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An empirical Kaiser criterion.

Johan Braeken1, Marcel A L M van Assen2

  • 1CEMO: Centre for Educational Measurement, Faculty of Educational Sciences, University of Oslo.

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Summary
This summary is machine-generated.

A new Empirical Kaiser Criterion for exploratory factor analysis (EFA) performs as well as parallel analysis for uncorrelated data and better for correlated, short scales. This eigenvalue-based method aids factor retention and sample size planning.

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Area of Science:

  • Psychometrics
  • Quantitative Psychology
  • Statistical Modeling

Background:

  • Exploratory Factor Analysis (EFA) relies on dimensionality assessment methods like screeplots, Kaiser criterion, and parallel analysis, primarily using correlation matrix eigenvalues.
  • Existing methods have limitations, necessitating further understanding and development of robust factor retention techniques.

Purpose of the Study:

  • To introduce and evaluate a novel factor retention method, the Empirical Kaiser Criterion, for exploratory factor analysis.
  • To compare the performance of the Empirical Kaiser Criterion against parallel analysis in typical research scenarios.

Main Methods:

  • Developed the Empirical Kaiser Criterion based on population and sample eigenvalue distributions derived from random matrix theory and Monte Carlo simulations.
  • Examined the performance of the Empirical Kaiser Criterion and parallel analysis in simulated research settings with varying scale correlations and lengths.

Main Results:

  • The Empirical Kaiser Criterion demonstrated comparable performance to parallel analysis when scales were uncorrelated.
  • The Empirical Kaiser Criterion significantly outperformed parallel analysis when scales were both correlated and short.

Conclusions:

  • The Empirical Kaiser Criterion is a powerful and promising factor retention method for EFA due to its theoretical grounding, strong performance, and ease of use.
  • This method is valuable for power analysis and sample size planning in EFA, especially in complex scenarios involving correlated and short scales.