RI- CLPM School-To-Prison Pipeline

Author

Ty Partridge

Code for running a random-intercept cross-lagged panel model

Step 1: set the working director and load the necessary packages

setwd("C:/Work Files/Lab/Conferences/SRA 2024/Jeanine")

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(lavaan)

This is lavaan 0.6-18
lavaan is FREE software! Please report any bugs.

library(semPlot)

Step 2: Call in the data set

I have a different file that I used to create the data set for this analysis.

SRA_S2P_Data <-read.csv("SRA_S2P_Data_New.csv")

Step 3: Data screening, cleaning, and preliminary analyses

Insert code blocks here for:

Descriptive Statistics
```
hist(SRA_S2P_Data$Gender.1)
```
Missing Data Analysis
Assumption Checking

Data corrections / adjustments to meet assumptions

SRA_S2P_Data <- SRA_S2P_Data %>% filter(Gender.1 < 3)

Step 4: Run the Measurement Models

# Measurement Model for Emotional Well-being 

EWB_Meas_Model <-'
  EWB_W1 =~   Q6f.1 + Q6g.2 + Q6h.1 + Q6i.1 + Q6f.1
             
  Resiliance_W1 =~  Q4a.1 + Q4b.1 + Q4c.1 + Q4d.1 + Q4e.1 + Q4f.1
  
  EWB_W2 =~  Q6f.2 + Q6g.2 + Q6h.2 + Q6i.2 + Q6f.2
  
  Resiliance_W2 =~ Q4a.2 + Q4b.2 + Q4c.2 + Q4d.2 + Q4e.2 + Q4f.2
             
  EWB_W4 =~  Q6f.4 + Q6g.4 + Q6h.4 + Q6i.4 + Q6f.4      
             
  Resiliance_W4 =~ Q4a.4 + Q4b.4 + Q4c.4 + Q4d.4 + Q4e.4 + Q4f.4
  
   '
EWB_Meas_Model_Fit <- sem(EWB_Meas_Model, estimator = "MLR", data=SRA_S2P_Data, mimic = "Mplus")

Warning: lavaan->lav_data_full():  
   some cases are empty and will be ignored: 507 553 576 1002 1230.

summary(EWB_Meas_Model_Fit, fit.measures = TRUE, standardized=TRUE)

lavaan 0.6-18 ended normally after 103 iterations

  Estimator                                         ML
  Optimization method                           NLMINB
  Number of model parameters                       103

                                                  Used       Total
  Number of observations                          1736        1741
  Number of missing patterns                       156            

Model Test User Model:
                                              Standard      Scaled
  Test Statistic                              2853.501    2180.012
  Degrees of freedom                               361         361
  P-value (Chi-square)                           0.000       0.000
  Scaling correction factor                                  1.309
    Yuan-Bentler correction (Mplus variant)                       

Model Test Baseline Model:

  Test statistic                             15298.830   11333.608
  Degrees of freedom                               406         406
  P-value                                        0.000       0.000
  Scaling correction factor                                  1.350

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.833       0.834
  Tucker-Lewis Index (TLI)                       0.812       0.813
                                                                  
  Robust Comparative Fit Index (CFI)                         0.830
  Robust Tucker-Lewis Index (TLI)                            0.809

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)             -41947.025  -41947.025
  Scaling correction factor                                  1.378
      for the MLR correction                                      
  Loglikelihood unrestricted model (H1)     -40520.275  -40520.275
  Scaling correction factor                                  1.324
      for the MLR correction                                      
                                                                  
  Akaike (AIC)                               84100.051   84100.051
  Bayesian (BIC)                             84662.363   84662.363
  Sample-size adjusted Bayesian (SABIC)      84335.142   84335.142

Root Mean Square Error of Approximation:

  RMSEA                                          0.063       0.054
  90 Percent confidence interval - lower         0.061       0.052
  90 Percent confidence interval - upper         0.065       0.056
  P-value H_0: RMSEA <= 0.050                    0.000       0.000
  P-value H_0: RMSEA >= 0.080                    0.000       0.000
                                                                  
  Robust RMSEA                                               0.077
  90 Percent confidence interval - lower                     0.074
  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                         0.087

Standardized Root Mean Square Residual:

  SRMR                                           0.057       0.057

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
  EWB_W1 =~                                                             
    Q6f.1             1.000                               0.542    0.516
    Q6g.2            -0.231    0.108   -2.144    0.032   -0.125   -0.106
    Q6h.1             1.801    0.101   17.797    0.000    0.977    0.840
    Q6i.1             1.424    0.086   16.510    0.000    0.773    0.745
  Resiliance_W1 =~                                                      
    Q4a.1             1.000                               0.394    0.545
    Q4b.1             1.105    0.079   14.011    0.000    0.436    0.575
    Q4c.1             1.112    0.074   15.116    0.000    0.439    0.606
    Q4d.1             0.959    0.072   13.347    0.000    0.378    0.428
    Q4e.1             1.077    0.070   15.394    0.000    0.425    0.615
    Q4f.1             1.293    0.079   16.388    0.000    0.510    0.687
  EWB_W2 =~                                                             
    Q6f.2             1.000                               0.615    0.568
    Q6g.2             1.646    0.123   13.371    0.000    1.012    0.860
    Q6h.2             1.711    0.099   17.332    0.000    1.052    0.869
    Q6i.2             1.296    0.090   14.457    0.000    0.797    0.726
  Resiliance_W2 =~                                                      
    Q4a.2             1.000                               0.382    0.524
    Q4b.2             1.067    0.096   11.121    0.000    0.407    0.550
    Q4c.2             1.025    0.099   10.389    0.000    0.391    0.549
    Q4d.2             1.021    0.092   11.066    0.000    0.389    0.444
    Q4e.2             1.329    0.100   13.238    0.000    0.507    0.700
    Q4f.2             1.521    0.108   14.043    0.000    0.580    0.734
  EWB_W4 =~                                                             
    Q6f.4             1.000                               0.628    0.583
    Q6g.4             1.551    0.102   15.270    0.000    0.974    0.788
    Q6h.4             1.688    0.107   15.710    0.000    1.060    0.868
    Q6i.4             1.487    0.096   15.546    0.000    0.934    0.817
  Resiliance_W4 =~                                                      
    Q4a.4             1.000                               0.415    0.547
    Q4b.4             1.282    0.100   12.782    0.000    0.532    0.675
    Q4c.4             1.200    0.099   12.065    0.000    0.498    0.683
    Q4d.4             1.043    0.090   11.609    0.000    0.433    0.503
    Q4e.4             1.417    0.111   12.793    0.000    0.588    0.743
    Q4f.4             1.432    0.109   13.088    0.000    0.594    0.733

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EWB_W1 ~~                                                             
    Resiliance_W1     0.138    0.014   10.214    0.000    0.646    0.646
    EWB_W2            0.225    0.023    9.623    0.000    0.674    0.674
    Resiliance_W2     0.116    0.012    9.713    0.000    0.560    0.560
    EWB_W4            0.183    0.021    8.757    0.000    0.536    0.536
    Resiliance_W4     0.115    0.013    8.646    0.000    0.512    0.512
  Resiliance_W1 ~~                                                      
    EWB_W2            0.129    0.014    9.173    0.000    0.532    0.532
    Resiliance_W2     0.122    0.013    9.218    0.000    0.814    0.814
    EWB_W4            0.105    0.013    8.003    0.000    0.424    0.424
    Resiliance_W4     0.106    0.013    8.336    0.000    0.645    0.645
  EWB_W2 ~~                                                             
    Resiliance_W2     0.156    0.016    9.922    0.000    0.665    0.665
    EWB_W4            0.246    0.027    9.275    0.000    0.639    0.639
    Resiliance_W4     0.131    0.015    8.747    0.000    0.513    0.513
  Resiliance_W2 ~~                                                      
    EWB_W4            0.125    0.014    8.592    0.000    0.520    0.520
    Resiliance_W4     0.113    0.014    8.333    0.000    0.712    0.712
  EWB_W4 ~~                                                             
    Resiliance_W4     0.192    0.021    8.964    0.000    0.737    0.737

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .Q6f.1             2.400    0.025   94.358    0.000    2.400    2.283
   .Q6g.2             2.048    0.034   60.971    0.000    2.048    1.741
   .Q6h.1             2.060    0.028   73.256    0.000    2.060    1.771
   .Q6i.1             1.668    0.025   66.341    0.000    1.668    1.609
   .Q4a.1             1.736    0.017   99.315    0.000    1.736    2.396
   .Q4b.1             1.804    0.018   98.266    0.000    1.804    2.378
   .Q4c.1             1.767    0.018  100.724    0.000    1.767    2.441
   .Q4d.1             2.102    0.021   97.978    0.000    2.102    2.376
   .Q4e.1             1.419    0.017   84.723    0.000    1.419    2.054
   .Q4f.1             1.516    0.018   84.335    0.000    1.516    2.041
   .Q6f.2             2.394    0.031   76.948    0.000    2.394    2.213
   .Q6h.2             2.116    0.034   62.267    0.000    2.116    1.748
   .Q6i.2             1.766    0.032   55.817    0.000    1.766    1.609
   .Q4a.2             1.727    0.021   82.586    0.000    1.727    2.371
   .Q4b.2             1.824    0.021   86.055    0.000    1.824    2.462
   .Q4c.2             1.794    0.021   87.437    0.000    1.794    2.521
   .Q4d.2             2.013    0.025   79.314    0.000    2.013    2.294
   .Q4e.2             1.479    0.021   70.201    0.000    1.479    2.043
   .Q4f.2             1.553    0.023   68.157    0.000    1.553    1.963
   .Q6f.4             2.340    0.033   70.962    0.000    2.340    2.172
   .Q6g.4             2.159    0.038   57.416    0.000    2.159    1.748
   .Q6h.4             2.101    0.037   56.806    0.000    2.101    1.721
   .Q6i.4             1.836    0.035   52.277    0.000    1.836    1.606
   .Q4a.4             1.843    0.023   79.551    0.000    1.843    2.429
   .Q4b.4             1.865    0.024   77.745    0.000    1.865    2.364
   .Q4c.4             1.788    0.022   80.130    0.000    1.788    2.450
   .Q4d.4             1.967    0.026   74.395    0.000    1.967    2.284
   .Q4e.4             1.580    0.025   64.138    0.000    1.580    1.995
   .Q4f.4             1.603    0.025   63.967    0.000    1.603    1.976

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .Q6f.1             0.811    0.034   23.509    0.000    0.811    0.734
   .Q6g.2             0.517    0.044   11.610    0.000    0.517    0.373
   .Q6h.1             0.399    0.035   11.467    0.000    0.399    0.295
   .Q6i.1             0.478    0.034   14.199    0.000    0.478    0.445
   .Q4a.1             0.369    0.016   23.196    0.000    0.369    0.703
   .Q4b.1             0.385    0.020   19.663    0.000    0.385    0.670
   .Q4c.1             0.332    0.016   21.149    0.000    0.332    0.633
   .Q4d.1             0.639    0.023   28.365    0.000    0.639    0.817
   .Q4e.1             0.297    0.017   17.420    0.000    0.297    0.622
   .Q4f.1             0.292    0.018   16.048    0.000    0.292    0.528
   .Q6f.2             0.793    0.043   18.503    0.000    0.793    0.677
   .Q6h.2             0.358    0.036    9.855    0.000    0.358    0.244
   .Q6i.2             0.570    0.041   14.049    0.000    0.570    0.473
   .Q4a.2             0.385    0.020   19.608    0.000    0.385    0.726
   .Q4b.2             0.383    0.024   16.069    0.000    0.383    0.698
   .Q4c.2             0.354    0.021   16.878    0.000    0.354    0.698
   .Q4d.2             0.618    0.026   23.811    0.000    0.618    0.803
   .Q4e.2             0.267    0.018   14.851    0.000    0.267    0.510
   .Q4f.2             0.289    0.020   14.113    0.000    0.289    0.462
   .Q6f.4             0.767    0.045   17.126    0.000    0.767    0.660
   .Q6g.4             0.578    0.053   10.864    0.000    0.578    0.379
   .Q6h.4             0.368    0.040    9.229    0.000    0.368    0.246
   .Q6i.4             0.435    0.034   12.689    0.000    0.435    0.333
   .Q4a.4             0.403    0.024   16.965    0.000    0.403    0.701
   .Q4b.4             0.339    0.024   14.046    0.000    0.339    0.545
   .Q4c.4             0.284    0.019   15.109    0.000    0.284    0.534
   .Q4d.4             0.554    0.027   20.563    0.000    0.554    0.747
   .Q4e.4             0.281    0.021   13.260    0.000    0.281    0.448
   .Q4f.4             0.305    0.024   12.733    0.000    0.305    0.463
    EWB_W1            0.294    0.031    9.519    0.000    1.000    1.000
    Resiliance_W1     0.156    0.016    9.614    0.000    1.000    1.000
    EWB_W2            0.378    0.043    8.842    0.000    1.000    1.000
    Resiliance_W2     0.146    0.019    7.705    0.000    1.000    1.000
    EWB_W4            0.394    0.049    8.077    0.000    1.000    1.000
    Resiliance_W4     0.172    0.023    7.458    0.000    1.000    1.000

# Measurement Model for Social Stressors

Soc_Stress_Model <-'
  Soc_Stress_1 =~ Q8a.1 + Q8b.1 + Q8c.1
  Soc_Stress_2 =~ Q8a.2 + Q8b.2 + Q8c.2
  Soc_Stress_4 =~ Q8a.4 + Q8b.4 + Q8c.4
'

Soc_Stress_Model_Fit <- sem(Soc_Stress_Model, estimator = "WLSMV", data=SRA_S2P_Data, mimic = "Mplus")

Warning: lavaan->lav_options_est_dwls():  
   estimator "DWLS" is not recommended for continuous data. Did you forget to 
   set the ordered= argument?

summary(Soc_Stress_Model_Fit, fit.measures = TRUE, standardized=TRUE)

lavaan 0.6-18 ended normally after 36 iterations

  Estimator                                       DWLS
  Optimization method                           NLMINB
  Number of model parameters                        30

                                                  Used       Total
  Number of observations                           848        1741

Model Test User Model:
                                              Standard      Scaled
  Test Statistic                                98.133     187.491
  Degrees of freedom                                24          24
  P-value (Chi-square)                           0.000       0.000
  Scaling correction factor                                  0.528
  Shift parameter                                            1.587
    simple second-order correction (WLSMV)                        

Model Test Baseline Model:

  Test statistic                              3032.703    1782.719
  Degrees of freedom                                36          36
  P-value                                        0.000       0.000
  Scaling correction factor                                  1.716

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.975       0.906
  Tucker-Lewis Index (TLI)                       0.963       0.860
                                                                  
  Robust Comparative Fit Index (CFI)                         0.975
  Robust Tucker-Lewis Index (TLI)                            0.963

Root Mean Square Error of Approximation:

  RMSEA                                          0.060       0.090
  90 Percent confidence interval - lower         0.048       0.078
  90 Percent confidence interval - upper         0.073       0.102
  P-value H_0: RMSEA <= 0.050                    0.078       0.000
  P-value H_0: RMSEA >= 0.080                    0.005       0.914
                                                                  
  Robust RMSEA                                               0.065
  90 Percent confidence interval - lower                     0.057
  90 Percent confidence interval - upper                     0.074
  P-value H_0: Robust RMSEA <= 0.050                         0.002
  P-value H_0: Robust RMSEA >= 0.080                         0.003

Standardized Root Mean Square Residual:

  SRMR                                           0.047       0.047

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
  Soc_Stress_1 =~                                                       
    Q8a.1             1.000                               0.514    0.502
    Q8b.1             1.153    0.111   10.370    0.000    0.593    0.563
    Q8c.1             1.425    0.118   12.114    0.000    0.732    0.660
  Soc_Stress_2 =~                                                       
    Q8a.2             1.000                               0.539    0.508
    Q8b.2             1.315    0.109   12.045    0.000    0.709    0.654
    Q8c.2             1.306    0.102   12.764    0.000    0.704    0.646
  Soc_Stress_4 =~                                                       
    Q8a.4             1.000                               0.550    0.510
    Q8b.4             1.176    0.116   10.183    0.000    0.647    0.611
    Q8c.4             1.418    0.125   11.344    0.000    0.780    0.699

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  Soc_Stress_1 ~~                                                       
    Soc_Stress_2      0.245    0.030    8.080    0.000    0.886    0.886
    Soc_Stress_4      0.224    0.029    7.815    0.000    0.792    0.792
  Soc_Stress_2 ~~                                                       
    Soc_Stress_4      0.224    0.028    7.906    0.000    0.756    0.756

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .Q8a.1             2.539    0.035   72.217    0.000    2.539    2.481
   .Q8b.1             2.031    0.036   56.183    0.000    2.031    1.930
   .Q8c.1             2.157    0.038   56.590    0.000    2.157    1.944
   .Q8a.2             2.508    0.036   68.785    0.000    2.508    2.363
   .Q8b.2             2.083    0.037   55.942    0.000    2.083    1.922
   .Q8c.2             2.179    0.037   58.232    0.000    2.179    2.001
   .Q8a.4             2.590    0.037   69.881    0.000    2.590    2.401
   .Q8b.4             2.072    0.036   56.919    0.000    2.072    1.956
   .Q8c.4             2.284    0.038   59.579    0.000    2.284    2.047

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .Q8a.1             0.783    0.033   23.830    0.000    0.783    0.748
   .Q8b.1             0.755    0.037   20.453    0.000    0.755    0.683
   .Q8c.1             0.695    0.042   16.719    0.000    0.695    0.564
   .Q8a.2             0.836    0.034   24.628    0.000    0.836    0.742
   .Q8b.2             0.672    0.041   16.244    0.000    0.672    0.572
   .Q8c.2             0.691    0.040   17.297    0.000    0.691    0.583
   .Q8a.4             0.860    0.041   20.772    0.000    0.860    0.740
   .Q8b.4             0.703    0.040   17.375    0.000    0.703    0.627
   .Q8c.4             0.636    0.045   14.025    0.000    0.636    0.511
    Soc_Stress_1      0.264    0.040    6.675    0.000    1.000    1.000
    Soc_Stress_2      0.290    0.042    6.977    0.000    1.000    1.000
    Soc_Stress_4      0.303    0.047    6.447    0.000    1.000    1.000

Step 5: Run the RI-CLPM

RI_CLPM <-'

 # Latent Variable Measurement Model
 ## Emotional Wellbeing
   EWB_W1 =~   Q6f.1 + Q6g.2 + Q6h.1 + Q6i.1 + Q6f.1
   EWB_W2 =~  Q6f.2 + Q6g.2 + Q6h.2 + Q6i.2 + Q6f.2
   EWB_W4 =~  Q6f.4 + Q6g.4 + Q6h.4 + Q6i.4 + Q6f.4      

 ## Social Stressors
 
  Soc_Stress_W1 =~ Q8a.1 + Q8b.1 + Q8c.1
  Soc_Stress_W2 =~ Q8a.2 + Q8b.2 + Q8c.2
  Soc_Stress_W4 =~ Q8a.4 + Q8b.4 + Q8c.4
  

 # Random Intercepts for each latent variable
  RI_SD =~ 1*SD_W1 + 1*SD_W2 + 1*SD_W4 # School Discipline
  RI_EWB =~ 1*EWB_W1 + 1*EWB_W2 + 1*EWB_W4 # Emotional Wellbeing
  RI_Soc_Stress =~ 1*Soc_Stress_W1 + 1*Soc_Stress_W2 + 1*Soc_Stress_W4 # Social Stress
  
  # Set intercept variances
  RI_SD ~~ RI_SD
  RI_EWB ~~ RI_EWB
  RI_Soc_Stress ~~ RI_Soc_Stress
  
  # Covariances among random intercepts
  RI_SD ~~ RI_EWB + RI_Soc_Stress
  RI_EWB ~~ RI_Soc_Stress

  # Autoregressive Paths
  SD_W2 ~ a1*SD_W1
  SD_W4 ~ a2*SD_W2
  
  EWB_W2 ~ b1*EWB_W1
  EWB_W4 ~ b2*EWB_W2
  
  Soc_Stress_W2 ~ c1*Soc_Stress_W1
  Soc_Stress_W4 ~ c2*Soc_Stress_W2
  
  # Cross-Lagged Paths
  SD_W2 ~ d1*EWB_W1 + e1*Soc_Stress_W1
  SD_W4 ~ d2*EWB_W2 + e2*Soc_Stress_W2
  
  EWB_W2 ~ f1*SD_W1 + g1*Soc_Stress_W1
  EWB_W4 ~ f2*SD_W2 + g2*Soc_Stress_W2
  
  Soc_Stress_W2 ~ h1*SD_W1 + i1*EWB_W1
  Soc_Stress_W4 ~ h2*SD_W2 + i2*EWB_W2
  
  # Residual Covariances within each time point
  SD_W1 ~~ EWB_W1 + Soc_Stress_W1
  EWB_W1 ~~ Soc_Stress_W1
  
  SD_W2 ~~ EWB_W2 + Soc_Stress_W2
  EWB_W2 ~~ Soc_Stress_W2
  
  SD_W4 ~~ EWB_W4 + Soc_Stress_W4
  EWB_W4 ~~ Soc_Stress_W4
  
  # Covariates on School Discipline
  
  SD_W1 ~ Gender.1 + Black_AfA.1 + White.1 + Hispanic.1
  SD_W2 ~ Gender.1 + Black_AfA.1 + White.1 + Hispanic.1
  SD_W4 ~ Gender.1 + Black_AfA.1 + White.1 + Hispanic.1
'


fit <- sem(RI_CLPM, data = SRA_S2P_Data, estimator = "MLR", mimic = "Mplus")

summary(fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-18 ended normally after 124 iterations

  Estimator                                         ML
  Optimization method                           NLMINB
  Number of model parameters                       112

  Number of observations                          1741
  Number of missing patterns                       148

Model Test User Model:
                                              Standard      Scaled
  Test Statistic                              1208.786    1068.222
  Degrees of freedom                               279         279
  P-value (Chi-square)                           0.000       0.000
  Scaling correction factor                                  1.132
    Yuan-Bentler correction (Mplus variant)                       

Model Test Baseline Model:

  Test statistic                             12190.755   10224.248
  Degrees of freedom                               345         345
  P-value                                        0.000       0.000
  Scaling correction factor                                  1.192

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.922       0.920
  Tucker-Lewis Index (TLI)                       0.903       0.901
                                                                  
  Robust Comparative Fit Index (CFI)                         0.922
  Robust Tucker-Lewis Index (TLI)                            0.904

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)             -36711.882  -36711.882
  Scaling correction factor                                  1.267
      for the MLR correction                                      
  Loglikelihood unrestricted model (H1)     -36107.489  -36107.489
  Scaling correction factor                                  1.170
      for the MLR correction                                      
                                                                  
  Akaike (AIC)                               73647.764   73647.764
  Bayesian (BIC)                             74259.532   74259.532
  Sample-size adjusted Bayesian (SABIC)      73903.718   73903.718

Root Mean Square Error of Approximation:

  RMSEA                                          0.044       0.040
  90 Percent confidence interval - lower         0.041       0.038
  90 Percent confidence interval - upper         0.046       0.043
  P-value H_0: RMSEA <= 0.050                    1.000       1.000
  P-value H_0: RMSEA >= 0.080                    0.000       0.000
                                                                  
  Robust RMSEA                                               0.052
  90 Percent confidence interval - lower                     0.049
  90 Percent confidence interval - upper                     0.056
  P-value H_0: Robust RMSEA <= 0.050                         0.120
  P-value H_0: Robust RMSEA >= 0.080                         0.000

Standardized Root Mean Square Residual:

  SRMR                                           0.052       0.052

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
  EWB_W1 =~                                                             
    Q6f.1             1.000                               0.544    0.518
    Q6g.2            -0.199    0.110   -1.812    0.070   -0.108   -0.092
    Q6h.1             1.806    0.102   17.743    0.000    0.983    0.845
    Q6i.1             1.410    0.084   16.711    0.000    0.767    0.740
  EWB_W2 =~                                                             
    Q6f.2             1.000                               0.617    0.570
    Q6g.2             1.610    0.124   12.959    0.000    0.993    0.847
    Q6h.2             1.703    0.098   17.435    0.000    1.050    0.873
    Q6i.2             1.263    0.086   14.716    0.000    0.779    0.713
  EWB_W4 =~                                                             
    Q6f.4             1.000                               0.625    0.581
    Q6g.4             1.564    0.103   15.257    0.000    0.978    0.793
    Q6h.4             1.696    0.108   15.643    0.000    1.061    0.871
    Q6i.4             1.473    0.095   15.462    0.000    0.921    0.807
  Soc_Stress_W1 =~                                                      
    Q8a.1             1.000                               0.478    0.468
    Q8b.1             1.323    0.090   14.754    0.000    0.633    0.585
    Q8c.1             1.616    0.101   15.942    0.000    0.773    0.692
  Soc_Stress_W2 =~                                                      
    Q8a.2             1.000                               0.498    0.464
    Q8b.2             1.365    0.110   12.459    0.000    0.680    0.631
    Q8c.2             1.467    0.108   13.634    0.000    0.731    0.663
  Soc_Stress_W4 =~                                                      
    Q8a.4             1.000                               0.524    0.488
    Q8b.4             1.300    0.110   11.858    0.000    0.681    0.631
    Q8c.4             1.585    0.122   12.977    0.000    0.830    0.737
  RI_SD =~                                                              
    SD_W1             1.000                               0.438    0.802
    SD_W2             1.000                               0.438    0.703
    SD_W4             1.000                               0.438    0.933
  RI_EWB =~                                                             
    EWB_W1            1.000                               0.694    0.694
    EWB_W2            1.000                               0.612    0.612
    EWB_W4            1.000                               0.604    0.604
  RI_Soc_Stress =~                                                      
    Soc_Stress_W1     1.000                               0.936    0.936
    Soc_Stress_W2     1.000                               0.899    0.899
    Soc_Stress_W4     1.000                               0.855    0.855

Regressions:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  SD_W2 ~                                                               
    SD_W1     (a1)    0.261    0.051    5.157    0.000    0.261    0.229
  SD_W4 ~                                                               
    SD_W2     (a2)   -0.402    0.055   -7.244    0.000   -0.402   -0.533
  EWB_W2 ~                                                              
    EWB_W1    (b1)    0.453    0.221    2.052    0.040    0.400    0.400
  EWB_W4 ~                                                              
    EWB_W2    (b2)    0.386    0.204    1.897    0.058    0.381    0.381
  Soc_Stress_W2 ~                                                       
    Sc_Str_W1 (c1)    0.073    0.154    0.476    0.634    0.071    0.071
  Soc_Stress_W4 ~                                                       
    Sc_Str_W2 (c2)   -0.075    0.126   -0.595    0.552   -0.071   -0.071
  SD_W2 ~                                                               
    EWB_W1    (d1)   -0.029    0.065   -0.451    0.652   -0.016   -0.026
    Sc_Str_W1 (e1)    0.004    0.069    0.052    0.958    0.002    0.003
  SD_W4 ~                                                               
    EWB_W2    (d2)    0.022    0.070    0.315    0.753    0.014    0.029
    Sc_Str_W2 (e2)   -0.018    0.077   -0.228    0.820   -0.009   -0.019
  EWB_W2 ~                                                              
    SD_W1     (f1)   -0.098    0.055   -1.775    0.076   -0.158   -0.086
    Sc_Str_W1 (g1)   -0.272    0.201   -1.353    0.176   -0.211   -0.211
  EWB_W4 ~                                                              
    SD_W2     (f2)   -0.074    0.052   -1.422    0.155   -0.118   -0.073
    Sc_Str_W2 (g2)   -0.287    0.203   -1.416    0.157   -0.229   -0.229
  Soc_Stress_W2 ~                                                       
    SD_W1     (h1)   -0.057    0.044   -1.308    0.191   -0.115   -0.063
    EWB_W1    (i1)   -0.032    0.115   -0.278    0.781   -0.035   -0.035
  Soc_Stress_W4 ~                                                       
    SD_W2     (h2)   -0.036    0.035   -1.020    0.308   -0.069   -0.043
    EWB_W2    (i2)    0.009    0.088    0.105    0.917    0.011    0.011
  SD_W1 ~                                                               
    Gender.1         -0.187    0.026   -7.205    0.000   -0.187   -0.171
    Blck_AA.1         0.153    0.047    3.259    0.001    0.153    0.126
    White.1          -0.179    0.044   -4.085    0.000   -0.179   -0.159
    Hispanc.1        -0.056    0.053   -1.058    0.290   -0.056   -0.028
  SD_W2 ~                                                               
    Gender.1         -0.185    0.027   -6.750    0.000   -0.185   -0.148
    Blck_AA.1         0.168    0.043    3.883    0.000    0.168    0.122
    White.1          -0.168    0.041   -4.115    0.000   -0.168   -0.130
    Hispanc.1        -0.050    0.049   -1.026    0.305   -0.050   -0.022
  SD_W4 ~                                                               
    Gender.1         -0.210    0.036   -5.751    0.000   -0.210   -0.223
    Blck_AA.1         0.102    0.064    1.604    0.109    0.102    0.098
    White.1          -0.213    0.062   -3.434    0.001   -0.213   -0.220
    Hispanc.1         0.025    0.074    0.339    0.735    0.025    0.014

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  RI_SD ~~                                                              
    RI_EWB            0.049    0.013    3.909    0.000    0.297    0.297
    RI_Soc_Stress     0.032    0.011    2.947    0.003    0.162    0.162
  RI_EWB ~~                                                             
    RI_Soc_Stress     0.149    0.018    8.207    0.000    0.880    0.880
 .EWB_W1 ~~                                                             
   .SD_W1            -0.014    0.010   -1.477    0.140   -0.037   -0.133
 .Soc_Stress_W1 ~~                                                      
   .SD_W1            -0.010    0.007   -1.404    0.160   -0.061   -0.222
 .EWB_W1 ~~                                                             
   .Soc_Stress_W1     0.041    0.015    2.774    0.006    0.617    0.617
 .EWB_W2 ~~                                                             
   .SD_W2             0.006    0.008    0.736    0.462    0.015    0.081
 .Soc_Stress_W2 ~~                                                      
   .SD_W2             0.001    0.006    0.129    0.897    0.004    0.023
 .EWB_W2 ~~                                                             
   .Soc_Stress_W2     0.049    0.013    3.611    0.000    0.688    0.688
 .EWB_W4 ~~                                                             
   .SD_W4             0.018    0.009    1.937    0.053    0.041    0.103
 .Soc_Stress_W4 ~~                                                      
   .SD_W4             0.020    0.009    2.362    0.018    0.063    0.160
 .EWB_W4 ~~                                                             
   .Soc_Stress_W4     0.098    0.016    5.964    0.000    0.701    0.701

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .Q6f.1             2.400    0.025   94.367    0.000    2.400    2.284
   .Q6g.2             2.085    0.041   51.355    0.000    2.085    1.779
   .Q6h.1             2.059    0.028   73.250    0.000    2.059    1.769
   .Q6i.1             1.668    0.025   66.356    0.000    1.668    1.608
   .Q6f.2             2.417    0.035   69.755    0.000    2.417    2.236
   .Q6h.2             2.155    0.042   51.872    0.000    2.155    1.791
   .Q6i.2             1.793    0.036   49.180    0.000    1.793    1.641
   .Q6f.4             2.369    0.039   60.466    0.000    2.369    2.203
   .Q6g.4             2.205    0.051   43.324    0.000    2.205    1.788
   .Q6h.4             2.150    0.052   40.973    0.000    2.150    1.765
   .Q6i.4             1.879    0.048   39.157    0.000    1.879    1.646
   .Q8a.1             2.656    0.025  108.137    0.000    2.656    2.600
   .Q8b.1             2.112    0.026   81.187    0.000    2.112    1.953
   .Q8c.1             2.251    0.027   83.546    0.000    2.251    2.013
   .Q8a.2             2.593    0.032   81.961    0.000    2.593    2.416
   .Q8b.2             2.160    0.033   64.952    0.000    2.160    2.003
   .Q8c.2             2.252    0.034   66.352    0.000    2.252    2.043
   .Q8a.4             2.649    0.034   77.217    0.000    2.649    2.468
   .Q8b.4             2.175    0.036   60.535    0.000    2.175    2.015
   .Q8c.4             2.401    0.039   62.221    0.000    2.401    2.130
   .SD_W1             0.601    0.061    9.836    0.000    0.601    1.100
   .SD_W2             0.602    0.063    9.605    0.000    0.602    0.965
   .SD_W4             0.774    0.089    8.707    0.000    0.774    1.647

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    RI_SD             0.192    0.020    9.735    0.000    1.000    1.000
    RI_EWB            0.143    0.031    4.597    0.000    1.000    1.000
    RI_Soc_Stress     0.201    0.027    7.429    0.000    1.000    1.000
   .Q6f.1             0.808    0.035   23.379    0.000    0.808    0.732
   .Q6g.2             0.518    0.046   11.306    0.000    0.518    0.377
   .Q6h.1             0.388    0.034   11.459    0.000    0.388    0.287
   .Q6i.1             0.486    0.032   15.035    0.000    0.486    0.452
   .Q6f.2             0.789    0.043   18.380    0.000    0.789    0.675
   .Q6h.2             0.345    0.036    9.552    0.000    0.345    0.238
   .Q6i.2             0.587    0.040   14.510    0.000    0.587    0.492
   .Q6f.4             0.766    0.045   16.940    0.000    0.766    0.662
   .Q6g.4             0.564    0.053   10.700    0.000    0.564    0.371
   .Q6h.4             0.358    0.039    9.148    0.000    0.358    0.241
   .Q6i.4             0.454    0.035   13.136    0.000    0.454    0.348
   .Q8a.1             0.815    0.022   36.363    0.000    0.815    0.781
   .Q8b.1             0.769    0.027   28.320    0.000    0.769    0.657
   .Q8c.1             0.652    0.032   20.120    0.000    0.652    0.522
   .Q8a.2             0.904    0.029   31.523    0.000    0.904    0.784
   .Q8b.2             0.701    0.034   20.796    0.000    0.701    0.602
   .Q8c.2             0.680    0.037   18.621    0.000    0.680    0.560
   .Q8a.4             0.878    0.034   26.108    0.000    0.878    0.762
   .Q8b.4             0.700    0.035   20.071    0.000    0.700    0.601
   .Q8c.4             0.582    0.039   14.902    0.000    0.582    0.458
   .SD_W1             0.077    0.013    5.899    0.000    0.077    0.257
   .SD_W2             0.033    0.008    3.972    0.000    0.033    0.085
   .SD_W4             0.158    0.013   12.229    0.000    0.158    0.716
   .EWB_W1            0.154    0.033    4.607    0.000    0.519    0.519
   .EWB_W2            0.167    0.032    5.204    0.000    0.440    0.440
   .EWB_W4            0.189    0.031    6.122    0.000    0.483    0.483
   .Soc_Stress_W1     0.028    0.015    1.875    0.061    0.123    0.123
   .Soc_Stress_W2     0.030    0.015    1.953    0.051    0.120    0.120
   .Soc_Stress_W4     0.103    0.021    4.962    0.000    0.376    0.376

This code will generate a figure but it isn’t very good.

semPaths(
  fit, 
  what = "std",               
  layout = "tree",            
  edge.label.cex = 0.8,       
  sizeMan = 5,                
  sizeLat = 6,                
  residuals = TRUE,           
  intercepts = FALSE,         
  nCharNodes = 8,             
  fade = TRUE,               
  title = TRUE,               
  nodeLabels = c(             
    "SD_W1", "EWB_W1", "Soc_Stress_W1",
    "SD_W2", "EWB_W2", "Soc_Stress_W2",
    "SD_W3", "EWB_W3", "Soc_Stress_W3",
    "RI_SD", "RI_EWB", "RI_Soc_Stress"
  )
)

Step 6: Run the model as a multigroup model for MH Clinic Users v. non-users

fit <- sem(RI_CLPM, data = SRA_S2P_Data, estimator = "MLR", mimic = "Mplus", group = "MH_Clinic_Use")

Warning: lavaan->lav_model_vcov():  
   The variance-covariance matrix of the estimated parameters (vcov) does not 
   appear to be positive definite! The smallest eigenvalue (= 1.216521e-14) 
   is close to zero. This may be a symptom that the model is not identified.

summary(fit, fit.measures = TRUE, standardized = TRUE)

lavaan 0.6-18 ended normally after 162 iterations

  Estimator                                         ML
  Optimization method                           NLMINB
  Number of model parameters                       233
  Number of equality constraints                    56

  Number of observations per group:                   
    0                                             1305
    1                                              436
  Number of missing patterns per group:               
    0                                              118
    1                                               58

Model Test User Model:
                                              Standard      Scaled
  Test Statistic                              1517.495    1369.536
  Degrees of freedom                               605         605
  P-value (Chi-square)                           0.000       0.000
  Scaling correction factor                                  1.108
    Yuan-Bentler correction (Mplus variant)                       
  Test statistic for each group:
    0                                          998.034     900.724
    1                                          519.460     468.812

Model Test Baseline Model:

  Test statistic                             12367.815   10661.962
  Degrees of freedom                               690         690
  P-value                                        0.000       0.000
  Scaling correction factor                                  1.160

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.922       0.923
  Tucker-Lewis Index (TLI)                       0.911       0.913
                                                                  
  Robust Comparative Fit Index (CFI)                         0.924
  Robust Tucker-Lewis Index (TLI)                            0.913

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)             -36619.205  -36619.205
  Scaling correction factor                                  0.945
      for the MLR correction                                      
  Loglikelihood unrestricted model (H1)     -35860.458  -35860.458
  Scaling correction factor                                  1.139
      for the MLR correction                                      
                                                                  
  Akaike (AIC)                               73592.410   73592.410
  Bayesian (BIC)                             74559.222   74559.222
  Sample-size adjusted Bayesian (SABIC)      73996.910   73996.910

Root Mean Square Error of Approximation:

  RMSEA                                          0.042       0.038
  90 Percent confidence interval - lower         0.039       0.036
  90 Percent confidence interval - upper         0.044       0.041
  P-value H_0: RMSEA <= 0.050                    1.000       1.000
  P-value H_0: RMSEA >= 0.080                    0.000       0.000
                                                                  
  Robust RMSEA                                               0.050
  90 Percent confidence interval - lower                     0.046
  90 Percent confidence interval - upper                     0.053
  P-value H_0: Robust RMSEA <= 0.050                         0.568
  P-value H_0: Robust RMSEA >= 0.080                         0.000

Standardized Root Mean Square Residual:

  SRMR                                           0.057       0.057

Parameter Estimates:

  Standard errors                             Sandwich
  Information bread                           Observed
  Observed information based on                Hessian


Group 1 [0]:

Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EWB_W1 =~                                                             
    Q6f.1             1.000                               0.520    0.501
    Q6g.2   (.p2.)   -0.188    0.106   -1.779    0.075   -0.098   -0.084
    Q6h.1   (.p3.)    1.799    0.102   17.602    0.000    0.935    0.829
    Q6i.1   (.p4.)    1.407    0.085   16.562    0.000    0.731    0.728
  EWB_W2 =~                                                             
    Q6f.2             1.000                               0.597    0.558
    Q6g.2   (.p6.)    1.603    0.121   13.235    0.000    0.958    0.823
    Q6h.2   (.p7.)    1.704    0.097   17.511    0.000    1.018    0.867
    Q6i.2   (.p8.)    1.263    0.086   14.699    0.000    0.755    0.713
  EWB_W4 =~                                                             
    Q6f.4             1.000                               0.596    0.557
    Q6g.4   (.10.)    1.564    0.103   15.135    0.000    0.932    0.779
    Q6h.4   (.11.)    1.694    0.110   15.349    0.000    1.010    0.853
    Q6i.4   (.12.)    1.476    0.097   15.208    0.000    0.880    0.802
  Soc_Stress_W1 =~                                                      
    Q8a.1             1.000                               0.468    0.456
    Q8b.1   (.14.)    1.331    0.092   14.528    0.000    0.622    0.585
    Q8c.1   (.15.)    1.613    0.103   15.596    0.000    0.754    0.685
  Soc_Stress_W2 =~                                                      
    Q8a.2             1.000                               0.485    0.452
    Q8b.2   (.17.)    1.387    0.114   12.173    0.000    0.673    0.630
    Q8c.2   (.18.)    1.480    0.111   13.278    0.000    0.718    0.657
  Soc_Stress_W4 =~                                                      
    Q8a.4             1.000                               0.531    0.484
    Q8b.4   (.20.)    1.285    0.108   11.851    0.000    0.683    0.634
    Q8c.4   (.21.)    1.567    0.121   12.904    0.000    0.832    0.737
  RI_SD =~                                                              
    SD_W1             1.000                               0.413    0.807
    SD_W2             1.000                               0.413    0.693
    SD_W4             1.000                               0.413    0.924
  RI_EWB =~                                                             
    EWB_W1            1.000                               0.705    0.705
    EWB_W2            1.000                               0.613    0.613
    EWB_W4            1.000                               0.614    0.614
  RI_Soc_Stress =~                                                      
    Sc_S_W1           1.000                               0.977    0.977
    Sc_S_W2           1.000                               0.942    0.942
    Sc_S_W4           1.000                               0.860    0.860

Regressions:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  SD_W2 ~                                                               
    SD_W1     (a1)    0.254    0.049    5.133    0.000    0.254    0.218
  SD_W4 ~                                                               
    SD_W2     (a2)   -0.414    0.055   -7.544    0.000   -0.414   -0.553
  EWB_W2 ~                                                              
    EWB_W1    (b1)    0.469    0.215    2.178    0.029    0.408    0.408
  EWB_W4 ~                                                              
    EWB_W2    (b2)    0.400    0.199    2.016    0.044    0.401    0.401
  Soc_Stress_W2 ~                                                       
    Sc_Str_W1 (c1)    0.020    0.156    0.127    0.899    0.019    0.019
  Soc_Stress_W4 ~                                                       
    Sc_Str_W2 (c2)   -0.092    0.132   -0.698    0.485   -0.084   -0.084
  SD_W2 ~                                                               
    EWB_W1    (d1)   -0.030    0.073   -0.413    0.679   -0.016   -0.026
    Sc_Str_W1 (e1)    0.013    0.076    0.169    0.866    0.006    0.010
  SD_W4 ~                                                               
    EWB_W2    (d2)    0.023    0.075    0.308    0.758    0.014    0.031
    Sc_Str_W2 (e2)   -0.015    0.084   -0.179    0.858   -0.007   -0.016
  EWB_W2 ~                                                              
    SD_W1     (f1)   -0.095    0.056   -1.698    0.090   -0.159   -0.082
    Sc_Str_W1 (g1)   -0.281    0.195   -1.441    0.150   -0.220   -0.220
  EWB_W4 ~                                                              
    SD_W2     (f2)   -0.074    0.053   -1.413    0.158   -0.125   -0.074
    Sc_Str_W2 (g2)   -0.293    0.194   -1.508    0.132   -0.239   -0.239
  Soc_Stress_W2 ~                                                       
    SD_W1     (h1)   -0.042    0.043   -0.981    0.326   -0.087   -0.044
    EWB_W1    (i1)    0.005    0.114    0.044    0.965    0.005    0.005
  Soc_Stress_W4 ~                                                       
    SD_W2     (h2)   -0.030    0.034   -0.879    0.379   -0.057   -0.034
    EWB_W2    (i2)    0.037    0.092    0.397    0.691    0.041    0.041
  SD_W1 ~                                                               
    Gender.1         -0.186    0.028   -6.745    0.000   -0.186   -0.181
    Blck_AA.1         0.168    0.054    3.102    0.002    0.168    0.148
    White.1          -0.145    0.050   -2.883    0.004   -0.145   -0.137
    Hispanc.1        -0.048    0.063   -0.758    0.448   -0.048   -0.024
  SD_W2 ~                                                               
    Gender.1         -0.190    0.030   -6.359    0.000   -0.190   -0.159
    Blck_AA.1         0.200    0.052    3.821    0.000    0.200    0.152
    White.1          -0.137    0.048   -2.836    0.005   -0.137   -0.111
    Hispanc.1        -0.028    0.061   -0.465    0.642   -0.028   -0.012
  SD_W4 ~                                                               
    Gender.1         -0.221    0.039   -5.733    0.000   -0.221   -0.247
    Blck_AA.1         0.144    0.070    2.041    0.041    0.144    0.146
    White.1          -0.173    0.067   -2.566    0.010   -0.173   -0.187
    Hispanc.1         0.018    0.087    0.206    0.837    0.018    0.010

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  RI_SD ~~                                                              
    RI_EWB            0.041    0.012    3.460    0.001    0.272    0.272
    RI_Soc_Stress     0.023    0.011    2.114    0.034    0.122    0.122
  RI_EWB ~~                                                             
    RI_Soc_Stress     0.145    0.018    7.913    0.000    0.869    0.869
 .EWB_W1 ~~                                                             
   .SD_W1            -0.014    0.010   -1.481    0.139   -0.039   -0.154
 .Soc_Stress_W1 ~~                                                      
   .SD_W1            -0.010    0.007   -1.525    0.127   -0.103   -0.408
 .EWB_W1 ~~                                                             
   .Soc_Stress_W1     0.031    0.015    2.109    0.035    0.847    0.847
 .EWB_W2 ~~                                                             
   .SD_W2             0.018    0.009    1.932    0.053    0.046    0.237
 .Soc_Stress_W2 ~~                                                      
   .SD_W2             0.003    0.006    0.523    0.601    0.025    0.126
 .EWB_W2 ~~                                                             
   .Soc_Stress_W2     0.048    0.014    3.431    0.001    0.898    0.898
 .EWB_W4 ~~                                                             
   .SD_W4             0.018    0.010    1.858    0.063    0.045    0.120
 .Soc_Stress_W4 ~~                                                      
   .SD_W4             0.020    0.010    2.060    0.039    0.062    0.164
 .EWB_W4 ~~                                                             
   .Soc_Stress_W4     0.088    0.017    5.087    0.000    0.679    0.679

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .Q6f.1   (.115)    2.359    0.026   90.354    0.000    2.359    2.276
   .Q6g.2   (.116)    2.030    0.043   47.683    0.000    2.030    1.744
   .Q6h.1   (.117)    1.987    0.031   64.178    0.000    1.987    1.763
   .Q6i.1   (.118)    1.611    0.026   61.079    0.000    1.611    1.606
   .Q6f.2   (.119)    2.377    0.036   66.716    0.000    2.377    2.221
   .Q6h.2   (.120)    2.087    0.044   47.508    0.000    2.087    1.778
   .Q6i.2   (.121)    1.741    0.038   46.390    0.000    1.741    1.645
   .Q6f.4   (.122)    2.334    0.040   58.720    0.000    2.334    2.182
   .Q6g.4   (.123)    2.151    0.052   40.980    0.000    2.151    1.797
   .Q6h.4   (.124)    2.090    0.054   38.436    0.000    2.090    1.766
   .Q6i.4   (.125)    1.827    0.049   37.347    0.000    1.827    1.665
   .Q8a.1   (.126)    2.610    0.026   99.199    0.000    2.610    2.544
   .Q8b.1   (.127)    2.051    0.028   73.522    0.000    2.051    1.930
   .Q8c.1   (.128)    2.178    0.029   73.893    0.000    2.178    1.978
   .Q8a.2   (.129)    2.549    0.033   76.725    0.000    2.549    2.376
   .Q8b.2   (.130)    2.102    0.035   59.820    0.000    2.102    1.965
   .Q8c.2   (.131)    2.189    0.036   60.913    0.000    2.189    2.002
   .Q8a.4   (.132)    2.617    0.036   71.764    0.000    2.617    2.383
   .Q8b.4   (.133)    2.133    0.038   56.273    0.000    2.133    1.981
   .Q8c.4   (.134)    2.351    0.042   56.264    0.000    2.351    2.083
   .SD_W1   (.135)    0.538    0.066    8.103    0.000    0.538    1.052
   .SD_W2   (.136)    0.546    0.069    7.902    0.000    0.546    0.916
   .SD_W4   (.137)    0.712    0.091    7.801    0.000    0.712    1.594

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    RI_SD             0.171    0.019    9.025    0.000    1.000    1.000
    RI_EWB            0.134    0.028    4.791    0.000    1.000    1.000
    RI_Soc_Stress     0.209    0.029    7.196    0.000    1.000    1.000
   .Q6f.1             0.804    0.040   20.069    0.000    0.804    0.749
   .Q6g.2             0.554    0.051   10.802    0.000    0.554    0.409
   .Q6h.1             0.397    0.039   10.238    0.000    0.397    0.312
   .Q6i.1             0.472    0.035   13.406    0.000    0.472    0.469
   .Q6f.2             0.789    0.049   15.932    0.000    0.789    0.688
   .Q6h.2             0.341    0.041    8.253    0.000    0.341    0.248
   .Q6i.2             0.550    0.047   11.797    0.000    0.550    0.491
   .Q6f.4             0.788    0.055   14.255    0.000    0.788    0.689
   .Q6g.4             0.564    0.062    9.147    0.000    0.564    0.394
   .Q6h.4             0.381    0.048    7.972    0.000    0.381    0.272
   .Q6i.4             0.430    0.038   11.219    0.000    0.430    0.357
   .Q8a.1             0.834    0.025   32.883    0.000    0.834    0.792
   .Q8b.1             0.743    0.029   25.197    0.000    0.743    0.657
   .Q8c.1             0.644    0.036   17.893    0.000    0.644    0.531
   .Q8a.2             0.916    0.033   27.614    0.000    0.916    0.795
   .Q8b.2             0.690    0.039   17.786    0.000    0.690    0.604
   .Q8c.2             0.679    0.041   16.531    0.000    0.679    0.568
   .Q8a.4             0.924    0.040   23.166    0.000    0.924    0.766
   .Q8b.4             0.693    0.039   17.720    0.000    0.693    0.598
   .Q8c.4             0.581    0.044   13.233    0.000    0.581    0.456
   .SD_W1             0.064    0.012    5.236    0.000    0.064    0.245
   .SD_W2             0.038    0.011    3.568    0.000    0.038    0.108
   .SD_W4             0.144    0.016    9.203    0.000    0.144    0.720
   .EWB_W1            0.136    0.030    4.483    0.000    0.503    0.503
   .EWB_W2            0.156    0.032    4.915    0.000    0.436    0.436
   .EWB_W4            0.164    0.029    5.740    0.000    0.461    0.461
   .Soc_Stress_W1     0.010    0.016    0.604    0.546    0.045    0.045
   .Soc_Stress_W2     0.018    0.018    1.026    0.305    0.078    0.078
   .Soc_Stress_W4     0.102    0.023    4.353    0.000    0.360    0.360


Group 2 [1]:

Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  EWB_W1 =~                                                             
    Q6f.1             1.000                               0.600    0.555
    Q6g.2   (.p2.)   -0.188    0.106   -1.779    0.075   -0.113   -0.095
    Q6h.1   (.p3.)    1.799    0.102   17.602    0.000    1.079    0.870
    Q6i.1   (.p4.)    1.407    0.085   16.562    0.000    0.844    0.758
  EWB_W2 =~                                                             
    Q6f.2             1.000                               0.658    0.595
    Q6g.2   (.p6.)    1.603    0.121   13.235    0.000    1.055    0.885
    Q6h.2   (.p7.)    1.704    0.097   17.511    0.000    1.121    0.887
    Q6i.2   (.p8.)    1.263    0.086   14.699    0.000    0.831    0.709
  EWB_W4 =~                                                             
    Q6f.4             1.000                               0.683    0.630
    Q6g.4   (.10.)    1.564    0.103   15.135    0.000    1.068    0.815
    Q6h.4   (.11.)    1.694    0.110   15.349    0.000    1.158    0.901
    Q6i.4   (.12.)    1.476    0.097   15.208    0.000    1.009    0.819
  Soc_Stress_W1 =~                                                      
    Q8a.1             1.000                               0.480    0.483
    Q8b.1   (.14.)    1.331    0.092   14.528    0.000    0.639    0.573
    Q8c.1   (.15.)    1.613    0.103   15.596    0.000    0.775    0.680
  Soc_Stress_W2 =~                                                      
    Q8a.2             1.000                               0.495    0.467
    Q8b.2   (.17.)    1.387    0.114   12.173    0.000    0.686    0.630
    Q8c.2   (.18.)    1.480    0.111   13.278    0.000    0.732    0.659
  Soc_Stress_W4 =~                                                      
    Q8a.4             1.000                               0.514    0.508
    Q8b.4   (.20.)    1.285    0.108   11.851    0.000    0.661    0.614
    Q8c.4   (.21.)    1.567    0.121   12.904    0.000    0.806    0.722
  RI_SD =~                                                              
    SD_W1             1.000                               0.498    0.797
    SD_W2             1.000                               0.498    0.729
    SD_W4             1.000                               0.498    0.963
  RI_EWB =~                                                             
    EWB_W1            1.000                               0.617    0.617
    EWB_W2            1.000                               0.562    0.562
    EWB_W4            1.000                               0.542    0.542
  RI_Soc_Stress =~                                                      
    Sc_S_W1           1.000                               0.863    0.863
    Sc_S_W2           1.000                               0.838    0.838
    Sc_S_W4           1.000                               0.806    0.806

Regressions:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  SD_W2 ~                                                               
    SD_W1     (a1)    0.254    0.049    5.133    0.000    0.254    0.232
  SD_W4 ~                                                               
    SD_W2     (a2)   -0.414    0.055   -7.544    0.000   -0.414   -0.547
  EWB_W2 ~                                                              
    EWB_W1    (b1)    0.469    0.215    2.178    0.029    0.428    0.428
  EWB_W4 ~                                                              
    EWB_W2    (b2)    0.400    0.199    2.016    0.044    0.385    0.385
  Soc_Stress_W2 ~                                                       
    Sc_Str_W1 (c1)    0.020    0.156    0.127    0.899    0.019    0.019
  Soc_Stress_W4 ~                                                       
    Sc_Str_W2 (c2)   -0.092    0.132   -0.698    0.485   -0.088   -0.088
  SD_W2 ~                                                               
    EWB_W1    (d1)   -0.030    0.073   -0.413    0.679   -0.018   -0.026
    Sc_Str_W1 (e1)    0.013    0.076    0.169    0.866    0.006    0.009
  SD_W4 ~                                                               
    EWB_W2    (d2)    0.023    0.075    0.308    0.758    0.015    0.029
    Sc_Str_W2 (e2)   -0.015    0.084   -0.179    0.858   -0.007   -0.014
  EWB_W2 ~                                                              
    SD_W1     (f1)   -0.095    0.056   -1.698    0.090   -0.145   -0.090
    Sc_Str_W1 (g1)   -0.281    0.195   -1.441    0.150   -0.205   -0.205
  EWB_W4 ~                                                              
    SD_W2     (f2)   -0.074    0.053   -1.413    0.158   -0.109   -0.074
    Sc_Str_W2 (g2)   -0.293    0.194   -1.508    0.132   -0.212   -0.212
  Soc_Stress_W2 ~                                                       
    SD_W1     (h1)   -0.042    0.043   -0.981    0.326   -0.085   -0.053
    EWB_W1    (i1)    0.005    0.114    0.044    0.965    0.006    0.006
  Soc_Stress_W4 ~                                                       
    SD_W2     (h2)   -0.030    0.034   -0.879    0.379   -0.059   -0.040
    EWB_W2    (i2)    0.037    0.092    0.397    0.691    0.047    0.047
  SD_W1 ~                                                               
    Gender.1         -0.191    0.056   -3.412    0.001   -0.191   -0.152
    Blck_AA.1         0.129    0.090    1.431    0.152    0.129    0.093
    White.1          -0.266    0.082   -3.246    0.001   -0.266   -0.207
    Hispanc.1        -0.115    0.094   -1.233    0.217   -0.115   -0.057
  SD_W2 ~                                                               
    Gender.1         -0.186    0.055   -3.376    0.001   -0.186   -0.136
    Blck_AA.1         0.103    0.079    1.308    0.191    0.103    0.068
    White.1          -0.250    0.075   -3.341    0.001   -0.250   -0.178
    Hispanc.1        -0.130    0.084   -1.544    0.123   -0.130   -0.059
  SD_W4 ~                                                               
    Gender.1         -0.185    0.069   -2.669    0.008   -0.185   -0.178
    Blck_AA.1         0.028    0.115    0.247    0.805    0.028    0.025
    White.1          -0.314    0.113   -2.782    0.005   -0.314   -0.296
    Hispanc.1         0.002    0.138    0.017    0.986    0.002    0.001

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  RI_SD ~~                                                              
    RI_EWB            0.051    0.020    2.510    0.012    0.277    0.277
    RI_Soc_Stress     0.029    0.017    1.716    0.086    0.142    0.142
  RI_EWB ~~                                                             
    RI_Soc_Stress     0.135    0.023    5.831    0.000    0.879    0.879
 .EWB_W1 ~~                                                             
   .SD_W1            -0.014    0.017   -0.822    0.411   -0.029   -0.089
 .Soc_Stress_W1 ~~                                                      
   .SD_W1            -0.008    0.013   -0.603    0.546   -0.032   -0.098
 .EWB_W1 ~~                                                             
   .Soc_Stress_W1     0.073    0.024    2.992    0.003    0.638    0.638
 .EWB_W2 ~~                                                             
   .SD_W2            -0.023    0.014   -1.706    0.088   -0.051   -0.364
 .Soc_Stress_W2 ~~                                                      
   .SD_W2            -0.005    0.011   -0.476    0.634   -0.020   -0.143
 .EWB_W2 ~~                                                             
   .Soc_Stress_W2     0.059    0.025    2.382    0.017    0.501    0.501
 .EWB_W4 ~~                                                             
   .SD_W4             0.019    0.020    0.987    0.324    0.039    0.089
 .Soc_Stress_W4 ~~                                                      
   .SD_W4             0.025    0.017    1.446    0.148    0.073    0.166
 .EWB_W4 ~~                                                             
   .Soc_Stress_W4     0.125    0.029    4.366    0.000    0.738    0.738

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .Q6f.1   (.115)    2.359    0.026   90.354    0.000    2.359    2.181
   .Q6g.2   (.116)    2.030    0.043   47.683    0.000    2.030    1.704
   .Q6h.1   (.117)    1.987    0.031   64.178    0.000    1.987    1.601
   .Q6i.1   (.118)    1.611    0.026   61.079    0.000    1.611    1.448
   .Q6f.2   (.119)    2.377    0.036   66.716    0.000    2.377    2.149
   .Q6h.2   (.120)    2.087    0.044   47.508    0.000    2.087    1.651
   .Q6i.2   (.121)    1.741    0.038   46.390    0.000    1.741    1.485
   .Q6f.4   (.122)    2.334    0.040   58.720    0.000    2.334    2.152
   .Q6g.4   (.123)    2.151    0.052   40.980    0.000    2.151    1.641
   .Q6h.4   (.124)    2.090    0.054   38.436    0.000    2.090    1.628
   .Q6i.4   (.125)    1.827    0.049   37.347    0.000    1.827    1.485
   .Q8a.1   (.126)    2.610    0.026   99.199    0.000    2.610    2.626
   .Q8b.1   (.127)    2.051    0.028   73.522    0.000    2.051    1.839
   .Q8c.1   (.128)    2.178    0.029   73.893    0.000    2.178    1.913
   .Q8a.2   (.129)    2.549    0.033   76.725    0.000    2.549    2.409
   .Q8b.2   (.130)    2.102    0.035   59.820    0.000    2.102    1.928
   .Q8c.2   (.131)    2.189    0.036   60.913    0.000    2.189    1.971
   .Q8a.4   (.132)    2.617    0.036   71.764    0.000    2.617    2.587
   .Q8b.4   (.133)    2.133    0.038   56.273    0.000    2.133    1.981
   .Q8c.4   (.134)    2.351    0.042   56.264    0.000    2.351    2.108
   .SD_W1   (.135)    0.538    0.066    8.103    0.000    0.538    0.861
   .SD_W2   (.136)    0.546    0.069    7.902    0.000    0.546    0.799
   .SD_W4   (.137)    0.712    0.091    7.801    0.000    0.712    1.378
   .EWB_W1            0.058    0.028    2.118    0.034    0.097    0.097
   .EWB_W2            0.026    0.033    0.810    0.418    0.040    0.040
   .EWB_W4            0.018    0.034    0.516    0.606    0.026    0.026
   .Sc_S_W1           0.066    0.023    2.838    0.005    0.138    0.138
   .Sc_S_W2           0.033    0.026    1.254    0.210    0.066    0.066
   .Sc_S_W4           0.015    0.028    0.545    0.586    0.030    0.030
    RI_SD             0.237    0.126    1.878    0.060    0.477    0.477
    RI_EWB            0.102    0.022    4.606    0.000    0.277    0.277
    RI_Sc_S           0.114    0.023    4.922    0.000    0.275    0.275

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    RI_SD             0.248    0.030    8.243    0.000    1.000    1.000
    RI_EWB            0.137    0.044    3.144    0.002    1.000    1.000
    RI_Soc_Stress     0.172    0.029    5.940    0.000    1.000    1.000
   .Q6f.1             0.811    0.059   13.819    0.000    0.811    0.693
   .Q6g.2             0.441    0.077    5.725    0.000    0.441    0.311
   .Q6h.1             0.375    0.058    6.522    0.000    0.375    0.244
   .Q6i.1             0.526    0.069    7.574    0.000    0.526    0.425
   .Q6f.2             0.791    0.081    9.712    0.000    0.791    0.646
   .Q6h.2             0.341    0.058    5.868    0.000    0.341    0.213
   .Q6i.2             0.683    0.077    8.910    0.000    0.683    0.497
   .Q6f.4             0.709    0.068   10.435    0.000    0.709    0.603
   .Q6g.4             0.576    0.092    6.254    0.000    0.576    0.335
   .Q6h.4             0.310    0.059    5.274    0.000    0.310    0.188
   .Q6i.4             0.498    0.068    7.331    0.000    0.498    0.329
   .Q8a.1             0.757    0.041   18.625    0.000    0.757    0.766
   .Q8b.1             0.836    0.053   15.811    0.000    0.836    0.672
   .Q8c.1             0.697    0.059   11.847    0.000    0.697    0.537
   .Q8a.2             0.876    0.049   17.936    0.000    0.876    0.782
   .Q8b.2             0.717    0.059   12.077    0.000    0.717    0.604
   .Q8c.2             0.698    0.067   10.433    0.000    0.698    0.566
   .Q8a.4             0.759    0.050   15.127    0.000    0.759    0.741
   .Q8b.4             0.723    0.062   11.719    0.000    0.723    0.623
   .Q8c.4             0.595    0.070    8.437    0.000    0.595    0.478
   .SD_W1             0.104    0.018    5.887    0.000    0.104    0.267
   .SD_W2             0.020    0.010    1.944    0.052    0.020    0.042
   .SD_W4             0.193    0.023    8.308    0.000    0.193    0.723
   .EWB_W1            0.223    0.057    3.931    0.000    0.620    0.620
   .EWB_W2            0.209    0.048    4.369    0.000    0.483    0.483
   .EWB_W4            0.249    0.055    4.549    0.000    0.532    0.532
   .Soc_Stress_W1     0.059    0.024    2.446    0.014    0.256    0.256
   .Soc_Stress_W2     0.066    0.028    2.352    0.019    0.272    0.272
   .Soc_Stress_W4     0.115    0.030    3.776    0.000    0.433    0.433