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Nested diagnostic classification models for multiple-choice items.

Authors
  • Liu, Ren1
  • Liu, Haiyan1
  • 1 Psychological Sciences, University of California, Merced, California, USA.
Type
Published Article
Journal
The British journal of mathematical and statistical psychology
Publication Date
May 01, 2021
Volume
74
Issue
2
Pages
257–285
Identifiers
DOI: 10.1111/bmsp.12214
PMID: 32700355
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

This study proposes and evaluates a diagnostic classification model framework for multiple-choice items. Models in the proposed framework have a two-level nested structure which allows for binary scoring (for correctness) and polytomous scoring (for distractors) at the same time. One advantage of these models is that they can provide distractor information while maintaining the statistical properties of the correct response option. We evaluated parameter recovery through a simulation study using Hamiltonian Monte Carlo algorithms in Stan. We also discussed three approaches to implementing the proposed modelling framework for different purposes and testing scenarios. We illustrated those approaches and compared them with a binary model and a traditional nominal model through an operational study. © 2020 British Psychological Society.

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