A guide to obtaining confidence intervals for effect sizes

Effect sizes, specify the magnitude of a statistical comparison. However, this does not tell us how precisely it is measured.

There are [http://www.latrobe.edu.au/psy/esci/ details by Geoff Cumming] and Steiger (2004) on combining these concepts by giving confidence intervals for effect sizes. Michael Smithson [https://www.anu.edu.au/psychology/people/smithson/details/CIstuff/CI.html has syntax] in SPSS and other statistical software to do the computations. There are also some [https://www.anu.edu.au/psychology/people/smithson/details/CIstuff/Noncoht2.pdf workshop notes] to explain what's going on.

There is also [http://core.ecu.edu/psyc/wuenschk/SPSS/SPSS-Programs.htm SPSS syntax], with an example, for obtaining a confidence interval for Cohen's d.

Keselman et al (2008) have some [http://www.apa.org/journals/supplemental/met_13_2_110/met_13_2_110_supp.html SAS V9.1 code with examples] to produce bootstrap confidence intervals for effect sizes (ie based on releated sampling) for a robust (winsorised) version of Cohen's d in mixed anovas.

References

Keselman, HJ, Algina, J, Lix, LM, Wilcox, RR, Deering, KN (2008) A generally robust approach for testing hypotheses and setting confidence intervals for effect sizes Psychological Methods 13(2) 110-129

Steiger, JH (2004) Beyond the F Test: Effect Size Confidence Intervals and Tests of Close Fit in the Analysis of Variance and Contrast Analysis. Psychological Methods 9(2) 164-182.