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Usage Statistics & Information Behaviors:

Usage Statistics & Information Behaviors: Understanding User Behavior with Quantitative Indicators John McDonald Assistant Director for User Services & Technology Innovation The Libraries of the Claremont Colleges November 2, 2007 NISO Usage Data Forum.

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Usage Statistics & Information Behaviors:

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  1. Usage Statistics & Information Behaviors: Understanding User Behavior with Quantitative Indicators John McDonald Assistant Director for User Services & Technology Innovation The Libraries of the Claremont Colleges November 2, 2007 NISO Usage Data Forum

  2. Correlation: Boba Fett and Ladybugs

  3. We have the data, now what do we do? • What we have done: • Cancel journals • Inform purchase decisions • What we should do: • Understand usage behaviors • Guide our decision making processes • Understand our impact on our patrons

  4. Information Usage Behaviors Ellis (1993), Ellis & Haugan (1997) & Meho & Tibbo (2003), McDonald (2007) Verifying Networking Monitoring Managing Manipulating Teaching Ending • Starting • Browsing • Accessing • Chaining • Differentiating • Extracting

  5. How Do We Observe & Measure these Behaviors? Accessing Chaining & Differentiating Managing & Ending Accessing & Browsing

  6. How do we observe & measure? • Pose a Question • How will a new service affect our users? • Develop a Theory • Explain what you think happened. • Test the Theory • Develop metrics, collect data, analyze.

  7. Example 1: Starting & Accessing • Question: How will a new service affect our users? • Theory: If we improve the user’s ability to identify relevant material (starting) and retrieve it (accessing), we either save them time or effort and allow them to access more material. • Test: There will be a significant increase in the usage of material.

  8. Starting & Accessing: Use Before & After OpenURL *significant at .05 level **significant at .01 level

  9. Example 2: Differentiating • Question: Do our choices affect our users ability to differentiate between resources? • Theory: If we group resources together, we allow users to identify relevant resources and provide efficient methods to differentiate between resources. • Test: There is significant increase in searches across common resource groupings.

  10. Differentiating: Federated Search Statistics

  11. Differentiating: OPAC Searches (2005 v. 2006)

  12. Differentiating: WorldCat Searches

  13. Example 3: Chaining • Question: Do our users move from one information resource to another? • Theory: If users are moving from resource to resource, usage of resources in the same environment (one provider) and results of that usage (citations) will increase. • Test: There will be a significant increase in the usage and/or results of usage of a resource’s material.

  14. Chaining: JSTOR Citations (2000 v. 2004)

  15. Example 4: Managing, Teaching • Question: Are our users managing or utilizing content differently? • Theory: A stable online archive allows users to re-access or re-use content more efficiently (utility usage or virtual vertical file), or utilize it for instructional purposes in different ways (virtual syllabus). • Test: There will be a significant increase in the systematic re-use of current, locally produced content.

  16. Managing, Teaching: Use of local content

  17. Example 5: Service Effects • Question: How do our choices in libraries affect user behavior? • Theory: When we change the display options (e.g. cataloging) for journals, did that affect either publisher usage or SFX usage? • Test: Changing cataloging results in decreased local journal usage as measured by the publisher and SFX.

  18. Service Effects: Usage of Journals (2005 v. 2006)

  19. Service Effects: SFX Clickthrough Rate (Local v. Shared)

  20. Example 5: Services Related Behaviors • What else do users want or need? • Are there services related behaviors that we can observe? Providing content is one option, but how are researchers using associated information services? • If we provide them the article they want in fulltext, we see that sometimes they ask for other types of things. • Can we match these things to those user behaviors?

  21. Services Related Behaviors

  22. What else could we be studying? • Monitoring • Many information providers have e-alerts, repeat saved searches, etc. • Networking • Users may want to email a citation to a colleague or another student. • Extracting • Passing the bibliographic information to another database to search. • Analyzing • Including user behavior information in the statistical measurement tools.

  23. Questions? John McDonald November 2, 2007

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