Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Recherche de test de modèle× | Recherche explicative× | |
|---|---|---|
| Domaine | Conception de la recherche | Conception de la recherche |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s | 1960s–1980s (codified in behavioral and social science methodology) |
| Auteur d'origine≠ | Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition | Formalized by Earl Babbie and Fred Kerlinger among others |
| Type≠ | Confirmatory quantitative research design | Non-experimental quantitative research design |
| Source fondatrice≠ | Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344 | Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417559 |
| Alias | model-based research, structural model testing, theory-testing research, MTR | analytical research, causal research, explanatory study, explanatory quantitative research |
| Apparentées | 5 | 5 |
| Résumé≠ | Model testing research is a confirmatory quantitative design in which the researcher specifies a theoretical model — depicting hypothesized relationships among constructs — and then tests how well that model fits empirical data. Drawing primarily on structural equation modeling (SEM) and confirmatory factor analysis (CFA), it evaluates whether the data-implied covariance structure is consistent with the theoretically derived one, yielding fit indices that indicate model-data correspondence. | Explanatory research is a non-experimental quantitative research design that goes beyond describing a phenomenon to identifying why it occurs — examining the relationships or mechanisms that account for observed patterns. Rooted in positivist social science methodology, it uses theory-driven hypotheses and statistical analysis to test whether specific variables explain variation in an outcome, without necessarily manipulating those variables. |
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