Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Recherche explicative multivariée× | Recherche causale-comparative× | |
|---|---|---|
| Domaine | Conception de la recherche | Conception de la recherche |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | Mid-to-late 20th century (consolidated ~1960s–1980s) | 1964 |
| Auteur d'origine≠ | Rooted in the multivariate statistics tradition (R.A. Fisher, Harold Hotelling) combined with explanatory research design conventions codified by Kerlinger and others | Fred N. Kerlinger |
| Type≠ | Quantitative research design | Non-experimental quantitative research design |
| Source fondatrice≠ | Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540 | Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston. link ↗ |
| Alias | multivariate explanatory design, explanatory multivariate research, multivariate causal-explanatory study, MER | ex post facto research, causal-comparative design, retrospective causal study, CCR |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | Multivariate explanatory research is a quantitative design that simultaneously examines multiple independent variables to explain variance in one or more outcomes. Rather than describing what exists or simply correlating pairs of variables, it seeks causal or structural explanations by testing theoretically grounded models with techniques such as multiple regression, MANOVA, or structural equation modeling on survey, administrative, or observational numeric data. | 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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