Carregando os pacotes
library(haven)
Warning message:
In do_once((if (is_R_CMD_check()) stop else warning)("The function xfun::isFALSE() will be deprecated in the future. Please ", :
The function xfun::isFALSE() will be deprecated in the future. Please consider using base::isFALSE(x) or identical(x, FALSE) instead.
library(lavaan)
library(semTools)
Banco de dados
data <- read_sav("~/Downloads/Banco espanha LA limpo.sav")
CFA PHQ9
model<-"
PHQ9 =~ PHQ9_1 + PHQ9_2 + PHQ9_3 + PHQ9_4 + PHQ9_5 + PHQ9_6 + PHQ9_7 + PHQ9_8 + PHQ9_9
"
fit<-cfa(model=model,data=data,ordered=T,missing="pairwise",estimator="wlsmv")
summary(fit,fit.measures=T,standardized=T,rsquare=T)
lavaan 0.6.15 ended normally after 16 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 36
Number of observations 4975
Number of missing patterns 1
Model Test User Model:
Standard Scaled
Test Statistic 461.617 857.267
Degrees of freedom 27 27
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.539
Shift parameter 1.576
simple second-order correction
Model Test Baseline Model:
Test statistic 58908.177 33015.598
Degrees of freedom 36 36
P-value 0.000 0.000
Scaling correction factor 1.785
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.993 0.975
Tucker-Lewis Index (TLI) 0.990 0.966
Robust Comparative Fit Index (CFI) 0.921
Robust Tucker-Lewis Index (TLI) 0.895
Root Mean Square Error of Approximation:
RMSEA 0.057 0.079
90 Percent confidence interval - lower 0.052 0.074
90 Percent confidence interval - upper 0.062 0.083
P-value H_0: RMSEA <= 0.050 0.006 0.000
P-value H_0: RMSEA >= 0.080 0.000 0.315
Robust RMSEA 0.117
90 Percent confidence interval - lower 0.110
90 Percent confidence interval - upper 0.125
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 0.049 0.049
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
PHQ9 =~
PHQ9_1 1.000 0.667 0.667
PHQ9_2 1.233 0.020 61.282 0.000 0.822 0.822
PHQ9_3 1.057 0.020 53.992 0.000 0.704 0.704
PHQ9_4 1.204 0.020 58.757 0.000 0.802 0.802
PHQ9_5 1.029 0.020 52.311 0.000 0.686 0.686
PHQ9_6 1.167 0.021 55.269 0.000 0.778 0.778
PHQ9_7 1.072 0.020 53.516 0.000 0.714 0.714
PHQ9_8 0.994 0.023 43.584 0.000 0.663 0.663
PHQ9_9 1.037 0.027 38.272 0.000 0.691 0.691
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PHQ9_1 0.000 0.000 0.000
.PHQ9_2 0.000 0.000 0.000
.PHQ9_3 0.000 0.000 0.000
.PHQ9_4 0.000 0.000 0.000
.PHQ9_5 0.000 0.000 0.000
.PHQ9_6 0.000 0.000 0.000
.PHQ9_7 0.000 0.000 0.000
.PHQ9_8 0.000 0.000 0.000
.PHQ9_9 0.000 0.000 0.000
PHQ9 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
PHQ9_1|t1 -0.436 0.018 -23.692 0.000 -0.436 -0.436
PHQ9_1|t2 0.805 0.020 40.184 0.000 0.805 0.805
PHQ9_1|t3 1.275 0.024 52.780 0.000 1.275 1.275
PHQ9_2|t1 -0.183 0.018 -10.244 0.000 -0.183 -0.183
PHQ9_2|t2 1.197 0.023 51.452 0.000 1.197 1.197
PHQ9_2|t3 1.678 0.031 54.760 0.000 1.678 1.678
PHQ9_3|t1 -0.485 0.019 -26.125 0.000 -0.485 -0.485
PHQ9_3|t2 0.577 0.019 30.542 0.000 0.577 0.577
PHQ9_3|t3 1.050 0.022 48.085 0.000 1.050 1.050
PHQ9_4|t1 -0.550 0.019 -29.269 0.000 -0.550 -0.550
PHQ9_4|t2 0.891 0.021 43.269 0.000 0.891 0.891
PHQ9_4|t3 1.445 0.026 54.601 0.000 1.445 1.445
PHQ9_5|t1 -0.266 0.018 -14.769 0.000 -0.266 -0.266
PHQ9_5|t2 0.706 0.019 36.232 0.000 0.706 0.706
PHQ9_5|t3 1.307 0.025 53.227 0.000 1.307 1.307
PHQ9_6|t1 0.249 0.018 13.836 0.000 0.249 0.249
PHQ9_6|t2 1.202 0.023 51.530 0.000 1.202 1.202
PHQ9_6|t3 1.640 0.030 54.910 0.000 1.640 1.640
PHQ9_7|t1 0.029 0.018 1.630 0.103 0.029 0.029
PHQ9_7|t2 1.131 0.023 50.073 0.000 1.131 1.131
PHQ9_7|t3 1.629 0.030 54.942 0.000 1.629 1.629
PHQ9_8|t1 0.576 0.019 30.486 0.000 0.576 0.576
PHQ9_8|t2 1.487 0.027 54.822 0.000 1.487 1.487
PHQ9_8|t3 1.934 0.037 52.146 0.000 1.934 1.934
PHQ9_9|t1 1.187 0.023 51.253 0.000 1.187 1.187
PHQ9_9|t2 1.825 0.034 53.582 0.000 1.825 1.825
PHQ9_9|t3 2.099 0.043 49.191 0.000 2.099 2.099
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PHQ9_1 0.556 0.556 0.556
.PHQ9_2 0.325 0.325 0.325
.PHQ9_3 0.504 0.504 0.504
.PHQ9_4 0.356 0.356 0.356
.PHQ9_5 0.530 0.530 0.530
.PHQ9_6 0.395 0.395 0.395
.PHQ9_7 0.490 0.490 0.490
.PHQ9_8 0.561 0.561 0.561
.PHQ9_9 0.523 0.523 0.523
PHQ9 0.444 0.014 32.639 0.000 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
PHQ9_1 1.000 1.000 1.000
PHQ9_2 1.000 1.000 1.000
PHQ9_3 1.000 1.000 1.000
PHQ9_4 1.000 1.000 1.000
PHQ9_5 1.000 1.000 1.000
PHQ9_6 1.000 1.000 1.000
PHQ9_7 1.000 1.000 1.000
PHQ9_8 1.000 1.000 1.000
PHQ9_9 1.000 1.000 1.000
R-Square:
Estimate
PHQ9_1 0.444
PHQ9_2 0.675
PHQ9_3 0.496
PHQ9_4 0.644
PHQ9_5 0.470
PHQ9_6 0.605
PHQ9_7 0.510
PHQ9_8 0.439
PHQ9_9 0.477
reliability(fit)
For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord" and the response by Zumbo & Kroc (2019). Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.
PHQ9
alpha 0.8522701
alpha.ord 0.9056191
omega 0.8646318
omega2 0.8646318
omega3 0.8736237
avevar 0.5289831
CFA EA ATIVO
model<-"
EA_ATIVO =~ EA_3 + EA_5 + EA_7 + EA_9 + EA_13
"
fit<-cfa(model=model,data=data,ordered=T,missing="pairwise",estimator="wlsmv",orthogonal=T)
summary(fit,fit.measures=T,standardized=T,rsquare=T)
lavaan 0.6.15 ended normally after 12 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 20
Number of observations 4975
Number of missing patterns 1
Model Test User Model:
Standard Scaled
Test Statistic 71.972 139.681
Degrees of freedom 5 5
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.516
Shift parameter 0.083
simple second-order correction
Model Test Baseline Model:
Test statistic 12409.932 9308.237
Degrees of freedom 10 10
P-value 0.000 0.000
Scaling correction factor 1.334
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.995 0.986
Tucker-Lewis Index (TLI) 0.989 0.971
Robust Comparative Fit Index (CFI) 0.969
Robust Tucker-Lewis Index (TLI) 0.938
Root Mean Square Error of Approximation:
RMSEA 0.052 0.074
90 Percent confidence interval - lower 0.042 0.063
90 Percent confidence interval - upper 0.063 0.084
P-value H_0: RMSEA <= 0.050 0.362 0.000
P-value H_0: RMSEA >= 0.080 0.000 0.168
Robust RMSEA 0.098
90 Percent confidence interval - lower 0.084
90 Percent confidence interval - upper 0.112
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.984
Standardized Root Mean Square Residual:
SRMR 0.032 0.032
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EA_ATIVO =~
EA_3 1.000 0.696 0.696
EA_5 1.012 0.022 45.575 0.000 0.704 0.704
EA_7 0.961 0.023 42.687 0.000 0.669 0.669
EA_9 1.066 0.023 45.909 0.000 0.742 0.742
EA_13 0.916 0.024 37.850 0.000 0.637 0.637
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EA_3 0.000 0.000 0.000
.EA_5 0.000 0.000 0.000
.EA_7 0.000 0.000 0.000
.EA_9 0.000 0.000 0.000
.EA_13 0.000 0.000 0.000
EA_ATIVO 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EA_3|t1 0.116 0.018 6.534 0.000 0.116 0.116
EA_3|t2 1.145 0.023 50.391 0.000 1.145 1.145
EA_3|t3 1.685 0.031 54.729 0.000 1.685 1.685
EA_5|t1 -0.141 0.018 -7.894 0.000 -0.141 -0.141
EA_5|t2 0.752 0.020 38.114 0.000 0.752 0.752
EA_5|t3 1.278 0.024 52.814 0.000 1.278 1.278
EA_7|t1 -0.057 0.018 -3.189 0.001 -0.057 -0.057
EA_7|t2 0.695 0.019 35.799 0.000 0.695 0.695
EA_7|t3 1.142 0.023 50.328 0.000 1.142 1.142
EA_9|t1 -0.285 0.018 -15.814 0.000 -0.285 -0.285
EA_9|t2 0.676 0.019 34.958 0.000 0.676 0.676
EA_9|t3 1.310 0.025 53.274 0.000 1.310 1.310
EA_13|t1 0.358 0.018 19.676 0.000 0.358 0.358
EA_13|t2 1.271 0.024 52.712 0.000 1.271 1.271
EA_13|t3 1.775 0.033 54.094 0.000 1.775 1.775
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EA_3 0.516 0.516 0.516
.EA_5 0.504 0.504 0.504
.EA_7 0.553 0.553 0.553
.EA_9 0.450 0.450 0.450
.EA_13 0.594 0.594 0.594
EA_ATIVO 0.484 0.016 29.647 0.000 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EA_3 1.000 1.000 1.000
EA_5 1.000 1.000 1.000
EA_7 1.000 1.000 1.000
EA_9 1.000 1.000 1.000
EA_13 1.000 1.000 1.000
R-Square:
Estimate
EA_3 0.484
EA_5 0.496
EA_7 0.447
EA_9 0.550
EA_13 0.406
reliability(fit)
For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord" and the response by Zumbo & Kroc (2019). Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.
EA_ATIVO
alpha 0.7514784
alpha.ord 0.8168347
omega 0.7624154
omega2 0.7624154
omega3 0.7654838
avevar 0.4766156
CFA EA EVASIVO
model<-"
EA_EVASIVO =~ EA_4 + EA_6 + EA_8 + EA_14
"
fit<-cfa(model=model,data=data,ordered=T,missing="pairwise",estimator="wlsmv",orthogonal=T)
summary(fit,fit.measures=T,standardized=T,rsquare=T)
lavaan 0.6.15 ended normally after 14 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 16
Number of observations 4975
Number of missing patterns 1
Model Test User Model:
Standard Scaled
Test Statistic 31.770 55.622
Degrees of freedom 2 2
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.571
Shift parameter 0.015
simple second-order correction
Model Test Baseline Model:
Test statistic 6221.761 5184.011
Degrees of freedom 6 6
P-value 0.000 0.000
Scaling correction factor 1.200
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.995 0.990
Tucker-Lewis Index (TLI) 0.986 0.969
Robust Comparative Fit Index (CFI) 0.981
Robust Tucker-Lewis Index (TLI) 0.942
Root Mean Square Error of Approximation:
RMSEA 0.055 0.073
90 Percent confidence interval - lower 0.039 0.058
90 Percent confidence interval - upper 0.072 0.091
P-value H_0: RMSEA <= 0.050 0.290 0.008
P-value H_0: RMSEA >= 0.080 0.008 0.279
Robust RMSEA 0.086
90 Percent confidence interval - lower 0.068
90 Percent confidence interval - upper 0.106
P-value H_0: Robust RMSEA <= 0.050 0.001
P-value H_0: Robust RMSEA >= 0.080 0.735
Standardized Root Mean Square Residual:
SRMR 0.023 0.023
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EA_EVASIVO =~
EA_4 1.000 0.672 0.672
EA_6 0.805 0.025 32.806 0.000 0.541 0.541
EA_8 1.015 0.027 37.869 0.000 0.682 0.682
EA_14 0.944 0.027 35.464 0.000 0.635 0.635
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EA_4 0.000 0.000 0.000
.EA_6 0.000 0.000 0.000
.EA_8 0.000 0.000 0.000
.EA_14 0.000 0.000 0.000
EA_EVASIVO 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EA_4|t1 -1.513 0.028 -54.919 0.000 -1.513 -1.513
EA_4|t2 -0.737 0.020 -37.525 0.000 -0.737 -0.737
EA_4|t3 0.308 0.018 17.027 0.000 0.308 0.308
EA_6|t1 -0.652 0.019 -33.922 0.000 -0.652 -0.652
EA_6|t2 0.362 0.018 19.873 0.000 0.362 0.362
EA_6|t3 1.146 0.023 50.412 0.000 1.146 1.146
EA_8|t1 -1.042 0.022 -47.876 0.000 -1.042 -1.042
EA_8|t2 -0.251 0.018 -13.949 0.000 -0.251 -0.251
EA_8|t3 0.681 0.019 35.175 0.000 0.681 0.681
EA_14|t1 -1.107 0.022 -49.530 0.000 -1.107 -1.107
EA_14|t2 -0.361 0.018 -19.817 0.000 -0.361 -0.361
EA_14|t3 0.691 0.019 35.609 0.000 0.691 0.691
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EA_4 0.548 0.548 0.548
.EA_6 0.707 0.707 0.707
.EA_8 0.534 0.534 0.534
.EA_14 0.597 0.597 0.597
EA_EVASIVO 0.452 0.017 25.903 0.000 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EA_4 1.000 1.000 1.000
EA_6 1.000 1.000 1.000
EA_8 1.000 1.000 1.000
EA_14 1.000 1.000 1.000
R-Square:
Estimate
EA_4 0.452
EA_6 0.293
EA_8 0.466
EA_14 0.403
reliability(fit)
For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord" and the response by Zumbo & Kroc (2019). Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.
EA_EVASIVO
alpha 0.6711989
alpha.ord 0.7255316
omega 0.6806421
omega2 0.6806421
omega3 0.6818561
avevar 0.4032597
CFA AUDITC
model<-"
AUDITC =~ ALC01_DC + ALC02_DC + ALC03_DC + ALC04_DC
"
fit<-cfa(model=model,data=data,ordered=T,missing="pairwise",estimator="wlsmv")
Warning: lavaan WARNING: some cases are empty and will be ignored:
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summary(fit,fit.measures=T,standardized=T,rsquare=T)
lavaan 0.6.15 ended normally after 13 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 19
Used Total
Number of observations 2078 4975
Number of missing patterns 1
Model Test User Model:
Standard Scaled
Test Statistic 9.943 18.982
Degrees of freedom 2 2
P-value (Chi-square) 0.007 0.000
Scaling correction factor 0.527
Shift parameter 0.117
simple second-order correction
Model Test Baseline Model:
Test statistic 8659.104 6362.580
Degrees of freedom 6 6
P-value 0.000 0.000
Scaling correction factor 1.361
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.999 0.997
Tucker-Lewis Index (TLI) 0.997 0.992
Robust Comparative Fit Index (CFI) 0.982
Robust Tucker-Lewis Index (TLI) 0.946
Root Mean Square Error of Approximation:
RMSEA 0.044 0.064
90 Percent confidence interval - lower 0.019 0.040
90 Percent confidence interval - upper 0.072 0.092
P-value H_0: RMSEA <= 0.050 0.591 0.159
P-value H_0: RMSEA >= 0.080 0.017 0.183
Robust RMSEA 0.151
90 Percent confidence interval - lower 0.101
90 Percent confidence interval - upper 0.208
P-value H_0: Robust RMSEA <= 0.050 0.001
P-value H_0: Robust RMSEA >= 0.080 0.989
Standardized Root Mean Square Residual:
SRMR 0.023 0.023
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
AUDITC =~
ALC01_DC 1.000 0.603 0.603
ALC02_DC 1.561 0.053 29.595 0.000 0.942 0.942
ALC03_DC 1.284 0.046 27.897 0.000 0.774 0.774
ALC04_DC 1.500 0.053 28.545 0.000 0.905 0.905
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.ALC01_DC 0.000 0.000 0.000
.ALC02_DC 0.000 0.000 0.000
.ALC03_DC 0.000 0.000 0.000
.ALC04_DC 0.000 0.000 0.000
AUDITC 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
ALC01_DC|t1 -0.776 0.031 -25.243 0.000 -0.776 -0.776
ALC01_DC|t2 0.299 0.028 10.687 0.000 0.299 0.299
ALC01_DC|t3 1.005 0.033 30.283 0.000 1.005 1.005
ALC01_DC|t4 1.613 0.045 35.527 0.000 1.613 1.613
ALC02_DC|t1 0.995 0.033 30.097 0.000 0.995 0.995
ALC02_DC|t2 1.668 0.047 35.416 0.000 1.668 1.668
ALC02_DC|t3 2.160 0.070 30.962 0.000 2.160 2.160
ALC03_DC|t1 0.448 0.029 15.690 0.000 0.448 0.448
ALC03_DC|t2 1.238 0.037 33.724 0.000 1.238 1.238
ALC03_DC|t3 1.562 0.044 35.546 0.000 1.562 1.562
ALC03_DC|t4 2.341 0.083 28.163 0.000 2.341 2.341
ALC04_DC|t1 1.021 0.033 30.577 0.000 1.021 1.021
ALC04_DC|t2 1.708 0.048 35.280 0.000 1.708 1.708
ALC04_DC|t3 2.124 0.068 31.460 0.000 2.124 2.124
ALC04_DC|t4 2.471 0.095 25.935 0.000 2.471 2.471
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.ALC01_DC 0.636 0.636 0.636
.ALC02_DC 0.113 0.113 0.113
.ALC03_DC 0.400 0.400 0.400
.ALC04_DC 0.182 0.182 0.182
AUDITC 0.364 0.024 15.109 0.000 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
ALC01_DC 1.000 1.000 1.000
ALC02_DC 1.000 1.000 1.000
ALC03_DC 1.000 1.000 1.000
ALC04_DC 1.000 1.000 1.000
R-Square:
Estimate
ALC01_DC 0.364
ALC02_DC 0.887
ALC03_DC 0.600
ALC04_DC 0.818
reliability(fit)
For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord" and the response by Zumbo & Kroc (2019). Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.
AUDITC
alpha NA
alpha.ord 0.8762058
omega 0.7615899
omega2 0.7615899
omega3 0.7619498
avevar 0.6671365
MODELO DE MEDIDA
model<-"
PHQ9 =~ PHQ9_1 + PHQ9_2 + PHQ9_3 + PHQ9_4 + PHQ9_5 + PHQ9_6 + PHQ9_7 + PHQ9_8 + PHQ9_9
#EA_ATIVO =~ EA_3 + EA_5 + EA_7 + EA_9 + EA_13
EA_EVASIVO =~ EA_4 + EA_6 + EA_8 + EA_14
AUDITC =~ ALC01_DC + ALC02_DC + ALC03_DC + ALC04_DC
#EA_ATIVO~~0*EA_EVASIVO
"
fit<-cfa(model=model,data=data,ordered=T,missing="pairwise",estimator="wlsmv")
summary(fit,fit.measures=T,standardized=T,rsquare=T)
lavaan 0.6.15 ended normally after 35 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 74
Number of observations 4975
Number of missing patterns 2
Model Test User Model:
Standard Scaled
Test Statistic 1403.270 1556.750
Degrees of freedom 116 116
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.919
Shift parameter 29.265
simple second-order correction
Model Test Baseline Model:
Test statistic 75647.478 41912.235
Degrees of freedom 136 136
P-value 0.000 0.000
Scaling correction factor 1.808
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.983 0.966
Tucker-Lewis Index (TLI) 0.980 0.960
Robust Comparative Fit Index (CFI) 0.929
Robust Tucker-Lewis Index (TLI) 0.916
Root Mean Square Error of Approximation:
RMSEA 0.047 0.050
90 Percent confidence interval - lower 0.045 0.048
90 Percent confidence interval - upper 0.049 0.052
P-value H_0: RMSEA <= 0.050 0.980 0.504
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.071
90 Percent confidence interval - lower 0.066
90 Percent confidence interval - upper 0.077
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.005
Standardized Root Mean Square Residual:
SRMR 0.055 0.055
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
PHQ9 =~
PHQ9_1 1.000 0.663 0.663
PHQ9_2 1.242 0.020 60.903 0.000 0.824 0.824
PHQ9_3 1.065 0.020 53.671 0.000 0.706 0.706
PHQ9_4 1.211 0.021 58.363 0.000 0.803 0.803
PHQ9_5 1.034 0.020 51.994 0.000 0.686 0.686
PHQ9_6 1.172 0.021 54.939 0.000 0.777 0.777
PHQ9_7 1.078 0.020 53.263 0.000 0.715 0.715
PHQ9_8 0.999 0.023 43.484 0.000 0.663 0.663
PHQ9_9 1.034 0.027 37.921 0.000 0.686 0.686
EA_EVASIVO =~
EA_4 1.000 0.582 0.582
EA_6 0.955 0.032 30.084 0.000 0.556 0.556
EA_8 1.368 0.043 32.068 0.000 0.796 0.796
EA_14 0.999 0.032 30.918 0.000 0.581 0.581
AUDITC =~
ALC01_DC 1.000 0.605 0.605
ALC02_DC 1.555 0.052 29.757 0.000 0.941 0.941
ALC03_DC 1.280 0.046 28.083 0.000 0.775 0.775
ALC04_DC 1.494 0.052 28.685 0.000 0.904 0.904
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
PHQ9 ~~
EA_EVASIVO 0.076 0.007 10.809 0.000 0.198 0.198
AUDITC -0.006 0.012 -0.498 0.619 -0.015 -0.015
EA_EVASIVO ~~
AUDITC -0.004 0.011 -0.388 0.698 -0.013 -0.013
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PHQ9_1 0.000 0.000 0.000
.PHQ9_2 0.000 0.000 0.000
.PHQ9_3 0.000 0.000 0.000
.PHQ9_4 0.000 0.000 0.000
.PHQ9_5 0.000 0.000 0.000
.PHQ9_6 0.000 0.000 0.000
.PHQ9_7 0.000 0.000 0.000
.PHQ9_8 0.000 0.000 0.000
.PHQ9_9 0.000 0.000 0.000
.EA_4 0.000 0.000 0.000
.EA_6 0.000 0.000 0.000
.EA_8 0.000 0.000 0.000
.EA_14 0.000 0.000 0.000
.ALC01_DC 0.000 0.000 0.000
.ALC02_DC 0.000 0.000 0.000
.ALC03_DC 0.000 0.000 0.000
.ALC04_DC 0.000 0.000 0.000
PHQ9 0.000 0.000 0.000
EA_EVASIVO 0.000 0.000 0.000
AUDITC 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
PHQ9_1|t1 -0.436 0.018 -23.692 0.000 -0.436 -0.436
PHQ9_1|t2 0.805 0.020 40.184 0.000 0.805 0.805
PHQ9_1|t3 1.275 0.024 52.780 0.000 1.275 1.275
PHQ9_2|t1 -0.183 0.018 -10.244 0.000 -0.183 -0.183
PHQ9_2|t2 1.197 0.023 51.452 0.000 1.197 1.197
PHQ9_2|t3 1.678 0.031 54.760 0.000 1.678 1.678
PHQ9_3|t1 -0.485 0.019 -26.125 0.000 -0.485 -0.485
PHQ9_3|t2 0.577 0.019 30.542 0.000 0.577 0.577
PHQ9_3|t3 1.050 0.022 48.085 0.000 1.050 1.050
PHQ9_4|t1 -0.550 0.019 -29.269 0.000 -0.550 -0.550
PHQ9_4|t2 0.891 0.021 43.269 0.000 0.891 0.891
PHQ9_4|t3 1.445 0.026 54.601 0.000 1.445 1.445
PHQ9_5|t1 -0.266 0.018 -14.769 0.000 -0.266 -0.266
PHQ9_5|t2 0.706 0.019 36.232 0.000 0.706 0.706
PHQ9_5|t3 1.307 0.025 53.227 0.000 1.307 1.307
PHQ9_6|t1 0.249 0.018 13.836 0.000 0.249 0.249
PHQ9_6|t2 1.202 0.023 51.530 0.000 1.202 1.202
PHQ9_6|t3 1.640 0.030 54.910 0.000 1.640 1.640
PHQ9_7|t1 0.029 0.018 1.630 0.103 0.029 0.029
PHQ9_7|t2 1.131 0.023 50.073 0.000 1.131 1.131
PHQ9_7|t3 1.629 0.030 54.942 0.000 1.629 1.629
PHQ9_8|t1 0.576 0.019 30.486 0.000 0.576 0.576
PHQ9_8|t2 1.487 0.027 54.822 0.000 1.487 1.487
PHQ9_8|t3 1.934 0.037 52.146 0.000 1.934 1.934
PHQ9_9|t1 1.187 0.023 51.253 0.000 1.187 1.187
PHQ9_9|t2 1.825 0.034 53.582 0.000 1.825 1.825
PHQ9_9|t3 2.099 0.043 49.191 0.000 2.099 2.099
EA_4|t1 -1.513 0.028 -54.919 0.000 -1.513 -1.513
EA_4|t2 -0.737 0.020 -37.525 0.000 -0.737 -0.737
EA_4|t3 0.308 0.018 17.027 0.000 0.308 0.308
EA_6|t1 -0.652 0.019 -33.922 0.000 -0.652 -0.652
EA_6|t2 0.362 0.018 19.873 0.000 0.362 0.362
EA_6|t3 1.146 0.023 50.412 0.000 1.146 1.146
EA_8|t1 -1.042 0.022 -47.876 0.000 -1.042 -1.042
EA_8|t2 -0.251 0.018 -13.949 0.000 -0.251 -0.251
EA_8|t3 0.681 0.019 35.175 0.000 0.681 0.681
EA_14|t1 -1.107 0.022 -49.530 0.000 -1.107 -1.107
EA_14|t2 -0.361 0.018 -19.817 0.000 -0.361 -0.361
EA_14|t3 0.691 0.019 35.609 0.000 0.691 0.691
ALC01_DC|t1 -0.776 0.031 -25.247 0.000 -0.776 -0.776
ALC01_DC|t2 0.299 0.028 10.688 0.000 0.299 0.299
ALC01_DC|t3 1.005 0.033 30.287 0.000 1.005 1.005
ALC01_DC|t4 1.613 0.045 35.532 0.000 1.613 1.613
ALC02_DC|t1 0.995 0.033 30.101 0.000 0.995 0.995
ALC02_DC|t2 1.668 0.047 35.421 0.000 1.668 1.668
ALC02_DC|t3 2.160 0.070 30.966 0.000 2.160 2.160
ALC03_DC|t1 0.448 0.029 15.692 0.000 0.448 0.448
ALC03_DC|t2 1.238 0.037 33.729 0.000 1.238 1.238
ALC03_DC|t3 1.562 0.044 35.551 0.000 1.562 1.562
ALC03_DC|t4 2.341 0.083 28.167 0.000 2.341 2.341
ALC04_DC|t1 1.021 0.033 30.582 0.000 1.021 1.021
ALC04_DC|t2 1.708 0.048 35.285 0.000 1.708 1.708
ALC04_DC|t3 2.124 0.067 31.465 0.000 2.124 2.124
ALC04_DC|t4 2.471 0.095 25.938 0.000 2.471 2.471
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PHQ9_1 0.560 0.560 0.560
.PHQ9_2 0.321 0.321 0.321
.PHQ9_3 0.501 0.501 0.501
.PHQ9_4 0.355 0.355 0.355
.PHQ9_5 0.530 0.530 0.530
.PHQ9_6 0.396 0.396 0.396
.PHQ9_7 0.489 0.489 0.489
.PHQ9_8 0.561 0.561 0.561
.PHQ9_9 0.530 0.530 0.530
.EA_4 0.661 0.661 0.661
.EA_6 0.691 0.691 0.691
.EA_8 0.366 0.366 0.366
.EA_14 0.662 0.662 0.662
.ALC01_DC 0.634 0.634 0.634
.ALC02_DC 0.114 0.114 0.114
.ALC03_DC 0.400 0.400 0.400
.ALC04_DC 0.182 0.182 0.182
PHQ9 0.440 0.014 32.323 0.000 1.000 1.000
EA_EVASIVO 0.339 0.016 20.751 0.000 1.000 1.000
AUDITC 0.366 0.024 15.212 0.000 1.000 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
PHQ9_1 1.000 1.000 1.000
PHQ9_2 1.000 1.000 1.000
PHQ9_3 1.000 1.000 1.000
PHQ9_4 1.000 1.000 1.000
PHQ9_5 1.000 1.000 1.000
PHQ9_6 1.000 1.000 1.000
PHQ9_7 1.000 1.000 1.000
PHQ9_8 1.000 1.000 1.000
PHQ9_9 1.000 1.000 1.000
EA_4 1.000 1.000 1.000
EA_6 1.000 1.000 1.000
EA_8 1.000 1.000 1.000
EA_14 1.000 1.000 1.000
ALC01_DC 1.000 1.000 1.000
ALC02_DC 1.000 1.000 1.000
ALC03_DC 1.000 1.000 1.000
ALC04_DC 1.000 1.000 1.000
R-Square:
Estimate
PHQ9_1 0.440
PHQ9_2 0.679
PHQ9_3 0.499
PHQ9_4 0.645
PHQ9_5 0.470
PHQ9_6 0.604
PHQ9_7 0.511
PHQ9_8 0.439
PHQ9_9 0.470
EA_4 0.339
EA_6 0.309
EA_8 0.634
EA_14 0.338
ALC01_DC 0.366
ALC02_DC 0.886
ALC03_DC 0.600
ALC04_DC 0.818
reliability(fit)
For constructs with categorical indicators, Zumbo et al.`s (2007) "ordinal alpha" is calculated in addition to the standard alpha, which treats ordinal variables as numeric. See Chalmers (2018) for a critique of "alpha.ord" and the response by Zumbo & Kroc (2019). Likewise, average variance extracted is calculated from polychoric (polyserial) not Pearson correlations.
PHQ9 EA_EVASIVO AUDITC
alpha 0.8522701 0.6711989 NA
alpha.ord 0.9056191 0.7255316 0.8762058
omega 0.8645731 0.6836231 0.7621202
omega2 0.8645731 0.6836231 0.7621202
omega3 0.8731777 0.6811235 0.7630602
avevar 0.5284564 0.4049198 0.6674271
semTools::discriminantValidity(fit)
Some of the latent variable variances are estimated instead of fixed to 1. The model is re-estimated by scaling the latent variables by fixing their variances and freeing all factor loadings.
MODELO ESTRUTURAL CORRELACIONAL
model<-"
PHQ9 =~ PHQ9_1 + PHQ9_2 + PHQ9_3 + PHQ9_4 + PHQ9_5 + PHQ9_6 + PHQ9_7 + PHQ9_8 + PHQ9_9
#EA_ATIVO =~ EA_3 + EA_5 + EA_7 + EA_9 + EA_13
EA_EVASIVO =~ EA_4 + EA_6 + EA_8 + EA_14
AUDITC =~ ALC01_DC + ALC02_DC + ALC03_DC + ALC04_DC
#EA_ATIVO~~0*EA_EVASIVO
AUDITC ~ SEXO + EDAD
AUDITC~~PHQ9
AUDITC~~EA_EVASIVO
"
fit<-sem(model=model,data=data,ordered=T,missing="pairwise",estimator="wlsmv",orthogonal=F)
summary(fit,fit.measures=T,standardized=T,rsquare=T)
lavaan 0.6.15 ended normally after 37 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 76
Number of observations 4975
Number of missing patterns 2
Model Test User Model:
Standard Scaled
Test Statistic 4101.639 4100.620
Degrees of freedom 148 148
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.010
Shift parameter 38.238
simple second-order correction
Model Test Baseline Model:
Test statistic 62457.959 35902.718
Degrees of freedom 136 136
P-value 0.000 0.000
Scaling correction factor 1.742
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.937 0.889
Tucker-Lewis Index (TLI) 0.942 0.898
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.073 0.073
90 Percent confidence interval - lower 0.071 0.071
90 Percent confidence interval - upper 0.075 0.075
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
P-value H_0: Robust RMSEA <= 0.050 NA
P-value H_0: Robust RMSEA >= 0.080 NA
Standardized Root Mean Square Residual:
SRMR 0.049 0.049
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
PHQ9 =~
PHQ9_1 1.000 0.621 0.621
PHQ9_2 1.303 0.025 52.507 0.000 0.810 0.810
PHQ9_3 1.079 0.023 46.258 0.000 0.670 0.670
PHQ9_4 1.257 0.025 50.396 0.000 0.781 0.781
PHQ9_5 1.052 0.023 44.970 0.000 0.653 0.653
PHQ9_6 1.221 0.026 47.607 0.000 0.759 0.759
PHQ9_7 1.123 0.024 46.882 0.000 0.697 0.697
PHQ9_8 1.056 0.027 39.754 0.000 0.656 0.656
PHQ9_9 1.078 0.032 33.877 0.000 0.670 0.670
EA_EVASIVO =~
EA_4 1.000 0.616 0.616
EA_6 0.889 0.029 30.567 0.000 0.547 0.547
EA_8 1.230 0.036 34.086 0.000 0.757 0.757
EA_14 0.975 0.030 32.148 0.000 0.600 0.600
AUDITC =~
ALC01_DC 1.000 0.638 0.631
ALC02_DC 1.511 0.050 30.480 0.000 0.965 0.939
ALC03_DC 1.230 0.044 28.238 0.000 0.785 0.771
ALC04_DC 1.460 0.049 29.653 0.000 0.932 0.909
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
AUDITC ~
SEXO 0.167 0.035 4.743 0.000 0.261 0.127
EDAD 0.010 0.001 7.084 0.000 0.016 0.204
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
PHQ9 ~~
.AUDITC 0.035 0.012 3.061 0.002 0.092 0.092
EA_EVASIVO ~~
.AUDITC 0.010 0.012 0.829 0.407 0.027 0.027
PHQ9 ~~
EA_EVASIVO 0.062 0.007 8.689 0.000 0.162 0.162
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PHQ9_1 0.000 0.000 0.000
.PHQ9_2 0.000 0.000 0.000
.PHQ9_3 0.000 0.000 0.000
.PHQ9_4 0.000 0.000 0.000
.PHQ9_5 0.000 0.000 0.000
.PHQ9_6 0.000 0.000 0.000
.PHQ9_7 0.000 0.000 0.000
.PHQ9_8 0.000 0.000 0.000
.PHQ9_9 0.000 0.000 0.000
.EA_4 0.000 0.000 0.000
.EA_6 0.000 0.000 0.000
.EA_8 0.000 0.000 0.000
.EA_14 0.000 0.000 0.000
.ALC01_DC 0.000 0.000 0.000
.ALC02_DC 0.000 0.000 0.000
.ALC03_DC 0.000 0.000 0.000
.ALC04_DC 0.000 0.000 0.000
PHQ9 0.000 0.000 0.000
EA_EVASIVO 0.000 0.000 0.000
.AUDITC 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
PHQ9_1|t1 -1.577 0.064 -24.706 0.000 -1.577 -1.577
PHQ9_1|t2 -0.265 0.062 -4.282 0.000 -0.265 -0.265
PHQ9_1|t3 0.222 0.063 3.525 0.000 0.222 0.222
PHQ9_2|t1 -1.280 0.066 -19.402 0.000 -1.280 -1.280
PHQ9_2|t2 0.159 0.065 2.466 0.014 0.159 0.159
PHQ9_2|t3 0.657 0.067 9.759 0.000 0.657 0.657
PHQ9_3|t1 -1.549 0.062 -24.914 0.000 -1.549 -1.549
PHQ9_3|t2 -0.435 0.060 -7.247 0.000 -0.435 -0.435
PHQ9_3|t3 0.061 0.061 0.999 0.318 0.061 0.061
PHQ9_4|t1 -1.767 0.066 -26.587 0.000 -1.767 -1.767
PHQ9_4|t2 -0.253 0.063 -4.013 0.000 -0.253 -0.253
PHQ9_4|t3 0.326 0.064 5.127 0.000 0.326 0.326
PHQ9_5|t1 -1.307 0.062 -21.047 0.000 -1.307 -1.307
PHQ9_5|t2 -0.294 0.061 -4.847 0.000 -0.294 -0.294
PHQ9_5|t3 0.328 0.063 5.240 0.000 0.328 0.328
PHQ9_6|t1 -0.696 0.067 -10.424 0.000 -0.696 -0.696
PHQ9_6|t2 0.298 0.067 4.454 0.000 0.298 0.298
PHQ9_6|t3 0.754 0.069 10.937 0.000 0.754 0.754
PHQ9_7|t1 -0.763 0.065 -11.704 0.000 -0.763 -0.763
PHQ9_7|t2 0.371 0.065 5.741 0.000 0.371 0.371
PHQ9_7|t3 0.883 0.066 13.282 0.000 0.883 0.883
PHQ9_8|t1 0.028 0.072 0.386 0.700 0.028 0.028
PHQ9_8|t2 0.952 0.074 12.865 0.000 0.952 0.952
PHQ9_8|t3 1.405 0.078 18.095 0.000 1.405 1.405
PHQ9_9|t1 0.513 0.089 5.783 0.000 0.513 0.513
PHQ9_9|t2 1.174 0.093 12.674 0.000 1.174 1.174
PHQ9_9|t3 1.456 0.095 15.267 0.000 1.456 1.456
EA_4|t1 -1.597 0.066 -24.045 0.000 -1.597 -1.597
EA_4|t2 -0.821 0.060 -13.695 0.000 -0.821 -0.821
EA_4|t3 0.225 0.059 3.807 0.000 0.225 0.225
EA_6|t1 -1.047 0.060 -17.483 0.000 -1.047 -1.047
EA_6|t2 -0.028 0.059 -0.484 0.628 -0.028 -0.028
EA_6|t3 0.760 0.059 12.794 0.000 0.760 0.760
EA_8|t1 -1.609 0.062 -26.079 0.000 -1.609 -1.609
EA_8|t2 -0.806 0.059 -13.685 0.000 -0.806 -0.806
EA_8|t3 0.135 0.059 2.300 0.021 0.135 0.135
EA_14|t1 -1.367 0.062 -22.214 0.000 -1.367 -1.367
EA_14|t2 -0.616 0.058 -10.554 0.000 -0.616 -0.616
EA_14|t3 0.439 0.058 7.538 0.000 0.439 0.439
ALC01_DC|t1 -0.055 0.090 -0.610 0.542 -0.055 -0.054
ALC01_DC|t2 1.064 0.091 11.678 0.000 1.064 1.051
ALC01_DC|t3 1.811 0.095 19.018 0.000 1.811 1.789
ALC01_DC|t4 2.468 0.105 23.511 0.000 2.468 2.438
ALC02_DC|t1 1.631 0.128 12.762 0.000 1.631 1.587
ALC02_DC|t2 2.315 0.135 17.120 0.000 2.315 2.253
ALC02_DC|t3 2.818 0.151 18.651 0.000 2.818 2.742
ALC03_DC|t1 1.127 0.104 10.878 0.000 1.127 1.106
ALC03_DC|t2 1.932 0.109 17.804 0.000 1.932 1.897
ALC03_DC|t3 2.261 0.112 20.237 0.000 2.261 2.220
ALC03_DC|t4 3.056 0.138 22.144 0.000 3.056 3.001
ALC04_DC|t1 1.724 0.132 13.080 0.000 1.724 1.681
ALC04_DC|t2 2.423 0.139 17.459 0.000 2.423 2.363
ALC04_DC|t3 2.851 0.155 18.419 0.000 2.851 2.780
ALC04_DC|t4 3.207 0.160 20.104 0.000 3.207 3.127
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PHQ9_1 0.614 0.614 0.614
.PHQ9_2 0.345 0.345 0.345
.PHQ9_3 0.551 0.551 0.551
.PHQ9_4 0.391 0.391 0.391
.PHQ9_5 0.573 0.573 0.573
.PHQ9_6 0.425 0.425 0.425
.PHQ9_7 0.514 0.514 0.514
.PHQ9_8 0.570 0.570 0.570
.PHQ9_9 0.552 0.552 0.552
.EA_4 0.621 0.621 0.621
.EA_6 0.700 0.700 0.700
.EA_8 0.427 0.427 0.427
.EA_14 0.640 0.640 0.640
.ALC01_DC 0.617 0.617 0.602
.ALC02_DC 0.125 0.125 0.118
.ALC03_DC 0.420 0.420 0.405
.ALC04_DC 0.183 0.183 0.174
PHQ9 0.386 0.014 27.798 0.000 1.000 1.000
EA_EVASIVO 0.379 0.017 22.278 0.000 1.000 1.000
.AUDITC 0.383 0.024 15.715 0.000 0.940 0.940
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
PHQ9_1 1.000 1.000 1.000
PHQ9_2 1.000 1.000 1.000
PHQ9_3 1.000 1.000 1.000
PHQ9_4 1.000 1.000 1.000
PHQ9_5 1.000 1.000 1.000
PHQ9_6 1.000 1.000 1.000
PHQ9_7 1.000 1.000 1.000
PHQ9_8 1.000 1.000 1.000
PHQ9_9 1.000 1.000 1.000
EA_4 1.000 1.000 1.000
EA_6 1.000 1.000 1.000
EA_8 1.000 1.000 1.000
EA_14 1.000 1.000 1.000
ALC01_DC 1.000 1.000 1.000
ALC02_DC 1.000 1.000 1.000
ALC03_DC 1.000 1.000 1.000
ALC04_DC 1.000 1.000 1.000
R-Square:
Estimate
PHQ9_1 0.386
PHQ9_2 0.655
PHQ9_3 0.449
PHQ9_4 0.609
PHQ9_5 0.427
PHQ9_6 0.575
PHQ9_7 0.486
PHQ9_8 0.430
PHQ9_9 0.448
EA_4 0.379
EA_6 0.300
EA_8 0.573
EA_14 0.360
ALC01_DC 0.398
ALC02_DC 0.882
ALC03_DC 0.595
ALC04_DC 0.826
AUDITC 0.060
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