Sheet1Table 1 temperature and impact energy values for steelTable.docx

Sheet1Table 1: temperature and impact energy values for
steelTable 3: width of the steel specimen prior to impact
(inches)steelTemp °CSpecimenWidth #1Width #2Width
#3Width Average-70-
44025100A0.37500.37500.37400.3747Impact energy (ft-
lbs)535577865B0.37450.37550.37700.3757732486983C0.37400.
37400.37900.37571826556794D0.37400.37700.37350.37481439
546880E0.37400.37800.37900.377010276775951030637690Tab
le 4: width of aluminum specimen prior to impact
(inches)516357694SpecimenWidth #1Width #2Width #3Width
Average6163476103A0.37650.37650.37700.3767516617694B0.
37650.37650.37700.3767617476673C0.37650.37800.37850.377
71319547085D0.37750.37700.38000.37828194166108E0.37600.
37600.37700.376313142790138Avg9.230769230823.538461538
549.461538461573.307692307792.4615384615Table 5: width of
the steel specimen after impact (inches)SpecimenWidth
#1Width #2Width #3Width
AverageA0.37400.37300.37400.3737Table 2: temperature and
impact energy values for
aluminumB0.37450.37450.37350.3742aluminumTemp
°CC0.37300.39650.37450.3813-70-
44025100D0.37250.37450.37400.3737Impact energy (ft-
lbs)910988E0.37350.37350.37350.37358989898989Table 6:
width of aluminum specimen after impact
(inches)98889SpecimenWidth #1Width #2Width #3Width
Average1010101010A0.37550.38400.37600.378591011108B0.3
7650.37950.37550.37728791111C0.37600.38100.37650.377888
788D0.37650.37800.37750.3773117879E0.37700.37600.37750.3
7688778767788Table 7: changes in width of St
specimen78888SpecimenInitial WidthFinal WidthΔ
Width711997A0.37470.3737-
0.0010Avg8.38461538468.46153846158.46153846158.6153846
1548.4615384615B0.37570.3742-
0.0015C0.37570.38130.0057D0.37480.3737-

0.0012E0.37700.3735-0.0035Table 8: changes in width of Al
specimenSpecimenInitial WidthFinal WidthΔ
WidthA0.37670.37850.0018B0.37670.37720.0005C0.37770.377
80.0002D0.37820.3773-0.0008E0.37630.37680.0005
Impact Energies vs Temperature
Steel-70.0 -44.0 0.0 25.0 100.0
9.23076923076923 23.53846153846154
49.46153846153846 73.30769230769231
92.46153846153846 Aluminum -70.0 -4 4.0
0.0 25.0 100.0 8.384615384615385
8.461538461538461 8.461538461538461
8.615384615384614 8.461538461538461
Temperature (°C)
Impact Energies
(ft-lbs)
Changes in Width vs Temperature
Steel0.00183333333333335 0.0005
0.000166666666666648 -0.000833333333333408 0.0005
9.23076923076923 23.53846153846154
49.46153846153846 73.30769230769231
92.46153846153846 Aluminum 0.00183333333333335
0.0005 0.000166666666666648 -
0.000833333333333408 0.0005 8.384615384615385
8.461538461538461 8.461538461538461
8.615384615384614 8.461538461538461
Temperature (°C)
Changes in Width
(in)
REFLECTION PAPER (50 points) Name: Click here to enter
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Perspectives on Gender Reflection Paper
50 points.
Learning Objectives This assignment is designed to (1) help you
reflect on meaningful content and perspectives
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https://www.youtube.com/watch?v=-PZyytKK2EE
https://www.youtube.com/watch?v=yQc3ig12jHo
John Grey
https://www.youtube.com/watch?v=yQc3ig12jHo (3 mins)
What is appealing about John Gray's assertions (i.e., why do so
many people believe them to be true)?
What does he suggest about men?
How does he position gender relations?
How does he construct gender differences?

Gender Differences
Gender differences hypothesis
Accentuation of ingroup similarities and outgroup differences
https://www.youtube.com/watch?v=-PZyytKK2EE
Gender Differences?
The Psychology of Sex Differences (Maccoby & Jacklin, 1974)
Reviewed 2,000+ studies of several psychological gender
differences (abilities, personality, social behavior, and
memory).
No support for many popular beliefs:
Girls > boys: social; suggestible; rote learning
Boys > than girls: self-esteem; higher level cognitive
processing; achievement motivation.
Gender Differences?
Concluded that gender differences were well established in only
four areas: verbal ability, visual-spatial ability, mathematical
ability, and aggression.
Overall, then, they found much evidence for gender similarities.
Gender Differences?
Secondary reports of their findings in textbooks and other
sources, however, focused almost exclusively on their
conclusions about gender differences.

Gender Similarities
Gender similarities hypothesis (Hyde, 2005)
Males and females are similar on most, but not all,
psychological variables.
Overall, men and women, as well as boys and girls, are more
alike than they are different.
Gender Similarities
Gender similarities hypothesis (Hyde, 2005):
Reviewed 46 meta-analyses
In terms of effect sizes, most psychological gender differences
will be in the close-to-zero (d ≤ 0.10) or small (0.11 < d < 0.35)
range, a few will be in the moderate range (0.36 < d < 0.65),
and very few will be large (d = 0.66 –1.00) or very large (d >
1.00).
Focusing on Similarities
124 effect sizes were classified into the size categories
30% of the effect sizes in the close-to-zero range.
48% in the small range.
Including mathematics performance, verbal ability, and
aggressive behavior.
Hyde: Differences or Similarities?
distribution of male and female performance for a small effect
size of 0.20.

124 effect sizes were classified into the size categories
Largest gender differences were for motor performance,
particularly for measures such as throwing velocity (d = 2.18)
and throwing distance (d = 1.98)
Bruce Kidd
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.
Gender difference in reported sexual satisfaction is close to
zero.
Self-Esteem
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.

Pushups
Zell et al. (2015)
Metasynthesis to assess psychological gender differences.
106 meta-analyses and 386 individual meta-analytic effects
Conclusion: majority of effects were either small (46%) or very
small (39%)
Actual Gender Differences
Eagly and Wood (1991): 9 gender differences in social behavior
across many studies
Women are better at sending and receiving messages non-
verbally
Women more likely to conform to group pressures
Women act more friendly and agree more with other group
member in small groups
Men are more strictly task-oriented in work groups
All female groups typically perform better than all male groups
Men are more likely to emerge as leaders in initially leaderless
groups
Men are more helpful in short-term interactions with strangers
Men behave more aggressively to others than women,
particularly when the aggression brings about physical harm or
pain

Women report more life satisfaction and happiness then men
What about Emotion?
Are women more emotional than men?
20
Who is more emotional?
Imagine that we give thousands of men and women a smart
phone with a journal app on it and track their emotional state
for a long period of time. At random times during the day, the
app beeps until the individuals record their current emotional
state: what they are feeling and how strong their feelings are.
What would you find?
Who would report more frequent emotional experiences: women
or men?
Who would report more intense emotional experiences: women
or men?

Now imagine you conduct another large-scale study asking men
and women about the times they have fallen in love, out of love,
and experienced unreciprocated love.
What would you find?
Who falls in love faster: women or men?
Who falls out of love faster: women or men?
Who suffers more intense emotional distress after a break up:
women or men?
Who is more likely to experience unrequited love?
Smart phone study (Larson & Pleck, 1999):
Contrary to stereotype, no gender differences exist
Men and women did not differ in frequency of emotion
Men and some did not differ in intensity of emotion
25
One large-scale research (Larson and Pleck, 1999) had married
men and women fill out a quick rating of their current mood and
emotional at different times throughout the day. The result? No
gender differences: “there was simply no evidence that the
husbands were less emotional than their wives,” concluded the
researchers.
Not only this but when married couples argue, it is the husbands

that show stronger and longer-lasting physiological emotion
than their wives. As a result, husbands tend to avoid marital
conflicts, whereas wives are more willing to argue and confront
their spouse with problems (Gottman, 1994).
The reason for this stereotype could be the fact that women feel
(due to societies norms and values) more willing to report their
emotions and claim to have stronger feelings. Social norms may
put pressure on men to stifle their emotions and not admit to
having stronger feelings.
What about love? The evidence also contradicts the view that
women love more than men. Men fall in love faster than women
and women fall out of love faster than men (Hill, Rubin &
Peplau, 1976; Huston, Surra, Fitzgerald, & Cate, 1981; Kanin,
Davidson, & Scheck, 1970). Furthermore men have more
experiences of loving someone who does not love them back,
while it is the opposite for women. Not only this, men suffer
more intense emotional distress than women, when a love
relationship breaks up (Hill et al., 1976)
Answers:
Men fall in love faster (Harrison & Shortall, 2011)
Women fall out of love faster (Harrison & Shortall, 2011)
Men suffer more intense emotional distress after a break up
(Simon & Barrett, 2010)
26
Answers:
Men have more experiences of loving someone who does not
reciprocate their love, whereas women have more experiences

of receiving love but not reciprocating it
27
Overall:
Men and women differ little in their experience of emotions
Men and women differ in their experiences with love
28
Were your answers correct?
What did you base your answers on?
Why do stereotypes regarding women’s greater emotionality
persist despite research findings to the contrary?
Women more emotional…
Attributional biases

Women more emotional…
Women more easily express their emotions and display more
emotional awareness
The expression of emotions is strongly determined by culturally
determined display rules
In the United States and many other cultures, women are
allowed a wider range of emotional expressiveness and
responsiveness than men
Why are there gender differences?
Evolution theory (David Buss)
Social structure theory (Eagly & Wood, 1999)
Social role theory (Alice Eagly)
Evolution Theory
Primary motive is reproductive success
We are instinctively attracted to features associated with
reproductive success
Parental investment theory
The sex that invests more is more selective.
Buss’s Cross Cultural Study

Rankings across 36 countries
Men ranked attractiveness higher than women
Women ranked good financial prospect higher than men
Women want to marry an older mate while men want to marry
younger mate
34
Social Structure Theory
Proposes that differences found by Buss can be explained by
social structure (Eagly & Wood, 1999)
In most countries, men control financial resources
Easiest way for women to access these resources is marry a man
with them
As women have increased access to resources, gender
differences in importance ratings decreased
Social Structure Theory
Social Role Theory
Almost all known behavioral and psychological differences
between males and females is the result of cultural stereotypes
about gender and the resulting social roles that are taught to
young people
Differences in parental investment

So which one is true…
Meta-analysis (Petersen & Hyde, 2010)
Analyzed gender differences in 30 reported sexual behaviors
and permissive attitudes
834 samples; 1,419,807 participants (682,863 male and 736,944
female)
87 countries from six continents
So which one is right?
Evolutionary Psychology Theory
Gender Similarities Hypothesis
Social Structural Theory
√
So which one is true…
Evolutionary Psychology
26 variables, men ≠ women
4 variables, men = women
√
Self-Reported Behaviors
Petting
Intercourse
Age at first sex

Number of partners
Oral sex
Anal sex
Casual sex
Same gender sex
Extramarital sex
Condom use
Masturbation
Pornography
Cybersex
M>
M>
M>
M>
M>
M>
M>
W>
M>
M>
M>
M>
M=F
Evolutionary Psychology √
Self-Reported Attitudes
Permissiveness
Premarital sex
Casual sex
Extramarital sex

Sex when engaged
Sex with commitment
Masturbation M=W
Condom use M>
Double standard M>
Fear/anxiety/guilt W>
Sexual satisfaction M>
M>
M>
M>
M=F
M>
W>
Self-Reported Behaviors
Petting
Intercourse
Age at first sex
Number of partners
Oral sex
Anal sex
Casual sex
Same gender sex
Extramarital sex
Condom use

Masturbation
Pornography
Cybersex
M>
M>
M>
M>
M>
M>
M>
W>
M>
M>
M>
M>
M=F
M>
M>
M>
Gender Similarities Hypothesis √
Self-Reported Attitudes
Permissiveness
Premarital sex
Casual sex
Extramarital sex
Sex when engaged
Sex with commitment

Masturbation M=W
Condom use M>
Double standard M>
Fear/anxiety/guilt W>
Sexual satisfaction M>
M>
M>
M>
M=F
M>
W>
M>
Gender Similarities Hypothesis √
Type of Mating Strategies
Short-term mating strategies were associated with significant
gender differences, but long-term mating strategies, especially
in adulthood were associated with a shift toward gender
similarities
Gender differences decreased with age of the sample for some
sexual behaviors and attitudes
Comparison across Nations
Nations and ethnic groups with greater gender equity had
smaller gender differences for some reported sexual behaviors
than nations and ethnic groups with less gender equity
Social Structural Theory √

Comparison across Nations
Differences between males and females across domains have
remained largely constant over the last several decades
Social Role Theory X
https://www.youtube.com/watch?v=6SLfOTgOTvM
Bad Research?
Hyde (2005)
Reviewed 46 meta-analyses
Zell et al. (2015) Metasynthesis
106 meta-analyses
Over 12 million participants
Male Nurses 2012
Percentage of Employed
2.2% of employed population = 3.32 Million
USA: People Employed in 2016 = 151 Million
31 countries higher IQ than US

51
Percentage of Employed
2.2% of employed population = 3.32 Million
1.8 Million Computer Scientists
1.6 Million Engineers
52
IQ Averages
Average IQ American = 100
115
130
100
145
Average IQ
Physicists = 130; Mathematics = 129
Computer Scientists = 128.5; Engineers = 125.5 to 127.5
53
Variability Hypothesis

Ed Hagen, a biological anthropologist at Washington State
University.
https://grasshoppermouse.github.io/2018/06/25/the-universal-
genetic-program-and-the-custom-built-phenotype-implications-
for-race-and-sex/
Things-People
Large sex differences along the Things–People dimension
Sue et al., (2009) Meta-analysis
d = .26 to .36 (within the small to moderate range according to
Cohen’s, 1988 benchmarks).
Why are there gender differences?
Many reasons
Much of social science (including social psychology) focus on
the self-fulfilling role of social/cultural beliefs
0
0.5
1
1.5
2
2.5
12345
Gender Empowerment Index
Gender Differences in Ratings
Importance

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:
[email protected]
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
582 September 2005 ● American Psychologist
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)
Table 1 (continued)
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
Table 1 (continued)
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)
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)
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
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.

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.

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
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.
REFERENCES
American Association of University Women. (1991).
Shortchanging girls,
shortchanging America: Full data report. Washington, DC:
Author.
Anderson, K. J., & Leaper, C. (1998). Meta-analyses of gender
effects on
conversational interruption: Who, what, when, where, and how.
Sex
Roles, 39, 225–252.
AnnOnline. (2005). Biography: Deborah Tannen. Retrieved
January 10,
2005, from http://www.annonline.com
Archer, J. (2004). Sex differences in aggression in real-world
setting: A
meta-analytic review. Review of General Psychology, 8, 291–
322.
Ashmore, R. D. (1990). Sex, gender, and the individual. In L.
A. Pervin
(Ed.), Handbook of personality: Theory and research (pp. 486 –

526).
New York: Guilford Press.
Barnett, R., & Rivers, C. (2004). Same difference: How gender
myths are
hurting our relationships, our children, and our jobs. New York:
Basic
Books.
Bettencourt, B. A., & Miller, N. (1996). Gender differences in
aggression
as a function of provocation: A meta-analysis. Psychological
Bulletin,
119, 422– 447.
Brown, R. P., & Josephs, R. A. (1999). A burden of proof:
Stereotype
relevance and gender differences in math performance. Journal
of
Personality and Social Psychology, 76, 246 –257.
Bussey, K., & Bandura, A. (1999). Social cognitive theory of
gender
development and differentiation. Psychological Review, 106,
676 –713.
Cohen, J. (1969). Statistical power analysis for the behavioral
sciences.
New York: Academic Press.
Cohen, J. (1988). Statistical power analysis for the behavioral
sciences
(2nd ed.). Hillsdale, NJ: Erlbaum.
Crick, N. R., & Grotpeter, J. K. (1995). Relational aggression,
gender, and

social–psychological adjustment. Child Development, 66, 710 –
722.
Deaux, K., & Major, B. (1987). Putting gender into context: An
interac-
tive model of gender-related behavior. Psychological Review,
94,
369 –389.
Dindia, K., & Allen, M. (1992). Sex differences in self-
disclosure: A
meta-analysis. Psychological Bulletin, 112, 106 –124.
Eagly, A. H., & Carli, L. L. (1981). Sex of researchers and sex-
typed
communications as determinants of sex differences in
influenceability:
A meta-analysis of social influence studies. Psychological
Bulletin, 90,
1–20.
Eagly, A. H., & Crowley, M. (1986). Gender and helping
behavior: A
meta-analytic review of the social psychological literature.
Psycholog-
ical Bulletin, 100, 283–308.
Eagly, A. H., Johannesen-Schmidt, M. C., & van Engen, M. L.
(2003).
Transformational, transactional, and laissez-faire leadership
styles: A
meta-analysis comparing women and men. Psychological
Bulletin, 129,
569 –591.
Eagly, A. H., & Johnson, B. T. (1990). Gender and leadership

style: A
meta-analysis. Psychological Bulletin, 108, 233–256.
Eagly, A. H., Karau, S. J., & Makhijani, M. G. (1995). Gender
and the
effectiveness of leaders: A meta-analysis. Psychological
Bulletin, 117,
125–145.
Eagly, A. H., Makhijani, M. G., & Klonsky, B. G. (1992).
Gender and the
evaluation of leaders: A meta-analysis. Psychological Bulletin,
111,
3–22.
Eagly, A. H., & Steffen, V. (1986). Gender and aggressive
behavior: A
meta-analytic review of the social psychological literature.
Psycholog-
ical Bulletin, 100, 309 –330.
Eagly, A. H., & Wood, W. (1999). The origins of sex
differences in
human behavior: Evolved dispositions versus social roles.
American
Psychologist, 54, 408 – 423.
Eaton, W. O., & Enns, L. R. (1986). Sex differences in human
motor
activity level. Psychological Bulletin, 100, 19 –28.
Epstein, C. F. (1988). Deceptive distinctions: Sex, gender, and
the social
order. New Haven, CT: Yale University Press.
Feingold, A. (1988). Cognitive gender differences are

disappearing.
American Psychologist, 43, 95–103.
Feingold, A. (1992). Sex differences in variability in
intellectual abilities:
A new look at an old controversy. Review of Educational
Research, 62,
61– 84.
Feingold, A. (1994). Gender differences in personality: A meta-
analysis.
Psychological Bulletin, 116, 429 – 456.
Feingold, A., & Mazzella, R. (1998). Gender differences in
body image
are increasing. Psychological Science, 9, 190 –195.
Frome, P. M., & Eccles, J. S. (1998). Parents’ influence on
children’s
achievement-related perceptions. Journal of Personality and
Social
Psychology, 74, 435– 452.
Gilligan, C. (1982). In a different voice: Psychological theory
and wom-
en’s development. Cambridge, MA: Harvard University Press.
Glass, G. V., McGaw, B., & Smith, M. L. (1981). Meta-analysis
in social
research. Beverly Hills, CA: Sage.
Gleitman, H. (1981). Psychology. New York: Norton.
Gottman, J. (1994). Why marriages succeed or fail. New York:
Simon &
Schuster.

Gray, J. (1992). Men are from Mars, women are from Venus: A
practical
guide for improving communication and getting what you want
in your
relationships. New York: HarperCollins.
Gray, J. (2005). John Gray, Ph.D. is the best-selling
relationship author of all
time. Retrieved January 10, 2005, from
http://www.marsvenus.com
Hedges, L. V., & Becker, B. J. (1986). Statistical methods in the
meta-
analysis of research on gender differences. In J. S. Hyde & M.
C. Linn
(Eds.), The psychology of gender: Advances through meta-
analysis (pp.
14 –50). Baltimore: Johns Hopkins University Press.
Hedges, L. V., & Friedman, L. (1993). Sex differences in
variability in
intellectual abilities: A reanalysis of Feingold’s results. Review
of
Educational Research, 63, 95–105.
Hedges, L. V., & Nowell, A. (1995, July 7). Sex differences in
mental test
scores, variability, and numbers of high-scoring individuals.
Science,
269, 41– 45.
Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-
analysis.
San Diego, CA: Academic Press.

Hollingworth, L. S. (1918). Comparison of the sexes in mental
traits.
Psychological Bulletin, 15, 427– 432.
Hyde, J. S. (1981). How large are cognitive gender differences?
A
meta-analysis using �2and d. American Psychologist, 36, 892–
901.
Hyde, J. S. (1984). How large are gender differences in
aggression? A
developmental meta-analysis. Developmental Psychology, 20,
722–736.
Hyde, J. S. (1985). Half the human experience: The psychology
of women
(3rd ed.). Lexington, MA: Heath.
Hyde, J. S. (1986). Gender differences in aggression. In J. S.
Hyde &
M. C. Linn (Eds.), The psychology of gender: Advances through
meta-
analysis (pp. 51– 66). Baltimore: Johns Hopkins University
Press.
Hyde, J. S., Fennema, E., & Lamon, S. (1990). Gender
differences in
mathematics performance: A meta-analysis. Psychological
Bulletin,
107, 139 –155.
Hyde, J. S., Fennema, E., Ryan, M., Frost, L. A., & Hopp, C.
(1990).
Gender comparisons of mathematics attitudes and affect: A
meta-
analysis. Psychology of Women Quarterly, 14, 299 –324.

Hyde, J. S., & Frost, L. A. (1993). Meta-analysis in the
psychology of
women. In F. L. Denmark & M. A. Paludi (Eds.), Psychology of
women: A handbook of issues and theories (pp. 67–103).
Westport, CT:
Greenwood Press.
Hyde, J. S., & Linn, M. C. (1988). Gender differences in verbal
ability: A
meta-analysis. Psychological Bulletin, 104, 53– 69.
Hyde, J. S., & Plant, E. A. (1995). Magnitude of psychological
gender
differences: Another side to the story. American Psychologist,
50,
159 –161.
Jaffee, S., & Hyde, J. S. (2000). Gender differences in moral
orientation:
A meta-analysis. Psychological Bulletin, 126, 703–726.
Kimball, M. M. (1989). A new perspective on women’s math
achieve-
ment. Psychological Bulletin, 105, 198 –214.
Kimball, M. M. (1995). Feminist visions of gender similarities
and
differences. Binghamton, NY: Haworth Press.
Kling, K. C., Hyde, J. S., Showers, C. J., & Buswell, B. N.
(1999). Gender
differences in self-esteem: A meta-analysis. Psychological
Bulletin,
125, 470 –500.

Knight, G. P., Guthrie, I. K., Page, M. C., & Fabes, R. A.
(2002).
Emotional arousal and gender differences in aggression: A
meta-anal-
ysis. Aggressive Behavior, 28, 366 –393.
Konrad, A. M., Ritchie, J. E., Lieb, P., & Corrigall, E. (2000).
Sex
differences and similarities in job attribute preferences: A meta-
analy-
sis. Psychological Bulletin, 126, 593– 641.
LaFrance, M., Hecht, M. A., & Paluck, E. L. (2003). The
contingent
smile: A meta-analysis of sex differences in smiling.
Psychological
Bulletin, 129, 305–334.
Leahey, E., & Guo, G. (2000). Gender differences in
mathematical tra-
jectories. Social Forces, 80, 713–732.
Leaper, C., & Smith, T. E. (2004). A meta-analytic review of
gender
variations in children’s language use: Talkativeness, affiliative
speech,
and assertive speech. Developmental Psychology, 40, 993–1027.
Lefrançois, G. R. (1990). The lifespan (3rd ed.). Belmont, CA:
Wadsworth.

Lightdale, J. R., & Prentice, D. A. (1994). Rethinking sex
differences in
aggression: Aggressive behavior in the absence of social roles.
Person-
ality and Social Psychology Bulletin, 20, 34 – 44.
Linn, M. C., & Petersen, A. C. (1985). Emergence and
characterization of
sex differences in spatial ability: A meta-analysis. Child
Development,
56, 1479 –1498.
Lummis, M., & Stevenson, H. W. (1990). Gender differences in
beliefs
and achievement: A cross-cultural study. Developmental
Psychology,
26, 254 –263.
Lynn, R., & Irwing, P. (2004). Sex differences on the
progressive matri-
ces: A meta-analysis. Intelligence, 32, 481– 498.
Maccoby, E. E., & Jacklin, C. N. (1974). The psychology of sex
differ-
ences. Stanford, CA: Stanford University Press.
Major, B., Barr, L., Zubek, J., & Babey, S. H. (1999). Gender
and
self-esteem: A meta-analysis. In W. B. Swann, J. H. Langlois, &
L. A.
Gilbert (Eds.) Sexism and stereotypes in modern society: The
gender
science of Janet Taylor Spence (pp. 223–253). Washington, DC:
Amer-
ican Psychological Association.

McClure, E. B. (2000). A meta-analytic review of sex
differences in facial
expression processing and their development in infants,
children, and
adolescents. Psychological Bulletin, 126, 424 – 453.
Murnen, S. K., & Stockton, M. (1997). Gender and self-reported
sexual
arousal in response to sexual stimuli: A meta-analytic review.
Sex
Roles, 37, 135–154.
Oliver, M. B., & Hyde, J. S. (1993). Gender differences in
sexuality: A
meta-analysis. Psychological Bulletin, 114, 29 –51.
Orenstein, P. (1994). Schoolgirls: Young women, self-esteem,
and the
confidence gap. New York: Anchor Books.
Pinquart, M., & Sörensen (2001). Gender differences in self-
concept and
psychological well-being in old age: A meta-analysis. Journal of
Ger-
ontology: Psychological Sciences, 56B, P195–P213.
Pipher, M. (1994). Reviving Ophelia: Saving the selves of
adolescent
girls. New York: Ballantine Books.
Quinn, D. M., & Spencer, S. J. (2001). The interference of
stereotype
threat with women’s generation of mathematical problem-
solving strat-
egies. Journal of Social Issues, 57, 55–72.

Rosenthal, R. (1991). Meta-analytic procedures for social
research (Rev.
ed.). Newbury Park, CA: Sage.
Rosenthal, R., & Rubin, D. B. (1982). A simple, general
purpose display
of magnitude of experimental effect. Journal of Educational
Psychol-
ogy, 74, 166 –169.
Rudman, L. A., & Glick, P. (1999). Feminized management and
backlash
toward agentic women: The hidden costs to women of a kinder,
gentler
image of middle managers. Journal of Personality and Social
Psychol-
ogy, 77, 1004 –1010.
Shields, S. A. (1975). Functionalism, Darwinism, and the
psychology of
women: A study in social myth. American Psychologist, 30, 739
–754.
Silverman, I. W. (2003). Gender differences in delay of
gratification: A
meta-analysis. Sex Roles, 49, 451– 463.
Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). Stereotype
threat and
women’s math performance. Journal of Experimental Social
Psychol-
ogy, 35, 4 –28.
Steele, C. M. (1997). A threat in the air: How stereotypes shape
intellec-
tual identity and performance. American Psychologist, 52, 613–

629.
Steele, C. M., & Aronson, J. (1995). Stereotype threat and the
intellectual
test performance of African Americans. Journal of Personality
and
Social Psychology, 69, 797– 811.
Stuhlmacher, A. C., & Walters, A. E. (1999). Gender
differences in
negotiation outcome: A meta-analysis. Personnel Psychology,
52,
653– 677.
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.
Tannen, D. (1991). You just don’t understand: Women and men
in
conversation. New York: Ballantine Books.
Thoma, S. J. (1986). Estimating gender differences in the
comprehension
and preference of moral issues. Developmental Review, 6, 165–
180.
Thomas, J. R., & French, K. E. (1985). Gender differences
across age in
motor performance: A meta-analysis. Psychological Bulletin,
98,
260 –282.
Thorndike, E. L. (1914). Educational psychology (Vol. 3). New

York:
Teachers College, Columbia University.
Twenge, J. M., & Nolen-Hoeksema, S. (2002). Age, gender,
race, socio-
economic status, and birth cohort differences on the Children’s
Depres-
sion Inventory: A meta-analysis. Journal of Abnormal
Psychology, 111,
578 –588.
Voyer, D., Voyer, S., & Bryden, M. P. (1995). Magnitude of sex
differ-
ences in spatial abilities: A meta-analysis and consideration of
critical
variables. Psychological Bulletin, 117, 250 –270.
Walker, L. J. (1984). Sex differences in the development of
moral rea-
soning: A critical review. Child Development, 55, 677– 691.
Walsh, M., Hickey, C., & Duffy, J. (1999). Influence of item
content and
stereotype situation on gender differences in mathematical
problem
solving. Sex Roles, 41, 219 –240.
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.
White, J. W., & Kowalski, R. M. (1994). Deconstructing the
myth of the
nonaggressive woman: A feminist analysis. Psychology of

Women
Quarterly, 18, 487–508.
Whitley, B. E. (1997). Gender differences in computer-related
attitudes
and behavior: A meta-analysis. Computers in Human Behavior,
13,
1–22.
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. E., Nelson, A. B., & Jones, C. J. (1999). Gender
differences
in cheating attitudes and classroom cheating behavior: A meta-
analysis.
Sex Roles, 41, 657– 677.
Wood, W., Rhodes, N., & Whelan, M. (1989). Sex differences
in positive
well-being: A consideration of emotional style and marital
status.
Psychological Bulletin, 106, 249 –264.
Woolley, H. T. (1914). The psychology of sex. Psychological
Bulletin, 11,
353–379.

(/)
Men and Women are from Earth
Reviewed by Barnett and Rivers
Do men and women come from two separate "communication
cultures"Â� that make it difficult for them to hear one another?
Are they doomed to moan,
eternally, "You just don't understand?"Â� Are women the
caring, sharing, open sex, while men are hardwired to be strong,
silent, self-absorbed, and
uncomfortable with emotions? This portrait of the sexes has
become conventional wisdom, promoted in best sellers like John
Gray's Men Are From Mars,
Women Are From Venus and Deborah Tannen's You Just Don't
Understand. The idea has also leached into the academic and
therapy arenas.
The textbook Language and Social Identity, by Daniel N. Maltz
and Ruth A. Borker, states flatly, "American men and women
come from different sociolinguistic
subcultures, having learned to do different things with words in
a conversation."

In this scenario, women are the relationship experts, holding
marriages and friendships together by putting others first,
avoiding conflict at the cost of their own
wishes, and not putting burdens on men by demanding intimacy
or understanding. Women speak "in a different voice,"Â� as
feminist psychologist Carol Gilligan
put it, because they are so tuned in to others. Men, in contrast,
lack empathy with others. If a friend or coworker approaches
them to talk about problems, they
change the subject or make a joke. In personal relationships
men don't have a clue, and as parents, they are the inferior sex.
They lack the inherent
communication abilities necessary for parenting that nature
confers on women.
Is this dismal picture accurate? Do the best sellers, some
textbooks, and the media's pop psychologists have it right? Are
men and women so hamstrung by
their communication styles that they are perpetually destined to
misunderstand each other--ships eternally passing in the night?
As poetic and familiar as this idea seems, it is more fantasy
than fact. New research tears this conventional wisdom to
shreds. Women and men are far more
alike than different in how they listen to people, the ways they
react to others who are in trouble, and their ability to be open
and honest in communication. As

University of Wisconsin psychologists Kathryn Dindia and
Mike Allen say, "It is time to stop perpetuating the myth that
there are large sex differences in men's
and women's self disclosure."
Women, Deborah Tannen says, use their unique conversational
style to show involvement, connection, and participation, while
men use speech to indicate
independence and position in a hierarchy. Women seek
connection and want to be liked, while men just want to press
their own agenda. Tannen also states as
fact that men always interrupt women. Is this really how men
and women behave? Sometimes yes, sometimes no. Tannen's
problem is that she often sees
gender as the main driver of communication, ignoring a whole
host of other important factors--such as power, individual
personalities, and the situation you're in
when you are about to speak. Do men always interrupt women?
In fact, the sex differences here are trivial, conclude researchers
Kristen Anderson and
Campbell Leaper of the University of California at Santa Cruz,
based on their meta-analyses of 43 studies. (A meta-analysis
summarizes the findings of many
studies.) The key to understanding interruptions is the situation-
-or, as the researchers put it, "The What, When, Where and
How."Â� Power is often the key.

Psychologist Elizabeth Aries of Amherst College found that
men often interrupt women in conversation. But, when
traditional power relationships are reversed--
such as in many contemporary couples where the woman is the
higher earner, or when male subordinates interact with female
superiors--speech patterns also
undergo a reversal. The person with less power interrupts less
and works harder to keep the conversation going, whether that
person is female or male. Those
with more power, male or female, are likely to take control of
the conversation. Do we really believe that Secretary of State
Condoleezza Rice is often
interrupted by her male aides? Or that a male law clerk would
break into the sentences of Justice Ruth Bader Ginsberg? It's
impossible to see the true
communication dynamic if we are blinded by the notion that
something buried deep in women's psyches chains them to the
speech styles of the powerless.
https://www.wcwonline.org/
https://www.wcwonline.org/search
As for men, are they in fact handcuffed by deep-seated
inabilities to engage in conversations about emotion? According
to essentialist theorists, who believe
differences between men and women are innate rather than
socially constructed, men are uncomfortable with any kind of

communication that has to do with
personal conflicts. They avoid talking about their problems.
They avoid responding too deeply to other people's problems,
instead giving advice, changing the
subject, making a joke, or giving no response. Unlike women,
they don't react to "troubles talk" by empathizing with others
and expressing sympathy. These
ideas are often cited in textbooks and in popular manuals, like
those written by John Gray, of Mars and Venus fame.
After Gray nabs us with his attention-grabbing titles, he tells us
that men are naturally programmed to go into their "caves"Â�
and not communicate with other
people. Women must never try to talk to men when they
withdraw, but must honor their behavior. A woman must not
offer help to a man, because it makes him
feel weak and incompetent. A woman must never criticize a man
or try to change his behavior. She should never show anger. If
she feels angry, she must wait
until she is "more loving and centered"Â� to talk to him. Only
when she is loving and forgiving can she share her feelings. If a
man pulls away from her, "he is just
fulfilling a valid need to take care of himself for a while."Â�
Gray cites the case of "Bill,"Â� who asks his wife, "Mary,"Â�
to make a phone call for him while he is sitting on the couch
watching TV. Mary reacts with a

frustrated and helpless tone of voice. She says, "I can't right
now, I already have too much to do, I have to change the baby's
diaper, I have to clean up this
mess, balance the checkbook, finish the wash, and tonight we
are going out to a movie. I have too much to do. I just can't do
it all!"
Bill goes back to watching TV and disconnects from her
feelings. Bill, says Gray, is angry at Mary for making him feel
like a failure. He retreats to his cave, "just
taking care of himself."Â� If Mary realized this, Gray suggests,
she would smile at his request to do still one more chore and
say sweetly that she's running
behind. As for Bill, all he has to do, Gray suggests, is to say
admiringly to Mary, "I just don't know how you do it!"Â� That
line might just get Bill a damp diaper in
the face. Gray does not suggest that Bill might A) change the
baby's diaper, B) help clean up the house, C) balance the
checkbook, or D) help with the wash.
Who has the power in this marriage--and isn't this really the
issue? Gray's scenario puts women in a tight bind, while
requiring little from men. Gray's
prescription for heterosexual relationships is for the woman to
leave the man alone while she supervises the kids' homework,
cooks dinner, and cleans up.
That's advice that gets couples into trouble, not out of it. A
woman who takes Gray's advice at face value may be at serious

risk for high stress. Unable to
express her anger openly and to ask for what she really needs,
always on edge because she must sense a man's every whim and
need, she is likely to turn her
anger inward. This situation makes her communication
inauthentic. She feels anger, but can't communicate it, and as a
result she feels worse and worse about
herself and her marriage. She is living a lie--and how good can
anyone feel about that? On top of that, her husband has no idea
of what she's feeling. There's
no way he could be the helpful person he may actually want to
be.
In fact, if women expected men to be unable to relate to other
people's problems, they would never bother talking to men
about such things.
Systematic research does not support those ideas. Erina L.
MacGeorge, of Purdue University, and her colleagues at the
University of Pennsylvania find no
support for the idea that women and men constitute different
"communication cultures."Â� Based on three studies that used
questionnaires and interviews to
sample 738 people--417 women and 321 men--they conclude
that: Both men and women view the provision of support as a
central element of close personal
relationships; both value the supportive communication skills of

their friends, lovers, and family members; both make similar
judgments about what counts as
sensitive, helpful support; and both respond quite similarly to
various support efforts.
When someone comes to men with a problem, they don't joke,
tell the person to cheer up, change the topic, or run away as
Gray and Tannen suggest they do.
Both sexes are instead likely to offer sympathy and advice. If
men are having a problem, and someone offers a sympathetic
response, they don't get angry,
minimize, or push the other person away. Like women, they are
comforted when people are concerned about them. There are no
differences in how men and
women handle emotional issues--theirs or others'.
Kristen Neff of the University of Texas at Austin and Susan
Harter of the University of Denver uncovered a similar lack of
difference in men's and women's styles
of conflict resolution. They found that 62 percent of men and 61
percent of women reported that they typically resolved conflicts
in their relationships by
compromising with their partners. This is a far cry from the idea
that women always retreat, and men always insist on getting
their way.
Similar doubts have also been raised about the "fact"Â� that

men can't share their feelings, and that they always retreat to
their caves or take refuge in silence. A
huge meta-analysis of 24,000 subjects in 205 peer-reviewed
studied found that women disclose slightly more that men--but
the effect was trivial. Practically
speaking, there were no differences between the sexes. Because
therapists (and others) contend that the ability to self-disclose is
crucial to success in therapy,
they believe that men's chances of being helped are relatively
poor. But this is not so, according these research findings. Even
more importantly, the idea that
women can't expect men to share, to lend a sympathetic ear, to
compromise in settling issues, or to be good listeners can have
disastrous consequences.
Women who cling to such stereotypes will miss out on close
relationships with their male partners and of course men will be
further pigeonholed as distant,
remote, and unavailable creatures. If that's how they are treated,
maybe that's what they will turn into. And if men assume that
women are too emotional to
discuss problems rationally, they will simply clam up and miss
the help and support they really need. Either way, rigid sex
stereotypes promote self-fulfilling
prophecies.

Unfortunately, the essentialist perspective has so colored the
dialogue about the sexes that there is scant room for any
narrative other than difference. As we've
seen, the difference rhetoric can harm both men and women.
Given how little empirical support exists for essentialist ideas,
it's high time to broaden the
dialogue. We believe that men and women are far more similar
than different, a provocative idea that is backed by considerable
research. We challenge the
conventional wisdom, and we encourage others to do the same.
Rosalind Barnett and Caryl Rivers are the authors of Same
Difference: How Gender Myths are Hurting Our Relationships,
Our Children, and Our Jobs.
Women's Review of Books
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Global Gender Differences Can Be Operationalized and Tested
Article in American Psychologist · October 2015
DOI: 10.1037/a0039590
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