Monthly Archives: March 2012
It being International Women’s Day today got me thinking about sex and computers. No, not like that, get your mind out of the gutter, I mean in terms of differences between males and females in our attitudes towards and interactions with technology. Such differences (if they exist) might be pertinent in a field like psychology, where the majority of undergraduates (often with ratios approaching 10:1) are female, but (as in most other fields) the majority of professors are male. By contrast computer science undergraduate courses are overwhelmingly male-dominated.
Obviously there are a whole host of social/economic/gender-political reasons why this might be the case, and one would hope that the balance these days might be shifting ever closer towards a more equal representation of the two sexes at all levels and fields in science. However, given that the majority of undergraduate psychologists are girls, and successful post-graduate research is to an extent dependent on computer skills, systematic differences in the way the two halves of the population treat and interact with computers might be worth paying attention to.
So, do systematic differences exist? The short answer, is… I’m not sure. Anecdotally, I’ve known plenty of people of both sexes who are programming ninjas, and equally, plenty of both sexes who are utterly hopeless with technology. In writing this piece I’ve tried to take a (quick) glance at some relevant research, but honestly, it seems a bit of a mess. There are quite a few studies out there, but a lot of them are old (I mean, old in terms of the computer industry – like pre-mid-90s) and things have clearly changed since then, particularly for the generation of ‘digital natives‘ that make up today’s undergraduate cohorts. One older meta-analytic study (from 1998) reported that gender differences in beliefs about computers and behaviour related to them were negligible, while finding that males showed more self-efficacy and more positive affect related to technology. A more recent (2007) study in a population of Greek school children reported similar results regarding self-efficacy. Another recent (2010) study (PDF) on internet use in Taiwanese students reported that boys and girls differed in the manner in which they used the internet – boys were more exploratory users of the web, while girls were more communicative users. This finding was also shown in a survey of male and female US college students from 2009. This study also revealed some other points of discrimination between the sexes in their internet use, with males showing a heavier usage pattern overall. However, female students spend a higher proportion of their time online actually doing academic work; males spent more time using the internet for leisure-related activities (checking sports scores, downloading music, visiting *ahem* ‘adult’ sites etc.).
The most recent, and perhaps most relevant study I found is from 2011 (PDF), and is a survey of Accountancy students who, like psychology, show a heavy female bias in their numbers. This study found a difference in attitudes early in the curriculum, but the gender difference disappeared on a more advanced course. This is good news, as it might suggest that some of the differences found in previous research have reduced or disappeared, perhaps as a result of the greater penetration of computers into everyday life.
The computer industry and the way we use its products changes in a heartbeat, and I can appreciate the problems involved in doing research which might seem out of date almost as soon as it’s published (a search for “gender differences +iPad” on Google scholar turns up nothing), nonetheless there seems to be a real paucity of research here. Most of the studies I found involve surveys on attitudes to computers, rather than skills – presumably because skills are harder to assess. Whatever differences there are between the sexes when it comes to technology (if there are any at all) we need to make sure that we’re giving the next generation of students of both sexes the training they need to be effective researchers, clinicians and members of the workforce.
Neuropolarbear just posted an interesting piece on his/her Giraffes, Elephants and Baboons blog, about the importance of learning some coding for graduate students in psychology/neuroscience. Needless to say, I couldn’t agree more and in fact the piece broadly echoes some of the points in my own previous post on the topic, as well as making some interesting and practical suggestions for teaching the right skills to future scientists – good stuff.
Prediction is very difficult, especially about the future.
– Neils Bohr
The way I use computing devices is currently something of a mess. I regularly work in two different locations and have a desktop machine at each place, plus a high-powered desktop and a lower-powered media PC at home, which all run Windows. I have a MacBook Pro which runs OS X (and, occasionally, Windows through Parallels), plus an iPhone, and I sometimes use my Wife’s iPad (both iOS, of course) and will probably get one myself at some point (or maybe a Kindle, not sure). Plus, there are a couple of desktop machines which I use fairly regularly in different labs for running experiments (Windows). All told then, there are roughly eight or nine different computing devices which I regularly use, with three or four different operating systems. Managing files and data so that what I need is accessible on any particular device at any point in time is a massive hassle. What I’ve been doing for the last two years is an ad-hoc mixture of cloud-based solutions (GMail, Google Docs, Evernote, Mendeley) and carrying around a 500Gb USB hard-drive which contains all my documents and experimental data. Wherever I am, I plug in my hard drive and have everything I need, and I don’t store anything locally on any of the machines.
This solution kind-of works, but is unsatisfactory in a number of ways. Firstly, it’s insecure – I’m reasonably careful about doing regular backups, but I live in constant terror of my USB hard-drive being lost, or just breaking. Secondly, I still have to deal with different operating systems and environments – I tend to take my MacBook everywhere with me as there are some Unix applications I use for data analysis that don’t work well on my (desktop) Windows machines. This pretty much defeats the purpose of having all my data on the (much more portable) USB hard drive. Thirdly, getting data on and off the iOS devices is a mega-hassle because of Apple’s teeth-grindingly-awful sync-everything-through-iTunes system.