Replication Data for: Hierarchical Bayesian Aldrich-McKelvey Scaling

Estimating the ideological positions of political actors is an important step towards answering a number of substantive questions in political science. Survey scales provide useful data for such estimation, but also present a challenge, as respondents tend to interpret the scales differently. The Aldrich-McKelvey model addresses this challenge, but the existing implementations of the model have notable shortcomings. Focusing on the Bayesian version of the model (BAM), the analyses in this article demonstrate that the model is prone to overfitting and yields poor results for a considerable share of respondents. The article addresses these shortcomings by developing a hierarchical Bayesian version of the model (HBAM). The new version treats self-placements as data to be included in the likelihood function, while also modifying the likelihood to allow for scale flipping. The resulting model outperforms the existing Bayesian version both on real data and in Monte Carlo simulations. An R package implementing the models in Stan is provided to facilitate future use.