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Commentary on “Extending the Basic Local Independence Model to Polytomous Data” by Stefanutti, de Chiusole, Anselmi, and Spoto

Author

Listed:
  • Chia-Yi Chiu

    (University of Minnesota)

  • Hans Friedrich Köhn

    (University of Illinois at Urbana-Champaign)

  • Wenchao Ma

    (University of Alabama)

Abstract

The Polytomous Local Independence Model (PoLIM) by Stefanutti, de Chiusole, Anselmi, and Spoto, is an extension of the Basic Local Independence Model (BLIM) to accommodate polytomous items. BLIM, a model for analyzing responses to binary items, is based on Knowledge Space Theory, a framework developed by cognitive scientists and mathematical psychologists for modeling human knowledge acquisition and representation. The purpose of this commentary is to show that PoLIM is simply a paraphrase of a DINA model in cognitive diagnosis for polytomous items. Specifically, BLIM is shown to be equivalent to the DINA model when the BLIM-items are conceived as binary single-attribute items, each with a distinct attribute; thus, PoLIM is equivalent to the DINA for polytomous single-attribute items, each with a distinct attribute.

Suggested Citation

  • Chia-Yi Chiu & Hans Friedrich Köhn & Wenchao Ma, 2023. "Commentary on “Extending the Basic Local Independence Model to Polytomous Data” by Stefanutti, de Chiusole, Anselmi, and Spoto," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 656-671, June.
    • Handle: RePEc:spr:psycho:v:88:y:2023:i:2:d:10.1007_s11336-022-09873-7
    DOI: 10.1007/s11336-022-09873-7
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    References listed on IDEAS

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    1. Hans-Friedrich Köhn & Chia-Yi Chiu, 2019. "Attribute Hierarchy Models in Cognitive Diagnosis: Identifiability of the Latent Attribute Space and Conditions for Completeness of the Q-Matrix," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 541-565, October.
    2. Luca Stefanutti & Debora Chiusole & Pasquale Anselmi & Andrea Spoto, 2020. "Extending the Basic Local Independence Model to Polytomous Data," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 684-715, September.
    3. Chia-Yi Chiu & Jeffrey Douglas & Xiaodong Li, 2009. "Cluster Analysis for Cognitive Diagnosis: Theory and Applications," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 633-665, December.
    4. Yuqi Gu & Gongjun Xu, 2019. "The Sufficient and Necessary Condition for the Identifiability and Estimability of the DINA Model," Psychometrika, Springer;The Psychometric Society, vol. 84(2), pages 468-483, June.
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