قارن الطرق

	Roll-Call Analysis	Wordfish Scaling
المجال	Political Science	Political Science
العائلة	Latent structure	Latent structure
سنة النشأة	—	2008
صاحب الطريقة	Spatial-voting tradition; Poole, Rosenthal, Clinton, Jackman, Rivers	Jonathan Slapin and Sven-Oliver Proksch
النوع	Scaling and analysis of legislative binary-choice data	Unsupervised latent-position model for word-count data
المصدر التأسيسي	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.
الأسماء البديلة	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
ذات صلة	3	4
الملخص	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.