A **criterion variable **is simply another name for a *dependent variable *or a *response variable*. This is the variable that is being predicted in a statistical analysis.

Just as explanatory variables have different names like *predictor variables *or *independent variables*, a response variable also has interchangeable names like *dependent variable *or ** criterion variable**.

**What are Some Examples of Criterion Variables?**

The following scenarios illustrate examples of criterion variables in several different settings.

**Example 1: Simple Linear Regression**

Simple linear regression is a statistical method we use to understand the relationship between two variables, x and y. One variable, x, is known as the predictor variable. The other variable, y, is known as the **criterion variable**, or *response variable*.

In simple linear regression, we find a “line of best fit” that describes the relationship between the predictor variable and the criterion variable.

For example, we may fit a simple linear regression model to a dataset using *hours studied *as the predictor variable and *test score *as the criterion variable. In this case, we would use simple linear regression to attempt to predict the value of our criterion variable *test score*.

Or, as another example, we may fit a simple linear regression model to a dataset using *weight *to predict the value for *height *for a group of people. In this case, our criterion variable is *height *since that’s the value we’re interested in predicting. If we plotted the values for height and weight on a scatterplot, the criterion variable *height *would be on the y-axis:

In general, the criterion variablewill be along the y-axis when we create a scatterplot and the predictor variable will be along the x-axis.

**Example 2: Multiple Linear Regression**

**Multiple linear regression **is similar to simple linear regression, except we use several predictor variables to predict the value of one criterion variable.

For example, we may use the predictor variables *hours studied *and *hours of sleep the night before the test* to predict the value of the criterion variable *test score*. In this case, our criterion variable is the variable being predicted in this analysis.

**Example 3: ANOVA**

An **ANOVA **(analysis of variance) is a statistical technique we use to find out if there is a statistically significant difference between the means of three or more independent groups.

For example, we may want to determine if three different exercise programs impact weight loss differently. The predictor variable we’re studying is *exercise program* and it has three *levels*. **The criterion variable** is *weight loss, *measured in pounds. We can conduct a one-way ANOVA to determine if there is a statistically significant difference between the resulting weight loss from the three programs.

In this case, we’re interested in understanding whether the value of the criterion variable *weight loss *differs among the three exercise programs.

If we instead analyzed *exercise program *and *average hours slept per night,* we would conduct a two-way ANOVA since we are interested in seeing how two factors impact weight loss. Once again, though, our **criterion variable** is still *weight loss *because we are interested in how the value of this variable differs for different levels of *exercise *and *sleep*.