Multivariate analysis of variance
Multivariate analysis of variance or multiple analysis of variance (MANOVA) is a statistical test procedure for comparing multivariate (population) means of several groups. Unlike univariate ANOVA, it uses the variancecovariance between variables in testing the statistical significance of the mean differences.
It is a generalized form of univariate analysis of variance (ANOVA). It is used when there are two or more dependent variables. It helps to answer: 1. do changes in the independent variable(s) have significant effects on the dependent variables?; 2. what are the interactions among the dependent variables? and 3. among the independent variables?^{1} Statistical reports, however, will provide individual pvalues for each dependent variable, indicating whether differences and interactions are statistically significant.
Where sums of squares appear in univariate analysis of variance, in multivariate analysis of variance certain positivedefinite matrices appear. The diagonal entries are the same kinds of sums of squares that appear in univariate ANOVA. The offdiagonal entries are corresponding sums of products. Under normality assumptions about error distributions, the counterpart of the sum of squares due to error has a Wishart distribution.
Analogous to ANOVA, MANOVA is based on the product of model variance matrix, and inverse of the error variance matrix, , or . The hypothesis that implies that the product .^{2} Invariance considerations imply the MANOVA statistic should be a measure of magnitude of the singular value decomposition of this matrix product, but there is no unique choice owing to the multidimensional nature of the alternative hypothesis.
The most common^{3}^{4} statistics are summaries based on the roots (or eigenvalues) of the matrix:
 Samuel Stanley Wilks' distributed as lambda (Λ)
 the PillaiM. S. Bartlett trace,
 the LawleyHotelling trace,
 Roy's greatest root (also called Roy's largest root),
Discussion continues over the merits of each, though the greatest root leads only to a bound on significance which is not generally of practical interest. A further complication is that the distribution of these statistics under the null hypothesis is not straightforward and can only be approximated except in a few lowdimensional cases.^{citation needed} The bestknown approximation for Wilks' lambda was derived by C. R. Rao.
In the case of two groups, all the statistics are equivalent and the test reduces to Hotelling's Tsquare.
Correlation of dependent variables
MANOVA is most effective when dependent variables are moderately correlated (0.4–0.7). If dependent variables are too highly correlated it could be assumed that they may be measuring the same variable.
See also
References
 ^ Stevens, J. P. (2002). Applied multivariate statistics for the social sciences. Mahwah, NJ: Lawrence Erblaum.
 ^ Carey, Gregory. "Multivariate Analysis of Variance (MANOVA): I. Theory". Retrieved 20110322.
 ^ Garson, G. David. "Multivariate GLM, MANOVA, and MANCOVA". Retrieved 20110322.
 ^ UCLA: Academic Technology Services, Statistical Consulting Group. "Stata Annotated Output  MANOVA". Retrieved 20110322.
External links
Wikiversity has learning materials about Multivariate analysis of variance 


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