AN ANALYSIS OF TYPES OF ERRORS IN THE SELECTION OF MINORITY COLLEGE STUDENTS

When standardized tests are used in the selection of applicants for college admission, they are intended to benefit both institutions and individuals. The benefit to the institution accrues from the possibility of improved accuracy of selection. This means that fewer of those who are admitted will fail. Thus, the institution will be less likely to waste its resources upon those students who are least able to profit from them. A similar benefit ought to occur to the individual, in that people who would be most likely to fail are not admitted-sparing them the personal trauma of failure. The institutional and individual benefits result from avoiding false-positive errors of selection. A false-positive error occurs when a selected individual fails. False-negative errors (the rejection of would-be successes) are also harmful. They deprive the institution, and hence society, of potential college graduates. They are especially harmful to the individual, for they prevent a potentially successful student from attending college. Until recently, discussions of fair selection were concerned primarily with the psychometric properties of predictors. When a best-fitting line, that is, the leastsquares regression of criterion on predictor(s), is calculated, the resulting regression equation can be used to generate a selection cutoff for any level of a continuous criterion. In general, the selection cutoff resulting from a least-squares regression will produce the smallest number of selection errors. The question of test bias has typically arisen when the regression of the criterion on the predictors differed as to slope and/or intercept for different definable demographic groups. Clear descriptions of the psychometrics of test bias are provided by Cleary (1968), Linn (1973), and Cleary, Humphreys, Wesman, and Kendrick (1975). We believe that the didactic intent of this paper is best served by omitting such a description. The reader is invited to consult the aforementioned articles for descriptions of test bias. The regression-based model of fair selection considers the absolute incidence of selection errors. The model incorporates a tacit assumption that reduction of selection errors of any type is a worthy goal. Thorndike (1971) publicized a hitherto little noticed property of selection of students using a regression-based model. This disturbing property can be stated simply: if members of a minority group tend, on the average, to score relatively lower on the predictor than on the criterion (as compared to the majority averages), then there will be a relatively higher incidence of false-negative errors of selection. When the condition described above prevails, the ratio of selectees to successes is lower for the minority group than for the majority group. This effect occurs even when the criterion-test regression is identical for the majority and minority groups. Thorndike concluded that selection using such a test could be fair to individual members of a minority group but unfair to the group as a whole. Imagine this example: The minority and majority groups means differ by a standard deviation on the criterion but by two standard deviations on the test. If success is defined as the majority mean score on the criterion, then about sixteen percent of the minority group members would suc-