Сравнение на методи

	Expert Survey	Wordfish Scaling
Област	Political Science	Political Science
Семейство	Process / pipeline	Latent structure
Година на възникване	—	2008
Създател	Comparative party-positioning research (Castles & Mair; Chapel Hill team)	Jonathan Slapin and Sven-Oliver Proksch
Тип	Survey of subject-matter experts to measure latent positions	Unsupervised latent-position model for word-count data
Основополагащ източник	Bakker, R., de Vries, C., Edwards, E., Hooghe, L., Jolly, S., Marks, G., Polk, J., Rovny, J., Steenbergen, M., & Vachudova, M. A. (2015). Measuring Party Positions in Europe: The Chapel Hill Expert Survey Trend File, 1999–2010. Party Politics, 21(1), 143–152.	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.
Други названия	Expert judgment survey, Party expert survey, Chapel Hill Expert Survey, Expert placement survey	Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation
Свързани	4	4
Резюме	An expert survey measures latent political quantities — most often parties' positions on policy dimensions — by asking a panel of country and subject-matter experts to place the objects of interest on structured numerical scales. Averaging many experts' judgments yields position estimates, while the spread across experts provides a built-in measure of uncertainty and reliability. The Chapel Hill Expert Survey is the leading example, producing comparable measures of European parties' positions on ideology, European integration, and many specific issues over time.	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.