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Dynamic fit index cutoffs for confirmatory factor analysis models.

Daniel McNeish1, Melissa G Wolf2

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Researchers can now use dynamic fit index cutoffs for better confirmatory factor analysis model evaluation. This simulation-based method provides tailored cutoffs, improving the validity of psychological assessments.

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Area of Science:

  • Psychometrics
  • Quantitative Psychology
  • Statistical Modeling

Background:

  • Confirmatory Factor Analysis (CFA) model fit assessment is crucial for evaluating psychological assessments.
  • Fit indices with fixed cutoffs (e.g., Hu & Bentler, 1999) are widely used but lack generalizability.
  • Fixed cutoffs are problematic as fit index meaning varies with model characteristics (factor reliability, item count, factor count).

Purpose of the Study:

  • To introduce a simulation-based method for dynamic fit index cutoffs.
  • To provide an accessible, open-source software application for implementing these dynamic cutoffs.
  • To extend cutoff derivations for multiple misspecification levels and address 1-factor models.

Main Methods:

  • Developed a simulation-based approach for deriving adaptive fit index cutoffs.
  • Created a Web-based Shiny application to automate the process, requiring no coding or simulation expertise.
  • Extended cutoff derivations to account for varying degrees of model misspecification and specific 1-factor model characteristics.

Main Results:

  • The dynamic fit index method generates cutoffs tailored to specific model and data characteristics.
  • The automated software application removes barriers to implementing simulation-based techniques.
  • New sets of cutoffs are available for multiple misspecification levels and for 1-factor models.

Conclusions:

  • Dynamic fit index cutoffs enhance the evaluation of CFA models and psychological assessments.
  • The user-friendly software promotes wider adoption of robust model fit assessment.
  • This approach helps fit indices function more accurately as effect sizes for quantifying misfit.