ScholarGate
Assistente

Confronta i metodi

Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.

Wordfish Scaling×Ideal Point Estimation×
CampoPolitical SciencePolitical Science
FamigliaLatent structureLatent structure
Anno di origine20082004
IdeatoreJonathan Slapin and Sven-Oliver ProkschClinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition)
TipoUnsupervised latent-position model for word-count dataLatent-variable spatial model of binary choice data
Fonte seminaleSlapin, 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. DOI ↗Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370. DOI ↗
AliasWordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimationIdeal point model, Item response theory for roll calls, Spatial voting model, Bayesian ideal points
Correlati44
SintesiWordfish 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.Ideal point estimation recovers the latent policy positions — ideal points — of political actors from their observed binary choices, most often legislators' yea/nay votes on roll calls. Building on the spatial theory of voting and formalized as a Bayesian item-response model by Clinton, Jackman, and Rivers in 2004, it places each legislator and each bill in a low-dimensional policy space and estimates positions so that the probability a legislator votes yea increases as the bill's 'yea' outcome moves closer to that legislator's ideal point.
ScholarGateInsieme di dati
  1. v1
  2. 3 Fonti
  3. PUBLISHED
  1. v1
  2. 3 Fonti
  3. PUBLISHED

Vai alla ricerca Scarica le diapositive

ScholarGateConfronta i metodi: Wordfish Scaling · Ideal Point Estimation. Consultato il 2026-06-24 da https://scholargate.app/it/compare