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Institutional performance benchmarking requires identifying a set of reference or comparator institutions. This paper describes a method by which an institution can identify other institutions that are most similar to itself using a methodology that identifies the nearest institutional neighbors based on a balanced set of metrics accessed from IPEDS [Integrated Postsecondary Education Data System] data. The Nearest Neighbor methodology is robust and flexible; it is easy to understand and to explain to others; and it is a hybrid method integrating judgment and analytical techniques. Use of the method is discussed, and it is compared to other methodologies such as Cluster Analysis. (Contains 1 table, 6 figures and 3 footnotes.)

Universities are coming under increasing pressure to demonstrate accountability in operations that affect student enrollment and that contribute to the increased cost of higher education. Institutional researchers are responding by working to provide strategic data-driven decision support that enables managers to evaluate the benefit of dollars spent on both instructional activities and non-classroom activities. While tools such as key performance indicators are useful for study of traditional activities, these tools frequently lack the flexibility to describe and generate all types of data required by the diverse, complex non-classroom activities of successful universities. This paper demonstrates how this problem can be addressed by involving relevant personnel in identifying mission-based success factors, indicators and learning assessments within key decision domains. A methodology is demonstrated that links assessed outcomes in Student Affairs to University strategic purposes. (Contains 5 exhibits and 3 figures.)