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Activity Number:
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307
- Challenges and Advances in Psychological and Behavioral Data Analysis
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Type:
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Topic-Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
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Sponsor:
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Mental Health Statistics Section
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Abstract #317391
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Title:
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A Higher-Order Cognitive Diagnosis Model with Ordinal Attributes for Dichotomous Response Data
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Author(s):
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Wenchao Ma*
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Companies:
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The University of Alabama
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Keywords:
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Cognitive diagnosis;
regularization;
higher-order;
Lasso;
IRT;
CDM
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Abstract:
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Most existing cognitive diagnosis models (CDMs) assume attributes are binary latent variables, which may be oversimplified in practice. This study 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.
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Authors who are presenting talks have a * after their name.
