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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
Language: English
Authors: Alexandra Aylward (ORCID 0000-0003-1321-6778), Alfredo J. Artiles (ORCID 0000-0001-5772-0787), Catherine Kramarczuk Voulgarides (ORCID 0000-0002-7649-8058), Adai Tefera, Sarah L. Alvarado, Pedro Noguera
Source: Exceptional Children. 2026 92(4):377-397.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 21
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Suburban Schools, Special Education, Equal Education, Educational Legislation, Federal Legislation, Students with Disabilities, Disproportionate Representation, School Segregation, Racial Segregation, School Districts, Compliance (Legal)
Laws, Policies and Program Identifiers: Individuals with Disabilities Education Act
DOI: 10.1177/00144029251408685
ISSN: 0014-4029
2163-5560
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.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1508196
Database: ERIC
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  Value: <anid>AN0194392929;exc01jul.26;2026Jun10.02:32;v2.2.500</anid> <title id="AN0194392929-1">Deepening the Study of Disproportionality in Special Education: A Contextual Analysis Within Suburban School Districts </title> <p>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.</p> <p>Keywords: Disproportionality; longitudinal analysis; context; segregation</p> <p>The Individuals with Disabilities Education Act (IDEA 1997, [<reflink idref="bib54" id="ref1">54</reflink>]) and its recent amendment through Public Law 114–95 within the December 2015 Every Student Succeeds Act was designed to ensure fair and equitable delivery of education to students with disabilities. The IDEA governs how states and public agencies provide early intervention, special education, and related services to more than 8 million eligible children with disabilities, approximately 15% of all public students ([<reflink idref="bib97" id="ref2">97</reflink>]). Under IDEA, schools and districts must meet the needs of children with disabilities and provide them a Free Appropriate Public Education (FAPE) in the least restrictive environment. The IDEA clearly states in Part D of Subchapter IV that the federal government has an ongoing obligation to support activities and strategies that improve the educational results for children with disabilities and contribute to positive results for children with disabilities ([<reflink idref="bib96" id="ref3">96</reflink>]).</p> <p>Furthermore, Section 618(d) of the IDEA requires states to collect and examine data to determine if significant disproportionality based on race and ethnicity is occurring in the state and the local educational agencies (LEAs) of the state with respect to: the identification of children as children with disabilities, including the identification of children as children with disabilities in accordance with a particular impairment; the placement in particular educational settings of such children; and the incidence, duration, and type of disciplinary actions, including suspensions and expulsions ([<reflink idref="bib68" id="ref4">68</reflink>]). Yet, racial disparities in high incidence disabilities (emotional and behavioral disorders, specific learning disability, mild intellectual disability, other health impairment, and disabilities related to speech and language impairment) at various levels of educational systems raise questions about the potential misidentification of these groups ([<reflink idref="bib56" id="ref5">56</reflink>]; [<reflink idref="bib97" id="ref6">97</reflink>]). Despite legal protections provided by IDEA, racial disparities in disability identification have persisted for decades ([<reflink idref="bib2" id="ref7">2</reflink>]; [<reflink idref="bib85" id="ref8">85</reflink>]), illustrating a prominent paradox in the history of special education ([<reflink idref="bib5" id="ref9">5</reflink>]).</p> <hd id="AN0194392929-2">Context as Central to Interpretation of IDEA</hd> <p>Although some nationally representative studies have indicated that students of color are often under-identified for special education services given their potential need for additional supports compared to similar White peers (e.g., [<reflink idref="bib49" id="ref10">49</reflink>]; [<reflink idref="bib62" id="ref11">62</reflink>]), until recently most studies have not captured how particular characteristics of settings interact with local interpretations of IDEA to produce and/or sustain racial disproportionality in special education. Inconsistencies in findings on under- and over-representation suggest local factors, such as interactions of district-wide and school-level contextual factors and policies, student factors, and disproportionality in educational opportunity, may shape patterns of disability identification across disability categories for different groups of students (e.g., [<reflink idref="bib8" id="ref12">8</reflink>]; [<reflink idref="bib7" id="ref13">7</reflink>]. [<reflink idref="bib30" id="ref14">30</reflink>]; [<reflink idref="bib35" id="ref15">35</reflink>], [<reflink idref="bib36" id="ref16">36</reflink>]; [<reflink idref="bib88" id="ref17">88</reflink>]).</p> <p>More recent scholarship indicates that the unique contexts of LEAs cited for disproportionality relate to how IDEA is locally negotiated within district contexts that have repeatedly experienced disproportionality citations ([<reflink idref="bib6" id="ref18">6</reflink>]; [<reflink idref="bib94" id="ref19">94</reflink>]; [<reflink idref="bib101" id="ref20">101</reflink>]). This nascent strand of research points to using a contextualized framing of disproportionality. We aim to contribute to this line of inquiry offering a more nuanced understanding of context and persistent levels of disproportionality in suburban districts.</p> <hd id="AN0194392929-3">IDEA Policy to Address Racial Disproportionality</hd> <p>The 1997 and 2004 reauthorizations of IDEA included three disproportionality indicators that track whether LEAs exceed a numerical threshold in the areas of suspension, identification, and disability category by race. If a "reasonable threshold" is surpassed, it serves as an indication that disproportionality is present, and a citation is issued to an LEA through the State Education Agency (SEA). However, IDEA does not define these thresholds, as the law only requires "states to use a standard methodology for analysis of disproportionality" (U.S. [<reflink idref="bib98" id="ref21">98</reflink>]), leading to substantial variability in the thresholds between SEAs ([<reflink idref="bib3" id="ref22">3</reflink>]; [<reflink idref="bib15" id="ref23">15</reflink>]).</p> <p>Procedurally, when an LEA is found to have disproportionality, it is required by federal law to monitor its compliance with IDEA procedural protections to address the issue through a formal process defined by each respective SEA. Relying upon traditional enrollment and identification rates found in much prior disproportionality research does not actually capture all LEAs which have been identified (or not identified) with racial disproportionality in special education, because SEAs have flexibility in determining thresholds, comparison groups, and minimum cell sizes to calculate risk ratios. As [<reflink idref="bib88" id="ref24">88</reflink>] note, classification rates, and therefore risk ratios, are particularly sensitive to the composition of the reference group (p. 16). Therefore, we used an operationally and conceptually different outcome of racial disproportionality in special education, or a state-determined indicator of disproportionality: a citation. We rely on event history analysis, building on prior work using discrete time hazard models (e.g., [<reflink idref="bib50" id="ref25">50</reflink>]; [<reflink idref="bib63" id="ref26">63</reflink>]). Event history models are advantageous because they more appropriately model duration until an important event, such as receipt of a citation, as a dynamic process (Hibel & Jasper, 2012), thus affording differential rates for different contexts to be appropriately modeled by allowing the use of time-varying covariates. We adapted Lauren Edelman's ([<reflink idref="bib27" id="ref27">27</reflink>], [<reflink idref="bib28" id="ref28">28</reflink>]) work, which centers organizational processes and law as outcomes of interest—in this case a citation—and applied the logic to the disproportionality problem, extending the work of [<reflink idref="bib100" id="ref29">100</reflink>].</p> <p>Consistent with a contextualized framing of and the complex nature of disproportionality, we focus on suburban LEAs cited for racial disproportionality in special education. Specifically, we investigate the role of sociodemographic characteristics, average academic performance, and important contextual characteristics. By including the measure of racial segregation within LEAs, which is on the rise in the United States ([<reflink idref="bib72" id="ref30">72</reflink>]), we enhance our understanding of the situated nature of disproportionality and opportunity gaps.</p> <hd id="AN0194392929-4">Predictors of Racial Disproportionality in Special Education</hd> <p>Scholarship has identified various predictors of disproportionality, which include a wide range of explanations such as the role of professionals' practices and beliefs (e.g., biased views about students and families; [<reflink idref="bib11" id="ref31">11</reflink>]; [<reflink idref="bib18" id="ref32">18</reflink>]; [<reflink idref="bib37" id="ref33">37</reflink>]; [<reflink idref="bib94" id="ref34">94</reflink>]), sociodemographic traits of students ([<reflink idref="bib63" id="ref35">63</reflink>]) and their interaction with school demographics ([<reflink idref="bib35" id="ref36">35</reflink>]; [<reflink idref="bib88" id="ref37">88</reflink>]), sociodemographic changes in LEAs ([<reflink idref="bib6" id="ref38">6</reflink>]), and sociocultural forces ([<reflink idref="bib46" id="ref39">46</reflink>]; [<reflink idref="bib95" id="ref40">95</reflink>]).</p> <hd id="AN0194392929-5">Student Body Sociodemographics</hd> <p>Research has documented relationships between sociodemographic factors and special education identification in distinctive settings ([<reflink idref="bib20" id="ref41">20</reflink>]). Multiple studies have found that as the enrollment of students of color increased, their relative risk of identification decreased ([<reflink idref="bib48" id="ref42">48</reflink>]; [<reflink idref="bib90" id="ref43">90</reflink>]). Gaps in identification rates between White and minoritized students play out differently based on the racial composition of schools, as risk is mediated by overall enrollment demographics ([<reflink idref="bib30" id="ref44">30</reflink>]; [<reflink idref="bib34" id="ref45">34</reflink>], 2019a; [<reflink idref="bib83" id="ref46">83</reflink>]). [<reflink idref="bib82" id="ref47">82</reflink>] found that in contrast to White students, who retained the highest rates of classification regardless of school racial composition, probabilities of classification were higher for minoritized students in schools with fewer Black, Indigenous, and other students of color, indicating racial bias, as risk of disability among minoritized students increased in schools where they are more distinctive (p. 10). Similarly, [<reflink idref="bib83" id="ref48">83</reflink>] found that even accounting for other child- and school-level differences, Black students' predicted probability of identification for special education is lower if they attend a school with a higher proportion of Black students.</p> <p>Varied risk of disability identification across racial groups has also been associated with child poverty rates ([<reflink idref="bib92" id="ref49">92</reflink>]), although findings have been mixed contingent upon how poverty is measured, such as at the district-level or child-level ([<reflink idref="bib20" id="ref50">20</reflink>]). Nonetheless, studies using district-level poverty measures paint a complex landscape. For instance, poverty level was positively <emph>or</emph> negatively associated with overrepresentation patterns depending on student race and disability category ([<reflink idref="bib19" id="ref51">19</reflink>]; [<reflink idref="bib75" id="ref52">75</reflink>]).</p> <p>Additionally, scholars have found that district size (i.e., student enrollment) has a significant moderate positive association with identification for special education, particularly in the categories of intellectual disability ([<reflink idref="bib91" id="ref53">91</reflink>]), learning disability, and speech or language impairment ([<reflink idref="bib86" id="ref54">86</reflink>]). Using event history analysis, [<reflink idref="bib100" id="ref55">100</reflink>]) found that a larger student body increased the likelihood that a LEA would receive a citation for disproportionality in one state.</p> <hd id="AN0194392929-6">Racial Segregation</hd> <p>Disproportionality research focused on sociodemographic factors can be enriched by attention to spatial indices, including racial segregation within districts. Racial segregation between schools has been implicated in racial disproportionality in education (e.g., [<reflink idref="bib29" id="ref56">29</reflink>]). Scholars have theorized that schools engage in practices such as disproportionate disability identification as a legal strategy to segregate students by race within schools ([<reflink idref="bib29" id="ref57">29</reflink>]; [<reflink idref="bib80" id="ref58">80</reflink>]), which is particularly salient when surrounded by fewer same-race peers, what [<reflink idref="bib35" id="ref59">35</reflink>] refers to as racial distinctiveness "Trends in segregated placements mirror[ed] historical redlining practices, suggesting the persistence of racial segregation that is enacted systematically and systemically via special education placements [sic], disability categories, and geography" ([<reflink idref="bib103" id="ref60">103</reflink>], p. 1), indicating a link between racial segregation and disproportionality. [<reflink idref="bib29" id="ref61">29</reflink>] found that between-school segregation measured using the Index of Dissimilarity, court pressure to desegregate, White flight to private schools, and a history of de jure segregation were associated with to the overrepresentation of Black students in some special education programs. However, the effects varied depending on context, or the proportion of Black students enrolled in the LEA. Although Morgan et al. ([<reflink idref="bib64" id="ref62">64</reflink>]) found that Black and Latinx students attending schools in Southern states with histories of de jure and de facto racial segregation were less likely to be identified as having disabilities than otherwise similar White students, the authors did not include an actual measure of segregation and are therefore limited in examining how <emph>current</emph> levels of racial segregation between schools within districts relates to varied special education identification rates.</p> <p>In the United States, residential segregation has become more prominent in suburban areas, as people of color move into older suburban communities ([<reflink idref="bib23" id="ref63">23</reflink>]), and is associated with negative social, economic, educational, and civic consequences that ultimately hinder educational opportunity ([<reflink idref="bib41" id="ref64">41</reflink>]). Education policies have been advanced for generations to remedy racial segregation and, although progress was made, recent evidence suggests an erosion of past gains ([<reflink idref="bib72" id="ref65">72</reflink>]). The question of how segregation in suburban communicates relates to persistent racial disproportionality is important, especially since the effect of between-school segregation on overrepresentation is stronger in more diverse districts ([<reflink idref="bib29" id="ref66">29</reflink>]). The state where this study was conducted is one of the most segregated in the nation in terms of exposure of Black students to their White counterparts, particularly in suburban schools ([<reflink idref="bib70" id="ref67">70</reflink>]). We examine one type of segregation—unevenness or racial imbalance ([<reflink idref="bib61" id="ref68">61</reflink>])—to assess how it might relate to disproportionality.</p> <hd id="AN0194392929-7">Academic Performance</hd> <p>There are many possible pathways between academic achievement in a district and disproportionality, such as the relation between academic achievement and risk of identification, as well as opportunity gaps reflected in test scores that might relate to a federal citation for racial disproportionality in special education. Differences in group performance on academic achievement measures have contributed to disproportionate representation of minoritized groups and were differentially predictive for different categories of special education ([<reflink idref="bib8" id="ref69">8</reflink>]; [<reflink idref="bib53" id="ref70">53</reflink>]). In their analysis of 2000 school districts nationwide, [<reflink idref="bib31" id="ref71">31</reflink>] found that racial disproportionality in special education was strongly related to districts' achievement gaps in both ELA and Math, where larger achievement gaps between Black and White students and Latinx and White students were associated with larger risk ratios between these groups ([<reflink idref="bib31" id="ref72">31</reflink>]). Yet, these risk ratios may not fully capture which districts actually receive a citation for racial disproportionality, as states are given latitude to determine who is in the reference group (which is not always White); the threshold that determines a citation; and minimum cell sizes.</p> <p>Additionally, systemic practices within districts and schools centered around academic achievement come to bear on the educational chances of poor and minoritized students to either expand or constrain their likelihood of achieving competitive educational outcomes. In a situated case study within Wisconsin, the Educational Services Leadership Team of an urban district conveyed that disproportionality had well-established roots in the classroom and was inextricably linked to widening achievement gaps ([<reflink idref="bib8" id="ref73">8</reflink>], p. 11). [<reflink idref="bib33" id="ref74">33</reflink>] documented that greater emphasis on high-stakes testing increased the likelihood that low-performing students and students from low socioeconomic backgrounds would be identified for special education. Relatedly, [<reflink idref="bib46" id="ref75">46</reflink>] found that wealthier communities did exert more pressure on LEAs to redirect struggling students towards pathways leading to special education classification. This may reflect pressure on district leaders to demonstrate greater proficiency levels which affects access to educational opportunity for certain student groups within a school.</p> <hd id="AN0194392929-8">Correlates of Student Engagement: Attendance and Dropout</hd> <p>Student engagement is directly tied to attendance, academic performance, and high school completion rates ([<reflink idref="bib57" id="ref76">57</reflink>], [<reflink idref="bib93" id="ref77">93</reflink>]). It is evident that low attendance and misbehavior and course failures are high-yield predictors of students falling off the graduation track ([<reflink idref="bib9" id="ref78">9</reflink>]). [<reflink idref="bib67" id="ref79">67</reflink>] found that that higher levels of school engagement specifically reduced the risk of dropout and low postsecondary attainment among Latinx students. Although students with disabilities experience higher dropout rates than their general education peers, few studies have examined the link between engagement, dropout, and disproportionality ([<reflink idref="bib52" id="ref80">52</reflink>]). One school leadership team in an urban school district in the Midwest identified that, through improved instructional practices and student engagement via the implementation of culturally responsive pedagogies and interventions, disproportionality would be reduced ([<reflink idref="bib8" id="ref81">8</reflink>]). We include measures of attendance and dropout rates to understand their relationship with a citation for racial disproportionality in special education.</p> <hd id="AN0194392929-9">Teacher Characteristics</hd> <p>Teacher quality is one of most powerful school-related influences on a child's academic performance ([<reflink idref="bib16" id="ref82">16</reflink>]; [<reflink idref="bib32" id="ref83">32</reflink>]; [<reflink idref="bib69" id="ref84">69</reflink>]). Research indicates that both student performance and social and emotional development benefit from teacher–student racial matches, especially for Black students ([<reflink idref="bib14" id="ref85">14</reflink>]; [<reflink idref="bib42" id="ref86">42</reflink>]). Although a few studies have not found that teacher race moderates racial disproportionality overall ([<reflink idref="bib17" id="ref87">17</reflink>]; [<reflink idref="bib49" id="ref88">49</reflink>]), teacher–student racial match has been identified as an important contextual factor in the identification of Black students for special education, where a higher proportion of Black teachers was associated with lower rates of identification with disabilities for Black children ([<reflink idref="bib65" id="ref89">65</reflink>]; Stiefel at al., 2024), particularly for Black boys in subjective disability categories ([<reflink idref="bib47" id="ref90">47</reflink>]). However, few studies have examined how other workforce characteristics are linked to disproportionality, specifically persistent citations for significant disproportionality in special education. This is a gap we address by including aggregate measures of educational attainment and attrition.</p> <hd id="AN0194392929-10">Disproportionality in Special Education in Suburban Communities</hd> <p>Suburbs are now often more racially, ethnically, and economically diverse than their nearest metropolitan cities ([<reflink idref="bib60" id="ref91">60</reflink>]), and, as a result, they are important areas to study the reproduction of racial inequality in schools and expand the ways we understand the intersections of race, place/space, and inequality ([<reflink idref="bib22" id="ref92">22</reflink>]). "Understanding whether the achievement gap, suspension gap, and other documented disparities vary by suburban versus urban or by suburban type helps map the terrain of inequality" ([<reflink idref="bib60" id="ref93">60</reflink>], p. 150). For this purpose, we focused on suburban districts where the respective SEA documented and cited disproportionality.</p> <p>Recent studies on suburban contexts shed light on a complex constellation of school and social factors that relate to and perpetuate racial disproportionality in special education ([<reflink idref="bib1" id="ref94">1</reflink>]; [<reflink idref="bib99" id="ref95">99</reflink>]). Research also shows that suburban LEAs located near urban centers consistently struggle to abate racial disproportionality in classifications, placements, and discipline suspensions and often entered, exited, and re-entered a citation since the 2004 reauthorization of IDEA in this state ([<reflink idref="bib100" id="ref96">100</reflink>]). Additionally, the recent sociodemographic changes of suburban schools and communities (e.g., [<reflink idref="bib39" id="ref97">39</reflink>]; [<reflink idref="bib40" id="ref98">40</reflink>]), and the historical legacies of segregation ([<reflink idref="bib23" id="ref99">23</reflink>]; [<reflink idref="bib38" id="ref100">38</reflink>]; [<reflink idref="bib103" id="ref101">103</reflink>]), make suburbs a rich space to investigate pressing educational equity issues ([<reflink idref="bib22" id="ref102">22</reflink>]; [<reflink idref="bib94" id="ref103">94</reflink>]; [<reflink idref="bib101" id="ref104">101</reflink>]). Suburban spaces are often not adequately prepared to meet the needs of an increasingly diverse student population ([<reflink idref="bib89" id="ref105">89</reflink>]) and focus on technical shifts in curriculum and instruction, instead of challenging school cultures characterized by deficit beliefs of students of color that often stifle meaningful shifts in practice ([<reflink idref="bib51" id="ref106">51</reflink>]).</p> <p>Therefore, in this study, we ask: How are district-level contextual factors associated with a federal citation for racial disproportionality in special education in suburban LEAs? Specifically, how does racial segregation between schools in suburban LEAs increase the likelihood of racial disproportionality? We hypothesize that segregation, an inequitable organizational process, may be a central factor related to persistent racial disproportionality.</p> <hd id="AN0194392929-11">Methods</hd> <p></p> <hd id="AN0194392929-12">Sample</hd> <p>The analysis is based on a longitudinal dataset of 215 public suburban districts (LEAs) in a Northern state for the 2004–2005 through 2011–2012 school years. We focus on LEAs labeled as suburban, or a territory outside a principal city and inside an urbanized area—as designated by the National Center for Education Statistics (NCES) Common Core Data ([<reflink idref="bib66" id="ref107">66</reflink>]).</p> <hd id="AN0194392929-13">District-Level Data</hd> <p>This analysis used district-level data on enrollment (log transformed to address skewness), racial demographics (percent of study body who identified as students of color), English Language Learner enrollment (percent), academic outcomes (e.g., math assessment results), attendance rates, dropout rates, and teacher attrition and educational level. The data are publicly available from the state education department from several distinct datasets, including the yearly State Report Cards and District Report Cards. Missing data among the LEA-level variables from these administrative datasets were not an issue.</p> <p>Teacher turnover is measured as the count of teachers in the prior school year who did not return to a teaching position in the district in the current school year, expressed as a percentage. Teacher educational level is operationalized as the percent of teachers with a master's degree or higher. District-level poverty estimates were obtained from the Small Area Income and Poverty Estimates (SAIPE) dataset, made available through the U.S. Census Bureau. To merge the SAIPE data with state report cards, we used the Common Core of Data.</p> <p>We also included a measure of district racial segregation, using school-level data on enrollment, also obtained from the State Report Cards, to calculate the Dissimilarity Index, which corresponds to the extent to which two groups are evenly distributed across schools within an LEA ([<reflink idref="bib26" id="ref108">26</reflink>]; [<reflink idref="bib79" id="ref109">79</reflink>]). This index can be interpreted as the proportion of minoritized students that would have to change schools to be evenly distributed across the LEA, where zero is no segregation and one is complete segregation. Although there are various measures of segregation, the Dissimilarity Index is an appropriate measure as segregation operates through exposing students to different school environments ([<reflink idref="bib79" id="ref110">79</reflink>]).</p> <hd id="AN0194392929-14">Outcome Variable</hd> <p>The SEA provided the authors' outcome data on citation status for each State Performance Plan <bold>(</bold>SPP) Indicator that are focused on racial disparities in special education outcomes—Indicators 4, 9, and 10 for all district-year observations for restricted use. IDEA section 618(d) requires states to collect and examine data to determine if significant disproportionality based on race and ethnicity is occurring in the state and in LEAs of the state. If a LEA is found to be racially disproportionate under state definitions of either Indicator 4, 9, or 10, it will receive a citation for racial disproportionality from their SEA. <emph>Indicator 9</emph> refers to the percentage of districts with disproportionate representation of racial and ethnic groups in special education and related services, due to inappropriate identification (20 U.S.C. 1416(a)(<reflink idref="bib3" id="ref111">3</reflink>)(C)). <emph>Indicator 10</emph> refers to disproportionality in specific disability categories due to inappropriate identification. <emph>Indicator 4A</emph> refers to significant discrepancy in the rate of suspensions and expulsions of greater than 10 days in a school year for children with Individualized Education Programs (IEPs), and <emph>Indicator 4B</emph> refers to significant discrepancy by race or ethnicity in the rate of suspensions and expulsions of greater than 10 days in a school year for children and noncompliance with IDEA procedural safeguards. These data were submitted yearly to the state education department by local LEA officials, and school superintendents were provided with an opportunity to review and correct summary reports based upon these data by the reporting deadline.</p> <p>The event of interest is the transition onto a first citation, a dichotomous indicator of any SPP citation or no citation in a given year. The time variable is duration (in years), and the analysis begins with the 2004–2005 school year during the reauthorization of IDEA, and ends in 2011–2012. Although 122 of the 215 suburban LEAs were never cited, among the 93 cited at least once during the study period, 18% had been on this status for more than 3 years.</p> <hd id="AN0194392929-15">Data Analysis Procedures</hd> <p>The paper uses discrete event history analysis methodology (EHA) to measure whether a suburban LEA experienced the event of a transition onto a citation during the study period of the 2004–2005 to 2011–2012 school years. In classical event history analysis (also called duration analysis or survival analysis), we examine the timing of a particular event, such as time to first marriage, by modeling the hazard or risk of the event of interest over time ([<reflink idref="bib81" id="ref112">81</reflink>]). Event history data also make it possible to gain an understanding of how theoretically important contextual variables relate to the event times: the factors that are associated with a greater probability of being cited for significant disproportionality during the study period in ways that a standard logit model with one record (or observation) per participant cannot address. How you model time to an event depends on whether or not the underlying process is discrete or continuous in the world. For this analysis, the discrete-time approach is more conceptually and technically appropriate, as a citation could occur sometime during discrete chunks of time —that is, the school year. The interpretation of a discrete hazard is a conditional probability, or the probability of the event of interest occurring in the next time period, given that it has not yet occurred ([<reflink idref="bib81" id="ref113">81</reflink>]).</p> <p>School years, the time unit we adopt, is a natural way to mark time in an educational setting. Moreover, much of our information on timing of citation is quite coarse. Citations can occur at any point during the school year, but we only observe citation in its grouped form—the school year. Therefore, the maximum likelihood estimation of the discrete time complementary log-log link model is most appropriate ([<reflink idref="bib21" id="ref114">21</reflink>]; [<reflink idref="bib84" id="ref115">84</reflink>]). More specifically, because survival times of citation incidence in this state were observed as the 1-year interval within which the incidence occurred, we used the complementary log–log model for this event history analysis. An advantage of the log–log link is that exponentiated coefficients from covariates in the model are multiplicative in the hazard, yielding a discrete model that mirrors the continuous-time Cox proportional hazards model. The complementary log-log transformation has a direct interpretation in terms of hazard ratios, and thus has practical applications in terms of hazard models. The complementary log-log link model for discrete-time EHA is provided in Equation 1. Equation 1: log(−log(1 – <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>λ</mi></math> </ephtml> (tj|<bold><emph>x</emph></bold>i))) = <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>α</mi></math> </ephtml><subs>j</subs> + <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup><mi>β</mi><mrow><mi mathvariant="normal">′</mi></mrow></msup></math> </ephtml> x<subs>i</subs> It assumes that the hazard <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>λ</mi></math> </ephtml> (<emph>t<subs>j</subs></emph>|<emph>x<subs>i</subs></emph>) for the outcome of a citation in the <emph>i</emph>th district (i = 1,..., n) in the <emph>j</emph>th time <emph>t</emph> interval (j = 1, ... J) with covariate vector x<subs>i</subs> and β was a parameter vector. More simply, it is the rate at which citations for any SPP 4a, 4b, 9, or 10 Indicator occur at that point in time, or the rate at which risk is accumulated. The model leads to the survival function adjusted by covariates at the <emph>j</emph>th time interval of</p> <p>Equation 2: <emph>S</emph>j = [<emph>S<subs>0j</subs></emph>]<sups>exp(</sups><ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup><mi>β</mi><mrow><mi mathvariant="normal">′</mi></mrow></msup></math> </ephtml><sups>x)</sups></p> <p>We used a non-parametric approach for the functional form for the baseline hazard function by including dummies for each period (year) defining new time-varying covariates which are functions of survival time <emph>t</emph> per district. The estimated coefficients on the duration interval dummies tell us about the shape of the estimated baseline hazard, or the probability that the LEA is cited in the next year, given that it survived (was not cited) to time <emph>t</emph>, obtained using the inverse cloglog transformation. The estimated coefficients on the duration dummy variables rise in magnitude as survival time increases, broadly speaking, which suggests that the hazard rises over time, but nonmonotonically. The baseline includes only the covariate of time, and as one may expect, the baseline hazard function increased over time and tend to 1.</p> <p>An event history analysis also allows for time varying and constant explanatory variables to be included in the model to determine the relative impact of contextual characteristics and thus provide explanations for the question of why some LEAs experience the event of citation while others do not. Based on this study's framework, we selected covariates from the research literature. The final model is provided in Equation 3: log(−log(1 – <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>λ</mi></math> </ephtml> (tj|<bold><emph>x</emph></bold>i))) = <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>α</mi></math> </ephtml><subs>j</subs> + <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup><mi>β</mi><mrow><mi mathvariant="normal">′</mi></mrow></msup></math> </ephtml> x<subs>i</subs> where x<subs>i</subs> was a vector of covariates that included Percent of enrolled students identifying as students of color; Child poverty rate; Total enrollment (log); Percent of students scoring Level 3–4 in Math 8 assessment; Percent of teachers with master's degree or higher; a categorical variable of Black/White racial segregation level (Low Segregation = reference group, Moderate Segregation, High Segregation); Attendance Rate, Dropout Rate; and Teacher Turnover Rate and <bold>β</bold> is a vector of parameters to be estimated and represents the effect of the covariates on the baseline hazard, which included dummies for each year.</p> <p>Additionally, in line with prior research, we also tested if there were significant interactions between (<reflink idref="bib1" id="ref116">1</reflink>) district-level poverty and Math 8 performance, as average poverty rates are consistently the single most powerful correlate of achievement gaps ([<reflink idref="bib78" id="ref117">78</reflink>]); (<reflink idref="bib2" id="ref118">2</reflink>) district-level poverty and proportion of students of color enrolled, as schools with concentrated poverty have a larger impact on Black, Indigenous, and Latinx students than on their White and Asian counterparts ([<reflink idref="bib71" id="ref119">71</reflink>]); and (<reflink idref="bib3" id="ref120">3</reflink>) the proportion of students of color enrolled and Math 8 performance, as racial segregation is linked to academic opportunity gaps in which segregation leads to differential exposure for Black, Indigenous, Latinx, and White students to opportunity ([<reflink idref="bib78" id="ref121">78</reflink>]). Despite the significance of each main effect, no interaction was significant, and thus they were not included in the final model.</p> <p>The descriptive statistics in Table 1 of the included variables provide evidence that there are notable differences between suburban LEAs that received one or more citations at some point and those that were never cited during the study period. Specifically, ever cited LEAs had higher enrollment rates of students of color and greater child poverty rates. Additionally, they had lower academic performance, operationalized as percent of students scoring at or above proficient on the Math 8 assessment and higher drop-out rates. Although test scores are an inadequate measure of academic ability, it can indicate differential access to higher-level math courses, which has implications for college access and future wages ([<reflink idref="bib10" id="ref122">10</reflink>]). Finally, LEAs receiving citations also had a higher percentage of teachers with master's degrees or higher.</p> <p>Table 1. Aggregate Descriptive Statistics of Cited Compared to Never-Cited Suburban Districts.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="left" /><col align="left" /><col align="left" /></colgroup><thead><tr><th align="left" /><th align="left"><bold>Suburban Districts</bold>215 (100%)</th><th align="left"><bold>Suburban Districts Receiving Citation</bold>93 (43.26%)</th><th align="left"><bold>Suburban Districts not Receiving Citation</bold>122 (56.74%)</th></tr></thead><tbody><tr><td><italic>Covariate</italic></td><td>Mean (<italic>SD</italic>)</td><td>Mean (<italic>SD</italic>)</td><td>Mean (<italic>SD</italic>)</td></tr><tr><td>Percent Students of Color</td><td>25.12 (24.41)</td><td>38.53 (29.34)**</td><td>15.06 (13.03)</td></tr><tr><td>Child Poverty Rate</td><td>7.75 (5.38)</td><td>9.59 (6.10)**</td><td>6.38 (4.28)</td></tr><tr><td>Total Enrollment (log)</td><td>8.18 (0.66)</td><td>8.28 (0.65)**</td><td>8.11 (0.65)</td></tr><tr><td>ELL Rate</td><td>3.69 (5.30)</td><td>6.07 (6.82)**</td><td>1.91 (2.54)</td></tr><tr><td>Percent Scoring Level 3–4 on Math 8</td><td>75.01 (16.83)</td><td>70.14 (19.23)**</td><td>78.60 (13.76)</td></tr><tr><td>Dissimilarity Index Black/White</td><td /><td /><td /></tr><tr><td> Low (D <=.30)</td><td>90.29%</td><td>89.25%**</td><td>91.09%</td></tr><tr><td> Moderate (D >.30 & D <.60)</td><td>7.62%</td><td>6.99%**</td><td>8.09%</td></tr><tr><td> High (D >=.60)</td><td>2.09%</td><td>3.76%**</td><td>0.82%</td></tr><tr><td>Dissimilarity Index Latinx/White</td><td /><td /><td /></tr><tr><td> Low (D <=.30)</td><td>92.73%</td><td>91.40%</td><td>93.75%</td></tr><tr><td> Moderate (D >.30 & D <.60)</td><td>5.17%</td><td>6.18%</td><td>4.41%</td></tr><tr><td> High (D >=.60)</td><td>2.09%</td><td>2.42%</td><td>1.84%</td></tr><tr><td>Attendance Rate</td><td>95.22 (1.40)</td><td>94.74 (1.71)**</td><td>95.57 (0.98)</td></tr><tr><td>Dropout Rate</td><td>1.31 (1.68)</td><td>1.79 (2.23)**</td><td>0.95 (0.97)</td></tr><tr><td>Teacher Turnover Rate</td><td>12.26 (4.09)</td><td>12.51 (4.11)*</td><td>12.07 (4.08)</td></tr><tr><td>Percent of Teachers with Master's Degree or Higher</td><td>38.44 (21.91)</td><td>41.22 (21.59)**</td><td>36.35 (21.93)</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note:</emph> For continuous variables, an independent group <emph>t</emph> test allowing for unequal variances, using Satterthwaite's degrees of freedom was used to test whether the difference in means was statistically significantly different from 0 between the group of suburban districts receiving citations during the study period and the group of suburban districts never receiving a citation during the study period for all predictors. For categorical predictors (e.g., segregation index D), we used a Pearson's chi-square test for independence. EEL = English Language Learner.</p> <p>2 * <emph>p</emph> <.05; **<emph>p</emph> <.01.</p> <p>As stated earlier, the state where this study was conducted is one of the most segregated in the nation in terms of exposure of Black students to their White counterparts, particularly in suburban schools ([<reflink idref="bib70" id="ref123">70</reflink>]). We examine one type of segregation, unevenness, or racial imbalance ([<reflink idref="bib61" id="ref124">61</reflink>]) to assess how it might relate to disproportionality. Unevenness refers to "the extent to which different racial/ethnic groups are evenly distributed across schools" ([<reflink idref="bib89" id="ref125">89</reflink>], p. 4), independent of changes in racial composition. Unevenness or racial imbalance is a consequential measure of segregation, particularly if it mediates contextual influences such as access to key educational resources (e.g., quality of services or learning opportunities). We use a categorical variable to assess racial imbalances, based on the commonly used Dissimilarity Index between Black and White students and Latinx and White students (D). The value of D represents the proportion of a group that would need to move to create a uniform distribution of population, where 0 indicates when the proportion of each group in schools is the same as the proportion in the LEA population as a whole. In our analysis, the index has three levels of low to mid to high segregation, based on prior research ([<reflink idref="bib61" id="ref126">61</reflink>]) where low segregation—the reference group—is equivalent to D ≤.3, moderate levels of segregation where D =.3 –.6, and high levels of segregation which includes LEAs where D ≥.6. Figure 1 presents the discrete hazard function for highest and lowest levels of D between Black and White students and provides evidence that the most segregated districts had higher probabilities of receiving citations under IDEA for disproportionality in special education, compared to the least segregated districts. We also considered the Dissimilarity Index between Latinx and White, but it was not a significant covariate, probably due to the lack of variation in levels of segregation between cited and non-cited suburban LEAs in this state.</p> <p>Graph: Figure 1. Probability of Citation for Low Versus Highly Segregated School Districts.</p> <p>Finally, we also include contextual variables beyond sociodemographics and achievement. We examine how teacher attrition, student attendance, and dropout rates related to a citation for disproportionality in special education.</p> <p>As these variables included in the final model can be related, we investigated whether multicollinearity was a concern. Although there were some moderate correlations (Table 2), no independent variables included in our final model were highly correlated. Additionally, no variance inflation factor (VIF) of each covariate was greater than 2.1.</p> <p>Table 2. Pairwise Correlation Matrix of Included Covariates.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Variables</th><th align="left">% Students of Color</th><th align="left">Child Poverty Rate</th><th align="left">Total Enrollment</th><th align="left">% Scoring Level 3–4 in Math 8</th><th align="left">Attendance Rate</th><th align="left">Dropout Rate</th><th align="left">Teacher Turnover Rate</th><th align="left">% Teachers With Master's or Higher</th><th align="left">Black/White Dissimilarity Index</th></tr></thead><tbody><tr><td>% Students of Color</td><td>1.000</td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Child Poverty Rate</td><td>0.411</td><td>1.000</td><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Total Enrollment</td><td>0.222</td><td>0.041</td><td>1.000</td><td /><td /><td /><td /><td /><td /></tr><tr><td>% scoring Level 3-4 in Math 8</td><td>−0.501</td><td>−0.647</td><td>−0.144</td><td>1.000</td><td /><td /><td /><td /><td /></tr><tr><td>Attendance Rate</td><td>−0.400</td><td>−0.558</td><td>−0.126</td><td>0.488</td><td>1.000</td><td /><td /><td /><td /></tr><tr><td>Dropout Rate</td><td>0.399</td><td>0.563</td><td>0.148</td><td>−0.532</td><td>−0.436</td><td>1.000</td><td /><td /><td /></tr><tr><td>Teacher Turnover Rate</td><td>0.147</td><td>0.010</td><td>−0.010</td><td>−0.072</td><td>−0.047</td><td>0.022</td><td>1.000</td><td /><td /></tr><tr><td>% Teachers with Master's or Higher</td><td>0.324</td><td>−0.300</td><td>0.101</td><td>0.123</td><td>0.090</td><td>−0.167</td><td>0.124</td><td>1.000</td><td /></tr><tr><td>Black/White Dissimilarity Index</td><td>0.082</td><td>−0.030</td><td>0.133</td><td>−0.045</td><td>−0.063</td><td>0.082</td><td>−0.008</td><td>0.073</td><td>1.000</td></tr></tbody></table> </ephtml> </p> <p>We report the hazard ratio (HR) for each variable calculated as exp (β), with 95% confidence intervals (CI). The parameters were estimated by maximum likelihood estimation with robust variance estimates, using Stata (Version 18).</p> <hd id="AN0194392929-16">Results</hd> <p>Of the 215 suburban LEAs in the study, over 50% (<emph>n</emph> = 122) had not transitioned onto a citation during the duration of the analysis. However, nearly half (<emph>n</emph> = 93) of the suburban LEAs from the state did transition onto their first citation sometime within the 8 years of the study. The most common sequence pattern was just one citation (<emph>n</emph> = 37). In contrast, 39 LEAs were cited for 3 or more years within the time period and often transitioned on and off a citation.</p> <p>The state under investigation used a relative risk ratio of 2.0 as the threshold for detecting disproportionality. Throughout the study period, suburban LEAs were most frequently cited for Indicator 10 and Indicator 4. Specifically, there were 114 citations under SPP Indicator 10, or disproportionality in specific disability categories that is the result of inappropriate identification; and 51 citations under Indicator 9, or disproportionality in special education and related services as a result of inappropriate identification. In addition, there were 43 citations under SPP Indicator 4A, indicating a significant discrepancy in the rate of suspensions and expulsions of greater than 10 days in a year for children with IEPs, and 60 citations under SPP Indicator 4B, indicating a significant discrepancy by race or ethnicity in the rate of suspensions and expulsions of greater than 10 days in a school year for children and noncompliance with IDEA procedural safeguards.</p> <p>To examine how district sociodemographic factors (e.g., childhood poverty rates, student racial demographics, LEA size) were associated with a citation for racial disproportionality in special education, the model estimates, which can be interpreted as hazard ratios, are provided in Table 3. Looking at the hazard ratios, the model which relied upon robust estimates of variance indicates that as the percent of students of color enrollment increased by one unit, and all other variables were held constant, the risk of citation increased by 2.4%. In other words, as enrollment of students of color increased, so did the likelihood of a citation. Similarly, as child poverty rate increased by one unit, and all other variables are held constant, the risk of suburban LEA citation increased by 7.9%. Moreover, holding all other variables constant, as student enrollment increased by one unit, the risk of citation increased by 33.6%. In other words, suburban LEAs with more students of color, a higher percentage of students living in poverty, and larger suburban LEAs were more likely to receive a citation for racial disproportionality.</p> <p>Table 3. Estimated Exponentiated Covariate Effects (Hazard Ratios) From Discrete Survival Model for Transition Onto Citation Among Suburban Districts.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Predictor</th><th align="left">Hazard Ratio</th><th align="left">Robust <italic>SE</italic></th><th align="left">95% Confidence Interval</th></tr></thead><tbody><tr><td>Student of Color Enrollment</td><td>1.024**</td><td>0.002</td><td>(1.020, 1.029)</td></tr><tr><td>Child Poverty</td><td>1.079**</td><td>0.015</td><td>(1.051, 1.108)</td></tr><tr><td>Total Enrollment (log)</td><td>1.336**</td><td>0.099</td><td>(1.154, 1.546)</td></tr><tr><td>Math 8, Level 3–4</td><td>1.028**</td><td>0.005</td><td>(1.017, 1.038)</td></tr><tr><td>Racial Segregation Level (Black/White)</td><td /><td /><td /></tr><tr><td><list list-type="Bullet"><list-item><p>Low Segregation</p></list-item></list></td><td>(ref.)</td><td /><td /></tr><tr><td><list list-type="Bullet"><list-item><p>Moderate Segregation</p></list-item></list></td><td>0.908</td><td>0.157</td><td>(0.646, 1.162)</td></tr><tr><td><list list-type="Bullet"><list-item><p>High Segregation</p></list-item></list></td><td>7.055**</td><td>2.226</td><td>(3.802, 13.093)</td></tr><tr><td>Attendance</td><td>0.923</td><td>0.038</td><td>(0.851, 1.001)</td></tr><tr><td>Dropout</td><td>1.091*</td><td>0.043</td><td>(1.011, 1.178)</td></tr><tr><td>Teacher Turnover</td><td>1.004</td><td>0.012</td><td>(0.980, 1.028)</td></tr><tr><td>Teachers with Master's or Higher</td><td>1.005</td><td>0.003</td><td>(0.999, 1.011)</td></tr></tbody></table> </ephtml> </p> <p>3 * <emph>p</emph> <.05; ** <emph>p</emph> <.01.</p> <p>We found that highly segregated LEAs, where the Black-White Index of Dissimilarity >=.60, were significantly more likely to receive a citation than LEAs with comparatively low segregation (See Figure 1). When holding all other predictors constant, the risk of citation in highly segregated LEAs was more than seven times (7.055) the risk in LEAs with low levels of Black–White segregation.</p> <p>Additionally, when controlling for all else, we found that as the percent of general education children scoring at or above proficient on the Math 8 assessment increased by one unit, the risk of citation increased by 2.8%. That is, higher performing districts were somewhat more likely to receive a citation for significant disproportionality (Table 3). Furthermore, holding all else constant, LEAs with higher levels of student dropout rates had an increased risk of citation. When the dropout rate increased by one unit, the risk of citation increased by 9.1%.</p> <p>Our analysis did not detect significant relationships between the annual rate of teacher turnover, teachers' educational level, and student attendance and risk of citation in suburban LEAs. These findings are likely due to the lack of variation between cited and non-cited LEAs in mean attendance rates, 94.74% or 95.57%, respectively, and similar average rates of teacher turnover in cited and non-cited LEAs, 12.51% and 12.07%, respectively.</p> <p>Finally, to ensure that LEAs with lower enrollments of children of color overall and within special education were not driving our results, we conducted a sensitivity analysis restricting the sample to districts that met the SEA's criteria for calculating racial disproportionality in special education (available in the online Supplemental Materials). The criteria for notification for SPP Indicators 9 and 10, based on relative risk ratios, require the following: at least 10 students with disabilities of a particular race and ethnicity enrolled in the district; at least 10 students with disabilities of all other races and ethnicities enrolled; at least 30 students of a particular race and ethnicity enrolled in the district; and at least 30 students of all other races and ethnicities enrolled. The risk ratios are a comparison of the risk of each race and ethnicity category identified for special education services compared to the risk of all other race and ethnicity categories identified for special education services (not just White students as the referent). LEAs were included in a given year in the sensitivity analysis if any minoritized racial group met the criteria, and, as a result, we lost 12.6% of the entire study sample.</p> <p>There were only minor differences in results between the two samples. First, the magnitude of the effect of enrollment decreased slightly, attendance rate became a statistically significant covariate, and the effect of segregation increased in magnitude. Holding all other variables constant, as student enrollment increased by one unit, the risk of citation increased by 17.2% instead of 33.6%. When controlling for all else, we find that as attendance rate increased by one unit, the risk of citation decreased by 8.1%. When holding all other predictors constant, the risk of citation in highly segregated LEAs is more than twenty times (20.041) the rate in LEAs with low levels of Black–White segregation.</p> <hd id="AN0194392929-17">Discussion</hd> <p>The purpose of this study was to examine how district-level contextual factors—specifically racial segregation between schools—were associated with a federal citation for racial disproportionality in special education in suburban LEAs. Based on previous research, we assume that disproportionality's magnitude and contours shift depending on the contexts in which the problem is located; in other words, we assume that disproportionality must be examined from a situated perspective. Our findings confirm the necessity of accounting for contextual conditions to understand the persistence of this problem, as the likelihood of a disproportionality citation is shaped by the interplay of contextual factors, particularly racial segregation. As such, our findings move beyond the oversimplifications of binary questions, such as whether disproportionality exists or whether the problem is over- or under-representation.</p> <hd id="AN0194392929-18">Sociodemographics and Racial Disproportionality in Special Education</hd> <p>In our study, the representation of students of color was related to the probability of disproportionality citations in suburban LEAs. Similar to what [<reflink idref="bib35" id="ref127">35</reflink>] described as racial distinctiveness, it is possible that disability identification might be influenced by a peer-reference logic whereby students of color stand out in spaces that have been historically populated by White students, and their disparate achievement levels might create conditions for higher referral rates for minoritized learners.</p> <p>Consistent with previous research using a district-based measure ([<reflink idref="bib20" id="ref128">20</reflink>]), we confirmed that poverty predicts the probability of disproportionality citations. This is an intriguing pattern given the mixed results reported in the past, which seem to be mediated by the way poverty is operationalized in the studies. For example, aggregate metrics (LEA-level) render overrepresentation patterns in wealthy settings while the disaggregated student-level measure of eligibility for free-reduced lunch produce mixed findings ([<reflink idref="bib20" id="ref129">20</reflink>]). Future studies need to examine this association using refined measures of poverty (NASEM, 2023a, 2023b) and a situated unit of analysis to document the circumstances that may shape the relationship between poverty and citation risk in distinct LEA settings. Moreover, future research using a dataset with disability categories would improve scholars' ability to map what determines classification rates.</p> <p>Our results also show that LEA size predicted the probability of suburban LEA citations. Larger LEAs had a higher probability to be cited for disproportionality. Previous studies have established this association ([<reflink idref="bib100" id="ref130">100</reflink>]) and the available evidence indicates that LEA size impacts funding, student resource allocation, school management systems, and LEA and school culture, which, in turn, are associated with student performance ([<reflink idref="bib12" id="ref131">12</reflink>]; [<reflink idref="bib87" id="ref132">87</reflink>]). We need additional research on the specific forces that influence citation risk in LEAs with different sizes and across types of locations.</p> <hd id="AN0194392929-19">Racial Segregation and Racial Disproportionality in Special Education</hd> <p>Racial segregation constitutes a major barrier to structural opportunities and is increasing in the U.S. educational system despite robust evidence about the significant long-term positive impact of desegregation efforts (e.g., positive outcomes in the job market and strong association with better health; [<reflink idref="bib72" id="ref133">72</reflink>]). This is unsettling because "school desegregation trends are key barometers of race relations in the U.S. not only because of the schools' historical role in race relations and American law, but also because it's now clear that the impacts are far reaching" ([<reflink idref="bib72" id="ref134">72</reflink>], p. 9). Segregation begets inequities. However, researchers have not systematically examined the role of racial segregation in disproportionality, although the available evidence makes it clear that racial segregation relates to the disproportionate representation of minoritized students in special education ([<reflink idref="bib29" id="ref135">29</reflink>]; [<reflink idref="bib30" id="ref136">30</reflink>]).</p> <p>We found that districts with higher Black–White segregation levels were far more likely to be cited, which attests to the importance of examining the geographic configurations and practices that act as "mechanism[s] through which opportunities and resources can be withheld <emph>for</emph> some children and withheld <emph>from</emph> other children" ([<reflink idref="bib43" id="ref137">43</reflink>], p. 782). Student enrollment demographics—commonly used in studies on racial disproportionality—do not capture the sorting of students within school districts that has clear implications for inequitable resource allocation ([<reflink idref="bib76" id="ref138">76</reflink>]), opportunity hoarding ([<reflink idref="bib77" id="ref139">77</reflink>]), and the inclusion of students with disabilities, where Whiteness is correlated with greater access to inclusive learning environments ([<reflink idref="bib103" id="ref140">103</reflink>]). [<reflink idref="bib102" id="ref141">102</reflink>] found that Black–White racial segregation is negatively related to funding disparities even after accounting for racial disparities in neighborhood child poverty contexts. The emerging evidence about the role of racial segregation in disproportionality is a stark reminder that we must study this complex problem in the broader sociohistorical contexts of racial relations in communities ([<reflink idref="bib94" id="ref142">94</reflink>]).</p> <p>Although the relationship between a citation and district-level student math achievement was small, it could reflect access to higher-level math, tracking and segregation within suburban LEAs. Schools, particularly suburban schools, create "school hierarchies (that) reinforce stereotypes and status beliefs because people conflate tracks with race" ([<reflink idref="bib58" id="ref143">58</reflink>], p. 86), where advantaged students enjoy more favorable interactions with teachers ([<reflink idref="bib25" id="ref144">25</reflink>]). As suburban districts become more diverse, they nevertheless systematically reinforce racial inequality through opportunity hoarding and actively discouraging families of minoritized children from active participation ([<reflink idref="bib59" id="ref145">59</reflink>]).</p> <hd id="AN0194392929-20">Emerging Evidence on Student Engagement</hd> <p>Our analyses show that LEA-level dropout rates were associated with the risk for a citation. Researchers have documented that high dropout rates signal lower levels of school connectedness among students ([<reflink idref="bib44" id="ref146">44</reflink>]), and students with negative feelings about school who do not have strong relationships with teachers are less likely to attend school and dropout ([<reflink idref="bib4" id="ref147">4</reflink>]; [<reflink idref="bib55" id="ref148">55</reflink>]). Students of color are less likely than White students to report feeling really cared about by an adult at school ([<reflink idref="bib13" id="ref149">13</reflink>]). However, this quantitative study cannot address the beliefs and values of the people in those districts. The association between dropout and disproportionality citations in the changing contexts of suburban schools raises more questions than answers. Districts should conduct surveys to gather evidence on student voice, student assets, and school culture that provide districts with critical insights about the contexts of disproportionality to guide meaningful reform efforts.</p> <hd id="AN0194392929-21">Limitations</hd> <p>There are a few limitations related to the data and analysis that bear additional discussion. We were unable to investigate disproportionality within disability categories, and this problem may have alternative contours depending on specific disability categories. Future analyses should examine patterns across these groups. Although our study examined possible interactions in the model, there may be ways in which the variables under investigation are intersecting and impacting collectively in some way that we cannot directly assess with the event history approach. Future studies should consider structural equation modeling and path analyses to elucidate potential influences among interacting variables.</p> <p>Additionally, while our study does not allow us to identify the specific mechanisms that result in the observed citation patterns, these findings have implications for retracing what is taking place between school personnel, students living in poverty, students of color, academic performance, disability classification, and policy. The timing of our study encompasses an impactful shift in racial equity policy under the reauthorization of IDEA. As suburban districts in this state continue to experience demographic shifts with more Black students and Latinx students enrolling, our results hold potential for future study and informing IDEA policy to consider context.</p> <p>Still, an additional key educational opportunity factor is funding, and adequate levels of funding, especially if allocated "to hire better-trained teachers who use more effective instructional strategies," is a factor associated with disproportionality ([<reflink idref="bib24" id="ref150">24</reflink>], p. 179), although findings vary by racial group ([<reflink idref="bib75" id="ref151">75</reflink>]). Therefore, it is important to conduct additional studies on the relationship between funding and disproportionality, as resource inequities may account for disparate rates of referrals for special education and/or related services. Although we controlled for a number of potential confounding variables, it is possible that unmeasured factors may be associated with a greater likelihood of a citation. Finally, while our findings are not generalizable to other areas of the country, our methodological approach provides evidence of and captures the complexity of contexts in relation to disproportionality in special education.</p> <hd id="AN0194392929-22">Implications</hd> <p>An important implication of this study is that <emph>where</emph> the problem is observed and beliefs, values and practices in those spaces must be examined systematically. Scholars and practitioners must assess and improve school climate and use culturally-responsive pedagogy. One older study showed that a combination of consistent discipline, a focus on classroom instruction where all staff members feel a sense of ownership, and high expectations for all was a strong predictor of low disproportionality ([<reflink idref="bib74" id="ref152">74</reflink>]), yet recent scholarship is lacking. Clearly, positive learning environments are necessary for students' social and emotional development and ability to be successful learners ([<reflink idref="bib45" id="ref153">45</reflink>]). Schools and LEAs that are culturally responsive and competent help create conditions necessary for learning and support engagement from students and families ([<reflink idref="bib73" id="ref154">73</reflink>]). Future research should examine district practices that mediate indicators such as student and teacher disengagement and attrition.</p> <p>To conclude, racial disparities in special education—whether over- or under-representation—is a major civil rights issue in U.S. education today, despite federal legislation and expanded oversight. Future studies should deepen our understanding of the contextual contingencies of this problem, how they are related to district location (suburban, urban, rural), staff views of communities of color and the cultural learning repertoires these students bring to school, and how IDEA is interpreted and implemented at the state and local levels to address racial disproportionality. Finally, we strongly recommend the pursuit of mixed-methods studies to provide a situated, context-specific perspective, as local patterns are obscured in national data.</p> <hd id="AN0194392929-23">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-ecx-10.1177_00144029251408685 for Deepening the Study of Disproportionality in Special Education: A Contextual Analysis Within Suburban School Districts by Alexandra Aylward, Alfredo J. Artiles, Catherine Kramarczuk Voulgarides, Adai Tefera, Sarah L. Alvarado and Pedro Noguera in Exceptional Children</p> <ref id="AN0194392929-24"> <title> References </title> <blist> <bibl id="bib1" idref="ref94" type="bt">1</bibl> <bibtext> Ahram R., Fergus E., Noguera P. (2011). Addressing racial/ethnic disproportionality in special education: Case studies of suburban school districts. Teachers College Record, 113(10), 2233–2266. https://doi.org/10.1177/0161468111113010</bibtext> </blist> <blist> <bibl id="bib2" idref="ref7" type="bt">2</bibl> <bibtext> Ahram R., Kramarczuk Voulgarides C., Cruz R. A. (2021). Understanding disability: High- quality evidence in research on special education disproportionality. Review of Research in Education, 45(1), 311–345. https://doi.org/10.3102/0091732X20985069</bibtext> </blist> <blist> <bibl id="bib3" idref="ref22" type="bt">3</bibl> <bibtext> Albrecht S. F., Skiba R. J., Losen D. J., Chung C. G., Middelberg L. (2012). Federal policy on disproportionality in special education: Is it moving us forward? Journal of Disability Policy Studies, 23(1), 14–25. https://doi.org/10.1177/10442073114079</bibtext> </blist> <blist> <bibl id="bib4" idref="ref147" type="bt">4</bibl> <bibtext> Alexander K. L., Entwisle D. R., Kabbani N. S. (2001). The dropout process in life course perspective: Early risk factors at home and school. Teachers College Record: The Voice of Scholarship in Education, 103(5), 760–823. https://doi.org/10.1111/0161-4681.00134</bibtext> </blist> <blist> <bibl id="bib5" idref="ref9" type="bt">5</bibl> <bibtext> Artiles A. J. (2011). Toward an interdisciplinary understanding of educational equity and difference: The case of the racialization of ability. Educational Researcher, 40(9), 431–445. https://doi.org/10.3102/0013189X11429391</bibtext> </blist> <blist> <bibl id="bib6" idref="ref18" type="bt">6</bibl> <bibtext> Aylward A., Barrio B., Kramarczuk Voulgarides C. (2021). Exclusion from educational opportunity in diversifying rural contexts. Rural Sociology, 86(3), 559–585. https://doi.org/10.1111/ruso.12381</bibtext> </blist> <blist> <bibl id="bib7" idref="ref13" type="bt">7</bibl> <bibtext> Bal A., Betters-Bubon J., Fish R. E. (2019). A multilevel analysis of statewide disproportionality in exclusionary discipline and the identification of emotional disturbance. Education and Urban Society, 51(2), 247–268. https://doi.org/10.1177/0013124517716260</bibtext> </blist> <blist> <bibl id="bib8" idref="ref12" type="bt">8</bibl> <bibtext> Bal A., Sullivan A. L., Harper J. (2014). A situated analysis of special education disproportionality for systemic transformation in an urban school district. Remedial and Special Education, 35(1), 3–14. https://doi.org/10.1177/0741932513507754</bibtext> </blist> <blist> <bibl id="bib9" idref="ref78" type="bt">9</bibl> <bibtext> Balfanz R., Herzog L., Iver M., J D. (2007). Preventing student disengagement and keeping students on the graduation path in urban middle-grades schools: Early identification and effective interventions. Educational Psychologist, 42(4), 223–235. https://doi.org/10.1080/00461520701621079</bibtext> </blist> <blist> <bibtext> Battey D. (2013). Access to mathematics: "A possessive investment in whiteness". Curriculum Inquiry, 43(3), 332–359. https://doi.org/10.1111/curi.12015</bibtext> </blist> <blist> <bibtext> Blanchett W. J., Mumford V., Beachum F. (2005). Urban school failure and disproportionality in a post-brown era: Benign neglect of the constitutional rights of students of color. Remedial and Special Education, 26(2), 70–81. https://doi.org/10.1177/074193250502600202</bibtext> </blist> <blist> <bibtext> Boser U. (2013). Size matters: A look at school-district consolidation. Center for American Progress. https://eric.ed.gov/?id=ED561073</bibtext> </blist> <blist> <bibtext> Bottiani J. H., Bradshaw C. P., Mendelson T. (2014). Promoting an equitable and supportive school climate in high schools: The role of school organizational health and staff burnout. Journal of School Psychology, 52(6), 567–582. https://doi.org/10.1016/j.jsp.2014.09.003</bibtext> </blist> <blist> <bibtext> Bristol T. J., Martin-Fernandez J. (2019). The added value of Latinx and Black teachers for Latinx and Black students: Implications for policy. Policy Insights from the Behavioral and Brain Sciences, 6(2), 147–153. https://doi.org/10.1177/2372732219862573</bibtext> </blist> <blist> <bibtext> Cavendish W., Artiles A. J., Harry B. (2014). Tracking inequality: Does policy legitimize the racialization of disability? Multiple Voices, 14(2), 30–40. https://doi.org/10.56829/2158-396X.14.2.30</bibtext> </blist> <blist> <bibtext> Chetty R., Friedman J. N., Rockoff J. E. (2014). Measuring the impacts of teachers II: Teacher value-added and student outcomes in adulthood. American Economic Review, 104(9), 2633–2679. https://doi.org/10.1257/aer.104.9.2633</bibtext> </blist> <blist> <bibtext> Cooc N. (2017). Examining racial disparities in teacher perceptions of student disabilities. Teachers College Record, 119(8), 1–32. https://doi.org/10.1177/016146811711900703</bibtext> </blist> <blist> <bibtext> Cooc N. (2018). Examining the underrepresentation of Asian Americans in special education: New trends from California school districts. Exceptionality, 26(1), 1–19. https://doi.org/10.1080/09362835.2016.1216847</bibtext> </blist> <blist> <bibtext> Coutinho M. J., Oswald D. P., Best A. M. (2002). The influence of sociodemographics and gender on the disproportionate identification of minority students as having learning disabilities. Remedial and Special Education, 23(1), 49–59. https://doi.org/10.1177/074193250202300107</bibtext> </blist> <blist> <bibtext> Cruz R. A., Rodl J. E. (2018). An integrative synthesis of literature on disproportionality in special education. The Journal of Special Education, 52(1), 50–63. https://doi.org/10.1177/0022466918758707</bibtext> </blist> <blist> <bibtext> DesJardins S. L., Ahlburg D. A., McCall B. P. (1999). An event history model of student departure. Economics of Education Review, 18(3), 375–339. https://doi.org/10.1016/S0272-7757(98)00049-1</bibtext> </blist> <blist> <bibtext> Diamond J. B., Posey-Maddox L., Velázquez M. (2021). Reframing suburbs: Race, place, and opportunity in suburban educational spaces. Educational Researcher, 50(4), 249–255. https://doi.org/10.3102/0013189X20972676</bibtext> </blist> <blist> <bibtext> Diem S., Cleary C., Ali N., Frankenberg E. (2014). The politics of maintaining diversity policies in suburban school districts. American Journal of Education, 120(3), 351–389. https://doi.org/10.1086/675532</bibtext> </blist> <blist> <bibtext> Donovan M. S., Cross C. T. (2002). Minority students in special and gifted education. National Academy Press. https://<ulink href="http://www.nationalacademies.org/publications/10128">www.nationalacademies.org/publications/10128</ulink></bibtext> </blist> <blist> <bibtext> Downey D. B., Pribesh S. (2004). When race matters: Teachers' evaluations of students' classroom behavior. Sociology of Education, 77(4), 267–282. https://doi.org/10.1177/003804070407700401</bibtext> </blist> <blist> <bibtext> Duncan O. D., Duncan B. (1955). A methodological analysis of segregation indexes. American Sociological Review, 20(2), 210–217. https://<ulink href="http://www.jstor.org/stable/2088328">www.jstor.org/stable/2088328</ulink>. https://doi.org/10.2307/2088328</bibtext> </blist> <blist> <bibtext> Edelman L. B. (1990). Legal environments and organizational governance: The expansion of due process in the American workplace. American Journal of Sociology, 95(6), 1401–1440. https://<ulink href="http://www.journals.uchicago.edu/doi/abs/10.1086/229459">www.journals.uchicago.edu/doi/abs/10.1086/229459</ulink> https://doi.org/10.1086/229459</bibtext> </blist> <blist> <bibtext> Edelman L. B. (2020). Working law: Courts, corporations, and symbolic civil rights. University of Chicago Press.</bibtext> </blist> <blist> <bibtext> Eitle T. M. (2002). Special education or racial segregation: Understanding variation in the representation of black students in educable mentally handicapped programs. Sociological Quarterly, 43(4), 575–605. https://doi.org/10.1111/j.1533-8525.2002.tb00067.x</bibtext> </blist> <blist> <bibtext> Elder T. E., Figlio D. N., Imberman S. A., Persico C. L. (2021). Segregation and racial gaps in special education: New evidence on the debate over disproportionality. Education Next, 21(2), 62–69. https://link.gale.com/apps/doc/A658584620/AONE?u=anon∼e9d9e81a&sid=googleScholar&xid=bf92f262</bibtext> </blist> <blist> <bibtext> Farkas G., Morgan P. L., Hillemeier M. M., Mitchell C., Woods A. D. (2020). District-level achievement gaps explain Black and Hispanic overrepresentation in special education. Exceptional Children, 86(4), 374–392. https://doi.org/10.1177/0014402919893695</bibtext> </blist> <blist> <bibtext> Feuer M. J., Floden R. E., Chudowsky N., Ahn J. (2013). Evaluation of teacher preparation programs: Purposes, methods, and policy options. National Academy of Education. https://files.eric.ed.gov/fulltext/ED565694.pdf</bibtext> </blist> <blist> <bibtext> Figlio D. N., Getzler L. S. (2002). Accountability, ability and disability: Gaming the system? National Bureau of Economic Research. https://<ulink href="http://www.nber.org/system/files/working%5fpapers/w9307/w9307.pdf">www.nber.org/system/files/working%5fpapers/w9307/w9307.pdf</ulink></bibtext> </blist> <blist> <bibtext> Fish R. E. (2017). The racialized construction of exceptionality: Experimental evidence of race/ethnicity effects on teachers' interventions. Social Science Research, 62, 317–334. https://doi.org/10.1016/j.ssresearch.2016.08.007</bibtext> </blist> <blist> <bibtext> Fish R. E. (2019a). Standing out and sorting in: Exploring the role of racial composition in racial disparities in special education. American Educational Research Journal, 56(6), 2573–2608. https://doi.org/10.3102/0002831219847966</bibtext> </blist> <blist> <bibtext> Fish R. E. (2019b). Teacher race and racial disparities in special education. Remedial and Special Education, 40(4), 213–224. https://doi.org/10.1177/0741932518810434</bibtext> </blist> <blist> <bibtext> Fish R. E. (2022). Stratified medicalization of schooling difficulties. Social Science & Medicine, 305, 115039. https://doi.org/10.1016/j.socscimed.2022.115039</bibtext> </blist> <blist> <bibtext> Frankenberg E., Orfield G.2012). The resegregation of suburban schools: A hidden crisis in American education. Harvard Education Press.</bibtext> </blist> <blist> <bibtext> Frey W. H. (2015). Diversity explosion: How new racial demographics are remaking America. Brookings Institution Press.</bibtext> </blist> <blist> <bibtext> Fry R. (2009). The rapid growth and changing complexion of suburban public schools. The Pew Charitable Trusts.</bibtext> </blist> <blist> <bibtext> Galster G., Sharkey P. (2017). Spatial foundations of inequality: A conceptual model and empirical overview. RSF: The Russell Sage Foundation Journal of the Social Sciences, 3(2), 1–33. https://doi.org/10.7758/rsf.2017.3.2.01</bibtext> </blist> <blist> <bibtext> Gershenson S., Hansen M., Lindsay C. A. (2021). Teacher diversity and student success: Why racial representation matters in the classroom. Harvard Education Press.</bibtext> </blist> <blist> <bibtext> Green T., Sánchez J., Germain E. (2017). Communities and school ratings: Examining geography of opportunity in an urban school district located in a resource-rich city. The Urban Review, 49(5), 777–804. https://doi.org/10.1007/s11256-017-0421-1</bibtext> </blist> <blist> <bibtext> Greeney B. S. (2010). High school size, student achievement, and school climate: A multi-year study. ProQuest Dissertations Publishing.</bibtext> </blist> <blist> <bibtext> Hamre B. K., Pianta R. C. (2005). Can instructional and emotional support in the first-grade classroom make a difference for children at risk of school failure? Child Development, 76(5), 949–967. https://doi.org/10.1111/j.1467-8624.2005.00889.x</bibtext> </blist> <blist> <bibtext> Harry B., Klingner J. (2014). Why are so many minority students in special education? Understanding race and disability in schools (2nd ed.). Teachers College Press.</bibtext> </blist> <blist> <bibtext> Hart C. M., Lindsay C. A. (2024). Teacher-student race match and identification for discretionary educational services. American Educational Research Journal, 61(3), 474–507. https://doi.org/10.3102/00028312241229413</bibtext> </blist> <blist> <bibtext> Hibel J., Faircloth S., Farkas G. (2008). Unpacking the placement of American Indian and Alaska native students in special education programs and services in the early grades: School readiness as a predictive variable. Harvard Educational Review, 78(3), 498–528. https://doi.org/10.17763/haer.78.3.8w010nq4u83348q5</bibtext> </blist> <blist> <bibtext> Hibel J., Farkas G., Morgan P. L. (2010). Who is placed into special education? Sociology of Education, 83(4), 312–332. https://doi.org/10.1177/0038040710383518</bibtext> </blist> <blist> <bibtext> Hibel J., Jasper A. D. (2012). Delayed special education placement for learning disabilities among children of immigrants. Social Forces, 91(2), 503–530. https://doi.org/10.1093/sf/sos092</bibtext> </blist> <blist> <bibtext> Holme J. J., Diem S., Welton A. (2014). Suburban school districts and demographic change: The technical, normative, and political dimensions of response. Educational Administration Quarterly, 50(1), 34–66. https://doi.org/10.1177/0013161X13484038</bibtext> </blist> <blist> <bibtext> Horowitz S. H., Rawe J., Whittaker M. C. (2017). The state of learning disabilities: Understanding the 1 in 5. National Center for Learning Disabilities. https://assets.ctfassets.net/p0qf7j048i0q/2Q2TsAzUSM9TXq22LPxQwY/658d54ed0529acddf70f89d367db7915/2017_State_of_LD_-_Executive_Summary_Final_Accessible.pdf</bibtext> </blist> <blist> <bibtext> Hosp J. L., Reschly D. J. (2004). Disproportionate representation of minority students in special education: Academic, demographic, and economic predictors. Exceptional Children, 70(2), 185–199. https://doi.org/10.1177/001440290407000204</bibtext> </blist> <blist> <bibtext> Individuals with Disabilities Education Improvement Act, 20 U.S.C. § 1400 et seq. (2004).</bibtext> </blist> <blist> <bibtext> Jia Y., Konold T. R., Cornell D. (2016). Authoritative school climate and high school dropout rates. School Psychology Quarterly, 31(2), 289–303. https://doi.org/10.1037/spq0000139</bibtext> </blist> <blist> <bibtext> Kilpatrick E. P., Ziomek-Daigle J., U. Nealy A. (2024). Postsecondary planning perspectives of black parents of young adults with high-incidence disabilities. Exceptional Children, 90(4), 442–460. https://doi.org/10.1177/00144029241247071</bibtext> </blist> <blist> <bibtext> Lee J. S. (2014). The relationship between student engagement and academic performance: Is it a myth or reality? The Journal of Educational Research, 107(3), 177–185. https://doi.org/10.1080/00220671.2013.807491</bibtext> </blist> <blist> <bibtext> Lewis A. E., Diamond J. B. (2015). Despite the best intentions: How racial inequality thrives in good schools. Oxford University Press.</bibtext> </blist> <blist> <bibtext> Lewis-McCoy R. L. (2014). Inequality in the promised land: Race, resources, and suburban schooling. Stanford University Press. https://doi.org/10.1177/0042085917747116</bibtext> </blist> <blist> <bibtext> Lewis-McCoy R. L. H. (2018). Suburban black lives matter. Urban Education, 53(2), 145–161. https://doi.org/10.1177/0042085917747116</bibtext> </blist> <blist> <bibtext> Massey D. S., Denton N. A. (1988). The dimensions of residential segregation. Social Forces, 67(2), 281–315. https://doi.org/10.2307/2579183</bibtext> </blist> <blist> <bibtext> Morgan P. L., Farkas G., Cook M., Strassfeld N. M., Hillemeier M. M., Pun W. H., Schussler D. L. (2017). Are black children disproportionately overrepresented in special education? A best-evidence synthesis. Exceptional Children, 83(2), 181–198. https://doi.org/10.1177/0014402916664042</bibtext> </blist> <blist> <bibtext> Morgan P. L., Farkas G., Hillemeier M. M., Mattison R., Maczuga S., Li H., Cook M., Li H., Cook M. (2015). Minorities are disproportionately underrepresented in special education: Longitudinal evidence across five disability conditions. Educational Researcher, 44(5), 278–292. https://doi.org/10.3102/0013189X15591157</bibtext> </blist> <blist> <bibtext> Morgan P. L., Woods A. D., Wang Y., Hillemeier M. M., Farkas G., Mitchell C. (2020). Are schools in the U.S. South using special education to segregate students by race? Exceptional Children, 86(3), 255–275. https://doi.org/10.1177/0014402919868486</bibtext> </blist> <blist> <bibtext> Murphy H., Cole C., Bolte H. (2024). Race placed: Special education identification and placement of black students. Educational Policy, 39(4), 854–877. https://doi.org/10.1177/08959048241268017</bibtext> </blist> <blist> <bibtext> National Center for Education Statistics (NCES), Common Core of Date. (n.d.). School and district glossary. https://nces.ed.gov/ccd/commonfiles/glossary.asp</bibtext> </blist> <blist> <bibtext> Niehaus K., Irvin M. J., Rogelberg S. (2016). School connectedness and valuing as predictors of high school completion and postsecondary attendance among Latino youth. Contemporary Educational Psychology, 44–45, 54–67. https://doi.org/10.1016/j.cedpsych.2016.02.003</bibtext> </blist> <blist> <bibtext> Office of Special Education and Rehabilitative Services (OSERS). (2025). OSEPmonitoring — Significant disproportionality reporting under IDEA part B. https://<ulink href="http://www.ed.gov/laws-and-policy/students-disabilities-laws-and-policy/osep-monitoring--significant-disproportionality-reporting-under-idea-part-b">www.ed.gov/laws-and-policy/students-disabilities-laws-and-policy/osep-monitoring--significant-disproportionality-reporting-under-idea-part-b</ulink></bibtext> </blist> <blist> <bibtext> Opper I. M. (2019). Does helping john help Sue? Evidence of spillovers in education. American Economic Review, 109(3), 1080–1115. https://doi.org/10.1257/aer.20161226</bibtext> </blist> <blist> <bibtext> Orfield G., Jarvie D. (2020). Black Segregation Matters: School resegregation and Black educational opportunity. Civil Rights Project / Proyecto Derechos Civiles at UCLA.</bibtext> </blist> <blist> <bibtext> Orfield G., Lee C. (2005). Why segregation matters: Poverty and educational inequality. Civil Rights Project at Harvard University.</bibtext> </blist> <blist> <bibtext> Orfield G., Pfleger R. (2024). The unfinished battle for integration in a multiracial America from Brown to now. The Civil Rights Project/Proyecto Derechos Civiles, UCLA.</bibtext> </blist> <blist> <bibtext> Osher D., Cantor P., Berg J., Steyer L., Rose T. (2020). Drivers of human development: How relationships and context shape learning and development. Applied Developmental Science, 24(1), 6–36. https://doi.org/10.1080/10888691.2017.1398650</bibtext> </blist> <blist> <bibtext> Osher D., Woodruff D., Sims A. E. (2002). Schools make a difference: The overrepresentation of African American youth in special education and the juvenile justice system. In Losen D. J., Orfield G. (Eds.), Racial inequality in special education (pp. 93–116). Harvard Education Press.</bibtext> </blist> <blist> <bibtext> Oswald D. P., Coutinho M. J., Best A. M., Nguyen N. (2001). Impact of sociodemographic characteristics on identification rates of minority students as having mental retardation. Mental Retardation, 39(5), 351–367. https://doi.org/10.1352/0047-6765(2001)039%3C0351:IOSCOT%3E2.0.CO;2</bibtext> </blist> <blist> <bibtext> Owens A. (2017). Income segregation between school districts and inequality in students' achievement. Sociology of Education, 91(1), 1–27. https://doi.org/10.1177/0038040717741180</bibtext> </blist> <blist> <bibtext> Posey-Maddox L., Powell S. N., Roda A., Lenhoff S. W., Miller E. O. (2025). Advantaged families' opportunity hoarding in U.S. K–12 education: A systematic review of the literature. Review of Educational Research. Advance online publication. https://doi.org/10.3102/00346543241304766</bibtext> </blist> <blist> <bibtext> Reardon S. F. (2016). School segregation and racial academic achievement gaps. The Russell Sage Foundation Journal of the Social Sciences, 2(5), 34–57. https://<ulink href="http://www.rsfjournal.org/content/2/5/34">www.rsfjournal.org/content/2/5/34</ulink> https://doi.org/10.7758/RSF.2016.2.5.03</bibtext> </blist> <blist> <bibtext> Reardon S. F., Owens A. (2014). 60 Years after brown: Trends and consequences of school segregation. Annual Review of Sociology, 40, 199–218. https://doi.org/10.1146/annurev-soc-071913-043152</bibtext> </blist> <blist> <bibtext> Reid D. K., Knight M. G. (2006). Disability justifies exclusion of minority students: A critical history grounded in disability studies. Educational Researcher, 35(6), 18–23. https://doi.org/10.3102/0013189X035006018</bibtext> </blist> <blist> <bibtext> Scott M. A., Kennedy B. B. (2005). Pitfalls in pathways: Some perspectives on competing risks event history analysis in education research. Journal of Educational and Behavioral Statistics, 30(4), 413–442. https://doi.org/10.3102/10769986030004413</bibtext> </blist> <blist> <bibtext> Shifrer D. (2018). Clarifying the social roots of the disproportionate classification of racial minorities and males with learning disabilities. The Sociological Quarterly, 59(3), 384–406. https://doi.org/10.1080/00380253.2018.1479198</bibtext> </blist> <blist> <bibtext> Shifrer D., Fish R. (2020). A multilevel investigation into contextual reliability in the designation of cognitive health conditions among US children. Society and Mental Health, 10(2), 180–197. https://doi.org/10.1177/2156869319847243</bibtext> </blist> <blist> <bibtext> Singer J. D., Willett J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford University Press.</bibtext> </blist> <blist> <bibtext> Skiba R. J., Artiles A. J., Kozleski E. B., Losen D. J., Harry E. G. (2016). Risks and consequences of oversimplifying educational inequities: A response to Morgan et al. (2015). Educational Researcher, 45(3), 221–225. https://doi.org/10.3102/0013189X16644606</bibtext> </blist> <blist> <bibtext> Skiba R. J., Poloni-Staudinger L., Simmons A. B., Feggins-Azziz L. R., Chung C.-G. (2005). Unproven links: Can poverty explain ethnic disproportionality in special education? The Journal of Special Education, 39(3), 130–144. https://doi.org/10.1177/00224669050390030101</bibtext> </blist> <blist> <bibtext> Stevenson K. R. (2006). School Size and Its Relationship to Student Outcomes and School Climate: A Review and Analysis of Eight South Carolina State-Wide Studies. National Clearinghouse for Educational Facilities. https://eric.ed.gov/?id=ED495953</bibtext> </blist> <blist> <bibtext> Stiefel L., Fatima S. S., Cimpian J. R., O'Hagan K. G. (2024). The role of school context in explaining racial disproportionality in special education. Educational Evaluation and Policy Analysis, 47(4), 1113–1135. https://doi.org/10.3102/01623737241271413</bibtext> </blist> <blist> <bibtext> Stroub K. J., Richards M. P. (2017). Suburbanizing segregation? Changes in racial/ethnic diversity and the geographic distribution of metropolitan school segregation, 2002–2012. Teachers College Record, 119(7), 1–40. https://doi.org/10.1177/016146811711900707</bibtext> </blist> <blist> <bibtext> Sullivan A., Artiles A. J. (2011). Theorizing racial inequity in special education: Applying structural inequity theory to disproportionality. Urban Education, 46(6), 1526–1552. https://doi.org/10.1177/0042085911416014</bibtext> </blist> <blist> <bibtext> Sullivan A. L. (2011). Disproportionality in special education identification and placement of English language learners. Exceptional Children, 77(3), 317–334. https://doi.org/10.1177/001440291107700304</bibtext> </blist> <blist> <bibtext> Sullivan A. L., Bal A. (2013). Disproportionality in special education: Effects of individual and school variables on disability risk. Exceptional Children, 79(4), 475–494. https://doi.org/10.1177/001440291307900406</bibtext> </blist> <blist> <bibtext> Tao Y., Meng Y., Gao Z., Yang X. (2022). Perceived teacher support, student engagement, and academic achievement: A meta-analysis. Educational Psychology, 42(4), 401–420. https://doi.org/10.1080/01443410.2022.2033168</bibtext> </blist> <blist> <bibtext> Tefera A. A., Artiles A. J., Kramarczuk Voulgarides C., Aylward A., Alvarado S. (2023). The aftermath of disproportionality citations: Situating disability-race intersections in historical, spatial, and sociocultural contexts. American Educational Research Journal, 60(2), 367–404. https://doi.org/10.3102/00028312221147007</bibtext> </blist> <blist> <bibtext> Thorius K. A. K., Tan P. (2015). Expanding analysis of educational debt: Considering intersections of dis/ability, race, and class. In Connor D. J., Ferri B., & Annamma S. A. (Eds.), Critical conversations about race, class, and dis/ability (pp. 87–100). Teacher College Press.</bibtext> </blist> <blist> <bibtext> U.S. Department of Education. (2004). Individuals with disabilities education act. Public Law, 108–446. https://<ulink href="http://www.congress.gov/bill/108th-congress/house-bill/1350/text">www.congress.gov/bill/108th-congress/house-bill/1350/text</ulink></bibtext> </blist> <blist> <bibtext> U.S. Department of Education. Office of Special Education and Rehabilitative Services, Office of Special Education Programs. (2023). 44th Annual Report to Congress on the Implementation of the Individuals with Disabilities Education Act, 2022, Washington, D.C. https://sites.ed.gov/idea/files/44th-arc-for-idea.pdf.</bibtext> </blist> <blist> <bibtext> U.S. Department of Education. Office of Special Education Programs. (2017). Significant disproportionality. https://<ulink href="http://www.ed.gov/news/press-releases/fact-sheet-equity-idea">www.ed.gov/news/press-releases/fact-sheet-equity-idea</ulink>.</bibtext> </blist> <blist> <bibtext> Voulgarides C. K. (2018). Does compliance matter in special education? IDEA and the hidden inequities of practice. Teachers College Press.</bibtext> </blist> <blist> <bibtext> Voulgarides C. K., Aylward A., Noguera P. A. (2013). Elusive quest for equity: An analysis of how contextual factors contribute to the likelihood of school districts being legally cited for racial disproportionality in special education. Journal of Law and Society, 15, Article 241. https://heinonline.org/HOL/P?h=hein.journals/jls15&i=257</bibtext> </blist> <blist> <bibtext> Voulgarides C. K., Aylward A., Tefera A., Artiles A. J., Alvarado S. L., Noguera P. A. (2021). Unpacking the logic of compliance in special education: Contextual influences on discipline racial disparities in suburban schools. Sociology of Education, 94(3), 208–226. https://doi.org/10.1177/00380407211013322</bibtext> </blist> <blist> <bibtext> Weathers E. S., Sosina V. E. (2022). Separate remains unequal: Contemporary segregation and racial disparities in school district revenue. American Educational Research Journal, 59(5), 905–938. https://doi.org/10.3102/00028312221079297</bibtext> </blist> <blist> <bibtext> White J. M., Li S., Ashby C. E., Ferri B., Wang Q., Bern P., Cosier M. (2019). Same as it ever was: The nexus of race, ability, and place in one urban school district. Educational Studies, 55(4), 453–472. https://doi.org/10.1080/00131946.2019.1630130</bibtext> </blist> </ref> <ref id="AN0194392929-25"> <title> Footnotes </title> <blist> <bibtext> The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the William T. Grant Foundation, (grant number 184607).</bibtext> </blist> <blist> <bibtext> Alexandra Aylward https://orcid.org/0000-0003-1321-6778 Alfredo J. Artiles https://orcid.org/0000-0001-5772-0787 Catherine Kramarczuk Voulgarides https://orcid.org/0000-0002-7649-8058</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> </ref> <aug> <p>By Alexandra Aylward; Alfredo J. Artiles; Catherine Kramarczuk Voulgarides; Adai Tefera; Sarah L. Alvarado and Pedro Noguera</p> <p>Reported by Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib54" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib97" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib96" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib68" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib56" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib85" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib49" firstref="ref10"></nolink> <nolink nlid="nl8" bibid="bib62" firstref="ref11"></nolink> <nolink nlid="nl9" bibid="bib30" firstref="ref14"></nolink> <nolink nlid="nl10" bibid="bib35" firstref="ref15"></nolink> <nolink nlid="nl11" bibid="bib36" firstref="ref16"></nolink> <nolink nlid="nl12" bibid="bib88" firstref="ref17"></nolink> <nolink nlid="nl13" bibid="bib94" firstref="ref19"></nolink> <nolink nlid="nl14" bibid="bib101" firstref="ref20"></nolink> <nolink nlid="nl15" bibid="bib98" firstref="ref21"></nolink> <nolink nlid="nl16" bibid="bib15" firstref="ref23"></nolink> <nolink nlid="nl17" bibid="bib50" firstref="ref25"></nolink> <nolink nlid="nl18" bibid="bib63" firstref="ref26"></nolink> <nolink nlid="nl19" bibid="bib27" firstref="ref27"></nolink> <nolink nlid="nl20" bibid="bib28" firstref="ref28"></nolink> <nolink nlid="nl21" bibid="bib100" firstref="ref29"></nolink> <nolink nlid="nl22" bibid="bib72" firstref="ref30"></nolink> <nolink nlid="nl23" bibid="bib11" firstref="ref31"></nolink> <nolink nlid="nl24" bibid="bib18" firstref="ref32"></nolink> <nolink nlid="nl25" bibid="bib37" firstref="ref33"></nolink> <nolink nlid="nl26" bibid="bib46" firstref="ref39"></nolink> <nolink nlid="nl27" bibid="bib95" firstref="ref40"></nolink> <nolink nlid="nl28" bibid="bib20" firstref="ref41"></nolink> <nolink nlid="nl29" bibid="bib48" firstref="ref42"></nolink> <nolink nlid="nl30" bibid="bib90" firstref="ref43"></nolink> <nolink nlid="nl31" bibid="bib34" firstref="ref45"></nolink> <nolink nlid="nl32" bibid="bib83" firstref="ref46"></nolink> <nolink nlid="nl33" bibid="bib82" firstref="ref47"></nolink> <nolink nlid="nl34" bibid="bib92" firstref="ref49"></nolink> <nolink nlid="nl35" bibid="bib19" firstref="ref51"></nolink> <nolink nlid="nl36" bibid="bib75" firstref="ref52"></nolink> <nolink nlid="nl37" bibid="bib91" firstref="ref53"></nolink> <nolink nlid="nl38" bibid="bib86" firstref="ref54"></nolink> <nolink nlid="nl39" bibid="bib29" firstref="ref56"></nolink> <nolink nlid="nl40" bibid="bib80" firstref="ref58"></nolink> <nolink nlid="nl41" bibid="bib103" firstref="ref60"></nolink> <nolink nlid="nl42" bibid="bib64" firstref="ref62"></nolink> <nolink nlid="nl43" bibid="bib23" firstref="ref63"></nolink> <nolink nlid="nl44" bibid="bib41" firstref="ref64"></nolink> <nolink nlid="nl45" bibid="bib70" firstref="ref67"></nolink> <nolink nlid="nl46" bibid="bib61" firstref="ref68"></nolink> <nolink nlid="nl47" bibid="bib53" firstref="ref70"></nolink> <nolink nlid="nl48" bibid="bib31" firstref="ref71"></nolink> <nolink nlid="nl49" bibid="bib33" firstref="ref74"></nolink> <nolink nlid="nl50" bibid="bib57" firstref="ref76"></nolink> <nolink nlid="nl51" bibid="bib93" firstref="ref77"></nolink> <nolink nlid="nl52" bibid="bib67" firstref="ref79"></nolink> <nolink nlid="nl53" bibid="bib52" firstref="ref80"></nolink> <nolink nlid="nl54" bibid="bib16" firstref="ref82"></nolink> <nolink nlid="nl55" bibid="bib32" firstref="ref83"></nolink> <nolink nlid="nl56" bibid="bib69" firstref="ref84"></nolink> <nolink nlid="nl57" bibid="bib14" firstref="ref85"></nolink> <nolink nlid="nl58" bibid="bib42" firstref="ref86"></nolink> <nolink nlid="nl59" bibid="bib17" firstref="ref87"></nolink> <nolink nlid="nl60" bibid="bib65" firstref="ref89"></nolink> <nolink nlid="nl61" bibid="bib47" firstref="ref90"></nolink> <nolink nlid="nl62" bibid="bib60" firstref="ref91"></nolink> <nolink nlid="nl63" bibid="bib22" firstref="ref92"></nolink> <nolink nlid="nl64" bibid="bib99" firstref="ref95"></nolink> <nolink nlid="nl65" bibid="bib39" firstref="ref97"></nolink> <nolink nlid="nl66" bibid="bib40" firstref="ref98"></nolink> <nolink nlid="nl67" bibid="bib38" firstref="ref100"></nolink> <nolink nlid="nl68" bibid="bib89" firstref="ref105"></nolink> <nolink nlid="nl69" bibid="bib51" firstref="ref106"></nolink> <nolink nlid="nl70" bibid="bib66" firstref="ref107"></nolink> <nolink nlid="nl71" bibid="bib26" firstref="ref108"></nolink> <nolink nlid="nl72" bibid="bib79" firstref="ref109"></nolink> <nolink nlid="nl73" bibid="bib81" firstref="ref112"></nolink> <nolink nlid="nl74" bibid="bib21" firstref="ref114"></nolink> <nolink nlid="nl75" bibid="bib84" firstref="ref115"></nolink> <nolink nlid="nl76" bibid="bib78" firstref="ref117"></nolink> <nolink nlid="nl77" bibid="bib71" firstref="ref119"></nolink> <nolink nlid="nl78" bibid="bib10" firstref="ref122"></nolink> <nolink nlid="nl79" bibid="bib12" firstref="ref131"></nolink> <nolink nlid="nl80" bibid="bib87" firstref="ref132"></nolink> <nolink nlid="nl81" bibid="bib43" firstref="ref137"></nolink> <nolink nlid="nl82" bibid="bib76" firstref="ref138"></nolink> <nolink nlid="nl83" bibid="bib77" firstref="ref139"></nolink> <nolink nlid="nl84" bibid="bib102" firstref="ref141"></nolink> <nolink nlid="nl85" bibid="bib58" firstref="ref143"></nolink> <nolink nlid="nl86" bibid="bib25" firstref="ref144"></nolink> <nolink nlid="nl87" bibid="bib59" firstref="ref145"></nolink> <nolink nlid="nl88" bibid="bib44" firstref="ref146"></nolink> <nolink nlid="nl89" bibid="bib55" firstref="ref148"></nolink> <nolink nlid="nl90" bibid="bib13" firstref="ref149"></nolink> <nolink nlid="nl91" bibid="bib24" firstref="ref150"></nolink> <nolink nlid="nl92" bibid="bib74" firstref="ref152"></nolink> <nolink nlid="nl93" bibid="bib45" firstref="ref153"></nolink> <nolink nlid="nl94" bibid="bib73" firstref="ref154"></nolink>
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  Data: Deepening the Study of Disproportionality in Special Education: A Contextual Analysis within Suburban School Districts
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  Data: <searchLink fieldCode="AR" term="%22Alexandra+Aylward%22">Alexandra Aylward</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1321-6778">0000-0003-1321-6778</externalLink>)<br /><searchLink fieldCode="AR" term="%22Alfredo+J%2E+Artiles%22">Alfredo J. Artiles</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5772-0787">0000-0001-5772-0787</externalLink>)<br /><searchLink fieldCode="AR" term="%22Catherine+Kramarczuk+Voulgarides%22">Catherine Kramarczuk Voulgarides</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7649-8058">0000-0002-7649-8058</externalLink>)<br /><searchLink fieldCode="AR" term="%22Adai+Tefera%22">Adai Tefera</searchLink><br /><searchLink fieldCode="AR" term="%22Sarah+L%2E+Alvarado%22">Sarah L. Alvarado</searchLink><br /><searchLink fieldCode="AR" term="%22Pedro+Noguera%22">Pedro Noguera</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Exceptional+Children%22"><i>Exceptional Children</i></searchLink>. 2026 92(4):377-397.
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: <searchLink fieldCode="DE" term="%22Suburban+Schools%22">Suburban Schools</searchLink><br /><searchLink fieldCode="DE" term="%22Special+Education%22">Special Education</searchLink><br /><searchLink fieldCode="DE" term="%22Equal+Education%22">Equal Education</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Legislation%22">Educational Legislation</searchLink><br /><searchLink fieldCode="DE" term="%22Federal+Legislation%22">Federal Legislation</searchLink><br /><searchLink fieldCode="DE" term="%22Students+with+Disabilities%22">Students with Disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Disproportionate+Representation%22">Disproportionate Representation</searchLink><br /><searchLink fieldCode="DE" term="%22School+Segregation%22">School Segregation</searchLink><br /><searchLink fieldCode="DE" term="%22Racial+Segregation%22">Racial Segregation</searchLink><br /><searchLink fieldCode="DE" term="%22School+Districts%22">School Districts</searchLink><br /><searchLink fieldCode="DE" term="%22Compliance+%28Legal%29%22">Compliance (Legal)</searchLink>
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  Data: <searchLink fieldCode="SU" term="%22Individuals+with+Disabilities+Education+Act%22">Individuals with Disabilities Education Act</searchLink>
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  Data: 10.1177/00144029251408685
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  Data: 0014-4029<br />2163-5560
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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.
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  Data: 2026
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  Data: EJ1508196
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1177/00144029251408685
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 377
    Subjects:
      – SubjectFull: Suburban Schools
        Type: general
      – SubjectFull: Special Education
        Type: general
      – SubjectFull: Equal Education
        Type: general
      – SubjectFull: Educational Legislation
        Type: general
      – SubjectFull: Federal Legislation
        Type: general
      – SubjectFull: Students with Disabilities
        Type: general
      – SubjectFull: Disproportionate Representation
        Type: general
      – SubjectFull: School Segregation
        Type: general
      – SubjectFull: Racial Segregation
        Type: general
      – SubjectFull: School Districts
        Type: general
      – SubjectFull: Compliance (Legal)
        Type: general
      – SubjectFull: Individuals with Disabilities Education Act
        Type: general
    Titles:
      – TitleFull: Deepening the Study of Disproportionality in Special Education: A Contextual Analysis within Suburban School Districts
        Type: main
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      – PersonEntity:
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            NameFull: Alexandra Aylward
      – PersonEntity:
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            NameFull: Alfredo J. Artiles
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          Name:
            NameFull: Catherine Kramarczuk Voulgarides
      – PersonEntity:
          Name:
            NameFull: Adai Tefera
      – PersonEntity:
          Name:
            NameFull: Sarah L. Alvarado
      – PersonEntity:
          Name:
            NameFull: Pedro Noguera
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 0014-4029
            – Type: issn-electronic
              Value: 2163-5560
          Numbering:
            – Type: volume
              Value: 92
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Exceptional Children
              Type: main
ResultId 1