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| Bayesian Panel Research× | Bayesian Konfirmatorische Forschung× | |
|---|---|---|
| Fachgebiet | Forschungsdesign | Forschungsdesign |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1990s–2000s (contemporary synthesis) | 1961 (Jeffreys); 2009–2018 (contemporary confirmatory formulation) |
| Urheber≠ | Building on Bayes (1763) and panel data econometrics; systematised by Hsiao, Lancaster, and others in the 1990s–2000s | Harold Jeffreys (theoretical foundation); Jeffrey Rouder, Eric-Jan Wagenmakers (applied confirmatory framework) |
| Typ≠ | Quantitative longitudinal research design with Bayesian inference | Quantitative hypothesis-testing framework |
| Wegweisende Quelle≠ | Lancaster, T. (2004). An Introduction to Modern Bayesian Econometrics. Blackwell Publishing. ISBN: 978-1405117868 | Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237. DOI ↗ |
| Aliasnamen | Bayesian longitudinal panel study, Bayesian panel data analysis, BPD research, Bayesian repeated-measures panel design | Bayesian hypothesis testing, confirmatory Bayesian analysis, Bayes factor hypothesis testing, BCR |
| Verwandt≠ | 4 | 1 |
| Zusammenfassung≠ | Bayesian panel research combines the longitudinal structure of panel data — where the same units (individuals, firms, countries) are observed at multiple time points — with Bayesian statistical inference. Rather than relying solely on the observed data and point estimates, it incorporates prior knowledge via probability distributions, updates those priors with repeated-measures data, and produces full posterior distributions over model parameters. This yields richer uncertainty quantification and principled handling of individual heterogeneity across waves. | Bayesian confirmatory research is a quantitative framework that tests pre-specified hypotheses by computing the Bayes factor — a ratio expressing how much more likely the observed data are under one hypothesis than another. Unlike classical null-hypothesis significance testing (NHST), it provides direct evidence for both the alternative and the null hypothesis, supports optional stopping rules under certain conditions, and updates prior beliefs with observed data through Bayes' theorem. |
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