A Quasi-Likelihood Approach to Assess Model Fit in Quadratic and Interaction SEM.

Multivariate Behavioral Research
Rebecca D Büchner, Andreas G Klein

Abstract

For the assessment of model fit in linear structural equation modeling (SEM), several fit measures have been developed that use an unconstrained mean and covariance structure, but cannot be readily applied to SEM with quadratic and interaction effects. In this article, we propose the novel quasi-likelihood ratio test (Q-LRT) to evaluate global fit of nonlinear SEM models. The Q-LRT is based on a simplification of the quasi-maximum likelihood method for the estimation of model parameters. An empirical application of the Q-LRT is demonstrated for data in a study about aging in men. Results from a Monte Carlo study show that the Q-LRT performs reliably when sample size is sufficiently large. Also, simulations suggest robustness of Q-LRT for moderately skewed latent exogenous variables.

References

Dec 23, 1998·Environmental Health Perspectives·R J Carroll, C D Galindo
Jun 14, 2003·The British Journal of Mathematical and Statistical Psychology·Melanie M Wall, Yasuo Amemiya
Mar 21, 2012·Psychological Methods·Jeffrey R HarringJui-Chen Hsu
Jul 10, 2013·Psychological Methods·Todd D LittleAlexander M Schoemann
Oct 5, 2013·Psychometrika·Xin-Yuan SongEdward Hak-Sing Ip
Jan 1, 2004·Multivariate Behavioral Research·Sik-Yum LeeWai-Yin Poon
Feb 1, 2015·Educational and Psychological Measurement·Xiulin MaoGregory R Hancock

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Software Mentioned

nlsem ”
QML
R
PoisNonNor
R package “ nlsem ”
R package “
R package ”
R Core Team
LRT
CompQuadForm ”

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