Men like vs. women like

Women don't share your autistic interests

What sort of things are women interested in compared to men? Maybe we should ask Mel Gibson. A more scientific way is to study this is to study something called vocational interests. Really, this just means what kind of job functions people like to do. Back in the early years of psychology when it was focused on solving practical problems rather than pushing left-wing policy, researchers were very interested in vocational guidance. So, how do you take a bunch of young people and provide them with reasonable career advice for further schooling, education? Well, naturally, you try to figure out how smart they are and what kind of jobs they like doing. Since intelligence tests had solved the first question well, the second question was to be solved by tests that measure vocational interests. The study of these have unfortunately fallen a lot of out fashion, but they are occasionally brought to the limelight. Recall that a few years ago, Google engineer James Damore pointed out that the science was saying something quite different than the management of Google was claiming it was saying. He wrote up a summary of this science: Google's Ideological Echo Chamber. The outcome was that, he didn't change management's views on the matter, but as a consolation prize he got fired. One of the key lines of argument by Damore was:

[Woman have more] Openness directed towards feelings and aesthetics rather than ideas. Women generally also have a stronger interest in people rather than things, relative to men (also interpreted as empathizing vs. systemizing).
○ These two differences in part explain why women relatively prefer jobs in social or artistic areas. More men may like coding because it requires systemizing and even within SWEs, comparatively more women work on front end, which deals with both people and aesthetics.

He cited a 2010 review by Richard Lippa (also featured in Hjernevask):

• Lippa, R. A. (2010). Gender differences in personality and interests: When, where, and why?. Social and personality psychology compass, 4(11), 1098-1110.

How big are gender differences in personality and interests, and how stable are these differences across cultures and over time? To answer these questions, I summarize data from two meta-analyses and three cross-cultural studies on gender differences in personality and interests. Results show that gender differences in Big Five personality traits are ‘small’ to ‘moderate,’ with the largest differences occurring for agreeableness and neuroticism (respective ds = 0.40 and 0.34; women higher than men). In contrast, gender differences on the people–things dimension of interests are ‘very large’ (d = 1.18), with women more people-oriented and less thing-oriented than men. Gender differences in personality tend to be larger in gender-egalitarian societies than in gender-inegalitarian societies, a finding that contradicts social role theory but is consistent with evolutionary, attributional, and social comparison theories. In contrast, gender differences in interests appear to be consistent across cultures and over time, a finding that suggests possible biologic influences.

The highlighted part is the one about vocational interests. Lippa describes it this way:

The dominant taxonomy in vocational interest research is Holland’s (1992), which proposes six main types of interests and vocations: realistic, investigative, artistic, social, enterprising, and conventional (see Figure 1 for a graphic depiction and description of each type). The six RIASEC domains define individual difference dimensions as well as interest types.

Factor analytic and multidimensional scaling studies suggest that two ‘super-factors’ underlie individual differences in interests (Lippa, 1998; Prediger, 1982): (i) the people–things dimension that taps the degree to which individuals are interested in people-oriented activities and occupations versus thing-oriented activities and occupations, and (ii) the ideas–data dimension data taps the degree to which individuals are interested in activities and occupations that require creative thought and intelligence versus activities and occupations that entail more routine tasks that are less cognitively demanding. Overwhelming evidence shows that men and women differ substantially on the people–things dimension of interests but little on the ideas–data dimension (more on this later).

It may still be a little too abstract, so I suggest simply taking an online version of the RIASEC scale. This isn't the best version because the questions are quite outdated, but you get the idea:

Test InstructionsThe test consists of 48 tasks that you will have to rate by how much you would enjoy performing each on a scale of (1) dislike (2) slightly dislike (3) neither like not dislike (4) slightly enjoy (5) enjoy. The test will take most five to ten minutes to complete.

First three questions:

1. Write books or plays

2. Help people with family-related problems

3. Do research on plants or animals

My results:

Unsurprising for a scientist, I score high on the Investigative interest.

Alright, so if we gave a large number of men and women, or boys and girls, these kind of vocational interest questions, they would probably show some differences in very unsurprising ways. Yes, that's what Lippa is summarizing. The most recent meta-analysis of sex differences in this high-level people vs. things is:

• Su, R., Rounds, J., & Armstrong, P. I. (2009). Men and things, women and people: a meta-analysis of sex differences in interests. Psychological bulletin, 135(6), 859.

The magnitude and variability of sex differences in vocational interests were examined in the present meta-analysis for Holland's (1959, 1997) categories (Realistic, Investigative, Artistic, Social, Enterprising, and Conventional), Prediger's (1982) Things-People and Data-Ideas dimensions, and the STEM (science, technology, engineering, and mathematics) interest areas. Technical manuals for 47 interest inventories were used, yielding 503,188 respondents. Results showed that men prefer working with things and women prefer working with people, producing a large effect size (d = 0.93) on the Things-People dimension. Men showed stronger Realistic (d = 0.84) and Investigative (d = 0.26) interests, and women showed stronger Artistic (d = -0.35), Social (d = -0.68), and Conventional (d = -0.33) interests. Sex differences favoring men were also found for more specific measures of engineering (d = 1.11), science (d = 0.36), and mathematics (d = 0.34) interests. Average effect sizes varied across interest inventories, ranging from 0.08 to 0.79. The quality of interest inventories, based on professional reputation, was not differentially related to the magnitude of sex differences. Moderators of the effect sizes included interest inventory item development strategy, scoring method, theoretical framework, and sample variables of age and cohort. Application of some item development strategies can substantially reduce sex differences. The present study suggests that interests may play a critical role in gendered occupational choices and gender disparity in the STEM fields.

So the differences are very large on interests as measured by these scales and in very obvious ways: women don't like engineering, they like helping, health and children. Insofar as we don't think these are perfectly reliable or construct valid measures, the differences must be larger still. (I mean, I guess you could hope for measurement invariance testing showing the gaps are not real, but it's an unwise gamble.) The US bureaucracy has created a list of jobs by their RIASEC categories. This means that one can give people RIASEC tests, and then present people with a plausible set of jobs, something that Linda Gottfredson has written about for some decades. Here's the O*NET results for someone interested mainly in Social Artistic and Investigative jobs (SAI, in that order):

As people also spend their own time on things they are interested in, we also find that men and women know different things. For instance, this 2001 Richard Lynn study on general knowledge shows that the sex gap varies quite a bit by topic:

Overall, men knew about 0.51 d more stuff, but the gap was only 0.08 d in art, and was -0.48 in cookery and -0.32 in medicine. Somehow there was near-neutrality in fashion knowledge, d = -0.05! The fact that knowledge differences vary by interest means that one can game a test to show larger or smaller test gaps by sampling items that measure things men or women know more. Thus, the issue of measuring overall knowledge gap becomes a thorny question of how to sample knowledge at random. Don't get too exited though, I very much doubt tinkering with the tests will change the picture much.

Finally, one might wonder if all this psychology test talk can be verified by some external approach. What if we just looked at what men and women actually talk about in private? This has been done too, with amusing results:

Tables 1 and 2 list the top 20 most female- and male-linked topics and their corresponding effect sizes (see Supplement 1 for full list of 1,281 topics and effect sizes). The most strongly female-linked topics included words describing positive emotions (e.g., “excited”, “happy”, “<3”, “love”,), social relationships (e.g., “friends”, “family”, “sister”), and intensive adverbs (e.g., “sooo”, “sooooo”, “ridiculously”). Strongly male-linked topics included words related to politics (e.g., “government”, “tax”, “political”), sports and competition (e.g., “football”, “season”, “win”, “battle”), and specific interests or activities, such as shooting guns, playing musical instruments, or playing video games. Note that topics are semantically-related clusters of words identified automatically by latent Dirichlet allocation. In Tables 1 and 2, Words are ranked in descending order of prevalence (weight) in each topic.

Male:

Female:

We see that the male topics include politics, war/sports/gaming/weapons/death/killing, swearing, music (especially metal/rock), work/science, metals. Women's topics are much more mundane. There's a lot of expression of emotions, especially positive. There's a lot of family talk shown by all the terms of human relationships (sister, daughter, nephew, brother, boyfriend etc.). Of interests, the main thing we see is food (cooking), and some shopping. In fact, it is surprisingly devoid of any abstract interests, I am surprised there are not more words related to clothing and child-rearing.

Overall we see that results are consistent across studies that men and women are interested in and talk about quite different things. It's amazing to live in a society that often pretends these differences are not real. But then again, when you ask people about their stereotypes about who works in which jobs, you get amazing accuracy like this:

It's not often you see a real correlation of .94 in social science, but here we are. One must wonder: how do normal people think these differences appear? Do the police show up in school and forces the girls to become nurses (10% male) and boys to become electricians (95% male)? Maybe the police tells boys to become murderers too (90% male). A similar level of accuracy can be seen for movie preferences:

Surely, movie studios must know about these, yet movie studios are among the most outspoken people trying to change gender norms. As they have been trying for several decades with counter-stereotype signaling in movies, and the gendered job market remains more or less the same, clearly their efforts are not working. The question we have to ask ourselves is how long our society must be stuck in these gender delusions? I wager quite a long time to come.