Porównaj metody

	Wordscores	Wordfish
Dziedzina	Psychometria	Psychometria
Rodzina	Latent structure	Latent structure
Rok powstania	2003	2008
Twórca	Michael Laver, Kenneth Benoit, John Garry	Jonathan Slapin, Svenja-Sophia Proksch
Typ	Text analysis and dimension reduction	Generative text model for dimension reduction
Źródło pierwotne	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.	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.
Inne nazwy	—	—
Pokrewne	5	5
Podsumowanie	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.	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.