Test Security

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. This report presents a novel algorithm to detect cluster aberrancy. The algorithm is general and applicable to all types of testing programs: paper-and-pencil testing, computer-based testing, multistage testing, and computerized adaptive testing; it can also be applied in areas outside of psychometrics, such as finance (e.g., detecting financial fraud). Both simulated and real data were used to study the performance of this algorithm.

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Additional reports in this collection

Detecting Groups of Test Takers Involved in Test...

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.

Detection of Invalid Test Scores: The Usefulness...

Most high-stakes testing programs apply methods to identify unlikely patterns of correct/incorrect responses to test questions. Some examples of why such patterns may occur include misinterpretation of questions, question preknowledge, answer copying, or guessing behavior. This report provides an overview of existing approaches to identifying atypical response patterns that fall into a class of analyses known as nonparametric statistics. Results of a simulation study comparing the different approaches, along with guidelines for applying these indices in practice, are also presented.