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      Modeling Item Revisit Behavior: The Hierarchical Speed–Accuracy–Revisits Model

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          Abstract

          This article presents a new approach to the analysis of how students answer tests and how they allocate resources in terms of time on task and revisiting previously answered questions. Previous research has shown that in high-stakes assessments, most test takers do not end the testing session early, but rather spend all of the time they were assigned to take the test. Rather than being an indication of speededness, this was found to be caused by test takers’ tendency to revisit previous items even if they already provided answers to all questions. In accordance with this information, the proposed approach models revisit patterns simultaneously with responses and response times to gain a better understanding of the relationship between speed, ability, and revisit tendency. The empirical data analysis revealed that examinees’ tendency to revisit items was strongly related to their speed and subgroups of examinees displayed different test-taking behaviors.

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          MCMC Using Hamiltonian Dynamics

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            Logistic latent trait models with linear constraints

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              Rating scale analysis

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                Author and article information

                Journal
                Educational and Psychological Measurement
                Educational and Psychological Measurement
                SAGE Publications
                0013-1644
                1552-3888
                April 2021
                August 31 2020
                April 2021
                : 81
                : 2
                : 363-387
                Affiliations
                [1 ]Teachers College, Columbia University, New York, NY, USA
                [2 ]Boston College, Chestnut Hill, MA, USA
                [3 ]National Board of Medical Examiners, Philadelphia, PA, USA
                Article
                10.1177/0013164420950556
                37929265
                d076e6a7-49d5-4bb3-bdd7-4cc8eb2d614e
                © 2021

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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