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Bayesian Procedures for Identifying Aberrant Response-Time Patterns in Adaptive Testing

Published online by Cambridge University Press:  01 January 2025

Wim J. van der Linden  and
Fanmin Guo
Wim J. van der Linden*
Affiliation:
University of Twente
Fanmin Guo
Affiliation:
Graduate Management Admission Council
*
Requests for reprints should be sent to Wim J. van der Linden, Department of Research Methodology, Measurement, and Data Analysis, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands. E-mail: [email protected]
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Abstract

In order to identify aberrant response-time patterns on educational and psychological tests, it is important to be able to separate the speed at which the test taker operates from the time the items require. A lognormal model for response times with this feature was used to derive a Bayesian procedure for detecting aberrant response times. Besides, a combination of the response-time model with a regular response model in an hierarchical framework was used in an alternative procedure for the detection of aberrant response times, in which collateral information on the test takers’ speed is derived from their response vectors. The procedures are illustrated using a data set for the Graduate Management Admission Test® (GMAT®). In addition, a power study was conducted using simulated cheating behavior on an adaptive test.

Keywords

adaptive testingBayesian predictive checkscheatingcollateral informationhierarchical modelingresponse times
Type
Theory and Methods
Information
Psychometrika , Volume 73 , Issue 3 , September 2008 , pp. 365 - 384
Copyright
Copyright © 2008 The Psychometric Society

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Footnotes

The authors have relied upon data supplied by the Graduate Management Admission Council® (GMAC®) to conduct the independent research that forms the basis for the findings and conclusions stated in this article. These findings and conclusions are the opinion of the authors only, and do not necessarily reflect the opinion of the GMAC®. The authors are indebted to Wim M.M. Tielen and Rinke H. Klein Entink for their computational support.

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