방법 비교

	Multidimensional Unfolding	Roll-Call Analysis
분야	Political Science	Political Science
계열	Latent structure	Latent structure
기원 연도	2000	—
창시자	Keith T. Poole (nonparametric optimal classification and unfolding)	Spatial-voting tradition; Poole, Rosenthal, Clinton, Jackman, Rivers
유형	Latent-space scaling model placing individuals and stimuli in a joint space	Scaling and analysis of legislative binary-choice data
원전	Poole, K. T. (2000). Nonparametric Unfolding of Binary Choice Data. Political Analysis, 8(3), 211–237.	Poole, K. T. (2000). Nonparametric Unfolding of Binary Choice Data. Political Analysis, 8(3), 211–237.
별칭	Unfolding analysis, Optimal classification, Preference unfolding, Joint-space scaling	Roll call voting analysis, Legislative vote scaling, Roll-call scaling, Optimal classification of votes
관련	5	3
요약	Multidimensional unfolding places both individuals and the stimuli they evaluate — candidates, parties, bills — in a single joint low-dimensional space, so that each person's preferences are explained by their proximity to the stimuli. In political science it underlies Keith Poole's nonparametric optimal classification of roll-call votes and the unfolding of thermometer ratings and rank orders, recovering legislators' and bills' positions from nothing but the pattern of choices. Unlike correlation-based scaling, unfolding treats preference as a single-peaked function of distance: you like what is close to you and dislike what is far.	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.