Banerjee, AniruddhaMcDonald, Joseph P.Dwyer, Owen J.Ward, Richard E.2011-03-162011-03-162011-03-16https://hdl.handle.net/1805/2514http://dx.doi.org/10.7912/C2/768Indiana University-Purdue University Indianapolis (IUPUI)The fundamental principles colleges and universities use to recruit students have remained largely unchanged for decades. Traditionally, admissions professionals visit high schools and attend college fairs, while colleges and universities hold preview days and publish viewbooks all in the interest of attracting a high-quality and diverse student population. The recruiting process has been greatly improved through the application of modern technology. The analytic abilities of technologies such as geographic information systems (GISystems), which allow for the visualization and analysis of spatial data, presents previously underutilized strategies for higher education recruiting methods. In addition, the incorporation of a Hierarchical Bayesian model will effectively model influential enrollment factors, which successful students possess. Hierarchical Bayesian models use the prior distribution, and likelihood of an events occurrence to create the posterior distribution or Bayesian inference. The intelligence created by combining traditional recruiting techniques with GISystems and Hierarchical Bayesian modeling will allow admissions professionals to improve the success rate of enrollment efforts and expenditures. This paper will explore the application of Hierarchical Bayesian models and GISystems within higher education recruiting.en-USEnrollmentEducation, Higher -- United StatesSchool enrollment -- United StatesGeographic information systemsThesis