Multivariate analysis of covariance: Difference between revisions

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Multivariate analysis of covariance (MANCOVA) is an extension of analysis of covariance (ANCOVA) methods to cover cases where there is more than one dependent variable and where the dependent variables cannot simply be combined. Multiple analysis of covariance (MANCOVA) is similar to multiple analysis of variance (MANOVA) , but allows you to control for the effects of supplementary continuous independent variables - covariates. If there are some covariates, MANCOVA should be used instead of MANOVA.

Covariates are variables which have effects on the dependent variables. In experimental design, covariates are usually the variables not controlled by the experimenter, but still having an effect on the dependent variables.

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 Multiple analysis of covariance (MANCOVA) is similar to multiple analysis of variance (MANOVA) , but allows you to control for the effects of supplementary continuous independent variables - covariates. If there are some covariates, MANCOVA should be used instead of MANOVA.
-Covariates are variables which have effects on the dependent variables, but their effects are not of interest. In experimental design, covariates are usually the variables not controlled by the experimenter, but still having an effect on the dependent variables.
+Covariates are variables which have effects on the dependent variables. In experimental design, covariates are usually the variables not controlled by the experimenter, but still having an effect on the dependent variables.
 ==See also==