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.

估算政治行为体的意识形态立场，是解答政治学领域诸多实质性研究问题的关键一环。调查量表可为这类估算提供有效数据，但同时也带来了挑战——受访者往往会以不同方式解读量表。奥德里奇-麦凯尔维模型（Aldrich-McKelvey model）可应对该挑战，但当前已有的该模型实现方案存在显著缺陷。本文聚焦于该模型的贝叶斯版本（BAM），分析显示该模型易于出现过拟合现象，且在相当比例的受访者样本中得出的结果欠佳。为此，本文提出了该模型的分层贝叶斯版本（HBAM）以弥补上述缺陷。新版本将自我定位数据纳入似然函数，并对似然函数进行修正以允许量表翻转。经优化后的模型在真实数据集与蒙特卡洛模拟（Monte Carlo simulations）实验中，均优于现有贝叶斯版本模型。本文还提供了一个基于Stan实现上述模型的R语言工具包，以方便后续研究使用。