Effect size use has been increasing in the past decade in many research areas. Confidence intervals associated with effect sizes are encouraged to be reported. Prior work has investigated the performance of confidence interval estimation with Cohen's d. This study extends this line of work to the analysis of variance case with more than two groups. Confidence interval estimation for the omega-squared (omega[superscript 2]) effect size measure was evaluated under a variety of simulated conditions with three methods where the primary interest was in the comparative performance of the three confidence interval calculation methods (i.e., parametric, percentile bootstrap, and bootstrap bias-corrected and accelerated confidence interval [BCA]) across a variety of real-life data situations. Results suggest that sample size and magnitude of the effect influence coverage rates and interval width. The percentile bootstrap method produced the widest intervals and had higher coverage rates in the smaller effect size conditions compared with the parametric and BCA approaches. (Contains 2 figures and 3 tables.)