مقایسهٔ روش‌ها

	Ideal Point Estimation	Roll-Call Analysis
حوزه	Political Science	Political Science
خانواده	Latent structure	Latent structure
سال پیدایش	2004	—
پدیدآور	Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition)	Spatial-voting tradition; Poole, Rosenthal, Clinton, Jackman, Rivers
نوع	Latent-variable spatial model of binary choice data	Scaling and analysis of legislative binary-choice data
منبع بنیادین	Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370.	Poole, K. T. (2000). Nonparametric Unfolding of Binary Choice Data. Political Analysis, 8(3), 211–237.
نام‌های دیگر	Ideal point model, Item response theory for roll calls, Spatial voting model, Bayesian ideal points	Roll call voting analysis, Legislative vote scaling, Roll-call scaling, Optimal classification of votes
مرتبط	4	3
خلاصه	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.	Roll-call analysis is the study of recorded legislative votes to recover the structure of political conflict — the ideological positions of legislators, the dimensionality of the issue space, and the cohesion of parties. It encompasses parametric spatial and item-response models that estimate latent ideal points, nonparametric scaling such as optimal classification that maximizes correctly classified votes without distributional assumptions, and descriptive cohesion statistics like the Rice index. Together these tools turn a matrix of yea/nay votes into a map of who agrees with whom and why.