Gender Gap in Science, Technology, Engineering, and Mathematics (STEM): Current Knowledge, Implications for Practice, Policy, and Future Directions

Abstract

Although the gender gap in math course-taking and performance has narrowed in recent decades, females continue to be underrepresented in math-intensive fields of Science, Technology, Engineering, and Mathematics (STEM). Career pathways encompass the ability to pursue a career as well as the motivation to employ that ability. Individual differences in cognitive capacity and motivation are also influenced by broader sociocultural factors. After reviewing research from the fields of psychology, sociology, economics, and education over the past 30 years, we summarize six explanations for US women’s underrepresentation in math-intensive STEM fields: (a) cognitive ability, (b) relative cognitive strengths, (c) occupational interests or preferences, (d) lifestyle values or work-family balance preferences, (e) field-specific ability beliefs, and (f) gender-related stereotypes and biases. We then describe the potential biological and sociocultural explanations for observed gender differences on cognitive and motivational factors and demonstrate the developmental period(s) during which each factor becomes most relevant. We then propose evidence-based recommendations for policy and practice to improve STEM diversity and recommendations for future research directions.

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References

  1. Ackerman, P. L., Bowen, K. R., Beier, M. E., & Kanfer, R. (2001). Determinants of individual differences and gender differences in knowledge. Journal of Educational Psychology, 93, 797–825.

    Article  Google Scholar 

  2. Alexander, G. M., Wilcox, T., & Woods, R. (2009). Sex differences in infants’ visual interest in toys. Archives of Sexual Behavior, 38, 427–433. doi:10.1007/s10508-008-9430-1.

    Article  Google Scholar 

  3. Allen, S. (2004). Designs for learning: studying science museum exhibits that do more than entertain. Science Education. doi:10.1002/sce.20016.

    Google Scholar 

  4. American Association of University Women Educational Foundation. (2008). Where the girls are: the facts about gender equity in education. Washington: Author.

    Google Scholar 

  5. Baker, M., & Milligan, K. (2013). Boy-girl differences in parental time investments: evidence from three countries. National Bureau of Economic Research (NBER) Working Paper 18893. Retrieved from http://www.nber.org/papers/w18893. doi: 10.3386/w18893

  6. Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: a longitudinal study and an intervention. Child Development, 78, 246–263. doi:10.1111/j.1467-8624.2007.00995.x.

    Article  Google Scholar 

  7. Bleeker, M. M., & Jacobs, J. E. (2004). Achievement in math and science: do mothers’ beliefs matter 12 years later? Journal of Educational Psychology, 96, 97–109. doi:10.1037/0022-0663.96.1.97.

    Article  Google Scholar 

  8. Ceci, S. J., & Williams, W. M. (2011). Understanding current causes of women’s underrepresentation in science. PNAS, 108, 3157–3162. doi:10.1073/pnas.1014871108.

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Ceci, S. J., Ginther, D. K., Kahn, S., & Williams, W. M. (2014). Women in academic science: a changing landscape. Psychological Science in the Public Interest, 15, 75–141. doi:10.1177/1529100614541236.

    Article  Google Scholar 

  11. Cheryan, S., & Plaut, V. C. (2010). Explaining underrepresentation: a theory of precluded interest. Sex Roles, 63, 475–488. doi:10.1007/s11199-010-9835-x.

    Article  Google Scholar 

  12. Cheryan, S., Plaut, V. C., Davies, P. G., & Steele, C. M. (2009). Ambient belonging: how stereotypical cues impact gender participation in computer science. Journal of Personality and Social Psychology, 97, 1045–1060. doi:10.1037/a0016239.

    Article  Google Scholar 

  13. Cheryan, S., Meltzoff, A. N., & Kim, S. (2011a). Classrooms matter: the design of virtual classrooms influences gender disparities in computer science classes. Computers & Education, 57, 1825–1835. doi:10.1016/j.compedu.2011.02.004.

    Article  Google Scholar 

  14. Cheryan, S., Siy, J. O., Vichayapai, M., Drury, B. J., & Kim, S. (2011b). Do female and male role models who embody STEM stereotypes hinder women’s anticipated success in STEM? Social Psychological and Personality Science, 2, 656–664. doi:10.1177/1948550611405218.

    Article  Google Scholar 

  15. Chow, A., Eccles, J. S., & Salmela-Aro, K. (2012). Task value profiles across subjects and aspirations to physical and IT-related sciences in the United States and Finland. Developmental Psychology, 48, 1612–1628. doi:10.1037/a0030194.

    Article  Google Scholar 

  16. Correll, S. J. (2001). Gender and the career choice process: the role of biased self‐assessments. American Journal of Sociology, 106, 1691–1730. doi:10.1086/321299.

    Article  Google Scholar 

  17. Crosnoe, R., Riegle-Crumb, C., Field, S., Frank, K., & Muller, C. (2008). Peer group contexts of girls’ and boys’ academic experiences. Child Development, 79, 139–155. doi:10.1111/j.1467-8624.2007.01116.x.

    Article  Google Scholar 

  18. Crowley, K., Callanan, M. A., Tenenbaum, H. R., & Allen, E. (2001). Parents explain more often to boys than to girls during shared scientific thinking. Psychological Science, 12, 258–261. doi:10.1111/1467-9280.00347.

    Article  Google Scholar 

  19. Cvencek, D., Meltzoff, A. N., & Greenwald, A. G. (2011). Math–gender stereotypes in elementary school children. Child Development, 82, 766–779. doi:10.1111/j.14678624.2010.01529.x.

    Article  Google Scholar 

  20. Deutsch, F. M. (2003). How small classes benefit high school students. NASSP Bulletin, 87, 35–44. doi:10.1177/019263650308763504.

    Article  Google Scholar 

  21. Diekman, A. B., Brown, E., Johnston, A., & Clark, E. (2010). Seeking congruity between goals and roles: a new look at why women opt out of STEM careers. Psychological Science, 21, 1051–1057. doi:10.1177/0956797610377342.

    Article  Google Scholar 

  22. Diekman, A. B., Clark, E. K., Johnston, A. M., Brown, E. R., & Steinberg, M. (2011). Malleability in communal goals and beliefs influences attraction to STEM careers: evidence for a goal congruity perspective. Journal of Personality and Social Psychology, 101, 902–918. doi:10.1037/a0025199.

    Article  Google Scholar 

  23. Dweck, C. S. (2002). The development of ability conceptions. In A. Wigfield & J. S. Eccles (Eds.), Development of achievement motivation. A volume in the educational psychology series: Vol. xvii, (pp. 57–88). San Diego, CA: Academic Press. doi:10.1016/B978-012750053-9/50005-X.

  24. Dweck, C. (2007). Is math a gift? Beliefs that put females at risk. In S. J. Ceci & W. M. Williams (Eds.), Why aren’t more women in 1460 science? Top researchers debate the evidence (pp. 47–55). Washington: APA Press. doi:10.1037/11546-004.

    Google Scholar 

  25. Eccles, J. S. (2009). Who am I and what am I going to do with my life? Personal and collective identities as motivators of action. Educational Psychologist, 44, 78–89. doi:10.1080/00461520902832368.

    Article  Google Scholar 

  26. Eccles, J. S., Wigfield, A., Harold, R. D., & Blumenfeld, P. (1993). Age and gender differences in children’s self- and task perceptions during elementary school. Child Development, 64, 830–847. doi:10.1111/j.1467-8624.1993.tb02946.x.

    Article  Google Scholar 

  27. Eccles, J. S., Barber, B., & Jozefowicz, D. (1999). Linking gender to educational, occupational, and recreational choice: applying the Eccles et al. model of achievement-related choices. In J. T. Spence (Ed.), Sexism and stereotypes in modern society: the gender science of Janet Taylor Spence (pp. 153–191). Washington: APA. doi:10.1037/10277-007.

    Google Scholar 

  28. 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, 103–127. doi:10.1037/a0018053.

    Article  Google Scholar 

  29. Ferriman, K., Lubinski, D., & Benbow, C. P. (2009). Work preferences, life values, and personal views of top math/science graduate students and the profoundly gifted: developmental changes and gender differences during emerging adulthood and parenthood. Journal of Personality and Social Psychology, 97, 517–532. doi:10.1037/a0016030.

    Article  Google Scholar 

  30. Freund, A. M., Weiss, D., & Wiese, B. S. (2012). Graduating from high school: the role of gender-related attitude, attributes, and motives for a central transition in late adolescence. Switzerland: Department of Psychology, University of Zurich. doi:10.1080/17405629.2013.772508. Unpublished manuscript.

    Google Scholar 

  31. Frick, A., & Wang, S. H. (2014). Mental spatial transformations in 14‐ and 16‐month‐old infants: effects of action and observational experience. Child Development, 85, 278–293. doi:10.1111/cdev.12116.

    Article  Google Scholar 

  32. Friedel, J. M., Cortina, K. S., Turner, J. C., & Midgley, C. (2007). Achievement goals, efficacy beliefs and coping strategies in mathematics: the role of perceived parent and teacher goal emphases. Contemporary Educational Psychology, 32, 434–458. doi:10.1016/j.cedpsych.2006.10.009.

    Article  Google Scholar 

  33. Glick, P., & Fiske, S. T. (1997). Hostile and benevolent sexism: measuring ambivalent sexist attitudes toward women. Psychology of Women Quarterly, 21, 119–135. doi:10.1111/j.1471-6402.1997.tb00104.x.

    Article  Google Scholar 

  34. Hakim, C. (2006). Women, careers, and work-life preferences. British Journal of Guidance and Counseling, 34, 279–294. doi:10.1080/03069880600769118.

    Article  Google Scholar 

  35. Hanson, S. L. (2004). African American women in science: experiences from high school through the post-secondary years and beyond. NWSA Journal, 16, 96–115. doi:10.1353/nwsa.2004.0033.

    Article  Google Scholar 

  36. Hanson, S. L. (2007). Success in science among young African American women: the role of minority families. Journal of Family Issues, 28, 3–33. doi:10.1177/0192513X06292694.

    Article  Google Scholar 

  37. Haughey, M., Snart, F., & da Costa, J. (2001). Literacy achievement in small grade 1 classes in high-poverty environments. Canadian Journal of Education, 26, 301–320. doi:10.2307/1602210.

    Article  Google Scholar 

  38. Hill, C., Corbett, C., & St. Rose, A. (2010). Why so few? Women in science, technology, engineering and mathematics. Washington: American Association of University Women.

    Google Scholar 

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

    Article  Google Scholar 

  40. Jacobs, J. E., & Eccles, J. S. (1992). The impact of mothers’ gender-role stereotypic beliefs on mothers’ and children’s ability perceptions. Journal of Personality and Social Psychology, 63, 932–944. doi:10.1037/0022-3514.63.6.932.

    Article  Google Scholar 

  41. Jacobs, J. E., & Winslow, S. E. (2004). Overworked faculty: job and stresses and family demands. Annals of American Political and Social Scientist, 596, 104–129. doi:10.1177/0002716204268185.

    Article  Google Scholar 

  42. Kelleher, C., Pausch, R., & Kiesler, S. (2007). Storytelling alice motivates middle school girls to learn computer programming. Proceeding of the SIGCHI Conference on Human Factors in Computing Systems, 1455-1464.

  43. Kena, G., Musu-Gillette, L., Robinson, J., Wang, X., Rathbun, A., Zhang, J., et al. (2015). The condition of education 2015 (NCES 2015–144). U.S. Department of Education, National Center for Education Statistics. Washington, DC. Retrieved from http://nces.ed.gov/pubsearch. Accessed 26 Aug 2015.

  44. King, D. K. (1992). Unraveling fabric, missing the beat: class and gender in Afro-American social issues. The Black Scholar, 22, 36–44.

    Article  Google Scholar 

  45. Knobloch-Westerwick, S., Glynn, C. J., & Huge, M. (2013). The Matilda effect in science communication: an experiment on gender bias in publication quality perceptions and collaboration interest. Science Communication, 35, 603–625. doi:10.1177/1075547012472684.

    Article  Google Scholar 

  46. Leaper, C., Anderson, K. J., & Sanders, P. (1998). Moderators of gender effects on parents’ talk to their children: a meta-analysis. Developmental Psychology, 34, 3–27. doi:10.1037/0012-1649.34.1.3.

    Article  Google Scholar 

  47. Leaper, C., Farkas, T., & Brown, C. S. (2012). Adolescent girls’ experiences and gender-related beliefs in relation to their motivation in math/science and English. Journal of Youth and Adolescence, 41, 268–282. doi:10.1007/s10964-011-9693-z.

    Article  Google Scholar 

  48. Leslie, S.-J., Cimpian, A., Meyer, M., & Freeland, E. (2015). Expectations of brilliance underlie gender distributions across academic disciplines. Science, 347, 262–265. doi:10.1126/science.1261375.

    Article  Google Scholar 

  49. Liben, L. S., & Coyle, E. F. (2014). Chapter three-developmental interventions to address the STEM gender gap: exploring intended and unintended consequences. Advances in Child Development and Behavior, 47, 77–115.

    Article  Google Scholar 

  50. Lindberg, S. M., Hyde, J. S., Petersen, J. L., & Linn, M. C. (2010). New trends in gender and mathematics performance: a meta-analysis. Psychological Bulletin, 136, 1123–1135. doi:10.1037/a0021276.

    Article  Google Scholar 

  51. Lippa, R. A., Collaer, M. L., & Peters, M. (2010). Sex differences in mental rotation and line angle judgments are positively associated with gender equality and economic development across 53 nations. Archives of Sexual Behavior, 39, 990–997. doi:10.1007/s10508-008-9460-8.

    Article  Google Scholar 

  52. Lohman, D. F., Gambrell, J., & Lakin, J. (2008). The commonality of extreme discrepancies in the ability profiles of academically gifted students. Psychology Science Quarterly, 50, 269–282.

    Google Scholar 

  53. Lubienski, S. T., Robinson, J. P., Crane, C. C., & Ganley, C. M. (2013). Girls’ and boys’ mathematics achievement, affect, and experiences: findings from ECLS-K. Journal for Research in Mathematics Education, 44, 634–645. doi:10.5951/jresematheduc.44.4.0634.

    Article  Google Scholar 

  54. Lubinski, D., & Benbow, C. P. (2006). Study of mathematically precocious youth after 35 years: uncovering antecedents for the development of math-science expertise. Perspectives on Psychological Science, 1, 316–345. doi:10.1111/j.1745-6916.2006.00019.x.

    Article  Google Scholar 

  55. Lubinski, D., Benbow, C. P., Webb, R. M., & Bleske-Rechek, A. (2006). Tracking exceptional human capital over two decades. Psychological Science, 17, 194–199. doi:10.1111/j.1467-9280.2006.01685.x.

    Article  Google Scholar 

  56. Maeda, Y., & Yoon, S. Y. (2013). A meta-analysis on gender differences in mental rotation ability measured by the Purdue spatial visualization tests: visualization of rotations (PSVT: R). Educational Psychology Review, 25, 69–94. doi:10.1007/s10648-012-9215-x.

    Article  Google Scholar 

  57. Maltese, A. V., & Tai, R. H. (2010). Eyeballs in the fridge: sources of early interest in science. International Journal of Science Education, 32, 669–685. doi:10.1080/09500690902792385.

    Article  Google Scholar 

  58. Maltese, A. V., & Tai, R. H. (2011). Pipeline persistence: examining the association of educational experiences with earned degrees in STEM among U.S. students. Science Education, 95, 877–907. doi:10.1002/sce.20441.

    Article  Google Scholar 

  59. Mason, M. A., & Goulden, M. (2004). Marriage and baby blues: redefining gender equity and the academy. Annals of the American Political and Social Sciences, 596, 86–103. doi:10.1177/000271620459600104.

    Article  Google Scholar 

  60. Meece, J. L., Anderman, E. M., & Anderman, L. H. (2006). Classroom goal structure, student motivation, and academic achievement. Annual Review of Psychology, 57, 387–503. doi:10.1146/annurev.psych.56.091103.070258.

    Article  Google Scholar 

  61. Meyer, M., Cimpian, A., & Leslie, S. J. (2015). Women are underrepresented in fields where success is believed to require brilliance. Frontiers in Psychology, 6, 1–12. doi:10.3389/fpsyg.2015.00235.

    Article  Google Scholar 

  62. Miller, D. I., & Halpern, D. F. (2014). The new science of cognitive sex differences. Trends in Cognitive Sciences, 18, 37–45. doi:10.1016/j.tics.2013.10.011.

    Article  Google Scholar 

  63. Miller, D. I., Eagly, A. H., & Linn, M. C. (2015). Women’s representation in science predicts national gender-science stereotypes: evidence from 66 nations. Journal of Educational Psychology, 107, 631–644.

    Article  Google Scholar 

  64. Möhring, W., & Frick, A. (2013). Touching up mental rotation: effects of manual experience on 6-month-old infants’ mental object rotation. Child Development, 84, 1554–1565. doi:10.1111/cdev.12065.

    Article  Google Scholar 

  65. Moore, D. S., & Johnson, S. P. (2008). Mental rotation in human infants: a sex difference. Psychological Science, 19, 1063–1066. doi:10.1111/j.1467-9280.2008.02200.x.

    Article  Google Scholar 

  66. Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012). Science faculty’s subtle gender biases favoring male students. PNAS, 109, 16474–16479. doi:10.1073/pnas.1211286109.

    Article  Google Scholar 

  67. Mueller, C. M., & Dweck, C. S. (1998). Praise for intelligence can undermine children’s motivation and performance. Journal of Personality and Social Psychology, 75, 33–52. doi:10.1037/0022-3514.75.1.33.

    Article  Google Scholar 

  68. National Science Foundation. (2011). Women, minorities, and persons with disabilities in science and engineering: 2011. Arlington: National Science Foundation.

    Google Scholar 

  69. Park, G., Lubienski, D., & Benbow, C. P. (2007). Contrasting intellectual patterns predict creativity in the arts and sciences. Psychological Science, 18, 948–952.

    Article  Google Scholar 

  70. Quinn, P. C., & Liben, L. S. (2008). A sex difference in mental rotation in young infants. Psychological Science, 19, 1067–1070. doi:10.1111/j.1467-9280.2008.02201.x.

    Article  Google Scholar 

  71. Reuben, E., Sapienza, P., & Zingales, L. (2014). How stereotypes impair women’s careers in science. PNAS, 111, 4403–4408. doi:10.1073/pnas.1314788111.

    Article  Google Scholar 

  72. Robinson, J. P., & Lubienski, S. T. (2011). The development of gender achievement gaps in mathematics and reading during elementary and middle school: examining direct cognitive assessments and teacher ratings. American Educational Research Journal, 48, 268–302. doi:10.3102/0002831210372249.

    Article  Google Scholar 

  73. Roseth, C. J., Johnson, D. W., & Johnson, R. T. (2008). Promoting early adolescents’ achievement and peer relationships: the effects of cooperative, competitive and individualistic goal structure. Psychological Bulletin, 134, 223–246. doi:10.1037/0033-2909.134.2.223.

    Article  Google Scholar 

  74. Sadik, A. (2008). Digital storytelling: A meaningful technology-integrated approach for engaged student learning. Educational Technology Research and Development, 56, 487–506. doi:10.1007/s11423-008-9091-8.

  75. Simpkins, S. D., Davis-Kean, P. E., & Eccles, J. S. (2006). Math and science motivation: a longitudinal examination of the links between choices and beliefs. Developmental Psychology, 42, 70–83. doi:10.1037/0012-1649.42.1.70.

    Article  Google Scholar 

  76. Spelke, E. S. (2005). Sex differences in intrinsic aptitude for mathematics and science? American Psychologist, 60, 950–958. doi:10.1037/0003-066X.60.9.950.

    Article  Google Scholar 

  77. Stake, J. E., & Nickens, S. D. (2005). Adolescent girls’ and boys’ science peer relationships and perceptions of the possible self as scientist. Sex Roles, 52, 1–11. doi:10.1007/s11199-005-1189-4.

    Article  Google Scholar 

  78. Stecher, B. M., & Bohrnstedt, G. W. (Eds.). (2002). Class size reduction in California: findings from 1999-00 and 2000-01. Sacramento: California Department of Education.

    Google Scholar 

  79. Stout, J. G., Dasgupta, N., Hunsinger, M., & McManus, M. A. (2011). STEMing the tide: using ingroup experts to inoculate women’s self-concept in science, technology, engineering, and mathematics (STEM). Journal of Personality and Social Psychology, 100, 255–270. doi:10.1037/a0021385.

    Article  Google Scholar 

  80. Su, R., Rounds, J., & Armstrong, P. I. (2009). Men and things, women and people: a meta-analysis of sex differences in interests. Psychological Bulletin, 135, 859–884. doi:10.1037/a0017364.

    Article  Google Scholar 

  81. Swim, J. K., & Cohen, L. L. (1997). Overt, covert, and subtle sexism: a comparison between attitudes toward women and modern sexism scales. Psychology of Women Quarterly, 21, 103–118. doi:10.1111/j.1471-6402.1997.tb00103.x.

    Article  Google Scholar 

  82. Swim, J. K., Aikin, K. J., Hall, W. S., & Hunter, B. A. (1995). Sexism and racism: old-fashioned and modern prejudices. Journal of Personality and Social Psychology, 68, 199–214. doi:10.1037/0022-3514.68.2.199.

    Article  Google Scholar 

  83. Swim, J. K., Mallett, R., Russo-Devosa, Y., & Stangor, C. (2005). Judgements of sexism: a comparison of the subtlety of sexism measures and sources of variability in judgements of sexism. Psychology of Women Quarterly, 29, 406–411. doi:10.1111/j.1471-6402.2005.00240.x.

    Article  Google Scholar 

  84. Tai, R. H., Liu, C. Q., Maltese, A. V., & Fan, X. (2006). Planning early for careers in science. Science, 312, 1143–1144. doi:10.1126/science.1128690.

    Article  Google Scholar 

  85. Tenenbaum, H. R. (2009). ‘You'd be good at that’: gender patterns in parent‐child talk about courses. Social Development, 18, 447–463. doi:10.1111/j.1467-9507.2008.00487.x.

    Article  Google Scholar 

  86. Tenenbaum, H. R., & Leaper, C. (2002). Are parents’ gender schemas related to their children’s gender-related cognitions? A meta-analysis. Developmental Psychology, 38, 615–630. doi:10.1037//0012-1649.38.4.615.

    Article  Google Scholar 

  87. Tiedemann, J. (2000a). Gender-related beliefs of teachers in elementary school mathematics. Educational Studies in Mathematics, 41, 191–207. doi:10.1023/A:1003953801526.

    Article  Google Scholar 

  88. Tiedemann, J. (2000b). Parents’ gender stereotypes and teachers’ beliefs as predictors of children's concept of their mathematical ability in elementary school. Journal of Educational Psychology, 92, 144–151. doi:10.1007/s11199-011-9996-2.

    Article  Google Scholar 

  89. Turner, J. C., & Patrick, H. (2004). Motivational influences on student participation in math classroom learning activities. Teachers College Record, 106, 1759–1785. doi:10.1111/j.1467-9620.2004.00404.x.

    Article  Google Scholar 

  90. U.S. Department of Education, National Center for Education Statistics. (2012). Higher education: gaps in access and persistence study. Retrieved from http://nces.ed.gov/pubs2012/2012046/index.asp

  91. U.S. Department of Education, National Center for Education Statistics (NCES). (2014). Digest of education statistics. Retrieved from https://nces.ed.gov/programs/digest/2014menu_tables.asp

  92. Valla, J., & 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 preference and cognition. Perspectives on Psychological Science, 6, 134–136. doi:10.1177/174569161140023.

    Article  Google Scholar 

  93. Valla, J. M., & Ceci, S. J. (2014). Breadth-based models of women’s underrepresentation in STEM fields: an integrative commentary on Schmidt (2011) and Nye et al. (2012). Perspectives on Psychological Science, 9, 219–224. doi:10.1177/1745691614522067.

    Article  Google Scholar 

  94. Voyer, D. (2011). Time limits and gender differences on paper-and-pencil tests of mental rotation: a meta-analysis. Psychonomic Bulletin & Review, 18, 267–277. doi:10.3758/s13423-010-0042-0.

    Article  Google Scholar 

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

    Article  Google Scholar 

  96. Voyer, D., Postma, A., Brake, B., & Imperato-McGinley, J. (2007). Gender differences in object location memory: a meta-analysis. Psychonomic Bulletin & Review, 14, 23–38. doi:10.3758/BF03194024.

    Article  Google Scholar 

  97. Wai, J., Lubinski, D., Benbow, C. P., & Steiger, J. H. (2010). Accomplishment in science, technology, engineering, and mathematics (STEM) and its relation to STEM educational dose: a 25-year longitudinal study. Journal of Educational Psychology, 102, 860–871. doi:10.1037/a0019454.

    Article  Google Scholar 

  98. Wai, J., Putallaz, M., & Makel, M. C. (2012). Studying intellectual outliers: Are there sex differences, and are the smart getting smarter? Current Directions in Psychological Science, 21, 382–390. doi:10.1177/0963721412455052.

  99. Wang, M. T. (2012). Educational and career interests in math: a longitudinal examination of the links between perceived classroom environment, motivational beliefs, and interests. Developmental Psychology, 48, 1643–1657. doi:10.1037/a0027247.

    Article  Google Scholar 

  100. Wang, M. T., & Degol, J. L. (2014a). Motivational pathways to STEM career choices: using expectancy-value perspective to understand individual and gender differences in STEM fields. Developmental Review, 33, 304–340. doi:10.1016/j.dr.2013.08.001.

    Article  Google Scholar 

  101. Wang, M. T., & Degol, J. L. (2014b). Staying engaged: knowledge and research needs in student engagement. Child Development Perspectives, 8, 137–143. doi:10.1111/cdep.12073.

    Article  Google Scholar 

  102. Wang, M. T., & Degol, J. L. (2015). School climate: a review of the definition, measurement, and impact on student outcomes. Educational Psychology Review. doi:10.1007/s10648-015-9319-1.

    Google Scholar 

  103. Wang, M. T., Degol, J. L., & Ye, F. (2015). Math achievement is important, but task values are critical too: Examining the intellectual and motivational factors leading to gender disparities in STEM careers. Frontiers in Psychology, 6, 1–9. doi:10.3389/fpsyq.2015.00036.

  104. Wang, M. T., Eccles, J. S., & Kenny, S. (2013). Not lack of ability but more choice: individual and gender differences in STEM career choice. Psychological Science, 24, 770–775. doi:10.1177/0956797612458937.

    Article  Google Scholar 

  105. Weisgram, E. S., & Bigler, R. S. (2006). Girls and science careers: the role of altruistic values and attitudes about scientific tasks. Journal of Applied Developmental Psychology, 27, 326–348. doi:10.1016/j.appdev.2006.04.004.

    Article  Google Scholar 

  106. Weisgram, E. S., & Bigler, R. S. (2007). Effects of learning about gender discrimination on adolescent girls’ attitudes toward and interest in science. Psychology of Women Quarterly, 31, 262–269. doi:10.1111/j.1471-6402.2007.00369.x.

    Article  Google Scholar 

  107. Williams, W. M., & Ceci, S. J. (2012). When scientists choose motherhood: a single factor goes a long way in explaining the dearth of women in math-intensive fields. How can we address it? American Scientist, 100, 138–145. doi:10.1511/2012.95.138.

    Article  Google Scholar 

  108. Wolters, C. A. (2004). Advancing achievement goal theory: using goal structures and goal orientations to predict students’ motivation, cognition, and achievement. Journal of Educational Psychology, 96, 236–250. doi:10.1037/0022-0663.96.2.236.

    Article  Google Scholar 

  109. Wong, W. I., Pasterski, V., Hindmarsh, P. C., Geffner, M. E., & Hines, M. (2012). Are there parental socialization effects on the sex-typed behavior of individuals with congenital adrenal hyperplasia? Archives of Sexual Behavior, 42, 381–391. doi:10.1007/s10508-012-9997-4.

    Article  Google Scholar 

  110. Wood, W., & Eagly, A. H. (2002). A cross-cultural analysis of the behavior of women and men: implications for the origins of sex differences. Psychological Bulletin, 128, 699–727. doi:10.1037//0033-2909.128.5.699.

    Article  Google Scholar 

  111. Yeager, D. S., & Dweck, C. S. (2012). Mindsets that promote resilience: when students believe that personal characteristics can be developed. Educational Psychologist, 47, 302–314. doi:10.1080/00461520.2012.722805.

    Article  Google Scholar 

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Acknowledgments

This project was supported by Grant DRL1315943 from the National Science Foundation and Grant HD HD074731-01 from the Eunice Kennedy Shriver National Institute of Child Health and Development (NICHD).

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Correspondence to Ming-Te Wang.

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Wang, M., Degol, J.L. Gender Gap in Science, Technology, Engineering, and Mathematics (STEM): Current Knowledge, Implications for Practice, Policy, and Future Directions. Educ Psychol Rev 29, 119–140 (2017). https://doi.org/10.1007/s10648-015-9355-x

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Keywords

  • Gender gap
  • STEM
  • Career preference
  • Lifestyle value
  • Relative cognitive strength
  • Motivation