Article

Clearer Analysis, Interpretation, and Communication in Organizational Research: A Bayesian Guide

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Abstract

Historically, organizational researchers fully embraced frequentist statistics and null hypothesis significance testing (NHST). Bayesian statistics is an underused alternative paradigm offering numerous benefits for organizational researchers and practitioners: e.g., accumulating direct evidence for the null hypothesis (vs. ‘fail to reject the null’), capturing uncertainty across a distribution of population parameters (vs. a 95% confidence interval on a single point estimate) – and through these benefits, communicating statistical findings more clearly. Although I-O and other organizational methodologists in the past have promoted Bayesian methods, only now is easy-to-use JASP statistical software available for more widespread implementation. Moreover, the software is free to download and use, is menu-driven, and is supported by an active multidisciplinary user community. Using JASP, our tutorial compares and contrasts frequentist and Bayesian approaches for two analyses: a multiple linear regression analysis and a linear mixed regression analysis.

Keywords: Bayesian statistics, Bayesian regression, Bayesian linear mixed model, statistical communication

How to Cite:

Courey, K. A., Oswald, F. L. & Culpepper, S. A., (2024) “Clearer Analysis, Interpretation, and Communication in Organizational Research: A Bayesian Guide”, Practical Assessment, Research, and Evaluation 29(1): 1. doi: https://doi.org/10.7275/pare.1975