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
Correlation: BobaFett and Ladybugs
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
How Do We Observe & Measure these Behaviors? Accessing Chaining & Differentiating Managing & Ending Accessing & Browsing
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.
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.
Starting & Accessing: Use Before & After OpenURL *significant at .05 level **significant at .01 level
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 will be a significant increase in searches across common resource groupings.
Differentiating: Federated Search Statistics
Differentiating: OPAC Searches (2005 v. 2006)
Differentiating: WorldCat Searches
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.
Chaining: JSTOR Citations (2000 v. 2004)
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.
Managing, Teaching: Use of local content
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.
Service Effects: Usage of Journals (2005 v. 2006)
Service Effects: SFX Clickthrough Rate (Local v. Shared)
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?
Services Related Behaviors
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.