Abstract
Our study is among the first to provide a comprehensive review of cross-national patterns of gender differences in various STEM-related constructs—achievement, beliefs, attitudes, aspirations, and participation, concerning country-level gender equality. We complement our review with empirical analyses utilizing rigorous methodologies and richer datasets from individual and country levels. Specifically, we examine gender differences in relative strength measures (e.g., strength in science relative to math and reading) and STEM aspirations and graduation, using PISA 2015 and PISA 2018 data from 78 countries/regions (N = 941,475). Our analysis corroborates our literature review, indicating that support for both the gender stratification hypothesis and the gender equality paradox (i.e., whether gender gaps favoring male students are smaller or larger in more gender-equal countries) is generally inconsistent and weak. Various factors contribute to this inconsistency, including specific outlier countries, different years of data collection, diverse data sources, a range of composite and domain-specific measures of gender equality, and statistical models. Our study also introduces a robust statistical model to compare performances in three subjects and evaluate the predictive power of relative strength measures for STEM aspirations at the student level. Our analyses reveal that general academic achievement and math achievement relative to reading are key predictors of STEM aspirations, compared with science achievement relative to math and reading. By juxtaposing both levels of analysis, our findings offer a more nuanced understanding of gender differences in decision-making processes that lead to careers in STEM-related fields.
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Data Availability
The present study is based on the analyses of secondary data that are freely available online (e.g., PISA data from https://www.oecd.org/pisa/data/).
Notes
We also conducted supplemental analyses to examine whether there was a non-linear relation between GGGI and gender differences in STEM aspirations. None of quadratic and cubic coefficients of GGGI were statistically significant.
References
Abu-Hilal, M. M. (2001). Correlates of achievement in the United Arab Emirates: A sociocultural study. In D. M. McInerney and S. Van Etten (Eds.), Research on sociocultural influences on motivation and learning (Vol. 1, pp. 205–230). Greenwich, CT: Information Age.
Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An Empiricist’s companion. Princeton: Princeton University Press.
Baker, D. P., & Jones, D. P. (1993). Creating gender equality: Cross-national gender stratification and mathematical performance. Sociology of Education, 66(2), 91–103.
Bandura, A. (1986). Social foundations of thought and action. Prentice Hall.
Bradley, K. (2000). The incorporation of women into higher education: Paradoxical outcomes? Sociological Education, 73, 1–18.
Breda, T., & Napp, C. (2019). Girls’ comparative advantage in reading can largely explain the gender gap in math-related fields. Proceedings of the National Academy of Sciences, 116(31), 15435–15440.
Breda, T., Jouini, E., Napp, C., & Thebault, G. (2020). Gender stereotypes can explain the gender-equality paradox. Proceedings of the National Academy of Sciences, 117(49), 31063–31069.
Breen, R., Karlson, K. B., & Holm, A. (2018). Interpreting and understanding logits, probits, and other nonlinear probability models. Annual Review of Sociology, 44, 39–54.
Brown, E. R., & Diekman, A. B. (2010). What will I be? Exploring gender differences in near and distant possible selves. Sex Roles, 63, 568–579.
Ceci, S. J., Williams, W. M., & Barnett, S. M. (2009). Women’s underrepresentation in science: Sociocultural and biological considerations. Psychological Bulletin, 135(2), 218.
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(3), 75–141.
Charles, M. (2011). A world of difference: International trends in women’s economic status. Annual Review of Sociology, 37, 355–371.
Charles, M. (2017). Venus, Mars, and math: Gender, societal affluence, and eighth graders’ aspirations for STEM. Socius, 3, 1–16.
Charles, M., & Bradley, K. (2009). Indulging our gendered selves? Sex segregation by field of study in 44 countries. American Journal of Sociology, 114, 924–976.
Charles, M., Harr, B., Cech, E., & Hendley, A. (2014). Who likes math where? Gender differences in eighth-graders’ attitudes around the world. International Studies in Sociology of Education, 24, 85–112.
Cheryan, S., Ziegler, S. A., Montoya, A. K., & Jiang, L. (2017). Why are some STEM fields more gender balanced than others? Psychological Bulletin, 143(1), 1.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Erlbaum.
Correll, S. J. (2004). Constraints into preferences: Gender, status, and emerging career aspirations. American Sociological Review, 69, 93–113.
Croft, A., Schmader, T., & Block, K. (2015). An underexamined inequality: Cultural and psychological barriers to men’s engagement with communal roles. Personality and Social Psychology Review, 19(4), 343–370.
Diekman, A. B., Brown, E. R., Johnston, A. M., & Clark, E. K. (2010). Seeking congruity between goals and roles: A new look at why women opt out of science, technology, engineering, and mathematics careers. Psychological Science, 21(8), 1051–1057.
Eagly, A. H. (1987). Sex differences in social behavior: A social-role interpretation. Erlbaum.
Eagly, A. H., & Wood, W. (2016). Social role theory of sex differences. In N. Naples, R. C. Hoogland, M. Wickramasinghe, & W. C. A. Wong (Eds.), The Wiley Blackwell Encyclopedia of Gender and Sexuality Studies. Oxford, England: Wiley-Blackwell.
Eccles, J. (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(2), 78–89.
Eccles, J. S., & Wang, M. T. (2016). What motivates females and males to pursue careers in mathematics and science? International Journal of Behavioral Development, 40(2), 100–106.
Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, 101859.
Else-Quest, N. M., & Grabe, S. (2012). The political is personal: Measurement and application of nation-level indicators of gender equity in psychological research. Psychology of Women Quarterly, 36, 131–144.
Else-Quest, N. M., & Hamilton, V. (2018). Measurement and analysis of nation-level gender equality in the psychology of women. Perspectives on women’s private and public livesIn C. B. Travis & J. W. White (Eds.), Handbook of the psychology of women (Vol. 2, pp. 545–563). American Psychological Association.
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.
Evans, C. D., & Diekman, A. B. (2009). On motivated role selection: Gender beliefs, distant goals, and career interest. Psychology of Women Quarterly, 33(2), 235–249.
Falk, A., & Hermle, J. (2018). Relationship of gender differences in preferences to economic development and gender equality. Science, 362(6412), eaas9899.
Faulkner, W. (2000). Dualisms, hierarchies and gender in engineering. Social Studies of Science, 30(5), 759–792.
Fryer, R. G., Jr., & Levitt, S. D. (2010). An empirical analysis of the gender gap in mathematics. American Economics Journal: Applied Economics, 2, 210–240.
Garthwaite, P. H., Critchley, F., Anaya-Izquierdo, K., & Mubwandarikwa, E. (2012). Orthogonalization of vectors with minimal adjustment. Biometrika, 99(4), 787–798.
Green, A., & Sanderson, D. (2018). The roots of STEM achievement: An analysis of persistence and attainment in STEM majors. The American Economist, 63(1), 79–93.
Guiso, L. F., Monte, P., Sapienza, P., & Zingales, L. (2008). Culture, gender, and math. Science, 320, 1164–1165.
Guo, J., Marsh, H. W., Morin, A. J., 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.
Guo, J., Marsh, H. W., Parker, P. D., Dicke, T., & Van Zanden, B. (2019). Countries, parental occupation, and girls’ interest in science. The Lancet, 393(10171), e6–e8.
Guo, J., Hu, X., Marsh, H. W., & Pekrun, R. (2022). Relations of epistemic beliefs with motivation, achievement, and aspirations in science: Generalizability across 72 societies. Journal of Educational Psychology, 114(4), 734–751.
Hyde, J. S. (2005). The gender similarities hypothesis. American Psychologist, 60(6), 581.
Hyde, J. S., & Mertz, J. E. (2009). Gender, culture, and mathematics performance. Proceedings of the National Academy of Sciences, 106, 8801–8807.
Hyde, J. S., Fennema, E., & Lamon, S. J. (1990). Gender differences in mathematics performance: A meta-analysis. Psychological Bulletin, 107(2), 139.
Hyde, J. S., Lindberg, S. M., Linn, M. C., Ellis, A. B., & Williams, C. C. (2008). Gender similarities characterize math performance. Science, 321(5888), 494–495.
Inglehart, R., & Welzel, C. (2005). Modernization, cultural change, and democracy: The human development sequence. New York: Cambridge University Press.
Jonsson, J. O. (1999). Explaining sex differences in educational choice an empirical assessment of a rational choice model. European Sociological Review, 15(4), 391–404.
Kane, J. M., & Mertz, J. E. (2012). Debunking myths about gender and mathematics performance. Notices of the AMS, 59(1), 10–21.
Laird, R. D., & De Los Reyes, A. (2013). Testing informant discrepancies as predictors of early adolescent psychopathology: Why difference scores cannot tell you what you want to know and how polynomial regression may. Journal of Abnormal Child Psychology, 41(1), 1–14.
Laird, R. D., & Weems, C. F. (2011). The equivalence of regression models using difference scores and models using separate scores for each informant: Implications for the study of informant discrepancies. Psychological Assessment, 23(2), 388.
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(6), 1123.
Machin, S., & Pekkarinen, T. (2008). Global sex differences in test score variability. Science, 322(5906), 1331–1332.
Marsh, H. W. (1986). Verbal and math self-concepts: An internal/external frame of reference model. American Educational Research Journal, 23(1), 129–149.
Marsh, H. W., & Hau, K. T. (2007). Applications of latent-variable models in educational psychology: The need for methodological-substantive synergies. Contemporary Educational Psychology, 32(1), 151–170.
Marsh, H. W., Abduljabbar, A. S., Morin, A. J. S., Parker, P., Abdelfattah, F., Nagengast, B., & Abu-Hilal, M. M. (2015). The big-fish-little-pond effect: Generalizability of social comparison processes over two age cohorts from Western, Asian, and Middle Eastern Islamic countries. Journal of Educational Psychology, 107(1), 258–271.
Marsh, H. W., Abduljabbar, A. S., Parker, P. D., Morin, A. J. S., Abdelfattah, F., Nagengast, B., & Abu-Hilal, M. M. (2015). The internal/external frame of reference model of self-concept and achievement relations: Age-cohort and cross-cultural differences. American Educational Research Journal, 52(1), 168–202.
Marsh, H. W., Van Zanden, B., Parker, P. D., Guo, J., Conigrave, J., & Seaton, M. (2019). Young women face disadvantage to enrollment in university STEM coursework regardless of prior achievement and attitudes. American Educational Research Journal, 56(5), 1629–1680.
Marsh, H. W., Parker, P. D., Guo, J., Basarkod, G., Niepel, C., & Van Zanden, B. (2021). Illusory gender-equality paradox, math self-concept, and frame-of-reference effects: New integrative explanations for multiple paradoxes. Journal of Personality and Social Psychology, 121(1), 168–183.
Marsh, H. W., Pekrun, R., Guo, J., Hattie, J., & Karin, E. (2023). Too much of a good thing might be bad: The double-edged sword of parental aspirations and the adverse effects of aspiration-expectation gaps. Educational Psychology Review, 35(2), 49.
Milfont, T. L., & Klein, R. A. (2018). Replication and reproducibility in cross-cultural psychology. Journal of Cross-Cultural Psychology, 49(5), 735–750.
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(3), 631.
Möller, J., & Marsh, H. W. (2013). Dimensional comparison theory. Psychological Review, 120(3), 544–560.
Muthén, L.K. and Muthén, B.O. (1998-2012). Mplus user’s guide (Seventh Edition). Los Angeles, CA: Muthén & Muthén.
Nicholls, G. M., Wolfe, H., Besterfield-Sacre, M., Shuman, L. J., & Larpkiattaworn, S. (2007). A method for identifying variables for predicting STEM enrollment. Journal of Engineering Education, 96(1), 33–44.
Niepel, C., Stadler, M., & Greiff, S. (2019). Seeing is believing: Gender diversity in STEM is related to mathematics self-concept. Journal of Educational Psychology, 111(6), 1119.
Noll, N. (2020). Gender equality ≠ gender neutrality: When a paradox is not so Paradoxical, after all. GenderSci Lab, Retrieved January 9, 2022 from https://www.genderscilab.org/blog/gender-equality-does-not-equal-gender-neutrality#_edn23
Nosek, B. A., Smyth, F. L., Sriram, N., Lindner, N., Devos, T., Ayala, A., & …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(26), 10593–10597.
OECD. (2016). PISA 2015 results (Volume I): Excellence and equity in education. PISA, OECD Publishing.
OECD. (2019). PISA 2018 assessment and analytical framework. OECD Publishing.
OECD. (2016a). PISA 2015 assessment and analytical framework: Science, reading, mathematic and financial literacy. Paris: OECD publishing. Retrieved September 6, 2020 from http://www.oecd.org/education/pisa-2015-assessment-and-analytical-framework-9789264281820-en
OECD. (2019b). PISA 2018 technical report. Paris: OECD publishing. Retrieved September 6, 2020 from http://www.oecd.org/pisa/data/pisa2018technicalreport/#d.en.423800
Osborne, J. W., & Waters, E. (2002). Multiple Regression Assumptions. ERIC Digest, Retrieved November 7, 2023 from https://files.eric.ed.gov/fulltext/ED470205.pdf
Penner, A. (2008). Gender differences in extreme mathematical achievement: An international perspective of biological and social forces. American Journal of Sociology, 114, 138–170.
Poortinga, Y. H., & Fontaine, J. R. (2022). Principles and practices of methodology and methods in cross-cultural psychology. Journal of Cross-Cultural Psychology, 53(7–8), 847–859.
Ramirez, F. O., & Wotipka, C. M. (2001). Slowly but surely? The global expansion of women’s participation in science and engineering fields of study, 1972–92. Sociological Education, 74, 231–251.
Reilly, D. (2012). Gender, culture, and sex-typed cognitive abilities. PLOS ONE, 7(7), e39904.
Richardson, S. S., Reiches, M. W., Bruch, J., Boulicault, M., Noll, N. E., & Shattuck-Heidorn, H. (2020). Is there a gender-equality paradox in science, technology, engineering, and math (STEM)? Commentary on the study by Stoet and Geary (2018). Psychological Science, 31(3), 338–341.
Schmader, T. (2023). Gender inclusion and fit in STEM. Annual Review of Psychology, 74(1), 219–243.
Schwab, K., Samans, R., Hausmann, R., Zahidi, S., Bekhouche, Y., Ugarte, P. P., & Ratcheva, V. (2015). The global gender gap report 2015. World Economic Forum. Retrieved December 28, 2020 from http://www3.weforum.org/docs/GGGR2015/cover.pdf
Shabuz, Z. R., & Garthwaite, P. H. (2019). Contribution of individual variables to the regression sum of squares. Model Assisted Statistics and Applications, 14(4), 281–296.
Shannon, G., Jansen, M., Williams, K., Cáceres, C., Motta, A., Odhiambo, A., & Mannell, J. (2019). Gender equality in science, medicine, and global health: Where are we at and why does it matter? The Lancet, 393(10171), 560–569.
Sikora, J., & Pokropek, A. (2012). Gender segregation of adolescent science career plans in 50 countries. Science Education, 96(2), 234–264.
Stoet, G., & Geary, D. C. (2015). Sex differences in academic achievement are not related to political, economic, or social equality. Intelligence, 48, 137–151.
Stoet, G., & Geary, D. C. (2018). The gender-equality paradox in science, technology, engineering, and mathematics education. Psychological Science, 29(4), 581–593.
Stoet, G., & Geary, D. C. (2019). A simplified approach to measuring national gender inequality. PloS One, 14(1), e0205349.
Stoet, G., & Geary, D. C. (2020). The gender-equality paradox is part of a bigger phenomenon: Reply to Richardson and colleagues (2020). Psychological Science, 31(3), 342–344.
Stoet, G., Bailey, D. H., Moore, A. M., & Geary, D. C. (2016). Countries with higher levels of gender equality show larger national sex differences in mathematics anxiety and relatively lower parental mathematics valuation for girls. PloS One, 11(4), e0153857.
United Nations Development Programme. (2016). Human development report 2016. Palgrave Macmillan.
Vishkin, A. (2022). Queen’s gambit declined: The gender-equality paradox in chess participation across 160 countries. Psychological Science, 33(2), 276–284.
Wan, S., Lauermann, F., Bailey, D. H., & Eccles, J. S. (2023). Girls’ comparative advantage in language arts explains little of the gender gap in math-related fields: A replication and extension. Proceedings of the National Academy of Sciences, 120(40), e2305629120.
Wang, M. T., & Degol, J. L. (2017). Gender gap in science, technology, engineering, and mathematics (STEM): Current knowledge, implications for practice, policy, and future directions. Educational Psychology Review, 29, 119–140.
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(5), 770–775.
Watt, H. M., Richardson, P. W., Klusmann, U., Kunter, M., Beyer, B., Trautwein, U., & Baumert, J. (2012). Motivations for choosing teaching as a career: An international comparison using the FIT-Choice scale. Teaching and Teacher Education, 28(6), 791–805.
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(4), 326–348.
Welzel, C., & Inglehart, R. (2010). Agency, values, and well-being: A human development model. Social Indicators Research, 97, 43–63.
Wigfield, A., Eccles, J. S., & Möller, J. (2020). How dimensional comparisons help to understand linkages between expectancies, values, performance, and choice. Educational Psychology Review, 32(3), 657–680.
Xie, Y., Fang, M., & Shauman, K. (2015). STEM education. Annual Review of Sociology, 41(1), 331–357.
Yalcinkaya, N. S., & Adams, G. (2020). A cultural psychological model of cross-national variation in gender gaps in STEM participation. Personality and Social Psychology Review, 24(4), 345–370.
Acknowledgements
This research was supported by a grant from the Ministry of Education of the People’s Republic of China (Grant No. 23YJC880038) and a grant from the Renmin University of China (Grant No. KYGJD2023006). We thank Sarah S. Richardson, David Geary, Gijsbert Stoet, Marion Boulicault, Joseph Bruch, and Nicole E. Noll for their constructive comments on the earlier version of the manuscript.
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Guo, J., Marsh, H.W., Parker, P.D. et al. Cross-Cultural Patterns of Gender Differences in STEM: Gender Stratification, Gender Equality and Gender-Equality Paradoxes. Educ Psychol Rev 36, 37 (2024). https://doi.org/10.1007/s10648-024-09872-3
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DOI: https://doi.org/10.1007/s10648-024-09872-3