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Noncompensatory MIRT For Passage-Based Tests

Published online by Cambridge University Press:  01 January 2025

Nana Kim Open the ORCID record for Nana Kim [Opens in a new window] ,
Daniel M. Bolt  and
James Wollack
Nana Kim*
Affiliation:
University of Wisconsin-Madison
Daniel M. Bolt
Affiliation:
University of Wisconsin-Madison
James Wollack
Affiliation:
University of Wisconsin-Madison
*
Correspondence should be made to Nana Kim, University of Wisconsin-Madison, 1025 W. Johnson St, Madison, WI53706, USA. Email: nkim84@wisc.edu
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Abstract

We consider a multidimensional noncompensatory approach for binary items in passage-based tests. The passage-based noncompensatory model (PB-NM) emphasizes two underlying components in solving passage-based test items: a passage-related component and a passage-independent component. An advantage of the PB-NM model over commonly applied compensatory models (e.g., bifactor model) is that the two components are parameterized in relation to difficulty as opposed to discrimination parameters. As a result, while simultaneously accounting for passage-related local item dependence, the model permits the assessment of how items based on the same passage may require varying levels of passage comprehension (as well as varying levels of passage-independent proficiency) to obtain a correct response. Through a simulation study, we evaluate the comparative fit of the PB-NM against the bifactor model and also illustrate the relationship between the difficulty parameters of the PB-NM and the discrimination parameters of the bifactor model. We further apply the PB-NM to an actual reading comprehension test to demonstrate the relevance of the model in understanding variation in the relative difficulty of the two components across different item types.

Keywords

multidimensional item response theory (MIRT)noncompensatory MIRTconjunctive Rasch modelbifactor modelpassage-based tests
Type
Application Reviews and Case Studies
Information
Psychometrika , Volume 87 , Issue 3 , September 2022 , pp. 992 - 1009
Copyright
Copyright © 2021 The Author(s) under exclusive licence to The Psychometric Society

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Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s11336-021-09826-6.

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