Elicited Priors for Bayesian Model Specification

We explain how to use elicited priors in Bayesian political science research. These are a form of prior information produced by previous knowledge from structured interviewswith subjective area experts who have little or no concern for the statistical aspects of the project. The purpose is to introduce qualitative and area-specific information into an empirical model in a systematic and organized manner in order to produce parsimonious yet realistic implications. Currently, there is no work in political science that articulates elicited priors in a Bayesian specification. We demonstrate the value of the approach by applying elicited priors to a problem in judicial comparative politics using data and elicitations we collected in Nicaragua. As quantitative political research becomes increasingly sophisticated, the more complex, but more capable, Bayesian approach is likely to grow in popularity. The Bayesian inferential engine is a coherent set of axioms that converts prior information to posterior evidence by conditioning on observed data. Thus, stipulating prior distributions for unknown quantities is a requirement, and this requirement has been a long-standing source of controversy. Bayesians statistics provides a number of ways to define prior information, and the strength of these prior assertions can var