Category Archives: Hypothesis

The Final Blog Post

It has been a short but extremely productive 6 months for the Library Impact Data Project Team. Before we report on what we have done and look to the future, we have to say a huge thank you to our partners. We thought we would be taking a lot on at the start of the project in getting eight universities to partner in a six month project; however, it has all gone extremely smoothly and as always everyone has put in far more effort and work than originally agreed. So thanks go to all the partners, in particular:

Phil Adams, Leo Appleton, Iain Baird, Polly Dawes, Regina Ferguson, Pia Krogh, Marie Letzgus, Dominic Marsh, Habby Matharoo, Kate Newell, Sarah Robbins, Paul Stainthorp

Also to Dave Pattern and Bryony Ramsden at Huddersfield.

So did we do what we said we would do

Is there is a statistically significant correlation across a number of universities between library activity data and student attainment?

There answer is a YES!

There is statistically significant relationship between both book loans and e-resources use and student attainment. And this is true across all of the universities in the study that provided data in these areas. In some cases this was more significant than in others, but our statistical testing shows that you can believe what you see when you look at our graphs and charts!

Where we didn’t find a statistical significance was in entries to the library, although it looks like there is a difference between students with a 1st and 3rd, there is not an overall significance. This is not surprising as many of us have group study facilities, lecture theatres, cafes and student services in the library. Therefore a student is as just likely to be entering the library for the above reasons than for studying purposes.

We want to stress here again that we realise THIS IS NOT A CAUSAL RELATIONSHIP!  Other factors make a difference to student achievement, and there are always exceptions to the rule, but we have been able to link use of library resources to academic achievement.

So what is our output?

Firstly we have provided all the partners in the project with short library director reports and are in the process of sending out longer in-depth reports. Regrettably, due to the nature of the content of these reports, we cannot share this data; however, we are in the process of anonymising partners graphs in order to release charts of averaged results for general consumption

Furthermore we are also planning to release the raw data from each partner for others to examine. Data will be released on an Open Data licence at

Finally, we have been astonished by how much interest there has been in our project. To date we have two articles ready for publication imminently and have another 2 in the pipeline. In addition by the end of October we will have delivered 11 conference papers on the project. All articles and conference presentations are accessibly at:

Next steps

Although this project has had a finite goal in proving or disproving the hypothesis, we would now like to go back to the original project which provided the inspiration. This was to seek to engage low/non users of library resources and to raise student achievement by increasing the use of library resources.
This has certainly been a popular theme in questions at the SCONUL and LIBER conferences, so we feel there is a lot of interest in this in the library community. Some of these ideas have also been discussed at the recent Business Librarians Association Conference

There are a number of ways of doing this, some based on business intelligence and others based on targeting staffing resources. However, we firmly believe that although there is a business intelligence string to what we would like to take forward, the real benefits will be achieved by actively engaging with the students to improve their experience. We think this could be covered in a number of ways.

  • Gender and socio-economic background? This came out in questions from library directors at SCONUL and LIBER. We need to re-visit the data to see whether there are any effects of gender, nationality (UK, other European and international could certainly be investigated) and socio-economic background in use and attainment.
  • We need to look into what types of data are needed by library directors, e.g. for the scenario ‘if budget cuts result in less resources, does attainment fall’? The Balanced Scorecard approach could be used for this?
  • We are keen to see if we add value as a library through better use of resources and we have thought of a number of possible scenarios in which we would like to investigate further:
    • Does a student who comes in with high grades leave with high grades? If so why? What do they use that makes them so successful?
    • What if a student comes in with lower grades but achieves a higher grade on graduation after using library resources? What did they do to show this improvement?
    • Quite often students who look to be heading for a 2nd drop to a 3rd in the final part of their course, why is this so?
    • What about high achievers that don’t use our resources? What are they doing in order to be successful and should we be adopting what they do in our resources/literacy skills sessions?
  • We have not investigated VLE use, and it would be interesting to see if this had an effect
  • We have set up meetings with the University of Wollongong (Australia) and Mary Ellen Davis (executive director of ACRL) to discuss the project further. In addition we have had interest from the Netherlands and Denmark for future work surrounding the improvement of student attainment through increased use of resources

In respect to targeting non/low users we would like to achieve the following:

  • Find out what students on selected ‘non-low use’ courses think to understand why students do not engage
  • To check the amount and type of contact subject teams have had with the specific courses to compare library hours to attainment (poor attainment does not reflect negatively on the library support!)
  • Use data already available to see if there is correlation across all years of the courses. We have some interesting data on course year, some courses have no correlation in year one with final grade, but others do. By delving deeper into this we could target our staffing resources more effectively to help students at the point of demand.
    • To target staffing resources
  • Begin profiling by looking at reading lists
    • To target resource allocation
    • Does use of resources + wider reading lead to better attainment – indeed, is this what high achievers actually do?
  • To flesh out themes from the focus groups to identify areas for improvement
    • To target promotion
    • Tutor awareness
    • Inductions etc.
  • Look for a connection between selected courses and internal survey results/NSS results
  • Create a baseline questionnaire or exercise for new students to establish level of info literacy skills
    • Net Generation students tend to overestimate their own skills and then demonstrate poor critical analysis once they get onto resources.
    • Use to inform use of web 2.0 technologies on different cohorts, e.g. health vs. computing
  • Set up new longitudinal focus groups or re-interview groups from last year to check progress of project
  • Use data collected to make informed decisions on stock relocation and use of space
  • Refine data collected and impact of targeted help
  • Use this information to create a toolkit which will offer best practice to a given profile
    • E.g. scenario based

Ultimately our goal will be to help increase student engagement with the library and its resources, which as we can now prove, leads to better attainment. This work would also have an impact on library resources, by helping to target our precious staff resources in the right place at the right time and to make sure that we are spending limited funds on the resources most needed to help improve student attainment.

How can others benefit?

There has been a lot of interest from other universities throughout the project. Some universities may want to take our research as proof in itself and just look at their own data; we have provided instructions on how to do this at We will also make available the recipes written with the Synthesis project in the documentation area of the blog, we will be adding specific recipes for different library management systems in the coming weeks:

For those libraries that want to do their own statistical analysis, this was a was a complex issue for the project, particularly given the nature of the data we could obtain vs. the nature of the data required to specifically find correlations. As a result, we used the Kruskal Wallis (KW) test, designed to measure whether there are differences between groups of non-normally distributed data. To confirm non-normal distribution, a Kolmogorov-Smirnov test was run. KW unfortunately does not tell us where differences are, the Mann Whitney test was used on specific couplings of degree results, selected based on visual data represented in boxplot graphs. The number of Mann Whitney tests have to be limited as the more tests conducted, the higher the significance value required, so we limited them to three (at a required significance value of 0.0167 (5% divided by 3)). Once Mann Whitney tests had been conducted, effect size of the difference was calculated. All tests other than effect size were run in PASW 18; effect size was calculated manually. It should be noted that we are aware the size of the samples we are dealing with could have indicated relationships where they do not exist, but we feel our visual data demonstrates relationships that are confirmed by the analytics, and thus that we have a stable conclusion in our discarding of the null hypothesis that there is no relationship between library use and degree result.

Full instructions of how the tests were run will first be made available to partner institutions and disseminated publicly through a toolkit in July/August

Lessons we learned during the project

The three major lessons learned were:

Forward planning for the retention of data. Make sure all your internal systems and people are communicating with each other. Do not delete data without first checking that other parts of the University require the data. Often this appears to be based on arbitrary decisions and not on institutional policy. You can only work with what you’re able to get!

Beware e-resources data. We always made it clear that the data we were collecting for e-resource use was questionable, during the project we have found that much of this data is not collected in the same way across an institution, let alone 8! Athens, Shibboleth and EZProxy data may all be handled differently – some may not be collected at all. If others find that there is no significance between e-resources data and attainment, they should dig deeper into their data before accepting the outcome.

Legal issues. For more details on this lesson, see our earlier blog on the legal stuff

Final thoughts

Although this post is labelled the final blog post, we will be back!

We are adding open data in the next few weeks and during August we will be blogging about the themes that have been brought out in the focus groups.

The intention is then to use this blog to talk about specific issues we come across with data etc. as we carry our findings forward. At our recent final project meeting, it was agreed that all 8 partners would continue to do this via the blog.

Finally a huge thank you to Andy McGregor for his support as Programme Manager and to the JISC for funding us.

Hypothesis musings.

Since the project began, I’ve been thinking about all the issues surrounding our hypothesis, and the kind of things we’ll need to consider as we go through our data collection and analysis.

For anyone who doesn’t know, the project hypothesis states that:

“There is a statistically significant correlation across a number of universities between library activity data and student attainment”

The first obvious thing here is that we realise there are other factors in attainment!  We do know that the library is only one piece in the jigsaw that makes a difference to what kind of grades students achieve.  However, we do feel we’ll find a correlation in there somewhere (ideally a positive one!).  Having thought about it beyond a basic level of “let’s find out”, the more I pondered, the more extra considerations leapt to mind!

Do we need to look at module level or overall degree?  There are all kinds of things that can happen that are module specific, so students may not be required to produce work that would link into library resources, but still need to submit something for marking.  Some modules may be based purely on their own reflection or creativity.  Would those be significant enough to need noting in overall results?  Probably not, but some degrees may have more of these types of modules than others, so could be worth remembering. 

My next thought was how much library resource usage counts as supportive for attainment.  Depending on the course, students may only need a small amount of material to achieve high grades.  Students on health sciences/medicine courses at Huddersfield are asked to work a lot at evidence based assignments, which would mean a lot of searching through university subscribed electronic resources, whereas a student on a history course might prefer to find primary sources outside of our subscriptions. 

On top of these, there all kinds of confounding factors that may play with how we interpret our results:

  • What happens if a student transfers courses or universities, and we can’t identify that?
  • What if teaching facilities in some buildings are poor and have an impact on student learning/grades?
  • Maybe a university has facilities other than the library through the library gates and so skews footfall statistics?
  • How much usage of the library facilities is for socialising rather than studying?
  • Certain groups of students may have an impact on data, such as distance learners and placement students, international students, or students with any personal specific needs.  For example some students may be more likely to use one specific kind of resource a lot out of necessity.  Will they be of a large enough number to skew results?
  • Some student groups are paid to attend courses and may have more incentive to participate in information literacy related elements e.g. nurses, who have information literacy classes with lots of access to e-resources as a compulsory part of their studies.

A key thing emerging here is that lots of resource access doesn’t always mean quality use of materials, critical thinking, good writing skills…  And even after all this we need to think about sample sizes – our samples are self-selected, and involve varying sizes of universities with various access routes to resources.  Will these differences between institutions be a factor as well?

All we can do for now is take note of these and remember them when we start getting data back, but for now I set to thinking about how I’d revise the hypothesis if we could do it again, with a what is admittedly a tiny percentage of these issues considered within it:

“There is a statistically significant correlation between library activity and student attainment at the point of final degree result”

So it considers library usage overall, degree result overall, and a lot of other factors to think about while we work on our data!

Our first project team meeting

On Friday the 11 March, all 8 project partners met for the first time. In a packed agenda we discussed the project in detail – we’ll be blogging the minutes soon.

We also approved the project plan and discussed the hypothesis in some detail – look out for our first blog on that soon too! We are now working on getting the focus group questions out to everyone in the next few days.

The meeting went well, and it was good to meet up face to face before we really get started on gathering the data, we started under a pile of biscuits and ended with a civilised drink in the Head of Steam at Huddersfield railway station before the long journey home for some.