Running Head: GENDER SIMILARITIES
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IN PRESS: American Psychologist
Evaluating Gender Similarities and Differences Using Metasynthesis
Ethan Zell
University of North Carolina at Greensboro
Zlatan Krizan
Iowa State University
Sabrina R. Teeter
Western Carolina University
Author Note
Address correspondence to Ethan Zell, Department of Psychology, University of North
Carolina at Greensboro, PO Box 26170, Greensboro, NC 27402. Email: [email protected]. We
thank Yanna Weisberg for comments on a previous version of this manuscript.
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Abstract
Despite the common lay assumption that males and females are profoundly different, Hyde
(2005) used data from 46 meta-analyses to demonstrate that males and females are highly
similar. Nonetheless, the gender similarities hypothesis has remained controversial. Since
Hyde’s provocative report, there has been an explosion of meta-analytic interest in psychological
gender differences. We utilized this enormous collection of 106 meta-analyses and 386
individual meta-analytic effects to re-evaluate the gender similarities hypothesis. Furthermore,
we employed a novel data-analytic approach called metasynthesis (Zell & Krizan, 2014) to
estimate the average difference between males and females and to explore moderators of gender
differences. The average, absolute difference between males and females across domains was
relatively small (d = 0.21, SD = 0.14), with the majority of effects being either small (46%) or
very small (39%). Magnitude of differences fluctuated somewhat as a function of the
psychological domain (e.g., cognitive variables, social and personality variables, well-being), but
remained largely constant across age, culture, and generations. These findings provide
compelling support for the gender similarities hypothesis, but also underscore conditions under
which gender differences are most pronounced.
Keywords: gender, gender differences, gender similarities, meta-analysis
Abstract word count: 182
Number of pages: 35
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Evaluating Gender Similarities and Differences Using Metasynthesis
All of us are aware of gender stereotypes that espouse profound differences between
males and females. Presumably, males are from Mars and females are from Venus, males are
tough and females are tender, males are competitive and females are cooperative, males are
dominant and females are submissive, males are stoic and females are emotional, males are quiet
and females are talkative, males are mathematical and females are verbal, and so on. The
assumption of these and many other prevalent gender stereotypes is that males and females are
vastly different in their personality, abilities, interests, attitudes, and behavioral tendencies. This
assumption is referred to as the gender differences hypothesis (Gray, 1992; Tannen, 1991). But is
it correct? Are males and females really that different? The current report aggregates data from
106 independent meta-analyses with over 12 million participants to test the gender differences
hypothesis and to explore moderators of gender differences. In doing so, we provide the most
comprehensive analysis of gender differences to date.
On the one hand, it is not surprising that people assume large differences between males
and females. Along with age and race, gender is perhaps the most salient category that guides
social perception (Macrae & Bodenhausen, 2000). Perceivers rapidly categorize a person’s
gender and immediately draw inferences about them using gender as a cue (Ito & Urland, 2003).
Moreover, there are typically obvious anatomical and biological differences between males and
females (i.e., biological sex). People may assume that males and females differ psychologically
to a similar extent that they differ physically. Finally, people are repeatedly exposed to cultural
stereotypes on supposed gender differences, starting in childhood (e.g., Browne, 1998). This
barrage of cultural messages may create the illusion that such stereotypes are correct and could
guide how people perceive and interpret the world around them.
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On the other hand, behavioral scientists have been conducting rigorous tests of the gender
differences hypothesis for several decades, and evidence for its core assumption has been sparse.
Researchers have increasingly used meta-analysis to test the gender differences hypothesis by
examining the overall, average difference between males and females across numerous studies in
a given domain. For example, highly cited meta-analyses have examined gender differences in
math performance (Hyde, Fennema, & Lamon, 1990), self-esteem (Kling, Hyde, Showers, &
Buswell, 1999), personality (Feingold, 1994), and aggression (Archer, 2000). In each of these
specific cases, gender differences were found to be relatively small. Based on emerging metaanalytic findings, theorists have proposed an alternative perspective, known as the gender
similarities hypothesis (Hyde, 2005). That is, males and females may be similar on most (but not
all) psychological dimensions, and when differences do arise they should typically be small in
magnitude.
Estimating Gender Differences
Meta-analysis has proven to be a powerful tool to assess gender differences in specific
domains (see Hyde, 2014). However, the fundamental question of how different males and
females are across domains remains largely unresolved. Indeed, prominent cultural stereotypes
not only pertain to supposed differences in specific domains (e.g., math ability or leadership
skill), but they also pertain to global differences between males and females across domains
(e.g., males are from Mars, females are from Venus). Although domain-specific findings may
help deflate bogus stereotypes in a given domain, other stereotypes as well as the general
impression that males and females are fundamentally different may remain. For example, when
confronted with findings demonstrating that females perform just as well as males on math tests
(Else-Quest, Hyde, & Linn, 2010; Lindberg, Hyde, Petersen, & Linn, 2010), people may revise
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or even abandon stereotypes about gender differences in math. However, they will most likely
retain gender stereotypes about other domains, as well as the more basic assertion that the
genders differ in profound ways. Thus, to address the more basic question of how males and
females differ across domains, researchers need to go beyond meta-analyses in specific domains.
As meta-analyses on gender differences have been accumulating, researchers have begun
to aggregate meta-analytic findings to derive more global estimates of the difference between
males and females. Richard, Bond, & Stokes-Zoota (2003) were the first group of scholars to
examine gender differences across domains by aggregating meta-analytic findings using
metasynthesis (i.e., second-order meta-analysis; see Johnson, Scott-Sheldon, & Carey, 2010; Zell
& Krizan, 2014). Specifically, Richard and colleagues aggregated data from 34 meta-analyses
examining topics related to social and personality psychology (e.g., attribution, relationships, and
nonverbal communication). The overall, absolute difference between males and females was
found to be a d of .24, which would be classified as a small effect using conventional standards
(Cohen, 1988).
Going further, Hyde (2005) collected data from 46 “major” meta-analyses examining
gender differences across several psychological domains, including social and personality
variables, cognitive variables, and psychological well-being. Although procedures were not used
to quantitatively aggregate the findings, 48% of the meta-analytic effects were small and 30%
were very small or close to zero. This pattern of results led Hyde to conclude that meta-analytic
findings provide stronger support for the gender similarities hypothesis than the gender
differences hypothesis. Hyde’s paper has become a classic in the psychological canon, as
evidenced by the fact that it has been cited 1,430 times according to Google Scholar and 620
times according to Scopus (as of August, 2014). However, the paper has also been regarded as
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highly controversial, as the basic question of whether existing data better support the gender
similarities hypothesis or the gender differences hypothesis remains hotly contested (see
Carothers & Reis, 2013; Eagly & Wood, 2013; Stewart-Williams & Thomas, 2013). Along these
lines, skeptics have noted that Hyde’s (2005) findings were limited by an analysis of only about
40 psychological domains, left out several key topics that had not been meta-analyzed at that
point, and broadly conflicted with large gender differences in mating preferences identified by
evolutionary psychologists (e.g., Buss, 1989, 2013; Schmitt et al., 2012).
The Current Report
Since the publication of Hyde’s (2005) analysis there has been an explosion of metaanalytic interest in gender differences. New meta-analyses have examined differences between
males and females in domains such as cooperation (Balliet, Li, Macfarlan, & Van Vugt, 2011),
impulsivity (Cross, Copping, & Campbell, 2011), self-conscious emotions (Else-Quest, Higgins,
Allison, & Morton, 2012), language use (Leaper & Ayres, 2007), and interests (Su, Rounds, &
Armstrong, 2010). In addition, new meta-analyses have been conducted on topics previously
studied (e.g., sexuality), using more sophisticated statistical techniques and a larger pool of data
(Petersen & Hyde, 2010). With this enormous and highly diverse collection of over 100 metaanalyses (described in more detail below), the psychological literature has crossed the threshold
whereby new insights about overall gender differences can now be made. The current study
utilizes this vast collection of meta-analytic findings to derive the most comprehensive test of the
gender similarities hypothesis to date. Further, the current report examined several potential
moderators of gender differences to ascertain their generalizability, including age, culture, and
time period, as well as the psychological domain.
Meta-Synthesis Method
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Before getting to our specific procedures and findings, it is important to note the unique
strengths and weaknesses of metasynthesis in this context (see Cooper & Koenka, 2002). On the
positive side, metasynthesis affords the opportunity to assess the global question of gender
differences across domains. Although this question could also be tested using meta-analysis, a
meta-analytic approach would be highly impractical due to the enormous amount of primary
studies (over 20,000). By using metasynthesis, we were able to incorporate data from these
studies without having to track down and code all of the original articles. Further, because
metasynthesis often utilizes extremely large pools of data and participants, it affords highly
reliable conclusions. At a time when psychological science has been criticized for underpowered
studies that sometimes fail to replicate (Simmons, Nelson, & Simohnson, 2011), metasynthesis is
a useful retort as it affords enormous power and confident conclusions.
On the negative side, given its broader approach metasynthesis cannot be used to evaluate
more finite micro-level hypotheses that are often of interest to psychological scientists (see Eagly
& Wood, 2013). Additionally, because individual meta-analyses often use different statistical
models to aggregate primary data (e.g., fixed-effect, random-effects), it is inappropriate to
formally aggregate meta-analytic findings to conduct tests of statistical significance. However,
given that metasynthesis typically has extremely high power, tests of statistical significance
would not be very informative, as even minute differences would yield a statistically significant
outcome. A final concern of metasynthesis is the overlapping of samples. Specifically, there are
occasions where a single study can be included in multiple, related meta-analyses. Therefore, if
metasynthesis incorporated all of these meta-analyses, the individual study would be counted
more than once violating assumptions regarding independence of observations. However, when
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meta-analyses share large amounts of data, researchers can simply select one of these metaanalyses for inclusion in the metasynthesis (e.g., the more recent one).
Identifying Meta-Analyses
We obtained meta-analyses on psychological gender differences, defined as gender
differences in mind and behavior (American Psychological Association, 2014), by using two
approaches. First, we scanned existing reviews of the gender differences literature for relevant
papers (e.g., Eagly & Wood, 2013; Hyde, 2005, 2014; Richard et al., 2003). Second, we searched
for meta-analyses using databases such as PsychInfo, Google Scholar, and Dissertations
Abstracts International during April of 2014. The search terms “sex differences” and “metaanalysis” were entered simultaneously. We also conducted a search using the terms “gender
differences” and “meta-analysis.” Together, these searches yielded over 500 relevant papers.
Many of the papers identified were immediately excluded on the basis that they were not
meta-analyses or did not examine gender differences. Papers that dealt with outcomes that were
not psychological in nature were excluded (e.g., medical outcomes, biological or anatomical
differences; Thomas & French, 1985). Papers that examined differences as a function of
perceived gender roles as opposed to gender per se were excluded. Gender roles involve the
degree to which people adopt stereotypically masculine versus feminine traits, behaviors, and
interests, rather than their gender identity (i.e., whether they identify as male or female; see
Reilly & Neumann, 2013). Papers were excluded if they examined gender only as a function of
other factors (i.e., did not report a direct gender comparison). Papers were also excluded if they
did not report an overall, mean difference between males and females using a standard metric
(e.g., Cohen’s d, Hedges’ g, or Pearson’s r). Finally, we excluded papers that focused on
specialized, non-representative populations (e.g., people with major psychoses or prisoners).
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However, we did not exclude papers on the basis of age or cultural group, so that the potential
influence of these variables could be tested in moderation analyses.
Next, we evaluated meta-analyses to address the possibility of overlapping samples. First,
meta-analyses were excluded if they were replaced by newer meta-analyses on the same topic
that incorporated additional studies or if they analyzed only a subset of studies of an existing
meta-analysis. Second, we compared the reference sections of related papers to quantify the
degree of sample overlap. Although there were several cases of sample overlap, in no instance
did the degree of overlap exceed 25%. This means that for each meta-analysis we examined, at
least 75% of its samples were unique (i.e., unshared with other meta-analyses). Therefore, the
meta-analyses incorporated into our model were mostly independent, in that they largely
contained unique data that was not shared by other meta-analyses.
After removing papers that did not fit the inclusion criteria, our search yielded a total of
106 meta-analyses examining psychological gender differences (see Supplemental Table 1). The
final set of papers covered an enormous range of topics such as interruption, risk taking, helping
behavior, leadership styles, body image, intelligence, occupational stress, jealousy, and morality,
among other topics (see Supplemental References). Across meta-analyses, the total number of
effects was 21,174. Several papers did not provide sufficient information to calculate sample size
(m = 10). However, among the remaining papers, the total sample size was 12,238,667
participants.
Study Treatment and Analyses
Meta-analytic effects were obtained from each paper as an estimate of effect size in a
given topic area (e.g., math ability, aggression). We focus on the absolute value of each metaanalytic effect since our purpose was to assess the magnitude of the difference between males
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and females regardless of the particular direction of difference (for a similar approach, see Hyde,
2005; Richard et al., 2003). Further, directional tests are more appropriate in traditional metaanalyses where the goal is to assess whether males score higher or lower than females in a given
topic area. Finally, the use of absolute values helps prevent misleadingly low estimates of gender
differences. If males score higher than females in one topic area (+0.5), and females score higher
than males in another topic area (-0.5), the average of these effects would be 0, implying no
overall gender difference. However, averaging absolute effects would yield an overall estimate
of 0.5, which we argue better represents the magnitude of global gender differences.
Where possible, we used the uncorrected, un-weighted difference between males and
females as an estimate of effect size (d). However, some meta-analyses only reported weighted
or corrected values (e.g., Hedges’ g). Effect sizes that were reported in the r metric were
converted to Cohen’s d. The model we employed used an un-weighted average of the individual
meta-analytic effects as an estimation of the population effect. In traditional meta-analyses, unweighted averages are robust and tend to outperform averages that weight by study sample size
(Bonett, 2009; Krizan, 2010; Shuster, 2010). We focus primarily on aggregate estimates and
their ranges, without formal computation of relevant confidence intervals. Our descriptive focus
is necessitated by the fact it is inappropriate to calculate confidence intervals by formally
aggregating estimates that derive separately from fixed-effect and random-effects models
(Hedges & Vevea, 1998). In addition, many papers did not provide information essential for such
computations (e.g., confidence intervals or standard errors).
We use Cohen’s (1988) conventions to interpret the magnitude of gender differences (d).
Cohen argued that the cutoff points of .2, .5, and .8 reflect small, medium, and large differences
respectively. Further, Hyde (2005) argued that effect sizes of .10 or below should be considered
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very small. When interpreting effects, we also present the percentage of overlap between male
and female distributions. Specifically, Cohen (1988) argued that effect sizes .2, .5, and .8
correspond to distributions that overlap by approximately 85%, 67%, and 52% respectively.
Distributions with a large amount of overlap can be viewed as highly similar. When describing
our results, we use the following symbols: d (average absolute difference between males and
females in standard deviation units), k (number of effects), and m (number of meta-analyses).
Magnitude of Gender Differences
Preliminary Analyses
Most meta-analyses provided only a single effect (m = 61), however, other meta-analyses
provided multiple effects (m = 45) with some reporting over 30 individual meta-analytic tests in
their report. For example, Petersen and Hyde (2010) conducted separate meta-analytic tests of
gender differences in 30 sexual attitudes and behaviors (e.g., condom use, premarital sex, samegender sex). In total, we obtained 386 meta-analytic effects, an amount that is several times
larger than the 124 obtained by Hyde (2005). As can be seen in Table 1, the majority of these
effects were either very small (39.4%) or small (46.1%); relatively few effects were medium
(11.9%), large (1.8%), or very large in size (0.8%).
Further, we scanned meta-analyses for information regarding the homogeneity of effects
(e.g., I2, T, T2, Q). Effects that are largely consistent (inconsistent) in size and direction are
considered homogeneous (heterogeneous). Direct homogeneity tests or statistics were not
reported for 83 effects. Of the remaining effects, 92 (30.4%) were homogeneous and 211
(69.6%) were heterogeneous. Therefore, although most of the meta-analytic effects obtained in
this report were small or very small, individual-study effects within these meta-analyses were
typically heterogeneous, suggesting that gender differences often vary by context.
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Primary Model
Our primary model averaged meta-analytic effects within papers when more than one
was provided. Several of the papers that derived multiple effects utilized some or all of the same
participants in multiple meta-analytic effects. Incorporating each of these effects into the model
would violate independence of observations. Thus, we incorporate only a single effect size from
each individual meta-analysis in our primary model as well as the moderation tests reported
below. Specifically, for papers that provided multiple effects in a given topic area (e.g.,
interests), we averaged the absolute value of each effect as an estimate of gender differences in
that topic area.
When considering the entire collection of 106 meta-analyses, the average absolute
difference between males and females across topic areas was a d of .21, typically regarded as a
small effect (see Figure 1). There was some dispersion in the individual effects (SD = .14), with
the smallest effect being a d of .02 and the largest being a d of .73 (see Table 2). Only one of the
meta-analytic averages would be characterized as large in size, and 81% of the effects were
between .01 and .30. Put simply, more than three quarters of the observed gender differences
reflected almost 80% overlap across distributions of males and females. This finding provides
strong support for the gender similarities hypothesis. That is, although theorists and laypersons
have long assumed that males and females are profoundly different (e.g., Gray, 1992; Tannen,
1991), our findings suggest that these assertions are likely inflated. We conducted a
supplemental analysis that weighted meta-analytic effects by the number of effect sizes. This
alternative, weighted model yielded an effect size (.19) that was highly similar to that obtained
by our primary model (.21). Therefore, we retained the use of an un-weighted model in
subsequent analyses.
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Although gender differences were typically small, there were several effects that were
moderate to large in size. The ten largest gender differences in the psychological literature are
presented in Table 3. According to these findings, males score higher than females on measures
of masculinity, mental rotation ability, importance of physical attractiveness in mate selection,
and aggression. Females score higher than males on measures of reactivity to painful (noxious)
stimuli, peer-attachment, and interest in people as opposed to things.
Moderators of Gender Differences
Theoretical Moderators
Psychological Domain. Gender differences research is broadly clustered into three
psychological domains: cognitive variables, social and personality variables, and well-being (see
Hyde, 2014). We examined whether the magnitude of gender differences varies across these
fundamental domains of psychological functioning. Cognitive variables were defined as mental
processes such as attention, memory, and problem solving, including variables such as math
performance, spatial performance, and verbal skills. Social and personality variables were
defined as constructs related to the study of individual differences or social behavior and they
included variables such as temperament, interests, aggression, interpersonal communication,
helping, sexuality, and leadership. Finally, well-being was defined as any variable related to
positive or negative mental health. This included variables such as depression, rumination, and
self-esteem. Two raters coded the domain of study for each meta-analysis (κ = .88), and
differences between raters were resolved through discussion.
There was some fluctuation in effect sizes by domain (see Table 4 for descriptive
statistics, including variability measures). Gender differences were comparable when examining
research on cognitive variables (.22) and social and personality variables (.22), but were
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somewhat smaller when examining research on psychological well-being (.14). Further, the
range of differences was more condensed for research on well-being. These findings show that
gender differences are relatively small regardless of domain, but that differences in well-being
are particularly small. Nonetheless, it is important to note that there were fewer meta-analyses on
psychological well-being (m = 11) than cognitive variables (m = 30) or social and personality
variables (m = 65), and thus it’s possible that the effect size in this domain will increase as more
meta-analyses are conducted.
Separate stem and leaf plots for each psychological domain show that the distribution of
effect sizes was similar across domains, albeit more condensed for studies on well-being (see
Supplemental Tables 2-4). Further, the effects of other moderators that we discuss below (age,
culture, and time period) were largely comparable when examining effects within each
psychological domain (see Supplemental Tables 5-7).
Age. Little is known about the degree to which gender differences change over the
lifespan. The gender differences hypothesis presumes that there are large differences between
males and females at all ages, but it is also possible that gender differences are small at all ages
or fluctuate as a function of age. Along these lines, meta-analyses in specific topic areas have
shown somewhat larger gender differences in adolescents and young adults relative to children
with regards to self-esteem (Kling et al., 1999), depression (Twenge & Nolen-Hoeksema, 2002),
and mathematics performance (Hyde et al., 1990). However, these findings do not indicate the
degree to which global differences between males and females change across the lifespan.
To further explore this issue, we scanned meta-analyses for data relevant to age. Most
meta-analyses utilized a mix of child, adolescent, and adult participants (m = 66). However,
some meta-analyses examined only adult participants (m = 32), and others examined only child
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and/or adolescent participants, defined as participants age 18 and below (m = 8). Effect sizes
were small in size regardless of the age of the samples utilized in meta-analyses. Specifically,
meta-analyses that examined gender differences in children and/or adolescents yielded an
average effect size of .17. Studies examining adults yielded a similar average effect size of .18.
Therefore, global differences between males and females are relatively small and appear to
remain constant from childhood to adulthood. Moreover, studies examining children and/or
adolescents covered a similar range of topics than studies examining adults (Supplemental
Tables 5-7), which suggests that age was likely not confounded with psychological domain.
Culture. We also tested whether global differences between males and females vary
across cultural groups. Some meta-analyses did not provide sufficient information to determine
the country or culture of the participants studied (m = 12), and one meta-analysis included only
participants from Turkey (Aydin, Sarier, & Uysal, 2011). Of the remaining meta-analyses,
samples consisted of only European-American participants (m = 46), or participants from at least
two different cultural groups (e.g., European-American, East-Asian, African, Middle-Eastern; m
= 47). Meta-analyses that examined only European-Americans yielded an effect size of .19.
Similarly, meta-analyses that examined multiple cultures yielded an effect size of .22. Thus,
differences between males and females across domains are relatively small and appear to remain
constant when comparing European-American samples to more diverse samples. However, few
of the multicultural meta-analyses indicated the proportion of effects from European-American
samples versus other samples. It is possible that these papers contained effects derived
predominantly from European-American samples.
Time Period. It has been argued that gender differences in specific domains such as
personality and competence may have declined over the last several decades, as women’s roles
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have expanded to become more comparable to that of men (Eagly & Wood, 2013; Hyde, 2005).
We examined whether global differences between males and females are increasing or
decreasing over time. The average difference between males and females was small whether
looking at meta-analyses published in the 1980s (.15), 1990s (.23), 2000s (.22), or 2010s (.19).
Further, the year of publication was not substantially correlated with effect size magnitude, r =
.03, p = .80. These findings suggest that differences between males and females across domains
have remained largely constant over the last several decades. It should be noted that our analysis
is not a strong test of differences over time, as newer meta-analyses typically incorporate both
newer and older data.
Methodological and Statistical Moderators
Quality. We examined whether meta-analyses that used more sophisticated
methodologies yielded different estimates of gender differences than those that used less
sophisticated methodologies (see Cooper & Koenka, 2012). Along these lines, we created a 5item checklist to evaluate the quality of each meta-analysis, by selecting the 5 most relevant
items from a larger checklist developed to evaluate meta-analyses in medicine (Higgins et al.,
2013). Specifically, two coders evaluated meta-analyses in terms of the search for articles,
inclusion/exclusion of articles, coding of articles, assessment of heterogeneity, and data synthesis
methodology (κs > .81).
Most meta-analyses scored favorably, with the average meta-analysis receiving a score of
4 out of 5 on the quality checklist (mean = 3.91, SD = 1.04). Although there was some
fluctuation in effect sizes as a function of methodological quality, effect sizes were small
regardless of quality (see Table 5). Further, a correlation analysis showed that effect sizes were
somewhat smaller in high quality than low quality meta-analyses, but this effect was not
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statistically significant, r = -.16, p = .10. Thus, the quality of the individual meta-analyses did not
appear to have a substantial influence on estimates of gender differences.
Publication Status. Meta-analyses that only incorporate published studies might
overestimate the magnitude of gender differences, since studies that obtain null findings might
go unpublished. To explore this possibility, we identified whether each meta-analysis
incorporated only published (m = 31) or both published and unpublished studies (m = 65). Metaanalyses that only incorporated published studies yielded somewhat larger effects (.24) than
those that incorporated both published and unpublished studies (.19), but this difference was
relatively small. In addition, although most of the meta-analyses that we obtained were published
articles or chapters (m = 102), a few were unpublished dissertations (m = 4). Published metaanalyses (effect size = .21, SD = .13) yielded comparable results to unpublished meta-analyses
(effect size = .16, SD = .17).
Model Type. We examined whether estimates of global gender differences fluctuated as
a function of the meta-analytic model used to aggregate effects. Along these lines, some metaanalyses used a fixed-effect model (m = 55) whereas other meta-analyses used a random-effects
model (m = 27). Some papers used a different model type (e.g., mixed-effects) or did not provide
sufficient information to determine the model used (m = 24). Effect size estimates were similar
when comparing fixed-effect (.22) to random-effects approaches (.21).
Effect-Size Metric. Differences between males and females did not fluctuate as a
function of the statistical metric used to calculate gender differences. Specifically, meta-analyses
using the Cohen’s d metric yielded an effect size of .21, meta-analyses using Hedges’ g yielded
an effect size of .21, and meta-analyses that utilized an r metric yielded a (converted) effect size
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of .20. The range of effects based on d was much larger than that based on r, but it also involved
a nine-fold increase in the number of effects generating that range.
General Discussion
We obtained an enormous collection of meta-analytic findings to provide a current,
highly comprehensive test of the magnitude of gender differences across domains. The obtained
findings were more consistent with the gender similarities hypothesis (Hyde, 2005) than the
gender differences hypothesis (Gray, 1992; Tannen, 1991). That is, of the 386 individual metaanalytic effects we obtained, 46.1% were small and 39.4% were very small. Further, when
aggregating across the 106 meta-analyses, the average absolute difference between males and
females was a small effect (d) of .21, reflecting approximately 84% overlap in distributions of
males and females. These findings inform recent debates regarding the overall difference
between males and females (Carothers & Reis, 2013; Stewart-Williams & Thomas, 2013).
In addition to estimating the magnitude of gender differences across domains, we
examined moderators of gender differences. First, gender differences were somewhat smaller
when comparing research on psychological well-being to research on cognitive variables and
social/personality variables, but fewer meta-analyses have been done on well-being. Second, we
showed that small gender differences are largely constant across age, culture, and time period.
However, prior meta-analyses sometimes did not report the culture of the samples utilized, and
more recent meta-analyses typically incorporated both newer and older data. Third, we showed
that methodological and statistical factors, such as the methodological quality of prior metaanalyses and type of meta-analytic model used, appear to have only minimal influence on
estimates of overall gender differences.
Interpretation of Results
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We utilize Cohen’s (1988) conventions to interpret the magnitude of gender differences.
Lipsey (1990) proposed a similar, but slightly more liberal rubric in which effects of .15 and .45
were labeled as small and medium respectively. However, it should be noted that effect size
cutoff points are somewhat arbitrary and that many if not most effects in psychological science
would be considered small in size. For example, foundational research in social psychology on
topics including attribution and social influence often yields small effects (Richard et al., 2003),
and recent estimates of the effect of violent video games on aggression yielded small effects
(Anderson et al., 2010). Therefore, although gender differences are typically small, they should
not be regarded as trivial, as even small effects can have important everyday consequences (e.g.,
passive smoking and lung cancer, calcium intake and bone mass, homework and academic
achievement; Bushman & Anderson, 2001). Further, small gender differences may accumulate
when summed across domains (Del Guidice, Booth, & Irwing, 2012).
In addition, caution is necessary when interpreting our global effect size estimate of .21.
Specifically, our findings should be interpreted as showing generally small differences between
males and females on measures of psychological outcomes (e.g., tests of math ability), rather
than on actual psychological outcomes (e.g., actual math ability). It’s possible that psychological
measures provide imperfect estimates of the constructs they are designed to measure, which
could reduce the apparent magnitude of gender differences. Although beyond the scope of the
present report, future study should evaluate the degree to which measurement error influences
estimates of overall gender differences in metasynthesis (see Schmidt & Oh, 2013).
Finally, although our results provide suggestive evidence that overall gender differences
may not fluctuate substantially across age, culture, and generations, we do not believe that these
data should be used to infer that gender differences are static or fixed. Indeed, prior research
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examining gender differences in specific domains has found important fluctuations in gender
effects as a function of age, culture, and time period (see Eagly & Wood, 2013; Hyde, 2014).
Scholars should continue to examine whether and when these potentially important theoretical
variables moderate psychological gender differences both within and across domains.
Limitations and Future Directions
The present findings help establish the overall difference between males and females
across domains, but do not pinpoint why gender differences occur. Therefore, an important next
step for future research will be to evaluate theories regarding why gender differences and
similarities occur (e.g., evolutionary, cognitive learning, sociocultural, and expectancy-value
theories; see Eagly & Wood, 2013; Hyde 2014). Additionally, by aggregating data across 106
meta-analyses that span very different content areas, some may find it difficult to interpret our
primary model testing gender differences across all domains. To be sure, cross-domain analyses
involve more complex interpretations than single-domain analyses. However, it should be noted
that the basic question of gender differences across domains has captivated both public and
scientific attention for many years (e.g., Gray, 1992; Hyde, 2005). Finally, a limitation of metaanalyses in general is that they sometimes combine studies that are not necessarily comparable
due to different populations, methodologies, and conceptualizations of key variables (i.e., the
“apples and oranges” problem; Sharpe, 1997). One could argue that this problem is exacerbated
in metasynthesis, where researchers combine meta-analyses that address different topics. In the
current study, however, this limitation likely did not have an undue influence since gender is
typically measured in a parallel fashion across studies.
In demonstrating an overall effect of .21, the current findings suggest that the
distributions of males and females on most variables overlap by about 84% (Cohen, 1988).
GENDER SIMILARITIES
21
However, the present findings cannot pinpoint the shape of these distributions (i.e., whether or
not they are skewed). Furthermore, although our analysis indicates that gender differences are
typically small when considering measures of central tendency, gender differences are more
pronounced when comparing the tails of male and female distributions. For example, research
indicates that gender differences in violence (Daly & Wilson, 1988) and spatial ability (Hedges
& Nowell, 1995) are more pronounced when examining the extreme right tails of male and
female distributions (i.e., those who score relatively high in violence or spatial abilities).
Additionally, research suggests that males are more variable than females on some intellectual
and cognitive variables (Machin & Pekkarinen, 2008).
In surveying a large collection of meta-analyses on gender differences, we also
uncovered methodological and reporting concerns that should be addressed in future studies.
First, researchers should specify the number of males and females they obtained across studies,
to allow for better estimates of the total number of participants included in future metasyntheses.
Second, researchers should specify the age and culture of the samples obtained and, if possible,
examine whether gender differences vary as a function of age and culture. Third, researchers
should specify the meta-analytic model they utilized to aggregate findings across studies (e.g.,
fixed-effect, random-effects), in addition to reporting confidence intervals and standard errors of
the observed meta-analytic effects to facilitate formal computations in future metasyntheses.
Finally, it is recommended that future meta-analyses examine whether the magnitude of
observed gender differences increases after adjusting effects for measurement error in the
criterion.
Coda
GENDER SIMILARITIES
22
Hyde (2005) proposed that males and females are far more similar than different. Since
then, gender differences and similarities have received an enormous amount of empirical
attention. We utilized data from over 20,000 individual studies and over 12 million participants
to re-evaluate the gender similarities hypothesis and found that its core proposition receives
strong support—across most topic areas in psychological science, the difference between males
and females is small or very small. However, there are important exceptions where moderate to
large gender differences arise. Further, there was a distinct, albeit small difference between
males and females when aggregating effects across domains. Thus, although our results suggest
that the overall difference between males and females is relatively small, we caution against the
conclusion that gender differences are trivial or non-existent.
In addition, despite substantial progress, future study is still needed before definitive
conclusions regarding overall gender differences can be made. Improvements in methodology
and reporting in future meta-analyses may provide better estimates of gender differences.
Moreover, future research using alternative empirical approaches, such as large population
studies (Else-Quest et al., 2010), cross-cultural studies (Schmitt et al., 2012), and archival studies
(Daly & Wilson, 1988) should complement our understanding of gender differences. Finally,
future research is needed to identify conditions under which gender differences are most
pronounced as well as factors that give rise to gender differences (see Eagly & Wood, 2013;
Hyde, 2014). Characterizing gender similarities and differences remains an exciting yet
challenging task that should occupy researchers for decades to come.
GENDER SIMILARITIES
23
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Table 1
Meta-Analytic Effect Sizes (m = 386) by Range of Magnitude
Effect size range
Effect sizes
Number
% of total
0—0.10
0.11—0.35
0.36—0.65
0.66—1.00
> 1.00
Very small
Small
Medium
Large
Very large
152
178
46
7
3
39.4%
46.1%
11.9%
1.8%
0.8%
GENDER SIMILARITIES
Table 2
Stem and Leaf Display of 106 Meta-Analytic Averages
Stem
Leaf
.7
3
.6
.5
1 3 6 7
.4
0 1 5 9
.3
0 0 0 1 4 4 4 4 5 6 7 7 8 9
.2
0 0 0 0 0 0 1 1 1 2 2 3 3 3 4 4 4 5 5 5 5 6 6 6 7 7 8 8 8
.1
0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 3 3 4 4 4 4 5 6 6 6 6 6 6 7 8 8 8 9 9 9
.0
2 2 2 2 3 3 4 4 5 5 5 5 5 7 7 8 8 8 9
Note. Values represent absolute effects sizes (Cohen’s d).
31
GENDER SIMILARITIES
32
Table 3
Ten Largest Gender Differences
Topic
d
k
Result
Reference
Masculine vs. feminine traits
.73
59
M>F
Twenge, 1997
Mental rotation ability
.57
70
M>F
Maeda & Yoon, 2013
Noxious stimulation
.56
26
F>M
Riley et al., 1998
Importance of beauty in mates
.53
28
M>F
Feingold, 1990
Peer attachment
.51
43
F>M
Gorrese & Ruggieri, 2012
Interest in people vs. things
.49
745
F>M
Su et al., 2009
Aggression
.45
197
M>F
Knight et al., 2002
Film induced fear
.41
95
F>M
Peck, 2000
Confidence in physical abilities
.40
46
M>F
Lirgg, 1991
Same-sex group performance
.39
64
M>F
Wood, 1987
Note: d = average absolute effect size; k = number of effects; M > F indicates that males scored
higher than females. Full references are provided in Appendix B.
GENDER SIMILARITIES
33
Table 4
Theoretical Moderators of Gender Differences
Moderator
m
k
d
SD
range
Cognitive
30
3,611
.22
.13
.05 to .57
Social/Personality
65
15,590
.22
.14
.02 to .73
Well-Being
11
1,973
.14
.09
.02 to .28
Child/Adolescent
8
1,914
.17
.10
.02 to .34
Adult
32
2,928
.18
.15
.02 to .73
Mixed
66
16,332
.23
.13
.02 to .57
Euro-American
46
10,508
.19
.14
.02 to .73
Multi-Cultural
47
8,902
.22
.11
.03 to .51
1980s
10
841
.15
.11
.02 to .39
1990s
34
4,759
.23
.16
.02 to .73
2000s
32
8,311
.22
.12
.02 to .49
2010s
30
7,263
.19
.13
.03 to .57
Research Domain
Age
Culture
Time Period
Note: m = number of meta-analyses, k = number of effects, d = average absolute effect size, SD =
standard deviation of the absolute effect size.
GENDER SIMILARITIES
34
Table 5
Methodological and Statistical Moderators of Gender Differences
Moderator
m
k
d
SD
range
Five (Highest)
34
12,748
.17
.13
.02 to .57
Four
41
5,983
.22
.12
.02 to .51
Three
20
1,790
.25
.16
.07 to .73
Two
6
366
.16
.14
.02 to .34
One (Lowest)
4
200
.28
.19
.16 to .56
Only Published
31
3,709
.24
.14
.02 to .51
Some Unpublished
65
16,748
.19
.11
.02 to .57
Fixed-Effect
55
10,489
.22
.14
.02 to .73
Random-Effects
27
4,300
.21
.14
.02 to .57
Other
24
6,385
.18
.11
.02 to .49
d
83
18,237
.21
.13
.02 to .73
g
14
2,427
.21
.15
.04 to .57
r
9
510
.20
.13
.02 to .34
Quality of Methods
Sources
Model Type
Effect Size Metric
Note: m = number of meta-analyses, k = number of effects, d = absolute effect size, SD =
standard deviation of the absolute effect size.
GENDER SIMILARITIES
Figure 1. Visual depiction of 2 distributions that differ by an effect size (d) of 0.21 (see
Magnusson, 2014).
35
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Supplemental Table 1
Meta-Analyses on Gender Differences
1st Author
Block
Boerner
Byrnes
Dean
Else-Quest
Herlitz
Hyde
Hyde
Irwing
Kirkland
Lindberg
Lynn
Maeda
Medland
Meehan
Richardson
Riley
Roth
Schram
Schredl
Severiens
Silverman
Silverman
Stuhlmacher
Syzmanowicz
Thoma
Thompson
Voyer
Voyer
Wood
Archer
Aven
Aydin
Balliet
Bornstein
Card
Chaplin
Year
2000
2014
1999
2008
2010
2013
1988
1990a
2005
2013
2010
2004
2013
2002
1984
1995
1998
2012
1996
2008
1998
2003
2006
1999
2011
1986
2014
1995
2007
1987
2000
1993
2011
2011
1995
2008
2013
Domain
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Cognitive
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Note: k = number of effects, d = absolute effect size.
d
.16
.23
.13
.19
.13
.36
.14
.20
.14
.18
.05
.25
.57
.10
.14
.20
.56
.10
.08
.22
.10
.11
.34
.09
.35
.12
.19
.37
.25
.39
.05
.02
.07
.05
.26
.34
.08
k
87
33
322
18
87
67
120
59
22
42
441
25
70
36
160
16
26
61
18
163
83
38
73
53
205
56
557
286
123
64
82
27
.15
272
121
148
445
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Supplemental Table 1 Continued
Meta-Analyses on Gender Differences
1st Author
Cohn
Cross
Daigle
Del Guidice
Dindia
Eagly
Eagly
Eagly
Eagly
Eagly
Else-Quest
Else-Quest
Feingold
Feingold
Feingold
Gentile
Gorrese
Guerin
Hall
Holt
Huang
Hyde
Jaffee
Johnson
Kite
Knight
Konrad
LaFrance
LaFrance
Leaper
Leaper
Leaper
Li
Lirgg
Lundquist
Miettunen
Miller
Year
1991
2011
1996
2011
1992
1981
1986
1990
1991
2003
2006
2012
1990
1994
1998
2009
2012
2012
2011
2005
2013
1990b
2000
2013
1996
2002
2000
2003
2009
1998
2004
2007
2006
1991
1994
2007
2008
Domain
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Note: k = number of effects, d = absolute effect size.
d
.22
.20
.05
.03
.18
.16
.07
.02
.30
.14
.16
.16
.53
.27
.37
.21
.51
.04
.17
.10
.08
.18
.24
.24
.23
.45
.12
.38
.30
.26
.16
.12
.21
.40
.11
.28
.28
k
113
741
111
113
205
148
181
370
184
111
891
697
28
147
730
428
43
123
36
142
247
330
255
59
97
197
2,540
418
84
59
194
174
93
46
257
32
70
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Supplemental Table 1 Continued
Meta-Analyses on Gender Differences
1st Author
Murnen
Nam
O’Connor
Paustian-Underdahl
Peck
Petersen
Randler
Rotundo
Sagarin
Schutte
Stier
Stockdale
Su
Twenge
Walters
Weinburgh
Whitley
Whitley
Whitley
Wilgenbusch
You
Davis
Kling
Martocchio
Miettunen
Pinquart
Purvanova
Schredl
Tamres
Twenge
Vemer
Vishnevsky
Year
1997
2010
1996
2012
2000
2010
2007
2001
2012
1997
1984
2013
2009
1997
1998
1995
1986
1997
1999
1999
2011
1999
1999
1989
2010
2001
2010
2011
2002
2002
1989
2010
Domain
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Personality/Social
Well-Being
Well-Being
Well-Being
Well-Being
Well-Being
Well-Being
Well-Being
Well-Being
Well-Being
Well-Being
Well-Being
Note: k = number of effects, d = absolute effect size.
d
.31
.34
.12
.05
.41
.19
.10
.30
.10
.34
.20
.25
.49
.73
.03
.20
.10
.23
.26
.24
.25
.12
.20
.02
.28
.11
.12
.15
.16
.02
.04
.27
k
62
16
33
99
95
1,870
52
66
199
36
10
20
745
59
79
31
58
104
66
66
20
119
216
19
88
617
409
118
207
85
25
70
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Supplemental Table 2
Stem and Leaf Display for Cognitive Meta-Analyses
Stem
Leaf
.7
.6
.5
6 7
.4
.3
4 5 6 7 9
.2
0 0 1 2 3 5 5
.1
0 0 0 1 3 3 4 4 4 6 8 9 9
.0
5 8 9
Note. Values represent absolute effects sizes (Cohen’s d).
S11
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Supplemental Table 3
Stem and Leaf Display for Social and Personality Meta-Analyses
Stem
Leaf
.7
3
.6
.5
1 3
.4
0 1 5 9
.3
0 0 0 1 4 4 4 7 8
.2
0 0 0 1 1 2 3 3 4 4 4 5 5 6 6 6 7 8 8
.1
0 0 0 0 1 2 2 2 4 6 6 6 6 7 8 8 9
.0
2 2 3 3 4 5 5 5 5 7 7 8 8
Note. Values represent absolute effects sizes (Cohen’s d).
S12
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Supplemental Table 4
Stem and Leaf Display for Well-Being Meta-Analyses
Stem
Leaf
.7
.6
.5
.4
.3
.2
0 7 8
.1
1 2 2 5 6
.0
2 2 4
Note. Values represent absolute effects sizes (Cohen’s d).
S13
ONLINE SUPPLEMENT
S14
Supplemental Table 5
Moderators for Cognitive Meta-Analyses
Moderator
m
k
d
SD
range
Child/Adolescent
2
120
.18
.07
.13 to .23
Adult
8
324
.17
.10
.08 to .39
Mixed
20
3,167
.24
.15
.05 to .57
Euro-American
12
1,414
.19
.08
.09 to .39
Multi-Cultural
13
1,986
.22
.11
.05 to .37
1980s
4
400
.22
.12
.14 to .39
1990s
8
1,063
.22
.17
.08 to .56
2000s
9
585
.20
.08
.10 to .34
2010s
9
1,563
.24
.16
.05 to .57
Age
Culture
Time Period
Note: m = number of meta-analyses, k = number of effects, d = average absolute effect size, SD =
standard deviation of the absolute effect size.
ONLINE SUPPLEMENT
S15
Supplemental Table 6
Moderators for Social and Personality Meta-Analyses
Moderator
m
k
d
SD
range
Child/Adolescent
5
1,709
.19
.10
.08 to .34
Adult
20
1,534
.20
.17
.02 to .73
Mixed
40
12,347
.23
.13
.02 to .53
Euro-American
26
7,495
.21
.17
.02 to .73
Multi-Cultural
31
6,542
.22
.12
.03 to .51
1980s
4
397
.13
.06
.07 to .20
1990s
24
3,361
.24
.16
.02 to .73
2000s
20
6,817
.24
.13
.05 to .49
2010s
17
5,015
.17
.13
.03 to .51
Age
Culture
Time Period
Note: m = number of meta-analyses, k = number of effects, d = average absolute effect size, SD =
standard deviation of the absolute effect size.
ONLINE SUPPLEMENT
S16
Supplemental Table 7
Moderators for Well-Being Meta-Analyses
Moderator
m
k
d
SD
range
Child/Adolescent
1
85
.02
--
--
Adult
4
1,070
.07
.05
.02 to .12
Mixed
6
818
.20
.07
.12 to .28
Euro-American
8
1,599
.09
.06
.02 to .16
Multi-Cultural
3
374
.25
.04
.20 to .28
1980s
2
44
.03
.01
.02 to .04
1990s
2
335
.16
.06
.12 to .20
2000s
3
909
.10
.07
.02 to .16
2010s
4
685
.21
.08
.12 to .28
Age
Culture
Time Period
Note: m = number of meta-analyses, k = number of effects, d = average absolute effect size, SD =
standard deviation of the absolute effect size.