A Higher-Order Cognitive Diagnosis Model with Ordinal Attributes for Dichotomous Response Data

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9 Scopus citations

Abstract

Most existing cognitive diagnosis models (CDMs) assume attributes are binary latent variables, which may be oversimplified in practice. This article introduces a higher-order CDM with ordinal attributes for dichotomous response data. The proposed model can either incorporate domain experts’ knowledge or learn from the data empirically by regularizing model parameters. A sequential item response model was employed for joint attribute distribution to accommodate the sequential mastery mechanism. The expectation-maximization algorithm was employed for model estimation, and a simulation study was conducted to assess the recovery of model parameters. A set of real data was also analyzed to assess the viability of the proposed model in practice.

Original languageEnglish (US)
Pages (from-to)408-421
Number of pages14
JournalMultivariate Behavioral Research
Volume57
Issue number2-3
DOIs
StatePublished - 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 Taylor & Francis Group, LLC.

Keywords

  • Cognitive diagnosis
  • higher-order
  • lasso
  • polytomous attribute
  • regularization
  • sequential IRT

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