Sammenlign metoder

	Metaanalytisk Økologisk Studie	Multilevelmodellering
Fagområde	Epidemiologi	Forskningsstatistik
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1990s	1992
Ophavsperson	Morgenstern, Blettner, and colleagues in epidemiology methodology	Anthony Bryk and Stephen Raudenbush
Type	Quantitative synthesis design	Method
Oprindelig kilde	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.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Aliasser	ecological meta-analysis, aggregate-level meta-analysis, meta-analytic ecologic design, population-level meta-analysis	HLM, mixed-effects models, random effects models, MLM
Relaterede	2	3
Resumé	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.	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.