A Bayesian Primer for the Organizational Sciences
面向组织科学初学者，用通俗方式解释贝叶斯统计如何结合先验信念与数据，并通过Excel插件BugsXLA演示贝叶斯逻辑回归模型的实际操作，以“工作流浪者”研究为例展示其相比传统方法的优势。
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.