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Researchers can use fewer simulation replications for dynamic fit index (DFI) cutoffs in factor analysis. This Monte Carlo study found 200 replications provide stable cutoffs, reducing computational time with minimal impact.

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Area of Science:

  • Behavioral Sciences
  • Psychometrics
  • Statistical Modeling

Background:

  • Factor analysis is crucial for measuring latent constructs in behavioral sciences.
  • Approximate fit indices are used for model fit and validity evidence.
  • Simulation-based methods offer customized cutoffs but are computationally intensive.

Purpose of the Study:

  • To determine the optimal number of simulation replications for stable dynamic fit index (DFI) cutoffs.
  • To assess the trade-off between computational efficiency and cutoff stability.
  • To provide guidance on simulation-based methods for factor analysis model fit.

Main Methods:

  • A Monte Carlo simulation study was conducted.
  • The study focused on dynamic fit index (DFI) cutoffs.
  • The number of simulation replications was varied to assess stability.

Main Results:

  • Dynamic fit index (DFI) cutoffs stabilize with 500 replications.
  • Fewer replications, particularly 200, are efficient for categorical data and simpler models (one-factor, three-factor).
  • Reducing replications significantly cuts computational time with minimal impact on results.

Conclusions:

  • The dynamic fit index (DFI) method can be more computationally efficient than previously thought.
  • 200 replications are often sufficient for stable DFI cutoffs in common factor analysis models.
  • Researchers can balance computational efficiency and statistical rigor by using fewer replications.