Módszerek összehasonlítása

	NOMINATE	Wordfish Scaling
Tudományterület	Political Science	Political Science
Módszercsalád	Latent structure	Latent structure
Keletkezés éve	1985	2008
Megalkotó	Keith T. Poole and Howard Rosenthal	Jonathan Slapin and Sven-Oliver Proksch
Típus	Spatial scaling model of roll-call voting	Unsupervised latent-position model for word-count data
Alapmű	Poole, K. T., & Rosenthal, H. (1985). A Spatial Model for Legislative Roll Call Analysis. American Journal of Political Science, 29(2), 357–384.	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.
Alternatív nevek	DW-NOMINATE, W-NOMINATE, Nominal Three-Step Estimation, Poole-Rosenthal scores	Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation
Kapcsolódó	3	4
Összefoglaló	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.	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.