Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Recherche par tests de modèles sur données de panel× | Recherche longitudinale× | |
|---|---|---|
| Domaine | Conception de la recherche | Conception de la recherche |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1970s–1980s (panel econometrics and SEM matured in parallel) | Late 19th–early 20th century; methodologically codified through the 20th century |
| Auteur d'origine≠ | Developed across econometrics (Hsiao, Hausman) and psychometrics (Jöreskog, Bollen) | No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett |
| Type≠ | Quantitative longitudinal research design | Quantitative (or mixed) observational research design |
| Source fondatrice≠ | Bollen, K. A. (1989). Structural Equations with Latent Variables. Wiley. ISBN: 978-0471011712 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841 |
| Alias | panel SEM, longitudinal model testing, panel structural equation modeling, panel-based hypothesis testing | longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study |
| Apparentées | 4 | 4 |
| Résumé≠ | Panel-based model testing research combines the longitudinal power of panel survey designs with the confirmatory rigor of structural model testing — such as structural equation modeling (SEM), path analysis, or confirmatory factor analysis — applied to data collected from the same units (individuals, firms, countries) across multiple time points. This approach enables researchers to test theoretically specified causal and mediation structures while controlling for unobserved unit-level heterogeneity and examining how relationships unfold over time. | 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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