Hello Terun. You listed SPSS as one of the topics for your question. I suspect that either file 6 or file 7 from the page linked below will give you what you want.

• https://sites.google.com/a/lakeheadu.ca/bweaver/Home/statistics/spss/my-spss-page/weaver_wuensch
• https://link.springer.com/article/10.3758%2Fs13428-012-0289-7

HTH.

My Dear you can from any software as SPSS is a good programe to compute corr.cof. (r) and test it with p-value; if p-value

You can do this online:

https://www.psychometrica.de/correlation.html

You could also run an analysis of covariance, i.e. a regression analysis:

post = pre + treatment (in that order, pre is the covariate)

There should not be an interaction 'pre*treatment'

I do not understand why you would want to compare 2 r values. Each is a measure of the degree of correlation between 2 variables of interest. If you want to see if the same correlation holds between 2 sets of data (perhaps different conditiond), then you should be comparing the slopes of the 2 correlations and also their intercepts for differences. Your regression fit should have provided you with SEMs for each fitted parameter, so those can be tested for significant differences, probably with a simple Student's t-test, if not too many comparisons are being made.

I also not fully understand your study design. If it is a crossover design (each participant gets treatment A and after some washout time treatment B or the opposite), repeated measures t-test may be used. If there are two independent groups of participants with pre-post and treatment A-B factors, a two way repeated measure ANOVA is ok and the effect of the interaction should be tested. If the pre-post variable is of continuous nature, then you may compare the slopes. Best method seems to be General Linear Model test for interaction of the slopes. Of course it all if the effects are linear and other assumptions of parametric tests are met.

Hope it helps. Good luck!