Сравнение методов

	Wordscores	Wordfish
Область	Психометрия	Психометрия
Семейство	Latent structure	Latent structure
Год появления	2003	2008
Автор метода	Michael Laver, Kenneth Benoit, John Garry	Jonathan Slapin, Svenja-Sophia Proksch
Тип	Text analysis and dimension reduction	Generative text model for dimension reduction
Основополагающий источник	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.
Другие названия	—	—
Связанные	5	5
Сводка	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.