library(lavaan)
cormat <- '
1.000
0.418 1.000
0.394 0.627 1.000
0.129 0.202 0.266 1.000
0.189 0.284 0.208 0.365 1.000
0.544 0.281 0.324 0.201 0.161 1.000
0.507 0.225 0.314 0.172 0.174 0.546 1.000
-0.357 -0.156 -0.038 -0.199 -0.277 -0.294 -0.174 1.000
'
sdev <- c(2.090, 3.430, 2.810, 1.950, 2.060, 2.160, 2.060, 3.650)
Cmat <- getCov(cormat)
Dmat <- diag(sdev)
covmat <- Dmat %*% Cmat %*% Dmat
colnames(covmat) <- c("Per1", "JS1", "JS2", "Mot1", "Mot2", "SE1", "SE2", "VI1")
rownames(covmat) <- colnames(covmat)
lv.pa.model <- '
PERFORM =~ 1*Per1
Per1 ~~ 0*Per1
SATIS =~ JS1 + JS2
ESTEEM =~ SE1 + SE2
VERBAL =~ VI1
VI1 ~~ 0*VI1
PERFORM ~ ESTEEM
SATIS ~ VERBAL + PERFORM
'
lv.pa.fit <- sem(lv.pa.model, sample.cov = covmat, sample.nobs = 122)
summary(lv.pa.fit, fit.measures = T, standardized = T)
## lavaan 0.6.15 ended normally after 65 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Number of observations 122
##
## Model Test User Model:
##
## Test statistic 11.747
## Degrees of freedom 7
## P-value (Chi-square) 0.109
##
## Model Test Baseline Model:
##
## Test statistic 212.828
## Degrees of freedom 15
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.976
## Tucker-Lewis Index (TLI) 0.949
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1641.545
## Loglikelihood unrestricted model (H1) -1635.672
##
## Akaike (AIC) 3311.090
## Bayesian (BIC) 3350.346
## Sample-size adjusted Bayesian (SABIC) 3306.081
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.075
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.147
## P-value H_0: RMSEA <= 0.050 0.251
## P-value H_0: RMSEA >= 0.080 0.511
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.049
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PERFORM =~
## Per1 1.000 2.081 1.000
## SATIS =~
## JS1 1.000 2.745 0.802
## JS2 0.799 0.151 5.280 0.000 2.194 0.783
## ESTEEM =~
## SE1 1.000 1.639 0.762
## SE2 0.860 0.139 6.198 0.000 1.409 0.687
## VERBAL =~
## VI1 1.000 3.635 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PERFORM ~
## ESTEEM 0.937 0.147 6.358 0.000 0.738 0.738
## SATIS ~
## VERBAL 0.049 0.072 0.678 0.498 0.065 0.065
## PERFORM 0.706 0.141 5.017 0.000 0.535 0.535
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ESTEEM ~~
## VERBAL -2.296 0.674 -3.408 0.001 -0.385 -0.385
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Per1 0.000 0.000 0.000
## .VI1 0.000 0.000 0.000
## .JS1 4.170 1.390 2.999 0.003 4.170 0.356
## .JS2 3.040 0.908 3.348 0.001 3.040 0.387
## .SE1 1.941 0.425 4.565 0.000 1.941 0.419
## .SE2 2.222 0.387 5.746 0.000 2.222 0.528
## .PERFORM 1.974 0.396 4.985 0.000 0.456 0.456
## .SATIS 5.496 1.464 3.754 0.000 0.729 0.729
## ESTEEM 2.687 0.639 4.204 0.000 1.000 1.000
## VERBAL 13.213 1.692 7.810 0.000 1.000 1.000
© Copyright 2024
@Yi Feng
and
@Gregory R. Hancock.