Extending Improvement-Over-Chance I-Index Effect Size Simulation Studies to Cover Some Small-Sample Cases

Abstract

All effect sizes are sensitive to design flaws and the failure to meet analytic assumptions. But some effect sizes appear to be more robust to assumption violations (e.g., homogeneity of variance). The present study extended prior Monte Carlo research by exploring the robustness of group overlap I indices at the relatively small sample sizes used in some research. I effects are statistically appealing because these indices can be applied across (a) both univariate and multivariate analyses and (b) conditions of either variance homogeneity or variance heterogeneity.