Decoding the data dichotomy: applying QuantCrit to understand racially conscience intersectional meta-analytic research

References

• Andon, A., Thompson, C.G., and Becker, B.J., 2014. A quantitative synthesis of the immigrant achievement gap across OECD countries. Large-scale assessments in education, 2 (1), 7–20. doi:10.1186/s40536-014-0007-2.
   Google Scholar
• Blank, M.A. and Antaki, J.F., 2017. Breast lesion elastography region of interest selection and quantitative heterogeneity: a systematic review and meta-analysis. Ultrasound in medicine & biology, 43 (2), 387–397. doi:10.1016/j.ultrasmedbio.2016.09.002.
   PubMedGoogle Scholar
• Bloom, H.S., et al., 2008. Performance trajectories and performance gaps as achievement effect-size benchmarks for educational interventions. Journal of research on educational effectiveness, 1 (4), 289–328. doi:10.1080/19345740802400072.
   Google Scholar
• Bohrnstedt, G., et al., 2015. School composition and the Black-White achievement gap. National Center for Education Statistics, NCES 2015-018. Available from: https://files.eric.ed.gov/fulltext/ED560723.pdf.
   Google Scholar
• Boykin, A.W. and Noguera, P., 2011. Creating the opportunity to learn. Alexandria, VA: ASCD.
   Google Scholar
• Caldera, A., Rizvi, S., Calderon-Berumen, F., and Lugo, M., 2020. When researching the “Other” intersects with the self: Women of color intimate research. Departures in Critical Qualitative Research, 9(1), 63–88. doi:10.1525/dcqr.2020.9.1.63
   Web of Science ®Google Scholar
• Carey, R.L., 2014. A cultural analysis of the achievement gap discourse: challenging the language and labels used in the work of school reform. Urban education, 49 (4), 440–468. doi:10.1177/0042085913507459.
   Web of Science ®Google Scholar
• Carpenter, D.M., Ramirez, A., and Severn, L., 2006. Gap or gaps: challenging the singular definition of the achievement gap. Education and urban society, 39 (1), 113–127. doi:10.1177/0013124506291792.
   Web of Science ®Google Scholar
• Collins, P.H., 1990. Black feminist thought in the matrix of domination. Black Feminist Thought: Knowledge, Consciousness, and the Politics of Empowerment, 138 (1990), 221–238. http://archive.cunyhumanitiesalliance.org/introsocspring20/wpcontent/uploads/sites/50/2019/03/Collins.Black-Feminist-Thought.pdf
   Google Scholar
• Curran, F.C. and Kellogg, A.T., 2016. Understanding science achievement gaps by race/ethnicity and gender in kindergarten and first grade. Educational researcher, 45 (5), 273–282. doi:10.3102/0013189X16656611.
   Web of Science ®Google Scholar
• Dotterer, A.M., Lowe, K., and McHale, S.M., 2014. Academic growth trajectories and family relationships among African American youth. Journal of research on adolescence, 24 (4), 734–747. doi:10.1111/jora.12080.
   PubMedGoogle Scholar
• Feingold, A., 1994. Gender differences in variability in intellectual abilities: a cross-cultural perspective. Sex roles, 30, 81–92. doi:10.1007/BF01420741.
   Web of Science ®Google Scholar
• Ferguson, C.J., 2016. An effect size primer: a guide for clinicians and researchers. In: A.E. Kazdin, ed. Methodological issues and strategies in clinical research. American Psychological Association, 301–310. doi:10.1037/14805-020.
   Google Scholar
• Garcia, N.M., López, N., and Vélez, V.N., 2018. QuantCrit: rectifying quantitative methods through critical race theory. Race ethnicity and education, 21, 149–157. doi:10.1080/13613324.2017.1377675.
   Web of Science ®Google Scholar
• Gillborn, D., Warmington, P., and Demack, S., 2018. QuantCrit: education, policy, ‘Big Data’and principles for a critical race theory of statistics. Race ethnicity and education, 21 (2), 158–179. doi:10.1080/13613324.2017.1377417.
   Web of Science ®Google Scholar
• Gorey, K.M., 2009. Comprehensive school reform: meta-analytic evidence of Black-White achievement gap narrowing. Education policy analysis archives, 17 (25), 1–17. Available from: http://epaa.asu.edu/ojs/article/view/708;.
   PubMedGoogle Scholar
• Gutiérrez, R., 2014. Improving education and the mistaken focus on “raising test scores” and “closing the achievement gap”. In: Paul C. Gorski and Kristien Zenkov, eds. The big lies of school reform. Routledge, 27–38.
   Google Scholar
• Gutsmiedl, K., et al., 2020. How well do elderly patients with major depressive disorder respond to antidepressants: a systematic review and single-group meta-analysis. BMC psychiatry, 20 (1), 1–12.
   PubMedGoogle Scholar
• Hanushek, E.A., et al., 2019. The achievement gap fails to close: half century of testing shows persistent divide between haves and have-nots. Education next, 19 (3), 8–17.
   Google Scholar
• Harris, D.N. and Herrington, C.D., 2006. Accountability, standards, and the growing achievement gap: lessons from the past half-century. American journal of education, 112 (2), 209–238. Available from: https://www.journals.uchicago.edu/doi/full/10.1086/498995.
   Web of Science ®Google Scholar
• Hattie, J., 2008. Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.
   Google Scholar
• Hill, C.J., et al., 2008. Empirical benchmarks for interpreting effect sizes in research. Child development perspectives, 2 (3), 172–177. doi:10.1111/j.1750-8606.2008.00061.x.
   Web of Science ®Google Scholar
• Hooks, B., 1989. Talking back: Thinking feminist, thinking black (Vol. 10). South End Press.
   Google Scholar
• Jeynes, W.H., 2010. Religiosity, religious schools, and their relationship with the achievement gap: a research synthesis and meta-analysis. The journal of negro education, 263–279. Available from: https://www.jstor.org/stable/20798348.
   Google Scholar
• Jeynes, W.H., 2015. A meta-analysis on the factors that best reduce the achievement gap. Education and urban society, 47 (5), 523–554. doi:10.1177/0013124514529155.
   Web of Science ®Google Scholar
• Kishi, T., et al., 2020. Recurrence of mania or depression among adult bipolar patients who continued using lithium: a single-group summary meta-analysis of randomized trials. Journal of clinical psychopharmacology, 40 (5), 468–474. doi: 10.1097/JCP.0000000000001264.
   PubMed Web of Science ®Google Scholar
• Krull, M., 2014. Time to end “separate and unequal” schools. Sojourners Magazine, 43 (9), 9–10.
   Google Scholar
• Lindberg, S.M., et al., 2010. New trends in gender and mathematics performance: a meta-analysis. Psychological bulletin, 136 (6), 1123–1135. doi:10.1037/a0021276.
   PubMed Web of Science ®Google Scholar
• McFarland, J., et al., 2019. The condition of education 2019. Washington, DC: National Center for Education Statistics, U.S. Department of Education, NCES 2019-144. Available from: https://nces.ed.gov/ pubsearch/pubsinfo.asp?pubid=2019144.
   Google Scholar
• Millin, T., Millin, M., and Pearce, J., 2020. Unpacking the efficacy of reading to learn using cognitive load theory. Journal of academic language and learning, 14 (1), 113–126. Available from: https://journal.aall.org.au/index.php/jall/article/view/693.
   Google Scholar
• Najafi, T.F., et al., 2015. Air pollution and quality of sperm: a meta-analysis. Iranian red crescent medical journal, 17 (4), doi:10.5812/ircmj.17(4)2015.26930.
   Google Scholar
• National Center for Education Statistics, 2009. The nation’s report card: mathematics 2009. Washington, DC: Institute of Education Sciences, U.S. Department of Education, NCES 2010-451.
   Google Scholar
• National Center for Education Statistics, 2013. The nation’s report card: trends in academic progress 2012. Washington, DC: Institute of Education Sciences, U.S. Department of Education, NCES 2013 456.
   Google Scholar
• National Center for Education Statistics, 2020. Achievement gaps. Available from: https://nces.ed.gov/nationsreportcard/studies/gaps/.
   Google Scholar
• National Research Council., 2004. Engaging schools: Fostering high school students’ motivation to learn. Washington, DC: The National Academies Press.
   Google Scholar
• Perie, M., Moran, R., and Lutkus, A.D., 2005. NAEP 2004 trends in academic progress: three decades of student performance in reading and mathematics. Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, Government Printing Office, NCES 2005-464.
   Google Scholar
• Rampey, B.D., Dion, G.S., and Donahue, P.L., 2009. NAEP 2008 trends in academic progress. Washington, DC: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education, NCES 2009-479.
   Google Scholar
• Reilly, D., Neumann, D.L., and Andrews, G., 2015. Sex differences in mathematics and science achievement: a meta-analysis of national assessment of educational progress assessments. Journal of educational psychology, 107 (3), 645–662. doi:10.1037/edu0000012.
   Web of Science ®Google Scholar
• Sablan, J.R., 2019. Can you really measure that? Combining critical race theory and quantitative methods. American educational research journal, 56 (1), 178–203. doi:10.3102/0002831218798325.
   Web of Science ®Google Scholar
• Shaw, C., et al., 2009. Systematic review of respite care in the frail elderly. Health technology assessment, 13 (20), 1–246. doi:10.3310/hta13200.
   PubMed Web of Science ®Google Scholar
• Soland, J. and Thum, Y.M., 2019. Effect sizes for measuring student and school growth in achievement: in search of practical significance. Annenberg Institute at Brown University, EdWorkingPaper No.19-60. Available from: http://edworkingpapers.com/ai19-60.
   Google Scholar
• Stevens, J., 2018. Using effect size to evaluate achievement gaps. Research brief. National Center on Assessment and Accountability for Special Education. Available from: https://files.eric.ed.gov/fulltext/ED584703.pdf.
   Google Scholar
• Taylor, E., 2006. A critical race analysis of the achievement gap in the United States: politics, reality, and hope. Leadership and policy in schools, 5 (1), 71–87. doi:10.1080/15700760500499025.
   Google Scholar
• Thompson, C.L. and O’Quinn, S.D., III, 2001. Eliminating the Black-White achievement gap: a summary of research. Available from: https://files.eric.ed.gov/fulltext/ED457250.pdf.
   Google Scholar
• Toldson, I.A., 2019. No BS (bad stats): Black people need people who believe in Black people enough not to believe every bad thing they hear about Black people. Boston, MA: Brill.
   Google Scholar
• Trusty, J., Thompson, B., and Petrocelli, J.V., 2004. Practical guide for reporting effect size in quantitative research in the Journal of Counseling & Development. Journal of counseling & development, 82 (1), 107–110. doi:10.1002/j.1556-6678.2004.tb00291.x.
   Web of Science ®Google Scholar
• U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, 2021a. National Assessment of Educational Progress (NAEP), various years, 1990–2019 Mathematics Assessments. Available from: https://www.nationsreportcard.gov/highlights/mathematics/2019/.
   Google Scholar
• U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, 2021b. National Assessment of Educational Progress (NAEP), various years, 1992–2019 Reading Assessments. Available from: https://www.nationsreportcard.gov/highlights/reading/2019/.
   Google Scholar
• Vanneman, A., et al., 2009. Achievement gaps: How Black and White students in public schools perform in mathematics and reading on the national assessment of educational progress. Washington, DC: U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences, NCES 2009-455.
   Google Scholar
• Webb, M. and Thomas, R., 2015. Teachers’ perceptions of educators’ and students’ role in closing the achievement gap. National forum of teacher education journal, 25 (3), 1–8. Available from: http://www.nationalforum.com/ Electronic%20Journal%20Volumes/Webb,%20Mary%20Teachers%20Perception s%20NFTEJ%20V25%20N3%202015.pdf.
   Google Scholar
• Young, J.L., Feille, K.K., and Young, J.R., 2017. Black girls as learners and doers of science: A single-group summary of elementary science achievement. Electronic Journal of Science Education, 21 (2), 1-20. https://files.eric.ed.gov/fulltext/EJ1187988.pdf
   Google Scholar