Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Manifesto Coding× | Ideal Point Estimation× | |
|---|---|---|
| Domeniu | Political Science | Political Science |
| Familie≠ | Process / pipeline | Latent structure |
| Anul apariției≠ | 2001 | 2004 |
| Autorul original≠ | Manifesto Research Group / Comparative Manifesto Project (CMP/MARPOR) | Clinton, Jackman & Rivers (Bayesian formulation); Poole & Rosenthal (spatial tradition) |
| Tip≠ | Quantitative content analysis of party manifestos | Latent-variable spatial model of binary choice data |
| Sursa seminală≠ | Budge, I., Klingemann, H.-D., Volkens, A., Bara, J., & Tanenbaum, E. (2001). Mapping Policy Preferences: Estimates for Parties, Electors, and Governments 1945–1998. Oxford: Oxford University Press. ISBN: 9780199244003 | Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370. DOI ↗ |
| Denumiri alternative | CMP coding, MARPOR coding, Manifesto content analysis, Party manifesto coding | Ideal point model, Item response theory for roll calls, Spatial voting model, Bayesian ideal points |
| Înrudite | 4 | 4 |
| Rezumat≠ | Manifesto coding is the quantitative content-analysis methodology of the Comparative Manifesto Project (CMP/MARPOR) for measuring parties' policy preferences from their election manifestos. Trained coders break each manifesto into quasi-sentences and assign every unit to one of a fixed set of policy categories. Counting how often each category appears yields salience measures, and combining pro- and anti- categories produces position scores such as the left–right RILE index, giving comparable estimates of party positions across more than fifty democracies since 1945. | 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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