Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Recherche explicative multivariée× | Modélisation par équations structurelles× | |
|---|---|---|
| Domaine≠ | Conception de la recherche | Statistiques de recherche |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | Mid-to-late 20th century (consolidated ~1960s–1980s) | 1921 |
| 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 | Sewall Wright |
| Type≠ | Quantitative research design | Method |
| 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 | Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm. link ↗ |
| Alias | multivariate explanatory design, explanatory multivariate research, multivariate causal-explanatory study, MER | SEM, path analysis, latent variable modeling, causal modeling |
| 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. | Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis. |
| ScholarGateJeu de données ↗ |
|
|