Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Multilevel Regression and Poststratification× | Kausālā mediācijas analīze (dabiski tiešie un netiešie efekti)× | |
|---|---|---|
| Nozare≠ | Political Science | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2004 | 2010 |
| Autors≠ | Gelman and Little (method); Park, Gelman & Bafumi (state-level application) | Pearl (2001); general framework by Imai, Keele & Tingley (2010) |
| Tips≠ | Survey small-area estimation model combining multilevel regression with census poststratification | Counterfactual causal decomposition |
| Pirmavots≠ | 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. DOI ↗ | Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420. link ↗ |
| Citi nosaukumi≠ | MRP, Mister P, Multilevel regression with poststratification, Small-area opinion estimation | natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation. |
| ScholarGateDatu kopa ↗ |
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