Sammenlign metoder

	Ideal Point Estimation	Wordfish Scaling
Fagområde	Political Science	Political Science
Familie	Latent structure	Latent structure
Oprindelsesår	2004	2008
Ophavsperson	Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition)	Jonathan Slapin and Sven-Oliver Proksch
Type	Latent-variable spatial model of binary choice data	Unsupervised latent-position model for word-count data
Oprindelig kilde	Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370.	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.
Aliasser	Ideal point model, Item response theory for roll calls, Spatial voting model, Bayesian ideal points	Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation
Relaterede	4	4
Resumé	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.	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.