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Meta-analysis is a powerful statistical tool that combines results from multiple studies to produce a pooled estimate, providing a clearer, more accurate picture of the research question. In behavioral research, meta-analysis of single proportions, such as the prevalence of specific behaviors or psychological conditions, is particularly useful for quantifying behavioral patterns across diverse populations. This article offers a practical guide to conducting meta-analyses of single proportions in R. Using the R package ‘meta’, this guide explains how to conduct a meta-analysis using the Generalized Linear Mixed Model (GLMM) approach, perform subgroup, sensitivity and meta-regression analyses, and detect publication bias. The step-by-step R scripts provided are designed to make meta-analysis accessible to behavioral researchers with varying levels of statistical expertise, particularly in low-resource settings. By synthesizing data from multiple smaller studies, meta-analysis helps bridge critical knowledge gaps and supports the development of evidence-based behavioral research and intervention strategies. To support the reproducibility of the analyses, the R script and a sample dataset are available in a GitHub repository.

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