NDLI: Bayesian IRT Guessing Models for Partial Guessing Behaviors

Content Provider	Springer Nature Link
Author	Cao, Jing Stokes, S. Lynne
Copyright Year	2007
Abstract	According to the recent Nation’s Report Card, 12th-graders failed to produce gains on the 2005 National Assessment of Educational Progress (NAEP) despite earning better grades on average. One possible explanation is that 12th-graders were not motivated taking the NAEP, which is a low-stakes test. We develop three Bayesian IRT mixture models to describe the results from a group of examinees including both nonguessers and partial guessers. The first assumes that the guesser answers questions based on his or her knowledge up to a certain test item, and guesses thereafter. The second model assumes that the guesser answers relatively easy questions based on his or her knowledge and guesses randomly on the remaining items. The third is constructed to describe more general low-motivation behavior. It assumes that the guesser gives less and less effort as he or she proceeds through the test. The models can provide not only consistent estimates of IRT parameters but also estimates of each examinee’s nonguesser/guesser status and degree of guessing behavior. We show results of a simulation study comparing the performance of the three guessing models to the 2PL-IRT model. Finally, an analysis of real data from a low-stakes test administered to university students is presented.
Starting Page	209
Ending Page	230
Page Count	22
File Format	PDF
ISSN	00333123
Journal	Psychometrika
Volume Number	73
Issue Number	2
e-ISSN	18600980
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-12-15
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian mixture model IRT model guessing behavior low motivation item location low-stakes test Psychometrics Assessment, Testing and Evaluation Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Statistical Theory and Methods
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Psychology

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