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Planning sample sizes for regression is hard. The Bias Uncertainty Corrected Sample Size (BUCSS) method helps researchers adjust effect sizes using prior data, accounting for bias and uncertainty in statistical power calculations.

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Area of Science:

  • Psychological research methodology
  • Statistical modeling

Background:

  • Statistical power is crucial in psychological studies, yet sample size planning for linear regression is complex.
  • Determining appropriate effect sizes is challenging due to predictor correlations and their impact on power.

Purpose of the Study:

  • To introduce and validate the Bias Uncertainty Corrected Sample Size (BUCSS) procedure for linear regression.
  • To provide a practical method for sample size planning that accounts for publication bias and uncertainty.

Main Methods:

  • The study proposes the BUCSS procedure, which adjusts prior sample effect sizes for bias and uncertainty.
  • A Monte Carlo simulation was conducted to evaluate BUCSS performance in regression contexts.

Main Results:

  • The BUCSS procedure is demonstrated as a valid approach for linear regression sample size planning.
  • Simulation results confirm BUCSS performs well in plausible regression scenarios.

Conclusions:

  • BUCSS offers a practical solution to the difficulties in regression sample size planning.
  • The article provides clear illustrations of BUCSS software application for common regression study scenarios.