Part IV. Analyzing the Qualitative Data

A. The form of the data

The survey generated scores of comments from customers. Here are some examples:

• "I would prefer that renewal not be solely via email as a book I checked out was recalled during a multi-week computer problem...."

• "I would make the policy recall policy known more -- promote it."

• "Why don't you put more automatic checkout and checkin machines like the ones at Main Library throughout other libraries? Is there a book drop which can be accessed from outside of buildings which can be used when the libraries are closed?"

• "Need more information on signs, sometimes it's confusing."

• "Another type of ID besides the CatCard should be sufficient to check out books and other things."

• "If you want everyone to have equal access to a fair playing field regardless of race, sex or economic background, you need to abolish your current lending policies. I'm surprised you haven't been sued yet."

One of the most daunting tasks for a needs assessment team is to take these seemingly random and contradictory comments and make them make sense. This task can be especially difficult when the comments seem unfair or even malicious. In this next part of the case study, we'll look at how the team worked with their qualitative data.

Let's continue.