Fitting growth curve models in the Bayesian framework

Zita Oravecz, Chelsea Muth

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Growth curve modeling is a popular methodological tool due to its flexibility in simultaneously analyzing both within-person effects (e.g., assessing change over time for one person) and between-person effects (e.g., comparing differences in the change trajectories across people). This paper is a practical exposure to fitting growth curve models in the hierarchical Bayesian framework. First the mathematical formulation of growth curve models is provided. Then we give step-by-step guidelines on how to fit these models in the hierarchical Bayesian framework with corresponding computer scripts (JAGS and R). To illustrate the Bayesian GCM approach, we analyze a data set from a longitudinal study of marital relationship quality. We provide our computer code and example data set so that the reader can have hands-on experience fitting the growth curve model.

Original languageEnglish (US)
Pages (from-to)235-255
Number of pages21
JournalPsychonomic Bulletin and Review
Issue number1
StatePublished - Feb 1 2018

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)


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