Predictive validity is a measurement of how well a test predicts future performance. It is a form of criterion validity, in which how well the test works is established by measuring it against known criteria. In order for a test to have predictive validity, there must be a statistically significant correlation between test scores and the criterion being used to measure the validity.
One of the classic examples of this is college entrance testing. When students apply to colleges, they are usually required to submit test scores from examinations such as the SAT or the ACT. These scores are used as a basis for comparison, with evaluators looking at the performance of students who have had similar tests in the past. The belief is that the test scores can predict how well a student will perform in college. High test scores tend to be correlated with strong college performance, making students with high scores appealing to admissions departments.
The college test scores example is also an excellent example of the weaknesses of predictive validity. Some students who take such tests do not go to college, which means that no data is generated to correlate their test scores and their college performance. This creates a hole in the data set, which can undermine the validity of such tests. Standardized testing has also been accused of some biases that can work against particular students, especially students in racial minorities. They may perform poorly on the test and well in college, skewing the results.
Statistical significance can be challenging to calculate. Huge numbers of factors can influence test results, especially when they involve data from a test and a criterion measure that are collected at different points. Predictive validity influences everything from health insurance rates to college admissions, with people using statistical data to try and predict the future for people based on information which can be gathered about them from testing.
Predictive validity is most commonly used when exploring data in the field of psychological study and analysis. It is used to collect information about various populations, and to create generalizations which may be useful when assessing individuals. For example, it is often used by big companies that administer a test to prospective employees, comparing test data from current employees to determine whether or not someone will be a good fit with the company.
By Mary McMahon
Ever since she began contributing to the site several years ago, Mary has embraced the exciting challenge of being a All The Science researcher and writer. Mary has a liberal arts degree from Goddard College and spends her free time reading, cooking, and exploring the great outdoors.