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ABSTRACT

Factors Affecting Multiple Regression and Discriminant Analysis with a Dichotomous Dependent Variable: Prediction, Explanation, and Classification

Social science researchers use multiple regression and/or discriminant analysis to predict, explain, or classify with a dichotomous dependent variable. This study outlines the steps a researcher should take to analyze data using each method, the concerns and emphasis of each method, and the issues involved. Basically, both statistical methodologies are affected by the magnitude of intercorrelation, marginal dependency, multivariate normality, variance-covariance in groups, prior probability of group membership, and misclassification. The usefulness of these two multivariable statistical methods can be enhanced if we better understand how these assumptions and issues impact their outcomes.

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