Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Veto Player Analysis× | Ideal Point Estimation× | |
|---|---|---|
| Nyanja | Political Science | Political Science |
| Familia≠ | MCDM | Latent structure |
| Mwaka wa asili≠ | 1995 | 2004 |
| Mwanzilishi≠ | George Tsebelis | Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition) |
| Aina≠ | Comparative institutional analysis framework | Latent-variable spatial model of binary choice data |
| Chanzo asilia≠ | Tsebelis, G. (2002). Veto Players: How Political Institutions Work. Princeton University Press. ISBN: 9780691091891 | Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370. DOI ↗ |
| Majina mbadala | Veto Players Theory, Veto Points Analysis, Tsebelis Veto Player Framework, Policy Stability Analysis | Ideal point model, Item response theory for roll calls, Spatial voting model, Bayesian ideal points |
| Zinazohusiana | 4 | 4 |
| Muhtasari≠ | Veto player analysis is a spatial-institutional framework, developed by George Tsebelis in his 1995 article and 2002 book, for predicting the capacity of a political system to change policy. A veto player is any individual or collective actor whose agreement is required to alter the status quo. The theory shows that the potential for policy change shrinks as the number of veto players grows, as the ideological distance between them widens, and as their internal cohesion increases — three structural variables that together determine a system's policy stability independently of constitutional labels such as presidentialism or parliamentarism. | 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. |
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