Evidence Based Library and Information Practice (Jun 2013)
Analyzing the MISO Data: Broader Perspectives on Library and Computing Trends
Abstract
Objective – To analyze data collected by 38 colleges and universities that participated in the Measuring Information Services Outcomes (MISO) survey between 2005 and 2010.Methods – The MISO survey is a Web-based quantitative survey designed to measure how faculty, students, and staff view library and computing services in higher education. Since 2005, over 10,000 faculty, 18,000 students, and 15,000 staff have completed the survey. To date, the MISO survey team has analyzed the data by faculty age group and student cohort. Much of the data analysis has focused on changes in the use, importance, and satisfaction with services over time.Results – Analysis of the data collected during 2008-2010 reveals marked differences in how faculty and students use the library. The most frequently used services by faculty are the online library catalog (3.39 on a 5-point scale), library databases (3.34), and the library website (3.29). In contrast, the most frequently used services by students are public computers in the library (3.61) and quiet work space in the library (3.29). Faculty reported a much higher use of online resources from off campus. Analysis of data from schools where the survey was administered more than once during 2005-2010 reveals that both faculty and students increased their utilization of databases over time. All other significant faculty trends reflected declines in usage, whereas, with the exception of use of the library website, all other student trends reflected no change or increased usage.Conclusion – As the MISO survey has continued and expanded over the years, the usefulness of rich comparable data from a set of peer institutions over time has increased tremendously. In addition to providing a rich source of data, MISO can serve as a model for how a group of schools can collaborate on a share assessment tool that meets the needs of individual institutions and provides a robust, aggregated dataset for deeper analysis.
