Bayesian analysis of longitudinal data using growth curve models

Zhiyong Zhang, Fumiaki Hamagami, Lijuan Wang, John R. Nesselroade, Kevin J. Grimm

Research output: Contribution to journalArticlepeer-review

108 Scopus citations


Bayesian methods for analyzing longitudinal data in social and behavioral research are recommended for their ability to incorporate prior information in estimating simple and complex models. We first summarize the basics of Bayesian methods before presenting an empirical example in which we fit a latent basis growth curve model to achievement data from the National Longitudinal Survey of Youth. This step-by-step example illustrates how to analyze data using both noninformative and informative priors. The results show that in addition to being an alternative to the maximum likelihood estimation (MLE) method, Bayesian methods also have unique strengths, such as the systematic incorporation of prior information from previous studies. These methods are more plausible ways to analyze small sample data compared with the MLE method.

Original languageEnglish (US)
Pages (from-to)374-383
Number of pages10
JournalInternational Journal of Behavioral Development
Issue number4
StatePublished - Jul 2007
Externally publishedYes


  • Bayesian analysis
  • Growth curve models
  • Informative priors
  • Longitudinal data
  • Pooling data
  • WinBUGS

ASJC Scopus subject areas

  • Social Psychology
  • Education
  • Developmental and Educational Psychology
  • Social Sciences (miscellaneous)
  • Developmental Neuroscience
  • Life-span and Life-course Studies


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