Exam 6: Multivariate Analysis of Variance and Covariance

A second MANOVA assumption is that the observations on at least one DV must follow a multivariate normal distribution in the group.

A third advantage of using MANOVA is that the overall Type I error rate is increased.

The null hypothesis being tested in MANCOVA is that the adjusted population mean vectors are not equal.

MANCOVA asks if there are statistically significant mean differences among groups after adjusting the newly created DV for differences on one or more covariates.

An assumption in MANCOVA is that linear relationships need not exist between all pairs of DVs, all pairs of covariates, and all DV-covariates in each cell.

If the null hypothesis is retained, it is common practice to stop the interpretation of the analysis at this point and conclude that the treatments or conditions have no effect on the DVs.

A third MANOVA assumption is that the relationships among all pairs of DVs for each cell in the data matrix must be normal.

MANOVA incorporates the interconnections of DVs into the analysis.

The most commonly used test statistic for MANOVA is Roy's Largest Root.

In MANCOVA, the effects of the covariates are added to the analysis, leaving the researcher with a clearer picture of the true effects of the IVs on the multiple DVs.