I have used a Log10 transformation on my data to transform some non-parametric data to normally distributed so that I could run a Pearsons correlation. Field (2013) states that only problematic variables need to be transformed to attempt to achieve normality, so this is what I did, and a pearsons correlation between my normal and transformed to nomrmality datasets and all seemed well.
However, now I want to calculate Cohen's d effect size using this data. I have averaged (mean) and calculated St. Dev for the data and have used this in the Cohen's d calculation (http://www.uccs.edu/~lbecker/), but obviously, comparing means and st.dev of transformed data (i.e. usually in minus figures) vs not transformed data is giving me some very strong effects.
If I compare non-logged data then the effect doesn't come from the same dataset as the data used in the pearsons correlation, but if I use the logged data, the effect sizes I am generating seem to be far too large.
Any thoughts/suggestions appreciated.