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Harmonizing the CBCL and SDQ ADHD scores by using linear equating, kernel equating, item response theory and machine

Miljan Jović1, Maryam Amir Haeri1, Andrew Whitehouse2

  • 1Department of Learning, Data Analytics and Technology, University of Twente, Enschede, Netherlands.

Frontiers in Psychology
|July 25, 2024
PubMed
Summary

Harmonizing ADHD scores from Child Behavior Checklist (CBCL) and Strengths and Difficulties Questionnaire (SDQ) is crucial for research. Regression methods using item-level data proved most effective for scale harmonization.

Keywords:
ADHDIRTdata harmonizationkernel equatinglinear equatingmachine learningtest equating

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Area of Science:

  • Psychometrics
  • Developmental Psychology
  • Clinical Psychology

Background:

  • Researchers often face challenges comparing results due to varied scales used by institutions.
  • Harmonizing scales is essential for meaningful data pooling and score comparison in research consortia.

Purpose of the Study:

  • To harmonize Attention-Deficit/Hyperactivity Disorder (ADHD) scores from the Child Behavior Checklist (CBCL) and Strengths and Difficulties Questionnaire (SDQ).
  • To evaluate and compare the efficacy of various test equating methods for scale harmonization.

Main Methods:

  • Utilized a common persons design with 1551 parent reports of children aged 10-11.5 years.
  • Applied linear equating, kernel equating, Item Response Theory (IRT), and machine learning methods (regression, random forest, Support Vector Machine).
  • Assessed method efficacy using root-mean-square error (RMSE) in cross-validation.

Main Results:

  • Regression approaches, particularly those treating outcomes as interval measurements and using item-level information, demonstrated superior performance.
  • Machine learning methods, including regression, showed promise in harmonizing ADHD scores effectively.

Conclusions:

  • Regression methods are recommended for harmonizing ADHD scores from CBCL and SDQ in single-group designs.
  • Item-level data and interval measurement assumptions enhance the accuracy of scale harmonization for ADHD constructs.