Discriminant analysis is a statistical technique used to analyze research data when the dependent variable is categorical and the independent variable is interval in nature. The objective of discriminant analysis is to develop discriminant functions that are linear combinations of independent variables that will discriminate between the categories of the dependent variable in a perfect manner. Discriminant analysis is used to classify observations into non-overlapping groups based on scores on one or more quantitative predictor variables.
Some key features of discriminant analysis include:
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Linear combinations: Discriminant analysis works by creating one or more linear combinations of predictors, creating a new latent variable for each function. These functions are called discriminant functions.
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Group membership: The primary function of discriminant analysis is to assign each observation to a particular group or category according to the data’s independent characteristics.
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Number of categories: Discriminant analysis is described by the number of categories that is possessed by the dependent variable.
Discriminant analysis is used in various fields, such as medicine, to assess the severity state of a patient and prognosis of disease outcome. It can also be used to differentiate between groups based on their consumption of certain products or their psychological attributes.
The steps involved in conducting discriminant analysis include formulating the problem, selecting the variables, testing assumptions, and interpreting the results. Discriminant analysis is a useful tool for researchers who want to analyze categorical data and identify differences among groups based on predictor variables.
