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    This study introduces equality constraints for multiple correspondence analysis, allowing prior knowledge integration. This method handles unequal category numbers and variable subsets effectively.

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    Area of Science:

    • Statistics
    • Data Analysis

    Background:

    • Multiple Correspondence Analysis (MCA) is a technique for analyzing categorical data.
    • Incorporating prior knowledge into MCA can enhance its interpretability and accuracy.

    Purpose of the Study:

    • To extend MCA by introducing equality constraints on variable categories.
    • To provide a method for handling unequal category numbers and subsets of variables within MCA.

    Main Methods:

    • The study outlines a procedure for applying equality constraints in MCA.
    • This involves specifying relationships between categories, which can be within or across variables.

    Main Results:

    • Equality constraints offer a simple yet powerful extension to MCA.
    • The proposed procedure effectively addresses challenges like unequal category numbers and variable subsets.

    Conclusions:

    • Equality constraints enhance MCA by allowing the incorporation of prior knowledge.
    • The method is versatile, applicable to various data structures and analytical goals.