RI-CLPM - Neighbourhood Safety
Ayla Pollmann
2023
Introduction
We will use a random intercept cross-lagged panel model (RI-CLPM) to investigate family relations and their relationship with youth behavioral and emotional problems & brain development, using the prospective, longitudinal Adolescent Brain Cognitive Development study (ABCD, N ≈ 12.000).
Analysis
In this script,
we conducted an RI-CLPM for family conflict.
we conduct two additional RI-CLPMs:
peer support as a resilience factor
gender differences
- Lastly, we constrain the lagged parameters that were significant in the RI-CLPM for family conflict.
All measures will be assessed at baseline/six-month follow-up, one-year follow-up and three/four-year follow-up in the ABCD study.
Adversity
- Neighborhood safety (ABCD Youth Neighborhood Safety/Crime Survey Modified from PhenX (NSC))
- Neighborhood safety based on parental perception (Neighborhood Safety Protocol: Mean of Parent Report)
Brain connectivity
- Average fractional anisotropy within all DTI atlas tract fibers.
Youth problems
- Externalizing and internalizing issues (Youth brief problem monitor)
Resilience
- Peer support (ABCD Other resilience scale)
Code Legend
1,2,3 = corresponding time point (time point 1,….)
b = brain (white matter connectivity)
Sa = Neighbourhood Safety
m = youth mental problems (behavioral and emotional problems)
Neighbourhood safety - Main Model
Neighbourhood safety x brain connectivity x youth problems
RICLPM_Safety <- '
# Create between components (random intercepts)
RIx =~ 1*a1Sa + 1*a2Sa + 1*a3Sa
RIy =~ 1*b1 + 1*b2 + 1*b3
RIz =~ 1*m1 + 1*m2 + 1*m3
# Create within-person centered variables
wx1 =~ 1*a1Sa
wx2 =~ 1*a2Sa
wx3 =~ 1*a3Sa
wy1 =~ 1*b1
wy2 =~ 1*b2
wy3 =~ 1*b3
wz1 =~ 1*m1
wz2 =~ 1*m2
wz3 =~ 1*m3
# Estimate lagged effects between within-person centered variables
wx2 + wy2 + wz2 ~ wx1 + wy1 + wz1
wx3 + wy3 + wz3 ~ wx2 + wy2 + wz2
# Estimate covariance between within-person centered variables at first wave
wx1 ~~ wy1 # Covariance
wx1 ~~ wz1
wy1 ~~ wz1
# Estimate covariances between residuals of within-person centered variables
# (i.e., innovations)
wx2 ~~ wy2
wx2 ~~ wz2
wy2 ~~ wz2
wx3 ~~ wy3
wx3 ~~ wz3
wy3 ~~ wz3
# Estimate variance and covariance of random intercepts
RIx ~~ RIx
RIy ~~ RIy
RIz ~~ RIz
RIx ~~ RIy
RIx ~~ RIz
RIy ~~ RIz
# Estimate (residual) variance of within-person centered variables
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wz1 ~~ wz1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wz2 ~~ wz2
wx3 ~~ wx3
wy3 ~~ wy3
wz3 ~~ wz3
'
RICLPM_Safety_fit <- lavaan::lavaan(RICLPM_Safety,
data = Data,
missing = "fiml",
meanstructure = TRUE,
int.ov.free = TRUE
)
summary(RICLPM_Safety_fit, standardized = TRUE)## lavaan 0.6.16 ended normally after 94 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 51
##
## Used Total
## Number of observations 9493 9495
## Number of missing patterns 70
##
## Model Test User Model:
##
## Test statistic 11.142
## Degrees of freedom 3
## P-value (Chi-square) 0.011
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIx =~
## a1Sa 1.000 0.479 0.481
## a2Sa 1.000 0.479 0.479
## a3Sa 1.000 0.479 0.481
## RIy =~
## b1 1.000 0.866 0.864
## b2 1.000 0.866 0.854
## b3 1.000 0.866 0.840
## RIz =~
## m1 1.000 0.498 0.501
## m2 1.000 0.498 0.497
## m3 1.000 0.498 0.497
## wx1 =~
## a1Sa 1.000 0.874 0.877
## wx2 =~
## a2Sa 1.000 0.879 0.878
## wx3 =~
## a3Sa 1.000 0.873 0.877
## wy1 =~
## b1 1.000 0.505 0.504
## wy2 =~
## b2 1.000 0.528 0.521
## wy3 =~
## b3 1.000 0.558 0.542
## wz1 =~
## m1 1.000 0.860 0.866
## wz2 =~
## m2 1.000 0.870 0.868
## wz3 =~
## m3 1.000 0.868 0.867
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wx2 ~
## wx1 0.078 0.027 2.924 0.003 0.078 0.078
## wy1 -0.166 0.073 -2.261 0.024 -0.095 -0.095
## wz1 0.117 0.025 4.602 0.000 0.115 0.115
## wy2 ~
## wx1 -0.039 0.019 -2.046 0.041 -0.064 -0.064
## wy1 0.317 0.093 3.400 0.001 0.303 0.303
## wz1 0.017 0.023 0.737 0.461 0.028 0.028
## wz2 ~
## wx1 0.049 0.020 2.441 0.015 0.050 0.050
## wy1 0.096 0.072 1.346 0.178 0.056 0.056
## wz1 0.330 0.031 10.487 0.000 0.327 0.327
## wx3 ~
## wx2 0.168 0.027 6.329 0.000 0.169 0.169
## wy2 -0.122 0.052 -2.358 0.018 -0.074 -0.074
## wz2 0.068 0.027 2.515 0.012 0.067 0.067
## wy3 ~
## wx2 -0.021 0.017 -1.261 0.207 -0.034 -0.034
## wy2 0.577 0.041 14.228 0.000 0.546 0.546
## wz2 0.009 0.019 0.480 0.631 0.014 0.014
## wz3 ~
## wx2 -0.016 0.022 -0.752 0.452 -0.016 -0.016
## wy2 -0.070 0.048 -1.454 0.146 -0.042 -0.042
## wz2 0.405 0.029 13.868 0.000 0.405 0.405
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wx1 ~~
## wy1 -0.048 0.020 -2.430 0.015 -0.108 -0.108
## wz1 0.083 0.021 4.042 0.000 0.111 0.111
## wy1 ~~
## wz1 0.011 0.023 0.467 0.640 0.025 0.025
## .wx2 ~~
## .wy2 -0.028 0.014 -2.010 0.044 -0.065 -0.065
## .wz2 0.114 0.015 7.795 0.000 0.161 0.161
## .wy2 ~~
## .wz2 0.010 0.014 0.769 0.442 0.025 0.025
## .wx3 ~~
## .wy3 0.003 0.011 0.299 0.765 0.008 0.008
## .wz3 0.057 0.014 3.990 0.000 0.083 0.083
## .wy3 ~~
## .wz3 -0.007 0.010 -0.742 0.458 -0.019 -0.019
## RIx ~~
## RIy 0.017 0.019 0.914 0.361 0.042 0.042
## RIz 0.088 0.019 4.573 0.000 0.370 0.370
## RIy ~~
## RIz -0.027 0.023 -1.186 0.235 -0.062 -0.062
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .a1Sa -0.001 0.010 -0.058 0.953 -0.001 -0.001
## .a2Sa 0.004 0.011 0.338 0.735 0.004 0.004
## .a3Sa 0.010 0.015 0.626 0.531 0.010 0.010
## .b1 -0.010 0.011 -0.921 0.357 -0.010 -0.010
## .b2 -0.026 0.012 -2.239 0.025 -0.026 -0.026
## .b3 -0.055 0.015 -3.703 0.000 -0.055 -0.053
## .m1 0.001 0.010 0.055 0.956 0.001 0.001
## .m2 0.006 0.011 0.602 0.547 0.006 0.006
## .m3 0.022 0.015 1.455 0.146 0.022 0.022
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIz 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## wz1 0.000 0.000 0.000
## .wz2 0.000 0.000 0.000
## .wz3 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIx 0.230 0.022 10.596 0.000 1.000 1.000
## RIy 0.750 0.034 21.745 0.000 1.000 1.000
## RIz 0.248 0.032 7.644 0.000 1.000 1.000
## wx1 0.763 0.024 31.870 0.000 1.000 1.000
## wy1 0.255 0.032 7.871 0.000 1.000 1.000
## wz1 0.740 0.034 21.967 0.000 1.000 1.000
## .wx2 0.748 0.022 34.190 0.000 0.969 0.969
## .wy2 0.251 0.019 13.439 0.000 0.899 0.899
## .wz2 0.668 0.019 35.239 0.000 0.884 0.884
## .wx3 0.728 0.021 34.066 0.000 0.955 0.955
## .wy3 0.217 0.009 24.614 0.000 0.696 0.696
## .wz3 0.632 0.019 34.108 0.000 0.838 0.838
## .a1Sa 0.000 0.000 0.000
## .a2Sa 0.000 0.000 0.000
## .a3Sa 0.000 0.000 0.000
## .b1 0.000 0.000 0.000
## .b2 0.000 0.000 0.000
## .b3 0.000 0.000 0.000
## .m1 0.000 0.000 0.000
## .m2 0.000 0.000 0.000
## .m3 0.000 0.000 0.000fitMeasures(RICLPM_Safety_fit, fit.measures = c("chisq","df","pvalue","rmsea","srmr","cfi"))## chisq df pvalue rmsea srmr cfi
## 11.142 3.000 0.011 0.017 0.007 0.999Multigroup analysis
Neighbourhood safety & Peer resilience
RICLPM_Safety_fit_Peers <- lavaan::lavaan(RICLPM_Safety,
data = Data,
missing = "fiml",
meanstructure = TRUE,
int.ov.free = TRUE,
group = "Resilience_Peers"
)
summary(RICLPM_Safety_fit_Peers, standardized = TRUE)## lavaan 0.6.16 ended normally after 198 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 102
##
## Number of observations per group:
## 1 4407
## 0 5070
## Number of missing patterns per group:
## 1 58
## 0 54
##
## Model Test User Model:
##
## Test statistic 15.217
## Degrees of freedom 6
## P-value (Chi-square) 0.019
## Test statistic for each group:
## 1 10.471
## 0 4.747
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIx =~
## a1Sa 1.000 0.459 0.465
## a2Sa 1.000 0.459 0.460
## a3Sa 1.000 0.459 0.463
## RIy =~
## b1 1.000 0.879 0.886
## b2 1.000 0.879 0.884
## b3 1.000 0.879 0.873
## RIz =~
## m1 1.000 0.467 0.471
## m2 1.000 0.467 0.461
## m3 1.000 0.467 0.471
## wx1 =~
## a1Sa 1.000 0.874 0.885
## wx2 =~
## a2Sa 1.000 0.887 0.888
## wx3 =~
## a3Sa 1.000 0.880 0.886
## wy1 =~
## b1 1.000 0.461 0.464
## wy2 =~
## b2 1.000 0.464 0.467
## wy3 =~
## b3 1.000 0.491 0.487
## wz1 =~
## m1 1.000 0.876 0.882
## wz2 =~
## m2 1.000 0.900 0.888
## wz3 =~
## m3 1.000 0.874 0.882
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wx2 ~
## wx1 0.103 0.039 2.622 0.009 0.102 0.102
## wy1 -0.134 0.117 -1.145 0.252 -0.070 -0.070
## wz1 0.201 0.037 5.453 0.000 0.198 0.198
## wy2 ~
## wx1 -0.045 0.031 -1.460 0.144 -0.085 -0.085
## wy1 0.091 0.154 0.594 0.552 0.091 0.091
## wz1 0.061 0.038 1.598 0.110 0.116 0.116
## wz2 ~
## wx1 0.093 0.031 3.022 0.003 0.090 0.090
## wy1 0.238 0.119 1.999 0.046 0.122 0.122
## wz1 0.347 0.047 7.463 0.000 0.338 0.338
## wx3 ~
## wx2 0.169 0.039 4.345 0.000 0.171 0.171
## wy2 -0.039 0.080 -0.483 0.629 -0.020 -0.020
## wz2 0.114 0.038 3.014 0.003 0.117 0.117
## wy3 ~
## wx2 -0.007 0.025 -0.277 0.782 -0.012 -0.012
## wy2 0.469 0.061 7.715 0.000 0.443 0.443
## wz2 0.025 0.028 0.889 0.374 0.045 0.045
## wz3 ~
## wx2 -0.007 0.031 -0.208 0.835 -0.007 -0.007
## wy2 -0.079 0.073 -1.083 0.279 -0.042 -0.042
## wz2 0.417 0.040 10.351 0.000 0.429 0.429
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wx1 ~~
## wy1 -0.038 0.024 -1.580 0.114 -0.094 -0.094
## wz1 0.142 0.032 4.453 0.000 0.185 0.185
## wy1 ~~
## wz1 0.039 0.029 1.332 0.183 0.097 0.097
## .wx2 ~~
## .wy2 -0.029 0.023 -1.261 0.207 -0.074 -0.074
## .wz2 0.159 0.021 7.519 0.000 0.223 0.223
## .wy2 ~~
## .wz2 0.038 0.023 1.639 0.101 0.102 0.102
## .wx3 ~~
## .wy3 0.033 0.015 2.204 0.028 0.088 0.088
## .wz3 0.092 0.021 4.408 0.000 0.136 0.136
## .wy3 ~~
## .wz3 -0.015 0.013 -1.141 0.254 -0.043 -0.043
## RIx ~~
## RIy -0.002 0.024 -0.070 0.944 -0.004 -0.004
## RIz 0.033 0.030 1.089 0.276 0.152 0.152
## RIy ~~
## RIz -0.061 0.029 -2.099 0.036 -0.149 -0.149
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .a1Sa -0.028 0.015 -1.889 0.059 -0.028 -0.028
## .a2Sa -0.030 0.016 -1.958 0.050 -0.030 -0.031
## .a3Sa -0.024 0.023 -1.068 0.286 -0.024 -0.024
## .b1 0.018 0.016 1.115 0.265 0.018 0.018
## .b2 0.020 0.017 1.206 0.228 0.020 0.020
## .b3 -0.015 0.021 -0.717 0.473 -0.015 -0.015
## .m1 -0.017 0.015 -1.120 0.263 -0.017 -0.017
## .m2 0.004 0.016 0.268 0.789 0.004 0.004
## .m3 0.007 0.022 0.310 0.757 0.007 0.007
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIz 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## wz1 0.000 0.000 0.000
## .wz2 0.000 0.000 0.000
## .wz3 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIx 0.211 0.033 6.477 0.000 1.000 1.000
## RIy 0.773 0.038 20.541 0.000 1.000 1.000
## RIz 0.218 0.049 4.470 0.000 1.000 1.000
## wx1 0.763 0.036 21.333 0.000 1.000 1.000
## wy1 0.212 0.034 6.264 0.000 1.000 1.000
## wz1 0.767 0.051 14.986 0.000 1.000 1.000
## .wx2 0.739 0.031 24.155 0.000 0.939 0.939
## .wy2 0.209 0.033 6.309 0.000 0.971 0.971
## .wz2 0.686 0.029 24.057 0.000 0.846 0.846
## .wx3 0.732 0.032 23.185 0.000 0.946 0.946
## .wy3 0.192 0.013 15.275 0.000 0.795 0.795
## .wz3 0.627 0.027 23.278 0.000 0.820 0.820
## .a1Sa 0.000 0.000 0.000
## .a2Sa 0.000 0.000 0.000
## .a3Sa 0.000 0.000 0.000
## .b1 0.000 0.000 0.000
## .b2 0.000 0.000 0.000
## .b3 0.000 0.000 0.000
## .m1 0.000 0.000 0.000
## .m2 0.000 0.000 0.000
## .m3 0.000 0.000 0.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIx =~
## a1Sa 1.000 0.483 0.482
## a2Sa 1.000 0.483 0.482
## a3Sa 1.000 0.483 0.484
## RIy =~
## b1 1.000 0.833 0.825
## b2 1.000 0.833 0.810
## b3 1.000 0.833 0.794
## RIz =~
## m1 1.000 0.512 0.515
## m2 1.000 0.512 0.517
## m3 1.000 0.512 0.509
## wx1 =~
## a1Sa 1.000 0.880 0.876
## wx2 =~
## a2Sa 1.000 0.878 0.876
## wx3 =~
## a3Sa 1.000 0.874 0.875
## wy1 =~
## b1 1.000 0.571 0.566
## wy2 =~
## b2 1.000 0.603 0.587
## wy3 =~
## b3 1.000 0.638 0.608
## wz1 =~
## m1 1.000 0.852 0.857
## wz2 =~
## m2 1.000 0.849 0.856
## wz3 =~
## m3 1.000 0.865 0.861
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wx2 ~
## wx1 0.051 0.037 1.384 0.167 0.051 0.051
## wy1 -0.221 0.092 -2.406 0.016 -0.144 -0.144
## wz1 0.040 0.036 1.121 0.262 0.039 0.039
## wy2 ~
## wx1 -0.043 0.024 -1.788 0.074 -0.062 -0.062
## wy1 0.502 0.109 4.593 0.000 0.476 0.476
## wz1 -0.005 0.029 -0.158 0.875 -0.006 -0.006
## wz2 ~
## wx1 0.015 0.028 0.544 0.586 0.016 0.016
## wy1 0.001 0.087 0.017 0.987 0.001 0.001
## wz1 0.311 0.044 7.041 0.000 0.312 0.312
## wx3 ~
## wx2 0.167 0.036 4.587 0.000 0.167 0.167
## wy2 -0.172 0.070 -2.476 0.013 -0.119 -0.119
## wz2 0.021 0.038 0.537 0.591 0.020 0.020
## wy3 ~
## wx2 -0.039 0.024 -1.628 0.104 -0.053 -0.053
## wy2 0.665 0.056 11.867 0.000 0.628 0.628
## wz2 0.002 0.027 0.084 0.933 0.003 0.003
## wz3 ~
## wx2 -0.028 0.030 -0.944 0.345 -0.028 -0.028
## wy2 -0.059 0.064 -0.927 0.354 -0.041 -0.041
## wz2 0.398 0.042 9.463 0.000 0.390 0.390
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wx1 ~~
## wy1 -0.070 0.035 -2.014 0.044 -0.139 -0.139
## wz1 0.037 0.028 1.333 0.183 0.050 0.050
## wy1 ~~
## wz1 -0.012 0.037 -0.314 0.754 -0.024 -0.024
## .wx2 ~~
## .wy2 -0.030 0.018 -1.699 0.089 -0.065 -0.065
## .wz2 0.072 0.021 3.461 0.001 0.103 0.103
## .wy2 ~~
## .wz2 -0.006 0.016 -0.365 0.715 -0.014 -0.014
## .wx3 ~~
## .wy3 -0.019 0.015 -1.285 0.199 -0.046 -0.046
## .wz3 0.026 0.019 1.343 0.179 0.038 0.038
## .wy3 ~~
## .wz3 0.001 0.014 0.083 0.934 0.003 0.003
## RIx ~~
## RIy 0.048 0.034 1.435 0.151 0.120 0.120
## RIz 0.130 0.026 4.951 0.000 0.526 0.526
## RIy ~~
## RIz 0.002 0.037 0.065 0.948 0.006 0.006
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .a1Sa 0.023 0.014 1.630 0.103 0.023 0.023
## .a2Sa 0.033 0.015 2.259 0.024 0.033 0.033
## .a3Sa 0.039 0.021 1.846 0.065 0.039 0.039
## .b1 -0.032 0.015 -2.160 0.031 -0.032 -0.032
## .b2 -0.063 0.016 -3.940 0.000 -0.063 -0.062
## .b3 -0.087 0.021 -4.174 0.000 -0.087 -0.083
## .m1 0.014 0.014 1.018 0.309 0.014 0.015
## .m2 0.008 0.014 0.535 0.592 0.008 0.008
## .m3 0.033 0.020 1.611 0.107 0.033 0.033
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIz 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## wz1 0.000 0.000 0.000
## .wz2 0.000 0.000 0.000
## .wz3 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIx 0.234 0.031 7.578 0.000 1.000 1.000
## RIy 0.694 0.069 10.004 0.000 1.000 1.000
## RIz 0.262 0.044 5.955 0.000 1.000 1.000
## wx1 0.774 0.034 22.793 0.000 1.000 1.000
## wy1 0.327 0.067 4.845 0.000 1.000 1.000
## wz1 0.726 0.045 15.949 0.000 1.000 1.000
## .wx2 0.749 0.032 23.655 0.000 0.973 0.973
## .wy2 0.277 0.021 13.469 0.000 0.762 0.762
## .wz2 0.649 0.026 24.545 0.000 0.902 0.902
## .wx3 0.727 0.029 24.829 0.000 0.951 0.951
## .wy3 0.242 0.013 18.758 0.000 0.594 0.594
## .wz3 0.635 0.025 24.914 0.000 0.848 0.848
## .a1Sa 0.000 0.000 0.000
## .a2Sa 0.000 0.000 0.000
## .a3Sa 0.000 0.000 0.000
## .b1 0.000 0.000 0.000
## .b2 0.000 0.000 0.000
## .b3 0.000 0.000 0.000
## .m1 0.000 0.000 0.000
## .m2 0.000 0.000 0.000
## .m3 0.000 0.000 0.000fitMeasures(RICLPM_Safety_fit_Peers, fit.measures = c("chisq","df","pvalue","rmsea","srmr","cfi"))## chisq df pvalue rmsea srmr cfi
## 15.217 6.000 0.019 0.018 0.008 0.999Neighbourhood safety & Gender
RICLPM_Safety_fit_Sex <- lavaan::lavaan(RICLPM_Safety,
data = Data,
missing = "fiml",
meanstructure = TRUE,
int.ov.free = TRUE,
group = "Sex"
)
summary(RICLPM_Safety_fit_Sex, standardized = TRUE)## lavaan 0.6.16 ended normally after 188 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 102
##
## Number of observations per group: Used Total
## 1 4953 4953
## 2 4522 4524
## Number of missing patterns per group:
## 1 63
## 2 52
##
## Model Test User Model:
##
## Test statistic 12.365
## Degrees of freedom 6
## P-value (Chi-square) 0.054
## Test statistic for each group:
## 1 4.797
## 2 7.568
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIx =~
## a1Sa 1.000 0.456 0.450
## a2Sa 1.000 0.456 0.451
## a3Sa 1.000 0.456 0.464
## RIy =~
## b1 1.000 0.872 0.868
## b2 1.000 0.872 0.852
## b3 1.000 0.872 0.826
## RIz =~
## m1 1.000 0.552 0.545
## m2 1.000 0.552 0.572
## m3 1.000 0.552 0.615
## wx1 =~
## a1Sa 1.000 0.904 0.893
## wx2 =~
## a2Sa 1.000 0.902 0.893
## wx3 =~
## a3Sa 1.000 0.870 0.886
## wy1 =~
## b1 1.000 0.498 0.496
## wy2 =~
## b2 1.000 0.536 0.523
## wy3 =~
## b3 1.000 0.596 0.564
## wz1 =~
## m1 1.000 0.850 0.838
## wz2 =~
## m2 1.000 0.792 0.820
## wz3 =~
## m3 1.000 0.708 0.788
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wx2 ~
## wx1 0.090 0.037 2.448 0.014 0.090 0.090
## wy1 -0.231 0.111 -2.088 0.037 -0.128 -0.128
## wz1 0.110 0.034 3.243 0.001 0.104 0.104
## wy2 ~
## wx1 -0.032 0.026 -1.233 0.218 -0.054 -0.054
## wy1 0.326 0.153 2.139 0.032 0.304 0.304
## wz1 0.015 0.030 0.494 0.621 0.024 0.024
## wz2 ~
## wx1 0.036 0.025 1.449 0.147 0.041 0.041
## wy1 0.105 0.096 1.091 0.275 0.066 0.066
## wz1 0.271 0.038 7.147 0.000 0.290 0.290
## wx3 ~
## wx2 0.211 0.035 5.945 0.000 0.219 0.219
## wy2 -0.140 0.069 -2.031 0.042 -0.086 -0.086
## wz2 0.044 0.039 1.136 0.256 0.040 0.040
## wy3 ~
## wx2 -0.016 0.022 -0.712 0.477 -0.024 -0.024
## wy2 0.642 0.056 11.566 0.000 0.578 0.578
## wz2 -0.015 0.027 -0.555 0.579 -0.020 -0.020
## wz3 ~
## wx2 -0.003 0.027 -0.093 0.926 -0.003 -0.003
## wy2 -0.060 0.060 -1.004 0.315 -0.045 -0.045
## wz2 0.293 0.042 7.044 0.000 0.328 0.328
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wx1 ~~
## wy1 -0.054 0.030 -1.770 0.077 -0.119 -0.119
## wz1 0.084 0.026 3.283 0.001 0.110 0.110
## wy1 ~~
## wz1 0.011 0.028 0.384 0.701 0.025 0.025
## .wx2 ~~
## .wy2 -0.036 0.020 -1.765 0.078 -0.079 -0.079
## .wz2 0.087 0.020 4.447 0.000 0.131 0.131
## .wy2 ~~
## .wz2 -0.002 0.018 -0.093 0.926 -0.004 -0.004
## .wx3 ~~
## .wy3 -0.013 0.014 -0.902 0.367 -0.032 -0.032
## .wz3 0.013 0.018 0.709 0.478 0.022 0.022
## .wy3 ~~
## .wz3 -0.008 0.012 -0.640 0.522 -0.024 -0.024
## RIx ~~
## RIy 0.029 0.029 0.987 0.324 0.072 0.072
## RIz 0.086 0.024 3.632 0.000 0.343 0.343
## RIy ~~
## RIz -0.026 0.027 -0.955 0.340 -0.053 -0.053
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .a1Sa 0.003 0.014 0.188 0.851 0.003 0.003
## .a2Sa -0.008 0.015 -0.522 0.602 -0.008 -0.008
## .a3Sa -0.051 0.021 -2.400 0.016 -0.051 -0.051
## .b1 -0.021 0.015 -1.413 0.158 -0.021 -0.021
## .b2 -0.035 0.016 -2.168 0.030 -0.035 -0.034
## .b3 -0.095 0.021 -4.519 0.000 -0.095 -0.090
## .m1 0.085 0.015 5.833 0.000 0.085 0.084
## .m2 -0.048 0.014 -3.362 0.001 -0.048 -0.049
## .m3 -0.183 0.018 -9.961 0.000 -0.183 -0.203
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIz 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## wz1 0.000 0.000 0.000
## .wz2 0.000 0.000 0.000
## .wz3 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIx 0.208 0.032 6.465 0.000 1.000 1.000
## RIy 0.761 0.056 13.623 0.000 1.000 1.000
## RIz 0.305 0.035 8.783 0.000 1.000 1.000
## wx1 0.818 0.036 23.000 0.000 1.000 1.000
## wy1 0.248 0.053 4.658 0.000 1.000 1.000
## wz1 0.722 0.037 19.774 0.000 1.000 1.000
## .wx2 0.782 0.032 24.615 0.000 0.961 0.961
## .wy2 0.258 0.028 9.081 0.000 0.900 0.900
## .wz2 0.569 0.024 23.408 0.000 0.907 0.907
## .wx3 0.709 0.029 24.517 0.000 0.936 0.936
## .wy3 0.235 0.012 19.247 0.000 0.662 0.662
## .wz3 0.447 0.022 20.502 0.000 0.892 0.892
## .a1Sa 0.000 0.000 0.000
## .a2Sa 0.000 0.000 0.000
## .a3Sa 0.000 0.000 0.000
## .b1 0.000 0.000 0.000
## .b2 0.000 0.000 0.000
## .b3 0.000 0.000 0.000
## .m1 0.000 0.000 0.000
## .m2 0.000 0.000 0.000
## .m3 0.000 0.000 0.000
##
##
## Group 2 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIx =~
## a1Sa 1.000 0.502 0.513
## a2Sa 1.000 0.502 0.507
## a3Sa 1.000 0.502 0.499
## RIy =~
## b1 1.000 0.856 0.856
## b2 1.000 0.856 0.853
## b3 1.000 0.856 0.855
## RIz =~
## m1 1.000 0.511 0.531
## m2 1.000 0.511 0.494
## m3 1.000 0.511 0.484
## wx1 =~
## a1Sa 1.000 0.840 0.858
## wx2 =~
## a2Sa 1.000 0.853 0.862
## wx3 =~
## a3Sa 1.000 0.872 0.867
## wy1 =~
## b1 1.000 0.517 0.517
## wy2 =~
## b2 1.000 0.525 0.523
## wy3 =~
## b3 1.000 0.519 0.518
## wz1 =~
## m1 1.000 0.816 0.848
## wz2 =~
## m2 1.000 0.899 0.870
## wz3 =~
## m3 1.000 0.924 0.875
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wx2 ~
## wx1 0.066 0.039 1.694 0.090 0.065 0.065
## wy1 -0.092 0.095 -0.966 0.334 -0.056 -0.056
## wz1 0.130 0.038 3.394 0.001 0.124 0.124
## wy2 ~
## wx1 -0.044 0.027 -1.615 0.106 -0.070 -0.070
## wy1 0.320 0.115 2.780 0.005 0.315 0.315
## wz1 0.018 0.035 0.509 0.611 0.027 0.027
## wz2 ~
## wx1 0.063 0.030 2.118 0.034 0.059 0.059
## wy1 0.065 0.094 0.693 0.488 0.037 0.037
## wz1 0.348 0.048 7.280 0.000 0.316 0.316
## wx3 ~
## wx2 0.119 0.040 2.985 0.003 0.116 0.116
## wy2 -0.085 0.078 -1.083 0.279 -0.051 -0.051
## wz2 0.071 0.038 1.893 0.058 0.073 0.073
## wy3 ~
## wx2 -0.027 0.025 -1.063 0.288 -0.044 -0.044
## wy2 0.521 0.059 8.786 0.000 0.527 0.527
## wz2 0.020 0.027 0.734 0.463 0.034 0.034
## wz3 ~
## wx2 -0.021 0.033 -0.632 0.527 -0.019 -0.019
## wy2 -0.075 0.073 -1.028 0.304 -0.043 -0.043
## wz2 0.416 0.040 10.458 0.000 0.404 0.404
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wx1 ~~
## wy1 -0.039 0.025 -1.555 0.120 -0.090 -0.090
## wz1 0.071 0.027 2.608 0.009 0.104 0.104
## wy1 ~~
## wz1 0.008 0.031 0.246 0.806 0.018 0.018
## .wx2 ~~
## .wy2 -0.017 0.019 -0.908 0.364 -0.042 -0.042
## .wz2 0.135 0.020 6.702 0.000 0.189 0.189
## .wy2 ~~
## .wz2 0.023 0.019 1.219 0.223 0.054 0.054
## .wx3 ~~
## .wy3 0.017 0.015 1.100 0.271 0.045 0.045
## .wz3 0.078 0.021 3.622 0.000 0.107 0.107
## .wy3 ~~
## .wz3 -0.025 0.014 -1.702 0.089 -0.066 -0.066
## RIx ~~
## RIy 0.003 0.025 0.125 0.900 0.007 0.007
## RIz 0.102 0.026 3.916 0.000 0.396 0.396
## RIy ~~
## RIz -0.023 0.031 -0.750 0.454 -0.053 -0.053
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .a1Sa -0.003 0.015 -0.239 0.811 -0.003 -0.004
## .a2Sa 0.016 0.015 1.055 0.291 0.016 0.016
## .a3Sa 0.073 0.023 3.215 0.001 0.073 0.073
## .b1 0.003 0.016 0.171 0.864 0.003 0.003
## .b2 -0.015 0.017 -0.880 0.379 -0.015 -0.015
## .b3 -0.012 0.021 -0.594 0.553 -0.012 -0.012
## .m1 -0.095 0.015 -6.534 0.000 -0.095 -0.099
## .m2 0.064 0.016 3.986 0.000 0.064 0.061
## .m3 0.244 0.023 10.711 0.000 0.244 0.231
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIz 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## wz1 0.000 0.000 0.000
## .wz2 0.000 0.000 0.000
## .wz3 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIx 0.252 0.029 8.836 0.000 1.000 1.000
## RIy 0.733 0.044 16.488 0.000 1.000 1.000
## RIz 0.261 0.045 5.772 0.000 1.000 1.000
## wx1 0.706 0.032 22.340 0.000 1.000 1.000
## wy1 0.267 0.041 6.474 0.000 1.000 1.000
## wz1 0.665 0.047 14.259 0.000 1.000 1.000
## .wx2 0.710 0.030 23.601 0.000 0.975 0.975
## .wy2 0.246 0.025 9.892 0.000 0.891 0.891
## .wz2 0.721 0.027 27.172 0.000 0.891 0.891
## .wx3 0.741 0.032 23.443 0.000 0.974 0.974
## .wy3 0.192 0.013 15.350 0.000 0.714 0.714
## .wz3 0.717 0.029 25.040 0.000 0.840 0.840
## .a1Sa 0.000 0.000 0.000
## .a2Sa 0.000 0.000 0.000
## .a3Sa 0.000 0.000 0.000
## .b1 0.000 0.000 0.000
## .b2 0.000 0.000 0.000
## .b3 0.000 0.000 0.000
## .m1 0.000 0.000 0.000
## .m2 0.000 0.000 0.000
## .m3 0.000 0.000 0.000fitMeasures(RICLPM_Safety_fit_Sex, fit.measures = c("chisq","df","pvalue","rmsea","srmr","cfi"))## chisq df pvalue rmsea srmr cfi
## 12.365 6.000 0.054 0.015 0.007 1.000Global differences - Constrained lagged-parameters for multi-groups
Multigroup Peers
MG_Safety <- '
RIx =~ 1*a1Sa + 1*a2Sa + 1*a3Sa
RIy =~ 1*b1 + 1*b2 + 1*b3
RIz =~ 1*m1 + 1*m2 + 1*m3
wx1 =~ 1*a1Sa
wx2 =~ 1*a2Sa
wx3 =~ 1*a3Sa
wy1 =~ 1*b1
wy2 =~ 1*b2
wy3 =~ 1*b3
wz1 =~ 1*m1
wz2 =~ 1*m2
wz3 =~ 1*m3
# Estimate lagged effects between within-person centered variables (constrain
# autoregressive effects across groups)
wx2 ~ c(a1, a1)*wx1 + c(b1, b1)*wy1 + c(c1, c1)*wz1
wy2 ~ c(d1, d1)*wx1 + c(e1, e1)*wy1 + c(f1, f1)*wz1
wz2 ~ c(g1, g1)*wx1 + c(h1, h1)*wy1 + c(i1, i1)*wz1
wx3 ~ c(a2, a2)*wx2 + c(b2, b2)*wy2 + c(c2, c2)*wz2
wy3 ~ c(d2, d2)*wx2 + c(e2, e2)*wy2 + c(f2, f2)*wz2
wz3 ~ c(g2, g2)*wx2 + c(h2, h2)*wy2 + c(i2, i2)*wz2
wx1 ~~ wy1
wx1 ~~ wz1
wy1 ~~ wz1
wx2 ~~ wy2
wx2 ~~ wz2
wy2 ~~ wz2
wx3 ~~ wy3
wx3 ~~ wz3
wy3 ~~ wz3
RIx ~~ RIx
RIy ~~ RIy
RIz ~~ RIz
RIx ~~ RIy
RIx ~~ RIz
RIy ~~ RIz
wx1 ~~ wx1
wy1 ~~ wy1
wz1 ~~ wz1
wx2 ~~ wx2
wy2 ~~ wy2
wz2 ~~ wz2
wx3 ~~ wx3
wy3 ~~ wy3
wz3 ~~ wz3'
MG_Safety_Peers_fit <- lavaan(MG_Safety,
data = Data,
missing = 'fiml',
meanstructure = TRUE,
int.ov.free = TRUE,
group = "Resilience_Peers"
)## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'Resilience_Peers' contains missing valuesanova(RICLPM_Safety_fit_Peers, MG_Safety_Peers_fit)##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## RICLPM_Safety_fit_Peers 6 154588 155318 15.217
## MG_Safety_Peers_fit 24 154577 155178 40.070 24.852 0.0089633 18 0.129Multigroup Gender
MG_Safety_Sex_fit <- lavaan(MG_Safety,
data = Data,
missing = 'fiml',
meanstructure = TRUE,
int.ov.free = TRUE,
group = "Sex"
)## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'Sex' contains missing values## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
## 4760 9337anova(RICLPM_Safety_fit_Sex, MG_Safety_Sex_fit)##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## RICLPM_Safety_fit_Sex 6 154011 154741 12.366
## MG_Safety_Sex_fit 24 153993 154594 29.940 17.575 0 18 0.484Neighbourhood safety - Main model - Parents
Neighbourhood safety x brain connectivity x youth problems
RICLPM_Safety_Parent <- '
# Create between components (random intercepts)
RIx =~ 1*a1SaPa + 1*a2SaPa + 1*a3SaPa
RIy =~ 1*b1 + 1*b2 + 1*b3
RIz =~ 1*m1 + 1*m2 + 1*m3
# Create within-person centered variables
wx1 =~ 1*a1SaPa
wx2 =~ 1*a2SaPa
wx3 =~ 1*a3SaPa
wy1 =~ 1*b1
wy2 =~ 1*b2
wy3 =~ 1*b3
wz1 =~ 1*m1
wz2 =~ 1*m2
wz3 =~ 1*m3
# Estimate lagged effects between within-person centered variables
wx2 + wy2 + wz2 ~ wx1 + wy1 + wz1
wx3 + wy3 + wz3 ~ wx2 + wy2 + wz2
# Estimate covariance between within-person centered variables at first wave
wx1 ~~ wy1 # Covariance
wx1 ~~ wz1
wy1 ~~ wz1
# Estimate covariances between residuals of within-person centered variables
# (i.e., innovations)
wx2 ~~ wy2
wx2 ~~ wz2
wy2 ~~ wz2
wx3 ~~ wy3
wx3 ~~ wz3
wy3 ~~ wz3
# Estimate variance and covariance of random intercepts
RIx ~~ RIx
RIy ~~ RIy
RIz ~~ RIz
RIx ~~ RIy
RIx ~~ RIz
RIy ~~ RIz
# Estimate (residual) variance of within-person centered variables
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wz1 ~~ wz1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wz2 ~~ wz2
wx3 ~~ wx3
wy3 ~~ wy3
wz3 ~~ wz3
'
RICLPM_Safety_Parent_fit <- lavaan::lavaan(RICLPM_Safety_Parent,
data = Data,
missing = "fiml",
meanstructure = TRUE,
int.ov.free = TRUE)
summary(RICLPM_Safety_Parent_fit, standardized = TRUE)## lavaan 0.6.16 ended normally after 87 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 51
##
## Used Total
## Number of observations 9493 9495
## Number of missing patterns 85
##
## Model Test User Model:
##
## Test statistic 18.998
## Degrees of freedom 3
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIx =~
## a1SaPa 1.000 0.713 0.712
## a2SaPa 1.000 0.713 0.707
## a3SaPa 1.000 0.713 0.703
## RIy =~
## b1 1.000 0.864 0.862
## b2 1.000 0.864 0.852
## b3 1.000 0.864 0.839
## RIz =~
## m1 1.000 0.500 0.503
## m2 1.000 0.500 0.499
## m3 1.000 0.500 0.499
## wx1 =~
## a1SaPa 1.000 0.703 0.702
## wx2 =~
## a2SaPa 1.000 0.713 0.707
## wx3 =~
## a3SaPa 1.000 0.721 0.711
## wy1 =~
## b1 1.000 0.508 0.507
## wy2 =~
## b2 1.000 0.532 0.524
## wy3 =~
## b3 1.000 0.560 0.544
## wz1 =~
## m1 1.000 0.859 0.864
## wz2 =~
## m2 1.000 0.868 0.867
## wz3 =~
## m3 1.000 0.868 0.867
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wx2 ~
## wx1 0.178 0.034 5.256 0.000 0.175 0.175
## wy1 -0.025 0.060 -0.411 0.681 -0.018 -0.018
## wz1 0.022 0.021 1.034 0.301 0.027 0.027
## wy2 ~
## wx1 -0.048 0.029 -1.664 0.096 -0.063 -0.063
## wy1 0.328 0.092 3.569 0.000 0.314 0.314
## wz1 0.018 0.023 0.785 0.433 0.030 0.030
## wz2 ~
## wx1 -0.017 0.031 -0.540 0.589 -0.014 -0.014
## wy1 0.090 0.071 1.275 0.202 0.053 0.053
## wz1 0.335 0.032 10.521 0.000 0.331 0.331
## wx3 ~
## wx2 0.176 0.031 5.682 0.000 0.175 0.175
## wy2 0.020 0.049 0.404 0.686 0.015 0.015
## wz2 0.027 0.025 1.058 0.290 0.032 0.032
## wy3 ~
## wx2 0.009 0.023 0.385 0.700 0.011 0.011
## wy2 0.584 0.040 14.422 0.000 0.554 0.554
## wz2 0.008 0.019 0.406 0.685 0.012 0.012
## wz3 ~
## wx2 -0.034 0.028 -1.214 0.225 -0.028 -0.028
## wy2 -0.066 0.048 -1.392 0.164 -0.041 -0.041
## wz2 0.402 0.029 13.850 0.000 0.402 0.402
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wx1 ~~
## wy1 -0.028 0.017 -1.641 0.101 -0.078 -0.078
## wz1 0.034 0.018 1.911 0.056 0.057 0.057
## wy1 ~~
## wz1 0.014 0.023 0.607 0.544 0.032 0.032
## .wx2 ~~
## .wy2 -0.014 0.012 -1.085 0.278 -0.038 -0.038
## .wz2 -0.001 0.013 -0.075 0.940 -0.002 -0.002
## .wy2 ~~
## .wz2 0.010 0.013 0.738 0.461 0.024 0.024
## .wx3 ~~
## .wy3 0.006 0.010 0.675 0.500 0.020 0.020
## .wz3 -0.020 0.012 -1.629 0.103 -0.036 -0.036
## .wy3 ~~
## .wz3 -0.006 0.010 -0.569 0.569 -0.015 -0.015
## RIx ~~
## RIy -0.003 0.018 -0.188 0.851 -0.005 -0.005
## RIz 0.068 0.018 3.791 0.000 0.190 0.190
## RIy ~~
## RIz -0.030 0.023 -1.309 0.191 -0.069 -0.069
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .a1SaPa 0.002 0.010 0.147 0.883 0.002 0.002
## .a2SaPa 0.011 0.011 1.021 0.307 0.011 0.011
## .a3SaPa 0.034 0.015 2.296 0.022 0.034 0.033
## .b1 -0.010 0.011 -0.909 0.363 -0.010 -0.010
## .b2 -0.026 0.012 -2.259 0.024 -0.026 -0.026
## .b3 -0.055 0.015 -3.699 0.000 -0.055 -0.053
## .m1 0.000 0.010 0.012 0.990 0.000 0.000
## .m2 0.005 0.011 0.495 0.621 0.005 0.005
## .m3 0.021 0.015 1.430 0.153 0.021 0.021
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIz 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## wz1 0.000 0.000 0.000
## .wz2 0.000 0.000 0.000
## .wz3 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIx 0.509 0.020 25.412 0.000 1.000 1.000
## RIy 0.746 0.035 21.597 0.000 1.000 1.000
## RIz 0.250 0.032 7.715 0.000 1.000 1.000
## wx1 0.494 0.020 25.293 0.000 1.000 1.000
## wy1 0.258 0.033 7.922 0.000 1.000 1.000
## wz1 0.738 0.034 21.977 0.000 1.000 1.000
## .wx2 0.492 0.017 28.276 0.000 0.967 0.967
## .wy2 0.253 0.018 13.728 0.000 0.893 0.893
## .wz2 0.669 0.019 34.953 0.000 0.887 0.887
## .wx3 0.503 0.017 29.931 0.000 0.968 0.968
## .wy3 0.217 0.009 24.522 0.000 0.692 0.692
## .wz3 0.631 0.019 33.998 0.000 0.838 0.838
## .a1SaPa 0.000 0.000 0.000
## .a2SaPa 0.000 0.000 0.000
## .a3SaPa 0.000 0.000 0.000
## .b1 0.000 0.000 0.000
## .b2 0.000 0.000 0.000
## .b3 0.000 0.000 0.000
## .m1 0.000 0.000 0.000
## .m2 0.000 0.000 0.000
## .m3 0.000 0.000 0.000fitMeasures(RICLPM_Safety_Parent_fit, fit.measures = c("chisq","df","pvalue","rmsea","srmr","cfi"))## chisq df pvalue rmsea srmr cfi
## 18.998 3.000 0.000 0.024 0.009 0.999Ayla Pollmann - 2024