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Ideal Point Estimation×Manifesto Coding×
分野Political SciencePolitical Science
系統Latent structureProcess / pipeline
提唱年20042001
提唱者Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition)Manifesto Research Group / Comparative Manifesto Project (CMP/MARPOR)
種類Latent-variable spatial model of binary choice dataQuantitative content analysis of party manifestos
原典Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370. DOI ↗Budge, I., Klingemann, H.-D., Volkens, A., Bara, J., & Tanenbaum, E. (2001). Mapping Policy Preferences: Estimates for Parties, Electors, and Governments 1945–1998. Oxford: Oxford University Press. ISBN: 9780199244003
別名Ideal point model, Item response theory for roll calls, Spatial voting model, Bayesian ideal pointsCMP coding, MARPOR coding, Manifesto content analysis, Party manifesto coding
関連44
概要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.Manifesto coding is the quantitative content-analysis methodology of the Comparative Manifesto Project (CMP/MARPOR) for measuring parties' policy preferences from their election manifestos. Trained coders break each manifesto into quasi-sentences and assign every unit to one of a fixed set of policy categories. Counting how often each category appears yields salience measures, and combining pro- and anti- categories produces position scores such as the left–right RILE index, giving comparable estimates of party positions across more than fifty democracies since 1945.
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ScholarGate手法を比較: Ideal Point Estimation · Manifesto Coding. 2026-06-25に以下より取得 https://scholargate.app/ja/compare