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A Machine Learning Approach to Assess Differential Item Functioning of the KINDL Quality of Life Questionnaire Across Children with and Without ADHD
Child Psychiatry & Human Development ( IF 2.776 ) Pub Date : 2021-05-07 , DOI: 10.1007/s10578-021-01179-6
Peyman Jafari 1 , Kamran Mehrabani-Zeinabad 1 , Sara Javadi 1 , Ahmad Ghanizadeh 2, 3 , Zahra Bagheri 1
Affiliation  

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.



中文翻译:

一种机器学习方法,用于评估患有和不患有 ADHD 的儿童的 KIDL 生活质量问卷的差异项目功能

本研究旨在调查患有和不患有注意力缺陷/多动障碍 (ADHD) 的儿童的儿童差异项目功能 (DIF) 和父母对 KIDL 测量的报告。样本包括 122 名患有多动症的儿童和 1086 名健康的同龄人,以及他们的父母中的 127 名和 1061 名。具有套索惩罚的广义部分学分模型作为一种机器学习方法,用于评估两组 KIDL 的 DIF。调查结果显示,24 项儿童报告中有 3 项和 24 项家长报告中有 7 项在患有和未患有多动症的儿童之间表现出 DIF。因此,患有和不患有多动症的伊朗儿童以及他们的父母对 kindl 中几乎所有的项目都有类似的看法。因此,

更新日期:2021-05-08
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