The impact of misspecifying class-specific residual variances in growth mixture models

Craig K. Enders, Davood Tofighi

Research output: Contribution to journalArticlepeer-review

108 Scopus citations


The purpose of this study was to examine the impact of misspecifying a growth mixture model (GMM) by assuming that Level-1 residual variances are constant across classes, when they do, in fact, vary in each subpopulation. Misspecification produced bias in the within-class growth trajectories and variance components, and estimates were substantially less precise than those obtained from a correctly specified GMM. Bias and precision became worse as the ratio of the largest to smallest Level-1 residual variances increased, class proportions became more disparate, and the number of class-specific residual variances in the population increased. Although the Level-1 residuals are typically of little substantive interest, these results suggest that researchers should carefully estimate and report these parameters in published GMM applications.

Original languageEnglish (US)
Pages (from-to)75-95
Number of pages21
JournalStructural Equation Modeling
Issue number1
StatePublished - Jan 1 2008

ASJC Scopus subject areas

  • General Decision Sciences
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)


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