I am performing a meta analysis on longitudinal cohort studies reporting outcomes in cognitive performance as a function of adherence to a nutritional variable. Most of them provide "standardized means differences" or "standardized beta regression coefficients" which I can easily convert to a correlation coefficient.
However, there are some studies that only report the results of their regression analysis as raw means differences (95% CI, p value) derived by using the highest tertile (vs. lowest and middle) as reference for comparison. These means differences are expressed in transformed scores, either T or Z scores.
How can I compute a Cohen's d coefficient with this information?
My first thought was to calculate SE/SD from the CI but given the fact that they are asymmetrical I don't think that makes any sense. Should I just divide the value by the SD of T-Scores (SD = 10) or Z-Scores (SD = 1)?