Successfully reported this slideshow.
Your SlideShare is downloading. ×

Effectively Applying Usage Statistics in E-Resource Collection Development

Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad

Check these out next

1 of 19 Ad
Advertisement

More Related Content

Slideshows for you (20)

Advertisement

Similar to Effectively Applying Usage Statistics in E-Resource Collection Development (20)

Recently uploaded (20)

Advertisement

Effectively Applying Usage Statistics in E-Resource Collection Development

  1. 1. Effectively Applying Usage Statistics in E-Resource Collection Development Using Evidence and Outreach in Decision- Making ACRL-MD – New Identities: Adapting the Academic Library November 14, 2014 Randy Lowe – Collection Development, Acquisition & Serials Librarian, Frostburg State University
  2. 2. Overview  Why E-Resources Assessment?  Usage Statistics – Types, Reports, Collection  Assessment: Evidence & Outreach ◦ Applying usage statistics to collection management decision-making ◦ Engaging librarians, faculty and administrators in the process
  3. 3. Why E-Resource Assessment?  Libraries have historically measured use of services (circulation statistics, re-shelving counts, gate counts, etc.)  The technology upon which e-resources reside inherently allows for extensive collection of usage data – and assessment of that use  Assessment of use data supports evidence-based collection management  Libraries operate in a challenging fiscal environment – demonstrating e-resource value and fiscal responsibility is a must
  4. 4. Effective E-Resources Assessment  Two essential elements in conducting effective e-resource assessments: ◦ Efficient and Accurate Data Collection ◦ Clear and Succinct Analysis  E-Resource assessment is more than just collecting usage statistics – it is applying them in the making of sound management decisions regarding library resources  Usage statistics measure volume, not value of resources
  5. 5. What Can You Do with E-Resources Usage Statistics?  Track usage / Assess overall collection use  Track expenditures / Figure cost-per-use  Track turnaways  Assess title, subject, publisher and other usage elements  Identify user behavior trends  Assist in making collection development decisions, including acquisition model selection  Effectively advocate for resources – especially if assessment is tied to institutional goals/strategic plan, curricular initiatives, student learning goals
  6. 6. Types of Usage Statistics Reports and When to Use Them  Vendor-Defined ◦ Analyzing usage data from a single vendor ◦ Obtaining cost information ◦ Comprehensive data files make it easy to analyze combinations of various data elements [Example] ◦ When COUNTER reports do not provide adequate detail  COUNTER-Compliant ◦ Analyzing usage data across multiple vendors ◦ Ensuring data integrity though adherence to recognized standards
  7. 7. Collecting Usage Data  Define Objectives ◦ What you need to know or are trying to find out should drive your data collection decisions ◦ Collecting Usage Statistics can be a major time commitment  Use your assessment objectives to help you to not only determine what data to collect, but when you have collected enough data to analyze  Properly balancing time and resources dedicated to both data collection and analysis is vital
  8. 8. Collecting Usage Data  Various vendors present data differently – this can present a challenge not only across vendors, but even with combining data elements from a single vendor  Manipulation / Formatting of raw data will likely be necessary  Example – COUNTER BR1 Report + Acquisition Type Data + Cost Data Compiled Manually = Data for Assessment  Schedule time(s) to collect data  Vendors’ archival policies for maintaining usage statistics vary
  9. 9. Assessing Usage Data You have usage data – What do you do with it?  It is easy to get overwhelmed in usage data – analysis should be guided by your assessment objectives ◦ What do you want/need to assess? ◦ What questions are you trying to answer? ◦ Who is your audience?  Have a purpose for using your data
  10. 10. Assessing Usage Data  Assessment is most powerful when it is tied to an action or potential action (including requests)  There is no single method for assessing usage statistics in every case – the “right data” to analyze and include in your report is that which will support your assessment objectives
  11. 11. Usage Data Analysis  Data analysis should be thorough, but presented succinctly  Conclusions, trends, etc. should be clear and verifiable  Beware of pre-conceived notions, perceptions or opinions – hypotheses can be both proven and refuted  State known limitations of the data you have collected and how they may affect your analysis
  12. 12. Using/Applying Evidence: Writing Your Report  Know your audience  Include a brief purpose/introduction  Write clearly and succinctly  Reported usage data should support the purpose of the assessment ◦ Only include data that supports your stated objectives – don’t include all collected data; it won’t be read by administrators
  13. 13. Using/Applying Evidence: Writing Your Report  Reported usage data should support the purpose of the assessment (continued) ◦ Include data within the text of your report where it is necessary and provides clear evidence for the points you are making ◦ It is usually more effective to include visual representations of (charts, graphs) rather than just figures within the text of reports ◦ Larger tables and data sets, if necessary to include, are best placed in appendices  Conclusions and recommendations should be easily identified and based on the evidence presented  State action and/or desired response clearly
  14. 14. Using/Applying Evidence: The Frostburg Experience  Effectively applying e-resources data to collection management has been an evolution  The lay of the land – 2007 ◦ We had data (searches & link resolver) ◦ Study to compare journal costs by format ◦ Data sat in a vacuum outside of annual database budgeting  Needed to establish a frame of reference to begin applying usage statistics in engaging faculty and administrators
  15. 15. Evidence & Outreach Example 1: Faculty Survey – 2007-2008  Faculty had not been previously engaged systematically in collection development efforts  User behavior as demonstrated in link resolver statistics indicated that online full-text was preferred by users  Library determined periodicals and standing orders should be migrated to online format, but which ones?  Fall 2007: Faculty surveyed regarding value (content) and usefulness (format) of journals, standing orders, databases.  Spring 2008: Results of survey matched link resolver usage statistics  Subscription Cancellations, additions, format migrations made over next 5 years
  16. 16. Evidence & Outreach Example 2: Underutilized Journals  Library began collecting full text article retrievals in 2009-2010 (and re-shelving counts in 2011-2012)  All journal subscriptions are reviewed by librarians annually  Faculty are involved in second level of review for underutilized subscriptions  Objective is to use the process as a means for continued dialogue with faculty in collection development
  17. 17. Evidence & Outreach Example 3: Collaboration with Academic Depts  Academic departments becoming increasingly engaged in e-resource subscription discussions, including funding ◦ Chemistry – CAS SciFinder ◦ Visual Arts – Artstor  Current collaboration is with Biology ◦ Department not satisfied with current e-resources ◦ No funds available for additional resources ◦ Reviewed use of current journal subscriptions and content of requested databases ◦ Department suggested journal cancellations to fund databases ◦ New e-resource scenarios developed
  18. 18. Evidence & Outreach Example 4: E-Book Assessment  Frostburg State University: Report overall use and expenditures of e-books over time; implement the most cost effective DDA acquisition model(s) [Report]  USMAI Consortial E-Book Pilot: Assess the effectiveness of a specific DDA acquisition model for the consortium; use and expenditures by consortium members and user types; identification of possible future program funding models [Report]
  19. 19. Thank You  Questions?  Contact Information: Randy Lowe Frostburg State University rlowe@frostburg.edu

×