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The Intersection of Gender, Social Class, and Cultural Context: a Meta-Analysis

  • Philip D. ParkerEmail author
  • Brooke Van Zanden
  • Herbert W. Marsh
  • Katherine Owen
  • Jasper J. Duineveld
  • Michael Noetel
Meta-Analysis

Abstract

Expectancy value theory is often evoked by educational psychologists to explain gender differences in Science, Technology, Engineering, and Mathematics (STEM) variables. Yet gender does not operate in isolation. Nor are gender effects likely to be context free. In the current meta-analysis, we explore gender differences in STEM-related expectancy for success, and the task values of intrinsic, utility, attainment, and cost. We find that gender differences were generally small in size. Invoking the concept of intersectionality, we find that heterogeneity in gender effect sizes are large and gender differences are moderated, primarily, by socioeconomic status, ethnic diversity, and somewhat by national gender equality.

Keywords

Gender differences Expectancy value theory STEM education Intersectionality 

Notes

Supplementary material

10648_2019_9493_MOESM1_ESM.docx (256 kb)
ESM 1 (DOCX 255 kb)

References

  1. Alon, S., & DiPrete, T. A. (2015). Gender differences in the formation of field of study choice set. Sociological Science, 2, 50–81.  https://doi.org/10.15195/v2.a5.CrossRefGoogle Scholar
  2. Auwarter, A. E., & Aruguete, M. S. (2008). Effects of student gender and socioeconomic status on teacher perceptions. The Journal of Educational Research, 101(4), 242–246.  https://doi.org/10.3200/joer.101.4.243-246.CrossRefGoogle Scholar
  3. Baker, D. P., & Jones, D. P. (1993). Creating gender equality: Cross-national gender stratification and mathematical performance. Sociology of Education, 66(2), 91–103.  https://doi.org/10.2307/2112795.CrossRefGoogle Scholar
  4. Bönte, W. (2015). Gender differences in competitive preferences: New cross-country empirical evidence. Applied Economics Letters, 22(1), 71–75.  https://doi.org/10.1080/13504851.2014.927560.CrossRefGoogle Scholar
  5. Bowleg, L. (2008). When Black+ lesbian+ woman≠ Black lesbian woman: The methodological challenges of qualitative and quantitative intersectionality research. Sex Roles, 59(5-6), 312–325.  https://doi.org/10.1007/s11199-008-9400-z.CrossRefGoogle Scholar
  6. Catsambis, S. (1994). The path to math: Gender and racial-ethnic differences in mathematics participation from middle school to high school. Sociology of Education, 199–215.  https://doi.org/10.2307/2112791.
  7. Catsambis, S. (1995). Gender, race, ethnicity, and science education in the middle grades. Journal of Research in Science Teaching, 32(3), 243–257.  https://doi.org/10.1002/tea.3660320305.CrossRefGoogle Scholar
  8. Charles, M., & Bradley, K. (2009). Indulging our gendered selves? Sex segregation by field of study in 44 countries. American Journal of Sociology., 114(4), 924–976.  https://doi.org/10.1086/595942.CrossRefGoogle Scholar
  9. 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(1), 85–112.  https://doi.org/10.1080/09620214.2014.895140.CrossRefGoogle Scholar
  10. 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), 1–35.  https://doi.org/10.1037/bul0000052.CrossRefGoogle Scholar
  11. Cheung, M. (2011). metaSEM: Meta-analysis: A structural equation modelling approach. R package version 0.5–3. Retrieved from http://courses.nus.edu.sg/course/psycwlm/Internet/metaSEM.
  12. Cheung, M. (2014). Modeling dependent effect sizes with three-level metaanalyses: A structural equation modeling approach. Psychological Methods, 19(2), 211–229.  https://doi.org/10.1037/a0032968.CrossRefGoogle Scholar
  13. Choo, H. Y., & Ferree, M. M. (2010). Practicing intersectionality in sociological research: A critical analysis of inclusions, interactions, and institutions in the study of inequalities. Sociological Theory, 28(2), 129–149.  https://doi.org/10.1111/j.1467-9558.2010.01370.x.
  14. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.Google Scholar
  15. Coley, R. J. (2001). Differences in the gender gap: Comparisons across racial/ethnic groups in education and work. ETS Policy Information Report. Princeton, NJ: educational testing service.Google Scholar
  16. Collier, A. (1994). Critical realism: An introduction to Roy Bhaskar’s philosophy. London: Verso.Google Scholar
  17. Collins, P. H. (1990). Black feminist thought: Knowledge, consciousness, and the politics of empowerment. New York: Routledge.Google Scholar
  18. Crenshaw, K. (1989). Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. U. Chi. Legal F., 139–67.Google Scholar
  19. Eagly, A. H., Eaton, A., Rose, S. M., Riger, S., & McHugh, M. C. (2012). Feminism and psychology: Analysis of a half-century of research on women and gender. American Psychologist, 67(3), 211–230.  https://doi.org/10.1037/a0027260.CrossRefGoogle Scholar
  20. Eccles, J. S. (1994). Understanding women’s educational and occupational choices. Psychology of Women Quarterly, 18(4), 585–609.  https://doi.org/10.1111/j.1471-6402.1994.tb01049.x.CrossRefGoogle Scholar
  21. Eccles, J. S. (2005). Subjective task value and the Eccles et al. model of achievement-related choices. Handbook of Competence and Motivation, 105–121.Google Scholar
  22. Eccles, J. S., & Hoffman, L. W. (1984). Socialization and the maintenance of a sex-segregated labor market. In H. W. Stevenson & A. E. Siegel (Eds.), Research in child development and social policy (Vol. 1, pp. 367–420). Chicago: University of Chicago Press.Google Scholar
  23. Eccles, J. S., & Jacobs, J. E. (1986). Social forces shape math attitudes and performance. Signs, 11(2), 367–380.  https://doi.org/10.1086/494229.CrossRefGoogle Scholar
  24. Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132.  https://doi.org/10.1146/annurev.psych.53.100901.135153.CrossRefGoogle Scholar
  25. Eccles, J. S., Jacobs, J. E., & Harold, R. D. (1990). Gender role stereotypes, expectancy effects, and parents' socialization of gender differences. Journal of Social Issues, 46(2), 183–201.  https://doi.org/10.1111/j.1540-4560.1990.tb01929.x.CrossRefGoogle Scholar
  26. 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(2), 131–144.  https://doi.org/10.1177/0361684312441592.CrossRefGoogle Scholar
  27. Else-Quest, N. M., & Hamilton, V. (2018). Measurement and analysis of nation-level gender equity in the psychology of women. In C. B. Travis & J. W. White (Eds.), APA handbook of the psychology of women (pp. 545–563). Washington, DC: APA Press.Google Scholar
  28. Else-Quest, N. M., & Hyde, J. S. (2016a). Intersectionality in quantitative psychological research I. Theoretical and epistemological issues. Psychology of Women Quarterly, 40(2), 155–170.  https://doi.org/10.1177/0361684316629797.CrossRefGoogle Scholar
  29. Else-Quest, N. M., & Hyde, J. S. (2016b). Intersectionality in quantitative psychological research II. Methods and techniques. Psychology of Women Quarterly, 40(3), 319–336.  https://doi.org/10.1177/0361684316647953.CrossRefGoogle Scholar
  30. 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.CrossRefGoogle Scholar
  31. Few-Demo, A. L. (2014). Intersectionality as the “new” critical approach in feminist family studies: Evolving racial/ethnic feminisms and critical race theories. Journal of Family Theory & Review, 6(2), 169–183.  https://doi.org/10.1111/jftr.12039.CrossRefGoogle Scholar
  32. Field, A. P. (2003). The problems in using fixed-effects models of metaanalysis on real-world data. Understanding Statistics: Statistical Issues in Psychology, Education, and the Social Sciences, 2(2), 105–124.  https://doi.org/10.1207/S15328031US0202_02.CrossRefGoogle Scholar
  33. Fox, D., Prilleltensky, I., & Austin, S. (Eds.). (2009). Critical psychology: An introduction. London: Sage.Google Scholar
  34. Fryer, R. G., Jr., & Levitt, S. D. (2010). An empirical analysis of the gender gap in mathematics. American Economic Journal: Applied Economics, 2(2), 210–240.Google Scholar
  35. Fu, R., Gartlehner, G., Grant, M., Shamliyan, T., Sedrakyan, A., Wilt, T. J., et al. (2011). Conducting quantitative synthesis when comparing medical interventions: AHRQ and the effective health care program. Journal of Clinical Epidemiology, 64, 1187–1197.CrossRefGoogle Scholar
  36. Glick, P., & Fiske, S. T. (2001). An ambivalent alliance: Hostile and benevolent sexism as complementary justifications for gender inequality. American Psychologist, 56(2), 109–118.  https://doi.org/10.1037/0003-066X.56.2.109.CrossRefGoogle Scholar
  37. Gneezy, U., Niederle, M., & Rustichini, A. (2003). Performance in competitive environments: Gender differences. Quarterly Journal of Economics, 118(3), 1049–1074.  https://doi.org/10.1162/00335530360698496.CrossRefGoogle Scholar
  38. Goldstein, H. (1995). Multilevel statistical models. London: Edward Arnold.Google Scholar
  39. Guiso, L., Monte, F., Sapienza, P., & Zingales, L. (2008). Culture, gender, and math. Science, 320(5880), 1164–1165.  https://doi.org/10.1126/science.1154094.CrossRefGoogle Scholar
  40. Gunnarsson, L. (2011). A defence of the category ‘women’. Feminist Theory, 12(1), 23–37.  https://doi.org/10.1177/1464700110390604.CrossRefGoogle Scholar
  41. 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.CrossRefGoogle Scholar
  42. 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.  https://doi.org/10.1016/S0140-6736(19)30210-7.CrossRefGoogle Scholar
  43. Haines, E., Deaux, K., & Lofaro, N. (2016). The times they are a-changing … or are they not? A comparison of gender stereotypes, 1983–2014. Psychology of Women Quarterly, 40(3), 353–363.  https://doi.org/10.1177/0361684316634081.CrossRefGoogle Scholar
  44. Hancock, A. (2016). Intersectionality: An intellectual history. Oxford University Press: Kindle Edition.Google Scholar
  45. Hawken, A., & Munck, G. L. (2013). Cross-national indices with gender-differentiated data: What do they measure? How valid are they? Social Indicators Research, 111(3), 801–838.  https://doi.org/10.1007/s11205-012-0035-7.CrossRefGoogle Scholar
  46. Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2-3), 61–83.  https://doi.org/10.1017/S0140525X0999152X.
  47. Higgins, J., Thompson, S., Deeks, J., & Altman, D. (2003). Measuring inconsistency in meta-analyses. British Medical Journal, 327(7414), 557–560.  https://doi.org/10.1136/bmj.327.7414.557.CrossRefGoogle Scholar
  48. Hull, G. T., Scott, P. B., & Smith, B. (1982). All the women are white, all the men are black, but some of us are brave. New York: Feminist.Google Scholar
  49. Hyde, J. S. (2005). The gender similarities hypothesis. American Psychologist, 60(6), 581–592.  https://doi.org/10.1037/0003-066X.60.6.581.CrossRefGoogle Scholar
  50. Hyde, J. S. (2007). New directions in the study of gender similarities and differences. Current Directions in Psychological Science, 16(5), 259–263.  https://doi.org/10.1111/j.1467-8721.2007.00516.x.CrossRefGoogle Scholar
  51. Hyde, J. S. (2012). Nation-level indicators of gender equality in psychological research: Theoretical and methodological issues. Psychology of Women Quarterly, 36(2), 145–148.  https://doi.org/10.1177/0361684312441448.CrossRefGoogle Scholar
  52. Hyde, J. S. (2013). Gender similarities and differences. Annual Review of Psychology, 65(1), 373–398.  https://doi.org/10.1146/annurev-psych-010213-115057.CrossRefGoogle Scholar
  53. Hyde, J. S., & Mertz, J. E. (2009). Gender, culture, and mathematics performance. Proceedings of the National Academy of Sciences, 106, 8801–8807.CrossRefGoogle Scholar
  54. Ireson, G. (2017). Gender achievement and social, political and economic equality: A European perspective. Educational Studies, 43(1), 40–50.CrossRefGoogle Scholar
  55. Jenkins, S. R. (2000). Introduction to the special issue: Defining gender, relationships, and power. Sex Roles, 42, 467–493.  https://doi.org/10.1023/A:1007010604246.CrossRefGoogle Scholar
  56. Kane, J. M., & Mertz, J. E. (2012). Debunking myths about gender and mathematics performance. Notices of the AMS, 59(01), 10–21.  https://doi.org/10.1090/noti790.CrossRefGoogle Scholar
  57. Kennedy, J. P., Lyons, T., & Quinn, F. (2014). The continuing decline of science and mathematics enrolments in Australian high schools. Teaching Science, 60, 34–46.Google Scholar
  58. Lareau, A. (2003). Unequal childhoods: Race, class and family life. Berkeley: University of California Press.Google Scholar
  59. Lauermann, F., Tsai, Y. M., & Eccles, J. S. (2017). Math-related career aspirations and choices within Eccles et al.’s expectancy–value theory of achievement-related behaviors. Developmental Psychology, 53(8), 1540–1559.CrossRefGoogle Scholar
  60. Lubienski, S. T., Crane, C. C., & Robinson, J. P. (2011). A longitudinal study of gender and mathematics using ECLS data. In Final report (grant# R305A080147) submitted to the National Center for Education Research. Washington, DC: Institute of Education Sciences.Google Scholar
  61. 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(4), 634–645.  https://doi.org/10.5951/jresematheduc.44.4.0634.CrossRefGoogle Scholar
  62. Lykes, M. B. (2000). Possible contributions of a psychology of liberation: Whither health and human rights? Journal of Health Psychology, 5(3), 383–397.  https://doi.org/10.1177/135910530000500312.CrossRefGoogle Scholar
  63. Mack, J. & Wilson, R. (2015). Trends in mathematics and science subject combinations in the NSW HSC 2001–2014 by gender. Technical paper, University of Sydney. Retrieved from: http://www.maths.usyd.edu.au/u/SMS/MMW2015.pdf.
  64. Mann, A., & DiPrete, T. A. (2016). The consequences of the national math and science performance environment for gender differences in STEM aspiration. Sociological Science, 3, 568.  https://doi.org/10.15195/v3.a25.CrossRefGoogle Scholar
  65. Marks, J. L., Lam, C. B., & McHale, S. M. (2009). Family patterns of gender role attitudes. Sex Roles, 61(3-4), 221–234.  https://doi.org/10.1007/s11199-009-9619-3.CrossRefGoogle Scholar
  66. Martinez Dy, A., Martin, L., & Marlow, S. (2014). Developing a critical realist positional approach to intersectionality. Journal of Critical Realism, 13(5), 447–466.  https://doi.org/10.1179/1476743014Z.00000000043.CrossRefGoogle Scholar
  67. Marsh, H. W., Bornmann, L., Mutz, R., Daniel, H. D., & O’Mara, A. (2009). Gender effects in the peer reviews of grant proposals: A comprehensive metaanalysis comparing traditional and multilevel approaches. Review of Educational Research, 79(3), 1290–1326.CrossRefGoogle Scholar
  68. McGraw, R., Lubienski, S. T., & Strutchens, M. E. (2006). A closer look at gender in NAEP mathematics achievement and affect data: Intersections with achievement, race/ethnicity, and socioeconomic status. Journal for Research in Mathematics Education, 37, 129–150 Retrieved from http://www.jstor.org/s7/30034845.Google Scholar
  69. Muntaner, C., & Augustinavicius, J. (2019). Intersectionality: A scientific realist critique. The American Journal of Bioethics, 19(2), 39–41.  https://doi.org/10.1080/15265161.2018.1557296.CrossRefGoogle Scholar
  70. Nagengast, B., Marsh, H. W., Scalas, L. F., Xu, M. K., Hau, K. T., & Trautwein, U. (2011). Who took the “×” out of expectancy-value theory? A psychological mystery, a substantive-methodological synergy, and a cross-national generalization. Psychological Science, 22(8), 1058–1066.CrossRefGoogle Scholar
  71. National Innovation and Science Agenda, Australian Government (2017). National innovation and science agenda. Retrieved from http://www.innovation.gov.au/page/agenda
  72. National Science and Technology Council, Office of science technology policy, US. Government Washington D.C. (2013). https://www.whitehouse.gov/sites/default/files/microsites/ostp/stem_stratplan_2013.pdf.
  73. National Science Foundation, Division of Science Resources Statistics. (2012). Women, minorities, and persons with disabilities in science and engineering (Special Report NSF 11-309). Arlington, VA. Retrieved from http://www.nsf.gov/statistics/wmpd/sex.
  74. Niederle, M., & Vesterlund, L. (2007). Do women shy away from competition? Do men compete too much? The Quarterly Journal of Economics, 122(3), 10671101–10671101.  https://doi.org/10.1162/qjec.122.3.1067.CrossRefGoogle Scholar
  75. Niederle, M., & Vesterlund, L. (2010). Explaining the gender gap in math test scores: The role of competition. The Journal of Economic Perspectives, 24(2), 129–144.  https://doi.org/10.1257/jep.24.2.129.CrossRefGoogle Scholar
  76. Office of the Chief Scientist (2014). Science, technology, engineering and mathematics: Australia’s future. Australian Government, Canberra. Retrieved from http://www.chiefscientist.gov.au/wp- content/uploads/STEM_AustraliasFuture_Sept2014_Web.pdf.
  77. Parker, R., Larkin, T., & Cockburn, J. (2017). A visual analysis of gender bias in contemporary anatomy textbooks. Social Science & Medicine, 180, 106–113.  https://doi.org/10.1016/j.socscimed.2017.03.032.CrossRefGoogle Scholar
  78. Parker, P. D., Marsh, H. W., Guo, J., Anders, J., Shure, N., & Dicke, T. (2018a). An information distortion model of social class differences in math self-concept, intrinsic value, and utility value. Journal of Educational Psychology, 110(3), 445–463.  https://doi.org/10.1037/edu0000215.CrossRefGoogle Scholar
  79. Parker, P. D., Van Zanden, B., & Parker, R. B. (2018b). Girls get smart, boys get smug: Historical changes in gender differences in math, literacy, and academic social comparison and achievement. Learning and Instruction, 54, 125–137.  https://doi.org/10.1016/j.learninstruc.2017.09.002.CrossRefGoogle Scholar
  80. Penner, A. M. (2008). Gender differences in extreme mathematical achievement: An international perspective on biological and social factors. American Journal of Sociology, 114(S1), S138–S170.  https://doi.org/10.1086/589252.CrossRefGoogle Scholar
  81. Prilleltensky, I. (2008). The role of power in wellness, oppression, and liberation: The promise of psychopolitical validity. Journal of Community Psychology, 36(2), 116–136.  https://doi.org/10.1002/jcop.20225.CrossRefGoogle Scholar
  82. Raudenbush, S. W., & Bryk, A. S. (1985). Empirical Bayes meta-analysis. Journal of Educational and Behavioral Statistics, 10(2), 75–98.  https://doi.org/10.2307/1164836.CrossRefGoogle Scholar
  83. Rosenthal, R. (1991). Meta-analytic procedures for social research (Vol. 6). Newbury Park: Sage.CrossRefGoogle Scholar
  84. Rosenthal, R. (1994). Parametric measures of effect size. In H. Cooper & L. Hedges (Eds.), The handbook of research synthesis (pp. 231–244). New York: Russell Sage Foundation.Google Scholar
  85. Sáinz, M., & López-Sáez, M. (2010). Gender differences in computer attitudes and the choice of technology-related occupations in a sample of secondary students in Spain. Computers & Education, 54(2), 578–587.  https://doi.org/10.1016/j.compedu.2009.09.007.CrossRefGoogle Scholar
  86. Sinclair, S., Hardin, C. D., & Lowery, B. S. (2006). Self-stereotyping in the context of multiple social identities. Journal of Personality and Social Psychology, 90(4), 529–542.  https://doi.org/10.1037/0022-3514.90.4.529.CrossRefGoogle Scholar
  87. Spierings, N. (2012). The inclusion of quantitative techniques and diversity in the mainstream of feminist research. European Journal of Women’s Studies, 19(3), 331–347.  https://doi.org/10.1177/1350506812443621.CrossRefGoogle Scholar
  88. Sterne, J. A. C., Egger, M., & Moher, D. (2011). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1. 0.Google Scholar
  89. Stevenson, H. W., Chen, C., & Booth, J. (1990). Influences of schooling and urban-rural residence on gender differences in cognitive abilities and academic achievement. Sex Roles, 23(9-10), 535–551.  https://doi.org/10.1007/BF00289767.CrossRefGoogle Scholar
  90. Stoet, G., & Geary, D. C. (2018). The gender-equality paradox in science, technology, engineering, and mathematics education. Psychological science, 29(4), 581–593.Google Scholar
  91. Stotsky, J., Shibuya, S., Kolovich, L., & Kebhaj, S. (2016). Trends in women’s advancement and gender equality. International Monentary Fund Working Paper, Washington DC, IMF).Google Scholar
  92. Trautwein, U., Marsh, H. W., Nagengast, B., Lüdtke, O., Nagy, G., & Jonkmann, K. (2012). Probing for the multiplicative term in modern expectancy–value theory: A latent interaction modeling study. Journal of Educational Psychology, 104(3), 763–777.CrossRefGoogle Scholar
  93. Van Den Noortgate, W., & Onghena, P. (2003). Multilevel meta-analysis: A comparison with traditional meta-analytical procedures. Educational and Psychological Measurement, 63(5), 765–790.  https://doi.org/10.1177/0013164402251027.CrossRefGoogle Scholar
  94. Van Zanden (2018). Understanding the psychological and social origins of gender disparities in self-beliefs, motivation, and educational attainment. Unpublished Dissertation. Google Scholar
  95. von Hippel, P. (2015). The heterogeneity statistic I2 can be biased in small meta-analyses. BMC Medical Research Methodology, 15(1), 35.  https://doi.org/10.1186/s12874-015-0024-z.CrossRefGoogle Scholar
  96. Wenner, G. (2003). Comparing poor, minority elementary students’ interest and background in science with that of their white, affluent peers. Urban Education, 38(2), 153–172.  https://doi.org/10.1177/0042085902250483.CrossRefGoogle Scholar
  97. Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81.CrossRefGoogle Scholar
  98. Zarrett, N., Malanchuk, O., Davis-Kean, P. E., & Eccles, J. (2006). Examining the gender gap in IT by race: Young adults’ decisions to pursue an IT career. In J. McGrath & W. Asprey (Eds.), Women and information technology: Research on underrepresentation. Cambridge: MIT Press.Google Scholar
  99. Zurbriggen, E. L., & Capdevila, R. (2010). The personal and the political are feminist: Exploring the relationships among feminism, psychology, and political life. Psychology of Women Quarterly, 34(4), 458–459.  https://doi.org/10.1111/j.1471-6402.2010.01595.x.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Institute for Positive Psychology and EducationAustralian Catholic UniversityNorth SydneyAustralia
  2. 2.SPRINTER Research Group and Prevention Research CollaborationUniversity of SydneySydneyAustralia

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