Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Recherche causale-comparative sur données de panel× | Modèle à effets fixes× | |
|---|---|---|
| Domaine≠ | Conception de la recherche | Économétrie |
| Famille≠ | Process / pipeline | Regression model |
| Année d'origine≠ | 1950s–1980s (formalized across educational and social science methodology literature) | 1971–1978 |
| Auteur d'origine≠ | Building on causal-comparative tradition (John W. Best, 1959) extended to panel data structures in social and educational research | Mundlak (1978); Nerlove (1971); classical panel econometrics |
| Type≠ | Quantitative observational research design | Panel regression estimator |
| Source fondatrice≠ | Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill. ISBN: 978-1260087840 | Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. ISBN: 978-3030538002 |
| Alias | panel causal-comparative design, longitudinal ex post facto research, panel ex post facto study, repeated-measures causal-comparative study | FE model, within estimator, least squares dummy variable, LSDV regression |
| Apparentées | 5 | 5 |
| Résumé≠ | Panel-based causal-comparative research is a quantitative observational design that tracks the same sample of participants or units across multiple time points and then compares pre-existing groups to identify differences in outcomes. By combining the temporal depth of a panel structure with the group-contrast logic of causal-comparative (ex post facto) methodology, it allows researchers to examine how naturally occurring conditions — such as treatment exposure, policy changes, or demographic characteristics — relate to outcomes over time, without experimental random assignment. | The fixed effects (FE) model is the workhorse estimator for panel data when unobserved unit-specific characteristics are suspected to correlate with the regressors. By absorbing each entity's time-invariant heterogeneity into a separate intercept, FE isolates the causal effect of within-unit variation and eliminates omitted-variable bias from time-constant confounders. |
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