eng TQMP The Quantitative Methods for Psychology 1913-4126 2026-09-20 22 2 102 118 10.20982/tqmp.22.2.p102 article Yadav, Vikas drvikasyadav@gmail.com a Trushna, Tanwi drvikasyadav@gmail.com a Goel, Akhil Dhanesh drvikasyadav@gmail.com b Mandal, Uday Kumar drvikasyadav@gmail.com a Sabde, Yogesh Damodar drvikasyadav@gmail.com a ICMR National Institute for Research in Environmental Health (ICMR-NIREH), Bhopal, India All India Institute of Medical Sciences (AIIMS), Jodhpur, India 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. https://www.tqmp.org/RegularArticles/vol22-2/p102/p102.pdf Prevalence; Meta-regression; Publication bias; Sensitivity analysis; Subgroup analysis; Baujat Plot; Radial plot; funnel plot; forest plot; Doi plot R