Regression analysis can be applied to establish criterion validity. An independent variables could be used as a predictor variable and a dependent variable, the criterion variable. The correlation coefficient between them is called validity coefficients.

For instance, scores of the driving test by simulation is the predictor variable while scores of the road test is the criterion variable. It is hypothesized that if the tester passes the simulation test, he/she should meet the criterion of being a safe driver. In othe words, if the simulation test scores could predict the road test scores in a regression model, the simulation test is claimed to have a high degree of criterion validity.

In short, criterion validity is about prediction rather than explanation. Predication is concerned with non-casual or mathematical dependence where as explanation is pertaining to causal or logical dependence. For example, one can predict the weather based on the height of mercury inside a thermometer. Thus, the height of mercury could satisfy the criterion validity as a predictor. However, one cannot explain why the weather changes by the change of mercury height. Because of this limitation of criterion validity, an evaluator has to conduct construct validation.