A ranking forced choice diagnostic classification model for psychological assessment using forced choice
Yi-An Zhu1, Jingwan Xu1, Daxun Wang1
1School of Psychology, Jiangxi Normal University, Nanchang, Jiangxi, China.
The British Journal of Mathematical and Statistical Psychology
|December 16, 2024
Summary
This study introduces a new diagnostic classification model (DCM) for forced-choice questionnaires (FCQ) to reduce response biases. The ranking FC-DCM improves upon existing models, showing accurate results in simulations.
Area of Science:
- Psychometrics
- Educational Measurement
- Latent Trait Theory
Background:
- Diagnostic classification models (DCM) are used for non-cognitive tests but can be affected by response biases in single-stimulus items.
- Forced-choice questionnaires (FCQ) mitigate response biases, but their integration with DCMs is limited.
- Existing DCMs for FC items have modeling and format limitations.
Purpose of the Study:
- To propose a novel ranking diagnostic classification model for forced-choice items (ranking FC-DCM).
- To address limitations of existing models regarding assumptions, FC formats, and attribute measurement.
- To evaluate the proposed model's performance in parameter recovery.
Main Methods:
- Development of a generalized ranking FC-DCM accommodating various FC formats.
- Simulation studies to assess person and item parameter recovery under different conditions.
- Application of the model to a real-world forced-choice questionnaire.
Main Results:
- The proposed ranking FC-DCM demonstrated satisfactory person and item parameter recovery across all simulated conditions.
- The model effectively handles different forced-choice formats and attribute measurement complexities.
- An illustrative example confirmed the model's practical utility and advantages.
Conclusions:
- The ranking FC-DCM is a viable and improved alternative for analyzing forced-choice data in non-cognitive assessments.
- This model offers enhanced diagnostic information while controlling for response biases.
- The study highlights the potential of the ranking FC-DCM for real-world psychometric applications.
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