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Bayesian Cohort Research×Panelstudien×
FachgebietForschungsdesignForschungsdesign
FamilieProcess / pipelineProcess / pipeline
EntstehungsjahrFormalised in health research from the 1990s onward1970s-1980s (econometric formalization); earlier social survey use from 1940s
UrheberSynthesis of cohort epidemiology (Doll & Hill, 1950s) with Bayesian inference (Bayes, Laplace, Jeffreys)Social science and econometric traditions; systematized by Cheng Hsiao and others from the 1970s-1980s
TypQuantitative longitudinal observational designQuantitative longitudinal observational design
Wegweisende QuelleIbrahim, J. G., & Chen, M. H. (2000). Power prior distributions for regression models. Statistical Science, 15(1), 46–60. DOI ↗Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717
AliasnamenBayesian cohort study, Bayesian prospective cohort, Bayesian longitudinal cohort analysis, Bayesian follow-up studypanel study, panel survey, longitudinal panel, repeated-measures panel
Verwandt43
ZusammenfassungBayesian cohort research follows a defined group of individuals over time to track outcomes, and uses Bayesian statistical inference to update beliefs about risk, incidence, or causal effects as follow-up data accumulate. Prior knowledge — from earlier studies, registries, or expert judgment — is formalised into a prior distribution and combined with the cohort's likelihood to yield a posterior distribution that quantifies uncertainty in a directly interpretable way.Panel research is a quantitative longitudinal design in which the same individuals, organizations, or other units are measured repeatedly across two or more time points. Unlike cross-sectional surveys that capture a single snapshot, a panel tracks change within units, enabling researchers to separate genuine within-unit change from between-unit differences and to model causal dynamics over time.
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ScholarGateMethoden vergleichen: Bayesian Cohort Research · Panel Research. Abgerufen am 2026-06-18 von https://scholargate.app/de/compare