Chapter 1 About this Guide

This guide shows you how to conduct meta-analyses in R from scratch. The focus of this guide is primarily on clinical outcome research in psychology. It was designed for staff and collaborators of the Protect Lab, which is headed by Prof. Dr. David D. Ebert.

The guide will show you how to:

  • Get R and RStudio set for your Meta-Analysis.
  • Get your data into R.
  • Prepare your data for the meta-analysis.
  • Install and use the dmetar R package we built specifically for this guide.
  • Perform fixed-effect and random-effects meta-analysis using the meta and metafor packages.
  • Analyse the heterogeneity of your results.
  • Tackle heterogeneity using subgroup analyses and meta-regression.
  • Check if selective outcome reporting (publication bias) or \(p\)-hacking is present in your data.
  • Summarize the risk of bias of your study material.
  • Calculate the power of a meta-analysis.
  • Convert effect sizes reported in original studies to the ones you need for your meta-analysis.
  • Do advanced types of meta-analyses, such as
    • multilevel meta-analyses
    • multivariate meta-analyses
    • meta-analytic structural equation modeling or
    • network meta-analyses.

What this guide will not cover

Although this guide will provide some information on the statistics behind meta-analysis, it will not give you an in-depth introduction into how meta-analyses are calculated statistically.

It is also beyond the scope of this guide to advise in detail which meta-analytical strategy is suited best in which contexts, and on how the search, study inclusion and reporting of meta-analyses should be conducted. The Cochrane Handbook for Systematic Reviews of Interventions, however, should be a great source to find more information on these topics.

Other sources to recommend when conducting Meta-Analyses

  • If you are looking for an easily digestable, hands-on introduction on how Meta-Analyses are conducted, we can recommend Pim Cuijpers’ online courses on meta-analysis. The courses are freely available on YouTube. To have a look, click here. The course is based on a book (Cuijpers 2016), which you can download for free here.
  • If you are interested in more details on how to conduct meta-analyses in R, you can either have a look at Wolfgang Viechtbauer’s webpage for the metafor package (Link). Or you can consult a book written by Schwarzer and colleagues on the meta package (Schwarzer, Carpenter, and Rücker 2015). We can also recommend a book written by Terri Pigott, which focuses more on the theoretical background of and advances in meta-analytic techniques (Pigott 2012).

How to get the R code for this guide

All code behind this book is available online on GitHub. We have created a website containing a download link for all material, and a quick guide on how to get the R code presented in this book running on your computer. The site can be found here.

How to cite this guide

Harrer, M., Cuijpers, P., Furukawa, T.A, & Ebert, D. D. (2019). Doing Meta-Analysis in R: A Hands-on Guide. DOI. Citation Download

To get started, proceed to the next chapter!


This guide is an open project, and we owe special thanks to our expert contributors who provided additional content in some of the sections of this guide.

Want to contribute to this guide yourself? Feel free to send Mathias () an E-mail and tell us about your proposed additions.


Cuijpers, Pim. 2016. “Meta-Analyses in Mental Health Research. A Practical Guide.” Amsterdam, the Netherlands: Pim Cuijpers Uitgeverij.

Schwarzer, Guido, James R Carpenter, and Gerta Rücker. 2015. Meta-Analysis with R. Springer.

Pigott, Terri. 2012. Advances in Meta-Analysis. Springer.