Comparar métodos

	Multilevel Regression and Poststratification	Ideal Point Estimation
Campo	Political Science	Political Science
Familia	Regression model	Latent structure
Año de origen	2004	2004
Autor original	Gelman and Little (method); Park, Gelman & Bafumi (state-level application)	Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition)
Tipo	Survey small-area estimation model combining multilevel regression with census poststratification	Latent-variable spatial model of binary choice data
Fuente seminal	Park, D. K., Gelman, A., & Bafumi, J. (2004). Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls. Political Analysis, 12(4), 375–385.	Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370.
Alias	MRP, Mister P, Multilevel regression with poststratification, Small-area opinion estimation	Ideal point model, Item response theory for roll calls, Spatial voting model, Bayesian ideal points
Relacionados	5	4
Resumen	Multilevel regression and poststratification (MRP) estimates opinion or behavior in small subpopulations — states, districts, demographic groups — from a single national survey that is far too small to support direct estimates in each unit. It first fits a multilevel model that predicts the outcome from individual demographic and geographic characteristics, borrowing strength across units through partial pooling, and then poststratifies the predicted values to known population counts of demographic-by-geographic cells. Introduced for state-level opinion by Park, Gelman, and Bafumi (2004) and shown by Lax and Phillips (2009) to outperform disaggregation, MRP has become the standard tool for subnational opinion estimation.	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.