Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Recherche explicative robuste× | Recherche causale-comparative× | |
|---|---|---|
| Domaine | Conception de la recherche | Conception de la recherche |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1960s–1980s (robust statistics foundations); applied to explanatory research from 1990s onward | 1964 |
| Auteur d'origine≠ | Peter J. Huber (robust statistics); applied to explanatory designs via Rand Wilcox and others | Fred N. Kerlinger |
| Type≠ | Quantitative research design | Non-experimental quantitative research design |
| Source fondatrice≠ | Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054 | Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston. link ↗ |
| Alias≠ | robust causal research, outlier-resistant explanatory design, robust regression-based explanatory study | ex post facto research, causal-comparative design, retrospective causal study, CCR |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | Robust explanatory research combines the explanatory goal of identifying why and how variables causally influence one another with robust statistical methods that remain valid when data violate classical assumptions — particularly normality, homoscedasticity, and the absence of influential outliers. Rather than discarding outliers or forcing data to conform to ordinary least squares assumptions, this design applies estimators and inferential procedures that down-weight or resist the distorting influence of extreme observations while preserving the explanatory aim of the study. | Causal-comparative research is a non-experimental quantitative design in which the researcher compares two or more groups that already differ on an independent variable — one that was not manipulated — to investigate possible causes or consequences of that difference. Because group membership is pre-existing rather than randomly assigned, the design can suggest causal relationships but cannot establish them with the certainty of a true experiment. It is widely used in education, psychology, and social sciences when experimental manipulation is impractical or unethical. |
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