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 language | English (US) |
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Pages (from-to) | 408-421 |
Number of pages | 14 |
Journal | Multivariate Behavioral Research |
Volume | 57 |
Issue number | 2-3 |
DOIs | |
State | Published - 2022 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2021 Taylor & Francis Group, LLC.
Keywords
- Cognitive diagnosis
- higher-order
- lasso
- polytomous attribute
- regularization
- sequential IRT