Extracting Pre-Post Correlations for Meta-Analyses of Repeated Measures Designs

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.

重复测量设计在众多科学领域内普遍存在，并已成为元分析综合研究的常见主题。对于重复测量设计计算效应量，一个至关重要的参数是干预前后的评分之间的相关性。尽管如此，干预前后的相关性在原始研究中常常未被报告。由于对计算干预前后相关性的替代方法缺乏认知，元分析者在缺乏可用数据的情况下，往往求助于使用固定值（例如，$r = .50$）来替代缺失的干预前后相关性。正如预期的那样，不准确的干预前后相关性将导致结果不准确，这突显了采用系统程序对干预前后相关性进行经验估计的必要性。本文旨在介绍在不同信息可用的情况下所需的各种方程和代码。