ANALYZING REPEATED-MEASURES OR RANDOMIZED BLOCK-DESIGNS USING TRIMMED MEANS

A well-known result is that the value of the standard error of the sample mean is sensitive to heavy tails (Tukey, 1960). In particular, moving slightly away from a normal distribution toward a heavier tailed distribution can increase the standard error by a considerable amount. As a result, methods for comparing means can have relatively low power. This problem is particularly serious in psychology because recent investigations indicate that the distributions associated with psychometric measures often have outliers and very heavy tails (Micceri, 1989; Wilcox, 1990a). One approach to this problem is to compare trimmed means instead. Yuen (1974) describes a method for comparing the trimmed means of two independent groups, and she demonstrated that her procedure can have considerably more power than Welch's test for means. This paper extends Yuen's solution to repeated measures and randomized block designs. Simulations indicate that the new procedure compares well to the usual F test using the Huynh-Feldt correction of the degrees of freedom.