American Psychologist Evaluating Gender Similarities and Differences ...

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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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).

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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

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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 References

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GENDER SIMILARITIES

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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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Psychology, 40, 993-1027. doi:10.1037/0012-1649.40.6.993 Li, Q. (2006). Computer-mediated communication: A meta-analysis of male and female attitudes and behaviors. International Journal on E-Learning, 5, 525-570. Lindberg, S. M., Hyde, J., Petersen, J. L., & Linn, M. C. (2010). New trends in gender and mathematics performance: A meta-analysis. Psychological Bulletin, 136, 1123-1135. doi:10.1037/a0021276 Lirgg, C. D. (1991). Gender differences in self-confidence in physical activity: A meta-analysis of recent studies. Journal of Sport & Exercise Psychology, 13, 294-310. Lundquist, A. (1994). Gender differences in the intimacy of romantic relationships: A metaanalysis. Dissertation Abstracts International, 54. Lustig, M. W., & Andersen, P. A. (1990). Generalizing about communication apprehension and avoidance: Multiple replications and meta-analyses. Journal of Social Behavior & Personality, 5, 309-340. Lynn, R., & Irwing, P. (2004). Sex differences on the progressive matrices: A meta-analysis. Intelligence, 32, 481-498. doi:10.1016/j.intell.2004.06.008 Maeda, Y., & Yoon, S. (2013). A meta-analysis on gender differences in mental rotation ability measured by the Purdue Spatial Visualization Tests: Visualization of rotations (PSVT:R). Educational Psychology Review, 25, 69-94. doi:10.1007/s10648-012-9215-x Martocchio, J. J., & O'Leary, A. M. (1989). Sex differences in occupational stress: A metaanalytic review. Journal of Applied Psychology, 74, 495-501. doi:10.1037/00219010.74.3.495 Medland, S. E., Geffen, G. G., & McFarland, K. K. (2002). Lateralization of speech production using verbal/manual dual tasks: Meta-analysis of sex differences and practice effects. Neuropsychologia, 40, 1233-1239. doi:10.1016/S0028-3932(01)00228-7 Meehan, A. M. (1984). A meta-analysis of sex differences in formal operational thought. Child Development, 55, 1110-1124. doi:10.2307/1130164 Miettunen, J., & Jääskeläinen, E. (2010). Sex differences in Wisconsin Schizotypy Scales—A meta-analysis. Schizophrenia Bulletin, 36, 347-358. doi:10.1093/schbul/sbn075 Miettunen, J., Veijola, J., Lauronen, E., Kantojärvi, L., & Joukamaa, M. (2007). Sex differences in Cloninger's temperament dimensions--A meta-analysis. Comprehensive Psychiatry, 48, 161-169. doi:10.1016/j.comppsych.2006.10.007 Miller, A. J., Worthington, E. r., & McDaniel, M. A. (2008). Gender and forgiveness: A metaanalytic review and research agenda. Journal of Social and Clinical Psychology, 27, 843876. doi:10.1521/jscp.2008.27.8.843 Murnen, S. K., & Stockton, M. (1997). Gender and self-reported sexual arousal in response to sexual stimuli: A meta-analytic review. Sex Roles, 37, 135-153. doi:10.1023/A:1025639609402 Nam, S., Chu, H., Lee, M., Lee, J., Kim, N., & Lee, S. (2010). A meta-analysis of gender differences in attitudes toward seeking professional psychological help. Journal of American College Health, 59, 110-116. doi:10.1080/07448481.2010.483714 O'Connor, W. E., Morrison, T. G., McLeod, L. D., & Anderson, D. (1996). A meta-analytic review of the relationship between gender and belief in a just world. Journal of Social Behavior & Personality, 11, 141-148. Paustian-Underdahl, S. C. (2012). A meta-analysis of gender differences in leadership effectiveness. Dissertation Abstracts International, 74. Peck, E. (2000). Gender differences in film-induced fear as a function of type of emotion

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measure and stimulus content: A meta-analysis and laboratory study. Dissertation Abstracts International Section A, 61. Petersen, J. L., & Hyde, J. (2010). A meta-analytic review of research on gender differences in sexuality, 1993–2007. Psychological Bulletin, 136, 21-38. doi:10.1037/a0017504 Pinquart, M., & Sörensen, S. (2001). Gender differences in self-concept and psychological wellbeing in old age: A meta-analysis. The Journals of Gerontology: Series B: Psychological Sciences and Social Sciences, 56B, P195-P213. doi:10.1093/geronb/56.4.P195 Purvanova, R. K., & Muros, J. P. (2010). Gender differences in burnout: A meta-analysis. Journal of Vocational Behavior, 77, 168-185. doi:10.1016/j.jvb.2010.04.006 Randler, C. (2007). Gender differences in morningness-eveningness assessed by self-report questionnaires: A meta-analysis. Personality and Individual Differences, 43, 1667-1675. doi:10.1016/j.paid.2007.05.004 Richardson, J. E. (1995). Gender differences in the Vividness of Visual Imagery Questionnaire: A meta-analysis. Journal of Mental Imagery, 19, 177-187. Riley, J., Robinson, M. E., Wise, E. A., Myers, C. D., & Fillingim, R. B. (1998). Sex differences in the perception of noxious experimental stimuli: A meta-analysis. Pain, 74, 181-187. doi:10.1016/S0304-3959(97)00199-1 Roth, P. L., Purvis, K. L., & Bobko, P. (2012). A meta-analysis of gender group differences for measures of job performance in field studies. Journal of Management, 38, 719-739. doi:10.1177/0149206310374774 Rotundo, M., Nguyen, D., & Sackett, P. R. (2001). A meta-analytic review of gender differences in perceptions of sexual harassment. Journal of Applied Psychology, 86, 914-922. doi:10.1037/0021-9010.86.5.914 Sagarin, B. J., Martin, A. L., Coutinho, S. A., Edlund, J. E., Patel, L., Skowronski, J. J., & Zengel, B. (2012). Sex differences in jealousy: A meta-analytic examination. Evolution and Human Behavior, 33, 595-614. doi:10.1016/j.evolhumbehav.2012.02.006 Schram, C. M. (1996). A meta-analysis of gender differences in applied statistics achievement. Journal of Educational and Behavioral Statistics, 21, 55-70. doi:10.2307/1165255 Schredl, M., & Reinhard, I. (2008). Gender differences in dream recall: A meta-analysis. Journal of Sleep Research, 17, 125-131. doi:10.1111/j.1365-2869.2008.00626.x Schredl, M., & Reinhard, I. (2011). Gender differences in nightmare frequency: A meta-analysis. Sleep Medicine Reviews, 15, 115-121. doi:10.1016/j.smrv.2010.06.002 Schutte, J. W., & Hosch, H. M. (1997). Gender differences in sexual assault verdicts: A metaanalysis. Journal of Social Behavior & Personality, 12, 759-772. Severiens, S., & ten Dam, G. (1998). A multilevel meta-analysis of gender differences in learning orientations. British Journal of Educational Psychology, 68, 595-608. doi:10.1111/j.2044-8279.1998.tb01315.x Silverman, I. W. (2003). Gender differences in delay of gratification: A meta-analysis. Sex Roles, 49, 451-463. doi:10.1023/A:1025872421115 Silverman, I. W. (2006). Sex differences in simple visual reaction time: A historical metaanalysis. Sex Roles, 54, 57-68. doi:10.1007/s11199-006-8869-6 Stier, D. S., & Hall, J. A. (1984). Gender differences in touch: An empirical and theoretical review. Journal of Personality and Social Psychology, 47, 440-459. doi:10.1037/00223514.47.2.440 Stockdale, L., Tackett, S., & Coyne, S. M. (2013). Sex differences in verbal aggression use in romantic relationships: A meta-analytic study and review. Journal of Aggression,

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Conflict and Peace Research, 5, 167-178. doi:10.1108/JACPR-09-2012-0003 Stuhlmacher, A. F., & Walters, A. E. (1999). Gender differences in negotiation outcome: A meta-analysis. Personnel Psychology, 52, 653-677. doi:10.1111/j.17446570.1999.tb00175.x Su, R., Rounds, J., & Armstrong, P. (2009). Men and things, women and people: A metaanalysis of sex differences in interests. Psychological Bulletin, 135, 859-884. doi:10.1037/a0017364 Syzmanowicz, A., & Furnham, A. (2011). Gender differences in self-estimates of general, mathematical, spatial and verbal intelligence: Four meta analyses. Learning and Individual Differences, 21, 493-504. doi:10.1016/j.lindif.2011.07.001 Tamres, L. K., Janicki, D., & Helgeson, V. S. (2002). Sex differences in coping behavior: A meta-analytic review and an examination of relative coping. Personality and Social Psychology Review, 6, 2-30. doi:10.1207/S15327957PSPR0601_1 Thoma, S. J. (1986). Estimating gender differences in the comprehension and preference of moral issues. Developmental Review, 6, 165-180. doi:10.1016/0273-2297(86)90010-9 Thompson, A. E., & Voyer, D. (2014). Sex differences in the ability to recognise non-verbal displays of emotion: A meta-analysis. Cognition and Emotion. Advance online publication. doi: 10.1080/02699931.2013.875889 Twenge, J. M. (1997). Changes in masculine and feminine traits over time: A meta-analysis. Sex Roles, 36, 305-325. doi:10.1007/BF02766650 Twenge, J. M., & Nolen-Hoeksema, S. (2002). Age, gender, race, socioeconomic status, and birth cohort difference on the children's depression inventory: A meta-analysis. Journal of Abnormal Psychology, 111, 578-588. doi:10.1037/0021-843X.111.4.578 Vemer, E., Coleman, M., Ganong, L. H., & Cooper, H. (1989). Marital satisfaction in remarriage: A meta-analysis. Journal of Marriage and the Family, 51, 713-725. doi:10.2307/352170 Vishnevsky, T., Cann, A., Calhoun, L. G., Tedeschi, R. G., & Demakis, G. J. (2010). Gender differences in self-reported posttraumatic growth: A meta-analysis. Psychology of Women Quarterly, 34, 110-120. doi:10.1111/j.1471-6402.2009.01546.x Voyer, D., Postma, A., Brake, B., & Imperato-McGinley, J. (2007). Gender differences in object location memory: A meta-analysis. Psychonomic Bulletin & Review, 14, 23-38. doi:10.3758/BF03194024 Voyer, D., Voyer, S., & Bryden, M. (1995). Magnitude of sex differences in spatial abilities: A meta-analysis and consideration of critical variables. Psychological Bulletin, 117, 250270. doi:10.1037/0033-2909.117.2.250 Walters, A. E., Stuhlmacher, A. F., & Meyer, L. L. (1998). Gender and negotiator competitiveness: A meta-analysis. Organizational Behavior and Human Decision Processes, 76, 1-29. doi:10.1006/obhd.1998.2797 Weinburgh, M. (1995). Gender differences in student attitudes toward science: A meta-analysis of the literature from 1970 to 1991. Journal of Research in Science Teaching, 32, 387398. doi:10.1002/tea.3660320407 Whitley, B. E., McHugh, M. C., & Frieze, I. H. (1986). Assessing the theoretical models for sex differences in causal attributions of success and failure. In J. S. Hyde & M. C. Linn (Eds.), The psychology of gender: Advances through meta-analysis, (pp. 102–135). Baltimore: Johns Hopkins University Press. Whitley, B. r. (1997). Gender differences in computer-related attitudes and behavior: A meta-

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analysis. Computers in Human Behavior, 13, 1-22. doi:10.1016/S0747-5632(96)00026-X Whitley, B. r., Nelson, A., & Jones, C. J. (1999). Gender differences in cheating attitudes and classroom cheating behavior: A meta-analysis. Sex Roles, 41, 657-680. doi:10.1023/A:1018863909149 Wilgenbusch, T., & Merrell, K. W. (1999). Gender differences in self-concept among children and adolescents: A meta-analysis of multidimensional studies. School Psychology Quarterly, 14, 101-120. doi:10.1037/h0089000 Wood, W. (1987). Meta-analytic review of sex differences in group performance. Psychological Bulletin, 102, 53-71. doi:10.1037/0033-2909.102.1.53 You, D., Maeda, Y., & Bebeau, M. J. (2011). Gender differences in moral sensitivity: A metaanalysis. Ethics & Behavior, 21, 263-282. doi:10.1080/10508422.2011.585591

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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

ONLINE SUPPLEMENT 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).

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ONLINE SUPPLEMENT 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

ONLINE SUPPLEMENT 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

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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.

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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.

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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.