Abstract
Statistics classes are required for postsecondary students in a variety of majors, in addition to students who are statistics majors themselves. Although many students will experience statistics class during college, there is very little research examining the state of diversity within these classes and within statistics programs. This study uses enrollment data from a large Midwestern university as well as nationally (USA) representative data collected from the United States National Center for Education Statistics (NCES) through the Integrated Postsecondary Education Data System (IPEDS) to examine the status of diversity within postsecondary statistics education. Results indicate that the distribution of statistics majors by gender is fairly equitable although females may be somewhat underrepresented. In addition, Black, Hispanic, and White students are shown to be underrepresented compared to the student population, while Asian students and International students are shown to be overrepresented among statistics majors when compared to the student population. Students majoring in the social sciences are often required to take statistics classes as well; results regarding these students indicate that males are underrepresented but that these students are fairly diverse with regard to race/ethnicity. Implications and associated pedagogical guidance are explored.
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References
Adiredja, A. P. (2019). Anti-deficit narratives: Engaging the politics of research on mathematical sense making. Journal for Research in Mathematics Education, 50(4), 401–435.
Adiredja, A. P., & Andrews-Larson, C. (2017). Taking the sociopolitical turn in postsecondary mathematics education research. International Journal of Research in Undergraduate Mathematics Education, 3(3), 444–465.
Barajas-Lopez, F., & Larnell, G. V. (2019). Unpacking the links between equitable teaching practices and standards for mathematical practice: Equity for whom and under what conditions? Journal for Research in Mathematics Education, 50(4), 349–361.
Chen, J. C., Whittinghill, D. C., & Kadlowec, J. A. (2010). Classes that click: Fast, rich feedback to enhance student learning and satisfaction. Journal of Engineering Education, 99, 159–168.
Chestnut, E. K., Lei, R. F., Leslie, S., & Cimpian, A. (2018). The myth that only brilliant people are good at math and its implications for diversity. Education Sciences, 8(2), 65.
Cvencek, D., Meltzoff, A. N., & Greenwald, A. G. (2011). Math-gender stereotypes in elementary school children. Child Development, 82(3), 766–779.
Devore, J. L. (2004). Probability and statistics for engineering and the sciences. Thomson: United States.
Fhloinn, E. N., Fitzmaurice, O., Bhaird, C. M., & O’Sullivan, C. (2016). Gender differences in the level of engagement with mathematics support in higher education in Ireland. International Journal of Research in Undergraduate Mathematics Education, 2(3), 297–317.
Garfield, J., & Ben-Zvi, D. (2007). How students learn statistics revisited: A current review of research on teaching and learning statistics. International Statistical Review, 75(3), 372–396.
Guiffrida, D. A., & Douthit, K. Z. (2010). The black student experience at predominantly white colleges: Implications for school and college counselors. Journal of Counseling & Development, 88, 311–318.
Hall, R. M., & Sandler, B.R. (1982). The campus climate: A chilly one for women? (Report of the project on the status and education of women). Washington, DC: Association of American Colleges.
Hayward, C. N., Kogan, M., & Laursen, S. L. (2016). Facilitating instructor adoption of inquiry-based learning in college mathematics. International Journal of Research in Undergraduate Mathematics Education, 2(1), 59–82.
Henry, W. J., Fowler, S. R., & West, N. M. (2011). Campus climate: An assessment of students’ perceptions in a College of Education. Urban Education, 46(4), 689–718.
Hsiung, C. (2012). The effectiveness of cooperative learning. Journal of Engineering Education, 101(1), 119–137.
Jett, C. C. (2019). Mathematical persistence among four African American male graduate students: A critical race analysis of their experiences. Journal for Research in Mathematics Education, 50(3), 311–340.
Lesser, L. M. (2010). Equity and the increasingly diverse tertiary student population: Challenges and opportunities in statistics education. In C. Reading (Ed.), Data and context in statistics education: Towards an evidence-based society. Proceedings of the Eighth International Conference on Teaching Statistics (ICOTS8, July, 2010), Ljubljana, Slovenia. Voorburg, International Statistical Institute.
Litzler, E., Samuelson, C. C., & Lorah, J. A. (2014). Breaking it down: Engineering student STEM confidence at the intersection of race/ethnicity and gender. Research in Higher Education, 55, 810–832.
Lubienski, S. T., & Bowen, A. (2000). Who’s counting? A survey of mathematics education research 1982-1998. Journal for Research in Mathematics Education, 31(5), 626–633.
Morton, K., & Riegle-Crumb, C. (2019). Who gets in? Examining inequality in eighth-grade algebra. Journal for Research in Mathematics Education, 50(5), 529–554.
National Center for Education Statistics. (2015). Integrated postsecondary education data system (IPEDS) [Data file]. Retrieved from https://nces.ed.gov/ipeds/use-the-data
National Science Board. (2016). Science and engineering indicators 2016. Arlington: National Science Foundation Retrieved from https://www.nsf.gov/statistics/2016/nsb20161/#/report.
R Core Team. (2018). R: A language and environment for statistical computing. In R Foundation for statistical computing. Vienna: Austria. URL https://www.R-project.org/.
Riegle-Crumb, C., & King, B. (2010). Questioning a white male advantage in STEM: Examining disparities in college major by gender and race/ethnicity. Educational Researcher, 39, 656–664.
Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual performance of African Americans. Journal of Personality and Social Psychology, 69(5), 797–811.
Stephenson, W. R. (2010). Diversity and differentiated instruction and learning. In data and context in statistics education: Towards an evidence-based society. In C. Reading (Ed.), Data and context in statistics education: Towards an evidence-based society. Proceedings of the Eighth International Conference on Teaching Statistics (ICOTS8, July, 2010), Ljubljana, Slovenia. Voorburg, International Statistical Institute.
Stinson, D. W. (2013). Negotiating the “white male math myth”: African American male students and success in school mathematics. Journal for Research in Mathematics Education, 44(1), 69–99.
Stinson, D. W. (2016). Absence of diversity in collegiate upper-level mathematics classrooms: Perpetuating the “white male math myth”. Journal of Urban Mathematics Education, 9(2), 1–4.
Verhoeven, P. S., & Tempelaar, D. T. (2014). Cultural diversity in statistics education: Bridging uniqueness. In sustainability in statistics education. In K. Makar, B. de Sousa, & R. Gould (Eds.), Sustainability in statistics education. Proceedings of the NinthInternational Conference on Teaching Statistics (ICOTS9, July, 2014), Flagstaff, Arizona, USA. Voorburg, International Statistical Institute.
Webel, C., Krupa, E. E., & McManus, J. (2017). The math emporium: Effective for whom, and for what? International Journal of Research in Undergraduate Mathematics Education, 3(2), 355–380.
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Lorah, J.A., Valdivia, M. Diversity in Statistics Education at Postsecondary Institutions. Int. J. Res. Undergrad. Math. Ed. 7, 21–32 (2021). https://doi.org/10.1007/s40753-020-00120-x
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DOI: https://doi.org/10.1007/s40753-020-00120-x