Differential item functioning in the autism behavior checklist in children with autism spectrum disorder based on a machine learning approach.

IF 3.2 3区医学 Q2 PSYCHIATRY Frontiers in Psychiatry Pub Date : 2024-09-16 eCollection Date: 2024-01-01 DOI:10.3389/fpsyt.2024.1447080

Kanglong Peng, Meng Chen, Libing Zhou, Xiaofang Weng

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Abstract

Aim: Our study utilized the Rasch analysis to examine the psychometric properties of the Autism Behavior Checklist (ABC) in children with autism spectrum disorder (ASD).

Methods: A total of 3,319 children (44.77 ± 23.52 months) were included. The Rasch model (RM) was utilized to test the reliability and validity of the ABC. The GPCMlasso model was used to test the differential item functioning (DIF).

Result: The response pattern of this sample showed acceptable fitness to the RM. The analysis supported the unidimensionality assumption of the ABC. Disordered category functions and DIF were found in all items in the ABC. The participants responded to the ABC items differently depending not only on autistic traits but also on age groups, gender, and symptom classifications.

Conclusion: The Rasch analysis produces reliable evidence to support that the ABC can precisely depict clinical ASD symptoms. Differences in population characteristics may cause unnecessary assessment bias and lead to overestimated or underestimated symptom severity. Hence, special consideration for population characteristics is needed in making an ASD diagnosis.

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目的：我们的研究采用 Rasch 分析方法，对自闭症谱系障碍（ASD）儿童的自闭症行为核对表（ABC）的心理测量特性进行了研究：共纳入 3,319 名儿童（44.77 ± 23.52 个月）。采用 Rasch 模型（RM）检验 ABC 的信度和效度。结果：结果：该样本的反应模式显示与 RM 的匹配性可以接受。分析结果支持 ABC 的单维假设。在 ABC 的所有项目中都发现了类别功能紊乱和 DIF。受试者对 ABC 项目的反应不仅因自闭症特征而异，还因年龄组、性别和症状分类而异：Rasch分析提供了可靠的证据，证明ABC能准确描述自闭症临床症状。人群特征的差异可能会造成不必要的评估偏差，导致症状严重程度被高估或低估。因此，在进行 ASD 诊断时需要特别考虑人群特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Psychiatry Medicine-Psychiatry and Mental Health

CiteScore

6.20

自引率

8.50%

发文量

2813

审稿时长

14 weeks

期刊介绍： Frontiers in Psychiatry publishes rigorously peer-reviewed research across a wide spectrum of translational, basic and clinical research. Field Chief Editor Stefan Borgwardt at the University of Basel is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide. The journal''s mission is to use translational approaches to improve therapeutic options for mental illness and consequently to improve patient treatment outcomes.