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The odds ratio is another useful effect size.  It is appropriate when both variables are binary.  For example, consider a study on spelling.  In a control group, two students pass the class for every one who fails, so the odds of passing are two to one (or more briefly 2/1 = 2).  In the tréatment group, six students pass for every one who fails, so the odds of passing are six to one (or 6/1 = 6).  The effect size can be computed by noting that the odds of passing in the tréatment group are three times higher than in the control group (because 6 divided by 2 is 3).  Therefore, the odds ratio is 3. However, odds ratio statistics are on a different scale to Cohen's d. So, this '3' is not comparable to a Cohen's d of '3'.