Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| NOMINATE× | Ideal Point Estimation× | |
|---|---|---|
| Área | Political Science | Political Science |
| Família | Latent structure | Latent structure |
| Ano de origem≠ | 1985 | 2004 |
| Autor original≠ | Keith T. Poole and Howard Rosenthal | Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition) |
| Tipo≠ | Spatial scaling model of roll-call voting | Latent-variable spatial model of binary choice data |
| Fonte seminal≠ | Poole, K. T., & Rosenthal, H. (1985). A Spatial Model for Legislative Roll Call Analysis. American Journal of Political Science, 29(2), 357–384. DOI ↗ | Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370. DOI ↗ |
| Outros nomes | DW-NOMINATE, W-NOMINATE, Nominal Three-Step Estimation, Poole-Rosenthal scores | Ideal point model, Item response theory for roll calls, Spatial voting model, Bayesian ideal points |
| Relacionados≠ | 3 | 4 |
| Resumo≠ | 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. | 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. |
| ScholarGateConjunto de dados ↗ |
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