MANOVA
Multivariate Analysis of Variance is a statistical method used to test for differences in two or more dependent variables simultaneously across multiple groups.
What is MANOVA?
While ANOVA tests for group differences on a single dependent variable, MANOVA creates a linear combination of multiple continuous dependent variables and tests whether the categorical independent variables have a significant effect on this composite construct. It accounts for the correlations among the dependent variables.
Why MANOVA Matters
It controls for the inflated Type I error rate that would occur from running multiple separate ANOVAs, and can detect multivariate patterns of group differences that might be invisible when examining dependent variables in isolation.
Example
A marketing researcher uses MANOVA to determine if three different advertising campaigns have significantly different effects on consumers' brand recall, purchase intention, and perceived product quality simultaneously.
Common Mistakes
- Including dependent variables that are highly correlated (multicollinearity), which reduces the power of the test.
- Failing to evaluate assumptions like multivariate normality and equality of covariance matrices (Box's M test).