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.
|Original language||English (US)|
|Number of pages||38|
|Journal||Journal of Public Administration Research and Theory|
|State||Published - Apr 2013|
All Science Journal Classification (ASJC) codes
- Sociology and Political Science
- Public Administration