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    Area of Science:

    • Biostatistics
    • Data Science
    • Psychometrics

    Background:

    • Analyzing relationships between factors often limited to within-study data.
    • Integrating findings across disparate studies presents significant methodological challenges.

    Purpose of the Study:

    • To develop a novel statistical method for establishing relationships between factors measured in different studies.
    • To provide a quantitative measure of association applicable to cross-study factor analysis.

    Main Methods:

    • Development of a novel mathematical framework for factor relationship assessment.
    • Application of the method to data from independent studies involving different participant cohorts.

    Main Results:

    • The developed method successfully quantifies relationships between factors across studies.
    • The resulting measure is interpretable as a correlation coefficient, indicating the strength and direction of association.

    Conclusions:

    • This approach enables robust cross-study factor comparison and integration.
    • The method offers a valuable tool for synthesizing evidence and advancing meta-analytic research.