方法对比
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| 生态学研究× | 多层模型× | |
|---|---|---|
| 领域≠ | 流行病学 | 研究统计学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 19th century (Snow 1854); formalised mid-20th century | 1992 |
| 提出者≠ | Various; foundational work by John Snow (1854) and systematised in modern form by Brian MacMahon and colleagues | Anthony Bryk and Stephen Raudenbush |
| 类型≠ | Observational epidemiological study | Method |
| 开创性文献≠ | Morgenstern, H. (1995). Ecologic studies in epidemiology: concepts, principles, and methods. Annual Review of Public Health, 16(1), 61–81. DOI ↗ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| 别名 | aggregate study, correlational study, ecological correlation study, population-level study | HLM, mixed-effects models, random effects models, MLM |
| 相关≠ | 5 | 3 |
| 摘要≠ | An ecological study is an observational epidemiological design in which the unit of analysis is a group or population — a country, region, city, or time period — rather than an individual. Exposures and outcomes are measured as aggregates (rates, proportions, or means) and then correlated across groups to generate or evaluate hypotheses about population-level associations between risk factors and disease. | 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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