We provide a step-by-step guide on conducting a quantitative systematic review (i.e., meta-analysis) using the open-source programming language R, as well as conducting a multilevel meta-analysis, in contexts where effect sizes are non-independent (e.g., multiple studies from the same lab). Quantitative systematic reviews offer researchers a method for synthesizing large bodies of literature, helping clarify inconsistent findings, identify research gaps, and refine theoretical models. However, existing tutorials often assume prior knowledge and/or experience, often overlooking foundational concepts. To address this gap, a comprehensive walkthrough of the systematic review process is presented, covering pre-registration, literature search and retrieval, screening, risk of bias assessment, and data extraction following the PRISMA framework. We then present detailed guidance on how to conduct both traditional and multilevel meta-analyses in R. Specifically, the tutorial explains how to estimate overall meta-analytic effect sizes when effect sizes are independent (traditional meta-analysis) and when effect sizes are nested within labs (multilevel meta-analysis). Procedures for assessing heterogeneity, testing for publication bias, and conducting moderation analyses are also covered. To accompany this tutorial, we supplement annotated R scripts and R notebooks to support transparency, reproducibility, and accessibility for researchers of all levels of experience.
Hu et al. (Tue,) studied this question.