Posts tagged with Survey in Blog Tiny Studies

Assessment and research activities focused on the U-M Library faculty, staff, and student experiences are happening regularly, and often the Library Human Resources (LHR) team is contributing to these activities if not leading the research. This work can focus on quantitative data, qualitative data, or take a hybrid approach, and can involve surveys, interviews, and/or some general number-crunching. This post looks over some recent HR assessment projects.

Assessing library impact on student learning is essential for demonstrating libraries’ integrated value and commitment to higher education. In 2018 the author investigated faculty perceptions of student learning in library instruction sessions, and as a result, revealed that faculty observe enhanced learning when their students participate in library instruction opportunities.

Continuing the discussion about survey design (see Let's Talk about Surveys, Part 1), you’ve decided a survey is an appropriate methodology for what you want to find out and are thinking about what questions you want to ask. But how you ask these questions and structure them within the survey itself, as well as the question formats and options you give people for responding all require careful consideration.

Doing a survey is often the default research method thought of when you need to answer questions about what people like, expect, or want, among other things. While surveys are likely to be considered the easiest option, you can’t conflate “easy to create” with “easy to create well.” Even if a survey is an appropriate methodology for the question you’re looking to answer, the questions you ask, the way you ask them, and the options you give people for responding all require a thoughtful approach.

In this study, engineering librarians Leena Lalwani, Jamie Niehof, and Paul Grochowski sought to learn from graduate students in the College of Engineering (CoE) how these students could benefit from more instruction on U-M Library resources.

In the second of two 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.

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.

Have you ever attended a workshop and promptly forgot most of what you learned a few days later? Given that library staff teach hundreds of library instruction sessions each semester through training workshops, course-integrated sessions, campus workshops, etc., this is an issue that is probably affecting those who attend our instruction sessions as well. Librarians explored a potential solution to this problem by testing an implementation of "Learning Boosters."

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 research methods to get the best data (described below).