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This study introduces a bootstrap-based method for statistical power analysis in mediation models, accommodating non-normal data. An R package, bmem, is provided for practical application in various mediation models.

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Area of Science:

  • Statistics
  • Psychometrics
  • Quantitative Psychology

Background:

  • Traditional statistical power analysis for mediation models often assumes data normality.
  • Existing methods frequently rely on the less powerful Sobel test, overlooking the advantages of bootstrap methods.

Purpose of the Study:

  • To propose a novel statistical power analysis method for mediation models using bootstrap techniques.
  • To accommodate non-normal data, including excessive skewness and kurtosis, in power estimations.
  • To introduce a user-friendly R package for implementing the proposed power analysis.

Main Methods:

  • Monte Carlo simulation to estimate statistical power.
  • Bootstrap method for mediation effect detection.
  • Development of the 'bmem' R package for practical application.

Main Results:

  • The proposed bootstrap method provides accurate statistical power estimation for mediation effects.
  • The method effectively handles non-normal data, expanding the applicability of power analysis.
  • The 'bmem' R package facilitates the implementation of advanced power analysis for diverse mediation models.

Conclusions:

  • The bootstrap method offers a more powerful and flexible approach to statistical power analysis in mediation models compared to traditional methods.
  • The developed 'bmem' R package serves as a valuable tool for researchers to conduct robust power analyses.
  • This approach enhances the reliability of mediation studies by enabling accurate power estimations, particularly with non-normal data.