Abstract
This chapter examines the extent to which socioeconomic and racial demographics are related to special education disproportionality and in so doing adds to the existing research on the impact of segregation and desegregation on student outcomes. Using district-level special education classification data, student racial demographic data, community socioeconomic data, and student academic achievement data, regression models were structured to analyze the relationship between the socioeconomic and racial composition of a school district and the likelihood that Black students would be disproportionately classified as disabled. The analysis shows that as the percentage of Black students in a school district decreased, the likelihood that Black students would be disproportionately classified as disabled and the likelihood that Black students would be classified as disabled in general both increased. This demonstrates that racial demographic characteristics of a school district are related to the overrepresentation of Black students in special education.
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Notes
- 1.
Odds ratios comparing the classification rates of Black students to White students were used as measures of relative likelihoods.
- 2.
This is counter to Oswald et al.’s (1999) findings.
- 3.
Their model also examined school-level variables, showing that suspension–expulsion rates are correlated to disproportionality in ED classifications, dropout rates are negatively correlated to disproportionality in MoMR classifications and positively correlated to disproportionality in SL classifications, achievement levels are positively correlated to disproportionality in MMR classifications and negatively correlated to disproportionality in SL classifications, and student–teacher ratios were positively correlated with disproportionality in MMR classifications. Moreover, the study’s logistic regression demonstrates that race (the proportion of African American students) was a better predictor of disproportionality than poverty and that school suspensions and expulsions proved to be the most significant predictor of disproportionality.
- 4.
MacMillan and Reschly (1998) raised concerns about Oswald et al.’s (1999), Oswald et al.’s (2001), and Coutinho et al.’s (2002) use of data from the Office of Civil Rights, arguing that it oversamples from large urban districts, thus limiting the ability to generalize the findings to a national level.
- 5.
The contradictory nature of these findings may put into question the validity of the hypothesis of differential susceptibility.
- 6.
The following district enrollment criteria were used to construct the final dataset: (a) at least 75 students with disabilities enrolled; (b) a minimum of 30 Black students (disabled and nondisabled) enrolled; (c) at least 75 non-Black students (disabled and nondisabled) enrolled; and (d) at least 10 Black students with disabilities.
- 7.
Based on the 649 school districts in the original dataset for which there is enrollment data, these 263 school districts serve 71.6Â % of the total number of students enrolled in the entire dataset. Moreover, based on the 652 school districts in the original dataset for which there is special education enrollment data, these 262 school districts served 73.8Â % of the total number of students with disabilities enrolled in the entire dataset.
- 8.
Originally, the academic performance of students from several grade levels was considered for this analysis, but they proved to be highly correlated with each other.
- 9.
This is a point of departure from Skiba et al. (2005), which used Z-scores.
- 10.
Reporting relative risk ratios alone can pose a problem in that relative risk values are not comparable to each other. A relative risk of 2.0 in one case is not the same as relative risk of 2.0 in another due to proportions. For example, in school district A, 10Â % of Black students are classified as disabled, while 5Â % of non-Black students are classified as disabled. At the same time, in school district B, 30Â % of Black students are classified as disabled, while 15Â % on non-Black students are classified as disabled. In both school district A and B, Black students are twice as likely to be classified as disabled compared to all other students, but Black students in school district B are three times more likely to be classified as disabled compared to Black students in school district A. For this reason, this study also will use the risk index of Black student being classified as disabled.
- 11.
The risk that non-Black students have of being classified as disabled will be used as an independent variable in this model.
- 12.
\( {\text{RR}}_{\text{Black}} = \frac{{{\text{SWD}}_{\text{Black}} /({\text{SWD}}_{\text{Black}} + {\text{GEN}}_{\text{Black}} )}}{{{\text{SWD}}_{\text{Other}} /({\text{SWD}}_{\text{Other}} + {\text{GEN}}_{\text{Other}} )}} \).
- 13.
As such, in the descriptive statistics, both the relative risk of Black students being classified with a disability as well as the natural log transformed relative risk. When reporting inferential statistics, only the natural log transformed relative risk is reported (though for the sake of clarity in the writing, the natural log transformed relative risk will be referred to as the relative risk).
- 14.
\( {\text{RI}}_{\text{Black}} = {\text{SWD}}_{\text{Black}} /({\text{SWD}}_{\text{Black}} + {\text{GEN}}_{\text{Black}} ) \).
- 15.
The interrelationship between the descriptive variables helps define the regression model. The proportion of Black students and the concentration of poverty in a school district were moderately correlated. Skiba et al. (2005) do, however, note that poverty and race may operate differently with respect to disproportionality. Therefore, both variables were included in the regression model. There was a moderate positive correlation between the proportion of Black students in a school district and the overall district enrollment, and a strong negative correlation between the proportion of Black students in a school district and the average ELA achievement.
- 16.
There is a moderate negative correlation between the risk index of non-Black students and the relative risk of Black students being classified as disabled, r = 0.3333, n = 263, p < 0.0001. This correlation is tautological to the definition of relative risk—as the risk of non-Black students decreases, the relative risk of Black students increases.
- 17.
Based on the number of univariate outliers and the skew of the descriptive variable, the decision was made to transform the several descriptive variables to reduce the number of outliers and improve the normality and homoscedasticity of the residuals. Natural log transformations were used on the percent of Black and African American students enrolled in district, the percent of children ages 5 through 17 living in families below the poverty line in district (concentration of poverty), and the total district enrollment variables. One case with missing data was removed from the data, but none of the outliers were removed, NÂ =Â 262. This did not have any significant impact on the correlations between the variables.
- 18.
Similar to risk ratio analysis, based on the number of univariate outliers and the skew of the descriptive variables, the decision was made to transform the several descriptive variables to reduce the number of outliers and improve the normality and homoscedasticity of the residuals. Natural log transformations were used on the risk index of Black students being classified as disabled, the risk index of non-Black students being classified as disabled, the percent of Black and African American students enrolled in district, the percent of children ages 5 through 17 living in families below the poverty line in district (concentration of poverty), and the total district enrollment variables. In one case, missing data was removed, but none of the outliers were removed (NÂ =Â 262). This did not have any significant impact on the correlations between the variables.
- 19.
This not a percentage point increase, but rather a percent change of the proportions. For example, a change from 75Â % Black student enrollment to 65Â % may represent a 10-percentage point change in the Black student enrollment, but it also represents a 15Â % change in the Black student enrollment. The 1Â % point change in the text refers to this second method of looking a percentage change and not the first.
- 20.
It should be noted that the risk index of Black students classified as disabled is not correlated with concentration of poverty in a school district, while there is a moderate positive correlation between the risk index of non-Black students and the concentration of poverty.
- 21.
They found that districts with the lower concentrations of students of color reported higher special education classification rates, suggesting that additional research is needed to understand referral and classification processes in schools where fewer non-White students are enrolled.
- 22.
Thus, educational practitioners might argue that by receiving a special education classification students are afforded additional resources and services that they might not otherwise get. Moreover, it could be argued that special education services provide additional support to struggling students and the overrepresentation of Black students in special education is indicative of response to need—providing struggling Black students with the supports they need to be successful in schools. Researchers enjoying this perspective have argued that Black students are in fact underrepresented in special education—indicating that despite being disproportionately overrepresented in special education, their educational needs (as defined both by their academic performance and their socioeconomic status) is such that Black students require additional special education services (Hibel, Farkas, & Morgan, 2010, Morgan et al., 2015). In a sense, this line of research argues that Black students (as well as Native American students, Hispanic Students, and language minority students) should be more disproportionately classified as disabled because they have a greater educational need. If this is the case, significant attention should be placed on the quality of those special education services being offered—as noted above research suggests special education services may not be an effective means of supporting student (Donovan & Cross, 2002; Fierros & Conroy, 2002; Gottlieb & Alter, 1994; Harry & Klingner, 2006), and carry with it the negative externalities of stigmatization (Donovan & Cross, 2002; Gartner & Lipsky, 1999; Wagner et al. 2007) and permanence (Fierros & Conroy, 2002; Harry & Klingner, 2006).
- 23.
References
Anderson-Levitt, K. M. (1984). Teacher interpretation of student behavior: Cognitive and social processes. The Elementary School Journal, 84(3), 315–337.
Anyon, J. (1980). Social class and the hidden curriculum of work. Journal of Education, 162(1), 67–72.
Anyon, J. (1981). Social class and school knowledge. Curriculum Inquiry, 11(1), 3–42.
Borman, G., & Dowling, M. (2010). Schools and inequality: A multilevel analysis of coleman’s equality of educational opportunity data. Teachers College Record, 112(5), 1201–1246.
Borman, K. M., Eitle, T. M., Michael, D., Eitle, D. J., Lee, R., Johnson, L., & Shircliffe, B. (2004). Accountability in a postdesegregation era: The continuing significance of racial segregation in Florida’s schools. American Educational Research Journal, 41(3), 605–631.
Brown v. Board of Education, 347 483 (US 1954).
Card, D., & Rothstein, J. (2007). Racial segregation and the black–white test score gap. Journal of Public Economics, 91(11), 2158–2184.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Laurence Earlbaum Associates.
Coutinho, M. J., Oswald, D. P., & Best, A. M. (2002). The influence of sociodemographics and gender on the disproportionate identification of minority students as having learning disabilities. Remedial and Special Education, 23(1), 49–59.
Darling-Hammond, L. (2013). Inequality and school resources: What will it take to close the opportunity gap. In P. L. Carter & K. G. Welner (Eds.), Closing the opportunity gap: What America must do to give every child an even chance (pp. 77–97). New York, NY: Oxford University Press.
Delpit, L. (2006). Other people’s children: Cultural conflict in the classroom. New York, NY: The New Press.
Donovan, M. S., & Cross, C. T. (2002). Minority students in special and gifted education. Washington, DC: National Academies Press.
Dunn, L. M. (1968). Special education for the mildly retarded: Is much of it justifiable? Exceptional Children, 35, 5–22.
Durlak, J. A. (2009). How to select, calculate, and interpret effect sizes. Journal of Pediatric Psychology, 34(9), 917–928. doi:10.1093/jpepsy/jsp004.
Eitle, T. M. (2002). Special education or racial segregation: Understanding variation in the representation of Black students in educable mentally handicapped programs. Sociological Quarterly, 43(4), 575–605. doi:10.1111/j.1533-8525.2002.tb00067.x.
Ferguson, R. F. (2003). Teachers’ perceptions and expectations and the black-white test score gap. Urban Education, 38(4), 460–507.
Ferri, B., & Connor, D. (2005). Tools of exclusion: Race, disability, and (re) segregated education. The Teachers College Record, 107(3), 453–474.
Fierros, E. G., & Conroy, J. W. (2002). Double jeopardy: An exploration of restrictiveness and race in special education. In D. J. Losen & G. Orfield (Eds.), Racial inequity in special education (pp. 39–70).
Figlio, D. N., & Getzler, L. S. (2002). Accountability, ability and disability: gaming the system (No. w9307). National Bureau of Economic Research.
Finn, J. D. (1982). Patterns in special education placement as revealed by the OCR surveys. In K. A. Heller, W. H. Holtzman, & S. Messick (Eds.), Placing children in special education: A strategy for equity (pp. 322–381). Washington, D.C.: National Academy Press.
Ford, D. Y., Harris, J. J, III, Tyson, C. A., & Trotman, M. F. (2001). Beyond deficit thinking: Providing access for gifted African american students. Roeper Review, 24(2), 52–58.
Frankenberg, E., & Orfield, G. (2012). The resegregation of suburban schools: A hidden crisis in American education. Cambridge, MA: Harvard Education Press.
Gartner, A., & Lipsky, D. K. (1999). Disability, human rights and education: The United States. In F. Armstrong & L. Barton (Eds.), Disability, human rights, and education: Cross cultural perspectives (pp. 100–118).
Gay, G. (2010). Culturally responsive teaching: Theory, research, and practice. New York: Teachers College Press.
Gottlieb, J., & Alter, M. (1994). Evaluation study of the overrepresentation of children of color referred to special education.
Gravois, T. A., & Rosenfield, S. (2002). A multi-dimensional framework for evaluation of instructional consultation teams. Journal of Applied School Psychology, 19(1), 5–29.
Gravois, T. A., & Rosenfield, S. A. (2006). Impact of instructional consultation teams on the disproportionate referral and placement of minority students in special education. Remedial and Special Education, 27(1), 42–52.
Greenwald, R., Hedges, L. V., & Laine, R. D. (1996). The effect of school resources on student achievement. Review of Educational Research, 66(3), 361–396.
Hale, J. E. (2001). Learning while black: Creating educational excellence for african american children. Baltimore, MD: JHU Press.
Hanushek, E. A., Kain, J. F., & Rivkin, S. G. (2002). New evidence about Brown v. Board of Education: The complex effects of school racial composition on achievement (No. w8741). Cambridge, MA: National Bureau of Economic Research.
Harry, B., Klingner, J., Sturges, K. M., & Moore, R. F. (2002). Of rocks and soft places: Using qualitative methods to investigate disproportionality. In D. J. Losen & G. Orfield (Eds.), Racial inequity in special education (pp. 71–92). Cambridge, MA: Harvard Education Press.
Harry, B., & Klingner, J. K. (2006). Why are so many minority students in special education?: Understanding race and disability in schools. New York, NY: Teachers College Press.
Harry, B., Klingner, J. K., Cramer, E. P., Sturges, K. M., & Moore, R. F. (2007). Case studies of minority student placement in special education. New York, NY: Teachers College Press.
Heath, S. B. (1983). Ways with words: Language, life and work in communities and classrooms. New York, NY: Cambridge University Press.
Heller, K., Holtzman, W., & Messick, S. (1982). Placing children in special education: A strategy for equity. Panel on selection and placement of students in programs for the mentally retarded, Committee on Child Development Research and Public Policy, Commission on Behavioral and Social Sciences and Education, National Research Council.
Hibel, J., Farkas, G., & Morgan, P. L. (2010). Who is placed into special education?. Sociology of Education, 83(4), 312–332.
Hosp, J. L., & Reschly, D. J. (2003). Referral rates for intervention or assessment: A meta-analysis of racial differences. The Journal of Special Education, 37(2), 67–80.
Irvine, J. J. (1990). Black students and school failure. Policies, practices, and prescriptions. Westport, CT: Greenwood Press, Inc.
King, J. E. (1991). Dysconscious racism: Ideology, identity, and the miseducation of teachers. The Journal of Negro Education, 60(2), 133–146.
Knotek, S. (2003). Bias in problem solving and the social process of student study teams: A qualitative investigation. The Journal of Special Education, 37(1), 2–14.
Ladner, M., & Hammons, C. (2001). Special but unequal: Race and special education. In C. E. Finn, A. Rotherham J. & C. R. Hokanson (Eds.), Rethinking special education for a new century (pp. 85–110). Washington, DC: Thomas B. Fordham Foundation: Progressive Policy Institute.
Ladson-Billings, G. (1999). Preparing teachers for diverse student populations: A critical race theory perspective. Review of Research in Education, 24, 211–247.
Lee, C. (2004). Racial segregation and educational outcomes in metropolitan Boston. Cambridge, MA: The Civil Rights Project.
Lewis, A. (2003). Race in the schoolyard: Negotiating the color line in classrooms and communities. Piscataway, NJ: Rutgers University Press.
Lipsey, M. W., & Wilson, D. B. (2000). Practical meta-analysis. Thousand Oaks, CA: Sage.
Losen, D. J., & Orfield, G. (2002). Racial inequity in special education. Cambridge, MA: Civil Rights Project, Harvard University.
Lutz, B. (2011). The end of court-ordered desegregation. American Economic Journal: Economic Policy, 3(2), 130–168.
MacMillan, D. L., & Reschly, D. J. (1998). Overrepresentation of minority students: the case for greater specificity of the variables examined. The Journal of Special Education, 32, 15–24.
Mehan, H. (1980). The competent student. Anthropology & Education Quarterly, 11(3), 131–152.
Mehan, H., Hertweck, A., & Meihls, J. L. (1986). Handicapping the handicapped: Decision making in students’ educational careers. Stanford: Stanford University Press.
Meier, K. J., Stewart Jr, J., & England, R. E. (1989). Race, class, and education: The politics of second generation discrimination. Madison: University of Wisconsin Press.
Mickelson, R. A. (2015). The cumulative disadvantages of first-and second-generation segregation for middle school achievement. American Educational Research Journal, 0002831215587933.
Mickelson, R. A., & Nkomo, M. (2012). Integrated schooling, life course outcomes, and social cohesion in multiethnic democratic societies. Review of Research in Education, 36(1), 197–238.
Moll, L. C., Amanti, C., Neff, D., & Gonzalez, N. (1992). Funds of knowledge for teaching: Using a qualitative approach to connect homes and classrooms. Theory into Practice, 31(2), 132–141.
Morgan, P. L., Farkas, G., Hillemeier, M. M., Mattison, R., Maczuga, S., Li, H., & Cook, M. (2015). Minorities are disproportionately underrepresented in special education longitudinal evidence across five disability conditions. Educational Researcher, 44(5), 278–292.
National Education Association of the United States, & National Association of School Psychologists. (2007). Truth in labeling: Disproportionality in special education. National Education Association.
Nieto, S. (2004). Affirming diversity: The sociopolitical context of multicultural education (4th ed.). Boston: Pearson/Allyn and Bacon.
O’Connor, C., & Fernandez, S. D. (2006). Race, class, and disproportionality: Reevaluating the relationship between poverty and special education placement. Educational Researcher, 35(6), 6–11.
Orfield, G. (2013). Housing segregation produces unequal schools: Causes and solutions. In P. L. Carter & K. G. Welner (Eds.), Closing the opportunity gap: What america must do to give every child an even chance (pp. 40–60). New York, NY: Oxford University Press.
Orfield, G., Frankenberg, E., Ee, J., & Kuscera, J. (2014). Brown at 60: great progress, a long retreat and an uncertain future. Los Angeles, CA: Civil Rights Project/Proyecto Derechos Civiles.
Orfield, G., & Lee, C. (2007). Historic reversals, accelerating resegregation, and the need for new integration strategies. Civil Rights Project/Proyecto Derechos Civiles.
Oswald, D. P., Coutinho, M. J., Best, A. M., & Nguyen, N. (2001). Impact of sociodemographic characteristics on the identification rates of minority students as having mental retardation. Journal Information, 39(5).
Oswald, D. P., Coutinho, M. J., Best, A. M., & Singh, N. N. (1999). Ethnic representation in special education the influence of school-related economic and demographic variables. The Journal of Special Education, 32(4), 194–206.
Reber, S. J. (2005). Court-ordered desegregation successes and failures integrating American schools since Brown versus Board of Education. Journal of Human Resources, 40(3), 559–590.
Reynolds, D., Teddlie, C., Creemers, B., Scheerens, J., & Townsend, T. (2000). An introduction to school effectiveness research. In D. Reynolds & C. Teddlie (Eds.), The international handbook of school effectiveness research (pp. 3–25). New York: Falmer Press.
Rothstein, R. (2004). Class and schools: Using social, economic, and educational reform to close the achievement gap. Washington, DC: Economic Policy Institute.
Skiba, R. J., Poloni-Staudinger, L., Simmons, A. B., Feggins-Azziz, L. R., & Chung, C. (2005). Unproven links can poverty explain ethnic disproportionality in special education? The Journal of Special Education, 39(3), 130–144.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). New York: Pearson Education Inc.
Tyler, K. M., Boykin, A. W., Miller, O., & Hurley, E. (2006). Cultural values in the home and school experiences of low-income African-American students. Social Psychology of Education, 9(4), 363–380.
U.S. Department of Education, Office of Special Education and Rehabilitative Services, Office of Special Education Programs. (2014). 36th annual report to congress on the implementation of the individuals with disabilities education act, 2014. Washington, DC: Author.
Valenzuela, A. (1999). Subtractive schooling: US-Mexican youth and the politics of caring. Albany, NY: SUNY Press.
Wagner, M., Newman, L., Cameto, R., Levine, P., & Marder, C. (2007). Perceptions and expectations of youth with disabilities. A special topic report of findings from the national longitudinal transition study-2 (NLTS2). NCSER 2007-3006. National Center for Special Education Research.
Zhang, D., & Katsiyannis, A. (2002). Minority representation in special education a persistent challenge. Remedial and Special Education, 23(3), 180–187.
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Ahram, R. (2016). The Diversity of School and Community Contexts and Implications for Special Education Classifications. In: Noguera, P., Pierce, J., Ahram, R. (eds) Race, Equity, and Education. Springer, Cham. https://doi.org/10.1007/978-3-319-23772-5_13
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