Do not do this. They are not equivalent unless they are uncorrelated. If you have the pxp correlation matrix of predictors, Rxx, along with the px1 vector of standardized betas B, then the p x 1 vector of validities Rxy for meta-analysis is = Rxx*B.

Dear Donaldo,

I think the peterson and Brown's work is a classic one. See also the attached paper.

You may consider using the regression coefficients in your analysis, and then performing a post-hoc sensitivity analysis to check the possible influence of those studies on the overall estimate.

Hope this helps,

Gianluca

See also this one

I am in favour of including standardized regression coefficients in meta-analysis if the correlation coefficients are not reported. As suggested earlier, do a sensitivity analysis to find out possible influence on your overall estimate.

Gianluca and Olu, thank you for your input.

Do not do this. They are not equivalent unless they are uncorrelated. If you have the pxp correlation matrix of predictors, Rxx, along with the px1 vector of standardized betas B, then the p x 1 vector of validities Rxy for meta-analysis is = Rxx*B.

Betas and r are different metrics, and, under normal conditions, the betas will be smaller than the rs (i.e., correlated without supression effects). If you are doing some form of null hypothesis testing and want to be superconservative, you could include betas. If you are interested in estimating an overall effect size, then only use rs.

Thank you Dr Oswald and Dr Hanson for your valuable comments

Is it also possible to use the formula by Peterson & Brown (2005) for logistic regression?