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Attention-Deficit/Hyperactivity Disorder

Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings.

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Related Experiment Video

Updated: Jul 20, 2026

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

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Published on: April 1, 2018

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

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:

ADHD IRT data harmonization kernel equating linear equating machine learning test equating

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Last Updated: Jul 20, 2026

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Published on: April 1, 2018

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The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

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Published on: June 12, 2020

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.