Introduction to Meta-Analysis

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Start - End
1 Feb - 3 Feb
Study Options
£ 225
Contact Name
Centre for Applied Statistics Courses
Contact Email

01 February 2021–03 February 2021, 9:30 am–1:00 pm

This course provides an overview of meta-analysis from a statistician's point of view, with an optional half day workshop in R.

NOTE: Due to the coronavirus outbreak, all courses will now be delivered online through a live video feed. You can expect the same level of group and individual support as you would have received in our face-to-face courses.


Day 1

Meta-analysis is "the statistical analysis of a large collection of analysis results from individual studies for the purpose of integrating the findings" (Glass, 1976)

We introduce the merits of meta-analysis and how it can form an important and informative part of a systematic review. We explain the most common statistical methods for conducting a meta-analysis and common issues that may be encountered along the way. At the end of the course, delegates should be able to conduct a meta-analysis of their own and interpret the results of meta-analyses published in journal articles.

The following topics are covered:

  • An introduction to meta-analysis and its place in evidence-based research.
  • Outcome measures and extracting relevant data from journal articles
  • Fixed effect and random-effects models
  • Heterogeneity between studies
  • How to identify and deal with publication bias.

Related topics that we don't cover on this course are (1) how to conduct a systematic search of the literature, and (2) assessing the quality of studies in a meta-analysis.

A basic level of statistical literacy is required as a prerequisite. In particular, delegates should have a basic understanding of standard errors, p-values and confidence intervals. Those who have completed the five-day 'Introduction to Statistics and Research Methods' course run frequently by the Centre for Applied Statistics Courses (CASC) team will be equipped.

R supplement (optional)

In the optional half-day of the course, the theory is put in practice with the use of R (Rstudio) and real-world datasets. A basic knowledge of R programming is recommended as a prerequisite (taught on our 1 day course - 'Introduction to R'). If you are not sure whether you have sufficient knowledge in R, please take our short test in the separate tab titled 'Prerequisite test for R workshop'.

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