Using Large-Scale Datasets to Amplify Equitable Learning in Urban Mathematics




large-scale data, methodology, NCES


Our purpose in this article was to provide researchers using NCES datasets to analyze secondary students’ mathematics achievement in urban schools with methodological considerations and analytical suggestions. We are hopeful that researchers interested in students’ mathematics achievement in urban schools will consider accessing and using NCES studies to deepen our collective understanding beyond IES summary statistics featured in reports such as NAEP and TIMMS and tables from The Condition of Education. We believe that studies of large-scale data that are attentive to the methodological considerations of complex sampling, data clustering and causal inference, will contribute nuanced perspectives of promising policy and practice directions for improving disparities in urban secondary school students’ inequitable mathematics achievement outcomes.


American Educational Research Association, American Psychological Association, National Council on Measurement in Education, & Joint Committee on Standards for Educational and Psychological Testing (US). (2014). Standards for educational and psychological test-ing. American Educational Research Association.

Capraro, M. M., Capraro, R. M. & Lewis, C. W. (2013). Improving urban schools: Equity and access in K–12 STEM education. Information Age Publishing.

Carpenter, T. P., Lindquist, M. M., Matthews, W., & Silver, E. A. (1983). Results of the third NAEP mathematics assessment: Secondary school. The Mathematics Teacher, 76(9), 652–659.

Celedón-Pattichis, S. (2008). “What does that mean?”: Drawing on Latino and Latina students’ language and culture to make mathematical meaning. In M. W. Ellis (Ed.), Mathematics for every student: Responding to diversity, Grades 6–8 (pp. 59–73). National Council of Teachers of Mathematics.

de Brey, C., Musu, L., McFarland, J., Wilkinson-Flicker, S., Diliberti, M., Zhang, A., Branstetter, C., & Wang, X. (2019). Status and trends in the education of racial and ethnic groups 2018 (NCES 2019-038). U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics.

Gándara, P., & Orfield, G. (2010). A return to the ‘Mexican room’: The segregation of Arizona’s English learners. The Civil Rights Project/Proyecto Derechos Civiles at UCLA.

Gifford B., & Valdés, G. (2006). The linguistic isolation of Hispanic students in California’s public schools: The challenge of reintegration. Yearbook of the National Society for the Study of Education, 105(2), 125–154.

Gutiérrez, R. (2007). (Re)defining equity: The importance of a critical perspective. In N. S. Nasir & P. Cobb (Eds), Improving access to mathematics: Diversity and equity in the classroom (pp. 37–50). Teachers College Press.

Ingels, S. J., Pratt, D. J., Herget, D. R., Burns, L. J., Dever, J. A., Ottem, R., Rogers, J. E., Jin, Y., & Leinwand, S. (2011). High School Longitudinal Study of 2009 (HSLS:09): Base-year data file documentation (NCES 2011-328). U.S. Department of Education, National Center for Education Statistics, National Center for Education Statistics, Elementary/Secondary & Libraries Studies Division.

Ingels, S. J., Pratt, D. J., Rogers, J. E., Siegel, P. H., & Stutts, E. S. (2004). Education Longitudinal Study of 2002: Base year data file user’s manual (NCES 2004-405). U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics.

Johnson, R. S. (2002). Using data to close the achievement gap: How to measure equity in schools. Corwin Press.

Kao, G., & Tienda, M. (1995). Optimism and achievement: The educational performance of immigrant youth. Social Science Quarterly, 76(1), 1–19.

Lippman, L., Burns, S., & McArthur, E. (1996). Urban schools: The challenge of location and poverty (NCES 96-184). U.S. Department of Education, Office of Educational Research and Improvement, National Center for Education Statistics, Data Development and Longitudinal Studies Group.

Maldonado, S. I., Mosqueda, E., & Bravo. M. A. (under review). Mathematical proficiency practices as predictor of Latinxs’ standardized test scores: A simultaneous analysis of student, teacher and school factors.

Martin, D. B., Gholson, M. L., & Leonard, J. (2010). Mathematics as gatekeeper: Power and privilege in the production of knowledge. Journal of Urban Mathematics Education, 3(2), 12–24.

Martiniello, M. (2008). Language and the performance of English language learners in math word problems. Harvard Educational Review, 78(2), 333–368.

McFarland, J., Hussar, B., Zhang, J., Wang, X., Wang, K., Hein, S., Diliberti, M., Cataldi, E. F., Mann, F. B., & Barmer, A. (2019). The condition of education 2019 (NCES 2019-144). U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics.

Milner, H. R. (2012). But what is urban education? Urban Education, 47(3), 556–561.

Moses, R. P., Cobb, C. E., Jr. (2001). Radical equations: Math literacy and civil rights. Beacon Press.

Mosqueda, E. (2010). Compounding inequalities: English proficiency and tracking and their relation to mathematics performance among Latina/o secondary school youth. Journal of Ur-ban Mathematics Education, 3(1), pp. 57–81.

Mosqueda, E. (2011). Teacher quality, academic tracking and the mathematics performance of Latino ELs. In K. Téllez, J. N. Moschkovich, & M. Civil (Eds.), Latinos and mathematics education: Research on learning and teaching in classrooms and communities (pp. 315–339). Information Age Publishing.

Mosqueda, E., & Maldonado, S. I. (2013). The effects of English language proficiency and curricular pathways: Latina/os' mathematics achievement in secondary schools. Equity & Excellence in Education 46(2), 202–219.

Mosqueda, E., Maldonado, S. I., Capraro, R., & Capraro, M. M. (under review). Systematized discrimination: The relationship between students’ linguistic minority status, race-ethnicity, opportunities to learn, and college preparatory mathematics.

Murnane, R. J., & Willet, J. B. (2011). Methods Matter: Improving causal inference in educational and social science research. Oxford University Press.

Muthén, B. O., & Satorra, A. (1995). Complex sample data in structural equation modeling. In P. Marsden (Ed.), Sociological Methodology (pp. 267–316). American Sociological Association.

Orfield, G., & Lee, C. (2007). Why segregation matters: Poverty and educational inequality. The Civil Rights Project at Harvard University.

Perry, T., Steele, C., & Hilliard, A., II (2003). Young, gifted and Black: Promoting high achievement among African American students. Beacon Press.

Portes, A., & Rumbaut, R. G. (2001). Legacies: The story of the immigrant second generation. University of California Press; Russell Sage Foundation.

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. (2nd ed.). Sage.

Rosenbaum P. R., & Rubin D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79(387), 516–524.

Rubin, D. B. (1997). Estimating causal effects from large datasets using propensity scores. Annals of Internal Medicine, 127(8, Part 2), 757–763.

Rumberger, R. W., & Palardy, G. J. (2005). Does segregation still matter? The impact of social composition on academic achievement in high school. Teachers College Record, 107, 1999–2045.

Schneider, B., Carnoy, M., Kilpatrick, J., Schmidt, W. H., & Shavelson, R. J. (2007). Estimating causal effects using experimental and observational designs [White paper]. American Educational Research Association.

Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford University Press.

Stevens, F. I., & Grymes, J. (1993). Opportunity to learn (OTL): Issues of equity for poor and minority students (ED356306). ERIC.

Tate, W. (2001). Science education as a civil right: Urban schools and opportunity-to-learn considerations. Journal for Research in Science Teaching, 38(9), 1015–1028.

Thomas, S. L., & Heck, R.H. (2001). Analysis of large-scale secondary data in higher education research: Potential perils associated with complex sampling designs. Research in Higher Education, 42(5), 517–540.

Thomas, S. L., Heck, R. H., & Bauer, K.W. (2005). Weighting and adjusting for design effects in secondary data analyses. New Directions for Institutional Research, 127, 51–72.

Trujillo, T. (2016). Restoring the federal commitment to equal educational opportunity: New directions for ESSA’s school improvement initiatives. Education Law & Policy Review, 3, 141–165. vol 3.pdf

Turner, E. E., & Celedón-Pattichis, S. (2011). Mathematical problem-solving among Latina/o kindergartners: An analysis of opportunities to learn. Journal of Latinos and Education, 10(2), 146–169.

Welsh, R. O., & Swain, W. A. (2020). (Re)Defining urban education: A conceptual review and empirical exploration of the definition of urban education. Educational Researcher, 49(2), 90–100.

Whitehurst, G. J. (2003). The institute of education sciences: New wine, new bottles (ED478983). ERIC.




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