Integrating Differential Evolution Optimization to Cognitive Diagnostic Model Estimation
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Date
2018
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Frontiers
Abstract
A log-linear cognitive diagnostic model (LCDM) is estimated via a global optimization approach- differential evolution optimization (DEoptim), which can be used when the traditional expectation maximization (EM) fails. The application of the DEoptim to LCDM estimation is introduced, explicated, and evaluated via a Monte Carlo simulation study in this article. The aim of this study is to fill the gap between the field of psychometric modeling and modern machine learning estimation techniques and provide an alternative solution in the model estimation.
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Keywords
differential evolution optimization, cognitive diagnostic model, LCDM, estimation, EM algorithm, ITEM RESPONSE THEORY, GLOBAL OPTIMIZATION, EM ALGORITHM, MIXTURE, FIT, PARAMETERS, FAMILY, Psychology, Multidisciplinary
Citation
Jiang, Z., & Ma, W. (2018). Integrating Differential Evolution Optimization to Cognitive Diagnostic Model Estimation. In Frontiers in Psychology (Vol. 9). Frontiers Media SA. https://doi.org/10.3389/fpsyg.2018.02142