How Gender and Race Stereotypes Impact the Advancement
of Scholars in STEM: Professors’Biased Evaluations of Physics
and Biology Post-Doctoral Candidates
Asia A. Eaton
&Jessica F. Saunders
&Ryan K. Jacobson
#Springer Science+Business Media, LLC, part of Springer Nature 2019
The current study examines how intersecting stereotypes about gender and race influence faculty perceptions of post-doctoral
candidates in STEM fields in the United States. Using a fully-crossed, between-subjects experimental design, biology and
physics professors (n= 251) from eight large, public, U.S. research universities were asked to read one of eight identical
curriculum vitae (CVs) depicting a hypothetical doctoral graduate applying for a post-doctoral position in their field, and rate
them for competence, hireability, and likeability. The candidate’s name on the CV was used to manipulate race (Asian, Black,
Latinx, and White) and gender (female or male), with all other aspects of the CV held constant across conditions. Faculty in
physics exhibited a gender bias favoring the male candidates as more competent and more hirable than the otherwise identical
female candidates. Further, physics faculty rated Asian and White candidates as more competent and hirable than Black and
Latinx candidates, while those in biology rated Asian candidates as more competent and hirable than Black candidates, and as
more hireable than Latinx candidates. An interaction between candidate gender and race emerged for those in physics, whereby
Black women and Latinx women and men candidates were rated the lowest in hireability compared to all others. Women were
rated more likeable than men candidates across departments. Our results highlight how understanding the underrepresentation of
women and racial minorities in STEM requires examining both racial and gender biases as well as how they intersect.
Keywords STEM .Prejudice .Gender gap .Racial discrimination .Academic settings .Intersectionality
Science, technology, engineering, and math (STEM) edu-
cation and innovation are considered essential for the
health and longevity of the United States (White House
2018). For this reason, leadership positions in the STEM
fields are among the most influential, lucrative, and pres-
tigious in the nation (National Science Foundation 2013;
Pew 2018). In keeping with women’s rising share of pow-
erful positions in management and politics (Catalyst,
2018,2019), the proportion of women earning doctorates
in many STEM fields has increased considerably over
recent decades. According to annual survey data collected
by the National Science Foundation, the percentage of
women earning doctorates in engineering as well as phys-
ical and earth sciences in the United States increased by
five points in the last 5 years, although the proportion of
women earning doctorates in mathematics and computer
sciences only grew by 1% in that time (National Science
However, despite the increased proportion of female doc-
torate recipients in many STEM fields, women remain under-
represented among STEM university faculty compared to
their male counterparts. Across all science and engineering
fields, women compose 42.5% of assistant professors and just
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s11199-019-01052-w) contains supplementary
material, which is available to authorized users.
*Asia A. Eaton
Department of Psychology, Florida International University, 11200
SW 8th St., DM 208, Miami, FL 33199, USA
Wom e n’s Research Institute of Nevada, University of Nevada Las
Vegas, Las Vegas, NV, USA
Department of Psychology, Goldsmiths University of London,
24.5% of full professors at four-year colleges and universities
in the U.S. (National Science Foundation 2018). The gap be-
tween the representation of women STEM Ph.D. recipients
and women tenured or tenure-track faculty in STEM is likely
due to myriad variables, including supply and demand-side
factors that involve the interaction of individual decisions with
social and cultural constraints and opportunities (Paustian-
Underdahl et al. 2019; Wright et al. 2015).
Because evidence suggests that gender differences in inher-
ent aptitudes for math and science are negligible or nonexis-
tent (Ceci and Williams 2011; Spencer et al. 1999;
Tomkiewicz and Bass 2008), much research has investigated
social and structural reasons for the underrepresentation of
women in academic STEM fields. Some of these include the
prevalence of highly masculine organizational cultures that
create a hostile climate for women, inadequate parental leave
policies for employees, and gender differences in work-family
balance and labor (Byars-Winston et al. 2011; Ceci and
Williams 2011). One line of research helping to explain the
gender gap in STEM centers around long-standing negative
stereotypes regarding women’s competence in science and
math (Moss-Racusin et al. 2012).
Gender Stereotypes and STEM
Stereotypes, or cultural beliefs about individuals based on
their social category membership, have profound effects on
our behavior toward others. When encountering a member of
a social category about which we hold stereotypic beliefs,
those beliefs are quickly and efficiently activated and can in-
fluence our emotions, thoughts, and actions (Cundiff et al.
2013). Gender and race are the strongest social bases upon
which we stereotype others (Wood and Eagly 2010), and they
are among the most widely studied by psychologists
(Bodenhausen and Richeson 2010). Despite significant cultur-
al shifts in women’s roles and opportunities over the last sev-
eral decades in the United States, stereotypic beliefs about
women’s and men’s traits, roles, occupations, and physical
characteristics have remained highly stable (Haines et al.
Descriptive stereotypes about women and men, or expec-
tations about what women and men are typically like
(Heilman 2012), portray women as generally less competent
than men (Diekman and Eagly 2000). Words like Bintelligent^
and Bcompetent^fall into the cluster of positive agentic traits
considered typical of men (Abele and Wojciszke 2007; Haines
et al. 2016), and not into the cluster of positive communal
traits that are seen as typical of women (Carli et al. 2016;
Eagly and Karau 2002). The stereotype content model, which
examines the perceived warmth and competence of societal
groups, also finds that women are generally regarded as less
competent than men (Fiske et al. 2002). These global gender
stereotypes can negatively affect evaluations of women’s
scholarly success compared to identical men, especially when
the target’s research and academic record is in its early stages
or is less than Bsuperb^(Steinpreis et al. 1999,p.524).
Women are also specifically stereotyped as being less com-
petent than men in STEM fields (Smeding 2012; Spencer et al.
1999). For example, teachers and parents believe that boys
have more natural talent in math than girls (Eccles et al.
1990;Li1999). Both women and men adults also rate women
as less descriptively similar to successful scientists than men
(Carli et al. 2016). In fact, in one study, participants from co-
ed universities showed no significant overlap in the traits they
ascribed to women and those they ascribed to scientists (Carli
et al. 2016). In another study, undergraduates perceived typi-
cal computer scientists as having traits that were incompatible
with the female gender role (Cheryan et al. 2013). The stereo-
type that men are typically better in math and science than
women is especially strong among men in male-dominated
fields and STEM fields (Banchefsky and Park 2018;Nosek
and Smyth 2011).
Unfortunately, gender-STEM stereotypes have tangible
negative implications for women’s success and leadership in
these fields by promoting prejudice, stereotyping, and dis-
crimination against women (Beasley and Fischer 2012;
Heilman 2012; Spencer et al. 1999; Tomkiewicz and Bass
2008). For example, research has found that both men and
women science faculty are less likely to hire a woman candi-
date compared to an identical man for a laboratory manager
position and that this bias is explained by perceptions of the
woman as less competent (Moss-Racusin et al. 2012).
Research also shows that national gender differences in sci-
ence and math achievement can be explained by national dif-
ferences in implicit gender-science stereotypes (Nosek et al.
2007). Specifically, the more a nation’s citizens implicitly as-
sociate men with science and women with the liberal arts, the
greater the gap between female and male adolescents’eighth
grade science achievement in that nation (Nosek et al. 2007).
Wom en’s and girls’persistence and felt belonging in
STEM are also negatively affected by gender-STEM stereo-
types. For example, women facing an experimentally-biased
chemistry department expected to feel a diminished sense of
belonging, more negative attitudes, and less trust and comfort
in that context than did male participants exposed to the same
biases (Moss-Racusin et al. 2018), and undergraduate women
who have been reminded of gender-STEM stereotypes are less
likely to aspire to STEM careers (Schuster and Martiny 2017).
The descriptive stereotype that females are less competent in
math and science than males has also been found to undercut
girls’and women’s math and science performance (Shaffer
et al. 2013; Schuster and Martiny 2017;Smeding2012).
This may be especially true for women who excel in and are
invested in math or science (Ambady et al. 2001;Steinberg
et al. 2012) or women who endorse gender stereotypes
(Schmader et al. 2004). In sum, a large body of evidence
shows that women are expected to be less competent and
successful in STEM fields than men, which may help to ex-
plain women’s underrepresentation in STEM.
Racial Stereotypes and STEM
Similar to women, there are significant holes in the STEM
pipeline for members of certain racial and ethnic groups.
Although women only compose about 35% of the full-time
STEM faculty at U.S. universities in 2015, the percentage of
African American and Latinx American STEM faculty is far
smaller and even more disproportionate- at less than 1% (U.S.
Department of Education, 2017). Asians and Whites, mean-
while, are overrepresented in the STEM workforce relative to
their overall share of the workforce, both in terms of their
representation in the U.S. population and among STEM doc-
torate holders (Kodel 2017). Given the substantive body of
empirical evidence indicating that racial and ethnic differences
in inherent aptitudes for math and science are nonexistent
(Gupta et al. 2011; Jimeno-Ingrum et al. 2009;Weyant
2005), racial stereotyping and discrimination have also been
proposed as barriers for the entry, retention, and success of
racial and ethnic minorities in STEM (Grossman & Porche,
In general, Black individuals are stereotyped in ways that
are incongruent with perceived success in the STEM fields.
African Americans, for example, are stereotyped as less com-
petent than Whites and Asians (Kellow and Jones 2008;
Wilson 1996), including in STEM (Blaine 2013). White uni-
versity students have been found to stereotype their Black
counterparts as unqualified for university study (Torres and
Charles 2004), and these stereotypes about the limited aca-
demic ability of Black students can reduces their intention to
major in STEM (Beasley and Fischer 2012; Kellow and Jones
Latinx individuals are also stereotyped as less competent
and lower in STEM ability than Whites and Asians (Blaine
2013; Jimeno-Ingrum et al. 2009). The stereotype that Latinxs
are less competent than Whites (Jimeno-Ingrum et al. 2009;
Weyant 2005) and do not value formal education (Valencia
and Black 2002) also has negative consequences for the aca-
demic performance of Latinx students. For example, Latinas’
concerns about how professors stereotype the academic ability
of their racial/ethnic group is significantly and negatively cor-
related withtheir college GPA (Valencia and Black 2002), and
middle school Latinx’s concerns about being judged on the
basis of their race at school are related to low feelings of
belonging at school (Sherman et al. 2013).
Individuals of Asian descent, on the other hand, are often
expected to be more competent than Whites (Berdahl and Min
2012), and to perform extremely well in STEM fields (Gupta
et al. 2011; Ho and Jackson 2001;Jacksonetal.1995).
Indeed, in research by Ghavami and Peplau (2013), the most
frequent attribute undergraduates used to describe both Asian
men and women was Bintelligent.^Asian Americans are also
over-represented in the U.S. STEM workforce and academia
(Landivar 2013; U.S. Department of Education, 2017). As
with all issues of occupational segregation, this over-
representation is likely due to the interaction of multiple fac-
tors throughout the social ecology (Wright et al. 2015), includ-
ing Confucian cultural traditions emphasizing effort, educa-
tion, and learning as a moral good (Cheng 1997;Li,2003;
Tweed and Lehman 2002). One factor Asian Americans do
not have to contend with in preparing for and working in
STEM fields, however, is negative stereotypes about their
ability and likelihood of success.
Intersection of Gender and Racial Stereotypes
Stereotype research to date has primarily focused on stereo-
types about a single social identity such as ethnicity or gender
(Fiske et al. 2002; Ghavami and Peplau 2013; Eagly and
Wood 2011; Wood and Eagly 2010). Although research on
global stereotypes about gender and race is vast, less is known
about how multiple group memberships interact to produce
particular stereotype profiles (Ghavami and Peplau 2013).
For example, stereotypes about women in general are distinct
from stereotypes about professional women (DeWall et al.
2005). Similarly, educated Black people are seen as distinct
from Black people in general (Czopp and Monteith 2006), as
are Black athletes and musicians (Walzer and Czopp 2011).
The ways in which multiple social identities intersect and
interlock to produce unique stereotypes and lived experiences
are captured through the concept of intersectionality (Cole
2009;Crenshaw1989). Psychological research in the last de-
cade finds that perceptions of and experiences at these inter-
sections are emergent rather than additive (Beasley and
Fischer 2012), and they cannot be adequately studied individ-
ually. Moreover, studying the effects of belonging to social
categories in isolation from one another results in the system-
atic understudy of certain minority groups, such as those who
are not considered prototypical for a single social group (Cole
2009). Studying barriers to STEM from an intersectional
framework provides the possibility of narrowing the gender
and racial gaps in a way that addresses the multifaceted deter-
rents of full STEM inclusion (Metcalf et al. 2018).
To our knowledge, no research to date has examined
perceptions of STEM scholars based on simultaneous var-
iability in their gender and racial identities. This is prob-
lematic because it creates overly broad classifications that
may not apply to those who are negatively affected by
two discursive groups (Steinbugler et al. 2006). The
intersecting categories of race and gender can create a
unique set of stereotypes that cannot be calculated by
summing their parts, and they can create both oppression
and opportunity (Ghavami and Peplau 2013; Steinbugler
et al. 2006). Consistent with intersectionality theory, the
results of a study conducted by Ghavami and Peplau
(2013) that examined and compared individuals’existing
perceived cultural stereotypes of 10 different gender-by-
ethnic groups (e.g., Black women or Asian American
men) found that different gender-by-ethnic group stereo-
types contained unique elements that were not merely
additive of gender stereotypes and ethnic group stereo-
types. For example, a White man likely enjoys certain
societal benefits because of his privileged gender and ra-
cial status. Additionally, because social categories are
cross-cutting, individuals can concurrently benefit from
particular identities and be disadvantaged by others
(Ghavami and Peplau 2013; Steinbugler et al. 2006). For
example, a Black man may benefit in some ways from his
gender, but be marginalized in other ways on account of
his race. Examining the intersection of race and gender
allows for understanding how race and gender simulta-
neously operate to produce unique perceptions of individ-
uals belonging to multiple disadvantaged groups and how
different levels of gender and racial group membership
interact to produce distinct levels and forms of bias
Although no known study to date has examined the
simultaneous role of racial and gender identities in
STEM hiring discrimination, there is a small, growing
body of literature examining these intersections in other
industries. For example, some research shows that em-
ployers hold slightly more favorable attitudes toward
hiring Black men than Black women (Steinbugler
et al. 2006). This may be because stereotypes that por-
tray Black women as single mothers, unreliable, and ill-
prepared are still commonly held beliefs in the labor
market (Kennelly 1999; Steinbugler et al. 2006).
vorable attitudes toward Latino men than toward Latina
women (Jimeno-Ingrum et al. 2009). For example, the
number of Hispanic/Latina women who hold positions
in higher education is even less than the number of
Hispanic/Latino men (U.S. Department of Education
that the intersection of marginalized gender and racial
group identities may lead to lower perceptions of com-
petence. Thus, Latina women and Black women may be
perceived as the least competent in the STEM fields
compared to all of the other intersecting racial and gen-
der groups (Steinbugler et al. 2006), a result of the
stereotypes associated with their intersecting minority
The Current Study
The primary purpose of the current study is to examine how
STEM candidates’gender and race, combined, influence per-
ceptions of STEM professors who evaluate those candidates.
Specifically, we examine U.S. biology and physics professors’
perceptions of the hireability and competence of post-doctoral
candidates for a tenure-track assistant professor position in
their same field, based on the candidate’s race and gender.
We modeled our study after landmark studies on job discrim-
ination in the evaluation of curriculum vitae (CVs) and re-
sumes (e.g., Moss-Racusin et al. 2012; Steinpreis et al.
1999), in which the applicant name on a single resume or
CV was varied while all else was held constant.
Based on the stereotype content model (Fiske et al. 2002),
as well as previous research examining faculty gender biases
in STEM (Moss-Racusin et al. 2012), we predict that male
post-doctoral candidates, overall, will be rated as higher in
competence and hireability than female post-doctoral candi-
dates across physics and biology departments (Hypothesis 1).
We also predicted that White and Asian candidates would be
rated as more competent and hireable than Black and Latinx
candidates across departments (Hypothesis 2). Furthermore,
consistent with intersectionality theory and prior research, we
predict that the White and Asian male candidates, coming
from multiple social backgrounds associated with success in
STEM, would be seen as the most competent and hireable of
all race-by-gender targets, whereas women candidates from
Black or Latina backgrounds, who face multiple descriptive
expectations to be low in STEM aptitude, would be rated the
least competent and hireable of all race-by-gender targets
(Hypothesis 3). Because some previous research suggests that
highly male-dominated fields are associated with greater gen-
der bias and inequity (Cheryan et al. 2017;Riegle-Crumband
King 2010), we also predict that the gender biases we observe
would be attenuated by department, with faculty from biology
departments showing a weaker preference for male post-
doctoral candidates than faculty from physics departments
Although we had no further formal hypotheses, we also
assessed perceptions of candidates’likeability. Research on
descriptive stereotypes suggests that female candidates may
be seen as generally more likeable than male candidates be-
cause communal traits, such being as caring and unselfish, are
believed to be more typical of women than men (Carli et al.
2016;WadeandBrewer2006). However, the women targets
being assessed were working in gender counter-stereotypic
fields and demonstrating some competence in that field, po-
tentially resulting in backlash. Backlash includes negative so-
cial and economic reactions individuals receive for violating
prescriptive and proscriptive norms (i.e., role-congruent
Bshoulds^and Bshould nots^; Moss-Racusin et al. 2010;
Rudman and Phelan 2010), such as women exhibiting high
levels of agency (Rudman et al. 2012). Thus, female targets in
STEM may be rated as less likeable than their male counter-
parts. Similarly, the ways race, as well as gender and race
together, would affect ratings of candidates’likeability was
left open. In sum, we analyzed candidates’perceived
likeability in an exploratory fashion.
Creation of the Department CVs
Social science literature suggests that stereotypes are most
likely to be expressed in the evaluation of ambiguous or aver-
age targets (Barrantes and Eaton 2018;Moss-Racusinetal.
2012; Steinpreis et al. 1999), which allow for multiple inter-
pretations. For this reason, the physics and biology CVs in the
current study were constructed to represent candidates whose
qualifications were average overall, but who also had conflict-
ing indications of competence. To ensure that the physics and
biology CVs both represented average postdoctoral candi-
dates with regard to positions at large, public universities of
the Highest Research Activity (R1s), we undertook extensive
pretesting at a large, public U.S. R1 not included in the final
pool of participating universities.
First, using input from multiple physics and biology facul-
ty, we identified two very common subfields in physics and
biology: nuclear physics and evolutionary biology, respective-
ly. These subfields were chosen so that the faculty participants
in our study would have the greatest opportunity possible to
feel qualified to render judgments on the CV and the candi-
date’s hireability and competence. Next, we solicited input on
CV content from two physics subject matter experts (a tenured
man and a tenured woman physics professor at the R1 used in
pretesting) and two biology subject matter experts (a tenured
man and a tenured woman biology professor from the same
R1). These subject matter experts (SMEs) were unaware of
our study’s hypotheses, and they were told the research team
needed assistance in creating Baverage^CVs for recent Ph.D.
graduates in their respective fields. The SMEs also provided
the research team with CVs of recent doctoral graduates from
their departments who had successfully attained post-doctoral
positions at a large public, R1 university. Similar to work by
Steinpreis and colleagues (Steinpreis et al. 1999), the bases of
the biology and physics CVs came from real-life scientists,
including real journal titles and national conferences.
Together, this content was used to draft a CV for the biology
and physics post-doctoral candidates. The CVs were revised
multiple times following the suggestions from the SMEs be-
fore quantitative pretesting.
Ambiguity in CVs’Indicators of Competence
Approximately 60% of the content in the CVs (publications,
conference presentations, the quality of the doctoral program,
etc.) was crafted to represent the competitiveness of an
average-level candidate. For example, the number of publica-
tions on the Physics CV (23 publications, 3 first-author) and
on the Biology CV (4 publications, 3 first-author) and their
journal titles were seen as average by the SMEs. However, as
we mentioned previously, findings in similar studies (e.g.,
Barrantes and Eaton 2018;Steinpreisetal.1999)haveindi-
cated that rater biases are expressed to a greater extent when
evaluating candidates whose performance is ambiguous or
still emerging. Thus, 20% of the remaining content in the
CVs was intended to represent noticeably superior signs of
achievement that indicate excellent performance, and the re-
maining 20% was intended to represent Bred flags^indicating
poor performance/low competence. As an example of excel-
lent performance, the candidate won a dissertation year fel-
lowship from their university and attended M.I.T. as an under-
graduate. As indicators of possible low performance, the can-
didate took 10 years to complete their Ph.D. and did not have
any significant external grant funding.
CV Pretest Results
After the CVs for each department were created with and
approved by the SMEs, they were quantitatively pretested
using a sample of 19 tenured and tenure-track biology profes-
sors and 15 tenured and tenure-track physics professors
employed at the same R1 from which the SMEs were drawn.
Pretest participants were asked to indicate the competitiveness
of the publication record section, the grants and award section,
and the honors section of the candidate’s CV, as well as their
Boverall perception of the applicant’s competence^on 9-point
Likert-type scales. Two short-answeritems were also included
to ensure faculty in both departments were able to identify the
notable accomplishments and red flags included in each CV.
The pretest of both CVs yielded mean ratings of overall
candidate competence that were in the middle of the 9-point
scales (Biology M=5.83,SD = 1.20; Physics M=6.00,SD =
1.81). When these means were tested against the scale mid-
point of 5, Physics professors’ratings of the candidates’com-
petence were not found to differ significantly from the mid-
point, t(14) = 2.13, p= .051. Although Biology professors rat-
ed the candidate as significantly above the mid-point of 5,
t(17) = 2.95, p= .009, the scores clustered close to the mid-
point (one standard deviation above and below the mean
ranged from 4.63 to 7.03). An independent-samples t-test with
faculty department as the independent variable and overall CV
competence as the dependent variable indicated that the
Biology CV did not significantly differ from the physics CV
in faculty perceptions of overall candidate competitiveness,
t(31) = .31, p=.75.(Seetheonline supplement for final CVs.)
Candidate Name Pretest Results
The eight candidate names selected to represent the eight race/
gender conditions were generated by choosing among the
most common first and last names indicated in the 2010
United States Census Bureau (U.S. Census Bureau, 2010)
for each of the eight race/gender groups. The names were as
follows: Bradley Miller (the White male condition), Claire
Miller (the White female condition), Zhang Wei [David] (the
Asian male condition), Wang Li [Lily] (the Asian female con-
dition), Jamal Banks (the Black male condition), Shanice
Banks (the Black female condition), José Rodriguez (the
Latino male condition), and Maria Rodriguez (the Latina fe-
The eight first and last name combinations were pretested
using a new sample of 20 biology and physics faculty mem-
bers from the same university where the CV pretesting was
done. Using a within-subjects design, the 20 biology and
physics pretest faculty were asked to indicate if each of the
eight candidate names was a male or female and whether it
was perceived as indicating a White, Latinx, Black, or Asian
candidate. Results of the name pretesting showed that 100%
of faculty member participants accurately indicated the
intended race and gender of each of the first and last name
combinations. Thus, the name pretesting supported our use of
the eight race/gender name combinations in our study to indi-
cate the intended gender and race of the candidate.
Our actual study employed a fully-crossed between-subjects
experimental design, using a large sample of U.S. male and
female biology and physics professors to understand how the
race and gender of post-doctoral candidates affects STEM
professors’evaluations of these candidates’competence and
hireability. We asked STEM professors in the Physics and
Biology departments of eight public research universities in
the United States to read and evaluate the CV of a recently
graduated, hypothetical Ph.D. student in their respective fields
(physics and biology) who was looking for a post-doctoral
position. The CVs varied only in terms of the gender (female
vs. male) and ethnicity (White vs. Latinx vs. Black vs. Asian)
of the candidate, which were indicated by the candidates’first
and last name.
Our participant pool included tenured and tenure-track pro-
fessors in the Physics and Biology departments at eight large
(i.e., more than 25,000 students), public, very high research
(RUVH), mostly-urban, U.S. universities that did not have
NSF ADVANCE IT grants as of mid-2016. Large universities
were chosen because they have large faculty bodies from
which we could sample. Universities in the same research tier
were chosen so that the standards for scholarly success across
schools were relatively uniform, allowing us to construct CVs
of recent graduates targeted at the average level of productiv-
ity for these types of schools. We chose RUVH schools be-
cause these universities have the least diverse faculty bodies
and yet are key organizations for advancing women and mi-
norities into high-level research positions in their fields
Schools from across the nation were selected to make the
results generalizable. Schools that had not had NSF
ADVANCE IT grants were chosen because these schools
may be less likely to guess the purpose of the study and be-
cause these schools have not yet benefitted from ADVANCE
IT grant consciousness-raising designed to increase the partic-
ipation and advancement of women pursuing academic sci-
ence and engineering careers (National Science Foundation
Prior research demonstrates a moderate effect of candi-
dates’gender on STEM professors’perceptions of candidates’
competence (Moss-Racusin et al. 2012). Thus, in order to
detect an effect of .03 (small η
) with .80 power, .05 proba-
bility, and 16 cells in a 2 (department: physics or biology) ×
2(candidate gender: male or female) × 4(candidate race:
White, Latinx, Black, or Asian) between-subjects design, we
attempted to achieve 14 individuals per cell for a total of 230
professors, 115 from each of the two departments. The total
number of tenured and tenure-track physics professors at the
institutions from which we recruited was 239 (M= 29.88,
SD = 9.11, range = 13–41), and the total in biology was 428
(M= 53.50, SD = 28.94, range = 24–106), making a total of
667 professors in both departments across all eight universi-
ties. However, 32 of the 667 mailed surveys sent to these
faculty were returned for invalid addresses and were removed
from our final participant pool, resulting in a final pool of 635
eligible physics and biology faculty members.
To maximize the response rate for each department to attain
a sufficiently large sample size of faculty participants from
each department, a $5.00 cash incentive was mailed to each
of the 635 potential faculty participants in the participant pool
along with a consent form, a survey, and a random version of
the CV in their field. All procedures were approved by the
Social and Behavioral Sciences IRB at the first author’s
Of the 635 tenured and tenure-track faculty in the participant
pool who were mailed surveys and study materials, a total of
251 faculty from both departments mailed back completed
surveys and were included as participants in our study, making
a response rate of 39.37% across departments. Based on pre-
cedents in the literature (e.g., Moss-Racusin et al., 2012;
Steinpreis et al. 1999), our attained response rate was typical
and sufficiently representative. Of the 251 faculty participants
included in our sample who completed the survey, 157
(62.55%, 38% response rate) were from a biology department
and 94 (37.45%, 41% response rate) were from a physics
department. Across both departments (n= 190), 22% (n=
43) of respondents self-identified as female and 78% (n=
147) self-identified as male. When examined by discipline,
90% (n= 84) of those in the physics department indicated they
were men, as did 65% (n= 63) of those in the biology depart-
ment. Regarding professional status, 57.22% (n=103)ofthe
faculty in the sample reported having the position of Full
Professor, 26.11% (n= 47) were associate professors,
13.33% (n= 24) were assistant professors, and 3.33% (n=6)
reported having another tenured or tenure-track professional
status. Lastly, nearly all (n=225, 89.62%) of the faculty in the
sample reported having previous experience hiring a post-
doctoral candidate at least once.
The gender and racial composition of male and female
faculty members included in the study were very similar to
the national average gender compositions for physics and bi-
ology departments (National Science Foundation 2014), with
the majority of faculty being men in both departments and
with the physics department being particularly male-
dominated compared to the biology department. Recent re-
search shows that, on average, 16% of physics faculty are
women (Ivie 2018), and nearly 90% of physics doctoral de-
grees earned in the United States between 2014 and 2016 were
earned by White students (Ivie 2018). In 2016, only 1.5% of
physics faculty were Black and 3.3% identified as Latinx (Ivie
Materials, Procedure, and Measures
Participants were first instructed to read and sign the consent
form. They were then asked to carefully review the CV they
were sent, which was described as B…a hypothetical CV that
was developed by combining various CVs of actual postdoc-
toral associates in your field. Please keep in mind that this is a
fictitious CV and not an actual individual.^In order to help
reduce demand characteristics and socially desirable
responding, participants were instructed that the main purpose
of the study was to examine how CV formatting and design
styles influenced science faculty’s perceptions of postdoctoral
candidates. To support this cover story, four questions on the
format of the CV were included at the beginning of the survey
before participants assessed the hireability, competence,
likeability, and competitiveness of the post-doctoral candidate.
To further support our cover story, the research interests of the
third author, who was described as the study’s principal inves-
tigator (PI), were altered while the study was running to reflect
an interest in CV and resume formatting. Thus, any partici-
pants who searched online for the PI’s research interests
would have found interests that matched the study’sostensible
Once the faculty participants were finished reading the
enclosed CV, they were instructed to complete the attached
survey. Participants first answered four items that examined
their perceptions of the format and design of the CV as part of
the cover story. Next, participants completed items measuring
their perceptions of the post-doctoral candidate’s overall com-
petitiveness, the likelihood he/she would be hired at their in-
stitution, and measures of his/her competence and likeability.
Participants were then instructed to mail back their completed
consent form, survey, and the CV using a stamped envelope
provided to them and addressed to the third author’sstudent
Four items at the beginning of the survey were used to assess
participants’perceptions of the formatting of the CV. The
items were: (1) BHow easy was it for you to navigate the
CV?,^(2) BHow complete or comprehensive was the infor-
mation in the CV?,^(3) How professional was the CV?,^and
(4) BHow well-designed was the CV?^These items were not
included in our analyses because they were only part of the
cover story and did not represent variables of interest.
Ratings of the candidate’s competence were created by using
the composite score from three items borrowed from Moss-
Racusin and colleagues(Moss-Racusin etal. 2012). The items
were: (a) BBased on the CV you read, did the candidate strike
you as competent?,^(b) BHow likely is it that the candidate
has the necessary skills for a postdoc job?,^and (c) BHow
qualified do you think the candidate is?^.Participantsused
9-point Likert-type scales to respond to these items, from 1
(not at all)to9(very much). Scores were averaged across
items such that higher scores denoted greater perceived com-
petence. Internal reliability for the competencecomposite was
Faculty ratings of the candidate’s hireability were created by
using the composite score of three hireability items from
Moss-Racusin and colleagues (Moss-Racusin et al. 2012).
Participants responded to the following three questions using
a1(not at all likely)to9(very likely) Likert-type scales: (a)
BHow likely do you think it would be for the candidate to
make the ‘first cut’(be in the top tier of candidates) if they
applied to an open postdoc position at an institution like yours
(large, public, R1)?^;(b)BHow likely do you think it would be
for the candidate to be selected for an interview if they applied
to an open postdoc position at an institution like yours?^;and
(c) BHow likely do you think it would be for the candidate to
be extended an official offer for an open postdoc position at an
institution like yours?^Scores were averaged across items
such that higher scores denoted greater perceived hireability.
Internal reliability for the hireability composite was high
Similar to the measure of competence, faculty ratings of can-
didate likability were calculated using the composite score of
three likeability items drawn from Moss-Racusin and col-
leagues (Moss-Racusin et al. 2012). The three items were:
(a) BBased on the CV you read, how much you did like the
candidate?^; (b) BWould you characterize the candidate as
someone you want to get to know better^; and (c) BWould
the candidate fit in well with other faculty members at your
institution?^Participants responded to these items using
Likert-type scales from 1 (not at all)to9(very much), and
internal consistency reliability for the likeability composite
was high (α= .93). Scores were averaged across items such
that higher scores denoted greater perceived likeability.
Preliminary Analyses and Analysis Plan
Data were first evaluated for missingness, skewedness, kurto-
sis, and outliers. A missing value analysis yielded a nonsig-
nificant value, Little’sMCARχ
(8) = 5.52, p= .70. The mul-
tiple imputation function in SPSS was used to impute values
for independent variables with missing values (see Treiman
2009, for a description of Bayesian multiple imputation). Ten
imputed datasets were created and pooled for our subsequent
analyses. Percentage of missing data on dependent variables
ranged from 12.6% to 16.7%. Multiple imputation has been
shown to provide unbiased estimates and standard errors when
missing data are either missing completely at random or miss-
ing at random, and the amount of missing data ranged from 10
to 20% (Schlomer et al. 2010).
To examine our hypotheses, data were analyzed in a three-
way factorial MANOVA with department, candidate gender,
and candidate race as the independent variables as well as
composite scores representing candidate competence and
hireability as the two dependent variables. Along with main
effects of race and gender (Hypotheses 1 and 2), our model
included a two-way interaction between race and gender
(Hypothesis 3) and between gender and department
(Hypothesis 4). We performed bootstrapping with 1000
resamples to allow for correlated error terms.
Hypothesis 1: Candidate Gender
Our results indicated a significant main effect of candidate
gender across both departments and all experimentally manip-
ulated target ethnicities on competence ratings, F(1, 246) =
11.18, p< .001, η
= .05. Consistent with a large body of
previous literature (Eagly and Mladinic 1994;Moss-Racusin
et al., 2012;Steinpreisetal.1999), faculty participants rated
the male candidates as being significantly more competent
than the equally qualified female candidates when averaging
across faculty departments, lending support to Hypothesis 1.
Further supporting Hypothesis 1, results from the three-way
factorial MANOVA, with candidate gender, candidate race,
and faculty department as the independent variables and can-
didate hireability as the dependent variable, indicated a signif-
icant main effect of candidate gender on faculty ratings of
hireability across departments and candidate ethnicities, F(1,
246) = 7.98, p=.005, η
= .03. Men were viewed as signifi-
cantly more hireable than their female counterparts. Although
exploratory, our analysis of likeability by gender showed a
significant main effect, F(1, 246) = 3.94, p=.048, η
Women were rated as significantly more likeable than men.
The mean competence, hireability, and likeability scores by
gender along with associated p-values and effect sizes are
reported in Table 1.
Hypothesis 2: Candidate Race/Ethnicity
In addition to the significant main effect of gender on faculty
ratings of candidate competence and hireability, there also
were significant main effects of candidate race on ratings of
competence, F(3, 246) = 7.78, p<.001,η
= .09, and candi-
date hireability, F(3, 246) = 10.77, p<.001, η
dicted by Hypothesis 2. White and Asian candidates were
rated as more competent and hireable than Black and Latinx
candidates across departments. Likeability ratings were not
found to differ significantly by applicant race, F(3,
246) = .12, p=.95,η
= .001. Mean competence, hireability,
and likeability ratings by race along with associated p-values
and effect sizes are reported in Table 1.
Hypotheses 3 and 4: Intersections and Department
Contrary to Hypothesis 3, there were no significant interac-
tions between race and gender on perceived competence, F(3,
243) = 1.01, p= .39, or hireability, F(3, 243) =1.13, p=.33.
We returned to this finding after testing Hypothesis 4. Results
for Hypothesis 4, examining the interaction between depart-
ment and gender, indicated that faculty department moderated
the effect of candidate gender on composite ratings of compe-
tence, F(1, 246) = 5.45, p=.02, η
=.02. More specifically,
faculty participants in the physics department rated male
candidates as significantly more competent than female can-
didates (see Table 1). Faculty participants’in the biology de-
partments competence ratings of male candidates did not sig-
nificantly differ from their competence ratings of female can-
didates. Likewise, the interaction between faculty department
and ratings of hireability was also significant, F(1, 246) =
= .07. Faculty participants in the physics
department rated male candidates as significantly more
hireable than female candidates, whereas faculty in the biolo-
gy department rated male and female candidatessimilarly (see
Table 1). Thus, consistent with Hypothesis 4, our results indi-
cated that only physics faculty appeared to exhibit gender bias
favoring male candidates in terms of both perceived compe-
tence and hireability. Faculty department did not moderate the
effect of candidate race, F(3, 246) = 1.13, p=.34, orgender,
F(1, 246) = .48, p= .49, on likeability.
Although there was a significant main effect of candidate
race on ratings of competence across departments, there was
not a significant interaction between candidate race and facul-
ty department on ratings of competence, F(3, 243) = 2.04,
p= .11. There was, however, a significant interaction between
candidate race and faculty department on hireability, F(3,
246) = 4.89, p= .03, η
= .06. More specifically, faculty in
the physics department exhibited a significant racial bias fa-
voring Asian and White candidates as more hirable compared
to equally qualified Black and Latinx candidates (see Table 1).
Those in biology also demonstrated a significant racial bias in
hireability, favoring the Asian candidates as more hirable than
equally qualified Black candidates. However, this was the
only significant racial bias in hireability exhibited by biology
faculty. Moreover, no significant three-way interaction was
found among participant department, candidate race, and
likeability, F(3, 243) = 1.13, p=.33.
Given the null finding for Hypothesis 3 and partial support for
Hypothesis 4 such that certain racial and gender groups were
rated lower by professors in physics, we examined whether a
three-way interaction among department, applicant gender, and
applicant race would reveal differences in ratings of competence
and hireability for Latinas and Black women compared to White
and Asian women as well as all men, regardless of men’s race.
Indeed, there was a significant three-way interaction among de-
partment, applicants’gender, and applicants’race on hireability,
F(3, 243) = 3.05, p=.03 η
=.04. Black (M=4.29, SE = .46,
p< .001) and Latinx (M=3.87,SE =.54, p< .001) female can-
didates, as well as Latino male candidates (M=4.67,SE = .61,
ps =.048 to < .001), were rated significantly lower than all other
candidates (Ms ranged 5.93–7.42) by physics faculty. The three-
way interactions on competence, F(3, 243) = 2.12, p= .09,
= .03, and likeability, F(3, 243) = 1.34, p=.25, η
were not significant. All means by gender, race, and faculty
Table 1 Descriptive statistics and gender and racial/ethnic group comparisons
Candidate Gender Candidate Asian vs. White vs.
Male Female Comparison Asian White Black Latinx Black Latinx Black Latinx
M(SD)M(SD)p, d M (SD)M(SD)M(SD)M(SD)p,d p,d p,d p,d
Overall 7.18 (1.40)
.006, .35 7.29 (1.41)
<.001, .63 .02, .43 <.001, .77 .003, .56
By Physics Faculty 7.46 (1.21)
<.001, .84 7.42 (1.20)
.008, .73 .002, 1.09 .004, .77 < .001, 1.15
By Biology Faculty 7.02 (1.48)
.70, .06 7.20 (1.53)
.014, .56 .42, .17 .003, .78 .16, .34
Overall 6.48 (1.90)
.03, .31 7.04 (1.51)
< .001, .73 .001, .78 .002, .52 .004, .53
By Physics Faculty 6.93 (1.77)
<.001, .92 6.86 (1.68)
.018, .63 <.001, 1.49 .008, .68 <.001, 1.59
By Biology Faculty 6.20 (1.94)
.18, .21 7.14 (1.41)
< .001, .81 .69, .13 .09, .36 .70, .09
Overall 5.88 (1.46)
<.001, .31 6.11 (1.29)
.38, .15 .51, .12 .54, .11 .33, .18
By Physics Faculty 5.85 (1.50)
.45, .16 6.03 (1.32)
.83, .07 .58, .18 .82, .06 .70, .17
By Biology Faculty 5.90 (1.45)
.02, .40 6.16 (1.29)
.33, .21 .30, .24 .56, .14 .56, .36
Means with different subscripts are significantly different across a row (a) within gender (i.e., comparing ratings of male and female candidates) and (b) within racial/ethnic groups (i.e., comparing ratings of
Asian, White, Black, and Latinx candidates)
department appear in supplementary Table 1s. Boxplots
displaying competence, hireability, and liking composite ratings
for each candidate CV in each department also are available in
the online supplement.
The present study examines how U.S. university professors’
perceptions of STEM post-doctoral candidates are affected by
gender and racial stereotypes. The present work goes beyond
previous examinations of stereotypes about STEMworkers by
applying an intersectional lens by exploring perceptions of
men and women STEM scholars in multiple racial/ethnic
identities across two STEM domains. We experimentally ma-
nipulated the racial and gender identities on the CVs of a
postdoctoral scholar applicant in either biology or chemistry.
Our hypotheses were generally supported by the data. A gen-
der bias (in physics), a racial bias (in both physics and biolo-
gy), and compounded gender and racial biases (in physics)
were evident in professors’evaluations of ambiguously qual-
ified post-doctoral candidates.
First, male post-doctoral candidate CVs were evaluated
more favorably by STEM professors in general, although this
effect was moderated by faculty department. Male favoritism in
the evaluation of STEM scholars is consistent with previous
evidence demonstrating gender bias in lab manager applica-
tions (Moss-Racusin et al., 2012), yet is potentially more dam-
aging because postdoctoral positions are increasingly necessary
for becoming a tenure-track research faculty member and
achieving the most prestigious opportunities in the field.
However, it is critical to note that only physics faculty, not
biology faculty, exhibited a general gender bias in their evalu-
ations of the candidates’competence and hireability. This mod-
eration by department was expected because biology is a more
gender-balanced field than physics (Cheryan et al. 2017).
The increased gender bias in physics compared to biology
may be due to a hostof factors. First, physics departments may
have more masculine cultures than biology departments, po-
tentially privileging male over female applicants (Cheryan
et al. 2017). Second, a large body of research suggests that
although both men and women hold sexist attitudes and gen-
der stereotypes, men hold stronger gender biases than women
do (Glick and Fiske 2001). Because 90% of our participants in
physics were men, compared with only 65% of participants in
biology, the gender bias observed in physics may be due, at
least in part, to participants’gender. Unfortunately, we were
not able to examine the potential moderating effects of partic-
ipants’gender on our dependent variables because there were
too few women faculty in our sample to examine interactions
among department, participants’gender, targets’gender, and
targets’race. Third, the presence of a gender bias in physics,
and not biology, may be due to the fact that physics is seen as a
Bharder^science than biology—one requiring very high levels
of mathematical and analytical intelligence (Hazari et al.
2007). Thus, the gender bias in physics may be due to a greater
presumed lack of fit between beliefs about typical women
candidates and the requirements of physics positions com-
pared to biology positions. All of these explanations may also
operate simultaneously, and they should be examined in future
The second main finding in the current study is that faculty
members in both departments demonstrated racial biases.
Biases in candidates’competence were similar in both depart-
ments, where Asian and White candidates were seen as more
competent than Black candidates. In terms of hireability, fac-
ulty in physics rated Asian and White candidates as more
hirable compared to Black and Latinx candidates, whereas
those in Biology rated the Asian candidates as more hirable
than the Black candidates. Our third finding, consistent with
intersectionality theory, was evidence for compounded gender
and racial biases among candidates in physics. Specifically,
Black and Latina female candidates, and Latino male candi-
dates, were rated significantly lower than all other candidates
on the measure of hireability by physics faculty.
Taken together, our findings lend experimental support to
the double bind and unique challenges faced by Women of
Color in science. Prior research has found that Women of
Color not only experience the bias patterns encountered by
White women, but also report biased experiences that differ
from those of White women (Williams and Dempsey 2014).
For example, Black women are more likely to experience
isolation in the academy than White women (Williams
and Dempsey 2014). Latinas, meanwhile, report levels of dis-
respect and accent discrimination not reported by other wom-
en (Williams and Dempsey 2014).
Limitations and Future Research
Although the current study helps shed new light on how
faculty’s biases may impede women and underrepresented
minority members from advancing in STEM disciplines, par-
ticularly in physics, there are some limitations in the current
research. First, although we examined how candidates’race
and gender affected STEM faculty’s ratings of post-doctoral
candidates, one of the main limitations was our inability to
analyze participants’gender and race because doing so would
have greatly reduced the statistical power of our model.
Examining how raters’own social identities may impact the
expression of stereotypes, including the extent to which they
share identities with a target, will be an important task for
future research on biases in STEM. Additionally, the attenu-
ating effect of department on racial and gender stereotypes in
the current study suggests that studying additional STEM de-
partments, as well as mediators of departmental differences in
biased evaluations, will be important for theory and practice
Next, we derived our predictions in the present paper from
literature on descriptive stereotypes (i.e., stereotypes about
what is typically true of group members) rather than on pre-
scriptive stereotypes (i.e., stereotypes about how group
members ought to be; Prentice and Carranza 2002).
Specifically, we expected the descriptive stereotypes that
women and underrepresented racial/ethnic minorities are less
competent in STEM than their counterparts would serve a
heuristic or energy-saving function (Heilman 2012) in the
evaluations of complex CVs that did not give the reader a clear
sense of the target’s competence. Whereas descriptive stereo-
types about the competence of women and racial/ethnic mi-
norities are well known (Fiske et al. 2002), there may also be
prescriptive stereotypes about the competence of these groups
in STEM that lead to backlash (Moss-Racusin et al. 2010).
Future research should further examine the effects of descrip-
tive and prescriptive stereotypes on evaluations of underrep-
resented groups in STEM, including the extent to which pre-
scriptive stereotypes about women’s competence and STEM
abilities might produce backlash. Our exploratory findings on
candidates’likeability, in which women candidates were seen
as more generally likeable than men, suggest that women
STEM candidates were not penalized in terms of their per-
ceived warmth. However, this is not conclusive evidence for
lack of a backlash effect because our candidates did not dem-
onstrate clearly superior achievement and ability that would
violate prescriptive norms for women and minorities to be less
intelligent and capable in STEM.
One potential criticism of our paper is that our CVs were
rather weak, generally sending a Bdon’t hire me^signal in
today’s highly competitive job market. Specifically, the red
flags in our CVs might be interpreted as Bbias amplifiers^
(Tetlock and Boettger 1989), leading faculty to be especially
suspicious of candidates with this mixed constellation of qual-
ities and to rely more heavily on stereotypes than they might
otherwise.Indeed, we constructed CVs that were intentionally
less than stellar and included some obvious drawbacks.
Nonetheless, the pretesting we performed with R1 physics
and biology faculty indicated that the CVs were rated as
slightly more competitive than average, with mean pretest
ratings of Bcompetitiveness^being above the scale midpoint
for both the biology and physics CVs. Second, although par-
ticipants in our study came all came from R1s where the ma-
jority of faculty participants had actual experience with hiring
post-doctorates, they were not among the top 20 R1s, where
the CVs we created might have been seen as especially low in
quality or problematic. The overall means for the competence
and hireability of the CVs in our study support this
A final issue to consider when situating our study in the
broader literature is the seeming divergence between our
findings and those from studies that do not reveal biases
against female applicants for academic positions in STEM
(Ceci and Williams 2015; National Research Council 2009;
Williams and Ceci, 2015). For example, experimental work by
Williams and Ceci (2015) found that faculty in biology, engi-
neering, and psychology significantly preferred women appli-
cants for assistant professor positions relative to men. We
believe the apparent disjuncture between our findings and
theirs can be resolved by considering the difference in the
strength of the application materials used in each study.
Williams and Ceci (2015) had professors evaluate applica-
tions for tenure-track positions that were Bunambiguously
strong,^whereas we intentionally developed materials that
were ambiguous in quality. It has been long known that ste-
reotypes are most likely to guide information processing and
evaluation in ambiguous situations, serving a schematic func-
tion (Barrantes and Eaton 2018;Heilman2012). In this way,
Williams and Ceci demonstrate a boundary condition in the
application of gender stereotypes by showing that scholars
with exceptionally strong records may be exempt from biases
in favor of men and, in fact, that excellent members of under-
represented groups may have a hiring advantage. Indeed, men
may not be prejudicially favored over women in STEM when
both are equally and highly qualified (Williams and Ceci,
2015) or when clear differences in strength between applicants
exists (Ceci and Williams 2015). However, when adjudicating
among moderately and equally qualified candidates, men may
be prejudicially advantaged. Because most Ph.D. graduates
have records that are moderate in quality, and include both
achievements and limitations, this is concerning, and adds
support to the adage that the evidence of true gender equity
will be B…when there will be equal numbers of mediocre
women [in positions of power] and mediocre men^
Many factors contribute to the maintenance of the gender and
racial gap in STEM, including push-and-pull factors ranging
from perceived ability to familial pressures (Watt et al. 2017).
The present work adds to the bodyof knowledge showing that
one likely contributor to this gap is prejudice in the evalua-
tions of women and underrepresented racial/ethnic minority
STEM scholars. To the extent that STEM professors see indi-
viduals of a certain gender and race as less competent and
hireable for STEM post-doctoral roles, they should be less
likely to recruit and hire such individuals. Ironically, biases
in recruitment and hiring can lead to a disproportionately low
representation of women and minorities in the STEM profes-
sion, reinforcing the perception that they are not appropriate
for or successful in these positions (Moss-Racusin et al. 2012).
One practical implication of our findings is that change to
evaluative processes and practices may be needed to
counteract gender and racial bias in STEM hiring (Sax et al.
2016). Several empirically tested interventions have improved
engagement at the undergraduate level for women and Black
students (Smith et al. 2012; Walton et al. 2015, but additional
interventions are needed to ensure women and minorities are
fairly evaluated and consistently engaged at the postdoctoral
level and beyond. One way to do this might be to have STEM
job candidates submit materials that do not include their full
names, but only surnames, which are inevitably present in
citations of publications and presentations. This may reduce
the operation of gender biases in the evaluation of candidates’
materials, although racial biases may still emerge as the result
of racially or ethnically linked surnames. Letter writers may
also wish to remove clear references to candidates’gender and
race in their letter of support to reduce the potential for bias.
A second suggestion to improve fairness in the evaluation
of post-doctoral candidates in STEM specifically is to change
post-doctoral hiring protocol to include additional checks and
balances. Presently, post-doctoral candidates are evaluated
and hired by Principal Investigators (PIs) only, rather than
by hiring committees composed of people with diverse per-
spectives and backgrounds. Including additional faculty mem-
bers in the evaluation of post-doctorates, from colleagues to
administrators, may help to expose and/or undermine the op-
eration of biases that an individual PI might have.
A third suggestion for STEM professorsand those who hire
STEM professionals is to develop anti-bias interventions that
are tailored to address issues specific to Women of Color
(Pietri et al. 2017). Although a number of trainings on bias
awareness and intervention exist (e.g., United States
Executive Office of the President/Office of Personnel
Management/Office of Science and Technology Policy,
2016) these tend to address single forms of bias, such as sex-
ism or racism. However, our study suggests that Latina and
Black women are at a greater disadvantage in physics than all
other candidates, and special attention should be paid in future
interventions to counteracting this unique and intersecting
form of disadvantage. A final suggestion for STEM profes-
sionals is to use clear and objective criteria for evaluating
STEM job applicants. Because stereotypes alter the weight
and attention we assign given aspects of a candidate’saccom-
plishments (Norton et al. 2004), having consistent standards
for the value of various accomplishments, as well as easy
ways to compare accomplishments across candidates, may
decrease the activation of stereotypes.
The current research provides novel and generalizable knowl-
edge about stereotypes thwarting women’s and minorities’
advancement in STEM fields. The fair evaluation and hiring
of postdoctoral racial minority and women candidates is likely
to increase the representation and success of these groups in
STEM. Our results indicate that future research should exam-
ine reasons for the differential expression of biases between
STEM fields such as biology and physics, as well as the ram-
ifications of violations of descriptive versus prescriptive
norms. In terms of practice, masking the gender and race of
candidates and implementing programs designed to decrease
bias against Women of Color in STEM are warranted. Lastly,
our findings highlight the importance of checks and balances
in the hiring process, as well as the need to establish clear,
objective evaluation criteria of postdoctoral candidates.
Acknowledgements The authors want to give a special thanks to Hannah
Schindler and Natalia Gutierrez who aided in the intensive data collection
process for the current study, and Natalia Martinez for her help assem-
bling the final submission.
Funding Funding for the present study was provided by the FIU Mine
Üçer Women in Science Fund.
Compliance with Ethical Standards
Conflict of Interest Asia A. Eaton declares no conflict of interest. Jessica
F. Saunders declares no conflict of interest. Ryan K. Jacobson declares no
conflict of interest. Keon West declares no conflict of interest.
Ethical Approval All procedures performed in studies involving human
participants were in accordance with the ethical standards of the institu-
tional and/or national research committee and with the 1964 Helsinki
declaration and its later amendments or comparable ethical standards.
Informed Consent Written informed consent was obtained from all in-
dividual participants included in the study.
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Department of Biological Sciences
University of North Texas
2005-2015 Ph.D. Biology, 2015 (December), University of North Texas, Denton, TX.
2001-2005 B.S. Ecology and Evolutionary Biology, 2005 (May), Massachusetts Institute
2015 Outstanding Dissertation Award, University of North Texas, 2015
2014 Dissertation Fellowship Award, University of North Texas, 2014.
2013 Graduate Student Research Competition, 1st Place, University of North Texas,
2005 Academic Excellence Award, Massachusetts Institute of Technology, 2005.
Grants and Awards
2015 Botanical Society of America (BSA) Annual Meeting 2015 Section Travel Award,
$500, Fall 2015.
2014 UNT Dissertation Year Fellowship, $25,000, Fall 2014.
2014 NSF Graduate Research Fellowship Program, not awarded, Fall 2014.
2011 UNT Biology Scholarship, $2,500, Spring 2011.
2011 Pearce Scholarship, UNT, $2,000, Spring 2011.
2010 Judith Evans Parker Travel Scholarship, UNT, $1,100, Spring 2010.
2010 UNT Academic Scholarship, $1,000, Fall 2010.
UNT, Department of Biological Sciences, Teaching Assistant: General Biology II Lab, 08/2013 –
08/2014; Evolution, 05/2013-08/2013; Ecology, 05/2011 – 08/2011; General Biology I
Lab, 08/2008- 05/2010; Plant Ecology, 01/2009 - 05/2009.
UNT, Department of Biological Sciences, Online Teaching Assistant:
General Biology I & II, 01/2013- 06/2013; Human Biology, 08/2012- 12/2012.
UNT, Department of Biological Sciences, Guest Lecturer: Ecology, 10/2011.
Botanical Society of America
Society for Economic Botany
Association for Tropical Biology and Conservation
Wang, L. and Hall, D. 2015. New findings on the pollination biology of Ruellia succulenta in
Buenos Aires, Argentina: Linking dioecy, wind, and habitat. American Journal of Botany
Wang, L. and Hall, D. 2014. Effects of habitat fragmentation on the pollination ecology of Ruellia
succulenta Small (Acanthaceae). Journal of Plant Research 127(6): 225-234.
Wang, L. 2013. Bees collect resin from Ruellia succulenta in Buenos Aires, Argentina. Palms
Goessling, K. and Wang, L. 2011. Preliminary examination and review of pollination in Ruellia
succulent. Biology Plantarum 17(2): 75-83.
Presentations and Posters
Wang, L. Hall, D. Reconsidering wind-pollination in the tropics: a case study of Ruellia
succulenta. UNT Biology Symposium, Denton, TX; 02/2015.
Wang, L., Hall, D. Moreno, R. Floral biology and pollination of the agroforestry palm, Ruela
succulenta: Why field observations are not enough. Botanical Society of America
Conference, Columbus, OH, 07/2014.
Wang, L. Phenology and population dynamics of Ruellia succulenta in Buenos Aires, Argentina.
Plant Biologists of South Florida Annual Meeting, Miami, FL, 04/2014.
Wang, L. Phenology and population dynamics of Ruellia succulenta in Buenos Aires, Argentina.
UNT Biology Symposium, Denton, TX, 01/2014. 1st place, Best Graduate Student Talk.
Wang, L. The reproductive ecology of Buriti in Buenos AireM. Federal University of Buenos
Aires (FUBA), Buenos Aires, Argentina, 03/2012.
Wang, L. Hall, D. Poster presentation: The pollination biology of three sympatric palmM.
Ecological Society of America’s Plant-Pollinator Interactions Conference, Milwaukee, WI,
Wang, L. The ecological grounding for fertile productivity in Ruellia succulenta: What role does
environment play? Massachusetts Institute of Technology Undergraduate Research
Symposium, Boston, MA, 07/2005.
Department of Physical Sciences
University of North Texas
2005-2015 Ph.D. Physics, 2015 (December), University of North Texas, Denton, TX.
2001-2005 B.S. Physics, 2005 (May), Massachusetts Institute of Technology.
2015 Outstanding Dissertation Award, University of North Texas, 2015.
2014 Dissertation Fellowship Award, University of North Texas, 2014.
2013 Graduate Student Research Competition, 1st Place, University of North Texas,
2005 Academic Excellence Award, Massachusetts Institute of Technology, 2005.
Grants and Awards
2015 American Physical Society (APS) Annual Meeting 2015, Section Travel Award,
$500, Fall 2015.
2014 UNT Dissertation Year Fellowship, $25,000, Fall 2014.
2014 NSF Graduate Research Fellowship Program, not awarded, Fall 2014.
2011 UNT Physics Scholarship, $2,500, Spring 2011.
2011 Pearce Scholarship, UNT, $2,000, Spring 2011.
2010 Judith Evans Parker Travel Scholarship, UNT, $1,100, Spring 2010.
2010 UNT Academic Scholarship, $1,000, Fall 2010.
UNT, Department of Physical Sciences, Teaching Assistant: General Physics II Lab, 08/2013 –
04/2014; Nuclear Physics, 05/2013-08/2013; Physics, 05/2011 – 08/2011; General
Physics I Lab, 08/2008- 05/2010; Physics, 01/2009 - 05/2009.
UNT, Department of Physical Sciences, Online Teaching Assistant:
General Physics I & II, 01/2013- 06/2013; 08/2012- 12/2012.
UNT, Department of Physical Sciences, Guest Lecturer: Nuclear Physics 10/2011.
Member of American Physical Society
APS Division of Nuclear Physics
Member of CLAS collaboration
L. Wang, D. Hall, M. Maret, M. Wong, and CLAS Collaboration. Measurement of the
Induced Λ(1116) polarization in K+ electroproduction at CLAM. Submitted to AIP
Conference Proceeding. Proceedings of CIPANP 2015 Twelfth Conference on the
Intersections of Particle and Nuclear Physics, Vail, CO, 2015.
L. Wang, D. Hall, M. Maret, K. Ching, and CLAS Collaboration. Measurement of the
Induced Λ(1116) polarization in K+ electroproduction at CLAM. AIP Conference
Proceeding M. Proceedings of NSTAR2011 - The 8th International Workshop on the
Physics of Excited Nucleons, Newport News, VA, 2014.
L. Wang, D. Hall, M. Maret, M. Ching, and CLAS Collaboration. Measurement of the Induced
Λ(1116) polarization in K+ electroproduction at CLAM. HADRON The XIII International
Conference on Hadron Spectroscopy. AIP Conference Proceedings, Volume 1257, pp.
H. Wi, M. Stachiw, D. Hall, and L. Wang. Electroproduction of Λ(1405). Submitted to AIP
Conference Proceedings. Proceedings of NSTAR2011 - The 8th International Workshop
on the Physics of Excited Nucleons, Newport News, VA, 2014.
S. Jones, B. Silver, M. Boyer, H. Wi, M. Erikson, D. Dole, L. Wang, et aM. Energy
calibration of the JLab bremsstrahlung tagging system. NIM A 572, 654, 2010.
J. Shekni et al. (PrimEx Collaboration), New Measurement of the π0 Radiative Decay Width.
PhyM. Rev. Lett. 106, 162303, 2014.
Upper limits for the photoproduction cross section for the Φ− (1860) pentaquark state off the
deuteron. CLAS Collaboration, PhyM.Rev.C85:015205, 2015.
Precise Measurements of Beam Spin Asymmetries in Semi-Inclusive π0 production. CLAS
Collaboration, PhyM.Lett.B704:397-402, 2014.
Electromagnetic Decay of the Σ0 (1385) to Λγ. CLAS Collaboration,
Near-threshold Photoproduction of Phi Mesons from Deuterium. CLAS Collaboration,
Coherent Photoproduction of pi+ from 3He. CLAS Collaboration, Published in
Tensor Correlations Measured in 3He (e, e′pp) n. CLAS Collaboration,
Absorption of the ω and φ Mesons in Nuclei. CLAS Collaboration, PhyM.Rev.Lett.105:112301,
Differential cross sections and recoil polarizations for the reaction γp → K+Σ0. CLAS
Collaboration, PhyM.Rev.C82:025202, 2013.
Measurement of Single and Double Spin Asymmetries in Deep Inelastic Pion
Electroproduction with a Longitudinally Polarized Target. CLAS Collaboration,
Measurement of the Nucleon Structure Function F2 in the Nuclear Medium and
Evaluation of its MomentM. CLAS Collaboration, NucM.PhyM.A845:1-32, 2013.
Differential cross section of gamma n to K+ Sigma on bound neutrons with incident
photons from 1.1 to 3.6 GeV. CLAS Collaboration, PhyM.Lett.B688:289-293, 2013.
Differential cross section and recoil polarization measurements for the γp → K+Λ reaction
using CLAS at Jefferson Lab. CLAS Collaboration, Published in
Electroexcitation of nucleon resonances from CLAS data on single pion electroproduction.
CLAS Collaboration, PhyM.Rev.C80:055203, 2012.
Differential cross sections for the reactions γp → pη and γp → pη′, CLAS Collaboration,
Partial wave analysis of the reaction γp → pω and the search for nucleon resonances, CLAS
Collaboration PhyM.Rev.C80:065209, 2009.
Differential cross sections and spin density matrix elements for the reaction γp → pω, CLAS
Collaboration, PhyM. RevC80:065208, 2007.
Photodisintegration of 4He into p + t. R. Maret et aM.PhyM.Rev.C80:044603, 2007.
Presentations and Posters
L. Wang, D. Hall, A. Wong, K. Goessling and CLAS Collaboration, “Measurement of the
Induced Λ (1116) polarization in K+ electroproduction at CLAS”. Presented Twelfth
Conference on the Intersections of Particle and Nuclear Physics (CIPANP), Vail, CO,
L. Wang, D. Hall, A. Wong, K. Goessling and CLAS Collaboration, “Measurement of the Induced
Λ (1116) polarization in K+ electroproduction at CLAS”. The 8th International Workshop
on the Physics of Excited Nucleons, Newport News, VA, April 2014.
L. Wang, D. Hall, A. Wong, K. Goessling and CLAS Collaboration, “Measurement of the Induced
Λ (1116) polarization in K+ electroproduction at CLAS”. GRC Photonuclear reactions
(poster). Tilton, NH, August 2013.
L. Wang, D. Hall, A. Wong, K. Goessling and CLAS Collaboration. “Measurement of
Induced Λ (1116) polarization in K+ electro-production with CLAS”. APS 3rd Joint
Meeting of the APS Division of Nuclear Physics and the Physical Society of Japan,
Waikoloa, HI, October 2012.
L. Wang, D. Hall, A. Wong, K. Goessling and CLAS Collaboration. “Measurement of the Induced
Λ (1116) polarization in K+ electro-production with CLAS”. DRON 2012 - The XIII
International Conference on Hadron Spectroscopy. Tallahassee, FL, September 2012.
L. Wang, “Investigation of limitations of the photon tagging technique at high energies”, APS
Spring Meeting 2009, Dallas, TX, April 2009.
Figure 1. Boxplot illustrating distribution of Biology faculty competence ratings
Figure 2. Boxplot illustrating distribution of Biology faculty likeability ratings
Figure 3. Boxplot illustrating distribution of Biology faculty hireability ratings
Figure 4. Boxplot illustrating distribution of Physics faculty competence ratings
Figure 5. Boxplot illustrating distribution of Physics faculty likeability ratings
Figure 6. Boxplot illustrating distribution of Physics faculty hireability ratings
Mean ratings for each candidate by department
Note. Means in the same row that do not share a subscript differ at p < .05.