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Using Machine Learning Methods to Develop a Short Tree-Based Adaptive Classification Test: Case Study With a

Yi Zheng1, Hyunjung Cheon1, Charles M Katz1

  • 1Arizona State University, Tempe, USA.

Applied Psychological Measurement
|September 27, 2021
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Summary

Machine learning developed short tree-based adaptive tests for juvenile delinquency risk assessment. These tests significantly reduce assessment length while maintaining high classification accuracy, offering a promising alternative to traditional methods.

Keywords:
adaptive testclassification treemachine learning

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Area of Science:

  • Psychometrics and Educational Measurement
  • Machine Learning Applications
  • Developmental Psychology

Background:

  • Traditional lengthy instruments pose challenges in assessments, especially with high-dimensional data and low-prevalence outcomes.
  • Item Response Theory (IRT)-based adaptive testing may struggle with instruments measuring numerous constructs and imbalanced datasets.
  • Machine learning offers alternative approaches for developing efficient adaptive tests.

Purpose of the Study:

  • To develop and evaluate short tree-based adaptive classification tests using machine learning.
  • To assess the feasibility of these tests for a juvenile delinquency risk assessment case study.
  • To compare the performance of tree-based adaptive tests against established benchmark methods.

Main Methods:

  • A cross-validation study was conducted comparing eight tree-based adaptive test constructions.
  • Benchmark methods included IRT scoring and random forest with balanced sampling.
  • Data from 3,975 subjects were used, with a focus on imbalanced training data due to low outcome prevalence.

Main Results:

  • The best tree-based adaptive tests demonstrated superior classification accuracy compared to IRT scoring.
  • Tree-based adaptive tests achieved comparable or better accuracy than random forest with balanced sampling.
  • These adaptive tests reduced instrument length by over 30-fold, administering only 3-6 items per individual.

Conclusions:

  • Tree-based adaptive tests are a highly effective machine learning approach for shortening instruments with numerous constructs.
  • This method shows significant potential for efficient and accurate risk assessment in fields like juvenile delinquency.
  • The developed tests offer a practical solution for reducing assessment burden while maintaining diagnostic precision.