1. 1 Introduction Description of the video series Meta-analysis in R with Metafor

2. Background Series of short videos on using metafor in R to compute a meta-analysis. The videos concern how to use the software ; they do not explain meta-analysis. Background you need: Univariate statistics (analysis of variance, correlation and regression). If you don’t have this, you need an introductory stats course. Meta-analysis theory. You must know meta-analytic theory (e.g., understand sampling error, I-squared, tau, mixed effects and meta-regression). If you don’t already have this background, read a book such as the following: Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Chichester, UK: Wiley.

3. Getting Started Metafor was written by Wolfgang Viechtbauer. Before you start, you should read (at least skim) his paper. Viechtbauer W. Conducting meta-analysis in R with the metafor package. Journal of Statistical Software 2010, 36 (3):1-48. This will help you understand the videos and help you see that metafor does many things in addition to what I cover in these videos. The software is pretty comprehensive. You will need a computer with an internet connection. Either Windows or Mac will do.

4. Website There is a website that supports these videos. It contains R code used in the videos that you may download and run Excel files with data The PowerPoint slides for the videos (like this one) http://faculty.cas.usf.edu/mbrannick/meta/index.html You may also discover a series of PowerPoint slides that I use when teaching a course, but these serve primarily as a supplement to the Borenstein et al. text, so they may or may not prove helpful.

5. 1 - Basics Basics Videos 1 introduction (this video) 2 Getting started – downloading and installing R and metafor 3. Uploading data from Excel to R 4. Preliminary calculations of effect sizes to achieve a common metric 5. Preferred upload formats – metafor likes certain kinds of data input (but will handle whatever you supply so long as it is common across studies)

6. 2 – Computing the Overall Mean Fixed-effects and Random-Effects Models 6a for r (correlation) and d (standardized mean difference) 6b for generic models 7a for generic binary data 7b for preferred r and d 8 for preferred binary 9 confidence and credibility or prediction intervals

7. Adding Complexity 10 Moderators 11 Funnel Plots Forest Plots 12 Forest plot 1 13 Forest plot 2 14 Forest plot 3 15 Sensitivity analysis – checking for the impact of bias, bad data, and consequential decisions on the implications