Vertaile menetelmiä

	Ideal Point Estimation	Manifesto Coding	Wordscores
Tieteenala	Political Science	Political Science	Psykometriikka
Menetelmäperhe	Latent structure	Process / pipeline	Latent structure
Syntyvuosi	2004	2001	2003
Kehittäjä	Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition)	Manifesto Research Group / Comparative Manifesto Project (CMP/MARPOR)	Michael Laver, Kenneth Benoit, John Garry
Tyyppi	Latent-variable spatial model of binary choice data	Quantitative content analysis of party manifestos	Text analysis and dimension reduction
Alkuperäislähde	Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370.	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	Laver, M., Benoit, K., & Garry, J. (2003). Extracting policy positions from political texts using words as data. American Political Science Review, 97(2), 311-331.
Rinnakkaisnimet	Ideal point model, Item response theory for roll calls, Spatial voting model, Bayesian ideal points	CMP coding, MARPOR coding, Manifesto content analysis, Party manifesto coding	—
Liittyvät	4	4	5
Tiivistelmä	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.	Wordscores is a text-based scaling method developed by Laver, Benoit, and Garry (2003) that estimates the policy positions of political actors based on word frequencies in their texts. By comparing word usage in reference texts of known positions with test texts, the method infers the latent political dimension of any document without requiring manual coding or training data.