Understanding donor behavior

An empirical study of statistical and non-parametric methods

Kenneth D. Lawrence, Dinesh Ramdas Pai, Ronald Klimberg, Sheila M. Lawrence

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)281-291
Number of pages11
JournalAdvances in Business and Management Forecasting
Volume5
DOIs
StatePublished - Jan 1 2008

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Nonparametric methods
Empirical study
Donation
Mathematical programming
Discriminant
Replication
Segmentation
Simulation experiment
Income level
Savings
Linear programming
Predictors

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)

Cite this

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Understanding donor behavior : An empirical study of statistical and non-parametric methods. / Lawrence, Kenneth D.; Pai, Dinesh Ramdas; Klimberg, Ronald; Lawrence, Sheila M.

In: Advances in Business and Management Forecasting, Vol. 5, 01.01.2008, p. 281-291.

Research output: Contribution to journalArticle

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