Evaluating Model Fit for Growth Curve Models: Integration of Fit Indices From SEM and MLM Frameworks

Wei Wu, Stephen West, Aaron B. Taylor

Research output: Contribution to journalArticlepeer-review

184 Scopus citations


Evaluating overall model fit for growth curve models involves 3 challenging issues. (a) Three types of longitudinal data with different implications for model fit may be distinguished: balanced on time with complete data, balanced on time with data missing at random, and unbalanced on time. (b) Traditional work on fit from the structural equation modeling (SEM) perspective has focused only on the covariance structure, but growth curve models have four potential sources of misspecification: within-individual covariance matrix, between-individuals covariance matrix, marginal mean structure, and conditional mean structure. (c) Growth curve models can be estimated in both the SEM and multilevel modeling (MLM) frameworks; these have different emphases for the evaluation of model fit. In this article, the authors discuss the challenges presented by these 3 issues in the calculation and interpretation of SEM- and MLM-based fit indices for growth curve models and conclude by identifying some lines for future research.

Original languageEnglish (US)
Pages (from-to)183-201
Number of pages19
JournalPsychological Methods
Issue number3
StatePublished - Sep 2009


  • growth curve modeling
  • longitudinal data
  • model fit
  • multilevel modeling
  • structural equation modeling

ASJC Scopus subject areas

  • Psychology (miscellaneous)


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