Multivariate Analysis of Covariance

Analysis of covariance (ANCOVA) assesses group differences on a dependent variable (DV) after the effects of one or more covariates are statistically removed. By utilizing the relationship between the covariate(s) and the DV, ANCOVA can increase the power of an analysis. MANCOVA is an extension of ANCOVA to relationships where a linear combination of DVs is adjusted for differences on one or more covariates. The adjusted linear combination of DVs is the combination that would be obtained if all participants had the same scores on the covariates. That is, MANCOVA is similar to MANOVA, but allows a researcher to control for the effects of supplementary continuous IVs, termed covariates. In an experimental design, covariates are usually the variables not controlled by the experimenter, but still affect the DVs. Consequently, although not as effective as random assignment, including covariates may reduce both systematic and within-group error by equalizing groups being compared on important characteristics. This chapter discusses the assumptions of MANCOVA, sample size requirements, and strengths and limitations of MANCOVA. An annotated example is also provided.