Understanding donor behavior: An empirical study of statistical and non-parametric methods

Abstract

In this chapter, we analyze donor behavior based on the general segmentation bases. In particular, we study the behavior of the individual donor group's support for higher education. There has been very little research to date that discriminates the donor behavior of individual donors on the bases of their donation levels. The existing literature is limited to a general treatment of donor behavior using one of the available classical statistical discriminant techniques. We investigate the individual donor behavior using both classical statistical techniques and a mathematical programming formulation. The study entails classifying individual donors based on their donation levels, a response variable. We use individuals’ income levels, savings, and age as predictor variables. For this study, we use the characteristics of a real dataset to simulate multiple datasets of donors and their characteristics. The results of a simulation experiment show that the weighted linear programming model consistently outperforms standard statistical approaches in attaining lower APparent Error Rates (APERs) for 100 replications in each of the three correlation cases.