Test Security

Comparison Study of Item Preknowledge Detectors (RR 14-01)

When a test taker has prior knowledge about an administered test question (item), then this event is called item preknowledge, the test taker is called aberrant, and the item is called compromised. Item preknowledge negatively affects the corresponding testing program and its test score users (universities, companies, government organizations) because the scores produced for aberrant test takers will be invalid. The performance of eight statistics for detection of item preknowledge (five existing, two modified, and one new) was studied via computer simulations. Three major factors that could potentially influence the performance of the statistics were considered: (a) the type of test (adaptive, in which the next administered item is selected based on the test taker’s responses to previously administered items; or nonadaptive, in which all test takers are administered the same group of items); (b) distribution of the aberrant population (normal or uniform); and (c) noise in the information about compromised items (since different groups of aberrant test takers may have prior knowledge of different groups of items). The last factor demonstrated the highest negative impact on the performance of all of the statistics: the greater the noise, the lower the detection of item preknowledge. Several methods to address this problem are discussed.

Request the full report

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.

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.