Replication data for: Essays in Political Methodology

This dissertation provides three novel methodologies to the field of political science. In the first chapter, I describe how to make causal inferences in the face of dynamic strategies. Traditional causal inference methods assume that these dynamic decisions are made all at once, an assumption that forces a choice between omitted variable bias and post-treatment bias. I resolve this dilemma by adapting methods from biostatistics and use these methods to estimate the effectiveness of an inherently dynamic process: a candidate's decision to ``go negative.'' Drawing on U.S. statewide elections (2000-2006), I find, in contrast to the previous literature, that negative advertising is an effective strategy for non-incumbents. In the second chapter, I develop a method for handling measurement error. Social scientists devote considerable effort to mitigating measurement error during data collection but then ignore the issue during data analysis. Although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. This chapter develops an easy-to-use alternative without these problems; it generalizes the popular multiple imputation framework by treating missing data problems as a special case of extreme measurement error and corrects for both. In the final chapter, I introduce a model for detecting changepoints in the distribution of contributions to candidates over the course of a campaign. This game-changers model is ideal for campaign contributions data because it allows for overdispersion, a key feature of contributions data. While many extant changepoint models force researchers to choose the number of changepoint ex ante, the game-changers model incorporates a Dirichlet process prior in order to estimate the number of changepoints along with th eir location. I demonstrate the usefulness of the model in data from the 2012 Republican primary and the 2008 U.S. Senate elections.

本博士学位论文为政治学领域贡献了三种创新性研究方法论。
在第一章中，本文阐述了面对动态策略时如何开展因果推断（causal inference）。传统因果推断方法假设这类动态决策为一次性完成，这一假设使得研究者不得不在遗漏变量偏误（omitted variable bias）与处理后偏误（post-treatment bias）之间做出权衡。本文通过借鉴生物统计学方法解决了这一困境，并运用这些方法估算了一项本质上属于动态过程的策略的有效性：候选人发起“负面竞选”的决策。本文基于2000年至2006年美国全州选举数据展开研究，与既有研究文献的结论相悖，我们发现负面广告策略对于非在任候选人而言是行之有效的竞选手段。
在第二章中，本文提出了一种处理测量误差的研究方法。社会科学家在数据收集阶段会投入大量精力以减轻测量误差，但在数据分析阶段却往往忽视该问题。尽管学界已提出诸多旨在降低测量误差引致偏误的统计方法，但由于假设前提脱离实际、模型依赖性过强、计算难度过高，抑或无法适用于多变量测量误差场景，这类方法鲜少得到广泛应用。本章提出了一种无需上述缺陷、易于使用的替代方案：通过将缺失数据问题视为极端测量误差的特例，对流行的多重插补（multiple imputation）框架进行推广，同时实现对缺失数据与测量误差的校正。
在最后一章中，本文介绍了一种用于检测竞选周期内候选人捐款分布变化点的模型。这款“游戏规则改变者”模型（game-changers model）非常适配竞选捐款数据，因为它能够处理数据过度离散（overdispersion）这一捐款数据的核心特征。当前诸多现存的变化点模型均要求研究者预先设定变化点的数量，而“游戏规则改变者”模型通过引入狄利克雷过程先验（Dirichlet process prior），能够同时估算变化点的数量及其位置。本文借助2012年美国共和党总统初选以及2008年美国参议院选举的数据集，验证了该模型的实用价值。