Bayesian Analytical Methods: A Methodological Prescription for Public Administration

• 成果类型: Article
• 署名作者: Gill, Jeff; Witko, Christopher
• 署名单位: Washington University (WUSTL); Saint Louis University
• 刊物名称: JOURNAL OF PUBLIC ADMINISTRATION RESEARCH AND THEORY
• ISSN/ISSBN: 1053-1858
• DOI: 10.1093/jopart/mus091
• 发表日期: 2013
• 页码: 457-494
• 关键词: STATISTICAL-INFERENCE; Political control; state agencies; test criteria; patterns; purposes; access; priors

摘要

In this article we describe in detail the Bayesian perspective on statistical inference and demonstrate that it provides a more principled approach to modeling public administration data. Because many datasets in public administration are population-level, one-time unique collections, or descriptive of fluid events, the Bayesian reliance on probability as a description of unknown quantities is a superior paradigm than that borrowed from Frequentist methods in the natural sciences where experimentation is routine. Here we provide a thorough, but accessible, introduction to Bayesian methods and then demonstrate our points with data on interest group influence in US state administrative agencies.