Discussion Assignment

Research Article

Gender Bias in STEM Fields: Variation in Prevalence and Links to STEM Self-Concept

Rachael D. Robnett 1

Abstract The current study focuses on girls’ and women’s reported experiences with gender bias in fields related to science, tech- nology, engineering, and math (STEM). In the first set of analyses, I examined whether the prevalence of self-reported gender bias varied depending on the educational context. I then examined whether experiencing gender bias was associated with lower STEM self-concept and, if so, whether having a supportive network of STEM peers would buffer this effect. Data were collected through a self-report survey that was administered to high school girls who aspired to have STEM careers, women in STEM undergraduate majors, and women in STEM doctoral programs. Overall, 61% of participants reported experiencing gender bias in the past year, but the prevalence rate varied according to their phase of education and field of study. In par- ticular, women in math-intensive undergraduate majors were especially likely to encounter gender bias, which predominately originated from male peers in their major. As expected, participants who encountered gender bias had lower STEM self- concept than participants who did not. However, this effect was attenuated for participants who also had a supportive network of STEM peers. These findings suggest that positive peer connections may be a valuable resource for girls and women in the STEM pipeline.

Keywords sex differences, sexism, academic achievement, academic self-concept

If we’re going to out-innovate and out-educate the rest of the

world, we’ve got to open doors for everyone. We need all

hands on deck, and that means clearing hurdles for women

and girls as they navigate careers in science, technology,

engineering, and math. (Michelle Obama, First Lady of the

United States, The White House Briefing Room, 2011)

The U.S. workforce has experienced an influx of women in

recent decades. Nonetheless, there remains a stubborn gender

gap in many careers related to science, technology, engineer-

ing, and math (STEM; American Association of University

Women [AAUW], 2010; National Science Foundation [NSF],

2012). This gap becomes larger as girls and women progress

from one phase of education to the next, and it is especially

pronounced in math and math-intensive fields such as physics

and engineering (NSF, 2012).

Many researchers have sought to understand why STEM

fields show continued gender disparities (see Halpern et al.,

2007). One controversial possibility is that gender bias is par-

tially responsible for pushing girls and women away from

STEM. Although some scholars have argued that gender bias

is no longer prevalent in STEM fields (e.g., Ceci, Williams, &

Barnett, 2009), recently scholars have provided evidence to

the contrary (e.g., Leaper & Brown, 2008; Moss-Racusin,

Dovidio, Brescoll, Graham, & Handelsman, 2012). Their

work strongly suggests that at least some girls and women

encounter bias in their pursuit of STEM careers.

In the current study, I examined girls’ and women’s

reported experiences with gender bias in STEM with two

goals. First, I sought to identify the most frequent perpetrators

of bias and to identify the educational contexts in which gen-

der bias is most prevalent. Second, I examined whether

experiencing gender bias would be associated with lower

STEM self-concept and, if so, whether having a supportive

network of STEM peers would buffer this effect. In the sec-

tions that follow, I begin with an overview of past research

examining gender bias in STEM. I then provide a rationale

for considering links among gender bias, STEM self-

concept, and peer supportiveness.

1 Department of Psychology, University of Nevada, Las Vegas, NV, USA

Corresponding Author:

Rachael D. Robnett, Department of Psychology, University of Nevada, Las

Vegas, 4505 S. Maryland Pkwy, Las Vegas, NV 89154, USA.

Email: rachael.robnett@unlv.edu

Psychology of Women Quarterly 2016, Vol. 40(1) 65-79 ª The Author(s) 2015 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0361684315596162 pwq.sagepub.com

Prevalence of Gender Bias in STEM

Many scholars agree that gender bias was once fairly com-

mon in STEM fields (see Ceci, Ginther, Kahn, & Williams,

2014; Wang & Degol, 2013). However, the question of

whether gender bias constitutes a contemporary barrier for

girls and women in STEM elicits more disagreement. For

example, after conducting an extensive review of the litera-

ture, Ceci and colleagues concluded that there is little evi-

dence of gender bias in STEM fields and claimed that the

gender gap in STEM cannot be attributed to gender bias (Ceci

et al., 2009; see also Ceci & Williams, 2010). However,

recent empirical work shows evidence of continued gender

bias in STEM fields. For example, Leaper and Brown

(2008) found that over half of the adolescent girls in their

sample had experienced academic discouragement in domains

related to math and science. Women in STEM undergraduate

majors and STEM graduate programs also report experien-

cing gender bias (Etzkowitz, Kemelgor, & Uzzi, 2000;

Herzig, 2004; Margolis, Fisher, & Miller, 2000; Steele,

James, & Barnett, 2002). Oftentimes the bias they encounter

is subtle (e.g., social isolation and exclusion from academic

discourse), but some women also report encountering overt

hostility (Etzkowitz et al., 2000; Margolis et al., 2000). These

self-report findings are consistent with findings from a recent

experimental study, which showed that STEM faculty mem-

bers rated applicants to a lab manager position more posi-

tively when the applicants were men as opposed to women

(Moss-Racusin et al., 2012). Thus, both self-report and

experimental research indicate that gender bias may be a

challenge for at least some girls and women in STEM fields.

One factor that contributes to the prevalence of gender

bias in STEM is that bias appears to come from a variety

of interpersonal sources (see Halpern et al., 2007). At the high

school level, some evidence suggests that male peers are the

most common source of gender bias (Leaper & Brown,

2008), but there is also evidence of bias originating from

female peers, teachers, and other adults (Breakwell,

Vignoles, & Robertson, 2003; Kessels, 2005; Leaper &

Brown, 2008). Similar to girls in high school, women in col-

lege and graduate school also report encountering gender bias

from multiple sources (e.g., Etzkowitz et al., 2000; Herzig,

2004). However, little is known about the relative occurrence

of bias originating from these sources because systematic

comparisons are uncommon. To shed light on this issue, the

current research compares the prevalence of bias originating

from four interpersonal sources that girls and women in

STEM fields are likely to encounter: male peers, female

peers, teachers/professors, and mentors. I draw a distinction

between male and female peers because prior research sug-

gests that male peers may be an especially common source

of gender bias (Leaper & Brown, 2008). A parallel distinction

is not made for teachers, professors, and mentors because I

did not have a priori expectations about gender differences

in the level of gender bias from these groups. This line of

reasoning is consistent with research suggesting that male

and female faculty may show similar levels of gender bias

(Moss-Racusin et al., 2012).

Gender bias and the educational context. As a whole, the research described thus far suggests that gender bias may

be an enduring problem in STEM fields. However, previous

work typically fails to consider whether the prevalence of

gender bias varies among STEM fields. Moreover, little is

known about whether gender bias differs in severity at differ-

ent points in the educational pipeline. There is good reason to

believe that the prevalence of gender bias varies as a function

of both factors. As described below, bias may be especially

prevalent in fields in which women are more severely under-

represented (Kabat-Farr & Cortina, 2014; Kanter, 1977) and,

relatedly, fields in which women’s presence constitutes a

greater challenge to existing status hierarchies (e.g., Rudman,

Moss-Racusin, Phelan, & Nauts, 2012). Examining this pos-

sibility is important because it may help researchers and pol-

icymakers develop interventions that target girls and women

who are at the greatest risk of encountering gender bias in

their pursuit of STEM careers (Sonnert, Fox, & Adkins,

2007; Wang & Degol, 2013).

Variation across STEM fields. Researchers who examine women’s standing in STEM fields often focus on a single

field or combine multiple fields into a monolithic STEM con-

struct (e.g., Herzig, 2004; Moss-Racusin et al., 2012). How-

ever, women’s representation and subjective experiences

are far from uniform across STEM fields (Ceci & Williams,

2010; Wang & Degol, 2013). Indeed, after finding clear evi-

dence of women’s uneven representation in STEM disci-

plines, Sonnert and colleagues (2007) argued that ‘‘gender

segregation by field is still in full force and shows no signs

of abating’’ (p. 1352).

On the whole, girls and women are better represented in

fields related to the life sciences than they are in math-

intensive fields such as the physical sciences, computer

science, engineering, and math itself (see Ceci & Williams,

2010; Perez-Felkner, McDonald, Schneider, & Grogan,

2012). This trend persists throughout the educational pipe-

line. For example, at the high school level, girls are less likely

than boys to take Advanced Placement exams in the physical

sciences, math, and computer science but are more likely to

take Advanced Placement exams in biology and environmen-

tal science (AAUW, 2010). Similar trends can be found in

STEM degree attainment among undergraduates and gradu-

ate students. For instance, women currently earn over half

of all doctoral degrees in biology, but this percentage drops

to less than one third in fields such as physics, engineering,

and math (AAUW, 2010).

After observing these patterns in a recent review, Wang

and Degol (2013) argued that understanding variation in

women’s standing across STEM fields should be a priority

for future research (see also Sonnert et al., 2007). In the cur-

rent study, I respond to this call by building on prior work that

66 Psychology of Women Quarterly 40(1)

has distinguished between women’s standing in the life

sciences and their standing in math and math-intensive

fields (Ceci & Williams, 2010; Perez-Felkner et al., 2012).

Specifically, analyses examine whether the prevalence of

gender bias is greater in math-intensive fields than in the life

sciences.

Variation across phases of education. It is uncommon for researchers to compare the prevalence of gender bias at dif-

ferent phases of education. However, several scholars have

speculated that gender differences in STEM attrition may

be due to increases in the amount of bias that girls and women

face as they move through the educational pipeline (e.g.,

Committee on Science, Engineering, and Public Policy,

2007; Etzkowitz et al., 2000; Herzig, 2004). This possibility

is consistent with theory and research indicating that women

may encounter negative reactions when they violate hierar-

chies in which men tend to hold positions of power (e.g.,

Glick & Fiske, 1996; Rudman et al., 2012). For instance,

Rudman and colleagues (2012) found that female leaders

were especially likely to encounter backlash when they

threatened the gender status quo by demonstrating high levels

of agency. By extension, gender bias may become a more

serious barrier as girls and women move into increasingly

prestigious, male-dominated academic spheres, which could

help to explain why the gender gap in STEM is wider at the

doctoral level than it is in earlier phases of education. I explore

this possibility by comparing rates of gender bias in STEM

across three phases of education: high school, college, and

graduate school.

Implications of Experiencing Gender Bias

A second goal of the current research was to shed light on the

implications of experiencing gender bias by examining

whether experiencing gender bias is associated with lower

STEM self-concept. STEM self-concept refers to the extent

to which individuals believe they are capable of excelling

in STEM fields (see Simpkins, Davis-Kean, & Eccles,

2006). A large body of theoretical and empirical work illus-

trates that self-concept plays a central role in academic deci-

sion making. For example, according to expectancy-value

theory, individuals’ beliefs about their ability to succeed in

a given domain are a key determinant of their academic and

career trajectories (Eccles & Wigfield, 2002). Similarly,

social–cognitive career theory argues that individuals’ beliefs

about their capabilities shape their career choices (Lent &

Brown, 1996). Both theories have generated an abundance

of empirical support, and much of this work illustrates that

self-concept predicts achievement and career aspirations in

STEM domains (DeBacker & Nelson, 2000; Robnett,

Chemers, & Zurbriggen, 2015; Robnett & Leaper, 2013;

Watt, 2006). This underscores the importance of identifying

factors such as gender bias that have the potential to nega-

tively influence girls’ and women’s STEM self-concept.

The models outlined in both expectancy-value theory and

social–cognitive career theory provide theoretical support for

the prediction that experiencing gender bias may lead to

lower STEM self-concept. Specifically, according to these

perspectives, self-concept is informed by the social context

and social interactions. For instance, Eccles (1994) proposed

that socializers such as teachers and peers play a key role in

shaping students’ academic self-concept (see also Wang &

Degol, 2013). Along a similar vein, Lent and colleagues

(2001) argued that self-efficacy can be influenced by contex-

tual barriers such as discrimination or a lack of social support.

It merits noting that these social–contextual barriers are

thought to present challenges regardless of whether they are

objectively documented or subjectively perceived (Lent &

Brown, 2006; Settles, Cortina, Buchanan, & Miner, 2013;

Wang & Degol, 2013).

My recent literature search indicates that only one prior

study has directly linked experiences with gender bias to

self-concept in STEM domains. Namely, Brown and Leaper

(2010) found that high school girls who experienced gender

bias felt less competent in math and science than did other

participants. I build on this work by assessing the implica-

tions of gender bias in a broader array of academic contexts.

Moreover, analyses are limited to girls and women who are

already in the STEM pipeline in order to shed light on how

gender bias influences those who have a vested interest in

their STEM achievement.

Supportive network of STEM peers as a buffer. If gender bias is indeed associated with lower STEM self-concept, it is

important to identify factors that can mitigate its effects.

Hence, the final goal of the current study was to examine

whether having a supportive network of STEM peers can buf-

fer the negative implications of experiencing gender bias.

Individuals often rely on social support when they encounter

general challenges as well as challenges that are specific to

bias and discrimination (e.g., Ayres & Leaper, 2013; Lazarus

& Folkman, 1984; Wasti & Cortina, 2002). Moreover, a num-

ber of studies from the field of social psychology indicate that

positive peer connections can promote academic retention for

underrepresented students (e.g., Walton & Cohen, 2007). The

proposed mechanism underlying this effect is belongingness

(e.g., Cheryan, Plaut, Davies, & Steele, 2009). That is, posi-

tive connections with STEM peers likely foster girls’ and

women’s sense of belongingness in STEM, which may in turn

enhance their likelihood of retention by tempering the self-

doubt that stems from negative social interactions (see Das-

gupta, 2011; Walton & Cohen, 2007).

On the basis of prior research focusing on social support

and belongingness, I examined whether the negative associ-

ation between experiencing gender bias and STEM self-

concept would be attenuated among girls and women who

report having a supportive network of STEM peers. I assumed

positive peer connections would be beneficial regardless of

the gender composition of the peer network. This assumption

Robnett 67

was guided by minimal group research demonstrating that

undergraduates’ math persistence and motivation improve

when they feel a sense of connection with their math peers,

even if they are not provided with information about their

peers’ gender or other background characteristics (Walton,

Cohen, Cwir, & Spencer, 2012, experiment 2).

The Current Study

To summarize, I sought to fill several gaps in prior research

that focuses on gender bias in STEM fields. First, I conducted

analyses to identify the most common perpetrators of gender

bias in STEM. Drawing from Leaper and Brown’s (2008)

study focusing on gender bias in an adolescent sample, the

following hypothesis was tested:

Hypothesis 1: Gender bias originating from male peers

will be significantly more common than gender bias origi-

nating from female peers, teachers/professors, or mentors.

I also examined whether the prevalence of gender bias var-

ied depending on the educational context. Because women

are especially likely to encounter gender bias when they are

in the numerical minority within a given setting (Kabat-

Farr & Cortina, 2014; Kanter, 1977; Rudman et al., 2012),

gender bias was expected to be most prevalent in STEM

domains that have lower levels of gender equity. Specifically,

the following hypotheses were tested:

Hypothesis 2a: Participants who are pursuing an education

in math-intensive fields (i.e., computer science, engineer-

ing, math, and physical sciences) will report experiencing

more gender bias than will participants who are pursuing

an education in the life sciences (i.e., biology, ecology, and

health sciences).

Hypothesis 2b: The prevalence of gender bias will

increase incrementally according to phase of education.

That is, women who are pursuing STEM doctoral degrees

will report experiencing the most gender bias, followed by

women who are pursuing STEM bachelor’s degrees.

STEM-focused high school girls will report experiencing

the least gender bias.

Hypothesis 2c: The two-way interaction between parti-

cipants’ phase of education and field of study will be

significant, such that women pursuing graduate degrees

in math-intensive fields will experience particularly high

rates of gender bias relative to other participants.

The current study also builds on prior research by examin-

ing the implications of experiencing gender bias. Given that

self-concept is thought to be shaped by the social context

(Eccles & Wigfield, 2002), I tested for a link between experi-

encing gender bias and participants’ STEM self-concept.

Thus, the third hypothesis is as follows:

Hypothesis 3: There will be a negative association

between gender bias and STEM self-concept, such that

participants who experience more bias will have lower

STEM self-concept.

Last, if experiencing gender bias is indeed associated with

lower STEM self-concept, it is important to identify factors

that may mitigate this effect. Prior research suggests that hav-

ing a supportive peer network may reduce the negative impli-

cations of experiencing gender bias (e.g., Ayres & Leaper,

2013). Hence, the final hypothesis is as follows:

Hypothesis 4: The association between experiencing gen-

der bias and STEM self-concept will be attenuated for par-

ticipants who have a supportive network of STEM peers.

Method

Participants

High school. Girls were recruited from math and science classes at two high schools in the western United States. As

an incentive, participants were entered into a raffle to win one

of several US$50 gift certificates. In total, 400 girls partici-

pated. However, analyses for the present study focused on a

subset of 108 girls who reported that they were interested

in pursuing a STEM career. Their mean age was 16.57 years

(SD ¼ .95). Sixty-eight (63%) participants planned to pursue a major in the life sciences, and 40 (37%) participants planned to pursue a major in math-intensive fields. With

respect to ethnic background, 37 (34%) participants identified as Asian American, 52 (48%) identified as European American, 15 (14%) identified as Latina, and 4 (4%) identified as mul- tiple/other.

1 Although a direct measure of socioeconomic

background was not obtained, the majority of participants

reported that their parents had completed at least a bachelor’s

degree (70 mothers [65%] and 78 fathers [72%]).

Undergraduate and graduate. Data collection at the under- graduate and graduate levels took place at a public, 4-year

university in the western United States. The university’s

Carnegie Classification indicates that it is more selective and

has very high research activity. The student body, collapsing

across undergraduates and graduate students, is 48% men and 52% women. The gender distribution in STEM majors is comparable to U.S. national averages. For instance, in phy-

sics, women earned 24% of the bachelor’s degrees (national average: 21%) and 20% of the doctoral degrees (national average: 17%) in the year when the data collection took place.

Undergraduate participants were recruited through

e-mails, course announcements, and flyers. Participants were

entered into a raffle to win one of several US$50 gift certif-

icates. To be included in the present study, participants

needed to be majoring (or premajoring) in a STEM field. In

total, 124 women participated. Their mean age was 20.28

68 Psychology of Women Quarterly 40(1)

years (SD ¼ 1.74). Sixty-three (51%) undergraduate partici- pants were pursuing degrees in the life sciences, and 61

(49%) were pursuing degrees in math-intensive fields. With respect to ethnic background, 29 (23%) participants identified as Asian American, 52 (42%) identified as European Ameri- can, 22 (18%) identified as Latina, and 21 (17%) identified as multiple/other. Over half of the participants reported that

their parents had obtained at least a bachelor’s degree (68

mothers [55%] and 64 fathers [52%]). Graduate participants were recruited through e-mails,

course announcements, and flyers. All graduate participants

received gift certificates that ranged in value from US$10

to US$20. (To improve the pace of recruiting, the incentive

was increased from US$10 to US$20 early in the data collec-

tion process.) To be included in the present study, participants

needed to be pursuing a doctoral degree in a field related to

STEM. In total, 102 women participated. Their mean age was

28.36 years (SD ¼ 5.05). Seventy-one (70%) participants were pursuing degrees in math-intensive fields, and 31 (30%) were pursuing degrees in the life sciences. With respect

to ethnic background, 19 (19%) participants identified as Asian American, 63 (62%) identified as European Ameri- can, 9 (9%) identified as Latina, and 10 (10%) identified as multiple/other. The majority of participants reported that

their parents had obtained at least a bachelor’s degree

(65 mothers [64%] and 77 fathers [75%]).

Procedure

Data collection occurred during the spring semesters of 2011

and 2012. The research team that was responsible for recruit-

ing and survey administration included 11 women; 1 was a

graduate student who identified as White, 8 were undergrad-

uates who identified as White, and 2 were undergraduates

who identified as Biracial. As described below, recruiting and

data collection differed somewhat depending on participants’

phase of education.

High school. Math and science teachers were provided with basic information about the study and the logistics of data

collection. They then received parental consent forms to send

home with their students. The consent forms explained that

students were invited to participate in a study that focuses

on links between students’ peer networks and their academic

interests. Approximately one month after the consent forms

were sent out, members of the research team returned to each

class to administer the survey. All high school participants

completed the survey during their math or science classes,

which were 50 minutes in duration. All of the students who

started the survey were able to finish within the class period.

Students who did not participate worked on other assignments.

Undergraduate and graduate. To recruit undergraduate and graduate students, the research team made announcements

during STEM courses, posted flyers in STEM departments,

and sent e-mails to students in STEM fields of study.

Prospective participants were told that the aim of the study

was to better understand students’ experiences in STEM

fields. Due to the nature of the recruiting process, it is not

possible to calculate the exact response rate. However, the

response rates for undergraduates and graduate students who

were recruited via e-mail were 124 (26%) and 102 (38%), respectively. Undergraduate and graduate students who

agreed to participate were provided with a link to an online

survey, which took about 40 minutes to complete. Attrition

rates were fairly low. Specifically, eight undergraduates

(6% of the full undergraduate sample) and three graduate stu- dents (3% of the full graduate sample) stopped on the first or second page of the survey. Their data are not included in the

forthcoming analyses.

Measures

Participants completed a survey that included questions about

their experiences with gender bias in STEM, their STEM

self-concept, and the supportiveness of their STEM peers.

The survey also included several additional constructs that

were not the focus of the current study. 2

There were slight

wording differences among the surveys used for high

school students, undergraduate students, and graduate stu-

dents. For simplicity, the examples provided throughout the

remainder of this section are for undergraduates majoring in

a science field.

Field of study. The current study distinguished between the life sciences and math-intensive STEM fields, a distinction

that has been made in prior theoretical and empirical work

(e.g., Ceci & Williams, 2010; Perez-Felkner et al., 2012; Son-

nert et al., 2007). In order to classify high school students

according to their field of study, participants were asked to

select their preferred college major from a list of 50 possible

majors. (As noted earlier, students who selected a non-STEM

major were not included in the current study.) College and

graduate students were classified according to their current

field of study. Disciplines including biology, ecology, and

health sciences were classified as life sciences, whereas dis-

ciplines including the physical sciences, math, engineering,

and computer science were classified as math-intensive.

Gender bias. Experiences with gender bias in STEM were assessed with an adaptation of a measure that Leaper and

Brown (2008) used in a study that focused on adolescent

girls’ experiences with gender bias in STEM. Thus, the adap-

tations made in the current study involved tailoring the word-

ing of prompt and response options so that the measure would

be suitable for students in all three phases of education. For

example, high school students were asked about bias originat-

ing from teachers, whereas college and graduate students

were asked about bias originating from professors.

Before completing the measure, participants were pre-

sented with the following prompt:

Robnett 69

Gender bias occurs when people treat women unfairly due to

their gender. Some women have experienced gender bias in sci-

ence fields, but others have not. We would like to know about

your experiences with gender bias in your major over the past

year. 3

Following the prompt were eight forms of academic gender

bias: (1) ‘‘Made negative comments about women’s science

abilities,’’ (2) ‘‘Expected less of you academically or profes-

sionally because of your gender,’’ (3) ‘‘Made you feel like

you had to work harder than male students to be taken seri-

ously,’’ (4) ‘‘Made you feel like your gender will make it dif-

ficult for you to succeed in STEM,’’ (5) ‘‘Excluded you from

a STEM study group because of your gender,’’ (6) ‘‘Excluded

you from a discussion about STEM because of your gender,’’

(7) ‘‘Made negative comments about your ability in STEM

because of your gender,’’ and (8) ‘‘Ignored your comments

or questions in STEM classes because of your gender.’’ For

each item, participants rated how frequently the following

people behaved in that manner: male peers from their major

(science classes, graduate program), female peers from their

major (science classes, graduate program), teachers/profes-

sors from their major (science classes, graduate program),

and a mentor/advisor. Participants made frequency ratings

on a 4-point scale (1 ¼ never, 2 ¼ once or twice, 3 ¼ several times, and 4 ¼ many times). The frequency ratings for each form of bias were averaged together to create mean gender

bias scores for each of the four perpetrators as well as an

overall gender bias score. Higher scores on these scales

reflect more frequent experiences with bias.

The internal reliability for gender bias originating from

each of the four perpetrators was acceptable for high school

students (male peers: a ¼ .85, female peers: a ¼ .62, teach- ers: a ¼ .78, and mentors: a ¼ .75), college students (male peers: a¼ .92, female peers: a¼ .87, professors: a¼ .84, and mentors: a ¼ .85), and graduate students (male peers: a ¼ .89, female peers: a ¼ .69, professors: a ¼ .86, and mentors: a ¼ .85). However, it merits noting that reliability was some- what lower for female peers than it was for the other three

sources of gender bias. The internal reliability for the overall

gender bias measure was also acceptable for high school stu-

dents (a ¼ .89), college students (a ¼ .94), and graduate stu- dents (a¼ .95). These internal reliabilities are consistent with values obtained in prior research. For example, in a study car-

ried out with high school students, Leaper and Brown (2008)

reported an a of .85 for their measure of gender bias.

STEM self-concept. The self-expectancy scale from Eccles’s expectancy-value measure (Eccles & Wigfield, 2002) was

used to assess participants’ self-concept in STEM. All items

were rated on a 4-point scale. The scale was composed of

10 items such as ‘‘If you were to order all of the students in

your major from the worst to the best, where would you

stand?’’ (1 ¼ one of the worst and 4 ¼ one of the best) and ‘‘How much effort would you need to do well in an advanced

science course?’’ (1 ¼ a lot and 4 ¼ almost none). Higher scores on this scale reflect higher STEM self-concept.

The internal reliability was acceptable for high school

students (a ¼ .91), undergraduate students (a ¼ .88), and graduate students (a ¼ .86). These internal reliabilities are consistent with values obtained in prior research. For

instance, in a study carried out with high school students,

Simpkins and colleagues (2006) reported an a of .85 for math/science self-concept. Similarly, in a study carried out

with college students, Durik, Shechter, Noh, Rozek, and Har-

ackiewicz (2015) reported an a of .84 for math self-concept.

Supportiveness of STEM peer network. To assess the suppor- tiveness of the STEM peer network, participants were first

presented with a prompt that asked them to respond to each

item with their STEM peers in mind. That is, high school

students rated peers from their math or science classes, under-

graduate students rated peers from their major, and graduate

students rated peers from their graduate program. Following

this prompt were 9 items that were adapted from a measure

that Stake and Mares (2001) used to assess peer relationships

that developed during a science enrichment program for high

school students. As before, the wording of this measure was

modified so that it would also be appropriate for students

in college and graduate school. Sample items include ‘‘My

interactions with other science majors have made me more

self-assured as a science student’’ and ‘‘My interactions with

other science majors have made studying science more enjoy-

able.’’ Each item was rated on a scale ranging from 1

(strongly disagree) to 6 (strongly agree), and higher score

reflects a more supportive STEM peer network.

The internal reliability of this measure was acceptable

for high school students (a ¼ .87), undergraduate students (a ¼ .83), and graduate students (a ¼ .88). These internal reliabilities are consistent with values obtained in prior

research. For example, in a study carried out with high school

students, Stake and Mares (2001) reported as of .85 (pretest) and .83 (posttest) for their measure of peer support.

Results

Preliminary Analyses

To test for ethnic differences in study variables, multivariate

analyses of variance (MANOVAs) compared mean levels of

self-concept, peer support, and gender bias for participants

who identified as Asian American, European American, or

Latina. The MANOVAs were carried out separately for par-

ticipants in high school, college, and graduate school. The

MANOVA for high school students was nonsignificant,

Wilks’ L ¼ .90, F(6, 196) ¼ 1.75, p ¼ .12, Zp2 ¼ .05, and the MANOVA for graduate students was only marginally

significant, Wilks’ L ¼ .87, F(6, 166) ¼ 2.02, p ¼ .07, Zp

2 ¼ .07. The MANOVA for undergraduates, however, was significant, Wilks’ L ¼ .84, F(6, 186) ¼ 2.76, p ¼ .02, Zp

2 ¼ .08. Follow-up univariate ANOVAs carried out with

70 Psychology of Women Quarterly 40(1)

the undergraduate participants indicated that there were

not significant ethnic differences in the extent to which parti-

cipants viewed their STEM peers as supportive, F(2, 101) ¼ .40, p ¼ .67, Zp2 ¼ .01, nor were there significant ethnic dif- ferences in the amount of gender bias that participants

reported experiencing, F(2, 101) ¼ .39, p ¼ .68, Zp2 ¼ .01. In contrast, the ANOVA for STEM self-concept was signifi-

cant, F(2, 101) ¼ 8.52, p < .001, Zp2 ¼ .15. Post hoc pairwise comparisons using the Bonferroni test revealed that among

undergraduates, STEM self-concept was significantly higher

in European American participants (M ¼ 2.46, SD ¼ .49) than it was in Asian American participants (M ¼ 2.06, SD ¼ .40, p ¼ .03). For this reason, all forthcoming analyses control for participants’ ethnic background.

A second MANOVA tested for field of study differences

in peer support and STEM self-concept. (Mean differences

in gender bias are reported in the main analyses below.)

The MANOVA was nonsignificant, Wilks’ L ¼ .99, F(2, 319) ¼ .80, p ¼ .45, Zp2 ¼ .01, which indicates that lev- els of peer support and self-concept did not significantly dif-

fer for participants in math-intensive fields versus the life

sciences. In contrast, a third MANOVA that tested for phase

of education differences in peer support and STEM self-

concept was significant, Wilks’ L ¼ .92, F(4, 658) ¼ 6.78, p < .001, Zp

2 ¼ .04. Follow-up univariate ANOVAs indicated that there were significant field of study differences in the

extent to which participants viewed their STEM peers as

supportive, F(2, 332) ¼ 7.40, p ¼ .001, Zp2 ¼ .04, and in par- ticipants’ STEM self-concept, F(2, 332) ¼ 4.83, p ¼ .01, Zp

2 ¼ .03. With respect to peer support, post hoc pairwise comparisons using the Bonferroni test revealed that partici-

pants in high school (M ¼ 4.15, SD ¼ .77) reported signifi- cantly lower levels of peer support than did participants in

college (M ¼ 4.46, SD ¼ .88, p ¼ .01) and graduate school (M ¼ 4.57, SD ¼ .79, p ¼ .001). With respect to STEM self-concept, post hoc pairwise comparisons using the Bon-

ferroni test revealed that participants in college (M ¼ 2.29, SD ¼ .49) had significantly lower STEM self-concept than did participants in high school (M ¼ 2.49, SD ¼ .57, p ¼ .01).

A final MANOVA was carried out among the graduate

students to examine whether participants who received a

US$10 gift card incentive differed from participants who

received a US$20 gift card incentive. The MANOVA was

nonsignificant, Wilks’ L ¼ .98, F(3, 91) ¼ .64, p ¼ .58, Zp

2 ¼ .02. Hence, all graduate students are grouped together in the forthcoming analyses.

Variation in the Prevalence of Gender Bias: Hypotheses 1 and 2

Descriptive statistics. Overall, 204 (61%) of girls and women in the current study reported experiencing gender bias

in STEM at least once over the course of the past year. The

most frequently experienced forms of gender bias included

feeling as though you had to work harder than male students

to be taken seriously (reported by 130 [39%] participants) and hearing negative comments about girls’ and women’s STEM

abilities (reported by 127 [38%] participants). The prevalence of gender bias is further broken down according to source,

phase of education, and field of study in Table 1. The percen-

tages in the table provide preliminary evidence of variation in

the prevalence of gender bias. For instance, 70% (n ¼ 43) of women in math-intensive undergraduate majors reported

experiencing gender bias from male peers; the corresponding

percentage for undergraduate women in the life sciences was

50% (n ¼ 32). Parametric analyses formally testing for varia- tion in gender bias are described next.

Mean differences in the prevalence of bias. A mixed repeated- measures ANCOVA was carried out to test Hypotheses

1 and 2. The goal of this analysis was to determine whether

mean levels of gender bias varied according to the source

of bias (Hypothesis 1) as well as according to participants’

field of study and phase of education (Hypothesis 2). The

within-subjects variable was the source of bias (male peer,

female peer, teacher/professor, and mentor); the between-

subjects variables were participants’ field of study (math-

intensive, life sciences) and their phase of education (high

school, college, and graduate). Ethnicity was included in the

model as a covariate. Below, findings pertaining to the source

of gender bias (within-subjects effects) are presented first,

followed by findings that address field of study and phase

of education differences in the prevalence of gender bias

(between-subjects effects).

Within-subjects effects. The repeated-measures ANCOVA revealed a main effect for source of gender bias, F(3, 909)

¼ 28.64, p < .001, Zp2 ¼ .09. Post hoc pairwise comparisons using the Bonferroni test provided support for Hypothesis 1.

Table 1. Percentage of Participants Who Experienced Gender Bias From Male Peers, Female Peers, Professors, and Mentors.

% Who Experienced at Least Once During the Past Year

High School College Graduate Overall

Male peers in STEM Life science 58 50 18 49 Math-intensive 46 70 41 52

Female peers in STEM Life science 28 32 27 30 Math-intensive 28 43 34 36

A STEM teacher or professor Life science 21 29 14 23 Math-intensive 18 43 42 37

A mentor or advisor in STEM Life science 30 24 18 26 Math-intensive 21 32 24 26

Note. Sources of gender bias are sorted in descending order according to their overall prevalence in the sample. STEM ¼ science, technology, engineering, and math.

Robnett 71

Specifically, the mean level of gender bias originating from

male peers (M ¼ 1.32, SD ¼ .51) was significantly higher than the mean level of bias originating from female peers

(M ¼ 1.11, SD ¼ .21, p < .001), teachers/professors (M ¼ 1.13, SD ¼ .29, p < .001), and mentors (M ¼ 1.11, SD ¼ .29, p < .001).

Between-subjects effects. Analyses also revealed significant between-subjects main effects. First, there was a main effect

for field of study, F(1, 303) ¼ 8.57, p ¼ .004, Zp2 ¼ .03. Con- sistent with Hypothesis 2a, girls and women in math-

intensive fields (M ¼ 1.20, SD ¼ .19) reported significantly higher rates of gender bias than did girls and women in the

life sciences (M ¼ 1.12, SD ¼ .19). There was also a main effect for phase of education, F(2, 303) ¼ 5.96, p ¼ .003, Zp

2 ¼ .04. Post hoc pairwise comparisons using the Bonfer- roni test provided partial support for Hypothesis 2b. Namely,

women in college (M ¼ 1.22, SD ¼ .31) reported experien- cing significantly more gender bias than did girls in high

school (M ¼ 1.13, SD ¼ .20, p ¼ .01), which was hypothe- sized, but they also reported experiencing significantly more

gender bias than did women in graduate school (M ¼ 1.15, SD ¼ .30, p ¼ .02), which was not hypothesized.

The two aforementioned main effects were qualified by a

two-way interaction between field of study and phase of edu-

cation, F(2, 303) ¼ 3.10, p ¼ .04, Zp2 ¼ .02. Figure 1 presents mean levels of gender bias as a function of participants’ field

of study and phase of education. To probe the interaction,

univariate ANOVAs were first carried out to assess field of

study effects separately for participants in high school, col-

lege, and graduate school. Findings illustrated that among

undergraduates, experiences with gender bias were signifi-

cantly more common among women in math-intensive fields

(M ¼ 1.32, SD ¼ .38) than they were among women in the life sciences (M ¼ 1.14, SD ¼ .22), F(1, 111) ¼ 9.23, p ¼ .003, Zp2 ¼ .08. The same pattern was also found for graduate students, although the mean difference was only

marginally significant, F(1, 91) ¼ 3.07, p ¼ .08, Zp2 ¼ .03. In contrast, rates of gender bias did not significantly differ

according to field of study for participants in high school,

F(1, 105) ¼ .003, p ¼ .96, Zp2 ¼ .00. To further examine the interaction, a second set of

univariate ANOVAs tested for phase of education effects

separately for participants in the life sciences and math-

intensive fields. A significant main effect was obtained for

participants in math-intensive fields, F(2, 165) ¼ 5.78, p ¼ .004, Zp2 ¼ .07. Post hoc pairwise comparisons with the Bonferroni test demonstrated that undergraduate women

in math-intensive fields (M ¼ 1.32, SD ¼ .38) reported experiencing significantly more gender bias than did

their counterparts in high school (M ¼ 1.12, SD ¼ .21, p ¼ .006) and graduate school (M ¼ 1.16, SD ¼ .29, p ¼ .05). Significant phase of education effects were not obtained for girls and women in the life sciences, F(2,

144) ¼ 1.64, p ¼ .20, Zp2 ¼ .02.

The findings from the phase of Education � Field of Study interaction indicate that the main effects of field of study and

phase of education were largely driven by undergraduate

women in math-intensive fields of study. Specifically,

women who were pursuing undergraduate degrees in math-

intensive fields reported significantly higher rates of gender

bias relative to other participants. This is inconsistent with

Hypothesis 2c, which predicted that graduate students, not

undergraduates, in math-intensive fields would experience

the highest rates of gender bias.

Gender Bias, STEM Self-Concept, and Peer Support: Hypotheses 3 and 4

Multiple regression was used to examine whether higher

rates of perceived gender bias were associated with lower

STEM self-concept (Hypothesis 3). Analyses also considered

whether the association between gender bias and STEM self-

concept would be attenuated for participants who had a sup-

portive network of STEM peers (Hypothesis 4). Control vari-

ables in the regression model included ethnicity, phase of

education, and field of study. Predictor variables of relevance

to the hypotheses included mean levels of gender bias and

peer support as well as the two-way interaction between

gender bias and peer support. Both continuous predictor vari-

ables were mean centered prior to computing the interaction

term.

Results of the multiple regression are reported in Table 2.

The overall model was significant, F(7, 310) ¼ 6.61, p < .001, and it accounted for 13% of the variance in participants’ STEM self-concept. Two of the control variables were signif-

icantly associated with STEM self-concept: European Amer-

ican participants had significantly higher STEM self-concept

than did participants from other ethnic groups, and under-

graduates had significantly lower STEM self-concept than

1

1.05

1.1

1.15

1.2

1.25

1.3

1.35

1.4

High School College Graduate

M ea

n L

ev el

o f G

en de

r B ia

s

Life Sciences Math-Intensive

* *

*

†

Figure 1. Depiction of the two-way interaction between phase of education and field of study. *p < .01.

y p < .10.

72 Psychology of Women Quarterly 40(1)

did high school students. In addition, having a more suppor-

tive STEM peer network was associated with significantly

higher STEM self-concept. Last, consistent with Hypothesis

3, higher rates of gender bias were associated with signifi-

cantly lower STEM self-concept. 4

The two-way interaction between gender bias and peer

support was also significant. To probe the interaction, the

simple slope for gender bias was assessed at 1 SD below

the peer support mean (�.84) and at 1 SD above the peer support mean (.84). Peer support was mean centered prior

to carrying out the regression. As illustrated in Figure 2,

findings provided support for the buffering effect pre-

dicted in Hypothesis 4. Specifically, gender bias was

negatively associated with STEM self-concept when parti-

cipants had a less supportive network of STEM peers

(b ¼ �.49, p < .001). In contrast, the association between gender bias and STEM self-concept was nonsignificant

when participants had a more supportive network of STEM

peers (b ¼ .01, p ¼ .93).

Discussion

In the current study, I focused on girls’ and women’s experi-

ences with gender bias in STEM fields. A key goal was to

examine whether perceptions of gender bias varied in preva-

lence depending on the source of bias as well as participants’

phase of education and field of study. Although the majority

of participants reported experiencing gender bias, experi-

ences with bias were especially common for women who

were enrolled in math-intensive undergraduate majors. The

present study also found that experiencing gender bias was

associated with lower STEM self-concept, but that this effect

was mitigated among participants who also had a supportive

network of STEM peers. Below, the findings are described in

more detail, and several implications for intervention and

outreach are highlighted.

Prevalence of Gender Bias

Overall, 61% of participants in the current study reported experiencing gender bias at least once during the past year.

This finding aligns with recent research indicating that girls

and women encounter gender bias in their pursuit of STEM

careers (e.g., Moss-Racusin et al., 2012) but differs from sev-

eral reviews that find little evidence of gender bias in STEM

fields (e.g., Ceci et al., 2009; Ceci & Williams, 2010). In

determining what might lead researchers to these disparate

conclusions, it may be informative to distinguish between

formal discrimination and interpersonal discrimination. For-

mal discrimination, which is often overt, pertains to unfair

treatment in hiring, promotion, and access to resources,

whereas interpersonal discrimination, which is often subtle,

pertains to negative interpersonal encounters (Hebl, Foster,

Mannix, & Dovidio, 2002). The current study focused on

interpersonal discrimination, which is a common focus of

research that finds evidence of gender bias in STEM fields

(e.g., Leaper & Brown, 2008; Steele et al., 2002). In contrast,

most of the studies that do not find evidence of gender bias

focus on formal discrimination (e.g., Ceci et al., 2009). Taken

together with prior research, findings from the present study

suggest that gender bias in STEM fields may be following a

trend that has been observed in society on the whole, whereby

interpersonal discrimination persists despite declines in more

formal forms of discrimination (see Hebl et al., 2002). This is

not to say that formal discrimination has been eradicated (see

Moss-Racusin et al., 2012; Settles et al., 2013), but rather that

it may be less common relative to interpersonal discrimination.

The results of the current study also add to a growing body

of evidence demonstrating that male peers are a more com-

mon source of gender bias in STEM than are female peers,

Table 2. Multiple Regression Testing Predictors of STEM Self-Concept.

b SE b t p

Ethnicity (0 ¼ ethnic minority, 1 ¼ White)

.24 .06 .23 3.85 <.001

Phase of education High school (0 ¼ college,

1 ¼ high school) .26 .07 .23 3.64 <.001

Graduate (0 ¼ college, 1 ¼ graduate)

.05 .07 .05 .71 .48

Field of study (0 ¼ life sciences, 1 ¼ math-intensive)

.07 .06 .07 1.17 .24

Peer support .09 .04 .15 2.63 .009 Gender bias �.24 .11 �.12 �2.23 .03 Bias � Support .30 .13 .13 2.33 .02

Note. Gender bias and peer support were centered prior to computing the interaction term. STEM ¼ science, technology, engineering, and math.

1

1.5

2

2.5

3

3.5

4

Low Gender Bias High Gender Bias

ST E

M S

el f-

C on

ce pt

Low Support High Support

Figure 2. Plot of the two-way interaction between gender bias and peer support. The simple slope for gender bias was assessed at 1 SD below the peer support mean (�.84) and at 1 SD above the peer support mean (.84). The simple slope is significant at 1 SD below the mean (‘‘low support’’), whereas it is nonsignificant at 1 SD above the mean (‘‘high support’’).

Robnett 73

teachers/professors, and mentors (for a similar pattern, see

Leaper & Brown, 2008). Notably, some prior work indicates

that at the college and graduate levels, it is not uncommon for

women to encounter male peers who make remarks about

women being accepted into STEM programs on the basis of

their gender rather than their academic credentials (Etzkowitz

et al., 2000; Margolis et al., 2000). It is possible that negative

interactions like these help to explain why over a third of par-

ticipants in the current study reported that others in STEM

made them feel as though they needed to work harder than

male students to be taken seriously. Beyond causing frustra-

tion, this type of double standard may contribute to the gender

gap in STEM fields to the extent that it erodes girls’ and

women’s sense of belongingness in their area of study (Cher-

yan et al., 2009; Murphy, Steele, & Gross, 2007).

Variation in the prevalence of gender bias. Findings also showed that the prevalence of gender bias varied depending

on the educational context. As expected, participants in

math-intensive fields reported higher rates of gender bias

relative to participants in the life sciences. Also, in partial

support of expectations, undergraduates reported higher rates

of gender bias than did participants in other phases of educa-

tion. However, an interaction effect illustrated that these two

main effects could be attributed to particularly high rates of

bias encountered by women in math-intensive undergraduate

majors. This pattern was somewhat unexpected because prior

research suggests that the prevalence of gender bias may be

higher in domains in which women are more underrepre-

sented (Kabat-Farr & Cortina, 2014; Kanter, 1977), thus lead-

ing to the prediction that women in math-intensive graduate

programs would experience the most bias.

There are several potential explanations for the high rates

of gender bias reported by undergraduates, as opposed to

graduate students, in math-intensive fields. First, this pattern

would be explained if women who perceive high levels of

gender bias exit the STEM pipeline before matriculating to

the doctoral level. On the other hand, it is also possible that

gender bias is simply more common in college than it is in

graduate school. To understand why this might be the case,

it is worthwhile to consider the competitive ‘‘weed out’’ cul-

ture that characterizes some math-intensive undergraduate

majors (see Etzkowitz et al., 2000). Specifically, undergrad-

uates in math-intensive majors are sometimes required

to prove themselves in difficult gateway courses that are

designed to filter out less capable students. Consequently,

some men may feel uncertain about their status in the major,

which could contribute to high rates of gender bias. This pos-

sibility is consistent with work indicating that men are espe-

cially likely to engage in backlash against women when their

status is threatened (Rudman et al., 2012). Along this vein,

Stake (2003) demonstrated that adolescent boys who were

low in their science self-confidence were especially likely

to hold negative views about women in science. Notably, her

findings also showed that if boys’ science self-confidence

increased over time, there was a corresponding reduction in

their negative views about women in science. Thus, if men

in math-intensive undergraduate majors feel less confident

in their academic status compared to their counterparts at the

graduate level, it could help to explain why undergraduate

women in math-intensive majors report experiencing particu-

larly high rates of gender bias. Of course, this possibility is

speculative and should be empirically tested.

Implications of Gender Bias

Beyond shedding light on the prevalence of gender bias, I

examined whether encountering bias is associated with nega-

tive implications for girls and women in STEM. Consistent

with hypotheses, experiencing higher levels of gender bias

was associated with lessened STEM self-concept. This pat-

tern accords with theoretical work proposing that features

of the social environment inform individuals’ self-concept

(Eccles, 1994; Lent & Brown, 2006). It is also a troubling pat-

tern, given that longitudinal research has linked self-concept

to important academic and career outcomes (see Wang &

Degol, 2013, for a review). For example, Watt (2006) found

that girls’ math self-concept predicted their future course-

taking and career aspirations in math. Similar longitudinal

work has illustrated that science self-efficacy predicts subse-

quent changes in the extent to which undergraduates identify

with science (Robnett et al., 2015). Thus, from an applied

standpoint, the negative association between gender bias and

STEM self-concept underscores the potential value of inter-

ventions and outreach that aim to reduce gender bias and

mitigate its negative effects.

These arguments raise the question of whether the associ-

ation between gender bias and STEM self-concept, although

statistically significant, has practical importance. Although it

is not possible to provide a definitive answer to this question

on the basis of the current study’s findings, there is good rea-

son to believe that even small statistical effects can have sub-

stantial real-world implications. For instance, Martell, Lane,

and Emrich (1996) used a computer simulation to demon-

strate that gender bias led to women being underrepresented

at the top of organizational hierarchies, even when the bias

accounted for only 1% of the variance in hiring decisions.

The buffering role of peer support. Having established that experiencing gender bias is associated with lessened STEM

self-concept, the current study next examined whether this

association would be attenuated for participants who had a

supportive network of STEM peers. This hypothesis was

grounded in research showing that positive peer connections

can foster a sense of belongingness, which tends to be espe-

cially important for students who are underrepresented in

their area of study (Cheryan et al., 2009; Dasgupta, 2011;

Walton & Cohen, 2007). Findings supported the hypothe-

sized buffering effect: Women who encountered gender bias

were relatively high in their STEM self-concept as long as

74 Psychology of Women Quarterly 40(1)

they also had a supportive network of STEM peers. Those

who did not have a supportive network, however, had rela-

tively low STEM self-concept. This pattern suggests that the

consequences of gender bias are lessened if girls and women

also have a supportive peer network in their area of study.

Future research should examine whether support networks

involving family members or friends outside STEM confer

similar benefits (see Ong, Wright, Espinsoa, & Orfield,

2011).

Practice Implications

Currently, many researchers, educators, and policymakers are

dedicating resources to intervention work that aims to correct

gender imbalances in STEM fields. Unfortunately, these

interventions too often have limited success (see Weisgram

& Bigler, 2007). Some researchers have suggested that inter-

vention efforts could be made more effective by tailoring

them to the challenges that girls and women encounter in spe-

cific STEM fields (see Sonnert et al., 2007). The results of the

current study indicate that this may be a worthwhile strategy.

Although many participants reported experiencing gender

bias regardless of their phase of education or field of study,

experiences with bias were especially common for women

who were pursuing undergraduate degrees in math-intensive

fields. Hence, interventions that aim to reduce the prevalence

of gender bias may be especially beneficial for women in

math-intensive undergraduate majors.

Interventions that target gender bias could take several

forms depending on whether the goal is to reduce gender bias

itself or to reduce its negative consequences. With respect to

reducing gender bias, efforts to educate faculty and students

about creating a more inclusive climate would be helpful.

Given that male peers were the most common source of gen-

der bias in the current study, it is particularly important to

reach them with messages about the value of promoting gen-

der equity. Research suggests that these messages may have a

particularly strong impact if they are explicitly endorsed by

departmental leadership (Fox, 2000).

Because some gender bias is likely to linger after even the

most intensive interventions, working to reduce its negative

implications is also important. The results of the present

study show that having a supportive network of STEM peers

may serve this purpose. Thus, interventions that aim to

foster social ties among STEM students may be useful

(e.g., Dasgupta, 2011). Another worthwhile approach would

be to promote existing outreach organizations such as Women

in Science and Engineering (e.g., https://wise.usc.edu/) that

bring STEM students together outside of the classroom.

Limitations and Future Directions

The current research has several limitations. First, the find-

ings should be evaluated with an eye toward their generaliz-

ability. Data were collected from students at two high schools

and one university in the western United States. Thus, it is

possible that the patterns of gender bias that were obtained

are specific to a particular region of the United States or to

particular institutions. A counterpoint to this concern is over-

lap between the gender bias prevalence rates reported in the

current study and the rates reported in other research. For

example, 61% of the participants in the current study reported experiencing gender bias, and prevalence rates in similar

studies range from around 50% to just over 70% (e.g., Konik & Cortina, 2008; Leaper & Brown, 2008; Sonnert, 1995).

It is also important to consider whether the present study’s

findings generalize to women outside of STEM. For example,

in the field of political science, women are underrepresented

(NSF, 2012) and may encounter negative stereotypes (e.g.,

Eagly & Karau, 2002). Thus, it would be worthwhile to

examine whether the current study’s findings can be repli-

cated among women pursuing degrees in political science and

other fields, such as economics and philosophy, in which

women are also underrepresented.

A second limitation of the current study pertains to its

focus on perceptions of gender bias. It is not possible to

directly assess how closely these perceptions align with

actual levels of gender bias in STEM fields. However, it bears

noting that the self-reported experiences with gender bias that

were examined in the current study may underestimate the

‘‘true’’ prevalence rate because individuals are often reluctant

to acknowledge that they have experienced unfair treatment

on the basis of their social category memberships (Crosby,

1984; Swim, Eyssell, Murdoch, & Ferguson, 2010). Such a

possibility is particularly likely in the current study, given

that the term gender bias was used in the survey measure.

This is because some individuals resist using terms like bias

or harassment even if they report experiencing behaviors that

are consistent with those terms (e.g., Magley, Hulin, Fitger-

ald, & DeNardo, 1999).

It is also important to keep in mind that although percep-

tions have an element of subjectivity, they nonetheless have

important implications for actual behavior. For example,

among adolescents, perceiving high levels of racial discrim-

ination is associated with worse academic outcomes (Benner

& Graham, 2013). Similarly, one meta-analysis found that

workers who perceive a negative workplace climate have les-

sened job performance and an enhanced likelihood of leaving

their jobs altogether (Carr, Schmidt, Ford, & DeShon, 2003).

It therefore follows that girls’ and women’s perceived experi-

ences with gender bias may have implications for their actual

retention in STEM fields. Longitudinal research would help

to shed light on whether this is indeed the case.

Longitudinal research would also provide insight into the

directionality of the association between gender bias and

STEM self-concept. According to expectancy-value theory

and social-cognitive career theory, social–contextual barriers

such as gender bias are antecedent to self-concept (Eccles,

1994; Lent & Brown, 2006). However, given the cross-

sectional nature of the present study, it is not possible to rule

Robnett 75

out the reverse causal direction. That is, perhaps girls and

women who have lower self-concept elicit greater levels of

gender bias from others in STEM. Longitudinal research

would clarify this question by assessing whether experien-

cing gender bias is associated with subsequent declines in

self-concept. As well, longitudinal research carried out at the

institutional level would provide insight into the potentially

cyclical nature of the association between gender bias and

women’s representation in STEM. For example, it seems

plausible that increasing the number of women in a given

STEM major could reduce the level of gender bias, which

may in turn draw more women into the major.

Another worthwhile direction for future research would be

to examine whether the outcomes of gender bias are influ-

enced by the gender of the perpetrator. Although the current

study distinguished between gender bias that originated from

male peers versus female peers, this distinction was not made

for gender bias that originated from professors and mentors.

Thus, future research should begin by establishing whether

male and female faculty members engage in differing levels

of gender bias. It would then be helpful to examine whether

the implications of gender bias differ depending on the gen-

der of the perpetrator. For example, perhaps gender bias

originating from male peers, teachers, and professors has a

particularly strong impact, given that men tend to have high

status within STEM departments. A related direction for

future research pertains to considering the gender composi-

tion of girls’ and women’s networks of STEM peers. Minimal

group research suggests that even the slightest sense of con-

nection to one’s peers can foster belongingness (e.g., Walton

et al., 2012), which implies that supportive peer ties should be

beneficial for girls and women in STEM regardless of

whether those ties are to male peers or female peers. It may

be, however, that the degree of benefit varies depending on

the gender composition of the peer group. In support of

this point, research indicates that connections to advanced,

same-gender peers can boost women’s self-concept and per-

sistence in STEM domains (Stout, Dasgupta, Hunsinger, &

McManus, 2011).

Conclusion

In conclusion, the current study’s findings build on prior

research in several ways. A key finding is that the prevalence

of gender bias appears to vary depending on girls’ and

women’s phase of education and field of study. In particular,

a relatively high proportion of women in math-intensive

majors reported experiencing gender bias, which was espe-

cially likely to come from male peers who were in their

major. Findings also add to a small body of research that has

linked experiences with gender bias to lessened STEM self-

concept. However, the negative implications of experiencing

gender bias were reduced for participants who also had a sup-

portive network of STEM peers. Collectively, the results of

the present study illustrate the importance of helping girls and

women forge positive connections with their peers who are

also in the STEM pipeline.

Acknowledgments

Timea Farkas, Campbell Leaper, and Paul Nelson are thanked for

their feedback on an earlier version of this article. I also thank Mary

Brabeck, Isis Settles, and three anonymous reviewers for their help-

ful comments during the review process. Gratitude is extended to

the participants and to the high school teachers and principals. The

following research assistants are thanked for their assistance:

Amanda Gerber, Bonnie Glenesk, Katrina Hoagland, Brittany

Hopkins, Alana Kivowitz, Kristina Lee, Nikki Luu, Megan Naides,

Alexa Paynter, Lauren Seidel, Stacey Storey, and Chaconne

Tatum-Diehl.

Author’s Note

An earlier version of this article was presented at the 2014 meeting

of the Gender & STEM Network conference in Berlin, Germany.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect

to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for

the research, authorship, and/or publication of this article: Prepara-

tion of this manuscript was supported by a grant from the American

Psychological Association to Rachael Robnett, as well as grants

from the UC Santa Cruz Academic Senate and the Paynter family

to Campbell Leaper.

Notes

1. Across the full sample (i.e., all phases of education and areas

of study), only four (1%) participants identified as African American. This low base rate precluded the use of a standalone

‘‘African American’’ ethnic group category in the analyses. Thus,

the African American girls and women in the sample are instead

included in the ‘‘multiple/other’’ ethnic group category.

2. The current study is part of a larger study that focuses on predic-

tors of students’ pursuit of science, technology, engineering, and

math (STEM) careers. Other constructs assessed in the larger

study include mentoring, social identity, work–family conflict,

and experiences with sexual harassment.

3. The sample prompt provided in text was used for undergraduates

who were majoring in science fields. Participants in high school

were asked about bias they experienced in their math or science

classes, and participants in graduate school were asked about bias

they experienced in their graduate program. In addition, the

wording of the prompt was tailored to students’ current field of

study or, in the case of high school students, to their desired field

of study. For example, for high school students who were inter-

ested in the life sciences, the last sentence of the prompt was as

follows: ‘‘We would like to know about your experiences with

gender bias in your science classes over the past year.’’ In con-

trast, for high school students who were interested in math-

intensive fields, the last sentence of the prompt was as follows:

‘‘We would like to know more about your experiences with gen-

der bias in your math classes over the past year.’’

76 Psychology of Women Quarterly 40(1)

4. The hypothesized association between gender bias and STEM

self-concept was also tested in a subset of participants who

reported their score on the SAT Math (high school and college

students) or the GRE Quantitative (graduate students). For these

participants, standardized test scores were included as a control

variable in the multiple regression model described above. Find-

ings replicated the results obtained in the full sample. The asso-

ciation between gender bias and self-concept was significant

(b ¼ �1.15, p ¼ .02), as was the two-way interaction between bias and peer support (b¼ 1.27, p ¼ .03).

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