An Evaluation of the Regression Discrepancy Method for Identifying Children With Learning Disabilities

Abstract

A learning disability (LD) exists if a child's academic achievement lags significantly behind intellectual ability and there is no other known cause for the discrepancy. The Regression Discrepancy Method using multiple regression for identifying LD children directly parallels the theoretical definition. It involves giving both an ability and an achievement test, which are normed together. An anticipated achievement score is computed for each child based on ability, grade level, and sex. Then, for each ability score, the 10% whose actual achievement is most discrepant from their anticipated achievement are identified as likely LD. In comparing this method with other identification techniques, the author discusses its advantages and limitations, pointing out that while it is conceptually and methodologically superior to other approaches, it is nonetheless seriously deficient as a sole criterion for LD identification. In an area so fraught with definitional and instrumentation problems, provision should be made to collect data independently and to trust only those diagnoses that are consistent and independently verifiable.