MANOVA vs Univariate ANOVAs

A Multivariate Analysis of Variance (MANOVA) takes into account inter-correlations between a group of outcomes. For example we could use a MANOVA if we have two or more highly correlated scores which measure attention and wish to see if these differ en bloc across patient groups.

If these scores were independent OR do not measure the same construct then a series of multiple univariate tests may be more applicable with each score representing a different outcome measure and score means compared across groups using separate anovas.

MANOVA has three advantages over univariate analyses. Firstly, it does not make the strong assumption of sphericity amongst levels of the repeated measures variable. Secondly, it takes into account inter-correlations between sets of outcome variables which are highly correlated. Thirdly it reduces the number of statistical tests by handling multiple outcome variables in the one analysis thus reducing type I error.

MANOVA does assume that variances and covariances in each of the groups are equal. This assumption can be tested by requesting a homogeneity of variance test labelled as Box's M test in the SPSS output.

• Understanding and interpreting a MANOVA using ''only'' between subjects differences

• Understanding and interpreting a MANOVA in the SPSS Repeated Measures procedure

• Warning about using MANOVA with covariates

References

Field A (2005) Discovering statistics using SPSS. 2nd Edition. Sage:London. An excellent primer with worked examples on both MANOVA and ANOVA. (In CBSU library).

Huberty CJ, Morris, JD (1989) Multivariate Analysis Versus Multiple Univariate Analyses. Psychological Bulletin 105(2) 302-308.