Skip to main content

Advertisement

SpringerLink
Log in

Compositional Effects, Segregation and Test Scores: Evidence From the National Assessment of Educational Progress

  • Published:
The Review of Black Political Economy

Abstract

A model of the effects of economic level and ethnicity on grade 8 mathematics scores both within and between schools found that both the economic composition and the ethnic composition of a school were directly related to the effectiveness of that school. Projection of the data suggests that if the nation's schools were completely desegregated economically (but not at all ethnically), the test-score gap between free lunch students and students paying full price for lunch would decline by 25 %. Ethnic compositional effects for black, Asian/Pacific Islander (API), and Hispanic students were reversed from their within-school effects, with positive effects for students in schools with larger proportions of black and Hispanic students and a strong negative effect for students in schools with larger proportions of Asian/Pacific Islander students.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. We follow Asa Hilliard III (2001) in preferring the word “ethnicity” to the compound word “race/ethnicity” used by some other researchers.

  2. Compositional effects are sometimes known as contextual effects. We follow Willms (2006) in reserving the term “contextual” effects to refer to factors describing “the physical features of the learning environment and its culture.”

  3. Just as an average national household can have 2.6 members even though no household can possible have exactly that number of members, so can a hypothetical nationally average student be about 2 % Indian, 10 % Hispanic, 15 % Black, 3 % Asian, and 70 % White and Other, according to the estimations of this NAEP dataset. This national average student would have a score of .39 on the free lunch scale, slightly above the reduced-price lunch income level. This nationally-average student would be poorer than the average student in a wealthy school and wealthier than the average student in a poor school. By focusing on this nationally average student, two-level analysis allows us to analyze school effects without confusing them with student characteristics. This analysis cannot substitute for analyses that look carefully at the effects of schools on very particular groups of students, but it is ideal for looking at the overall effects of school segregation on students.

  4. Using mathematical language, Willms (2006) calls socioeconomic total effects the “overall socioeconomic gradient slope;” Sirin (2005) and White (1982) call them “student-level” effects because they are based on the student as the single level of analysis, but we prefer the language of Raudenbush and Bryk (2002, figure 5.1) – total effects, because they represent the total effect of socioeconomic status on student test scores in a given society.

  5. There are many ways to estimate total effects, between-school effects, and within-school effects. For details, the reader is referred to Willms (2006), Raudenbush and Bryk (2002), and Snijders and Bosker (2000). All of these sources assume that the school-level variables are aggregates of the student-level variables, and are thus using the same scale. They also describe the importance of centering in the estimation of between and composition effects. We use grand-mean centering, but identical results can be found using group-mean centering. Equation (2) can be found on page 50 of Willms, as equation 5.38 of Raudenbush and Bryk, and as equation 3.28 of Snijders and Bosker. Snijders and Bosker make clear that estimations of this equation provide exact results when the number of students in each school is the same; when the data set is not balanced in this way, the two sides of the equation may not balance exactly.

  6. Willms’ data sources were the Progress in International Reading Literacy Study (PIRLS) and the Programme for International Student Assessment (PISA).

  7. Future analyses building on the analysis in this paper can and should add school sector as part of an elaborated structural equation model in order to investigate whether access to private schooling is part of the mechanism leading to unequal results for children based on their economic level and race/ethnicity.

  8. Of the 6,334 schools in the NAEP schools dataset, 193 are not included in the national sample. The schools and students dataset are best combined using SCHID as the variable that links the two datasets. This is the correct choice, not SCRPSU, despite the fact that SCRPSU is the variable recommended for this purpose by section 5.6 of the NAEP Data Companion (Rogers & Stoeckel, 2004). In the 2003 Mathematics dataset, some schools share SCRPSU ids. SCHID, on the other hand, is unique. Per NCES policy, this report rounds unweighted sample size numbers to the nearest ten to prevent disclosure of student identity.

  9. As of 2011, the NAEP ethnic categories were changed and now reflect Asian as its own group, with Native Hawaiian and other Pacific Islanders as a separate group. Unfortunately, this advantage for more recent data is outweighed for researchers by the loss of many important covariates in the reduced-size student and teacher surveys.

  10. It is important to note that this estimate makes two simplifying assumptions: one is that the results of desegregation would be linear, even at the extremes; the other is that economic desegregation could be accomplished without accompanying ethnic desegregation.

References

  • Ballon E. Racial differences in high school math track assignment. Journal of Latinos and Education. 2008;7(4):272–87.

    Article  Google Scholar 

  • Bankston III C, Caldas SJ. Majority African American schools and social injustice: the influence of de facto segregation on academic achievement. Social Forces. 1996;75(2):535–56.

    Article  Google Scholar 

  • Berry III, RQ. Voices of African-American male students: a portrait of successful middle school mathematics students. PhD dissertation. Chapel Hill, NC: School of Education, University of North Carolina. 2002.

  • Betts JR, Rueben KS, Danenberg A. Equal resources, equal outcomes? The distribution of school resources and student achievement in California. San Francisco: Public Policy Institute of California; 2000.

    Google Scholar 

  • Blackorby J, Schiller E, Mallik S, Hebbeler K, Huang T, Javitz H, et al. Patterns in the identification of and outcomes for children and youth with disabilities. NCEE 2010-4005Congress. 2010.

  • Boger JC, Orfield G, editors. School resegregation: must the South turn back? Chapel Hill: University of North Carolina Press; 2005.

    Google Scholar 

  • Bollen KA, Glanville J, Stecklov G. Socioeconomic status and class in studies of fertility and health in developing countries. Annu Rev Sociol. 2001;27:153–85.

    Article  Google Scholar 

  • Borman KM, Eitle TMN, Michael D, Eitle DJ, Lee R, Johnson L, et al. Accountability in a Postdesegregation Era: the continuing significance of racial segregation in Florida's schools. Am Educ Res J. 2004;41(3):605–31.

    Article  Google Scholar 

  • Bourdieu P, Passeron J-C. Reproduction in education, society, and culture. Translated by R. Nice. Beverly Hills, CA: Sage; 1977.

  • Braswell JS, Lutkus AD, Grigg WS, Santapau SL, Tay-Lim BS-H, Johnson MS. The nation's report card: mathematics 2000. Vol. NCES 2001–617. Washington: U.S. Department of Education; Office of Educational Research and Improvement; 2001.

    Google Scholar 

  • Braswell JS, Dion GS, Daane MC, Jin Y. The nation's report card: mathematics 2003. Vol. NCES 2005–451. Washington: U.S. Government Printing Office; 2005.

    Google Scholar 

  • Braun H, Jenkins F, Grigg W. A closer look at charter schools using hierarchical linear modeling. Vol: NCES 2006–460. Washington: U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences; 2006.

    Google Scholar 

  • Card D, Rothstein J. Racial segregation and the black-white test score gap. Cambridge: National Bureau of Economic Research; 2006.

    Book  Google Scholar 

  • Cashin S. The failures of integration: how race and class are undermining the American dream. New York: Public Affairs; 2004.

    Google Scholar 

  • Crain RI, Mahard RE. Desegregation and black achievement: a review of the research. Law and Contemporary Problems. 1978;23(3):17–56.

    Article  Google Scholar 

  • Darling-Hammond L. The color line in American education: race, resources, and student achievement. Du Bois Review. 2004;1(2):213–46.

    Article  Google Scholar 

  • Darling-Hammond L. Securing the right to learn: policy and practice for powerful teaching and learning. Educ Res. 2006;35(7):13–24.

    Article  Google Scholar 

  • Delpit LD. Other people's children. New York: New Press; 1995.

    Google Scholar 

  • Efron B. Bootstrap methods: another look at the jackknife. Ann Stat. 1979;7:1–26.

    Article  Google Scholar 

  • Everson HT, Millsap RE. Beyond individual differences: exploring School effects on sat scores, vol. 3. New York: College Entrance Examination Board; 2004.

    Google Scholar 

  • Ferguson RF. Paying for public education: new evidence on how and why money matters. Harvard Journal on Legislation. 1991;28:465–98.

    Google Scholar 

  • Ferguson RF. Teachers' perceptions and expectations and the Black-White test score gap. In: Jencks C, Phillips M, editors. The Black-White test score gap. Washington: Brookings Institution Press; 1998. p. 318–74.

    Google Scholar 

  • Flinspach SL, Banks KE. Moving beyond race: socioeconomic diversity as a race-neutral approach to desegregation in the Wake County schools. In: Boger JC, Orfield G, editors. School resegregation: must the South turn back? Chapel Hill: University of North Carolina Press; 2005. p. 261–80.

    Google Scholar 

  • Fordham S, Ogbu JU. Black students' school success: coping with the burden of ‘Acting White’. Urban Rev. 1986;18(3):176–206.

    Article  Google Scholar 

  • Goe, Laura. 2002. "Legislating Equity: The Distribution of Emergency Permit Teachers in California." Education Policy Analysis Archives 10 (42).

  • Grissmer D, Flanagan A, Williamson S. Why did the Black-White score gap narrow in the 1970s and 1980s? In: Jencks C, Phillips M, editors. The Black-White test score gap. Washington: Brookings Institute; 1998. p. 182–226.

    Google Scholar 

  • Grissmer D, Flanagan A, Kawata J, Williamson S. Improving student achievement: what State Naep test scores tell us. Santa Monica: RAND; 2000.

    Google Scholar 

  • Hanushek E, Kain J, Rivkin S. New evidence about ‘Brown V. Board of Education’: the complex effects of school racial composition on achievement. J Labor Econ. 2009.

  • Harris L. Report on the status of public school education in California 2004. Los Angeles: UCLA Institute for Democracy, Education, and Access; 2004.

    Google Scholar 

  • Harris MM, Willomer DJ. Principal's optimism and perceived school effectiveness. J Educ Adm. 1998;36:353–61.

    Article  Google Scholar 

  • Hilliard III AG. Race, identity, hegemony, and education: what do we need to know now? In: Watkins WH, Lewis JH, Chou V, editors. Race and education: the roles of history and society in educating African American students. Boston: Allyn and Bacon; 2001. p. 7–25.

    Google Scholar 

  • Jones LV, Olkin I. The nation's report card: evolution and perspectives. Bloomington: Phi Delta Kappa; 2004.

    Google Scholar 

  • Kozol J. Savage inequalities: children in America's schools. New York: Crown; 1991.

    Google Scholar 

  • Kozol J. The shame of the nation: the restoration of Apartheid Schooling in America. New York: Crown Publishers; 2005.

    Google Scholar 

  • Lee J. Racial and ethnic achievement gap trends: reversing the progress toward equity? Educ Res. 2002;31(1):3–12.

    Article  Google Scholar 

  • Lee VE, Bryk AS. A multilevel model of the social distribution of high school achievement. Sociol Educ. 1989;62:172–92.

    Article  Google Scholar 

  • Little RJA, Rubin DB. Statistical analysis with missing data. Hoboken: Wiley - Interscience; 2002.

    Book  Google Scholar 

  • Lubienski C, Lubienski ST. Charter, private, public schools and academic achievement: new evidence from Naep mathematics data. New York: National Center for the Study of Privatization in Education; 2006.

    Google Scholar 

  • Lubienski ST, Shelley II MC. A closer look at U.S. mathematics instruction and achievement: examinations of race and ses in a decade of Naep data. In: American Educational Research Association. Chicago: EDRS; 2003. p. 50.

    Google Scholar 

  • Malloy C, Malloy W. Issues of culture in mathematics teaching and learning. Urban Rev. 1998;30:245–57.

    Article  Google Scholar 

  • Marsh HW, Lüdtke O, Robitzsch A, Trautwein U, Asparouhov T, Muthén B, et al. Doubly-latent models of school contextual effects: integrating multilevel and structural equation approaches to control measurement and sampling error. Multivar Behav Res. 2009;44:764–802.

    Article  Google Scholar 

  • Martin DB. In: Schoenfeld AH, editor. Mathematics success and failure among African-American youth: the roles of sociohistorical context, community forces, school influence, and individual agency. Mahwah: Lawrence Erlbaum Associates; 2000.

    Google Scholar 

  • McMillian MM, Fuller S, Hill Z, Duch K, Darity W. Racial composition and student achievement under wake county's race- and income-based school assignment policies. 2014.

  • Mickelson RA, Bottia M. Integrated education and mathematics outcomes: a synthesis of social science research. North Carolina Law Review. 2010;88:993–1090.

    Google Scholar 

  • Mickelson RA, Bottia MC, Lambert R. Effects of school racial composition on K-12 mathematics outcomes: a metaregression analysis. Rev Educ Res. 2013;83(1):121–58.

    Article  Google Scholar 

  • Mislevy RJ, Beaton AE, Kaplan B, Sheehan KM. Estimating population characteristics from sparse matrix samples of item responses. J Educ Meas. 1992;29(2):133–61.

    Article  Google Scholar 

  • Muthén, Linda K. and Bengt O. Muthén. 1998–2005. Mplus User's Guide. Los Angeles: Muthén and Muthén.

  • National Center for Education Statistics. Naep Data Tool: National Center for Education Statistics. 2004. Retrieved November 4, 2004 (http://nces.ed.gov/nationsreportcard/naepdata).

  • National Center for Education Statistics. Mapping 2005 state proficiency standards onto the Naep ScaleCongress. 2007. Retrieved 6/07/07 (http://nces.ed.gov/nationsreportcard/pdf/studies/2007482.pdf).

  • National Center for Education Statistics. Naep Overview. Washington, DC: National Center for Education Statistics; 2008. Retrieved May 8, 2008, 2008 (http://nces.ed.gov/nationsreportcard/about/).

  • Oakes J, Rogers J, Silver D, Goode J. Separate and unequal 50 years after Brown: California's racial ‘Opportunity Gap’. Los Angeles: UCLA/IDEA; 2004.

    Google Scholar 

  • Olson L. New bills would prod states to take national view on standards. Educ Week. 2007. p. 4.

  • Orfield G. The growth of segregation: African Americans, Latinos, and unequal education. In: Hill H, James J, Jones E, editors. Dismantling desegregation: the quiet reversal of Brown V. board of education. Madison: University of Wisconsin Press; 1996. p. 234–62.

    Google Scholar 

  • Orfield G, DeBray EH, editors. Hard work for good schools: facts not fads in Title I Reform. Cambridge: The Civil Rights Project; 1999.

    Google Scholar 

  • Orfield G, Yun JT. Resegregation in American schools. Cambridge: The Civil Rights Project, Harvard University; 1999.

    Google Scholar 

  • Orfield G. Schools more separate. Rethinking schools online. 2001;16(1):10.

    Google Scholar 

  • Raudenbush SW, Bryk A. Hierarchical linear models: applications and data analysis methods. Thousand Oaks: Sage; 2002.

    Google Scholar 

  • Reardon SF, Yun JT, Kurlaender M. Implications of income-based school assignment policies for racial school segregation. Educational Evaluation and Policy Analysis. 2006;28(1):49–75.

    Article  Google Scholar 

  • Reardon SF, Grewal ET, Kalogrides D, Greenberg E. Brown fades: the end of court-ordered school desegregation and the resegregation of American public schools. Journal of Policy Analysis and Management. 2012;31(4):876–904. doi:10.1002/pam.21649.

    Article  Google Scholar 

  • Regan R. The relationship between school socioeconomic composition and academic performance: a comparative analysis of elementary schools in the five largest North Carolina school districts 2002–2003. In: Annual Meeting of the North Carolina Association for Research in Education. Chapel Hill, NC: not published; 2005.

  • Rist RC. Student social class and teacher expectations: the self-fulfilling prophecy in Ghetto education. Harv Educ Rev. 1970;40(3):411–51.

    Google Scholar 

  • Rogers AM, Stoeckel JJ. Naep 2003 mathematics and reading assessments secondary-use data files data companion. Washington: National Center for Education Statistics; 2004.

    Google Scholar 

  • Rogoff B. The cultural nature of human development. New York: Oxford University Press; 2003.

    Google Scholar 

  • Rothstein R. Class and schools: using social, economic, and educational reform to close the Black-White achievement gap. Washington: Economic Policy Institute; 2004.

    Google Scholar 

  • Rumberger RW, Palardy GJ. Does resegregation matter? In: Boger JC, Orfield G, editors. School resegregation: must the South turn back? Chapel Hill: University of North Carolina Press; 2005. p. 127–47.

    Google Scholar 

  • Schellenberg SJ. Concentration of poverty and the ongoing need for Title I. In: Orfield G, DeBray EH, editors. Hard work for good schools: facts not fads in Title I Reform. Cambridge: The Civil Rights Project; 1999. p. 130–46.

    Google Scholar 

  • Sharma A, Joyner AM, Osment A. Adverse Impact of racial isolation on student performance: a study in North Carolina. Education Policy Analysis Archives. 2014;22(14).

  • Sirin SR. Socioeconomic status and academic achievement: a meta-analytic review of research. Rev Educ Res. 2005;75(3):417–53.

    Article  Google Scholar 

  • Snijders T, Bosker R. Multilevel analysis: an introduction to basic and advanced multilevel modeling. Trowbridge: The Cromwell Press, Ltd.; 2000.

    Google Scholar 

  • Spring J. Deculturalization and the struggle for equality. New York: McGraw Hill; 2004.

    Google Scholar 

  • Stancavage FB, Mitchell JH, de Mello VP, Gaertner FE, Spain AK, Rahal L. National Indian education study, part Ii: The educational experiences of fourth- and eighth-grade American Indian and Alaskan Native Students, vol. 2007–454. Washington, DC: National Center for Education Statistics; 2006.

    Google Scholar 

  • Street P. Segregated schools: educational apartheid in post-civil rights America. New York: Routledge; 2005.

    Google Scholar 

  • Stroub KJ, Richards MP. From resegregation to reintegration: trends in the Racial/Ethnic Segregation of Metropolitan Public Schools. Am Educ Res J. 2013;50(3):497–531.

    Article  Google Scholar 

  • Strutchens ME, STLubienski RMG, Westbrook SK. Naep findings regarding race and ethnicity: students' performance, school experiences, attitudes and beliefs, and family influences. In: Kloosterman P, Frank J, Lester K, editors. Results and interpretations of the 1990–2000 mathematics assessments of the National Assessment of Educational Progress. Reston: National Council of Teachers of Mathematics; 2004. p. 269–304.

    Google Scholar 

  • Sullivan AL, Bal A. Disproportionality in special education: effects of Individual and school variables on disability risk. Except Child. 2013;79(4):475–94.

    Google Scholar 

  • Teranishi R, Allen WR, Solórzano DG. Opportunity at the crossroads: racial inequality, school segregation, and higher education in California. Teach Coll Rec. 2004;106(11):2224–45.

    Article  Google Scholar 

  • Walseman KM, Bell B, Goosby B. Effect of school racial composition on trajectories of depressive symptoms from adolescence through early adulthood. Race and Social Problems. 2011;3(3):131–45.

    Article  Google Scholar 

  • White KR. The relation between socioeconomic status and academic achievement. Psychol Bull. 1982;91:461–81.

    Article  Google Scholar 

  • Willis P. Elements of a culture. In: Learning to labor: how working class kids get working class jobs. New York: Columbia University Press; 1981.

    Google Scholar 

  • Willms JD. Social class segregation and its relationship to Pupils' examination results in Scotland. Am Sociol Rev. 1986;51:224–41.

    Article  Google Scholar 

  • Willms JD. Learning divides: ten policy questions about the performance and equity of schools and schooling systems, vol. UIS/WP/06-02. Montreal: UNESCO Institute for Statistics; 2006.

    Google Scholar 

Download references

Acknowledgements

This article was made possible by the generous support of Dr. and Mrs. Thomas Royster, through the Royster Fellows program at the University of North Carolina - Chapel Hill. Address correspondence to Tom Munk, Westat, Inc., 1009 Slater Road, Durham, NC 27703-8446; e-mail: tommunk@westat.com.

Author information

Authors and Affiliations

  1. Westat, Westat, Inc., 1009 Slater Road, Durham, NC, 27703-8446, USA

    Tom E. Munk

  2. Morgan State University, Baltimore, MD, USA

    M. Monique McMillian

  3. University of Hawaii at Manoa, Honolulu, HI, USA

    Nicole R. Lewis

Authors
  1. Tom E. Munk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Monique McMillian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicole R. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Monique McMillian.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Munk, T.E., McMillian, M.M. & Lewis, N.R. Compositional Effects, Segregation and Test Scores: Evidence From the National Assessment of Educational Progress. Rev Black Polit Econ 41, 433–454 (2014). https://doi.org/10.1007/s12114-014-9200-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12114-014-9200-3

Keywords

Search

Navigation