The Role of Opportunity to Learn and School Socioeconomic Composition in Reducing Racial and Gendered Disparities in Mathematics Achievement

References

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Barnard-Brak, L., Lan, W. Y., & Yang, Z. (2018). Differences in mathematics achievement according to opportunity to learn: A 4pL item response theory examination. Studies in Educational Evaluation, 56, 1–7. https://doi.org/10.1016/j.stueduc.2017.11.002
   Web of Science ®Google Scholar
• Bauer, G. R., & Scheim, A. I. (2019). Advancing quantitative intersectionality research methods: Intracategorical and intercategorical approaches to shared and differential constructs. Social Science & Medicine, 226, 260–262. https://doi.org/10.1016/j.socscimed.2019.03.018
   PubMed Web of Science ®Google Scholar
• Benito, R., Alegre, M. À., & Gonzàlez-Balletbò, I. (2014). School segregation and its effects on educational equality and efficiency in 16 OECD comprehensive school systems. Comparative Education Review, 58(1), 104–134. https://doi.org/10.1086/672011
   Web of Science ®Google Scholar
• Bjorklund-Young, A., & Plasman, J. S. (2020). Reducing the achievement gap: Middle grades mathematics performance and improvement. RMLE Online, 43(10), 25–45. https://doi.org/10.1080/19404476.2020.1836467
   Google Scholar
• Bottia, M. C., Mickelson, R. A., Giersch, J., Stearns, E., & Moller, S. (2018). The role of high school racial composition and opportunities to learn in students’ STEM college participation. Journal of Research in Science Teaching, 55(3), 446–476. https://doi.org/10.1002/tea.21426
   Web of Science ®Google Scholar
• Bozick, R., Alexander, K., Entwisle, D., Dauber, S., & Kerr, K. (2010). Framing the future: Revisiting the place of educational expectations in status attainment. Social Forces, 88(5), 2027–2052. https://doi.org/10.1353/sof.2010.0033
   Web of Science ®Google Scholar
• Broer, M., Bai, Y., Fonseca, F., Broer, M., Bai, Y., & Fonseca, F. (2019). A review of the literature on socioeconomic status and educational achievement. In Socioeconomic inequality and educational outcomes: Evidence from twenty years of TIMSS (pp. 7–17). Springer.
   Google Scholar
• Brown, B. A., Boda, P., Lemmi, C., & Monroe, X. (2019). Moving culturally relevant pedagogy from theory to practice: Exploring teachers’ application of culturally relevant education in science and mathematics. Urban Education, 54(6), 775–803. https://doi.org/10.1177/0042085918794802
   Web of Science ®Google Scholar
• Bullock, E. C. (2018). Intersectional analysis in critical mathematics education research: A response to figure hiding. Review of Research in Education, 42(1), 122–145. https://www.jstor.org/stable/44668716 https://doi.org/10.3102/0091732X18759039
   Google Scholar
• Carter, P. L., & Welner, K. G. (Eds.). (2013). Closing the opportunity gap: What America must do to give every child an even chance. Oxford University Press.
   Google Scholar
• Collins, P. H. (1990). Black feminist thought in the matrix of domination. In U. Hyman (Ed.), Black feminist thought: Knowledge, consciousness, and the politics of empowerment (Vol. 138, pp. 221–238). HarperCollins.
   Google Scholar
• Collins, P. H. (2015). Intersectionality’s definitional dilemmas. Annual Review of Sociology, 41(1), 1–20. https://doi.org/10.1146/annurev-soc-073014-112142
   Web of Science ®Google Scholar
• Collins, P. H., & Bilge, S. (2016). Intersectionality. Polity Press.
   Google Scholar
• Copur-Gencturk, Y., Cimpian, J. R., Lubienski, S. T., & Thacker, I. (2020). Teachers’ bias against the mathematical ability of female, Black, and Hispanic students. Educational Researcher, 49(1), 30–43. https://doi.org/10.3102/0013189X19890577
   Web of Science ®Google Scholar
• Covay Minor, E., Desimone, L. M., Spencer, K., & Phillips, K. J. (2015). A new look at the opportunity-to-learn gap across race and income. American Journal of Education, 121(2), 241–269. https://doi.org/10.1086/679392
   Web of Science ®Google Scholar
• Crenshaw, K. (1989). Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum, 1989(1), 139–167.
   Google Scholar
• Crenshaw, K. (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Review, 43(6), 1241–1299. https://doi.org/10.2307/1229039
   Google Scholar
• 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. National Center for Education Statistics.
   Google Scholar
• Estrada, P., Wang, H., & Farkas, T. (2020). Elementary English learner classroom composition and academic achievement: The role of classroom-level segregation, number of English proficiency levels, and opportunity to learn. American Educational Research Journal, 57(4), 1791–1836. https://doi.org/10.3102/0002831219887137
   Web of Science ®Google Scholar
• Floden, R. E. (2002). The measurement of opportunity to learn. In A. C. Porter & A. Gamoran (Eds.), Methodological advances in cross-national surveys of educational achievement (p. 231). National Academy Press.
   Google Scholar
• Gevrek, Z. E., Gevrek, D., & Neumeier, C. (2020). Explaining the gender gaps in mathematics achievement and attitudes: The role of societal gender equality. Economics of Education Review, 76, 101978. https://doi.org/10.1016/j.econedurev.2020.101978
   Web of Science ®Google Scholar
• Gibson, C. E., Losee, J., & Vitiello, C. (2014). A replication attempt of stereotype susceptibility (Shih, Pittinsky, & Ambady, 1999). Social Psychology, 45(3), 194–198. https://doi.org/10.1027/1864-9335/a000184
   Web of Science ®Google Scholar
• Goldsmith-Pinkham, P., & Imbens, G. W. (2013). Social networks and the identification of peer effects. Journal of Business & Economic Statistics, 31(3), 253–264. https://doi.org/10.1080/07350015.2013.801251
   Web of Science ®Google Scholar
• Harwell, M., & LeBeau, B. (2010). Student eligibility for a free lunch as an SES measure in education research. Educational Researcher, 39(2), 120–131. https://doi.org/10.3102/0013189X10362578
   Web of Science ®Google Scholar
• Henry, D. A., Betancur Cortés, L., & Votruba-Drzal, E. (2020). Black–white achievement gaps differ by family socioeconomic status from early childhood through early adolescence. Journal of Educational Psychology, 112(8), 1471–1489. https://doi.org/10.1037/edu0000439
   Web of Science ®Google Scholar
• Hsieh, T. Y., Simpkins, S. D., & Eccles, J. S. (2021). Gender by racial/ethnic intersectionality in the patterns of adolescents’ math motivation and their math achievement and engagement. Contemporary Educational Psychology, 66, 101974. https://doi.org/10.1016/j.cedpsych.2021.101974
   Web of Science ®Google Scholar
• Huang, F. L. (2018). Using cluster bootstrapping to analyze nested data with a few clusters. Educational and Psychological Measurement, 78(2), 297–318. https://doi.org/10.1177/0013164416678980
   PubMed Web of Science ®Google Scholar
• Husen, T. (Ed.). (1967a). International study of achievement in mathematics: A comparison of twelve countries (Vol. I). John Wiley & Sons.
   Google Scholar
• Ireland, D. T., Freeman, K. E., Winston-Proctor, C. E., DeLaine, K. D., McDonald Lowe, S., & Woodson, K. M. (2018). (Un)hidden figures: A synthesis of research examining the intersectional experiences of Black women and girls in STEM education. Review of Research in Education, 42(1), 226–254. https://doi.org/10.3102/0091732X18759072
   Web of Science ®Google Scholar
• Jackson, J. W., & VanderWeele, T. J. (2018). Decomposition analysis to identify intervention targets for reducing disparities. Epidemiology, 29(6), 825–835. https://doi.org/10.1097/EDE.0000000000000901
   Web of Science ®Google Scholar
• Jackson, J. W., Williams, D. R., & VanderWeele, T. J. (2016). Disparities at the intersection of marginalized groups. Social Psychiatry and Psychiatric Epidemiology, 51(10), 1349–1359. https://doi.org/10.1007/s00127-016-1276-6
   PubMed Web of Science ®Google Scholar
• Jencks, C., & Mayer, S. (1990). The social consequences of growing up in a poor neighborhood. In L. E. Lynn, & M. F. H. McGeary (Eds.), Inner-city poverty in the United States (pp. 111–186). National Academy Press.
   Google Scholar
• Kang, H., & Cogan, L. (2022). The differential role of socioeconomic status in the relationship between curriculum-based mathematics and mathematics literacy: The link between TIMSS and PISA. International Journal of Science and Mathematics Education, 20(1), 133–148. https://doi.org/10.1007/s10763-020-10133-2
   Web of Science ®Google Scholar
• Kang, S., & Park, S. (2021). Causal.decomp: Causal decompostion analysis. R package version 0.0.1.
   Google Scholar
• Kaufman, J. S. (2008). Epidemiologic analysis of racial/ethnic disparities: Some fundamental issues and a cautionary example. Social Science & Medicine, 66(8), 1659–1669. https://doi.org/10.1016/j.socscimed.2007.11.046
   PubMed Web of Science ®Google Scholar
• Kraft, M. A. (2020). Interpreting effect sizes of education interventions. Educational Researcher, 49(4), 241–253. https://doi.org/10.3102/0013189X20912798
   Web of Science ®Google Scholar
• Leung, M., Cardichon, J., Scott, C., & Darling-Hammond, L. (2021). Inequitable opportunity to learn: Access to advanced mathematics and science courses. Learning Policy Institute.
   Google Scholar
• Leyva, L. A., Quea, R., Weber, K., Battey, D., & López, D. (2021). Detailing racialized and gendered mechanisms of undergraduate precalculus and calculus classroom instruction. Cognition and Instruction, 39(1), 1–34. https://doi.org/10.1080/07370008.2020.1849218
   Web of Science ®Google Scholar
• Little, R. J. (1988). Missing-data adjustments in large surveys. Journal of Business & Economic Statistics, 6(3), 287–296. https://doi.org/10.2307/1391878
   Web of Science ®Google Scholar
• Ma, J., Pender, M., & Welch, M. (2019). Education Pays 2019: The benefits of higher education for individuals and society. College Board.
   Google Scholar
• McGraw, R., Lubienski, S. T., & Strutchens, M. E. (2006). A closer look at gender in NAEP mathematics achievement and affect data: Intersections with achievement, race/ethnicity, and socioeconomic status. Journal for Research in Mathematics Education, 37(2), 129–150. https://doi.org/10.2307/30034845
   Web of Science ®Google Scholar
• Muench, R., Wieczorek, O., & Dressler, J. (2022). Equity lost: Sweden and Finland in the struggle for PISA scores. European Educational Research Journal, 22(3), 413–432. https://doi.org/10.1177/14749041211069240
   Web of Science ®Google Scholar
• National Center for Education Statistics. (2018). 2017 NAEP mathematics and reading assessments: Highlighted results at grades 4 and 8 for the nation, states, and districts. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2018037
   Google Scholar
• National Science Foundation. (2017). Women, minorities, and persons with disabilities in science and engineering (2017 NSF 17-310). National Center for Science and Engineering Statistics Directorate for Social, Behavioral and Economic Sciences.
   Google Scholar
• Ngo, F. J., & Velasquez, D. (2020). Inside the math trap: Chronic math tracking from high school to community college. Urban Education, 58(8), 1629–1657. https://doi.org/10.1177/0042085920908912
   Web of Science ®Google Scholar
• OECD. (2003). Assessment framework: Mathematics, reading, science and problem solving knowledge and skills. PISA, OECD Publishing.
   Google Scholar
• OECD. (2014). PISA 2012 results: What students know and can do – Student Performance in mathematics, reading and science (Vol. I, Revised ed.). PISA, OECD Publishing.
   Google Scholar
• Owens, A. (2018). Income segregation between school districts and inequality in students’ achievement. Sociology of Education, 91(1), 1–27. https://doi.org/10.1177/003804071774118
   Web of Science ®Google Scholar
• Owens, A. (2020). Unequal opportunity: School and neighborhood segregation in the USA. Race and Social Problems, 12(1), 29–41. https://doi.org/10.1007/s12552-019-09274-z
   Web of Science ®Google Scholar
• Owens, A., & Tom, T. (2022). Intersecting inequalities: Racial/ethnic and socioeconomic differences in math achievement and school contexts in California. Social Problems. Advance online publication. https://doi.org/10.1093/socpro/spac028
   Web of Science ®Google Scholar
• Palardy, G. J. (2013). High school socioeconomic segregation and student attainment. American Educational Research Journal, 50(4), 714–754. https://doi.org/10.3102/0002831213481240
   Web of Science ®Google Scholar
• Palardy, G. J. (2015). High school socioeconomic composition and college choice: Multilevel mediation via organizational habitus, school practices, peer and staff attitudes. School Effectiveness and School Improvement, 26(3), 329–353. https://doi.org/10.1080/09243453.2014.965182
   Web of Science ®Google Scholar
• Palardy, G. J. (2019). School peer non-academic skills and academic performance in high school. Frontiers in Education, 4, 57. https://doi.org/10.3389/feduc.2019.00057
   Google Scholar
• Palardy, G. J. (2020). The impact of socioeconomic segregation in U.S. high schools on achievement, behavior, and attainment and the mediating effects of peers and school practices. In J.E. Hall, A. Lindorff, & P. Sammons (Eds.), International perspectives in educational effectiveness research (pp. 109–132). Springer.
   Google Scholar
• Palardy, G., Rumberger, R., & Butler, T. (2015). The effect of high school socioeconomic, racial, and linguistic segregation on academic performance and school behaviors. Teachers College Record, 117(12), 1–52. https://doi.org/10.1177/016146811511701206
   Web of Science ®Google Scholar
• Park, S., Kang, S., & Lee, C. (2023). Choosing an optimal method for causal decomposition analysis: A better practice for identifying contributing factors to health disparities. Sociological Methodology, accepted. arXiv preprint arXiv:2109.06940.
   Google Scholar
• Park, S., Kang, S., Lee, C., & Ma, S. (2023). Sensitivity analysis for causal decomposition analysis: Assessing robustness toward omitted variable bias. Journal of Causal Inference, 11(1), 20220031. https://doi.org/10.1515/jci-2022-0031
   Web of Science ®Google Scholar
• Reardon, S. F., Fahle, E. M., Kalogrides, D., Podolsky, A., & Zárate, R. C. (2019). Gender achievement gaps in U.S. school districts. American Educational Research Journal, 56(6), 2474–2508. https://doi.org/10.3102/0002831219843824
   Web of Science ®Google Scholar
• Riegle-Crumb, C. (2006). The path through math: Course sequences and academic performance at the intersection of race/ethnicity and gender. American Journal of Education, 113(1), 101–122. https://doi.org/10.1086/506495
   PubMed Web of Science ®Google Scholar
• 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: The Voice of Scholarship in Education, 107(9), 1999–2045. https://doi.org/10.1177/016146810510700905
   Web of Science ®Google Scholar
• Schiller, K. S., Schmidt, W. H., Muller, C., & Houang, R. T. (2010). Hidden disparities: How courses and curricula shape opportunities in mathematics during high school. Equity & Excellence in Education, 43(4), 414–433. https://doi.org/10.1080/10665684.2010.517062
   Google Scholar
• Schmidt, W. H., Burroughs, N. A., Zoido, P., & Houang, R. T. (2015). The role of schooling in perpetuating educational inequality: An international perspective. Educational Researcher, 44(7), 371–386. https://doi.org/10.3102/0013189X1560398
   Web of Science ®Google Scholar
• Schmidt, W. H., Guo, S., & Houang, R. T. (2021). The role of opportunity to learn in ethnic inequality in mathematics. Journal of Curriculum Studies, 53(5), 579–600. https://doi.org/10.1080/00220272.2020.1863475
   Web of Science ®Google Scholar
• Schmidt, W., Zoido, P., & Cogan, L. (2014). Schooling matters: Opportunity to learn in PISA 2012 (OECD Education Working Papers, No. 95). OECD Publishing. https://doi.org/10.1787/5k3v0hldmchl-en
   Google Scholar
• Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. https://doi.org/10.3102/00346543075003417
   Web of Science ®Google Scholar
• Skovsmose, O. (2020). Critical mathematics education (pp. 154–159). Springer International Publishing.
   Google Scholar
• Stearns, E., Bottia, M. C., Giersch, J., Mickelson, R. A., Moller, S., Jha, N., & Dancy, M. (2020). Do relative advantages in STEM grades explain the gender gap in selection of a STEM major in college? A multimethod answer. American Educational Research Journal, 57(1), 218–257. https://doi.org/10.3102/000283121985353
   Web of Science ®Google Scholar
• Takashiro, N. (2017). A multilevel analysis of Japanese middle school student and school socioeconomic status influence on mathematics achievement. Educational Assessment, Evaluation and Accountability, 29(3), 247–267. https://doi.org/10.1007/s11092-016-9255-8
   Web of Science ®Google Scholar
• Urick, A., Liu, Y., Ford, T. G., & Wilson, A. S. (2019). Does instructional leadership mediate effects of student home resources on opportunity to learn and math reasoning skills? A cross-national comparison of 4th grade students. International Journal of Leadership in Education, 24(6), 876–914. https://doi.org/10.1080/13603124.2019.1629697
   Web of Science ®Google Scholar
• Urick, A., Wilson, A. S., Ford, T. G., Frick, W. C., & Wronowski, M. L. (2018). Testing a framework of math progress indicators for ESSA: How opportunity to learn and instructional leadership matter. Educational Administration Quarterly, 54(3), 396–438. https://doi.org/10.1177/0013161X18761343
   Web of Science ®Google Scholar
• Van Ewijk, R., & Sleegers, P. (2010). The effect of peer socioeconomic status on student achievement: A meta-analysis. Educational Research Review, 5(2), 134–150. https://doi.org/10.1016/j.edurev.2010.02.001
   Web of Science ®Google Scholar
• VanderWeele, T. J., & Robinson, W. R. (2014). On causal interpretation of race in regressions adjusting for confounding and mediating variables. Epidemiology, 25(4), 473–484. https://doi.org/10.1097/EDE.0000000000000105
   PubMed Web of Science ®Google Scholar
• Walkowiak, T. A., Pinter, H. H., & Berry, R. Q. (2017). A reconceptualized framework for ‘opportunity to learn’ in school mathematics. Journal of Mathematics Education at Teachers College, 8(1), 7–18. https://doi.org/10.7916/jmetc.v8i1.800
   Google Scholar
• Wronowski, M. L., Thornton, M., Razavi-Maleki, B., Witcher, A. W., & Duarte, B. J. (2022). Beyond tracking: The relationship of opportunity to learn and diminished math outcomes for US high school students. Teachers College Record: The Voice of Scholarship in Education, 124(6), 196–224. https://doi.org/10.1177/01614681221113473
   Web of Science ®Google Scholar
• Xuan, X., Xue, Y., Zhang, C., Luo, Y., Jiang, W., Qi, M., & Wang, Y. (2019). Relationship among school socioeconomic status, teacher-student relationship, and middle school students’ academic achievement in China: Using the multilevel mediation model. PLoS One, 14(3), e0213783. https://doi.org/10.1371/journal.pone.0213783
   PubMed Web of Science ®Google Scholar
• Yang Hansen, K., & Strietholt, R. (2018). Does schooling actually perpetuate educational inequality in mathematics performance? A validity question on the measures of opportunity to learn in PISA. ZDM, 50(4), 643–658. https://doi.org/10.1007/s11858-018-0935-3
   Google Scholar
• Young, J., & Cunningham, J. A. (2021). Repositioning black girls in mathematics disposition research: New perspectives from QuantCrit. Investigations in Mathematics Learning, 13(1), 29–42. https://doi.org/10.1080/19477503.2020.1827664
   Google Scholar