An introduction to Bayesian statistics in health psychology

Sarah Depaoli, Holly Rus, James Clifton, A.G.J. van de Schoot, Jitske Tiemensma

Research output: Contribution to journalArticleAcademicpeer-review


The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of Health Psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in Health Psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programs and an extensive sensitivity analysis examining the impact of priors.
Original languageEnglish
Pages (from-to)248-264
JournalHealth Psychology Review
Issue number3
Publication statusPublished - 2017


  • Bayesian statistics
  • convergence
  • posterior
  • prior distributions


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