Participants’ age was used as a covariate, as previous research indicated that it is related to satisfaction. The outcome variable is satisfaction with their training experience and each condition implemented a specific learning strategy. In this (real) example, adult learners were assigned to one of four educational conditions: a control and three experimental conditions. Once again, rather than focus on the “rules”, think about what an interaction tells you about the effects of the independent variable, and how you can best communicate the results. So what does it mean, and what should you do, if you find an interaction between the categorical IV and the continuous covariate? In a previous post, I showed a detailed example for an observational study where the first assumption is irrelevant, but I have gotten a number of questions about the second. There is no interaction between independent variable and the covariate.
The independent variable and the covariate are independent of each other.Ģ. There are two oft-cited assumptions for Analysis of Covariance (ANCOVA), which is used to assess the effect of a categorical independent variable on a numerical dependent variable while controlling for a numerical covariate:ġ.
