Multivariate Analysis of Variance (MANOVA) is a popular statistical tool in the social sciences, allowing for the comparison of mean vectors across groups. MANOVA rests on three primary assumptions regarding the population: (a) multivariate normality, (b) equality of group population covariance matrices and (c) independence of errors. When these assumptions are violated, MANOVA does not perform well with respect to Type I error and power. There are several alternative test statistics that can be considered including robust statistics and the use of the structural equation modeling (SEM) framework. This simulation study focused on comparing the performance of the P test statistics with fifteen other test statistics across seven manipulated factors. These statistics were evaluated across 12,076 different conditions in terms of Type I error and power. Results suggest that when assumptions were met, the standard MANOVA test functioned well. However, when assumptions were violated, it performed poorly, whereas several of the alternatives performed better. Discussion focuses on advice for selecting alternatives in practice. This study’s focus on all these in one simulation and the 3 group case should be helpful to the practitioner making methodological sections.
Finch, Holmes and French, Brian
"A Monte Carlo Comparison of Robust MANOVA Test Statistics,"
Journal of Modern Applied Statistical Methods:
2, Article 4.
Available at: http://digitalcommons.wayne.edu/jmasm/vol12/iss2/4