Model Criticism of Growth Curve Models via Posterior Predictive Model Checking

Derek M. Fay, Roy Levy, Ann C. Schulte

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Longitudinal data structures are frequently encountered in a variety of disciplines in the social and behavioral sciences. Growth curve modeling offers a highly extensible framework that allows for the exploration of rich hypotheses. However, owing to the presence of interrelated sources of potential data-model misfit at multiple levels, the matter of model criticism remains challenging for even foundational growth curve models. Through a simulation study and an applied example, the performance of six discrepancy measures was investigated using posterior predictive model checking as the framework for model criticism. The likelihood ratio and the standardized generalized dimensionality discrepancy measure outperformed the other discrepancy measures under consideration and show promise for future study and use.

Original languageEnglish (US)
Pages (from-to)191-212
Number of pages22
JournalJournal of Experimental Education
Issue number1
StatePublished - 2022


  • Bayesian analysis
  • growth curve modeling (GCM)
  • hierarchical Bayesian models
  • latent growth modeling (LGM)
  • multilevel modeling (MLM)
  • posterior predictive model checking

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology


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