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Modern Sequential Analysis and Its Applications to Computerized Adaptive Testing

Published online by Cambridge University Press:  01 January 2025

Jay Bartroff ,
Matthew Finkelman  and
Tze Leung Lai
Jay Bartroff*
Affiliation:
University of Southern California
Matthew Finkelman
Affiliation:
Harvard University
Tze Leung Lai
Affiliation:
Stanford University
*
Requests for reprints should be sent to Jay Bartroff, Department of Mathematics, University of Southern California, 3620 S Vermont Ave., KAP 108, Los Angeles, CA 90089, USA. E-mail: bartroff@usc.edu
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Abstract

After a brief review of recent advances in sequential analysis involving sequential generalized likelihood ratio tests, we discuss their use in psychometric testing and extend the asymptotic optimality theory of these sequential tests to the case of sequentially generated experiments, of particular interest in computerized adaptive testing. We then show how these methods can be used to design adaptive mastery tests, which are asymptotically optimal and are also shown to provide substantial improvements over currently used sequential and fixed length tests.

Keywords

sequential analysiscomputerized adaptive testingmastery testinggeneralized likelihood ratio statisticsitem response theory

Information

Type
Theory and Methods
Information
Psychometrika , Volume 73 , Issue 3 , September 2008 , pp. 473 - 486
Copyright
Copyright © 2008 The Psychometric Society

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