And so, our exciting rollercoaster of findings gets underway with a quick look at some of the demographic factors which seem to affect usage of library resources. Now, I’ve already posted about some early findings, which hadn’t been tested for statistical significance. These new findings have, and I’m only showing the results which are significant: i.e. where we can be confident at the agreed level (which varies from test to test but is in every case a standard statistical method) that the results represent real differences within a wider population, and aren’t just a coincidence within the sample of data that we’ve got.
We’re looking here at final year students who have finished their degrees, were full-time students and whose courses were based at the Huddersfield campus. This helps us to exclude a few variables that might possibly confound our overall findings: for example, we might find that mature students have less library usage, but if lots of our mature students are part-time students (and we haven’t tested for this, so I don’t know – it’s just an example) then we wouldn’t be able to tell whether it’s their maturity or their part-time status that limits their use of the library. Now, one thing we haven’t been able to do is to control for the different variables that we want to test – so we don’t necessarily know whether, say, our Asian students are disproportionately male, and it’s their gender rather than their race that makes them use the library more often (again, just an example – don’t quote me on this). This is a bit of a problem, but it’s not unusual in statistics and with the sample that we’ve got there’s no way round it other than to shrug our shoulders and make sure we acknowledge this when we report the findings. (Hence this paragraph of caveat!)
For each finding, I’m showing the effect size. For the tests that we’ve used (Mann-Whitney U, fact fans), these are generally reckoned as follows: anything up to .3 is a small effect, between .3 and .5 is a medium sized effect, and anything over .5 is a large effect. (Ignore the minus signs by the way – they’re just a function of the test and don’t mean anything.) You’ll notice that most of the demographic variables only show small effect sizes but don’t worry – it gets a lot more exciting when we look at subjects.
I think most of the dimensions of use are fairly self-explanatory, but I should probably clarify what we mean by the three that refer to the ‘number of e-resources accessed’. This metric shows how many of Huddersfield’s 240-odd e-resources, which range from large journal platforms and databases down to individual journal subscriptions, a student has logged into during the year, once, at least five times or at least 25 times.
So, without any further ado, let’s crack on!
Figure 1: Age and usage
Figure 1 shows the relationship between age and usage. We’ve separated our students into mature – those who entered the university aged 21 or older – and non-mature students (it was VERY difficult to come up with a non-pejorative name for that group!). As you can see, mature students have higher library usage than non-mature students on every dimension except library visits and hours spent logged into the library PCs. Remember, these are all full-time students, so it’s not that they’re illicitly logging in from work rather than visiting the physical library. We wondered whether the mature students can afford their own laptops and therefore have less need of the library PCs: it might also have something to do with the way younger students treat the library as more of a social space to hang out with friends. Most departments also have resource centres where students can access computers, and may seem like a less daunting study environment for some students.
Figure 2: Gender and usage
Figure 2 looks at gender and usage. Again, we see differences here on almost every metric – hours logged into the library PCs and number of e-resources accessed 25 or more times are the only exceptions. And on almost every one, women are bigger users than men – the only exception is the number of visits to the library, where men dominate.
Now, we’re looking at something a bit different for the next few figures. Rather than comparing each group to every other, we’ve chosen one group as a control and compared all the rest to them (this is for reasons to do with our statistical methods). In each case, our control is simply the biggest group: this allows us to compare minorities with the majority and hopefully identify behaviour that might otherwise get lost.
Figure 3: Ethnicity and usage
Figure 3 looks at ethnicity and usage; the control group here is ‘white’. (For more information on how we constructed our ethnicity categories, go back to my earlier post.) You’ll notice that there are fewer significant differences here, and almost none to do with e-resources. The only exception is the number of e-resources accessed by Chinese students – we’ll come back to that. Asian students are big users of the libraries, and especially the library PCs, but aren’t using their high levels of use to borrow more items. Facebook, anyone?! Black students are also big library users, and they are borrowing more than their white counterparts. Chinese students, as we’ve said, are borrowing less and accessing fewer e-resources than white students: there may be an issue here to do with breadth of reading which (as we’ll see in a few posts’ time) is important.
Figure 4: Country of domicile and usage
Figure 4 looks at country of domicile (Huddersfield-ese for ‘where do you live when you’re not at university?’), with the control group being students based in the UK. There are more significant differences here on several dimensions of use. Notably, students from the new EU (member states which joined in 2004 or later) are very keen on computers: they spend more time logged into e-resources, download more content and use more resources more often than UK-based students. Students from the old EU (pre-2004 member states) and the very broad ‘rest of the world’ category visit the library less than UK students, but borrow more items and use the PCs more often when they are there. Students hailing originally from China show lower usage on a number of dimensions: alongside Figure 3, this suggests that Chinese students are systematically lower users of library resources.
All this is very interesting, but how useful is it in helping librarians to develop services that meet the needs of their users? In truth, it’s really only a first step. We now know where the differences in usage are, but we don’t know why they exist, and that’s what we need to understand if we are to tailor services to students. Maybe the Chinese students are getting all their information from alternative sources and so it doesn’t matter that their usage is much lower. Perhaps the high use of e-resources by students from the new EU doesn’t indicate thorough, broad reading but rather very inefficient search and discovery strategies. In order to really understand what’s going on behind the numbers, we will need to take a more qualitative approach, running focus groups and case studies to explore students’ behaviours and the reasons for those behaviours. Only then can we attempt to understand what we should do to ensure the library’s doing everything it can to support all its users.