A Critical Quantitative Analysis of Disproportionality of Disability Identification

Abstract

In this dissertation, I took a critical approach to examine the longstanding issue of disproportionality in disability identification for racially and ethnically minoritized students in special education. I used Quantitative Critical Race Theory (QuantCrit) to frame my research design and interpretation. I leveraged data from the Florida Department of Education to model disproportionality for Black, Hispanic, and Asian students. Using data from the 2024–2025 school year, I examined school and district-level factors associated with disproportionality, and whether disproportionality is associated with student reading performance. I found that school and district-level factors differentially predicted disproportionality by racial and disability groupings, which further emphasized the importance of disaggregated analysis in research on disproportionality. Furthermore, I identified four unique profiles of schools based on their patterns of over and underidentification for Black students with disabilities and Hispanic students with disabilities. Implications, limitations, and future research directions are also discussed.