Direct comparison

Multivariable vs. Multivariate

Multivariable has one outcome and many predictors; multivariate has multiple outcomes analyzed jointly — the distinction most papers get wrong.

Written and maintained by CASRAI Editorial Board

Last updated 1 September 2026

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In summary

Multivariable and multivariate are not interchangeable statistical terms, even though research papers and textbooks routinely swap them. A multivariable model has one outcome (dependent variable) and multiple predictors (independent variables) -- ordinary multiple linear regression, multivariable logistic regression, and Cox regression with several covariates are all multivariable, not multivariate, models. A multivariate model has two or more outcomes analyzed jointly -- MANOVA, factor analysis, and multivariate regression with a vector of dependent variables are genuinely multivariate. The distinction matters for reporting accuracy: writing 'multivariate logistic regression' when a study actually adjusted for several predictors against a single binary outcome is technically incorrect, even though it is extremely common usage.

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