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07-05-2021 | Original Article

A Machine Learning Approach to Assess Differential Item Functioning of the KINDL Quality of Life Questionnaire Across Children with and Without ADHD

Auteurs: Peyman Jafari, Kamran Mehrabani-Zeinabad, Sara Javadi, Ahmad Ghanizadeh, Zahra Bagheri

Gepubliceerd in: Child Psychiatry & Human Development | Uitgave 5/2022

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Abstract

This study aimed to investigate differential item functioning (DIF) of the child and parent reports of the KINDL measure across children with and without Attention-deficit/hyperactivity disorder (ADHD). The sample included 122 children with ADHD and 1086 healthy peers, alongside 127 and 1061 of their parents, respectively. The generalized partial credit model with lasso penalization, as a machine learning method, was used to assess DIF of the KINDL across the two groups. The findings showed that three out of 24 items of the child reports and seven out of 24 items of the parent reports of the KINDL exhibited DIF between children with and without ADHD. Accordingly, Iranian children with and without ADHD along with their parents perceive almost all items in the KINDL similarly. Hence, the observed difference in quality of life scores between children with and without ADHD is a real difference and not a reflection of measurement bias.
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Metagegevens
Titel
A Machine Learning Approach to Assess Differential Item Functioning of the KINDL Quality of Life Questionnaire Across Children with and Without ADHD
Auteurs
Peyman Jafari
Kamran Mehrabani-Zeinabad
Sara Javadi
Ahmad Ghanizadeh
Zahra Bagheri
Publicatiedatum
07-05-2021
Uitgeverij
Springer US
Gepubliceerd in
Child Psychiatry & Human Development / Uitgave 5/2022
Print ISSN: 0009-398X
Elektronisch ISSN: 1573-3327
DOI
https://doi.org/10.1007/s10578-021-01179-6

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