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Description: Repeated measures designs are prevalent across various scientific disciplines and have become a frequent subject of meta-analytic syntheses. An essential parameter to calculate effect sizes for repeated measures designs is the correlation between pre and post intervention scores. Despite this, pre-post correlations are frequently unreported in primary studies. As a result of the lack of awareness of alternative methods for calculating pre-post correlations, meta-analysts often resort to the use of fixed values (e.g., $r = .50$) to replace unavailable pre-post correlations. As you would expect, innacurate pre-post correlations will lead to innacurate results, highlighting the need for a systematic procedure for empirically estimating pre-post correlations. The purpose of this paper is to present the necessary equations and code for various scenarios where different information may be available.