In two blog posts, Informationists from the Taubman Health Sciences Library share their research project to improve library integration within the U-M School of Nursing curriculum. Using a mixed methods approach, they are investigating undergraduate student information seeking needs and behaviors.
Posts tagged "qualitative"
Ask a Librarian email and instant messaging (IM) service providers targeted current users of our virtual reference services during 2016-2017, to gather feedback about our online research and reference service. We wanted to know more about users' motivation for seeking help via email and via IM, as well as users' satisfaction with their online interactions. Additionally, we were interested in gathering users' ideas for future IM service enhancements.
Like many academic research libraries, the University of Michigan Library has a promotion process for its librarians. And, like many libraries, the policies need to be reviewed on occasion. The Promotion and Appointment of Librarians (PAL) Task Force was charged by the Librarians’ Forum with reviewing our promotion process and making recommendations to better align what we do with the goals of both individuals and the Library. This Task Force utilized various qualitative and quantitative...
There are many ways to record and analyze what is happening in the University of Michigan libraries over time. The more we understand how users are engaging with our spaces, the more we can do to meet their needs. But how do you get a handle on such a big question (library space use)? What data do you collect and how do you break it down?
The U-M Shapiro Undergraduate Library (UGL) collection serves the course-related and extracurricular information needs of U-M undergraduate students. This collection encourages students to explore new ideas, gain research skills, and become lifelong learners. How can we tailor this small collection (approximately 175,000 volumes) to meet their current needs?
Maybe you’ve heard of or lived with a roommate who never washed the dishes, who talked loudly on the phone late into the night or who stiffed you on rent. Not fun. Bias in our research isn’t fun either. It distorts the nature of the data we collect, analyze and share.
Quantitative data gives you the hard numbers: what, how many times, when, generally who, and where. Quantitative data also leaves out the biggest and possibly most important factor: why.
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