Common Misconceptions Concerning The Analysis Of Covariance

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Abstract
Four misconceptions about the requirements for proper use of analysis of covariance (ANCOVA) are examined by means of Monte Carlo simulation. Conclusions are that ANCOVA does not require covariates to be measured without error, that ANCOVA can be used effectively to adjust for initial group differences that result from nonrandom assignment which is dependent on observed covariate scores, that ANCOVA does not provide unbiased estimates of true treatment effects where initial group differences are due to nonrandom assignment which is dependent on the true latent covariable if the covariate contains measurement error, and that ANCOVA requires no assumption concerning the equality of within-groups and between-groups regression. Where treatments actually influence covariate scores, the hypothesis tested by ANCOVA concerns a weighted combination of effects on covariate and dependent variables.

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