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Better power by design: Permuted-subblock randomization boosts power in repeated-measures experiments.

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Temporal variations in experiments reduce statistical power. Permuted-subblock randomization (PSR) balances conditions over time, boosting experimental power by an average of 13% and controlling false positives. An R package, explan, implements this method.

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Area of Science:

  • Experimental Psychology
  • Research Methodology
  • Statistical Power

Background:

  • Participant performance can vary during experimental sessions due to learning, fatigue, or attention fluctuations.
  • This temporal variation can negatively impact measurement accuracy and reduce statistical power in research.
  • Standard randomization methods may not adequately address time-dependent changes in participants.

Purpose of the Study:

  • To introduce and evaluate a restricted randomization algorithm, permuted-subblock randomization (PSR), designed to mitigate temporal variations in experimental sessions.
  • To assess the effectiveness of PSR in boosting statistical power while maintaining control over false positive rates.
  • To provide a practical tool for researchers to implement PSR in their experimental designs.

Main Methods:

  • Utilized Monte Carlo simulations to evaluate PSR performance under various time-dependent error scenarios.
  • Simulated error types included exponential decay (learning effects), Gaussian random walk, pink noise, and a mixture of these.
  • Assessed the impact of PSR on statistical power and false positive rates across different study designs.

Main Results:

  • Permuted-subblock randomization (PSR) demonstrated an average increase in statistical power of approximately 13%.
  • The power boost ranged from 4% to 45% across various simulated study designs.
  • PSR effectively controlled the false positive rate when no time-dependent variation was present.

Conclusions:

  • PSR is an effective restricted randomization technique for enhancing statistical power in experiments with temporal variations.
  • The algorithm successfully balances experimental conditions throughout a session, improving measurement reliability.
  • The R package 'explan' offers a user-friendly implementation of PSR for experimental planning.