Sammenlign metoder

	Wordfish Scaling	Ideal Point Estimation	Wordfish
Fagområde	Political Science	Political Science	Psykometri
Familie	Latent structure	Latent structure	Latent structure
Oprindelsesår	2008	2004	2008
Ophavsperson	Jonathan Slapin and Sven-Oliver Proksch	Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition)	Jonathan Slapin, Svenja-Sophia Proksch
Type	Unsupervised latent-position model for word-count data	Latent-variable spatial model of binary choice data	Generative text model for dimension reduction
Oprindelig kilde	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.	Slapin, J. B., & Proksch, S. O. (2008). A scaling model for estimating time-series party positions from texts. Journal of Politics, 70(3), 554-569.
Aliasser	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	—
Relaterede	4	4	5
Resumé	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.	Wordfish is a statistical model for scaling documents on latent dimensions, developed by Slapin and Proksch (2008). Unlike reference-based methods like Wordscores, Wordfish uses a Poisson generative model to jointly estimate word frequencies and document positions without requiring reference texts or manual annotation. It is particularly useful for estimating time-series changes in policy positions and can scale documents from multiple languages simultaneously.