Attribute Hierarchy Models in Cognitive Diagnosis: Identifiability of the Latent Attribute Space and Conditions for Completeness of the Q-Matrix

Hans Friedrich Koehn, Chia Yi Chiu

Research output: Contribution to journalArticle

Abstract

Educational researchers have argued that a realistic view of the role of attributes in cognitively diagnostic modeling should account for the possibility that attributes are not isolated entities, but interdependent in their effect on test performance. Different approaches have been discussed in the literature; among them the proposition to impose a hierarchical structure so that mastery of one or more attributes is a prerequisite of mastering one or more other attributes. A hierarchical organization of attributes constrains the latent attribute space such that several proficiency classes, as they exist if attributes are not hierarchically organized, are no longer defined because the corresponding attribute combinations cannot occur with the given attribute hierarchy. Hence, the identification of the latent attribute space is often difficult—especially, if the number of attributes is large. As an additional complication, constructing a complete Q-matrix may not at all be straightforward if the attributes underlying the test items are supposed to have a hierarchical structure. In this article, the conditions of identifiability of the latent space if attributes are hierarchically organized and the conditions of completeness of the Q-matrix are studied.

Original languageEnglish (US)
JournalJournal of Classification
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Q-matrix
Identifiability
Completeness
Attribute
Research Personnel
diagnostic
organization
Model
performance
Hierarchical Structure
Hierarchy
Performance Test
Complications
Proposition
literature
Diagnostics

Keywords

  • Attribute hierarchy
  • Cognitive diagnosis
  • Completeness
  • DINA model
  • General DCMs
  • Latent attribute apace
  • Q-Matrix

ASJC Scopus subject areas

  • Mathematics (miscellaneous)
  • Psychology (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

Cite this

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