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The Time Has Come: Bayesian Methods for Data Analysis in the Organizational Sciences

机译:时机已到:组织科学中的贝叶斯数据分析方法

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摘要

The use of Bayesian methods for data analysis is creating a revolution in fields ranging from genetics to marketing. Yet, results of our literature review, including more than 10,000 articles published in 15 journals from January 2001 and December 2010, indicate that Bayesian approaches are essentially absent from the organizational sciences. Our article introduces organizational science researchers to Bayesian methods and describes why and how they should be used. We use multiple linear regression as the framework to offer a step-by-step demonstration, including the use of software, regarding how to implement Bayesian methods. We explain and illustrate how to determine the prior distribution, compute the posterior distribution, possibly accept the null value, and produce a write-up describing the entire Bayesian process, including graphs, results, and their interpretation. We also offer a summary of the advantages of using Bayesian analysis and examples of how specific published research based on frequentist analysis-based approaches failed to benefit from the advantages offered by a Bayesian approach and how using Bayesian analyses would have led to richer and, in some cases, different substantive conclusions. We hope that our article will serve as a catalyst for the adoption of Bayesian methods in organizational science research.
机译:使用贝叶斯方法进行数据分析正在从遗传学到市场营销领域掀起一场革命。但是,我们文献综述的结果(包括从2001年1月到2010年12月在15种期刊上发表的10,000多篇文章)表明,组织科学基本上没有贝叶斯方法。我们的文章向组织科学研究人员介绍了贝叶斯方法,并描述了为什么以及如何使用它们。我们使用多元线性回归作为框架来提供有关如何实现贝叶斯方法的分步演示,包括软件的使用。我们解释并说明了如何确定先验分布,计算后验分布,可能接受空值并产生描述整个贝叶斯过程(包括图形,结果及其解释)的描述。我们还提供了使用贝叶斯分析的优势的摘要,并举例说明了基于基于频繁度分析的方法的特定已发表研究如何无法从贝叶斯方法的优势中受益以及使用贝叶斯分析如何导致更丰富的研究,以及在某些情况下,得出不同的实质性结论。我们希望我们的文章将成为在组织科学研究中采用贝叶斯方法的催化剂。

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