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Criterion Validity
Also known as criterion-related validity, or sometimes predictive or concurrent validity, criterion validity is the general term to describe how well scores on one measure (i.e., a predictor) predict scores on another measure of interest (i.e., the criterion). In other words, a particular criterion or outcome measure is of interest to the researcher; examples could include (but are not limited to) ratings of job performance, grade point average (GPA) in school, a voting outcome, or a medical diagnosis. Criterion validity, then, refers to the strength of the relationship between measures intended to predict the ultimate criterion of interest and the criterion measure itself. In academic settings, for example, the criterion of interest may be GPA, and the predictor being studied is the score on a standardized math test. Criterion validity, in this context, would be the strength of the relationship (e.g., the correlation coefficient) between the scores on the standardized math test and GPA.
Some care regarding the use of the term criterion validity needs to be employed. Typically, the term is applied to predictors, rather than criteria; researchers often refer to the “criterion validity” of a specific predictor. However, this is not meant to imply that there is only one “criterion validity” estimate for each predictor. Rather, each predictor can have different “criterion validity” estimates for many different criteria. Extending the above example, the standardized math test may have one criterion validity estimate for overall GPA, a higher criterion validity estimate for science ability, and a lower criterion validity estimate for artistic appreciation; all three are valid criteria of interest. Additionally, each of these estimates may be moderated by (i.e., have different criterion validity estimates for) situational, sample, or research design characteristics. In this entry the criterion, the research designs that assess criterion validity, effect sizes, and concerns that may arise in applied selection are discussed.
Nature of the Criterion
Again, the term criterion validity typically refers to a specific predictor measure, often with the criterion measure assumed. Unfortunately, this introduces substantial confusion into the procedure of criterion validation. Certainly, a single predictor measure can predict an extremely wide range of criteria, as Christopher Brand has shown with general intelligence, for example. Using the same example, the criterion validity estimates for general intelligence vary quite a bit; general intelligence predicts some criteria better than others. This fact further illustrates that there is no single criterion validity estimate for a single predictor. Additionally, the relationship between one predictor measure and one criterion variable can vary depending on other variables (i.e., moderator variables), such as situational characteristics, attributes of the sample, and particularities of the research design. Issues here are highly related to the criterion problem in predictive validation studies.
Research Design
There are four broad research designs to assess the criterion validity for a specific predictor: predictive validation, quasi-predictive validation, concurrent validation, and postdictive validation. Each of these is discussed in turn.
Predictive Validation
When examining the criterion validity of a specific predictor, the researcher is often interested in selecting persons based on their scores on a predictor (or set of predictor measures) that will predict how well the people will perform on the criterion measure. In a true predictive validation design, predictor measure or measures are administered to a set of applicants, and the researchers select applicants completely randomly (i.e., without regard to their scores on the predictor measure or measures.) The correlation between the predictor measure(s) and the criterion of interest is the index of criterion validity. This design has the advantage of being free from the effects of range restriction; however, it is an expensive design, and unfeasible in many situations, as stakeholders are often unwilling to forgo selecting on potentially useful predictor variables.
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