Test Security

Detecting Groups of Test Takers Involved in Test Collusion as Unusually Large Cliques in a Graph (RR 18-01)

by Dmitry I. Belov, Law School Admission Council; and James A. Wollack, University of Wisconsin–Madison

Test collusion (TC) is the sharing of test materials or answers to test questions (items) before or during a test. Because of the potentially large advantages for the test takers involved, TC poses a serious threat to the validity of score interpretations. The proposed approach applies graph theory methodology to response similarity analyses to identify groups involved in TC while minimizing the false-positive detection rate. The new approach is illustrated and compared with a recently published method using real and simulated data. The results of computational studies demonstrate advantages of the new approach, particularly a remarkable robustness to the multiple-comparison problem while still demonstrating good power to detect moderate to high amounts of collusion.

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A New Approach to Detecting Cluster Aberrancy (RR 16-05)

This report addresses a general type of cluster aberrancy in which a subgroup of test takers has an unfair advantage on some subset of administered items. Examples of cluster aberrancy include item preknowledge and test collusion. In general, cluster aberrancy is hard to detect due to the multiple unknowns involved: Unknown subgroups of test takers have an unfair advantage on unknown subsets of items. The issue of multiple unknowns makes the detection of cluster aberrancy a challenging problem from the standpoint of applied mathematics.

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