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Bayesian Joint Modeling of Response Times with Dynamic Latent Ability in Educational Testing
Xiaojing Wang1, Abhisek Saha2, Dipak K Dey1
1Department of Statistics, https://ror.org/02der9h97University of Connecticut, Storrs, United States.
Psychometrika
|December 1, 2025
Summary
This study introduces new models that combine item responses and response times for better educational testing. These models reduce bias and improve ability estimation accuracy in educational assessments.
Area of Science:
- Educational Measurement
- Psychometrics
- Data Science
Background:
- Traditional educational testing primarily uses item responses to infer ability, often overlooking response time data.
- Ignoring response time may lead to less accurate ability estimations.
Purpose of the Study:
- To develop advanced state space models that integrate both item responses and response times for enhanced ability inference.
- To investigate the relationship between examinee ability, item difficulty, and response times in educational testing.
Main Methods:
- Developed a novel class of state space models for conjointly analyzing dichotomous item responses and response times.
- Conducted simulations to evaluate the performance of the proposed models.
- Performed an empirical study using EdSphere datasets to compare different response time models.
Main Results:
- Simulations showed that the new models significantly reduce bias in ability estimation.
- The proposed models demonstrated improved precision in ability estimation compared to traditional methods.
- An inverted U-shape relationship between ability-difficulty distance and response time provided a better fit for the EdSphere data.
Conclusions:
- Integrating response time data into educational testing models enhances the accuracy and precision of ability estimations.
- The inverted U-shape relationship offers a more psychologically plausible explanation for examinee behavior during assessments.
- These findings suggest a more comprehensive approach to educational measurement by incorporating temporal data.
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