A Signal Detection Approach in a Multiple Cohort Study: Different Admission Tools Uniquely Select Different Successful Students

Abstract

Using multiple admission tools in university admission procedures is common practice. This is particularly useful if different admission tools uniquely select different subgroups of students who will be successful in university programs. A signal-detection approach was used to investigate the accuracy of Secondary School grade point average (SSGPA), an admission test score (ACS), and a non-cognitive score (NCS) in uniquely selecting successful students. This was done for three consecutive first year cohorts of a broad psychology program. Each applicant's score on SSGPA, ACS, or NCS alone—and on seven combinations of these scores, all considered separate “admission tools”—was compared at two different (medium and high) cut-off scores (criterion levels). Each of the tools selected successful students who were not selected by any of the other tools. Both sensitivity and specificity were enhanced by implementing multiple tools. The signal-detection approach distinctively provided useful information for decisions on admission instruments and cut-off scores.