Practical workshop on conducting open and reproducible meta-analyses using ‘metafor’ R package

Recently, there has been growing concern about the reproducibility of scientific research, particularly in the social sciences. Meta-analysis is one of the widely used statistical technique to synthesize results from numerous studies to estimate the overall effect size of a particular phenomenon. Conducting open and reproducible meta-analyses is critical for ensuring the transparency and validity of research findings. To achieve this, R, a free and open-source statistical software can be utilized as it provides various statistical options for conducting meta-analyses. R allows researchers to fully document their analysis process, making it simpler for others to replicate the study and validate its results. In addition, R provides a range of packages that allow for advanced meta-analytic techniques, such as meta-regression and reliability generalization meta-analysis. In this workshop, we will walk through the steps for conducting an open and reproducible meta-analysis using ‘the metafor R package. We will cover data acquisition and management, effect size estimation, publication bias assessment, and sensitivity analyses. By the end of this workshop, you will have the necessary skills to conduct a meta-analysis using R that is transparent, reproducible, and robust.   


Reasons for conducting a meta-analysis

Benefits of conducting a meta-analysis      

Typical research questions a meta-analysis address  

Importance of open and reproducible meta-analyses      

Conducting a meta-analysis - Search, coding, and pre-registration    

Issues and challenges in conducting meta-analyses  

Open-Source Framework templates 

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