Regression Analysis > Covariate

## What is a Covariate?

In general terms, covariates are **characteristics **(excluding the actual treatment) of the participants in an experiment. If you collect data on characteristics before you run an experiment, you could use that data to see how your treatment affects different groups or populations. Or, you could use that data to control for the influence of any covariate.

Covariates may affect the **outcome in a study**. For example, you are running an experiment to see how corn plants tolerate drought. Level of drought is the actual “treatment”, but it isn’t the only factor that affects how plants perform: size is a known factor that affects tolerance levels, so you would run plant size as a covariate.

A covariate can be an independent variable (i.e. of direct interest) or it can be an unwanted, confounding variable. Adding a covariate to a model can increase the accuracy of your results.

## Meaning in ANCOVA

In ANCOVA, the independent variables are categorical variables. For example, you might look at the effect of several different treatments for depression; the independent variables would be the type of treatment received. The dependent variable is a countable variable, like a score on a self-scoring depression scale. However, within the group, there is a lot of unexplained variation going on. In this example, people don’t enter the experiment with exactly the same levels of depression. The initial level of depression is something you need to control for.

In this context, the covariate is always:

- Observed/measured (as opposed to a manipulated variable)
- A control variable
- A continuous variable.

Another example (from Penn State): Let’s say you are comparing the salaries of men and women to see who earns more. One factor that you need to control for is that people tend to earn more the longer they are out of college. Years out of college in this case is a covariate.

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