Educational Aspirations of Diverse Groups among Undergraduate Statistics Majors

Julie Lorah; Angie Miller

doi:10.21423/jume-v16i2a548

Authors

Julie Lorah Clemson University https://orcid.org/0000-0002-0353-2323
Angie Miller https://orcid.org/0000-0002-5828-235X

DOI:

https://doi.org/10.21423/jume-v16i2a548

Keywords:

statistics education, postsecondary education, educational aspirations, diversity

Abstract

Understanding the educational aspirations of diverse groups among statistics majors provides insight into the discipline of statistics. This study utilizes multi-institution data from the 2019 and 2020 administrations of the National Survey of Student Engagement (NSSE) to explore educational aspirations for statistics majors through comparisons to other major types as well as among gender identity and race/ethnicity in a sample of 225,892 seniors, including 521 majoring in statistics. Preliminary results from a series of chi-squared analyses suggest that while other STEM majors are more likely to aspire for doctoral or professional degrees, statistics majors are more likely to aspire for a master’s degree. However, among the statistics majors there were no significant differences in educational aspiration by gender identity and race/ethnicity.

References

Anaya, G. (1999). College impact on student learning: Comparing the use of self-reported gains, standardized test scores, and college grades. Research in Higher Education, 40, 499-526. https://doi.org/10.1023/A:1018744326915

Battey, D. & Coleman, M. A. (2021) Antiracist work in mathematics classrooms: The case of policing. Journal of Urban Mathematics Education, 14(1B). DOI: https://doi.org/10.21423/jume-v14i1Ba410

Brown, E. N. & Kass, R. E. (2009). What is statistics? The American Statistician, 63(2), 105-110. https://doi.org/10.1198/tast.2009.0019

Carter, D. F. (1999). The impact of institutional choice and environments on African-American and White students’ degree expectations. Research in Higher Education, 40(1), 17–41. https://doi.org/10.1023/A:1018770210560

Carter, D. F. (2002). College students’ degree aspirations: A theoretical model and literature re-view with a focus on African American and Latino students. In J. Smart (Ed.), Higher edu-cation: Handbook of theory and research (vol. 17, pp. 129–171). New York, NY: Agathon Press.

Ceci, S. J., Williams, W. M., & Barnett, S. M. (2009). Women's underrepresentation in science: Sociocultural and biological considerations. Psychological Bulletin, 135(2), 218–261. https://doi.org/10.1037/a0014412

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155

Cuellar, M. G. & Gonzalez, A. M. (2021). Beyond the baccalaureate: Factors shaping Latina/o graduate degree aspirations. Journal of Hispanic Higher Education, 20(1), 59-74. https://doi.org/10.1177/1538192719830082.

Dempster, M & McCorry, N.K. (2017). The role of previous experience and attitudes toward sta-tistics in statistics assessment outcomes among undergraduate psychology students. Jour-nal of Statistics Education, 17(2), https://doi.org/10.1080/10691898.2009.11889515

Eagan, M. K., Hurtado, S., Chang, M. J., Garcia, G. A., Herrera, F. A., & Garibay, J. C. (2013). Making a difference in science education: The impact of undergraduate research programs. American Educational Research Journal, 50(4), 683–713. https://doi.org/10.3102/0002831213482038

Edwin, M., Prescod, D. J., & Bryan, J. (2018). Profiles of high school students’ STEM career aspirations. The Career Development Quarterly, 67, 255-263. https://doi.org/10.1002/cdq.12194

Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London: Sage Publications.

Garriott, P. (2020). A critical cultural wealth model for first-generation and economically margin-alized college students’ academic and career development. Journal of Career Development, 47(1), 80-95. https://journals.sagepub.com/doi/pdf/10.1177/0894845319826266

Gignac, G. E., & Szodorai, E. T. (2016). Effect size guidelines for individual differences research-ers. Personality and Individual Differences, 102, 74–78. https://doi.org/10.1016/j.paid.2016.06.069

Greene, B. A. (2015). Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research. Educational Psychologist, 50(1), 14-30. https://doi.org/10.1080/00461520.2014.989230

Guo, J., Marsh, H. W., Morin, A. J. S., Parker, P. D., & Kaur, G. (2015). Directionality of the associations of high school expectancy-value, aspirations, and attainment: A longitudinal study. American Educational Research Journal, 52(2), 371-402. https://doi.org/10.3102/0002831214565786

Higgins, J. J. (1999). Nonmathematical statistics: A new direction for the undergraduate disci-pline. The American Statistician, 53(1), 1-6. https://doi.org/10.1080/00031305.1999.10474418

Holmes, K., Gore, J., Smith, M., & Lloyd, A. (2018). An integrated analysis of school stu-dents’ aspirations for STEM careers: Which student and school factors are most predictive? International journal of science and math education, 16, 655-675. https://doi.org/10.1007/s10763-016-9793-z

Horton, N. J. (2015). Challenges and opportunities for statistics and statistical education: looking back, looking forward. The American Statistician, 69(2), 138-145. https://doi.org/10.1080/00031305.2015.1032435

Hossler, D., & Stage, F. K. (1992). Family and high school experience influences on the postsec-ondary educational plans of ninth-grade students. American Educational Research Journal, 29(2), 425-451. https://doi.org/10.3102/00028312029002425

Jones, D. S., Gillette, D. D., Cooper, P. E., Salinas, R. Y., Hill, J. L., Black, S. J., Lew, D. J., & Canelas, D. A. (2022). Cultivating PhD aspirations during college. CBE – Life Scienc-es Education, 21, 1-19. https://doi.org/10.1187/cbe.20-06-0111

Kao, G., & Tienda, M. (1998). Educational aspirations of minority youth. American Journal of Education, 106(3), 349–384. https://doi.org/10.1086/444188

Ladson-Billings, G. (2021). Does that count? How mathematics education can support justice-focused anti-racist teaching and learning. Journal of Urban Mathematics Education, 14(1B), 1-5. https://doi.org/10.21423/jume-v14i1Ba444

Litzler, E., & Lorah, J. A. (2018). Degree aspirations of undergraduate engineering students at the intersection of race/ethnicity and gender. Journal of Women and Minorities in Science and Engineering 24(2), 165-193. https://doi.org/10.1615/JWomenMinorScienEng.2018017998

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 Edu-cation, 55, 810–832. https://doi.org/10.1007/s11162-014-9333-z

Lorah, J. A. & Valdivia, M. (2021). Diversity in statistics education at postsecondary institu-tions. International Journal of Research in Undergraduate Mathematics Education, 7, 21-32. https://doi.org/10.1007/s40753-020-00120-x

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. https://doi.org/10.2307/749890

Martin, D.B. & Larnell, G.V. (2013). Urban Mathematics Education. In H.R. Milner & K. Lo-motey (Eds.), Handbook of Urban Education (373‐393). New York: Routledge.

Mamedova, S., & Pawlowski, E. (2021). International comparisons of adult literacy and numera-cy skills over time. Data Point. NCES 2022-005. National Center for Education Statis-tics. https://files.eric.ed.gov/fulltext/ED616505.pdf

Matthews, L. E. (2008). Illuminating urban excellence: A movement of change within mathemat-ics education [Editorial]. Journal of Urban Mathematics Education, 1(1), 1–4.

Mau, W. J. & Li, J. (2018). Factors influencing STEM career aspirations of underrepresented high school students. The Career Development Quarterly, 66, 246-258. https://doi.org/10.1002/cdq.12146

Miller, A. L. (2012). Investigating social desirability bias in student self-report surveys. Educa-tional Research Quarterly, 36(1), 30-47. https://eric.ed.gov/?id=EJ1061958

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

Minton, P. D. (1983). The visibility of statistics as a discipline. The American Statistician, 37(4), 284-289. https://doi.org/10.2307/2682765

Moore, D. S. (2001). Undergraduate programs and the future of academic statistics. The American Statistician, 55(1), 1-6. https://doi.org/10.1198/000313001300339860

National Survey of Student Engagement. (2018). NSSE Conceptual Framework (2013) (NSSE Psychometric Portfolio Report). Bloomington, IN: Center for Postsecondary Research, In-diana University, School of Education. Available online: https://nsse.indiana.edu/nsse/psychometric-portfolio/index.html

NSSE 2019 Overview. (2019). Bloomington, IN: Center for Postsecondary Research, Indiana University, School of Education. Retrieved from https://scholarworks.iu.edu/dspace/bitstream/handle/2022/25756/NSSE%202019%20Overview.pdf?sequence=23&isAllowed=y

NSSE 2020 Overview. (2020). Bloomington, IN: Center for Postsecondary Research, Indiana University, School of Education. Retrieved from https://scholarworks.iu.edu/dspace/bitstream/handle/2022/25756/NSSE%202020%20Overview.pdf?sequence=24&isAllowed=y

Pike, G. R. (1995). The relationship between self-reports of college experiences and achievement test scores. Research in Higher Education, 36(1), 1-22. https://doi.org/10.1007/BF02207764

Raygoza, M. C. (2016). Striving toward transformational resistance: Youth participatory action research in the mathematics classroom. Journal of Urban Mathematics Education, 9(2), 122–152.https://doi.org/10.21423/jume-v9i2a286

Rocconi L. M., Gonyea R. M. (2018). Contextualizing effect sizes in the national survey of stu-dent engagement: An empirical analysis. Research & Practice in Assessment, 13(Summer/Fall), 22–38. Retrieved from http://www.rpajournal.com/contextualizing-effect-sizes-in-the-national-survey-of-student-engagement-an-empirical-analysis/ [Google Scholar]

Scriven, M. (2008). The concept of a transdiscipline: And of evaluation as a transdiscipline. Journal of MultiDisciplinary Evaluation, 5(10), 65-66.

Uttl, B. & Smibert, D. (2017). Student evaluations of teaching: Teaching quantitative courses can be hazardous to one's career. PeerJ, 5. https://doi.org/10.7717/peerj.3299

Wiley. (2022). The state of the student 2022. https://www.wiley.com/en-us/network/trending-stories/the-state-of-the-student-adjusting-to-the-new-normal-and-all-that-comes-with-it

Wofford, A. M., Sax, L. J., George, K. L., Ramirez, D., & Nhien, C. (2022). Advancing equity in graduate pathways: Examining the factors that sustain and develop computing graduate aspirations. The Journal of Higher Education, 93(1), 110-136, https://doi.org/10.1080/00221546.2021.1930840

Young, J., Raygoza, M. C., Madkins, T., Lawler, B., & Roberts, T. (2022). Revisiting Urban Mathematics Education: Towards Robust Theoretical, Conceptual, and Analytical Methods. Journal of Urban Mathematics Education, 15(2), 1-7.

Zysberg, L. (2010). Statistics for cowards: a field study of the effects of re-designing order and presentation in statistics courses for social science college students. Procedia - Social and Behavioral Sciences, 2(10), 595-599. https://doi.org/10.1016/j.sbspro.2010.03.0