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Simulation-based power analyses offer a practical solution for estimating statistical power in mixed-effects models, crucial for reliable research design and sample size planning when analytical methods fall short.

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Area of Science:

  • Statistics
  • Psychology
  • Data Science

Background:

  • Mixed-effects models are widely used but lack analytical power solutions.
  • Estimating statistical power is vital for sample size planning and ensuring reliable research.
  • Simulation-based power analyses present a flexible alternative to analytical approaches.

Purpose of the Study:

  • To provide guidance on conducting simulation-based power analyses for mixed-effects models.
  • To demonstrate practical applications in sample size and stimuli determination.
  • To facilitate sound research design using mixed-effects models.

Main Methods:

  • Discussing simulation-based power analysis for mixed-effects models.
  • Illustrating use cases: sample size with existing data, determining stimuli, and planning without data.
  • Providing code and resources for linear and generalized linear models.

Main Results:

  • Demonstrated how to estimate power for mixed-effects models across various scenarios.
  • Provided practical examples and code for simulation-based power analyses.
  • Highlighted the utility of simulation for sample size planning.

Conclusions:

  • Simulation-based power analysis is a valuable tool for mixed-effects models.
  • This tutorial aids researchers in designing studies with adequate statistical power.
  • The work supports informed decision-making in research design and sample sizing.