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I have looked at different web tools for calculating confidence intervals for effect size like Cohen's d for within-subject design. My experience from this survey tells me that there are different ways to estimate within SD because results I got differs on both effect size and confidence intervals. They usually use correlations between variable to estimate pooled variance. Can anyone point me to adequate way of calculating these things?

In addition I mainly use effect size confidence intervals to determine when intervals are non-overlapping the zero effect as hypothesis testing. Does this produce the same results as using a standard confidence interval for the difference directly?



marked as duplicate by whuber Dec 23 '18 at 14:30

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  • $\begingroup$ There is no need to sign posts here -- your signature is appended automatically. $\endgroup$ – user88 Jan 13 '12 at 15:01

This is the formula that I use: within-subjects

d = t / sqrt(df)

Hope that helps!


This is an old post, but a recent paper went through the computation of Cohen's $d$ (unbiased as well as the biased version) for both within and between-subject design. It also offers a R function that does all the computation provided that the data are available.

See https://doi.org/10.20982/tqmp.14.4.p242

  • $\begingroup$ Thank you for answering. Because this post is identical to your answer in another thread, that supports a long-outstanding suggestion that these threads are duplicates. Generally, if you ever feel inclined to post the same answer to two different questions, then instead please flag one of the questions as a duplicate. $\endgroup$ – whuber Dec 23 '18 at 14:32

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