Several standardised measures of effect are used within the context of ANOVA to describe the degree of relationship between an predictor or set of predictors and the dependent variable.

η2: Eta-squared Eta-squared describes the percentage of variance explained in the dependent variable by a predictor variable. It is a biased estimate of population variance explained.

partial η2: Partial eta-squared Partial eta-squared describes the percentage of variance explained in the dependent variable by a predictor controlling for other predictors. It is a biased estimate of the variance explained in the population. The following rules of thumb have emerged: small = 0.01; medium = 0.06; large = 0.14 These rules were taken from: Kittler, J. E., Menard, W., & Phillips, K., A. (2007). Weight concerns in individuals with body dysmorphic disorder. Eating Behaviors, 8, 115-120.

Omega Squared: Omega squared provides a relatively unbiased estimate of the variance explained in the population by a predictor variable.

Cohen's f This measure of effect size is frequently encountered when performing power analysis calculations. Conceptually it represents the square root of variance explained over variance not explained

http://en.wikipedia.org/wiki/Analysis_of_variance