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Systematic and Systematic-like Reviews

Extract the data

Once you have decided which articles to include in your review, you need to extract relevant data from those studies.  This should be done in a structured and systematic way and is usually presented in tables in the final review report.


  • Extract only the data that is relevant to your research question
  • Create a table or data collection form so that you can summarise the data consistently for each study
  • Convert all the data to the same units of measurement, where possible
  • Make sure your table headings can be easily interpreted by others
  • Make sure any abbreviations or acronyms used in the table are explained in footnotes
  • Pilot your data extraction method before you start – especially if more than one person is extracting the data.

Useful Tools

You can extract your data into a simple table, but if you want a more detailed approach there are a number of analysis tools that can help:

  • NVivo (CSU supported and great for qualitative data analysis)
  • Dovetail - alternative to NVivo, without the steep learning curve. Use it for qualitative data analysis plus it transcribes audio. There is a free version available. Get started with Dovetail for beginners.
  • PSPP (Open source software)
  • JASP (Open source software)
  • GRADEpro software has been developed by the GRADE working group to support the creation of Summary of Findings Tables for Cochrane Reviews and Evidence Profiles. More information about the GRADE approach to assessing the quality of evidence can be found in the Cochrane Handbook for Systematic Reviews of Interventions. Watch how to use GRADEpro or view the handbook for more information.
  • Elamin, M. B., Flynn, D. N., Bassler, D., Briel, M., Alonso-Coello, P., Karanicolas, P. J., Guyatt, G. H., Malaga, G., Furukawa, T. A., & Kunz, R. (2009). Choice of data extraction tools for systematic reviews depends on resources and review complexity. Journal of Clinical Epidemiology, 62(5), 506-510.


Synthesise the data

Generic forest plotYou will need to develop a synthesis, or critical overview, which integrates the key findings from all your included studies, while considering the methodological quality and other pertinent features of each of those studies (such as the sample size, population, or the context in which each one was conducted).     


  • Narrative, or descriptive. If you are not completing a full systematic review, or if you are comparing different study designs, this could be the best option. There are no strict rules, but you should try to minimise bias and maximise credibility by carefully exploring the relationships between studies and assessing the robustness of the synthesis.
  • Qualitative synthesis methods: Meta-synthesis, Meta-study or Meta-aggregation. These methods synthesise the findings from very different types of studies, clustering common characteristics into categories or using interpretative tools.
  • Meta-analysis: Used to synthesise the results of quantitative studies, this statistical technique results in an estimate of the “average” intervention effect. Data from different studies are weighted depending on the sample size and relevant criteria, and evaluated to work out a cumulative outcome. The results of a meta-analysis are often summarised in a forest plot. See:



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