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Large-Scale Datasets and Social Justice: Measuring Inequality in Opportunities to Learn

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Research Methods for Social Justice and Equity in Education

Abstract

Large-scale datasets allow for the tracking of persistent patterns of inequality and inequity in education. This chapter demonstrates how inequality in students’ learning opportunities compounds in high schools. This chapter uses the Civil Rights Data Collection (CRDC) of Advanced Placement (AP) and International Baccalaureate (IB) curricula to demonstrate how a four-part chain of events in curriculum opportunities exacerbate inequality of education in the US. This census dataset allows for small numbers of historically marginalized voices to be heard among the many. With these voices, researchers can begin to listen to the social injustices that undertow our society and begin to enact change through educational policy. These findings move forward the educational opportunity and tracking discussions in the twenty-first century to understand the nested spaces of opportunity along curricular pipelines.

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Notes

  1. 1.

    There are inherent social justice issues related to the forced categorization of persons into a handful of racial or ethnic categories. This discussion holds much merit but is beyond the scope of this chapter. For a good discussion, see Zuberi and Bonilla-Silva (2008).

  2. 2.

    The Civil Rights Data Collection (CRDC) reports at the school-level regarding each school’s student body population. Students who identify with more than one racial or ethnic heritage are recorded at the school-level as “multi-racial/ethnic” students. Thus, any counts reported for a racial or ethnic group other than “multi-racial/ethnic” are restricted to students who self-declare heritage to a single racial or ethnic identity.

  3. 3.

    This chapter uses the term “American Indian” whenever the reference is a National Center for Education Statistics (NCES) database since that is the descriptor used in those databases.

  4. 4.

    Since these are census data, there is no need for statistical tests of significance because there is no sampling error or confidence interval to estimate (Knoke et al., 2002).

  5. 5.

    It is not the case that these schools are simply homogenous schools with only one racial or ethnic student body population (for an in-depth analysis, see Price, forthcoming).

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Correspondence to Heather E. Price .

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Suggested Readings

Suggested Readings

  • Espinoza, O. (2007). Solving the equity–equality conceptual dilemma: A new model for analysis of the educational process. Educational Research, 49(4), 343–363. https://doi.org/10.1080/00131880701717198

This article reviews the literature on the “meaning, goals, and assumptions of the concepts ‘equity’ and ‘equality’, and their implications for social and public policy” (p. 343). It then develops an equality-equity model and provides some ideas about “how ‘equity’ (i.e. ‘equity for equal needs’, ‘equity for equal potential’ and ‘equity for equal achievement’) and ‘equality’ (i.e. ‘equality of opportunity’, ‘equality for all’ and ‘equality on average across social groups’) could be treated and measured in future research in relation to different features of the educational process (availability of resources, access, survival, output and outcome)” (p. 343).

  • Orfield, G., & Eaton, S. E. (1996). Dismantling desegregation. The quiet reversal of Brown v. Board of Education. New York, NY: The New Press.

This book speaks to the steady resegregation of American schools. The issue of equality and equity are discussed through the lens of legal rulings on school segregation and integration. It discusses the impact of community on equal access due to residential segregation, white flight, and gentrification. Lastly, the impact of school choice and education politics on the framing of equality and equity in the public sphere is considered.

  • Secada, W. G. (Ed.). (1989). Equity and education. New York, NY: Falmer.

This book takes a critical stance on the formulation of the terms of equality and equity. It reviews how the terms have been redefined not by educators, but by politicians. It provides alternative ways to think of the terms and imagines the impact that the different conceptual definitions might have on students, schooling, and educational outcomes.

  • Reardon, S. F., & Firebaugh, G. (2002). Measures of multigroup segregation. Sociological Methodology, 32(1), 33–67.

Using the example of segregation, this article shows the formulaic differences in measures of inequality. It demonstrates how the metrics produced from different measures can alter the findings of inequality and thus change the implications from the research. It emphasizes the importance of clear concepts in research when choosing a measure to represent inequality. Although technical, the article can be used as a reference guide for choosing measures of inequality.

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Price, H.E. (2019). Large-Scale Datasets and Social Justice: Measuring Inequality in Opportunities to Learn. In: Strunk, K.K., Locke, L.A. (eds) Research Methods for Social Justice and Equity in Education. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-05900-2_17

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  • DOI: https://doi.org/10.1007/978-3-030-05900-2_17

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