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ORIGINAL RESEARCH article

Front. Psychol.
Sec. Quantitative Psychology and Measurement
Volume 15 - 2024 | doi: 10.3389/fpsyg.2024.1345406
This article is part of the Research Topic Artificial Intelligence and Mental Health Care View all 12 articles

Harmonizing the CBCL and SDQ ADHD scores by using Linear equating, Kernel equating, Item Response Theory and Machine Learning methods

Provisionally accepted
Miljan Jovic Miljan Jovic 1*Maryam Amir Haeri Maryam Amir Haeri 1Andrew Whitehouose Andrew Whitehouose 2Stephanie M. Van Den Berg Stephanie M. Van Den Berg 1
  • 1 University of Twente, Enschede, Netherlands
  • 2 Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia

The final, formatted version of the article will be published soon.

    A problem that applied researchers and practitioners often face is the fact that different institutions within research consortia use different scales to evaluate the same construct which makes comparison of the results and pooling challenging. In order to meaningfully pool and compare the scores, the scales should be harmonized. The aim of this paper is to use different test equating methods to harmonize the ADHD scores from Child Behavior Checklist (CBCL) and Strengths and Difficulties Questionnaire (SDQ) and to see which method leads to the result. Sample consists of 1551 parent reports of children aged 10-11.5 years from Raine study on both CBCL and SDQ (common persons design). We used linear equating, kernel equating, Item Response Theory (IRT), and the following machine learning methods: regression (linear and ordinal), random forest (regression and classification) and Support Vector Machine (regression and classification). Efficacy of the methods is operationalized in terms of the root-mean-square error (RMSE) of differences between predicted and observed scores in cross-validation. Results showed that with single group design, it is the best to use the methods that use item level information and that treat the outcome as interval measurement level (regression approach).

    Keywords: data harmonization, test equating, machine learning, IRT, Linear equating, kernel equating, ADHD

    Received: 13 Dec 2023; Accepted: 17 Jun 2024.

    Copyright: © 2024 Jovic, Amir Haeri, Whitehouose and Van Den Berg. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Miljan Jovic, University of Twente, Enschede, Netherlands

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