1 / 20

What Does Usage Data Tell Us?

What Does Usage Data Tell Us?. Carol Tenopir University of Tennessee ctenopir@utk.edu. Logs and opinion surveys give much useful data, but…. Logs don’t show why Sessions may be difficult to differentiate or compare across vendors Logs show only a fraction of total use

dawsons
Download Presentation

What Does Usage Data Tell Us?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. What Does Usage Data Tell Us? Carol Tenopir University of Tennessee ctenopir@utk.edu

  2. Logs and opinion surveys give much useful data, but… • Logs don’t show why • Sessions may be difficult to differentiate or compare across vendors • Logs show only a fraction of total use • Opinion and general surveys don’t give outcomes or values of specific readings

  3. Critical Incident Added to General Surveys and Logs • Specific (last incident of reading) • Includes all reading--e & print, library & personal • Purpose, motivation, outcomes • Last reading=random sample of readings • Has been show to match logs

  4. Tenopir & King Data From: • 25,000+ scientists, engineers, physicians, and social scientists • 1977 to the present • University and non-university settings • Surveys use critical incident plus demographic and some recollection

  5. Recollection and demographic questions only go so far… …add longitudinal to get a picture of trends …add critical incident and you get a more detailed picture

  6. Scholarly Article Reading Updated June 2004

  7. Average Time Spent and Number of Articles Read Per Year Per Scientist

  8. Sources of Readings 21.4 % 36 % 42.9 % 49 % 35.7 % 15 % 15.6 % University Faculty Astronomers 22.1 % 62.3% Medical Faculty

  9. Print or Electronic 20 % 37 % 63 % 80 % 25 % University faculty Astronomers 75 % Medical Faculty

  10. Means of Learning About Articles Read 20.8% 21% 32.3% 37% 50% 16.9% 62.3% 39% 17.6 EngineeringFaculty MedicalFaculty Astronomers

  11. Source of Readings of Scholarly Articles

  12. How Scientists Learned About Articles Early Evolving Advanced 1990-1995 2000-2001 2001- Browsing 58% 46% 21% Online Search 9% 14% 39% Colleagues 16% 22% 21% Citations 6% 13% 16% Other 11% 5% 3%

  13. Older Readings on Average are Judged to be More Valuable Sample Size: Total = 397, Scientists = 300, Non-Scientists = 97 Source: University of Tennessee (2000), Drexel University (2002), University of Pittsburgh (2003)

  14. #2 #4 #5 #3 #1 Purpose and Ranking of Importance: Pittsburgh

  15. #3 #5 #4 #2 #1 Purpose and Ranking of Importance: Medical Faculty (UT)

  16. Usefulness & Value of Reading • Library provided and older articles more valuable • Articles affect the principal purpose in many ways • Achievers read more from library collections • Readers are more productive than non-readers

  17. Subject experts (overall): • read more in not much more time • use many sources to locate and read information • rely more on library provided articles • prefer convenience • Differ in choice of print or electronic, personal or library by field and workplace

  18. Learning About Users and Usage Opinions, preferences (individual) Critical incident (readings), Experimental Usage logs

  19. Tenopir, Use and Users of Electronic Library Resources, 2003. www.clir.org/pub/reports/pub120/pub120.pdf Tenopir & King, Communication Patterns of Engineers, 2004. IEEE/Wiley and Towards Electronic Journals, 2000. SLA.

  20. New Three-Year Project • Maximizing Library Investments in Digital Collections Through Better Data Gathering Analysis (MaxData) • Funded by U.S. Institute of Museum and Library Services (IMLS) (2005-2007) • With David Nicholas, Ciber, University College, London

More Related