Discriminant analysis is a statistical procedure related to multiple correlation. It uses a number of predictor variables to classify subjects into two or more distinct groups such as dropouts versus persisters, successful versus unsuccessful students, delinquents versus nondelinquents, and so on. The criterion in discriminant analysis is a person’s group membership. The procedure results in an equation, or discriminant function, where the scores on the predictors are multiplied by weights to predict the classification of subjects into groups. When there are just two groups, the discriminant function is essentially a multiple correlation equation with the group membership criterion coded 0 or 1. But with three or more groups as the criterion, discriminant analysis goes beyond multiple correlation.
Discriminant analysis might be used to identify predictors of success in a school of education doctoral program. You could identify the variables that discriminated membership into one of two groups: those who successfully completed doctoral study and those who did not. A number of different predictors might be used: Miller Analogies Test (MAT) scores, Graduate Record Examination (GRE) scores, undergraduate GPA, graduate GPA, time lapse between the master’s degree and entrance into the doctoral program, doctoral major, age at entrance, gender, race/ethnicity, and marital status. Complex correlational analysis would produce an equation showing the variables that were significant in predicting the criterion, success or lack of success in a doctoral program.