방법 비교

	메타분석 생태학 연구	생태학 연구	다수준 모형
분야	역학	역학	연구 통계
계열	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	1990s	19th century (Snow 1854); formalised mid-20th century	1992
창시자	Morgenstern, Blettner, and colleagues in epidemiology methodology	Various; foundational work by John Snow (1854) and systematised in modern form by Brian MacMahon and colleagues	Anthony Bryk and Stephen Raudenbush
유형	Quantitative synthesis design	Observational epidemiological study	Method
원전	Blettner, M., Sauerbrei, W., Schlehofer, B., Scheuchenpflug, T., & Friedenreich, C. (1999). Traditional reviews, meta-analyses and pooled analyses in epidemiology. International Journal of Epidemiology, 28(1), 1–9.	Morgenstern, H. (1995). Ecologic studies in epidemiology: concepts, principles, and methods. Annual Review of Public Health, 16(1), 61–81.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
별칭	ecological meta-analysis, aggregate-level meta-analysis, meta-analytic ecologic design, population-level meta-analysis	aggregate study, correlational study, ecological correlation study, population-level study	HLM, mixed-effects models, random effects models, MLM
관련	2	5	3
요약	A meta-analytic ecological study synthesises data from multiple populations or geographic units — rather than from individual patients — to estimate associations between exposures and health outcomes. By pooling aggregate-level statistics across studies or regions, it extends the reach of ecological reasoning to a wider evidence base, enabling detection of exposure-outcome relationships that single-population ecological analyses may miss due to limited variability or sample size.	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.