Comparer des méthodes

	Régression logistique ordinale	Multinomial Logistic Regression
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1980	1966–1974
Auteur d'origine	Peter McCullagh	Cox (1966); Theil (1969); formalized by McFadden (1974)
Type	Ordinal regression / GLM	Generalized linear model
Source fondatrice	McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142.	Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. ISBN: 978-0471360933
Alias	proportional-odds model, cumulative link model, ordered logit, OLR	polytomous logistic regression, softmax regression, multinomial logit, nominal logistic regression
Apparentées	6	4
Résumé	Ordinal logistic regression — most commonly the proportional-odds model — estimates the relationship between one or more predictors and an ordered categorical outcome (e.g., Likert scales, disease severity grades, educational attainment levels). It models cumulative log-odds across the ordered categories while assuming a single shared effect of each predictor at all thresholds.	Multinomial logistic regression extends binary logistic regression to outcomes with three or more unordered categories. It models the log-odds of each category relative to a chosen reference category as a linear function of the predictors, and estimates all parameters simultaneously via maximum likelihood. It is the standard choice when the dependent variable is nominal with multiple levels.