Methoden vergelijken

	Wordfish Scaling	Ideal Point Estimation	Manifesto Coding
Vakgebied	Political Science	Political Science	Political Science
Familie	Latent structure	Latent structure	Process / pipeline
Jaar van ontstaan	2008	2004	2001
Grondlegger	Jonathan Slapin and Sven-Oliver Proksch	Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition)	Manifesto Research Group / Comparative Manifesto Project (CMP/MARPOR)
Type	Unsupervised latent-position model for word-count data	Latent-variable spatial model of binary choice data	Quantitative content analysis of party manifestos
Oorspronkelijke bron	Slapin, J. B., & Proksch, S.-O. (2008). A Scaling Model for Estimating Time-Series Party Positions from Texts. American Journal of Political Science, 52(3), 705–722.	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
Aliassen	Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation	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
Verwant	4	4	4
Samenvatting	Wordfish scaling is an unsupervised text-as-data method that estimates a single latent position for each political document — a party manifesto, a legislative speech, a press release — directly from its word frequencies, without any reference texts or hand coding. Introduced by Slapin and Proksch in 2008, it models word counts as draws from a Poisson distribution whose rate depends on a document position and word-specific parameters, recovering, for example, a left–right ordering of parties purely from how often each word appears in each text.	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.