Compară metode

	Bayesian Item Response Theory in Politics	Roll-Call Analysis
Domeniu	Political Science	Political Science
Familie	Latent structure	Latent structure
Anul apariției	2004	—
Autorul original	Clinton, Jackman & Rivers (political IRT formulation); Treier & Jackman (latent-trait measurement)	Spatial-voting tradition; Poole, Rosenthal, Clinton, Jackman, Rivers
Tip	Latent-variable measurement model for binary and ordinal items	Scaling and analysis of legislative binary-choice data
Sursa seminală	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.
Denumiri alternative	Bayesian IRT, Political item response model, Latent trait measurement model, Bayesian latent measurement in politics	Roll call voting analysis, Legislative vote scaling, Roll-call scaling, Optimal classification of votes
Înrudite	5	3
Rezumat	Bayesian item response theory (IRT) in political science measures latent traits — such as ideology, level of democracy, or political knowledge — from observed binary or ordinal items, treating each item's response probability as a function of a respondent's position on the latent scale. Formalized for politics by Clinton, Jackman, and Rivers (2004) for roll-call votes and extended by Treier and Jackman (2008) to measure democracy as a latent variable, the approach combines item characteristic curves with prior distributions and estimates everything jointly by Markov chain Monte Carlo, yielding full posterior uncertainty for every subject's latent score.	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.