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Related Experiment Videos

Transformations for within-subject designs: a Monte Carlo investigation

L K Bush1, U Hess, G Wolford

  • 1Department of Psychology and Counseling, Dartmouth College, Hanover, New Hampshire 03755.

Psychological Bulletin
|May 1, 1993
PubMed
Summary

Transformations like Z-scores and range correction significantly boost statistical power in within-subject designs, outperforming simple averaging for reaction time and psychophysiological data.

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Area of Science:

  • Psychology
  • Statistics
  • Experimental Design

Background:

  • Within-subject designs often collect multiple measures per participant per condition.
  • Averaging these measures is common but may not optimize statistical power.
  • Alternative data transformations exist but their impact on error rates is less understood.

Purpose of the Study:

  • To evaluate the effectiveness of various data transformations in enhancing statistical power for within-subject designs.
  • To compare the influence of transformations on Type I and Type II error probabilities.
  • To identify optimal transformations for different data distributions, including skewed data.

Main Methods:

  • Utilized Monte Carlo simulations to rigorously assess transformation effects.

Related Experiment Videos

  • Compared simple averaging against Z-score and range correction transformations.
  • Investigated performance under both normal and highly skewed data distributions.
  • Main Results:

    • Z-score and range correction transformations substantially increased power with normally distributed data compared to averaging.
    • These transformations also performed well across various conditions with highly skewed data.
    • Outlier correction, particularly trimming, proved beneficial for increasing power.

    Conclusions:

    • Data transformations, specifically Z-scores and range correction, offer significant advantages for improving statistical power in within-subject designs.
    • These methods are robust across different data distributions, including skewed data common in psychological research.
    • Employing appropriate transformations and outlier handling (trimming) is recommended for more sensitive and reliable experimental findings.