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| Badania podłużne przyczynowo-porównawcze× | Badania panelowe× | |
|---|---|---|
| Dziedzina | Projektowanie badań | Projektowanie badań |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1970s–1980s (as an established combined design in educational and social research) | 1970s-1980s (econometric formalization); earlier social survey use from 1940s |
| Twórca≠ | Synthesized from causal-comparative tradition (Kerlinger, 1973) and longitudinal design frameworks (Goldstein, 1979) | Social science and econometric traditions; systematized by Cheng Hsiao and others from the 1970s-1980s |
| Typ≠ | Non-experimental quantitative research design | Quantitative longitudinal observational design |
| Źródło pierwotne≠ | Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (7th ed.). McGraw-Hill. ISBN: 978-0073525532 | Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717 |
| Inne nazwy | longitudinal ex post facto design, longitudinal causal-comparative design, repeated-measures causal-comparative research, prospective causal-comparative study | panel study, panel survey, longitudinal panel, repeated-measures panel |
| Pokrewne≠ | 4 | 3 |
| Podsumowanie≠ | Longitudinal causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on one or more dependent variables across multiple measurement points over time. Unlike true experiments, the researcher does not manipulate the independent variable; instead, naturally occurring group differences (e.g., gender, socioeconomic status, diagnostic category) are examined to explore their relationship to outcomes as they evolve longitudinally. | 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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