Four Categories of Academic Libraries: A Cluster Analysis Based on Collections, Expenditures, and Circulation per Student Data

Abstract

The purpose of this study is to determine whether hidden categories of academic libraries exist based on collections, circulations, and expenditures per student. This study utilizes k-means clustering analysis to identify categories of academic libraries based on three variables: number of library resources per enrolled students, library circulation per enrolled student, and library expenditures per enrolled student. Four distinct clusters are identified: high circulation per student, high expenditures per student, large collections per student, and standard circulation, expenditures, and collections. Attributes of these clusters of libraries (institution type, university endowment, year established) are explored. These findings indicate new means of classifying libraries based on funding and use statistics as opposed to characteristics of the institution of higher education which the library serves.

Keywords:

• library collections
• library expenditures
• circulation
• academic libraries
• cluster analysis
• machine learning
• data mining
• university libraries
• collections data
• user data
• spending
• finances
• faculty

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Brady D. Lund

Brady Lund is a doctoral student at Emporia State University’s School of Library and Information Management, Emporia, Kansas, USA. His work has been published in dozens of LIS journals and conference proceedings. He may be reached at [email protected].