Vertaile menetelmiä

	Expert Survey	Ideal Point Estimation
Tieteenala	Political Science	Political Science
Menetelmäperhe	Process / pipeline	Latent structure
Syntyvuosi	—	2004
Kehittäjä	Comparative party-positioning research (Castles & Mair; Chapel Hill team)	Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition)
Tyyppi	Survey of subject-matter experts to measure latent positions	Latent-variable spatial model of binary choice data
Alkuperäislähde	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.	Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370.
Rinnakkaisnimet	Expert judgment survey, Party expert survey, Chapel Hill Expert Survey, Expert placement survey	Ideal point model, Item response theory for roll calls, Spatial voting model, Bayesian ideal points
Liittyvät	4	4
Tiivistelmä	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.	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.