Chapter 11:
Phil Wood
2025-08-01
Figure 11.1 Two Factor Oblique Model Simulated Alcohol Use Data
Load Simulated Alcohol Data
<a href=“https://docs.google.com/document/d/1SZ44w8P1oq6C_kMhAdBE8WX9z14-u3NNKQDCVkNmNK4/edit?usp=sharing>
Oblique Two Factor Model Figure11.1
require(lavaan)## Loading required package: lavaan## This is lavaan 0.6-19
## lavaan is FREE software! Please report any bugs.AlcUseData <- read.table("singlegroupalcsim.txt", header = TRUE) ;
Fig11.1Model<-"
! regressions
TypAlc=~L2*qpls5mw1
TypAlc=~L1*alcf3mw1
LstWkAlc=~L5*typqsaw1
LstWkAlc=~L4*typqfrw1
LstWkAlc=~L3*typqthw1
! residuals, variances and covariances
alcf3mw1 ~~ VAlc*alcf3mw1
typqfrw1 ~~ VFr*typqfrw1
typqsaw1 ~~ VSa*typqsaw1
typqthw1 ~~ VTh*typqthw1
qpls5mw1 ~~ VPls5*qpls5mw1
TypAlc ~~ 1.0*TypAlc
LstWkAlc ~~ 1.0*LstWkAlc
TypAlc ~~ r*LstWkAlc
! observed means
alcf3mw1~1;
typqfrw1~1;
typqsaw1~1;
typqthw1~1;
qpls5mw1~1;
"
Fig11.1Result<-lavaan(Fig11.1Model, data=AlcUseData, fixed.x=FALSE, missing="FIML")
summary(Fig11.1Result, fit.measures=TRUE,standardized=TRUE);## lavaan 0.6-19 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 16
##
## Number of observations 2558
## Number of missing patterns 1
##
## Model Test User Model:
##
## Test statistic 96.222
## Degrees of freedom 4
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 5285.686
## Degrees of freedom 10
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.983
## Tucker-Lewis Index (TLI) 0.956
##
## Robust Comparative Fit Index (CFI) 0.983
## Robust Tucker-Lewis Index (TLI) 0.956
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -28383.767
## Loglikelihood unrestricted model (H1) -28335.655
##
## Akaike (AIC) 56799.533
## Bayesian (BIC) 56893.085
## Sample-size adjusted Bayesian (SABIC) 56842.248
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.095
## 90 Percent confidence interval - lower 0.079
## 90 Percent confidence interval - upper 0.112
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.939
##
## Robust RMSEA 0.095
## 90 Percent confidence interval - lower 0.079
## 90 Percent confidence interval - upper 0.112
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.939
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.021
##
## 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
## TypAlc =~
## qpls5mw1 (L2) 0.897 0.020 45.559 0.000 0.897 0.810
## alcf3mw1 (L1) 2.380 0.056 42.802 0.000 2.380 0.770
## LstWkAlc =~
## typqsaw1 (L5) 3.246 0.075 43.123 0.000 3.246 0.775
## typqfrw1 (L4) 2.931 0.069 42.475 0.000 2.931 0.766
## typqthw1 (L3) 1.591 0.054 29.682 0.000 1.591 0.578
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## TypAlc ~~
## LstWkAlc (r) 0.966 0.011 90.243 0.000 0.966 0.966
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .alcf3mw1 3.976 0.061 65.047 0.000 3.976 1.286
## .typqfrw1 2.267 0.076 29.992 0.000 2.267 0.593
## .typqsaw1 2.653 0.083 32.049 0.000 2.653 0.634
## .typqthw1 1.077 0.054 19.783 0.000 1.077 0.391
## .qpls5mw1 0.666 0.022 30.426 0.000 0.666 0.602
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .alcf3m1 (VAlc) 3.891 0.150 26.020 0.000 3.891 0.407
## .typqfr1 (VFr) 6.031 0.231 26.123 0.000 6.031 0.413
## .typqsw1 (VSa) 6.992 0.274 25.534 0.000 6.992 0.399
## .typqth1 (VTh) 5.051 0.155 32.585 0.000 5.051 0.666
## .qpls5m1 (VPl5) 0.420 0.019 22.467 0.000 0.420 0.343
## TypAlc 1.000 1.000 1.000
## LstWkAl 1.000 1.000 1.000require(lavaangui)## Loading required package: lavaangui## Warning: package 'lavaangui' was built under R version 4.5.1## This is lavaangui 0.2.5
## lavaangui is BETA software! Please report any bugs at https://github.com/karchjd/lavaangui/issues#plot_lavaan(Fig11.1Result)