Why Disaggregating Data Matters for Addressing Educational Inequality
Robert T. Teranishi
Bach Mai Dolly Nguyen
Cynthia M. Alcantar
Edward R. Curammeng
James A. Banks
Teachers College Press
The United States demography is changing rapidly. How are we capturing these shifts? Do the racial categories that exist accurately represent the individuals who fall into them? Have long-standing categories hindered our understanding of racial inequality? These questions are particularly significant in education, where a precise view of students—who achieves and who requires greater resources—is critical. This volume brings together the expertise of scholars from a range of disciplines to explore the current state of racial heterogeneity, data practice, and educational inequality. They offer recommendations to guide future research, practice, and policy with the goal of better understanding and meeting the needs of our diverse student population in the years to come.
Contributes both conceptual and practical knowledge toward understanding the relevance of data practices that impact racial inequality—important for both researchers and practitioners.
Highlights the relevance of racial heterogeneity broadly, but also its significance for particular racial groups—for example, Pacific Islanders and mixed-race/multiracial students—who are largely understudied.
Offers recommendations that include the importance of promoting collaboration between researchers, advocates, practitioners, and policymakers.
Robert T. Teranishi is professor of education and codirector of the Institute for Immigration, Globalization, and Education at UCLA Graduate School of Education & Information Studies. Bach Mai Dolly Nguyen is an assistant professor of education at Oregon State University. Cynthia M. Alcantar is an assistant professor of higher education leadership at the University of Nevada, Reno. Edward R. Curammeng is assistant professor of teacher education at California State University, Dominguez Hills.