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
ACHIEVE =~ Mot1 + Mot2
SATIS =~ JS1 + JS2
ESTEEM =~ SE1 + SE2
VERBAL =~ VI1
VI1 ~~ 0*VI1
PERFORM ~ ESTEEM
SATIS ~ ACHIEVE + 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 84 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 122
##
## Model Test User Model:
##
## Test statistic 14.191
## Degrees of freedom 15
## P-value (Chi-square) 0.511
##
## Model Test Baseline Model:
##
## Test statistic 258.619
## Degrees of freedom 28
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.007
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2134.734
## Loglikelihood unrestricted model (H1) -2127.638
##
## Akaike (AIC) 4311.468
## Bayesian (BIC) 4370.353
## Sample-size adjusted Bayesian (SABIC) 4303.955
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.081
## P-value H_0: RMSEA <= 0.050 0.770
## P-value H_0: RMSEA >= 0.080 0.055
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.035
##
## 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
## ACHIEVE =~
## Mot1 1.000 1.105 0.569
## Mot2 1.169 0.336 3.484 0.000 1.292 0.630
## SATIS =~
## JS1 1.000 2.704 0.787
## JS2 0.834 0.134 6.221 0.000 2.254 0.801
## ESTEEM =~
## SE1 1.000 1.648 0.766
## SE2 0.861 0.137 6.283 0.000 1.419 0.692
## VERBAL =~
## VI1 1.000 3.635 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PERFORM ~
## ESTEEM 0.922 0.144 6.425 0.000 0.730 0.730
## SATIS ~
## ACHIEVE 1.179 0.452 2.609 0.009 0.482 0.482
## VERBAL 0.175 0.085 2.065 0.039 0.235 0.235
## PERFORM 0.583 0.138 4.223 0.000 0.449 0.449
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ACHIEVE ~~
## ESTEEM 0.741 0.294 2.516 0.012 0.407 0.407
## VERBAL -1.614 0.585 -2.761 0.006 -0.402 -0.402
## ESTEEM ~~
## VERBAL -2.297 0.675 -3.402 0.001 -0.383 -0.383
##
## 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
## .Mot1 2.550 0.473 5.388 0.000 2.550 0.676
## .Mot2 2.540 0.568 4.472 0.000 2.540 0.604
## .JS1 4.480 1.161 3.860 0.000 4.480 0.380
## .JS2 2.834 0.790 3.588 0.000 2.834 0.358
## .SE1 1.913 0.419 4.564 0.000 1.913 0.413
## .SE2 2.196 0.383 5.730 0.000 2.196 0.522
## .PERFORM 2.023 0.391 5.170 0.000 0.467 0.467
## ACHIEVE 1.222 0.494 2.473 0.013 1.000 1.000
## .SATIS 3.893 1.190 3.273 0.001 0.533 0.533
## ESTEEM 2.715 0.638 4.256 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.