A spatial perspective for predicting enrollment in a regional pharmacy school |
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| Authors: | Ke Chen Jason Kennedy John M Kovacs Chunhua Zhang |
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| Institution: | (1) Department of Geoscience, East Tennessee State University, Johnson City, TN 37614, USA;(2) Department of Geography, Nipissing University, North Bay, ON, Canada, P1B 8L7 |
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| Abstract: | Having the ability to predict enrollment is an important task for any school’s recruiting team. The purpose of this study
was to identify significant factors that can be used to predict the spatial distribution of enrollments. As a case study,
we used East Tennessee State University (ETSU) pharmacy school, a regional pharmacy school located in the Appalachian Mountains.
Through the application of a negative binomial regression model, we found that the most important indicators of enrollment
volume for the ETSU pharmacy school were Euclidean distance, probability (based on competing pharmacy schools’ prestige, driving
distance between schools and home and tuition costs), and the natural barrier of the Appalachian Mountains. Using these factors,
together with other control variables, we successfully predicted the spatial distribution of enrollments for ETSU pharmacy
school. Interestingly, gender also surfaced as a variable for predicting the pharmacy school’s enrollment. We found female
students are more sensitive to the geographic proximity of home to school. |
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| Keywords: | Pharmacy education Enrollment GIS Gravity model Negative binomial regression |
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