Where’s the confusion? Covariates as Continuous Predictor Variables So you get a clearer picture of whether people do well on the final test due to the training or due to the math ability they had coming in. So if you use pretest math score as a covariate, you can adjust for where people started out. Having a lot of unexplained variation makes it pretty tough to see the actual effect of the training–it gets lost in all the noise. If you don’t adjust for that, it is just unexplained variation. The dependent variable is their math score after receiving the training.īut even within each training group, there is going to be a lot of variation in people’s math ability.
The independent variable is the training condition–whether participants received the math training or some irrelevant training. observations weren’t randomly assigned its values, you just measured what was there).Ī simple example is a study looking at the effect of a training program on math ability. In this context, the covariate is always continuous, never the key independent variable, and always observed (i.e. The most precise definition is its use in Analysis of Covariance, a type of General Linear Model in which the independent variables of interest are categorical, but you also need to adjust for the effect of an observed, continuous variable–the covariate. And these different ways of using the term have BIG implications for what your model means. Covariate is a tricky term in a different way than hierarchical or beta, which have completely different meanings in different contexts.Ĭovariate really has only one meaning, but it gets tricky because the meaning has different implications in different situations, and people use it in slightly different ways.