{"title":"Differential item functioning in the autism behavior checklist in children with autism spectrum disorder based on a machine learning approach.","authors":"Kanglong Peng, Meng Chen, Libing Zhou, Xiaofang Weng","doi":"10.3389/fpsyt.2024.1447080","DOIUrl":null,"url":null,"abstract":"<p><strong>Aim: </strong>Our study utilized the Rasch analysis to examine the psychometric properties of the Autism Behavior Checklist (ABC) in children with autism spectrum disorder (ASD).</p><p><strong>Methods: </strong>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).</p><p><strong>Result: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":12605,"journal":{"name":"Frontiers in Psychiatry","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11440003/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Psychiatry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fpsyt.2024.1447080","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"PSYCHIATRY","Score":null,"Total":0}
Differential item functioning in the autism behavior checklist in children with autism spectrum disorder based on a machine learning approach.
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.
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.