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Regularized Exploratory Bifactor Analysis With Small Sample Sizes.

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  • 1School of Management, Kyung Hee University, Seoul, South Korea.

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PubMed
Summary
This summary is machine-generated.

Regularized exploratory factor analysis (REFA) outperforms unweighted least squares factor analysis (ULS-FA) for small sample sizes in behavioral research. REFA is particularly recommended when dealing with low factor loadings or few variables per factor.

Keywords:
Monte Carlo simulationexploratory bifactor analysisregularized exploratory factor analysissmall sample sizeunweighted least squares

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Area of Science:

  • Behavioral Science
  • Psychology
  • Behavior Genetics
  • Quantitative Psychology

Background:

  • Small sample sizes are common in behavioral science disciplines, posing estimation challenges for factor analysis.
  • Unweighted least squares factor analysis (ULS-FA) and regularized exploratory factor analysis (REFA) are popular methods for small samples (N < 50).
  • The performance of ULS-FA and REFA in exploratory bifactor modeling with small samples remains unclear.

Purpose of the Study:

  • To evaluate the small sample performance of ULS-FA and REFA in exploratory bifactor modeling.
  • To compare bifactor structure recovery under various experimental conditions.

Main Methods:

  • A comprehensive simulation study was conducted.
  • Evaluated ULS-FA and REFA under conditions of varying sample size, factor loading, number of variables per factor, number of factors, and factor correlation.
  • Focused on bifactor structure recovery.

Main Results:

  • REFA demonstrated superior performance compared to ULS-FA in small sample bifactor analysis.
  • REFA's advantage was particularly evident under conditions with low factor loadings.
  • REFA was also recommended when there were few group factors or a small number of variables per factor.

Conclusions:

  • REFA is the recommended approach over ULS-FA for exploratory bifactor modeling with small sample sizes.
  • The findings provide practical guidance for researchers in behavioral sciences facing small sample estimation problems.
  • REFA offers better bifactor structure recovery in challenging conditions common in behavioral research.