Analysis of Covariance (ANCOVA) is the inclusion of a continuous variable in addition to the variables of interest (i.e., the dependent and independent variable) as means for control. Because the ANCOVA is an extension of the ANOVA, the researcher can still can assess main effects and interactions to answer their research hypotheses. The difference between an ANCOVA and an ANOVA is that an ANCOVA model includes a “covariate” that is correlated with the dependent variable and means on the dependent variable are adjusted due to the effects the covariate has on it. Covariates can be used in many ANOVA based designs – such as between-subjects, within-subjects (repeated measures), mixed (between – and within – designs) etc. Thus, this technique answers the question: Are mean differences or interactive effects likely to have occurred by chance after scores have been adjusted on the dependent variable because of the effect of the covariate?
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Tabachnick and Fidell (2013) review three general applications for an Analysis of Covariance include:
The researcher can go about interpreting main effects and interactions as they typically would. The difference is that the regression of the covariate on the dependent variable is estimated first before the variance in scores is partitioned into differences between and within group; however, the error term is adjusted from the regression line derived from the covariate on the DV vs. running through the means in ANOVA designs.
Tabachnick, B., & Fidell, L. (2013). Using multivariate statistics (6th ed.)Upper Saddle
River, NJ: Pearson.