Ancova

ANOVA can be extended to include one or more continuous variables that predict the outcome (or dependent variable). Continuous variables such as these, that are not part of the main experimental manipulation but have an influence on the dependent variable, are known as covariates and they can be included in an ANOVA analysis. For example, in the Viagra example from Field (2013), we might expect there to be other things that influence a person’s libido other than Viagra. Some possible influences on libido might be the libido of the participant’s sexual partner (after all ‘it takes two to tango’), other medication that suppresses libido (such as antidepressants), and fatigue. If these variables are measured, then it is possible to control for the influence they have on the dependent variable by including them in the model. What, in effect, happens is that we carry out a hierarchical regression in which our dependent variable is the outcome, and the covariate is entered in the first block. In a second block, our experimental manipulations are entered (in the form of what are called Dummy variables). So, we end up seeing what effect an independent variable has after the effect of the covariate. Field (2013) explains the similarity between ANOVA and regression and this is useful reading to understand how ANCOVA works.