From Jeff Gill and Christopher Witko, now on JPART Advance Access:
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.
Full disclosure: Ken Meier and I are editing a special issue of JPART that will include a collection of methods papers.