November 2016: Minority Representation in Special Education

Overview

A number of studies have found that racial-, ethnic-, and language-minority schoolchildren in the United States are overidentified as having a disability and are thus disproportionately overrepresented in special education (e.g., Artiles, 2003; Dunn, 1968; Harry, Arnaiz, Klingner, & Sturges, 2008; Oswald, Coutinho, Best, & Singh, 1999; Sullivan & Bal, 2013). The prevalence and relative consistency of this finding have resulted in federal policies designed to reduce disproportionality (e.g., Posner, 2007; U.S. Department of Education, Office of Civil Rights, 2009). For example, local educational agencies are required to report to the U.S. Department of Education whether minority children are significantly overrepresented in special education. If overrepresentation is reported, the local educational agency in question is required to allocate 15% of its IDEA Part B funds to early intervention services for children from minority groups who are overidentified for special education.

However, there is also evidence in the other direction; several researchers have reported that minority children are underidentified as disabled (e.g., Hibel, Farkas, & Morgan, 2010; Morgan, Farkas, Hillemeier, & Maczuga, 2012; Morgan, Staff, Hillemeier, Farkas, & Maczuga, 2013; Shifrer, Muller, & Callahan, 2011; Sullivan, 2013) and thus underrepresented in special education (Delgado & Scott, 2006). Morgan et al. (2015) reconsider this issue using data from the Early Childhood Longitudinal Survey and advanced statistical models that control for the effects of correlated but confounding factors.

Substantive Causes of Disproportionality

A number of causes have been proposed to explain the disproportionality of minority students in special education. For example, minority children are believed to experience systemic prejudice such that their abilities and behaviors are unjustifiably characterized as nontypical and problematic (Coutinho & Oswald, 2000; Harry et al., 2008; Hays, Prosek, & McLeod, 2010; Lorsen & Orfield, 2002; O’Connor & Fernandez, 2006), leading to overrepresentation. Others have argued that schools use academic and behavioral standards more aligned with white, English-speaking, middle-class, privileged populations (Blanchett, Klingner, & Harry, 2009), pathologizing racial-, ethnic-, and language-minority differences and increasing the probability that children from these groups are identified as disabled (e.g., Peske & Haycock, 2006). It is also the case that children from less privileged circumstances are more likely than more privileged peers to be exposed to harmful biological and environmental factors in early childhood that disproportionately increase their risk for impaired cognitive, academic, and behavioral functioning and disability (Annie E. Casey Foundation, 2014; Donovan & Cross, 2002; Mann, McCartney, & Park, 2007). Because racial- and language-minority groups are more prevalent in lower-income settings, it follows that children from these groups are more likely to experience later disability.

To explain evidence of minority underrepresentation in special education (e.g., Hibel et al., 2010; Morgan et al., 2012; Rosenberg, Zhang, & Robinson, 2008; Samson & Lesaux, 2009), investigators have argued that socioeconomic, linguistic, and/or cultural obstacles may limit access to or use of special education services by families of children who are from cultural-, racial-, or linguistic-minority groups (Coll, Crnic, Lamberty, & Wasik, 1996; Danesco, 1997; Harry, 1992; O’Hara, 2003; Pena & Fiestas, 2009). Coll et al. (1996) have found that some cultural groups prefer to rely on their extended families rather than public resources to support special-needs children. Accordingly, they may decline evaluation requests for special education. Members of minority groups may also attribute lower academic or behavioral performance to systemic prejudice or other nonbiological, child-level factors (Danesco, 1997; Yeh, Forness, Ho, McCabe, & Hough, 2004; Yeh, Hough, McCabe, Lau, & Garland, 2004) and decline the suggestion of special education referral. Relatedly, the stigma associated with disability may discourage families from racial-, ethnic-, or language-minority groups to have their child identified as disabled (Hervey-Jumper, Dougan, & Franco, 2008; O’Hara, 2003; Zuckerman et al., 2014). Finally, children attending disadvantaged schools may be less likely to be identified as disabled (Delpit, 1995), particularly for disorders that are less apparent (e.g., learning disability), because low performance may be less atypical in the context of the local school (see Hibel et al., 2010, and the “frog pond” effects in regard to special education eligibility).

Methodological Causes of Disproportionality

The underrepresentation–overrepresentation conundrum may also have methodological roots. Early studies failed to account for potential confounding factors prior to estimating minority children’s risk of being identified as disabled because the statistical software and computing power for doing so were difficult to access (Donovan & Cross, 2002; MacMillian & Reschly, 1998). Educational studies that use covariate adjustments tend to report that racial-, ethnic-, and language-minority children are underidentified as disabled when controlling for child, family, and school factors (e.g., Hibel et al., 2010; Morgan et al., 2012; Morgan et al., 2013; Shifrer et al., 2011; Yeh, Forness et al., 2004), a finding that also is prevalent in the public health literature (e.g., Bussing, Zima, Gary, & Garvan, 2003), where covariate adjustments are typically used to investigate disparities in disability identification and treatment. For example, children who are black are less likely to be diagnosed with autism, learning disabilities, and attention-deficit/hyperactivity disorder following covariate adjustment for IQ, prior academic achievement and behavior, maternal education, and additional factors (e.g., Bussing et al., 2003; Mandell, Listerud, Levy, & Pinto-Martin, 2002; Morgan et al., 2013; Pastor & Reuben, 2005). Furthermore, children from minority groups who are diagnosed with learning disabilities, attention deficit hyperactivity disorder, etc., tend to be disproportionately less likely than otherwise similar white children to participate in special education (Morgan et al., 2013). Additional methodological and substantive limitations characterize the extant empirical work.

Findings

When controlling for potential confounding variables, Morgan et al. (2015) found that minority children were consistently less likely than otherwise similar white, English-speaking children to be identified as disabled and to receive special education services. From kindergarten entry to the end of middle school, racial- and ethnic-minority children were less likely to be identified as having (a) learning disabilities, (b) speech or language impairments, (c) intellectual disabilities, (d) health impairments, or (e) emotional disturbances. Language-minority children were less likely to be identified as having (a) learning disabilities or (b) speech or language impairments. In essence, the findings suggest that when other causes of disability identification are constrained as equal (e.g., controlled) across racial, ethnic, and language groups, children from minority groups are less likely to be identified as having a disability or to participate in special education. The authors do not suggest that black children, for example, are less frequently identified than children from other groups. Their argument is that although racial-, ethnic, and language-minority students may have higher rates of identification, these students are comparatively underrepresented when controlling for the other causes of identification.

Implications

These findings have implications for researchers and practitioners.

1. These findings need to be replicated in the Early Childhood Longitudinal Survey data. The statistical models that Morgan et al. (2015) used are sophisticated, and they require sometimes-subjective decisions by the investigators. Like many advanced statistical procedures, the improved accuracy they promise is often balanced by the uncertainty surrounding many of the decisions required by the investigator. For example, the Early Childhood Longitudinal Survey is a probability-based sample that requires the use of sampling weights to obtain a correct estimate of the population value. There is disagreement (or at least very little evidence-based guidance) on the concurrent use of time-varying covariates and sampling weights in hazards models. Morgan et al. report their method in transparent terms. However, theirs is not the only approach. Another method of combing covariate weighting and sample weighting might lead to different findings, a possibility that deserves attention, given the policy decisions that this line of research informs.
2. For practitioners, the findings underscore the importance of collecting data on students’ response to intervention. Children who are truly disabled are best identified by their pattern of response to effective instruction. Child, family, and school variables are certainly factors in the complex decision making that underlies disability status when considered in the population of students in U.S. public schools. However, for individual children who struggle to learn, the best approach is provide increasingly intensive supports, regardless of ethnicity, race, or primary language.

References

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