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A more general model for testing measurement invariance and differential item functioning.

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    Moderated nonlinear factor analysis (MNLFA) offers a flexible approach to measurement invariance testing. This method combines strengths of existing models for comprehensive differential item functioning assessment across diverse individual differences.

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    Area of Science:

    • Psychometrics
    • Statistical Modeling
    • Cross-Cultural Psychology

    Background:

    • Measurement invariance is crucial for valid comparisons across groups.
    • Existing methods like multiple groups and MIMIC models have limitations in assessing invariance.
    • Differential item functioning (DIF) detection is key for scale validity.

    Purpose of the Study:

    • Introduce moderated nonlinear factor analysis (MNLFA) as a superior method for measurement invariance.
    • Demonstrate MNLFA's ability to overcome limitations of traditional models.
    • Facilitate a more robust assessment of DIF across multiple individual difference variables.

    Main Methods:

    • Utilized moderated nonlinear factor analysis (MNLFA).
    • Compared MNLFA with multiple groups and MIMIC models.
    • Employed mathematical proofs and empirical demonstration.

    Main Results:

    • MNLFA integrates and surpasses the capabilities of multiple groups and MIMIC models.
    • MNLFA allows simultaneous assessment of measurement invariance and DIF.
    • MNLFA accommodates multiple categorical and continuous individual difference variables.

    Conclusions:

    • MNLFA provides a more flexible and comprehensive framework for measurement invariance.
    • This approach enhances the validity and comparability of measurements in diverse populations.
    • Researchers can achieve a fuller understanding of DIF using MNLFA.