Multivariate statistics for multiple outcomes

Compare independent groups on multiple outcomes concurrently

Multivariate statistics for multiple outcomes are used when attempting to control for increased Type I error rates, or experimentwise error rates, when testing multiple hypotheses concurrently. Furthermore, the multivariate and bivariate associations between predictor, confounding, and outcome variables can be assessed and understood within a theoretical or conceptual framework when using multivariate statistics for multiple outcomes. The statistical assumptions of multivariate statistics for multiple outcomes such as MANOVA and MANCOVA can be hard to meet but the findings yielded from these analyses are powerful.

What type of multivariate statistical test for multiple outcomes will answer your research question?

Researchers are comparing independent groups or levels of a categorical variable on several continuous outcomes at the same time.
MANOVA
Researchers are adjusting values of several outcomes when comparing independent groups or levels of a categorical variable at the same time.
MANCOVA
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There are two multivariate statistics for multiple outcomes: Multivariate Analysis of Variance (MANOVA) and Multivariate Analysis of Covariance (MANCOVA).