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This study enhances parallel analysis for factor retention by considering eigenvalue variability from observed data. The revised method provides a proportion of samples suggesting k factors, improving accuracy, especially with small sample sizes.

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Area of Science:

  • Psychometrics
  • Statistical Analysis
  • Quantitative Research Methods

Background:

  • Parallel analysis is a leading method for factor retention in factor analysis.
  • Traditional methods like Kaiser's rule have limitations in addressing eigenvalue sampling variability.
  • Existing parallel analysis accounts for variability from the identity matrix but not observed data.

Purpose of the Study:

  • To propose a revised parallel analysis method that incorporates sampling variability of eigenvalues from observed data.
  • To provide practitioners with information on the variability of factor numbers across random samples.
  • To enhance the accuracy of factor retention, particularly in scenarios with limited sample sizes.

Main Methods:

  • Developed a revised parallel analysis technique.
  • Simulated data to test the proposed strategy.
  • Compared the proportion of random samples suggesting k factors against traditional methods.

Main Results:

  • The revised parallel analysis effectively addresses sampling variability from observed data.
  • Simulation results demonstrate the utility of the proposed strategy.
  • The method is particularly beneficial for research with small sample sizes.

Conclusions:

  • The revised parallel analysis offers a more robust approach to factor retention.
  • Considering eigenvalue variability from observed data improves the reliability of factor determination.
  • This enhanced method is recommended for researchers dealing with sampling fluctuations and limited data.