References
- Aguilar, M. S., Rosas, A., Zavaleta, J. G. M., and Romo-Vázquez, A. (2016), “Exploring High-Achieving Students’ Images of Mathematicians,” International Journal of Science and Mathematics Education, 14, 527–548. DOI: https://doi.org/10.1007/s10763-014-9586-1.
- Batanero, C., Merino, B., and Díaz, C. (2003), “Assessing Secondary School Student’s Understanding of Average,” European Research in Mathematics Education III, 3, 1–9.
- Berkeley School of Information (2020), “What is Data Science?” Available at https://datascience.berkeley.edu/about/what-is-data-science/.
- Blackwell, J. E. (1988), “Faculty Issues: The Impact on Minorities,” The Review of Higher Education, 11, 417–434. DOI: https://doi.org/10.1353/rhe.1988.0013.
- Boaler, J., and Levitt, S. D. (2019), “Modern High School Math Should be about Data Science — not Algebra 2.” Los Angeles Times. Available at https://www.latimes.com/opinion/story/2019-10-23/math-high-school-algebra-data-statistics.
- Boas, L. V. (2020), “Diversity in Data Science: A Systemic Inequality How FAANG Companies are Dealing with this Structural Problem. Towards Data Science,” Ävailable at https://towardsdatascience.com/diversity-in-data-science-a-systemic-inequality-b97a0e953f6e.
- Bodzin, A., and Gehringer, M. (2001), “Breaking Science Stereotypes,” Science and Children, 38, 36–41.
- Brooks, C. I. (1986), “Female Superiority in Statistics Achievement,” Teaching of Psychology, 14, 45–46. DOI: https://doi.org/10.1207/s15328023top1401_13.
- Brown, P. L., Concannon, J. P., Marx, D., Donaldson, C. W., and Black, A. (2016), “An Examination of Middle School Students’ STEM Self-efficacy with Relation to Interest and Perceptions of STEM,” Journal of STEM Education, 17, 27–38.
- Buck, J. L. (1985), “A Failure to Find Gender Differences in Statistics Achievement,” Teaching of Psychology, 12, 100. DOI: https://doi.org/10.1207/s15328023top1202_13.
- Burns, H. D., and Lesseig, K. (2017), “Infusing Empathy into Engineering Design: Supporting Under-represented Student Interest and Sense of Belongingness,” in American Society for Engineering Education Annual Conference & Exposition, Columbus, Ohio.
- Carr, M., and Jessup, D. L. (1997), “Gender Differences in First-Grade Mathematics Strategy Use: Social and Metacognitive Influences,” Journal of Educational Psychology, 89, 318–328.
- Chambers, D. W. (1983), “Stereotypic Images of the Scientist: The Draw-a-Scientist Test,” Science Education, 67, 255–265. DOI: https://doi.org/10.1002/sce.3730670213.
- Charrad, M., Ghazzali, N., Boiteau, V., and Niknafs, A. (2014), “NbClust: An R Package for Determining the Relevant Number of Clusters in a Student,” Journal of Statistical Software, 61, 1–36. DOI: https://doi.org/10.18637/jss.v061.i06.
- Columbus, L. (2017), “IBM Predicts Demand For Data Scientists Will Soar 28% By 2020.” Available at https://www.forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-soar-28-by-2020/{\#}2f3902ea7e3b.
- Criado-Perez, C. (2019), Invisible Women: Exposing Data Bias in a World Designed for Men, New York: Abrams Press.
- Dasgupta, N., and Asgari, S. (2004), “Seeing is Believing: Exposure to Counterstereotypic Women Leaders and its Effect on the Malleability of Automatic Gender Stereotyping,” Journal of Experimental Social Psychology, 40, 642–658. DOI: https://doi.org/10.1016/j.jesp.2004.02.003.
- Datta, S., Datta, S., Pihur, V., and Brock, G. (2008), “clValid: An R Package for Cluster Validation,” Journal of Statistical Software, 25, 1–22.
- Dosad, M. (2020), “The Gender Gap in Data Analytics,” Available at https://www.harnham.com/us/the-gender-gap-in-data-analytics
- Es, C. V., and Weaver, M. M. (2018), “Race, Sex, and their Influences on Introductory Statistics Education,” Journal of Statistics Education, 26, 48–54. DOI: https://doi.org/10.1080/10691898.2018.1434426.
- Everitt, B. S., Landau, S., Leese, M., and Stahl, D. (2011), Cluster Analysis (5th ed.), Chichester: Wiley.
- Farland-Smith, D. (2012), “Development and Field Test of the modified Draw-A-Scientist Test and the Draw-A-Scientist Rubric,” School Science and Mathematics, 112, 109–116. DOI: https://doi.org/10.1111/j.1949-8594.2011.00124.x.
- Finson, K. D. (2002), “Drawing a Scientist: What we do and do not know after Fifty Years of Drawings,” School Science and Mathematics, 102, 335–345. DOI: https://doi.org/10.1111/j.1949-8594.2002.tb18217.x.
- Finson, K. D., Beaver, J. B., and Cramond, B. L. (1995), “Development and Field Test of a Checklist for the Draw-A-Scientist Test,” School Science and Mathematics, 95, 195–205. DOI: https://doi.org/10.1111/j.1949-8594.1995.tb15762.x.
- Forbes Magazine. (2020), “Fifteen Most Valuable College Majors,” Available at https://www.forbes.com/pictures/lmj45jgfi/no-15-statistics/{#}148e884a4e31
- Glassdoor (2020), “50 Best Jobs in America.” Available at https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm
- Good, C., Aronson, J., and Harder, J. A. (2008), “Problems in the Pipeline: Stereotype Threat and Women’s Achievement in High-Level Math Courses,” Journal of Applied Developmental Psychology, 29, 17–28. DOI: https://doi.org/10.1016/j.appdev.2007.10.004.
- Goodenow, C. (1993), “Classroom Belonging Among Early Adolescent Students: Relationships to Motivation and Achievement,” The Journal of Early Adolescence, 13, 21–43. DOI: https://doi.org/10.1177/0272431693013001002.
- Haley, M. R., Johnson, M. F., and Kuennen, E. W. (2007), “Student and Professor Gender Effects in Introductory Business Statistics,” Journal of Statistics Education, 15, 1–19.
- Hastie, T., Tibshirani, R., and Friedman, J. (2017), The Elements of Statistical Learning, Springer: New York.
- Jacobs, J. E. (2005), “Twenty-Five Years of Research on Gender and Ethnic Differences in Math and Science Career Choices: What Have We Learned?,” New Directions for Child and Adolescent Development, 2005, 85–94. DOI: https://doi.org/10.1002/cd.151.
- James, G., Witten, D., Hastie, T., and Tibshirani, R. (2017), An Introduction to Statistical Learning: With Applications in R, New York, NY: Springer.
- Kiefer, A. K., and Sekaquaptewa, D. (2007), “Implicit Stereotypes, Gender Identification, and Math-Related Outcomes: A Prospective Study of Female College students,” Psychological Science, 18, 13–18. DOI: https://doi.org/10.1111/j.1467-9280.2007.01841.x.
- Lanza, S. T., Tan, X., and Bray, B. C. (2013), “Latent Class Analysis with Distal Outcomes: A Flexible Model-based Approach,” Structural Equation Modeling: A Multidisciplinary Journal, 20, 1–26. DOI: https://doi.org/10.1080/10705511.2013.742377.
- Latu, I. M., Mast, M. S., Lammers, J., and Bombari, D. (2013), “Successful Female Leaders Empower Women’s Behavior in Leadership Tasks,” Journal of Experimental Social Psychology, 49, 444–448. DOI: https://doi.org/10.1016/j.jesp.2013.01.003.
- Lewis, C. M., Anderson, R. E., and Yasuhara, K. (2016), “‘I Don’t Code All Day’ Fitting in Computer Science When the Stereotypes Don’t Fit.” in Proceedings of the 2016 ACM Conference on International Computing Education Research, pp. 23–32.
- Luong, K. T., and Knobloch-Westerwick, S. (2017), “Can the Media Help Women be Better at Math? Stereotype Threat, Selective Exposure, Media Effects, and Women’s Math Performance,” Human Communication Research, 43, 193–213. DOI: https://doi.org/10.1111/hcre.12101.
- Marx, D. M., and Roman, J. S. (2002), “Female Role Models: Protecting Women’s Math Test Performance,” Personality and Social Psychology Bulletin, 28, 1183–1193. DOI: https://doi.org/10.1177/01461672022812004.
- Miller, D. I., Nolla, K. M., Eagly, A. H., and Uttal, D. H. (2018), “The Development of Children’s Gender-Science Stereotypes: A Meta-Analysis of 5 Decades of US Draw-a-Scientist Studies,” Child Development, 89, 1943–1955.
- Mufti, G. B., Bertrand, P., and Moubarki, E. L. (2005), “Determining the Number of Groups from Measures of Cluster Stability.” in Proceedings of International Symposium on Applied Stochastic Models and Data Analysis, pp. 17–20.
- Murphy, M. C., Gopalan, M., Carter, E. R., Emerson, K. T., Bottoms, B. L., and Walton, G. M. (2020), “A Customized Belonging Intervention Improves Retention of Socially Disadvantaged Students at a Broad-Access University,” Science Advances, 6, eaba4677. DOI: https://doi.org/10.1126/sciadv.aba4677.
- National Science Foundation (2021), “Women, Minorities, and Persons with Disabilities.” National Center for Science and Engineering Statistics Directorate for Social, Behavioral and Economic Sciences Report. Retrieved June 10, 2021.
- O’Connell, C., and McKinnon, M. (2021), “Perceptions of Barriers to Career Progression for Academic Women in STEM,” Societies, 11, 1–20. DOI: https://doi.org/10.3390/soc11020027.
- Piatek-Jimenez, K., Nouhan, M., and Williams, M. (2020), “‘College Students’ Images of Mathematicians and Mathematical Careers,” Journal of Humanistic Mathematics, 10, 66–100. DOI: https://doi.org/10.5642/jhummath.202001.06.
- Picho, K., and Schmader, T. (2018), “When do Gender Stereotypes Impair Math Performance? A Study of Stereotype Threat Among Ugandan Adolescents,” Sex Roles, 78, 295–306. DOI: https://doi.org/10.1007/s11199-017-0780-9.
- Picker, S. H., and Berry, J. S. (2000), “Investigating Pupils’ Images of Mathematicians,” Educational Studies in Mathematics, 43, 65–94. DOI: https://doi.org/10.1023/A:1017523230758.
- Pilotti, M. A. (2021), “What Lies beneath Sustainable Education? Predicting and Tackling Gender Differences in STEM Academic Success,” Sustainability, 13, 1–15. DOI: https://doi.org/10.3390/su13041671.
- R Core Team (2020), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing. Available at https://www.R-project.org/.
- Rattan, A., Good, C., and Dweck, C. S. (2012), “It’s ok—Not Everyone Can be Good at Math: Instructors with an Entity Theory Comfort (and Demotivate) Students,” Journal of Experimental Social Psychology, 48, 731–737.
- Rencher, A. C., and Christensen, W. F. (2012), Methods of Multivariate Analysis (3rd ed.), Hoboken, NJ: Wiley.
- Rock, D., and Shaw, J. M. (2000), “Exploring Children’s Thinking about Mathematicians and their Work,” Teaching Children Mathematics, 6, 550–555. DOI: https://doi.org/10.5951/TCM.6.9.0550.
- Rossman, A., Chance, B., Medina, E., and Obispo, C. P. S. L. (2006), “Some Key Comparisons between Statistics and Mathematics, and Why Teachers Should Care,” in Thinking and Reasoning with Data and Chance: Sixty-Eighth Annual Yearbook of the National Council of Teachers of Mathematics, 323–333.
- Rousseeuw, P. J. (1987), “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis,” Journal of Computational and Applied Mathematics, 20, 53–65. DOI: https://doi.org/10.1016/0377-0427(87)90125-7.
- Saidi, S. S., and Siew, N. M. (2019), “Assessing Students’ Understanding of the Measures of Central Tendency and Attitude towards Statistics in Rural Secondary Schools,” International Electronic Journal of Mathematics Education, 14, 73–86.
- Schau, C., and Emmioğlu, E. (2012), “Do Introductory Statistics Courses in the United States Improve Students’ Attitudes?” Statistics Education Research Journal, 11, 86–94. DOI: https://doi.org/10.52041/serj.v11i2.331.
- Schram, C. M. (1996), “A Meta-Analysis of Gender Differences in Applied Statistics Achievement,” Journal of Educational and Behavioral Statistics, 21, 55–70. DOI: https://doi.org/10.3102/10769986021001055.
- Silge, J., and Robinson, D. (2017), Text Mining with R: A Tidy Approach, Sebastopol: OReilly Media.
- Simon, S., and Hoyt, C. L. (2013), “Exploring the Effect of Media Images on Women’s Leadership Self-perceptions and Aspirations,” Group Processes & Intergroup Relations, 16, 232–245.
- Songsore, E., and White, B. J. (2018), “Students’ Perceptions of the Future Relevance of Statistics after Completing an Online Introductory Statistics Course,” Statistics Education Research Journal, 17, 120–140. DOI: https://doi.org/10.52041/serj.v17i2.162.
- Spencer, S. J., Steele, C. M., and Quinn, D. M. (1999), “Stereotype Threat and Women’s Math Performance,” Journal of Experimental Social Psychology, 35, 4–28. DOI: https://doi.org/10.1006/jesp.1998.1373.
- Steele, C. M. (1997), “A Threat in the Air: How Stereotypes Shape Intellectual Identity and Performance,” American Psychologist, 52, 613–629. DOI: https://doi.org/10.1037/0003-066X.52.6.613.
- Stout, J., and Tamer, B. (2016), “Collaborative Learning Eliminates the Negative Impact of Gender Stereotypes on Women’s Self-concept.” in Proceedings of the 47th ACM Technical Symposium on Computing Science Education, pp. 496–496.
- Susbiyanto, S., Kurniawan, D. A., Perdana, R., and Riantoni, C. (2019), “Identifying the Mastery of Research Statistical Concept by Using Problem-Based Learning,” International Journal of Evaluation and Research in Education, 8, 461–469. DOI: https://doi.org/10.11591/ijere.v8i3.20252.
- Tellhed, U., Bäckström, M., and Björklund, F. (2017), “Will I fit in and Do Well? The Importance of Social Belongingness and Self-efficacy for Explaining Gender Differences in Interest in STEM and HEED Majors,” Sex Roles, 77, 86–96. DOI: https://doi.org/10.1007/s11199-016-0694-y.
- Thomas, M. D., Henley, T. B., and Snell, C. M. (2006), “The Draw a Scientist Test: A Different Population and a Somewhat Different Story,” College Student Journal, 40, 140–149.
- Witherspoon, E. B., and Schunn, C. D. (2020), “Locating and Understanding the Largest Gender Differences in Pathways to Science Degrees,” Science Education, 104, 144–163. DOI: https://doi.org/10.1002/sce.21557.
- Woehlke, P. L., and Leitner, D. W. (1980), “Gender Differences in Performance on Variables Related to Achievement in Graduate-Level Educational Statistics,” Psychological Reports, 47, 1119–1125. DOI: https://doi.org/10.2466/pr0.1980.47.3f.1119.