Category Archives: Dissemination

A brief history of library analytics

We are just finalizing a chapter for a forthcoming Facet publication, the following section didn’t make the final cut, but we thought we would reproduce it here for anyone interested.

The literature shows an interest in the relationship between library use and undergraduate attainment stretching back to the 1960s and 1970s (Barkey, 1965; Lubans, 1971; Mann, 1974), however, until recently literature reviews looking into this area have found little evidence of more research until the last few years.

Some studies have investigated the relationship between university library usage and undergraduate student outcomes (De Jager, 2002a; De Jager, 2002b; Emmons and Wilkinson, 2011; Han, Wong and Webb, 2011), however, all lack information on electronic resource information use, De Jager points out that further investigation is necessary to discover where electronic resources play a part in achievement. Additionally, recent research has considered the relationship between library value and impact on research and learning (Oakleaf, 2010; Tenopir and Volentine, 2012). These studies have found that the library supports key academic research activities and thus can be considered to make a vital contribution to university value.

Over the past few years’ more detailed research on ‘library analytics’ has been gathered in the UK, US and Australia; Huddersfield (Stone, Pattern and Ramsden, 2012; Stone and Ramsden, 2013; Stone and Collins, 2013; Collins and Stone, 2014), Wollongong (Cox and Jantti, 2012; Jantti and Cox, 2013) and Minnesota (Soria, Fransen, and Nackerud, 2013; Nackerud, Fransen, Peterson and Mastel, 2013). These three projects have all independently suggested a correlation or statistical significance between library usage  – e-resources use, book loans and gate entries – and student attainment. It is important to note, however, that this relationship cannot be considered a causal one.

The advantage of a more data driven approach over surveys (Chrzatowski , 2006, Whitmire, 2002) is that data can be captured for every student in an institution, or across institutions, which removes the issue of low survey return rates and potential bias in survey responses or interpretation. Another benefit of using linked data from student registry systems is that far more information can be interpreted, for example demographic characteristics and discipline in addition to degree classifications and grade point average. Student retention can also be investigated using this data.

With regards to research into demographic data in academic libraries, a number of studies have been undertaken in the United States (Whitmire, 2003; Jones, Johnson-Yale, Millermaier and Perez, 2009; Green, 2012).  Of the more analytics driven studies, Cox and Jantti (2012) reported on gender and age differences. Many of the more recent studies have also looked at discipline, in some cases producing consistent finding, for example, arts and humanities are usually found to be the biggest users of physical library materials (De Jager, 2002a; Maughan, 1999; Whitmore, 2002) and many studies have found engineering students to be the least engaged library users across resources (Kramer and Kramer, 1968, Bridges, 2008 and Cox and Jantti, 2012, Nackerud et al, 2013).

The references included here can be in the bibliography of library analytics maintained by this blog.

Activity data – delivering benefits from the data deluge


Earlier this month Jisc published a paper on activity data, featuring 6 case studies including LIDP.

Executive Summary

‘Activity data’ is the record of human actions in the online or physical world that can be captured by computer. The analysis of such data leading to ‘actionable insights’ is broadly known as ‘analytics’ and is part of the bigger picture of corporate business intelligence. In global settings (such as Facebook), this data can become extremely large over time – hence the nickname of ‘big data’ – and is therefore associated with storage and management approaches such as ‘data warehousing’.

This executive overview offers higher education decision-makers an introduction to the potential of activity data – what it is, how it can contribute to mission-critical objectives – and proposes how institutions may respond to the associated opportunities and challenges. These themes and recommendations are explored in further detail in the supporting advisory paper, which draws on six institutional cases studies as well as evidence and outputs from a range of Jisc-supported projects in activity data and business intelligence.

Read the whole report here

LIDP Toolkit: Phase 2

We are starting to wrap up the loose ends of LIDP 2. You will have seen some bonus blogs from us today, and we have more about reading lists and focus groups to come – plus more surprises!

Here is something we said we would do from the outset – a second version of the toolkit to reflect the work we have done in Phase 2 and to build on the Phase 1 Toolkit:

Stone, Graham and Collins, Ellen (2012) Library Impact Data Project Toolkit: Phase 2. Manual. University of Huddersfield, Huddersfield.

The second phase of the Library Impact Data Project set out to explore a number of relationships between undergraduate library usage, attainment and demographic factors. There were six main work packages:

  1. Demographic factors and library usage: testing to see whether there is a relationship between demographic variables (gender, ethnicity, disability, discipline etc.) and all measures of library usage;
  2. Retention vs non-retention: testing to see whether there is a relationship between patterns of library usage and retention;
  3. Value added: using UCAS entry data and library usage data to establish whether use of library services has improved outcomes for students;
  4. VLE usage and outcome: testing to see whether there is a relationship between VLE usage and outcome (subject to data availability);
  5. MyReading and Lemon Tree: planning tests to see whether participation in these social media library services had a relationship with library usage;
  6. Predicting final grade: using demographic and library usage data to try and build a model for predicting a student’s final grade.

This toolkit explains how we reached our conclusions in work packages 1, 2 and 6 (the conclusions themselves are outlined on the project blog. Our aim is to help other universities replicate our findings. Data were not available for work package 4, but should this data become available it can be tested in the same way as in the first phase of the project, or in the same way as the correlations outlined below. Work package 6 was also a challenge in terms of data, and we made some progress but not enough to present full results.

The toolkit aims to give general guidelines about:

1. Data Requirements
2. Legal Issues
3. Analysis of the Data
4. Focus Groups
5. Suggestions for Further Analysis
6. Release of the Data

Publications from Phase 1 of LIDP

We now have a complete list of publications from Phase I of the project, which took place from January-July 2011.

The College and Research Libraries article is now the definitive article for Phase 1 and would be the best one to cite if you feel that way inclined!

We hope to publish at least two papers on Phase II in 2013.