827
## .dsq_pssv_ggrss 7.163 1.117 6.415 0.000 7.163 0.698
## .dsq_projection 9.913 1.494 6.637 0.000 9.913 0.693
## .dsq_devaluatin 5.482 0.992 5.528 0.000 5.482 0.540
## .dsq_splitting 9.663 1.600 6.040 0.000 9.663 0.667
## Mature 7.582 2.386 3.177 0.001 1.000 1.000
## Neurotic 2.161 1.987 1.087 0.277 1.000 1.000
## Combined_Immtr 3.146 1.511 2.083 0.037 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.429
## dsq_suppressin 0.167
## dsq_sublimatin 0.313
## dsq_anticipatn 0.414
## dsq_rationlztn 0.251
## dsq_rctn_frmtn 0.152
## dsq_idealizatn 0.161
## dsq_psed_ltrsm 0.312
## dsq_undoing 0.152
## dsq_tstc_fntsy 0.173
## dsq_pssv_ggrss 0.302
## dsq_projection 0.307
## dsq_devaluatin 0.460
## dsq_splitting 0.333modindices(fit3f_combined_adj_X, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 90 dsq_humor ~~ dsq_projection 15.532 -3.946 -3.946 -0.395 -0.395
## 78 Combined_Immature =~ dsq_pseudo_altruism 15.004 -0.853 -1.513 -0.500 -0.500
## 55 Mature =~ dsq_undoing 12.185 -0.780 -2.147 -0.540 -0.540
## 58 Mature =~ dsq_projection 11.917 -0.424 -1.169 -0.309 -0.309
## 54 Mature =~ dsq_pseudo_altruism 10.662 0.634 1.747 0.577 0.577
## 103 dsq_suppression ~~ dsq_devaluation 9.102 2.464 2.464 0.297 0.297
## 109 dsq_sublimation ~~ dsq_pseudo_altruism 8.276 2.264 2.264 0.294 0.294
## 72 Combined_Immature =~ dsq_suppression 7.739 0.567 1.006 0.259 0.259
## 71 Combined_Immature =~ dsq_humor 7.245 -0.568 -1.007 -0.240 -0.240
## 79 Combined_Immature =~ dsq_undoing 6.450 0.662 1.174 0.295 0.295
## 160 dsq_undoing ~~ dsq_splitting 6.058 2.650 2.650 0.233 0.233
## 169 dsq_projection ~~ dsq_splitting 5.836 -2.709 -2.709 -0.277 -0.277
## 68 Neurotic =~ dsq_projection 5.754 -0.689 -1.012 -0.268 -0.268
## 57 Mature =~ dsq_passive_aggression 5.500 0.249 0.685 0.214 0.214
## 122 dsq_anticipation ~~ dsq_passive_aggression 5.411 1.464 1.464 0.234 0.234
## 142 dsq_reaction_formation ~~ dsq_splitting 5.329 -2.324 -2.324 -0.215 -0.215
## 140 dsq_reaction_formation ~~ dsq_projection 4.963 2.265 2.265 0.207 0.207
## 137 dsq_reaction_formation ~~ dsq_undoing 4.958 2.744 2.744 0.216 0.216
## 61 Neurotic =~ dsq_humor 4.727 -0.916 -1.346 -0.320 -0.320
## 156 dsq_undoing ~~ dsq_autistic_fantasy 4.483 2.710 2.710 0.191 0.191
## 135 dsq_reaction_formation ~~ dsq_idealization 4.051 -2.364 -2.364 -0.194 -0.194
## 87 dsq_humor ~~ dsq_undoing 3.899 -2.308 -2.308 -0.199 -0.199
## 149 dsq_idealization ~~ dsq_splitting 3.847 2.001 2.001 0.184 0.184fit1_ds <- cfa(model_1f, data = ds_df, estimator = "mlr", missing = "fiml")
summary(fit1_ds,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 72 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 60
##
## Number of observations 210
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 495.165 477.113
## Degrees of freedom 170 170
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.038
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 724.594 635.432
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.140
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.392 0.311
## Tucker-Lewis Index (TLI) 0.320 0.229
##
## Robust Comparative Fit Index (CFI) 0.402
## Robust Tucker-Lewis Index (TLI) 0.332
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10930.821 -10930.821
## Scaling correction factor 1.259
## for the MLR correction
## Loglikelihood unrestricted model (H1) -10683.239 -10683.239
## Scaling correction factor 1.096
## for the MLR correction
##
## Akaike (AIC) 21981.642 21981.642
## Bayesian (BIC) 22182.469 22182.469
## Sample-size adjusted Bayesian (SABIC) 21992.354 21992.354
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.095 0.093
## 90 Percent confidence interval - lower 0.086 0.083
## 90 Percent confidence interval - upper 0.105 0.102
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.995 0.985
##
## Robust RMSEA 0.096
## 90 Percent confidence interval - lower 0.084
## 90 Percent confidence interval - upper 0.107
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.988
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.121 0.121
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Factor1 =~
## dsq_sublimatin 1.000 1.627 0.409
## dsq_humor 1.409 0.658 2.141 0.032 2.293 0.510
## dsq_anticipatn 1.259 0.319 3.942 0.000 2.049 0.482
## dsq_suppressin 1.108 0.368 3.014 0.003 1.803 0.445
## dsq_psed_ltrsm 0.430 0.219 1.963 0.050 0.700 0.188
## dsq_idealizatn 1.058 0.334 3.163 0.002 1.721 0.424
## dsq_rctn_frmtn 1.019 0.457 2.227 0.026 1.657 0.412
## dsq_undoing 0.698 0.392 1.781 0.075 1.136 0.278
## dsq_rationlztn 1.675 0.725 2.311 0.021 2.726 0.667
## dsq_projection -0.127 0.890 -0.143 0.886 -0.207 -0.052
## dsq_pssv_ggrss -0.064 0.796 -0.081 0.935 -0.105 -0.027
## dsq_acting_out 0.048 0.842 0.057 0.954 0.078 0.016
## dsq_isolation 0.083 0.705 0.118 0.906 0.135 0.028
## dsq_tstc_fntsy 0.136 1.065 0.128 0.898 0.221 0.042
## dsq_denial 0.666 0.347 1.923 0.055 1.084 0.323
## dsq_displacmnt -0.110 0.551 -0.200 0.841 -0.180 -0.044
## dsq_dissociatn 1.018 0.274 3.718 0.000 1.657 0.530
## dsq_splitting 0.335 0.540 0.621 0.535 0.545 0.133
## dsq_devaluatin 0.142 0.529 0.268 0.789 0.231 0.071
## dsq_somatizatn -0.451 0.712 -0.634 0.526 -0.735 -0.154
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_sublimatin 9.796 0.283 34.584 0.000 9.796 2.462
## .dsq_humor 8.238 0.319 25.833 0.000 8.238 1.830
## .dsq_anticipatn 11.805 0.304 38.891 0.000 11.805 2.777
## .dsq_suppressin 6.755 0.289 23.402 0.000 6.755 1.667
## .dsq_psed_ltrsm 11.027 0.264 41.736 0.000 11.027 2.967
## .dsq_idealizatn 8.089 0.289 28.012 0.000 8.089 1.993
## .dsq_rctn_frmtn 8.948 0.286 31.249 0.000 8.948 2.223
## .dsq_undoing 9.445 0.291 32.462 0.000 9.445 2.309
## .dsq_rationlztn 8.428 0.290 29.028 0.000 8.428 2.064
## .dsq_projection 8.142 0.283 28.732 0.000 8.142 2.037
## .dsq_pssv_ggrss 8.743 0.277 31.579 0.000 8.743 2.245
## .dsq_acting_out 10.707 0.348 30.788 0.000 10.707 2.189
## .dsq_isolation 9.833 0.348 28.215 0.000 9.833 2.004
## .dsq_tstc_fntsy 10.292 0.377 27.295 0.000 10.292 1.940
## .dsq_denial 5.379 0.239 22.479 0.000 5.379 1.602
## .dsq_displacmnt 10.026 0.291 34.492 0.000 10.026 2.447
## .dsq_dissociatn 5.251 0.223 23.515 0.000 5.251 1.679
## .dsq_splitting 9.998 0.292 34.183 0.000 9.998 2.431
## .dsq_devaluatin 7.171 0.230 31.166 0.000 7.171 2.214
## .dsq_somatizatn 11.506 0.389 29.568 0.000 11.506 2.412
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_sublimatin 13.188 1.647 8.007 0.000 13.188 0.833
## .dsq_humor 14.998 2.721 5.513 0.000 14.998 0.740
## .dsq_anticipatn 13.875 1.674 8.290 0.000 13.875 0.768
## .dsq_suppressin 13.174 1.906 6.913 0.000 13.174 0.802
## .dsq_psed_ltrsm 13.328 1.320 10.097 0.000 13.328 0.965
## .dsq_idealizatn 13.509 2.406 5.615 0.000 13.509 0.820
## .dsq_rctn_frmtn 13.459 1.796 7.493 0.000 13.459 0.830
## .dsq_undoing 15.443 2.260 6.834 0.000 15.443 0.923
## .dsq_rationlztn 9.254 3.360 2.754 0.006 9.254 0.555
## .dsq_projection 15.937 1.390 11.466 0.000 15.937 0.997
## .dsq_pssv_ggrss 15.160 1.440 10.531 0.000 15.160 0.999
## .dsq_acting_out 23.918 1.747 13.695 0.000 23.918 1.000
## .dsq_isolation 24.050 1.813 13.262 0.000 24.050 0.999
## .dsq_tstc_fntsy 28.087 2.042 13.752 0.000 28.087 0.998
## .dsq_denial 10.102 1.422 7.102 0.000 10.102 0.896
## .dsq_displacmnt 16.750 1.485 11.281 0.000 16.750 0.998
## .dsq_dissociatn 7.031 1.131 6.214 0.000 7.031 0.719
## .dsq_splitting 16.622 1.819 9.140 0.000 16.622 0.982
## .dsq_devaluatin 10.432 1.083 9.637 0.000 10.432 0.995
## .dsq_somatizatn 22.225 2.313 9.610 0.000 22.225 0.976
## Factor1 2.648 1.383 1.915 0.055 1.000 1.000
##
## R-Square:
## Estimate
## dsq_sublimatin 0.167
## dsq_humor 0.260
## dsq_anticipatn 0.232
## dsq_suppressin 0.198
## dsq_psed_ltrsm 0.035
## dsq_idealizatn 0.180
## dsq_rctn_frmtn 0.170
## dsq_undoing 0.077
## dsq_rationlztn 0.445
## dsq_projection 0.003
## dsq_pssv_ggrss 0.001
## dsq_acting_out 0.000
## dsq_isolation 0.001
## dsq_tstc_fntsy 0.002
## dsq_denial 0.104
## dsq_displacmnt 0.002
## dsq_dissociatn 0.281
## dsq_splitting 0.018
## dsq_devaluatin 0.005
## dsq_somatizatn 0.024fit3_ds <- cfa(model_3f, data = ds_df, estimator = "mlr", missing = "fiml")
summary(fit3_ds,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 178 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 63
##
## Number of observations 210
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 374.566 335.018
## Degrees of freedom 167 167
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.118
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 724.594 635.432
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.140
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.612 0.623
## Tucker-Lewis Index (TLI) 0.558 0.571
##
## Robust Comparative Fit Index (CFI) 0.638
## Robust Tucker-Lewis Index (TLI) 0.589
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10870.522 -10870.522
## Scaling correction factor 1.036
## for the MLR correction
## Loglikelihood unrestricted model (H1) -10683.239 -10683.239
## Scaling correction factor 1.096
## for the MLR correction
##
## Akaike (AIC) 21867.044 21867.044
## Bayesian (BIC) 22077.911 22077.911
## Sample-size adjusted Bayesian (SABIC) 21878.291 21878.291
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.077 0.069
## 90 Percent confidence interval - lower 0.067 0.059
## 90 Percent confidence interval - upper 0.087 0.079
## P-value H_0: RMSEA <= 0.050 0.000 0.001
## P-value H_0: RMSEA >= 0.080 0.322 0.040
##
## Robust RMSEA 0.075
## 90 Percent confidence interval - lower 0.063
## 90 Percent confidence interval - upper 0.087
## P-value H_0: Robust RMSEA <= 0.050 0.001
## P-value H_0: Robust RMSEA >= 0.080 0.262
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.103 0.103
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.003 0.445
## dsq_suppressin 0.912 0.256 3.559 0.000 1.826 0.451
## dsq_sublimatin 0.711 0.282 2.522 0.012 1.424 0.358
## dsq_anticipatn 1.169 0.313 3.736 0.000 2.343 0.551
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.492 0.371
## dsq_idealizatn 1.286 0.460 2.796 0.005 1.918 0.473
## dsq_psed_ltrsm 0.543 0.278 1.954 0.051 0.810 0.218
## dsq_undoing 1.115 0.602 1.851 0.064 1.663 0.407
## Immature =~
## dsq_rationlztn 1.000 0.483 0.118
## dsq_isolation -3.517 3.719 -0.946 0.344 -1.697 -0.346
## dsq_dissociatn -0.414 1.026 -0.403 0.687 -0.200 -0.064
## dsq_devaluatin -3.160 3.359 -0.941 0.347 -1.525 -0.471
## dsq_splitting -2.905 3.305 -0.879 0.379 -1.402 -0.341
## dsq_denial -1.658 2.105 -0.788 0.431 -0.800 -0.238
## dsq_tstc_fntsy -7.033 7.302 -0.963 0.335 -3.394 -0.640
## dsq_displacmnt -3.223 3.242 -0.994 0.320 -1.555 -0.380
## dsq_pssv_ggrss -5.152 5.236 -0.984 0.325 -2.486 -0.638
## dsq_somatizatn -3.369 3.386 -0.995 0.320 -1.626 -0.341
## dsq_acting_out -5.167 5.476 -0.944 0.345 -2.493 -0.510
## dsq_projection -5.163 5.228 -0.987 0.323 -2.491 -0.623
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 2.841 1.258 2.258 0.024 0.950 0.950
## Immature 0.009 0.167 0.054 0.957 0.009 0.009
## Neurotic ~~
## Immature -0.194 0.150 -1.299 0.194 -0.270 -0.270
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 8.247 0.319 25.813 0.000 8.247 1.832
## .dsq_suppressin 6.762 0.288 23.479 0.000 6.762 1.669
## .dsq_sublimatin 9.803 0.283 34.666 0.000 9.803 2.463
## .dsq_anticipatn 11.813 0.302 39.081 0.000 11.813 2.779
## .dsq_rctn_frmtn 8.958 0.286 31.312 0.000 8.958 2.225
## .dsq_idealizatn 8.101 0.288 28.128 0.000 8.101 1.996
## .dsq_psed_ltrsm 11.032 0.264 41.790 0.000 11.032 2.968
## .dsq_undoing 9.453 0.290 32.580 0.000 9.453 2.311
## .dsq_rationlztn 8.435 0.289 29.152 0.000 8.435 2.065
## .dsq_isolation 9.848 0.349 28.228 0.000 9.848 2.007
## .dsq_dissociatn 5.259 0.222 23.650 0.000 5.259 1.682
## .dsq_devaluatin 7.185 0.230 31.224 0.000 7.185 2.219
## .dsq_splitting 10.012 0.292 34.286 0.000 10.012 2.434
## .dsq_denial 5.391 0.239 22.516 0.000 5.391 1.605
## .dsq_tstc_fntsy 10.323 0.376 27.429 0.000 10.323 1.946
## .dsq_displacmnt 10.039 0.291 34.539 0.000 10.039 2.450
## .dsq_pssv_ggrss 8.764 0.277 31.694 0.000 8.764 2.249
## .dsq_somatizatn 11.555 0.387 29.840 0.000 11.555 2.423
## .dsq_acting_out 10.729 0.348 30.873 0.000 10.729 2.193
## .dsq_projection 8.163 0.283 28.859 0.000 8.163 2.042
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 16.243 1.886 8.613 0.000 16.243 0.802
## .dsq_suppressin 13.088 2.015 6.494 0.000 13.088 0.797
## .dsq_sublimatin 13.807 1.628 8.484 0.000 13.807 0.872
## .dsq_anticipatn 12.584 1.779 7.073 0.000 12.584 0.696
## .dsq_rctn_frmtn 13.979 2.052 6.813 0.000 13.979 0.863
## .dsq_idealizatn 12.791 1.754 7.294 0.000 12.791 0.777
## .dsq_psed_ltrsm 13.161 1.355 9.716 0.000 13.161 0.953
## .dsq_undoing 13.966 1.832 7.623 0.000 13.966 0.835
## .dsq_rationlztn 16.448 1.470 11.192 0.000 16.448 0.986
## .dsq_isolation 21.193 1.878 11.284 0.000 21.193 0.880
## .dsq_dissociatn 9.737 0.941 10.347 0.000 9.737 0.996
## .dsq_devaluatin 8.165 0.893 9.143 0.000 8.165 0.778
## .dsq_splitting 14.957 1.620 9.235 0.000 14.957 0.884
## .dsq_denial 10.638 0.971 10.954 0.000 10.638 0.943
## .dsq_tstc_fntsy 16.636 2.314 7.188 0.000 16.636 0.591
## .dsq_displacmnt 14.367 1.355 10.604 0.000 14.367 0.856
## .dsq_pssv_ggrss 9.000 1.362 6.605 0.000 9.000 0.593
## .dsq_somatizatn 20.110 2.234 9.001 0.000 20.110 0.884
## .dsq_acting_out 17.718 1.826 9.705 0.000 17.718 0.740
## .dsq_projection 9.783 1.345 7.274 0.000 9.783 0.612
## Mature 4.014 1.805 2.223 0.026 1.000 1.000
## Neurotic 2.226 1.761 1.264 0.206 1.000 1.000
## Immature 0.233 0.481 0.484 0.629 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.198
## dsq_suppressin 0.203
## dsq_sublimatin 0.128
## dsq_anticipatn 0.304
## dsq_rctn_frmtn 0.137
## dsq_idealizatn 0.223
## dsq_psed_ltrsm 0.047
## dsq_undoing 0.165
## dsq_rationlztn 0.014
## dsq_isolation 0.120
## dsq_dissociatn 0.004
## dsq_devaluatin 0.222
## dsq_splitting 0.116
## dsq_denial 0.057
## dsq_tstc_fntsy 0.409
## dsq_displacmnt 0.144
## dsq_pssv_ggrss 0.407
## dsq_somatizatn 0.116
## dsq_acting_out 0.260
## dsq_projection 0.388fit4_ds <- cfa(model_4f, data = ds_df, estimator = "mlr", missing = "fiml")## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use lavInspect(fit, "cov.lv") to investigate.summary(fit4_ds,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 159 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 66
##
## Number of observations 210
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 288.040 265.405
## Degrees of freedom 164 164
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.085
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 724.594 635.432
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.140
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.768 0.772
## Tucker-Lewis Index (TLI) 0.731 0.736
##
## Robust Comparative Fit Index (CFI) 0.797
## Robust Tucker-Lewis Index (TLI) 0.765
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10827.259 -10827.259
## Scaling correction factor 1.121
## for the MLR correction
## Loglikelihood unrestricted model (H1) -10683.239 -10683.239
## Scaling correction factor 1.096
## for the MLR correction
##
## Akaike (AIC) 21786.518 21786.518
## Bayesian (BIC) 22007.427 22007.427
## Sample-size adjusted Bayesian (SABIC) 21798.301 21798.301
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.060 0.054
## 90 Percent confidence interval - lower 0.048 0.043
## 90 Percent confidence interval - upper 0.071 0.066
## P-value H_0: RMSEA <= 0.050 0.076 0.263
## P-value H_0: RMSEA >= 0.080 0.001 0.000
##
## Robust RMSEA 0.057
## 90 Percent confidence interval - lower 0.042
## 90 Percent confidence interval - upper 0.070
## P-value H_0: Robust RMSEA <= 0.050 0.207
## P-value H_0: Robust RMSEA >= 0.080 0.002
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.084 0.084
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.202 0.489
## dsq_suppressin 0.856 0.249 3.431 0.001 1.885 0.465
## dsq_sublimatin 0.721 0.212 3.408 0.001 1.588 0.399
## dsq_anticipatn 0.962 0.233 4.137 0.000 2.119 0.498
## dsq_rationlztn 1.274 0.240 5.306 0.000 2.806 0.687
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.599 0.397
## dsq_idealizatn 1.148 0.359 3.198 0.001 1.836 0.452
## dsq_psed_ltrsm 0.574 0.264 2.172 0.030 0.917 0.247
## dsq_undoing 1.012 0.474 2.136 0.033 1.617 0.395
## Immature =~
## dsq_tstc_fntsy 1.000 3.324 0.626
## dsq_displacmnt 0.493 0.118 4.194 0.000 1.639 0.400
## dsq_pssv_ggrss 0.744 0.123 6.071 0.000 2.474 0.635
## dsq_somatizatn 0.499 0.154 3.244 0.001 1.657 0.348
## dsq_acting_out 0.726 0.138 5.264 0.000 2.414 0.493
## dsq_projection 0.778 0.120 6.498 0.000 2.586 0.647
## Image_Distorting =~
## dsq_isolation 1.000 1.764 0.360
## dsq_dissociatn 0.648 0.633 1.024 0.306 1.143 0.366
## dsq_devaluatin 0.870 0.252 3.452 0.001 1.535 0.474
## dsq_splitting 0.763 0.330 2.313 0.021 1.346 0.327
## dsq_denial 0.865 0.546 1.586 0.113 1.527 0.455
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 3.088 1.139 2.712 0.007 0.877 0.877
## Immature -1.208 0.924 -1.307 0.191 -0.165 -0.165
## Image_Distrtng 1.628 0.877 1.857 0.063 0.419 0.419
## Neurotic ~~
## Immature 1.276 0.883 1.446 0.148 0.240 0.240
## Image_Distrtng 1.252 0.590 2.123 0.034 0.444 0.444
## Immature ~~
## Image_Distrtng 3.982 2.770 1.438 0.150 0.679 0.679
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 8.244 0.319 25.815 0.000 8.244 1.832
## .dsq_suppressin 6.760 0.288 23.488 0.000 6.760 1.668
## .dsq_sublimatin 9.801 0.283 34.646 0.000 9.801 2.463
## .dsq_anticipatn 11.810 0.302 39.076 0.000 11.810 2.778
## .dsq_rationlztn 8.435 0.290 29.133 0.000 8.435 2.065
## .dsq_rctn_frmtn 8.958 0.286 31.327 0.000 8.958 2.225
## .dsq_idealizatn 8.100 0.288 28.125 0.000 8.100 1.996
## .dsq_psed_ltrsm 11.032 0.264 41.786 0.000 11.032 2.968
## .dsq_undoing 9.453 0.290 32.580 0.000 9.453 2.311
## .dsq_tstc_fntsy 10.323 0.376 27.444 0.000 10.323 1.945
## .dsq_displacmnt 10.040 0.291 34.522 0.000 10.040 2.450
## .dsq_pssv_ggrss 8.765 0.276 31.700 0.000 8.765 2.250
## .dsq_somatizatn 11.561 0.388 29.778 0.000 11.561 2.425
## .dsq_acting_out 10.729 0.347 30.900 0.000 10.729 2.193
## .dsq_projection 8.165 0.283 28.834 0.000 8.165 2.042
## .dsq_isolation 9.844 0.349 28.227 0.000 9.844 2.006
## .dsq_dissociatn 5.265 0.223 23.615 0.000 5.265 1.684
## .dsq_devaluatin 7.181 0.230 31.232 0.000 7.181 2.217
## .dsq_splitting 10.008 0.292 34.261 0.000 10.008 2.433
## .dsq_denial 5.393 0.239 22.543 0.000 5.393 1.606
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 15.407 1.860 8.285 0.000 15.407 0.761
## .dsq_suppressin 12.872 1.983 6.491 0.000 12.872 0.784
## .dsq_sublimatin 13.313 1.510 8.815 0.000 13.313 0.841
## .dsq_anticipatn 13.583 1.510 8.996 0.000 13.583 0.752
## .dsq_rationlztn 8.805 1.386 6.355 0.000 8.805 0.528
## .dsq_rctn_frmtn 13.649 1.908 7.154 0.000 13.649 0.842
## .dsq_idealizatn 13.101 1.892 6.925 0.000 13.101 0.795
## .dsq_psed_ltrsm 12.976 1.307 9.927 0.000 12.976 0.939
## .dsq_undoing 14.116 1.818 7.764 0.000 14.116 0.844
## .dsq_tstc_fntsy 17.106 2.528 6.767 0.000 17.106 0.608
## .dsq_displacmnt 14.101 1.419 9.939 0.000 14.101 0.840
## .dsq_pssv_ggrss 9.061 1.373 6.601 0.000 9.061 0.597
## .dsq_somatizatn 19.989 2.221 8.999 0.000 19.989 0.879
## .dsq_acting_out 18.106 1.946 9.305 0.000 18.106 0.757
## .dsq_projection 9.302 1.373 6.776 0.000 9.302 0.582
## .dsq_isolation 20.958 2.302 9.105 0.000 20.958 0.871
## .dsq_dissociatn 8.471 1.967 4.306 0.000 8.471 0.866
## .dsq_devaluatin 8.132 1.424 5.709 0.000 8.132 0.775
## .dsq_splitting 15.109 1.745 8.657 0.000 15.109 0.893
## .dsq_denial 8.947 1.878 4.764 0.000 8.947 0.793
## Mature 4.850 1.887 2.570 0.010 1.000 1.000
## Neurotic 2.556 1.642 1.557 0.119 1.000 1.000
## Immature 11.048 2.577 4.287 0.000 1.000 1.000
## Image_Distrtng 3.113 2.102 1.481 0.139 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.239
## dsq_suppressin 0.216
## dsq_sublimatin 0.159
## dsq_anticipatn 0.248
## dsq_rationlztn 0.472
## dsq_rctn_frmtn 0.158
## dsq_idealizatn 0.205
## dsq_psed_ltrsm 0.061
## dsq_undoing 0.156
## dsq_tstc_fntsy 0.392
## dsq_displacmnt 0.160
## dsq_pssv_ggrss 0.403
## dsq_somatizatn 0.121
## dsq_acting_out 0.243
## dsq_projection 0.418
## dsq_isolation 0.129
## dsq_dissociatn 0.134
## dsq_devaluatin 0.225
## dsq_splitting 0.107
## dsq_denial 0.207cov2cor(lavInspect(fit4_ds, "cov.lv"))## Mature Neurtc Immatr Img_Ds
## Mature 1.000
## Neurotic 0.877 1.000
## Immature -0.165 0.240 1.000
## Image_Distorting 0.419 0.444 0.679 1.000# Likelihood Ratio Tests (Nested)
anova(fit1_ds, fit3_ds)## Warning: lavaan->lav_test_diff_SatorraBentler2001():
## scaling factor is negative##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit3_ds 167 21867 22078 374.57
## fit1_ds 170 21982 22183 495.16 3anova(fit1_ds, fit4_ds)## Warning: lavaan->lav_test_diff_SatorraBentler2001():
## scaling factor is negative##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit4_ds 164 21787 22007 288.04
## fit1_ds 170 21982 22183 495.16 6# BIC (Non-Nested)
fitMeasures(fit3_ds, "bic")## bic
## 22077.91fitMeasures(fit4_ds, "bic")## bic
## 22007.43modindices(fit4_ds, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 86 Mature =~ dsq_dissociation 28.530 0.709 1.561 0.499 0.499
## 116 Immature =~ dsq_dissociation 26.190 -0.627 -2.085 -0.667 -0.667
## 102 Neurotic =~ dsq_dissociation 19.875 1.014 1.621 0.518 0.518
## 321 dsq_dissociation ~~ dsq_denial 18.808 3.126 3.126 0.359 0.359
## 125 Image_Distorting =~ dsq_reaction_formation 14.406 -1.081 -1.907 -0.474 -0.474
## 111 Immature =~ dsq_reaction_formation 13.789 -0.405 -1.348 -0.335 -0.335
## 245 dsq_idealization ~~ dsq_splitting 10.814 3.550 3.550 0.252 0.252
## 227 dsq_reaction_formation ~~ dsq_acting_out 10.608 -3.939 -3.939 -0.251 -0.251
## 117 Immature =~ dsq_devaluation 9.521 0.410 1.362 0.420 0.420
## 319 dsq_dissociation ~~ dsq_devaluation 9.473 -2.147 -2.147 -0.259 -0.259
## 87 Mature =~ dsq_devaluation 8.578 -0.417 -0.918 -0.284 -0.284
## 114 Immature =~ dsq_undoing 8.275 0.319 1.060 0.259 0.259
## 101 Neurotic =~ dsq_isolation 8.091 -1.013 -1.619 -0.330 -0.330
## 231 dsq_reaction_formation ~~ dsq_devaluation 7.941 -2.348 -2.348 -0.223 -0.223
## 75 Mature =~ dsq_reaction_formation 7.154 1.003 2.209 0.549 0.549
## 150 dsq_humor ~~ dsq_dissociation 7.130 2.353 2.353 0.206 0.206
## 85 Mature =~ dsq_isolation 7.034 -0.552 -1.215 -0.248 -0.248
## 78 Mature =~ dsq_undoing 6.871 -0.998 -2.197 -0.537 -0.537
## 253 dsq_pseudo_altruism ~~ dsq_projection 6.855 2.293 2.293 0.209 0.209
## 213 dsq_rationalization ~~ dsq_acting_out 6.831 2.829 2.829 0.224 0.224
## 196 dsq_anticipation ~~ dsq_passive_aggression 6.191 2.268 2.268 0.204 0.204
## 128 Image_Distorting =~ dsq_undoing 5.685 0.689 1.216 0.297 0.297
## 149 dsq_humor ~~ dsq_isolation 5.558 -3.263 -3.263 -0.182 -0.182
## 118 Immature =~ dsq_splitting 5.401 0.372 1.236 0.300 0.300
## 119 Immature =~ dsq_denial 4.902 -0.301 -1.002 -0.298 -0.298
## 159 dsq_suppression ~~ dsq_pseudo_altruism 4.886 -2.179 -2.179 -0.169 -0.169
## 164 dsq_suppression ~~ dsq_somatization 4.814 -3.191 -3.191 -0.199 -0.199
## 280 dsq_displacement ~~ dsq_passive_aggression 4.650 2.085 2.085 0.184 0.184
## 176 dsq_sublimation ~~ dsq_pseudo_altruism 4.542 2.096 2.096 0.160 0.160
## 136 dsq_humor ~~ dsq_sublimation 4.493 -2.413 -2.413 -0.169 -0.169
## 103 Neurotic =~ dsq_devaluation 4.477 -0.525 -0.840 -0.259 -0.259
## 284 dsq_displacement ~~ dsq_isolation 4.308 -2.686 -2.686 -0.156 -0.156
## 93 Neurotic =~ dsq_anticipation 4.119 1.010 1.614 0.380 0.380
## 204 dsq_anticipation ~~ dsq_denial 4.089 -1.770 -1.770 -0.161 -0.161
## 302 dsq_somatization ~~ dsq_splitting 4.009 3.110 3.110 0.179 0.179
## 308 dsq_acting_out ~~ dsq_splitting 3.971 2.519 2.519 0.152 0.152
## 126 Image_Distorting =~ dsq_idealization 3.913 0.597 1.054 0.260 0.260
## 316 dsq_isolation ~~ dsq_devaluation 3.901 2.159 2.159 0.165 0.165
## 230 dsq_reaction_formation ~~ dsq_dissociation 3.897 1.617 1.617 0.150 0.150model_4f_adj_ds_V <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation + dsq_rationalization
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_undoing
Immature =~ dsq_autistic_fantasy + dsq_displacement + dsq_passive_aggression + dsq_somatization + dsq_acting_out + dsq_projection
Image_Distorting =~ dsq_isolation + dsq_dissociation + dsq_devaluation + dsq_splitting + dsq_denial
'
fit4_adjds_V <- cfa(model_4f_adj_ds_V, data = ds_df, estimator = "mlr", missing = "fiml")
summary(fit4_adjds_V,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 156 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 63
##
## Number of observations 210
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 254.087 235.533
## Degrees of freedom 146 146
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.079
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 682.859 596.718
## Degrees of freedom 171 171
## P-value 0.000 0.000
## Scaling correction factor 1.144
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.789 0.790
## Tucker-Lewis Index (TLI) 0.753 0.754
##
## Robust Comparative Fit Index (CFI) 0.816
## Robust Tucker-Lewis Index (TLI) 0.785
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10290.234 -10290.234
## Scaling correction factor 1.135
## for the MLR correction
## Loglikelihood unrestricted model (H1) -10163.191 -10163.191
## Scaling correction factor 1.096
## for the MLR correction
##
## Akaike (AIC) 20706.468 20706.468
## Bayesian (BIC) 20917.336 20917.336
## Sample-size adjusted Bayesian (SABIC) 20717.716 20717.716
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.059 0.054
## 90 Percent confidence interval - lower 0.047 0.041
## 90 Percent confidence interval - upper 0.071 0.066
## P-value H_0: RMSEA <= 0.050 0.103 0.284
## P-value H_0: RMSEA >= 0.080 0.002 0.000
##
## Robust RMSEA 0.056
## 90 Percent confidence interval - lower 0.041
## 90 Percent confidence interval - upper 0.070
## P-value H_0: Robust RMSEA <= 0.050 0.245
## P-value H_0: Robust RMSEA >= 0.080 0.002
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.084 0.084
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.239 0.497
## dsq_suppressin 0.865 0.255 3.391 0.001 1.936 0.478
## dsq_sublimatin 0.685 0.205 3.348 0.001 1.533 0.385
## dsq_anticipatn 0.938 0.230 4.080 0.000 2.100 0.494
## dsq_rationlztn 1.245 0.237 5.244 0.000 2.788 0.683
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.555 0.386
## dsq_idealizatn 1.257 0.406 3.093 0.002 1.954 0.482
## dsq_undoing 1.019 0.549 1.856 0.063 1.584 0.387
## Immature =~
## dsq_tstc_fntsy 1.000 3.305 0.623
## dsq_displacmnt 0.501 0.119 4.222 0.000 1.654 0.404
## dsq_pssv_ggrss 0.756 0.124 6.099 0.000 2.498 0.641
## dsq_somatizatn 0.499 0.155 3.211 0.001 1.650 0.346
## dsq_acting_out 0.739 0.139 5.329 0.000 2.444 0.500
## dsq_projection 0.774 0.118 6.565 0.000 2.559 0.640
## Image_Distorting =~
## dsq_isolation 1.000 1.742 0.355
## dsq_dissociatn 0.672 0.654 1.028 0.304 1.171 0.375
## dsq_devaluatin 0.864 0.255 3.390 0.001 1.505 0.465
## dsq_splitting 0.784 0.336 2.335 0.020 1.365 0.332
## dsq_denial 0.882 0.563 1.566 0.117 1.536 0.457
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 3.002 1.230 2.441 0.015 0.862 0.862
## Immature -1.224 0.933 -1.312 0.190 -0.165 -0.165
## Image_Distrtng 1.669 0.860 1.941 0.052 0.428 0.428
## Neurotic ~~
## Immature 1.220 0.893 1.367 0.172 0.237 0.237
## Image_Distrtng 1.411 0.560 2.520 0.012 0.521 0.521
## Immature ~~
## Image_Distrtng 3.867 2.817 1.373 0.170 0.672 0.672
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 8.244 0.319 25.815 0.000 8.244 1.832
## .dsq_suppressin 6.760 0.288 23.487 0.000 6.760 1.668
## .dsq_sublimatin 9.801 0.283 34.647 0.000 9.801 2.463
## .dsq_anticipatn 11.810 0.302 39.076 0.000 11.810 2.778
## .dsq_rationlztn 8.435 0.290 29.132 0.000 8.435 2.065
## .dsq_rctn_frmtn 8.958 0.286 31.338 0.000 8.958 2.225
## .dsq_idealizatn 8.100 0.288 28.140 0.000 8.100 1.996
## .dsq_undoing 9.453 0.290 32.582 0.000 9.453 2.311
## .dsq_tstc_fntsy 10.322 0.376 27.443 0.000 10.322 1.945
## .dsq_displacmnt 10.040 0.291 34.522 0.000 10.040 2.450
## .dsq_pssv_ggrss 8.765 0.277 31.698 0.000 8.765 2.250
## .dsq_somatizatn 11.563 0.388 29.769 0.000 11.563 2.425
## .dsq_acting_out 10.729 0.347 30.900 0.000 10.729 2.193
## .dsq_projection 8.164 0.283 28.840 0.000 8.164 2.042
## .dsq_isolation 9.844 0.349 28.225 0.000 9.844 2.006
## .dsq_dissociatn 5.265 0.223 23.616 0.000 5.265 1.684
## .dsq_devaluatin 7.181 0.230 31.229 0.000 7.181 2.217
## .dsq_splitting 10.008 0.292 34.262 0.000 10.008 2.433
## .dsq_denial 5.393 0.239 22.543 0.000 5.393 1.606
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 15.244 1.901 8.018 0.000 15.244 0.753
## .dsq_suppressin 12.677 2.025 6.259 0.000 12.677 0.772
## .dsq_sublimatin 13.485 1.517 8.891 0.000 13.485 0.852
## .dsq_anticipatn 13.662 1.514 9.022 0.000 13.662 0.756
## .dsq_rationlztn 8.911 1.475 6.039 0.000 8.911 0.534
## .dsq_rctn_frmtn 13.786 2.008 6.865 0.000 13.786 0.851
## .dsq_idealizatn 12.651 2.057 6.149 0.000 12.651 0.768
## .dsq_undoing 14.222 1.822 7.806 0.000 14.222 0.850
## .dsq_tstc_fntsy 17.231 2.536 6.794 0.000 17.231 0.612
## .dsq_displacmnt 14.049 1.406 9.995 0.000 14.049 0.837
## .dsq_pssv_ggrss 8.942 1.349 6.628 0.000 8.942 0.589
## .dsq_somatizatn 20.015 2.218 9.025 0.000 20.015 0.880
## .dsq_acting_out 17.961 1.933 9.293 0.000 17.961 0.750
## .dsq_projection 9.442 1.352 6.984 0.000 9.442 0.591
## .dsq_isolation 21.036 2.301 9.143 0.000 21.036 0.874
## .dsq_dissociatn 8.406 2.015 4.172 0.000 8.406 0.860
## .dsq_devaluatin 8.221 1.417 5.801 0.000 8.221 0.784
## .dsq_splitting 15.056 1.782 8.449 0.000 15.056 0.890
## .dsq_denial 8.918 1.913 4.662 0.000 8.918 0.791
## Mature 5.013 1.940 2.584 0.010 1.000 1.000
## Neurotic 2.419 1.787 1.354 0.176 1.000 1.000
## Immature 10.922 2.578 4.236 0.000 1.000 1.000
## Image_Distrtng 3.034 2.093 1.450 0.147 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.247
## dsq_suppressin 0.228
## dsq_sublimatin 0.148
## dsq_anticipatn 0.244
## dsq_rationlztn 0.466
## dsq_rctn_frmtn 0.149
## dsq_idealizatn 0.232
## dsq_undoing 0.150
## dsq_tstc_fntsy 0.388
## dsq_displacmnt 0.163
## dsq_pssv_ggrss 0.411
## dsq_somatizatn 0.120
## dsq_acting_out 0.250
## dsq_projection 0.409
## dsq_isolation 0.126
## dsq_dissociatn 0.140
## dsq_devaluatin 0.216
## dsq_splitting 0.110
## dsq_denial 0.209modindices(fit4_adjds_V, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 82 Mature =~ dsq_dissociation 28.809 0.715 1.600 0.512 0.512
## 111 Immature =~ dsq_dissociation 27.251 -0.640 -2.114 -0.676 -0.676
## 98 Neurotic =~ dsq_dissociation 21.551 1.175 1.827 0.584 0.584
## 296 dsq_dissociation ~~ dsq_denial 18.388 3.095 3.095 0.357 0.357
## 120 Image_Distorting =~ dsq_reaction_formation 16.168 -1.249 -2.176 -0.540 -0.540
## 107 Immature =~ dsq_reaction_formation 14.332 -0.419 -1.385 -0.344 -0.344
## 232 dsq_idealization ~~ dsq_splitting 10.979 3.566 3.566 0.258 0.258
## 112 Immature =~ dsq_devaluation 10.600 0.429 1.416 0.437 0.437
## 215 dsq_reaction_formation ~~ dsq_acting_out 10.333 -3.893 -3.893 -0.247 -0.247
## 294 dsq_dissociation ~~ dsq_devaluation 9.512 -2.150 -2.150 -0.259 -0.259
## 219 dsq_reaction_formation ~~ dsq_devaluation 8.998 -2.517 -2.517 -0.236 -0.236
## 72 Mature =~ dsq_reaction_formation 8.691 1.129 2.527 0.628 0.628
## 83 Mature =~ dsq_devaluation 8.626 -0.417 -0.934 -0.288 -0.288
## 97 Neurotic =~ dsq_isolation 8.055 -1.121 -1.743 -0.355 -0.355
## 109 Immature =~ dsq_undoing 7.938 0.317 1.048 0.256 0.256
## 81 Mature =~ dsq_isolation 7.249 -0.560 -1.255 -0.256 -0.256
## 74 Mature =~ dsq_undoing 7.104 -1.038 -2.324 -0.568 -0.568
## 143 dsq_humor ~~ dsq_dissociation 6.992 2.321 2.321 0.205 0.205
## 202 dsq_rationalization ~~ dsq_acting_out 6.319 2.730 2.730 0.216 0.216
## 186 dsq_anticipation ~~ dsq_passive_aggression 5.898 2.214 2.214 0.200 0.200
## 142 dsq_humor ~~ dsq_isolation 5.684 -3.295 -3.295 -0.184 -0.184
## 99 Neurotic =~ dsq_devaluation 5.329 -0.631 -0.981 -0.303 -0.303
## 122 Image_Distorting =~ dsq_undoing 5.265 0.725 1.263 0.309 0.309
## 113 Immature =~ dsq_splitting 5.072 0.359 1.188 0.289 0.289
## 114 Immature =~ dsq_denial 4.676 -0.294 -0.971 -0.289 -0.289
## 156 dsq_suppression ~~ dsq_somatization 4.634 -3.121 -3.121 -0.196 -0.196
## 259 dsq_displacement ~~ dsq_isolation 4.384 -2.708 -2.708 -0.158 -0.158
## 255 dsq_displacement ~~ dsq_passive_aggression 4.312 2.011 2.011 0.179 0.179
## 130 dsq_humor ~~ dsq_sublimation 4.219 -2.352 -2.352 -0.164 -0.164
## 291 dsq_isolation ~~ dsq_devaluation 4.188 2.230 2.230 0.170 0.170
## 194 dsq_anticipation ~~ dsq_denial 4.167 -1.788 -1.788 -0.162 -0.162
## 277 dsq_somatization ~~ dsq_splitting 4.151 3.163 3.163 0.182 0.182
## 283 dsq_acting_out ~~ dsq_splitting 4.084 2.546 2.546 0.155 0.155
## 205 dsq_rationalization ~~ dsq_dissociation 3.860 1.472 1.472 0.170 0.170
## 89 Neurotic =~ dsq_anticipation 3.843 1.035 1.610 0.379 0.379
## 110 Immature =~ dsq_isolation 3.841 0.375 1.239 0.253 0.253model_4f_adj_ds_VI <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation + dsq_rationalization
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_undoing
Immature =~ dsq_autistic_fantasy + dsq_displacement + dsq_passive_aggression + dsq_somatization + dsq_acting_out + dsq_projection
Image_Distorting =~ dsq_isolation + dsq_dissociation + dsq_devaluation + dsq_denial
'
fit4_adjds_VI <- cfa(model_4f_adj_ds_VI, data = ds_df, estimator = "mlr", missing = "fiml")
summary(fit4_adjds_VI,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 154 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 60
##
## Number of observations 210
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 213.824 200.844
## Degrees of freedom 129 129
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.065
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 630.831 550.238
## Degrees of freedom 153 153
## P-value 0.000 0.000
## Scaling correction factor 1.146
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.822 0.819
## Tucker-Lewis Index (TLI) 0.789 0.785
##
## Robust Comparative Fit Index (CFI) 0.849
## Robust Tucker-Lewis Index (TLI) 0.821
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9735.150 -9735.150
## Scaling correction factor 1.159
## for the MLR correction
## Loglikelihood unrestricted model (H1) -9628.238 -9628.238
## Scaling correction factor 1.095
## for the MLR correction
##
## Akaike (AIC) 19590.301 19590.301
## Bayesian (BIC) 19791.127 19791.127
## Sample-size adjusted Bayesian (SABIC) 19601.013 19601.013
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.056 0.051
## 90 Percent confidence interval - lower 0.042 0.038
## 90 Percent confidence interval - upper 0.069 0.065
## P-value H_0: RMSEA <= 0.050 0.224 0.413
## P-value H_0: RMSEA >= 0.080 0.001 0.000
##
## Robust RMSEA 0.052
## 90 Percent confidence interval - lower 0.035
## 90 Percent confidence interval - upper 0.068
## P-value H_0: Robust RMSEA <= 0.050 0.390
## P-value H_0: Robust RMSEA >= 0.080 0.001
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.083 0.083
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.291 0.509
## dsq_suppressin 0.845 0.250 3.379 0.001 1.936 0.478
## dsq_sublimatin 0.661 0.191 3.459 0.001 1.513 0.380
## dsq_anticipatn 0.889 0.221 4.014 0.000 2.036 0.479
## dsq_rationlztn 1.226 0.229 5.344 0.000 2.809 0.688
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.601 0.398
## dsq_idealizatn 1.207 0.353 3.423 0.001 1.932 0.476
## dsq_undoing 0.982 0.539 1.822 0.068 1.573 0.384
## Immature =~
## dsq_tstc_fntsy 1.000 3.230 0.609
## dsq_displacmnt 0.540 0.122 4.425 0.000 1.745 0.426
## dsq_pssv_ggrss 0.790 0.131 6.018 0.000 2.551 0.655
## dsq_somatizatn 0.510 0.167 3.057 0.002 1.649 0.346
## dsq_acting_out 0.746 0.147 5.061 0.000 2.411 0.493
## dsq_projection 0.785 0.122 6.433 0.000 2.535 0.634
## Image_Distorting =~
## dsq_isolation 1.000 1.506 0.307
## dsq_dissociatn 1.079 1.300 0.830 0.407 1.625 0.520
## dsq_devaluatin 0.794 0.277 2.863 0.004 1.195 0.369
## dsq_denial 1.328 1.100 1.207 0.228 1.999 0.595
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 3.157 1.228 2.570 0.010 0.861 0.861
## Immature -1.254 0.931 -1.346 0.178 -0.169 -0.169
## Image_Distrtng 1.778 0.584 3.045 0.002 0.516 0.516
## Neurotic ~~
## Immature 1.175 0.933 1.259 0.208 0.227 0.227
## Image_Distrtng 1.149 0.647 1.775 0.076 0.477 0.477
## Immature ~~
## Image_Distrtng 2.048 3.213 0.637 0.524 0.421 0.421
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 8.244 0.319 25.812 0.000 8.244 1.832
## .dsq_suppressin 6.760 0.288 23.487 0.000 6.760 1.668
## .dsq_sublimatin 9.801 0.283 34.646 0.000 9.801 2.463
## .dsq_anticipatn 11.810 0.302 39.075 0.000 11.810 2.778
## .dsq_rationlztn 8.435 0.290 29.127 0.000 8.435 2.065
## .dsq_rctn_frmtn 8.958 0.286 31.342 0.000 8.958 2.225
## .dsq_idealizatn 8.100 0.288 28.142 0.000 8.100 1.996
## .dsq_undoing 9.453 0.290 32.579 0.000 9.453 2.311
## .dsq_tstc_fntsy 10.322 0.376 27.439 0.000 10.322 1.945
## .dsq_displacmnt 10.041 0.291 34.519 0.000 10.041 2.451
## .dsq_pssv_ggrss 8.765 0.277 31.681 0.000 8.765 2.250
## .dsq_somatizatn 11.565 0.388 29.806 0.000 11.565 2.426
## .dsq_acting_out 10.728 0.347 30.892 0.000 10.728 2.193
## .dsq_projection 8.164 0.283 28.833 0.000 8.164 2.042
## .dsq_isolation 9.839 0.349 28.211 0.000 9.839 2.005
## .dsq_dissociatn 5.264 0.223 23.634 0.000 5.264 1.683
## .dsq_devaluatin 7.176 0.230 31.225 0.000 7.176 2.216
## .dsq_denial 5.391 0.239 22.527 0.000 5.391 1.605
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 15.009 1.940 7.738 0.000 15.009 0.741
## .dsq_suppressin 12.677 2.013 6.297 0.000 12.677 0.772
## .dsq_sublimatin 13.545 1.524 8.888 0.000 13.545 0.855
## .dsq_anticipatn 13.926 1.489 9.350 0.000 13.926 0.771
## .dsq_rationlztn 8.790 1.440 6.104 0.000 8.790 0.527
## .dsq_rctn_frmtn 13.641 2.032 6.714 0.000 13.641 0.842
## .dsq_idealizatn 12.738 1.968 6.473 0.000 12.738 0.773
## .dsq_undoing 14.260 1.872 7.615 0.000 14.260 0.852
## .dsq_tstc_fntsy 17.716 2.551 6.945 0.000 17.716 0.629
## .dsq_displacmnt 13.741 1.415 9.708 0.000 13.741 0.819
## .dsq_pssv_ggrss 8.674 1.309 6.628 0.000 8.674 0.571
## .dsq_somatizatn 20.013 2.266 8.831 0.000 20.013 0.880
## .dsq_acting_out 18.120 1.984 9.133 0.000 18.120 0.757
## .dsq_projection 9.564 1.448 6.604 0.000 9.564 0.598
## .dsq_isolation 21.802 3.221 6.769 0.000 21.802 0.906
## .dsq_dissociatn 7.138 2.948 2.422 0.015 7.138 0.730
## .dsq_devaluatin 9.058 1.987 4.560 0.000 9.058 0.864
## .dsq_denial 7.283 1.991 3.659 0.000 7.283 0.646
## Mature 5.248 1.976 2.656 0.008 1.000 1.000
## Neurotic 2.564 1.815 1.413 0.158 1.000 1.000
## Immature 10.436 2.552 4.089 0.000 1.000 1.000
## Image_Distrtng 2.267 3.004 0.755 0.450 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.259
## dsq_suppressin 0.228
## dsq_sublimatin 0.145
## dsq_anticipatn 0.229
## dsq_rationlztn 0.473
## dsq_rctn_frmtn 0.158
## dsq_idealizatn 0.227
## dsq_undoing 0.148
## dsq_tstc_fntsy 0.371
## dsq_displacmnt 0.181
## dsq_pssv_ggrss 0.429
## dsq_somatizatn 0.120
## dsq_acting_out 0.243
## dsq_projection 0.402
## dsq_isolation 0.094
## dsq_dissociatn 0.270
## dsq_devaluatin 0.136
## dsq_denial 0.354modindices(fit4_adjds_VI, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 79 Mature =~ dsq_dissociation 25.095 0.757 1.735 0.555 0.555
## 107 Immature =~ dsq_devaluation 21.316 0.446 1.441 0.445 0.445
## 106 Immature =~ dsq_dissociation 18.159 -0.417 -1.346 -0.431 -0.431
## 94 Neurotic =~ dsq_dissociation 15.815 1.101 1.763 0.564 0.564
## 273 dsq_dissociation ~~ dsq_devaluation 14.462 -2.742 -2.742 -0.341 -0.341
## 102 Immature =~ dsq_reaction_formation 13.010 -0.414 -1.337 -0.332 -0.332
## 204 dsq_reaction_formation ~~ dsq_acting_out 11.149 -4.055 -4.055 -0.258 -0.258
## 208 dsq_reaction_formation ~~ dsq_devaluation 10.553 -2.759 -2.759 -0.248 -0.248
## 78 Mature =~ dsq_isolation 10.073 -0.709 -1.625 -0.331 -0.331
## 80 Mature =~ dsq_devaluation 9.025 -0.446 -1.022 -0.316 -0.316
## 274 dsq_dissociation ~~ dsq_denial 8.992 2.947 2.947 0.409 0.409
## 69 Mature =~ dsq_reaction_formation 8.732 1.098 2.514 0.625 0.625
## 104 Immature =~ dsq_undoing 8.339 0.334 1.078 0.263 0.263
## 105 Immature =~ dsq_isolation 8.076 0.414 1.337 0.273 0.273
## 71 Mature =~ dsq_undoing 7.420 -1.018 -2.331 -0.570 -0.570
## 271 dsq_isolation ~~ dsq_devaluation 7.317 2.964 2.964 0.211 0.211
## 192 dsq_rationalization ~~ dsq_acting_out 6.519 2.765 2.765 0.219 0.219
## 136 dsq_humor ~~ dsq_isolation 6.202 -3.457 -3.457 -0.191 -0.191
## 177 dsq_anticipation ~~ dsq_passive_aggression 6.017 2.241 2.241 0.204 0.204
## 137 dsq_humor ~~ dsq_dissociation 5.967 2.082 2.082 0.201 0.201
## 114 Image_Distorting =~ dsq_reaction_formation 5.500 -0.867 -1.305 -0.324 -0.324
## 184 dsq_anticipation ~~ dsq_denial 5.211 -1.966 -1.966 -0.195 -0.195
## 85 Neurotic =~ dsq_anticipation 4.847 1.120 1.793 0.422 0.422
## 93 Neurotic =~ dsq_isolation 4.793 -0.826 -1.322 -0.270 -0.270
## 229 dsq_undoing ~~ dsq_devaluation 4.659 1.868 1.868 0.164 0.164
## 124 dsq_humor ~~ dsq_sublimation 4.418 -2.391 -2.391 -0.168 -0.168
## 266 dsq_projection ~~ dsq_isolation 4.348 2.422 2.422 0.168 0.168
## 149 dsq_suppression ~~ dsq_somatization 4.166 -2.958 -2.958 -0.186 -0.186
## 165 dsq_sublimation ~~ dsq_acting_out 3.985 2.379 2.379 0.152 0.152
## 253 dsq_passive_aggression ~~ dsq_devaluation 3.966 1.460 1.460 0.165 0.165model_4f_adj_ds_VII <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation + dsq_rationalization
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_undoing
Immature =~ dsq_autistic_fantasy + dsq_displacement + dsq_passive_aggression + dsq_somatization + dsq_acting_out + dsq_projection
Image_Distorting =~ dsq_dissociation + dsq_devaluation + dsq_denial
'
fit4_adjds_VII <- cfa(model_4f_adj_ds_VII, data = ds_df, estimator = "mlr", missing = "fiml")
summary(fit4_adjds_VII,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 156 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 57
##
## Number of observations 210
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 180.983 166.948
## Degrees of freedom 113 113
## P-value (Chi-square) 0.000 0.001
## Scaling correction factor 1.084
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 589.461 511.888
## Degrees of freedom 136 136
## P-value 0.000 0.000
## Scaling correction factor 1.152
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.850 0.856
## Tucker-Lewis Index (TLI) 0.820 0.827
##
## Robust Comparative Fit Index (CFI) 0.875
## Robust Tucker-Lewis Index (TLI) 0.850
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9143.556 -9143.556
## Scaling correction factor 1.124
## for the MLR correction
## Loglikelihood unrestricted model (H1) -9053.065 -9053.065
## Scaling correction factor 1.097
## for the MLR correction
##
## Akaike (AIC) 18401.113 18401.113
## Bayesian (BIC) 18591.898 18591.898
## Sample-size adjusted Bayesian (SABIC) 18411.289 18411.289
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.054 0.048
## 90 Percent confidence interval - lower 0.039 0.032
## 90 Percent confidence interval - upper 0.068 0.062
## P-value H_0: RMSEA <= 0.050 0.332 0.589
## P-value H_0: RMSEA >= 0.080 0.001 0.000
##
## Robust RMSEA 0.050
## 90 Percent confidence interval - lower 0.030
## 90 Percent confidence interval - upper 0.067
## P-value H_0: Robust RMSEA <= 0.050 0.499
## P-value H_0: Robust RMSEA >= 0.080 0.001
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.080 0.080
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.364 0.525
## dsq_suppressin 0.794 0.230 3.460 0.001 1.877 0.463
## dsq_sublimatin 0.648 0.181 3.591 0.000 1.533 0.385
## dsq_anticipatn 0.838 0.188 4.450 0.000 1.980 0.466
## dsq_rationlztn 1.197 0.209 5.714 0.000 2.829 0.693
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.624 0.404
## dsq_idealizatn 1.204 0.347 3.466 0.001 1.956 0.482
## dsq_undoing 0.948 0.547 1.734 0.083 1.541 0.377
## Immature =~
## dsq_tstc_fntsy 1.000 3.203 0.604
## dsq_displacmnt 0.556 0.122 4.559 0.000 1.780 0.434
## dsq_pssv_ggrss 0.803 0.135 5.935 0.000 2.572 0.660
## dsq_somatizatn 0.523 0.169 3.099 0.002 1.675 0.352
## dsq_acting_out 0.756 0.148 5.091 0.000 2.420 0.495
## dsq_projection 0.780 0.125 6.227 0.000 2.500 0.625
## Image_Distorting =~
## dsq_dissociatn 1.000 2.077 0.664
## dsq_devaluatin 0.331 0.441 0.750 0.453 0.687 0.212
## dsq_denial 0.895 0.448 1.997 0.046 1.859 0.554
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 3.276 1.233 2.657 0.008 0.853 0.853
## Immature -1.296 0.945 -1.371 0.170 -0.171 -0.171
## Image_Distrtng 3.144 1.586 1.982 0.048 0.640 0.640
## Neurotic ~~
## Immature 1.136 0.964 1.178 0.239 0.218 0.218
## Image_Distrtng 1.807 1.129 1.600 0.110 0.535 0.535
## Immature ~~
## Image_Distrtng 1.453 1.553 0.936 0.349 0.218 0.218
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 8.244 0.319 25.811 0.000 8.244 1.832
## .dsq_suppressin 6.760 0.288 23.487 0.000 6.760 1.668
## .dsq_sublimatin 9.801 0.283 34.646 0.000 9.801 2.463
## .dsq_anticipatn 11.810 0.302 39.074 0.000 11.810 2.778
## .dsq_rationlztn 8.435 0.290 29.129 0.000 8.435 2.065
## .dsq_rctn_frmtn 8.958 0.286 31.343 0.000 8.958 2.225
## .dsq_idealizatn 8.100 0.288 28.143 0.000 8.100 1.996
## .dsq_undoing 9.453 0.290 32.582 0.000 9.453 2.311
## .dsq_tstc_fntsy 10.322 0.376 27.439 0.000 10.322 1.945
## .dsq_displacmnt 10.041 0.291 34.539 0.000 10.041 2.451
## .dsq_pssv_ggrss 8.766 0.277 31.683 0.000 8.766 2.250
## .dsq_somatizatn 11.563 0.388 29.837 0.000 11.563 2.426
## .dsq_acting_out 10.729 0.347 30.895 0.000 10.729 2.193
## .dsq_projection 8.164 0.283 28.828 0.000 8.164 2.042
## .dsq_dissociatn 5.262 0.223 23.631 0.000 5.262 1.683
## .dsq_devaluatin 7.173 0.230 31.204 0.000 7.173 2.215
## .dsq_denial 5.388 0.239 22.498 0.000 5.388 1.604
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 14.670 1.810 8.106 0.000 14.670 0.724
## .dsq_suppressin 12.900 2.034 6.343 0.000 12.900 0.785
## .dsq_sublimatin 13.486 1.512 8.921 0.000 13.486 0.852
## .dsq_anticipatn 14.151 1.446 9.786 0.000 14.151 0.783
## .dsq_rationlztn 8.677 1.355 6.406 0.000 8.677 0.520
## .dsq_rctn_frmtn 13.567 2.057 6.596 0.000 13.567 0.837
## .dsq_idealizatn 12.645 1.987 6.363 0.000 12.645 0.768
## .dsq_undoing 14.359 1.931 7.437 0.000 14.359 0.858
## .dsq_tstc_fntsy 17.890 2.543 7.034 0.000 17.890 0.635
## .dsq_displacmnt 13.619 1.380 9.866 0.000 13.619 0.811
## .dsq_pssv_ggrss 8.564 1.304 6.565 0.000 8.564 0.564
## .dsq_somatizatn 19.903 2.289 8.695 0.000 19.903 0.876
## .dsq_acting_out 18.075 1.995 9.058 0.000 18.075 0.755
## .dsq_projection 9.740 1.475 6.605 0.000 9.740 0.609
## .dsq_dissociatn 5.463 2.681 2.038 0.042 5.463 0.559
## .dsq_devaluatin 10.014 1.165 8.599 0.000 10.014 0.955
## .dsq_denial 7.821 1.589 4.921 0.000 7.821 0.693
## Mature 5.587 1.871 2.986 0.003 1.000 1.000
## Neurotic 2.639 1.864 1.416 0.157 1.000 1.000
## Immature 10.262 2.509 4.090 0.000 1.000 1.000
## Image_Distrtng 4.314 2.772 1.556 0.120 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.276
## dsq_suppressin 0.215
## dsq_sublimatin 0.148
## dsq_anticipatn 0.217
## dsq_rationlztn 0.480
## dsq_rctn_frmtn 0.163
## dsq_idealizatn 0.232
## dsq_undoing 0.142
## dsq_tstc_fntsy 0.365
## dsq_displacmnt 0.189
## dsq_pssv_ggrss 0.436
## dsq_somatizatn 0.124
## dsq_acting_out 0.245
## dsq_projection 0.391
## dsq_dissociatn 0.441
## dsq_devaluatin 0.045
## dsq_denial 0.307modindices(fit4_adjds_VII, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 101 Immature =~ dsq_devaluation 26.658 0.446 1.429 0.441 0.441
## 75 Mature =~ dsq_dissociation 16.778 1.067 2.522 0.807 0.807
## 100 Immature =~ dsq_dissociation 12.940 -0.367 -1.177 -0.376 -0.376
## 97 Immature =~ dsq_reaction_formation 12.570 -0.412 -1.321 -0.328 -0.328
## 193 dsq_reaction_formation ~~ dsq_acting_out 11.417 -4.100 -4.100 -0.262 -0.262
## 196 dsq_reaction_formation ~~ dsq_devaluation 11.049 -2.881 -2.881 -0.247 -0.247
## 250 dsq_dissociation ~~ dsq_devaluation 10.727 -2.454 -2.454 -0.332 -0.332
## 66 Mature =~ dsq_reaction_formation 9.332 1.109 2.622 0.651 0.651
## 99 Immature =~ dsq_undoing 8.587 0.340 1.090 0.266 0.266
## 77 Mature =~ dsq_denial 8.187 -0.672 -1.587 -0.473 -0.473
## 68 Mature =~ dsq_undoing 7.842 -1.012 -2.392 -0.585 -0.585
## 91 Neurotic =~ dsq_denial 7.322 -1.354 -2.199 -0.655 -0.655
## 76 Mature =~ dsq_devaluation 6.491 -0.446 -1.054 -0.325 -0.325
## 182 dsq_rationalization ~~ dsq_acting_out 6.361 2.709 2.709 0.216 0.216
## 168 dsq_anticipation ~~ dsq_passive_aggression 5.912 2.229 2.229 0.202 0.202
## 118 dsq_humor ~~ dsq_sublimation 5.347 -2.602 -2.602 -0.185 -0.185
## 81 Neurotic =~ dsq_anticipation 5.333 1.169 1.899 0.447 0.447
## 215 dsq_undoing ~~ dsq_devaluation 5.258 2.033 2.033 0.170 0.170
## 89 Neurotic =~ dsq_dissociation 5.188 1.317 2.139 0.684 0.684
## 223 dsq_autistic_fantasy ~~ dsq_devaluation 5.047 2.346 2.346 0.175 0.175
## 236 dsq_passive_aggression ~~ dsq_devaluation 4.922 1.657 1.657 0.179 0.179
## 146 dsq_suppression ~~ dsq_devaluation 4.353 1.771 1.771 0.156 0.156
## 142 dsq_suppression ~~ dsq_somatization 3.978 -2.897 -2.897 -0.181 -0.181
## 157 dsq_sublimation ~~ dsq_acting_out 3.971 2.370 2.370 0.152 0.152model_4f_adj_ds_VIII <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation + dsq_anticipation + dsq_rationalization
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_undoing
Immature =~ dsq_autistic_fantasy + dsq_displacement + dsq_passive_aggression + dsq_somatization + dsq_acting_out + dsq_projection
Image_Distorting =~ dsq_dissociation + dsq_denial
'
fit4_adjds_VIII <- cfa(model_4f_adj_ds_VIII, data = ds_df, estimator = "mlr", missing = "fiml")
summary(fit4_adjds_VIII,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 154 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 54
##
## Number of observations 210
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 127.559 114.498
## Degrees of freedom 98 98
## P-value (Chi-square) 0.024 0.122
## Scaling correction factor 1.114
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 532.796 457.883
## Degrees of freedom 120 120
## P-value 0.000 0.000
## Scaling correction factor 1.164
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.928 0.951
## Tucker-Lewis Index (TLI) 0.912 0.940
##
## Robust Comparative Fit Index (CFI) 0.956
## Robust Tucker-Lewis Index (TLI) 0.946
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -8631.576 -8631.576
## Scaling correction factor 1.084
## for the MLR correction
## Loglikelihood unrestricted model (H1) -8567.797 -8567.797
## Scaling correction factor 1.103
## for the MLR correction
##
## Akaike (AIC) 17371.153 17371.153
## Bayesian (BIC) 17551.897 17551.897
## Sample-size adjusted Bayesian (SABIC) 17380.793 17380.793
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.038 0.028
## 90 Percent confidence interval - lower 0.015 0.000
## 90 Percent confidence interval - upper 0.055 0.047
## P-value H_0: RMSEA <= 0.050 0.865 0.975
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA 0.030
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.053
## P-value H_0: Robust RMSEA <= 0.050 0.915
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.066 0.066
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 2.394 0.532
## dsq_suppressin 0.763 0.210 3.628 0.000 1.825 0.450
## dsq_sublimatin 0.646 0.180 3.579 0.000 1.546 0.389
## dsq_anticipatn 0.829 0.184 4.496 0.000 1.985 0.467
## dsq_rationlztn 1.184 0.206 5.738 0.000 2.835 0.694
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.652 0.410
## dsq_idealizatn 1.191 0.335 3.554 0.000 1.966 0.485
## dsq_undoing 0.917 0.529 1.734 0.083 1.515 0.370
## Immature =~
## dsq_tstc_fntsy 1.000 3.196 0.602
## dsq_displacmnt 0.560 0.125 4.495 0.000 1.790 0.437
## dsq_pssv_ggrss 0.811 0.138 5.882 0.000 2.591 0.665
## dsq_somatizatn 0.528 0.172 3.081 0.002 1.689 0.354
## dsq_acting_out 0.758 0.149 5.103 0.000 2.423 0.495
## dsq_projection 0.774 0.126 6.121 0.000 2.475 0.619
## Image_Distorting =~
## dsq_dissociatn 1.000 2.490 0.796
## dsq_denial 0.653 0.218 2.998 0.003 1.626 0.484
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 3.360 1.200 2.801 0.005 0.850 0.850
## Immature -1.300 0.952 -1.365 0.172 -0.170 -0.170
## Image_Distrtng 3.679 0.928 3.966 0.000 0.617 0.617
## Neurotic ~~
## Immature 1.109 0.987 1.124 0.261 0.210 0.210
## Image_Distrtng 2.067 1.041 1.986 0.047 0.503 0.503
## Immature ~~
## Image_Distrtng 0.602 1.035 0.582 0.561 0.076 0.076
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 8.244 0.319 25.812 0.000 8.244 1.832
## .dsq_suppressin 6.760 0.288 23.486 0.000 6.760 1.668
## .dsq_sublimatin 9.801 0.283 34.647 0.000 9.801 2.463
## .dsq_anticipatn 11.810 0.302 39.073 0.000 11.810 2.778
## .dsq_rationlztn 8.435 0.290 29.130 0.000 8.435 2.065
## .dsq_rctn_frmtn 8.958 0.286 31.346 0.000 8.958 2.225
## .dsq_idealizatn 8.100 0.288 28.145 0.000 8.100 1.996
## .dsq_undoing 9.452 0.290 32.586 0.000 9.452 2.311
## .dsq_tstc_fntsy 10.322 0.376 27.440 0.000 10.322 1.945
## .dsq_displacmnt 10.042 0.291 34.542 0.000 10.042 2.451
## .dsq_pssv_ggrss 8.766 0.277 31.684 0.000 8.766 2.250
## .dsq_somatizatn 11.561 0.388 29.823 0.000 11.561 2.427
## .dsq_acting_out 10.729 0.347 30.892 0.000 10.729 2.193
## .dsq_projection 8.164 0.283 28.827 0.000 8.164 2.042
## .dsq_dissociatn 5.259 0.222 23.666 0.000 5.259 1.682
## .dsq_denial 5.385 0.239 22.530 0.000 5.385 1.604
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 14.527 1.762 8.246 0.000 14.527 0.717
## .dsq_suppressin 13.092 2.009 6.517 0.000 13.092 0.797
## .dsq_sublimatin 13.445 1.508 8.917 0.000 13.445 0.849
## .dsq_anticipatn 14.131 1.444 9.788 0.000 14.131 0.782
## .dsq_rationlztn 8.645 1.347 6.420 0.000 8.645 0.518
## .dsq_rctn_frmtn 13.478 2.039 6.610 0.000 13.478 0.832
## .dsq_idealizatn 12.604 1.964 6.419 0.000 12.604 0.765
## .dsq_undoing 14.438 1.922 7.512 0.000 14.438 0.863
## .dsq_tstc_fntsy 17.939 2.570 6.980 0.000 17.939 0.637
## .dsq_displacmnt 13.584 1.381 9.836 0.000 13.584 0.809
## .dsq_pssv_ggrss 8.466 1.304 6.495 0.000 8.466 0.558
## .dsq_somatizatn 19.845 2.322 8.546 0.000 19.845 0.874
## .dsq_acting_out 18.062 1.992 9.067 0.000 18.062 0.755
## .dsq_projection 9.865 1.483 6.653 0.000 9.865 0.617
## .dsq_dissociatn 3.579 1.959 1.827 0.068 3.579 0.366
## .dsq_denial 8.634 1.188 7.267 0.000 8.634 0.766
## Mature 5.730 1.851 3.095 0.002 1.000 1.000
## Neurotic 2.728 1.879 1.451 0.147 1.000 1.000
## Immature 10.214 2.523 4.048 0.000 1.000 1.000
## Image_Distrtng 6.198 2.102 2.949 0.003 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.283
## dsq_suppressin 0.203
## dsq_sublimatin 0.151
## dsq_anticipatn 0.218
## dsq_rationlztn 0.482
## dsq_rctn_frmtn 0.168
## dsq_idealizatn 0.235
## dsq_undoing 0.137
## dsq_tstc_fntsy 0.363
## dsq_displacmnt 0.191
## dsq_pssv_ggrss 0.442
## dsq_somatizatn 0.126
## dsq_acting_out 0.245
## dsq_projection 0.383
## dsq_dissociatn 0.634
## dsq_denial 0.234modindices(fit4_adjds_VIII, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 92 Immature =~ dsq_reaction_formation 12.464 -0.413 -1.320 -0.328 -0.328
## 182 dsq_reaction_formation ~~ dsq_acting_out 11.519 -4.113 -4.113 -0.264 -0.264
## 63 Mature =~ dsq_reaction_formation 10.156 1.180 2.826 0.702 0.702
## 94 Immature =~ dsq_undoing 8.958 0.346 1.107 0.271 0.271
## 65 Mature =~ dsq_undoing 7.862 -1.023 -2.448 -0.598 -0.598
## 172 dsq_rationalization ~~ dsq_acting_out 6.340 2.700 2.700 0.216 0.216
## 95 Immature =~ dsq_dissociation 5.951 -0.311 -0.994 -0.318 -0.318
## 96 Immature =~ dsq_denial 5.951 0.203 0.649 0.193 0.193
## 72 Mature =~ dsq_dissociation 5.915 1.372 3.285 1.051 1.051
## 73 Mature =~ dsq_denial 5.915 -0.896 -2.145 -0.639 -0.639
## 112 dsq_humor ~~ dsq_sublimation 5.890 -2.725 -2.725 -0.195 -0.195
## 159 dsq_anticipation ~~ dsq_passive_aggression 5.648 2.174 2.174 0.199 0.199
## 77 Neurotic =~ dsq_anticipation 4.961 1.135 1.874 0.441 0.441
## 149 dsq_sublimation ~~ dsq_acting_out 4.038 2.388 2.388 0.153 0.153
## 135 dsq_suppression ~~ dsq_somatization 3.934 -2.892 -2.892 -0.179 -0.179fit1_kn <- cfa(model_1f_norat, data = kn_df, estimator = "mlr", missing = "fiml")
summary(fit1_kn,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 149 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 57
##
## Number of observations 306
## Number of missing patterns 12
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 480.575 456.078
## Degrees of freedom 152 152
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.054
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 774.640 708.842
## Degrees of freedom 171 171
## P-value 0.000 0.000
## Scaling correction factor 1.093
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.456 0.435
## Tucker-Lewis Index (TLI) 0.388 0.364
##
## Robust Comparative Fit Index (CFI) 0.464
## Robust Tucker-Lewis Index (TLI) 0.396
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -15573.356 -15573.356
## Scaling correction factor 1.055
## for the MLR correction
## Loglikelihood unrestricted model (H1) -15333.068 -15333.068
## Scaling correction factor 1.054
## for the MLR correction
##
## Akaike (AIC) 31260.712 31260.712
## Bayesian (BIC) 31472.957 31472.957
## Sample-size adjusted Bayesian (SABIC) 31292.179 31292.179
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.084 0.081
## 90 Percent confidence interval - lower 0.076 0.073
## 90 Percent confidence interval - upper 0.093 0.089
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.790 0.576
##
## Robust RMSEA 0.083
## 90 Percent confidence interval - lower 0.074
## 90 Percent confidence interval - upper 0.092
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.700
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.087 0.087
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Factor1 =~
## dsq_sublimatin 1.000 0.309 0.080
## dsq_humor -1.114 3.052 -0.365 0.715 -0.345 -0.094
## dsq_anticipatn 1.758 2.525 0.696 0.486 0.544 0.169
## dsq_suppressin -1.851 3.961 -0.467 0.640 -0.573 -0.144
## dsq_psed_ltrsm 3.143 4.029 0.780 0.435 0.972 0.290
## dsq_idealizatn 4.425 6.058 0.730 0.465 1.369 0.344
## dsq_rctn_frmtn 3.938 5.619 0.701 0.483 1.218 0.352
## dsq_undoing 6.155 9.238 0.666 0.505 1.904 0.484
## dsq_projection 5.008 8.324 0.602 0.547 1.549 0.430
## dsq_pssv_ggrss 6.372 10.333 0.617 0.537 1.971 0.560
## dsq_acting_out 2.419 3.612 0.670 0.503 0.748 0.192
## dsq_isolation 1.380 2.979 0.463 0.643 0.427 0.096
## dsq_tstc_fntsy 4.401 7.665 0.574 0.566 1.362 0.261
## dsq_denial 2.964 4.371 0.678 0.498 0.917 0.316
## dsq_displacmnt 5.528 8.506 0.650 0.516 1.710 0.452
## dsq_dissociatn 1.285 1.641 0.783 0.434 0.398 0.157
## dsq_splitting 4.580 7.327 0.625 0.532 1.417 0.399
## dsq_devaluatin 2.888 5.129 0.563 0.573 0.893 0.300
## dsq_somatizatn 5.902 8.986 0.657 0.511 1.826 0.424
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_sublimatin 9.549 0.220 43.320 0.000 9.549 2.476
## .dsq_humor 11.523 0.211 54.732 0.000 11.523 3.129
## .dsq_anticipatn 10.706 0.184 58.190 0.000 10.706 3.326
## .dsq_suppressin 7.435 0.228 32.558 0.000 7.435 1.868
## .dsq_psed_ltrsm 9.632 0.192 50.247 0.000 9.632 2.877
## .dsq_idealizatn 6.304 0.227 27.714 0.000 6.304 1.584
## .dsq_rctn_frmtn 9.111 0.199 45.870 0.000 9.111 2.634
## .dsq_undoing 9.314 0.225 41.370 0.000 9.314 2.365
## .dsq_projection 8.023 0.206 38.883 0.000 8.023 2.226
## .dsq_pssv_ggrss 7.341 0.202 36.318 0.000 7.341 2.085
## .dsq_acting_out 10.797 0.224 48.222 0.000 10.797 2.766
## .dsq_isolation 8.098 0.254 31.891 0.000 8.098 1.829
## .dsq_tstc_fntsy 11.666 0.299 39.043 0.000 11.666 2.239
## .dsq_denial 5.415 0.166 32.646 0.000 5.415 1.866
## .dsq_displacmnt 8.673 0.216 40.077 0.000 8.673 2.291
## .dsq_dissociatn 4.413 0.145 30.333 0.000 4.413 1.744
## .dsq_splitting 7.452 0.203 36.649 0.000 7.452 2.100
## .dsq_devaluatin 7.258 0.170 42.612 0.000 7.258 2.436
## .dsq_somatizatn 10.118 0.246 41.088 0.000 10.118 2.349
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_sublimatin 14.773 0.963 15.346 0.000 14.773 0.994
## .dsq_humor 13.445 1.003 13.403 0.000 13.445 0.991
## .dsq_anticipatn 10.062 0.884 11.380 0.000 10.062 0.971
## .dsq_suppressin 15.522 1.107 14.016 0.000 15.522 0.979
## .dsq_psed_ltrsm 10.262 1.005 10.209 0.000 10.262 0.916
## .dsq_idealizatn 13.959 1.540 9.064 0.000 13.959 0.882
## .dsq_rctn_frmtn 10.480 1.036 10.120 0.000 10.480 0.876
## .dsq_undoing 11.883 1.260 9.432 0.000 11.883 0.766
## .dsq_projection 10.594 1.193 8.882 0.000 10.594 0.815
## .dsq_pssv_ggrss 8.518 1.118 7.621 0.000 8.518 0.687
## .dsq_acting_out 14.678 0.973 15.090 0.000 14.678 0.963
## .dsq_isolation 19.413 1.151 16.866 0.000 19.413 0.991
## .dsq_tstc_fntsy 25.302 1.976 12.802 0.000 25.302 0.932
## .dsq_denial 7.579 0.601 12.604 0.000 7.579 0.900
## .dsq_displacmnt 11.406 0.912 12.507 0.000 11.406 0.796
## .dsq_dissociatn 6.249 0.541 11.548 0.000 6.249 0.975
## .dsq_splitting 10.583 0.974 10.866 0.000 10.583 0.841
## .dsq_devaluatin 8.079 0.784 10.302 0.000 8.079 0.910
## .dsq_somatizatn 15.221 1.365 11.154 0.000 15.221 0.820
## Factor1 0.096 0.294 0.325 0.745 1.000 1.000
##
## R-Square:
## Estimate
## dsq_sublimatin 0.006
## dsq_humor 0.009
## dsq_anticipatn 0.029
## dsq_suppressin 0.021
## dsq_psed_ltrsm 0.084
## dsq_idealizatn 0.118
## dsq_rctn_frmtn 0.124
## dsq_undoing 0.234
## dsq_projection 0.185
## dsq_pssv_ggrss 0.313
## dsq_acting_out 0.037
## dsq_isolation 0.009
## dsq_tstc_fntsy 0.068
## dsq_denial 0.100
## dsq_displacmnt 0.204
## dsq_dissociatn 0.025
## dsq_splitting 0.159
## dsq_devaluatin 0.090
## dsq_somatizatn 0.180fit3_kn <- cfa(model_3f_norat, data = kn_df, estimator = "mlr", missing = "fiml")
summary(fit3_kn,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 150 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 60
##
## Number of observations 306
## Number of missing patterns 12
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 392.122 374.564
## Degrees of freedom 149 149
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.047
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 774.640 708.842
## Degrees of freedom 171 171
## P-value 0.000 0.000
## Scaling correction factor 1.093
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.597 0.581
## Tucker-Lewis Index (TLI) 0.538 0.519
##
## Robust Comparative Fit Index (CFI) 0.609
## Robust Tucker-Lewis Index (TLI) 0.551
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -15529.129 -15529.129
## Scaling correction factor 1.072
## for the MLR correction
## Loglikelihood unrestricted model (H1) -15333.068 -15333.068
## Scaling correction factor 1.054
## for the MLR correction
##
## Akaike (AIC) 31178.259 31178.259
## Bayesian (BIC) 31401.674 31401.674
## Sample-size adjusted Bayesian (SABIC) 31211.382 31211.382
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.073 0.070
## 90 Percent confidence interval - lower 0.064 0.062
## 90 Percent confidence interval - upper 0.082 0.079
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.099 0.034
##
## Robust RMSEA 0.071
## 90 Percent confidence interval - lower 0.062
## 90 Percent confidence interval - upper 0.081
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.066
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.079 0.079
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 1.807 0.491
## dsq_suppressin 1.010 0.684 1.477 0.140 1.825 0.458
## dsq_sublimatin 1.034 0.814 1.270 0.204 1.868 0.484
## dsq_anticipatn 0.082 0.316 0.259 0.795 0.148 0.046
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.932 0.558
## dsq_idealizatn 0.804 0.251 3.207 0.001 1.553 0.390
## dsq_psed_ltrsm 0.714 0.184 3.886 0.000 1.379 0.412
## dsq_undoing 1.208 0.206 5.873 0.000 2.333 0.592
## Immature =~
## dsq_isolation 1.000 0.569 0.128
## dsq_dissociatn 0.481 0.586 0.820 0.412 0.273 0.108
## dsq_devaluatin 1.944 1.211 1.606 0.108 1.106 0.371
## dsq_splitting 2.556 2.074 1.233 0.218 1.453 0.410
## dsq_denial 1.494 1.118 1.337 0.181 0.849 0.293
## dsq_tstc_fntsy 2.799 1.832 1.527 0.127 1.591 0.305
## dsq_displacmnt 2.900 2.300 1.260 0.208 1.649 0.436
## dsq_pssv_ggrss 3.743 2.662 1.406 0.160 2.128 0.605
## dsq_somatizatn 2.987 2.362 1.265 0.206 1.699 0.394
## dsq_acting_out 1.206 1.212 0.995 0.320 0.686 0.176
## dsq_projection 3.176 2.311 1.375 0.169 1.806 0.501
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 1.047 0.814 1.287 0.198 0.300 0.300
## Immature -0.290 0.464 -0.626 0.532 -0.282 -0.282
## Neurotic ~~
## Immature 0.600 0.459 1.309 0.191 0.546 0.546
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 11.523 0.211 54.732 0.000 11.523 3.129
## .dsq_suppressin 7.426 0.228 32.542 0.000 7.426 1.865
## .dsq_sublimatin 9.549 0.220 43.320 0.000 9.549 2.476
## .dsq_anticipatn 10.706 0.184 58.190 0.000 10.706 3.326
## .dsq_rctn_frmtn 9.107 0.199 45.846 0.000 9.107 2.632
## .dsq_idealizatn 6.304 0.227 27.714 0.000 6.304 1.584
## .dsq_psed_ltrsm 9.634 0.192 50.275 0.000 9.634 2.878
## .dsq_undoing 9.314 0.225 41.370 0.000 9.314 2.365
## .dsq_isolation 8.098 0.254 31.894 0.000 8.098 1.829
## .dsq_dissociatn 4.413 0.145 30.343 0.000 4.413 1.743
## .dsq_devaluatin 7.258 0.170 42.612 0.000 7.258 2.436
## .dsq_splitting 7.453 0.203 36.640 0.000 7.453 2.101
## .dsq_denial 5.415 0.166 32.646 0.000 5.415 1.866
## .dsq_tstc_fntsy 11.665 0.299 39.036 0.000 11.665 2.239
## .dsq_displacmnt 8.673 0.216 40.077 0.000 8.673 2.291
## .dsq_pssv_ggrss 7.341 0.202 36.339 0.000 7.341 2.085
## .dsq_somatizatn 10.118 0.246 41.088 0.000 10.118 2.349
## .dsq_acting_out 10.797 0.224 48.221 0.000 10.797 2.766
## .dsq_projection 8.023 0.206 38.895 0.000 8.023 2.226
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.299 2.572 4.004 0.000 10.299 0.759
## .dsq_suppressin 12.523 2.223 5.634 0.000 12.523 0.790
## .dsq_sublimatin 11.379 2.763 4.119 0.000 11.379 0.765
## .dsq_anticipatn 10.336 0.893 11.570 0.000 10.336 0.998
## .dsq_rctn_frmtn 8.244 0.967 8.525 0.000 8.244 0.688
## .dsq_idealizatn 13.420 1.422 9.435 0.000 13.420 0.848
## .dsq_psed_ltrsm 9.304 0.939 9.904 0.000 9.304 0.830
## .dsq_undoing 10.066 1.396 7.210 0.000 10.066 0.649
## .dsq_isolation 19.271 1.172 16.438 0.000 19.271 0.983
## .dsq_dissociatn 6.332 0.546 11.597 0.000 6.332 0.988
## .dsq_devaluatin 7.655 0.739 10.352 0.000 7.655 0.862
## .dsq_splitting 10.473 0.895 11.701 0.000 10.473 0.832
## .dsq_denial 7.698 0.615 12.523 0.000 7.698 0.914
## .dsq_tstc_fntsy 24.620 1.995 12.344 0.000 24.620 0.907
## .dsq_displacmnt 11.612 0.991 11.720 0.000 11.612 0.810
## .dsq_pssv_ggrss 7.864 0.928 8.472 0.000 7.864 0.635
## .dsq_somatizatn 15.669 1.400 11.195 0.000 15.669 0.844
## .dsq_acting_out 14.768 0.953 15.489 0.000 14.768 0.969
## .dsq_projection 9.730 1.072 9.077 0.000 9.730 0.749
## Mature 3.264 2.662 1.226 0.220 1.000 1.000
## Neurotic 3.732 1.056 3.535 0.000 1.000 1.000
## Immature 0.323 0.459 0.705 0.481 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.241
## dsq_suppressin 0.210
## dsq_sublimatin 0.235
## dsq_anticipatn 0.002
## dsq_rctn_frmtn 0.312
## dsq_idealizatn 0.152
## dsq_psed_ltrsm 0.170
## dsq_undoing 0.351
## dsq_isolation 0.017
## dsq_dissociatn 0.012
## dsq_devaluatin 0.138
## dsq_splitting 0.168
## dsq_denial 0.086
## dsq_tstc_fntsy 0.093
## dsq_displacmnt 0.190
## dsq_pssv_ggrss 0.365
## dsq_somatizatn 0.156
## dsq_acting_out 0.031
## dsq_projection 0.251fit4_kn <- cfa(model_4f_norat, data = kn_df, estimator = "mlr", missing = "fiml")## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use lavInspect(fit, "cov.lv") to investigate.summary(fit4_kn,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 156 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 63
##
## Number of observations 306
## Number of missing patterns 12
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 387.267 373.735
## Degrees of freedom 146 146
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.036
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 774.640 708.842
## Degrees of freedom 171 171
## P-value 0.000 0.000
## Scaling correction factor 1.093
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.600 0.577
## Tucker-Lewis Index (TLI) 0.532 0.504
##
## Robust Comparative Fit Index (CFI) 0.611
## Robust Tucker-Lewis Index (TLI) 0.545
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -15526.702 -15526.702
## Scaling correction factor 1.095
## for the MLR correction
## Loglikelihood unrestricted model (H1) -15333.068 -15333.068
## Scaling correction factor 1.054
## for the MLR correction
##
## Akaike (AIC) 31179.404 31179.404
## Bayesian (BIC) 31413.990 31413.990
## Sample-size adjusted Bayesian (SABIC) 31214.183 31214.183
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.073 0.071
## 90 Percent confidence interval - lower 0.065 0.063
## 90 Percent confidence interval - upper 0.082 0.080
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.117 0.055
##
## Robust RMSEA 0.072
## 90 Percent confidence interval - lower 0.062
## 90 Percent confidence interval - upper 0.081
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.079
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.079 0.079
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 1.659 0.450
## dsq_suppressin 1.208 0.596 2.027 0.043 2.004 0.503
## dsq_sublimatin 1.154 0.584 1.976 0.048 1.914 0.496
## dsq_anticipatn 0.074 0.264 0.279 0.780 0.123 0.038
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.937 0.560
## dsq_idealizatn 0.786 0.234 3.363 0.001 1.522 0.382
## dsq_psed_ltrsm 0.724 0.186 3.895 0.000 1.403 0.419
## dsq_undoing 1.202 0.212 5.679 0.000 2.329 0.591
## Immature =~
## dsq_tstc_fntsy 1.000 1.695 0.325
## dsq_displacmnt 0.955 0.339 2.815 0.005 1.619 0.428
## dsq_pssv_ggrss 1.213 0.376 3.229 0.001 2.057 0.584
## dsq_somatizatn 1.012 0.357 2.832 0.005 1.715 0.398
## dsq_acting_out 0.396 0.235 1.687 0.092 0.672 0.172
## dsq_projection 1.020 0.368 2.772 0.006 1.728 0.479
## Image_Distorting =~
## dsq_isolation 1.000 0.629 0.142
## dsq_dissociatn 0.751 0.788 0.953 0.341 0.472 0.186
## dsq_devaluatin 1.823 1.223 1.490 0.136 1.146 0.385
## dsq_splitting 2.387 2.299 1.039 0.299 1.501 0.423
## dsq_denial 1.533 1.265 1.212 0.226 0.964 0.332
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 0.887 0.756 1.174 0.240 0.276 0.276
## Immature -0.956 0.857 -1.116 0.265 -0.340 -0.340
## Image_Distrtng -0.045 0.331 -0.136 0.892 -0.043 -0.043
## Neurotic ~~
## Immature 1.903 0.668 2.850 0.004 0.580 0.580
## Image_Distrtng 0.580 0.500 1.159 0.246 0.476 0.476
## Immature ~~
## Image_Distrtng 1.010 0.866 1.167 0.243 0.948 0.948
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 11.523 0.211 54.732 0.000 11.523 3.129
## .dsq_suppressin 7.426 0.228 32.547 0.000 7.426 1.865
## .dsq_sublimatin 9.549 0.220 43.320 0.000 9.549 2.476
## .dsq_anticipatn 10.706 0.184 58.190 0.000 10.706 3.326
## .dsq_rctn_frmtn 9.107 0.199 45.853 0.000 9.107 2.632
## .dsq_idealizatn 6.304 0.227 27.714 0.000 6.304 1.584
## .dsq_psed_ltrsm 9.634 0.192 50.277 0.000 9.634 2.878
## .dsq_undoing 9.314 0.225 41.370 0.000 9.314 2.365
## .dsq_tstc_fntsy 11.665 0.299 39.043 0.000 11.665 2.239
## .dsq_displacmnt 8.673 0.216 40.077 0.000 8.673 2.291
## .dsq_pssv_ggrss 7.340 0.202 36.337 0.000 7.340 2.085
## .dsq_somatizatn 10.118 0.246 41.088 0.000 10.118 2.349
## .dsq_acting_out 10.797 0.224 48.224 0.000 10.797 2.766
## .dsq_projection 8.023 0.206 38.900 0.000 8.023 2.226
## .dsq_isolation 8.098 0.254 31.896 0.000 8.098 1.829
## .dsq_dissociatn 4.412 0.145 30.330 0.000 4.412 1.743
## .dsq_devaluatin 7.258 0.170 42.612 0.000 7.258 2.436
## .dsq_splitting 7.452 0.203 36.637 0.000 7.452 2.101
## .dsq_denial 5.415 0.166 32.646 0.000 5.415 1.866
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.812 1.638 6.600 0.000 10.812 0.797
## .dsq_suppressin 11.835 2.159 5.481 0.000 11.835 0.747
## .dsq_sublimatin 11.207 1.874 5.979 0.000 11.207 0.754
## .dsq_anticipatn 10.343 0.894 11.565 0.000 10.343 0.999
## .dsq_rctn_frmtn 8.223 0.938 8.763 0.000 8.223 0.687
## .dsq_idealizatn 13.517 1.391 9.718 0.000 13.517 0.854
## .dsq_psed_ltrsm 9.237 0.974 9.487 0.000 9.237 0.824
## .dsq_undoing 10.083 1.462 6.896 0.000 10.083 0.650
## .dsq_tstc_fntsy 24.279 2.095 11.587 0.000 24.279 0.894
## .dsq_displacmnt 11.710 0.989 11.836 0.000 11.710 0.817
## .dsq_pssv_ggrss 8.165 1.015 8.043 0.000 8.165 0.659
## .dsq_somatizatn 15.615 1.400 11.150 0.000 15.615 0.842
## .dsq_acting_out 14.787 0.960 15.408 0.000 14.787 0.970
## .dsq_projection 10.008 1.113 8.991 0.000 10.008 0.770
## .dsq_isolation 19.199 1.246 15.403 0.000 19.199 0.980
## .dsq_dissociatn 6.183 0.623 9.925 0.000 6.183 0.965
## .dsq_devaluatin 7.565 0.949 7.975 0.000 7.565 0.852
## .dsq_splitting 10.333 0.952 10.858 0.000 10.333 0.821
## .dsq_denial 7.490 0.703 10.654 0.000 7.490 0.890
## Mature 2.751 1.673 1.645 0.100 1.000 1.000
## Neurotic 3.753 1.018 3.685 0.000 1.000 1.000
## Immature 2.872 1.518 1.892 0.059 1.000 1.000
## Image_Distrtng 0.395 0.666 0.593 0.553 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.203
## dsq_suppressin 0.253
## dsq_sublimatin 0.246
## dsq_anticipatn 0.001
## dsq_rctn_frmtn 0.313
## dsq_idealizatn 0.146
## dsq_psed_ltrsm 0.176
## dsq_undoing 0.350
## dsq_tstc_fntsy 0.106
## dsq_displacmnt 0.183
## dsq_pssv_ggrss 0.341
## dsq_somatizatn 0.158
## dsq_acting_out 0.030
## dsq_projection 0.230
## dsq_isolation 0.020
## dsq_dissociatn 0.035
## dsq_devaluatin 0.148
## dsq_splitting 0.179
## dsq_denial 0.110# Likelihood Ratio Tests (Nested)
anova(fit1_kn, fit3_kn)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit3_kn 149 31178 31402 392.12
## fit1_kn 152 31261 31473 480.58 63.488 3 1.056e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit1_kn, fit4_kn)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit4_kn 146 31179 31414 387.27
## fit1_kn 152 31261 31473 480.58 63.062 6 1.072e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# BIC (Non-Nested)
fitMeasures(fit3_kn, "bic")## bic
## 31401.67fitMeasures(fit4_kn, "bic")## bic
## 31413.99modindices(fit3_kn, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 72 Mature =~ dsq_dissociation 24.169 0.581 1.050 0.415 0.415
## 190 dsq_idealization ~~ dsq_splitting 21.908 3.424 3.424 0.289 0.289
## 222 dsq_isolation ~~ dsq_devaluation 21.634 3.377 3.377 0.278 0.278
## 199 dsq_pseudo_altruism ~~ dsq_isolation 17.973 -3.406 -3.406 -0.254 -0.254
## 177 dsq_reaction_formation ~~ dsq_splitting 15.562 -2.427 -2.427 -0.261 -0.261
## 70 Mature =~ dsq_undoing 14.018 -0.711 -1.284 -0.326 -0.326
## 233 dsq_dissociation ~~ dsq_denial 13.591 1.515 1.515 0.217 0.217
## 201 dsq_pseudo_altruism ~~ dsq_devaluation 11.151 -1.732 -1.732 -0.205 -0.205
## 253 dsq_splitting ~~ dsq_acting_out 10.908 2.500 2.500 0.201 0.201
## 76 Mature =~ dsq_autistic_fantasy 10.204 -0.761 -1.375 -0.264 -0.264
## 87 Neurotic =~ dsq_dissociation 9.897 0.398 0.769 0.304 0.304
## 207 dsq_pseudo_altruism ~~ dsq_somatization 9.796 2.333 2.333 0.193 0.193
## 142 dsq_sublimation ~~ dsq_idealization 9.317 2.467 2.467 0.200 0.200
## 123 dsq_suppression ~~ dsq_sublimation 8.700 3.803 3.803 0.319 0.319
## 131 dsq_suppression ~~ dsq_devaluation 8.607 1.825 1.825 0.186 0.186
## 83 Neurotic =~ dsq_suppression 8.342 -0.483 -0.934 -0.234 -0.234
## 99 Immature =~ dsq_sublimation 7.985 1.526 0.868 0.225 0.225
## 146 dsq_sublimation ~~ dsq_dissociation 7.960 1.496 1.496 0.176 0.176
## 229 dsq_isolation ~~ dsq_acting_out 7.922 -2.763 -2.763 -0.164 -0.164
## 113 dsq_humor ~~ dsq_dissociation 7.642 1.399 1.399 0.173 0.173
## 173 dsq_reaction_formation ~~ dsq_undoing 7.550 2.756 2.756 0.303 0.303
## 245 dsq_devaluation ~~ dsq_somatization 7.469 -1.874 -1.874 -0.171 -0.171
## 128 dsq_suppression ~~ dsq_undoing 7.288 -2.197 -2.197 -0.196 -0.196
## 272 dsq_passive_aggression ~~ dsq_projection 7.273 1.878 1.878 0.215 0.215
## 112 dsq_humor ~~ dsq_isolation 7.238 -2.374 -2.374 -0.169 -0.169
## 75 Mature =~ dsq_denial 7.220 0.356 0.643 0.222 0.222
## 171 dsq_reaction_formation ~~ dsq_idealization 7.112 -2.148 -2.148 -0.204 -0.204
## 242 dsq_devaluation ~~ dsq_autistic_fantasy 6.972 2.221 2.221 0.162 0.162
## 106 dsq_humor ~~ dsq_sublimation 6.721 -3.343 -3.343 -0.309 -0.309
## 114 dsq_humor ~~ dsq_devaluation 6.700 -1.479 -1.479 -0.167 -0.167
## 84 Neurotic =~ dsq_sublimation 6.581 0.423 0.817 0.212 0.212
## 104 Immature =~ dsq_undoing 6.491 1.802 1.025 0.260 0.260
## 237 dsq_dissociation ~~ dsq_somatization 6.315 -1.506 -1.506 -0.151 -0.151
## 187 dsq_idealization ~~ dsq_isolation 6.246 -2.395 -2.395 -0.149 -0.149
## 100 Immature =~ dsq_anticipation 6.084 1.017 0.578 0.180 0.180
## 90 Neurotic =~ dsq_denial 6.067 0.350 0.677 0.233 0.233
## 92 Neurotic =~ dsq_displacement 5.985 0.447 0.864 0.228 0.228
## 183 dsq_reaction_formation ~~ dsq_acting_out 5.907 -1.707 -1.707 -0.155 -0.155
## 273 dsq_somatization ~~ dsq_acting_out 5.643 2.183 2.183 0.143 0.143
## 129 dsq_suppression ~~ dsq_isolation 5.620 2.280 2.280 0.147 0.147
## 225 dsq_isolation ~~ dsq_autistic_fantasy 5.503 3.022 3.022 0.139 0.139
## 140 dsq_sublimation ~~ dsq_anticipation 5.453 1.716 1.716 0.158 0.158
## 174 dsq_reaction_formation ~~ dsq_isolation 5.399 1.861 1.861 0.148 0.148
## 210 dsq_undoing ~~ dsq_isolation 5.373 2.084 2.084 0.150 0.150
## 96 Neurotic =~ dsq_projection 5.284 -0.398 -0.770 -0.214 -0.214
## 192 dsq_idealization ~~ dsq_autistic_fantasy 5.106 -2.487 -2.487 -0.137 -0.137
## 215 dsq_undoing ~~ dsq_autistic_fantasy 4.923 2.300 2.300 0.146 0.146
## 69 Mature =~ dsq_pseudo_altruism 4.808 0.338 0.611 0.183 0.183
## 220 dsq_undoing ~~ dsq_projection 4.793 -1.508 -1.508 -0.152 -0.152
## 208 dsq_pseudo_altruism ~~ dsq_acting_out 4.758 1.538 1.538 0.131 0.131
## 85 Neurotic =~ dsq_anticipation 4.711 0.275 0.531 0.165 0.165
## 255 dsq_denial ~~ dsq_autistic_fantasy 4.677 1.791 1.791 0.130 0.130
## 252 dsq_splitting ~~ dsq_somatization 4.552 1.740 1.740 0.136 0.136
## 94 Neurotic =~ dsq_somatization 4.366 0.436 0.843 0.196 0.196
## 107 dsq_humor ~~ dsq_anticipation 4.336 -1.466 -1.466 -0.142 -0.142
## 121 dsq_humor ~~ dsq_acting_out 4.334 1.613 1.613 0.131 0.131
## 110 dsq_humor ~~ dsq_pseudo_altruism 4.263 1.339 1.339 0.137 0.137
## 108 dsq_humor ~~ dsq_reaction_formation 4.137 1.352 1.352 0.147 0.147
## 152 dsq_sublimation ~~ dsq_passive_aggression 4.132 1.365 1.365 0.144 0.144
## 88 Neurotic =~ dsq_devaluation 4.076 -0.292 -0.565 -0.190 -0.190
## 261 dsq_autistic_fantasy ~~ dsq_displacement 4.034 -2.126 -2.126 -0.126 -0.126
## 274 dsq_somatization ~~ dsq_projection 3.889 -1.620 -1.620 -0.131 -0.131
## 226 dsq_isolation ~~ dsq_displacement 3.887 -1.796 -1.796 -0.120 -0.120model_3f_adj_kn_V <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Immature =~ dsq_isolation + dsq_dissociation + dsq_devaluation
+ dsq_splitting + dsq_denial + dsq_autistic_fantasy + dsq_displacement
+ dsq_passive_aggression + dsq_somatization + dsq_acting_out + dsq_projection
'
fit3_adjkn_V <- cfa(model_3f_adj_kn_V, data = kn_df, estimator = "mlr", missing = "fiml")
summary(fit3_adjkn_V,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 145 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 57
##
## Number of observations 306
## Number of missing patterns 12
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 368.430 351.270
## Degrees of freedom 132 132
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.049
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 750.691 691.208
## Degrees of freedom 153 153
## P-value 0.000 0.000
## Scaling correction factor 1.086
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.604 0.593
## Tucker-Lewis Index (TLI) 0.541 0.528
##
## Robust Comparative Fit Index (CFI) 0.613
## Robust Tucker-Lewis Index (TLI) 0.552
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -14737.387 -14737.387
## Scaling correction factor 1.035
## for the MLR correction
## Loglikelihood unrestricted model (H1) -14553.171 -14553.171
## Scaling correction factor 1.045
## for the MLR correction
##
## Akaike (AIC) 29588.773 29588.773
## Bayesian (BIC) 29801.017 29801.017
## Sample-size adjusted Bayesian (SABIC) 29620.240 29620.240
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.077 0.074
## 90 Percent confidence interval - lower 0.067 0.065
## 90 Percent confidence interval - upper 0.086 0.083
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.275 0.131
##
## Robust RMSEA 0.075
## 90 Percent confidence interval - lower 0.065
## 90 Percent confidence interval - upper 0.085
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.215
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.080 0.080
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 1.884 0.512
## dsq_suppressin 0.948 0.541 1.752 0.080 1.787 0.449
## dsq_sublimatin 0.946 0.536 1.764 0.078 1.783 0.462
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.941 0.561
## dsq_idealizatn 0.795 0.236 3.373 0.001 1.543 0.388
## dsq_psed_ltrsm 0.710 0.178 3.997 0.000 1.377 0.411
## dsq_undoing 1.203 0.203 5.918 0.000 2.334 0.593
## Immature =~
## dsq_isolation 1.000 0.576 0.130
## dsq_dissociatn 0.463 0.547 0.846 0.397 0.267 0.105
## dsq_devaluatin 1.926 1.178 1.635 0.102 1.110 0.372
## dsq_splitting 2.520 2.006 1.256 0.209 1.452 0.409
## dsq_denial 1.465 1.063 1.378 0.168 0.844 0.291
## dsq_tstc_fntsy 2.777 1.796 1.547 0.122 1.600 0.307
## dsq_displacmnt 2.851 2.217 1.286 0.198 1.643 0.434
## dsq_pssv_ggrss 3.698 2.583 1.432 0.152 2.130 0.605
## dsq_somatizatn 2.950 2.294 1.286 0.198 1.700 0.395
## dsq_acting_out 1.182 1.168 1.012 0.311 0.681 0.174
## dsq_projection 3.138 2.245 1.398 0.162 1.808 0.502
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 1.080 0.809 1.336 0.182 0.295 0.295
## Immature -0.330 0.409 -0.806 0.420 -0.304 -0.304
## Neurotic ~~
## Immature 0.609 0.456 1.336 0.182 0.545 0.545
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 11.523 0.211 54.732 0.000 11.523 3.129
## .dsq_suppressin 7.426 0.228 32.549 0.000 7.426 1.865
## .dsq_sublimatin 9.549 0.220 43.320 0.000 9.549 2.476
## .dsq_rctn_frmtn 9.107 0.199 45.844 0.000 9.107 2.632
## .dsq_idealizatn 6.304 0.227 27.714 0.000 6.304 1.584
## .dsq_psed_ltrsm 9.634 0.192 50.272 0.000 9.634 2.878
## .dsq_undoing 9.314 0.225 41.370 0.000 9.314 2.365
## .dsq_isolation 8.098 0.254 31.895 0.000 8.098 1.829
## .dsq_dissociatn 4.413 0.145 30.343 0.000 4.413 1.743
## .dsq_devaluatin 7.258 0.170 42.612 0.000 7.258 2.436
## .dsq_splitting 7.453 0.203 36.640 0.000 7.453 2.101
## .dsq_denial 5.415 0.166 32.646 0.000 5.415 1.866
## .dsq_tstc_fntsy 11.665 0.299 39.038 0.000 11.665 2.239
## .dsq_displacmnt 8.673 0.216 40.077 0.000 8.673 2.291
## .dsq_pssv_ggrss 7.341 0.202 36.336 0.000 7.341 2.085
## .dsq_somatizatn 10.118 0.246 41.088 0.000 10.118 2.349
## .dsq_acting_out 10.797 0.224 48.221 0.000 10.797 2.766
## .dsq_projection 8.023 0.206 38.896 0.000 8.023 2.226
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.013 2.008 4.986 0.000 10.013 0.738
## .dsq_suppressin 12.661 2.134 5.933 0.000 12.661 0.799
## .dsq_sublimatin 11.689 1.939 6.029 0.000 11.689 0.786
## .dsq_rctn_frmtn 8.210 0.935 8.783 0.000 8.210 0.686
## .dsq_idealizatn 13.453 1.385 9.713 0.000 13.453 0.850
## .dsq_psed_ltrsm 9.310 0.936 9.948 0.000 9.310 0.831
## .dsq_undoing 10.060 1.373 7.326 0.000 10.060 0.649
## .dsq_isolation 19.262 1.172 16.430 0.000 19.262 0.983
## .dsq_dissociatn 6.336 0.543 11.665 0.000 6.336 0.989
## .dsq_devaluatin 7.647 0.734 10.417 0.000 7.647 0.861
## .dsq_splitting 10.477 0.896 11.691 0.000 10.477 0.833
## .dsq_denial 7.707 0.611 12.616 0.000 7.707 0.915
## .dsq_tstc_fntsy 24.592 1.990 12.357 0.000 24.592 0.906
## .dsq_displacmnt 11.633 0.984 11.816 0.000 11.633 0.812
## .dsq_pssv_ggrss 7.856 0.919 8.549 0.000 7.856 0.634
## .dsq_somatizatn 15.666 1.397 11.218 0.000 15.666 0.844
## .dsq_acting_out 14.775 0.953 15.501 0.000 14.775 0.970
## .dsq_projection 9.723 1.065 9.126 0.000 9.723 0.748
## Mature 3.550 2.086 1.702 0.089 1.000 1.000
## Neurotic 3.767 1.025 3.674 0.000 1.000 1.000
## Immature 0.332 0.461 0.720 0.472 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.262
## dsq_suppressin 0.201
## dsq_sublimatin 0.214
## dsq_rctn_frmtn 0.314
## dsq_idealizatn 0.150
## dsq_psed_ltrsm 0.169
## dsq_undoing 0.351
## dsq_isolation 0.017
## dsq_dissociatn 0.011
## dsq_devaluatin 0.139
## dsq_splitting 0.167
## dsq_denial 0.085
## dsq_tstc_fntsy 0.094
## dsq_displacmnt 0.188
## dsq_pssv_ggrss 0.366
## dsq_somatizatn 0.156
## dsq_acting_out 0.030
## dsq_projection 0.252modindices(fit3_adjkn_V, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 69 Mature =~ dsq_dissociation 24.187 0.563 1.061 0.419 0.419
## 167 dsq_idealization ~~ dsq_splitting 21.929 3.427 3.427 0.289 0.289
## 199 dsq_isolation ~~ dsq_devaluation 21.531 3.367 3.367 0.277 0.277
## 176 dsq_pseudo_altruism ~~ dsq_isolation 17.931 -3.402 -3.402 -0.254 -0.254
## 154 dsq_reaction_formation ~~ dsq_splitting 15.499 -2.421 -2.421 -0.261 -0.261
## 67 Mature =~ dsq_undoing 13.853 -0.674 -1.271 -0.323 -0.323
## 210 dsq_dissociation ~~ dsq_denial 13.683 1.520 1.520 0.218 0.218
## 178 dsq_pseudo_altruism ~~ dsq_devaluation 11.096 -1.727 -1.727 -0.205 -0.205
## 230 dsq_splitting ~~ dsq_acting_out 10.963 2.506 2.506 0.201 0.201
## 117 dsq_suppression ~~ dsq_sublimation 10.254 3.851 3.851 0.317 0.317
## 83 Neurotic =~ dsq_dissociation 10.061 0.398 0.772 0.305 0.305
## 73 Mature =~ dsq_autistic_fantasy 9.968 -0.729 -1.373 -0.263 -0.263
## 184 dsq_pseudo_altruism ~~ dsq_somatization 9.875 2.342 2.342 0.194 0.194
## 134 dsq_sublimation ~~ dsq_idealization 9.584 2.508 2.508 0.200 0.200
## 124 dsq_suppression ~~ dsq_devaluation 8.607 1.828 1.828 0.186 0.186
## 138 dsq_sublimation ~~ dsq_dissociation 8.186 1.523 1.523 0.177 0.177
## 95 Immature =~ dsq_sublimation 7.997 1.505 0.867 0.225 0.225
## 206 dsq_isolation ~~ dsq_acting_out 7.936 -2.765 -2.765 -0.164 -0.164
## 72 Mature =~ dsq_denial 7.758 0.358 0.674 0.232 0.232
## 107 dsq_humor ~~ dsq_dissociation 7.691 1.400 1.400 0.176 0.176
## 222 dsq_devaluation ~~ dsq_somatization 7.549 -1.883 -1.883 -0.172 -0.172
## 80 Neurotic =~ dsq_suppression 7.372 -0.449 -0.871 -0.219 -0.219
## 106 dsq_humor ~~ dsq_isolation 7.278 -2.374 -2.374 -0.171 -0.171
## 150 dsq_reaction_formation ~~ dsq_undoing 7.233 2.712 2.712 0.298 0.298
## 121 dsq_suppression ~~ dsq_undoing 7.197 -2.185 -2.185 -0.194 -0.194
## 249 dsq_passive_aggression ~~ dsq_projection 7.158 1.860 1.860 0.213 0.213
## 148 dsq_reaction_formation ~~ dsq_idealization 7.044 -2.137 -2.137 -0.203 -0.203
## 219 dsq_devaluation ~~ dsq_autistic_fantasy 6.878 2.205 2.205 0.161 0.161
## 81 Neurotic =~ dsq_sublimation 6.784 0.419 0.814 0.211 0.211
## 99 Immature =~ dsq_undoing 6.662 1.789 1.031 0.262 0.262
## 108 dsq_humor ~~ dsq_devaluation 6.458 -1.450 -1.450 -0.166 -0.166
## 101 dsq_humor ~~ dsq_sublimation 6.416 -3.254 -3.254 -0.301 -0.301
## 86 Neurotic =~ dsq_denial 6.305 0.354 0.687 0.237 0.237
## 164 dsq_idealization ~~ dsq_isolation 6.224 -2.392 -2.392 -0.149 -0.149
## 214 dsq_dissociation ~~ dsq_somatization 6.206 -1.492 -1.492 -0.150 -0.150
## 88 Neurotic =~ dsq_displacement 6.187 0.450 0.874 0.231 0.231
## 160 dsq_reaction_formation ~~ dsq_acting_out 5.927 -1.708 -1.708 -0.155 -0.155
## 122 dsq_suppression ~~ dsq_isolation 5.701 2.300 2.300 0.147 0.147
## 250 dsq_somatization ~~ dsq_acting_out 5.682 2.190 2.190 0.144 0.144
## 202 dsq_isolation ~~ dsq_autistic_fantasy 5.449 3.006 3.006 0.138 0.138
## 151 dsq_reaction_formation ~~ dsq_isolation 5.441 1.866 1.866 0.148 0.148
## 187 dsq_undoing ~~ dsq_isolation 5.352 2.080 2.080 0.149 0.149
## 92 Neurotic =~ dsq_projection 5.247 -0.393 -0.763 -0.212 -0.212
## 169 dsq_idealization ~~ dsq_autistic_fantasy 5.109 -2.488 -2.488 -0.137 -0.137
## 192 dsq_undoing ~~ dsq_autistic_fantasy 4.870 2.286 2.286 0.145 0.145
## 197 dsq_undoing ~~ dsq_projection 4.849 -1.517 -1.517 -0.153 -0.153
## 185 dsq_pseudo_altruism ~~ dsq_acting_out 4.769 1.540 1.540 0.131 0.131
## 66 Mature =~ dsq_pseudo_altruism 4.708 0.319 0.602 0.180 0.180
## 232 dsq_denial ~~ dsq_autistic_fantasy 4.683 1.792 1.792 0.130 0.130
## 115 dsq_humor ~~ dsq_acting_out 4.596 1.657 1.657 0.136 0.136
## 229 dsq_splitting ~~ dsq_somatization 4.555 1.740 1.740 0.136 0.136
## 90 Neurotic =~ dsq_somatization 4.303 0.429 0.833 0.193 0.193
## 104 dsq_humor ~~ dsq_pseudo_altruism 4.122 1.317 1.317 0.136 0.136
## 84 Neurotic =~ dsq_devaluation 4.096 -0.290 -0.563 -0.189 -0.189
## 238 dsq_autistic_fantasy ~~ dsq_displacement 4.046 -2.128 -2.128 -0.126 -0.126
## 144 dsq_sublimation ~~ dsq_passive_aggression 4.043 1.353 1.353 0.141 0.141
## 112 dsq_humor ~~ dsq_displacement 4.005 1.431 1.431 0.133 0.133
## 203 dsq_isolation ~~ dsq_displacement 3.920 -1.804 -1.804 -0.120 -0.120
## 251 dsq_somatization ~~ dsq_projection 3.919 -1.625 -1.625 -0.132 -0.132
## 142 dsq_sublimation ~~ dsq_autistic_fantasy 3.880 -2.102 -2.102 -0.124 -0.124model_3f_adj_kn_VI <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Immature =~ dsq_isolation + dsq_devaluation
+ dsq_splitting + dsq_denial + dsq_autistic_fantasy + dsq_displacement
+ dsq_passive_aggression + dsq_somatization + dsq_acting_out + dsq_projection
'
fit3_adjkn_VI <- cfa(model_3f_adj_kn_VI, data = kn_df, estimator = "mlr", missing = "fiml")
summary(fit3_adjkn_VI,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 145 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 54
##
## Number of observations 306
## Number of missing patterns 10
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 310.965 292.089
## Degrees of freedom 116 116
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.065
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 690.998 628.448
## Degrees of freedom 136 136
## P-value 0.000 0.000
## Scaling correction factor 1.100
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.649 0.642
## Tucker-Lewis Index (TLI) 0.588 0.581
##
## Robust Comparative Fit Index (CFI) 0.660
## Robust Tucker-Lewis Index (TLI) 0.602
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -14027.158 -14027.158
## Scaling correction factor 1.023
## for the MLR correction
## Loglikelihood unrestricted model (H1) -13871.676 -13871.676
## Scaling correction factor 1.051
## for the MLR correction
##
## Akaike (AIC) 28162.316 28162.316
## Bayesian (BIC) 28363.390 28363.390
## Sample-size adjusted Bayesian (SABIC) 28192.127 28192.127
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.074 0.070
## 90 Percent confidence interval - lower 0.064 0.061
## 90 Percent confidence interval - upper 0.084 0.080
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.171 0.054
##
## Robust RMSEA 0.072
## 90 Percent confidence interval - lower 0.062
## 90 Percent confidence interval - upper 0.083
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.120
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.076 0.076
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 1.924 0.522
## dsq_suppressin 0.914 0.503 1.817 0.069 1.757 0.441
## dsq_sublimatin 0.910 0.492 1.849 0.064 1.750 0.454
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.947 0.562
## dsq_idealizatn 0.788 0.233 3.379 0.001 1.535 0.386
## dsq_psed_ltrsm 0.707 0.177 3.997 0.000 1.377 0.411
## dsq_undoing 1.199 0.202 5.936 0.000 2.335 0.593
## Immature =~
## dsq_isolation 1.000 0.589 0.133
## dsq_devaluatin 1.873 1.118 1.676 0.094 1.103 0.370
## dsq_splitting 2.450 1.920 1.276 0.202 1.442 0.407
## dsq_denial 1.379 0.972 1.418 0.156 0.812 0.280
## dsq_tstc_fntsy 2.744 1.749 1.569 0.117 1.616 0.310
## dsq_displacmnt 2.792 2.148 1.300 0.194 1.644 0.434
## dsq_pssv_ggrss 3.605 2.482 1.453 0.146 2.123 0.603
## dsq_somatizatn 2.939 2.262 1.299 0.194 1.730 0.402
## dsq_acting_out 1.142 1.121 1.019 0.308 0.672 0.172
## dsq_projection 3.065 2.159 1.420 0.156 1.804 0.501
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 1.114 0.799 1.395 0.163 0.298 0.298
## Immature -0.377 0.426 -0.887 0.375 -0.333 -0.333
## Neurotic ~~
## Immature 0.618 0.456 1.355 0.175 0.539 0.539
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 11.523 0.211 54.732 0.000 11.523 3.129
## .dsq_suppressin 7.426 0.228 32.550 0.000 7.426 1.865
## .dsq_sublimatin 9.549 0.220 43.320 0.000 9.549 2.476
## .dsq_rctn_frmtn 9.107 0.199 45.845 0.000 9.107 2.632
## .dsq_idealizatn 6.304 0.227 27.714 0.000 6.304 1.584
## .dsq_psed_ltrsm 9.634 0.192 50.271 0.000 9.634 2.878
## .dsq_undoing 9.314 0.225 41.370 0.000 9.314 2.365
## .dsq_isolation 8.098 0.254 31.894 0.000 8.098 1.829
## .dsq_devaluatin 7.258 0.170 42.612 0.000 7.258 2.436
## .dsq_splitting 7.453 0.203 36.637 0.000 7.453 2.101
## .dsq_denial 5.415 0.166 32.646 0.000 5.415 1.866
## .dsq_tstc_fntsy 11.665 0.299 39.036 0.000 11.665 2.239
## .dsq_displacmnt 8.673 0.216 40.077 0.000 8.673 2.291
## .dsq_pssv_ggrss 7.341 0.202 36.336 0.000 7.341 2.085
## .dsq_somatizatn 10.118 0.246 41.088 0.000 10.118 2.349
## .dsq_acting_out 10.798 0.224 48.220 0.000 10.798 2.766
## .dsq_projection 8.023 0.206 38.897 0.000 8.023 2.226
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.863 1.948 5.063 0.000 9.863 0.727
## .dsq_suppressin 12.766 2.064 6.186 0.000 12.766 0.805
## .dsq_sublimatin 11.806 1.852 6.375 0.000 11.806 0.794
## .dsq_rctn_frmtn 8.187 0.933 8.772 0.000 8.187 0.684
## .dsq_idealizatn 13.477 1.377 9.788 0.000 13.477 0.851
## .dsq_psed_ltrsm 9.310 0.935 9.956 0.000 9.310 0.831
## .dsq_undoing 10.058 1.361 7.392 0.000 10.058 0.649
## .dsq_isolation 19.248 1.179 16.331 0.000 19.248 0.982
## .dsq_devaluatin 7.661 0.740 10.358 0.000 7.661 0.863
## .dsq_splitting 10.505 0.902 11.652 0.000 10.505 0.835
## .dsq_denial 7.760 0.615 12.615 0.000 7.760 0.922
## .dsq_tstc_fntsy 24.541 1.997 12.288 0.000 24.541 0.904
## .dsq_displacmnt 11.628 0.987 11.777 0.000 11.628 0.811
## .dsq_pssv_ggrss 7.888 0.919 8.585 0.000 7.888 0.636
## .dsq_somatizatn 15.561 1.397 11.140 0.000 15.561 0.839
## .dsq_acting_out 14.786 0.952 15.539 0.000 14.786 0.970
## .dsq_projection 9.737 1.069 9.108 0.000 9.737 0.749
## Mature 3.700 2.021 1.830 0.067 1.000 1.000
## Neurotic 3.789 1.025 3.698 0.000 1.000 1.000
## Immature 0.347 0.474 0.732 0.464 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.273
## dsq_suppressin 0.195
## dsq_sublimatin 0.206
## dsq_rctn_frmtn 0.316
## dsq_idealizatn 0.149
## dsq_psed_ltrsm 0.169
## dsq_undoing 0.351
## dsq_isolation 0.018
## dsq_devaluatin 0.137
## dsq_splitting 0.165
## dsq_denial 0.078
## dsq_tstc_fntsy 0.096
## dsq_displacmnt 0.189
## dsq_pssv_ggrss 0.364
## dsq_somatizatn 0.161
## dsq_acting_out 0.030
## dsq_projection 0.251modindices(fit3_adjkn_VI, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 157 dsq_idealization ~~ dsq_splitting 22.167 3.450 3.450 0.290 0.290
## 186 dsq_isolation ~~ dsq_devaluation 21.357 3.355 3.355 0.276 0.276
## 166 dsq_pseudo_altruism ~~ dsq_isolation 17.900 -3.398 -3.398 -0.254 -0.254
## 145 dsq_reaction_formation ~~ dsq_splitting 15.385 -2.413 -2.413 -0.260 -0.260
## 64 Mature =~ dsq_undoing 13.742 -0.653 -1.257 -0.319 -0.319
## 208 dsq_splitting ~~ dsq_acting_out 11.084 2.522 2.522 0.202 0.202
## 167 dsq_pseudo_altruism ~~ dsq_devaluation 10.856 -1.709 -1.709 -0.202 -0.202
## 111 dsq_suppression ~~ dsq_sublimation 10.434 3.676 3.676 0.299 0.299
## 173 dsq_pseudo_altruism ~~ dsq_somatization 10.039 2.358 2.358 0.196 0.196
## 127 dsq_sublimation ~~ dsq_idealization 9.655 2.520 2.520 0.200 0.200
## 69 Mature =~ dsq_autistic_fantasy 9.094 -0.688 -1.324 -0.254 -0.254
## 117 dsq_suppression ~~ dsq_devaluation 8.695 1.841 1.841 0.186 0.186
## 68 Mature =~ dsq_denial 8.324 0.367 0.706 0.243 0.243
## 193 dsq_isolation ~~ dsq_acting_out 7.958 -2.769 -2.769 -0.164 -0.164
## 200 dsq_devaluation ~~ dsq_somatization 7.844 -1.919 -1.919 -0.176 -0.176
## 90 Immature =~ dsq_sublimation 7.696 1.463 0.861 0.223 0.223
## 227 dsq_passive_aggression ~~ dsq_projection 7.400 1.891 1.891 0.216 0.216
## 115 dsq_suppression ~~ dsq_undoing 7.188 -2.184 -2.184 -0.193 -0.193
## 94 Immature =~ dsq_undoing 7.147 1.782 1.049 0.266 0.266
## 101 dsq_humor ~~ dsq_isolation 7.128 -2.344 -2.344 -0.170 -0.170
## 142 dsq_reaction_formation ~~ dsq_undoing 7.039 2.684 2.684 0.296 0.296
## 140 dsq_reaction_formation ~~ dsq_idealization 6.978 -2.127 -2.127 -0.202 -0.202
## 76 Neurotic =~ dsq_suppression 6.934 -0.431 -0.839 -0.211 -0.211
## 81 Neurotic =~ dsq_denial 6.905 0.365 0.711 0.245 0.245
## 197 dsq_devaluation ~~ dsq_autistic_fantasy 6.832 2.198 2.198 0.160 0.160
## 83 Neurotic =~ dsq_displacement 6.636 0.459 0.894 0.236 0.236
## 77 Neurotic =~ dsq_sublimation 6.592 0.409 0.796 0.206 0.206
## 96 dsq_humor ~~ dsq_sublimation 6.292 -3.147 -3.147 -0.292 -0.292
## 155 dsq_idealization ~~ dsq_isolation 6.194 -2.387 -2.387 -0.148 -0.148
## 102 dsq_humor ~~ dsq_devaluation 6.039 -1.402 -1.402 -0.161 -0.161
## 151 dsq_reaction_formation ~~ dsq_acting_out 5.959 -1.713 -1.713 -0.156 -0.156
## 116 dsq_suppression ~~ dsq_isolation 5.816 2.325 2.325 0.148 0.148
## 228 dsq_somatization ~~ dsq_acting_out 5.700 2.191 2.191 0.144 0.144
## 143 dsq_reaction_formation ~~ dsq_isolation 5.450 1.867 1.867 0.149 0.149
## 189 dsq_isolation ~~ dsq_autistic_fantasy 5.357 2.979 2.979 0.137 0.137
## 176 dsq_undoing ~~ dsq_isolation 5.266 2.062 2.062 0.148 0.148
## 159 dsq_idealization ~~ dsq_autistic_fantasy 5.028 -2.468 -2.468 -0.136 -0.136
## 185 dsq_undoing ~~ dsq_projection 4.991 -1.540 -1.540 -0.156 -0.156
## 210 dsq_denial ~~ dsq_autistic_fantasy 4.872 1.830 1.830 0.133 0.133
## 109 dsq_humor ~~ dsq_acting_out 4.792 1.689 1.689 0.140 0.140
## 174 dsq_pseudo_altruism ~~ dsq_acting_out 4.792 1.545 1.545 0.132 0.132
## 180 dsq_undoing ~~ dsq_autistic_fantasy 4.772 2.262 2.262 0.144 0.144
## 63 Mature =~ dsq_pseudo_altruism 4.731 0.312 0.599 0.179 0.179
## 70 Mature =~ dsq_displacement 4.688 0.352 0.676 0.179 0.179
## 87 Neurotic =~ dsq_projection 4.618 -0.363 -0.708 -0.196 -0.196
## 136 dsq_sublimation ~~ dsq_passive_aggression 4.546 1.440 1.440 0.149 0.149
## 106 dsq_humor ~~ dsq_displacement 4.433 1.506 1.506 0.141 0.141
## 207 dsq_splitting ~~ dsq_somatization 4.407 1.711 1.711 0.134 0.134
## 85 Neurotic =~ dsq_somatization 4.329 0.424 0.825 0.191 0.191
## 229 dsq_somatization ~~ dsq_projection 4.298 -1.702 -1.702 -0.138 -0.138
## 216 dsq_autistic_fantasy ~~ dsq_displacement 4.196 -2.167 -2.167 -0.128 -0.128
## 190 dsq_isolation ~~ dsq_displacement 4.035 -1.830 -1.830 -0.122 -0.122
## 99 dsq_humor ~~ dsq_pseudo_altruism 3.986 1.294 1.294 0.135 0.135model_3f_adj_kn_VII <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Immature =~ dsq_devaluation
+ dsq_splitting + dsq_denial + dsq_autistic_fantasy + dsq_displacement
+ dsq_passive_aggression + dsq_somatization + dsq_acting_out + dsq_projection
'
fit3_adjkn_VII <- cfa(model_3f_adj_kn_VII, data = kn_df, estimator = "mlr", missing = "fiml")
summary(fit3_adjkn_VII,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 118 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 51
##
## Number of observations 306
## Number of missing patterns 9
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 246.221 231.112
## Degrees of freedom 101 101
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.065
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 622.809 566.624
## Degrees of freedom 120 120
## P-value 0.000 0.000
## Scaling correction factor 1.099
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.711 0.709
## Tucker-Lewis Index (TLI) 0.657 0.654
##
## Robust Comparative Fit Index (CFI) 0.724
## Robust Tucker-Lewis Index (TLI) 0.672
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -13145.278 -13145.278
## Scaling correction factor 1.020
## for the MLR correction
## Loglikelihood unrestricted model (H1) -13022.168 -13022.168
## Scaling correction factor 1.050
## for the MLR correction
##
## Akaike (AIC) 26392.556 26392.556
## Bayesian (BIC) 26582.459 26582.459
## Sample-size adjusted Bayesian (SABIC) 26420.711 26420.711
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.069 0.065
## 90 Percent confidence interval - lower 0.058 0.054
## 90 Percent confidence interval - upper 0.079 0.076
## P-value H_0: RMSEA <= 0.050 0.003 0.012
## P-value H_0: RMSEA >= 0.080 0.042 0.009
##
## Robust RMSEA 0.066
## 90 Percent confidence interval - lower 0.055
## 90 Percent confidence interval - upper 0.078
## P-value H_0: Robust RMSEA <= 0.050 0.011
## P-value H_0: Robust RMSEA >= 0.080 0.027
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.070 0.070
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 1.864 0.506
## dsq_suppressin 0.970 0.487 1.990 0.047 1.808 0.454
## dsq_sublimatin 0.963 0.474 2.030 0.042 1.795 0.466
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.915 0.553
## dsq_idealizatn 0.821 0.237 3.464 0.001 1.573 0.395
## dsq_psed_ltrsm 0.737 0.179 4.125 0.000 1.411 0.421
## dsq_undoing 1.203 0.199 6.036 0.000 2.303 0.585
## Immature =~
## dsq_devaluatin 1.000 1.037 0.348
## dsq_splitting 1.427 0.417 3.421 0.001 1.479 0.417
## dsq_denial 0.765 0.278 2.757 0.006 0.793 0.273
## dsq_tstc_fntsy 1.492 0.425 3.507 0.000 1.547 0.297
## dsq_displacmnt 1.626 0.466 3.492 0.000 1.685 0.445
## dsq_pssv_ggrss 2.043 0.436 4.689 0.000 2.118 0.602
## dsq_somatizatn 1.715 0.609 2.817 0.005 1.778 0.413
## dsq_acting_out 0.707 0.372 1.900 0.057 0.732 0.188
## dsq_projection 1.732 0.355 4.877 0.000 1.796 0.498
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 1.069 0.761 1.404 0.160 0.299 0.299
## Immature -0.621 0.471 -1.320 0.187 -0.322 -0.322
## Neurotic ~~
## Immature 1.081 0.281 3.841 0.000 0.544 0.544
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 11.523 0.211 54.732 0.000 11.523 3.129
## .dsq_suppressin 7.426 0.228 32.550 0.000 7.426 1.865
## .dsq_sublimatin 9.549 0.220 43.320 0.000 9.549 2.476
## .dsq_rctn_frmtn 9.107 0.199 45.843 0.000 9.107 2.632
## .dsq_idealizatn 6.304 0.227 27.714 0.000 6.304 1.584
## .dsq_psed_ltrsm 9.634 0.192 50.271 0.000 9.634 2.878
## .dsq_undoing 9.314 0.225 41.370 0.000 9.314 2.365
## .dsq_devaluatin 7.258 0.170 42.612 0.000 7.258 2.436
## .dsq_splitting 7.453 0.203 36.638 0.000 7.453 2.101
## .dsq_denial 5.415 0.166 32.646 0.000 5.415 1.866
## .dsq_tstc_fntsy 11.664 0.299 39.033 0.000 11.664 2.238
## .dsq_displacmnt 8.673 0.216 40.077 0.000 8.673 2.291
## .dsq_pssv_ggrss 7.343 0.202 36.356 0.000 7.343 2.086
## .dsq_somatizatn 10.118 0.246 41.088 0.000 10.118 2.349
## .dsq_acting_out 10.797 0.224 48.217 0.000 10.797 2.766
## .dsq_projection 8.022 0.206 38.892 0.000 8.022 2.226
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.090 1.743 5.788 0.000 10.090 0.744
## .dsq_suppressin 12.587 1.999 6.296 0.000 12.587 0.794
## .dsq_sublimatin 11.647 1.750 6.654 0.000 11.647 0.783
## .dsq_rctn_frmtn 8.310 0.930 8.932 0.000 8.310 0.694
## .dsq_idealizatn 13.359 1.364 9.794 0.000 13.359 0.844
## .dsq_psed_ltrsm 9.217 0.925 9.968 0.000 9.217 0.822
## .dsq_undoing 10.206 1.365 7.477 0.000 10.206 0.658
## .dsq_devaluatin 7.803 0.713 10.939 0.000 7.803 0.879
## .dsq_splitting 10.398 0.892 11.660 0.000 10.398 0.826
## .dsq_denial 7.790 0.612 12.738 0.000 7.790 0.925
## .dsq_tstc_fntsy 24.760 1.954 12.673 0.000 24.760 0.912
## .dsq_displacmnt 11.491 0.968 11.869 0.000 11.491 0.802
## .dsq_pssv_ggrss 7.907 0.946 8.360 0.000 7.907 0.638
## .dsq_somatizatn 15.394 1.401 10.987 0.000 15.394 0.830
## .dsq_acting_out 14.702 0.949 15.486 0.000 14.702 0.965
## .dsq_projection 9.768 1.080 9.040 0.000 9.768 0.752
## Mature 3.474 1.803 1.927 0.054 1.000 1.000
## Neurotic 3.667 1.009 3.633 0.000 1.000 1.000
## Immature 1.075 0.456 2.358 0.018 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.256
## dsq_suppressin 0.206
## dsq_sublimatin 0.217
## dsq_rctn_frmtn 0.306
## dsq_idealizatn 0.156
## dsq_psed_ltrsm 0.178
## dsq_undoing 0.342
## dsq_devaluatin 0.121
## dsq_splitting 0.174
## dsq_denial 0.075
## dsq_tstc_fntsy 0.088
## dsq_displacmnt 0.198
## dsq_pssv_ggrss 0.362
## dsq_somatizatn 0.170
## dsq_acting_out 0.035
## dsq_projection 0.248modindices(fit3_adjkn_VII, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 147 dsq_idealization ~~ dsq_splitting 21.396 3.376 3.376 0.286 0.286
## 136 dsq_reaction_formation ~~ dsq_splitting 15.462 -2.422 -2.422 -0.261 -0.261
## 61 Mature =~ dsq_undoing 13.142 -0.660 -1.231 -0.312 -0.312
## 156 dsq_pseudo_altruism ~~ dsq_devaluation 11.094 -1.734 -1.734 -0.204 -0.204
## 187 dsq_splitting ~~ dsq_acting_out 10.312 2.426 2.426 0.196 0.196
## 120 dsq_sublimation ~~ dsq_idealization 9.572 2.504 2.504 0.201 0.201
## 65 Mature =~ dsq_autistic_fantasy 9.533 -0.725 -1.351 -0.259 -0.259
## 105 dsq_suppression ~~ dsq_sublimation 9.375 3.653 3.653 0.302 0.302
## 162 dsq_pseudo_altruism ~~ dsq_somatization 9.216 2.250 2.250 0.189 0.189
## 134 dsq_reaction_formation ~~ dsq_undoing 8.822 2.912 2.912 0.316 0.316
## 110 dsq_suppression ~~ dsq_devaluation 8.408 1.815 1.815 0.183 0.183
## 85 Immature =~ dsq_sublimation 7.958 0.845 0.876 0.227 0.227
## 64 Mature =~ dsq_denial 7.918 0.368 0.686 0.236 0.236
## 72 Neurotic =~ dsq_suppression 7.828 -0.470 -0.900 -0.226 -0.226
## 176 dsq_devaluation ~~ dsq_autistic_fantasy 7.818 2.364 2.364 0.170 0.170
## 206 dsq_passive_aggression ~~ dsq_projection 7.767 1.943 1.943 0.221 0.221
## 132 dsq_reaction_formation ~~ dsq_idealization 7.201 -2.162 -2.162 -0.205 -0.205
## 109 dsq_suppression ~~ dsq_undoing 7.154 -2.183 -2.183 -0.193 -0.193
## 179 dsq_devaluation ~~ dsq_somatization 7.131 -1.833 -1.833 -0.167 -0.167
## 76 Neurotic =~ dsq_denial 6.966 0.377 0.722 0.249 0.249
## 73 Neurotic =~ dsq_sublimation 6.896 0.429 0.822 0.213 0.213
## 96 dsq_humor ~~ dsq_devaluation 6.854 -1.502 -1.502 -0.169 -0.169
## 89 Immature =~ dsq_undoing 6.229 0.951 0.986 0.250 0.250
## 78 Neurotic =~ dsq_displacement 6.223 0.457 0.874 0.231 0.231
## 142 dsq_reaction_formation ~~ dsq_acting_out 6.072 -1.732 -1.732 -0.157 -0.157
## 91 dsq_humor ~~ dsq_sublimation 5.723 -2.968 -2.968 -0.274 -0.274
## 189 dsq_denial ~~ dsq_autistic_fantasy 5.280 1.911 1.911 0.138 0.138
## 168 dsq_undoing ~~ dsq_autistic_fantasy 5.267 2.389 2.389 0.150 0.150
## 207 dsq_somatization ~~ dsq_acting_out 5.055 2.058 2.058 0.137 0.137
## 149 dsq_idealization ~~ dsq_autistic_fantasy 5.054 -2.477 -2.477 -0.136 -0.136
## 208 dsq_somatization ~~ dsq_projection 4.904 -1.821 -1.821 -0.149 -0.149
## 103 dsq_humor ~~ dsq_acting_out 4.820 1.695 1.695 0.139 0.139
## 82 Neurotic =~ dsq_projection 4.765 -0.380 -0.727 -0.202 -0.202
## 128 dsq_sublimation ~~ dsq_passive_aggression 4.569 1.444 1.444 0.150 0.150
## 163 dsq_pseudo_altruism ~~ dsq_acting_out 4.495 1.490 1.490 0.128 0.128
## 173 dsq_undoing ~~ dsq_projection 4.475 -1.463 -1.463 -0.147 -0.147
## 66 Mature =~ dsq_displacement 4.330 0.347 0.646 0.171 0.171
## 100 dsq_humor ~~ dsq_displacement 4.219 1.467 1.467 0.136 0.136
## 94 dsq_humor ~~ dsq_pseudo_altruism 4.200 1.328 1.328 0.138 0.138
## 80 Neurotic =~ dsq_somatization 4.124 0.424 0.812 0.189 0.189
## 60 Mature =~ dsq_pseudo_altruism 4.089 0.300 0.559 0.167 0.167
## 195 dsq_autistic_fantasy ~~ dsq_displacement 3.989 -2.114 -2.114 -0.125 -0.125
## 139 dsq_reaction_formation ~~ dsq_displacement 3.910 1.288 1.288 0.132 0.132model_3f_adj_kn_VIII <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Immature =~ dsq_devaluation
+ dsq_splitting + dsq_denial + dsq_autistic_fantasy + dsq_displacement
+ dsq_passive_aggression + dsq_somatization + dsq_projection
'
fit3_adjkn_VIII <- cfa(model_3f_adj_kn_VIII, data = kn_df, estimator = "mlr", missing = "fiml")
summary(fit3_adjkn_VIII,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 113 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 48
##
## Number of observations 306
## Number of missing patterns 8
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 217.212 206.089
## Degrees of freedom 87 87
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.054
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 586.594 536.796
## Degrees of freedom 105 105
## P-value 0.000 0.000
## Scaling correction factor 1.093
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.730 0.724
## Tucker-Lewis Index (TLI) 0.674 0.667
##
## Robust Comparative Fit Index (CFI) 0.741
## Robust Tucker-Lewis Index (TLI) 0.688
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -12303.494 -12303.494
## Scaling correction factor 1.024
## for the MLR correction
## Loglikelihood unrestricted model (H1) -12194.888 -12194.888
## Scaling correction factor 1.043
## for the MLR correction
##
## Akaike (AIC) 24702.987 24702.987
## Bayesian (BIC) 24881.719 24881.719
## Sample-size adjusted Bayesian (SABIC) 24729.485 24729.485
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.070 0.067
## 90 Percent confidence interval - lower 0.058 0.055
## 90 Percent confidence interval - upper 0.082 0.078
## P-value H_0: RMSEA <= 0.050 0.003 0.009
## P-value H_0: RMSEA >= 0.080 0.080 0.030
##
## Robust RMSEA 0.068
## 90 Percent confidence interval - lower 0.056
## 90 Percent confidence interval - upper 0.080
## P-value H_0: Robust RMSEA <= 0.050 0.010
## P-value H_0: Robust RMSEA >= 0.080 0.056
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.070 0.070
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 1.923 0.522
## dsq_suppressin 0.914 0.477 1.915 0.055 1.757 0.441
## dsq_sublimatin 0.911 0.470 1.940 0.052 1.751 0.454
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.950 0.564
## dsq_idealizatn 0.792 0.225 3.525 0.000 1.544 0.388
## dsq_psed_ltrsm 0.712 0.168 4.236 0.000 1.389 0.415
## dsq_undoing 1.184 0.194 6.097 0.000 2.310 0.587
## Immature =~
## dsq_devaluatin 1.000 1.074 0.361
## dsq_splitting 1.313 0.361 3.633 0.000 1.411 0.398
## dsq_denial 0.748 0.263 2.841 0.005 0.804 0.277
## dsq_tstc_fntsy 1.460 0.404 3.616 0.000 1.569 0.301
## dsq_displacmnt 1.556 0.431 3.615 0.000 1.672 0.442
## dsq_pssv_ggrss 1.994 0.404 4.941 0.000 2.142 0.608
## dsq_somatizatn 1.589 0.538 2.950 0.003 1.707 0.396
## dsq_projection 1.699 0.337 5.042 0.000 1.825 0.506
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 1.132 0.783 1.447 0.148 0.302 0.302
## Immature -0.694 0.504 -1.376 0.169 -0.336 -0.336
## Neurotic ~~
## Immature 1.135 0.277 4.092 0.000 0.542 0.542
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 11.523 0.211 54.732 0.000 11.523 3.129
## .dsq_suppressin 7.426 0.228 32.550 0.000 7.426 1.865
## .dsq_sublimatin 9.549 0.220 43.320 0.000 9.549 2.476
## .dsq_rctn_frmtn 9.107 0.199 45.841 0.000 9.107 2.632
## .dsq_idealizatn 6.304 0.227 27.714 0.000 6.304 1.584
## .dsq_psed_ltrsm 9.634 0.192 50.271 0.000 9.634 2.878
## .dsq_undoing 9.314 0.225 41.370 0.000 9.314 2.365
## .dsq_devaluatin 7.258 0.170 42.612 0.000 7.258 2.436
## .dsq_splitting 7.453 0.203 36.636 0.000 7.453 2.101
## .dsq_denial 5.415 0.166 32.646 0.000 5.415 1.866
## .dsq_tstc_fntsy 11.665 0.299 39.037 0.000 11.665 2.239
## .dsq_displacmnt 8.673 0.216 40.077 0.000 8.673 2.291
## .dsq_pssv_ggrss 7.343 0.202 36.358 0.000 7.343 2.086
## .dsq_somatizatn 10.118 0.246 41.088 0.000 10.118 2.349
## .dsq_projection 8.022 0.206 38.892 0.000 8.022 2.226
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.866 1.842 5.357 0.000 9.866 0.727
## .dsq_suppressin 12.767 1.993 6.404 0.000 12.767 0.805
## .dsq_sublimatin 11.801 1.790 6.593 0.000 11.801 0.794
## .dsq_rctn_frmtn 8.173 0.915 8.933 0.000 8.173 0.682
## .dsq_idealizatn 13.449 1.352 9.945 0.000 13.449 0.849
## .dsq_psed_ltrsm 9.277 0.917 10.119 0.000 9.277 0.828
## .dsq_undoing 10.173 1.316 7.729 0.000 10.173 0.656
## .dsq_devaluatin 7.724 0.710 10.881 0.000 7.724 0.870
## .dsq_splitting 10.596 0.882 12.015 0.000 10.596 0.842
## .dsq_denial 7.774 0.617 12.604 0.000 7.774 0.923
## .dsq_tstc_fntsy 24.693 1.960 12.599 0.000 24.693 0.909
## .dsq_displacmnt 11.535 0.989 11.663 0.000 11.535 0.805
## .dsq_pssv_ggrss 7.807 0.917 8.511 0.000 7.807 0.630
## .dsq_somatizatn 15.642 1.348 11.603 0.000 15.642 0.843
## .dsq_projection 9.662 1.062 9.100 0.000 9.662 0.744
## Mature 3.697 1.909 1.937 0.053 1.000 1.000
## Neurotic 3.804 0.999 3.808 0.000 1.000 1.000
## Immature 1.154 0.449 2.572 0.010 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.273
## dsq_suppressin 0.195
## dsq_sublimatin 0.206
## dsq_rctn_frmtn 0.318
## dsq_idealizatn 0.151
## dsq_psed_ltrsm 0.172
## dsq_undoing 0.344
## dsq_devaluatin 0.130
## dsq_splitting 0.158
## dsq_denial 0.077
## dsq_tstc_fntsy 0.091
## dsq_displacmnt 0.195
## dsq_pssv_ggrss 0.370
## dsq_somatizatn 0.157
## dsq_projection 0.256modindices(fit3_adjkn_VIII, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 138 dsq_idealization ~~ dsq_splitting 21.978 3.445 3.445 0.289 0.289
## 128 dsq_reaction_formation ~~ dsq_splitting 15.303 -2.415 -2.415 -0.259 -0.259
## 58 Mature =~ dsq_undoing 13.108 -0.637 -1.224 -0.311 -0.311
## 146 dsq_pseudo_altruism ~~ dsq_devaluation 10.847 -1.712 -1.712 -0.202 -0.202
## 99 dsq_suppression ~~ dsq_sublimation 10.319 3.639 3.639 0.296 0.296
## 152 dsq_pseudo_altruism ~~ dsq_somatization 9.844 2.338 2.338 0.194 0.194
## 113 dsq_sublimation ~~ dsq_idealization 9.630 2.516 2.516 0.200 0.200
## 62 Mature =~ dsq_autistic_fantasy 9.008 -0.686 -1.320 -0.253 -0.253
## 104 dsq_suppression ~~ dsq_devaluation 8.502 1.825 1.825 0.184 0.184
## 61 Mature =~ dsq_denial 8.357 0.368 0.708 0.244 0.244
## 80 Immature =~ dsq_sublimation 7.702 0.804 0.864 0.224 0.224
## 126 dsq_reaction_formation ~~ dsq_undoing 7.530 2.745 2.745 0.301 0.301
## 164 dsq_devaluation ~~ dsq_autistic_fantasy 7.390 2.294 2.294 0.166 0.166
## 124 dsq_reaction_formation ~~ dsq_idealization 7.349 -2.187 -2.187 -0.209 -0.209
## 103 dsq_suppression ~~ dsq_undoing 7.231 -2.193 -2.193 -0.192 -0.192
## 72 Neurotic =~ dsq_denial 7.056 0.370 0.721 0.249 0.249
## 167 dsq_devaluation ~~ dsq_somatization 6.989 -1.818 -1.818 -0.165 -0.165
## 68 Neurotic =~ dsq_suppression 6.971 -0.432 -0.843 -0.212 -0.212
## 188 dsq_passive_aggression ~~ dsq_projection 6.748 1.837 1.837 0.212 0.212
## 69 Neurotic =~ dsq_sublimation 6.632 0.410 0.800 0.208 0.208
## 84 Immature =~ dsq_undoing 6.558 0.933 1.003 0.255 0.255
## 74 Neurotic =~ dsq_displacement 6.547 0.457 0.891 0.235 0.235
## 86 dsq_humor ~~ dsq_sublimation 6.246 -3.117 -3.117 -0.289 -0.289
## 91 dsq_humor ~~ dsq_devaluation 6.172 -1.421 -1.421 -0.163 -0.163
## 175 dsq_denial ~~ dsq_autistic_fantasy 5.126 1.882 1.882 0.136 0.136
## 157 dsq_undoing ~~ dsq_autistic_fantasy 5.099 2.348 2.348 0.148 0.148
## 161 dsq_undoing ~~ dsq_projection 5.003 -1.546 -1.546 -0.156 -0.156
## 140 dsq_idealization ~~ dsq_autistic_fantasy 4.927 -2.448 -2.448 -0.134 -0.134
## 77 Neurotic =~ dsq_projection 4.915 -0.376 -0.734 -0.204 -0.204
## 173 dsq_splitting ~~ dsq_somatization 4.894 1.810 1.810 0.141 0.141
## 63 Mature =~ dsq_displacement 4.893 0.359 0.691 0.183 0.183
## 121 dsq_sublimation ~~ dsq_passive_aggression 4.839 1.490 1.490 0.155 0.155
## 95 dsq_humor ~~ dsq_displacement 4.636 1.540 1.540 0.144 0.144
## 57 Mature =~ dsq_pseudo_altruism 4.502 0.304 0.585 0.175 0.175
## 76 Neurotic =~ dsq_somatization 4.373 0.427 0.832 0.193 0.193
## 189 dsq_somatization ~~ dsq_projection 4.324 -1.715 -1.715 -0.139 -0.139
## 180 dsq_autistic_fantasy ~~ dsq_displacement 4.059 -2.135 -2.135 -0.127 -0.127
## 89 dsq_humor ~~ dsq_pseudo_altruism 3.974 1.293 1.293 0.135 0.135model_3f_adj_kn_VIV <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Immature =~ dsq_devaluation
+ dsq_splitting + dsq_autistic_fantasy + dsq_displacement
+ dsq_passive_aggression + dsq_somatization + dsq_projection
'
fit3_adjkn_VIV <- cfa(model_3f_adj_kn_VIV, data = kn_df, estimator = "mlr", missing = "fiml")
summary(fit3_adjkn_VIV,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 114 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 45
##
## Number of observations 306
## Number of missing patterns 8
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 195.143 183.570
## Degrees of freedom 74 74
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.063
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 548.554 497.001
## Degrees of freedom 91 91
## P-value 0.000 0.000
## Scaling correction factor 1.104
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.735 0.730
## Tucker-Lewis Index (TLI) 0.674 0.668
##
## Robust Comparative Fit Index (CFI) 0.747
## Robust Tucker-Lewis Index (TLI) 0.689
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -11551.314 -11551.314
## Scaling correction factor 1.022
## for the MLR correction
## Loglikelihood unrestricted model (H1) -11453.743 -11453.743
## Scaling correction factor 1.047
## for the MLR correction
##
## Akaike (AIC) 23192.628 23192.628
## Bayesian (BIC) 23360.189 23360.189
## Sample-size adjusted Bayesian (SABIC) 23217.470 23217.470
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.073 0.070
## 90 Percent confidence interval - lower 0.061 0.057
## 90 Percent confidence interval - upper 0.086 0.082
## P-value H_0: RMSEA <= 0.050 0.001 0.005
## P-value H_0: RMSEA >= 0.080 0.191 0.083
##
## Robust RMSEA 0.071
## 90 Percent confidence interval - lower 0.058
## 90 Percent confidence interval - upper 0.084
## P-value H_0: Robust RMSEA <= 0.050 0.006
## P-value H_0: Robust RMSEA >= 0.080 0.140
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.070 0.070
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 1.985 0.539
## dsq_suppressin 0.858 0.441 1.942 0.052 1.702 0.428
## dsq_sublimatin 0.858 0.438 1.960 0.050 1.703 0.442
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.947 0.563
## dsq_idealizatn 0.792 0.228 3.473 0.001 1.542 0.388
## dsq_psed_ltrsm 0.712 0.170 4.189 0.000 1.387 0.414
## dsq_undoing 1.190 0.200 5.956 0.000 2.317 0.588
## Immature =~
## dsq_devaluatin 1.000 1.089 0.366
## dsq_splitting 1.336 0.359 3.726 0.000 1.455 0.410
## dsq_tstc_fntsy 1.361 0.386 3.529 0.000 1.482 0.284
## dsq_displacmnt 1.489 0.408 3.646 0.000 1.622 0.428
## dsq_pssv_ggrss 1.999 0.398 5.017 0.000 2.177 0.618
## dsq_somatizatn 1.526 0.516 2.956 0.003 1.663 0.386
## dsq_projection 1.719 0.335 5.136 0.000 1.873 0.520
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 1.178 0.776 1.519 0.129 0.305 0.305
## Immature -0.803 0.520 -1.545 0.122 -0.371 -0.371
## Neurotic ~~
## Immature 1.083 0.269 4.020 0.000 0.510 0.510
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 11.523 0.211 54.732 0.000 11.523 3.129
## .dsq_suppressin 7.427 0.228 32.551 0.000 7.427 1.865
## .dsq_sublimatin 9.549 0.220 43.320 0.000 9.549 2.476
## .dsq_rctn_frmtn 9.108 0.199 45.846 0.000 9.108 2.632
## .dsq_idealizatn 6.304 0.227 27.714 0.000 6.304 1.584
## .dsq_psed_ltrsm 9.634 0.192 50.269 0.000 9.634 2.878
## .dsq_undoing 9.314 0.225 41.370 0.000 9.314 2.365
## .dsq_devaluatin 7.258 0.170 42.612 0.000 7.258 2.436
## .dsq_splitting 7.452 0.203 36.643 0.000 7.452 2.100
## .dsq_tstc_fntsy 11.665 0.299 39.035 0.000 11.665 2.238
## .dsq_displacmnt 8.673 0.216 40.077 0.000 8.673 2.291
## .dsq_pssv_ggrss 7.344 0.202 36.360 0.000 7.344 2.086
## .dsq_somatizatn 10.118 0.246 41.088 0.000 10.118 2.349
## .dsq_projection 8.022 0.206 38.894 0.000 8.022 2.226
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.622 1.845 5.214 0.000 9.622 0.709
## .dsq_suppressin 12.954 1.923 6.736 0.000 12.954 0.817
## .dsq_sublimatin 11.970 1.751 6.837 0.000 11.970 0.805
## .dsq_rctn_frmtn 8.185 0.930 8.803 0.000 8.185 0.683
## .dsq_idealizatn 13.453 1.348 9.982 0.000 13.453 0.850
## .dsq_psed_ltrsm 9.282 0.923 10.060 0.000 9.282 0.828
## .dsq_undoing 10.141 1.340 7.569 0.000 10.141 0.654
## .dsq_devaluatin 7.691 0.699 11.004 0.000 7.691 0.866
## .dsq_splitting 10.468 0.870 12.039 0.000 10.468 0.832
## .dsq_tstc_fntsy 24.958 1.932 12.915 0.000 24.958 0.919
## .dsq_displacmnt 11.702 0.984 11.890 0.000 11.702 0.817
## .dsq_pssv_ggrss 7.656 0.908 8.431 0.000 7.656 0.618
## .dsq_somatizatn 15.790 1.338 11.806 0.000 15.790 0.851
## .dsq_projection 9.484 1.078 8.798 0.000 9.484 0.730
## Mature 3.941 1.928 2.044 0.041 1.000 1.000
## Neurotic 3.792 1.012 3.746 0.000 1.000 1.000
## Immature 1.187 0.445 2.668 0.008 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.291
## dsq_suppressin 0.183
## dsq_sublimatin 0.195
## dsq_rctn_frmtn 0.317
## dsq_idealizatn 0.150
## dsq_psed_ltrsm 0.172
## dsq_undoing 0.346
## dsq_devaluatin 0.134
## dsq_splitting 0.168
## dsq_tstc_fntsy 0.081
## dsq_displacmnt 0.183
## dsq_pssv_ggrss 0.382
## dsq_somatizatn 0.149
## dsq_projection 0.270modindices(fit3_adjkn_VIV, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 129 dsq_idealization ~~ dsq_splitting 22.388 3.469 3.469 0.292 0.292
## 120 dsq_reaction_formation ~~ dsq_splitting 14.848 -2.377 -2.377 -0.257 -0.257
## 55 Mature =~ dsq_undoing 13.292 -0.619 -1.228 -0.312 -0.312
## 93 dsq_suppression ~~ dsq_sublimation 11.008 3.545 3.545 0.285 0.285
## 136 dsq_pseudo_altruism ~~ dsq_devaluation 10.375 -1.674 -1.674 -0.198 -0.198
## 141 dsq_pseudo_altruism ~~ dsq_somatization 10.267 2.396 2.396 0.198 0.198
## 106 dsq_sublimation ~~ dsq_idealization 9.813 2.543 2.543 0.200 0.200
## 98 dsq_suppression ~~ dsq_devaluation 8.344 1.811 1.811 0.181 0.181
## 69 Neurotic =~ dsq_displacement 8.217 0.496 0.966 0.255 0.255
## 151 dsq_devaluation ~~ dsq_autistic_fantasy 7.867 2.375 2.375 0.171 0.171
## 58 Mature =~ dsq_autistic_fantasy 7.772 -0.630 -1.250 -0.240 -0.240
## 118 dsq_reaction_formation ~~ dsq_undoing 7.765 2.852 2.852 0.313 0.313
## 75 Immature =~ dsq_sublimation 7.434 0.797 0.868 0.225 0.225
## 116 dsq_reaction_formation ~~ dsq_idealization 7.363 -2.209 -2.209 -0.211 -0.211
## 97 dsq_suppression ~~ dsq_undoing 7.216 -2.192 -2.192 -0.191 -0.191
## 79 Immature =~ dsq_undoing 7.053 0.915 0.997 0.253 0.253
## 154 dsq_devaluation ~~ dsq_somatization 6.764 -1.795 -1.795 -0.163 -0.163
## 81 dsq_humor ~~ dsq_sublimation 6.515 -3.164 -3.164 -0.295 -0.295
## 65 Neurotic =~ dsq_sublimation 6.416 0.403 0.785 0.204 0.204
## 64 Neurotic =~ dsq_suppression 6.317 -0.411 -0.800 -0.201 -0.201
## 59 Mature =~ dsq_displacement 6.297 0.403 0.800 0.211 0.211
## 145 dsq_undoing ~~ dsq_autistic_fantasy 5.674 2.486 2.486 0.156 0.156
## 113 dsq_sublimation ~~ dsq_passive_aggression 5.588 1.610 1.610 0.168 0.168
## 71 Neurotic =~ dsq_somatization 5.559 0.466 0.907 0.211 0.211
## 86 dsq_humor ~~ dsq_devaluation 5.476 -1.337 -1.337 -0.155 -0.155
## 149 dsq_undoing ~~ dsq_projection 5.364 -1.600 -1.600 -0.163 -0.163
## 169 dsq_passive_aggression ~~ dsq_projection 5.177 1.668 1.668 0.196 0.196
## 89 dsq_humor ~~ dsq_displacement 4.973 1.601 1.601 0.151 0.151
## 159 dsq_splitting ~~ dsq_somatization 4.880 1.814 1.814 0.141 0.141
## 54 Mature =~ dsq_pseudo_altruism 4.664 0.299 0.593 0.177 0.177
## 130 dsq_idealization ~~ dsq_autistic_fantasy 4.568 -2.366 -2.366 -0.129 -0.129
## 170 dsq_somatization ~~ dsq_projection 4.462 -1.754 -1.754 -0.143 -0.143
## 100 dsq_suppression ~~ dsq_autistic_fantasy 4.105 -2.263 -2.263 -0.126 -0.126
## 122 dsq_reaction_formation ~~ dsq_displacement 4.015 1.310 1.310 0.134 0.134
## 72 Neurotic =~ dsq_projection 4.005 -0.329 -0.640 -0.178 -0.178model_3f_adj_kn_IX <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Immature =~ dsq_devaluation
+ dsq_splitting + dsq_autistic_fantasy + dsq_displacement
+ dsq_passive_aggression + dsq_somatization + dsq_projection
'
fit3_adjkn_IX <- cfa(model_3f_adj_kn_IX, data = kn_df, estimator = "mlr", missing = "fiml")
summary(fit3_adjkn_IX,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 114 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 45
##
## Number of observations 306
## Number of missing patterns 8
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 195.143 183.570
## Degrees of freedom 74 74
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.063
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 548.554 497.001
## Degrees of freedom 91 91
## P-value 0.000 0.000
## Scaling correction factor 1.104
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.735 0.730
## Tucker-Lewis Index (TLI) 0.674 0.668
##
## Robust Comparative Fit Index (CFI) 0.747
## Robust Tucker-Lewis Index (TLI) 0.689
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -11551.314 -11551.314
## Scaling correction factor 1.022
## for the MLR correction
## Loglikelihood unrestricted model (H1) -11453.743 -11453.743
## Scaling correction factor 1.047
## for the MLR correction
##
## Akaike (AIC) 23192.628 23192.628
## Bayesian (BIC) 23360.189 23360.189
## Sample-size adjusted Bayesian (SABIC) 23217.470 23217.470
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.073 0.070
## 90 Percent confidence interval - lower 0.061 0.057
## 90 Percent confidence interval - upper 0.086 0.082
## P-value H_0: RMSEA <= 0.050 0.001 0.005
## P-value H_0: RMSEA >= 0.080 0.191 0.083
##
## Robust RMSEA 0.071
## 90 Percent confidence interval - lower 0.058
## 90 Percent confidence interval - upper 0.084
## P-value H_0: Robust RMSEA <= 0.050 0.006
## P-value H_0: Robust RMSEA >= 0.080 0.140
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.070 0.070
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 1.985 0.539
## dsq_suppressin 0.858 0.441 1.942 0.052 1.702 0.428
## dsq_sublimatin 0.858 0.438 1.960 0.050 1.703 0.442
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.947 0.563
## dsq_idealizatn 0.792 0.228 3.473 0.001 1.542 0.388
## dsq_psed_ltrsm 0.712 0.170 4.189 0.000 1.387 0.414
## dsq_undoing 1.190 0.200 5.956 0.000 2.317 0.588
## Immature =~
## dsq_devaluatin 1.000 1.089 0.366
## dsq_splitting 1.336 0.359 3.726 0.000 1.455 0.410
## dsq_tstc_fntsy 1.361 0.386 3.529 0.000 1.482 0.284
## dsq_displacmnt 1.489 0.408 3.646 0.000 1.622 0.428
## dsq_pssv_ggrss 1.999 0.398 5.017 0.000 2.177 0.618
## dsq_somatizatn 1.526 0.516 2.956 0.003 1.663 0.386
## dsq_projection 1.719 0.335 5.136 0.000 1.873 0.520
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 1.178 0.776 1.519 0.129 0.305 0.305
## Immature -0.803 0.520 -1.545 0.122 -0.371 -0.371
## Neurotic ~~
## Immature 1.083 0.269 4.020 0.000 0.510 0.510
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 11.523 0.211 54.732 0.000 11.523 3.129
## .dsq_suppressin 7.427 0.228 32.551 0.000 7.427 1.865
## .dsq_sublimatin 9.549 0.220 43.320 0.000 9.549 2.476
## .dsq_rctn_frmtn 9.108 0.199 45.846 0.000 9.108 2.632
## .dsq_idealizatn 6.304 0.227 27.714 0.000 6.304 1.584
## .dsq_psed_ltrsm 9.634 0.192 50.269 0.000 9.634 2.878
## .dsq_undoing 9.314 0.225 41.370 0.000 9.314 2.365
## .dsq_devaluatin 7.258 0.170 42.612 0.000 7.258 2.436
## .dsq_splitting 7.452 0.203 36.643 0.000 7.452 2.100
## .dsq_tstc_fntsy 11.665 0.299 39.035 0.000 11.665 2.238
## .dsq_displacmnt 8.673 0.216 40.077 0.000 8.673 2.291
## .dsq_pssv_ggrss 7.344 0.202 36.360 0.000 7.344 2.086
## .dsq_somatizatn 10.118 0.246 41.088 0.000 10.118 2.349
## .dsq_projection 8.022 0.206 38.894 0.000 8.022 2.226
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.622 1.845 5.214 0.000 9.622 0.709
## .dsq_suppressin 12.954 1.923 6.736 0.000 12.954 0.817
## .dsq_sublimatin 11.970 1.751 6.837 0.000 11.970 0.805
## .dsq_rctn_frmtn 8.185 0.930 8.803 0.000 8.185 0.683
## .dsq_idealizatn 13.453 1.348 9.982 0.000 13.453 0.850
## .dsq_psed_ltrsm 9.282 0.923 10.060 0.000 9.282 0.828
## .dsq_undoing 10.141 1.340 7.569 0.000 10.141 0.654
## .dsq_devaluatin 7.691 0.699 11.004 0.000 7.691 0.866
## .dsq_splitting 10.468 0.870 12.039 0.000 10.468 0.832
## .dsq_tstc_fntsy 24.958 1.932 12.915 0.000 24.958 0.919
## .dsq_displacmnt 11.702 0.984 11.890 0.000 11.702 0.817
## .dsq_pssv_ggrss 7.656 0.908 8.431 0.000 7.656 0.618
## .dsq_somatizatn 15.790 1.338 11.806 0.000 15.790 0.851
## .dsq_projection 9.484 1.078 8.798 0.000 9.484 0.730
## Mature 3.941 1.928 2.044 0.041 1.000 1.000
## Neurotic 3.792 1.012 3.746 0.000 1.000 1.000
## Immature 1.187 0.445 2.668 0.008 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.291
## dsq_suppressin 0.183
## dsq_sublimatin 0.195
## dsq_rctn_frmtn 0.317
## dsq_idealizatn 0.150
## dsq_psed_ltrsm 0.172
## dsq_undoing 0.346
## dsq_devaluatin 0.134
## dsq_splitting 0.168
## dsq_tstc_fntsy 0.081
## dsq_displacmnt 0.183
## dsq_pssv_ggrss 0.382
## dsq_somatizatn 0.149
## dsq_projection 0.270modindices(fit3_adjkn_IX, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 129 dsq_idealization ~~ dsq_splitting 22.388 3.469 3.469 0.292 0.292
## 120 dsq_reaction_formation ~~ dsq_splitting 14.848 -2.377 -2.377 -0.257 -0.257
## 55 Mature =~ dsq_undoing 13.292 -0.619 -1.228 -0.312 -0.312
## 93 dsq_suppression ~~ dsq_sublimation 11.008 3.545 3.545 0.285 0.285
## 136 dsq_pseudo_altruism ~~ dsq_devaluation 10.375 -1.674 -1.674 -0.198 -0.198
## 141 dsq_pseudo_altruism ~~ dsq_somatization 10.267 2.396 2.396 0.198 0.198
## 106 dsq_sublimation ~~ dsq_idealization 9.813 2.543 2.543 0.200 0.200
## 98 dsq_suppression ~~ dsq_devaluation 8.344 1.811 1.811 0.181 0.181
## 69 Neurotic =~ dsq_displacement 8.217 0.496 0.966 0.255 0.255
## 151 dsq_devaluation ~~ dsq_autistic_fantasy 7.867 2.375 2.375 0.171 0.171
## 58 Mature =~ dsq_autistic_fantasy 7.772 -0.630 -1.250 -0.240 -0.240
## 118 dsq_reaction_formation ~~ dsq_undoing 7.765 2.852 2.852 0.313 0.313
## 75 Immature =~ dsq_sublimation 7.434 0.797 0.868 0.225 0.225
## 116 dsq_reaction_formation ~~ dsq_idealization 7.363 -2.209 -2.209 -0.211 -0.211
## 97 dsq_suppression ~~ dsq_undoing 7.216 -2.192 -2.192 -0.191 -0.191
## 79 Immature =~ dsq_undoing 7.053 0.915 0.997 0.253 0.253
## 154 dsq_devaluation ~~ dsq_somatization 6.764 -1.795 -1.795 -0.163 -0.163
## 81 dsq_humor ~~ dsq_sublimation 6.515 -3.164 -3.164 -0.295 -0.295
## 65 Neurotic =~ dsq_sublimation 6.416 0.403 0.785 0.204 0.204
## 64 Neurotic =~ dsq_suppression 6.317 -0.411 -0.800 -0.201 -0.201
## 59 Mature =~ dsq_displacement 6.297 0.403 0.800 0.211 0.211
## 145 dsq_undoing ~~ dsq_autistic_fantasy 5.674 2.486 2.486 0.156 0.156
## 113 dsq_sublimation ~~ dsq_passive_aggression 5.588 1.610 1.610 0.168 0.168
## 71 Neurotic =~ dsq_somatization 5.559 0.466 0.907 0.211 0.211
## 86 dsq_humor ~~ dsq_devaluation 5.476 -1.337 -1.337 -0.155 -0.155
## 149 dsq_undoing ~~ dsq_projection 5.364 -1.600 -1.600 -0.163 -0.163
## 169 dsq_passive_aggression ~~ dsq_projection 5.177 1.668 1.668 0.196 0.196
## 89 dsq_humor ~~ dsq_displacement 4.973 1.601 1.601 0.151 0.151
## 159 dsq_splitting ~~ dsq_somatization 4.880 1.814 1.814 0.141 0.141
## 54 Mature =~ dsq_pseudo_altruism 4.664 0.299 0.593 0.177 0.177
## 130 dsq_idealization ~~ dsq_autistic_fantasy 4.568 -2.366 -2.366 -0.129 -0.129
## 170 dsq_somatization ~~ dsq_projection 4.462 -1.754 -1.754 -0.143 -0.143
## 100 dsq_suppression ~~ dsq_autistic_fantasy 4.105 -2.263 -2.263 -0.126 -0.126
## 122 dsq_reaction_formation ~~ dsq_displacement 4.015 1.310 1.310 0.134 0.134
## 72 Neurotic =~ dsq_projection 4.005 -0.329 -0.640 -0.178 -0.178model_3f_adj_kn_X <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Immature =~ dsq_devaluation
+ dsq_splitting + dsq_displacement
+ dsq_passive_aggression + dsq_somatization + dsq_projection
'
fit3_adjkn_X <- cfa(model_3f_adj_kn_X, data = kn_df, estimator = "mlr", missing = "fiml")
summary(fit3_adjkn_X,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 103 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 42
##
## Number of observations 306
## Number of missing patterns 7
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 166.202 159.161
## Degrees of freedom 62 62
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.044
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 503.165 459.774
## Degrees of freedom 78 78
## P-value 0.000 0.000
## Scaling correction factor 1.094
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.755 0.746
## Tucker-Lewis Index (TLI) 0.692 0.680
##
## Robust Comparative Fit Index (CFI) 0.766
## Robust Tucker-Lewis Index (TLI) 0.705
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10626.295 -10626.295
## Scaling correction factor 1.030
## for the MLR correction
## Loglikelihood unrestricted model (H1) -10543.194 -10543.194
## Scaling correction factor 1.039
## for the MLR correction
##
## Akaike (AIC) 21336.589 21336.589
## Bayesian (BIC) 21492.980 21492.980
## Sample-size adjusted Bayesian (SABIC) 21359.775 21359.775
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.074 0.072
## 90 Percent confidence interval - lower 0.061 0.058
## 90 Percent confidence interval - upper 0.088 0.085
## P-value H_0: RMSEA <= 0.050 0.002 0.005
## P-value H_0: RMSEA >= 0.080 0.249 0.157
##
## Robust RMSEA 0.072
## 90 Percent confidence interval - lower 0.058
## 90 Percent confidence interval - upper 0.087
## P-value H_0: Robust RMSEA <= 0.050 0.007
## P-value H_0: Robust RMSEA >= 0.080 0.194
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.068 0.068
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 1.929 0.524
## dsq_suppressin 0.900 0.521 1.727 0.084 1.736 0.436
## dsq_sublimatin 0.911 0.539 1.691 0.091 1.757 0.456
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.938 0.560
## dsq_idealizatn 0.813 0.240 3.390 0.001 1.576 0.396
## dsq_psed_ltrsm 0.725 0.176 4.107 0.000 1.404 0.420
## dsq_undoing 1.176 0.195 6.028 0.000 2.278 0.579
## Immature =~
## dsq_devaluatin 1.000 1.014 0.340
## dsq_splitting 1.455 0.394 3.691 0.000 1.475 0.416
## dsq_displacmnt 1.667 0.461 3.613 0.000 1.690 0.447
## dsq_pssv_ggrss 2.189 0.438 5.002 0.000 2.219 0.630
## dsq_somatizatn 1.609 0.565 2.850 0.004 1.631 0.379
## dsq_projection 1.889 0.357 5.294 0.000 1.915 0.531
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 1.159 0.806 1.439 0.150 0.310 0.310
## Immature -0.613 0.499 -1.229 0.219 -0.313 -0.313
## Neurotic ~~
## Immature 1.005 0.257 3.903 0.000 0.511 0.511
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 11.523 0.211 54.732 0.000 11.523 3.129
## .dsq_suppressin 7.427 0.228 32.549 0.000 7.427 1.865
## .dsq_sublimatin 9.549 0.220 43.320 0.000 9.549 2.476
## .dsq_rctn_frmtn 9.108 0.199 45.842 0.000 9.108 2.632
## .dsq_idealizatn 6.304 0.227 27.714 0.000 6.304 1.584
## .dsq_psed_ltrsm 9.634 0.192 50.268 0.000 9.634 2.878
## .dsq_undoing 9.314 0.225 41.370 0.000 9.314 2.365
## .dsq_devaluatin 7.258 0.170 42.612 0.000 7.258 2.436
## .dsq_splitting 7.450 0.203 36.638 0.000 7.450 2.100
## .dsq_displacmnt 8.673 0.216 40.077 0.000 8.673 2.291
## .dsq_pssv_ggrss 7.347 0.202 36.355 0.000 7.347 2.086
## .dsq_somatizatn 10.118 0.246 41.088 0.000 10.118 2.349
## .dsq_projection 8.022 0.206 38.893 0.000 8.022 2.226
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 9.841 2.093 4.701 0.000 9.841 0.726
## .dsq_suppressin 12.838 2.071 6.199 0.000 12.838 0.810
## .dsq_sublimatin 11.781 2.016 5.843 0.000 11.781 0.792
## .dsq_rctn_frmtn 8.220 0.952 8.634 0.000 8.220 0.686
## .dsq_idealizatn 13.348 1.350 9.890 0.000 13.348 0.843
## .dsq_psed_ltrsm 9.234 0.928 9.955 0.000 9.234 0.824
## .dsq_undoing 10.319 1.363 7.569 0.000 10.319 0.665
## .dsq_devaluatin 7.850 0.690 11.383 0.000 7.850 0.884
## .dsq_splitting 10.412 0.867 12.015 0.000 10.412 0.827
## .dsq_displacmnt 11.474 0.982 11.690 0.000 11.474 0.801
## .dsq_pssv_ggrss 7.476 0.938 7.973 0.000 7.476 0.603
## .dsq_somatizatn 15.894 1.325 11.997 0.000 15.894 0.857
## .dsq_projection 9.324 1.104 8.448 0.000 9.324 0.718
## Mature 3.722 2.169 1.716 0.086 1.000 1.000
## Neurotic 3.756 1.034 3.634 0.000 1.000 1.000
## Immature 1.028 0.409 2.514 0.012 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.274
## dsq_suppressin 0.190
## dsq_sublimatin 0.208
## dsq_rctn_frmtn 0.314
## dsq_idealizatn 0.157
## dsq_psed_ltrsm 0.176
## dsq_undoing 0.335
## dsq_devaluatin 0.116
## dsq_splitting 0.173
## dsq_displacmnt 0.199
## dsq_pssv_ggrss 0.397
## dsq_somatizatn 0.143
## dsq_projection 0.282modindices(fit3_adjkn_X, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 120 dsq_idealization ~~ dsq_splitting 21.690 3.408 3.408 0.289 0.289
## 112 dsq_reaction_formation ~~ dsq_splitting 15.018 -2.393 -2.393 -0.259 -0.259
## 52 Mature =~ dsq_undoing 12.710 -0.633 -1.221 -0.310 -0.310
## 87 dsq_suppression ~~ dsq_sublimation 11.133 3.871 3.871 0.315 0.315
## 126 dsq_pseudo_altruism ~~ dsq_devaluation 10.120 -1.662 -1.662 -0.195 -0.195
## 130 dsq_pseudo_altruism ~~ dsq_somatization 9.881 2.355 2.355 0.194 0.194
## 99 dsq_sublimation ~~ dsq_idealization 9.660 2.520 2.520 0.201 0.201
## 110 dsq_reaction_formation ~~ dsq_undoing 9.201 3.066 3.066 0.333 0.333
## 108 dsq_reaction_formation ~~ dsq_idealization 8.240 -2.359 -2.359 -0.225 -0.225
## 70 Immature =~ dsq_sublimation 8.080 0.872 0.885 0.229 0.229
## 64 Neurotic =~ dsq_displacement 7.446 0.481 0.932 0.246 0.246
## 91 dsq_suppression ~~ dsq_undoing 7.396 -2.222 -2.222 -0.193 -0.193
## 76 dsq_humor ~~ dsq_sublimation 7.338 -3.542 -3.542 -0.329 -0.329
## 92 dsq_suppression ~~ dsq_devaluation 6.987 1.665 1.665 0.166 0.166
## 60 Neurotic =~ dsq_suppression 6.871 -0.439 -0.850 -0.214 -0.214
## 61 Neurotic =~ dsq_sublimation 6.803 0.427 0.827 0.215 0.215
## 81 dsq_humor ~~ dsq_devaluation 6.577 -1.475 -1.475 -0.168 -0.168
## 66 Neurotic =~ dsq_somatization 5.673 0.480 0.930 0.216 0.216
## 67 Neurotic =~ dsq_projection 5.313 -0.388 -0.751 -0.208 -0.208
## 141 dsq_devaluation ~~ dsq_somatization 5.197 -1.584 -1.584 -0.142 -0.142
## 74 Immature =~ dsq_undoing 5.078 0.846 0.858 0.218 0.218
## 145 dsq_splitting ~~ dsq_somatization 5.022 1.849 1.849 0.144 0.144
## 55 Mature =~ dsq_displacement 4.751 0.352 0.680 0.180 0.180
## 83 dsq_humor ~~ dsq_displacement 4.741 1.560 1.560 0.147 0.147
## 152 dsq_somatization ~~ dsq_projection 4.725 -1.822 -1.822 -0.150 -0.150
## 137 dsq_undoing ~~ dsq_projection 4.572 -1.481 -1.481 -0.151 -0.151
## 105 dsq_sublimation ~~ dsq_passive_aggression 4.503 1.442 1.442 0.154 0.154
## 51 Mature =~ dsq_pseudo_altruism 4.421 0.305 0.589 0.176 0.176
## 113 dsq_reaction_formation ~~ dsq_displacement 4.046 1.313 1.313 0.135 0.135
## 79 dsq_humor ~~ dsq_pseudo_altruism 3.944 1.289 1.289 0.135 0.135model_3f_adj_kn_XI <- '
Mature =~ dsq_humor + dsq_suppression + dsq_sublimation
Neurotic =~ dsq_reaction_formation + dsq_idealization + dsq_pseudo_altruism + dsq_undoing
Immature =~ dsq_splitting + dsq_displacement
+ dsq_passive_aggression + dsq_somatization + dsq_projection
'
fit3_adjkn_XI <- cfa(model_3f_adj_kn_XI, data = kn_df, estimator = "mlr", missing = "fiml")
summary(fit3_adjkn_XI,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 102 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 39
##
## Number of observations 306
## Number of missing patterns 7
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 136.275 130.527
## Degrees of freedom 51 51
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.044
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 449.892 408.535
## Degrees of freedom 66 66
## P-value 0.000 0.000
## Scaling correction factor 1.101
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.778 0.768
## Tucker-Lewis Index (TLI) 0.713 0.700
##
## Robust Comparative Fit Index (CFI) 0.787
## Robust Tucker-Lewis Index (TLI) 0.725
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9869.689 -9869.689
## Scaling correction factor 1.032
## for the MLR correction
## Loglikelihood unrestricted model (H1) -9801.552 -9801.552
## Scaling correction factor 1.039
## for the MLR correction
##
## Akaike (AIC) 19817.378 19817.378
## Bayesian (BIC) 19962.598 19962.598
## Sample-size adjusted Bayesian (SABIC) 19838.908 19838.908
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.074 0.071
## 90 Percent confidence interval - lower 0.059 0.057
## 90 Percent confidence interval - upper 0.089 0.086
## P-value H_0: RMSEA <= 0.050 0.005 0.010
## P-value H_0: RMSEA >= 0.080 0.265 0.178
##
## Robust RMSEA 0.072
## 90 Percent confidence interval - lower 0.056
## 90 Percent confidence interval - upper 0.088
## P-value H_0: Robust RMSEA <= 0.050 0.013
## P-value H_0: Robust RMSEA >= 0.080 0.216
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.065 0.065
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature =~
## dsq_humor 1.000 1.799 0.488
## dsq_suppressin 1.030 0.495 2.083 0.037 1.853 0.465
## dsq_sublimatin 1.027 0.504 2.038 0.042 1.847 0.479
## Neurotic =~
## dsq_rctn_frmtn 1.000 1.877 0.542
## dsq_idealizatn 0.875 0.262 3.335 0.001 1.642 0.413
## dsq_psed_ltrsm 0.778 0.194 4.022 0.000 1.461 0.436
## dsq_undoing 1.183 0.190 6.209 0.000 2.220 0.564
## Immature =~
## dsq_splitting 1.000 1.477 0.416
## dsq_displacmnt 1.201 0.282 4.258 0.000 1.773 0.468
## dsq_pssv_ggrss 1.441 0.314 4.595 0.000 2.128 0.604
## dsq_somatizatn 1.219 0.276 4.422 0.000 1.801 0.418
## dsq_projection 1.214 0.323 3.754 0.000 1.793 0.498
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mature ~~
## Neurotic 1.046 0.723 1.446 0.148 0.310 0.310
## Immature -0.741 0.559 -1.326 0.185 -0.279 -0.279
## Neurotic ~~
## Immature 1.551 0.397 3.902 0.000 0.559 0.559
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 11.523 0.211 54.732 0.000 11.523 3.129
## .dsq_suppressin 7.426 0.228 32.551 0.000 7.426 1.865
## .dsq_sublimatin 9.549 0.220 43.320 0.000 9.549 2.476
## .dsq_rctn_frmtn 9.108 0.199 45.839 0.000 9.108 2.632
## .dsq_idealizatn 6.304 0.227 27.714 0.000 6.304 1.584
## .dsq_psed_ltrsm 9.634 0.192 50.267 0.000 9.634 2.878
## .dsq_undoing 9.314 0.225 41.370 0.000 9.314 2.365
## .dsq_splitting 7.451 0.203 36.640 0.000 7.451 2.100
## .dsq_displacmnt 8.673 0.216 40.077 0.000 8.673 2.291
## .dsq_pssv_ggrss 7.349 0.202 36.351 0.000 7.349 2.086
## .dsq_somatizatn 10.118 0.246 41.088 0.000 10.118 2.349
## .dsq_projection 8.022 0.206 38.894 0.000 8.022 2.226
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_humor 10.327 1.683 6.137 0.000 10.327 0.761
## .dsq_suppressin 12.420 1.951 6.367 0.000 12.420 0.783
## .dsq_sublimatin 11.457 1.788 6.408 0.000 11.457 0.771
## .dsq_rctn_frmtn 8.451 0.982 8.602 0.000 8.451 0.706
## .dsq_idealizatn 13.135 1.353 9.709 0.000 13.135 0.830
## .dsq_psed_ltrsm 9.071 0.928 9.774 0.000 9.071 0.810
## .dsq_undoing 10.580 1.413 7.487 0.000 10.580 0.682
## .dsq_splitting 10.409 0.913 11.405 0.000 10.409 0.827
## .dsq_displacmnt 11.186 1.001 11.179 0.000 11.186 0.781
## .dsq_pssv_ggrss 7.877 1.035 7.609 0.000 7.877 0.635
## .dsq_somatizatn 15.312 1.383 11.075 0.000 15.312 0.825
## .dsq_projection 9.777 1.141 8.566 0.000 9.777 0.752
## Mature 3.236 1.718 1.884 0.060 1.000 1.000
## Neurotic 3.524 1.042 3.381 0.001 1.000 1.000
## Immature 2.181 0.809 2.695 0.007 1.000 1.000
##
## R-Square:
## Estimate
## dsq_humor 0.239
## dsq_suppressin 0.217
## dsq_sublimatin 0.229
## dsq_rctn_frmtn 0.294
## dsq_idealizatn 0.170
## dsq_psed_ltrsm 0.190
## dsq_undoing 0.318
## dsq_splitting 0.173
## dsq_displacmnt 0.219
## dsq_pssv_ggrss 0.365
## dsq_somatizatn 0.175
## dsq_projection 0.248modindices(fit3_adjkn_XI, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 110 dsq_idealization ~~ dsq_splitting 20.185 3.294 3.294 0.282 0.282
## 103 dsq_reaction_formation ~~ dsq_splitting 15.640 -2.471 -2.471 -0.263 -0.263
## 102 dsq_reaction_formation ~~ dsq_undoing 12.056 3.272 3.272 0.346 0.346
## 49 Mature =~ dsq_undoing 11.697 -0.653 -1.174 -0.298 -0.298
## 134 dsq_passive_aggression ~~ dsq_projection 9.400 2.373 2.373 0.270 0.270
## 92 dsq_sublimation ~~ dsq_idealization 9.267 2.461 2.461 0.201 0.201
## 65 Immature =~ dsq_sublimation 9.095 0.635 0.938 0.243 0.243
## 56 Neurotic =~ dsq_suppression 9.082 -0.529 -0.992 -0.249 -0.249
## 81 dsq_suppression ~~ dsq_sublimation 8.833 3.909 3.909 0.328 0.328
## 100 dsq_reaction_formation ~~ dsq_idealization 8.303 -2.348 -2.348 -0.223 -0.223
## 119 dsq_pseudo_altruism ~~ dsq_somatization 8.226 2.134 2.134 0.181 0.181
## 57 Neurotic =~ dsq_sublimation 7.734 0.476 0.894 0.232 0.232
## 85 dsq_suppression ~~ dsq_undoing 7.208 -2.198 -2.198 -0.192 -0.192
## 62 Neurotic =~ dsq_projection 6.117 -0.466 -0.874 -0.242 -0.242
## 71 dsq_humor ~~ dsq_sublimation 5.705 -3.075 -3.075 -0.283 -0.283
## 135 dsq_somatization ~~ dsq_projection 5.678 -2.041 -2.041 -0.167 -0.167
## 59 Neurotic =~ dsq_displacement 5.399 0.457 0.858 0.227 0.227
## 74 dsq_humor ~~ dsq_pseudo_altruism 4.379 1.357 1.357 0.140 0.140
## 64 Immature =~ dsq_suppression 4.109 -0.437 -0.646 -0.162 -0.162
## 77 dsq_humor ~~ dsq_displacement 4.083 1.452 1.452 0.135 0.135
## 69 Immature =~ dsq_undoing 4.068 0.559 0.826 0.210 0.210
## 104 dsq_reaction_formation ~~ dsq_displacement 3.972 1.314 1.314 0.135 0.135
## 97 dsq_sublimation ~~ dsq_passive_aggression 3.920 1.366 1.366 0.144 0.144
## 61 Neurotic =~ dsq_somatization 3.858 0.439 0.824 0.191 0.191# Specify CFA models
model_2f <- '
PA1 =~ dsq_autistic_fantasy + dsq_somatization + dsq_passive_aggression + dsq_displacement
+ dsq_acting_out + dsq_projection + dsq_undoing + dsq_devaluation + dsq_splitting + dsq_denial
+ dsq_dissociation + dsq_reaction_formation
PA2 =~ dsq_suppression + dsq_humor + dsq_rationalization + dsq_anticipation + dsq_sublimation
+ dsq_pseudo_altruism + dsq_idealization + dsq_isolation
'
# Specify CFA models for Knekt Study (no Rationalization)
model_2f_norat <- '
PA1 =~ dsq_autistic_fantasy + dsq_somatization + dsq_passive_aggression + dsq_displacement
+ dsq_acting_out + dsq_projection + dsq_undoing + dsq_devaluation + dsq_splitting + dsq_denial
+ dsq_dissociation + dsq_reaction_formation
PA2 =~ dsq_suppression + dsq_humor + dsq_anticipation + dsq_sublimation
+ dsq_pseudo_altruism + dsq_idealization + dsq_isolation
'# Fit models
fit2_comb <- cfa(model_2f, data = DSQ_Data_mg, group = "StudyID", estimator = "mlr", missing = "fiml")
# Inspect 2-factor
summary(fit2_comb,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 165 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 122
##
## Number of observations per group:
## Dekker_2008/Van_2009 151
## Dos_Santos_2020 210
## Number of missing patterns per group:
## Dekker_2008/Van_2009 18
## Dos_Santos_2020 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 699.307 642.840
## Degrees of freedom 338 338
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.088
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## Dekker_2008/Van_2009 312.288 312.288
## Dos_Santos_2020 330.552 330.552
##
## Model Test Baseline Model:
##
## Test statistic 1390.926 1256.752
## Degrees of freedom 380 380
## P-value 0.000 0.000
## Scaling correction factor 1.107
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.643 0.652
## Tucker-Lewis Index (TLI) 0.598 0.609
##
## Robust Comparative Fit Index (CFI) 0.660
## Robust Tucker-Lewis Index (TLI) 0.618
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -18649.159 -18649.159
## Scaling correction factor 1.011
## for the MLR correction
## Loglikelihood unrestricted model (H1) -18299.505 -18299.505
## Scaling correction factor 1.068
## for the MLR correction
##
## Akaike (AIC) 37542.317 37542.317
## Bayesian (BIC) 38016.760 38016.760
## Sample-size adjusted Bayesian (SABIC) 37629.712 37629.712
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.077 0.071
## 90 Percent confidence interval - lower 0.069 0.063
## 90 Percent confidence interval - upper 0.085 0.079
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.273 0.026
##
## Robust RMSEA 0.076
## 90 Percent confidence interval - lower 0.067
## 90 Percent confidence interval - upper 0.085
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.240
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.099 0.099
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
##
## Group 1 [Dekker_2008/Van_2009]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PA1 =~
## dsq_tstc_fntsy 1.000 1.775 0.416
## dsq_somatizatn 0.707 0.214 3.303 0.001 1.255 0.355
## dsq_pssv_ggrss 0.982 0.315 3.120 0.002 1.743 0.545
## dsq_displacmnt 0.688 0.302 2.280 0.023 1.222 0.298
## dsq_acting_out 0.955 0.361 2.646 0.008 1.695 0.402
## dsq_projection 1.021 0.281 3.630 0.000 1.812 0.479
## dsq_undoing 0.803 0.241 3.333 0.001 1.426 0.359
## dsq_devaluatin 1.202 0.294 4.083 0.000 2.133 0.669
## dsq_splitting 1.307 0.349 3.746 0.000 2.319 0.609
## dsq_denial 0.624 0.247 2.522 0.012 1.107 0.338
## dsq_dissociatn 0.879 0.258 3.401 0.001 1.560 0.423
## dsq_rctn_frmtn 0.336 0.218 1.542 0.123 0.597 0.158
## PA2 =~
## dsq_suppressin 1.000 1.715 0.441
## dsq_humor 1.529 0.475 3.218 0.001 2.622 0.624
## dsq_rationlztn 0.870 0.254 3.424 0.001 1.492 0.502
## dsq_anticipatn 1.101 0.340 3.234 0.001 1.888 0.619
## dsq_sublimatin 1.275 0.311 4.097 0.000 2.187 0.592
## dsq_psed_ltrsm 0.847 0.269 3.155 0.002 1.453 0.479
## dsq_idealizatn 0.736 0.266 2.767 0.006 1.261 0.329
## dsq_isolation 0.212 0.293 0.722 0.470 0.363 0.080
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PA1 ~~
## PA2 0.312 0.482 0.648 0.517 0.103 0.103
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_tstc_fntsy 9.190 0.352 26.088 0.000 9.190 2.153
## .dsq_somatizatn 11.442 0.289 39.623 0.000 11.442 3.236
## .dsq_pssv_ggrss 7.748 0.268 28.865 0.000 7.748 2.421
## .dsq_displacmnt 8.151 0.342 23.839 0.000 8.151 1.992
## .dsq_acting_out 9.436 0.347 27.182 0.000 9.436 2.239
## .dsq_projection 9.351 0.313 29.911 0.000 9.351 2.470
## .dsq_undoing 11.142 0.332 33.523 0.000 11.142 2.805
## .dsq_devaluatin 9.764 0.262 37.301 0.000 9.764 3.064
## .dsq_splitting 9.717 0.311 31.276 0.000 9.717 2.552
## .dsq_denial 6.887 0.269 25.571 0.000 6.887 2.101
## .dsq_dissociatn 9.586 0.303 31.623 0.000 9.586 2.599
## .dsq_rctn_frmtn 9.899 0.310 31.946 0.000 9.899 2.629
## .dsq_suppressin 8.564 0.320 26.759 0.000 8.564 2.204
## .dsq_humor 9.300 0.346 26.880 0.000 9.300 2.212
## .dsq_rationlztn 8.846 0.245 36.114 0.000 8.846 2.976
## .dsq_anticipatn 10.127 0.253 40.092 0.000 10.127 3.321
## .dsq_sublimatin 10.030 0.308 32.593 0.000 10.030 2.713
## .dsq_psed_ltrsm 12.697 0.254 50.044 0.000 12.697 4.187
## .dsq_idealizatn 7.960 0.314 25.323 0.000 7.960 2.078
## .dsq_isolation 8.592 0.378 22.718 0.000 8.592 1.887
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_tstc_fntsy 15.072 1.792 8.410 0.000 15.072 0.827
## .dsq_somatizatn 10.925 1.094 9.987 0.000 10.925 0.874
## .dsq_pssv_ggrss 7.204 0.987 7.299 0.000 7.204 0.703
## .dsq_displacmnt 15.259 1.668 9.147 0.000 15.259 0.911
## .dsq_acting_out 14.882 1.789 8.317 0.000 14.882 0.838
## .dsq_projection 11.054 1.281 8.626 0.000 11.054 0.771
## .dsq_undoing 13.745 1.408 9.761 0.000 13.745 0.871
## .dsq_devaluatin 5.605 0.882 6.352 0.000 5.605 0.552
## .dsq_splitting 9.123 1.635 5.579 0.000 9.123 0.629
## .dsq_denial 9.523 1.016 9.372 0.000 9.523 0.886
## .dsq_dissociatn 11.168 1.504 7.425 0.000 11.168 0.821
## .dsq_rctn_frmtn 13.822 1.525 9.065 0.000 13.822 0.975
## .dsq_suppressin 12.157 1.780 6.831 0.000 12.157 0.805
## .dsq_humor 10.804 1.871 5.774 0.000 10.804 0.611
## .dsq_rationlztn 6.613 0.893 7.408 0.000 6.613 0.748
## .dsq_anticipatn 5.734 1.005 5.705 0.000 5.734 0.617
## .dsq_sublimatin 8.888 1.652 5.380 0.000 8.888 0.650
## .dsq_psed_ltrsm 7.084 0.990 7.151 0.000 7.084 0.770
## .dsq_idealizatn 13.086 1.392 9.402 0.000 13.086 0.892
## .dsq_isolation 20.606 1.767 11.661 0.000 20.606 0.994
## PA1 3.150 1.469 2.144 0.032 1.000 1.000
## PA2 2.940 1.366 2.153 0.031 1.000 1.000
##
## R-Square:
## Estimate
## dsq_tstc_fntsy 0.173
## dsq_somatizatn 0.126
## dsq_pssv_ggrss 0.297
## dsq_displacmnt 0.089
## dsq_acting_out 0.162
## dsq_projection 0.229
## dsq_undoing 0.129
## dsq_devaluatin 0.448
## dsq_splitting 0.371
## dsq_denial 0.114
## dsq_dissociatn 0.179
## dsq_rctn_frmtn 0.025
## dsq_suppressin 0.195
## dsq_humor 0.389
## dsq_rationlztn 0.252
## dsq_anticipatn 0.383
## dsq_sublimatin 0.350
## dsq_psed_ltrsm 0.230
## dsq_idealizatn 0.108
## dsq_isolation 0.006
##
##
## Group 2 [Dos_Santos_2020]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PA1 =~
## dsq_tstc_fntsy 1.000 3.299 0.622
## dsq_somatizatn 0.510 0.156 3.274 0.001 1.684 0.353
## dsq_pssv_ggrss 0.772 0.116 6.638 0.000 2.548 0.654
## dsq_displacmnt 0.496 0.104 4.763 0.000 1.635 0.399
## dsq_acting_out 0.808 0.132 6.143 0.000 2.665 0.545
## dsq_projection 0.717 0.112 6.384 0.000 2.367 0.592
## dsq_undoing 0.359 0.129 2.784 0.005 1.185 0.290
## dsq_devaluatin 0.466 0.098 4.778 0.000 1.537 0.475
## dsq_splitting 0.412 0.108 3.818 0.000 1.358 0.330
## dsq_denial 0.227 0.099 2.298 0.022 0.748 0.223
## dsq_dissociatn 0.052 0.105 0.493 0.622 0.170 0.054
## dsq_rctn_frmtn -0.167 0.133 -1.260 0.208 -0.551 -0.137
## PA2 =~
## dsq_suppressin 1.000 1.665 0.411
## dsq_humor 1.396 0.424 3.290 0.001 2.325 0.517
## dsq_rationlztn 1.728 0.501 3.452 0.001 2.879 0.705
## dsq_anticipatn 1.290 0.319 4.037 0.000 2.148 0.505
## dsq_sublimatin 0.956 0.310 3.083 0.002 1.593 0.400
## dsq_psed_ltrsm 0.477 0.281 1.700 0.089 0.794 0.214
## dsq_idealizatn 0.905 0.289 3.128 0.002 1.506 0.371
## dsq_isolation -0.140 0.346 -0.405 0.685 -0.234 -0.048
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PA1 ~~
## PA2 -0.188 0.913 -0.205 0.837 -0.034 -0.034
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_tstc_fntsy 10.323 0.376 27.428 0.000 10.323 1.946
## .dsq_somatizatn 11.558 0.387 29.860 0.000 11.558 2.423
## .dsq_pssv_ggrss 8.766 0.277 31.687 0.000 8.766 2.250
## .dsq_displacmnt 10.040 0.291 34.549 0.000 10.040 2.451
## .dsq_acting_out 10.731 0.348 30.880 0.000 10.731 2.193
## .dsq_projection 8.163 0.283 28.859 0.000 8.163 2.041
## .dsq_undoing 9.460 0.290 32.579 0.000 9.460 2.313
## .dsq_devaluatin 7.186 0.230 31.213 0.000 7.186 2.219
## .dsq_splitting 10.012 0.292 34.289 0.000 10.012 2.434
## .dsq_denial 5.391 0.239 22.520 0.000 5.391 1.605
## .dsq_dissociatn 5.259 0.222 23.657 0.000 5.259 1.682
## .dsq_rctn_frmtn 8.950 0.286 31.341 0.000 8.950 2.223
## .dsq_suppressin 6.762 0.288 23.482 0.000 6.762 1.669
## .dsq_humor 8.247 0.319 25.827 0.000 8.247 1.832
## .dsq_rationlztn 8.439 0.290 29.132 0.000 8.439 2.066
## .dsq_anticipatn 11.812 0.302 39.108 0.000 11.812 2.779
## .dsq_sublimatin 9.803 0.283 34.670 0.000 9.803 2.463
## .dsq_psed_ltrsm 11.030 0.264 41.771 0.000 11.030 2.967
## .dsq_idealizatn 8.095 0.288 28.071 0.000 8.095 1.995
## .dsq_isolation 9.833 0.349 28.208 0.000 9.833 2.004
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_tstc_fntsy 17.270 2.301 7.506 0.000 17.270 0.613
## .dsq_somatizatn 19.921 2.251 8.850 0.000 19.921 0.875
## .dsq_pssv_ggrss 8.690 1.289 6.743 0.000 8.690 0.572
## .dsq_displacmnt 14.114 1.296 10.887 0.000 14.114 0.841
## .dsq_acting_out 16.832 1.807 9.313 0.000 16.832 0.703
## .dsq_projection 10.387 1.381 7.523 0.000 10.387 0.650
## .dsq_undoing 15.330 1.584 9.678 0.000 15.330 0.916
## .dsq_devaluatin 8.127 0.870 9.343 0.000 8.127 0.775
## .dsq_splitting 15.079 1.606 9.388 0.000 15.079 0.891
## .dsq_denial 10.718 0.981 10.923 0.000 10.718 0.950
## .dsq_dissociatn 9.748 0.946 10.306 0.000 9.748 0.997
## .dsq_rctn_frmtn 15.902 1.432 11.106 0.000 15.902 0.981
## .dsq_suppressin 13.650 2.022 6.749 0.000 13.650 0.831
## .dsq_humor 14.853 1.950 7.616 0.000 14.853 0.733
## .dsq_rationlztn 8.395 1.831 4.586 0.000 8.395 0.503
## .dsq_anticipatn 13.458 1.665 8.080 0.000 13.458 0.745
## .dsq_sublimatin 13.299 1.555 8.552 0.000 13.299 0.840
## .dsq_psed_ltrsm 13.186 1.315 10.031 0.000 13.186 0.954
## .dsq_idealizatn 14.201 1.770 8.022 0.000 14.201 0.862
## .dsq_isolation 24.014 1.747 13.749 0.000 24.014 0.998
## PA1 10.883 2.354 4.623 0.000 1.000 1.000
## PA2 2.774 1.310 2.118 0.034 1.000 1.000
##
## R-Square:
## Estimate
## dsq_tstc_fntsy 0.387
## dsq_somatizatn 0.125
## dsq_pssv_ggrss 0.428
## dsq_displacmnt 0.159
## dsq_acting_out 0.297
## dsq_projection 0.350
## dsq_undoing 0.084
## dsq_devaluatin 0.225
## dsq_splitting 0.109
## dsq_denial 0.050
## dsq_dissociatn 0.003
## dsq_rctn_frmtn 0.019
## dsq_suppressin 0.169
## dsq_humor 0.267
## dsq_rationlztn 0.497
## dsq_anticipatn 0.255
## dsq_sublimatin 0.160
## dsq_psed_ltrsm 0.046
## dsq_idealizatn 0.138
## dsq_isolation 0.002# Likelihood Ratio Tests (Nested)
anova(fit1_comb, fit2_comb)## Warning: lavaan->lav_test_diff_SatorraBentler2001():
## scaling factor is negative##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit2_comb 338 37542 38017 699.31
## fit1_comb 340 37774 38241 935.28 2anova(fit1_comb, fit3_comb)## Warning: lavaan->lav_test_diff_SatorraBentler2001():
## scaling factor is negative##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit3_comb 334 37575 38065 724.23
## fit1_comb 340 37774 38241 935.28 6anova(fit1_comb, fit4_comb)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit4_comb 328 37471 37984 608.10
## fit1_comb 340 37774 38241 935.28 732.49 12 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# BIC (Non-Nested)
fitMeasures(fit2_comb, "bic")## bic
## 38016.76fitMeasures(fit3_comb, "bic")## bic
## 38065.24fitMeasures(fit4_comb, "bic")## bic
## 37984.45fit2_dekker <- cfa(model_2f, data = dekker_df, estimator = "mlr", missing = "fiml")
# Inspect 2-factor
summary(fit2_dekker,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 80 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 61
##
## Number of observations 151
## Number of missing patterns 18
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 339.719 321.488
## Degrees of freedom 169 169
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.057
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 666.331 620.879
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.073
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.642 0.646
## Tucker-Lewis Index (TLI) 0.597 0.602
##
## Robust Comparative Fit Index (CFI) 0.649
## Robust Tucker-Lewis Index (TLI) 0.605
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7786.126 -7786.126
## Scaling correction factor 0.992
## for the MLR correction
## Loglikelihood unrestricted model (H1) -7616.266 -7616.266
## Scaling correction factor 1.040
## for the MLR correction
##
## Akaike (AIC) 15694.252 15694.252
## Bayesian (BIC) 15878.306 15878.306
## Sample-size adjusted Bayesian (SABIC) 15685.248 15685.248
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.082 0.077
## 90 Percent confidence interval - lower 0.069 0.065
## 90 Percent confidence interval - upper 0.094 0.090
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.603 0.371
##
## Robust RMSEA 0.082
## 90 Percent confidence interval - lower 0.069
## 90 Percent confidence interval - upper 0.096
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.618
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.095 0.095
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PA1 =~
## dsq_tstc_fntsy 1.000 1.775 0.416
## dsq_somatizatn 0.707 0.214 3.304 0.001 1.255 0.355
## dsq_pssv_ggrss 0.982 0.315 3.120 0.002 1.743 0.545
## dsq_displacmnt 0.688 0.302 2.280 0.023 1.222 0.298
## dsq_acting_out 0.955 0.361 2.646 0.008 1.695 0.402
## dsq_projection 1.021 0.281 3.630 0.000 1.812 0.479
## dsq_undoing 0.803 0.241 3.333 0.001 1.426 0.359
## dsq_devaluatin 1.202 0.294 4.084 0.000 2.133 0.669
## dsq_splitting 1.307 0.349 3.746 0.000 2.319 0.609
## dsq_denial 0.624 0.247 2.522 0.012 1.107 0.338
## dsq_dissociatn 0.879 0.258 3.401 0.001 1.560 0.423
## dsq_rctn_frmtn 0.336 0.218 1.542 0.123 0.597 0.158
## PA2 =~
## dsq_suppressin 1.000 1.715 0.441
## dsq_humor 1.529 0.475 3.218 0.001 2.622 0.624
## dsq_rationlztn 0.870 0.254 3.424 0.001 1.492 0.502
## dsq_anticipatn 1.101 0.340 3.234 0.001 1.888 0.619
## dsq_sublimatin 1.275 0.311 4.097 0.000 2.187 0.592
## dsq_psed_ltrsm 0.847 0.269 3.155 0.002 1.453 0.479
## dsq_idealizatn 0.736 0.266 2.767 0.006 1.261 0.329
## dsq_isolation 0.212 0.293 0.722 0.470 0.363 0.080
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PA1 ~~
## PA2 0.312 0.482 0.648 0.517 0.103 0.103
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_tstc_fntsy 9.190 0.352 26.088 0.000 9.190 2.153
## .dsq_somatizatn 11.442 0.289 39.623 0.000 11.442 3.236
## .dsq_pssv_ggrss 7.748 0.268 28.865 0.000 7.748 2.421
## .dsq_displacmnt 8.151 0.342 23.839 0.000 8.151 1.992
## .dsq_acting_out 9.436 0.347 27.182 0.000 9.436 2.239
## .dsq_projection 9.351 0.313 29.911 0.000 9.351 2.470
## .dsq_undoing 11.142 0.332 33.523 0.000 11.142 2.805
## .dsq_devaluatin 9.764 0.262 37.301 0.000 9.764 3.064
## .dsq_splitting 9.717 0.311 31.276 0.000 9.717 2.552
## .dsq_denial 6.887 0.269 25.571 0.000 6.887 2.101
## .dsq_dissociatn 9.586 0.303 31.623 0.000 9.586 2.599
## .dsq_rctn_frmtn 9.899 0.310 31.946 0.000 9.899 2.629
## .dsq_suppressin 8.564 0.320 26.759 0.000 8.564 2.204
## .dsq_humor 9.300 0.346 26.880 0.000 9.300 2.212
## .dsq_rationlztn 8.846 0.245 36.114 0.000 8.846 2.976
## .dsq_anticipatn 10.127 0.253 40.092 0.000 10.127 3.321
## .dsq_sublimatin 10.030 0.308 32.593 0.000 10.030 2.713
## .dsq_psed_ltrsm 12.697 0.254 50.044 0.000 12.697 4.187
## .dsq_idealizatn 7.960 0.314 25.323 0.000 7.960 2.078
## .dsq_isolation 8.592 0.378 22.718 0.000 8.592 1.887
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_tstc_fntsy 15.072 1.792 8.410 0.000 15.072 0.827
## .dsq_somatizatn 10.925 1.094 9.987 0.000 10.925 0.874
## .dsq_pssv_ggrss 7.204 0.987 7.299 0.000 7.204 0.703
## .dsq_displacmnt 15.259 1.668 9.147 0.000 15.259 0.911
## .dsq_acting_out 14.882 1.789 8.317 0.000 14.882 0.838
## .dsq_projection 11.054 1.281 8.626 0.000 11.054 0.771
## .dsq_undoing 13.745 1.408 9.761 0.000 13.745 0.871
## .dsq_devaluatin 5.605 0.882 6.352 0.000 5.605 0.552
## .dsq_splitting 9.123 1.635 5.579 0.000 9.123 0.629
## .dsq_denial 9.523 1.016 9.372 0.000 9.523 0.886
## .dsq_dissociatn 11.168 1.504 7.425 0.000 11.168 0.821
## .dsq_rctn_frmtn 13.822 1.525 9.065 0.000 13.822 0.975
## .dsq_suppressin 12.157 1.780 6.831 0.000 12.157 0.805
## .dsq_humor 10.804 1.871 5.774 0.000 10.804 0.611
## .dsq_rationlztn 6.613 0.893 7.408 0.000 6.613 0.748
## .dsq_anticipatn 5.734 1.005 5.705 0.000 5.734 0.617
## .dsq_sublimatin 8.888 1.652 5.380 0.000 8.888 0.650
## .dsq_psed_ltrsm 7.084 0.991 7.151 0.000 7.084 0.770
## .dsq_idealizatn 13.086 1.392 9.402 0.000 13.086 0.892
## .dsq_isolation 20.606 1.767 11.661 0.000 20.606 0.994
## PA1 3.150 1.469 2.144 0.032 1.000 1.000
## PA2 2.940 1.366 2.153 0.031 1.000 1.000
##
## R-Square:
## Estimate
## dsq_tstc_fntsy 0.173
## dsq_somatizatn 0.126
## dsq_pssv_ggrss 0.297
## dsq_displacmnt 0.089
## dsq_acting_out 0.162
## dsq_projection 0.229
## dsq_undoing 0.129
## dsq_devaluatin 0.448
## dsq_splitting 0.371
## dsq_denial 0.114
## dsq_dissociatn 0.179
## dsq_rctn_frmtn 0.025
## dsq_suppressin 0.195
## dsq_humor 0.389
## dsq_rationlztn 0.252
## dsq_anticipatn 0.383
## dsq_sublimatin 0.350
## dsq_psed_ltrsm 0.230
## dsq_idealizatn 0.108
## dsq_isolation 0.006# Likelihood Ratio Tests (Nested)
anova(fit1_dekker, fit2_dekker)## Warning: lavaan->lav_test_diff_SatorraBentler2001():
## scaling factor is negative##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit2_dekker 169 15694 15878 339.72
## fit1_dekker 170 15793 15974 440.11 1anova(fit1_dekker, fit3_dekker)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit3_dekker 167 15708 15898 349.66
## fit1_dekker 170 15793 15974 440.11 49.38 3 1.083e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit1_dekker, fit4_dekker)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit4_dekker 164 15685 15884 320.06
## fit1_dekker 170 15793 15974 440.11 104.13 6 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# BIC (Non-Nested)
fitMeasures(fit2_dekker, "bic")## bic
## 15878.31fitMeasures(fit3_dekker, "bic")## bic
## 15898.29fitMeasures(fit4_dekker, "bic")## bic
## 15883.74fit2_ds <- cfa(model_2f, data = ds_df, estimator = "mlr", missing = "fiml")
summary(fit2_ds,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 91 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 61
##
## Number of observations 210
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 359.587 321.355
## Degrees of freedom 169 169
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.119
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 724.594 635.432
## Degrees of freedom 190 190
## P-value 0.000 0.000
## Scaling correction factor 1.140
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.643 0.658
## Tucker-Lewis Index (TLI) 0.599 0.615
##
## Robust Comparative Fit Index (CFI) 0.671
## Robust Tucker-Lewis Index (TLI) 0.630
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10863.032 -10863.032
## Scaling correction factor 1.031
## for the MLR correction
## Loglikelihood unrestricted model (H1) -10683.239 -10683.239
## Scaling correction factor 1.096
## for the MLR correction
##
## Akaike (AIC) 21848.065 21848.065
## Bayesian (BIC) 22052.239 22052.239
## Sample-size adjusted Bayesian (SABIC) 21858.955 21858.955
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.073 0.066
## 90 Percent confidence interval - lower 0.063 0.055
## 90 Percent confidence interval - upper 0.084 0.076
## P-value H_0: RMSEA <= 0.050 0.000 0.008
## P-value H_0: RMSEA >= 0.080 0.150 0.009
##
## Robust RMSEA 0.071
## 90 Percent confidence interval - lower 0.059
## 90 Percent confidence interval - upper 0.083
## P-value H_0: Robust RMSEA <= 0.050 0.004
## P-value H_0: Robust RMSEA >= 0.080 0.122
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.102 0.102
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PA1 =~
## dsq_tstc_fntsy 1.000 3.299 0.622
## dsq_somatizatn 0.510 0.156 3.274 0.001 1.684 0.353
## dsq_pssv_ggrss 0.772 0.116 6.638 0.000 2.548 0.654
## dsq_displacmnt 0.496 0.104 4.763 0.000 1.635 0.399
## dsq_acting_out 0.808 0.132 6.143 0.000 2.665 0.545
## dsq_projection 0.717 0.112 6.384 0.000 2.367 0.592
## dsq_undoing 0.359 0.129 2.784 0.005 1.185 0.290
## dsq_devaluatin 0.466 0.098 4.778 0.000 1.537 0.475
## dsq_splitting 0.412 0.108 3.818 0.000 1.358 0.330
## dsq_denial 0.227 0.099 2.298 0.022 0.748 0.223
## dsq_dissociatn 0.052 0.105 0.493 0.622 0.170 0.054
## dsq_rctn_frmtn -0.167 0.133 -1.260 0.208 -0.551 -0.137
## PA2 =~
## dsq_suppressin 1.000 1.665 0.411
## dsq_humor 1.396 0.424 3.290 0.001 2.325 0.517
## dsq_rationlztn 1.728 0.501 3.452 0.001 2.879 0.705
## dsq_anticipatn 1.290 0.319 4.037 0.000 2.148 0.505
## dsq_sublimatin 0.956 0.310 3.083 0.002 1.593 0.400
## dsq_psed_ltrsm 0.477 0.281 1.700 0.089 0.794 0.214
## dsq_idealizatn 0.905 0.289 3.128 0.002 1.506 0.371
## dsq_isolation -0.140 0.346 -0.405 0.685 -0.234 -0.048
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PA1 ~~
## PA2 -0.188 0.913 -0.205 0.837 -0.034 -0.034
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_tstc_fntsy 10.323 0.376 27.428 0.000 10.323 1.946
## .dsq_somatizatn 11.558 0.387 29.860 0.000 11.558 2.423
## .dsq_pssv_ggrss 8.766 0.277 31.687 0.000 8.766 2.250
## .dsq_displacmnt 10.040 0.291 34.549 0.000 10.040 2.451
## .dsq_acting_out 10.731 0.348 30.880 0.000 10.731 2.193
## .dsq_projection 8.163 0.283 28.859 0.000 8.163 2.041
## .dsq_undoing 9.460 0.290 32.579 0.000 9.460 2.313
## .dsq_devaluatin 7.186 0.230 31.213 0.000 7.186 2.219
## .dsq_splitting 10.012 0.292 34.289 0.000 10.012 2.434
## .dsq_denial 5.391 0.239 22.520 0.000 5.391 1.605
## .dsq_dissociatn 5.259 0.222 23.657 0.000 5.259 1.682
## .dsq_rctn_frmtn 8.950 0.286 31.341 0.000 8.950 2.223
## .dsq_suppressin 6.762 0.288 23.482 0.000 6.762 1.669
## .dsq_humor 8.247 0.319 25.827 0.000 8.247 1.832
## .dsq_rationlztn 8.439 0.290 29.132 0.000 8.439 2.066
## .dsq_anticipatn 11.812 0.302 39.108 0.000 11.812 2.779
## .dsq_sublimatin 9.803 0.283 34.670 0.000 9.803 2.463
## .dsq_psed_ltrsm 11.030 0.264 41.771 0.000 11.030 2.967
## .dsq_idealizatn 8.095 0.288 28.071 0.000 8.095 1.995
## .dsq_isolation 9.833 0.349 28.208 0.000 9.833 2.004
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_tstc_fntsy 17.270 2.301 7.506 0.000 17.270 0.613
## .dsq_somatizatn 19.921 2.251 8.850 0.000 19.921 0.875
## .dsq_pssv_ggrss 8.690 1.289 6.743 0.000 8.690 0.572
## .dsq_displacmnt 14.114 1.296 10.887 0.000 14.114 0.841
## .dsq_acting_out 16.832 1.807 9.313 0.000 16.832 0.703
## .dsq_projection 10.387 1.381 7.523 0.000 10.387 0.650
## .dsq_undoing 15.330 1.584 9.678 0.000 15.330 0.916
## .dsq_devaluatin 8.127 0.870 9.343 0.000 8.127 0.775
## .dsq_splitting 15.079 1.606 9.388 0.000 15.079 0.891
## .dsq_denial 10.718 0.981 10.923 0.000 10.718 0.950
## .dsq_dissociatn 9.748 0.946 10.306 0.000 9.748 0.997
## .dsq_rctn_frmtn 15.902 1.432 11.106 0.000 15.902 0.981
## .dsq_suppressin 13.650 2.022 6.749 0.000 13.650 0.831
## .dsq_humor 14.853 1.950 7.616 0.000 14.853 0.733
## .dsq_rationlztn 8.395 1.831 4.586 0.000 8.395 0.503
## .dsq_anticipatn 13.458 1.665 8.080 0.000 13.458 0.745
## .dsq_sublimatin 13.299 1.555 8.552 0.000 13.299 0.840
## .dsq_psed_ltrsm 13.186 1.315 10.031 0.000 13.186 0.954
## .dsq_idealizatn 14.201 1.770 8.022 0.000 14.201 0.862
## .dsq_isolation 24.014 1.747 13.749 0.000 24.014 0.998
## PA1 10.883 2.354 4.623 0.000 1.000 1.000
## PA2 2.774 1.310 2.118 0.034 1.000 1.000
##
## R-Square:
## Estimate
## dsq_tstc_fntsy 0.387
## dsq_somatizatn 0.125
## dsq_pssv_ggrss 0.428
## dsq_displacmnt 0.159
## dsq_acting_out 0.297
## dsq_projection 0.350
## dsq_undoing 0.084
## dsq_devaluatin 0.225
## dsq_splitting 0.109
## dsq_denial 0.050
## dsq_dissociatn 0.003
## dsq_rctn_frmtn 0.019
## dsq_suppressin 0.169
## dsq_humor 0.267
## dsq_rationlztn 0.497
## dsq_anticipatn 0.255
## dsq_sublimatin 0.160
## dsq_psed_ltrsm 0.046
## dsq_idealizatn 0.138
## dsq_isolation 0.002# Likelihood Ratio Tests (Nested)
anova(fit1_ds, fit2_ds)## Warning: lavaan->lav_test_diff_SatorraBentler2001():
## scaling factor is negative##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit2_ds 169 21848 22052 359.59
## fit1_ds 170 21982 22183 495.16 1anova(fit1_ds, fit3_ds)## Warning: lavaan->lav_test_diff_SatorraBentler2001():
## scaling factor is negative##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit3_ds 167 21867 22078 374.57
## fit1_ds 170 21982 22183 495.16 3anova(fit1_ds, fit4_ds)## Warning: lavaan->lav_test_diff_SatorraBentler2001():
## scaling factor is negative##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit4_ds 164 21787 22007 288.04
## fit1_ds 170 21982 22183 495.16 6# BIC (Non-Nested)
fitMeasures(fit2_ds, "bic")## bic
## 22052.24fitMeasures(fit3_ds, "bic")## bic
## 22077.91fitMeasures(fit4_ds, "bic")## bic
## 22007.43fit2_kn <- cfa(model_2f_norat, data = kn_df, estimator = "mlr", missing = "fiml")
summary(fit2_kn,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE)## lavaan 0.6-19 ended normally after 134 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 58
##
## Number of observations 306
## Number of missing patterns 12
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 458.252 445.277
## Degrees of freedom 151 151
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.029
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 774.640 708.842
## Degrees of freedom 171 171
## P-value 0.000 0.000
## Scaling correction factor 1.093
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.491 0.453
## Tucker-Lewis Index (TLI) 0.424 0.380
##
## Robust Comparative Fit Index (CFI) 0.500
## Robust Tucker-Lewis Index (TLI) 0.433
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -15562.195 -15562.195
## Scaling correction factor 1.119
## for the MLR correction
## Loglikelihood unrestricted model (H1) -15333.068 -15333.068
## Scaling correction factor 1.054
## for the MLR correction
##
## Akaike (AIC) 31240.389 31240.389
## Bayesian (BIC) 31456.357 31456.357
## Sample-size adjusted Bayesian (SABIC) 31272.408 31272.408
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.082 0.080
## 90 Percent confidence interval - lower 0.073 0.071
## 90 Percent confidence interval - upper 0.090 0.088
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.626 0.494
##
## Robust RMSEA 0.080
## 90 Percent confidence interval - lower 0.071
## 90 Percent confidence interval - upper 0.089
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.521
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.088 0.088
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PA1 =~
## dsq_tstc_fntsy 1.000 1.338 0.257
## dsq_somatizatn 1.262 0.527 2.396 0.017 1.689 0.392
## dsq_pssv_ggrss 1.524 0.473 3.224 0.001 2.038 0.579
## dsq_displacmnt 1.286 0.494 2.605 0.009 1.720 0.454
## dsq_acting_out 0.535 0.303 1.763 0.078 0.716 0.183
## dsq_projection 1.235 0.450 2.747 0.006 1.652 0.458
## dsq_undoing 1.352 0.510 2.652 0.008 1.808 0.459
## dsq_devaluatin 0.716 0.233 3.076 0.002 0.958 0.322
## dsq_splitting 1.026 0.417 2.461 0.014 1.373 0.387
## dsq_denial 0.701 0.249 2.817 0.005 0.938 0.323
## dsq_dissociatn 0.328 0.189 1.735 0.083 0.438 0.173
## dsq_rctn_frmtn 0.913 0.418 2.184 0.029 1.222 0.353
## PA2 =~
## dsq_suppressin 1.000 0.686 0.172
## dsq_humor 1.300 1.207 1.077 0.282 0.892 0.242
## dsq_anticipatn 1.097 1.909 0.574 0.566 0.753 0.234
## dsq_sublimatin 2.389 2.502 0.955 0.340 1.640 0.425
## dsq_psed_ltrsm 2.453 4.199 0.584 0.559 1.684 0.503
## dsq_idealizatn 2.872 5.148 0.558 0.577 1.971 0.495
## dsq_isolation -0.976 1.733 -0.563 0.573 -0.670 -0.151
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PA1 ~~
## PA2 0.313 0.283 1.107 0.268 0.341 0.341
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_tstc_fntsy 11.665 0.299 39.039 0.000 11.665 2.239
## .dsq_somatizatn 10.118 0.246 41.088 0.000 10.118 2.349
## .dsq_pssv_ggrss 7.341 0.202 36.344 0.000 7.341 2.085
## .dsq_displacmnt 8.673 0.216 40.077 0.000 8.673 2.291
## .dsq_acting_out 10.797 0.224 48.218 0.000 10.797 2.766
## .dsq_projection 8.023 0.206 38.872 0.000 8.023 2.226
## .dsq_undoing 9.314 0.225 41.370 0.000 9.314 2.365
## .dsq_devaluatin 7.258 0.170 42.612 0.000 7.258 2.436
## .dsq_splitting 7.453 0.203 36.651 0.000 7.453 2.100
## .dsq_denial 5.415 0.166 32.646 0.000 5.415 1.866
## .dsq_dissociatn 4.413 0.145 30.346 0.000 4.413 1.744
## .dsq_rctn_frmtn 9.110 0.199 45.863 0.000 9.110 2.634
## .dsq_suppressin 7.431 0.228 32.561 0.000 7.431 1.866
## .dsq_humor 11.523 0.211 54.732 0.000 11.523 3.129
## .dsq_anticipatn 10.706 0.184 58.190 0.000 10.706 3.326
## .dsq_sublimatin 9.549 0.220 43.320 0.000 9.549 2.476
## .dsq_psed_ltrsm 9.635 0.192 50.250 0.000 9.635 2.878
## .dsq_idealizatn 6.304 0.227 27.714 0.000 6.304 1.584
## .dsq_isolation 8.102 0.254 31.881 0.000 8.102 1.830
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .dsq_tstc_fntsy 25.367 1.905 13.318 0.000 25.367 0.934
## .dsq_somatizatn 15.703 1.457 10.781 0.000 15.703 0.846
## .dsq_pssv_ggrss 8.246 1.112 7.413 0.000 8.246 0.665
## .dsq_displacmnt 11.372 0.958 11.865 0.000 11.372 0.793
## .dsq_acting_out 14.726 0.975 15.102 0.000 14.726 0.966
## .dsq_projection 10.264 1.259 8.151 0.000 10.264 0.790
## .dsq_undoing 12.239 1.326 9.227 0.000 12.239 0.789
## .dsq_devaluatin 7.960 0.757 10.513 0.000 7.960 0.897
## .dsq_splitting 10.706 0.936 11.435 0.000 10.706 0.850
## .dsq_denial 7.539 0.600 12.566 0.000 7.539 0.895
## .dsq_dissociatn 6.214 0.538 11.542 0.000 6.214 0.970
## .dsq_rctn_frmtn 10.473 1.008 10.394 0.000 10.473 0.875
## .dsq_suppressin 15.380 1.737 8.856 0.000 15.380 0.970
## .dsq_humor 12.767 1.500 8.514 0.000 12.767 0.941
## .dsq_anticipatn 9.791 0.887 11.038 0.000 9.791 0.945
## .dsq_sublimatin 12.180 2.792 4.362 0.000 12.180 0.819
## .dsq_psed_ltrsm 8.372 1.517 5.519 0.000 8.372 0.747
## .dsq_idealizatn 11.947 2.683 4.452 0.000 11.947 0.755
## .dsq_isolation 19.147 1.188 16.119 0.000 19.147 0.977
## PA1 1.790 1.143 1.565 0.118 1.000 1.000
## PA2 0.471 1.450 0.325 0.745 1.000 1.000
##
## R-Square:
## Estimate
## dsq_tstc_fntsy 0.066
## dsq_somatizatn 0.154
## dsq_pssv_ggrss 0.335
## dsq_displacmnt 0.207
## dsq_acting_out 0.034
## dsq_projection 0.210
## dsq_undoing 0.211
## dsq_devaluatin 0.103
## dsq_splitting 0.150
## dsq_denial 0.105
## dsq_dissociatn 0.030
## dsq_rctn_frmtn 0.125
## dsq_suppressin 0.030
## dsq_humor 0.059
## dsq_anticipatn 0.055
## dsq_sublimatin 0.181
## dsq_psed_ltrsm 0.253
## dsq_idealizatn 0.245
## dsq_isolation 0.023# Likelihood Ratio Tests (Nested)
anova(fit1_kn, fit2_kn)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit2_kn 151 31240 31456 458.25
## fit1_kn 152 31261 31473 480.58 4.6856 1 0.03042 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit1_kn, fit3_kn)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit3_kn 149 31178 31402 392.12
## fit1_kn 152 31261 31473 480.58 63.488 3 1.056e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit1_kn, fit4_kn)##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## 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)
## fit4_kn 146 31179 31414 387.27
## fit1_kn 152 31261 31473 480.58 63.062 6 1.072e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# BIC (Non-Nested)
fitMeasures(fit2_kn, "bic")## bic
## 31456.36fitMeasures(fit3_kn, "bic")## bic
## 31401.67fitMeasures(fit4_kn, "bic")## bic
## 31413.99