Bayesian Versus Frequentist Estimation for Structural Equation Models in Small Sample Contexts: A Systematic Review

Sanne C. Smid, Daniel McNeish, Milica Miočević, Rens van de Schoot

Research output: Contribution to journalReview articlepeer-review

35 Scopus citations

Abstract

In small sample contexts, Bayesian estimation is often suggested as a viable alternative to frequentist estimation, such as maximum likelihood estimation. Our systematic literature review is the first study aggregating information from numerous simulation studies to present an overview of the performance of Bayesian and frequentist estimation for structural equation models with small sample sizes. We conclude that with small samples, the use of Bayesian estimation with diffuse default priors can result in severely biased estimates–the levels of bias are often even higher than when frequentist methods are used. This bias can only be decreased by incorporating prior information. We therefore recommend against naively using Bayesian estimation when samples are small, and encourage researchers to make well-considered decisions about all priors. For this purpose, we provide recommendations on how to construct thoughtful priors.

Original languageEnglish (US)
Pages (from-to)131-161
Number of pages31
JournalStructural Equation Modeling
Volume27
Issue number1
DOIs
StatePublished - Jan 2 2020

Keywords

  • Small samples
  • informative priors
  • structural equation models
  • systematic review

ASJC Scopus subject areas

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

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