Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Riskiga kohandatud ökoloogiline uuring× | Multilevel Modeling× | |
|---|---|---|
| Valdkond≠ | Epidemioloogia | Uurimisstatistika |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 1980s–1990s | 1992 |
| Looja≠ | Extension of ecological study methodology; risk adjustment concepts formalized by Morgenstern (1982) and developed further in health outcomes research | Anthony Bryk and Stephen Raudenbush |
| Tüüp≠ | Observational ecological design with statistical confounding control | Method |
| Algallikas≠ | Morgenstern, H. (1982). Uses of ecologic analysis in epidemiologic research. American Journal of Public Health, 72(12), 1336–1344. DOI ↗ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Rööpnimetused | risk-adjusted ecological analysis, confounder-adjusted ecological study, ecological regression with risk adjustment, adjusted area-level study | HLM, mixed-effects models, random effects models, MLM |
| Seotud≠ | 4 | 3 |
| Kokkuvõte≠ | A risk-adjusted ecological study is an observational epidemiological design that examines associations between exposures and outcomes measured at the group or area level — such as regions, hospitals, or countries — while statistically controlling for known risk factors also measured at that level. By incorporating risk adjustment through ecological regression or standardization, the design reduces (though cannot eliminate) confounding from group-level variables, enabling more valid comparisons across populations or settings. | Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies. |
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