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    Area of Science:

    • Psychometrics
    • Statistical Modeling

    Background:

    • Factor analysis is a common statistical technique.
    • Oblique factor rotation is used when factors are expected to correlate.
    • Understanding factor invariance is crucial for reliable analysis.

    Purpose of the Study:

    • To investigate the invariance of oblique factors under random data conditions.
    • To determine the influence of sample size, number of variables, and number of extracted factors on oblique factor invariance.

    Main Methods:

    • The study employed random data simulations.
    • Factor invariance was assessed across varying sample sizes (50, 100, 200).
    • Analyses considered different numbers of variables (15, 30, 45) and extracted factors (5, 10).

    Main Results:

    • Oblique factor invariance demonstrated sensitivity to the tested parameters.
    • Increasing sample size generally improved factor invariance.
    • The number of variables and extracted factors also influenced the stability of oblique solutions.

    Conclusions:

    • The findings highlight the importance of adequate sample size and variable selection for stable oblique factor solutions.
    • Researchers should carefully consider these parameters when conducting factor analyses.
    • These results contribute to a better understanding of oblique factor rotation robustness.