JOURNAL ARTICLE

Outcome-Reporting Bias in Special Education Intervention Research Using Experimental and Quasi-Experimental Group Designs: A Conceptual Replication.

  • Published In: Remedial & Special Education, 2026, v. 47, n. 1. P. 25 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Talbott, Elizabeth; Maggin, Daniel M.; Chua, Meveryn; Ashley, Lauren; Chen, Xiaohong; Chin, Philippa A.; Curry, Mary Kate 3 of 3

Abstract

This article focuses on a conceptual replication of a study examining outcome-reporting bias in special education intervention research by comparing statistically significant and nonsignificant outcomes reported in unpublished dissertations with those in corresponding published journal articles. Analyzing 40 special education dissertations with matched publications from 2006 to 2015, the study found that statistically significant outcomes were 1.48 times more likely to be published than nonsignificant ones, indicating the presence of outcome-reporting bias, though smaller than previously reported. Significant moderators of this bias included academic intervention outcomes, randomized controlled trial designs, participant race (samples with ≥50% non-White), and high-incidence disability status. The authors highlight the under-publication of dissertations and recommend their inclusion in systematic reviews and meta-analyses to reduce publication bias and better inform special education research and practice.

Additional Information

  • Source:Remedial & Special Education. 2026/02, Vol. 47, Issue 1, p25
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2026
  • ISSN:0741-9325
  • DOI:10.1177/07419325241240067
  • Accession Number:190817740
  • Copyright Statement:Copyright of Remedial & Special Education 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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