On the Performance of Bayesian Approaches in Small Samples: A Comment on Smid, McNeish, Miocevic, and van de Schoot (2020). Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Zitzmann, Steffen; Lüdtke, Oliver; Robitzsch, Alexander; Hecht, Martin
• Is Part Of: Structural equation modeling. Volume 28:Issue 1(2021)
• Page Start: 40
• Page End: 50
• Publication Date: 2021-01-02
• Subjects: Bayesian estimation -- Markov chain Monte Carlo -- structural equation modeling -- small sample; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2020.1752216
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: This journal recently published a systematic review of simulation studies on the performance of Bayesian approaches for estimating latent variable models in small samples. The authors of this review highlighted that Bayesian approaches can perform poorly (i.e., by exhibiting bias) when the prior distributions are not thoughtfully constructed on the basis of previous knowledge. In this comment, we question whether the bias is the most important criterion when the sample size is small. We argue that the variability is more important and should therefore not be ignored. Moreover, because one of the most important selling points of Bayesian approaches was not addressed in the article, we argue that although somewhat biased, Bayesian approaches allow for more accurate estimates (i.e., a smaller mean squared error) than Maximum Likelihood (ML) in small samples, and we show one such approach that is more accurate than ML.