Replication data for: Deep Interactions with MRP: Election Turnout and Voting Patterns Among Small Electoral Subgroups

Using multilevel regression and poststratification (MRP), we estimate voter turnout and vote choice within deeply interacted subgroups: subsets of the population that are defined by multiple demographic and geographic characteristics. This article lays out the models and statistical procedures we use, along with the steps required to fit the model for the 2004 and 2008 Presidential elections. Though MRP is an increasingly popular method, we improve upon it in numerous ways: deeper levels of covariate interaction, allowing for non-linearity and non-monotonicity, accounting for unequal inclusion probabilities that are conveyed in survey weights, post-estimation adjustments to turnout and voting levels, and informative multidimensional graphical displays as a form of model checking. We use a series of examples to demonstrate the flexibility of our method, including an illustration of turnout and vote choice as subgroups become increasingly detailed, and an analysis of both vote choice changes and turnout changes from 2004 to 2008.

本研究采用多层回归与后分层（multilevel regression and poststratification, MRP）方法，对由多个人口统计学与地理学特征共同定义的深度交互子群中的选民投票率与投票选择进行估计。本文详述了研究所采用的模型与统计流程，以及针对2004年与2008年美国总统大选拟合模型所需的全部步骤。尽管多层回归与后分层方法的应用愈发广泛，但本研究从多个维度对其进行了改进：采用更高阶的协变量交互设置、允许模型呈现非线性与非单调性特征、考量调查权重中体现的入样概率不均等问题、对选民投票率与投票结果进行估计后调整，以及采用信息丰富的多维图形展示作为模型检验手段。我们通过一系列实例验证了所提方法的灵活性，包括展示子群划分愈发精细时的选民投票率与投票选择变化，以及分析2004年至2008年间投票选择与选民投票率的双重变迁。