Over Chapters 9-15 we have seen how the general linear model can be used to detect group differences on a single outcome. However, there may be circumstances in which we are interested in several outcomes and in these cases the simple ANOVA model is inadequate. Instead, we can use multivariate analysis of variance (or MANOVA). MANOVA can be thought of as ANOVA for situations in which there are several dependent variables. The principles of ANOVA extend to MANOVA in that we can use MANOVA when there is only one independent variable or when there are several, we can look at interactions between independent variables, and we can even do contrasts to see which groups differ from each other. ANOVA can be used only in situations in which there is one dependent variable (or outcome) and so is known as a univariate test (meaning 'one variable'); MANOVA is designed to look at several dependent variables (outcomes) simultaneously and so is a multivariate test (meaning 'many variables'). There is a fairly lengthy theory section to try to explain the workings of MANOVA, but for those of you who value the little time you have on Earth, skip this section and we'll look how to do MANOVA in SPSS and interpret the output. This process will lead us to another statistical test known as discriminant function analysis or just discriminant analysis.