CritSEM: Advancing QuantCrit to Examine Racialized Resegregation in Special Education
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| Title: | CritSEM: Advancing QuantCrit to Examine Racialized Resegregation in Special Education |
|---|---|
| Language: | English |
| Authors: | Nicholas S. Bell, Zachary Collier, Verónica N. Vélez, Donna Y. Ford |
| Source: | Journal of Research on Educational Effectiveness. 2025 18(2):390-422. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 33 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Special Education, African American Students, Hispanic American Students, School Resegregation, Racism, Disproportionate Representation, Critical Race Theory, Children, Longitudinal Studies, Surveys, Social Justice, Structural Equation Models |
| Assessment and Survey Identifiers: | Early Childhood Longitudinal Survey |
| DOI: | 10.1080/19345747.2024.2408538 |
| ISSN: | 1934-5747 1934-5739 |
| Abstract: | The field of education urgently needs research, aligned with QuantCrit, to disrupt the racialized overrepresentation [resegregation] of Black and Latinx students in special education. Our concern about how statistics have been (mis)applied motivated us to consider the possibility of quantitative methods for use in educational research that aims to dismantle white supremacy. Therefore, we developed "CritSEM (Critical Structural Equation Modeling)" as a methodological intervention for the purpose of telling a Critical Race Counterstory. Specifically, we examined the extent that anti-racist educators can disrupt processes causing the resegregation of Black and Latinx students in special education, using restricted data from the 2011 Early Childhood Longitudinal Study. Our study concludes with a discussion and implications about working toward an anti-racist special education system and applying QuantCrit in advanced statistical methods. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1493720 |
| Database: | ERIC |
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| FullText | Links: – Type: pdflink Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwHhumCJFRQnoLvJHI-6M39vAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDAHDDUMeKwnOB9hgUwIBEICBm-w_8Ce3twmQa_3pRrMkZhCYm1UuV8PTgsTjLctDPpS84ZUptovcfeoO7gXMoai7L5k97Nv-YPs_uYTObrNM-mU6ANVlrXwLGjqDNvayJSYRCbAkOC5njNE8ynaD7GjTNa5YzNE2e9sbDVkHImOO7U5T_I_TOcATUaXZZpz_XRPS-TEhlOaRpnZoxsoh2n4zmndYhk0LkI9rA3lX Text: Availability: 1 Value: <anid>AN0186502988;[5ew9]01apr.25;2025Jul11.03:25;v2.2.500</anid> <title id="AN0186502988-1">CritSEM: Advancing QuantCrit to Examine Racialized Resegregation in Special Education </title> <p>The field of education urgently needs research, aligned with QuantCrit, to disrupt the racialized overrepresentation [resegregation] of Black and Latinx students in special education. Our concern about how statistics have been (mis)applied motivated us to consider the possibility of quantitative methods for use in educational research that aims to dismantle white supremacy. Therefore, we developed CritSEM (Critical Structural Equation Modeling) as a methodological intervention for the purpose of telling a Critical Race Counterstory. Specifically, we examined the extent that anti-racist educators can disrupt processes causing the resegregation of Black and Latinx students in special education, using restricted data from the 2011 Early Childhood Longitudinal Study. Our study concludes with a discussion and implications about working toward an anti-racist special education system and applying QuantCrit in advanced statistical methods.</p> <p>Keywords: QuantCrit; special education; overrepresentation; anti-racism; resegregation; Black and Latinx Students</p> <p>Informed by theory, legal decisions, and empirical research, policymakers have challenged the overrepresentation of Students of Color[<reflink idref="bib1" id="ref1">1</reflink>] in special education, grounded in assumptions that overrepresentation equates to another form of racial segregation (Connor et al., [<reflink idref="bib27" id="ref2">27</reflink>]; Larry P v Riles, [<reflink idref="bib79" id="ref3">79</reflink>]; Skiba et al., [<reflink idref="bib111" id="ref4">111</reflink>]). This critical issue is concerning given that 2024 is the 70th anniversary of <emph>Brown v. Board of Education</emph>. Even with policy shifts, overrepresentation in special education has been difficult to disrupt. In fact, it has become normalized and ubiquitous in public schools and classrooms more specifically (Cruz et al., [<reflink idref="bib145" id="ref5">145</reflink>]; Mayes, [<reflink idref="bib85" id="ref6">85</reflink>]). Overrepresentation is unsurprising given that special education was created to advance and protect white racial interests, in response to school integration efforts, resulting in a nefarious (overt and covert) system designed to segregate Black students from the general education system (Mayes, [<reflink idref="bib85" id="ref7">85</reflink>]).</p> <p>Throughout the article, we use the term <emph>resegregation</emph> to describe the overrepresentation of Students of Color in special education to underscore the ongoing and pervasive role of white supremacy[<reflink idref="bib2" id="ref8">2</reflink>] shaping the educational experiences, trajectories, and outcomes of Students of Color, particularly in special education. To frame it any other way dismisses the historical context and centrality of race and racism in decisions to remove students from the general education system, forcing them into a system that limits their opportunities and adds the weight of another socially constructed identity. For example, Students of Color in special education are disproportionately separated from their peers through pull-out placements, further exacerbating racially segregated conditions in schools, despite the robust literature base supporting the inclusion of students identified with disabilities (Franklin, [<reflink idref="bib51" id="ref9">51</reflink>]; Mayes, [<reflink idref="bib85" id="ref10">85</reflink>]; National Center for Learning Disabilities, [<reflink idref="bib96" id="ref11">96</reflink>]; U.S. Department of Education, [<reflink idref="bib126" id="ref12">126</reflink>]; Osgood, [<reflink idref="bib99" id="ref13">99</reflink>], [<reflink idref="bib100" id="ref14">100</reflink>]; Sailor, [<reflink idref="bib108" id="ref15">108</reflink>]). Overrepresentation has lifelong consequences and contributes to racial hierarchies <emph>beyond</emph> schooling. Thus, our use of the term <emph>resegregation</emph> is intentional.</p> <p>Despite the need to focus on structural solutions for resegregation, the field of Special Education has been engrossed by research advancing arguments of underrepresentation. Contrary to hundreds of studies and reports, including by the Office for Civil Rights, researchers claim that Black and Latinx students are underrepresented in special education (Morgan, Farkas, Cook, et al., [<reflink idref="bib89" id="ref16">89</reflink>]; Morgan, Farkas, Hillemeier, et al., [<reflink idref="bib90" id="ref17">90</reflink>]; Morgan et al., [<reflink idref="bib88" id="ref18">88</reflink>]; Shifrer et al., [<reflink idref="bib110" id="ref19">110</reflink>]; Wiley et al., [<reflink idref="bib129" id="ref20">129</reflink>]). However, these claims have been debunked by other scholars who have expressed serious methodological concerns about the (mis)use of statistics in this research (Cavendish et al., [<reflink idref="bib21" id="ref21">21</reflink>]; Collins et al., [<reflink idref="bib25" id="ref22">25</reflink>]; Skiba et al., [<reflink idref="bib111" id="ref23">111</reflink>]; Toldson &amp; Ford, [<reflink idref="bib124" id="ref24">124</reflink>]). For example, a major concern is the use of achievement covariates in statistical models that fail to adequately and systematically account for structural inequities, but instead rely on culturally deficit rationales (Gillborn, [<reflink idref="bib55" id="ref25">55</reflink>]).</p> <p>We argue that these methodological (mis)applications support policy decisions to over-refer and over-identify Students of Color for special education; thereby, promoting white supremacy. Scholars fighting for racial justice in special education see their efforts unraveling from opposing research on underrepresentation, because of its potential impact on policy and practice via a majoritarian story, steeped in white supremacy (Blanchett, [<reflink idref="bib12" id="ref26">12</reflink>]; Collins et al., [<reflink idref="bib25" id="ref27">25</reflink>]; Connor et al., [<reflink idref="bib27" id="ref28">27</reflink>]; Ford &amp; Russo, [<reflink idref="bib50" id="ref29">50</reflink>]).</p> <p>Our concern about how statistics have been (mis)applied motivated us to consider the use of quantitative methods in special education research with the aim to challenge and, preferably, dismantle white supremacy. The field of special education urgently needs research aligned with QuantCrit—Quantitative Critical Race Theory—to disrupt power in normalized processes that continue to promote and reproduce the resegregation of Black and Latinx students. In addition, we seek to understand the impact of <emph>anti-racist educators</emph>, as system level change agents, who have been shown to rally around a wide range of issues adversely impacting Students of Color, including special education (Kulkarni, [<reflink idref="bib74" id="ref30">74</reflink>]; Picower, [<reflink idref="bib106" id="ref31">106</reflink>]).</p> <p>Anti-racist educators have developed the equity beliefs, knowledge, and skills to fight for social and racial justice in schools (Bell et al., [<reflink idref="bib10" id="ref32">10</reflink>]; Bell &amp; Codding, [<reflink idref="bib9" id="ref33">9</reflink>]; Ford et al., [<reflink idref="bib49" id="ref34">49</reflink>]). In the context of special education, an anti-racist educator: (a) pushes back against a system inherently designed to over-identify Students of Color for special education, recognizing and preventing the pervasive causes of racialized overrepresentation; (b) engages in justice-producing pedagogies (e.g., culturally relevant and sustaining teaching) and high quality instruction (i.e., conceptually-based, rigorous, and equitable instruction); and (c) creates an inclusive classroom setting where all students can experience success, eliminating restrictive and segregated educational placements (Bell &amp; Codding, [<reflink idref="bib9" id="ref35">9</reflink>]).</p> <p>For our investigation, we <emph>extend</emph> QuantCrit to guide the application of advanced methodological considerations in structural equation modeling. We introduce CritSEM (<emph>Critical Structural Equation Modeling</emph>) as a methodological approach and intervention for quantitative data analyses. CritSEM combines the tenets of QuantCrit with structural equation modeling to tell a Critical Race Counterstory. Analyzing restricted data from the 2011 Early Childhood Longitudinal Study (ECLS-K) Study, we develop a Critical Race Counterstory that challenges resegregation in special education and reveals the importance of anti-racist educators for promoting social and racial justice in general and special education. Our empirical study counters the research of others, citing <emph>underrepresentation</emph>, who also used ECLS-K data, and other nationally representative data sets. We ask the following: (a) <emph>How do anti-racist educators disrupt the resegregation of Black and Latinx students in special education?</emph> and (<emph>b) How can we extend the methodological application of QuantCrit via CritSEM for the purpose of conducting anti-racist research?</emph> Finally, we discuss working toward an anti-racist special education system and present implications for the future of QuantCrit to frame the use of advanced statistical methods in educational research.</p> <hd id="AN0186502988-2">Researcher Positionality</hd> <p>Our collaboration began when Author 1 invited the coauthors to co-analyze data from his broader body of work, that (a) applies Critical Race Theory (CRT) to study the resegregation of Black, Latinx, and Multilingual students in special education; and (b) explores anti-racist special education policy and practices. The first author's research underscores the troubling consequences of how overrepresentation is measured. Based on his work, it became clear that few <emph>quantitative</emph> studies to date appropriately contextualize the historical and political landscape that necessarily shape the educational realities under investigation.</p> <p>A vital part of any research is articulating what influences our analysis. Smith et al. ([<reflink idref="bib113" id="ref36">113</reflink>]) emphasize naming positionality for challenging the notion of knowledge as separate from self, community, and place. Toward that end, it is vital to articulate our positionalities—the social location from which we designed our study and collectively engaged in data analysis.</p> <p>Nicholas S. Bell is a white cis-gendered man who spent 10 years working as a special education teacher. During that time, he witnessed and confronted rampant racism, ableism, and sexism in elementary and high schools. These experiences shaped his pursuit of research on overrepresentation in special education through the lens of Critical Race Theory, particularly as it intersects with disability. For Author 1, the persistence and pervasiveness of white supremacy in special education has been at the heart of systemic injustices in education, since the concept of special education became an institutionalized practice. This history and his own experiences in elementary and high school classrooms has motivated Author 1 to advance racial and social justice in research on special education through QuantCrit.</p> <p>Zachary Collier, a Black Southern man, is an assistant professor in the field of measurement and statistics, which centers on the systematic study and application of methodologies for research, variable measurement, and outcome evaluation across diverse domains. With a background as a middle school special education teacher preceding his PhD in measurement and statistics, Author 2 brings a unique blend of practical experience and academic expertise to the current study. His contributions infuse the research with methodological rigor, analytical acumen, and a steadfast commitment to ethical practice, amplifying efforts to confront the resegregation of students in special education.</p> <p>Verónica N. Vélez is the US-born daughter of Latinx immigrant parents. As a young student, she witnessed countless moments of disdain and judgment on behalf of her teachers toward her and her family for not learning English quickly enough. This prompted a referral to special education, against which her mother fought ardently. Though she did not have the language then, Author 3 understood at an early age how schools actively work to subtract her family's culture (Valenzuela, [<reflink idref="bib128" id="ref37">128</reflink>]) and envelope her, and others like her, in whitestream ideologies (Grande, [<reflink idref="bib59" id="ref38">59</reflink>]) while masking these efforts in practices of "inclusivity" through special education referrals. These early schooling experiences inform her current work that centers CRT to deepen theoretical and methodological skills, particularly computational and spatial approaches, for conducting research on race and racism in education.</p> <p>Donna Y. Ford is a Black female and Distinguished Professor who has dedicated over 30 years to recruiting and retaining Black and other minoritized students in gifted and talented education. Also addressed has been special education overrepresentation, especially among Black males. She has also written extensively on anti-racist, equitable, culturally responsive educators and education. She is the author of the Bloom-Banks Matrix which is designed to help educators develop curricula that is rigorous and multicultural/culturally relevant. She has written over a dozen books and over 300 articles and chapters, all in the spirit of advocating for Students of Color, their families, and their communities, and disrupting racial injustices.</p> <p>The four of us came together, inspired by the potential of quantitative approaches to contribute and amplify the contextualization of educational phenomena, a research outcome most associated with qualitative research. Recent educational scholarship about how quantitative methods could be employed to advance projects of racial justice, opened the door for us to further theorize, innovate, and use statistical tools in alignment with our philosophical aims (Gillborn et al., [<reflink idref="bib56" id="ref39">56</reflink>]; Pérez Huber et al., [<reflink idref="bib102" id="ref40">102</reflink>]; Sablan, [<reflink idref="bib107" id="ref41">107</reflink>]). Like these scholars, we refused to shy away from quantitative approaches, while being mindful of well-merited critiques of their misuse to study and frame Communities of Color, especially in education. Engaging in QuantCrit also meant a refusal of superficial nods to Critical Race Theory (CRT) in quantitative research that fails to demonstrate adequate theoretical application throughout the inquiry process. We aspired to explore <emph>advanced</emph> statistical approaches to demonstrate the utility of QuantCrit in quantitative research design and analysis.</p> <hd id="AN0186502988-3">Theory</hd> <p></p> <hd id="AN0186502988-4">Critical Race Theory, QuantCrit and the Use of Statistics for Social and Racial Justice</hd> <p>Critical Race Theory (CRT) originated in the late 1970s from the work of lawyers, activists, and legal scholars as a new strategy for dealing with the emergence of a post-Civil Rights racial structure in the United States. The structure, they argued, was maintained by a color-evasive[<reflink idref="bib3" id="ref42">3</reflink>] ideology that hides and protects white privilege while masking racism in rhetoric of "meritocracy" and "fairness" (Bonilla-Silva, [<reflink idref="bib16" id="ref43">16</reflink>]; Delgado &amp; Stefancic, [<reflink idref="bib39" id="ref44">39</reflink>]; Ladson-Billings &amp; Tate, [<reflink idref="bib77" id="ref45">77</reflink>]; Solórzano &amp; Yosso, [<reflink idref="bib115" id="ref46">115</reflink>]). Premised on the belief that the Civil Rights struggle and numerous legal decisions, such as <emph>Brown v. Board of Education of Topeka</emph> ([<reflink idref="bib18" id="ref47">18</reflink>]), granted equal opportunities to all citizens, color-evasive ideology argues that race is no longer a decisive factor in the distribution of resources. The danger of color-evasive "racism," though, is that it disregards the "... enormous and multifarious implications of the massive existing racial inequality" (Bonilla-Silva, [<reflink idref="bib16" id="ref48">16</reflink>], p. 80). Not only does it deny that institutions continue to perpetuate racism, its "reasonable" and politically correct style has made it both a popular and "moral" position, creating an "... almost impenetrable defense of postmodern white supremacy" (Bonilla-Silva, [<reflink idref="bib16" id="ref49">16</reflink>], p. 162). CRT powerfully emerged as a theory aimed at undermining color-evasive ideology through a deconstruction of this racist premise. Although its origins are found in the law, CRT has extended into several disciplines, including education, as researchers look to expose and challenge the ways in which white supremacy mediates norms and structures to produce educational inequality both in and out of the classroom (Bartlett &amp; Brayboy, [<reflink idref="bib6" id="ref50">6</reflink>]; Howard &amp; Navarro, [<reflink idref="bib66" id="ref51">66</reflink>]; Ladson-Billings &amp; Tate, [<reflink idref="bib77" id="ref52">77</reflink>]; Solórzano &amp; Yosso, [<reflink idref="bib114" id="ref53">114</reflink>], [<reflink idref="bib115" id="ref54">115</reflink>]). In educational scholarship, Critical Race research centers Students and Families of Color, leaning predominantly on qualitative approaches to communicate counterstories that challenge deeply rooted, deficit-oriented analyses about these communities and, by extension, narratives of the superiority of white people (Carey, [<reflink idref="bib20" id="ref55">20</reflink>]; Solórzano &amp; Yosso, [<reflink idref="bib115" id="ref56">115</reflink>]).</p> <p>Anchored within CRT in education, <bold><emph>QuantCrit</emph></bold> applies key aspects of qualitative research methods to subvert the notion of objectivity in <emph>quantitative</emph> methods. QuantCrit argues that "numbers" <emph>also</emph> tell stories, but mainstream narratives typically advance causal understandings of individualized behaviors rather than structural conditions. There is plenty of historical evidence of how quantitative data is used to misrepresent, silence, oppress, and ultimately blame People of Color for their purported failures toward social and educational progress (Ladson-Billings, [<reflink idref="bib76" id="ref57">76</reflink>]; Zuberi &amp; Bonilla-Silva, [<reflink idref="bib133" id="ref58">133</reflink>]). For example, discussions of the achievement gap, school "dropouts," and failing schools, often leverage statistics to "prove" a culture of poverty and intellectual inferiority among Students of Color and their families (Valencia, [<reflink idref="bib127" id="ref59">127</reflink>]; Ladson-Billing, [<reflink idref="bib76" id="ref60">76</reflink>]). Given that "[n]umbers are increasingly used to justify policy priorities and to label teachers, schools, districts, and even entire countries, as educational successes and failures" (Gillborn et al., [<reflink idref="bib56" id="ref61">56</reflink>], p. 161), the urgency to rethink quantitative approaches in educational research is clear, particularly when People of Color are targeted.</p> <p>QuantCrit also has its intellectual roots in sociology, where Scholars of Color have considered the limitations, as well as the possibilities, of statistical analyses for race conscious inquiry (Cruse, [<reflink idref="bib35" id="ref62">35</reflink>]; Du Bois, [<reflink idref="bib42" id="ref63">42</reflink>]; Zuberi, [<reflink idref="bib132" id="ref64">132</reflink>]; Zuberi &amp; Bonilla-Silva, [<reflink idref="bib133" id="ref65">133</reflink>]). These scholars point to the racist origins of social statistics. Racial classifications often reflect essentialist interpretations of race as "unalterable characteristics of individuals" (Zuberi, [<reflink idref="bib131" id="ref66">131</reflink>], p. 178) tied to a biological reality, rather than a conceptualization of race as a social construct mediated by white supremacy. Categories bear the marks of early classification schemes, which sought to subdivide humans according to morphological traits and then to rank those subdivisions hierarchically—a reflection of colonization-era constructions of race for the purposes of justifying the enslavement of Africans and Indigenous Americans genocide and dispossession of land (Grosfoguel, [<reflink idref="bib61" id="ref67">61</reflink>]). Not only do such categories "naturalize particular understandings of race" (James, [<reflink idref="bib68" id="ref68">68</reflink>], p. 36), but also bolster inappropriate causal inferences that treat race—rather than racism—as a cause of disparate educational and societal outcomes, reinforcing an ideology of racial inferiority (Zuberi, [<reflink idref="bib131" id="ref69">131</reflink>]). This interpretation of statistical results is reflected in much present-day research on educational "gaps," as noted above.</p> <p>Arguably as the first QuantCrit scholar, Du Bois ([<reflink idref="bib42" id="ref70">42</reflink>]) demonstrated how statistical analyses could disrupt racist data narratives to center a more critical narrative of Black life in the United States. Morris ([<reflink idref="bib92" id="ref71">92</reflink>]) highlights how Du Bois relied on statistical and comparative data, analyzed and displayed through powerful visuals in the form of charts, graphs, and maps, to demonstrate how Blacks and Europeans behaved similarly in shared social circumstances. His work challenged social Darwinist beliefs of white scholars at the time, which later came to shape eugenicist ideologies, pointing instead to structural conditions. As Morris ([<reflink idref="bib92" id="ref72">92</reflink>]) notes, Du Bois, "refut[ed] the notion that [B]lack people were intellectually inferior, uninterested, and incapable of learning. Du Bois' contributions signal a long trajectory of scholars questioning, while simultaneously reimagining, the use of quantitative methods toward anti-racist ends.</p> <p>Building off Du Bois and extending his work into educational inquiry, Covarrubias ([<reflink idref="bib28" id="ref73">28</reflink>]), advanced intersectional inquiry (Collins, [<reflink idref="bib26" id="ref74">26</reflink>]; Crenshaw, [<reflink idref="bib33" id="ref75">33</reflink>]) to examine interlocking systems of power among Chicanxs to display trends of "gender-based discrimination, patriarchy, class inequality, nativist racism and their interconnected effects" (p. 103). Covarrubias and Vélez ([<reflink idref="bib30" id="ref76">30</reflink>]) subsequently conceptualized <emph>Critical Race Quantitative Intersectionality</emph> to cross-examine how descriptive statistical data has culturally perpetuated deficit perspectives affecting educational research, policy, and practice. QuantCrit draws explicitly from this work (Garcia et al., [<reflink idref="bib52" id="ref77">52</reflink>]; Gillborn et al., [<reflink idref="bib56" id="ref78">56</reflink>]) to propose a methodological framework that both reckons with the eugenicist history of quantitative methods (Zuberi, [<reflink idref="bib132" id="ref79">132</reflink>]) and simultaneously opens a productive dialogue for considering quantitative methodologies in Critical Race educational research (Crawford, [<reflink idref="bib32" id="ref80">32</reflink>]; Knowles &amp; Hawkman, [<reflink idref="bib73" id="ref81">73</reflink>]; Pérez Huber et al., [<reflink idref="bib102" id="ref82">102</reflink>]; Sablan, [<reflink idref="bib107" id="ref83">107</reflink>]). It is guided by the following tenets (Gillborn et al., [<reflink idref="bib56" id="ref84">56</reflink>]):</p> <p></p> <ulist> <item> The centrality of racism as a complex and deeply rooted aspect of society that is not readily amenable to quantification. That is, though quantitative methods are poorly suited for capturing the "nuances of the numerous social processes that shape and legitimate race inequity," QuantCrit recognizes both that quantitative analysis can intervene to identify "wider structures" that reproduce racism, and that without critical intervention, "quantitative analysis will tend to remake and legitimate existing race inequalities" (Gillborn et al., [<reflink idref="bib56" id="ref85">56</reflink>], p. 169). After all, that is what statistical analysis was made to do.</item> <p></p> <item> The acknowledgment that numbers are not neutral, and they should be interrogated for their role in promoting deficit analyses that serve white racial interests. QuantCrit sees as a central task not just the analysis of data, but also the challenging of "the past and current ways in which quantitative research has served" white supremacy.</item> <p></p> <item> The reality that categories are neither "natural" nor given and so the units and forms of analysis must be critically evaluated. QuantCrit critiques treatments of "'race' as a cause in its own right as if the minoritized group is inherently deficient" (Gillborn et al., [<reflink idref="bib56" id="ref86">56</reflink>], p. 171). QuantCrit intentionally replaces terms like "race" with "race/racism," pointing to the structural origins of disparate outcomes.</item> <p></p> <item> The recognition that voice and insight are vital: data cannot "speak for itself" and critical analyses should be informed by the experiential knowledge of marginalized groups. Collins ([<reflink idref="bib26" id="ref87">26</reflink>]) describes the "outsider within" as a unique "standpoint" afforded by marginality. QuantCrit affirms the importance of experiential knowledge in validating, situating, challenging, and shaping the purpose of the analysis and interpretation of quantitative data on race/racism.</item> <p></p> <item> The understanding that statistical analyses have no inherent value, but they can play a role in struggles for social justice, just as Du Bois' work and the work of other critical scholars.</item> </ulist> <p>Taken together, the tenets guide both the political aims and technical approach to QuantCrit. They anchor our <emph>use</emph> and <emph>critique</emph> of statistical methods for analyzing white supremacy in special education, attending to how quantitative approaches can inadvertently reify power dynamics, by homogenizing and/or flattening the complexity of experiences "behind the numbers." As a methodological intervention, our goal is to advance statistical applications and analyses that strengthen Critical Race Counterstories for "exposing, analyzing, and challenging the majoritarian stories" (Solórzano &amp; Yosso, [<reflink idref="bib115" id="ref88">115</reflink>], p. 31) about the resegregation of Black and Latinx students in special education.</p> <p>While we are well-aware of other critical quantitative approaches in education (e.g., CritQuant, quantitative criticism), our alignment with the tenets of QuantCrit reflects an "... epistemological genealogy rooted in Critical Race Theory" (Tabron &amp; Thomas, [<reflink idref="bib118" id="ref89">118</reflink>], p. 771) that guides our inquiry. We recognize that prior to the emergence of the term "QuantCrit," quantitative scholars were using CRT to guide their statistical research designs (Teranishi, [<reflink idref="bib120" id="ref90">120</reflink>]). Nonetheless, we avoid conflating QuantCrit with other critical quantitative approaches (Tabron &amp; Thomas, [<reflink idref="bib118" id="ref91">118</reflink>]). QuantCrit ties us explicitly to political commitments at the core of CRT, wherein "critical" signifies an insistence to surface white supremacy as a structural and institutional condition shaping the educational experiences and trajectories of Communities of Color and engages quantitative research as an effort to end racism and its connection to other forms of subordination.</p> <hd id="AN0186502988-5">Literature</hd> <p>We apply QuantCrit to examine and (re)frame the resegregation of Black and Latinx students in special education, demonstrating how quantitative methods continue to be leveraged to support this outcome.</p> <hd id="AN0186502988-6">(Re)Framing the Resegregation of Students of Color in Special Education Using QuantCrit</hd> <p>Black and Latinx students are separated from their peers in the general education system at disproportionate rates and placed into a special education system that can cause short-term and long-term harm (National Center for Learning Disabilities, [<reflink idref="bib96" id="ref92">96</reflink>]). Students' initial identification for special education is just the beginning of resegregation patterns. Individual Education Plan (IEP) teams hold the authority to place students in more restrictive, segregated learning environments, ranging from partial to full pull-out placements. Segregated placements have become a common and acceptable practice in schools, despite their ineffectiveness, notably for students identified with high incidence disabilities (Sailor, [<reflink idref="bib108" id="ref93">108</reflink>]). Additionally, Black and Latinx students report negative experiences from segregated (pull-out) placements, including discrimination and microaggressions, poor-quality teaching, teacher bullying, lack of teacher quality, teachers' low expectations, limited academic growth and opportunities, and feelings of isolation (Connor, [<reflink idref="bib135" id="ref94">135</reflink>]; Craft &amp; Howley, [<reflink idref="bib31" id="ref95">31</reflink>]; Dávila, [<reflink idref="bib136" id="ref96">136</reflink>]; Ferri &amp; Connor, [<reflink idref="bib137" id="ref97">137</reflink>]; Harry, Klingner, et al., [<reflink idref="bib140" id="ref98">140</reflink>]; Hart et al., [<reflink idref="bib141" id="ref99">141</reflink>]; Rogers, [<reflink idref="bib145" id="ref100">145</reflink>]; Tefera, [<reflink idref="bib146" id="ref101">146</reflink>]). Aside from placements, the most restrictive and debilitating form of resegregation comes when teachers assign students IEP diplomas, an alternative path to conventional educational pathways, which, in effect, prevents students from earning a high school diploma.</p> <p>We argue that resegregation reflects broader patterns of an educational system entrenched in systematic and institutional racism. We want to make clear that providing students with disabilities access to an education that meets their diverse needs is a key priority. Our critique of special education should not be misunderstood to argue that students with disabilities do not require a range of supports and services. Rather, we claim that special education is entangled with efforts to preserve white supremacy through the daily operation of normative processes and practices in schools that keep students from obtaining requisite skills (Battey &amp; Leyva, [<reflink idref="bib7" id="ref102">7</reflink>]). QuantCrit provides a theoretical and methodological frame to examine our claim, particularly how resegregation in special education—and the quantitative studies that support it—is a major reason that Black and Latinx students are prevented from accessing quality instruction in public schools.</p> <p>From a QuantCrit perspective, the use of statistical research methods to over-identify Black and Latinx students for special education is naïve, problematic, and dangerous (Cavendish et al., [<reflink idref="bib21" id="ref103">21</reflink>]; Collins et al., [<reflink idref="bib25" id="ref104">25</reflink>]; Connor et al., [<reflink idref="bib27" id="ref105">27</reflink>]). This practice can be traced to the eugenics movement in the United States, starting in the late nineteenth century, that fed off the fear of children with learning and behavioral differences as well as the fear of non-white populations (Cohen, [<reflink idref="bib23" id="ref106">23</reflink>], Osgood, [<reflink idref="bib99" id="ref107">99</reflink>]). Researchers, scholars, and clinicians who embraced eugenics believed in a superior race void of genetic defects, supporting solutions to eradicate inferior beings with "bad genes"—the poor, People of Color, and immigrants from southern and eastern Europe (Gould, [<reflink idref="bib58" id="ref108">58</reflink>]; Nielsen, [<reflink idref="bib97" id="ref109">97</reflink>]; Osgood, [<reflink idref="bib99" id="ref110">99</reflink>]). Overall, white society in the United States believed [perceived] genetic defects resulted from poverty, family homelife, immigration, and cultural deprivation, which in turn, created inferior human beings, contributing to a cycle of societal issues (Gould, [<reflink idref="bib58" id="ref111">58</reflink>]; Osgood, [<reflink idref="bib99" id="ref112">99</reflink>]).</p> <p>The racist and inequitable processes established during the eugenics movement in the name of science, became foundational pillars for a hidden special education system designed to over-identify students (Mayes, [<reflink idref="bib85" id="ref113">85</reflink>]). For example, teachers' subjective and deficit-based evaluations (e.g., academic) of students and inappropriate usage of academic tests are still being used (Mayes, [<reflink idref="bib85" id="ref114">85</reflink>]). These "objective" processes helped solidify ways to sort, classify, and segregate students. Today, the resegregation of students via special education is further abetted by quantitative research, which leans on arguments of "objective and traditional" science, to further solidify practices and policies rooted in a racist system (Morgan, Farkas, Cook, et al., [<reflink idref="bib89" id="ref115">89</reflink>]; Morgan et al., [<reflink idref="bib88" id="ref116">88</reflink>]). We argue white supremacy is reproduced when quantitative research infers overstated conclusions from models statistically significant yet lacking an understanding of race as a social construction, such as Morgan et al.'s research citing underrepresentation. Without a critical theory orientation to surface these issues, they easily become invisible in taken-for-granted positivist arguments that underlie quantitative approaches.</p> <p>We were compelled to intervene by conducting research <emph>at the intersection of race and special education</emph>, using QuantCrit. Our aim is not only to showcase the immediate insights gained from our study, but also to set a broader precedent for the multifaceted applications of <emph>Critical Structural Equation Modeling</emph> (CritSEM). Thus, our inquiry on resegregation began by closely examining critical qualitative research and scholarship on the topic (Ahram et al., [<reflink idref="bib2" id="ref117">2</reflink>]; Bal et al., [<reflink idref="bib134" id="ref118">134</reflink>]; Blanchett, [<reflink idref="bib12" id="ref119">12</reflink>]; Connor, [<reflink idref="bib135" id="ref120">135</reflink>], Craft &amp; Howley, [<reflink idref="bib31" id="ref121">31</reflink>]; Dávila, [<reflink idref="bib136" id="ref122">136</reflink>]; Ferri &amp; Connor, [<reflink idref="bib137" id="ref123">137</reflink>]; Ford &amp; Russo, [<reflink idref="bib50" id="ref124">50</reflink>]; Harry, Sturges, et al., [<reflink idref="bib138" id="ref125">138</reflink>]; Harry et al., [<reflink idref="bib139" id="ref126">139</reflink>]; Harry &amp; Klingner, [<reflink idref="bib64" id="ref127">64</reflink>]; Hart et al., [<reflink idref="bib141" id="ref128">141</reflink>]; Tefera, [<reflink idref="bib146" id="ref129">146</reflink>]). Findings revealed (a) reasons for overrepresentation which included teachers' lack of equity and social justice preparation, resulting in subjective and deficit-based evaluations of students, and reliance on [biased] tests, and (b) Black and Latinx students' negative experiences from being in special education. We also explored the ways in which quantitative research used descriptive and inferential statistics to show the magnitude of the problem (Cruz et al., [<reflink idref="bib36" id="ref130">36</reflink>]; National Center for Learning Disabilities, [<reflink idref="bib96" id="ref131">96</reflink>]). Our initial inquiry of the phenomena guided and informed the development of Critical Structural Equation Models.</p> <hd id="AN0186502988-7">Methods</hd> <p>We argue for <emph>data sensitivity approaches</emph><bold>,</bold> defined as the statistical approaches that researchers use to incorporate, and test, critical theory and qualitative inputs in models. These analytical approaches are sensitive enough to detect the theoretical underpinnings of QuantCrit. In our study, we used CritSEM as our data sensitivity approach to analyze secondary data from the ECLS-K Study (e.g., Early Childhood Longitudinal Study-K 2011 Restricted Data).</p> <p>Prior to our research, we defined a political purpose for employing statistics (e.g., CritSEM) in alignment with QuantCrit to establish the critical nature of the research and underscore a commitment to dismantle white supremacy throughout the research process. Our investigation is meant to counter uncritical forms of research in special education (Morgan, Farkas, Cook, et al., [<reflink idref="bib89" id="ref132">89</reflink>]; Morgan, Farkas, Hillemeier, et al., [<reflink idref="bib90" id="ref133">90</reflink>]; Morgan et al., [<reflink idref="bib88" id="ref134">88</reflink>]). The use of achievement covariates in statistical models erroneously leads to universal claims that Black and Latinx students are underrepresented. This research reifies white supremacy in special education, which, we argue, leads to over-identifying Students of Color using covert and normalized processes, enigmatic in education, that get carried out by educators, lacking preparation (Bell &amp; Codding, [<reflink idref="bib9" id="ref135">9</reflink>]; Gould, [<reflink idref="bib58" id="ref136">58</reflink>]; Mayes, [<reflink idref="bib85" id="ref137">85</reflink>]; Selden, [<reflink idref="bib109" id="ref138">109</reflink>]; Sleeter, [<reflink idref="bib112" id="ref139">112</reflink>]). In response, we conducted empirical research, refusing decontextualized and acultural approaches, in favor of merging critical theory with statistical and probability theory. From a methodological perspective, we sought to advance QuantCrit via CritSEM for the purposes of understanding the extent that anti-racist educators can disrupt processes that cause resegregation, using the ECLS-K 2011 Restricted Data. This purpose aligns with <emph>our political aim to advance racial and social justice in special education</emph> (Cavendish et al., [<reflink idref="bib21" id="ref140">21</reflink>]; Collins et al., [<reflink idref="bib25" id="ref141">25</reflink>]; Connor et al., [<reflink idref="bib27" id="ref142">27</reflink>]).</p> <hd id="AN0186502988-8">Critical Structural Equation Modeling (CritSEM)</hd> <p>Structural equation modeling (SEM) is an applied methodological approach that incorporates and tests <emph>a priori</emph> theory (Bollen &amp; Pearl, [<reflink idref="bib15" id="ref143">15</reflink>]; Kline, [<reflink idref="bib71" id="ref144">71</reflink>]; Montfort, [<reflink idref="bib87" id="ref145">87</reflink>]; Morgan, [<reflink idref="bib91" id="ref146">91</reflink>]). With SEM, researchers use theory and prior research to conceptualize structural paths to indicate the causal directionality of measured and/or latent variables, which represent theoretical constructs defined by the correlations among measured, dependent variables (Bollen, [<reflink idref="bib14" id="ref147">14</reflink>]). Bollen and Pearl ([<reflink idref="bib15" id="ref148">15</reflink>]) wrote, "Structural equation modeling is an inference engine that takes in two inputs, qualitative causal assumptions and data, and derives two logical consequences of these inputs: quantitative causal conclusions and statistical measures of fit for the testable implications of the assumptions" (p. 309). Theory is explicated up front in the form of hypothesized path diagrams that indicate—based on the theoretical underpinnings of the model—directionality between the [latent] variables. To test that theory, models produce fit statistics to evaluate the overall structure of the model, in addition to statistical estimates. Poor fit statistics require a reevaluation of the overall structure, involving an iterative approach, to further understand the data and theory.</p> <p>SEM is emerging as a robust data sensitivity approach to extend the methodological application of QuantCrit. For example, the first author developed and analyzed structural equation models to investigate overrepresentation in special education, after considering potential applications of QuantCrit and CRT, derived from the literature, such as the incorporation of qualitative research in quantitative research and structural equation modeling as a technique for telling a Critical Race Counterstory (Bell, [<reflink idref="bib8" id="ref149">8</reflink>]). Stewart et al. ([<reflink idref="bib116" id="ref150">116</reflink>]) critically examined race in an educational policy study that explored educators' critical pedagogy efficacy beliefs via structural equation models, showing the utility of mediation analyses via direct and indirect effects. This research serves as an entry point to the present study. We combine the tenets of QuantCrit with the methodological considerations of SEM to introduce <emph>Critical Structural Equation Modeling (CritSEM).</emph></p> <p>We believe CritSEM disrupts racism and other forms of injustices in research through explicit theoretical and methodological steps that critically integrate and unify qualitative and quantitative methodologies. To be clear, structural equation modeling is not a singular statistical technique, but rather, an analytical process including the conceptualization of the model, identification, and estimation of parameters and assessment of model fit (Mueller &amp; Hancock, [<reflink idref="bib93" id="ref151">93</reflink>]). QuantCrit provides a lens to analyze how race intersects with social systems, influencing access to resources and outcomes. When incorporated into SEM, QuantCrit ensures that models not only capture statistical relationships, but also consider the social realities and power dynamics influencing those connections.</p> <p>Our naming of CritSEM aims to provide a clear label of our methodology, which looks to critique and improve the quality of quantitative research findings via SEM. Categorizing statistical methods like CritSEM is essential. This practice helps scholars gain a comprehensive understanding of various statistical techniques, including their underlying assumptions and applicability across diverse research contexts.</p> <p>It is vital to stress that CritSEM is not just a methodological tool; it represents an intervention in quantitative practice, capable of informing anti-racist solutions, due to its causal properties. CritSEM considers both the causal implications [conclusions] of SEM (Bollen &amp; Pearl, [<reflink idref="bib15" id="ref152">15</reflink>]) and critical research. Drawing from Zuberi's ([<reflink idref="bib132" id="ref153">132</reflink>]) groundbreaking work on white logics in quantitative social science research, Stewart et al. ([<reflink idref="bib116" id="ref154">116</reflink>]) underlines the importance of defining causality in terms of "more complex, nuanced, and justice-oriented discussions on examining social phenomena" instead of an overreliance on covariates and other so called gold standard norms (p. 11). A necessary feature of CrtiSEM for causal implications and conclusions is the development of a <emph>Critical Race Counterstory</emph>.</p> <p>QuantCrit argues that "numbers" tell stories, but mainstream narratives will typically advance understandings of individualized behaviors rather than structural conditions. For example, quantitative research on Students of Color who drop out of school will leverage statistics to tell or infer stories about poor academic motivation, patterns of inappropriate behavior that lead to increased suspensions, or lack of parental involvement that contribute to dropping out. This locates the cause of disparate educational outcomes in the bodies and homes of Students of Color rather than in the structures and ideologies that shape schooling. Statistical analyses can tell powerful <emph>counte</emph>rstories, when inquiry pivots to examine power in social structures and institutions (Covarrubias &amp; Vélez, [<reflink idref="bib30" id="ref155">30</reflink>]). This is where CritSEM comes in. It offers a framework for applying a critical lens to quantitative research through the development of models and interpretation of results that uniquely positions data <emph>as a story</emph> through its merger of qualitative and quantitative approaches. We argue that the <emph>counter</emph>stories, emerging from CritSEM, challenge dominant paradigms and amplify historically marginalized voices (Dixson and Rousseau, [<reflink idref="bib41" id="ref156">41</reflink>]) within quantitative research. For our study, we developed a Critical Race Counterstory via CritSEM about resegregation in special education to disrupt and dismantle the white supremacy in research and policy upholding this practice.</p> <hd id="AN0186502988-9">Methodological Considerations for Conducting CritSEM</hd> <p>With CritSEM, researchers test the theoretical underpinnings of the models they construct, using QuantCrit, qualitative research, and justice-based scholarship. As an overall analytical approach, we recommend CritSEM to incorporate other statistical models. Below, we present methodological considerations for CritSEM.</p> <p> <emph>First</emph>, every aspect of the research design is theoretically driven via QuantCrit (Garcia &amp; Mayorga, [<reflink idref="bib53" id="ref157">53</reflink>]). The entry point for statistical analyses should never be methodological, but rather political, grounded in and guided by a theoretical framework. Critical theory is necessary for all research, but especially quantitative research, given problematic claims to "neutrality" and "objectivity" often associated with statistics. And if justice is what we seek, then we must insist on contextualizing research studies within a landscape of power. Therefore, the first step in CritSEM is for researchers to theoretically ground their research in the literature review section of the article, and throughout, with QuantCrit and critical theory to fully understand the injustice, prior to statistical analyses.</p> <p> <emph>Second</emph>, CritSEM tells a Critical Race Counterstory, using the incorporation of counternarratives via path models and measurement models, for the function of discrediting deficit-based research and/or promoting asset-based research. Scholars posit and explicate QuantCrit and critical theory in their construction of structural equation model(s), that if confirmed, repudiates opposing forms of uncritical research. The phantom variable approach offers an added dimension for telling a Critical Race Counterstory in CritSEM, whereby researchers can explore ways to disrupt the structural conditions that result in injustices, while empowering those seeking justice. Our study pioneers the application of the phantom variable approach within CritSEM, contributing to the narration of a Critical Race Counterstory. By <emph>haunting</emph>[<reflink idref="bib4" id="ref158">4</reflink>] the dominant narrative with this approach, we shed light on overlooked perspectives.</p> <p>In CritSEM, a phantom variable (see definition and description below) symbolizes anti-racist efforts, based on justified and theoretical notions, using extant and extensive literature. It is incorporated into structural equation models so that researchers can understand changes in the direct and indirect effects of designated paths. Such changes can reveal disruptions in power dynamics, perpetuated by white supremacy, which continue to influence policies and practices. Evidence from this analysis proposes anti-racist and justice-oriented solutions to help inform clinicians in the field or even future experiments.</p> <p> <emph>Third</emph>, the inclusion of qualitative approaches minimizes concerns of QuantCrit scholars about the sole usage of quantitative methods (Covarrubias et al., [<reflink idref="bib29" id="ref159">29</reflink>]; Pérez Huber et al., [<reflink idref="bib102" id="ref160">102</reflink>]). We identify two ways of applying qualitative approaches within structural equation modeling. The first is an in-depth analysis of existing critical qualitative research and justice-oriented scholarship as researchers conceptualize their Critical Structural Equation Models and discuss results. These qualitative and scholarly findings ground statistics in the cultural intuition (Delgado Bernal, [<reflink idref="bib11" id="ref161">11</reflink>]) and experiences of marginalized communities to develop a more complete understanding of the data so that <emph>critical analyses are informed by the experiential knowledge of marginalized groups</emph> (Covarrubias, [<reflink idref="bib28" id="ref162">28</reflink>]). Additionally, a qualitative meta-analysis—an analysis of qualitative studies via coding procedures to understand a research phenomenon—powerfully informs the development of a Critical Race Counterstory underlying the path model (Timulak &amp; Creaner, [<reflink idref="bib122" id="ref163">122</reflink>]). The same qualitative meta-analysis can also ground and problematize the quantitative results. In this way, CritSEM is considered a <emph>multiple methods approach</emph>.</p> <p>Fourth, it is important to note that while QuantCrit is interested in the centrality of race and racism, it also aims to examine how race and racism <emph>intersect</emph> with other social locations (i.e., identities) and forms of subordination (e.g., sexism, classism, ableism). These intersections in CritSEM can be applied to specify independent variables and the centering of a reference group. They provide a deeper theoretical understanding, rather than analyses of isolated identities (Covarrubias, [<reflink idref="bib28" id="ref164">28</reflink>]; López et al., [<reflink idref="bib83" id="ref165">83</reflink>]), of how identities converge on individuals.</p> <hd id="AN0186502988-10">Critical Structural Equation Modeling to Investigate Resegregation in Special Education</hd> <p>For this investigation, we analyze data from the ECLS-K Study and develop a Critical Race Counterstory about resegregation in special education. We asked the following research questions: (a) How do anti-racist educators disrupt the resegregation of Black and Latinx students in special education? and (b) How can we extend the methodological application of QuantCrit via CritSEM for the purpose of conducting anti-racist research?</p> <hd id="AN0186502988-11">ECLS-K 2011 Restricted Data</hd> <p>We used restricted data from the Early Childhood Longitudinal Study-K 2011 (ECLS-K). The ECLS-K Study[<reflink idref="bib5" id="ref166">5</reflink>] consists of longitudinal data, from kindergarten to fifth grade (2011–2016), on a cohort of students attending over 1,000 schools in the United States. Data was collected at different time points from a variety of sources, including students, general education teachers, special education teachers, and parents. The intention of the study was to create a nationally representative sample of students in the United States. The sample was selected using a multistage, stratified clustered design (Tourangeau et al., [<reflink idref="bib125" id="ref167">125</reflink>]). As a result of the complex survey design, sampling weights are necessary to create a national sample for analyses, which in turn, also accounts for non-responses, missing data (Tourangeau et al., [<reflink idref="bib125" id="ref168">125</reflink>]). We applied replicate weights and used jackknife replication variance estimation methods to adjust for standard errors. To further address missing data, we used multiple imputation methods (Enders, [<reflink idref="bib44" id="ref169">44</reflink>]). The overall analytical (unweighted) sample that we used for analyses consisted of 17, 730 students.</p> <p>Related to our study, the longitudinal design of the ECLS-K data supported the examination of theoretical causes of resegregation and possible interventions to address it. For example, the Kindergarten Academic Ratings Scales and Student Administered Assessments represented the causes. We discuss the psychometric scales and assessments[<reflink idref="bib6" id="ref170">6</reflink>] from the ECLS-K Study, forming the variables for this investigation, in the Critical Structural Equation Modeling 1 and 2 descriptions.</p> <hd id="AN0186502988-12">Limitations and Consequences of Research</hd> <p>We made the conscious decision to discuss the limitations and consequences of our research, before presenting the Critical Structural Equation Models. A major limitation of our study was the analysis of secondary data. Quantitative data (i.e., experimental data, simulated data, and secondary data) provide ample opportunities to conduct asset-based research, expose white supremacy, confront uncritical uses of data, and/or contribute to methodological advancements. Yet, despite these opportunities, the data should be interrogated (Gillborn et al., [<reflink idref="bib56" id="ref171">56</reflink>]). Secondary data require the most scrutiny since the researcher was not involved in the construction of the data or data collection instruments, and presumably the data was created devoid of a critical lens (Garcia &amp; Mayorga, [<reflink idref="bib53" id="ref172">53</reflink>]; Gillborn et al., [<reflink idref="bib56" id="ref173">56</reflink>]). From a QuantCrit perspective, the ECLS-K Study precluded key data for exploring the phenomenon of resegregation, such as educators' usage of social and racial justice practices and beliefs. Further, secondary analyses run the potential risk of (mis)representing the issue of resegregation in special education without a critical framing and without anchoring in qualitative research. To alleviate these limitations, we took several steps. We applied QuantCrit in the formation of Critical Structural Equation Models. Additionally, we used a phantom variable approach to measure the extent that anti-racist educators can reduce resegregation. Finally, we decided early on to merge our data analyses with a qualitative meta-analysis on resegregation in special education.</p> <hd id="AN0186502988-13">Critical Structural Equations Models: A Critical Race Counterstory</hd> <p>We hypothesized <emph>a priori</emph> Critical Structural Equation Models in the hopes of garnering evidence on reducing and, ideally, eliminating resegregation via a Critical Race Counterstory (Bollen, [<reflink idref="bib13" id="ref174">13</reflink>]; Greenland, [<reflink idref="bib60" id="ref175">60</reflink>]; Morgan, [<reflink idref="bib91" id="ref176">91</reflink>]). Our hypotheses focused on the causes of resegregation in special education in early elementary school and ways to disrupt the unjust identification process. Specifically, we hypothesized that (a) teachers' deficit-based evaluations and usage of early assessments, during kindergarten, will result in resegregation; and (b) anti-racist educators, during kindergarten, will reduce the resegregation of Black and Latinx boys and girls in special education. We constructed four Critical Structural Equation Models to test our hypotheses, incorporating the phantom variable approach for two of the models. Lastly, we grounded our findings from the Critical Structural Equation Models (described below) with a qualitative meta-analysis on overrepresentation.</p> <hd id="AN0186502988-14">CritSEM: Examining Reasons for Resegregation</hd> <p>In Figure 1, we show the Critical Structural Equation Models, denoted by solid and dotted lines. Using CritSEM, we examined teachers' deficit-based evaluations and inappropriate usage of early assessments, during kindergarten, as predictors of resegregation in special education.</p> <p>Graph: Figure 1. Examining the causes of resegregation in special education. Note: In the figure, circles represented latent variables and squares/rectangles were the variables. The arrows signified hypothesized paths. TE (Teacher Evaluations), EA (Early Assessments), RS (Resegregation in Special Education); Independent Variable: REG (Race/ethnicity and Gender); Path Models: first model (REG→TE→RS) and second model REG→EA→RS)</p> <p>From a QuantCrit perspective, the causes of resegregation in these models represent the normalized, pervasive, and racist functions of schooling that shape how teachers (mis)identify Black and Latinx students for special education. Scholars discuss teachers' deficit-based and subjective evaluations of students, and use of biased assessments, as major causes of resegregation (Ahram et al., [<reflink idref="bib2" id="ref177">2</reflink>]; Blanchett, [<reflink idref="bib12" id="ref178">12</reflink>]; Craft &amp; Howley, [<reflink idref="bib31" id="ref179">31</reflink>]; Ford &amp; Russo, [<reflink idref="bib50" id="ref180">50</reflink>]; Harry et al., [<reflink idref="bib139" id="ref181">139</reflink>]; Harry &amp; Klingner, [<reflink idref="bib64" id="ref182">64</reflink>]; Hart et al., [<reflink idref="bib141" id="ref183">141</reflink>]; Kearns et al., [<reflink idref="bib142" id="ref184">142</reflink>]; Klingner &amp; Harry, [<reflink idref="bib143" id="ref185">143</reflink>]; Knotek, [<reflink idref="bib72" id="ref186">72</reflink>]; Orosco &amp; Klingner, [<reflink idref="bib144" id="ref187">144</reflink>]). In these instances, teachers fail to consider the structural inequalities, masked in classrooms, that manifest into the appearance of disparate outcomes, according to white middle-class norms. In fact, perceived learning difficulties may result from limited access to academic opportunities, a lack of culturally relevant teaching, low quality teaching, and structural inequalities, such as a lack of resources (Ford, [<reflink idref="bib45" id="ref188">45</reflink>], [<reflink idref="bib46" id="ref189">46</reflink>], [<reflink idref="bib47" id="ref190">47</reflink>], [<reflink idref="bib48" id="ref191">48</reflink>]). These causes, while now implicit, were once explicit when notions of eugenics became prominent in education. Proponents of eugenics relied on the convictions of educators to classify students for special education, via their evaluations and use of intelligence testing (Bruinius, [<reflink idref="bib19" id="ref192">19</reflink>]; Gould, [<reflink idref="bib58" id="ref193">58</reflink>]; Nielsen, [<reflink idref="bib97" id="ref194">97</reflink>]; Osgood, [<reflink idref="bib98" id="ref195">98</reflink>]; Selden, [<reflink idref="bib109" id="ref196">109</reflink>]). In our models, teachers' usage of early assessments and academic evaluations represent the eugenics-based processes still causing resegregation in special education today.</p> <p>Lastly, it is important to note that in our CritSEM Models, the reference group consisted of white girls, since this group has not been historically considered overrepresented in special education. We were intentional about using a comparative analysis given our focus on exposing white supremacy in special education. We examined if teachers' evaluations and early assessments predicted the resegregation of Black and Latinx boys and girls in special education, in comparison to white girls. Although white boys were included in the models, we only reported these results in the data tables, since this population was not the focus of our study. In the discussion, our aim was to reflect on whether these multiple layers of socially constructed identities, imposed on students, target Black and Latinx students in special education. To reiterate, although explicitly centered in race and racism, QuantCrit calls upon researchers to understand how racism intersects with other forms of power (Garcia et al., [<reflink idref="bib52" id="ref197">52</reflink>]; Gillborn et al., [<reflink idref="bib56" id="ref198">56</reflink>]).</p> <hd id="AN0186502988-15">Critical Structural Equation Model 1: Teachers' Deficit-Based and Subjective Evaluations</hd> <p>In the first structural equation model (solid line), we examined the probabilities of Black and Latinx boys and girls being resegregated in special education, in comparison to white girls, by third grade, because of their teachers' academic evaluations in math and literacy. If significant, these [indirect] effects would suggest that Black and Latinx boys and girls are resegregated in special education, due to their teachers' evaluations and discretions.</p> <p>The teacher evaluation latent variable was defined by teachers' literacy (reading, writing, and language) and math evaluations of their students in kindergarten, using Academic Rating Scales from the ECLS-K Study (Tourangeau et al., [<reflink idref="bib125" id="ref199">125</reflink>]). For the resegregation in special education latent variable, we transformed a categorical variable, consisting of four categories, into a latent response variable that measured the degree students are resegregated in special education by third grade. Categories comprising the variable, prior to the transformation, consisted of students' educational placements in third grade: general education for students who were not identified for special education; full inclusion for students identified for and who received special education services in the general education classroom; and segregated special education, when students were pulled out into segregated learning environments. A <emph>latent response variable transformation</emph> changes a categorical dependent variable into a continuous latent variable, which deviates from a crude measurement of categorical variables, resulting in a substantive and meaningful interpretation on a continuous scale (Agresti, [<reflink idref="bib1" id="ref200">1</reflink>]; Lee et al., [<reflink idref="bib80" id="ref201">80</reflink>]; Masyn et al., [<reflink idref="bib84" id="ref202">84</reflink>]; Muthén &amp; Asparouhov, [<reflink idref="bib94" id="ref203">94</reflink>]).</p> <p>Transformations have several distinct advantages in structural equation modeling. Latent response variables: (a) represent the measurement of a continuous latent variable, underlying a theoretical notion, that is not confined by raw categorical cut points; (b) can be used as independent and dependent variables; and (c) in the context of indirect effects, whether the latent response variable is an independent or dependent variable, can be estimated. Model statistics involving the latent response variable as the dependent variable are probit coefficients. In our model, the latent response variable, as a dependent outcome, represented the resegregation in special education latent variable.</p> <hd id="AN0186502988-16">Critical Structural Equation Model 2: Teachers' Inappropriate Usage of Early Assessments</hd> <p>In the second structural equation model, we wanted to understand the extent that early assessments predicted resegregation in special education for Black and Latinx boys and girls, in comparison to white girls. Significant results would suggest that teachers' perception and usage of early assessments is an additional cause of resegregation.</p> <p>The early assessments latent variable was defined by assessments (reading, math, and working memory), specifically designed for the ECLS-K Study, that were administered to students in early kindergarten (Tourangeau et al., [<reflink idref="bib125" id="ref204">125</reflink>]); the assessments were intended to measure students' knowledge and skills in math (conceptual knowledge, procedural knowledge, and problem solving), reading (basic skills, vocabulary knowledge, and reading comprehension), and executive function (working memory). These assessments provide us with tangible measures, which we can then use to understand the underlying construct of early assessments.</p> <hd id="AN0186502988-17">Phantom Variable within CritSEM: Exploring How to Disrupt Resegregation</hd> <p>The next two structural equation models added the phantom variable of anti-racist educators to the prior models (see Figure 2). Anti-racist educators could be one way to reduce resegregation. Within the field of teacher education, research that establishes the necessity to prepare teachers as activists and advocates of social and racial justice has been well documented (Bell et al., [<reflink idref="bib10" id="ref205">10</reflink>], Bell &amp; Codding, [<reflink idref="bib9" id="ref206">9</reflink>]; Cochran-Smith, [<reflink idref="bib22" id="ref207">22</reflink>]; Picower, [<reflink idref="bib103" id="ref208">103</reflink>], [<reflink idref="bib104" id="ref209">104</reflink>], [<reflink idref="bib105" id="ref210">105</reflink>]). The impetus has been driven by resistance to increasing neoliberal and corporate takeovers of P-12 education, insisting on preparing teachers as technicians to operate narrowed curriculum standards that align with high-stakes tests that disproportionately impact Students of Color (Au &amp; Ferrare, [<reflink idref="bib4" id="ref211">4</reflink>]). Given the push to increase testing and assessment at younger and younger ages, research makes clear that preparing critical, anti-racist, and socially just teachers in early childhood is imperative (Heineke et al., [<reflink idref="bib65" id="ref212">65</reflink>]).</p> <p>Graph: Figure 2. Examining the impact of anti-racist educators to disrupt resegregation. Note: In the figure, circles represented latent variables and squares/rectangles were the variables. The arrows signified hypothesized paths. TE (Teacher Evaluations), EA (Early Assessments), RS (Resegregation in Special Education), ARE PV (Anti-Racism Educator Phantom variable); Independent Variable: REG (Race/ethnicity and Gender variables); Path Models: first model (REG→TE→RS←ARE PV) and second model (REG→EA→RS←ARE PV).</p> <p>We examined the impact of anti-racist educators as a phantom variable to reduce resegregation. More specifically, we ran several models with varied magnitudes of the effect of anti-racist educators, to determine how large the relation between the anti-racist and deficit-oriented approaches would have to be for the inference to be affected.</p> <p>A phantom variable is added to represent an unmeasured variable that may be influencing the relationships between observed variables. The approach redirects our focus toward variables that may not have been initially collected or accessible to researchers conducting the analysis. In essence, it acknowledges and potentially mitigates unrealistic expectations that quantitative analyses should encompass all the appropriate and relevant variables needed to account for the complexities of social and political conceptions of race and ethnicity. The phantom variable approach can even be used to quantify the potential impact of unobserved variables in models, which is how we use it via CritSEM to tell a Critical Race Counterstory.</p> <p>We formulated and followed a structured series of steps termed the phantom variable "fixed-parameter approach" in Harring et al. ([<reflink idref="bib63" id="ref213">63</reflink>]):</p> <p>Step (<reflink idref="bib1" id="ref214">1</reflink>) In the context of SEM, the researcher identifies a phantom variable that confounds the relations among variables specified in the initial model (Leite et al., [<reflink idref="bib81" id="ref215">81</reflink>]). The path coefficients from the phantom variable to the other variables quantify the hypothetical linkages between a previously unidentified confounder and the original variables in the model. For example, we focused on understanding the sensitivity of paths, meaning changes in path coefficients, involving the direct and indirect paths [effects] of teachers' evaluations and early assessments. To identify and establish the phantom variable, we conducted a comprehensive qualitative meta-analysis of empirical qualitative research on the causes of resegregation, students' experiences in special education, and how educators can disrupt resegregation in special education. The meta-analysis revealed to us that anti-racist educators, if prepared, could disrupt the pervasive causes of resegregation, and therefore prevent Black and Latinx students from unjust experiences in special education, because of their agency, advocacy, and actions. Thus, anti-racist educators became the phantom variable in the model. As a check for trustworthiness, we employed a process that compared and contrasted justice-oriented scholarship on overrepresentation to the meta-analysis results (Levitt et al., [<reflink idref="bib82" id="ref216">82</reflink>]; Timulak, [<reflink idref="bib121" id="ref217">121</reflink>]), which added clarity and helped confirm the validity of results.</p> <p>Step (<reflink idref="bib2" id="ref218">2</reflink>) Traditionally, the anti-racist latent factor would be defined by indicators connected to what defines anti-racism in this context. The phantom variable approach does not require such manifest variables of the phantom variable to examine sensitivity associated with model structure and parameters. Rather, researchers need to have a clear understanding of the types of uncertainty that the phantom variable approach addresses, to give a correct interpretation of model results. We relied on the theoretical emergence of the anti-racist educator construct, derived from the qualitative meta-analysis and further checked using justice-based scholarship with Critical Race underpinnings.</p> <p>We meticulously defined the characteristics of our anti-racist educator variable, based on the meta-analysis. Pertaining to this investigation, <emph>anti-racist educators</emph> recognize and confront the pervasive causes of racialized overrepresentation; employ justice-producing pedagogies (e.g., culturally relevant and sustaining teaching) and high quality instruction (i.e., conceptually-based, rigorous, and equitable instruction); and eliminate segregated settings inside and outside of the general education classroom through inclusive classroom practices and pedagogy that create the circumstances for students to experience success (Blanchett, [<reflink idref="bib12" id="ref219">12</reflink>]; Ford, [<reflink idref="bib47" id="ref220">47</reflink>], [<reflink idref="bib48" id="ref221">48</reflink>]; Ford &amp; Russo, [<reflink idref="bib50" id="ref222">50</reflink>]; Ladson-Billings, [<reflink idref="bib75" id="ref223">75</reflink>]; Paris, [<reflink idref="bib101" id="ref224">101</reflink>]).</p> <p>Step (<reflink idref="bib3" id="ref225">3</reflink>) The anti-racist educator latent construct, dubbed a "phantom variable" or "phantom construct," was seamlessly integrated into our model and linked to observed variables.</p> <p>We used the meta-analysis to determine the direction and strength, whether positive or negative, of potential path coefficients between our newly introduced phantom variable, anti-racist educators, and other variables specified in our initial SEM (Leite et al., [<reflink idref="bib81" id="ref226">81</reflink>]).</p> <p>For the model, we set the mean and variance of the phantom variable as constant values, following the recommendations of existing literature; by definition, the phantom variable is a latent variable with a mean of zero, a variance set to one, and no manifest indicators (Harring et al., [<reflink idref="bib63" id="ref227">63</reflink>]).</p> <p>Step (<reflink idref="bib4" id="ref228">4</reflink>) Next, we executed the updated Critical Structural Equation Model to probe potential variations in the strength of specific paths of interest (Leite et al., [<reflink idref="bib81" id="ref229">81</reflink>]). This step allowed us to assess how sensitive our model's results were to changes in the anti-racist educator phantom variable. Varying these parameters allows researchers to investigate the results' trustworthiness and generalizability. That is, the sensitivity to a missing confounder and other forms of external misspecification (Harring et al., [<reflink idref="bib63" id="ref230">63</reflink>]). The meta-analysis enabled us to evaluate the magnitude of effects, qualitatively, associated with these path coefficients across various scenarios. For example, we felt that a strong anti-racist educator, according to our definition, would signify the highest effect. We manipulated the effects of anti-racist educators by setting values of 0.25, 0.5, and 0.8, representing small, medium, and large effects according to Cohen's ([<reflink idref="bib24" id="ref231">24</reflink>]) guidelines. Testing these three different effect sizes allows for a comprehensive understanding of how varying levels of anti-racist educator influence can impact the model's outcomes.</p> <p>Step (<reflink idref="bib5" id="ref232">5</reflink>) Finally, we pinpointed which parameters within our SEM exhibited heightened sensitivity to variations in the phantom variable. This sensitivity analysis provided valuable insights into areas of the model most influenced by characteristics of the anti-racist educator variable.</p> <p>The implications of these findings extend beyond the immediate study. By identifying the parameters most affected by the phantom variable, future interventions can be more precisely targeted to address the most sensitive aspects of the model. This can lead to the development of effective anti-racist strategies and educational practices. Furthermore, understanding the influence of phantom variables can inform future studies by highlighting critical pathways that warrant further investigation. Researchers can build upon these insights to explore new hypotheses and refine theoretical models, enhancing the overall robustness and applicability of their work.</p> <p>In practice, the application of phantom variables in models can help practitioners and policymakers design interventions that are more nuanced and context-specific. By acknowledging and addressing the subtleties revealed through a sensitivity analysis, efforts to combat systemic racism and promote social justice can be strategically implemented, resulting in more impactful and sustainable outcomes. By methodically following these steps, we were able to comprehensively investigate the impact of anti-racist educators on reducing resegregation in special education.</p> <hd id="AN0186502988-18">Critical Structural Equation Models 3 and 4: Anti-Racist Educators Phantom Variable</hd> <p>We examined the impact [direct effect] of anti-racist educators, during kindergarten, on reducing resegregation in special education by third grade (see Figure 2). Additionally, we examined if the latent propensity of resegregation decreased [indirect effect] for Black and Latinx boys and girls in comparison to white girls, due to their teachers' evaluations and usage of early assessments, during kindergarten. First, we examined the impact of anti-racist educators in the teacher evaluation model (solid line in Figure 2) and then the early assessment model (dotted line in Figure 2). We also interpreted estimates of the regression paths for the teacher evaluation and early assessment latent variables leading to the resegregation in special education latent variable. Examining changes in the regression paths after systematically altering the effects of anti-racist educators allowed us to test hypotheses about the relations between these variables.</p> <p>In our illustration, we investigated three sources of sensitivity due to external misspecification: changes in path coefficients from early assessments to resegregation in special education (EA→RS); changes in path coefficients from teacher evaluations to resegregation in special education (TE→RS); and changes in indirect effects of race/ethnicity and gender (REG→TE→RS; REG→EA→RS). We also examined the direct effect of anti-racist educators reducing resegregation in special education.</p> <hd id="AN0186502988-19">Evaluating Model Fit, and Estimates of Models</hd> <p>For each structural equation model, we assessed model fit to see if the statistics met <emph>a priori</emph> thresholds: RMSEA &lt; 0.06, CFI &gt;.90, TLI &gt; 0.90, and SRMR &lt; 0.06 (Hu &amp; Bentler, [<reflink idref="bib67" id="ref233">67</reflink>]; McDonald &amp; Ho, [<reflink idref="bib86" id="ref234">86</reflink>]; Kenny &amp; McCoach, [<reflink idref="bib70" id="ref235">70</reflink>]; Lai &amp; Green, [<reflink idref="bib78" id="ref236">78</reflink>]). Acceptable fit indicates that the theoretical and hypothesized models explained the relationships set forth in our models. We would only interpret parameter coefficients of models, if fit was deemed acceptable.</p> <hd id="AN0186502988-20">Grounding Quantitative Findings in Qualitative Research</hd> <p>We synthesized our findings from Critical Structural Equation Models (described below) with a qualitative meta-analysis on resegregation. Our meta-analysis included 20 qualifying studies (see Appendix A). We used open coding methods and an inductive process (Strauss &amp; Corbin, [<reflink idref="bib117" id="ref237">117</reflink>]) to develop eight coding categories, derived from the findings of qualitative research studies. Next, we generated two broader coding categories (themes), using constant-comparison methods (Glaser &amp; Strauss, [<reflink idref="bib57" id="ref238">57</reflink>]). The first theme, containing five coding categories, showed the complex set of intricate processes causing overrepresentation: educators inadequately prepared for equity and social justice; reasons teachers referred students for special education; prereferral, referral and evaluation: errors, subjectivity, and biases; limited family and community partnerships resulted in biases; and impact of race/ethnicity, class, language status, and gender on special education identification. The second theme, signifying students' unjust experiences in special education, consisted of three categories: disability label subjugates students' identities; experiences inside special education; and advocacy, resilience, and resistance. While the primary purpose of a qualitative meta-analysis is to uncover unique and standalone findings, scholars have applied meta-analyses to ground statistics (Harden &amp; Thomas, [<reflink idref="bib62" id="ref239">62</reflink>]; Timulak &amp; Creaner, [<reflink idref="bib122" id="ref240">122</reflink>]).</p> <p>The application of qualitative meta-analyses in statistics, from a QuantCrit perspective, helps ensure quantitative research is advancing racial and social justice in the <emph>counter</emph>story (Solórzano &amp; Yosso, [<reflink idref="bib115" id="ref241">115</reflink>]). Anchoring our inquiry in a meta-analysis amplified the voices of those "at the margins" (Dixson, &amp; Rousseau, [<reflink idref="bib41" id="ref242">41</reflink>]; Solórzano &amp; Yosso, [<reflink idref="bib115" id="ref243">115</reflink>]) as we were intentional in identifying research centered on the experiential experiences of People of Color, in alignment with CRT. This made plausible the emergence of a Critical Race Counterstory via CritSEM in our research. Moreover, in the synthesis, we wanted to reflect on points of divergence and convergence in the integration of quantitative and qualitative findings. We felt that the quantitative findings could be amplified and substantiated if we found points of convergence. For the synthesis, we followed a systematic set of methodological steps outlined by Johnson et al. ([<reflink idref="bib69" id="ref244">69</reflink>]).</p> <hd id="AN0186502988-21">Results</hd> <p></p> <hd id="AN0186502988-22">Causes of Resegregation in Special Education</hd> <p>First, we hypothesized that teachers' deficit-based evaluations and usage of early assessments, during kindergarten, will result in resegregation (see Figure 1). Our finding (see No PV Effects, Table 1) shows that, in comparison to white girls, Black boys, Black girls, Latinx boys, and Latinx girls had increased propensities of being resegregated in special education by third grade, due to their teachers' kindergarten evaluations in math and literacy. Similarly, our second finding (see No PV Effects, Table 2) suggests that early assessments, during kindergarten, predicted the resegregation of Black boys, Black girls, Latinx boys, and Latinx girls in special education, in comparison to white girls, by third grade. The fit of the first structural equation model (RMSEA = 0.03, CFI = 0.98, TLI = 0.97, SRMR = 0.01) and second structural equation model (RMSEA = 0.04, CFI = 0.98, TLI = 0.97, SRMR = 0.05) was acceptable, confirming how well our hypothesized models, as shown in Figures 1 and 2, generalize to observations in the dataset.</p> <p>Table 1. Changes in standardized path estimates for different magnitudes of effects using the Phantom Variable Approach (Teacher Evaluations Model).</p> <p> <ephtml> &lt;table&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;SEM direct and indirect effects&lt;/td&gt;&lt;td&gt;No PV effects&lt;/td&gt;&lt;td&gt;Small effects (.25)&lt;/td&gt;&lt;td&gt;Medium effects (.5)&lt;/td&gt;&lt;td&gt;Large effects (.8)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody valign="top"&gt;&lt;tr&gt;&lt;td&gt;ARE &amp;#8594; RS&lt;/td&gt;&lt;td&gt;&amp;#8211;&lt;/td&gt;&lt;td char="."&gt;&amp;#8722;0.18&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;&amp;#8722;0.40&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;&amp;#8722;0.72&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;ARE &amp;#8594; TE&lt;/td&gt;&lt;td&gt;&amp;#8211;&lt;/td&gt;&lt;td char="."&gt;0.20&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.45&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.83&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;TE &amp;#8594; RS&lt;/td&gt;&lt;td char="."&gt;&amp;#8722;0.62&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;&amp;#8722;0.58&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;&amp;#8722;0.44&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.01&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;BG&amp;#8594;TE&amp;#8594;RS&lt;/td&gt;&lt;td char="."&gt;0.15&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&lt;/td&gt;&lt;td char="."&gt;0.14&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.10&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.00&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;LG&amp;#8594;TE&amp;#8594;RS&lt;/td&gt;&lt;td char="."&gt;0.12&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.11&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.08&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.00&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;WB&amp;#8594;TE&amp;#8594;RS&lt;/td&gt;&lt;td char="."&gt;0.04&lt;/td&gt;&lt;td char="."&gt;0.04&lt;/td&gt;&lt;td char="."&gt;0.03&lt;/td&gt;&lt;td char="."&gt;0.00&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;BB&amp;#8594;TE&amp;#8594;RS&lt;/td&gt;&lt;td char="."&gt;0.22&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.20&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.15&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.00&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;LB&amp;#8594;TE&amp;#8594;RS&lt;/td&gt;&lt;td char="."&gt;0.21&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.19&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.15&lt;xref ref-type="table-fn" rid="tfn1"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.00&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt; </ephtml> </p> <p>1 *<emph>p</emph> &lt; 0.05, **<emph>p</emph> &lt; 0.005.</p> <ulist> <item>2 <emph>Note.</emph> Latent factors: ARE (anti-racist educator), RS (resegregation in special education), TE (teacher evaluations).</item> <item>3 REG (race/ethnicity and gender) variables: BG (Black girls), LG (Latinx girls), WB (white boys), BB (Black boys), LB (Latinx boys).</item> <item>4 Source: U.S. Department of Education, National Center for Education Statistics, Early Childhood Longitudinal Study 2011(ECLS-K 2001) Kindergarten-Fifth Grade, 2011-2016.</item> </ulist> <p>Table 2. Changes in standardized path estimates for different magnitudes of effects using the Phantom Variable Approach (Early Assessments Model).</p> <p> <ephtml> &lt;table&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;SEM direct and indirect effects&lt;/td&gt;&lt;td&gt;No PV effects&lt;/td&gt;&lt;td&gt;Small effects (.25)&lt;/td&gt;&lt;td&gt;Medium effects (.5)&lt;/td&gt;&lt;td&gt;Large effects (.8)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody valign="top"&gt;&lt;tr&gt;&lt;td&gt;ARE &amp;#8594;RS&lt;/td&gt;&lt;td&gt;&amp;#8211;&lt;/td&gt;&lt;td char="."&gt;&amp;#8722;0.20&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;&amp;#8722;0.41&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;&amp;#8722;0.79&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;ARE &amp;#8594;EA&lt;/td&gt;&lt;td&gt;&amp;#8211;&lt;/td&gt;&lt;td char="."&gt;0.02&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.05&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.09&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;EA&amp;#8594;RS&lt;/td&gt;&lt;td char="."&gt;&amp;#8722;0.60&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;&amp;#8722;0.59&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;&amp;#8722;0.58&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;&amp;#8722;0.52&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;BG&amp;#8594;EA&amp;#8594;RS&lt;/td&gt;&lt;td char="."&gt;0.38&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.38&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.37&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.33&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;LG&amp;#8594;EA&amp;#8594;RS&lt;/td&gt;&lt;td char="."&gt;0.39&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.39&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.38&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.34&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;WB&amp;#8594;EA&amp;#8594;RS&lt;/td&gt;&lt;td char="."&gt;0.03&lt;/td&gt;&lt;td char="."&gt;0.03&lt;/td&gt;&lt;td char="."&gt;0.02&lt;/td&gt;&lt;td char="."&gt;0.02&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;BB&amp;#8594;EA&amp;#8594;RS&lt;/td&gt;&lt;td char="."&gt;0.36&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.35&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.34&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.31&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;LB&amp;#8594;EA&amp;#8594;EP&lt;/td&gt;&lt;td char="."&gt;0.43&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.43&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.42&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;td char="."&gt;0.38&lt;xref ref-type="table-fn" rid="tfn5"&gt;&amp;#42;&lt;/xref&gt;&amp;#42;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt; </ephtml> </p> <ulist> <item>5 *<emph>p</emph> &lt; 0.05, **<emph>p</emph> &lt; 0.005.</item> <item>6 <emph>Note.</emph> Latent factors: ARE (anti-racist educator), RS (resegregation in special education), EA (early assessments).</item> <item>7 REG (race/ethnicity and gender) variables: BG (Black girls), LG (Latinx girls), WB (white boys), BB (Black boys), LB (Latinx boys).</item> <item>8 Source: U.S. Department of Education, National Center for Education Statistics, Early Childhood Longitudinal Study 2011(ECLS-K 2001) Kindergarten-Fifth Grade, 2011-2016.</item> </ulist> <p>Mapping this finding onto our working definition of CritSEM (which, again, draws from QuantCrit), our analysis builds a Critical Race Counterstory by centering not just race (in the demographic categories for students), but <emph>racism,</emph> underscoring the relationship between evaluations and early assessments with resegregation. This is key for engaging quantitative approaches for anti-racist ends. CritSEM is not only positioned to recognize the "wider structures" that reproduce racism, but it also serves as a necessary methodological intervention, without which "quantitative analysis will tend to remake and legitimate existing race inequalities" (Gillborn et al., [<reflink idref="bib56" id="ref245">56</reflink>], p. 169).</p> <hd id="AN0186502988-23">Anti-Racist Educators Disrupt Processes Causing Resegregation</hd> <p>Next, we hypothesized that anti-racist educators, during kindergarten, will reduce resegregation in special education for Black and Latinx boys and girls (see Figure 2). We compared models that varied the magnitude of effects from anti-racist educators to resegregation in special education and deficit- and eugenics-based approaches (academic evaluations and early assessments). We manipulated the effects of anti-racist educators by setting values of 0.25, 0.5, and 0.8. The results of our analysis are presented in Tables 1 and 2, which provide a comparison of path estimates under each of these conditions. Of relevance to our research question were the changes in the indirect effects of resegregation on teachers' evaluations and early assessments. For Black and Latinx boys and girls, the indirect effects moved closer to zero, especially for the teacher evaluation model, indicating that anti-racist educators in kindergarten can reduce the resegregation of racially minoritized students in special education. Additionally, anti-racist educators directly reduced resegregation in special education and lessened the impact of the causes of resegregation.</p> <p>Whether the effects are small or large, the findings underscore the importance of anti-racist educators in early childhood education. If small effects are chosen, the results indicate that anti-racist interventions by educators have a modest impact. While the influence on reducing resegregation is positive, it may be limited and require additional support or complementary strategies to achieve significant change. Small effects highlight the importance of early interventions, but also suggest that systemic issues contributing to resegregation may need broader, more comprehensive approaches. Choosing large effects suggests that anti-racist educators have a substantial impact on reducing resegregation. For example, the indirect effects of the teacher evaluation model (see Table 1) revealed there were not significant differences between Black and Latinx boys and girls, in comparison to white girls, for being resegregated in special education, due to teacher evaluations. This underscores the critical role of anti-racist interventions in combating systemic inequities and emphasizes the potential for significant positive outcomes through targeted anti-racist practices.</p> <p>Moreover, it is important to note that this finding signals another important alignment between CritSEM and QuantCrit. Specifically, the argument that statistical analyses have no inherent value, but can play a role in struggles for social justice. Our results indicate that anti-racist actions among educators are key in mitigating resegregation of students in special education. Thus, CritSEM reflects an important commitment to social and racial justice that is shared among Critical Race Scholars. Innovating quantitative approaches, such as CritSEM, that explore the extent to which anti-racist actions challenge school-based policies and practices rooted in white supremacy, enhance the role of research, particularly quantitative research, in the fight for social and racial justice.</p> <hd id="AN0186502988-24">Synthesis of Results</hd> <p>The integration of findings from the synthesis confirmed <emph>why</emph> Students of Color are resegregated in special education. Findings from the structural equation models and qualitative meta-analysis aligned to fully reveal a Critical Race Counterstory about the placement of Students of Color in special education. First, students were inappropriately referred to special education. Common causes for these inappropriate referrals include teachers' unjust evaluations and inappropriate usage of assessments, based on the convergence of intersecting identities. Second, while the structural equation models showed that anti-racist educators are one way to disrupt resegregation, the qualitative meta-analysis showed a lack of culturally relevant teaching, deficit-based perspectives, and limited understanding of the issue (i.e., overrepresentation) in classrooms as reasons for resegregation. We used the qualitative meta-analysis to validate the CritSEM results by looking for points of convergence and divergence and adding depth and richness to the findings. Our quantitative models uphold and strengthen the meta-analysis, coming together to provide a greater perspective, about the core issues at stake in this research. Collectively, these aspects of our research design via CritSEM make abundantly clear that anti-racist educators are <emph>essential</emph> in classrooms to prevent resegregation.</p> <hd id="AN0186502988-25">Discussion</hd> <p>Results of the Critical Structural Equation Models suggest that teachers' deficit-based evaluations and usage of early assessments, during kindergarten, are determining a <emph>de facto</emph> segregated placement for Black and Latinx students. Informed by the structural and ideological framing that CRT provides, we argue that these students are targeted for special education and separated from general education <emph>because of their racialized identities</emph>, which, in turn, denies them educational opportunities–further cementing racism in U.S. public schooling. Teachers are permitted to carry out these processes, under the guise and protection of white supremacy, which results in students being subjected to unjust and racialized experiences, based on the intersection of race/ethnicity and notions of (dis)ability in the United States. As noted above, Black and Latinx students report negative experiences from segregated (pull-out) placements, including discrimination and microaggressions, poor-quality teaching, teacher bullying, lack of teacher quality, teachers' low expectations, limited academic growth and opportunities, and feelings of isolation (Connor, [<reflink idref="bib135" id="ref246">135</reflink>]; Craft &amp; Howley, [<reflink idref="bib31" id="ref247">31</reflink>]; Dávila, [<reflink idref="bib136" id="ref248">136</reflink>]; Ferri &amp; Connor, [<reflink idref="bib137" id="ref249">137</reflink>]; Ford, [<reflink idref="bib45" id="ref250">45</reflink>], [<reflink idref="bib46" id="ref251">46</reflink>]; Harry, Klingner, et al., [<reflink idref="bib140" id="ref252">140</reflink>]; Hart et al., [<reflink idref="bib141" id="ref253">141</reflink>]; Rogers, [<reflink idref="bib145" id="ref254">145</reflink>]; Tefera, [<reflink idref="bib146" id="ref255">146</reflink>]). And this list doesn't measure the debilitating impact of IEP diplomas, an alternative path to conventional educational pathways, preventing students from earning a high school diploma.</p> <p>The underlying question becomes, <emph>what can anti-racist educators do to confront resegregation?</emph> Our Critical Race Counterstory shows that educators can push back against an unjust special education system, using anti-racist practices, but to do so, they will need to develop the requisite knowledge, skills, and beliefs of an anti-racist educator (see definition above). Yet, even with anti-racist practices in the classroom, Black and Latinx boys and girls still had increased propensities for being resegregated in special education, due to early assessments. While anti-racism in the classroom mitigated the impact, it was not enough to completely confront white supremacist structures in the educational system. Educators can help advance an anti-racist special education system, but they will also need supporting anti-racist, administrative leadership and policies.</p> <hd id="AN0186502988-26">Implications</hd> <p>Previous research using achievement covariates to determine special education placement not only blamed Students of Color for academic disparities but also justified their overrepresentation in these programs (Cavendish et al., [<reflink idref="bib21" id="ref256">21</reflink>]; Collins et al., [<reflink idref="bib25" id="ref257">25</reflink>]; Toldson &amp; Ford, [<reflink idref="bib124" id="ref258">124</reflink>]). Using QuantCrit to analyze this body of work makes clear that, far from "neutral" and "objective," the (mis)use of statistics conceals racist assumptions. We agree with Gillborn et al. ([<reflink idref="bib56" id="ref259">56</reflink>]), that "statistics are frequently mobilized to obfuscate, camouflage, and even to further legitimate racist inequities" (p. 160). The use of statistics is made even more dangerous in the current context of neoliberalism, led largely by white liberals,[<reflink idref="bib7" id="ref260">7</reflink>] that assumes "natural" differences in intelligence, motivation, and moral character (Gillborn, [<reflink idref="bib55" id="ref261">55</reflink>]) are what inherently explain differences in educational performance (Toldson, [<reflink idref="bib123" id="ref262">123</reflink>]). We argue the absence of critical theories and approaches, like QuantCrit, not only create the potential for research to weaponize statistics to amplify culturally deficit rationales about the academic achievement of Students of Color, but also contribute to what some scholars are calling <emph>new eugenics</emph> (Baker, [<reflink idref="bib5" id="ref263">5</reflink>]; Gillborn, [<reflink idref="bib54" id="ref264">54</reflink>]). The violence is premised on quantitative research that fundamentally sees intelligence or ability as <emph>generalized</emph> (e.g., those with superior intelligence are assumed to be superior in all areas), <emph>measurable</emph> (e.g., ability is assumed to be uniform and easily quantified), and <emph>relatively fixed</emph> (e.g., those deemed gifted are assumed to remain so throughout their lives) (Gillborn, [<reflink idref="bib54" id="ref265">54</reflink>]).</p> <p>Our application and extension of QuantCrit via CritSEM demonstrates a powerful <emph>Critical Race</emph> C<emph>ounterstory</emph> to quantitative research that justifies the resegregation of Black and Latinx students in special education. We present noteworthy and empirical evidence that disputes the research of others, citing <emph>underrepresentation</emph>, who also use ECLS-K data and other nationally representative data sets.</p> <p>We believe there is a dire need to rethink and reconsider research in special education. Therefore, we call for anti-racist research in special education to advance justice by transforming, reshaping, and/or rethinking the status quo of the current special education system and the aspects of white supremacy that shape it. We believe CritSEM is an important methodological advancement toward this end. Given its alignment with QuantCrit, which is anchored in CRT, CritSEM is sensitive enough to test critical theory, backed by qualitative inputs in the development of models. These analytical approaches allow us to examine the theoretical underpinnings underlying models and test the assumptions of that theory. In this way, CritSEM provides Critical Race Researchers with a tool to quantitatively examine the structural conditions that racism produces and understand how anti-racist interventions might shape these conditions.</p> <p>Our collaboration to study the resegregation of Black and Latinx students identified for special education offered so much more than an affirmation that anti-racist educators <emph>indeed</emph> matter. It offered an opportunity to showcase the benefits of QuantCrit empirically, extending its reach into advanced statistical techniques, such as CritSEM. While we believe these developments are exciting and vitally important, we refuse any methodological interest in statistical modeling, including CritSEM, before considering the theoretical frameworks and political anchors that <emph>should</emph> guide the work. The desire to know the technical elements of methodological tools, particularly quantitative tools, cannot override the need to understand their epistemological underpinnings. Although one needs to consider the range of what advanced quantitative research tools can do—the statistical computations, formulas, and analyses—we argue that it is imperative to center and amplify one's theoretical and political practices before imagining one's technical practices. Without attending to the white supremacist logics that shape statistical approaches, we are likely to reproduce them, even when our commitments to racial justice are clear and sincere.</p> <hd id="AN0186502988-27">Conclusion</hd> <p>We recognize that the work we do is often met with opposition from those that would claim our approach violates "objectivity" in a methodological field that has traditionally prided itself on this characterization. CritSEM makes clear that no research—whether quantitative or qualitative—is neutral and bias-free. CritSEM pivots advanced statistical modeling toward a structural analysis of power with the intention of remedying injustice in all its forms. It does this by helping quantitative researchers become more aware of and explicitly examine how race and racism are likely to be operating in their models. As it integrates qualitative and quantitative approaches to produce more nuanced and accurate models of the relationships among variables and increase the understanding of the complex and multilayered systems of power in society, CritSEM is unapologetic in its anti-racist aims. Some quantitative scholars might see this as a methodological contradiction that cannot be remedied, or a bias that should not be allowed in "rigorous" quantitative educational scholarship. Instead, we see this as one of CritSEM's greatest strengths. Not only is it technically sophisticated in its approach, but it also fundamentally positions research in service of the public good, which cannot be achieved without advancing justice-producing actions.</p> <p>While QuantCrit scholars have adequately exposed the flawed arguments in defense of these claims, less sufficiently addressed have been those critiques from critical <emph>qualitative</emph> scholars, including Critical Race scholars, who are yet to be convinced that quantitative approaches can be rectified to support anti-racist ends. We agree with many of their critiques, which guided how we developed CritSEM. Additionally, we also see their appraisals as an invitation to deepen our engagement with a range of statistical approaches. There is much more work to be done. We humbly offer a step forward here, propelled by an unwavering commitment as Critical Race scholars to continue addressing the theoretical and methodological tensions in quantitative research on Students of Color, their families, and their communities.</p> <hd id="AN0186502988-28">Acknowledgement</hd> <p>We are thankful for Maria Haji‑Georgi's brilliant drawing of the phantom variable in Figure 2.</p> <hd id="AN0186502988-29">Disclosure Statement</hd> <p>No potential conflict of interest was reported by the author(s).</p> <hd id="AN0186502988-30">Open Research Statements</hd> <p></p> <hd id="AN0186502988-31">Study and Analysis Plan Registration</hd> <p>There is no registration associated with this study.</p> <hd id="AN0186502988-32">Data, Code, and Materials Transparency</hd> <p>This study used restricted data from the ECLS-K, 2011 Study. Researchers can apply for a restricted data license to access this data with the National Center for Educational Statistics (NCES), using the following link: https://nces.ed.gov/statprog/instruct.asp. Requirements for a restricted data license can also be found on the NCES site via the link. The materials and code associated with this study are not publicly available.</p> <hd id="AN0186502988-33">Design and Analysis Reporting Guidelines</hd> <p>Not applicable.</p> <hd id="AN0186502988-34">Transparency Declaration</hd> <p>The lead author (the manuscript's guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.</p> <hd id="AN0186502988-35">Replication Statement</hd> <p>This manuscript reports an original study.</p> <hd id="AN0186502988-36">Appendix A. 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This choice is informed by critical scholarship and activism, such as that by Dumas ([43]) who writes that Black is a "self-determined name of a racialized social group that shares a specific set of histories, cultural processes, and imagined and performed kinships" (p. 12), white, on the other hand, is a socially constructed category that was created for the purposes of dominance and exclusion; it "does not describe a group with a sense of common experiences or kinship outside of acts of Incolonization or terror" (p. 13).</bibtext> </blist> <blist> <bibtext> In contrast to a meaning of white supremacy that focuses on overt acts of racialized hatred, when we use the term "white supremacy," we mean the everyday enactments of "the systematic maintenance of the dominant position that produces [w]hite privilege" (Battey &amp; Leyva, [7], p. 50) – the multitude of mechanisms by which whiteness stays at the center.</bibtext> </blist> <blist> <bibtext> We use color-evasive in lieu of color-blind, the more traditional term for this ideology to (a) problematize an assumption that equates blindness with ignorance that inaccurately conveys and distorts the unique way blind individuals interact with the world; and, (b) rethink and remove ableist language as core to our explicit efforts toward social justice in all aspects of our work, particularly in research and scholarship (Annamma et al., [3]).</bibtext> </blist> <blist> <bibtext> In our design and application of a "phantom" variable, we are inspired by the work of Bozalek et al. ([17]) and Zembylas et al. ([130]) who draw from the well-known work of Derrida ([40]) and his theorizing of "hauntology." As a play on ontology, hauntology engages the relationship between then/now, presence/absence, bringing forward the "ghosts" to challenge the past and chart a different future. As Taylor and Fairchild ([119]) note, "the promise of hauntological analyses is that a justice-to-come moves beyond/outside the calculations of current hegemonic formations to engage with the incalculable. justice-to-come requires an openness and an attunement to new possibilities." We believe phantom variables, as we have conceptualized them in this paper, move toward a hauntological analysis for quantitative methods, offering new possibilities for reimagining hegemonic norms in quantitative educational research.</bibtext> </blist> <blist> <bibtext> Details about the ECLS-K study can be found online (Tourangeau et al., [125]).</bibtext> </blist> <blist> <bibtext> Information about the psychometric properties of scales and assessments can be found in the NCES Psychometric Report (Najarian et al., [95]).</bibtext> </blist> <blist> <bibtext> white liberalism maintains the appearance of advancing civil rights for marginalized communities out of self-interests, and not core beliefs of racial and social justice, that only continues to cover up the underlying truths about the foundational racism and inequity in the United States" (Crenshaw, [34]; Delgado, [37], [38]; Delgado &amp; Stefancic, [39]).</bibtext> </blist> </ref> <aug> <p>By Nicholas S. 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| Header | DbId: eric DbLabel: ERIC An: EJ1493720 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: CritSEM: Advancing QuantCrit to Examine Racialized Resegregation in Special Education – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nicholas+S%2E+Bell%22">Nicholas S. Bell</searchLink><br /><searchLink fieldCode="AR" term="%22Zachary+Collier%22">Zachary Collier</searchLink><br /><searchLink fieldCode="AR" term="%22Verónica+N%2E+Vélez%22">Verónica N. Vélez</searchLink><br /><searchLink fieldCode="AR" term="%22Donna+Y%2E+Ford%22">Donna Y. Ford</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Journal+of+Research+on+Educational+Effectiveness%22"><i>Journal of Research on Educational Effectiveness</i></searchLink>. 2025 18(2):390-422. – Name: Avail Label: Availability Group: Avail Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 33 – Name: DatePubCY Label: Publication Date Group: Date Data: 2025 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Research – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Special+Education%22">Special Education</searchLink><br /><searchLink fieldCode="DE" term="%22African+American+Students%22">African American Students</searchLink><br /><searchLink fieldCode="DE" term="%22Hispanic+American+Students%22">Hispanic American Students</searchLink><br /><searchLink fieldCode="DE" term="%22School+Resegregation%22">School Resegregation</searchLink><br /><searchLink fieldCode="DE" term="%22Racism%22">Racism</searchLink><br /><searchLink fieldCode="DE" term="%22Disproportionate+Representation%22">Disproportionate Representation</searchLink><br /><searchLink fieldCode="DE" term="%22Critical+Race+Theory%22">Critical Race Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+Studies%22">Longitudinal Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Surveys%22">Surveys</searchLink><br /><searchLink fieldCode="DE" term="%22Social+Justice%22">Social Justice</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+Equation+Models%22">Structural Equation Models</searchLink> – Name: SubjectThesaurus Label: Assessment and Survey Identifiers Group: Su Data: <searchLink fieldCode="SU" term="%22Early+Childhood+Longitudinal+Survey%22">Early Childhood Longitudinal Survey</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1080/19345747.2024.2408538 – Name: ISSN Label: ISSN Group: ISSN Data: 1934-5747<br />1934-5739 – Name: Abstract Label: Abstract Group: Ab Data: The field of education urgently needs research, aligned with QuantCrit, to disrupt the racialized overrepresentation [resegregation] of Black and Latinx students in special education. Our concern about how statistics have been (mis)applied motivated us to consider the possibility of quantitative methods for use in educational research that aims to dismantle white supremacy. Therefore, we developed "CritSEM (Critical Structural Equation Modeling)" as a methodological intervention for the purpose of telling a Critical Race Counterstory. Specifically, we examined the extent that anti-racist educators can disrupt processes causing the resegregation of Black and Latinx students in special education, using restricted data from the 2011 Early Childhood Longitudinal Study. Our study concludes with a discussion and implications about working toward an anti-racist special education system and applying QuantCrit in advanced statistical methods. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2026 – Name: AN Label: Accession Number Group: ID Data: EJ1493720 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1493720 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/19345747.2024.2408538 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 33 StartPage: 390 Subjects: – SubjectFull: Special Education Type: general – SubjectFull: African American Students Type: general – SubjectFull: Hispanic American Students Type: general – SubjectFull: School Resegregation Type: general – SubjectFull: Racism Type: general – SubjectFull: Disproportionate Representation Type: general – SubjectFull: Critical Race Theory Type: general – SubjectFull: Children Type: general – SubjectFull: Longitudinal Studies Type: general – SubjectFull: Surveys Type: general – SubjectFull: Social Justice Type: general – SubjectFull: Structural Equation Models Type: general – SubjectFull: Early Childhood Longitudinal Survey Type: general Titles: – TitleFull: CritSEM: Advancing QuantCrit to Examine Racialized Resegregation in Special Education Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nicholas S. Bell – PersonEntity: Name: NameFull: Zachary Collier – PersonEntity: Name: NameFull: Verónica N. Vélez – PersonEntity: Name: NameFull: Donna Y. Ford IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1934-5747 – Type: issn-electronic Value: 1934-5739 Numbering: – Type: volume Value: 18 – Type: issue Value: 2 Titles: – TitleFull: Journal of Research on Educational Effectiveness Type: main |
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