How to Perform Discriminant Analysis in Medical Research? Explained with Illustrations

Discriminant function analysis is the statistical analysis used to analyze data when the dependent variable or outcome is categorical and independent variable or predictor variable is parametric. It is a parametric technique to determine which weightings of quantitative variables or predictors best discriminates between two or more than two categories of dependent variables and does so better than chance. Discriminant analysis is used to find out the accuracy of a given classification system in predicting the sample into a particular group. Discriminant analysis includes the development of discriminant functions for each sample and deriving a cutoff score that is used for classifying the samples into different groups. Discriminant function analysis is a statistical analysis used to find out the accuracy of a given classification system or predictor variables. This article explains the basic assumptions, uses, and necessary requirements of discriminant analysis with a real-life clinical example. Whenever a new classification system is introduced, discriminant function analysis can be used to find out the accuracy with which the classification is able to differentiate a particular sample into different groups. Thus, it is a very useful tool in medical research where classification is required.