Replication Data for: Ends Against the Middle: Measuring Latent Traits When Opposites Respond the Same Way for Antithetical Reasons

Standard methods for measuring latent traits from categorical data assume that response functions are monotonic. This assumption is violated when individuals from both extremes respond identically but for conflicting reasons. Two survey respondents may “disagree” with a statement for opposing motivations, liberal and conservative justices may dissent from the same Supreme Court decision but provide ideologically contradictory rationales, and in legislative settings, ideological opposites may join together to oppose moderate legislation in pursuit of antithetical goals. In this article, we introduce a scaling model that accommodates ends against the middle responses and provide a novel estimation approach that improves upon existing routines. We apply this method to survey data, voting data from the United States Supreme Court, and the 116th Congress, and show it outperforms standard methods in terms of both congruence with qualitative insights and model fit. This suggests that our proposed method may offer improved one-dimensional estimates of latent traits in many important settings.

从分类数据中测度潜在特质（latent traits）的标准方法，均假设响应函数（response functions）具有单调性。当来自两个极端的个体因相互冲突的动机给出相同应答时，这一假设便不再成立。例如，两名被调查者可能因对立的动机对同一表述表示“不同意”；自由派与保守派大法官可能对同一项美国最高法院判决提出异议，但给出意识形态层面相互矛盾的判决理由；在立法场景中，意识形态对立的双方可能联手反对温和派法案，以追求截然相反的政策目标。本文提出一种可适配两端应答趋同、中间应答分化模式的标度模型，并提出一种优于现有常规方法的全新估计路径。我们将该方法应用于调查数据、美国最高法院投票数据及第116届美国国会投票数据，结果显示，无论在与定性研究见解的契合度还是模型拟合效果上，该方法均优于标准方法。这表明，在诸多重要研究场景中，我们提出的方法可对潜在特质提供更精准的一维估计结果。