Relative efficiency of confidence interval methods around effect sizes

Reporting effect sizes and corresponding confidence intervals is increasingly demanded, which generates interest to analyze the performance of confidence intervals around effect sizes. As effect sizes take on the value zero in case of no effect per definition, not only the inclusion of the population effect, but also the exclusion of the value zero are therefore performance criteria for these intervals. This study is the first to compare the performance of confidence interval methods applying these two criteria via determining their finite relative efficiency. Computing the quotient of two methods' minimum required sample sizes to achieve levels of both criteria allows to account for the problem of limitations in available observations, which often occurs in the educational, behavioral or social sciences. Results indicate that confidence intervals based on a noncentral t-distribution around the robust effect size proposed by Algina et al. (2005) possess high relative efficiency.