The MANOVA (multivariate analysis of variance) is a type of multivariate analysis used to analyze data that involves more than one dependent variable at a time. MANOVA allows us to test hypotheses regarding the effect of one or more independent variables on two or more dependent variables.
A MANOVA analysis generates a p-value that is used to determine whether or not the null hypothesis can be rejected. See Statistical Data Analysis for more information.
Suppose we have a hypothesis that a new teaching style is better than the standard method for teaching math. We may want to look at the effect of teaching style (independent variable) on the average values of several dependent variables such as student satisfaction, number of student absences and math scores. A MANOVA procedure allows us to test our hypothesis for all three dependent variables at once.
Like the example above, a MANOVA is often used to detect differences in the average values of the dependent variables between the different levels of the independent variable. Interestingly, in addition to detecting differences in the average values, a MANOVA test can also detect differences in correlations among the dependent variables between the different levels of the independent variable.
MANOVA is simply one of many multivariate analyses that can be performed using SPSS. The SPSS MANOVA procedure is a standard, well accepted means of performing this analysis.
Multiple Linear Regression is another type of multivariate analysis, which is described in its own tutorial topic.
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