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Assessing multiple abilities through process data in computer-based assessments: The multidimensional sequential response model (MSRM)
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DOI：10.3758/s13428-025-02658-7.png)
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Abstract
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With the advent of computer-based assessment (CBA), process data have assumed an increasingly pivotal role in estimating examinees' latent abilities by capturing detailed records of their response processes. This study introduces the Multidimensional sequential response model (MSRM), a novel model for assessing multiple abilities through process data in computer-based cognitive and psychological assessments. A Bayesian estimation method for the MSRM is proposed and examined through a Monte Carlo simulation study across varying conditions. The results suggest that the MSRM's parameter estimation demonstrates adequate accuracy and computational efficiency, with estimation quality improving as sample sizes and sequence lengths increase. We demonstrate the practical utility of MSRM through two empirical studies, showing that it can be effectively applied in various contexts. This methodology provides valuable insights for tailored instruction by offering detailed assessments of ability mastery across multiple dimensions, thereby supporting more targeted educational interventions.
Keywords:
Multidimensional sequential response model (MSRM)
Process data
Computer-based assessments
Bayesian estimation
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3.9
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695
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