Porównaj metody

	Wordfish Scaling	Wordscores
Dziedzina	Political Science	Psychometria
Rodzina	Latent structure	Latent structure
Rok powstania	2008	2003
Twórca	Jonathan Slapin and Sven-Oliver Proksch	Michael Laver, Kenneth Benoit, John Garry
Typ	Unsupervised latent-position model for word-count data	Text analysis and dimension reduction
Źródło pierwotne	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.	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.
Inne nazwy	Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation	—
Pokrewne	4	5
Podsumowanie	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.	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.