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Multiple Comparison Tests01:13

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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ANALYSIS OF MULTITRAIT-MULTIMETHOD MATRICES: A TWO STEP PRINCIPAL COMPONENTS PROCEDURE.

S L Golding, E Seidman

    Multivariate Behavioral Research
    |January 13, 2016
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    Summary

    A new two-step principal components analysis method effectively assesses variable convergence across domains. This technique offers a promising alternative to Jackson's method for analyzing multitrait-multimethod data.

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    Area of Science:

    • Psychometrics
    • Multivariate Statistics

    Background:

    • Assessing convergence across different measurement domains is crucial in psychology and social sciences.
    • Existing methods like Jackson's multi-method factor analysis have limitations.

    Purpose of the Study:

    • To present and evaluate a novel technique, two-step principal components analysis, for assessing cross-domain variable convergence.
    • To compare the proposed method with Jackson's (1969) multi-method factor analysis.

    Main Methods:

    • The proposed method involves empirical orthogonalization of each domain into components.
    • Convergence is analyzed among these components across different domains.
    • Data from personality, vocational interest, and aptitude domains were analyzed.

    Main Results:

    • Both the two-step procedure and Jackson's method provided evidence of cross-domain convergence.
    • Jackson's method exhibited undesirable mathematical and interpretational consequences.
    • The two-step procedure demonstrated a systematic and empirical approach.

    Conclusions:

    • The two-step principal components analysis is a promising technique for analyzing multitrait-multimethod matrices.
    • It offers advantages over traditional methods in terms of mathematical properties and interpretability.
    • This method facilitates a more robust assessment of construct convergence across diverse measurement contexts.