Suchergebnisse

When first learning Bayesian statistics, the organizational scholar may be confronted by a number of conceptual and practical challenges. The present article seeks to minimize these by first explicating how the Bayesian process can be understood simply as the combination of two complementary sources of information: prior beliefs and data. In turn, we describe how each source is derived from Bayes's theorem and mathematically formalized, essential knowledge for the Bayesian analyst. However, the beginner can also be undermined by practical difficulties such as software implementation. To this end, we offer a walkthrough of how a Bayesian logistic regression model is coded within BugsXLA, a user-friendly Excel add-in for Bayesian estimation. The data for this example come from a previously published study that identified a subpopulation of "job hobos," individuals characterized by their frequent voluntary turnover and positive attitudes toward quitting. In the original frequentist analysis, exploring the predictors of hoboism proved to be inefficient and inconclusive. We contrast this standard approach with Bayesian estimation, whose results provide rich and novel insights on the topic.