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A summary of a lecture about sex differences including a handout of the 28 Notable Psychological Sex Differences found repeatedly in Meta-Analyses.
Are men from mars and women from Venus? Are we really categorically different - like from different planets - or are we essentially the same regardless of gender. Perhaps no topic that pervades developmental psychology, social psychology, and other branches of psychology is more controversial. Developmental psychologists regularly teach about the major theories of gender role development: evolutionary psychology, social learning theory, and gender schema theory, Knowing what sex differences exist and how big they are is important to situate the theories into context. As an example when introdcing sex differences, I mention that claim that boys are better than girls at math.
I begin rhetorically with the question, "Are men from mars and women from Venus?" and quickly move a description about how findings we have in psychology about group differences are statistical. To make it concrete I show the following graph of real data about the heights of adult men and women.
Men are taller than women. But of course there are some women who are taller than some men. Differences are statistical, not categorical. To describe just how big a statistical difference is, psychologists report an effect size. When I explain effect sizes, I use r-squared because it strikes me as most intuitive and it matches the change in r-squared reported by regressions. It ranges from 0 (no effect) to 1 (complete effect). The number is the amount of variation in one variable accounted for by the other. For example, an effect size of .33 means that 33% of our height can be predicted from knowing our sex. A nice advantage of r-squared is that we immediately see that about 67% of our height is predicted from something we have not yet measured. Maybe nutrition accounts for the largest proportion of variance. Most research into sex differences reports cohen's d, probably because the math underneath it most closely matches a t-test. Cohen's d and r-squared say the same thing in different ways and just like you would get the same statistical results using a yard stick or meter stick, the particular statistic does not matter. A mathematical formula lets you switch between the two. In case you're curious the formula assuming both groups have roughly the same sample size is: r-squared = (cohen's d squared) / (cohen's d squard + 4) (see Cohen, 1988, Statistical Power Analysis for the Behavioral Sciences). In the height example cohen's d is 1.41, cohen's d squared is 1.9881, 1.9881 divided by 5.9881 is 0.33200848 which is r-squared. While showing this graph I emphasize that the very real physical gender difference we see here is about twice as big as the biggest of the psychological sex differences we will soon see.
Whenever we read a single result from a single study, we can not be completely sure the result is true. Afterall, before conducting our analyses we set our statistical bar where we know 5% of the results were just luck. In some areas of research we have lots of studies on the same topic. We can be more confident in results when they are found over and over again by different researchers using somewhat different methods. There is even a statistical technique for summarizing similar studies. Rather than a study of people, we have a study of the studies of people called a meta-analysis. Here I usually remind students of other places we used the prefix meta like in "meta-cognition" (our thoughts about our thoughts).
Janet Shibley Hyde (2005) reviewed all the meta-analyses of sex differences. You might call it a meta-meta-analysis! To summarize sex differences, I provide a handout of all the sex differences found in meta-analyses with an effect size of at least 2.5%. Though I don't usually explain this in depth to students, I chose 2.5% because it was small enough for me to fill an entire handout with sex differences but not so small that our class would become boring with a litany of sex differences. In the following slides and on the handout I put the effect size as an r-square in parentheses after each difference. As we review them, I occasionally reiterate how seeing, for example, a sex difference of 5% means 95% of who we are in this way is accounted for by something other than sex. I also remind students of difference we saw earlier in the semester and others we will discuss in more depth later in the semester.
Adolescent boys are better at mechanical reasoning (12.6%) and mental rotation (9.4%). Adolescent girls are better at spelling (4.8%) and language arts (3.8%), whereas boys are better at science (2.5%) and computers (3.3%).
Adolescent and adult men are more assertive (6.1%) and aggressive (3.6%). They are more aggressive verbally (2.8%) and especially physically (7.1%), whereas women are more likely to use indirect aggression (2.9%).
Adolescent and adult women are more trusting (3.0%), agreeable and tender (17.2%). Women are more likely to smile (3.8%), especially in social situations (5.0%). Though they are also more neurotic and anxious (2.5%). Women are better at speaking (2.7%) and recognizing the emotions of others (3.0%). Men are more likely to interrupt during a conversation (2.7%). At least when others are around, men are more likely to help others in distress (12.0%).
Body image is a greater concern for women in forming their self-esteem (7.8%). Men are more likely to masturbate (18.7%). Casual sex is more appealing to men than women (14.1%).
There really are sex differences. Above we have summarized the 28 notable (effect size of r-square > 2.5%) psychological sex differences found repeatedly. The biggest psychological sex differences are in masturbation (18.7%), agreeableness (17.2%), casual sex (14.1%) and mechanical reasoning (12.6%). To clarify how I made this summary I return the opening example that "everybody knows" that "boys are better at math than girls." It's not missing for lack of meta-analyses. There actually have been 5 different meta-analytic results about math abilities but the highest of their effect sizes was just under 1%. This difference that everybody knows, one that lay persons think of as so pervasive and maybe even categorical, accounts for less than 1% of who we are. Even just looking the notable, replicable, psychological sex differences reveal sex accounts for only about 7% of who we are. That means over 93% of who we are is accounted for by something other than our sex. Differences within sexes are far greater than differences between sexes.
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|K. H. Grobman||k h g @ d e v p s y . o r g||© 2003 - 2008|