Three of us from City went to this event, all from the Management Information Group – we hoped it would help us to understand some of the things that we could do with the statistics we’re collecting, and also help us to identify what the right sort of things to collect are. I’d say it was successful in both of these aims. I confess I’m still a little hazy on some of the actual maths involved but I understand what it’s for much more clearly now!
Claire Creaser, the speaker, is based at LISU at Loughborough – it’s the Library and Information Statistics Unit, and it has been helping LIS professionals and institutions to collect, publish, understand and use statistics since the 1980s. They run a consultancy service which includes training like this event as well as statistical research within libraries – there’s more information about them here.
We started the day with a refresher on some statistical concepts, and I was relieved that I had heard and understood most of them before – I think this might be a side effect of both half-remembering GCSE maths and also being a business librarian; Cass students do a lot of statistics. We started with the basic idea that stats are numbers with a context to them, such as a relationship (“spending on eresources is 3 times higher than on print books”, for instance), or how a quantity changes over time.
Claire gave us some very sound advice on where to start with statistics that will, and indeed has already, inform our work in the Management Information Group:
- Work out what you want to find out from the statistics first. Why do you need these statistics?
- Make sure that what you collect really is relevant to what you’re trying to find out or achieve. Don’t collect things because they’re easy to collect, collect them because they’re useful, relevant and as current as possible.
- Consider where your statistics come from – your LMS, logging enquiries, etc. – and work out what type they are so you’ll know how to analyse them.
The three types are:
- Categorical – labels that describe things, with no inherent order e.g. types of enquiries, gender, loan periods.
- Ordinal- has an inherent order, e.g. “I found this service to be…. poor, adequate, good, excellent”.
- Ratio and Interval data – apparently this last one is actually two categories but for LIS purposes they tend to be the same. You can divide this data in meaningful ways e.g. expenditure information, the number of acquisitions. Includes time series data – temperature would be another example.
We then went through all sorts of things you can do with these data types, and Claire warned us that the quality of the data is really important – as with so many things in life, you have to put good data into your analysis, or you’ll get rubbish out!
The session I found most useful for the work we currently have in hand in the MI Group was the next one, on using statistics to provide evidence to management. We heard how using statistics as part of Evidence-Based Librarianship can help us be more economic, more efficent, and more effective in achieving our goals. Gathering and using clear, meaningful and accurate stats will help us to improve our services to our users, and there are various frameworks and standards that we could use to help. We also talked about SLAs/KPIs contribute to this.
During the rest of the day we also heard and talked about how to use statistics in business cases, how to visually present the data clearly and without misleading people once you’ve got it, more about how surveys work, benchmarking using stats, and we looked in detail at the SCONUL reporting tool. I was particularly interested in sampling when you’re doing surveys: we learned that often, sampling in a carefully-designed way can actually help you get a more accurate picture of what people really think than the more commonly-used “send everyone the survey and hope they fill it in” approach.
I enjoyed this event and found it useful for the work we’re doing at the moment, and we will be looking into some of the frameworks and standards mentioned when we’re looking at and advising on things like KPIs and benchmarking as well as statistical collection methods.