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Best Design for Multidimensional Computerized Adaptive Testing With the Bifactor Model.

Dong Gi Seo1, David J Weiss2

  • 1National Registry of Emergency Medical Technicians, Columbus, OH, USA.

Educational and Psychological Measurement
|May 26, 2018
PubMed
Summary
This summary is machine-generated.

This study explored multidimensional computerized adaptive testing (MCAT) using bifactor models. D s-optimality improved general factor estimates, while D- or A-optimality enhanced group factor estimates in MCAT.

Keywords:
bifactor modelcomputerized adaptive testingfull information item factor analysismultidimensional item response theory

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Psychological Assessment

Background:

  • Traditional computerized adaptive tests (CATs) often rely on unidimensional item response theory.
  • Many psychological constructs are inherently multidimensional, necessitating advanced modeling approaches.
  • Multidimensional CAT (MCAT) offers a potential improvement for assessing complex psychological variables.

Purpose of the Study:

  • To investigate the accuracy, fidelity, and efficiency of a fully multidimensional CAT algorithm (MCAT).
  • To evaluate MCAT performance using a bifactor model with simulated data.
  • To compare different item selection and estimation methods within the MCAT framework.

Main Methods:

  • Simulated data were generated based on two multidimensional item response theory models.
  • A fully multidimensional CAT algorithm (MCAT) was implemented with a bifactor model.
  • Four item selection strategies (D s-optimality, D-optimality, A-optimality) and two estimation methods (MAP, EAP) were compared across three bifactor pattern designs.

Main Results:

  • D s-optimality item selection enhanced general factor (θ) estimates.
  • D- or A-optimality improved the estimation of group factors across different bifactor designs.
  • MCAT models without a guessing parameter outperformed those with a guessing parameter.
  • Maximum a posteriori (MAP) estimation yielded more accurate θ estimates and lower standard errors than expected a posteriori (EAP) estimation in most scenarios.

Conclusions:

  • The findings support the utility of MCAT with bifactor models for assessing multidimensional psychological constructs.
  • Specific item selection and estimation methods demonstrate differential performance in capturing general and group factors.
  • The study highlights the importance of model specification, particularly regarding the guessing parameter, for optimal MCAT performance.