title: Transformations for within-subject designs: a Monte Carlo investigation.
abstract: We explored the use of transformations to improve power in within-subject designs in which multiple observations are collected for each S in each condition, such as reaction time and psychophysiological experiments. Often, the multiple measures within a treatment are simply averaged to yield a single number, but other transformations have been proposed. Monte Carlo simulations were used to investigate the influence of those transformations on the probabilities of Type I and Type II errors. With normally distributed data, Z and range correction transformations led to substantial increases in power over simple averages. With highly skewed distributions, the optimal transformation depended on several variables, but Z and range correction performed well across conditions. Correction for outliers was useful in increasing power, and trimming was more effective than eliminating all points beyond a criterion.
authors.affiliation: Department of Psychology and Counseling, Dartmouth College, Hanover, New Hampshire 03755.
authors.forename: L K
authors.initials: LK
authors.lastname: Bush
authors.forename: U
authors.initials: U
authors.lastname: Hess
authors.forename: G
authors.initials: G
authors.lastname: Wolford
mesh_terms: D000328:Adult; D005260:Female; D006339:Heart Rate; D006801:Humans; D007256:Information Systems; D008297:Male; D009010:Monte Carlo Method; D011603:Psychophysiology