Getting started with meta-analysis

Summary

1. Meta-analysis is a powerful and informative tool for basic and applied research. It provides a statistical framework for synthesizing and comparing the results of studies which have all tested a particular hypothesis. Meta-analysis has the potential to be particularly useful for ecologists and evolutionary biologists, as individual experiments often rely on small sample sizes due to the constraints of time and manpower, and therefore have low statistical power.

2. The rewards of conducting a meta-analysis can be significant. It can be the basis of a systematic review of a topic that provides a powerful exploration of key hypotheses or theoretical assumptions, thereby influencing the future development of a field of research. Alternatively, for the applied scientist, it can provide robust answers to questions of ecological, medical or economic significance. However, planning and conducting a meta-analysis can be a daunting prospect and the analysis itself is invariably demanding and labour intensive. Errors or omissions made at the planning stage can create weeks of extra work.

3. While a range of useful resources is available to help the budding meta-analyst on his or her way, much of the key information and explanation is spread across different articles and textbooks. In order to help the reader use the available information as efficiently as possible (and so avoid making time-consuming errors) this article aims to provide a ‘road map’ to the existing literature. It provides a brief guide to planning, organizing and implementing a meta-analysis which focuses more on logic and implementation than on maths; it is intended to be a first port of call for those interested in the topic and should be used in conjunction with the more detailed books and articles referenced. In the main, references are cited and discussed with an emphasis on useful reading order rather than a chronological history of meta-analysis and its uses.

4. No prior knowledge of meta-analysis is assumed in the current article, though it is assumed that the reader is familiar with anova and regression-type statistical models.