Psychology Women and Science, Technology, Engineering, and Math (STEM)
Sylvia Beyer
  • LAST REVIEWED: 10 May 2019
  • LAST MODIFIED: 30 October 2019
  • DOI: 10.1093/obo/9780199828340-0161


A nation’s prosperity depends to a significant degree on a highly educated workforce in science, technology, engineering, and math (STEM). In 2017 only 29 percent of the US STEM workforce was female, even though women represent 51.5 percent of the population (see National Center for Science and Engineering Statistics 2019, cited under Statistical Compendia). If more women were to enter STEM, this would not only relieve the shortage of STEM workers, but also provide lucrative jobs to women, and include their perspectives, fostering innovation and scientific progress. Shortages of women in STEM exist in other countries and are being addressed with varying levels of success (see Cross-Cultural Findings). However, the majority of research efforts examining the reasons behind women’s underrepresentation in STEM have been conducted in the United States, often funded by the US government (e.g., by the National Science Foundation’s Broadening Participation in Computing program and Research on Gender in Science and Engineering program). The Theories researchers employ focus on different kinds of explanations for female underrepresentation in STEM with varying ramifications and implications for interventions. For example, some researchers focus on biological explanations, attributing female underrepresentation in STEM to gender differences in Quantitative, Spatial, and Verbal Abilities. Other researchers focus on psychological factors such as Stereotype Threat, women’s low Self-Efficacy in male-dominated fields, a lack of Sense of Belonging or Identification with a STEM Field, and negative Stereotypes about People in STEM and the Field of STEM that conflict with women’s Gender Roles and Values. Furthermore, there exist cultural and institutional barriers that deter women or make it difficult for them to succeed in STEM fields. These include a lack of Role Models, the Role of Parents in encouraging females, Pedagogical Issues, General Workplace Issues such as a chilly climate, problems with Work-Life Balance that disproportionately affect women who typically are the primary caretakers of children and elderly parents, and outright Bias and Discrimination. Only in the early 21st century have researchers started to pay attention to Intersectionality. Gender intersects with race, ethnicity, sexual orientation, socioeconomic status, first-generation college student status, and many more. We now know that these intersectionalities affect outcomes in important ways. Furthermore, it is important to discuss Best Practices for Intervention Strategies. This article also examines Cross-Cultural Findings regarding the phenomenon of women’s underrepresentation in STEM. Striving for currency, this article will focus on work that has been published within the early 21st century. Rather than presenting research on individual STEM disciplines separately, this article discusses the major issues and causes across the disciplines. This provides for a less repetitive presentation and facilitates comparisons within one topic across disciplines (e.g., under the heading Self-Efficacy, the reader can compare research on computer science, technology, and engineering). It is also worth noting that certain STEM fields are overrepresented among research on specific causes. For example, most research on Stereotype Threat focuses on math. And certain STEM fields have received more research attention than others. Computer science, science as a general area, and engineering have been well studied. Math has been studied well in K–12 samples, but less well in higher education. Specific science fields like physics, astronomy, chemistry, or the geosciences have received much less attention.

General Overviews

The literature on women and STEM is vast and complex. This section presents overviews that help orient the reader to the subject matter. Beyer 2016 reviews the major factors implicated in female underrepresentation in computer science. Bilimoria and Lord 2015 is an edited volume with a focus on issues facing women in academia and professional careers in STEM. Ceci, et al. 2014 presents an extensive summary of the most recent research on causes of female underrepresentation in STEM from high school course taking through full professor salaries. Cheryan, et al. 2017 is a review paper summarizing the major obstacles for women in STEM. Hill, et al. 2010 presents eight research projects that suggest empirically based Best Practices for Intervention Strategies. This report is highly readable for those less familiar with the topic. Kanny, et al. 2014 reviews forty years of research to identify five main factors affecting female underrepresentation in STEM. Wang and Degol 2013 provides an excellent theoretical and empirical overview of research on female underrepresentation in STEM.


Many articles investigating applied or educational topics regarding the underrepresentation in STEM are published in discipline-specific journals such as Computer Science Education or the Journal of Engineering Education. The Journal of Women and Minorities in Science and Engineering is devoted to research on gender and minority issues in STEM, whereas the International Journal of Gender, Science, and Technology focuses on women in STEM. Proceedings of the National Academy of Sciences of the United States of America publishes articles in the natural and social sciences. Many of the research articles on underrepresentation issues in STEM are published in peer-reviewed journals in social psychology such as the Journal of Personality and Social Psychology, or psychology in general such as Psychological Science. Sex Roles is by far the most commonly used outlet for research articles on female underrepresentation in STEM. Furthermore, some journals publish special issues relevant to women in STEM. The journal Social Sciences is one such example with its publication of special issues Charles and Thébaud 2016 and Beyer 2017.

Statistical Compendia

For those seeking detailed data, there exist statistical compendia that facilitate comparisons across STEM disciplines, age groups, time, etc. National Center for Science and Engineering Statistics 2019, the most recent edition of the National Science Foundation’s biennial compendium, is a must-have for every researcher investigating underrepresentation issues. National Science Board 2018, a report on science and engineering indicators, provides the most up-to-date data on education, research, and workforce issues in the STEM fields in the United States.

Nonprofit Advocacy Groups for Women in STEM Fields

Two excellent nonprofits whose emphasis is on women in certain STEM fields are described here. The National Center for Women in Information Technology (NCWIT) website contains a plethora of resources including intervention suggestions and how-to information in easy-to-understand language and downloadable kits pertaining to females in information technology. The strength of Women in Engineering ProActive Network (WEPAN) is the Women in STEM Knowledge Center it sponsors, which puts information on women in STEM at the reader’s fingertips.

Causes of Female Underrepresentation

Researchers investigating female underrepresentation in STEM are often asked to explain what the reason for underrepresentation is and how it can be solved. Unfortunately, there is no simple answer because there is not one single cause. The reasons are complex and multi-determined, making solutions all the more elusive. The metaphor used most commonly is that of a pipeline with leaks all along the way, so that at the end of the pipe, water flow slows down to a trickle. Females are lost throughout the school system. Those who make it through high school with an interest in STEM intact often do not pursue STEM majors in college or drop out, and even fewer pursue graduate studies in STEM. Finally, those women working in STEM careers who made it so far in the pipeline are again more likely to leave the field, leaving behind a small minority of women working in STEM. It is important to recognize that at different points of this developmental trajectory, females leave or fail to be attracted to STEM for different reasons. For example, the reasons why girls decide to forego taking non-required math courses in high school are distinct from the reasons why women decide to leave a STEM career, adding further complexity to this research topic. Before discussing the multitude of causes, the reader should be aware of the major Theories in the field. Theories can be divided into those favoring biological explanations, such as gender differences in Quantitative, Spatial, and Verbal Abilities, and psychological theories emphasizing individual differences such as in the experience of Stereotype Threat, Self-Efficacy, Sense of Belonging or Identification with a STEM Field, and Stereotypes about People in STEM and the Field of STEM that conflict with Gender Roles and Values. Thirdly, there are theories emphasizing cultural and institutional barriers such as a lack of Role Models, the Role of Parents, Pedagogical Issues, difficulties in General Workplace Issues, Work-Life Balance, and Bias and Discrimination.


The articles in this section present theories that vary both in outlook (e.g., focus on biology versus psychology) and in generality (e.g., some theories are very circumscribed and aspire to explaining only a small aspect of underrepresentation while some are overarching). Ceci, et al. 2009 thoroughly reviews major classes of theories and then proposes a causal model. Cheryan and Plaut 2010 presents a psychological theory emphasizing the importance of developing a sense of belonging. The Dasgupta 2011 model focuses on how females could become “inoculated” against gender stereotypes that deter them from STEM. Diekman, et al. 2016 is a good introduction for those interested in learning more about the role congruity model (see Gender Roles and Values). Lent, et al. 2017 presents a meta-analysis of research on social-cognitive career theory, one of the major career theories that is frequently employed to understand female underrepresentation in STEM. Quesenberry and Trauth 2012 introduces a theory that considers women as individuals rather than as a social group.

  • Ceci, Stephen J., Wendy M. Williams, and Susan M. Barnett. 2009. Women’s underrepresentation in science: Sociocultural and biological considerations. Psychological Bulletin 135:218–261.

    DOI: 10.1037/a0014412Save Citation »Export Citation » Share Citation »

    This article reviews the evidence for biological (e.g., gender differences in Quantitative, Spatial, and Verbal Abilities) and sociocultural causes of female underrepresentation in STEM, proposing a causal model. The authors also present Cross-Cultural Findings.

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  • Cheryan, Sapna, and Victoria C. Plaut. 2010. Explaining underrepresentation: A theory of precluded interest. Sex Roles 63:475–488.

    DOI: 10.1007/s11199-010-9835-xSave Citation »Export Citation » Share Citation »

    The authors introduce a theory whereby individuals’ lack of interest in a major is due to a lack of perceived similarity to those in the major, leading to a sense of not belonging (see Sense of Belonging or Identification with a STEM Field). Women feel even less similarity to those in computer science programs than men do, and hence show very little interest in the field.

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  • Dasgupta, Nilanjana. 2011. Ingroup experts and peers as social vaccines who inoculate the self-concept: The stereotype inoculation model. Psychological Inquiry 22:231–246.

    DOI: 10.1080/1047840X.2011.607313Save Citation »Export Citation » Share Citation »

    The author details how the stereotype inoculation model explains female underrepresentation in STEM. According to the model, exposure to Role Models inoculates those in the minority against negative stereotypes and doubts about their belonging (see Sense of Belonging or Identification with a STEM Field). The author presents a literature review to support her model with empirical findings.

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  • Diekman, Amanda B., Mia Steinberg, Elizabeth R. Brown, Aimee L. Belanger, and Emily K. Clark. 2016. A goal congruity model of role entry, engagement, and exit: Understanding communal goal processes in STEM gender gaps. Personality and Social Psychology Review 21.2: 1–34.

    DOI: 10.1177/1088868316642141Save Citation »Export Citation » Share Citation »

    This paper details the role congruity model and how it can explain female underrepresentation in STEM. Its focus is on perceived communal goal affordances (see Gender Roles and Values). Research and policy implications are also discussed. This is an excellent overview paper for those interested in learning more about the role congruity model.

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  • Lent, Robert W., Hung-Bin Sheu, Matthew J. Miller, Megan E. Cusick, Lee T. Penn, and Nancy N. Truong. 2017. Predictors of science, technology, engineering, and mathematics choice options: A meta-analytic path analysis of the social-cognitive choice model by gender and race/ethnicity. Journal of Counseling Psychology 65.1: 17–35.

    DOI: 10.1037/cou0000243Save Citation »Export Citation » Share Citation »

    This article is a meta-analysis of thirty years’ worth of studies on social-cognitive career theory in the realm of predicting STEM career or major choice. Anyone interested in learning more about the empirical evidence for one of the most influential theories of career choice should turn to this article.

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  • Quesenberry, Jeria L., and Eileen M. Trauth. 2012. The (dis)placement of women in the IT workforce: An investigation of individual career values and organizational interventions. Information Systems Journal 22:457–473.

    DOI: 10.1111/j.1365-2575.2012.00416.xSave Citation »Export Citation » Share Citation »

    The authors present an individual differences theory of gender and information technology, which focuses on how individual differences in women affect career satisfaction and turnover intentions. Through their interviews they discover that professional women in information technology, for whom lifestyle integration is important, show high career satisfaction and little intention to leave, whereas women who self-identify with managerial competence show only moderate career satisfaction and express greater intentions to leave.

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Quantitative, Spatial, and Verbal Abilities

The argument that female underrepresentation in science is due to gender differences in quantitative abilities is not new. It became infamous when in 2005 then–Harvard University president Lawrence Summers gave a speech commenting that women are underrepresented in top university science departments because of their lower math ability. This speech outraged and galvanized many STEM researchers. In this section there are articles espousing this view and others challenging it. Ackerman, et al. 2013 finds that although cognitive ability predicts STEM persistence, other variables can enhance prediction of persistence versus attrition. Ceci and Williams 2011 argues that gender differences in quantitative ability might play some role in female underrepresentation in STEM, although the researchers identify other variables as more important. Halpern 2012 is written by one of the leading experts on gender differences in cognitive abilities. Her book reviews cutting-edge research and presents it in an evenhanded manner. Lubinski, et al. 2014 finds that high math ability is predictive of later high achievement. The authors still find that high-ability women earn less and are more likely to prefer part-time work due to differences in values (see Gender Roles and Values). Mullis, et al. 2016 reviews the major findings of the international Trends in International Mathematics and Science Study (TIMSS) (see Cross-Cultural Findings), which finds strong evidence that the gender gap in mathematics and science performance in children is narrowing. Schmidt 2011 examines technical aptitude and finds that the gender difference is due to females’ lack of interest. Stoet and Geary 2018 reveals that there is generally no gender difference in science performance among fifteen-year-olds in most countries (see Cross-Cultural Findings), thus female underrepresentation in STEM is not due to a lack of ability.

  • Ackerman, Phillip L., R. Ruth Kanfer, and Margaret E. Beier. 2013. Trait complex, cognitive ability, and domain knowledge predictors of baccalaureate success, STEM persistence, and gender differences. Journal of Educational Psychology 105.3: 911–927.

    DOI: 10.1037/a0032338Save Citation »Export Citation » Share Citation »

    This study of first-year undergraduates at Georgia Tech finds that several variables can predict grades, graduation, attrition, and STEM persistence eight years later. While high school GPA and Advanced Placement test scores were important predictors, certain personality variables and self-concept were also of importance, albeit to different extents for female and male students.

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  • Ceci, Stephen J., and Wendy M. Williams. 2011. Understanding current causes of women’s underrepresentation in science. Proceedings of the National Academy of Sciences of the United States of America 108.3: 3157–3162.

    DOI: 10.1073/pnas.1014871108Save Citation »Export Citation » Share Citation »

    This article concludes that gender Bias and Discrimination in science is not the cause of the underrepresentation of women in science. Rather, they claim that gender differences in resources (because women are overrepresented among institutions with a high teaching load), quantitative abilities, and reproductive choices (because having children hampers the careers of women; see Work-Life Balance) account for underrepresentation. A must-read for anyone interested in the topic, even if the conclusions may be controversial.

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  • Halpern, Diane F. 2012. Sex differences in cognitive abilities. 4th ed. New York: Psychology Press.

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    This book by one of the leading authorities on gender differences in cognitive abilities is in its fourth edition. Anyone interested in the size and causes of gender differences in quantitative, spatial, and verbal abilities will find something of value in this book, which presents a very evenhanded approach.

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  • Lubinski, David, Camilla P. Benbow, and Harrison J. Kell. 2014. Life paths and accomplishments of mathematically precocious males and females four decades later. Psychological Science 25:2217–2232.

    DOI: 10.1177/0956797614551371Save Citation »Export Citation » Share Citation »

    In this longitudinal study, the researchers followed a group of thirteen-year-old highly mathematically talented children for forty years. Mathematical talent translated into high achievement for many of the participants. However, men outearned women with high math talent. The researchers also found gender differences in values (see Gender Roles and Values).

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  • Mullis, Ina V. S., Michael O. Martin, and Tom Loveless. 2016. 20 years of TIMSS: International trends in mathematics and science achievement, curriculum, and instruction. Boston: Trends in International Mathematics and Science Study.

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    Published in collaboration with PIRLS International Study Center, the Trends in International Mathematics and Science Study (TIMSS) is conducted every four years. The latest study finds that gender differences in math and science performance in the fourth and eighth grades have narrowed considerably over the years, with girls actually outperforming boys in some countries. The data are presented in great detail and the interested reader will find a treasure trove of information about mathematics and science performance of school-aged children in an international comparison (see Cross-Cultural Findings).

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  • Schmidt, Frank. 2011. A theory of sex differences in technical aptitude and some supporting evidence. Perspectives on Psychological Science 6:560–573.

    DOI: 10.1177/1745691611419670Save Citation »Export Citation » Share Citation »

    The gender difference in technical aptitude stems from gender differences in interest, which prevent women from gaining more Self-Efficacy and experience.

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  • Stoet, Gijsbert, and David C. Geary. 2018. The gender-equality paradox in science, technology, engineering, and mathematics education. Psychological Science 29.4: 581–593.

    DOI: 10.1177/0956771774119Save Citation »Export Citation » Share Citation »

    Using the 2015 results from the Program for International Student Assessment (PISA) study of fifteen-to-sixteen-year-old students, the authors find that in most countries (see Cross-Cultural Findings) girls do as well as, or even better, than boys. However, science is often not girls’ best subject, so even if they receive higher science grades than boys, they often feel lower Self-Efficacy. Interestingly, countries with less gender equity have more female STEM majors than more gender egalitarian countries (see Bias and Discrimination).

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Stereotype Threat

Research on stereotype threat has proliferated in psychology since the introduction of the concept in the 1990s. Stereotype threat happens in high-stakes situations such as achievement testing when a group is negatively stereotyped in the domain. For example, African Americans taking an intelligence test and women taking a difficult math test may experience stereotype threat because they are members of social groups for whom negative stereotypes in the achievement domain exist. Studies on stereotype threat have demonstrated its negative effects, especially among those who are highly identified with the domain. Casad, et al. 2019 delineates a complex chain of events including stereotype threat, which eventually leads to female disengagement from STEM. Schuster and Martiny 2017 shows how stereotype threat decreases positive affect and thereby decreases interest in STEM fields. Shaffer, et al. 2013 illustrates how informing students that women are underrepresented in STEM can actually set up a stereotype threat condition for women. Tellhed and Adolfsson 2017 illustrates how stereotype threat conditions decrease students’ abilities to gauge their own performances accurately.

  • Casad, Bettina J., Zachary W. Petzel, and Emily A. Ingalls. 2019. A model of threatening academic environments predicts women STEM majors’ self-esteem and engagement in STEM. Sex Roles 80:469–488.

    DOI: 10.1007/s11199-018-0942-4Save Citation »Export Citation » Share Citation »

    This study of female undergraduate STEM majors attempts to delineate the causal chain of events that leads to disengagement from STEM. The authors find support for gender stigma consciousness affecting gender rejection sensitivity, which predicts negative perceptions of campus climate, which further predicts stereotype threat, lower perceived control, and eventually lower engagement with STEM.

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  • Schuster, Carolin, and Sarah E. Martiny. 2017. Not feeling good in STEM: Effects of stereotype activation and anticipated affect on women’s career aspirations. Sex Roles 76:40–55.

    DOI: 10.1007/s11199-016-0665-3Save Citation »Export Citation » Share Citation »

    German eleventh and twelfth graders and university students, when presented with stereotype threat–inducing stimuli (low representation of their gender in a field), experience reduced positive affect and interest in a STEM field. Self-Efficacy for STEM also positively relates to a desire to major in a STEM field.

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  • Shaffer, Emily S., David M. Marx, and Radmila Prislin. 2013. Mind the gap: Framing of women’s success and representation in STEM affects women’s math performance under threat. Sex Roles 68:454–463.

    DOI: 10.1007/s11199-012-0252-1Save Citation »Export Citation » Share Citation »

    When American women are told that women have reached parity in math, they perform much better on a math test than if they are told that parity has not been reached. In fact they perform as well as men. Thus, emphasizing that women are still underrepresented in STEM may set up stereotype threat conditions for women.

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  • Tellhed, Una, and Caroline Adolfsson. 2017. Competence and confusion: How stereotype threat can make you a bad judge of your competence. European Journal of Social Psychology 48:189–197.

    DOI: 10.1002/ejsp.2307Save Citation »Export Citation » Share Citation »

    This study finds that under stereotype threat conditions, female and male high school students alike inaccurately evaluate their performance on an important test. On the other hand, in the control condition, students were fairly accurate in their performance evaluations.

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Self-efficacy refers to a person’s confidence that s/he can accomplish a specific behavioral goal such as believing that one has excellent math ability and hence will do well on math tests. Self-efficacy is assessed in many of the studies presented in other sections of this article (see the cross-references), attesting to the importance of this psychological construct. It can affect performance, course and major selection, occupational choices, and persistence, as demonstrated by the articles in this section. Beyer 2014 finds that gender differences in computer self-efficacy predict women’s lower likelihood of taking a computer science course. In Cech, et al. 2011, female engineering majors have lower professional efficacy than male majors, which predicts their attrition from the field. He and Freeman 2010 points out the interrelations among computer experience, computer anxiety, and computer self-efficacy. In a study of engineering majors, Jagacinski 2013 finds that low engineering self-efficacy predicts course grades. Sax, et al. 2015 examines females entering college and finds that their math self-concept is considerably lower than their male counterparts’. Tellhed, et al. 2017 investigates Swedish high school students, finding that girls’ lower self-efficacy and feelings of belonging in STEM (see Sense of Belonging or Identification with a STEM Field) lower their interest in STEM. The longitudinal study Watt, et al. 2017 finds that for female high school students, math self-concept is a better predictor of later interest in a STEM career than it is for males. Woodcock and Bairaktarova 2015 exposes first-year engineering students to an engineering task. Women underestimate their performance on the task, whereas men evaluate their performance more accurately.

  • Beyer, Sylvia. 2014. Why are women underrepresented in computer science? Gender differences in stereotypes, self-efficacy, values, and interests and predictors of future CS course taking and grades. Computer Science Education 24.2–3: 153–192.

    DOI: 10.1080/08993408.2014.963363Save Citation »Export Citation » Share Citation »

    Computer self-efficacy is related to computer science course taking. Female college students’ lower self-efficacy, therefore, deters them from computer science. The importance of positive experiences in computer science courses (see Pedagogical Issues) for future course taking is also examined.

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  • Cech, Erin, Brian Rubineau, Susan Silbey, and Carroll Seron. 2011. Professional role confidence and gendered persistence in engineering. American Sociological Review 76.5: 641–666.

    DOI: 10.1177/0003122411420815Save Citation »Export Citation » Share Citation »

    American female engineering majors show twice the attrition rate as men. Men show higher math self-efficacy and professional efficacy, and believe they have a better career fit (personal values are compatible with engineering) than women despite an absence of gender differences in grades or SAT scores. Professional efficacy and perceived career fit, but not math self-efficacy, predict engineering persistence.

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  • He, Jun, and Lee A. Freeman. 2010. Are men more technology-oriented than women? The role of gender on the development of general computer self-efficacy of college students. Journal of Information Systems Education 21.2: 203–212.

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    Among American business majors, those with less computer experience and greater anxiety have lower computer self-efficacy. Female students have less computer experience and greater anxiety and hence show lower computer self-efficacy.

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  • Jagacinski, Carolyn M. 2013. Women engineering students: Competence perceptions and achievement goals in the freshman engineering course. Sex Roles 69:644–657.

    DOI: 10.1007/s11199-013-0325-9Save Citation »Export Citation » Share Citation »

    American female engineering majors have lower perceptions of their abilities and show stronger performance-avoidance goals than male engineering majors or female and male psychology majors. This negatively affects their course grades.

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  • Sax, Linda J., M. Allison Kanny, Tiffani A. Riggers-Piehl, Hanna Whang, and Laura N. Paulson. 2015. “But I’m not good at math”: The changing salience of mathematical self-concept in shaping women’s and men’s STEM aspirations. Research in Higher Education 56:813–842.

    DOI: 10.1007/s11162-015-9375-xSave Citation »Export Citation » Share Citation »

    Data from the Cooperative Institutional Research Program (CIRP) data set indicate that women starting college have lower math self-concept than men in most STEM disciplines, which negatively affects their selection of a STEM major.

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  • Tellhed, Una, Martin Bäckström, and Fredrik Björklund. 2017. Will I fit in and do well? The importance of social belongingness and self-efficacy for explaining differences in interest in STEM and HEED majors. Sex Roles 77:86–96.

    DOI: 10.1007/s11199-016-0694-ySave Citation »Export Citation » Share Citation »

    Swedish high school girls have lower self-efficacy and lower feelings of belonging in STEM (see Sense of Belonging or Identification with a STEM Field) than boys, which relates to their lower levels of interest in STEM.

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  • Watt, Helen M. G., Janet S. Hyde, Jennifer Petersen, Zoe A. Morris, Christopher S. Rozek, and Judith M. Harackiewicz. 2017. Mathematics—a critical filter for STEM-related career choices? A longitudinal examination among Australian and U.S. adolescents. Sex Roles 77:254–271.

    DOI: 10.1007/s11199-016-0711-1Save Citation »Export Citation » Share Citation »

    This study assessed Australian and US ninth graders’ survey responses to predict their interest in a STEM career at the end of high school. Results varied somewhat by country (see Cross-Cultural Findings). For females, math self-concept was important for interest in a STEM major, whereas for males, math interest was more important.

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  • Woodcock, Anna, and Diana Bairaktarova. 2015. Gender-biased self-evaluations of first-year engineering students. Journal of Women and Minorities in Science and Engineering 21.3: 255–269.

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    This study of first-year engineering students finds that women underestimate their performance on an engineering task, whereas men evaluate themselves more accurately. This pattern of self-doubt regarding their performance may contribute to women’s attrition from engineering.

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Sense of Belonging or Identification with a STEM Field

Sense of belonging is a relatively recently identified psychological construct that relates to female underrepresentation in STEM. In essence, in order to consider a major or occupation one must have a sense that one belongs or at least could belong. The perception that one is or would be an outsider serves as a deterrent. The studies in this section examine the effect of sense of belonging on women’s presence in STEM majors and fields. Cheryan, et al. 2009 finds that the physical environment exudes clues related to belonging and non-belonging, concluding that classrooms and labs need to be designed and decorated in a female-friendly way. Good, et al. 2012 presents a scale that measures sense of belonging. This article also demonstrates the importance of a sense of belonging for intentions to pursue math. For engineering students in Jones, et al. 2013, identification with the field predicts retention in the major. London, et al. 2011 finds that sense of belonging and social support are critical in sustaining female science majors through a negative performance experience. Master, et al. 2015 demonstrates how the physical classroom environment affects girls’ sense of belonging and thereby their interest in taking a computer science class. Rosenthal, et al. 2011 suggests that single-sex programs might benefit female science and engineering majors by promoting a greater sense of belonging.

  • Cheryan, Sapna, Victoria C. Plaut, Paul G. Davies, and Claude M. Steele. 2009. Ambient belonging: How stereotypical cues impact gender participation in computer science. Journal of Personality and Social Psychology 97:1045–1060.

    DOI: 10.1037/a0016239Save Citation »Export Citation » Share Citation »

    This study of American college students demonstrates that a stereotypically “techy” environment (e.g., room with Star Trek posters) makes women feel they do not belong and dissuades them, but not men, from considering majoring in computer science or working for certain companies.

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  • Good, Catherine, Aneeta Rattan, and Carol S. Dweck. 2012. Why do women opt out? Sense of belonging and women’s representation in mathematics. Journal of Personality and Social Psychology 102.4: 700–717.

    DOI: 10.1037/a0026659Save Citation »Export Citation » Share Citation »

    The authors present psychometric information on a newly developed Sense of Belonging Scale. They demonstrate that having a sense of belonging increases American college students’ intention to pursue math, a belief in the utility of math, and their Self-Efficacy, while decreasing math anxiety. Women who endorse a malleable view of intelligence have a stronger sense of belonging in math and higher grades.

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  • Jones, Brett D., Chloe Ruff, and Marie C. Paretti. 2013. The impact of engineering identification and stereotypes on undergraduate women’s achievement and persistence in engineering. Social Psychology of Education 16:471–493.

    DOI: 10.1007/s11218-013-9222-xSave Citation »Export Citation » Share Citation »

    For female American undergraduate engineering majors, the only predictor of the likelihood to leave engineering is their identification with the field. Engineering Self-Efficacy is positively related to engineering achievement, and unlike what is typical in studies of women’s self-efficacy in STEM, female undergraduates in this sample have higher self-efficacy than men. Men endorse stronger gender stereotypes about engineering than women do.

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  • London, Bonita E., Lisa Rosenthal, Sheri R. Levy, and Marci Lobel. 2011. The influences of perceived identity compatibility and social support on women in nontraditional fields during the college transition. Basic and Applied Social Psychology 33.4: 304–321.

    DOI: 10.1080/01973533.2011.614166Save Citation »Export Citation » Share Citation »

    A study of American female first-year science majors reports that perceived social support and identification with STEM protect women’s motivation after a poor performance. Perceived social support also positively affects sense of belonging.

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  • Master, Allison, Sapna Cheryan, and Andrew N. Meltzoff. 2015. Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science. Journal of Educational Psychology 108:424–437.

    DOI: 10.1037/edu0000061Save Citation »Export Citation » Share Citation »

    High school girls who are exposed to a stereotypical (see Stereotypes about People in STEM and the Field of STEM) as compared to a non-stereotypical computer science classroom are much less interested in taking a computer science class. The stereotypical classroom negatively affects girls’ sense of belonging. The physical classroom environment does not affect boys’ sense of belonging or interest in a computer science class.

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  • Rosenthal, Lisa, Bonita E. London, Sheri R. Levy, and Marci Lobel. 2011. The roles of perceived identity compatibility and social support for women in a single-sex STEM program at a co-educational university. Sex Roles 65.9–10: 725–736.

    DOI: 10.1007/s11199-011-9945-0Save Citation »Export Citation » Share Citation »

    In this study American undergraduate women enrolled in a Women in Science and Engineering program show higher perceived identity compatibility and social support and have a stronger sense of belonging in STEM. The authors conclude that single-sex programs (see also Pedagogical Issues) promote a sense of belonging.

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Stereotypes about People in STEM and the Field of STEM

There exist many negative stereotypes about STEM majors, occupations, and the individuals in those fields. The psychological literature differentiates between implicit and explicit stereotypes. Implicit stereotypes are assessed via different reaction times to stimuli that are either paired in a stereotypical way (e.g., men and science) or not (e.g., women and science). Implicit measures of stereotypes usually use the Implicit Association Test and have the advantage of not being subject to social desirability biases as participants are unaware that their stereotypes are being assessed. Explicit stereotypes are assessed via surveys, and participants can usually discern what is being measured, possibly affecting their responses. Stereotypes about STEM can serve as deterrents to entry into STEM, especially for women. The studies in this section explain how and why. Banchefsky, et al. 2016 reveals that feminine appearance is considered atypical for scientists. It was assumed that feminine-looking women were teachers rather than scientists. Bian, et al. 2017 finds evidence that at the tender age of six, girls, but not boys, are already less likely to associate brilliance with members of their sex. For the case of computer science, Cvencek, et al. 2011 finds that elementary-aged girls already associate math with males and not themselves. Lane, et al. 2012 finds that men but not women associate themselves with science. These stereotypical associations predict STEM major choices. Leslie, et al. 2015 asks academics whether brilliance is required for success in their field and finds that these perceptions are negatively correlated with the number of women in the field. Miller, et al. 2015 finds that gender stereotypes at the national level closely mirror the representation of women in science (see Cross-Cultural Findings). Miller, et al. 2018 is a meta-analysis of studies that examine children’s drawings of scientists. More recent studies find young girls becoming more likely to draw female scientists than in the past, but older girls still show very traditional stereotypes of scientists as male. Nosek and Smyth 2011 compares implicit and explicit stereotypes of math and finds that implicit stereotypes are better predictors of math attitudes and anxiety than are explicit stereotypes.

  • Banchefsky, Sarah, Jacob Westfall, Bernadette Park, and Charles M. Judd. 2016. But you don’t look like a scientist! Women scientists with feminine appearance are deemed less likely to be scientists. Sex Roles 75:95–109.

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    MTurk workers rated the likelihood that photos of actual scientists depicted scientists, teachers, or journalists. Real female scientists who were rated as appearing very feminine by participants were considered more likely to be a teacher or journalist than a scientist. On the other hand, male scientists who were either feminine or not feminine in rated appearance were equally likely considered to be scientists.

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  • Bian, Lin, Sarah-Jane Leslie, and A. Andrei Cimpian. 2017. Gender stereotypes about intellectual ability emerge early and influence children’s interests. Science 355:389–391.

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    Girls as young as six and seven years of age already associated girls less with being brilliant than boys did. This was not yet the case at five years of age. Girls were also less likely than boys to want to play a game for “smart children,” suggesting possibly lower Self-Efficacy. Such stereotypes and perceptions of lower ability can influence future interests.

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  • Cvencek, Dario, Andrew N. Meltzoff, and Anthony G. Greenwald. 2011. Math-gender stereotypes in elementary-school children. Child Development 82:766–779.

    DOI: 10.1111/j.1467-8624.2010.01529.xSave Citation »Export Citation » Share Citation »

    On implicit (measured via reaction times on the Implicit Association Test) and explicit (assessed via surveys) measures of math stereotypes, American girls in grades one through five are less likely to associate math with themselves than boys do.

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  • Lane, Kristin A., Jin X. Goh, and Erin Driver-Linn. 2012. Implicit science stereotypes mediate the relationship between gender and academic participation. Sex Roles 66.3–4: 220–234.

    DOI: 10.1007/s11199-011-0036-zSave Citation »Export Citation » Share Citation »

    On an Implicit Association Test, American undergraduate men associate science with themselves, whereas American women do not. Associations between science and one’s gender are predictive of plans to pursue a STEM major. Students also believe that men are better at science than women and that women are better in the humanities.

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  • Leslie, Sarah-Jane, Andrei Cimpian, Meredith Meyer, and Edward Freeland. 2015. Expectations of brilliance underlie gender distributions across academic disciplines. Science 347.6219: 262–265.

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    Faculty, postdocs, and graduate students indicate whether brilliance is required to excel in their discipline. Regardless of whether a field was a STEM or non-STEM field, the fewer women in the field, the more academics in the field felt that brilliance was important. The authors discuss how stereotypes of women as lacking brilliance can dissuade women from entering career fields.

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  • Miller, David I., Alice H. Eagly, and Marcia C. Linn. 2015. Women’s representation in science predicts national gender-science stereotypes: Evidence from 66 nations. Journal of Educational Psychology 107:631–644.

    DOI: 10.1037/edu0000005Save Citation »Export Citation » Share Citation »

    This study of over 350,000 participants who took the Implicit Association Test finds that a country’s representation of women in science (see Cross-Cultural Findings) is correlated with its citizens’ stereotypes about women’s lack of science ability. This held even when taking nations’ gender equities into account.

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  • Miller, David I., Kyle M. Nolla, Alice H. Eagly, and David H. Uttal. 2018. The development of children’s gender-science stereotypes: A meta-analysis of 5 decades of U.S. draw-a-scientist studies. Child Development 89:1943–1955.

    DOI: 10.1111/cdev.13039Save Citation »Export Citation » Share Citation »

    This meta-analysis reviews fifty years of research on draw-a-scientist studies. The authors find that children, especially girls, are more likely to draw women as scientists now than in the past. However, among older children, most drawings still depicted male scientists, revealing persistent stereotypes about gender and science.

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  • Nosek, Brian A., and Frederick L. Smyth. 2011. Implicit social cognitions predict sex differences in math engagement and achievement. American Educational Research Journal 48:1125–1156.

    DOI: 10.3102/0002831211410683Save Citation »Export Citation » Share Citation »

    Female participants in an online survey, who implicitly associate math with males, feel less identified with and hold more negative attitudes toward math and have more math anxiety. STEM majors, particularly female ones, are more positive toward math than non-STEM majors. Male STEM majors strongly associate math with males, while female STEM majors do not. Females with weak rather than strong implicit stereotypes do better on math achievement tests and are more engaged in math.

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Gender Roles and Values

Gender roles and values can affect course-taking patterns and selection of a major and occupation, as illustrated in the citations in this section. Brown, et al. 2015 finds that communal affordance predict STEM motivation. Cheryan 2011 explains how Stereotypes about People in STEM and the Field of STEM conflict with women’s gender roles and communal values, that is, their people orientation and desire to make a difference. Diekman, et al. 2010 finds that undergraduates do not believe that STEM can satisfy communal values, deterring women. Eccles and Wang 2016 shows that values in twelfth grade are a better predictor than math scores of gender differences in STEM occupations at age twenty-nine. Kyte and Riegle-Crumb 2017 is noteworthy for its focus on an understudied group of students: low income and mostly minority. It reports that the perception that STEM is socially relevant increases female students’ interest in pursuing a STEM major. Su, et al. 2009 presents a meta-analysis of interests and values finding consistent gender differences explaining females’ lower interest in math. Weisgram, et al. 2011 finds gender differences in values and also explains how femininity is related to traditional career choices. Zafar 2009 also finds gender differences in values that relate to occupational choices.

  • Brown, Elizabeth R., Dustin B. Thoman, Jessi L. Smith, and Amanda B. Diekman. 2015. Closing the communal gap: The importance of communal affordances in science career motivation. Journal of Applied Social Psychology 45.12: 662–673.

    DOI: 10.1111/jasp.12327Save Citation »Export Citation » Share Citation »

    In three studies with undergraduates, MTurk workers, and research assistants, STEM communal affordances, that is, the extent to which STEM fields are perceived to allow individuals to work with and help others, predicted STEM motivation.

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  • Cheryan, Sapna. 2011. Understanding the paradox in math-related fields: Why do some gender gaps remain while others do not? Sex Roles 66:184–190.

    DOI: 10.1007/s11199-011-0060-zSave Citation »Export Citation » Share Citation »

    This article reviews reasons why women are underrepresented in STEM. Stereotypes about People in STEM and the Field of STEM, including stereotypes about scientists, scientific work, and science careers, abound and conflict with women’s gender roles and values. These stereotypes also inhibit a Sense of Belonging or Identification with a STEM Field. The article mentions initiatives to counteract these stereotypes to make STEM more compatible with women’s gender roles and values.

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  • Diekman, Amanda B., Elizabeth R. Brown, Amanda M. Johnston, and Emily K. Clark. 2010. Seeking congruity between goals and roles: A new look at why women opt out of science, technology, engineering, and mathematics careers. Psychological Science 21:1051–1057.

    DOI: 10.1177/0956797610377342Save Citation »Export Citation » Share Citation »

    American undergraduates believe that STEM careers do not satisfy communal values, which inhibits their interest in STEM careers. Since women score higher on communal values than men, this can in part explain their reluctance to enter STEM majors.

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  • Eccles, Jacquelynne S., and Ming-Te Wang. 2016. What motivates females and males to pursue careers in mathematics and science? International Journal of Behavioral Development 40:100–106.

    DOI: 10.1177/0165025415616201Save Citation »Export Citation » Share Citation »

    This longitudinal study examines what predicts twelfth graders’ employment in STEM occupations at age twenty-nine. The study is based on Eccles’s expectancy-value model. Occupational values in twelfth grade were a stronger predictor of gender differences in STEM occupations at age twenty-nine than were math scores on an aptitude test (see Quantitative, Spatial, and Verbal Abilities).

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  • Kyte, Sarah B., and Catherine Riegle-Crumb. 2017. Perceptions of the social relevance of science: Exploring the implications for gendered patterns in expectations of majoring in STEM fields. Social Sciences 6.1: 19.

    DOI: 10.3390/socsci6010019Save Citation »Export Citation » Share Citation »

    This study of students in a low-income, mostly minority-serving school district finds that female eighth- and ninth-grade students who perceive STEM fields as socially relevant show an increased interest in pursuing a STEM major in college.

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  • Su, Rong, James Rounds, and Patrick I. Armstrong. 2009. Men and things, women and people: A meta-analysis of sex differences in interests. Psychological Bulletin 135:859–884.

    DOI: 10.1037/a0017364Save Citation »Export Citation » Share Citation »

    A meta-analysis finds that males are much more interested in things and females in people. Males are somewhat more interested in math and much more interested in engineering than females due to differences in values.

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  • Weisgram, Erica S., Lisa M. Dinella, and Megan Fulcher. 2011. The role of masculinity/femininity, values, and occupational value affordances in shaping young men and women’s occupational choices. Sex Roles 65.3–4: 243–258.

    DOI: 10.1007/s11199-011-9998-0Save Citation »Export Citation » Share Citation »

    Female college students and those high in femininity show stronger altruistic and family values, whereas males and those high in masculinity are more focused on monetary rewards in future careers. The more feminine and less masculine a woman, the more likely she is to avoid male-dominated careers.

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  • Zafar, Basit. 2009. College major choice and the gender gap. New York: Federal Reserve Bank of New York Staff Reports.

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    Students choose a major mostly because they enjoy the coursework and anticipate enjoying working in the field and gaining the approval of their parents (see Role of Parents). Men value earning potential more than women.

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Role Models

Role models illustrate to individuals that people similar to them can be successful. This serves as a source of inspiration, encouragement, and Sense of Belonging or Identification with a STEM Field and reduces the effect of negative Stereotypes about People in STEM and the Field of STEM. The selected articles present a range of findings on role models, revealing greater complexity in the effect of role models than originally anticipated. Betz and Sekaquaptewa 2012 details how femininity of STEM role models relates to middle school girls’ interest in STEM. In a study of the effect of computer science role models on female nonmajors’ interest in computer science, Cheryan, et al. 2011 illustrates that role models who match the stereotype of a computer scientist actually decrease women’s interest in the field. Fuesting and Diekman 2017 finds that the communal behaviors of role models are more important than their gender in fostering a Sense of Belonging or Identification with a STEM Field for students. Herrmann, et al. 2016 demonstrates that short exposure to a role model who details her own initial struggles with the subject matter increases female students’ course grades in chemistry and psychology. Rosenthal, et al. 2013 finds that female undergraduates exposed to a successful female role model show an increased Sense of Belonging or Identification with a STEM Field. Stout, et al. 2011 finds that female STEM role models increase STEM majors’ Self-Efficacy and Sense of Belonging or Identification with a STEM Field. Young, et al. 2013 finds benefits of exposure to positive female role models for female students, with no disadvantages accruing to male students.

Role of Parents

Parents play a critical role in their children’s lives. Their encouragement or discouragement and stereotypical perceptions about their children can affect their children’s Self-Efficacy and course and major selection. Gniewosz and Watt 2017 finds that parents’ overestimations of their children’s math abilities predicts their children’s intrinsic math motivations three years later. Gunderson, et al. 2012 finds that parents and teachers’ stereotypes and anxieties about math can profoundly affect children. Harackiewicz, et al. 2012 points out how critical the role of mothers is in course selections and identifies them as an untapped resource. Jacobs, et al. 2017 points out how strongly parents influence the career choices of their children. Over time, the impact of mothers who are engineers on their daughters’ career choices has increased. Lang 2010 also accords parents a crucial role as influencers of their children’s interest.

Pedagogical Issues

Pedagogical issues such as teachers, curriculum, and types of classrooms can have either positive or negative effects on the likelihood that students enroll in STEM courses, receive high grades, or choose STEM careers. Beilock, et al. 2010 illustrates that female teachers who are highly math anxious negatively affect elementary school-aged girls’, but not boys’, math grades. Cheryan, et al. 2011 illustrates how classrooms containing paraphernalia stereotypically associated with men negatively affect women’s Sense of Belonging or Identification with a STEM Field. The review Knight, et al. 2011 shows that women do well when their teaching appeals to students’ communal values. A study of physics students in Miyake, et al. 2010 shows how a simple class exercise on values affirmation can improve female students’ physics grades. Pahlke, et al. 2014 uses a meta-analytic approach to examine whether single-sex education is superior to coeducational programs for math and science performance. Roach 2011 finds that female students are highly influenced by the quality and nature of their first information technology course and instructor. Watt, et al. 2017 examines how math teachers create a mastery or performance focus in their classrooms. Mastery focus relates to students’ development of math engagement.

  • Beilock, Sian L., Elizabeth A. Gunderson, Gerardo Ramirez, and Susan C. Levine. 2010. Female teachers’ math anxiety affects girls’ math achievement. Proceedings of the National Academy of Sciences of the United States of America 107.5: 1860–1863.

    DOI: 10.1073/pnas.0910967107Save Citation »Export Citation » Share Citation »

    American female math teachers’ math anxiety negatively predicts their first- and second-grade female, but not male, students’ math achievement. This is mediated by girls’ stereotypes that boys excel at math. Boys’ stereotypes about math and their math performance are not affected by their female teachers’ math anxiety.

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  • Cheryan, Sapna, Andrew N. Meltzoff, and Saenam Kim. 2011. Classrooms matter: The design of virtual classrooms influences gender disparities in computer science classes. Computers & Education 57:1825–1835.

    DOI: 10.1016/j.compedu.2011.02.004Save Citation »Export Citation » Share Citation »

    When given a choice, American women, but not men, prefer a non-stereotypical to a stereotypical computer science classroom with video games and science fiction paraphernalia. They experience a greater Sense of Belonging or Identification with a STEM Field in the non-stereotypical classroom and expect to perform better in that environment. Thus, the physical environment can have a demonstrable effect on women’s sense of belonging.

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  • Knight, David B., Ellen F. Mappen, and Stephanie L. Knight. 2011. A review of the literature on increasing the representation of women undergraduates in STEM disciplines through civic engagement pedagogies. Science Education and Civic Engagement 3.1: 36–47.

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    This article reviews studies that indicate that women undergraduates thrive when science is taught in an engaging way that focuses on real-world applications, when there are community service components, and when values like the social good of the discipline are emphasized.

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  • Miyake, Akira, Lauren E. Kost-Smith, Noah D. Finkelstein, Steven J. Pollock, Geoffrey L. Cohen, and Tiffany A. Ito. 2010. Reducing the gender achievement gap in college science: A classroom study of values affirmation. Science 330.6008: 1234.

    DOI: 10.1126/science.1195996Save Citation »Export Citation » Share Citation »

    This study examines the effect of a two-time (beginning and end of semester) values affirmation intervention (writing about values important to them) on the grades of physics students. The female students in the values affirmation condition do almost as well as the male students, whereas there is a large gender gap favoring men in the control condition. This simple in-class, but non-physics-related, intervention is particularly helpful to women who most strongly endorse gender stereotypes.

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  • Pahlke, Erin, Janet S. Hyde, and Carlie M. Allison. 2014. The effects of single-sex compared with coeducational schooling on students’ performance and attitudes: A meta-analysis. Psychological Bulletin 140.4: 1042–1072.

    DOI: 10.1037/a0035740Save Citation »Export Citation » Share Citation »

    This meta-analysis of 1.6 million students from twenty-one countries (see Cross-Cultural Findings) finds, contrary to popular belief, that single-sex education does not lead to greater math or science performance in either girls or boys.

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  • Roach, David. 2011. Gender within the IT major: A retrospective study of factors that lead students to select an IT major. International Journal of Business Information Systems 7:149–165.

    DOI: 10.1504/IJBIS.2011.038509Save Citation »Export Citation » Share Citation »

    American students majoring in information technology at three public and one private university participated. Female majors attach greater importance to social influences in their choice of major than male students. Female majors indicate lower computer Self-Efficacy and higher computer anxiety. All students, but particularly females, are strongly affected by their first information technology course and instructor.

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  • Watt, Helen M. G., Colin Carmichael, and Rosemary Callingham. 2017. Students’ engagement profiles in mathematics according to learning environment dimensions: Developing an evidence base for best practice in mathematics education. School Psychology International 38:166–183.

    DOI: 10.1177/0143034316688373Save Citation »Export Citation » Share Citation »

    The math engagement of Australian students in grades three through nine is examined in this study. Classrooms with a mastery (rather than performance) focus with enthusiastic teachers are most likely to have students with high math engagement, indicating that teachers and the classroom environment can have a positive impact on students.

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General Workplace Issues

The leakage of women out of the STEM pipeline continues until after completion of a degree. Retention of female STEM employees has proved problematic for a variety of reasons discussed in the articles in this section. Fouad and Singh 2011, a report on female graduates from engineering programs, finds that women leave engineering occupations mostly for reasons related to negative employment conditions. Glass, et al. 2013, a study examining a longitudinal data set, compares the attrition rate of female STEM workers to that of female professional workers and finds it to be much higher, especially for those with children. Hunt 2012 finds that the exit rate for female compared to male engineers is greater in comparison to the natural sciences. Singh, et al. 2014 finds that a major factor that differentiates female engineers who leave the field from those who stay is negative behavior directed toward them. Stewart, et al. 2016 finds, perhaps unsurprisingly, that departments that lack an interest in gender parity compared to departments that value diversity do not greatly improve the representation of women in the department.

  • Fouad, Nadya, and Romila Singh. 2011. Stemming the tide: Why women leave engineering. Milwaukee: Univ. of Wisconsin–Milwaukee.

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    This study of female alumnae from engineering programs finds that many female engineers leave the field due to a negative workplace climate, lack of work support, poor advancement opportunities, and inflexible work schedules. Fifteen percent of engineering graduates never even entered the field due to lack of interest and lack of work flexibility. They did enter the labor force using their skills in a variety of different occupations, especially in management.

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  • Glass, Jennifer L., Sharon Sassler, Yael Levitte, and Katherine M. Michelmore. 2013. What’s so special about STEM? A comparison of women’s retention in STEM and professional occupations. Social Forces 92.2: 723–756.

    DOI: 10.1093/sf/sot092Save Citation »Export Citation » Share Citation »

    This study targets women who participated in the National Longitudinal Survey of Youth and eventually obtained a college degree. Women in STEM leave their field (but not the workforce) in greater numbers (>50 percent) than professional women (>20 percent). Higher educational level, children, and marriage (except when it is to another STEM-employed individual) increase the likelihood that STEM women leave the field more than for professional women.

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  • Hunt, Jennifer. 2012. Why do women leave science and engineering? Working Paper 15853. Cambridge, MA: National Bureau of Economic Research.

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    This study uses National Surveys of College Graduates (NSCG) to compare women who leave science to those leaving engineering. Female engineers have a higher attrition rate than male engineers. This gender differential is higher than for science fields. Women leave science due to having children to a greater extent than they leave engineering. The greater the percentage of males in an occupation, the greater the likelihood of excess female exits.

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  • Singh, Romila, Nadya A. Fouad, Mary Fitzpatrick, Catia Figueriredo, and Wen H. Chang. 2014. To stay or to leave: Factors that differentiate women currently working in engineering from those who left the profession. In Women in STEM careers: International perspectives on increasing workforce participation, advancement and leadership. Edited by Diana Bilimoria and Linley Lord, 39–56. Cheltenham, UK, and Northampton, MA: Edward Elgar.

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    This study set out to determine what differentiates women engineers who stay in engineering from those who leave the field. There are no differences in Self-Efficacy; however, women who leave engineering are much more likely to report having encountered negative, condescending workplace environments.

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  • Stewart, Abigail J., Janet E. Malley, and Keith A. Herzog. 2016. Increasing the representation of women faculty in STEM departments: What makes a difference? Journal of Women and Minorities in Science and Engineering 22:23–47.

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    This qualitative study uses interviews with senior faculty at an R1 institution to determine what factors promote or hinder increasing diversity in a department. Departments that recognized the problem and committed to making changes were much more successful in increasing gender parity than departments that did not prioritize diversity. This points out the importance of commitment to diversity to succeed in increasing gender parity.

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Work-Life Balance

Work-life balance issues contribute both to a lack of attraction to STEM for those whose life values include having children and to attrition from STEM for those who have become mothers. Goulden, et al. 2009 examines what happens to STEM graduate students and postdocs’ interest in academic careers at R1 universities, finding that women’s desire for children causes them to lower their ambitions. Sassler, et al. 2017 finds that work-life balance issues can explain why more female engineers leave the field, but this explanation does not hold for female computer scientists. Gender persistence is also affected by race and ethnicity (see Intersectionality). Sax, et al. 2016, like Sassler, et al. 2017, finds that engineering is less attractive to women who have strong family goals. Shauman 2017 examines outcomes for women with doctorates in STEM fields and finds that the presence of young children decreases the likelihood of employment in STEM fields. Williams and Ceci 2012, authored by well-known authorities in the field, blames the leakage of female STEM faculty on their desire to have children. Xu 2016 delineates the financial difficulties and work-life balance issues women, compared to men who want to pursue graduate study in STEM, face.

  • Goulden, Marc, Karie Frash, and Mary A. Mason. 2009. Staying competitive: Patching America’s leaky pipeline in the sciences. Berkeley: Univ. of California, Berkeley Center on Health, Economic & Family Security.

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    The authors of this report find that family formation factors, especially motherhood and to a lesser extent marriage, negatively affect the STEM pipeline. Female graduate students and postdocs are dissuaded from continuing into R1 university research careers due to the unrelenting demands of the tenure process that conflict with childrearing. R1 research faculty positions are considered the least family friendly career choice.

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  • Sassler, Sharon, Katherine Michelmore, and Kristin Smith. 2017. A tale of two majors: Explaining the gender gap in STEM employment among computer science and engineering degree holders. Social Sciences 6:69.

    DOI: 10.3390/socsci6030069Save Citation »Export Citation » Share Citation »

    Data from the Scientists and Engineers Statistical Data System (SESTAT) data set indicate that women with degrees in computer science or engineering are much less likely to be working in their respective disciplines than their male counterparts. However, the disparity depends on race and ethnicity (see Intersectionality). For engineering but not computer science, some of the gender gap seems to be due to work-life balance issues.

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  • Sax, Linda J., M. Allison Kanny, Jerry A. Jacobs, Hannah Whang, Dayna S. Weintraub, and Jerry A. Hroch. 2016. Understanding the changing dynamics of the gender gap in undergraduate engineering majors: 1971–2011. Research in Higher Education 57.5: 570–600.

    DOI: 10.1007/s11162-015-9396-5Save Citation »Export Citation » Share Citation »

    The Cooperative Institutional Research Program (CIRP) data set of entering college students reveals that women with family goals are much less likely to pursue an engineering major than their male counterparts are. Intersectionality issues are also relevant in that African American women have become more underrepresented over time.

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  • Shauman, Kimberly A. 2017. Gender differences in the early employment outcomes of STEM doctorates. Social Sciences 6:129–154.

    DOI: 10.3390/socsci6010024Save Citation »Export Citation » Share Citation »

    This study of individuals with doctorates in STEM finds that women with young children are much less likely to be in the labor force than their male counterparts. There is also a gender pay gap (see Bias and Discrimination) in STEM occupations.

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  • Williams, Wendy J., and Stephen J. Ceci. 2012. When scientists choose motherhood. American Scientist 100.2: 138–145.

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    The authors argue that none of the traditional explanations of women’s underrepresentation such as lower quantitative abilities, stereotypes, or Bias and Discrimination can account for the phenomenon. Instead they believe that fertility choices are the major reason explaining women’s underrepresentation in STEM in academic careers at research universities. Women who have children and bear the majority of childcare responsibilities are at a serious disadvantage in the tenure system compared to men.

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  • Xu, Yonghong J. 2016. Advance to graduate school in the US: How the path is different for women in STEM. International Journal of Gender, Science and Technology 8:420–441.

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    This study employs a large US data set of college graduates and follows them longitudinally to determine who eventually enrolls in graduate school. For women in STEM compared to women in non-STEM fields, marriage was a deterrent to enrolling in a PhD program. This was not the case for men. Women compared to men in STEM fields were less likely to receive assistantships in graduate programs or tuition assistance from their employers (see Bias and Discrimination), adding a large financial burden.

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Bias and Discrimination

There is a lot of research suggesting that subtle and not-so-subtle biases and outright discrimination have highly negative consequences for females’ interests, Sense of Belonging or Identification with a STEM Field, Self-Efficacy, and satisfaction in a major or occupation. Even the expectation that one will be the victim of bias and discrimination can have a deterring effect. The articles in this section illustrate the various kinds of biases and discrimination and their effects on females of various age groups. Blair-Loy, et al. 2017 uncovers evidence that female job applicants for academic engineering positions face more interruptions and audience questions during job talks than male applicants do. Brown and Leaper 2010 cautions how destructive sexist comments by others can be, deterring adolescent girls from math and science. Ceci and Williams 2011 claims that the effect of bias and discrimination is vastly overrated and that other factors are more prominent causes of female underrepresentation in STEM. Dutt, et al. 2016 suggests that strong negative biases exist in letters of recommendations written for female postdocs in the geosciences. Griffith and Dasgupta 2018 shows that women in STEM departments with few women are less satisfied due to negative interpersonal experiences and inequities. LaCosse, et al. 2016 finds that exposure to negative treatment of a female decreases female STEM students’ Sense of Belonging or Identification with a STEM Field and intention to enter a STEM career. Moss-Racusin, et al. 2012 is a disturbing reminder of how deep-seated biases and discrimination still exist even among female scientists, as they discriminate against a female applicant. Moss-Racusin, et al. 2015 examines online comments to the 2012 study by the author, finding gender differences in the negativity of comments. Men were much more likely than women to deny the existence of biases, attributing the status quo to gender differences in science ability (see Quantitative, Spatial, and Verbal Abilities).


Women are not the only group that is underrepresented in STEM. Many racial and ethnic groups are underrepresented as well. And it is important to recognize that females of color may face somewhat different issues and challenges than white women or men of color. Gender also intersects with sexual orientation, first-generation student status, and socioeconomic status. This section presents research on different kinds of intersectionality. Blaney and Stout 2017 shines a light on first-generation female college students’ low Self-Efficacy and Sense of Belonging or Identification with a STEM Field. Litzler and Lorah 2018 illustrates the importance of investigating intersectionality in this sample of engineering majors at fifteen universities. Aspirations for postgraduate education differed significantly for white women compared to women of color. Ma and Liu 2017 also finds that white women, women of color, and men of color differ consistently in participation rates and retention rates in STEM majors. Nix and Perez-Felkner 2019 provides evidence of the intersectionality between race and gender in the effect of math difficulty orientation on pursuit of a STEM major. Sax, et al. 2017 is a qualitative study with interviews of fifteen computer science chairs who have committed to increasing gender and racial diversity in their departments. Sax, et al. 2018 finds that Sense of Belonging or Identification with a STEM Field is affected by gender and race. Pedagogical Issues are also addressed.

Best Practices for Intervention Strategies

Given the many Causes of Female Underrepresentation in STEM, it should come as no big surprise that researchers, companies, and those charged with teaching from kindergarten all the way through graduate school have attempted to implement intervention strategies. Some were tried based on mere hunches and had no evaluation plan. The citations in this section only represent interventions that have been thoroughly evaluated and determined to be best practices. Researchers and those working in the trenches alike can find a variety of evidence-based intervention strategies here that suit their needs. Bilimoria and Liang 2014 summarizes best practices aimed at increasing female representation in STEM. Brown, et al. 2015 provides evidence of the efficacy of a communal value utility intervention. Dasgupta, et al. 2015 demonstrates the effectiveness of an intervention, placing female engineering students in majority female groups. Liben and Coyle 2014 summarizes intervention approaches and also provides suggestions for improving the evaluation of intervention efficacy. Min, et al. 2011 is a statistical analysis of a very large data set revealing factors related to attrition. Based on the results, the authors make intervention recommendations, emphasizing the critical importance of early intervention. Pietri, et al. 2019 reveals that interventions to increase awareness of bias can have adverse consequences by lowering Sense of Belonging or Identification with a STEM Field. Valla and Williams 2012 is a useful review of successful intervention programs in STEM. Van den Hurk, et al. 2019 finds that only 9 out of 119 studies on interventions allow for strong conclusions due to methodological shortcomings in the majority of studies.

  • Bilimoria, Diana, and Xiangfen Liang. 2014. Effective practices to increase women’s participation, advancement and leadership in US academic STEM. In Women in STEM careers: International perspectives on increasing workforce participation, advancement and leadership. Edited by Diana Bilimoria and Linley Lord, 146–165. Cheltenham, UK, and Northampton, MA: Edward Elgar.

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    This chapter summarizes strategies and practices for increasing female representation in STEM. The authors also summarize the efforts at institutional transformation funded by the National Science Foundation’s ADVANCE program.

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  • Brown, Elizabeth R., Jessi L. Smith, Dustin B. Thoman, Jill M. Allen, and Gregg Muragishi. 2015. From bench to bedside: A communal utility value intervention to enhance students’ biomedical science motivation. Journal of Educational Psychology 107.4: 1116–1135.

    DOI: 10.1037/edu0000033Save Citation »Export Citation » Share Citation »

    The authors present four studies assessing the efficacy of a communal utility value (see Gender Roles and Values) intervention for the biomedical field (i.e., emphasizing working with and helping others). Undergraduates and research assistants in the biomedical field showed greater motivation in the communal utility value intervention condition compared to a control condition.

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  • Dasgupta, Nilanjana, Melissa M. Scircle, and Matthew Hunsinger. 2015. Female peers in small work groups enhance women’s motivation, verbal participation, and career aspirations in engineering. Proceedings of the National Academy of Science 112:4988–4993.

    DOI: 10.1073/pnas.1422822112Save Citation »Export Citation » Share Citation »

    This study of female engineering majors used an experimental design to evaluate the effectiveness of an intervention that placed women in groups of varying percentages of female peers: 25 percent, 50 percent, or 75 percent. Female students participated more in the majority female groups, and female first-year students also reported less anxiety than when placed in majority male groups.

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  • Liben, Lynn S., and Emily F. Coyle. 2014. Developmental interventions to address the STEM gender gap: Exploring intended and unintended consequences. In Advances in child development and behavior. Vol. 47. Edited by Lynn S. Liben and Rebecca S. Bigler, 77–115. Amsterdam and Boston: Elsevier.

    DOI: 10.1016/bs.acdb.2014.06.001Save Citation »Export Citation » Share Citation »

    This book chapter categorizes different intervention strategies to increase female representation in STEM, their advantages and disadvantages, and ends with recommendations for implementing successful interventions, as well as suggestions for improving research on the efficacy of various interventions.

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  • Min, Youngkyoung, Gouili Zhang, Russell A. Long, Timothy J. Anderson, and Matthew W. Ohland. 2011. Nonparametric survival analysis of the loss rate of undergraduate engineering students. Journal of Engineering Education 100:349–373.

    DOI: 10.1002/j.2168-9830.2011.tb00017.xSave Citation »Export Citation » Share Citation »

    In this study of data from over one hundred thousand engineering majors from nine public universities in the United States, low math SAT score (see Quantitative, Spatial, and Verbal Abilities), being female, and being white are negatively related to attrition. Most majors drop out early on, making early intervention programs important for retention.

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  • Pietri, Evava S., Erin P. Hennes, John F. Dovidio, et al. 2019. Addressing unintended consequences of gender diversity interventions on women’s sense of belonging in STEM. Sex Roles 80:527–547.

    DOI: 10.1007/s11199-018-0952-2Save Citation »Export Citation » Share Citation »

    This study examines the outcome of interventions aimed at increasing awareness of gender inequities. The authors discover that while such interventions increase bias literacy, a side effect is social identity threat, which lowers Sense of Belonging or Identification with a STEM Field. However, presenting positive female scientist Role Models mitigated this effect.

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  • Valla, Jeffrey M., and Wendy M. Williams. 2012. Increasing achievement and higher-education representation of under-represented groups in science, technology, engineering, and mathematics fields: A review of current K–12 intervention programs. Journal of Women and Minorities in Science and Engineering 18.1: 21–53.

    DOI: 10.1615/JWomenMinorScienEng.2012002908Save Citation »Export Citation » Share Citation »

    This paper reviews intervention strategies aimed at increasing the representation of girls and underrepresented minorities in STEM. It provides information on which intervention strategies are most likely to be successful. The paper laments the paucity of high-quality program evaluations. This paper is of great value to those interested in an empirical evaluation of intervention strategies.

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  • van den Hurk, Anniek, Martina Meelissen, and Annemarie van Langen. 2019. Interventions in education to prevent STEM pipeline leakage. International Journal of Science Education 41.2: 150–164.

    DOI: 10.1080/09500693.2018.1540897Save Citation »Export Citation » Share Citation »

    This meta-review of 119 studies on interventions to increase female representation in STEM finds that the great majority of studies are not methodologically rigorous, making drawing conclusions difficult. Only nine studies were deemed to be designed in a way that allows for causal conclusions to be drawn. Interventions aimed at increasing experience and knowledge, motivation, and Sense of Belonging or Identification with a STEM Field appear to be most promising.

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Cross-Cultural Findings

Much of the research on causes of female underrepresentation in STEM and interventions is conducted in the United States. Of course, research on women in STEM is being conducted in other parts of the world and this section will present some of that research. Brown, et al. 2018 demonstrates that Asians’ stronger belief in STEM’s ability to satisfy communal values (see Gender Roles and Values) explains their greater positivity toward STEM. Nosek, et al. 2009 is an impressive analysis of three hundred thousand online participants and finds that implicit stereotypes predict a nation’s gender difference in math and science scores (see Quantitative, Spatial, and Verbal Abilities). Shinohara and Fujimoto 2016 attempts to understand why female Japanese engineers are less likely to stay in their occupation than their male counterparts. However, none of the variables they assessed could successfully predict this gender difference in persistence. Van den Brink and Stobbe 2009 illustrates the contradictory behavior of female Dutch geosciences students who deny the presence of gender Bias and Discrimination while simultaneously discounting the seriousness of female peers who display a more feminine gender role. Varma 2011 presents an interesting analysis of female Indian computer science students who, unlike most American women, find that the field fits their values because it allows financial independence and the ability to support their families.

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