Clearer analysis, interpretation, and communication in organizational research: A Bayesian guide

Historically, industrial-organizational (IO) psychology has fully embraced frequentist statistics and null hypothesis significance testing (NHST). Bayesian statistics is an underused alternative paradigm offering numerous benefits for IO researchers and practitioners: eg, 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 IO 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 has an active multidisciplinary user community. Using JASP, our tutorial compares and contrasts frequentist and Bayesian approaches for three basic analyses: the independent samples t-test, one-way analysis of variance, and multiple linear regression analysis. We conclude by discussing how Bayesian analyses can be incorporated into organizational research and graduate education in the future. Ultimately, we hope that organizational researchers, practitioners, and graduate students will be as enthusiastic as we are about “going Bayes.”

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