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A Monte Carlo Comparison of Seven ε-Adjustment Procedures in Repeated Measures Designs with Small Sample Sizes
Stephen M. Quintana and Scott E. Maxwell
Journal of Educational Statistics
Vol. 19, No. 1 (Spring, 1994), pp. 57-71
Stable URL: http://www.jstor.org/stable/1165177
Page Count: 15
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The purpose of this study was to evaluate seven univariate procedures for testing omnibus null hypotheses for data gathered from repeated measures designs. Five alternate approaches are compared to the two more traditional adjustment procedures (Geisser and Greenhouse's ε̂ and Huynh and Feldt's ε̃), neither of which may be entirely adequate when sample sizes are small and the number of levels of the repeated factors is large. Empirical Type I error rates and power levels were obtained by simulation for conditions where small samples occur in combination with many levels of the repeated factor. Results suggested that alternate univariate approaches were improvements to the traditional approaches. One alternate approach in particular was found to be most effective in controlling Type I error rates without unduly sacrificing power.
Journal of Educational Statistics © 1994 American Educational Research Association