JOURNAL ARTICLE

Validating the Child Behavior Checklist 1.5-5 as a screening tool for autism spectrum disorder.

  • Published In: Autism: The International Journal of Research & Practice, 2025, v. 29, n. 4. P. 1072 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Kitanishi, Norton; Bordini, Daniela; Ribeiro, Marcos V V; Paula, Cristiane Silvestre; Brentani, Helena; Portelese, Joana; Surkan, Pamela J; Martins, Silvia S.; Jesus Mari, Jair de; Martins Okuda, Paola Matiko; Caetano, Sheila C 3 of 3

Abstract

This article focuses on validating the Child Behavior Checklist for ages 1.5–5 (CBCL 1.5-5) as a cost-effective level 1 screening tool for autism spectrum disorder (ASD) in Brazilian children. Using data from a representative sample of 1,292 children aged 3–5 years and 70 children diagnosed with ASD aged 1–5 years, the study assessed the construct validity and reliability of the CBCL's Autism Spectrum Problems and Withdrawn Syndrome subscales. Both scales demonstrated good model fit, internal consistency, and strong diagnostic accuracy, with area under the curve (AUC) values of 0.93 and sensitivities and specificities above 80%. The findings support the CBCL 1.5-5 as a reliable and valid screening instrument for early ASD identification in low- and middle-income country settings, though further research is recommended to confirm its stability across diverse populations and over time.

Additional Information

  • Source:Autism: The International Journal of Research & Practice. 2025/04, Vol. 29, Issue 4, p1072
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:1362-3613
  • DOI:10.1177/13623613241293088
  • Accession Number:184233764
  • Copyright Statement:Copyright of Autism: The International Journal of Research & Practice is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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