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Statistical power analysis and sample size planning for moderated mediation models.

Ziqian Xu1, Fei Gao2, Anqi Fa2

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Summary
This summary is machine-generated.

This study introduces new simulation-based methods for statistical power analysis in conditional process models, specifically moderated mediation. The developed methods aid researchers in sample-size planning for complex behavioral science research.

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Area of Science:

  • Behavioral Science
  • Statistics
  • Psychometrics

Background:

  • Conditional process models, including moderated mediation and mediated moderation, are frequently employed in behavioral science.
  • Existing statistical power analysis methods and software packages inadequately address these complex models.
  • Accurate sample-size planning is crucial for the validity of research findings.

Purpose of the Study:

  • To introduce novel simulation-based methods for conducting statistical power analysis in conditional process models.
  • To focus on developing and validating power analysis techniques for moderated mediation models.
  • To provide accessible tools for researchers to perform sample-size planning.

Main Methods:

  • Development of simulation-based approaches for power analysis.
  • Application of methods to five common moderated mediation models.
  • Assessment of method performance through simulation studies.
  • Implementation in the WebPower R package and web applications.

Main Results:

  • The proposed simulation-based methods offer intuitive sample-size planning for moderated mediation models.
  • Performance assessment across five models demonstrates the utility and reliability of the methods.
  • The WebPower R package and web apps facilitate the practical application of these power analysis techniques.

Conclusions:

  • The introduced methods enhance statistical power analysis for moderated mediation models in behavioral research.
  • Accessible software implementations (WebPower R package, web apps) lower the barrier for researchers to conduct power analyses.
  • These advancements support more rigorous and well-powered studies in the field.