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CSU-Wide Library Assessment Toolkit

This Toolkit contains resources to help you with library assessment needs for the following 3 areas: Information Literacy, Library Collections, and Space Usage.

Target Audience

Identifying Target Audience

By this point you should have defined what you are measuring. Whatever it is you are measuring, you need to identify and define the user population you will be measuring. The ‘what’ and ‘who’ here clearly are interlinked and will influence from both ends how you design your methodology.

Populations can be tricky. Our users are usually a very diverse group with many quirky variables any of which can skew data considerably. For students, these groupings can include some of the following:

·      Where in the graduation pipe are they? Incoming freshmen, transfers or seniors?

·      What is their ethnicity? Might that have an influence?

·      Native English speaker or ESL? Domestic student or international?

·      What’s their major, subject emphasis or discipline?

·      Commuter or on campus housing?

·      A special group like honors students or student athletes?

·      Identified as an at risk population?

·      Primarily a library user or a remote user?

All of these are variables are important to consider. For example, if you are trying to ascertain how important your library is as a study/socializing/group space for students, you may well have a significant skew away from capturing science majors in any given sample since “their” lab won’t be the library. Science students by the whole tend to heavily skew toward being remote users of science databases rather than visiting the library to circulate pint collections. How then does this potentially skew your data?

Once you are aware of these population differences and their potential variables, you should further identify your population(s) by methodological selection.

·      What is a valid sample size for your identified user population?

·      Define what you mean by ‘random sample’. Is it whoever randomly walks in/shows up or a centrally controlled sample randomly selected? Be absolutely clear when designing your methodology what you mean by this.

·      How self-selected is your sample? In other words, if you are analyzing how use of your print collections impacts some rubric of student success, all students who have checked out a book are self-selected.

·      Understand what a ‘control group’ is and how you might use one effectively in your assessment. Generally, a control group in an assessment study is one that does not receive some sort of library service or outreach, and is then used as a benchmark to measure how some other tested users do who benefitted from library services or outreach.

·      Is your population a defined cohort or set of cohorts? If so, what kind of cohort? It may include “all incoming freshmen” or some other subset.

·      Whether randomly selected or self-selected, choosing to use cohorts has added benefits in that they become de facto control groups where you can test impacts of different services on each. For example one course class section gets library information via online tutorials, the other the same content but by f-2-f instruction. Compare the difference using a defined rubric (e.g. class grade or successful completion of assignment).

One of the most vexing populations of students to study is the “non-user”. We generally mean by that someone who neither visits the library nor checks out our analog collections. Of course, neither of these mean they don’t ‘use’ the library, it’s just they generally don’t visit it physically. So how can we reach them?

 

  1. Class rosters (e.g. who shows up in the library, who doesn’t)
  2. Freshman (> Senior) class data sets (e.g. against the whole incoming Freshman class who among them used the library, who didn’t)
  3. Compare random samples of total population non-users with random samples of inhouse users
  4. Other

 

Finally, if you think you’ll get best results by using swipe technologies, make sure you have campus backing and support to administer swipes anywhere, and that you get permission to compare your swipe data to larger university data repositories where even richer student data are stored (e.g. Peoplesoft).