Hierarchical Bayesian Prediction Methods in Election Politics: Introduction and Major Test

Abstract

The authors build a collection of latent construct models, one per voter, in a representative sample of the voting age population. A given voter's model is an algebraic expression of how that person integrates information about candidates and issues to arrive at a vote decision. Modeling individual decisions—called agent-based modeling—avoids aggregation fallacies and yields diagnostic insights unavailable from traditional pre-election polling methods. This article summarizes tests of the predictive accuracy of the method using data from 10 battleground states in the 2004 U.S. presidential election. Results are presented for the popular vote, the Electoral College vote, and within person. (Each person's predicted vote is compared to that person's actual vote obtained from a post-election survey.) Model accuracy is assessed relative to a standard voting intention model and to a meta-poll approach that combines multiple traditional polls over time, within state. The model's predictive accuracy compares favorably with other models tested, increasing confidence in its insights about voting behavior. A companion article expands on these diagnostics and reviews applications of the approach to campaign strategy and planning.