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Explanatory Cognitive Diagnostic Models: Incorporating Latent and Observed Predictors
Yoon Soo Park1, Kuan Xing1, Young-Sun Lee2
1The University of Illinois at Chicago, USA.
Applied Psychological Measurement
|July 24, 2018
Summary
This study integrates latent and observed variables into cognitive diagnostic models (CDMs) for educational testing. Findings show stable parameter recovery and accurate attribute classification, enhancing understanding of student performance factors.
Area of Science:
- Educational Measurement
- Psychometrics
- Cognitive Science
Background:
- Large-scale educational testing generates rich data on students, schools, and resources.
- Understanding how these factors influence test performance is crucial for educational research.
- Existing cognitive diagnostic models (CDMs) can be enhanced by incorporating explanatory variables.
Purpose of the Study:
- To investigate methods for including latent and observed explanatory variables as predictors in CDMs.
- To simultaneously estimate these predictors alongside attribute mastery.
- To demonstrate the application using real-world international testing data and simulations.
Main Methods:
- Developed and tested approaches for specifying and estimating latent and observed predictors within CDMs.
- Utilized item response theory (IRT) for estimating latent variables based on dichotomous and polytomous item indicators.
- Conducted simulation studies with varying sample sizes and attribute numbers to assess model performance.
Main Results:
- Demonstrated stable parameter recovery for both latent and observed predictors.
- Showed consistent and reliable attribute classification across different simulation conditions.
- Validated the proposed methods using large-scale international educational testing data.
Conclusions:
- Simultaneously estimating latent and observed predictors in CDMs is feasible and effective.
- The proposed approach enhances the explanatory power of CDMs for understanding student performance.
- Findings have implications for model specification and the interpretation of latent constructs in educational assessment.
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