Predictive Validity Definition
Predictive validity is the degree to which a test accurately predicts the behavior or performance that it is designed to measure. In order for a test to have predictive validity, it must first have construct validity. This means that the test must actually measure the construct that it is purporting to measure. Once a test has been shown to have construct validity, researchers can then assess its predictive validity by looking at how well scores on the test predict future behavior or performance.
There are two types of predictive validity: concurrent and prospective. Concurrent predictive validity occurs when the predictor variable (i.e., the test score) and the criterion variable (i.e., the behavior or performance being predicted) are measured at the same time. Prospective predictive validity occurs when the predictor variable is measured before the criterion variable.
Predictive validity is important because it allows us to know how well a test will actually predict future behavior or performance. It is important to note, however, that no test can be perfectly accurate in its predictions; there will always be some error. The goal is to create a test with as much predictive validity as possible so that we can minimize prediction error.
