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    Area of Science:

    • Statistics
    • Data Analysis

    Background:

    • Categorical response variables present challenges in statistical analysis.
    • Transforming these variables is crucial for applying various analytical techniques.

    Purpose of the Study:

    • To describe and illustrate three distinct methods for transforming unordered categorical response variables.
    • To compare the application of these methods using real-world data.

    Main Methods:

    • Dummy variable transformation: A standard approach for representing categorical data.
    • Simultaneous analysis (Overall & Woodward): Utilizes eigenanalysis of scaled frequency patterns for all variables at once.
    • Separate analysis (Fisher & Lancaster): Analyzes each variable individually, generating scale values for maximal correlation with a grouping variable.

    Main Results:

    • Demonstration of the practical application of each of the three transformation methods.
    • Illustrative results from two distinct real data sets are presented.

    Conclusions:

    • The study provides a comparative overview of different techniques for handling categorical variables.
    • These methods offer valuable tools for researchers working with categorical data in statistical modeling.