Power in Bayesian Mediation Analysis for Small Sample Research

Milica Miočević, David Mackinnon, Roy Levy

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


Bayesian methods have the potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This article compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N ≤ 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N ≥ 100 and the effects were small, N < 60 and the effects were large, and N < 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results.

Original languageEnglish (US)
Pages (from-to)666-683
Number of pages18
JournalStructural Equation Modeling
Issue number5
StatePublished - Sep 3 2017


  • Bayesian statistics
  • power
  • single mediator model
  • small sample sizes

ASJC Scopus subject areas

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


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