Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Анализ на причинно-следствена медиация (естествени преки и косвени ефекти)× | Йерархично линейно моделиране (HLM / Многостепенно моделиране)× | |
|---|---|---|
| Област≠ | Причинно-следствено заключение | Статистика |
| Семейство≠ | Regression model | Hypothesis test |
| Година на възникване≠ | 2010 | 1986 |
| Създател≠ | Pearl (2001); general framework by Imai, Keele & Tingley (2010) | Raudenbush & Bryk (popularized); Goldstein (parallel development) |
| Тип≠ | Counterfactual causal decomposition | Parametric nested-data regression |
| Основополагащ източник≠ | Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420. link ↗ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 |
| Други названия≠ | natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation | HLM, MLM, multilevel modeling, multilevel analysis |
| Свързани≠ | 5 | 4 |
| Резюме≠ | 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. | Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels. |
| ScholarGateНабор от данни ↗ |
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