Using R for Meta-Analysis in Business Research Context


  • Gamon Savatsomboon Mahasarakham Business School, Mahasarakham University, Mahasarakham 44150, Thailand
  • Ong-art Chanprasitchai Mahasarakham Business School, Mahasarakham University, Mahasarakham 44150, Thailand
  • Anirut Pongklee Faculty of Agriculture and Technology, Nakhon Phanom University, Nakhon Phanom 48000, Thailand
  • Sujin Butdisuwan School of Liberal Arts, Shinawatra University, Pathum Thani 12160, Thailand
  • Piyapun Santaveesuk School of Liberal Arts, Shinawatra University, Pathum Thani 12160, Thailand
  • Kanokwan Noppan Independent Researcher


Meta-analysis, PICO, PRISMA, R, meta Package, dmetar Package


R is widely used to perform meta-analysis for international publication. However, R has been rarely used by Thai academics and researchers to conduct meta-analyses for research and publication. This could be viewed as a practice gap. Thus, the goal of this paper is to alleviate this gap by encouraging the Thai academic and research community to use R for meta-analysis in the business research context. This article will demonstrate that R is just as effective as other programs at performing a basic meta-analysis, and beyond. The article is divided broadly into two main parts: systematic review and meta-analysis. The idea of systematic reviews is explained in the first part of the article, which comprises two subsections. The primary goal of Subsection 1 is the application of SRQ (instead of PICO), a framework for precisely articulating the research question that serves as the basis for additional literature inclusion in the meta-analysis at hand. Subsection 2, which defines the criteria for accepting or rejecting studies to be included in a meta-analysis study at hand, deals with the application of PRISMA. The remaining part of the paper focuses on how meta-analysis is carried out in R, as well as related topics, starting with the import of datasets for analysis from an Excel file. Examples of R scripts are provided to conduct meta-analysis using R. Text outputs of all required statistics are generated, for example, pooled effect sizes in the form of common (fixed) and random effects for assessing the heterogeneity of studies included in the meta-analysis at hand. In addition, R can generate both forest and funnel plots. The paper also discusses how to use the funnel plot to determine the publication bias of the study. R is fully capable of doing comprehensive meta-analyses, being on par with or even surpassing other programs in some areas. Thai researchers and academics can utilize R with confidence. Thus, Thai researchers and academics are strongly encouraged to use R for meta-analysis and share their findings, like other international researchers.


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How to Cite

Savatsomboon, G., Chanprasitchai, O.- art, Pongklee, A., Butdisuwan, S., Santaveesuk, P., & Noppan, K. (2024). Using R for Meta-Analysis in Business Research Context . Journal of Research Methodology, 37(1), 67–88. Retrieved from



Academic Article