ABSTRACT
In this chapter, we introduce multivariate analysis of variance (MANOVA), which is a complex statistic similar to ANOVA but with multiple dependent variables analyzed together. The dependent variables should be related conceptually, and they should be correlated with one another at a low to moderate level. If they are too highly correlated, one runs the risk of multicollinearity. If they are uncorrelated, there is usually no reason to analyze them together. The General Linear Model program in SPSS provides you with a multivariate F based on the linear combination of dependent variables that maximally distinguishes your groups. This multivariate result is the MANOVA. SPSS also automatically prints out univariate Fs for the separate univariate ANOVAs for each dependent variable. Typically, these ANOVA results are not examined unless the multivariate results (the MANOVA) are significant, and some statisticians believe that they should not be used at all.
