Bayesian Estimation and Inference

TLDR

The Bayesian revolution is spreading across disciplines but remains largely absent from organizational research. The authors review Bayesian estimation fundamentals, illustrate their application in two empirical studies—one structural equation model of testosterone effects on team status and one ANOVA-style ego depletion experiment—demonstrating how Bayesian methods overcome frequentist limitations, and provide data, Mplus code, and tables for result presentation. Bayesian methods offer many benefits with minimal drawbacks, the main barrier being researchers’ unfamiliarity.

Abstract

This paper introduces the “Bayesian revolution” that is sweeping across multiple disciplines but has yet to gain a foothold in organizational research. The foundations of Bayesian estimation and inference are first reviewed. Then, two empirical examples are provided to show how Bayesian methods can overcome limitations of frequentist methods: (a) a structural equation model of testosterone’s effect on status in teams, where a Bayesian approach allows directly testing a traditional null hypothesis as a research hypothesis and allows estimating all possible residual covariances in a measurement model, neither of which are possible with frequentist methods; and (b) an ANOVA-style model from a true experiment of ego depletion’s effects on performance, where Bayesian estimation with informative priors allows results from all previous research (via a meta-analysis and other previous studies) to be combined with estimates of study effects in a principled manner, yielding support for hypotheses that is not obtained with frequentist methods. Data are available from the first author, code for the program Mplus is provided, and tables illustrate how to present Bayesian results. In conclusion, the many benefits and few hindrances of Bayesian methods are discussed, where the major hindrance has been an easily solvable lack of familiarity by organizational researchers.