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Schools and Inequality: A Multilevel Analysis of Coleman’s Equality of Educational Opportunity Data

by Geoffrey D. Borman & Maritza Dowling - 2010

Background/Context: The Equality of Educational Opportunity study is widely recognized as one of the most important studies on schooling ever performed. The findings from the report have shaped the field of education, national education policies, and wider public and scholarly opinion regarding the contributions of schools and schooling to equality and productivity in the United States. Despite past reanalyses of the data and decades of research on the effects of schools as organizations, the report’s fundamental finding—that a student’s family background is far more important than school social composition and school resources for understanding student outcomes—still retains much of its currency. Purpose/Objective/Research Question/Focus of Study: Using the original Equality of Educational Opportunity data, this study replicated Coleman’s statistical models but also applied a two-level hierarchical linear model (HLM) to measure the effects of school-level social composition, resources, teacher characteristics, and peer characteristics on ninth-grade students’ verbal achievement.

Research Design: HLM allows researchers to disentangle how schools and students’ family backgrounds contribute to learning outcomes. The methodology offers a clearer interpretation of the relative effects of school characteristics, including racial/ethnic composition, and family background, including race/ethnicity and social class, on students’ academic outcomes.

Findings/Results: Our results suggest that schools do indeed matter, in that when one examines the outcomes across the national sample of schools, fully 40% of the differences in achievement can be found between schools. Even after statistically taking into account students’ family background, a large proportion of the variation among true school means is related to differences explained by school characteristics. Within-school inequalities in the achievement outcomes for African American and White students and students from families of higher and lower social class are explained in part by teachers’ biases favoring middle-class students and by schools’ greater reliance on curriculum differentiation through the use of academic and nonacademic tracking.

Conclusions/Recommendations: Formal decomposition of the variance attributable to individual background and the social composition of the schools suggests that going to a high-poverty school or a highly segregated African American school has a profound effect on a student’s achievement outcomes, above and beyond the effect of individual poverty or minority status. Specifically, both the racial/ethnic and social class composition of a student’s school are 1 3/4 times more important than a student’s individual race/ethnicity or social class for understanding educational outcomes.

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Cite This Article as: Teachers College Record Volume 112 Number 5, 2010, p. 1201-1246
https://www.tcrecord.org ID Number: 15664, Date Accessed: 7/24/2021 2:30:38 PM

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About the Author
  • Geoffrey Borman
    University of Wisconsin–Madison
    E-mail Author
    GEOFFREY BORMAN is a professor of education at the University of Wisconsin–Madison. His main substantive research interests revolve around the social distribution of the outcomes of schooling and the ways in which policies and practices can help address and overcome educational inequality. Dr. Borman’s primary methodological interests include the synthesis of research evidence, the design of quasi-experimental and experimental studies of educational innovations, and the specification of school-effects models. His recent publications include articles appearing in Review of Education Research, Educational Evaluation and Policy Analysis, and the American Educational Research Journal.
  • Maritza Dowling
    University of Wisconsin–Madison
    MARITZA DOWLING is a faculty member in the Department of Biostatistics and Medical Informatics at the University of Wisconsin–Madison. She holds a master’s degree in measurement and statistics and a PhD in quantitative methods. Most of her training has focused on the convergence of quantitative methodology, psychometrics, and cognition. She is particularly committed to bridging the gap between emerging developments and substantive applications of novel analytic methods using cross-sectional and longitudinal data. Her substantive interests include uses and extensions of quantitative models (e.g., multilevel, latent growth, Markov chain, and item-response modeling) to address existing problems in the study of cognitive developmental and stage-sequential processes. Dr. Dowling’s recent publications include articles appearing in Review of Education Research and Educational Evaluation and Policy Analysis.
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