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Multidimensional Unfolding×Ideal Point Estimation×
领域Political SciencePolitical Science
方法族Latent structureLatent structure
起源年份20002004
提出者Keith T. Poole (nonparametric optimal classification and unfolding)Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition)
类型Latent-space scaling model placing individuals and stimuli in a joint spaceLatent-variable spatial model of binary choice data
开创性文献Poole, K. T. (2000). Nonparametric Unfolding of Binary Choice Data. Political Analysis, 8(3), 211–237. DOI ↗Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370. DOI ↗
别名Unfolding analysis, Optimal classification, Preference unfolding, Joint-space scalingIdeal point model, Item response theory for roll calls, Spatial voting model, Bayesian ideal points
相关54
摘要Multidimensional unfolding places both individuals and the stimuli they evaluate — candidates, parties, bills — in a single joint low-dimensional space, so that each person's preferences are explained by their proximity to the stimuli. In political science it underlies Keith Poole's nonparametric optimal classification of roll-call votes and the unfolding of thermometer ratings and rank orders, recovering legislators' and bills' positions from nothing but the pattern of choices. Unlike correlation-based scaling, unfolding treats preference as a single-peaked function of distance: you like what is close to you and dislike what is far.Ideal point estimation recovers the latent policy positions — ideal points — of political actors from their observed binary choices, most often legislators' yea/nay votes on roll calls. Building on the spatial theory of voting and formalized as a Bayesian item-response model by Clinton, Jackman, and Rivers in 2004, it places each legislator and each bill in a low-dimensional policy space and estimates positions so that the probability a legislator votes yea increases as the bill's 'yea' outcome moves closer to that legislator's ideal point.
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ScholarGate方法对比: Multidimensional Unfolding · Ideal Point Estimation. 于 2026-06-24 检索自 https://scholargate.app/zh/compare