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| Bayesiläinen paneelitutkimus× | Paneelitutkimus× | |
|---|---|---|
| Tieteenala | Tutkimusasetelma | Tutkimusasetelma |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1990s–2000s (contemporary synthesis) | 1970s-1980s (econometric formalization); earlier social survey use from 1940s |
| Kehittäjä≠ | Building on Bayes (1763) and panel data econometrics; systematised by Hsiao, Lancaster, and others in the 1990s–2000s | Social science and econometric traditions; systematized by Cheng Hsiao and others from the 1970s-1980s |
| Tyyppi≠ | Quantitative longitudinal research design with Bayesian inference | Quantitative longitudinal observational design |
| Alkuperäislähde≠ | Lancaster, T. (2004). An Introduction to Modern Bayesian Econometrics. Blackwell Publishing. ISBN: 978-1405117868 | Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717 |
| Rinnakkaisnimet | Bayesian longitudinal panel study, Bayesian panel data analysis, BPD research, Bayesian repeated-measures panel design | panel study, panel survey, longitudinal panel, repeated-measures panel |
| Liittyvät≠ | 4 | 3 |
| Tiivistelmä≠ | 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. | 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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