Blackwell Reference Online

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The main aim of discriminant function analysis is to predict group membership of an object or a person by using as few characteristics (or set of predictors) as possible. Additionally, discriminant analysis is used to classify elements according to their characteristic properties (→  Statistics, Descriptive ). So if you know the answers a subject might give to a crucial set of questions and/or the scores that subject might achieve on an important set of characteristics, discriminant analysis enables you to predict whether the subject is or will likely be a voter or nonvoter, buyer or nonbuyer, college dropout or graduate, researcher or lecturer, master or servant, winner or loser, television viewer or nonviewer, opinion-leader or follower, etc. For example, a product manager is interested in the critical characteristics of the target group that distinguish the buyers of the product from the nonbuyers. The question is: what makes a person a buyer or a nonbuyer? What are the relevant factors that are responsible for paying money for the product or not? And which of the factors is mainly responsible for the difference; which are more important than others? Do buyers differ from nonbuyers predominantly in their socio-demographic background, their psychological profile, their attitudes, or their personal interests? If the answers a subject might give to the crucial questions are known, ... log in or subscribe to read full text