Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse factorielle× | Diagnostics d'influence (Distance de Cook, DFFITS, Effet de levier)× | |
|---|---|---|
| Domaine≠ | Statistiques de recherche | Statistique |
| Famille≠ | Process / pipeline | Regression model |
| Année d'origine≠ | 1931 | 1977 |
| Auteur d'origine≠ | Louis Leon Thurstone | R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage) |
| Type≠ | Method | Regression diagnostic |
| Source fondatrice≠ | Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press. DOI ↗ | Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18. DOI ↗ |
| Alias≠ | EFA, CFA, latent variable modeling | Cook's distance, DFFITS, leverage, influential observation detection |
| Apparentées≠ | 3 | 5 |
| Résumé≠ | Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data. | Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients. |
| ScholarGateJeu de données ↗ |
|
|