What Is Discriminant Analysis, Factor Analysis And Cluster Analysis?


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Discriminant analysis helps to identify the independent variables that discriminate a nominally scaled dependent variable of interest. The linear combination of independent variables indicates the discriminating function showing the large difference that exists in the two group means. In other words the independent variables measured on an interval or ratio scale discriminate the groups of interest to study.

Factor analysis helps to reduce a vast number of variables to a meaningful, interpretable, and manageable set of factors. A principle component analyses transform all the variables into a set of composite variables that are not correlated to one another. Suppose we have measured in a questionnaire the four concepts of mental health, job satisfaction, life satisfaction, and job involvement with seven questions tapping each. When we factor analyze these 28 items, we should find four factors with the right variables loading on each factor, confirming that we have measured the concepts correctly.

The cluster analysis is used to classify objects or individuals into mutually exclusive and collectively exhaustive groups with high homogeneity within clusters and low homogeneity between clusters. In other words cluster analysis helps to identify objects that are similar to one another, based on some specified criterion. Cluster analysis will cluster individuals by their preferences for each of the different brands.

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