So a MANOVA is typically seen as an extension of an ANOVA that has more than one continuous variable. The typical assumptions of an ANOVA should be checked, such as normality, equality of variance, and univariate outliers. However, there are additional assumptions that should be checked when conducting a MANOVA.
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Absence of multivariate outliers is checked by assessing Mahalanobis Distances among the participants. To do this in SPSS, run a multiple linear regression with all of the dependent variables of the MANOVA as the independent variables of the multiple linear regression. The dependent variable would be simply an ID variable. There is an option in SPSS to save the Mahalanobis Distances when running the regression. Once this is done, sort the Mahalanobis Distances from greatest to least. To identify an outlier, the critical chi square value must be known. This is derived from the critical chi square value at p = .001 with the degrees of freedom being the number of dependent variables. With 3 variables, the critical value is 16.27, so any participants with a Mahalanobis Distance value greater than 16.27 should be removed.
Linearity assumes that all of the dependent variables are linearly related to each other. This can be checked by conducting a scatterplot matrix between the dependent variables. Linearity should be met for each group of the MANOVA separately.
Absence of multicollinearity is checked by conducting correlations among the dependent variables. The dependent variables should all be moderately related, but any correlation over .80 presents a concern for multicollinearity.
Equality of covariance matrices is an assumption checked by running a Box’s M test. Unlike most tests, the Box’s M test tends to be very strict, and thus the level of significance is typically .001. So as long as the p value for the test is above .001, the assumption is met.