Wednesday, October 17, 2012

Why Aren't There More Female Programmers?

I usually don't get too excited about days dedicated to some historic figure, but yesterday National Geographic published an article about the October 16 being the day dedicated to the first person to write a computer program.  Why is this so interesting?  Because the first person to write a computer program was none other than poet Lord Byron's daughter, Ada Lovelace in 1843.  Very interesting considering computer most computer programmers in the US are men.

Ada Lovelace Day is marketed as a day to recognize the contributions of women in science, technology, engineering, and mathematics (STEM) fields. While you can read all about the many accomplishments of women in STEM fields elsewhere, what I wonder is why did something that was developed by a woman become a field entirely dominated by men?  And why aren't there more women programmers?

It could be that women just aren't good at learning how to program, and Ada Lovelace was just kind of strange.  While she may have been strange, it's unlikely that other women can't be good programmers.  Time and time again, studies show that women don't lack the brain capacity needed for programming.  First off, women are better at learning new languages than men, down to the biological level.  Second, women now score higher on IQ tests than men.  Third, women and men show no difference in the old analytical reasoning section of the GRE nor in the new analytical writing section.  So we really shouldn't be seeing differences in ability to program by gender.

Maybe women don't want to be programmers.  That's what Justin James argues when he writes that "women are not attracted to programming at all."  But if that were really so, we wouldn't see any women in programming, and it doesn't explain why 30 years ago there were nearly equal numbers of men and women in computer science courses at universities.

If it's not ability or preference, then it must be something outside a woman's control.  I don't have all the answers or explanations.  But, my earlier post on stereotype threat and organizational culture discusses how the culture of technology firms and university departments systematically excludes women, leading many to abandon the career path they otherwise would have wanted.

So on this day that at least a few people are talking about Ada Lovelace, it's a good time to think about what we can do to diversify the programming workforce and create an environment where all people can and do pursue their desired career.

1 comment:

  1. You are making a classic error here when citing

    Sex differences in quantitative and analytical GRE performance: An exploratory study

    in support of your conclusions.

    That error is assuming that measures of central tendency (mean, median, etc) are the default when attempting to explain observed differences in groups.

    With measurements such as GRE analytical score, the distribution of scores is very important to tackling questions such as what you have set out for yourself here. The means of the two groups can be exactly the same yet one group be the majority in a field based upon greater spread in the distribution. That is, if you assume a threshold score above which high achievement in a field is possible, then a more dispersed population will have more above that threshold. In other words, let's say that being a great programmer requires an analytical GRE score above 700. If men are more dispersed, more very high and very low scores on this test, then those above 700 will be a majority men. Think this threw by drawing a few sample bell curves and make the male curve less peaked and with thicker tails, which describes a situation of greater variance.

    It turns out that on most things men are more varied than women. This includes many tests of mental capacity as well as things like height.

    I downloaded and skimmed the results of the paper I mentioned above that you referenced. The authors do not give measures for variance of groups, instead they focus on gender effect based upon median scores.

    Achieving statistical significance in a difference in median or another measure does not address the issue I stated previously related to variance.

    Skimming the data tables though, I can tentatively predict that their data showed greater variance among men. How can I tell? In the analytical section, the men's mean is higher than that for women, however the women's median is higher than that for men. Also, the men's mean is around 76 points higher than the median while women show around a 38 point difference between the two.

    This indicates that there are more men at the highest score levels and this pulls their mean upward but would not influence the median. The mean's median is in fact lower.

    How did I figure this out and you didn't? Well, either it is some built in bias or I can thank those thick tails.