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Abstract Spatial voting models are widely used in political science to analyze legislators’ preferences and voting behavior. Traditional models assume that legislators’ ideal points are static across different types of votes. This article extends the Bayesian spatial voting model to incorporate hierarchical Bayesian methods, allowing for the identification of covariates that explain differences in legislators’ ideal points across voting domains. We apply this model to procedural and final passage votes in the U.S. House of Representatives from the 93rd through 113th Congresses. Our findings indicate that legislators in the minority party and those representing moderate constituencies are more likely to exhibit different ideal points between procedural and final passage votes. This research advances the methodology of ideal point estimation by simultaneously scaling ideal points and explaining variation in these points, providing a more nuanced understanding of legislative voting behavior. 

Abstract We develop a new class of spatial voting models for binary preference data that can accommodate both monotonic and non-monotonic response functions, and are more flexible than alternative “unfolding” models previously introduced in the literature. We then use these models to estimate revealed preferences for legislators in the U.S. House of Representatives and justices on the U.S. Supreme Court. The results from these applications indicate that the new models provide superior complexity-adjusted performance to various alternatives and that the additional flexibility leads to preferences’ estimates that more closely match the perceived ideological positions of legislators and justices. 

Abstract Latent factor models are widely used in the social and behavioural sciences as scaling tools to map discrete multivariate outcomes into low-dimensional, continuous scales. In political science, dynamic versions of classical factor models have been widely used to study the evolution of justices’ preferences in multi-judge courts. In this paper, we discuss a new dynamic factor model that relies on a latent circular space that can accommodate voting behaviours in which justices commonly understood to be on opposite ends of the ideological spectrum vote together on a substantial number of otherwise closely divided opinions. We apply this model to data on nonunanimous decisions made by the US Supreme Court between 1937 and 2021, and show that for most of this period, voting patterns can be better described by a circular latent space.