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Determining Sample Size Requirements in EFA Solutions: A Simple Empirical Proposal.

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This study proposes a new method to estimate the necessary sample size for exploratory factor analysis (EFA). It provides a practical approach for determining adequate sample sizes for specific datasets, moving beyond general rules of thumb.

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Area of Science:

  • Statistics
  • Psychometrics

Background:

  • Existing sample size recommendations for exploratory factor analysis (EFA) are often general rules of thumb or based on simulation studies.
  • These general recommendations lack direct applicability to specific datasets due to varying conditions.

Purpose of the Study:

  • To propose a method for estimating the required sample size for exploratory factor analysis (EFA) tailored to a specific dataset.
  • To provide a practical and data-driven approach for determining adequate sample sizes in EFA.

Main Methods:

  • Developed a novel estimation procedure for sample size determination in EFA.
  • Utilized an intensive simulation process using sample correlation matrices to generate pseudo-population datasets.
  • Employed a criterion based on the closeness between pseudo-population and sample reproduced correlation matrices to determine required sample size.

Main Results:

  • The proposed method effectively estimates the necessary sample size for a given dataset and EFA model.
  • Simulation results indicate the proposal performs well in practice.
  • Identified determinants of sample size align with existing literature.

Conclusions:

  • The proposed data-driven method offers a more practical and precise way to determine sample size for EFA.
  • This approach enhances the reliability and stability of EFA solutions by ensuring adequate sample sizes.
  • The findings support the use of this method for researchers conducting EFA.