Since its creation in the 18th century, Bayesian inference has had a fairly complex history. In its darker times it has fallen into obscurity, while brighter days have seen it applied to projects such as cracking Enigma in World War II. Fortunately, scholarship in the last two decades has embraced Bayesian probability and statistics and applied it to various fields of study. In their February Special Issue, Journal of Management adds to the conversation on Bayesian probability and statistics both inside and outside of management research.
Michael J. Zyphur, Frederick L. Oswald, and Deborah E. Rupp collaborated on the introduction to the Special Issue:
Bayesian estimation and inference have been core features of scientific knowledge generation since the work of Sir Thomas Bayes was built upon by Pierre-Simone Laplace from the late 1700s through the early 1800s. Although present-day statistical analysis in organizational research is “frequentist” in nature (due to the influence of scholars such as Sir Ronald Fisher and Jerzey Neyman), the past 20 years has seen a veritable explosion of Bayesian applications across the social and physical sciences. This special issue highlights these applications and the many opportunities they carry, including precise and flexible methods for testing hypotheses and very intuitive ways of describing results.
For this special issue, three editorial commentaries were solicited from world-renowned experts in statistics, probability, and their historical and current applications. These papers offer a view from outside management, giving fresh insight into topics that are rarely covered in management research, including critical perspectives on existing paradigms in our field and recommendations for improvements in statistical methods and research design. The topics covered relate to (a) the apparent desire for universal or default methods of inquiry and inference—whether Bayesian or frequentist—which narrows researchers’ focus and reduces their ability to develop and deploy a larger “toolbox” of methods and approaches; (b) the many limitations of existing frequentist tools, which tend to be underestimated or ignored because of their institutionalized and habitual nature; and (c) the existence and importance of multiple theories of probability that are available for scientific inference and the benefits of acknowledging the importance of researchers’ educations and beliefs about such theories.
You can read the Special Issue on Bayesian Probability and Statistics in Management Research from Journal of Management for free for through the end of February! Click here to access the Table of Contents. Want to know when all the latest research from Journal of Management becomes available? Click here to sign up for e-alerts!