World Library  
Flag as Inappropriate
Email this Article

Women in STEM fields

Many scholars and policy makers have noted that women have historically been underrepresented in the fields of science, technology, engineering, and math (STEM fields). Scholars are exploring the various reasons for the existence of this gender gap in STEM fields and are also seeking ways to increase diversity within STEM fields.[1][2][3] [4][5]


  • Gender imbalance in STEM fields 1
    • Representation of women 1.1
    • Men's and women's earnings 1.2
  • Recent advances in technology 2
  • Explanations for low representation of women 3
    • Lack of female interest 3.1
    • Biological explanations 3.2
    • Structural explanations 3.3
      • Effects of age 3.3.1
      • Territorial segregation 3.3.2
      • Leaky pipeline 3.3.3
        • Reasons for the leakage
      • Gender and work 3.3.4
    • Social-psychological explanations 3.4
      • Stereotypes and heuristics 3.4.1
      • Discrimination 3.4.2
        • Implicit discrimination
      • Stereotype threat 3.4.3
      • Black Sheep effect 3.4.4
      • Queen Bee effect 3.4.5
  • Strategies for increasing representation of women 4
    • Social-psychological interventions 4.1
      • Role models 4.1.1
      • Self-affirmation 4.1.2
  • See also 5
  • References 6
    • Notes 6.1
    • Further reading 6.2

Gender imbalance in STEM fields

Studies suggest that many factors contribute to the attitudes and achievement of young women in mathematics and science including encouragement from parents, interaction with mathematics and science teachers, curriculum content, hands-on laboratory experiences, high school achievement in mathematics and science, and resources available at home.[6] In the United States, research findings are mixed concerning the grade in which boys’ and girls’ attitudes about mathematics and science diverge. Analyzing several nationally representative longitudinal studies, one researcher found few differences in girls' and boys' attitudes towards science in the early secondary school years.[6]

Students’ aspirations to pursue careers in mathematics and science influence both the courses they choose to take in those areas as well as the level of effort put forth in these courses. A report by the U.S. Department of Education found that the gap in the career aspirations of boys and girls in science or engineering fields exists as early as eighth grade. Among the eighth grade class of 1988, boys were more than twice as likely as girls to aspire to be scientists or engineers (9 and 3 percent, respectively), although girls were more likely than boys to aspire to professional, business, or managerial occupations (38 and 20 percent respectively). While male and female high school seniors are equally likely to expect a career in science or mathematics, male seniors are much more likely than their female counterparts to expect a career in engineering.[7]

A 1996 study of college freshmen by the Higher Education Research Institute shows that men and women differ greatly in their intended fields of study. Of first-time college freshmen in 1996, 20 percent of men and 4 percent of women planned to major in computer science and engineering, while similar percentages of men and women planned to major in biology or physical sciences. The differences in the intended majors between male and female first-time freshmen directly relate to the differences in the fields in which men and women earn their degree. At the post-secondary level, women are less likely than men to earn a degree in mathematics, physical sciences, and computer sciences and engineering. The exception to this gender imbalance is in the life sciences.[8]

Representation of women

According to the Census Bureau’s 2009 American Community Survey, women comprise 48 percent of the U.S. workforce but just 24 percent of workers in STEM fields. Half as many women are working in STEM jobs as would be expected if gender representation in STEM professions mirrored the overall workforce. This underrepresentation has remained fairly consistent over the past decade, even as women’s share of the college-educated workforce has increased. Among STEM jobs, women’s representation has varied over time. While the percentage of women has declined in computer and math jobs, their percentage has risen in other occupations. In 2009, women comprised 27 percent of the computer and math workforce (the largest of the four STEM components), a drop of 3 percentage points since 2000. Engineers are the second largest STEM occupational group, but only about one out of every seven engineers is female.[9]

Men are much more likely than women to have a STEM career regardless of educational attainment. Women in STEM fields earn considerably less than men, even after controlling for a wide set of characteristics such as education and age. On average, men in STEM jobs earn $36.34 per hour while women in STEM jobs earn $31.11 per hour.[9]

Percentage distribution of probable fields of study among first-time college freshmen, by sex (fall 1996)
Probable major field of study Men Women
Arts and humanities 9.4 10.5
Biology 6.5 7.4
Business 18.1 13.8
Education 6.3 14.2
Engineering 15.2 2.6
Physical Sciences 2.7 2.0
Professional 9.8 20.2
Social sciences 6.1 11.7
Technical 3.7 1.4
Computer Sciences 4.3 1.2
Undecided 7.4 8.8
Other 10.5 6.5

The physical sciences include fields such as astronomy, chemistry, earth science, mathematics, and physics. The professional category includes fields such as architecture and health technologies. Women are more likely to hold jobs that are less prestigious and have lower wages than those held by men. While many prestigious fields such as engineering, chemistry, physics, and computer science are dominated by men, women are the majority in the social sciences and life sciences.

Men's and women's earnings

Although female college graduates shared in the earnings growth of all college graduates in the 1980s, they earned less on average than male college graduates. Some of the differences in salary are related to the differences in occupations entered by women and men. Among recent science and engineering bachelor’s degree recipients, women were less likely than men to be employed in science and engineering occupations. There remains an earnings gap between men and women in comparable scientific positions. Among more experienced scientists and engineers, the gender gap in salaries is larger than for recent graduates.[10] Salaries are highest in mathematics, computer science, and engineering, fields in which women are not highly represented. Among recent college graduates who majored in the natural sciences, women earned less than men did. However, there was no measureable difference between the starting salaries of men and women who majored in computer sciences and engineering.[11] Even though women make up about half of the labor market, they are underrepresented in jobs in scientific fields and tend to be paid less than men.[11]

Recent advances in technology

Abbiss states that "the ubiquity of computers in everyday life has seen the breaking down of gender distinctions in preferences for and the use of different applications, particularly in the use of the internet and email." [12] Both genders have acquired skills, competencies and confidence in using a variety of technological, mobile and application tools for personal, educational and professional use at high school level, but the gap still remains when it comes to enrollment of girls in computer science classes, which declines from grades 10 to 12 and to post-secondary level program options.

Explanations for low representation of women

Many people have attempted to make sense of the relatively low numbers of women in STEM fields, leading to the rise of a number of biological, structural, and social-psychological explanations.[13][14][15]

Lack of female interest

A meta-analysis concluded that men prefer working with things and women prefer working with people. When interests were classified by RIASEC type (Realistic, Investigative, Artistic, Social, Enterprising, Conventional), Men showed stronger Realistic and Investigative interests, and women showed stronger Artistic, Social, and Conventional interests. Sex differences favoring men were also found for more specific measures of engineering, science, and mathematics interests.[16]

In their 3-year interview study, Seymour and Hewitt (1997) found that perceptions that non-STEM academic majors offered better education options and better matched their interests was the most common (46%) reason provided by female students for switching majors from STEM areas to non-STEM areas. The second most frequently cited reason given for switching to non-STEM areas was a reported loss of interest in the women’s chosen STEM majors. Additionally, 38% of female students who remained in STEM majors expressed concerns that there were other academic areas that might be a better fit for their interests. Preston’s (2004) survey of 1,688 individuals who had left sciences also showed that 30% of the women endorsed “other fields more interesting” as their reason for leaving. [16]

Biological explanations

Biological explanations tend to focus on gender differences in areas such as spatial skills. Spatial sense is considered a critical component to success in engineering and other branches of science, and men are sometimes found to outscore women in tests of spatial ability. However, studies have shown that spatial skills can be quickly developed through a small amount of training.[13] If girls are brought up in an environment where they are encouraged to use and develop their spatial skills, this gender gap in spatial sense will likely narrow.

Structural explanations

Margaret Rossiter offers two possible structural explanations for the low number of women in STEM fields: hierarchical segregation and territorial segregation. She describes "hierarchical segregation" as a decrease in the number of women as one "moves up the ladder of power and prestige." [14] There tends to be a lack of gender diversity in the upper echelons of many occupations where the highest positions are typically held by men. Rossiter also puts forth the concept of "territorial segregation."[17] This is the idea that women "cluster" in certain fields of study. For example, "women are more likely to teach and do research in the humanities and social sciences than in the natural sciences and engineering." [17] The majority of women in college tend to choose majors such as psychology, education, English, performing arts, and nursing.[18] One reason that women tend to form these "clusters" is because of a lack of support in STEM fields where they are outnumbered by men.

Effects of age

Among all workers from ages 16 to 34, women receive a little over 90 percent of men's earnings.[19] However, this ratio drops for women ages 35 to 64, who earn 80 percent or less of men's wages. While this pattern may signal workplace changes among younger cohorts, women do tend to fall behind men as their careers progress. It thus remains to be seen whether this is an age or generational phenomenon.[19]

Territorial segregation

The term territorial segregation (or occupational segregation) refers to how the STEM fields have traditionally been dominated by men, making it difficult for women to enter these occupations. Even within the STEM fields, women tend to be concentrated in the "soft" sciences: life, behavioral, and social sciences. Jobs in these fields often have lower salaries and are accorded less prestige. Only 9 percent of U.S. physicists are women.[20] Over the last four decades, the relative proportion of women Ph.D. recipients has increased more than 100-fold in engineering (from 0.2 percent in 1966 to 22.5 percent in 2006), 12-fold in the geosciences (from 3 percent to 36.6 percent), and 8-fold in the physical sciences (from 3.7 percent to 27.9 percent).[21] Even though many more women are now earning Ph.D.s in STEM fields, they still hold far fewer full professorships than do men. For example, although women held 62 percent of the Ph.D.s in psychology in 1994, only 19 percent of tenured positions were given to women.[22]

Leaky pipeline

The metaphor of the leaky pipeline has been used to describe how women drop out of STEM fields at all stages of their careers. Statistician Berry Vetter claims that 280 of any 2,000 9th grade boys and 210 of any 2,000 9th grade girls will have taken enough math to pursue a technical career. Of these, 143 of the men and 45 of the women will major in science in college. Forty-four of these men and 20 of these women will complete their degrees in science. Five of these men and one of these women will go on to obtain PhDs in science.[22]

According to A. N. Pell, the pipeline has several major leaks spanning the time from elementary school to retirement.[23] One of the most important periods is adolescence. Teachers often give boys more opportunity to figure out the solution to a problem by themselves while telling the girls to follow the rules.[24] Teachers are also more likely to accept questions from boys while telling girls to wait for their turns.[23] This is partly due to gender expectations that boys will be active but that girls should be quiet and obedient.[25] Girls also have less laboratory experience because they are given fewer opportunities to gain such experience than are boys.[23] In middle and high school, courses dealing with mechanics and computers as well as the more rigorous science and mathematics courses are mainly taken by male students and also tend to be taught by male teachers.[26] Girls' lack of opportunities to practice their math and science skills can lead to a loss of self-esteem in their math and science abilities. Such low self-esteem may prevent women and girls from entering science and math fields. Many girls will end up not taking enough math classes to qualify for three-quarters of majors in college.[23]

Reasons for the leakage

This leakage may be due to discrimination, both overt and covert, faced by women in STEM fields. Schiebinger claims that women are twice as likely to leave their jobs than men in science and engineering.[27] The reasons behind these decisions to leave include not being invited to professional meetings, the use of sexually discriminating standards against women, the struggle to balance family and work, the perceived need to hide pregnancies, and inflexible working conditions. The New England Journal of Medicine suggests that three-quarters of women students and residents are harassed at least once during their medical training.[27] In engineering and science education, women make up almost 50 percent of non-tenure track lecturer and instructor jobs, but only 10 percent of tenured or tenure-track professors. In addition, the number of female department chairs in medical schools has not changed for the past 20 years [23] this lack of women at the highest levels of a profession may be due to the so-called "glass ceiling." The glass ceiling is "the unseen, yet unbreachable barrier that keeps minorities and women from rising to the upper rungs of the corporate ladder, regardless of their qualifications or achievements."[28] Moreover, women who do make it to these high levels may face the difficulties associated with holding a token status. Because these highly ranked women are such an anomaly, they may lack support from colleagues and may face antagonism from peers and supervisors.[25]

Gender and work

Both men and women who work in nontraditional occupations may encounter discrimination, but the forms and consequences of this discrimination are very different. Although women entering traditionally male professions face negative stereotypes suggesting that they are not real women, these stereotypes do not seem to deter women to the same degree that similar stereotypes may deter men from pursuing nontraditional professions. There is ample historical evidence that women flock to male-identified occupations once opportunities are available.[29] On the other hand, examples of occupations changing from predominantly female to predominantly male are very rare in our history. The few existing cases—such as medicine—suggest that redefinition of the occupations as appropriately masculine is necessary before men will consider joining them.[30]

Although men in female-dominated occupations may contend with negative stereotypes about their masculinity, they may also experience certain benefits. Women, particularly those in male-dominated occupations, tend to hit a glass ceiling; while men in female-dominated occupations may hit a "glass escalator." [31] While the glass ceiling can make it difficult for women and minorities to reach the top of an occupation, the glass escalator allows men to excel in a profession that is female dominated. Since STEM fields tend to be male-dominated, it is likely that women will hit the glass ceiling.[32]

Social-psychological explanations

Psychologists have long studied issues related to discrimination, motivation, and performance. In more recent years, social psychologists have examined how certain social-psychological phenomena may apply directly to the STEM fields, and may explain the relative lack of gender diversity within these fields.

Stereotypes and heuristics

A heuristic is a cognitive shortcut that people use to make decisions.[33] Stereotypes, or commonly held beliefs about certain groups, are often employed as heuristics when making decisions in social situations. Stereotypes about what someone in a STEM field should look and act like may cause established members of these fields to overlook individuals who may be highly competent but may not fit people's idea of how a person in a STEM field should appear.[34] The stereotypical scientist or individual in another STEM profession is usually thought to be male.[35] This indicates that women in STEM fields may not fit individuals' conceptualization of what a scientist, engineer, or mathematician "should" look like and may thus be overlooked or penalized. The role congruity theory of prejudice states that perceived incongruity between gender stereotypes and the stereotypes associated with a particular role or occupation can result in negative evaluations.[36][37][38] In addition, negative stereotypes about women's quantitative abilities may lead people to devalue their work or discourage these women from continuing in STEM fields.[39]

Individuals of a particular gender are often perceived to be better suited to particular careers or areas of study than those of the other gender.[40][41] A study by Gaucher et al.[40] found that job advertisements for male-dominated careers tended to use more agentic words (or words denoting agency, such as "leader" and "goal-oriented") associated with male stereotypes. If individuals are given information about a prospective student’s gender, they may infer that he or she possesses traits consistent with stereotypes for that gender.[42] Social role theory states that men are expected to display agentic qualities and women to display communal qualities.[43] These expectations can influence hiring decisions.[44] Madera et al.[44] found that women tended to be described in more communal terms and men in more agentic terms in letters of recommendation. These researchers also found that communal characteristics were negatively related to hiring decisions in academia.[44]


Some researchers have demonstrated a general evaluative bias against women.[45] In an audit study in which they sent email requests to meet to professors in doctoral programs at the top 260 U.S. universities, researchers found evidence for discrimination against ethnic minorities and women relative to Caucasian men.[46] While it was impossible to determine whether any particular individual in this study was exhibiting discrimination, since each participant only viewed a request from one potential graduate student, the overall tendency to favor Caucasian men over all other groups indicates that discrimination is still very much an issue. In another study, science faculty were sent the materials of student who was applying for a lab manager position at their university.[47] The materials were the same for each participant, but each participant was randomly assigned either a male or a female name. The researchers found that faculty members rated the male candidate as both more competent and more hirable than the female candidate, despite the fact that the applications were identical except for the applicant's gender.[47] Again, it is impossible to say whether any of the individual faculty members were acting in a discriminatory fashion, but it is apparent that there is still a widespread bias against women in science fields.

Implicit discrimination

In highly competitive STEM fields, the support and encouragement of a mentor can make a lot of difference in women’s decisions of whether or not to continue pursuing a career in their discipline [48][49] This may be particularly true for younger individuals who may face many obstacles early on in their careers.[5] Since these younger individuals often look to those who are more established in their discipline for help and guidance, the responsiveness of these potential mentors and their willingness to help is incredibly important. Regardless of whether the majority of those in STEM fields outwardly agree with importance of increasing the representation of women in these areas, they may still hold biases—conscious or not—that affect how they interact with women looking to enter their particular discipline. If these biases manifest themselves in the differential treatment of women, particularly in respect to their willingness to assist young women in their respective field of study, this could impact the number of women choosing to enter into, and persist in, STEM careers.

Stereotype threat

Stereotype threat arises from the fear that one’s actions will confirm a negative stereotype about one’s in-group. This fear creates additional stress, consuming valuable cognitive resources and lowering task performance in the threatened domain.[50][51][52] Individuals are susceptible to stereotype threat whenever they are assessed in a domain for which there exists a negative stereotype about a group to which they belong. Stereotype threat has been shown to undermine the academic performance of women and girls in math and science to the extent that standard measures of academic achievement often underestimate the abilities of women and girls in these subjects.[15][39] Laboratory experiments have also found that individuals who identify strongly with a certain area (e.g. math) are more likely to have their performance in that area hampered by stereotype threat than those who identify less strongly with the area.[52] This means that even highly motivated students from negatively stereotyped minority groups are likely to be adversely affected by stereotype threat and, as a result, may come to disengage from the stereotyped domain.[52] Negative stereotypes about girls’ capabilities in mathematics and science drastically lower their performance in mathematics and science courses as well as their interest in pursuing a STEM career.[53] Studies have found that this gender difference in performance disappears if students are told that there are no gender differences on a particular mathematics test.[15] This indicates that the learning environment can greatly impact women's success in a course.

Black Sheep effect

The Black Sheep effect occurs when individuals are likely to evaluate members of their in-group more favorably than members of their out-group when those members are highly qualified.[54][55][56][57] However, when an individual’s in-group members have average or below average qualities, he or she is likely to evaluate them much lower than out-group members with equivalent qualifications.[54][55][56][57] This might suggest that women who are already established in STEM fields will be especially likely to help women who are earlier on in their career trajectories when these younger women display qualifications but will be less likely than their male colleagues to help younger women who do not display such qualifications.

Queen Bee effect

The Queen Bee effect is similar to the Black Sheep effect but applies only to women. It explains why higher-status women, particularly in male-dominated professions, may actually be far less likely to help other women than their male colleagues might be.[58][59] The study by Ellemers et al.[59] found that while doctoral students in a number of different disciplines did not exhibit any gender differences in work commitment or work satisfaction, faculty members at the same university believed that female students were less committed to their work than male students. What was particularly surprising was that these beliefs by faculty members were most strongly endorsed by female faculty members, rather than male faculty members.[59] One potential explanation for this finding is that individual mobility for a member of a negatively-stereotyped group is often accompanied by a social and psychological distancing of oneself from the group. This implies that women who are successful in male-dominated careers don't see their own success as proof that negative stereotypes about women's quantitative and analytical abilities are wrong but rather as proof that they personally are exceptions to the rule.[59] Thus, such women may actually play a role in perpetuating, rather than abolishing, these negative stereotypes.

Strategies for increasing representation of women

There are a multitude of factors that may explain the low representation of women in STEM careers. Anne-Marie Slaughter, the first woman to hold the position of Director of Policy Planning for the United States Department of State,[60] has recently suggested some strategies to the corporate and political environment to support women to fulfill to the best of their abilities the many roles and responsibilities that they undertake.[61] The academic and research environment for women may benefit by applying some of the suggestions she has made to help women excel, while maintaining a work-life balance.

Social-psychological interventions

A number of researchers have tested interventions to alleviate stereotype threat for women in situations where their math and science skills are being evaluated. The hope is that by combating stereotype threat, these interventions will boost women's performance, encouraging a greater number of them to persist in STEM careers.

One simple intervention is simply educating individuals about the existence of stereotype threat. Researchers found that women who were taught about stereotype threat and how it could negatively impact women's performance in math performed as well as men on a math test, even when stereotype threat was induced. These women also performed better than women who were not taught about stereotype threat before they took the math test.[62]

Role models

One of the proposed methods for alleviating stereotype threat is through introducing role models. One study found that women who took a math test that was administered by a female experimenter did not suffer a drop in performance when compared to women whose test was administered by a male experimenter.[63] Additionally, these researchers found that it was not the physical presence of the female experimenter but rather learning about her apparent competence in math that buffered participants against stereotype threat.[63] The findings of another study suggest that role models do not necessarily have to be individuals with authority or high status, but can also be drawn from peer groups. This study found that girls in same-gender groups performed better on a task that measured math skills than girls in mixed-gender groups.[64] This was due to the fact that girls in the same-gender groups had greater access to positive role models, in the form of their female classmates who excelled in math, than girls in mixed-gender groups.[64] Similarly, another experiment showed that making groups achievements salient helped buffer women against stereotype threat. Female participants who read about successful women, even though these successes were not directly related to performance in math, performed better on a subsequent math test than participants who read about successful corporations rather than successful women.[65] A study investigating the role of textbook images on science performance found that women demonstrated better comprehension of a passage from a chemistry lesson when the text was accompanied by a counter-stereotypic image (i.e., of a female scientist) than when the text was accompanied by a stereotypic image (i.e., of a male scientist).[35] Other scholars distinguish between the challenges of both recruitment and retention in increasing women's participation in STEM fields. These researchers suggest that although both female and male role models can be effective in recruiting women to STEM fields, female role models are more effective at promoting the retention of women in these fields.[66]


Researchers have investigated the usefulness of self-affirmation in alleviating stereotype threat. One study found that women who affirmed a personal value prior to experiencing stereotype threat performed as well on a math test as men and as women who did not experience stereotype threat.[67] A subsequent study found that a short writing exercise in which college students, who were enrolled in an introductory physics course, wrote about their most important values substantially decreased the gender performance gap and boosted women's grades.[68] Scholars believe that the effectiveness of such values-affirmation exercises is their ability to help individuals view themselves as complex individuals, rather than through the lens of a harmful stereotype. Supporting this hypothesis, another study found that women who were encouraged to draw self-concept maps with many nodes did not experience a performance decrease on a math test.[69] However, women who did not draw self-concept maps or only drew maps with a few nodes did perform significantly worse than men on the math test.[69] The effect of these maps with many nodes was to remind women of their "multiple roles and identities," that were unrelated to, and would thus not be harmed by, their performance on the math test.[69]

See also



  1. ^ Gürer, Denise and Camp, Tracy (2001). Investigating the Incredible Shrinking Pipeline for Women in Computer Science. Final Report – NSF Project 9812016.
  2. ^ Ceci, S.J.; Williams, W.M. (2010). "Sex Differences in Math-Intensive fields". Current Directions in Psychological Science 19 (5): 275–279.  
  3. ^ Ceci, S.J.; Williams, W.M.; Barnett, S.M. (2009). "Women's underrepresentation in science: Sociocultural and biological considerations". Psychological Bulletin 135 (2): 218–261.  
  4. ^ Diekman, A.B.; Brown, E.R.; Johnston, A.M.; Clark, E.K. (2010). "Seeking Congruity Between Goals and Roles". Psychological Science 21 (8): 1051–1057.  
  5. ^ a b Griffith, A.L. (2010). "Persistence of women and minorities in STEM field majors: Is it the school that matters?". Economics of Education Review 29 (6): 911–922.  
  6. ^ a b S.L. Hanson, "Lost Talent, Women in the Sciences", Philadelphia, PA.: Temple University Press, 1996.
  7. ^ U.S. Department of Education, National Center for Education Statistics, A Profile of the American Eighth-Grader: NELS:88 Student Descriptive Summary, Washington, D.C.: 1990, table 4.6
  8. ^ a b Higher Education Research Institute, Graduate School of Education and Information Studies, The American Freshman: National Norms for Fall 1996, University of California, Los Angeles, 1996.
  9. ^ a b Beede, David, Tiffany Julian, David Langdon, George McKittrick, Beethika Khan, and Mark Doms. U.S. Department of Commerce Economics and Statistics Administrations. "Women in STEM: A Gender Gap to Innovation", 2009.
  10. ^ National Science Foundation, Women, Minorities and Persons with Disabilities in Science and Engineering: 1996, Washington, D.C.: 1996, appendix table 5-8.
  11. ^ a b Bae, Yupin, "The Condition of Education, 1997." National Center for Education Statistics, Washington, D.C., 1997.
  12. ^ Abbiss, Jane (2011). "Boys and Machines" (PDF). Gender and Education 23 (5): 601–617.  
  13. ^ a b Gender Gap in Spatial Ability Can Be Reduced Through Training, Science Daily, 16 September 2010
  14. ^ a b Has feminism changed science? Londa Schiebinger Cambridge: Harvard University Press, 1999 p. 33
  15. ^ a b c Spencer, S.J.; Steele, C.M.; Quinn, D.M. (1999). "Stereotype threat and women's math performance". Journal of Experimental Social Psychology 35 (1): 4–28.  
  16. ^ a b Su, Rong; Rounds, James; Armstrong, Patrick (2009). "Men and Things, Women and People: A Meta-Analysis of Sex Differences in Interests". Psychological Bulletin 135 (6): 859–884.  
  17. ^ a b Has feminism changed science ? Londa Schiebinger Cambridge: Harvard University Press, 1999 p. 34
  18. ^ Ruchika Tulshyan. "Top 10 College Majors For Women - 10: Liberal Arts and Sciences, General Studies, Humanities". Archived from the original on 2013-01-23. Retrieved 2013-03-07. 
  19. ^ a b U.S. Bureau of Labor Statistics, "Highlights of Women’s Earnings in 2010," Report 1031, July 2011
  20. ^ Schiebinger, Londa (1999). Has Feminism Changed Science?. FIrst Harvard University. p. 35. 
  21. ^ Mason, Marry Ann. "Keeping Women in the Science Pipeline". Retrieved 3 December 2012. 
  22. ^ a b Schiebinger, Londa (1999). Has Feminism Changed Science?. FIrst Harvard University. pp. 54–55. 
  23. ^ a b c d e Pell, A. N. "Fixing the leaky pipeline: women scientists in academia.". Journal of Animal Science. Retrieved 4 December 2012. 
  24. ^ Schiebinger, Londa (1999). Has Feminism Changed Science?. FIrst Harvard University. p. 56. 
  25. ^ a b Lips, Hilary (2008). Sex & Gender: An Introduction Sixth Edition. New York: McGraw Hill. 
  26. ^ [1]
  27. ^ a b Schiebinger, Londa (1999). Has Feminism Changed Science?. Harvard University Press. p. 51. 
  28. ^ Federal Glass Ceiling Commission. .Solid Investments: Making Full Use of the Nation's Human Capital Washington, D.C.: U.S. Department of Labor, November 1995, p. 4.
  29. ^ Cohn, Samuel. 1985. The Process of Occupational Sex-Typing. Philadelphia: Temple University Press.
  30. ^ Ehrenreich, Barbara, and Deirdre English. 1978. For Her Own Good: 100 Years of Expert Advice to Women. Garden City, N.Y.: Anchor Press.
  31. ^ Williams, Christine (1992). "The Glass Escalator: Hidden Advantages for Men in the 'Female' Professions". Social Problems 39: 253–267.  
  32. ^ Reskin, Barbara, and Patricia Roos.1990. Job Queues, Gender Queues: Explaining Women's Inroads into Male Occupations. Philadelphia: Temple University Press.
  33. ^ Tversky, Amos; Kahneman, Daniel (1973). "Availability: A heuristic for judging frequency and probability". Cognitive Psychology 5 (2): 207–232.  
  34. ^ Wells, Gary L. (2007). "The Conjunction Error and the Representativeness Heuristic". Social Cognition 3 (3): 266–279.  
  35. ^ a b Good, Jessica J.; Woodzicka, Julie A.; Wingfield, Lylan C. (2010). "The Effects of Gender Stereotypic and Counter-Stereotypic Textbook Images on Science Performance". Journal of Social Psychology 150 (2): 132–147.  
  36. ^ Eagly, A.H.; Karau, S.J. (2002). "Role congruity theory of prejudice toward female leaders". Psychological Review 109 (3): 573–598.  
  37. ^ Garcia-Retamero, R.; Lopez-Zafra, E. (2006). "Prejudice against Women in Male-congenial Environments: Perceptions of Gender Role Congruity in Leadership". Sex Roles 55 (1-2): 51–61.  
  38. ^ Ritter, B.A.; Yoder, J.D. (2004). "Gender Differences in Leader Emergence Persist Even for Dominant Women: An Updated Confirmation of Role Congruity Theory". Psychology of Women Quarterly 28 (3): 187–193.  
  39. ^ a b Miyake, A.; Kost-Smith, L.E.; Finkelstein, N.D.; Pollock, S.J.; Cohen, G.L.; Ito, T.A. (2010). "Reducing the Gender Achievement Gap in College Science: A Classroom Study of Values Affirmation". Science 330 (6008): 1234–1237.  
  40. ^ a b Gaucher, D.; Friesen, J.; Kay, A.C. (2011). "Evidence that gendered wording in job advertisements exists and sustains gender inequality". Journal of Personality and Social Psychology 101 (1): 109–128.  
  41. ^ Lyness, K.S.; Heilman, M.E. (2006). "When fit is fundamental: Performance evaluations and promotions of upper-level female and male managers". Journal of Applied Psychology 91 (4): 777–785.  
  42. ^ Deaux, K.; Lewis, L.L. (1984). "Structure of gender stereotypes: Interrelationships among components and gender label". Journal of Personality and Social Psychology 46 (5): 991–1004.  
  43. ^ Eagly, A.H.; Wood, W. (1991). "Explaining Sex Differences in Social Behavior: A Meta-Analytic Perspective". Personality and Social Psychology Bulletin 17 (3): 306–315.  
  44. ^ a b c Madera, J.M.; Hebl, M.R.; Martin, R.C. (2009). "Gender and letters of recommendation for academia: Agentic and communal differences". Journal Applied Psychology 94 (6): 1591–1599.  
  45. ^ Swim, J.; Borgida, E.; Maruyama, G.; Myers, D.G. (1989). "Joan McKay versus John McKay: Do gender stereotypes bias evaluations?". Psychological Bulletin 105 (3): 409–429.  
  46. ^ Milkman, K.L; Akinola, M.; Chugh, D. (2012). "Temporal Distance and Discrimination: An Audit Study in Academia". Psychological Science 23 (7): 710–717.  
  47. ^ a b Moss-Racusin, C.A.; Dovidio, J.F.; Brescoll, V.L.; Graham, M.; Handelsman, J. (2012). "Science faculty's subtle gender biases favor male students". Proceedings of the National Academy of Sciences 109: 16474–16479.  
  48. ^ Sonnert, G.; Fox, M.F.; Adkins, K. (2007). "Undergraduate Women in Science and Engineering: Effects of Faculty, Fields, and Institutions Over Time". Social Science Quarterly 88 (5): 1333–1356.  
  49. ^ Stout, J.G.; Dasgupta, N.; Hunsinger, M.; McManus, M.A. (2011). "STEMing the tide: Using ingroup experts to inoculate women's self-concept in science, technology, engineering, and mathematics (STEM)". Journal of Personality and Social Psychology 100 (2): 255–270.  
  50. ^ Schmader, T.; Johns, M. (2003). "Converging evidence that stereotype threat reduces working memory capacity". Journal of Personality and Social Psychology 85 (3): 440–452.  
  51. ^ Steele, C.M.; Aronson, J. (1995). "Stereotype Threat and the Intellectual Test Performance of African Americans". Journal of Personality and Social Psychology 69 (5): 797–811.  
  52. ^ a b c Steele, C.M.; Spencer, S.J.; Aronson, J. (2002). "Contending with group image: The psychology of stereotype and social identity threat". Advances in Experimental Social Psychology 34: 379–440.  
  53. ^ Christine Bork, Women in science, Huffington Post
  54. ^ a b Eidelman, S.; Biernat, M. (2003). "Derogating black sheep: Individual or group protection?". Journal of Experimental Social Psychology 39 (6): 602–609.  
  55. ^ a b Kerr, N.L.; Hymes, R.W.; Anderson, A.B.; Weathers, J.E. (1995). "Defendant-juror similarity and mock juror judgments". Law and Human Behavior 19 (6): 545–567.  
  56. ^ a b Marques, J.; Abrams, D.; Serodio, R.G. (2001). "Being better by being right: Subjective group dynamics and derogation of in-group deviants when generic norms are undermined". Journal of Personality and Social Psychology 81 (3): 436–447.  
  57. ^ a b Taylor, T.S.; Hosch, H.M. (2004). "An examination of jury verdicts for evidence of a similarity-leniency effect, an out-group punitiveness effect or a black sheep effect". Law and Human Behavior 28 (5): 587–598.  
  58. ^ Cooper, V.W. (1997). "Homophily or the Queen Bee Syndrome". Small Group Research 28 (4): 483–499.  
  59. ^ a b c d Ellemers, N.; Van den Heuvel, H.; de Gilder, D.; Maass, A.; Bonvini, A. (2004). "The underrepresentation of women in science: Differential commitment or the queen bee syndrome?". British Journal of Social Psychology 43 (3): 315–338.  
  60. ^ Slaughter, A
  61. ^ Finding a work-life-balance, Toronto Star, 11 July 2012
  62. ^ Johns, Michael; Schmader, Toni; Martens, Andy (2005). "Knowing Is Half the Battle: Teaching Stereotype Threat as a Means of Improving Women's Math Performance". Psychological Science 16 (3): 175–179.  
  63. ^ a b Marx, D.M.; Roman, J.S. (2002). "Female role models: Protecting women's math performance". Personality and Social bulletin 28 (9): 1183–1193.  
  64. ^ a b Huguet, P.; Regner, I. (2007). "Stereotype threat among schoolgirls in quasi-ordinary classroom circumstances". Journal of Educational Psychology 99 (3): 545–560.  
  65. ^ McIntyre, R.B.; Paulson, R.M.; Lord, C.G. (2003). "Alleviating women's mathematics stereotype threat through salience of group achievements". Journal of Experimental Social Psychology 39 (1): 83–90.  
  66. ^ Drury, Benjamin J.; Siy, John Oliver; Cheryan, Sapna (2011). "When Do Female Role Models Benefit Women? The Importance of Differentiating Recruitment From Retention in STEM". Psychological Inquiry 22 (4): 265–269.  
  67. ^ Martens, A.; Johns, M.; Greenberg, J.; Schimel, J. (2006). "Combating stereotype threat: The effect of self-affirmation on women's intellectual performance". Journal of Experimental Social Psychology 42 (2): 236–243.  
  68. ^ Miyake, A.; Kost-Smith, L.E.; Finkelstein, N.D.; Pollock, S.J.; Cohen, G.L.; Ito, T.A. (2010). "Reducing the Gender Achievement Gap in College Science: A Classroom Study of Values Affirmation". Science 330 (6008): 1234–1237.  
  69. ^ a b c Gresky, D.M.; Eyck, L.L.T.; Lord, C.G.; McIntyre, R.B. (2005). "Effects of salient multiple identities on women's performance under mathematics stereotype threat". Sex Roles 53 (9-10): 703–716.  

Further reading

  • American Association of University Women (2010). Why So Few?
  • At last, proof women are smarter than men, Toronto Star, July 17, 2012

This article was sourced from Creative Commons Attribution-ShareAlike License; additional terms may apply. World Heritage Encyclopedia content is assembled from numerous content providers, Open Access Publishing, and in compliance with The Fair Access to Science and Technology Research Act (FASTR), Wikimedia Foundation, Inc., Public Library of Science, The Encyclopedia of Life, Open Book Publishers (OBP), PubMed, U.S. National Library of Medicine, National Center for Biotechnology Information, U.S. National Library of Medicine, National Institutes of Health (NIH), U.S. Department of Health & Human Services, and, which sources content from all federal, state, local, tribal, and territorial government publication portals (.gov, .mil, .edu). Funding for and content contributors is made possible from the U.S. Congress, E-Government Act of 2002.
Crowd sourced content that is contributed to World Heritage Encyclopedia is peer reviewed and edited by our editorial staff to ensure quality scholarly research articles.
By using this site, you agree to the Terms of Use and Privacy Policy. World Heritage Encyclopedia™ is a registered trademark of the World Public Library Association, a non-profit organization.

Copyright © World Library Foundation. All rights reserved. eBooks from World eBook Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.