পদ্ধতির তুলনা করুন

	Ideal Point Estimation	Wordscores
ক্ষেত্র	Political Science	মনোমিতি
পরিবার	Latent structure	Latent structure
উদ্ভবের বছর	2004	2003
প্রবর্তক	Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition)	Michael Laver, Kenneth Benoit, John Garry
ধরন	Latent-variable spatial model of binary choice data	Text analysis and dimension reduction
মৌলিক উৎস	Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370.	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.
অপর নাম	Ideal point model, Item response theory for roll calls, Spatial voting model, Bayesian ideal points	—
সম্পর্কিত	4	5
সারসংক্ষেপ	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.	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.