Núm. 9 (2019): PISA como evaluación educativa supranacional: luces y sombras en equidad
Monográfico

LA BRECHA DE GÉNERO EN MATEMÁTICAS Y EN LECTURA: LA PERSPECTIVA DEL ESTUDIANTE

Joel Rapp
Universidad Autónoma de Madrid (UAM), España
Publicado 18 diciembre 2019

Palabras clave:

PISA, Brecha de género en matemáticas, Brecha de género en lectura, Educación comparada
Cómo citar
Rapp, J., & Borgonovi, F. (2019). LA BRECHA DE GÉNERO EN MATEMÁTICAS Y EN LECTURA: LA PERSPECTIVA DEL ESTUDIANTE. Journal of Supranational Policies of Education, (9), 6–56. https://doi.org/10.15366/jospoe2019.9.001

Resumen

La brecha de género en matemáticas, que favorece a los chicos sobre las chicas en clase, ha adquirido cada vez más importancia en las últimas décadas. Teniendo en cuenta que la competencia matemática es crítica para las carreras STEM y para integrarse adecuadamente a través de profesiones relacionadas con las matemáticas en el mercado laboral, esta brecha es una fuente de preocupación social. Se ha debatido ampliamente la existencia y el origen de esta brecha de género. Los investigadores que representan un enfoque socio-cultural han resaltado el hecho de que se ha ido reduciendo a lo largo de los años, así como su variabilidad entre países y la correlación entre la amplitud de la brecha y los diferentes factores socio-culturales, es decir, las medidas de desigualdad de género por país, para desmontar las explicaciones basadas en diferencias biológicas. Sin embargo, a pesar de las docenas de publicaciones sobre el tema, la cuestión parece estar lejos de resolverse.

La brecha inversa en lectura se ha documentado de forma consistente comparando países y edades. La brecha que favorece a las chicas ha recibido, sin embargo, menos atención, aun cuando la competencia lingüística es tan crucial para la carrera profesional y el mercado laboral como las matemáticas. La comparación de las brechas de género en lectura y en matemáticas ha mostrado que están muy correlacionadas entre países y a lo largo del tiempo, y que existe un orden de rango consistente por su magnitud. Más aún, cuando la brecha en matemáticas de reduce, normalmente la brecha inversa en lectura aumenta. Debido a esta reciprocidad, en todas las circunstancias, incluso cuando las chicas tienen mejor rendimiento que los chicos en matemáticas, las chicas tienden a tener mejor rendimiento en lectura que en matemáticas y los chicos tienden a hacerlo mejor en matemáticas que en lectura.

En este estudio, se ha realizado un enfoque comparativo para explorar la relación mutua de las brechas en matemáticas y lectura de manera integradora, aplicando la perspectiva intra-grupo al nivel personal. Utilizando los datos de PISA 2012 de alrededor de medio millón de estudiantes de 15 años, procedentes de 10 000 centros educativos de 63 países, hemos examinado la diferencia entre estudiantes entre el rendimiento en matemáticas y lectura, además de una serie de correlatos potencialmente explicativos. De entre docenas de factores personales y escolares, el género se identificó como el predictor más dominante de la diferencia entre estudiantes. Los resultados fueron consistentes en la comparación internacional, y pueden explicar la persistencia de estereotipos de género en el rendimiento de matemáticas y en la menor representación de las mujeres en carreras relacionadas con STEM.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Abedi, J., & Lord, C. (2001). The language factor in mathematics tests. Applied Measurement in Education, 14, 219-234. https://doi.org/10.1207/S15324818AME1403_2

AERA, APA, NCME. (1999). Standards for Educational and Psychological Testing. Washington, DC: AERA.

Aiken, L. R. (1971). Verbal factors and mathematics learning: A review of research. Journal for Research in Mathematics Education, 2(4), 304-313. https://doi.org/10.2307/748485

Aiken, L. R. (1972). Language factors in learning mathematics. Review of Education Research, 42, 359-385. https://doi.org/10.3102/00346543042003359

Baker, D.P., Jones, D.P. (1993). Creating Gender Equality: Cross-national gender stratification and mathematical performance, Sociology of Education, 66, 91-103. https://doi.org/10.2307/2112795

Berenbaum, S. A., Martin, C. L, Hanish, L. D., Briggs, P. T. & Fabes, R. A. (2008). Sex differences in children's play. In: Becker, J. B., Berkley, K. J., Geary, N., Hampson, E., Herman, J. P. & Young, E. A, (editors). Sex differences in the brain: from genes to behavior. New York: Oxford University Press; https://doi.org/10.1093/acprof:oso/9780195311587.003.0014

Berenbaum, S. A., & Resnick, S. M. (2007). The seeds of career choices: Prenatal sex hormone effects on psychological sex differences. In S. J. Ceci & W. M. Williams (Eds.), Why Aren't More Women in Science? (pp. 147-157). Washington DC: APA Books. https://doi.org/10.1037/11546-012

Bowers, J. M., Perez-Pouchoulen, M., Edwards, N.S., & McCarthy, M.M, (2013). Foxp2 mediates sex differences in ultrasonic vocalization by rat pups and directs order of maternal retrieval. The Journal of Neuroscience, 33(8), 3276-3283. https://doi.org/10.1523/JNEUROSCI.0425-12.2013

Breda, T., Jouini, E. & Napp C. (2018) "Societal inequalities amplify gender gaps in math", Science, 16 Mar 2018: Vol. 359, Issue 6381, pp. 1219-1220. https://doi.org/10.1126/science.aar2307

Breda, T., & Napp (2019). Girls' comparative advantage in reading can largely explain the gender gap in math-intensive fields, PNAS (Proceedings of the National Academy of Science of the United States of America), July. https://doi.org/10.1073/pnas.1905779116

Brody, L., & Mills, C. (2005). Talent search research: What have we learned? High Ability Studies,16, 97-111. https://doi.org/10.1080/13598130500115320

Burrelli, J. (2008). Thirty-Three Years of Women in S&E Faculty Positions, InfoBrief, Science Resources Statistics, NSF 08-308, National Science Foundation Directorate for Social, Behavioral, and Economic Sciences. Retrieved from: http://www.nsf.gov/statistics/infbrief/nsf08308/nsf08308.pdf.

Cahan, S., Barneron, M. & Kassim, S. (2014). Gender differences in school achievement: a within-class perspective. International Studies in Sociology of Education, 24:1,3-23. https://doi.org/10.1080/09620214.2014.895132

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

Ceci S. J., & Williams W. M. (2010). The Mathematics of Sex: How Biology and Society Conspire to Limit Talented Women and Girls. Oxford University Press.

Chen, F. (2010). Differential Language Influence on Math Achievement. Ph.D. Dissertation, The University of North Carolina at Greensboro. Retrieved from: http://libres.uncg.edu/ir/uncg/f/Chen_uncg_0154D_10511.pdf.

Cole, N. (1997). The ETS gender study: How females and males perform in educational settings. Princeton, NJ: Educational Testing Service.

Del Pero, A. S., & Bytchkova A. (2013), A Bird's Eye View of Gender Differences in Education in OECD Countries,OECD Social, Employment and Migration. Working Papers, No. 149, OECD Publishing. http://dx.doi.org/10.1787/5k40k706tmtb-en. https://doi.org/10.1787/5k40k706tmtb-en

Eagly, H. A., & Wood W. (2013). The Nature-Nurture Debates: 25 Years of Challenges in Understanding the Psychology of Gender. Perspectives on Psychological Science 8(3), 340-357. https://doi.org/10.1177/1745691613484767

Eccles, J. S., Vida, M. N., & Barber, B. (2004). The relation of early adolescents' college plans and both academic ability and task-value beliefs to subsequent college enrollment. Journal of Early Adolescence, 24, 63-77. https://doi.org/10.1177/0272431603260919

Ellison, G., & Swanson, A. (2010). The gender gap in secondary school mathematics at high achievement levels: Evidence from the American Mathematics Competitions, The Journal of Economic Perspectives, 24, 109-128. https://doi.org/10.1257/jep.24.2.109

Else-Quest, N. M., Hyde, J. S., & Linn, M. C. (2010). Cross-National Patterns of Gender Differences in Mathematics: A Meta-Analysis. Psychological Bulletin, 136(1), 103-127. https://doi.org/10.1037/a0018053

Estyn (2008). Closing the gap between boys' and girls' achievement in schools. Her Majesty's Inspectorate for Education and Training in Wales, Cardiff, United Kingdom

Fox, M.F., Sonnert G., & Nikiforova I. (2011). Programs for undergraduate women in science and engineering: Issues, problems and solutions. Gender and Society 25 (5): 589-615. https://doi.org/10.1177/0891243211416809

Fryer R.G., & Levitt S.D. (2010). An Empirical analysis of the Gender Gap in Mathematics. American Economic Journal: Applied Economics, 2(2010): 210-240. https://doi.org/10.1257/app.2.2.210

Gallagher, A.M., De Lisi, R., Holst, PC, McGillicuddy-De Lisi, A. V., Morely, M., & Calahan, C. (2000). Gender differences in advanced mathematical problem solving. Journal of Experimental Child Psychology, 75, 165-190. https://doi.org/10.1006/jecp.1999.2532

Gallagher, A. M., & Kaufman, J. C. (Eds.). (2005). Gender differences in mathematics: An integrative psychological approach. New York, NY, US: Cambridge University Press. https://doi.org/10.1017/CBO9780511614446

Geary, D. C., (1996). Sexual selection and sex differences in mathematical abilities. Behavioral and Brain Sciences, 19, 229-247. https://doi.org/10.1017/S0140525X00042400

Geary, D.C. (2010). Male, Female: The Evolution of Human Sex Differences, 2nd edition. Washington DC: American Psychological Association. https://doi.org/10.1037/12072-000

Goldman, A.D., & Penner, A.M. (2014). Exploring international gender differences in mathematics self-concept. International Journal of Adolescence and Youth, 21:4, 403-418. https://doi.org/10.1080/02673843.2013.847850

Green, B., & Alkhateeb, H. M. (2001). Gender differences in mathematics achievement among high school students in the United Arab Emirates (1991-2000). School Science and Mathematics, 101(1), 5-9. https://doi.org/10.1111/j.1949-8594.2001.tb18184.x

Guiso, L., Monte, F., Sapienza, P., & Zingales, L. (2008). Culture, gender, and math. Science, 320, 1164-1165. https://doi.org/10.1126/science.1154094

Haladyna, T.M., & Downing, S.M. (2004). Construct-irrelevant variance in high-stake testing. Educational Measurement: Issues and Practice, 23(1), 17-27. https://doi.org/10.1111/j.1745-3992.2004.tb00149.x

Halpern, D. F., Benbow, C. P., Geary, D. C., Gur, R. C., Hyde, J. S., & Gernsbacher, M. A. (2007). The science of sex differences in science and mathematics. Psychological Science in the Public Interest 8(1), 1-51. https://doi.org/10.1111/j.1529-1006.2007.00032.x

Harasty, J., Double, K. L., Halliday, G.M., Kril, J. J., & McRitchie, D.A. (1997). Language-associated cortical regions are proportionally larger in the female brain. Archives of Neurology. 54(2), 171-176. https://doi.org/10.1001/archneur.1997.00550140045011

Hyde, J.S. (2005). The gender similarities hypothesis. American Psychologist, 60(6), 581-592. https://doi.org/10.1037/0003-066X.60.6.581

Hyde, J. S., Fennema, E., & Lamon, S. (1990). Gender differences in mathematics performance: A meta-analysis. Psychological Bulletin, (107)2, 139-155. https://doi.org/10.1037/0033-2909.107.2.139

Hyde, J.S., & Linn, M.C. (1988). Gender differences in verbal ability: A meta-analysis. Psychological Bulletin, 104, 53-69. https://doi.org/10.1037/0033-2909.104.1.53

Hyde, J.S., Lindberg, S. M., Linn, M. C., Ellis, A., & Williams, C. (2008). Gender similarities characterize math performance, Science, 321, 494-495. https://doi.org/10.1126/science.1160364

Ingalhalikar, M., Smith, A., Parker, D., Satterthwaite, T. D., Elliott, M.A., Ruparel, K., Hakonarson, H., Gur, R. E., Gur, R. C., & Verma, R. (2014). Sex differences in the structural connectome of the human brain. Proceeding of the National Academy of Science, U. S. A., 111(2), 823-828; https://doi.org/10.1073/pnas.1316909110

Kane, J. M., & Mertz, J. E. (2012). Debunking Myths about Gender and Mathematics. Notices of the AMS, 59, 10-21. https://doi.org/10.1090/noti790

Kieffer, M.J., Lesaux, N.K., Rivera, M., &Francis, D.J. (2009). Accommodations for English language learners taking large-scale assessments: A meta-analysis on effectiveness and validity. Review of Educational Research, 79(3), 1168-1201. https://doi.org/10.3102/0034654309332490

Kimura, D. (1999). Sex and Cognition. MIT Press, Cambridge MA. https://doi.org/10.7551/mitpress/6194.001.0001

Lavy, V., & Sand E., (2015). On the origins of gender human capital gaps: short and long term consequences of teachers' stereotypical biases. NBER Working Paper No. 20909. https://doi.org/10.3386/w20909

Lietz, P. (2006). A meta-analysis of gender differences in reading achievement at the secondary school level. Studies in Educational Evaluation,32, 317-344. https://doi.org/10.1016/j.stueduc.2006.10.002

Lindberg, S.M., Hyde, J. S., Peterson, J. L., & Linn, M. C. (2010). New trends in gender and mathematics performance: A meta-analysis, Psychological bulletin,136, 1123-1135. https://doi.org/10.1037/a0021276

Maccoby, E. E., & Jacklin, C.N. (1974). The Psychology of Sex Differences. Stanford University Press: Stanford, California.

Marks, G. N., (2008). Accounting for the gender gaps in student performance in reading and mathematics: evidence from 31 countries. Oxford Review of Education, 34, 89-109; https://doi.org/10.1080/03054980701565279

Marsh, H.W., (1986). Verbal and math self-concepts: An internal/external frame of reference model. American Educational Research Journal, 23, 129-149; https://doi.org/10.3102/00028312023001129

Marsh, H.W., (1989). Age and sex effects in multiple dimensions of self-concept: Preadolescence to early adulthood. Journal of educational Psychology, 81, 417-430; https://doi.org/10.1037/0022-0663.81.3.417

Marsh, H.W., (2007). Self-Concept theory, measurement and research into practice: the role of self-concept in educational psychology. Leicester: British Psychological Society.

Marsh, H.W., Lüdtke, O., Nagengast, B., Trautwein, U., Abduljabbar, A.S., Abdelfattah, F., & Jansen, M. (2015). Dimensional Comparison Theory: Paradoxical relations between self-beliefs and achievements in multiple domains. Learning and Instruction, 35, 16-32; https://doi.org/10.1016/j.learninstruc.2014.08.005

Martin, M.O., Mullis, I.V.S., Foy, P. & Stanco, G.M. (2012). TIMSS 2011 International Results in Science. Chestnut Hill, MA: Boston College, TIMSS and PIRLS International Study Center

Möller, J. & Marsh, H.W. (2013). Dimensional comparison theory. Psychological Review, 120(3), 544-560. https://doi.org/10.1037/a0032459

Mosconi, N. (2001). Comment les pratiques enseignantes fabriquent de l'inégalité entre les sexes. Les Dossiers des Sciences de l'Education, 5 (in French). https://doi.org/10.3406/dsedu.2001.953

Mui, F. L. L., Yeung, A. S., Low, R., & Jin, P. T. (2000). Academic self-concept of talented students: Factor structure and applicability of the internal/external frame of reference model. Journal for the education of the gifted 23(3), 343-367.

Mullis, I.V.S., Martin, M.O., Foy, P., & Drucker, K.T., (2012). PIRLS 2011 International Results in Reading. Chestnut Hill, MA: Boston College, TIMSS and PIRLS International Study Center.

Mullis, I.V.S., Martin, M.O., Foy, P. & Arora, A. (2012). TIMSS 2011 International Results in Mathematics. Chestnut Hill, MA: Boston College, TIMSS and PIRLS International Study Center.

Mullis, I.V.S., Martin, M.O. & Foy, P. (2013). The impact of reading ability on TIMSS mathematics and science achievement at the fourth grade: an analysis by item treading demands. In TIMSS and PIRLS 2011: Relationships among Reading, Mathematics and Science Achievement at the Fourth Grade - Implications for Early Learning. Michael O. Martin and Ina V.S. Mullis, Editors. TIMSS and PIRLS International Study Center. Lynch school of education, Boston College and International Association for the Evaluation of educational Achievment (IEA).

Nagy, G., Trautwein, U., Baumert, J., Koller, O., & Garrett, J. (2006). Gender and course selection in upper secondary education: Effects of academic self-concept and intrinsic value. Educational Research & Evaluation, 12, 323-345. https://doi.org/10.1080/13803610600765687

Niederle, M., & Vesterlund, L. (2010). Explaining the gender gap in math test scores: The role of competition. Journal of Economic Perspectives, 24(2), 129-144. https://doi.org/10.1257/jep.24.2.129

Nosek, B. A., & Smyth, F. L. (2011). Implicit social cognitions predict sex differences in math engagement and achievement. American Educational Research Journal, 48, 1124-1154. https://doi.org/10.3102/0002831211410683

Nosek, B. A., Smyth, F. L., Sriram, N., Lindner, N. M., Devos, T., Ayala, A., Bar-Anan, Y., Bergh, R., Cai, H., Gonsalkorale, K., Kesebir, S., Maliszewski, N., Neto, F., Olli, E., Park, J., Schnabel, K., Shiomura, K., Tulbure, B., Wiers, R. W., Somogyi, M., Akrami, N., Ekehammar, B., Vianello, M., Banaji, M. R., & Greenwald, A. G. (2009). National differences in gender-science stereotypes predict national sex differences in science and math achievement. Proceedings of the National Academy of Sciences, 106, 10593-10597. https://doi.org/10.1073/pnas.0809921106

OECD (2010), PISA 2009 Results: What Students Know and Can Do - Student Performance in Reading, Mathematics and Science (Volume I) https://doi.org/10.1787/9789264091450-en

OECD (2012). PISA 2009 Technical Report, PISA, OECD Publishing. https://doi.org/10.1787/9789264167872-en

OECD (2014), PISA 2012 Results: What Students Know and Can Do - Student Performance in Mathematics, Reading and Science (Volume I, Revised edition, February 2014), PISA, OECD Publishing. https://doi.org/10.1787/9789264201118-en

OECD (2015). The ABC of gender equality in education: aptitude, behavior, confidence, PISA, OECD Publishing. https://doi.org/10.1787/9789264229945-en

OECD (2016), PISA 2015 Results (Volume I): Excellence and Equity in Education, PISA, OECD Publishing, Paris. https://doi.org/10.1787/9789264266490-en

Park, G., Lubinski, D., & Benbow, C. P. (2007). Contrasting intellectual patterns predict creativity in the arts and sciences: tracking intellectually precocious youth over 25 years. Psychological Science, 18, 948-952. https://doi.org/10.1111/j.1467-9280.2007.02007.x

Parker, P.D., Marsh, H.W., Ciarrochi, J., Marshall, S., & Abduljabbar, A.S. (2014). Juxtaposing math self-efficacy and self-concept as predictors of long-term achievement outcomes. Educational Psychology, 34(1), 29-48 https://doi.org/10.1080/01443410.2013.797339

Parker, P. D., Marsh, H. W., Morin, A. J. S., Seaton, M., & Van Zanden, B. (2015). If one goes up the other must come down: Examining ipsative relationships between math and English self-concept trajectories across high school. British Journal of Educational Psychology, 85(2), 172-191 https://doi.org/10.1111/bjep.12050

Parker, P. D., Schoon, I., Tsai, Y-M., Nagy, G., Trautwein, U., & Eccles, J. S. (2012). Achievement, agency, gender, and socioeconomic background as predictors of postschool choices: A multicontext study. Developmental Psychology, 48(6), 1629-1642. https://doi.org/10.1037/a0029167

Penner, A. (2008). Gender differences in extreme mathematical achievement: An international perspective on biological and social factors. American Journal of Sociology, 114: 138-170. https://doi.org/10.1086/589252

Plucker, J. A., & Stocking, V. B. (2001). Looking outside and inside: Self-concept development of gifted adolescents. Exceptional Children, 67(4), 535-548. https://doi.org/10.1177/001440290106700407

Preckel, F., Goetz, T., Pekrun, R., & Kleine, M. (2008). Gender differences in gifted and average-ability students: Comparing girl's and boy's achievement, self-concept, interest, and motivation in mathematics. Gifted Child Quarterly, 52(2), 146-159. https://doi.org/10.1177/0016986208315834

Rapp, J. (2015). Gender gaps in mathematics and language in Israel-what can be learned from the Israeli case? National Authority for measurement and evaluation in Education (RAMA) Report. Ramat-Gan, Israel.

Reilly, D., (2012). Gender, culture, and sex-typed cognitive abilities. PLoS ONE 7(7): e39904. doi:10.1371/journal.pone.0039904. https://doi.org/10.1371/journal.pone.0039904

Sato, E., Rabinowitz, S., Gallagher, C., & Huang, C.W. (2010). Accommodations for English Language Learner Students: The Effect of Linguistic Modification of Math Test Item Sets. NCEE 2009-4079. U.S. Department of Education.

Secada, W.G. (1992). Race, ethnicity, social class, language, and achievement in mathematics. In D. A. Grouws (Ed.), Handbook of Research on Mathematics Teaching and Learning (pp. 623-660). New York: Macmillan.

Sells, L. (1973). High School Mathematics as the Critical Filter in the Job Market. Unpublished PH.D. thesis. Berkeley CA: University of California.

Sfard, A. (2012). "Linguistic and Mathematical Literacy - What is the Connection?" in: Between Language and Disciplinary Literacy: Workshop Report," Pollak, I. (ed.). Initiative for Applied Education Research, Israeli Academy of Sciences and Humanities. (in Hebrew).

Schmidt, F. L. (2011). A theory of sex differences in technical aptitude and some supporting evidence. Perspectives on Psychological Science, 6, 560 - 573. https://doi.org/10.1177/1745691611419670

Smith, J. L., Lewis, K. L., Hawthorne, L. & Hodges, S.D. (2013). When trying hard isn't natural: Women's belonging with and motivation for male-dominated STEM fields as a function of effort expenditure concerns personality and social. Psychology Bulletin 39(2), 131-143. https://doi.org/10.1177/0146167212468332

Spelke, E.S. (2005). Sex differences in intrinsic aptitude for mathematics and science: A critical review. American Psychologist, 60(9), 950-958. https://doi.org/10.1037/0003-066X.60.9.950

Stoet, G., & Geary, D. C. (2013). Sex differences in mathematics and reading achievement are inversely related: within- and across-nation assessment of 10 years of PISA data. PLoS ONE 8(3): e57988 https://doi.org/10.1371/journal.pone.0057988

Stoet, G., & Geary, D. C. (2015). Sex differences in academic achievement are not related to political, economic, or social equality. Intelligence 48, 137-151. https://doi.org/10.1016/j.intell.2014.11.006

Thoman, D.B., Arizaga, J. A., Smith, J.L. Story, T.L. & Soncuya, G. (2014). The Grass Is Greener in Non-Science, Technology, Engineering, and Math Classes: Examining the Role of Competing Belonging to Undergraduate Women's Vulnerability to Being Pulled Away From Science. Psychology of Women Quarterly, 38 (2), 246-258. https://doi.org/10.1177/0361684313499899

Valla, J. M., & Ceci, S. J. (2011). Can sex differences in science be tied to the Long reach of prenatal hormones? Brain organization theory, digit ratio (2D/4D), and sex differences in preferences and cognition. Perspectives on Psychological Science, 6(2), 134-146. https://doi.org/10.1177/1745691611400236

Valian, V. (2007). Women at the top in science--And Elsewhere. In S.J. Ceci and W.M. Williams (Eds), Why aren't more women in science: Top researchers debate the evidence, (pp. 27-37). Washington, DC, US: American Psychological Association. https://doi.org/10.1037/11546-002

Voyer, D., & Voyer, S. D. (2014,). Gender differences in scholastic achievement: a meta-analysis. Psychological Bulletin. 140, 1174-1204. https://doi.org/10.1037/a0036620

Wai, J., Cacchio, M., Putallaz, M., & Makel, M. C. (2010). Sex differences in the right tail of cognitive abilities: A 30-Year examination. Intelligence, 38, 412-423. https://doi.org/10.1016/j.intell.2010.04.006

Wang, M.T., Eccles, J.S., & Kenny, S. (2013). Not lack of ability but more choice: individual and gender differences in choice of careers in science, technology, engineering, and mathematics. Psychological Science, 24, 770-775; https://doi.org/10.1177/0956797612458937