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Termination Criteria for Grid Multiclassification Adaptive Testing With Multidimensional Polytomous Items.

Zhuoran Wang1, Chun Wang2, David J Weiss3

  • 1National Council of State Boards of Nursing (NCSBN), Chicago, IL, USA.

Applied Psychological Measurement
|September 22, 2022
PubMed
Summary
This summary is machine-generated.

Adaptive classification testing (ACT) efficiently classifies individuals into multiple groups using grid classification. New termination criteria, like the grid classification generalized likelihood ratio (GGLR), improve classification accuracy and efficiency.

Keywords:
adaptive classification testingcomputerized adaptive testinggrid multiclassificationpolytomous itemssequential probability ratio test, termination criteria

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Computerized Adaptive Testing

Background:

  • Adaptive classification testing (ACT) is a variation of computerized adaptive testing (CAT).
  • Multidimensional multiclassification involves classifying examinees into multiple categories across several dimensions.
  • Grid classification is proposed for multidimensional multiclassification to provide clearer examinee standing and facilitate interventions.

Purpose of the Study:

  • To implement the sequential probability ratio test (SPRT) and confidence interval method in grid multiclassification ACT.
  • To propose two new termination criteria: grid classification generalized likelihood ratio (GGLR) and simplified GGLR for grid multiclassification ACT.
  • To evaluate the efficiency of grid multiclassification ACT compared to measurement CAT.

Main Methods:

  • Implementation of SPRT and confidence interval method within the ACT framework.
  • Development and proposal of GGLR and simplified GGLR as novel termination criteria.
  • Simulation studies using both simulated and real item banks with polytomous multidimensional items.

Main Results:

  • Grid multiclassification ACT demonstrates higher efficiency than CAT focused on trait estimate precision.
  • The GGLR criterion was most effective in terminating grid multiclassification ACT and classifying examinees.
  • Both simulated and real item bank studies confirmed the efficiency of the proposed methods.

Conclusions:

  • Grid multiclassification ACT offers a more efficient approach for classifying examinees into multiple groups compared to traditional CAT.
  • The GGLR criterion is a highly efficient method for terminating grid multiclassification ACT, especially with high-quality item banks.
  • The developed methods enhance the precision and efficiency of examinee classification in multidimensional settings.