{ social psychology }

  • On the Social Psychology of Sexism

    As somebody interested in gender equality in CS, one thing that’s proved quite illuminating for me is to read up on the psychology of sexism. Why does sexism persist in society? What social and psychological structures keep it in place?

    Sexism in some ways is unlike other forms of discrimination. When it comes to race, or class, or disability, the social psychology literature will frequently talk about social distance. But when it comes to women, men “can’t live without ‘em” [1] – and so things tend to be a bit different.

    Ambivalent Sexism

    It turns out that sexism has two faces: good old hostile sexism – and the more palatable benevolent sexism. Benevolent sexism is the notion that women are wonderful, caring, nurturing and beautiful creatures – and so must be protected and provided for. (Note the “creatures” – not “people”.)

    The evidence on the psychology of sexism is that the people who espouse hostile sexism are also benevolently sexist. They think women shouldn’t work – women should stay home and care for the children because women are so good at mothering. And they get hostile when their regressive worldview clashes with attempts of women and men to change the status quo.

    Furthermore, the more you’re exposed to benevolent sexism, the more likely you are to later take on hostile sexist views. The more you’re primed to believe that women should fill the magical-traditional role, the more likely you’ll try to thwart any attempt to move away from this view.

    A vital thing to note about benevolent sexism is how frequently it is embraced by women. Many women will argue that women are better at being nurturing, or communicating, etc. After all, why would you turn down a worldview that argues you’re wonderful and worthy of protection?

    Hammond et al’s 2013 “The Allure of Sexism: Psychological Entitlement Fosters Women’s Endorsement of Benevolent Sexism Over Time“ gives the most up-to-date review I’ve seen on ambivalent sexism in its introduction – the rest of this paper is largely a summary of their literature review.

    Benevolent Sexism

    Benevolent sexism tends to be a fairly palatable type of sexism since it doesn’t seem sexist [2] – indeed women often see it as chivalry or intimacy rather than sexism [3]. After all, women “complete” men and are their “better halves”.

    While hostile sexism “works to suppress direct challenges to men’s power by threatening women who taken on career roles or seek political reform” [2], benevolent sexism works to incentivise women’s adoption of traditional, patriarchal gender roles. Women are revered as special and caring – and deserving of protection.

    And indeed, the main way in which benevolent sexism stops gender inequality is through women’s adoption of these views. Benevolent sexism incentizises women to stay in those special, caring, protection-worthy gender roles – and at the same time makes men’s social advantages seem more fair [4].

    Effects on Women

    A longitudinal study of New Zealanders found that psychological entitlement in women leads to greater endorsement of benevolent sexism. (Hammond et al, 2013). The more a woman believes she is deserving of good things, praise, and material wealth – the more benevolent sexism will appeal to her. After all, benevolent sexism both reveres women as being men’s “better halves” – and at the same time promises women that they will be protected and financially provided for by their male partners.

    Women who espouse benevolent sexism have greater life satisfaction [5]. They feel good about their focus on care-giving and appreciate being provided for by their male partners. They have more power to influence their male partners [6], and hence have indirect access to resources and power. 

    At the same time, however, benevolent sexism holds women back when it comes to gaining social power. Women who hold benevolently sexist beliefs have less ambition for education and their careers [7]. They are more likely to defer to their partners on career decisions [8]. They believe their careers should take the back seat to their male partner’s careers [9].

    And furthermore, exposure to benevolently sexist statements leads women to perform poorly at tasks and feel lower competence [10]. They’re more likely to believe that men and women have an equal chance of success in society [4]. And they’re less likely to support gender-based collective action [11].

    Effects on Men

    For men, espousing benevolent sexism requires sacrifice in relationships [12]. They have to provide for their female partners, and live up to the romantic notion of the ‘’white knight’’.

    But this sacrifice does come with benefits. Men who are portrayed as benevolently sexist are viewed as more attractive [13]. And men who actually agree with benevolent sexism are more caring and satified relationship partners [6].

    Furthermore, women who espouse benevolent sexism are likely to be hostile/resistant to men who aren’t on the same page [6]. And women who espouse benevolent sexism are more let down when their male partners fail to live up to the fantasy of the white knight [14].

    A Tragedy of the Commons

    For women, benevolent sexism gives them dyadic power [1]. They have more power in relationships, and more satisfaction in them. As a result, women have something to lose if the status quo is disrupted [12].

    But getting the individual benefits of benevolent sexism means agreement with the attitudes that perpetuate gender inequality. The women who agree with benevolent sexism are more likely to hold themselves back in their careers and less likely to support feminist causes.

    If we want to get more women into non-traditional careers like computer science, sexism is something we need to tackle. We spend a lot of time trying to break down hostile sexism in STEM – but what about benevolent sexism? 


    1. Glick, Peter, and Susan T. Fiske. “Hostile and benevolent sexism measuring ambivalent sexist attitudes toward women.” Psychology of Women Quarterly 21.1 (1997): 119-135.
    2. Hammond, Matthew D., Chris G. Sibley, and Nickola C. Overall. “The Allure of Sexism Psychological Entitlement Fosters Women’s Endorsement of Benevolent Sexism Over Time.” Social Psychological and Personality Science (2013): 1948550613506124.
    3. Barreto, M., & Ellemers, N. (2005). The burden of benevolent sexism: How it contributes to the maintenance of gender inequalities. European Journal of Social Psychology, 35, 633-642. doi:10.1002/ejsp.270
    4. Jost, J. T., & Kay, A. C. (2005). Exposure to benevolent sexism and complementary gender stereotypes: Consequences for specific and diffuse forms of system justification. Journal of Personality and Social Psychology, 88, 498–509. doi:10.1037/0022-3514.88.3.498
    5. Hammond, M. D., & Sibley, C. G. (2011). Why are benevolent sexists happier? Sex Roles, 65, 332–343. doi:10.1007/s11199-011-0017-2
    6. Overall, N. C., Sibley, C. G., & Tan, R. (2011). The costs and benefits of sexism: Resistance to influence during relationship conflict. Journal of Personality and Social Psychology, 101, 271–290. doi:10.1037/a0022727
    7. Fernandez, M., Castro, Y., Otero, M., Foltz, M., & Lorenzo, M. (2006). Sexism, vocational goals, and motivation as predictors of men’s and women’s career choice. Sex Roles, 55, 267–272. doi:10.1007/s11199-006-9079-y
    8. Moya, M., Glick, P., Exposito, F., de Lemus, S., & Hart, J. (2007). It’s for your own good: Benevolent sexism and women’s reactions to protectively justified restrictions. Personality and Social Psychology Bulletin, 33, 1421–1434. doi:10.1177/0146167207304790
    9. Chen, Z., Fiske, S. T., & Lee, T. L. (2009). Ambivalent Sexism and power-related gender-role ideology in marriage. Sex Roles, 60, 765–778. doi:10.1007/s11199-009-9585-9
    10. Dardenne, B., Dumont, M., & Bollier, T. (2007). Insidious dangers of benevolent sexism: consequences for women’s performance. Journal of Personality and Social Psychology, 93, 764–779. doi:10.1037/0022-3514.93.5.764
    11. Becker, J. C., & Wright, S. C. (2011). Yet another dark side of chivalry: Benevolent sexism undermines and hostile sexism motivates collective action for social change. Journal of Personality and Social Psychology, 101, 62–77. doi:10.1037/a0022615
    12. Glick, P., & Fiske, S. T. (1996). The ambivalent sexism inventory: Differentiating hostile and benevolent sexism. Journal of Personality and Social Psychology, 70, 491–512. doi:10.1037//0022-3514.70.3.491
    13. Kilianski, S. E., & Rudman, L. A. (1998). Wanting it both ways: Do women approve of benevolent sexism? Sex Roles, 39, 333–352. doi:10.1023/A:1018814924402
    14. Hammond, M. D., & Overall, N. C. (2013). When relationships do not live up to benevolent ideals: Women’s benevolent sexism and sensitivity to relationship problems. European Journal of Social Psychology, 43, 212–223. doi:10.1002/ejsp.1939
  • Correspondence tests: uncovering biases against women in science

    Part of the controversy surrounding affirmative action and other systems which give preferential treatment to minority groups comes from the ideal notion that people are judged on their merits – and not their gender/race/etc [6]. In such an ideal world, for instance, a female scientist would be equally likely to be hired, given tenure, or accolades as an identical male scientist.

    Science likes to bill itself as a meritocracy, in which scientists are evaluated only their work. A lot of the unease many scientists have about preferential treatment is that it goes against that ideal of meritocratic science [5]. So, it’s worth asking: is a female scientist equally likely to be hired/tenured/etc as an identical male scientist?

    Probably the best study design for probing this type of question are correspondence tests. These refer to studies where you describe either a female individual or a male individual to a group of participants – keeping everything but gender (or race, ethnicity, etc) constant – and see if participants respond differently to to the woman/man.

    Correspondence tests are generally easier to run than audit studies, where you hire actors to be identical to one another except for gender/race/etc. Both types of studies are useful for identifying discrimination against particular groups. Another approach is to pair real male and female scientists with equal on-paper qualifications and see whether they are equally likely to be given tenure. This approach, however, suffers from the problem of pairing: are that female and male scientist really identical except for on-paper qualifications?

    In this post, I’ll be describing the results of three correspondence tests looking at discrimination against women in science. These three studies are also the only such studies that I know of to have been published since the 90s. (There’s an older one from the 70s that is now a bit dated.)

    The effect of gender on tenurability

    Published in 1999, Steinpreis et al [1] ran a correspondence test looking at the effect of gender on tenure. They sent out hundreds of questionnaires to academic psychologists (randomly selected from the Directory of the American Psychological Association). The paper describes two studies, one on tenurability, and one on hirability. Over a hundred questionnaires were returned on the tenurability study of the paper.

    Each questionnaire contained a CV, and participants were asked to rate the CV: would they tenure this individual? Participants were told only that this was part of a study on how CVs are reviewed for tenure decisions.

    The CV was that of a real psychologist, who had been given early tenure – a random half of the participants got a version of the CV with the name changed to “Karen Miller” – the other half of the participants got “Brian Miller”. (The questionnaire also asked if the participant recognized any of the names on the CV – such participants were removed from the analysis.)

    Each questionnaire had a hidden code on the sheet that indicated the gender and institution of the participant it was sent to – this way the researchers did not have to ask their participants for their gender (which could bias them by getting them consciously thinking about gender).

    The results? “Brian Miller” and “Karen Miller” were equally likely to be offered tenure – but participants were also four times more likely to write cautionary comments about “Karen” than “Brian”, such as “I would need to see evidence that she had gotten these grants and publications on her own” and “We would have to see her job talk”.

    A caveat of the study, however, is that this was the CV of a psychologist who had been offered early tenure – in short, this was an unambiguously competent applicant. In correspondence studies used in non-science contexts, ambiguity has been repeatedly found to play a large role: a minority applicant who is ambiguously qualified for a job (or loan) is less likely to receive the job than a majority applicant – but in the face of unambiguous qualifications, biases are muted [2].

    The effect of gender on hirability for a tenure-track position

    The Steinpreis et al paper contains another correspondence test, looking at the effect of gender on applying for tenure-track jobs. They used roughly the same approach for this as they did for the other correspondence test, also with over a hundred participants.

    Here, the CV was that of the same individual, but at an earlier stage in her career; the dates were shifted to make it seem recent. Like the other study, participants either saw the CV as that of “Karen Miller” or “Brian Miller”. Participants were asked if they would hire the applicant, and what starting salary they would suggest.

    Unlike the tenure study, significant differences were seen between the ratings of “Karen” and “Brian”. “Brian” was significantly more likely to be hired, and he was significantly more likely to be rated as having adequate research experience, along with adequate teaching experience and adequate service experience. He was offered a larger starting salary.

    Both female and male participants demonstrated these biases – there was no effect of the participant’s own gender in any part of Steinpreis et al’s two studies.

    The effect of gender on hirability for a lab manager position

    Thirteen years after the Steinpreis et al study, Moss-Racusin et al ran a correspondence study looking at the effect of gender on the hirability of a canditate for a lab manager position [3]. The candidate here is somebody with a Bachelors degree – this is a lower-position job than the tenure-track job in the Steinpres et al paper.

    Moss-Racusin et al sent job packets to 547 tenure-track/tenured faculty in biology, chemistry and physics departments at American R1 institutions, found through the websites of those departments. 127 respondents fully completed the study.

    Each job packet contained the resume and references of the applicant – who was randomly assigned either the name “Jennifer” or “John”. Unlike the Steinpres et al study which used a real scientist’s CV, this job packet was created specifically for the study. The applicant was designed to reflect what the authors described as a “slightly ambiguous competence”.

    “John”/“Jennifer” was “in the ballpark” for the position but not an obvious star [3]. They had two years of research experience and a journal publication – but a mediocre GPA and mixed references.

    Similar to the Steinpreis et al study on hirability, “John” was statistically significantly more likely to be hired than “Jennifer” and was offered a larger starting salary. He was also rated as more competent than “Jennifer”. Participants also indicated a greater inclination to mentor “John” than “Jennifer”. While the differences in the ratings between “John” and “Jennifer” were not huge, the effect sizes were all moderate to large (d = 0.60-0.75).

    And similar to the Steinpreis et al study on hirability, both female and male participants were equally likely to rate “John” above “Jennifer”.

    The effect of gender on perceived publication quality and collaboration interest

    In the two papers I’ve described above, the correspondence studies all used job application materials for the “correspondence”. Knobloch-Westerwick et al [4] took a look at gender discrimination through a different lens: by having participants rate conference abstracts whose authors were rotated as female or male.

    Participants in this study were graduate students (n=243), all of whom were in communications programmes; abstracts were all taken from the 2010 annual conference of the International Communication Association. Participants had not attended this conference.

    Unlike the Steinpreis et al and Moss-Racusin et al papers, participants evaluated multiple “correspondences” – they each saw 15 abstracts. The participants rated each article on a scale of 0-10 for how interesting, relevant, rigourous, and publishable the abstract was. Participants also rated abstracts on how much they would like to chat to the author, and potentially collaborate with them.

    Overall, they found that abstracts with male authors were rated as having statistically significantly higher scientific quality than when these abstracts were presented with female authors. Abstracts with male authors were more likely to be deemed worthy of talking to – and collaborating with – the author. The gender of the participant did not have an effect on the ratings they gave.

    Given the other studies I’ve described here, this probably isn’t surprising. What I found quite neat about the paper is they then broke it down by subfield. There’s two steps to this analysis. Before the ran the main study, they ran a preliminary study with assistant professors, which involved these participants rating whether a given abstract fell into a female-typed subfield, or into a male-typed subfield. Knobloch-Westerwick et al then rigged the abstract selection in the main study to show equal numbers of abstracts from these three categories. Female-typed subfields turned out to be communications relating to children, parenting and body image; male-typed subfields were political communication, computer-mediated communication, news, and journalism. Health communication, intercultural communication were rated as gender-neutral.

    In female-typed subfields, female authors were rated higher than male authors. In male-typed subfields, the male authors were rated higher than female authors. And in the gender-neutral areas, female and male authors were rated equally. (It should also be noted that the female-typed abstracts were rated less favourably than gender-neutral and male-typed abstracts.)

    This brings us to role congruity theory. Role congruity theory looks at gender through the social construct of gender roles – gender roles not only represent beliefs about the attributes of women and men, but also normative expectations about their behaviour [4]. What we saw in the subfield results is that people who are role-incongruous are discriminated against.

    Per the article, “Role congruity theory postulates that bias against female scientists originates in differences between a female gender role and the common expectations towards individuals in a scientist role.” [4] In short, where women go against societal gender norms, they’re viewed less favourably.


    From the articles here, two things emerge for whether we’ll see discrimination against women in a correspondence study: whether they’re role-congruent or role-incongruent, and the level of ambiguity in the study.

    When faced with an extraordinary tenure candidate, it doesn’t matter whether they’re role-congruent or role-incongruent. Bias is more likely when the applicant is ambiguously qualified (which is really most of the time). Often, this will mean that a female scientist needs to be more qualified to get the same job: a male scientist can get it for being “good enough” – but the woman needs to be amazing.

    The three papers together paint a fairly clear picture that subtle bias occurs against women in science – and that female academics are just as prone to bias as their male colleagues.

    While there are a number of issues with affirmative action and other preferential treatment systems for minorities (see [2]) – the notion that scientists are rated independent of gender isn’t one of them. These biases exist and add up quickly over an entire discipline – and over the course of an individual’s life.


    1. Steinpreis, R. E., Anders, K. A., & Ritzke, D. (1999). The impact of gender on the review of the curricula vitae of job applicants and tenure candidates: A national empirical study. Sex roles, _41_(7-8), 509-528.
    2. Heilman, M. E., Block, C. J., & Stathatos, P. (1997). The affirmative action stigma of incompetence: Effects of performance information ambiguity. Academy of Management Journal, _40_(3), 603-625.
    3. Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012). Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences, 109(41), 16474-16479.
    4. Knobloch-Westerwick, S., Glynn, C. J., & Huge, M. (2013). The Matilda Effect in Science Communication: An Experiment on Gender Bias in Publication Quality Perceptions and Collaboration Interest. Science Communication.
    5. van den Brink, M., & Benschop, Y. (2012). Gender practices in the construction of academic excellence: Sheep with five legs. Organization, _19_(4), 507-524.
    6. Crosby, F., & Clayton, S. (1990). Affirmative action and the issue of expectancies. Journal of Social issues, _46_(2), 61-79.
  • Subtyping, Subgrouping, and Stereotype Change

    There’s been a fair bit of research finding that negative stereotypes are part of what deters women and racial minorities from computer science and STEM in general (e.g. [1]). These stereotypes make it harder for women and minorities to personally identify with computer science, and amplify some of the biases that they face in CS. So for this post, I’ll be going over observed phenomena in social psychology and sociology that pertain to stereotype change.


    Stereotypes are really hard to change. They’re reinforced from many sources (media, individuals, groups, etc). But even more than that, stereotypes are schema: they are how we mentally organize information about social groups, and how we can determine whether we are “in” or “out” of a group. Schema allow us to process information effortlessly, and are pretty deeply ingrained once they’re there.

    The human brain is not very good at changing schema. When we see evidence that contradicts our schema, our brains will do all sorts of mental gymnastics to avoid confronting or changing the incorrect schema. Most frequently, we forget that we saw it all. Sometimes our misconceptions even get stronger [2].

    This happens with stereotypes. Betz and Sekaquaptewa did a study where they showed role models to young girls, to try to motivate the girls’ interest in STEM [3]. Role models were either gender-neutral, or feminine. The result? Gender-neutral role models boosted interest – and feminine (counterstereotypic) role models actually reduced girls’ interest in science. To these girls, the feminine scientist – a stereotype violator – is aberrant.

    Stereotype violators are not viewed favourably by others. Indeed, in laboratory settings, people go out of their way to punish stereotype violators [4]. Stereotype violators are seen as less likable, and less competent. Not surprisingly, women in science are rated as less likable and less competent than otherwise identical men [5, 6].


    So, let’s say instead of being exposed to just one woman scientist, you are exposed to a bunch of them. Regularly. That will change your schema, right? Nope.

    The human brain does a thing that social psychologists call subtyping. Instead of changing your mental model of what a scientist is (white male), you instead create a new category: the woman scientist [7].

    And the evidence is that this is what happens to female scientists, and to female engineers [3]. Furthermore, the stereotype of the woman scientist is of an unfeminine woman. The unfeminine label in of itself is costly: these women are seen as less likable, less attractive, less competent, and less confident [3].

    Perceived Variability

    So how can we change stereotypes, then? It turns out a thing called “perceived variability” is key: it’s how much variation we perceive in an out-group [7]. “Out-group” here refers to any group that a person does not identify with; an “in-group” is one that that person identifies with. Humans systematically underestimate the variability within an out-group, particularly in comparison to the variability within the in-group (e.g. men see women as more homogenous than they are; whites see aboriginals as more homogenous; etc).

    This is known as the Out-group homogeneity effect. We mentally exaggerate the stereotypical qualities of outgroups (and outgroup members), and ignore the counterstereotypical qualities.

    We stop paying attention to stereotypes when we perceive greater variability in the group that’s been stereotyped [7]. For example, it’s a lot harder to think about aboriginals in terms of generalizations and stereotypes when you’re used to thinking about the differences between Inuit, Metis and First Nations, and differences between the Haida, Salish, Blackfoot, Anishinaabe, Innu, Mi’kmaq, Dene, etc.


    So how can we increase the perceived variability of an outgroup? Subgrouping refers to the process in which both people members are brought together around common goals or interests, and can include both in-group and out-group members. For example, creating a study group in a computer science class in which both women and men are represented  – or joining a robotics club which has a mix of white, Asian, black, and hispanic students.

    Subgrouping “allows for a more varied cognitive representations of group members” [7] – it leads you to start seeing the members of your subgroup around their membership in your common subgroup – rather than their membership in any in-group or out-group. Richards and Hewstone have a very nice literature review about subgrouping and subtyping, showing how dozens of studies have consistently found that subgrouping leads to increased perceived group variability, and stereotype change.

    The Contact Hypothesis in sociology gets at subgrouping: the observed effect that being familiar with a member of an outgroup (eg. homosexuals) increases your acceptance of the outgroup. Having a friend, classmate or family member who is queer means you a share with a subgroup with them (friend group, class, family, etc).

    For subgroups to form effectively, they need to have meaningful cohesion to those in the subgroup. One study by Park et al that is described by Richards and Hewstone found that they could not form a subgroup around all engineering students: “[they] were all hardworking and bright, but in very different ways. Some were motivated only by money, some by parental expectations, and some by larger environmental goals.” Instead, there subgroups of engineering students formed, around those three motivators. [7]

    Similarly, Park et al found that they could not form subgroups around continuous variables (high/moderate/high) or arbitrary bases [7]. And other studies in the Richards and Hewstone review found that trying to form subgroups around having minority status (e.g. clubs for women in STEM, study groups for black students) either did not change stereotypes about their group, or intensified them [7].

    Indeed, I wouldn’t be surprised that part of why instructional techniques such as Peer Instruction disproportionately helps female CS/physics students is because they encourage subgrouping. When you have your whole class together, collaborating in small groups for class activities, you’re having them bond as classmates – rather than as members of in-groups or out-groups.


    This is one reason I’m always a bit iffy about Women in CS/Science clubs: they don’t promote stereotype change, but instead promote subtyping. Instead of changing the notion of what a computer scientist is, they reinforce the subcategory of woman computer scientist.

    But stereotypes aren’t the only thing that affect minorities. Part of why Women in CS clubs are so popular is that they provide a sense of community to these women. This is really important when you’re a minority member! The sense of isolation that many women experience in STEM is why many of them leave.

    And, as always, is evidence that girls only schooling can be good for encouraging young girls’ interest in math and science [8]. It’s somewhat of a tragedy of the commons problem: putting all the women together in a club helps those individual women cope with a culture in which they are negatively stereotyped – but it doesn’t change the actual stereotype.

    [1] Cheryan, Sapna, et al. “The Stereotypical Computer Scientist: Gendered Media Representations as a Barrier to Inclusion for Women.” Sex roles 69.1-2 (2013): 58-71.
    [2] McRaney. The Backfire Effect. http://youarenotsosmart.com/2011/06/10/the-backfire-effect/
    [3] Betz, Diana E., and Denise Sekaquaptewa. “My fair physicist? Feminine math and science role models demotivate young girls.” Social Psychological and Personality Science 3.6 (2012): 738-746.
    [4] Rudman, Laurie A., and Kimberly Fairchild. “Reactions to counterstereotypic behavior: the role of backlash in cultural stereotype maintenance.” Journal of personality and social psychology 87.2 (2004): 157.
    [5] Steinpreis, Rhea E., Katie A. Anders, and Dawn Ritzke. “The impact of gender on the review of the curricula vitae of job applicants and tenure candidates: A national empirical study.” Sex roles 41.7-8 (1999): 509-528.
    [6] Moss-Racusin, Corinne A., et al. “Science faculty’s subtle gender biases favor male students.” Proceedings of the National Academy of Sciences 109.41 (2012): 16474-16479.
    [7] Richards, Zoë, and Miles Hewstone. “Subtyping and subgrouping: Processes for the prevention and promotion of stereotype change.” Personality and Social Psychology Review 5.1 (2001): 52-73.
    [8] Barinaga, Marcia. “Surprises across the cultural divide.” Science 263.5152 (1994): 1468-1470.