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Deepening the Study of Disproportionality in Special Education: A Contextual Analysis Within Suburban School Districts.

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Title: Deepening the Study of Disproportionality in Special Education: A Contextual Analysis Within Suburban School Districts.
Authors: Aylward, Alexandra (AUTHOR), Artiles, Alfredo J. (AUTHOR), Voulgarides, Catherine Kramarczuk (AUTHOR), Tefera, Adai (AUTHOR), Alvarado, Sarah L. (AUTHOR), Noguera, Pedro (AUTHOR)
Source: Exceptional Children. Jul2026, Vol. 92 Issue 4, p377-397. 21p.
Subjects: Special education, Segregation, Event history analysis, School districts, Annotations & citations (Law), Racial inequality, United States. Individuals with Disabilities Education Act, Education policy
Abstract: Despite a policy framework aiming to provide equal opportunity and high-quality educational services, racially disparate outcomes persist within education. Under the Individual with Disabilities Education Act, states are mandated to identify and cite districts with "significant disproportionality" in special education. Notwithstanding policy, school districts continue to receive citations for disproportionality. We explored how district-level contextual variables related to the likelihood of a legal citation for racial disproportionality in special education among suburban districts, and how these factors covary with changes in citation status. Building on extant research on racial composition, we used discrete-time event history analysis methodology (EHA) to specifically examine how district-level racial segregation, measured with the dissimilarity index, related to the experience of a citation during the 2004–2005 to 2011–2012 school years. The results indicate that districts with higher Black–White segregation levels were far more likely to be cited. The findings suggest that national data obscure the actual situated, localized patterns of racial disproportionality. [ABSTRACT FROM AUTHOR]
Copyright of Exceptional Children 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.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Despite a policy framework aiming to provide equal opportunity and high-quality educational services, racially disparate outcomes persist within education. Under the Individual with Disabilities Education Act, states are mandated to identify and cite districts with "significant disproportionality" in special education. Notwithstanding policy, school districts continue to receive citations for disproportionality. We explored how district-level contextual variables related to the likelihood of a legal citation for racial disproportionality in special education among suburban districts, and how these factors covary with changes in citation status. Building on extant research on racial composition, we used discrete-time event history analysis methodology (EHA) to specifically examine how district-level racial segregation, measured with the dissimilarity index, related to the experience of a citation during the 2004–2005 to 2011–2012 school years. The results indicate that districts with higher Black–White segregation levels were far more likely to be cited. The findings suggest that national data obscure the actual situated, localized patterns of racial disproportionality. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Exceptional Children 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.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Event history analysis
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              Text: Jul2026
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