In meta-analysis, heterogeneity in statistical measures across primary studies can significantly affect the efficiency of data synthesis and the accuracy of result interpretation. Such inconsistencies may introduce bias in effect size estimation and increase the complexity of pooled analyses. Therefore, establishing standardized approaches for data type transformation and harmonizing different statistical measures has become a critical step in ensuring the quality of meta-analyses. To achieve efficient and scientifically rigorous data integration, researchers need to master systematic data transformation techniques and develop standardized processing strategies. Based on this need, this study provides a comprehensive summary of effect size transformation methods in meta-analysis, focusing on standardizing binary and continuous variables. It offers practical guidance to support researchers in applying these methods consistently and accurately.
