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Planning a meta-analysis with dependent effect sizes is now more accessible. This study introduces practical guidance and the POMADE R package to conduct power analysis for complex research syntheses.

Keywords:
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Area of Science:

  • Statistics
  • Research Methodology
  • Social Sciences

Background:

  • Sample size and statistical power are critical for research synthesis planning.
  • Traditional power analysis methods were limited to simple data structures with independent effect sizes.
  • Meta-analyses often involve multiple, dependent effect size estimates, particularly in social science research.

Purpose of the Study:

  • To provide practical guidance for conducting power analysis in meta-analyses with dependent effect sizes.
  • To introduce the POMADE R package, designed to facilitate power analysis for complex meta-analytic data structures.
  • To address practical challenges in applying recent power approximation formulas for planning research syntheses.

Main Methods:

  • Development and application of power approximation formulas for meta-analyses with dependent effect sizes.
  • Introduction of the POMADE R package for conducting power analysis.
  • Presentation of resources for identifying necessary study design features and model parameters.
  • Use of detailed worked examples to illustrate the application of the POMADE package.
  • Emphasis on graphical tools, including a novel 'traffic light power plot', for presenting power analysis findings.

Main Results:

  • The POMADE R package offers a practical tool for power analysis in meta-analyses with dependent effect sizes.
  • Comprehensive guidance and examples are provided for applying power analysis in complex research synthesis planning.
  • Graphical tools, such as the traffic light power plot, enhance the communication of power analysis results and assumptions.

Conclusions:

  • This work facilitates the application of advanced power analysis techniques for meta-analyses involving dependent effect sizes.
  • The POMADE package and practical guidance empower researchers to better plan their syntheses, improving statistical power.
  • Effective visualization of power analysis results aids in understanding and communicating the certainty of assumptions.