基于机器学习方法的自闭症谱系障碍儿童自闭症行为检查表中的项目功能差异
Differential item functioning in the autism behavior checklist in children with autism spectrum disorder based on a machine learning approach.
作者信息
Peng Kanglong, Chen Meng, Zhou Libing, Weng Xiaofang
机构信息
Rehabilitation Department, Shenzhen Children's Hospital, Shenzhen, China.
Rehabilitation Department, Luohu District Maternal and Child Health Care Hospital, Shenzhen, China.
出版信息
Front Psychiatry. 2024 Sep 16;15:1447080. doi: 10.3389/fpsyt.2024.1447080. eCollection 2024.
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.
目的
我们的研究采用拉施分析来检验自闭症谱系障碍（ASD）儿童的自闭症行为量表（ABC）的心理测量特性。
方法
共纳入3319名儿童（44.77±23.52个月）。采用拉施模型（RM）来检验ABC的信度和效度。使用广义部分信贷模型套索（GPCMlasso）模型来检验项目功能差异（DIF）。
结果
该样本的反应模式显示出对RM可接受的拟合度。分析支持了ABC的单维性假设。在ABC的所有项目中均发现了类别功能紊乱和DIF。参与者对ABC项目的反应不仅因自闭症特征不同，还因年龄组、性别和症状分类而异。
结论
拉施分析产生了可靠的证据，支持ABC能够精确描述临床ASD症状。人群特征的差异可能会导致不必要的评估偏差，并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。
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