Ends Against the Middle: Measuring Latent Traits when Opposites Respond the Same Way for Antithetical Reasons

IF 4.7 2区 社会学 Q1 POLITICAL SCIENCE Political Analysis Pub Date : 2023-01-09 DOI:10.1017/pan.2022.33
JBrandon Duck-Mayr, J. Montgomery
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引用次数: 9

Abstract

Abstract 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 U.S. Supreme Court, and the 116th Congress, and show that 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.
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两端对中:当对手出于对立原因以相同方式回应时,测量潜在特征
从分类数据中测量潜在特征的标准方法假设响应函数是单调的。当来自两个极端的个人做出相同的反应,但出于相互矛盾的原因时,这一假设就被违反了。两名受访者可能出于相反的动机“不同意”一项声明,自由派和保守派法官可能对最高法院的同一裁决持不同意见,但提供了意识形态上相互矛盾的理由,在立法环境中,意识形态上的对立可能会联合起来反对温和的立法,以追求相反的目标。在这篇文章中,我们介绍了一个缩放模型,该模型可以适应中间响应的末端,并提供了一种新的估计方法,该方法改进了现有的例程。我们将这种方法应用于调查数据、美国最高法院和第116届国会的投票数据,并表明它在与定性见解的一致性和模型拟合方面都优于标准方法。这表明,我们提出的方法可以在许多重要环境中提供对潜在性状的改进的一维估计。
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来源期刊
Political Analysis
Political Analysis POLITICAL SCIENCE-
CiteScore
8.80
自引率
3.70%
发文量
30
期刊介绍: Political Analysis chronicles these exciting developments by publishing the most sophisticated scholarship in the field. It is the place to learn new methods, to find some of the best empirical scholarship, and to publish your best research.
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