Bayesian IRT Guessing Models for Partial Guessing Behaviors

S. Lynne Stokes

68 citations0/0 files storedPsychometrika

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Psychometric Methodologies and Testing Educational Assessment and Pedagogy Statistical Methods and Bayesian Inference

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.

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References

2005Response Time Effort: A New Measure of Examinee Motivation in Computer-Based Tests 2005Low Examinee Effort in Low-Stakes Assessment: Problems and Potential Solutions 1990Modeling Item Responses When Different Subjects Employ Different Solution Strategies 2006An Application of Item Response Time: The Effort‐Moderated IRT Model 2007The Nation's Report Card[TM]: 12th-Grade Reading and Mathematics, 2005. NCES 2007-468.2006IRT Models for Ability-Based Guessing 1995ESTIMATING THE EFFECTS OF TEST LENGTH AND TEST TIME ON PARAMETER ESTIMATION USING THE HYBRID MODEL

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