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SOLOMON: a method for splitting a sample into equivalent subsamples in factor analysis.

Urbano Lorenzo-Seva1

  • 1Universitat Rovira i Virgili, ctra de Valls s/n, 43007, Tarragona, Spain. urbano.lorenzo@urv.cat.

Behavior Research Methods
|December 17, 2021
PubMed
Summary
This summary is machine-generated.

Researchers often split data for factor analysis. This study proposes a new method for creating equivalent subsamples, improving upon random splitting for more reliable exploratory and confirmatory factor analysis.

Keywords:
Duplex methodKMO indexRSPSSconfirmatory factor analysisexploratory factor analysisreplicationsample splitting

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Area of Science:

  • Psychometrics
  • Statistical Analysis
  • Multivariate Statistics

Background:

  • Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) are crucial sequential steps in statistical modeling.
  • Current practice involves splitting a single dataset randomly for EFA and CFA, lacking a standardized method for ensuring subsample equivalence.
  • This limitation can impact the reliability and validity of the derived factor structures.

Purpose of the Study:

  • To introduce a novel method for splitting single samples into equivalent subsamples for factor analysis.
  • To provide an alternative to the conventional random split approach in EFA and CFA.
  • To enhance the robustness of the overall analysis process from data exploration to hypothesis confirmation.

Main Methods:

  • A new sample splitting technique is proposed, drawing inspiration from methods used in multivariate regression analysis.
  • The proposed method aims to generate subsamples that are statistically equivalent prior to conducting factor analysis.
  • Validation was performed using both simulation studies and analyses of real-world datasets.

Main Results:

  • The proposed splitting method demonstrated its efficacy in creating equivalent subsamples.
  • Simulation studies indicated improved reliability compared to random splitting.
  • Application to real datasets confirmed the practical utility of the method.

Conclusions:

  • The developed sample splitting technique offers a more rigorous approach for factor analysis workflows.
  • Adoption of this method can lead to more dependable hypothesis generation in EFA and testing in CFA.
  • This contributes to advancing best practices in statistical analysis for psychological and social sciences.