방법 비교

	NOMINATE	Roll-Call Analysis	Wordfish Scaling
분야	Political Science	Political Science	Political Science
계열	Latent structure	Latent structure	Latent structure
기원 연도	1985	—	2008
창시자	Keith T. Poole and Howard Rosenthal	Spatial-voting tradition; Poole, Rosenthal, Clinton, Jackman, Rivers	Jonathan Slapin and Sven-Oliver Proksch
유형	Spatial scaling model of roll-call voting	Scaling and analysis of legislative binary-choice data	Unsupervised latent-position model for word-count data
원전	Poole, K. T., & Rosenthal, H. (1985). A Spatial Model for Legislative Roll Call Analysis. American Journal of Political Science, 29(2), 357–384.	Poole, K. T. (2000). Nonparametric Unfolding of Binary Choice Data. Political Analysis, 8(3), 211–237.	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.
별칭	DW-NOMINATE, W-NOMINATE, Nominal Three-Step Estimation, Poole-Rosenthal scores	Roll call voting analysis, Legislative vote scaling, Roll-call scaling, Optimal classification of votes	Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation
관련	3	3	4
요약	NOMINATE — Nominal Three-step Estimation — is the family of spatial scaling procedures developed by Keith Poole and Howard Rosenthal to recover legislators' ideological positions from roll-call votes. Each legislator and the yea and nay outcomes of each vote are placed in a low-dimensional space, and a normal (Gaussian) deterministic utility plus a random shock governs choices. Fitted by maximum likelihood, NOMINATE produces the canonical ideal-point coordinates used to chart polarization across two centuries of the U.S. Congress, with the dynamic DW-NOMINATE variant allowing positions to drift smoothly over time.	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.	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.