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What is a covariate?
A covariate is a variable that is known to have an effect on the Dependent variable, but not on the Independent Variable. In study of ANCOVA they are also known as Intervening or Confounding variables.
What is the purpose of adding a covariate to an analysis of variance?
The purpose of adding a covariate in to analysis of variance is that, the group means in ANOVA are adjusted based on the magnitude of effect which the covariate may be adding, in other words the formula looks at the relationship between the covariate and outcome, and comes out with an actual numeric value and remove that value from the group mean. Which controls the probability of type 2 error.
Should the covariate be related to the IV?
No, covariate should not be related to the Independent Variable, since this is the one of the critical assumption of ANCOVA. But if so happens then one should remove the covariate from the analysis
Should the covariate be related to the DV?
Yes, the covariate should be related to DV, this is also a critical assumption for ANCOVA, however this assumption needs to be checked group wise as well, i.e. if the relationship of covariate and DV differs group wise then the overall regression model is said to be inaccurate this assumption is known as homogeneity of regression slopes.
What types of covariates (level of measurement) can you use?
We can you use any covariate i.e. both categorical(properly-dummy) and continuous, however aligning with the assumption that they must be related to DV...
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