On Using Bayesian Methods to Address Small Sample Problems

Research output: Contribution to journalArticlepeer-review

307 Scopus citations


As Bayesian methods continue to grow in accessibility and popularity, more empirical studies are turning to Bayesian methods to model small sample data. Bayesian methods do not rely on asympotics, a property that can be a hindrance when employing frequentist methods in small sample contexts. Although Bayesian methods are better equipped to model data with small sample sizes, estimates are highly sensitive to the specification of the prior distribution. If this aspect is not heeded, Bayesian estimates can actually be worse than frequentist methods, especially if frequentist small sample corrections are utilized. We show with illustrative simulations and applied examples that relying on software defaults or diffuse priors with small samples can yield more biased estimates than frequentist methods. We discuss conditions that need to be met if researchers want to responsibly harness the advantages that Bayesian methods offer for small sample problems as well as leading small sample frequentist methods.

Original languageEnglish (US)
Pages (from-to)750-773
Number of pages24
JournalStructural Equation Modeling
Issue number5
StatePublished - Sep 2 2016
Externally publishedYes


  • Bayes
  • prior distribution
  • small sample

ASJC Scopus subject areas

  • General Decision Sciences
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)


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