I am interested in an average effect size between heart rate and anxiety. Anxiety is separated in 2 factors (F1 and F2), and I would like to combine the correlations of those factors into one correlation. In addition, I also have the correlations between heart rate and anxiety during 4 stages(H1-H4) of an experiment for each of the factors. Now my question is, would it be possible to obtain one average effect size (r) which summarizes all this information?

Factor 1 H1=-.12 H2=-.18 H3=-.32 H4=-.24 Factor 2 H1=-.12 H2=-.03 H3=-.12 H4=-.12

Sample size =76. Now, I know I can calculate the average correlation from two studies by transforming them with a Fisher’s Z transformation, average the z, and then calculate the r. However, I don’t know if this would be a valid approach within the experimental conditions. Any help is much appreciated.

Thanks, ]]>

I have extracted data from longitudinal studies that have collected data on pre and post time points. All studies have same time period.

So I have got

N_pre

Mean_Pre

SD_Pre

N_post

Mean_Post

SD_Post

In order to compute the standardised or unstandardised Mean differences, I need to use the denominator SD. What I know is I need the SD of mean difference. Unfortunately this is not mentioned available for many studies. Also it may be computed based on (r=correlation coefficient if it is provided, or other statistics like t etc.)

But I have seen some papers that ignore the above rule and just use SD_Pre or SD_Pooled: (SD_Pre and SD_Post). I am so confused, are we allowed to do that?

Can anyone please guide.

Thank you.

Kind regards

Aziz ]]>