This journal recently published a systematic review of simulation studies on the performance of Bayesianapproaches for estimating latent variable models in small samples. The authors of this review high-lighted that Bayesian approaches can perform poorly (i.e., by exhibiting bias) when the prior distribu-tions are not thoughtfully constructed on the basis of previous knowledge. In this comment, wequestion whether the bias is the most important criterion when the sample size is small. We arguethat the variability is more important and should therefore not be ignored. Moreover, because one ofthe most important selling points of Bayesian approaches was not addressed in the article, we arguethat although somewhat biased, Bayesian approaches allow for more accurate estimates (i.e., a smallermean squared error) than Maximum Likelihood (ML) in small samples, and we show one such approachthat is more accurate than ML.
Original languageEnglish
JournalStructural Equation Modeling: A Multidisciplinary Journal
Issue number1
Pages (from-to)40-50
Number of pages11
Publication statusPublished - 02.01.2021
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    Research areas

  • Bayesian estimation, Markov chain Monte Carlo, structural equation modeling, small sample

ID: 1388370