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A Meta-Analysis of Gender Stereotypes and Bias in Experimental
Simulations of Employment Decision Making
Amanda J. Koch
Human Resources Research Organization,
Minneapolis, Minnesota
Susan D. D’Mello
Korn Ferry, Minneapolis, Minnesota
Paul R. Sackett
University of Minnesota
Gender bias continues to be a concern in many work settings, leading researchers to identify factors that
influence workplace decisions. In this study we examine several of these factors, using an organizing
framework of sex distribution within jobs (including male- and female-dominated jobs as well as
sex-balanced, or integrated, jobs). We conducted random effects meta-analyses including 136 indepen-
dent effect sizes from experimental studies (N⫽22,348) and examined the effects of decision-maker
gender, amount and content of information available to the decision maker, type of evaluation, and
motivation to make careful decisions on gender bias in organizational decisions. We also examined study
characteristics such as type of participant, publication year, and study design. Our findings revealed that
men were preferred for male-dominated jobs (i.e., gender-role congruity bias), whereas no strong
preference for either gender was found for female-dominated or integrated jobs. Second, male raters
exhibited greater gender-role congruity bias than did female raters for male-dominated jobs. Third,
gender-role congruity bias did not consistently decrease when decision makers were provided with
additional information about those they were rating, but gender-role congruity bias was reduced when
information clearly indicated high competence of those being evaluated. Fourth, gender-role congruity
bias did not differ between decisions that required comparisons among ratees and decisions made about
individual ratees. Fifth, decision makers who were motivated to make careful decisions tended to exhibit
less gender-role congruity bias for male-dominated jobs. Finally, for male-dominated jobs, experienced
professionals showed smaller gender-role congruity bias than did undergraduates or working adults.
Keywords: gender bias, gender stereotypes, employment discrimination, personnel evaluation,
employment decisions
Substantial progress toward gender equality in the United States
continues to be made. Today, women are more likely than men to
complete high school, attain bachelor’s degrees, and earn ad-
vanced degrees, and this gap between men and women has been
steadily increasing over the past 30 years (Aud et al., 2011).
However, educational progress has not always translated into
equality in the workplace for women. Their salaries and organi-
zational ranks continue to lag behind those of men (Aud et al.,
2011), suggesting the possibility of continued gender discrimina-
tion.
There is a long history of research on gender bias in workplace
decisions. Exemplifying the classic trade-off between experimen-
tal control and concerns for realism, one research tradition focuses
on true experiments, typically in settings that simulate employment
decisions (e.g., Nieva & Gutek, 1980;Rosen & Jerdee, 1974). In
this research, decision makers are presented with information
about job applicants (e.g., résumés, performance evaluations) in
which applicant gender and other features are manipulated. In con-
trast, a second research tradition examines differences in employ-
ment evaluations in field settings, such as job performance and
promotability ratings. In meta-analyses of field studies, both Bo-
wen, Swim, and Jacobs (2000) and Roth, Purvis, and Bobko
(2012) reported small overall effect sizes for male–female differ-
ences (overall dvalues of ⫺.01 and ⫺.11, respectively, with
females receiving higher scores than males), but gender differ-
ences varied across moderators such as gender stereotypicality of
the rating measure, rater gender, and type of rating. One advantage
of these meta-analyses of field studies assessing gender differences
in ratings is the use of actual employee evaluations, allowing for
increased confidence in the generalizability of findings. However,
one drawback is the inability to unambiguously attribute gender
differences in field studies to any particular cause, such as gender
bias or true gender differences in performance. As Bowen et al.
(2000) noted, “One of the primary limitations of this meta-analysis
is that differences in ratings of women and men could have
This article was published Online First May 26, 2014.
Amanda J. Koch, Human Resources Research Organization, Minneap-
olis, Minnesota; Susan D. D’Mello, Korn Ferry, Minneapolis, Minnesota;
Paul R. Sackett, Department of Psychology, University of Minnesota.
We thank Adam Beatty and Phil Walmsley for helpful comments on
drafts of this article.
Correspondence concerning this article should be addressed to Amanda
J. Koch, Human Resources Research Organization, 100 Washington Av-
enue South, Suite 1660, Minneapolis, MN 55401. E-mail: akoch@
humrro.org
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Applied Psychology © 2014 American Psychological Association
2015, Vol. 100, No. 1, 128–161 0021-9010/15/$12.00 http://dx.doi.org/10.1037/a0036734
128
occurred because of actual performance differences between the
two genders” (pp. 2207–2210). Similarly, Roth et al. (2012) noted,
“We did not have access to ‘true scores’ for job performance....We
did not directly address ratings bias (as might be possible in the
laboratory)” (p. 733).
The current study reports meta-analyses of the experimental
literature on gender bias in work-related decisions, with the goal of
comparing and integrating findings with those from the above-
mentioned meta-analyses of field studies. Although there have
been other meta-analytic investigations of gender bias in experi-
mental settings (most recently Davison & Burke, 2000), the pres-
ent study makes several important and novel contributions. First,
we conduct moderator analyses within different categories of sex
distributions within jobs. Given the possibility of changes in the
direction of bias based on the sex distribution within a job (e.g.,
higher ratings for males when jobs are male dominated and higher
ratings for females when jobs are female dominated), it is impor-
tant to examine moderator effects separately for each job type.
Although others have studied gender bias for jobs with different
stereotypes as well as for moderators such as rater gender and
individuating information (e.g., Davison & Burke, 2000;Eagly,
Makhijani, & Klonsky, 1992;Tosi & Einbender, 1985), they have
not examined these moderators within each category of job ste-
reotype. Second, we examine contextual variables that have not
been examined meta-analytically in a gender bias context, includ-
ing ambiguity of individuating information, response scale, and
motivation to make careful decisions. Finally, the present meta-
analyses are the most comprehensive quantitative analysis of gen-
der bias in experimental contexts to date, with more than twice as
many independent samples as the most recent meta-analysis on the
topic by Davison and Burke (2000).
Background and Hypotheses
Gender Stereotypes
Stereotypes are category-based traits or attributes that are often
applied to a group of people as a result of accepted beliefs about
the members of the group (Agars, 2004;Welle & Heilman, 2007).
Group stereotypes lead to expectations about how members of the
group should and do behave. Although they can be functional,
automatic, unintentional, and accurate in the aggregate (e.g.,
Devine, 1989;Goodwin, Gubin, Fiske, & Yzerbyt, 2000;Macrae,
Milne, & Bodenhausen, 1994), stereotypes can result in bias, an
inaccurate evaluation reflecting a generalization rather than an
individual’s true qualities.
As a social category that is easily observed, gender is a common
cue for stereotypical thinking, with gender stereotypes being
quickly and automatically activated (e.g., Banaji & Hardin, 1996;
Banaji, Hardin, & Rothman, 1993;Blair & Banaji, 1996). Factor
analytic research has revealed that the majority of stereotypic
gender beliefs tend to fall into two categories: communal and
agentic (e.g., Broverman, Vogel, Broverman, Clarkson, & Rosen-
krantz, 1972;Eagly & Steffen, 1984). Communal attributes are
associated more with females and relate to concern for others, such
as being helpful, kind, nurturing, emotionally expressive, and
affectionate (Eagly & Karau, 2002). Agentic attributes are more
strongly associated with males and express a tendency to be
assertive and controlling, such as being dominant, ambitious, in-
dependent, and confident (Eagly & Karau, 2002). Stereotypes can
be descriptive and describe how men and women are, but they can
also be prescriptive and describe how men and women should be
(Eagly, 1987).
Job Stereotypes and Sex Distributions Within Jobs
Gender bias at work can arise when people judge men and
women differently as a result of the use of gender stereotypes. One
proposed explanation for gender bias in the workplace is a role
congruity theory (Eagly & Karau, 2002), which explains bias in
terms of the congruence between stereotypes held about job re-
quirements and stereotypes held about gender groups. The greater
the incongruence between stereotypical gender traits and the gen-
der stereotype of a job, the greater the gender bias. For example,
the characteristics seen as necessary to succeed as a CEO may
include agentic traits such as dominance, aggression, and emo-
tional toughness, which are more strongly associated with males
than with females (e.g., Schein, 2001). Heilman’s (1983,2001,
2012) lack-of-fit model makes similar predictions, for example,
that a lack of fit between stereotypical gender characteristics and
stereotypical job requirements leads to the conclusion that certain
individuals will be unable to succeed in jobs in which gender
stereotypes of jobs are at odds with the individual’s gender.
In a similar vein, some propose a backlash effect that causes bias
against those who deviate from gender norms (e.g., Kark & Eagly,
2010;Rudman & Fairchild, 2004). The social category of gender
induces norms against which the ratee is judged, and when the
ratee does not align with the norm, he or she may be viewed as
inadequate (Kobrynowicz & Biernat, 1998). This perception of
inadequacy to meet gender norms can lead to bias (Heilman &
Okimoto, 2007). For example, agentic female job applicants, who
are diverging from the communal, nonagentic stereotype of
women, may be seen as highly qualified for a masculine-
stereotyped job but also as deviant and unlikable, leading to
penalties such as hiring discrimination. This leads to a dilemma, as
women must exhibit some traditionally masculine traits to appear
qualified for masculine-stereotyped jobs, yet they may be penal-
ized for doing so. Backlash may have different effects on gender
bias when different outcomes are used. Highly qualified women
may be judged to be just as competent as men, but these women
still may be less liked and less likely to be hired than men
(Rudman, Moss-Racusin, Phelan, & Nauts, 2012).
Whereas the previous explanations for gender bias at work focus
on the stereotypes associated with job requirements or with groups
of people, another explanation for employment bias focuses on sex
segregation in the labor force. Although sex segregation has been
found to be declining over time, particularly in professional occu-
pations (see Pettit & Hook, 2009), there continues to be a dispro-
portionate distribution of men and women in various occupations
(U.S. Department of Labor, 2011). It is reasonable to believe that
people hold stereotypes about workers in certain jobs based not
only on job requirements but also on the sex of the typical
incumbent. Even if there is no mismatch between a ratee’s gender
and the gender stereotype of job requirements, bias can result due
to an unbalanced proportion of males and females in a job (Glick,
1991). For example, if a person is making a hiring decision for a
job that is typically held by men but does not necessarily require
masculine or agentic characteristics, the image of a successful
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129
GENDER STEREOTYPES AND EMPLOYMENT DECISION BIAS
worker that comes to mind still may be of a male worker, leading
the decision maker to see a male applicant as a better fit for the job
than a female applicant. Although the perceived gender stereotype
of job requirements and the sex distribution within a job are
conceptually distinct, they are often highly related (Cejka & Eagly,
1999). In the present study, we use sex distribution within a job as
an indicator of the gender stereotypes of the job, recognizing that
sex distribution may represent gender stereotypes of job require-
ments as well as gender-based stereotypes based on the sex of
typical incumbents.
All of these explanations for employment bias lead to the same
prediction: People who are pursuing jobs that have stereotypes
consistent with their own gender will be evaluated more positively
than those whose gender is inconsistent with job stereotypes.
Meta-analyses consistently reveal higher ratings for men than for
women in masculine jobs (e.g., Davison & Burke, 2000;Eagly et
al., 1992;Swim, Borgida, Maruyama, & Myers, 1989). Due to the
finding that females are underrepresented in certain occupations
that confer high status, power, and pay (Carli & Eagly, 2001),
much research has focused on bias against females in high-status,
male-dominated jobs (e.g., leadership positions; Eagly et al.,
1992). However, a female advantage for feminine-stereotyped jobs
has also been documented (e.g., Davison & Burke, 2000). Regard-
ing jobs without clear gender stereotypes or without extreme sex
imbalances, previous meta-analyses have found a small pro-male
bias (Swim et al., 1989) or a small pro-female bias (Eagly et al.,
1992). The employment discrimination theories we discussed do
not address these jobs that have no strong gender stereotypes.
Thus, we take an exploratory approach regarding gender bias in
sex-balanced, or integrated, jobs.
Hypothesis 1: Gender-role congruity bias will be found, with
men being rated more favorably than women for male-
dominated jobs and women being rated more favorably than
men for female-dominated jobs.
Rater Gender
Some have found that compared to women, men are more likely
to hold traditional stereotypes about women (e.g., passive, timid;
Massengill & DiMarco, 1979), have less favorable attitudes to-
ward gender egalitarianism (e.g., Eagly & Mladinic, 1989;Spence
& Hahn, 1997), endorse hostile sexism (Glick et al., 2000), and
view leadership positions as more masculine and less feminine
(e.g., Brenner, Tomkiewicz, & Schein, 1989;Koenig, Eagly,
Mitchell, & Ristikari, 2011;Schein, 2001). Thus, men may be
more likely than women to see women as incompatible with male-
dominated or masculine roles. A meta-analysis of experimental
studies requiring evaluation of leaders was consistent with this
prediction, with male decision makers exhibiting a pro-male bias
and female decision makers exhibiting minimal bias (Eagly et al.,
1992). Men’s views on gender equality and gender stereotypes also
lead us to believe that male raters, compared to female raters, will
show larger gender-role congruity bias for female-dominated jobs
(i.e., male raters will exhibit a larger pro-female bias than female
raters for female-dominated jobs). Perhaps due to a stronger desire
to maintain a segregated occupational system where women do not
challenge men for male-dominated high-status jobs, men may have
a stronger preference than women for selecting women into
female-dominated jobs, creating less competition from women in
male-dominated jobs. Therefore, we expect male raters to exhibit
stronger gender-role congruity bias than female raters for both
male- and female-dominated jobs.
Hypothesis 2: Male raters will exhibit stronger gender-role
congruity bias than female raters.
Individuating Information
Some argue that stereotyping against women may be likely
when there is little to no other information available to differen-
tiate among candidates but that stereotyping effects disappear
when decision makers have access to individuating information
(e.g., Landy, 2008). That is, the more a decision maker has access
to information about credentials, skills, relevant experience, and
the like, the less the decision maker relies on gender as the basis
for decision. Based on findings that people ignore base rates when
making judgments (Kahneman & Tversky, 1973), some have
proposed that stereotypes, like prior probabilities, are often ig-
nored when decisions are made (Locksley, Borgida, Brekke, &
Hepburn, 1980). Similarly, expectation states theory (EST; Berger,
Rosenholtz, & Zelditch, 1980;Correll & Ridgeway, 2006), which
describes how performance expectations are formed, has been used
to specify situations in which individuating information should
outweigh stereotypes based on gender. According to EST, diffuse
status cues, such as gender, age, or race, carry broad expectations
for competence in a wide variety of situations. On the other hand,
specific status cues, such as skills or abilities needed for a partic-
ular task, carry expectations for competence in a small number of
clearly-defined situations where the information is relevant. When
specific, job-relevant status cues are available to a decision maker,
they may be given more weight than the beliefs associated with
diffuse status cues because of their relevance to the decision. For
example, the presence of specific status cues indicating that a
woman earned an MBA and has received excellent performance
reviews in her current leadership role are likely to be seen as more
relevant and salient to a decision about promotion into upper-level
management than is the diffuse cue of gender. As a result, diffuse
cues are expected to become less influential as the number of
specific status cues (i.e., individuating information) increases. If
individuating information decreases bias, many workplace deci-
sions that happen after long periods of interaction between raters
and ratees (e.g., performance appraisals, promotion decisions)
should be nearly bias-free. Therefore, there are important practical
implications concerning the effects of individuating information
on bias.
Evidence regarding the relationship between the amount of
individuating information and gender bias is mixed. An early
meta-analysis containing 21 experimental studies (Tosi & Ein-
bender, 1985) supported the proposition that when organizational
decision makers have more job-relevant information about a job
applicant, they make less biased decisions. Some support was also
offered by Swim et al. (1989), who found in their meta-analysis of
experimental studies that gender bias was slightly greater when
participants were provided with no individuating information (i.e.,
a ratee’s name only) than when they had additional information. In
more recent meta-analyses, Eagly et al. (1992) found no relation-
ship between the amount of information and gender bias and
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130 KOCH, D’MELLO, AND SACKETT
Davison and Burke (2000) found negative but nonsignificant ef-
fects. However, these meta-analyses did not differentiate between
male-dominated, female-dominated, and integrated jobs when ex-
amining the effects of the amount of individuating information on
gender bias. We hypothesize that as more job-relevant information
becomes available, gender-role congruity bias will decrease.
The content of the information can also affect the size of gender
bias in decision making. Numerous studies have shown that when
individuating information is ambiguous regarding a trait or role in
question, decision makers rely heavily on stereotypes (see Kunda
& Thagard, 1996). Unambiguous information reduces the need for
inference, as the “correct” decision is more obvious (e.g., if a job
applicant is obviously qualified for a position, there is less need for
conjecture about the applicant’s future performance in the role). In
an organizational context, individuating information that does not
clearly indicate a ratee’s competence for a particular role may not
change the size of gender bias that results when decisions are made
about that person for that role (e.g., Heilman, 1984). Because of
people’s tendency to seek information that confirms expectations
(Nickerson, 1998), information that is ambiguous can be distorted
to confirm gender stereotypes (Kunda & Thagard, 1996). Addi-
tionally, a backlash effect could lead to gender bias in cases where
individuating information clearly indicates that ratees do not con-
form to typical gender stereotypes, leading to penalties for devi-
ating from gender norms (see Rajecki, De Graaf-Kaser, & Ras-
mussen, 1992). Therefore, we do not expect gender-role congruity
bias to be completely eliminated when individuating information is
indicative of competence for the job in question. Rather, we
predict that gender-role congruity bias will be larger when indi-
viduating information is mixed or ambiguous regarding ratees’
competence or ability to succeed in a position than when individ-
uating information is highly diagnostic of success.
Hypothesis 3: As more job-relevant information becomes
available, gender-role congruity bias will decrease.
Hypothesis 4: Gender-role congruity bias will be largest when
individuating information is ambiguous or not clearly diag-
nostic of success in the job.
Type of Evaluation
Although meta-analytic research findings indicate that gender
bias results when a person’s gender and the gender stereotype of a
job are incongruent, some researchers have found that the reverse
is true in some cases. For example, a reverse-stereotyping effect of
pro-female bias for masculine jobs has been found under certain
conditions (e.g., Biernat & Kobrynowicz, 1997;Biernat & Manis,
1994). One explanation for this effect is provided by the shifting
standards model (Biernat, 2003,2012), which suggests that people
make within-category comparisons (e.g., within-gender compari-
sons) when making subjective judgments. That is, gender stereo-
types serve as standards against which individuals are evaluated.
As a result, a “good” rating for a woman may mean something
different from a “good” rating for a man. A more lenient standard
for women in a masculine-stereotyped job may lead to a qualified
appraisal of a woman as being “good . . . for a woman” in a
nontraditional role. One key point to make about the shifting
standards model is that reverse-stereotyping does not imply that
stereotypes are not influencing decisions; reverse effects occur
because of the differing gender expectations represented by ste-
reotypes (e.g., Biernat & Manis, 1994;Heilman, Martell, & Simon,
1988).
The present study examines the propositions of the shifting
standards model by examining the type of comparisons that raters
make. In some cases, a rater is asked to make decisions about
ratees without explicitly comparing them to other ratees (e.g.,
rating each candidate’s competence on several dimensions). Alter-
natively, decision makers may make direct comparisons of ratees,
for example, rank ordering a group of candidates for hiring.
Because comparative judgments are common-rule ratings that re-
quire decision makers to use a scale that has the same meaning for
both male and female ratees (e.g., choosing to hire a person has the
same meaning if that person is a man or a woman), they may be
more likely than individual ratings to show gender-role congruity
stereotyping effects. For example, when rating the competence of
an individual, a decision maker may rate a female applicant high
on an individual rating scale because she is competent for a
woman, but the same rater may still select a male when asked to
choose between a male and female candidate for the job; the
female candidate may be good for a woman, but she still may not
be as good as the male candidate when they are directly compared.
We expect a reduction in pro-female bias for female-dominated
jobs and a reduction in pro-male bias for male-dominated jobs
when individual (compared to comparative) ratings are made.
Hypothesis 5: Gender-role congruity bias will be larger for
comparative than for individual ratings.
Motivation to Make Careful Decisions
Although the use of stereotypes is often described as being
automatic and unintentional, there are certain conditions that may
reduce the likelihood that decision makers will rely on stereotypes.
When people are motivated to make accurate decisions, they invest
more time in information processing, pay attention to a wider
range of potentially useful information, and engage in deeper
processing of information, which can reduce or eliminate the
influence of cognitive biases (Kunda, 1990;Lerner & Tetlock,
1999). One situation in which decision makers are motivated to
make accurate, careful judgments occurs when they are held ac-
countable for their decisions. A feeling of accountability can be
created when decision makers are encouraged to be accurate, are
told they will have to justify their decisions, or expect their
decisions to affect other people’s lives (Kunda, 1990). Another
situation that may motivate decision makers to make careful judg-
ments occurs when they become aware of or are reminded of
fairness norms. Making decision makers aware of organizational
or personal values regarding equity can motivate them to spend
more time on decision making and to avoid stereotyping (see
Fiske, 1993). Therefore, we expect to find smaller gender-role
congruity bias in studies with conditions that increase decision
makers’ motivation to make careful decisions.
Hypothesis 6: Gender-role congruity bias will be smaller for
studies in which (a) decision makers feel accountable for their
decisions, (b) decision makers believe their decisions will
have consequences that affect others, or (c) decision makers
are informed of equity norms.
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131
GENDER STEREOTYPES AND EMPLOYMENT DECISION BIAS
Additional Study Characteristics
In an exploratory manner, we examine several characteristics of
studies as potential moderators of the magnitude of gender-role
congruity bias. First, when evidence from experimental gender
bias studies is used to make inferences about workplace discrim-
ination, some question whether this type of research reveals any-
thing about actual workplace discrimination (e.g., Landy, 2008).
One of the criticisms of experimental research on gender bias is
that studies are often conducted with undergraduate samples with
little to no training or experience in making workplace decisions.
It is argued that in actual employment settings, decision makers
have training and experience that should reduce the amount of
gender bias exhibited in workplace decisions. On the other hand,
given the automatic, unintentional, and pervasive nature of stereo-
types (e.g., Devine, 1989), it may also be the case that gender bias
is not reduced with additional experience. Because these are both
reasonable possibilities, we take an exploratory approach to ex-
amining gender bias exhibited by different types of samples.
Second, due to advances in women’s rights and changes in the
approval of traditional gender roles over time (e.g., Spence &
Hahn, 1997), we chose to examine publication year as a moderator.
It may be that as opportunities for women in the workplace
increase over time, gender bias against women in masculine jobs
decreases. However, in their meta-analysis, Eagly et al. (1992)
found that anti-female bias was actually larger in more recent
studies, but when included in a meta-regression with other vari-
ables, publication year was not a significant predictor of bias.
Although it seems reasonable to assume that gender-role congruity
bias has decreased as women’s participation in the labor force has
increased, some research suggests that this is not necessarily the
case.
Finally, we examine study design as a potential moderator.
Ratee gender as a between-person versus within-person variable
(i.e., a rater evaluates one target versus multiple targets of both
genders) could be a moderator for a few reasons, although the
expected direction of the effect is unclear. On one hand, one might
expect gender to be more salient for within-person designs, espe-
cially when the amount of individuating information is low, mak-
ing raters aware of the study’s gender focus and less likely to
exhibit bias. On the other hand, the amount of information to
review may be greater for participants in studies with within-
person designs, as those raters are rating multiple candidates, and
these higher information processing requirements may lead to
increased reliance on stereotypes. Additionally, within-person de-
signs may lead to comparisons of candidates of different genders,
making the evaluations more likely to occur on the same scale and
to lead to greater bias. Therefore, we do not have hypotheses
regarding study design.
Method
Literature Search
We searched several online databases (Academic Search Pre-
mier, Business Source Premier, Google Scholar, Index to Theses,
JSTOR, Proquest Dissertations and Theses, PsycINFO, Social
Science Citation Index, Sociological Abstracts, Web of Science)
using combinations of the following key words: gender differ-
ences, gender bias, gender stereotypes, sex discrimination, job
application, employment discrimination, personnel recruitment,
hiring decisions, performance appraisal, employment selection,
management decision making, personnel selection, performance
ratings, performance evaluation, and résumé evaluation. We
searched reference lists from previous meta-analyses on gender
bias (e.g., Davison & Burke, 2000). We also examined references
in review articles and meta-analyses that focused on different
variables in experimental decision-making studies, such as phys-
ical attractiveness (Hosoda, Stone-Romero, & Coats, 2003),
weight (Rudolph, Wells, Weller, & Baltes, 2009), and age (Morge-
son, Reider, Campion, & Bull, 2008). Our search was limited to
studies published in English. The first two authors performed the
literature searches, examining study titles and abstracts and retain-
ing those that could possibly be included in the meta-analyses.
After generating an initial list of potentially eligible studies, the
same two authors examined the full text of each study to decide
whether it could be included. Disagreements about study inclusion
were settled through discussion with all three authors.
Inclusion Criteria
Several criteria for inclusion were adopted. First, the studies had
to be experimental. Most studies were simulations of employment
contexts, with the typical paradigm being the evaluation of résu-
més that were identical or very similar other than the gender of the
applicants. Studies in which the ratees were intended to differ
systematically by gender (e.g., the female applicant was always
more qualified) were excluded. Thus, any differences in ratings of
hireability, salary, promotion, and so on, could be attributed to the
applicant’s gender. Although most studies were conducted in lab-
oratory settings, audit studies in which résumés or job applications
were sent to actual employers were also included if job applicants
were matched on all characteristics besides gender. Second, stud-
ies had to include job-related ratings about a ratee (e.g., hireability,
salary, competence, promotion). Participant evaluations of non-
job-related variables such as liking, attraction, or attributions for
behavior were excluded. Also excluded were direct ratings of
stimulus materials (e.g., evaluations of the quality of essays), as
these did not involve decisions about the ratees themselves. Third,
studies had to provide enough data to allow for the computation of
Cohen’s d(e.g., means, standard deviations, and sample sizes for
repeated-measures or independent groups designs; correlations or
tvalues for independent group designs). Studies that reported
results only for subsamples or treatment conditions with statisti-
cally significant values were excluded to avoid an upward bias in
effect sizes. Fourth, studies were excluded if their samples con-
sisted of participants from nonnormal populations (e.g., prisoners).
Participants included students, working adults, managers, and per-
sonnel specialists. No constraints were placed on the time period or
on the geographical locations in which the studies were conducted.
Coding
The first two authors coded the studies on characteristics of
interest. Initially, 20 studies were coded by both raters; agreement
between raters was 65% for calculated dvalues and 100% for all
other variables. This disagreement was found to result from using
different decision rules (e.g., choosing the single most relevant
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132 KOCH, D’MELLO, AND SACKETT
dependent variable versus computing a composite of all relevant
dependent variables). Discussion easily reconciled all differences.
Both raters coded all variables of interest in the remaining studies.
Agreement between raters was 97% for the remaining studies. Any
coding differences were discussed and agreed upon by the first two
authors, and any remaining disagreements were settled through
discussions with all authors.
Sex distribution within job. Each job used in a study was
coded as male-dominated, female-dominated, or integrated based
on the proportion of men and women who held that job in the
country and during the time period in which the study was con-
ducted. National labor statistics were acquired from census data,
government publications, and online databases with national em-
ployment statistics (Australian Bureau of Statistics, 2012;Central
Bureau of Statistics, 2012a,2012b;Destatis Federal Statistics
Office, 2012;International Labour Organization, 2012;Ontario
Network of Women in Engineering, 2012;Statistics Canada, 2012;
Statistics Sweden, 2012;U.S. Census Bureau, 2012a,2012b;U.S.
Department of Labor, 2004,2005,2006,2007,2008,2009,2010,
2011). We located annual employment statistics ranging in date
from 1960 to 2010 for the countries in our database, and each job’s
sex distribution was derived from the available national labor
statistics that preceded the article’s publication date by as few
years as possible. Job titles used in original studies were matched
as closely as possible to job titles in the labor statistics publications
and databases, according to the specificity of the job title in the
original article. For example, the sex distribution of a job in a study
specifying a role of “Manager” was based on labor statistics for the
category of “Management Occupations,” whereas a study specify-
ing a role of “Corporate Training Manager” was based on labor
statistics for “Human Resources Managers.” When a job listed in
a study did not have an exact or near-exact match in a labor
statistics database, the first two authors independently chose the
best match for the job, and differences were reconciled through
discussion. Because we did not expect the impact of sex distribu-
tion to be a linear phenomenon (e.g., a change from 40% to 60%
female was not expected to have the same effect as a change from
60% to 80% female), we put all jobs into three categories. Based
on Kanter’s (1977) classification of skewed and tilted proportional
representation in groups, we classified jobs as female-dominated if
the percentage of women in the job was greater than 65%, inte-
grated if the percentage of women was between 35% and 65%, and
male-dominated if the percentage of women was less than 35%
(see Appendix A for a list of job titles in each sex distribution
category).
Rater gender. When possible, we coded dvalues for male
and female rater groups. In cases where separate dvalues were not
provided by rater gender, the sample was labeled as mixed/not
specified and was not included in rater gender moderator analyses.
Individuating information. The amount of individuating in-
formation was coded as the number of pieces of information
provided to a decision maker, with the categories of pieces of
information being résumé or job application, recommendation
letter, videotape or transcript of interview, work sample or simu-
lation, and performance appraisal (e.g., résumé plus recommenda-
tion letter ⫽2). Studies that provided single pieces of information
that contained substantially less information than one would find
in a résumé, recommendation letter, and so on, were coded as
limited, indicating that no sizable pieces of information were
provided. No study had more than three sources of information,
and because only four studies had three pieces of information, we
combined these four studies with the studies providing two pieces
of information for analyses.
The content of individuating information was coded as indicat-
ing high competence, ambiguous or average competence, or low
competence. To be labeled as high or low competence, studies had
to provide consistent information about ratees that clearly indi-
cated their level of qualifications or competence. Examples of
information indicating high competence included an excellent
grade point average in college, job-related awards, high ratings on
performance reviews, and positive letters of recommendation.
Studies with information indicating poor or below average perfor-
mance on all job-related attributes were labeled as low compe-
tence. When studies provided different pieces of information,
some indicating high competence and some indicating low com-
petence, they were labeled as ambiguous. Studies were labeled as
average competence when the individuating information indicated
that the ratee was average in all job-related ways. We combined
ambiguous and average competence into one category because in
neither case did the information clearly indicate whether the ratee
would be successful in the position. Some studies did not provide
enough details about study materials to determine the content of
the individuating information; these studies were not included in
these moderator analyses.
Type of evaluation. We coded two moderators related to the
type of evaluation: type of comparison and type of employment
rating. Type of comparison was coded as comparative or individ-
ual. Decisions that required a direct comparison of ratees, such as
choosing one of several candidates to hire or making a short list for
hiring, were coded as comparative. Decisions that required eval-
uations of a single ratee, without any explicit reference to other
ratees, were coded as individual. Types of employment ratings
were hiring, promotion, compensation, reward (e.g., allocation of
desirable training), penalty (e.g., termination), job performance,
and competence.
Motivation to make careful decisions. We coded each study
for contextual variables that would increase participants’ motiva-
tion to make careful decisions. These contextual variables were (a)
whether participants expected to justify or explain their decisions
during the study, increasing accountability (k⫽4); (b) whether
participants believed their decisions had real consequences outside
of the research study (k⫽16); and (c) whether participants were
informed of organizational equity norms or were instructed to
make fair or meritocratic decisions (k⫽5). Because of the small
number of studies in each of these categories, we combined them
into one motivation to make careful decisions category for analy-
ses. Due to some studies including more than one of these condi-
tions, a total of 23 samples had conditions representing increased
motivation to make careful decisions.
Type of participant. The type of participant was coded as
undergraduate, working adult, or experienced professional. Expe-
rienced professional samples consisted of people with experience
relevant to the decision-making task in the study. For example,
everyone in the sample had experience making hiring decisions
when the study outcome was hiring, or everyone had experience
evaluating job performance when the study outcome was job
performance ratings. Working adult samples were not specified as
having this relevant experience.
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133
GENDER STEREOTYPES AND EMPLOYMENT DECISION BIAS
Research design. Studies were coded as between- or within-
person, based on whether gender was manipulated between par-
ticipants (i.e., each participant evaluated a ratee of only one gen-
der) or within participants (i.e., each participant evaluated ratees of
both genders) for the calculation of dvalues.
dvalue. We obtained dvalues for all samples, either by taking
them directly from articles or by computing them when articles
reported other statistics that could be converted to dvalues. When
means, sample sizes, and standard deviations were provided, we
computed Cohen’s dvalues (Cohen, 1969) using the following
formula:
d⫽MM⫺MF
冑
共
NM⫺1
兲
SDM
2⫹
共
NF⫺1
兲
SDF
2
NM⫹NF⫺2
,
where M
M
is the mean rating of the male ratee, M
F
is the mean
rating of the female ratee, N
M
is the number of participants who
rated the male ratee, N
F
is the number of participants who rated
the female ratee, SD
M
is the standard deviation of the ratings of the
male ratee, and SD
F
is the standard deviation of the ratings of the
female ratee. We chose to use the type of dvalue that uses ratee as
the unit of analysis and represents a between-units design (i.e.,
what Hönekopp, Becker, & Oswald, 2006 label as d
1
) due to its
relevance to the research question, its clarity and familiarity, and
its usefulness both in describing the strength of effects and in
moderator analyses (see Hönekopp et al., 2006). This dvalue
represents the difference, in standard deviation units, between
ratings of a male ratee and a female ratee. When multiple ratees of
the same gender were used in a study, we verified that ratees of
both genders were matched on other characteristics of interest in
the study (e.g., race, competence) for d-value computations to
avoid ambiguity in the meaning of dvalues (e.g., dvalues were
computed for differences between ratings of Black female ratees
and Black male ratees, not for differences between Black female
ratees and White male ratees). For studies using between-person
designs, we used formulas provided by Hunter and Schmidt (2004,
pp. 278–279) and by Lipsey and Wilson (2001, pp. 172–189) to
obtain the appropriate dvalues from correlations, tvalues, and F
ratios. For studies using within-person designs, we intended to
convert tvalues and Fratios to the preferred type of dvalue (as
described by Morris & DeShon, 2002, p. 111) but were unable to
do so because the primary studies with repeated measure tvalues
and Fratios lacked information (i.e., correlations between groups)
required to convert to dvalues that would be in the same metric as
the ratee-based, between-group dvalues. As a result, we included
only those within-person studies that provided means, standard
deviations, and sample sizes for ratees in order to maintain a
common, raw-score, between-ratee metric across study designs
(see Hönekopp et al., 2006).
1
Some samples made multiple types of ratings (e.g., both indi-
vidual and comparative, both salary and hiring ratings) or rated
multiple types of jobs (e.g., both female- and male-dominated).
We included multiple ratings from the same sample only when
they were categorized into different values of a moderator. When
a study contained more than one measure of the same value of a
moderator, the measures were combined into a composite. If
information was not available to create a composite (e.g., correla-
tions between measures), dvalues were averaged to obtain one
study dvalue to enter into the meta-analyses. For example, a
sample that made both comparative and individual ratings for a
male-dominated job would contribute two effect sizes when the
moderator being examined was type of comparison (one for com-
parative and one for individual) but only one effect size when the
moderator being examined was sex distribution (the composite d
value from the study). Thus, each sample contributed no more than
one dvalue to any value of a moderator.
Correlation between ratings of male and female ratees. To
compute sampling error variance for within-person studies, we
needed an estimate of the correlation between ratings of male and
female ratees in studies where participants rated both male and
female ratees (see Morris & DeShon, 2002). Two research assis-
tants searched all of the within-person studies included in the
meta-analyses to code for this correlation. However, none of the
within-person studies provided the correlation between ratings of
male and female ratees. We instead assumed a correlation of zero
between ratings of male and female ratees when computing sam-
pling error variance for within-person studies, resulting in the use
of the same sampling error variance formula as for between-person
studies. Conceptually, we find it reasonable to assume that the
correlation between ratings of male and female ratees is close to
zero, as the manipulation of ratee gender should be the major
source of rating differences.
Analyses
We used Hunter and Schmidt’s (2004) random effects meta-
analytic method to conduct our meta-analyses. Bare-bones
meta-analyses were conducted with a Microsoft Excel macro
based on Hunter and Schmidt’s dvalue meta-analysis program.
No corrections were made for artifacts such as range restriction
or measurement error because we were interested in actual
decisions made, analogous to how decisions would be used
operationally (i.e., operational decisions are based on uncor-
rected ratings; however, we do not know whether ratings made
in simulated settings are consistently more or less reliable than
those made in operational settings). Mean sample size weighted
dvalues were computed overall and for moderator variables.
Confidence and credibility intervals were calculated with for-
mulas provided by Hunter and Schmidt. To examine publication
bias, we used the “meta” package in R statistical software
(Schwarzer, 2012) to apply random effects trim and fill methods
(Duval & Tweedie, 2000a,2000b).
1
In two samples, it was unclear whether the means and standard devi-
ations represented the preferred ratee-based, between-person dvalue or a
rater-based, between-person dvalue (i.e., what Hönekopp et al., 2006, label
as d
2
), and in two samples, it was unclear whether the means and standard
deviations represented the preferred type of dvalue or a ratee-based,
within-person dvalue (i.e., what Hönekopp et al. label as d
3
). Based on the
recommendation by Hönekopp et al. to conduct separate meta-analyses for
different types of dvalues, we excluded these four samples and reran our
primary analyses, finding that all dvalues changed by less than .01.
Because the results did not change when including these potentially dif-
ferent dvalues, we chose to include them in further analyses.
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134 KOCH, D’MELLO, AND SACKETT
Results
Our initial literature searches in various search engines (conducted
in May 2012) identified several thousand potential sources. Review of
the titles and abstracts reduced this number to over 500, and review of
the full publications further reduced the number to 111 sources that
met the inclusion criteria. This usable database included 18 disserta-
tions and 93 journal articles published between 1970 and 2012.
Because of multiple independent samples within studies (e.g., differ-
ent populations, multiple experiments reported in one article), the 111
journal articles and dissertations provided 136 independent samples
and a total sample size of 22,348 (see Appendix B). Studies that were
considered but did not meet the inclusion criteria are listed in Appen-
dix C. The average overall dvalue (across all moderators) was .08,
indicating bias in favor of males. We found no evidence of publication
bias based on a funnel plot or the trim and fill procedure (Duval &
Tweedie, 2000a,2000b).
Sex Distributions Within Jobs
First, we examined the sex distribution of the job as a moderator
(see Table 1), finding a small but positive average effect size for
male-dominated jobs (d
⫽.13), indicating males received higher
ratings than females. We found near zero effect sizes for female-
dominated and integrated jobs (d
sof⫺.02 and .02, respectively).
The pro-male bias for male-dominated jobs supported Hypothesis
1, whereas the lack of a pro-female bias for female-dominated jobs
did not support this hypothesis.
Rater Gender
Next, we examined rater gender. Across all job types, female
raters exhibited a near-zero bias (d
⫽.04), and males exhibited a
larger pro-male bias (d
⫽.21). However, we found different
patterns when examining jobs with different sex distributions
separately (see Table 2). For male-dominated jobs, male raters
showed a stronger gender-role congruity bias (i.e., pro-male bias)
than female raters, in support of Hypothesis 2. Both male and
female raters exhibited a pro-male bias for female-dominated jobs,
contrary to our expectations. However, it should be noted that k
and nfor female-dominated job analyses were quite small. For
integrated jobs, bias did not differ for male and female raters (i.e.,
confidence intervals were overlapping).
Individuating Information
Overall, we did not find a consistent linear pattern in mean dvalue
changes when adding additional pieces of information. For male-
dominated jobs, the mean dvalue decreased when going from limited
information to one piece of information (from .45 to .09) but increased to
.22 for two or more pieces of information (see Table 3). Nevertheless,
pro-male bias was smaller when participants had one or more pieces of
information than when information was limited, providing some support
for Hypothesis 3 for male-dominated jobs. There was no consistent trend
for female-dominated jobs, with a mean dvalue that changed from a
slight pro-female bias with limited information (d
⫽⫺.03) to a slight
pro-male bias with one piece of information (d
⫽.05) and back to a small
pro-female bias with two or more pieces of information (d
⫽⫺.09); all
confidence intervals were overlapping, so Hypothesis 3 was not sup-
ported for female-dominated jobs. Similarly, the mean dvalue for inte-
grated jobs switched signs from limited information (d
⫽.14) to one
piece of information (d
⫽⫺.05) and then returned to its original direction
and approximate size for two or more pieces of information (d
⫽.17).
Across all job types, Hypothesis 3 received only partial support and only
in the male-dominated job sample, where the large pro-male bias asso-
ciated with limited information decreased when decision makers had
additional information.
Regarding the content of the individuating information, our results
for male- and female-dominated jobs supported Hypothesis 4. That is,
we found the largest gender-role congruity bias when individuating
information was ambiguous, compared to when information was
unambiguous and signaled high or low competence. For male-
dominated jobs, the pro-male bias found when information was am-
biguous (d
⫽.29) decreased to near zero when information indicated
high competence (d
⫽.02). For female-dominated jobs, the gender-
role congruity bias that was found when information was ambiguous
(d
⫽⫺.12) turned into a pro-male bias when information indicated
high competence (d
⫽.16). For integrated jobs, bias was larger when
individuating information signaled high competence (d
⫽.18) than
when it was ambiguous (d
⫽⫺.02).
Type of Evaluation
The findings regarding type of comparison did not confirm our
expectations in Hypothesis 5 (see Table 4). Instead of finding
larger gender-role congruity effects for comparative ratings than
for individual ratings, we found no significant differences in gen-
der bias for the two types of comparisons for any job type.
Although our results followed the expected pattern for male-
dominated jobs, confidence intervals were overlapping. For
female-dominated jobs, mean dvalues were near zero for individ-
ual and comparative ratings (d
s of .01 and ⫺.01, respectively). We
found a similar pattern for integrated jobs (d
⫽.03 for individual
ratings and d
⫽⫺.06 for comparative ratings, with overlapping
confidence intervals). Thus, none of our results supported Hypoth-
esis 5.
When examining various types of employment ratings, we found
different patterns of results for female-dominated, male-dominated,
and integrated jobs. For male-dominated jobs, the largest gender-role
congruity bias resulted for hiring decisions (d
⫽.26), followed by
evaluations of competence (d
⫽.16) and compensation (d
⫽.13). For
female-dominated jobs, the largest bias resulted for job performance
ratings (d
⫽⫺.32), followed by competence ratings (d
⫽⫺.22) and
hiring decisions (d
⫽⫺.14). For integrated jobs, there was no sig-
nificant bias for hiring, compensation, or job performance ratings and
a pro-male bias for competence ratings (d
⫽.08) and promotion
ratings (d
⫽.28).
Motivation to Make Careful Decisions
As predicted in Hypothesis 6, contextual variables that were
expected to increase participants’ motivation to make careful de-
cisions did reduce gender-role congruity bias for male-dominated
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135
GENDER STEREOTYPES AND EMPLOYMENT DECISION BIAS
jobs, with an average dvalue of .01 for studies where participants
were motivated to make careful decisions and an average dvalue
of .15 for studies without any of the motivating characteristics. We
did not discover the same trend for female-dominated or integrated
jobs (see Table 5). However, for both of these job types, very few
studies included the characteristics expected to increase decision
accuracy (k⫽5 for female-dominated jobs and k⫽4 for inte-
grated jobs).
Table 1
Overall Analyses
Variable kn d
SD SD
d
80% CV
lower 80% CV
upper 90% CI
lower 90% CI
upper
All samples 136 22,348 0.08 0.29 0.25 ⫺0.24 0.40 0.06 0.11
Sex distribution
Male-dominated 80 12,900 0.13 0.31 0.27 ⫺0.21 0.48 0.10 0.16
Female-dominated 28 2,772 ⫺0.02 0.43 0.38 ⫺0.50 0.47 ⫺0.08 0.05
Integrated 46 7,486 0.02 0.32 0.28 ⫺0.33 0.38 ⫺0.02 0.06
Rater gender
Male raters 49 3,499 0.21 0.42 0.34 ⫺0.23 0.65 0.16 0.27
Female raters 38 3,025 0.04 0.38 0.31 ⫺0.35 0.43 ⫺0.02 0.10
Amount of information
Limited 14 2,279 0.24 0.33 0.29 ⫺0.13 0.61 0.17 0.31
1 97 16,393 0.05 0.27 0.22 ⫺0.24 0.33 0.02 0.07
2 or more 28 3,676 0.16 0.35 0.31 ⫺0.24 0.55 0.10 0.21
Content of information
High competence 49 5,231 0.12 0.37 0.32 ⫺0.29 0.52 0.07 0.16
Ambiguous or average competence 51 6,494 0.12 0.37 0.32 ⫺0.29 0.53 0.08 0.16
Low competence 15 1,012 0.06 0.25 0.07 ⫺0.03 0.15 ⫺0.05 0.16
Type of comparison
Comparative 48 5,327 0.10 0.44 0.40 ⫺0.41 0.60 0.05 0.14
Individual 102 18,267 0.08 0.27 0.22 ⫺0.21 0.36 0.05 0.10
Type of employment rating
Hiring 98 14,261 0.11 0.32 0.28 ⫺0.25 0.46 0.08 0.13
Promotion 10 1,165 0.05 0.32 0.26 ⫺0.28 0.38 ⫺0.04 0.15
Compensation 20 3,656 0.06 0.20 0.14 ⫺0.11 0.24 0.01 0.12
Reward 3 454 0.07 0.21 0.13 ⫺0.09 0.23 ⫺0.08 0.23
Penalty 4 619 0.04 0.29 0.24 ⫺0.27 0.36 ⫺0.09 0.18
Job performance 25 6,182 0.04 0.28 0.25 ⫺0.28 0.35 ⫺0.01 0.08
Competence 38 4,954 0.08 0.23 0.15 ⫺0.12 0.27 0.03 0.12
Motivation to make careful decisions
Yes 23 2,538 ⫺0.05 0.26 0.18 ⫺0.27 0.18 ⫺0.11 0.02
No 115 19,513 0.10 0.30 0.26 ⫺0.23 0.43 0.08 0.12
Type of participant
Undergraduates 81 12,112 0.13 0.29 0.25 ⫺0.18 0.45 0.10 0.16
Working adults 15 2,376 0.05 0.34 0.30 ⫺0.34 0.44 ⫺0.01 0.12
Experienced professionals 34 6,720 0.03 0.23 0.18 ⫺0.20 0.26 ⫺0.01 0.07
Year of publication
1970s 24 5,431 0.03 0.19 0.13 ⫺0.14 0.20 ⫺0.02 0.07
1980s 29 4,345 0.14 0.35 0.30 ⫺0.25 0.53 0.09 0.19
1990s 33 4,774 0.13 0.32 0.28 ⫺0.23 0.48 0.08 0.18
2000s 50 7,798 0.06 0.30 0.25 ⫺0.26 0.39 0.03 0.10
Study design
Between 68 14,551 0.07 0.27 0.24 ⫺0.23 0.38 0.05 0.10
Within 68 7,797 0.11 0.33 0.27 ⫺0.24 0.46 0.07 0.14
Note.k⫽number of effect sizes; n⫽sample size; d
⫽average sample size weighted effect size (positive values indicate bias in favor of males and
negative values indicate bias in favor of females); SD ⫽standard deviation of observed effect sizes; SD
d
⫽estimated standard deviation of corrected effect
sizes; 80% CV ⫽lower and upper limits of 80% credibility interval; 90% CI ⫽lower and upper limits of 90% confidence interval.
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136 KOCH, D’MELLO, AND SACKETT
Additional Study Characteristics
When comparing bias exhibited by undergraduate, working
adult, and experienced professional samples, we found some sup-
port for the notion that with increased experience and training of
raters, gender-role congruity bias tends to be reduced (see Table
6). For male-dominated jobs, undergraduates and working adults
exhibited a larger pro-male bias (d
s⫽.19) than experienced
professionals (d
⫽.04). This trend was reversed for female-
dominated jobs, with experienced professionals showing the larg-
est pro-female bias, though the sample of experienced profession-
als was small (n⫽167, k⫽5). Undergraduates and experienced
professionals exhibited similar levels of bias when making decisions
about integrated jobs (d
s⫽.07 and .05, respectively). Thus, findings
on bias exhibited by different types of participants were mixed.
Table 2
Moderator Analyses for Rater Gender
Variable kn d
SD SD
d
80% CV
lower 80% CV
upper 90% CI
lower 90% CI
upper
Male-dominated jobs
Male raters 33 1,927 0.30 0.47 0.39 ⫺0.20 0.80 0.22 0.38
Female raters 22 1,112 0.01 0.45 0.34 ⫺0.43 0.45 ⫺0.09 0.11
Female-dominated jobs
Male raters 6 394 0.18 0.48 0.41 ⫺0.34 0.70 0.01 0.35
Female raters 7 624 0.32 0.53 0.49 ⫺0.31 0.94 0.19 0.45
Integrated jobs
Male raters 14 1,281 0.14 0.39 0.33 ⫺0.28 0.56 0.04 0.23
Female raters 12 1,498 ⫺0.03 0.19 0.08 ⫺0.13 0.07 ⫺0.11 0.06
Note.k⫽number of effect sizes; n⫽sample size; d
⫽average sample size weighted effect size (positive values indicate bias in favor of males and
negative values indicate bias in favor of females); SD ⫽standard deviation of observed effect sizes; SD
d
⫽estimated standard deviation of corrected effect
sizes; 80% CV ⫽lower and upper limits of 80% credibility interval; 90% CI ⫽lower and upper limits of 90% confidence interval.
Table 3
Moderator Analyses for Individuating Information
Variable kn d
SD SD
d
80% CV
lower 80% CV
upper 90% CI
lower 90% CI
upper
Amount of information
Male-dominated jobs
Limited 6 852 0.45 0.40 0.36 ⫺0.02 0.91 0.33 0.56
1 57 9,932 0.09 0.26 0.21 ⫺0.18 0.35 0.05 0.12
2 or more 20 2,086 0.22 0.43 0.38 ⫺0.27 0.71 0.15 0.30
Female-dominated jobs
Limited 4 400 ⫺0.03 0.16 0.00 ⫺0.03 ⫺0.03 ⫺0.19 0.13
1 17 1,348 0.05 0.38 0.31 ⫺0.34 0.44 ⫺0.04 0.14
2 or more 8 1,024 ⫺0.09 0.56 0.53 ⫺0.77 0.59 ⫺0.19 0.01
Integrated jobs
Limited 6 1,227 0.14 0.25 0.20 ⫺0.12 0.40 0.05 0.24
1 30 4,877 ⫺0.05 0.30 0.25 ⫺0.37 0.27 ⫺0.10 0.00
2 or more 10 1,382 0.17 0.37 0.33 ⫺0.26 0.59 0.08 0.26
Content of information
Male-dominated jobs
High competence 29 2,223 0.02 0.41 0.34 ⫺0.41 0.45 ⫺0.05 0.09
Ambiguous or average competence 35 3,492 0.29 0.43 0.38 ⫺0.19 0.78 0.24 0.35
Low competence 13 710 0.10 0.28 0.05 0.03 0.16 ⫺0.03 0.22
Female-dominated jobs
High competence 6 753 0.16 0.38 0.33 ⫺0.26 0.59 0.04 0.28
Ambiguous or average competence 12 1,375 ⫺0.12 0.49 0.46 ⫺0.70 0.47 ⫺0.21 ⫺0.03
Low competence 0 — — — — — — — —
Integrated jobs
High competence 17 2,304 0.18 0.35 0.30 ⫺0.21 0.56 0.11 0.25
Ambiguous or average competence 15 2,673 ⫺0.02 0.22 0.16 ⫺0.23 0.19 ⫺0.08 0.04
Low competence 2 150 ⫺0.08 0.27 0.14 ⫺0.25 0.10 ⫺0.34 0.19
Note.k⫽number of effect sizes; n⫽sample size; d
⫽average sample size weighted effect size (positive values indicate bias in favor of males and
negative values indicate bias in favor of females); SD ⫽standard deviation of observed effect sizes; SD
d
⫽estimated standard deviation of corrected effect
sizes; 80% CV ⫽lower and upper limits of 80% credibility interval; 90% CI ⫽lower and upper limits of 90% confidence interval. Dashes indicate that
values are not reported for any moderator with a kof0or1.
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GENDER STEREOTYPES AND EMPLOYMENT DECISION BIAS
We did not find a consistent trend for changes in bias over time.
The average effect size for male-dominated jobs in the 1970s was
quite small, .05, but increased to .24 in the 1980s and to .26 in the
1990s before decreasing to .08 in the 2000s. The pro-female bias
for female-dominated jobs increased from ⫺.14 in the 1970s
to ⫺.32 in the 1980s before changing to a pro-male bias of .33 in
the 1990s and a near-zero bias of .03 in the 2000s. Bias for
integrated jobs remained close to zero throughout all decades, with
the largest average effect size being .09 in the 1980s. For all job
types, bias was quite small, .08 or smaller, in studies published
during the 2000s. Overall, findings did not indicate a clear trend in
gender bias by study year.
We found that effect sizes were similar when ratee gender was
manipulated between- and within-person. Mean effect sizes for
within- and between-person designs differed by only .01 for male-
dominated jobs, by .03 for female-dominated jobs, and by .08 for
integrated jobs. Confidence intervals for between- and within-person
designs were overlapping for all job types. Findings did not support a
difference in gender bias for different types of study designs.
Discussion
Summary of Findings
Findings regarding all hypotheses and research questions are
summarized in Table 7. Our results supported the predictions made
by role congruity theory (Eagly & Karau, 2002;Kark & Eagly,
2010), which suggests that the greater the incongruence between
stereotypical gender traits and the gender stereotype of a job, the
greater the gender bias, particularly for masculine jobs (repre-
sented by male-dominated jobs in our study). Our findings suggest
that women may be more likely to face discrimination in male-
dominated environments, whereas, on average, neither gender has
an advantage in female-dominated or integrated environments. The
extent to which a job is female dominated is negatively related to
occupational salary and prestige (Glick, 1991;Lyness, 2002), so
women may tend to face the most discrimination in jobs that
generally produce the highest pay and status.
Table 4
Moderator Analyses for Type of Evaluation
Variable kn d
SD SD
d
80% CV
lower 80% CV
upper 90% CI
lower 90% CI
upper
Type of comparison
Male-dominated jobs
Comparative 31 3,113 0.19 0.40 0.35 ⫺0.26 0.63 0.13 0.25
Individual 58 10,424 0.12 0.28 0.23 ⫺0.18 0.42 0.09 0.15
Female-dominated jobs
Comparative 12 856 ⫺0.01 0.29 0.17 ⫺0.22 0.20 ⫺0.12 0.10
Individual 19 2,095 0.01 0.47 0.43 ⫺0.54 0.56 ⫺0.06 0.08
Integrated jobs
Comparative 13 1,820 ⫺0.06 0.51 0.48 ⫺0.68 0.56 ⫺0.14 0.02
Individual 37 6,096 0.03 0.31 0.27 ⫺0.31 0.37 ⫺0.01 0.07
Type of employment rating
Male-dominated jobs
Hiring 55 6,987 0.26 0.38 0.33 ⫺0.17 0.68 0.22 0.30
Promotion 7 829 ⫺0.04 0.31 0.24 ⫺0.35 0.27 ⫺0.16 0.07
Compensation 12 1,725 0.13 0.22 0.15 ⫺0.07 0.32 0.05 0.20
Reward 3 454 0.07 0.21 0.13 ⫺0.09 0.23 ⫺0.08 0.23
Penalty 3 566 ⫺0.03 0.17 0.08 ⫺0.13 0.07 ⫺0.17 0.11
Job performance 14 4,704 0.09 0.33 0.31 ⫺0.31 0.49 0.04 0.14
Competence 21 2,647 0.16 0.29 0.23 ⫺0.13 0.45 0.10 0.23
Female-dominated jobs
Hiring 21 2,022 ⫺0.14 0.50 0.46 ⫺0.72 0.45 ⫺0.21 ⫺0.07
Promotion 0 — — — — — — — —
Compensation 3 678 0.04 0.43 0.41 ⫺0.48 0.57 ⫺0.08 0.17
Reward 0 — — — — — — — —
Penalty 0 — — — — — — — —
Job performance 7 736 ⫺0.32 0.41 0.36 ⫺0.78 0.14 ⫺0.44 ⫺0.20
Competence 10 911 ⫺0.22 0.35 0.28 ⫺0.58 0.15 ⫺0.33 ⫺0.11
Integrated jobs
Hiring 39 6,384 0.00 0.30 0.26 ⫺0.33 0.33 ⫺0.04 0.04
Promotion 3 336 0.28 0.22 0.10 0.16 0.41 0.10 0.47
Compensation 8 1,745 0.00 0.15 0.05 ⫺0.07 0.07 ⫺0.08 0.08
Rewards 0 — — — — — — — —
Penalty 1 — — — — — — — —
Job performance 9 1,416 0.05 0.43 0.40 ⫺0.46 0.57 ⫺0.03 0.14
Competence 16 2,212 0.08 0.29 0.23 ⫺0.22 0.37 0.01 0.15
Note.k⫽number of effect sizes; n⫽sample size; d
⫽average sample size weighted effect size (positive values indicate bias in favor of males and
negative values indicate bias in favor of females); SD ⫽standard deviation of observed effect sizes; SD
d
⫽estimated standard deviation of corrected effect
sizes; 80% CV ⫽lower and upper limits of 80% credibility interval; 90% CI ⫽lower and upper limits of 90% confidence interval. Dashes indicate that
values are not reported for any moderator with a kof0or1.
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138 KOCH, D’MELLO, AND SACKETT
In our examination of rater gender, we found that male raters
tended to favor males, regardless of the sex distribution within the
job. The finding that male raters exhibited stronger gender-role
congruity bias than female raters for male-dominated jobs is con-
sistent with the idea that men may be sensitive to changes in the
traditional gender hierarchy and may disapprove of women work-
ing in male-dominated, high-status occupations. Because the
workplace has historically been the male domain, males may feel
as though their roles are being threatened by women entering the
workforce, especially when women seek male-dominated jobs. It
may also be that males, compared to females, tend to see male-
dominated positions as more masculine or tend to adhere more
strongly to gender stereotypes. Our results show that female raters
did not exhibit a large bias for male-dominated jobs. This finding
could have resulted partially from the tendency of women (com-
pared to men) to hold less traditional stereotypes about women, to
see women as having more masculine traits, and to view some
traditionally masculine jobs as more feminine (e.g., Brenner et al.,
1989;Koenig et al., 2011;Massengill & DiMarco, 1979;Schein,
2001), leading women to be more likely than men to believe that
women are compatible with masculine or male-dominated roles.
Rater gender analyses also revealed a surprising pro-male bias by
both male and female raters for female-dominated jobs. This
finding is consistent with a “glass escalator” effect, where men in
female-dominated professions enjoy advantages such as being
more likely to be hired, to be promoted, and to earn pay raises than
women in the same occupations (see Williams, 1992). Explana-
tions for this effect include men being steered toward more mas-
culine positions or specialties within female-dominated occupa-
tions, which include managerial and administrative roles that tend
to be higher paying and more prestigious.
Our results offer limited support for the claim that providing
more individuating information decreases gender bias in work-
place decisions (e.g., Landy, 2008). Compared to when informa-
tion was limited, one or more substantial pieces of information
decreased bias, particularly for male-dominated jobs. However,
mean effect sizes for the number of pieces of information sug-
gested that bias did not always decrease when adding each addi-
tional piece of information. Perhaps more information leads to a
higher cognitive load, resulting in the decision maker failing to
consider the information and relying on stereotypes. The findings
on the content of individuating information may also shed light on
why we did not discover the expected pattern of bias for the
amount of information. If information was ambiguous regarding a
ratee’s potential for success in a job, it did not tend to reduce
gender-role congruity bias. Even if a decision maker had a large
amount of information about a ratee, if that information was
ambiguous, gender-role congruity bias did not decrease. This
supports the idea that individuating information must be highly
diagnostic to counteract stereotypes.
These results do not support predictions about a backlash effect
where highly competent ratees are punished for violating tradi-
tional stereotypes. In the case of female-dominated jobs, males
were rewarded rather than punished for being highly competent in
gender-incongruent jobs. This appears consistent with what Kunda
and Thagard (1996) called a contrast effect, in which unambiguous
information indicating a counterstereotypical trait of a ratee leads
the ratee to be viewed as extreme on that trait. For male-dominated
jobs, neither gender was favored when ratees were highly compe-
tent. This appears to be an encouraging sign for competent women
wishing to enter male-dominated professions.
Our findings failed to provide support for the shifting standards
model (Biernat, 2003), which predicts smaller gender-role congru-
ity bias for individual ratings than for comparative ratings. We
found no substantial differences between individual and compar-
ative ratings. It appears that the within-gender comparisons for
individual, subjective rating scales thought to induce the shifting
standards pattern (e.g., “this person is competent...fora
woman”) are not consistently made.
One encouraging finding from our study comes from the mod-
erator analyses on characteristics expected to increase participants’
motivation to make careful decisions, particularly for male-
dominated jobs. When participants felt accountable for their deci-
sions, believed their decisions had real-life consequences, or were
reminded of equity norms, they tended to make less biased deci-
sions about male-dominated jobs than when none of these features
were present. This finding provides support for the idea that when
held accountable, decision makers are more careful and thorough
about processing information, leading to more accurate decisions.
Our findings suggest that increasing feelings of accountability or
Table 5
Moderator Analyses for Motivation to Make Careful Decisions
Variable kn d
SD SD
d
80% CV
lower 80% CV
upper 90% CI
lower 90% CI
upper
Male-dominated jobs
Yes 15 1,518 0.01 0.30 0.22 ⫺0.27 0.29 ⫺0.07 0.09
No 68 11,382 0.15 0.31 0.27 ⫺0.20 0.50 0.12 0.18
Female-dominated jobs
Yes 5 176 0.22 0.34 0.03 0.19 0.26 ⫺0.03 0.47
No 24 2,596 ⫺0.03 0.44 0.40 ⫺0.54 0.48 ⫺0.10 0.03
Integrated jobs
Yes 4 910 ⫺0.21 0.18 0.12 ⫺0.36 ⫺0.05 ⫺0.32 ⫺0.10
No 41 6,279 0.04 0.33 0.28 ⫺0.32 0.40 0.00 0.08
Note.Yes indicates one of the following conditions was present: participants felt accountable for their decisions, participants believed their judgments had
real consequences, or participants were aware of equity norms. No indicates none of those three conditions was present in the study. k⫽number of effect
sizes; n⫽sample size; d
⫽average sample size weighted effect size (positive values indicate bias in favor of males and negative values indicate bias in
favor of females); SD ⫽standard deviation of observed effect sizes; SD
d
⫽estimated standard deviation of corrected effect sizes; 80% CV ⫽lower and
upper limits of 80% credibility interval; 90% CI ⫽lower and upper limits of 90% confidence interval.
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139
GENDER STEREOTYPES AND EMPLOYMENT DECISION BIAS
highlighting equity norms in an organization may help to reduce
gender bias in decision making, specifically for male-dominated
jobs.
We found some evidence indicating that professionals with
experience and/or training in organizational decision making ex-
hibit less bias than untrained working adults or undergraduate
students. For male-dominated jobs, experienced professionals
tended to show smaller gender-role congruity bias than working
adults or undergraduates, providing some support for ideas ex-
pressed by those who question the generalizability of findings
from laboratory studies with undergraduate participants to real-life
employment settings (e.g., Landy, 2008). It may be that experi-
enced decision makers have learned to avoid stereotypical thinking
or are more aware of norms that discourage them from appearing
biased.
Experimental Simulations Versus Field Studies
In the introduction we noted the tension between the exper-
imental control gained in simulations versus the realism of field
studies. Some argue that laboratory studies using “paper peo-
ple” are not similar enough to actual organizational scenarios to
allow one to generalize laboratory findings to the workplace.
On the other hand, the lack of experimental control of field-
based studies leaves one unable to rule out other plausible
explanations when it is found that one gender receives higher
ratings than the other (e.g., are there true gender differences?).
Therefore, it is useful to note points of convergence between the
present study and meta-analyses of field studies of gender bias.
Both Bowen et al. (2000) and Roth et al. (2012) reported small
overall gender differences in measures of job performance and
Table 6
Moderator Analyses for Study Characteristics
Variable kn d
SD SD
d
80% CV
lower 80% CV
upper 90% CI
lower 90% CI
upper
Type of participant
Male-dominated jobs
Undergraduates 50 6,543 0.19 0.31 0.26 ⫺0.13 0.52 0.15 0.24
Working adults 10 1,187 0.19 0.43 0.39 ⫺0.31 0.70 0.10 0.29
Experienced professionals 18 4,970 0.04 0.24 0.21 ⫺0.23 0.30 ⫺0.01 0.08
Female-dominated jobs
Undergraduates 20 2,436 0.02 0.42 0.38 ⫺0.47 0.51 ⫺0.05 0.09
Working adults 1 — — — — — — — —
Experienced professionals 5 167 ⫺0.32 0.56 0.43 ⫺0.88 0.23 ⫺0.58 ⫺0.07
Integrated jobs
Undergraduates 24 3,871 0.07 0.30 0.26 ⫺0.27 0.40 0.01 0.12
Working adults 4 1,142 ⫺0.09 0.08 0.00 ⫺0.09 ⫺0.09 ⫺0.19 0.00
Experienced professionals 14 1,655 0.05 0.33 0.27 ⫺0.30 0.40 ⫺0.03 0.13
Year of publication
Male-dominated jobs
1970s 20 4,871 0.05 0.24 0.20 ⫺0.21 0.31 0.00 0.09
1980s 22 3,169 0.24 0.39 0.35 ⫺0.21 0.69 0.18 0.30
1990s 15 1,919 0.26 0.33 0.28 ⫺0.09 0.61 0.18 0.33
2000s 23 2,941 0.08 0.23 0.15 ⫺0.11 0.28 0.02 0.15
Female-dominated jobs
1970s 4 266 ⫺0.14 0.33 0.22 ⫺0.42 0.14 ⫺0.34 0.06
1980s 6 764 ⫺0.32 0.28 0.22 ⫺0.60 ⫺0.04 ⫺0.44 ⫺0.20
1990s 10 613 0.33 0.47 0.39 ⫺0.17 0.83 0.20 0.47
2000s 8 1,129 0.03 0.35 0.31 ⫺0.36 0.43 ⫺0.06 0.13
Integrated jobs
1970s 6 466 ⫺0.03 0.31 0.22 ⫺0.31 0.24 ⫺0.18 0.12
1980s 8 1,356 0.09 0.34 0.30 ⫺0.30 0.48 0.00 0.18
1990s 10 1,738 ⫺0.07 0.21 0.15 ⫺0.26 0.12 ⫺0.15 0.01
2000s 22 3,926 0.05 0.34 0.31 ⫺0.35 0.44 ⫺0.01 0.10
Study design
Male-dominated jobs
Between 38 8,781 0.13 0.28 0.24 ⫺0.18 0.44 0.10 0.17
Within 42 4,119 0.14 0.37 0.31 ⫺0.26 0.54 0.09 0.19
Female-dominated jobs
Between 13 1,712 ⫺0.03 0.51 0.48 ⫺0.64 0.59 ⫺0.11 0.05
Within 15 1,060 0.00 0.25 0.09 ⫺0.11 0.12 ⫺0.10 0.10
Integrated jobs
Between 26 4,628 ⫺0.01 0.32 0.28 ⫺0.36 0.35 ⫺0.05 0.04
Within 20 2,858 0.07 0.32 0.27 ⫺0.28 0.42 0.01 0.13
Note.k⫽number of effect sizes; n⫽sample size; d
⫽average sample size weighted effect size (positive values indicate bias in favor of males and
negative values indicate bias in favor of females); SD ⫽standard deviation of observed effect sizes; SD
d
⫽estimated standard deviation of corrected effect
sizes; 80% CV ⫽lower and upper limits of 80% credibility interval; 90% CI ⫽lower and upper limits of 90% confidence interval. Dashes indicate that
values are not reported for any moderator with a kof0or1.
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140 KOCH, D’MELLO, AND SACKETT
promotability, consistent with overall effect sizes in the present
study. Roth et al. and Bowen et al. also identified moderators
that influenced the size of gender differences in evaluations. For
example, like the present study, Bowen et al. found that pro-
male bias on measures of job performance was larger when
raters were males. Although overall gender differences may be
small in both field-based and laboratory studies, both types of
research have identified similar moderator variables that can
have an impact on the size of gender differences in employment
evaluations. Because of the inherent ambiguity in explaining
gender differences in field studies, laboratory studies are criti-
cal for isolating gender bias as a cause of gender differences in
evaluations.
Study Limitations
One limitation is that we are unable to test many of the proposed
mechanisms underlying the effects reported here. For example,
although we can document that female raters give lower ratings to
women than to men in female-dominated jobs, intervening causal
mechanisms cannot be tested meta-analytically unless those mech-
anisms are systematically explored in primary studies. We view
this as reflecting the state of our knowledge rather than as a failing
of the present study. Our hope is that highlighting various effects
via meta-analysis will motivate research that explores the mecha-
nisms underlying the effects, thus informing subsequent meta-
analyses.
Another limitation is that we cannot draw strong conclusions
about some moderator analyses, especially for female-dominated
jobs, due to a lack of studies and small sample sizes. Because of
women’s later entry into the labor force and their disproportionate
segregation into lower paying and lower status jobs, it is not
surprising that researchers typically choose to focus on gender
discrimination in male-dominated rather than female-dominated
jobs. Nevertheless, the smaller literature on female-dominated jobs
limits the conclusions we can draw about them.
Finally, there are multiple ways to identify gender stereotypes of
jobs. We chose to determine job stereotypes based on the sex
distribution within jobs, but in some cases the gender stereotype
associated with a job’s requirements may not align with the sex
distribution in that job. For example, human resource positions in
the United States were male dominated in the 1970s but integrated
(i.e., filled by roughly equal numbers of men and women) in the
1980s, and they have been female-dominated since the 1990s
(International Labour Organization, 2012). It is not necessarily the
case that the requirements for human resource jobs have changed
in terms of the masculine or feminine attributes required to be
successful in the job. Rather, there could be a mismatch between
the sex distribution and the gender stereotype of the job require-
ments. If we had classified jobs based on the gender stereotypes
associated with job requirements (e.g., whether jobs require in-
cumbents to exhibit agentic or communal behaviors), some jobs
may have been placed into different job stereotype categories.
Nevertheless, job stereotypes based on the sex distributions of
incumbents and those based on stereotypes of job requirements are
often the same, and both are related to gender bias exhibited by
decision makers (e.g., Glick, 1991).
Table 7
Summary of Findings
Hypothesis Support for hypothesis
H1: Gender-role congruity bias will be found, with men being rated
more favorably than women for male-dominated jobs and women
being rated more favorably than men for female-dominated jobs.
Only for male-dominated jobs; not for female-dominated jobs.
H2: Male raters will exhibit stronger gender-role congruity bias than
female raters. Only for male-dominated jobs; not for female-dominated jobs.
H3: As more job-relevant information becomes available, gender-role
congruity bias will decrease. Only for limited information compared to one or more pieces
of information for male-dominated jobs; not for female-
dominated jobs.
H4: Gender-role congruity bias will be largest when individuating
information is ambiguous or not clearly diagnostic of success in
the job.
Yes, for both male- and female-dominated jobs.
H5: Gender-role congruity bias will be larger for comparative than
for individual ratings. No, not for male- or female-dominated jobs.
H6: Gender-role congruity bias will be smaller for studies in which
(a) decision makers feel accountable for their decisions, (b)
decision makers believe their decisions will have consequences
that affect others, or (c) decision makers are informed of equity
norms.
Only for male-dominated jobs; not for female-dominated jobs.
Research question Finding
Does gender-role congruity bias vary by sample type (i.e.,
undergraduates vs. working adults vs. experienced professionals)? For male-dominated jobs, experienced professionals exhibited
less gender-role congruity bias than undergraduates and
working adults; this pattern did not hold for female-
dominated or integrated jobs.
Does gender bias vary by publication year? No support for a difference in gender bias by publication
year.
Does gender bias vary by study design (i.e., when ratee gender is
manipulated between-person vs. within-person)? No support for a difference in gender bias by study design.
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GENDER STEREOTYPES AND EMPLOYMENT DECISION BIAS
Future Research
These results suggest several avenues that should be addressed
in future research. First, further understanding the conditions under
which different stereotyping effects occur would be practically and
theoretically useful. For many analyses, credibility intervals were
quite wide, indicating that bias varied substantially across studies,
and additional moderator variables may help to explain this vari-
ation. For example, identifying other contextual variables that
reduce reliance on stereotypes would be worthwhile. Trying to
explain why competent men may be preferred over competent
women in female-dominated jobs is another interesting topic.
Second, our finding that males exhibit pro-male bias for all job
types is worrisome, and it is certainly no easy task to change negative
evaluations of women. However, the processes responsible for male
raters’ biases could be illuminated further, for example, by examining
the effects of gender differences in perceived job stereotypes, per-
ceived gender stereotypes, and perceived fit between the person and
the occupation, organization, and/or job. Additionally, the effect sizes
for the male rater samples had large credibility intervals that often
included zero, so trying to identify conditions under which these raters
exhibit pro-male, pro-female, or near-zero bias for different job types
is worthwhile. Because people belong to many social categories
associated with stereotypes (e.g., race, gender, age), a deeper under-
standing of bias in employment settings requires the study of the
intersection of multiple categories.
Third, whereas most studies present estimates of individuals’
gender biases at one time point, it would be informative to examine
how the impact of gender stereotypes change over time for indi-
viduals (e.g., how initial impressions of people are revised over
time, whether gender bias increases or decreases after certain
amounts of time or following certain events). Along the same
lines, the content of individuating information should be examined
in more detail in terms of both personal and job-related informa-
tion. Managers may have non-job-related personal information
about their employees (e.g., family situations, financial need) that
could impact decisions about outcomes such as raises or promo-
tions. With regard to job-related information, our findings suggest
diagnostic information demonstrating high levels of competence is
most likely to result in less bias. More specific résumé information
should be systematically manipulated in future laboratory studies
to better pinpoint the optimal type of information that should be
gathered by employers in order to reduce bias.
Finally, in addition to the above substantive research issues, we
wish to highlight the finding of reduced gender bias in settings where
raters are motivated to make effective decisions. We suggest that
future studies, even if in simulated settings, carefully attend to this
finding. Various design features merit consideration, including the
use of designs in which raters believe that the ratings they provide are
actually consequential or the use of designs in which raters believe
they will have to justify their decisions to others. Motivation to rate
carefully can be designed into laboratory studies of gender bias.
Practical Implications and Conclusions
Identifying specific contextual variables that both individuals and
organizations may alter to reduce gender bias is an important avenue
of study, and our findings provide such evidence. We focus specifi-
cally on implications for male-dominated jobs, because we found the
largest gender bias for these jobs and because they tend to be the
highest paying and most prestigious jobs. Although stereotyping is a
complex cognitive process, the extent to which decision makers rely
on stereotypes rather than on individuating information appears to
depend on several contextual variables under the control of an orga-
nization. When decision makers are motivated to make careful deci-
sions, they are better able to avoid stereotyping. Organizations can try
to increase decision makers’ motivation to make accurate decisions by
imposing certain conditions, including making sure decision makers
(a) expect to justify their judgments to others, (b) have a vested
interest in the outcome such that their own success depends on the
decision (e.g., they will have to work with a new hire), (c) expect their
decisions to be made public, (d) are aware of organizational values
regarding equity, or (e) expect their judgments to be evaluated (Fiske,
1993;Kunda, 1990). Finally, there is evidence that professionals with
training and/or experience make less biased decisions than undergrad-
uates or working adults without relevant decision-making experience.
Providing decision makers with training or practice in making partic-
ular types of decisions may help to reduce gender bias.
Our findings also have implications for women in male-dominated
occupations. Gender bias does not necessarily decrease with addi-
tional individuating information, but it does tend to decrease when the
individuating information unambiguously indicates a woman’s com-
petence for a job. It is not simply the quantity of information but the
quality of information that can reduce bias. By providing unequivocal,
job-related information about their qualifications for a job, women
can try to counteract the biasing effects of gender stereotypes. Orga-
nizations can also use this research finding when gathering informa-
tion about job applicants. Using standardized selection tools and
procedures that elicit job-relevant information about candidates (e.g.,
job knowledge tests, structured interviews) may help organizations to
gather clear evidence about candidates’ qualifications and to reduce
any reliance on gender stereotypes in decision making.
In conclusion, our study explores the magnitude and direction of
gender bias under various conditions. We highlight the importance
of considering the gender stereotypes or sex distributions within
jobs when examining gender bias and challenge the assumption
that additional information will decrease or remove gender bias.
Additionally, our findings support rater gender, content of indi-
viduating information, and motivation to make careful decisions as
important variables that impact bias. We hope these findings will
lead to further investigation of situational variables that can help to
reduce gender bias in the workplace.
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(Appendices follow)
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147
GENDER STEREOTYPES AND EMPLOYMENT DECISION BIAS
Appendix A
Job Titles by Sex Distribution Within Job
Male-dominated jobs Study year
Executive position at a clothing manufacturing company 1974
Stock worker in a grocery store 1974
Engineer at a chemical company active 1975
Head of a furniture department in a large department store
a
1975
Management trainee 1975
Personnel technician 1975
Stock person in a grocery store 1976
Assistant director of child daycare center 1977
Automobile salesperson 1977
General management trainee 1977
Life insurance agent 1977
Management trainee in mechanical engineering 1977
Manager in a public utility 1977
Managerial position 1977
Semiskilled positions such as machine operator, working on a production line, inspection
operations, setup operations, feeding equipment, or handling materials 1977
Trainee in sales management 1977
Wholesale hardware shipping and receiving clerk 1977
Beginning management position 1978
Electrician 1978
Management trainee 1978
Managerial position in claims department of an insurance company 1979
Sales management trainee 1980
Accounting and financial positions 1982
Business partner for a new export/import business 1982
Entry-level management trainee 1983
Teaching position at a junior college in the department of accounting 1983
Third-level manager at a commercial bank 1983
Supermarket manager 1984
Automobile salesperson
a
1985
Electrician assistant
a
1985
Head of a furniture department in a department store 1985
Middle-level manager in a technological manufacturing organization 1986
Petroleum engineer 1986
Computer analyst-programmer 1987
Computer operator 1987
Computer programmer 1987
Entry level management position in materials management area 1987
Gardener 1987
Management accountant 1987
Division manager in the sales division of a marketing firm 1988
Managerial trainee at a department store 1988
Photographer 1988
Assistant district attorney 1989
Police officer 1989
Computer science graduate student 1990
Director at children’s daycare center 1991
Police officer 1991
Administrative officer 1992
Store manager at a computer products company 1993
Employee in the stock-trading division of a financial corporation 1994
Staff photographer for a sports magazine 1994
Systems analyst 1994
Engineering intern 1995
National yacht racing teams 1996
Police officer 1996
Sales position at a brokerage firm 1996
Firefighter 1997
(Appendices continue)
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
148 KOCH, D’MELLO, AND SACKETT
Appendix A (continued)
Male-dominated jobs Study year
Senior architect 1997
Regional director of a real estate company 1999
Short order cook 1999
University computer lab manager 1999
Mechanichal engineering intern 2001
University computer lab manager 2001
Assistant vice president of an operations department in a consumer goods company 2004
Assistant vice president of sales in an aircraft company 2004
Line supervisor at an industrial company 2004
Chairperson of a district doctors’ association 2005
Employee in software manufacturing firm 2005
Police chief 2005
Police chief
a
2006
Factory manager 2007
Police officer 2007
Computer lab manager 2008
Development staff member 2008
Junior position in civil/environmental engineering 2008
Vice president of development
b
2008
Vice president of operations
a
2008
Factory worker 2009
CEO of a supermarket chain 2010
Research and information technology consultant working in a product development unit 2010
Leadership position 2011
Assistant vice president of sales 2012
Junior position in civil/environmental engineering 2012
Junior position in geological/civil engineering 2012
Female-dominated jobs Study year
Government documents director at a regional research library
a
1976
Office receptionist 1977
Telephone operator 1977
Secretary 1978
Campus library worker 1980
Teaching position at a junior college in the department of nursing 1983
Office receptionist
a
1985
Switchboard operator
a
1985
Administrative assistant in the sales division of a marketing firm 1988
Second grade teacher 1989
Social worker 1989
Teacher at children’s daycare center 1991
Entry-level administrative assistant
a
1993
Retail sales representative 1994
Nurse 1996
Waiter/waitress at high-priced restaurant 1996
Waiter/waitress at low-priced restaurant 1996
Waiter/waitress at medium-priced restaurant 1996
Secretary 1997
Bank clerk 1998
Personnel management project manager at a consulting company 2004
School social worker
a
2005
Women’s studies professor 2006
College student summer intern at a commercial bank 2007
Nurse 2007
Human resources position at a toy company 2008
Corporate training manager 2011
Integrated jobs Study year
Psychology professor 1970
(Appendices continue)
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
149
GENDER STEREOTYPES AND EMPLOYMENT DECISION BIAS
Appendix A (continued)
Female-dominated jobs Study year
School counselor 1970
Editorial assistant 1975
Assistant copy editor of a city newspaper 1977
Salesperson 1978
Nonmanagerial position in claims department of an insurance company 1979
Newswriter for local newspaper 1981
Position at a school that requires travel and principal 1982
School psychologist 1985
Shoe salesperson
a
1985
Telephone solicitor
a
1985
Counselor in an employee assistance program 1987
Director of counseling in an employee assistance program 1987
Industrial relations officer 1987
Payroll clerk 1987
Benefits officer at a men’s shoe company 1991
Finance officer at a men’s shoe company 1991
Loan counselor at a bank 1991
Loan officer at a bank 1991
Benefits officer at a men’s clothing company 1994
Finance officer at a men’s clothing company 1994
Entry-level marketing associate 1995
Journalist 1996
Management trainee in a financial organization 1996
Personnel analyst 1997
Entry-level accountant 1998
Mutual fund manager 1999
Research assistant at a consumer research organization 1999
Accountant 2001
Assistant bank manager 2001
Dean of a business school 2001
Insurance salesperson 2001
Office supervisor for an insurance company 2001
Entry-level accountant 2002
Mail sorting intern 2003
Marketing intern 2003
Preparatory school teacher 2003
Technical writer 2003
Intern at U.S. Senator’s office 2005
Position requiring creation of an investment portfolio 2005
Position requiring development of an appropriate budget for a computer software company
a
2005
Retail pharmacist 2005
Sales supervisor
a
2005
Management position
a
2007
Personal trainer for a fitness organization 2007
Associate editor 2008
Community organization manager 2008
Mail superintendent 2008
Technical writer 2008
Project manager 2009
Personal trainer for a fitness organization 2010
Marketing director 2011
a
Two samples rated this job.
b
Three samples rated this job.
(Appendices continue)
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150 KOCH, D’MELLO, AND SACKETT
Appendix B
Studies Included in Meta-Analyses
Author (year) nadbSex
distributioncRater
genderdAmount of
informationeContent of
informationfType of
comparisong
Type of
employment
ratinghMotivationiType of
participantjStudy
designkPublication
sourcelCountry
Alksnis (2001) 311 ⫺0.17 I Yes 1 A I 1, 3, 7 No U B D Canada
Ashkanasy (1994) 252 0.07 F, M No 1 H, L I 3 No U W J Australia
Behr (2001) 171 0.11 I No 1 A I 1 No U B D United States
(US)
Bell & Klein (2001) 186 ⫺0.11 I No 2 A I 1, 3, 7 No E B J US
Bieber (1988) 90 0.93 M No 1 A C 1 No U W D US
Biernat & Fuegen
(2001) 64 0.05 M Yes 2 No C 1 Yes U W J US
Bigoness (1976) 60 ⫺0.41 M Males 1 H, L I 6 No U W J US
Bonds (1980) 32 0.16 M Males 1 H, L I, C 1 No E W D US
Bosak & Sczesny
(2011) 102 0.02 M Yes 1 H, A I 1 No U B J Germany
Bowles et al.
(2007), Study 1 119 0.16 F No 2 H I 1 No U B J US
Bowles et al.
(2007), Study 2 236 ⫺0.08 I No 1 H I 1 No W B J US
Bowles et al.
(2007), Study 3 247 ⫺0.24 I Yes 1 H I 1 No W B J US
Brief & Wallace
(1976),
undergraduate
sample 113 0.06 F No L No I 6, 7 No U B J US
Brief & Wallace
(1976), working
adult sample 47 0.06 F No L No I 6, 7 No W B J US
Bruckmüller &
Branscombe
(2010) 118 0.04 M No L No C 1 No U W J US
Bundens (1986) 165 ⫺0.13 M No 2 H, L I 2, 3 No W B D US
Butler & Skattebo
(2004) 96 ⫺0.08 M No 1 No I 3, 6 No W B J US
Cash et al. (1977) 72 0.11 F, M No 2 A I 1, 7 No E B J US
Cash & Kilcullen
(1985) 64 0.16 M Yes 1 H, L C 1 No U W J US
Castilla & Benard
(2010) 229 ⫺0.05 M No 1 H I, C 1, 2, 3, 5 No W W J US
Cohen & Bunker
(1975) 150 0.02 M, I Males 3 No I 1 Yes E B J US
Crow et al. (1998) 548 ⫺0.05 I No L No C 1 No W W J US
Cunningham et al.
(2010) 106 0.18 I No 1 H I 1 No U B J US
Devlin (1997) 86 0.61 M Yes 1 No I 1 No W B J US
Dipboye et al.
(1977) 96 0.79 M Yes 1 H, L C 1 No U W J US
Dipboye et al.
(1975),
undergraduate
sample 30 0.35 M Males 1 H, A, L I, C 1 No U W J US
Dipboye et al.
(1975),
professional
sample 30 0.25 M Males 1 H, A, L I, C 1 No E W J US
Drogosz & Levy
(1996) 180 ⫺0.05 F, M, I No 1 A I 6 No U W J US
Etaugh & Kasley
(1981) 368 0.38 I Yes 2 No I 6 No U B J US
Etaugh & Riley
(1983) 160 ⫺0.07 F, M Yes 1 No I 7 No U B J US
Fidell (1970) 147 0.12 I No 1 No I 1 No E W J US
Firth (1982) 146 0.50 M No 1 No C 1 Yes E W J United Kingdom
Foschi et al. (1995) 96 ⫺0.02 M Yes 1 H, A I 3 Yes U B J Canada
Foschi &
Valenzuela
(2008) 49 ⫺0.23 M Yes 1 A I, C 1, 7 Yes U W J Canada
Foschi &
Valenzuela
(2012) 53 0.11 M Yes 1 H, L I, C 1, 7 Yes U W J Canada
Foster et al. (1996) 80 ⫺0.11 M Yes 1 H, A C 1 No U W J US
Francesco (1978) 102 0.07 F, M, I No 2 A I, C 1, 7 No U W D US
(Appendices continue)
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151
GENDER STEREOTYPES AND EMPLOYMENT DECISION BIAS
Appendix B (continued)
Author (year) nadbSex
distributioncRater
genderdAmount of
informationeContent of
informationfType of
comparisong
Type of
employment
ratinghMotivationiType of
participantjStudy
designkPublication
sourcelCountry
Frank & Drucker
(1977) 55 ⫺0.07 M Yes 1 A I 6 No U B J US
Frasher et al. (1982) 113 0.77 I No L H I 1, 2 No E B J US
Fusilier & Hitt
(1983) 523 0.02 M No 1 No I 1 No U B J US
Gallois et al. (1992) 56 ⫺0.28 M No 1 A I 1 Yes E W J Australia
Ghumman &
Jackson (2008) 412 ⫺0.14 I Yes 1 A I 1 U B J US
Gill (2004) 43 ⫺0.04 F No 1 No C 1 Yes U W J US
Gordon & Owens
(1988) 96 0.23 F, M No 1, 2 No I 1, 7 No E B J US
Gully (1993) 256 0.00 M No 1 No I 2, 3 No U B D US
Güngör & Biernat
(2009) 115 0.43 M No 1 No C 1 No U B J US
Haefner (1977) 286 0.41 M No L H, A I 1 No E W J US
Hamner et al.
(1974) 36 ⫺0.43 M Yes 1 H, L I 6 No U W J US
Hardin et al. (2002) 159 0.09 I Yes L H I 1, 3 No E B J US
Heilman & Guzzo
(1978) 23 ⫺0.13 M No 1 H, A I 2 No W B J US
Heilman & Haynes
(2005), Study 1 60 0.69 I No 2 H I 6, 7 No U B J US
Heilman & Haynes
(2005), Study 2 61 0.71 I No 2 H I 6, 7 No U W J US
Heilman & Haynes
(2005), Study 3 90 0.93 I No 2, 3 H I 6, 7 No U B J US
Heilman et al.
(1993), Study 1 76 0.25 F Yes 1 No I, C 1, 7 No U W J US
Heilman et al.
(1993), Study 2 69 0.35 F Females 1 No I, C 1, 7 No U W J US
Heilman et al.
(1988) 233 0.21 M No 1 H, A I 7 No U B J US
Heilman &
Saruwatari
(1979) 41 ⫺0.09 M, I No 1 No C 1 No U W J US
Heilman et al.
(2004), Study 1 48 0.94 M No 1, 2 H, A I, C 7 No U W J US
Heilman et al.
(2004), Study 3 131 0.22 M No 2 H, A I 3, 4, 7 No W B J US
Heneman (1977) 144 0.24 M No 1 H, A, L I 1, 3 No U B J US
Hmurovic (2012) 287 ⫺0.03 M No 2 A I 4, 5, 6, 7 No U B D US
Horvath & Ryan
(2003) 236 0.26 I No 1 H I 1 No U W J US
Hosoda et al. (2003) 196 ⫺0.88 I No 1 No I, C 1 No E W J US
Jackson (1999),
Study 1 116 0.17 I No 1 A I 1 No U W D Canada
Jackson (1999),
Study 2 130 0.28 M Yes 1 A I, C 1 No U W D Canada
Jawahar & Mattsson
(2005), Study 1 213 0.08 F, I No 1 H C 1 No E W J US
Jawahar & Mattsson
(2005), Study 2 61 ⫺0.04 F, I No 1 H C 1 No E W J US
Jones (1970) 90 0.17 I Males 1 No I, C 1, 7 No E B D US
Judd & Oswald
(1997) 80 0.22 F, M No L No I 1 No U B J US
Juodvalkis et al.
(2003) 68 0.10 I No 1 No I 1, 7 No U B J US
Katz (1987) 152 0.13 M Males 1 No I 1, 3 Yes E B J US
Kawakami et al.
(2005) 70 0.23 M No 1 No C 1 No U W J Netherlands
Knez & Enmarker
(1998) 80 0.24 F No 1 A I 6 No U W J Sweden
Koenig (1989) 217 ⫺0.07 F, M Yes 1 A C 1 No U W J US
Kryger & Shikiar
(1978) 75 ⫺0.45 M Males 1 H, L I 1, 7 No E B J US
Kushnir (1982) 133 1.24 M Yes L A C 1 No W W J Israel
Lee et al. (1997) 78 0.25 I Yes 1 No I 7 No U B J US
Levin et al. (2005),
Study 1 153 0.06 M Yes L No C 1, 5 No U W J US
Levin et al. (2005),
Study 2 104 0.40 I Yes L A C 1, 5 No U W J US
Liberman &
Okimoto (2008),
Study 1 76 ⫺0.03 M No 2 H I 7 No U W J US
(Appendices continue)
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152 KOCH, D’MELLO, AND SACKETT
Appendix B (continued)
Author (year) nadbSex
distributioncRater
genderdAmount of
informationeContent of
informationfType of
comparisong
Type of
employment
ratinghMotivationiType of
participantjStudy
designkPublication
sourcelCountry
Liberman &
Okimoto (2008),
Study 2 64 ⫺0.05 M No 2 H I 7 No U W J US
Liberman &
Okimoto (2008),
Study 3 120 ⫺0.16 M No 2 H, A I 7 No U W J US
London & Stumpf
(1983) 72 ⫺0.62 M No 3 H C 2 Yes E W J US
Manshor et al.
(2003) 156 0.26 I Yes L No C 1 No E W J Malaysia
Marlowe et al.
(1996) 112 0.31 I No 1 H I, C 1, 2 No E W J US
Martell (1991) 202 0.57 M No 1 A I 6 No U B J US
Mayer & Bell
(1975) 150 0.12 M Yes 1 No I 7 No U W J US
McGovern (1977) 52 ⫺0.23 M Males 2 A I 7 No E B D US
McRae (1991) 134 0.07 I No 2 No I 1 No E B J US
McRae (1994) 131 ⫺0.12 I No 1 No I 1, 7 No E B J US
Mettrick & Cowan
(1996) 122 0.39 M No L H I 6 No U B J US
Miller & Routh
(1985) 152 ⫺0.50 I No 1 No I 1 No E B J US
Moore (1984) 69 0.43 M No 1 H, L I 6 No W W J US
Muchinsky & Harris
(1977) 100 ⫺0.05 M, I Yes 1 H, A, L I 1 No U W J US
Musumeci (1995) 111 ⫺0.01 I Yes 1 H I 1, 2, 3, 7 No W W D US
Neumark (1996),
low-price
restaurants 21 ⫺0.43 F No 1 A C 1 Yes E W J US
Neumark (1996),
medium-price
restaurants 21 0.39 F No 1 A C 1 Yes E W J US
Neumark (1996),
high-price
restaurants 23 0.75 F No 1 H C 1 Yes E W J US
Ng & Wiesner
(2007) 201 ⫺0.21 F, M No 1 No C 1 Yes U W J US
Nicklin & Roch
(2008) 244 ⫺0.06 F No 2 A I 1 No U B J US
Norton et al. (1977) 3,261 ⫺0.04 M No 1 No I 6 No E B J US
Pazy (1986) 48 0.99 M No 1 A C 2, 3, 6 No E W J Israel
Phelan et al. (2008) 428 0.32 M No 1 No I 1 No U B J US
Pingitore et al.
(1994) 320 0.95 F, M Yes 2 A I 1 No U B J US
Plake et al. (1987) 85 ⫺0.15 I No 1 A I 1, 6, 7 No E B J US
Riach & Rich
(1987), computer
programmer 115 ⫺0.07 M No 1 No C 1 Yes E W J Australia
Riach & Rich
(1987), computer
operator 99 0.04 M No 1 No C 1 Yes E W J Australia
Riach & Rich
(1987),
management
accountant 211 0.08 M No 1 No C 1 Yes E W J Australia
Riach & Rich
(1987), gardener 148 0.14 M No 1 No C 1 Yes E W J Australia
Riach & Rich
(1987), computer
analyst-
programmer 152 0.15 M No 1 No C 1 Yes E W J Australia
Riach & Rich
(1987), industrial
relations officer 94 ⫺0.04 I No 1 No C 1 Yes E W J Australia
Riach & Rich
(1987), payroll
clerk 172 ⫺0.01 I No 1 No C 1 Yes E W J Australia
Riegelhaupt
(1985), Study 1 68 0.32 F, M, I No 2 A I 1, 3, 6, 7 No U B D US
Riegelhaupt (1985),
Study 2 304 ⫺0.03 F, M, I No 2 A I 1, 3, 6, 7 No U B D US
Robbins & DeNisi
(1993) 70 0.02 F, M No 1 A I 6 No U W J US
Rosen & Jerdee
(1974) 235 0.25 M Males 1 No I 1 No U B J US
(Appendices continue)
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153
GENDER STEREOTYPES AND EMPLOYMENT DECISION BIAS
Appendix B (continued)
Author (year) nadbSex
distributioncRater
genderdAmount of
informationeContent of
informationfType of
comparisong
Type of
employment
ratinghMotivationiType of
participantjStudy
designkPublication
sourcelCountry
Rudman & Glick
(1999) 234 0.33 M No 1 No I 1, 7 No U B J US
Rudman & Glick
(2001) 179 0.18 M No 1 No I 1, 7 No U B J US
Sartore &
Cunningham
(2007) 96 ⫺0.15 I No 1 H, L I 1 No U B J US
Schalm (2001) 324 ⫺0.13 I Yes 1 A I 1 Yes U B D Canada
Schmitt & Lappin
(1980) 64 0.07 F Yes 1 No I 6 No U W J US
Shahani-Denning et
al. (2011) 297 0.29 I No 1 H I 1, 3, 7 No E W J US
Snipes et al. (1998) 246 0.06 F, M Yes 1 No I 1 No U B J US
Stockdale (1990) 44 ⫺0.05 M No 2 H, A I 1 No E W D US
Terborg & Ilgen
(1975) 36 0.38 M Males 2 H I 1, 2, 3, 4, 6 No U B J US
Triana (2011) 306 0.52 F No 1 H I 3 No U B J US
Tyler &
McCullough
(2009) 240 0.38 I Yes 1 A I 1, 7 No U B J US
Uhlmann (2006),
Study 1 112 ⫺0.47 F Yes 1 A I 1 No U B D US
Uhlmann (2006),
Study 2 114 0.21 M Yes 1 A I 1 No W B D US
Uhlmann (2006),
Study 4 34 0.36 M Males 1 A I 1 No U B D US
Uhlmann & Cohen
(2005) 72 0.43 M Yes 1 A I 1 No U B J US
Uhlmann & Cohen
(2007) 141 ⫺0.18 M Yes 1 A I 1 No W B J US
White & White
(1994) 288 ⫺0.02 M No 1, 2, 3 H, A I 1, 7 No U B J US
Wood (1999) 320 ⫺0.44 I No 1 H, A I 3, 6 Yes U B D US
Zebrowitz et al.
(1991), Study 1 64 0.12 F, M No 1 No C 1 No U W D US
Zebrowitz et al.
(1991), Study 2 128 ⫺0.10 I No 1 No C 1 No U W D US
a
Sample size.
b
Overall effect size from the article. Positive dvalues indicate bias in favor of males and negative dvalues indicate bias in favor of
females.
c
F⫽female-dominated, M ⫽male-dominated, I ⫽integrated.
d
No ⫽effect sizes were provided for a mixed or unknown gender group,
Yes ⫽separate effect sizes were provided for male and female raters, Males ⫽sample was all male, Females ⫽sample was all female.
e
Number of pieces
of information provided to participants: L ⫽limited information, 1 ⫽1 piece of information, 2 ⫽2 pieces of information, 3 ⫽3 pieces of
information.
f
Competence of ratee indicated by individuating information: H ⫽high competence, A ⫽average or ambiguous competence, L ⫽low
competence.
g
C⫽comparative, I ⫽individual.
h
Rating made by study participants: 1 ⫽hiring, 2 ⫽promotion, 3 ⫽compensation, 4 ⫽reward, 5 ⫽
penalty, 6 ⫽job performance, 7 ⫽competence.
i
Motivation to make careful decisions: Yes ⫽provided dvalue for one of the motivating conditions,
No ⫽did not provide dvalue for a motivating condition.
j
U⫽undergraduates, W ⫽working adults, E ⫽experienced professionals.
k
B⫽
between-person design, W ⫽within-person design.
l
D⫽dissertation, J ⫽journal.
(Appendices continue)
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154 KOCH, D’MELLO, AND SACKETT
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Received February 20, 2013
Revision received February 11, 2014
Accepted March 18, 2014 䡲
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