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Step-by-Step Meta-Analysis in R

Learn how to run meta-analysis using R and explore moderation techniques in this introductory guide by Dr. Morgana Lizzio-Wilson. Set up data, install metafor package, run analysis, and interpret results efficiently.

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Step-by-Step Meta-Analysis in R

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  1. Meta-analysis in R:An introductory guide Dr Morgana Lizzio-Wilson Postdoctoral Research FellowSchool of PsychologyThe University of Queensland

  2. Our agenda • How to run meta-analysis using R • Briefly touch on moderation in meta-analysis using R

  3. You will need… • R (see Andy Field’s book for installation and setup procedures) • Metafor package for R • Metafor documentation • Your data organised in a .csv file (see example on slide 4)

  4. Set up your data Study no. Effect size SD/SE

  5. Install and run metafor Installs metafor package from the CRAN (an online repository that stores versions of code for R)

  6. Install and run metafor Tells R to use this package (run each time you open R and want to conduct a meta-analysis)

  7. Load datafile Loads the datafile and gives it a name that you will use in subsequent code

  8. Run the meta-analysis Runs the meta-analysis Effect size Datafile • “FE” means we are running a fixed-effects model(which provides an inference about the average effect in the set of the studies included in the meta-analysis) • We could also run a random-effects model(see next slide) SD/SE

  9. Run the meta-analysis • “REML” means we are running a random-effects model(which provides an inference about the average effect in the entire population of studies from which the included studies are assumed to be a random selection) • Decide what inferences you want to make before running the analysis

  10. Interpret the output Measures variation in outcome variable(s) between studies. A non-significant result indicates that the effect sizes included in the analysis are homogeneous. A significant result indicates that the effects are heterogeneous, which could be due to sampling error or moderators. The p value is < .05 and the CIs do not cross zero. This means that the meta-analysis is significant (woot!) so we can say that there is a significant effect across the four studies

  11. Example write-up To estimate the overall indirect effect, we conducted a meta-analysis of the 4 effect sizes across the 4 studies. A fixed-effects model was specified using the metafor package for R (Viechtbauer, 2010).  Results showed a significant overall indirect effect of subtype on intra-gender hostility via collective threat, aggregate IE = .03, SE = .01, CIs [.01, .04]. Note: The Q statistic is usually reported in a table with the effect sizes for each study rather than in text.

  12. Moderation in meta-analysis • Code and output vary depending on whether you’re using continuous or categorical moderator(s) • Consult the metafor documentation for help with code and output interpretation:

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