JOURNAL ARTICLE

Informant Discrepancies in the Assessment of Social Skills and Behaviors of Children With Autism Spectrum Disorder.

  • Published In: Focus on Autism & Other Developmental Disabilities, 2026, v. 41, n. 2. P. 94 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Stanford, Samantha E.; Nickerson, Amanda B.; Lopata, Christopher; Fredrick, Stephanie S.; Donnelly, James P.; Rodgers, Jonathan D.; Thomeer, Marcus L. 3 of 3

Abstract

This article focuses on examining discrepancies between parent and teacher ratings on the Adapted Skillstreaming Checklist (ASC), a measure assessing social skills and behavioral flexibility/regulation in children with autism spectrum disorder (ASD) without intellectual disability (ID). In a sample of 124 U.S. children aged 6 to 11 years, no significant mean differences were found between parent and teacher ASC scores, though correlations between informants were modest (intraclass correlation coefficient = .30; Pearson r = .18). The study found no systematic trends in discrepancies across the score range and no child or parent variables moderated these differences. The findings suggest that while group-level comparisons using the ASC may not be affected by informant discrepancies, individual-level ratings show less consistency, indicating the need for practitioners to follow up on differing reports to better understand contextual factors and improve intervention planning.

Additional Information

  • Source:Focus on Autism & Other Developmental Disabilities. 2026/06, Vol. 41, Issue 2, p94
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2026
  • ISSN:1088-3576
  • DOI:10.1177/10883576251353482
  • Accession Number:193226597
  • Copyright Statement:Copyright of Focus on Autism & Other Developmental Disabilities 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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