![]() It is useful to control for a covariate like IQ scores, but we are not really interested in the relationship between IQ and math scores. The only research question is about whether the math scores differed on average among the three programs. Students need to be randomly assigned to one of the three programs. The key independent variable here is the learning program. The only hypothesis tests of interest are about the independent variables, controlling for the effects of the nuisance covariate.Ī typical example is a study to compare the math scores of students who were enrolled in three different learning programs at the end of the school year. There are no research questions about how this covariate itself affects or relates to the dependent variable. The covariate–continuous and observed–is considered a nuisance variable. The key situation is the independent variables are categorical and manipulated, not observed. ANCOVA for Experimental DataĪnalysis of Covariance was developed for experimental situations and some of the assumptions and definitions of ANCOVA apply only to those experimental situations. This committee member is, in the strictest sense of how analysis of covariance is used, correct.Īnd yet, they over-applied that assumption to an inappropriate situation. Specifically, the assumption in question is that the covariate has to be uncorrelated with the independent variable. Just recently, a client got some feedback from a committee member that the Analysis of Covariance (ANCOVA) model she ran did not meet all the assumptions. ![]()
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