SSCI

The Gender Similarities Hypothesis

Janet Shibley Hyde University of Wisconsin—Madison

The differences model, which argues that males and fe- males are vastly different psychologically, dominates the popular media. Here, the author advances a very different view, the gender similarities hypothesis, which holds that males and females are similar on most, but not all, psy- chological variables. Results from a review of 46 meta- analyses support the gender similarities hypothesis. Gen- der differences can vary substantially in magnitude at different ages and depend on the context in which mea- surement occurs. Overinflated claims of gender differences carry substantial costs in areas such as the workplace and relationships.

Keywords: gender differences, gender similarities, meta- analysis, aggression

The mass media and the general public are captivatedby findings of gender differences. John Gray’s(1992) Men Are From Mars, Women Are From Venus, which argued for enormous psychological differ- ences between women and men, has sold over 30 million copies and been translated into 40 languages (Gray, 2005). Deborah Tannen’s (1991) You Just Don’t Understand: Women and Men in Conversation argued for the different cultures hypothesis: that men’s and women’s patterns of speaking are so fundamentally different that men and women essentially belong to different linguistic communi- ties or cultures. That book was on the New York Times bestseller list for nearly four years and has been translated into 24 languages (AnnOnline, 2005). Both of these works, and dozens of others like them, have argued for the differ- ences hypothesis: that males and females are, psychologi- cally, vastly different. Here, I advance a very different view—the gender similarities hypothesis (for related state- ments, see Epstein, 1988; Hyde, 1985; Hyde & Plant, 1995; Kimball, 1995).

The Hypothesis The gender similarities hypothesis holds that males

and females are similar on most, but not all, psychological variables. That is, men and women, as well as boys and girls, are more alike than they are different. In terms of effect sizes, the gender similarities hypothesis states that most psychological gender differences are in the close-to- zero (d � 0.10) or small (0.11 � d � 0.35) range, a few are in the moderate range (0.36 � d � 0.65), and very few are large (d � 0.66 –1.00) or very large (d � 1.00).

Although the fascination with psychological gender differences has been present from the dawn of formalized psychology around 1879 (Shields, 1975), a few early re-

searchers highlighted gender similarities. Thorndike (1914), for example, believed that psychological gender differences were too small, compared with within-gender variation, to be important. Leta Stetter Hollingworth (1918) reviewed available research on gender differences in men- tal traits and found little evidence of gender differences. Another important reviewer of gender research in the early 1900s, Helen Thompson Woolley (1914), lamented the gap between the data and scientists’ views on the question:

The general discussions of the psychology of sex, whether by psychologists or by sociologists show such a wide diversity of points of view that one feels that the truest thing to be said at present is that scientific evidence plays very little part in produc- ing convictions. (p. 372)

The Role of Meta-Analysis in Assessing Psychological Gender Differences

Reviews of research on psychological gender differences began with Woolley’s (1914) and Hollingworth’s (1918) and extended through Maccoby and Jacklin’s (1974) wa- tershed book The Psychology of Sex Differences, in which they reviewed more than 2,000 studies of gender differ- ences in a wide variety of domains, including abilities, personality, social behavior, and memory. Maccoby and Jacklin dismissed as unfounded many popular beliefs in psychological gender differences, including beliefs that girls are more “social” than boys; that girls are more suggestible; that girls have lower self-esteem; that girls are better at rote learning and simple tasks, whereas boys are better at higher level cognitive processing; and that girls lack achievement motivation. Maccoby and Jacklin con- cluded that gender differences were well established in only four areas: verbal ability, visual-spatial ability, math- ematical ability, and aggression. Overall, then, they found much evidence for gender similarities. Secondary reports of their findings in textbooks and other sources, however, focused almost exclusively on their conclusions about gen- der differences (e.g., Gleitman, 1981; Lefrançois, 1990).

Preparation of this article was supported in part by National Science Foundation Grant REC 0207109. I thank Nicole Else-Quest, Sara Lind- berg, Shelly Grabe, and Jenni Petersen for reviewing and commenting on a draft of this article.

Correspondence concerning this article should be addressed to Janet Shibley Hyde, Department of Psychology, University of Wisconsin— Madison, 1202 West Johnson Street, Madison, WI 53706. E-mail: jshyde@wisc.edu

581September 2005 ● American Psychologist Copyright 2005 by the American Psychological Association 0003-066X/05/$12.00 Vol. 60, No. 6, 581–592 DOI: 10.1037/0003-066X.60.6.581

Shortly after this important work appeared, the statistical method of meta-analysis was developed (e.g., Glass, McGaw, & Smith, 1981; Hedges & Olkin, 1985; Rosenthal, 1991). This method revolutionized the study of psychological gender differences. Meta-analyses quickly appeared on issues such as gender differences in influenceability (Eagly & Carli, 1981), abilities (Hyde, 1981; Hyde & Linn, 1988; Linn & Petersen, 1985), and aggression (Eagly & Steffen, 1986; Hyde, 1984, 1986).

Meta-analysis is a statistical method for aggregating research findings across many studies of the same question (Hedges & Becker, 1986). It is ideal for synthesizing re- search on gender differences, an area in which often dozens or even hundreds of studies of a particular question have been conducted.

Crucial to meta-analysis is the concept of effect size, which measures the magnitude of an effect—in this case, the magnitude of gender difference. In gender meta-anal- yses, the measure of effect size typically is d (Cohen, 1988):

d � MM � MF

sw ,

where MM is the mean score for males, MF is the mean score for females, and sw is the average within-sex standard deviation. That is, d measures how far apart the male and female means are in standardized units. In gender meta- analysis, the effect sizes computed from all individual studies are averaged to obtain an overall effect size reflect- ing the magnitude of gender differences across all studies. In the present article, I follow the convention that negative values of d mean that females scored higher on a dimen- sion, and positive values of d indicate that males scored higher.

Gender meta-analyses generally proceed in four steps: (a) The researcher locates all studies on the topic being reviewed, typically using databases such as PsycINFO and carefully chosen search terms. (b) Statistics are extracted from each report, and an effect size is computed for each study. (c) A weighted average of the effect sizes is com- puted (weighting by sample size) to obtain an overall assessment of the direction and magnitude of the gender difference when all studies are combined. (d) Homogeneity analyses are conducted to determine whether the group of effect sizes is relatively homogeneous. If it is not, then the studies can be partitioned into theoretically meaningful groups to determine whether the effect size is larger for some types of studies and smaller for other types. The researcher could ask, for example, whether gender differ- ences are larger for measures of physical aggression com- pared with measures of verbal aggression.

The Evidence To evaluate the gender similarities hypothesis, I collected the major meta-analyses that have been conducted on psy- chological gender differences. They are listed in Table 1, grouped roughly into six categories: those that assessed cognitive variables, such as abilities; those that assessed verbal or nonverbal communication; those that assessed social or personality variables, such as aggression or lead- ership; those that assessed measures of psychological well- being, such as self-esteem; those that assessed motor be- haviors, such as throwing distance; and those that assessed miscellaneous constructs, such as moral reasoning. I began with meta-analyses reviewed previously by Hyde and Plant (1995), Hyde and Frost (1993), and Ashmore (1990). I updated these lists with more recent meta-analyses and, where possible, replaced older meta-analyses with more up-to-date meta-analyses that used larger samples and bet- ter statistical methods.

Hedges and Nowell (1995; see also Feingold, 1988) have argued that the canonical method of meta-analysis— which often aggregates data from many small convenience samples—should be augmented or replaced by data from large probability samples, at least when that is possible (e.g., in areas such as ability testing). Test-norming data as well as data from major national surveys such as the National Longitudinal Study of Youth provide important information. Findings from samples such as these are in- cluded in the summary shown in Table 1, where the num- ber of reports is marked with an asterisk.

Inspection of the effect sizes shown in the rightmost column of Table 1 reveals strong evidence for the gender similarities hypothesis. These effect sizes are summarized in Table 2. Of the 128 effect sizes shown in Table 1, 4 were unclassifiable because the meta-analysis provided such a wide range for the estimate. The remaining 124 effect sizes were classified into the categories noted earlier: close-to- zero (d � 0.10), small (0.11 � d � 0.35), moderate (0.36 � d � 0.65), large (d � 0.66 –1.00), or very large (�1.00). The striking result is that 30% of the effect sizes are in the close-to-zero range, and an additional 48% are in the small range. That is, 78% of gender differences are

Janet Shibley Hyde

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Table 1 Major Meta-Analyses of Research on Psychological Gender Differences

Study and variable Age No. of reports d

Cognitive variables

Hyde, Fennema, & Lamon (1990) Mathematics computation All 45 �0.14 Mathematics concepts All 41 �0.03 Mathematics problem solving All 48 �0.08

Hedges & Nowell (1995) Reading comprehension Adolescents 5* �0.09 Vocabulary Adolescents 4* �0.06 Mathematics Adolescents 6* �0.16 Perceptual speed Adolescents 4* �0.28 Science Adolescents 4* �0.32 Spatial ability Adolescents 2* �0.19

Hyde, Fennema, Ryan, et al. (1990) Mathematics self-confidence All 56 �0.16 Mathematics anxiety All 53 �0.15

Feingold (1988) DAT spelling Adolescents 5* �0.45 DAT language Adolescents 5* �0.40 DAT verbal reasoning Adolescents 5* �0.02 DAT abstract reasoning Adolescents 5* �0.04 DAT numerical ability Adolescents 5* �0.10 DAT perceptual speed Adolescents 5* �0.34 DAT mechanical reasoning Adolescents 5* �0.76 DAT space relations Adolescents 5* �0.15

Hyde & Linn (1988) Vocabulary All 40 �0.02 Reading comprehension All 18 �0.03 Speech production All 12 �0.33

Linn & Petersen (1985) Spatial perception All 62 �0.44 Mental rotation All 29 �0.73 Spatial visualization All 81 �0.13

Voyer et al. (1995) Spatial perception All 92 �0.44 Mental rotation All 78 �0.56 Spatial visualization All 116 �0.19

Lynn & Irwing (2004) Progressive matrices 6–14 years 15 �0.02 Progressive matrices 15–19 years 23 �0.16 Progressive matrices Adults 10 �0.30

Whitley et al. (1986) Attribution of success to ability All 29 �0.13 Attribution of success to effort All 29 �0.04 Attribution of success to task All 29 �0.01 Attribution of success to luck All 29 �0.07 Attribution of failure to ability All 29 �0.16 Attribution of failure to effort All 29 �0.15 Attribution of failure to task All 29 �0.08 Attribution of failure luck All 29 �0.15

Communication

Anderson & Leaper (1998) Interruptions in conversation Adults 53 �0.15 Intrusive interruptions Adults 17 �0.33

Leaper & Smith (2004) Talkativeness Children 73 �0.11 Affiliative speech Children 46 �0.26 Assertive speech Children 75 �0.11

(table continues)

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Table 1 (continued)

Study and variable Age No. of reports d

Communication (continued )

Dindia & Allen (1992) Self-disclosure (all studies) — 205 �0.18 Self-disclosure to stranger — 99 �0.07 Self-disclosure to friend — 50 �0.28

LaFrance et al. (2003) Smiling Adolescents and adults 418 �0.40 Smiling: Aware of being observed Adolescents and adults 295 �0.46 Smiling: Not aware of being observed Adolescents and adults 31 �0.19

McClure (2000) Facial expression processing Infants 29 �0.18 to �0.92 Facial expression processing Children and adolescents 89 �0.13 to �0.18

Social and personality variables

Hyde (1984, 1986) Aggression (all types) All 69 �0.50 Physical aggression All 26 �0.60 Verbal aggression All 6 �0.43

Eagly & Steffen (1986) Aggression Adults 50 �0.29 Physical aggression Adults 30 �0.40 Psychological aggression Adults 20 �0.18

Knight et al. (2002) Physical aggression All 41 �0.59 Verbal aggression All 22 �0.28 Aggression in low emotional arousal context All 40 �0.30 Aggression in emotional arousal context All 83 �0.56

Bettencourt & Miller (1996) Aggression under provocation Adults 57 �0.17 Aggression under neutral conditions Adults 50 �0.33

Archer (2004) Aggression in real-world settings All 75 �0.30 to �0.63 Physical aggression All 111 �0.33 to �0.84 Verbal aggression All 68 �0.09 to �0.55 Indirect aggression All 40 �0.74 to �0.05

Stuhlmacher & Walters (1999) Negotiation outcomes Adults 53 �0.09

Walters et al. (1998) Negotiator competitiveness Adults 79 �0.07

Eagly & Crowley (1986) Helping behavior Adults 99 �0.13 Helping: Surveillance context Adults 16 �0.74 Helping: No surveillance Adults 41 �0.02

Oliver & Hyde (1993) Sexuality: Masturbation All 26 �0.96 Sexuality: Attitudes about casual sex All 10 �0.81 Sexual satisfaction All 15 �0.06 Attitudes about extramarital sex All 17 �0.29

Murnen & Stockton (1997) Arousal to sexual stimuli Adults 62 �0.31

Eagly & Johnson (1990) Leadership: Interpersonal style Adults 153 �0.04 to �0.07 Leadership: Task style Adults 154 0.00 to �0.09 Leadership: Democratic vs. autocratic Adults 28 �0.22 to �0.34

Eagly et al. (1992) Leadership: Evaluation Adults 114 �0.05

Eagly et al. (1995) Leadership effectiveness Adults 76 �0.02

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Table 1 (continued)

Study and variable Age No. of reports d

Social and personality variables (continued) Eagly et al. (2003)

Leadership: Transformational Adults 44 �0.10 Leadership: Transactional Adults 51 �0.13 to �0.27 Leadership: Laissez-faire Adults 16 �0.16

Feingold (1994) Neuroticism: Anxiety Adolescents and adults 13* �0.32 Neuroticism: Impulsiveness Adolescents and adults 6* �0.01 Extraversion: Gregariousness Adolescents and adults 10* �0.07 Extraversion: Assertiveness Adolescents and adults 10* �0.51 Extraversion: Activity Adolescents and adults 5 �0.08 Openness Adolescents and adults 4* �0.19 Agreeableness: Trust Adolescents and adults 4* �0.35 Agreeableness: Tendermindedness Adolescents and adults 10* �0.91 Conscientiousness Adolescents and adults 4 �0.18

Psychological well-being Kling et al. (1999, Analysis I)

Self-esteem All 216 �0.21 Kling et al. (1999, Analysis II)

Self-esteem Adolescents 15* �0.04 to �0.16 Major et al. (1999)

Self-esteem All 226 �0.14 Feingold & Mazzella (1998)

Body esteem All — �0.58 Twenge & Nolen-Hoeksema (2002)

Depression symptoms 8–16 years 310 �0.02 Wood et al. (1989)

Life satisfaction Adults 17 �0.03 Happiness Adults 22 �0.07

Pinquart & Sörensen (2001) Life satisfaction Elderly 176 �0.08 Self-esteem Elderly 59 �0.08 Happiness Elderly 56 �0.06

Tamres et al. (2002) Coping: Problem-focused All 22 �0.13 Coping: Rumination All 10 �0.19

Motor behaviors Thomas & French (1985)

Balance 3–20 years 67 �0.09 Grip strength 3–20 years 37 �0.66 Throw velocity 3–20 years 12 �2.18 Throw distance 3–20 years 47 �1.98 Vertical jump 3–20 years 20 �0.18 Sprinting 3–20 years 66 �0.63 Flexibility 5–10 years 13 �0.29

Eaton & Enns (1986) Activity level All 127 �0.49

Miscellaneous Thoma (1986)

Moral reasoning: Stage Adolescents and adults 56 �0.21 Jaffee & Hyde (2000)

Moral reasoning: Justice orientation All 95 �0.19 Moral reasoning: Care orientation All 160 �0.28

Silverman (2003) Delay of gratification All 38 �0.12

Whitley et al. (1999) Cheating behavior All 36 �0.17 Cheating attitudes All 14 �0.35

(table continues)

585September 2005 ● American Psychologist

small or close to zero. This result is similar to that of Hyde and Plant (1995), who found that 60% of effect sizes for gender differences were in the small or close-to-zero range.

The small magnitude of these effects is even more striking given that most of the meta-analyses addressed the classic gender differences questions—that is, areas in which gender differences were reputed to be reliable, such as mathematics performance, verbal ability, and aggressive behavior. For example, despite Tannen’s (1991) assertions, gender differences in most aspects of communication are small. Gilligan (1982) has argued that males and females speak in a different moral “voice,” yet meta-analyses show that gender differences in moral reasoning and moral ori- entation are small (Jaffee & Hyde, 2000).

The Exceptions As noted earlier, the gender similarities hypothesis does not assert that males and females are similar in absolutely every domain. The exceptions—areas in which gender dif- ferences are moderate or large in magnitude—should be recognized.

The largest gender differences in Table 1 are in the domain of motor performance, particularly for measures such as throwing velocity (d � 2.18) and throwing distance (d � 1.98) (Thomas & French, 1985). These differences

are particularly large after puberty, when the gender gap in muscle mass and bone size widens.

A second area in which large gender differences are found is some— but not all—measures of sexuality (Oliver & Hyde, 1993). Gender differences are strikingly large for incidences of masturbation and for attitudes about sex in a casual, uncommitted relationship. In contrast, the gender difference in reported sexual satisfaction is close to zero.

Across several meta-analyses, aggression has repeat- edly shown gender differences that are moderate in mag- nitude (Archer, 2004; Eagly & Steffen, 1986; Hyde, 1984, 1986). The gender difference in physical aggression is particularly reliable and is larger than the gender difference in verbal aggression. Much publicity has been given to gender differences in relational aggression, with girls scor- ing higher (e.g., Crick & Grotpeter, 1995). According to the Archer (2004) meta-analysis, indirect or relational ag- gression showed an effect size for gender differences of �0.45 when measured by direct observation, but it was only �0.19 for peer ratings, �0.02 for self-reports, and �0.13 for teacher reports. Therefore, the evidence is am- biguous regarding the magnitude of the gender difference in relational aggression.

The Interpretation of Effect Sizes The interpretation of effect sizes is contested. On one side of the argument, the classic source is the statistician Cohen (1969, 1988), who recommended that 0.20 be considered a small effect, 0.50 be considered medium, and 0.80 be considered large. It is important to note that he set these guidelines before the advent of meta-analysis, and they have been the standards used in statistical power analysis for decades.

In support of these guidelines are indicators of overlap between two distributions. For example, Kling, Hyde, Showers, and Buswell (1999) graphed two distributions differing on average by an effect size of 0.21, the effect size they found for gender differences in self-esteem. This graph is shown in Figure 1. Clearly, this small effect size

Table 1 (continued)

Study and variable Age No. of reports d

Whitley (1997) Computer use: Current All 18 �0.33 Computer self-efficacy All 29 �0.41

Konrad et al. (2000) Job attribute preference: Earnings Adults 207 �0.12 Job attribute preference: Security Adults 182 �0.02 Job attribute preference: Challenge Adults 63 �0.05 Job attribute preference: Physical work environment Adults 96 �0.13 Job attribute preference: Power Adults 68 �0.04

Note. Positive values of d represent higher scores for men and/or boys; negative values of d represent higher scores for women and/or girls. Asterisks indicate that data were from major, large national samples. Dashes indicate that data were not available (i.e., the study in question did not provide this information clearly). No. � number; DAT � Differential Aptitude Test.

Table 2 Effect Sizes (n � 124) for Psychological Gender Differences, Based on Meta-Analyses, Categorized by Range of Magnitude

Effect sizes

Effect size range

0–0.10 0.11–0.35 0.36–0.65 0.66–1.00 �1.00

Number 37 59 19 7 2 % of total 30 48 15 6 2

586 September 2005 ● American Psychologist

reflects distributions that overlap greatly—that is, that show more similarity than difference. Cohen (1988) devel- oped a U statistic that quantifies the percentage of nonover- lap of distributions. For d � 0.20, U � 15%; that is, 85% of the areas of the distributions overlap. According to another Cohen measure of overlap, for d � 0.20, 54% of individuals in Group A exceed the 50th percentile for Group B.

For another way to consider the interpretation of effect sizes, d can also be expressed as an equivalent value of the Pearson correlation, r (Cohen, 1988). For the small effect size of 0.20, r � .10, certainly a small correlation. A d of 0.50 is equivalent to an r of .24, and for d � 0.80, r � .37.

Rosenthal (1991; Rosenthal & Rubin, 1982) has ar- gued the other side of the case—namely, that seemingly small effect sizes can be important and make for impressive applied effects. As an example, he took a two-group ex- perimental design in which one group is treated for cancer and the other group receives a placebo. He used the method of binomial effect size display (BESD) to illustrate the consequences. Using this method, for example, an r of .32 between treatment and outcome, accounting for only 10% of the variance, translates into a survival rate of 34% in the placebo group and 66% in the treated group. Certainly, the effect is impressive.

How does this apply to the study of gender differ- ences? First, in terms of costs of errors in scientific decision making, psychological gender differences are quite a dif- ferent matter from curing cancer. So, interpretation of the magnitude of effects must be heavily conditioned by the costs of making Type I and Type II errors for the particular question under consideration. I look forward to statisticians developing indicators that take these factors into account.

Second, Rosenthal used the r metric, and when this is translated into d, the effects look much less impressive. For example, a d of 0.20 is equivalent to an r of 0.10, and Rosenthal’s BESD indicates that that effect is equivalent to cancer survival increasing from 45% to 55%— once again, a small effect. A close-to-zero effect size of 0.10 is equiv- alent to an r of .05, which translates to cancer survival rates increasing only from 47.5% to 52.5% in the treatment group compared with the control group. In short, I believe that Cohen’s guidelines provide a reasonable standard for the interpretation of gender differences effect sizes.

One caveat should be noted, however. The foregoing discussion is implicitly based on the assumption that the variabilities in the male and female distributions are equal. Yet the greater male variability hypothesis was originally proposed more than a century ago, and it survives today (Feingold, 1992; Hedges & Friedman, 1993). In the 1800s, this hypothesis was proposed to explain why there were more male than female geniuses and, at the same time, more males among the mentally retarded. Statistically, the combination of a small average difference favoring males and a larger standard deviation for males, for some trait such as mathematics performance, could lead to a lopsided gender ratio favoring males in the upper tail of the distri- bution reflecting exceptional talent. The statistic used to investigate this question is the variance ratio (VR), the ratio of the male variance to the female variance. Empirical investigations of the VR have found values of 1.00 –1.08 for vocabulary (Hedges & Nowell, 1995), 1.05–1.25 for mathematics performance (Hedges & Nowell), and 0.87– 1.04 for self-esteem (Kling et al., 1999). Therefore, it appears that whether males or females are more variable depends on the domain under consideration. Moreover, most VR estimates are close to 1.00, indicating similar variances for males and females. Nonetheless, this issue of possible gender differences in variability merits continued investigation.

Developmental Trends Not all meta-analyses have examined developmental trends and, given the preponderance of psychological research on college students, developmental analysis is not always pos- sible. However, meta-analysis can be powerful for identi- fying age trends in the magnitude of gender differences. Here, I consider a few key examples of meta-analyses that have taken this developmental approach (see Table 3).

At the time of the meta-analysis by Hyde, Fennema, and Lamon (1990), it was believed that gender differences in mathematics performance were small or nonexistent in childhood and that the male advantage appeared beginning around the time of puberty (Maccoby & Jacklin, 1974). It was also believed that males were better at high-level mathematical problems that required complex processing, whereas females were better at low-level mathematics that required only simple computation. Hyde and colleagues addressed both hypotheses in their meta-analysis. They found a small gender difference favoring girls in compu- tation in elementary school and middle school and no gender difference in computation in the high school years.

Figure 1 Graphic Representation of a 0.21 Effect Size

Note. Two normal distributions that are 0.21 standard deviations apart (i.e., d � 0.21). This is the approximate magnitude of the gender difference in self-esteem, averaged over all samples, found by Kling et al. (1999). From “Gender Differences in Self-Esteem: A Meta-Analysis,” by K. C. Kling, J. S. Hyde, C. J. Showers, and B. N. Buswell, 1999, Psychological Bulletin, 125, p. 484. Copyright 1999 by the American Psychological Association.

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There was no gender difference in complex problem solv- ing in elementary school or middle school, but a small gender difference favoring males emerged in the high school years (d � 0.29). Age differences in the magnitude of the gender effect were significant for both computation and problem solving.

Kling et al. (1999) used a developmental approach in their meta-analysis of studies of gender differences in self- esteem, on the basis of the assertion of prominent authors such as Mary Pipher (1994) that girls’ self-esteem takes a nosedive at the beginning of adolescence. They found that the magnitude of the gender difference did grow larger from childhood to adolescence: In childhood (ages 7–10), d � 0.16; for early adolescence (ages 11–14), d � 0.23; and for the high school years (ages 15–18), d � 0.33. However, the gender difference did not suddenly become large in early adolescence, and even in high school, the difference was still not large. Moreover, the gender differ- ence was smaller in older samples; for example, for ages 23–59, d � 0.10.

Whitley’s (1997) analysis of age trends in computer self-efficacy are revealing. In grammar school samples, d � 0.09, whereas in high school samples, d � 0.66. This dramatic trend leads to questions about what forces are at work transforming girls from feeling as effective with computers as boys do to showing a large difference in self-efficacy by high school.

These examples illustrate the extent to which the magnitude of gender differences can fluctuate with age. Gender differences grow larger or smaller at different times in the life span, and meta-analysis is a powerful tool for detecting these trends. Moreover, the fluctuating magnitude of gender differences at different ages argues against the differences model and notions that gender differences are large and stable.

The Importance of Context Gender researchers have emphasized the importance of context in creating, erasing, or even reversing psychologi- cal gender differences (Bussey & Bandura, 1999; Deaux & Major, 1987; Eagly & Wood, 1999). Context may exert influence at numerous levels, including the written instruc- tions given for an exam, dyadic interactions between par- ticipants or between a participant and an experimenter, or the sociocultural level.

In an important experiment, Lightdale and Prentice (1994) demonstrated the importance of gender roles and social context in creating or erasing the purportedly robust gender difference in aggression. Lightdale and Prentice used the technique of deindividuation to produce a situation that removed the influence of gender roles. Deindividuation refers to a state in which the person has lost his or her individual identity; that is, the person has become anony- mous. Under such conditions, people should feel no obli-

Table 3 Selected Meta-Analyses Showing Developmental Trends in the Magnitude of Gender Differences

Study and variable Age (years) No. of reports d

Hyde, Fennema, & Lamon (1990) Mathematics: Complex problem solving 5–10 11 0.00

11–14 21 �0.02 15–18 10 �0.29 19–25 15 �0.32

Kling et al. (1999) Self-esteem 7–10 22 �0.16

11–14 53 �0.23 15–18 44 �0.33 19–22 72 �0.18 23–59 16 �0.10 �60 6 �0.03

Major et al. (1999) Self-esteem 5–10 24 �0.01

11–13 34 �0.12 14–18 65 �0.16

19 or older 97 �0.13 Twenge & Nolen-Hoeksema (2002)

Depressive symptoms 8–12 86 �0.04 13–16 49 �0.16

Thomas & French (1985) Throwing distance 3–8 — �1.50 to �2.00

16–18 — �3.50

Note. Positive values of d represent higher scores for men and/or boys; negative values of d represent higher scores for women and/or girls. Dashes indicate that data were not available (i.e., the study in question did not provide this information clearly). No. � number.

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gation to conform to social norms such as gender roles. Half of the participants, who were college students, were assigned to an individuated condition by having them sit close to the experimenter, identify themselves by name, wear large name tags, and answer personal questions. Par- ticipants in the deindividuation condition sat far from the experimenter, wore no name tags, and were simply told to wait. All participants were also told that the experiment required information from only half of the participants, whose behavior would be monitored, and that the other half would remain anonymous. Participants then played an in- teractive video game in which they first defended and then attacked by dropping bombs. The number of bombs dropped was the measure of aggressive behavior.

The results indicated that in the individuated condi- tion, men dropped significantly more bombs (M � 31.1) than women did (M � 26.8). In the deindividuated condi- tion, however, there were no significant gender differences and, in fact, women dropped somewhat more bombs (M � 41.1) than men (M � 36.8). In short, the significant gender difference in aggression disappeared when gender norms were removed.

Steele’s (1997; Steele & Aronson, 1995) work on stereotype threat has produced similar evidence in the cognitive domain. Although the original experiments con- cerned African Americans and the stereotype that they are intellectually inferior, the theory was quickly applied to gender and stereotypes that girls and women are bad at math (Brown & Josephs, 1999; Quinn & Spencer, 2001; Spencer, Steele, & Quinn, 1999; Walsh, Hickey, & Duffy, 1999). In one experiment, male and female college students with equivalent math backgrounds were tested (Spencer et al., 1999). In one condition, participants were told that the math test had shown gender difference in the past, and in the other condition, they were told that the test had been shown to be gender fair—that men and women had per- formed equally on it. In the condition in which participants had been told that the math test was gender fair, there were no gender differences in performance on the test. In the condition in which participants expected gender differ- ences, women underperformed compared with men. This simple manipulation of context was capable of creating or erasing gender differences in math performance.

Meta-analysts have addressed the importance of con- text for gender differences. In one of the earliest demon- strations of context effects, Eagly and Crowley (1986) meta-analyzed studies of gender differences in helping behavior, basing the analysis in social-role theory. They argued that certain kinds of helping are part of the male role: helping that is heroic or chivalrous. Other kinds of helping are part of the female role: helping that is nurturant and caring, such as caring for children. Heroic helping involves danger to the self, and both heroic and chivalrous helping are facilitated when onlookers are present. Wom- en’s nurturant helping more often occurs in private, with no onlookers. Averaged over all studies, men helped more (d � 0.34). However, when studies were separated into those in which onlookers were present and participants were aware of it, d � 0.74. When no onlookers were

present, d � �0.02. Moreover, the magnitude of the gender difference was highly correlated with the degree of danger in the helping situation; gender differences were largest favoring males in situations with the most danger. In short, the gender difference in helping behavior can be large, favoring males, or close to zero, depending on the social context in which the behavior is measured. Moreover, the pattern of gender differences is consistent with social-role theory.

Anderson and Leaper (1998) obtained similar context effects in their meta-analysis of gender differences in con- versational interruption. At the time of their meta-analysis, it was widely believed that men interrupted women con- siderably more than the reverse. Averaged over all studies, however, Anderson and Leaper found a d of 0.15, a small effect. The effect size for intrusive interruptions (excluding back-channel interruptions) was larger: 0.33. It is important to note that the magnitude of the gender difference varied greatly depending on the social context in which interrup- tions were studied. When dyads were observed, d � 0.06, but with larger groups of three or more, d � 0.26. When participants were strangers, d � 0.17, but when they were friends, d � �0.14. Here, again, it is clear that gender differences can be created, erased, or reversed, depending on the context.

In their meta-analysis, LaFrance, Hecht, and Paluck (2003) found a moderate gender difference in smiling (d � �0.41), with girls and women smiling more. Again, the magnitude of the gender difference was highly dependent on the context. If participants had a clear awareness that they were being observed, the gender difference was larger (d � �0.46) than it was if they were not aware of being observed (d � �0.19). The magnitude of the gender dif- ference also depended on culture and age.

Dindia and Allen (1992) and Bettencourt and Miller (1996) also found marked context effects in their gender meta-analyses. The conclusion is clear: The magnitude and even the direction of gender differences depends on the context. These findings provide strong evidence against the differences model and its notions that psychological gender differences are large and stable.

Costs of Inflated Claims of Gender Differences The question of the magnitude of psychological gender differences is more than just an academic concern. There are serious costs of overinflated claims of gender differ- ences (for an extended discussion of this point, see Barnett & Rivers, 2004; see also White & Kowalski, 1994). These costs occur in many areas, including work, parenting, and relationships.

Gilligan’s (1982) argument that women speak in a different moral “voice” than men is a well-known example of the differences model. Women, according to Gilligan, speak in a moral voice of caring, whereas men speak in a voice of justice. Despite the fact that meta-analyses discon- firm her arguments for large gender differences (Jaffee & Hyde, 2000; Thoma, 1986; Walker, 1984), Gilligan’s ideas

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have permeated American culture. One consequence of this overinflated claim of gender differences is that it reifies the stereotype of women as caring and nurturant and men as lacking in nurturance. One cost to men is that they may believe that they cannot be nurturant, even in their role as father. For women, the cost in the workplace can be enor- mous. Women who violate the stereotype of being nur- turant and nice can be penalized in hiring and evaluations. Rudman and Glick (1999), for example, found that female job applicants who displayed agentic qualities received considerably lower hireability ratings than agentic male applicants (d � 0.92) for a managerial job that had been “feminized” to require not only technical skills and the ability to work under pressure but also the ability to be helpful and sensitive to the needs of others. The researchers concluded that women must present themselves as compe- tent and agentic to be hired, but they may then be viewed as interpersonally deficient and uncaring and receive biased work evaluations because of their violation of the female nurturance stereotype.

A second example of the costs of unwarranted vali- dation of the stereotype of women as caring nurturers comes from Eagly, Makhijani, and Klonsky’s (1992) meta- analysis of studies of gender and the evaluation of leaders. Overall, women leaders were evaluated as positively as men leaders (d � 0.05). However, women leaders por- trayed as uncaring autocrats were at a more substantial disadvantage than were men leaders portrayed similarly (d � 0.30). Women who violated the caring stereotype paid for it in their evaluations. The persistence of the stereotype of women as nurturers leads to serious costs for women who violate this stereotype in the workplace.

The costs of overinflated claims of gender differences hit children as well. According to stereotypes, boys are better at math than girls are (Hyde, Fennema, Ryan, Frost, & Hopp, 1990). This stereotype is proclaimed in mass media headlines (Barnett & Rivers, 2004). Meta-analyses, however, indicate a pattern of gender similarities for math performance. Hedges and Nowell (1995) found a d of 0.16 for large national samples of adolescents, and Hyde, Fen- nema, and Lamon (1990) found a d of �0.05 for samples of the general population (see also Leahey & Guo, 2000). One cost to children is that mathematically talented girls may be overlooked by parents and teachers because these adults do not expect to find mathematical talent among girls. Parents have lower expectations for their daughters’ math success than for their sons’ (Lummis & Stevenson, 1990), despite the fact that girls earn better grades in math than boys do (Kimball, 1989). Research has shown repeat- edly that parents’ expectations for their children’s mathe- matics success relate strongly to outcomes such as the child’s mathematics self-confidence and performance, with support for a model in which parents’ expectations influ- ence children (e.g., Frome & Eccles, 1998). In short, girls may find their confidence in their ability to succeed in challenging math courses or in a mathematically oriented career undermined by parents’ and teachers’ beliefs that girls are weak in math ability.

In the realm of intimate heterosexual relationships, women and men are told that they are as different as if they came from different planets and that they communicate in dramatically different ways (Gray, 1992; Tannen, 1991). When relationship conflicts occur, good communication is essential to resolving the conflict (Gottman, 1994). If, however, women and men believe what they have been told—that it is almost impossible for them to communicate with each other—they may simply give up on trying to resolve the conflict through better communication. Thera- pists will need to dispel erroneous beliefs in massive, unbridgeable gender differences.

Inflated claims about psychological gender differ- ences can hurt boys as well. A large gender gap in self- esteem beginning in adolescence has been touted in popular sources (American Association of University Women, 1991; Orenstein, 1994; Pipher, 1994). Girls’ self-esteem is purported to take a nosedive at the beginning of adoles- cence, with the implication that boys’ self-esteem does not. Yet meta-analytic estimates of the magnitude of the gender difference have all been small or close to zero: d � 0.21 (Kling et al., 1999, Analysis I), d � 0.04 – 0.16 (Kling et al., 1999, Analysis II), and d � 0.14 (Major, Barr, Zubek, & Babey, 1999). In short, self-esteem is roughly as much a problem for adolescent boys as it is for adolescent girls. The popular media’s focus on girls as the ones with self- esteem problems may carry a huge cost in leading parents, teachers, and other professionals to overlook boys’ self- esteem problems, so that boys do not receive the interven- tions they need.

As several of these examples indicate, the gender similarities hypothesis carries strong implications for prac- titioners. The scientific evidence does not support the belief that men and women have inherent difficulties in commu- nicating across gender. Neither does the evidence support the belief that adolescent girls are the only ones with self-esteem problems. Therapists who base their practice in the differences model should reconsider their approach on the basis of the best scientific evidence.

Conclusion The gender similarities hypothesis stands in stark contrast to the differences model, which holds that men and women, and boys and girls, are vastly different psychologically. The gender similarities hypothesis states, instead, that males and females are alike on most— but not all—psy- chological variables. Extensive evidence from meta-analy- ses of research on gender differences supports the gender similarities hypothesis. A few notable exceptions are some motor behaviors (e.g., throwing distance) and some aspects of sexuality, which show large gender differences. Aggres- sion shows a gender difference that is moderate in magnitude.

It is time to consider the costs of overinflated claims of gender differences. Arguably, they cause harm in numerous realms, including women’s opportunities in the workplace, couple conflict and communication, and analyses of self- esteem problems among adolescents. Most important, these claims are not consistent with the scientific data.

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