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Nested Logit Models for Multiple-Choice Item Response Data

Published online by Cambridge University Press:  01 January 2025

Youngsuk Suh  and
Daniel M. Bolt
Youngsuk Suh*
Affiliation:
University of Texas at Austin
Daniel M. Bolt
Affiliation:
University of Wisconsin-Madison
*
Requests for reprints should be sent to Youngsuk Suh, Department of Educational Psychology, University of Texas at Austin, 1 University Station D5800, Austin, TX 78712, USA. E-mail: yssuh327@gmail.com
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Abstract

Nested logit item response models for multiple-choice data are presented. Relative to previous models, the new models are suggested to provide a better approximation to multiple-choice items where the application of a solution strategy precedes consideration of response options. In practice, the models also accommodate collapsibility across all distractor categories, making it easier to allow decisions about including distractor information to occur on an item-by-item or application-by-application basis without altering the statistical form of the correct response curves. Marginal maximum likelihood estimation algorithms for the models are presented along with simulation and real data analyses.

Keywords

Multiple-choice itemsmultiple-choice modelsnested logit modelsnominal response modelmarginal maximum likelihood estimationitem informationdistractor selection informationdistractor category collapsibility

Information

Type
Original Paper
Information
Psychometrika , Volume 75 , Issue 3 , September 2010 , pp. 454 - 473
Copyright
Copyright © 2010 The Psychometric Society

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