A largely univariate framework for understanding multivariate analysis of variance

Multivariate analysis of variance (MANOVA) has a long history of use in psychological science, is a staple of many advanced statistics textbooks and classes, and remains widely used in diverse areas of psychology. However, the way in which it is typically explained and taught relies on multivariate concepts, terms, and procedures that may be highly nonintuitive for many students, teachers, and applied researchers. This may limit many individuals' ability to learn, teach, interpret, use, and communicate MANOVA effectively and comfortably. Fortunately, MANOVA can be understood via a largely univariate perspective that is likely intuitive and accessible for many who are interested in MANOVA. Although other sources allude to this alternative perspective, those allusions are minimal and are not comprehensive, systematic, or illustrated in accessible ways. Moreover, no existing sources illustrate how, or even whether, this perspective applies to factorial MANOVA. The current tutorial explains and illustrates a largely univariate framework for understanding both one-way and factorial MANOVA. From this perspective, MANOVA begins with combinations of dependent variables, followed by a univariate analysis of variance (ANOVA) conducted on each combination, and by aggregation of effect sizes obtained from those ANOVAs to obtain multivariate effect sizes and multivariate inferential statistics. Thus, all key MANOVA results can be interpreted in relation to familiar results emerging from univariate ANOVA. This alternative perspective may help many students, teachers, and researchers gain a more intuitive understanding of how MANOVA works, what its results mean, and when/how to use it effectively. (PsycInfo Database Record (c) 2026 APA, all rights reserved).