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library(modsem)
This vignette demonstrates how to evaluate and compare model fit for latent interaction models estimated via
using the modsem package (v≥1.0.8). Because standard Chi-square statistics are not available under LMS/QML, we assess fit by:
If the baseline model shows acceptable fit and adding the latent interaction significantly improves fit, the interaction model can also be deemed well-fitting.
We define a model with three latent variables (X, Y, Z) and their interaction (X:Z):
<- "
m1 # Outer (measurement) model
X =~ x1 + x2 + x3
Y =~ y1 + y2 + y3
Z =~ z1 + z2 + z3
# Inner (structural) model
Y ~ X + Z + X:Z
"
# Estimate the full (H1) model via LMS
<- modsem(m1, oneInt, method = "lms")
est_h1
# Estimate the baseline (H0) model without interaction
<- estimate_h0(est_h1, calc.se = FALSE) # std.errors are not needed est_h0
To get fit measures for the baseline model you can use the fit_modsem_da()
function.
fit_modsem_da(est_h0)
It can also be used to get fit measures for the full model, but should be pared with chisq = FALSE
to avoid the Chi-square test. If it is set to TRUE
it will calculate the Chi-square test while ignoring the interaction terms in the model.
fit_modsem_da(est_h1, chisq = FALSE)
Compare H0 vs. H1 using a log-likelihood ratio test:
compare_fit(est_h0, est_h1)
A significant p-value indicates the latent interaction term significantly improves model fit.
For convenience, you can also use the modsem_inspect()
function with what = "fit"
to get fit indices for both models, and comparative fit in one go.
modsem_inspect(est_h1, what = "fit")
Klein, A., & Moosbrugger, H. (2000).
<doi:10.1007/BF02296338>.
"Maximum likelihood estimation of latent interaction effects with the LMS method."
Klein, A. G., & Muthén, B. O. (2007).
<doi:10.1080/00273170701710205>.
"Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects."
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They may not be fully stable and should be used with caution. We make no claims about them.