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Sensitivity of Fit Indices to Misspecification in Growth Curve Models
Authors:Wei Wu  Stephen G. West
Affiliation:1. University of Kansas;2. Arizona State University
Abstract:This study investigated the sensitivity of fit indices to model misspecification in within-individual covariance structure, between-individual covariance structure, and marginal mean structure in growth curve models. Five commonly used fit indices were examined, including the likelihood ratio test statistic, root mean square error of approximation, standardized root mean square residual, comparative fit index, and Tucker-Lewis Index. The fit indices were found to have differential sensitivity to different types of misspecification in either the mean or covariance structures with severity of misspecification controlled. No fit index was always more (or less) sensitive to misspecification in the marginal mean structure relative to those in the covariance structure. Specifying the covariance structure to be saturated can substantially improve the sensitivity of fit indices to misspecification in the marginal mean structure; this result might help identify the sources of specification error in a growth curve model. An empirical example of children's growth in math achievement (Wu, West, &; Hughes, 2008 Wu, W., West, S. G. and Hughes, J. N. 2008. Effect of retention in first grade on children's achievement trajectories over four years: A piecewise growth analysis using propensity score matching.. Journal of Educational Psychology, 100: 727740. [Crossref], [PubMed], [Web of Science ®] [Google Scholar]) was used to illustrate the results.
Keywords:
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