مقایسهٔ روش‌ها

	NOMINATE	Roll-Call Analysis
حوزه	Political Science	Political Science
خانواده	Latent structure	Latent structure
سال پیدایش	1985	—
پدیدآور	Keith T. Poole and Howard Rosenthal	Spatial-voting tradition; Poole, Rosenthal, Clinton, Jackman, Rivers
نوع	Spatial scaling model of roll-call voting	Scaling and analysis of legislative binary-choice data
منبع بنیادین	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.
نام‌های دیگر	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
مرتبط	3	3
خلاصه	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.