On June 1, 2015, the Olympic decathlon champion formerly known as Bruce Jenner revealed a new gender identity and name, Caitlyn Jenner. She was just the most recent high-profile example: others have included actress Laverne Cox; Chelsea Manning, at the center of the Wikileaks case; and Chaz Bono, son of entertainer Cher and the late Sonny Bono.

When covering issues related to the transgender community, the media often focus on gender non-conformity and gender fluidity. Given this coverage, attention has been drawn to questions about differences between gender and sex; whether and how gender is socially constructed; and what, if any, gender stereotypes represent real and observable differences between men and women.

Previous research on gender has put forth two major hypotheses: First, the gender-differences hypothesis assumes that males and females are strikingly different across multiple major domains (for example, interests, personality characteristics, abilities and behaviors). Conversely, the gender-similarities hypothesis assumes that they’re highly similar across many domains.

Researchers at the University of North Carolina, Iowa State University and Western Carolina University sought to examine these two hypotheses in a 2015 metastudy, “Evaluating Gender Similarities and Differences Using Metasynthesis.” The researchers — Ethan Zell, Zlatan Krizan and Sabrina R. Teeter — identified and systematically analyzed 106 metastudies and 21,174 different findings, for a total sample size of more than 12.2 million individuals. The findings were aggregated to determine absolute differences between genders on multiple psychological domains rather than domain-specific ones:

Although domain-specific findings may help deflate bogus stereotypes in a given domain, other stereotypes as well as the general impression that males and females are fundamentally different may remain. For example, when confronted with findings demonstrating that females perform just as well as males on math tests, people may revise or even abandon stereotypes about gender differences in math. However, they will most likely retain gender stereotypes about other domains, as well as the more basic assertion that the genders differ in profound ways. Thus, to address the more basic question of how males and females differ across domains, researchers need to go beyond meta-analyses in specific domains.

The researchers focused primarily on the size of the differences (rather than whether or not they were statistically significant) and used a measure of difference called “Cohen’s d,” which includes criteria for interpreting whether a d (difference) is small, moderate or large.

The findings include:

• Overall, differences between the genders were found to be “relatively small” across the 386 domains considered. For more than 75% of them, there was an almost 80% overlap between the distributions of men and women on a given domain.
• Of the domains considered, observed differences in 85.5% were considered small or very small. “Magnitude of differences fluctuated somewhat as a function of the psychological domain (e.g., cognitive variables, social and personality variables, well-being), but remained largely constant across age, culture and generations.”
• Areas in which the greatest differences between genders were observed included: masculinity, mental rotation ability, importance of physical attractiveness in mate selection and aggression (for which men scored higher); and reactivity to painful stimuli, peer attachment, and interest in people as opposed to things (for which women scored higher).
• Meta-analyses that incorporated only published studies reported larger absolute differences between genders than those that included published and unpublished works. This could be a reflection of “publication bias,” a phenomenon in which research that shows statistically significant results is more likely to be published than that which does not. Thus a paper showing no statistically significant difference between men and women in a given area may be substantially less likely to be published than one that shows a difference, even if they use the same methods.

Overall, researchers found greater support for the gender-similarities hypothesis than the gender-differences hypothesis. However, while overall observed difference between genders was small, they caution that it would be incorrect to conclude that the difference was nonexistent or trivial. They also suggest that, although the distributions of men and women overlapped on the majority of variables, they are unable to assume that the distributions had identical shapes. For example, data suggest that men may demonstrate more variability than women on some intellectual and cognitive domains, meaning that on these domains, more men score further away from the average score for men than women do from the average score for women.

Further reading: A wide range of research has also been conducted on cultural issues related to gender roles. For examples, see a 2012 study, “Men Rule: The Continued Under-Representation of Women in U.S. Politics,” which showed that women were substantially less likely to believe that they had traits qualifying them to hold elected office. A 2013 study, “Gender and 21st-Century Corporate Crime: Female Involvement and the Gender Gap in Enron-Era Corporate Frauds,” found substantial differences between the type of corporate crime women perpetrate and the rates of involvement. A 2009 study, “The Paradox of Declining Female Happiness,” found decreases in women’s happiness over time compared to men’s.

 

Keywords: gender, sex, psychology, psychological gender differences, gender differences hypothesis, gender similarities hypothesis, nature, nurture, metastudy, metasynthesis, LGBT, gay issues