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| Badania konfirmacyjne oparte na panelach× | Badania podłużne× | |
|---|---|---|
| Dziedzina | Projektowanie badań | Projektowanie badań |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1960s–1980s (formalization of panel methods with confirmatory inference) | Late 19th–early 20th century; methodologically codified through the 20th century |
| Twórca≠ | Multiple contributors; panel data analysis formalized by Yair Mundlak, Zvi Griliches, and Edwin Kuh in the 1960s–1970s; confirmatory integration developed across econometrics and SEM traditions | No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett |
| Typ≠ | Quantitative longitudinal research design | Quantitative (or mixed) observational research design |
| Źródło pierwotne≠ | Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841 |
| Inne nazwy | confirmatory panel design, longitudinal confirmatory study, panel confirmatory analysis, PBCR | longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study |
| Pokrewne | 4 | 4 |
| Podsumowanie≠ | Panel-based confirmatory research combines the longitudinal power of panel data — repeated observations of the same units over time — with a pre-specified, hypothesis-driven analytic framework. Instead of exploring patterns post-hoc, the researcher commits to theoretical propositions before data collection and uses the panel structure to test causal or directional claims while controlling for unobserved time-invariant confounders. It is widely used in economics, sociology, epidemiology, and organizational research. | Longitudinal research is an observational design in which the same participants, groups, or units are measured repeatedly over an extended period. Rather than capturing a single snapshot, it tracks change, stability, and temporal sequencing of variables — making it the primary non-experimental strategy for studying development, growth, decline, and the unfolding of causal processes across time. |
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