Proper and Useful Distractors in Multiple-Choice Diagnostic Classification Models

Hans Friedrich Köhn, Chia Yi Chiu, Yu Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The multiple-choice (MC) item format has been implemented in educational assessments that are used across diverse content domains. MC items comprise two components: the stem that provides the context with a motivating narrative, and the collection of response options consisting of the correct answer, called the “key,” and several incorrect alternatives, the “distractors.” The MC-DINA model was the first diagnostic classification model for MC items that used distractors explicitly as potential sources of diagnostic information. However, the MC-DINA model requires that the q-vectors of the distractors are nested within each other and that of the key, which poses a serious constraint on item development. Consequently, later adaptations of the MC item format to cognitive diagnosis dropped the nestedness condition. The relaxation of the nestedness-condition, however, comes at a price: distractors may become redundant (i.e., they do not contribute to any further diagnostic differentiation between examinees), and they may induce undesirable diagnostic ambiguity (i.e., they are equally likely to be chosen by an examinee, but their q-vectors point at different diagnostic classifications). In this article, two criteria, useful and proper, are proposed to identify redundant and diagnostically ambiguous distractors.

Original languageEnglish (US)
Title of host publicationQuantitative Psychology - The 87th Annual Meeting of the Psychometric Society, 2022
EditorsMarie Wiberg, Dylan Molenaar, Jorge González, Jee-Seon Kim, Heungsun Hwang
PublisherSpringer
Pages97-106
Number of pages10
ISBN (Print)9783031277801
DOIs
StatePublished - 2023
Event87th Annual Meeting of the Psychometric Society, IMPS 2022 - Bologna, Italy
Duration: Jul 11 2022Jul 15 2022

Publication series

NameSpringer Proceedings in Mathematics and Statistics
Volume422
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017

Conference

Conference87th Annual Meeting of the Psychometric Society, IMPS 2022
Country/TerritoryItaly
CityBologna
Period7/11/227/15/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Cognitive diagnosis
  • MC-DINA
  • MC-NPC
  • Nonparametric cognitive diagnosis
  • Polytomous items

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