The Effects of Nonnormal Distributions on Confidence Intervals Around the Standardized Mean Difference: Bootstrap and Parametric Confidence Intervals

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Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs (2013, methodology): “There are also bootstrapping procedures to calculate Cohen’s d s when the data are not normally distributed, which can provide a less biased point estimate (Kelley, 2005).”
Confidence Interval Coverage for Cohen's Effect Size Statistic (2006, methodology): “Table 2 presents summary results when data were nonnormal in just one of the two groups (Kelley, 2005, also investigated such a case).”
Changing philosophies and tools for statistical inferences in behavioral ecology (2009, background): “One can therefore calculate the effect size for thousands of bootstrap samples drawn from the original empirical sample and then obtain the 95% CI by determining the range of frequency distribution which includes 95% of those effect sizes (Manly 1991; Kelley 2005).”