Combining Scores in Multiple-Criteria Assessment Systems

Abstract

Best practice in gifted and talented identification procedures involves making decisions on the basis of multiple measures. However, very little research has investigated the impact of different methods of combining multiple measures. This article examines the consequences of the conjunctive (“and”), disjunctive/complementary (“or”), and compensatory (“mean”) models for combining scores from multiple assessments. It considers the impact of rule choice on the size of the student population, the ability heterogeneity of the identified students, and the psychometric performance of such systems. It also uses statistical simulation to examine the performance of the state of Georgia’s mandated and complex multiple-criteria assessment system.