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Summary-statistics-based power analysis: A new and practical method to determine sample size for mixed-effects

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A new summary-statistics-based power analysis method simplifies power calculations for mixed-effects models with nested data. This approach, validated by simulations, offers a practical alternative for researchers needing efficient power analysis.

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Area of Science:

  • Statistics
  • Psychometrics
  • Data Analysis

Background:

  • Power analysis is crucial for mixed-effects modeling, but existing methods can be complex.
  • Two-level nested data structures are common in various research fields.

Purpose of the Study:

  • To introduce a practical summary-statistics-based power analysis method for mixed-effects modeling.
  • To complement existing formula-based and simulation-based power analysis techniques.
  • To provide an accessible tool for applied researchers with limited information.

Main Methods:

  • The method leverages conditional equivalence between summary-statistics and mixed-effects models.
  • Power analysis is simplified to that of a one-sample t-test.
  • Utilizes existing software like G*Power and R's pwr package.

Main Results:

  • Analytic proofs and simulations demonstrate the validity and robustness of the proposed method.
  • Illustrative examples using published research are provided.
  • A web application is available to facilitate its use.

Conclusions:

  • The summary-statistics-based power analysis offers a convenient alternative for mixed-effects models with nested data.
  • It requires minimal input, making power analysis more accessible.
  • While having limitations, it serves as a valuable tool for applied researchers.