Personality and Gender

The so-called “Big Five” personality traits are often misunderstood. They all have catchy names, expressed by the acronym CANOE (or OCEAN), but in fact all they are is a summary of answers to certain kinds of personality questions:

• Conscientiousness: I pay attention to details; I follow a schedule; …
• Agreeableness: I am interested in people; I feel the emotions of others; …
• Neuroticism: I get upset easily; I worry about things; …
• Openness to experience: I am full of ideas; I am interested in abstractions; …
• Extraversion: I am the life of the party; I start conversations; … (this last one is also measured by the MBTI test)

These tests work in multiple cultures. In this article, I am using data from the Dutch version of the test, the “Vijf PersoonlijkheidsFactoren Test” developed by Elshout and Akkerman. Specifically, I am using data from 8,954 psychology freshmen at the University of Amsterdam during 1982–2007 (Smits, I.A.M., Dolan, C.V., Vorst, H.C., Wicherts, J.M. and Timmerman, M.E., 2013. Data from ‘Cohort Differences in Big Five Personality Factors Over a Period of 25 Years’. Journal of Open Psychology Data, 1(1), p.e2). In my analysis, I have compensated for missing data and for the fact that the sample was 69% female.

The Dutch test consists of 70 items, in 5 groups of 14. The following tree diagram (click to zoom) is the result of UPGMA hierarchical clustering on pairwise correlations between all 70 items. It can be seen that they naturally cluster into 5 groups corresponding almost perfectly to the “Big Five” personality traits – the exception being item A11, which fits extraversion slightly better (r = 0.420) than its own cluster of agreeableness (r = 0.406). This lends support to the idea that the test is measuring five independent things, and that these five things are real.

On tests like this, women consistently score, on average, a little higher than men in conscientiousness, agreeableness, neuroticism, and extraversion (and in this dataset, on average, a little lower in openness to experience). Mean values for conscientiousness in this dataset (on a scale of 14 to 98) were 60.3 for women and 56.1 for men (a difference of 4.2). For agreeableness, they were 70.6 for women and 67.6 for men (a difference of 3.0). There are also small age effects for conscientiousness, agreeableness, and openness to experience (over the 18–25 age range), which I have ignored.

The chart below (click to zoom) shows distributions of conscientiousness and agreeableness among men and women, and the relative frequency of different score ranges (compensating for the fact that the sample was 69% female). Thus, based on this data, a random sample of people with both scores in the range 81 to 90 would be 74% female. With both scores in the range 41 to 50, the sample would be 72% male. This reflects a simple mathematical truth – small differences in group means can produce substantial differences at the tails of the distribution.

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Gender and Solar Car Teams

Solar Team Twente, led by Irene van den Hof, arrives at the World Solar Challenge 2015 finish line in 2^nd place (photo: Anthony Dekker)

As a keen follower of international solar car racing, it’s interesting to explore the so-called “Gender-Equality Paradox” in Science, Technology, Engineering, and Mathematics (see Stoet and Geary, 2018) as it relates to solar car teams – although I realise that this is a controversial subject.

In countries with high gender equality, such as Sweden, female participation in the STEM professions is paradoxically low. In part, this seems to be due to the fact that young women with STEM skills and interests often have other skills and interests as well, and these drive their educational and career choices (and within STEM fields, women appear to preferentially choose medicine over engineering). One can hardly force women to make other choices, though!

Solar car racing is in some ways engineering at its most intense – a difficult challenge requiring a substantial sacrifice of free time (much like an engineering start-up company). In the chart below, I plot the UN Gender Inequality Index for various countries against the average percentage of women in the engineering segment of solar car teams from those countries (I include team leaders in the count, but not dedicated media or public relations personnel). The colour of the dots for each nation indicate whether team leaders are mostly women (pink) or mostly men (blue).

The results are not statistically very significant (p = 0.05 and 0.09 for the two coefficients at the individual team level), but there is an interesting inverted parabolic fit here. For countries with high gender equality (Sweden, Belgium, Germany, and the Netherlands), only about 6.7% of the engineering segment of solar car teams is female. This is compared to 11.3% for other countries. On the other hand, Germany and the Netherlands do have mostly female team leaders.

In part, these results may reflect the fact that when a team attempts to make an optimum assignment of people to roles, the best people to carry out public relations and leadership roles are often the female team members (some people have suggested psychometric reasons for this). In fact, women are exactly twice as likely to be team leaders as you would expect based on the composition of the engineering segment of teams.

Obviously this small-scale study doesn’t settle anything, but it does raise some interesting questions for further investigation. And, of course, it would be fatal to believe that the man or woman building the car’s suspension was doing a more worthwhile job than the man or woman raising the sponsorship money that the team needs to survive. Success requires being good at everything, and that requires a diverse team.

Edit: This analysis may have missed a few women who were not included on team web pages.

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Bridges, gender, and Benjamin Lee Whorf

I’ve long been fascinated by the Sapir-Whorf hypothesis – the idea that the structure of language determines (or at least influences) the way that you think. I first read Whorf’s book several decades ago.

A friend recently pointed me at this TED talk by Lera Boroditsky. After years of being sneered at, it seems that Whorf is back in fashion.

And there’s certainly something to Whorf’s ideas. For example, there is solid evidence that the way that you name colours influences the way that you see them (slightly, anyway). There is some exaggeration in the TED talk, though. Australian aboriginal speakers of Kuuk Thaayorre have a unique way of describing directions (in absolute, rather than relative terms, e.g. “there is an ant on your northern leg”). They also navigate well across their tribal lands. But is there a causal relationship? Do aboriginal people with this linguistic feature navigate better than those without it? No, they don’t.

Even stranger is the idea that Spanish speakers, for whom a bridge is masculine (el puente), are less likely to describe a bridge as “beautiful,” and more likely to describe it as “strong,” than German speakers, for whom a bridge is feminine (die Brücke). There really are way too many confounding factors there – people who speak different languages differ in other ways too. So I thought I’d try a quick-and-dirty experiment of my own.

For a set of 17 languages, I counted Google hits for the phrases “beautiful bridge” (e.g. French: beau pont, German: schöne Brücke) and “strong bridge” (e.g. Greek: ισχυρή γέφυρα, Dutch: sterke brug), divided one set of numbers by the other, and took the logarithms of those ratios. The chart below summarises the results. Languages in pink have a feminine bridge, languages in blue have a masculine bridge, and languages in grey have a bridge which is neither (for example, English has no gender, while Dutch and Swedish have merged masculine and feminine into a “common” gender).

The mean values there are 0.95, 1.14, and 1.60, where positive numbers mean more hits with “beautiful bridge” (i.e. the trend runs the opposite way from the prediction), but none of the differences are statistically significant (p > 0.4). Gender does not seem to influence perceptions of bridges.

Interestingly, if we exclude the international languages English and Spanish, there is actually a statistically significant (but weak) correlation with GDP of the relevant nation (p = 0.029, r = 0.58). On the whole, poorer countries are more likely to describe a bridge as “strong,” and wealthier countries as “beautiful.” That makes sense, if you think about it (although Iceland is an exception to this pattern).

How about you? Is the bridge beautiful, or strong?

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