This report presents a new algorithm for detecting groups of test takers (aberrant groups) who had access to subsets of test questions (aberrant subsets) prior to an exam. This method is in line with the development of statistical methods for detecting test collusion, a new research direction in test security. Test collusion may be described as the large-scale sharing of test materials, including answers to test questions. The algorithm employs several new statistics to perform a sequence of statistical tests to identify aberrant groups. The algorithm is flexible and can be easily modified to detect other types of test collusion. It can also be applied within all major modes of testing: paper-and-pencil testing, computer-based testing, multiple-stage testing, and computerized adaptive testing. A simulation study demonstrates the advantages of using the algorithm in computerized adaptive testing.
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