1. Conversions of Effect Sizes

2. Log Odds and d
From log odds to d:
From d to log odds:

3. Converting r and d From r to d From d to r
If n1 and n2 are unknown, use a = 4.

4. Other conversions
If you want to move from log odds to correlation, go through d. Consider whether the results of the studies should really be combined or not.
Many meta-analysis programs assume that you have only one effect size in your data (CMA is an exception). The data analysis requires that all the effect sizes are in the same metric. You will probably have more than one effect size in your database after you find all your studies. I suggest that you use Excel (or some other program) to convert all the effect sizes to a common metric before input to R. You can create a flag for kind or type of effect size if you like and treat it as a moderator.

5. Class Exercise 2
Create an Excel sheet that converts r to d and also converts d to r.
What values of d if r = .3, .6, .9?
What values of r if d = 0, .5, 1, 1.5?
If r = .3 and N = 100, what is the variance of d?
If d = .5, and N1 = N2 = 25, what is the variance of r?