Psychologist John Holland devised his theory of vocational choice in 1959. It posited six clusters of orientations. He did this with no regard to sex differences, but — as Charles Murray points out in Human Diversity: The Biology of Gender, Race, and Class — there are huge differences in where men and women land on some of those orientations, on average:
The authors assembled a database from 81 samples that amounted to 243,670 men and 259,518 women. On average, women’s vocational interests tilted toward occupations involving work with or understanding of other people; men’s vocational interests tilted toward working with things.
The biggest tilts involved the Realistic orientation — a male preference — with an effect size of –0.84, and the Social orientation — a female preference — with an effect size of +0.68.
On the Data-Ideas dimension, there was virtually no sex difference.
On the People-Things dimension, the effect size was +0.93, meaning that women were on the People end and men were on the Things end of the dimension — a large effect size by any standard.
I’m typically a fan of clever new ideas for spectra or grids, political of otherwise, for all that every one of them imposes a framework and excludes conceptual possibilities. I quite like this one. The people v things axis is familiar, but the pairing with data v ideas is new to me. It fits.
It allows a wide range of possibilities even to male and female archetypes, and even fits stereotypes.
By which I mean that it is actually unsurprising that there might be notable difference between men and women on people v things, but not on ideas v data, especially as shown here.
The artistic woman is an archetype so widely recognized as to hardly need exposition, even if it is sometimes realized as a flaky personality. The data-driven woman has also always existed, albeit recognized more in the 20th century when people started to think more in numerical terms. It becomes immediately obvious when framed as here, as Administration and sales or as Business Operations.
Think of the sheer numbers of women employed as “computers” on one scale or another. And not just in aerospace or other engineering enterprises- my mother’s great pride was that when an office girl at a Scottish bus company she could manage columns of figures faster than anyone else. She stayed pretty good with numbers.