Selecting prospective programs in higher education can be a problematic and inefficient task for applicants. In particular, one of the most significant challenges entails locating a specific subset of programs likely to be a good fit. In this paper, clustering techniques are employed in evaluating a specifically created data set of AACSB-accredited doctoral programs in accounting so as to aggregate them by type. In so doing, one is then better positioned to identify which schools best align with his/her relevant characteristics and objectives, thus gaining insight concerning the most appropriate subset of schools to initially consider for application purposes. This approach provides meaningful differentiation between the various program types, offers a means for improving productivity relative to the university application process, and demonstrates promise as an initial foundation for eventual construction of a program recommendation system for use in ostensibly any program application initiative within the higher education domain.

You do not currently have access to this content.