Handling effect size dependency in meta-analysis

The statistical synthesis of quantitative effects within primary studies via meta-analysis is an important analytical technique in the scientific toolkit of modern researchers. As with any scientific method or technique, knowledge of the weaknesses that might render findings limited or potentially erroneous as well as strategies by which to mitigate these biases is essential for high-quality scientific evidence. In this paper, we focus on one prevalent consideration for meta-analytical investigations, namely dependency among effects. We provide readers with a non-technical introduction to and overview of statistical solutions for handling dependent effects for their efforts to integrate evidence within primary studies. This goal is achieved via a series of seven reflective questions that scholars might consider when planning and executing a meta-analysis in which some degree of dependency among effect sizes from primary studies may exist. We also provide an example application of the recommendations with real-world data, including an analytical script that readers can adapt for their own purposes.