correction and the Huynh–Feldt correction. The Greenhouse–Geisser correction is more conservative, but addresses a common issue of increasing variability over time in a repeated-measures design. The Huynh–Feldt correction is less conservative, but does not address issues of increasing variability. It has been suggested that lower Huynh–Feldt be used with smaller departures from sphericity, while Greenhouse–Geisser be used when the departures are large. Multivariate Test—This test does not assume sphericity, but is also highly conservative. Effect size One of the most commonly reported effect size statistics for rANOVA is partial eta-squared (ηp2). It is also common to use the multivariate η2 when the assumption of sphericity has been violated, and the multivariate test statistic is reported. A third effect size statistic that is reported is the generalized η2, which is comparable to ηp2 in a one-way repeated measures ANOVA. It has been shown to be a better estimate of effect size with other within-subjects tests. Cautions rANOVA is not always the best statistical analysis for repeated measure designs. The rANOVA is vulnerable to effects from missing values, imputation, unequivalent time points between subjects and violations of sphericity. These issues can result in sampling bias and inflated rates of Type I error. In such cases it may be better to consider use of a linear mixed model. See also Notes References Design and analysis of experiments Exploration of longitudinal data (Comprehensive treatment of theory and practice) Conaway, M. (1999, October 11). Repeated Measures Design. Retrieved February 18, 2008, from http://biostat.mc.vanderbilt.edu/twiki/pub/Main/ClinStat/repmeas.PDF Minke, A. (1997, January). Conducting Repeated Measures Analyses: Experimental Design Considerations. Retrieved February 18, 2008, from Ericae.net: http://ericae.net/ft/tamu/Rm.htm Shaughnessy, J. J. (2006). Research Methods in Psychology. New York: McGraw-Hill. External links Examples of all ANOVA and ANCOVA models with up to three treatment factors, including randomized block, split plot, repeated measures, and Latin squares, and their analysis in R (University of Southampton) Design of experiments Science experiments Analysis of variance Statistical reliability