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    Area of Science:

    • Psychometrics
    • Multivariate Statistics

    Background:

    • Traditional factor analysis methods for determining the number of factors often rely on eigenvalues.
    • These methods can lack clarity and directness in identifying reliable factors.

    Purpose of the Study:

    • To present split-half factor comparabilities as a direct and unambiguous method for factor retention.
    • To provide a technique for assessing appropriate factor rotation.
    • To address factor analysis in taxonomic contexts and non-homogenous populations.

    Main Methods:

    • Utilizing factor scores to calculate split-half factor comparabilities.
    • Applying principal components analysis with synthetic and real semantic response data.
    • Considering both random and sub-population-based split-halves for non-homogenous groups.

    Main Results:

    • Split-half factor comparabilities offer a direct and unambiguous approach to factor retention.
    • The method effectively aids in selecting reliable factors and appropriate rotations.
    • The importance of considering sub-population split-halves in non-homogenous samples is demonstrated.

    Conclusions:

    • Split-half factor comparabilities provide a robust and interpretable method for factor analysis.
    • This technique is particularly valuable for taxonomic factor analysis and classificatory measures.
    • The study validates the method using diverse datasets and analytical approaches.