Data extraction involves the collection of relevant information from studies objectively and accurately in a consistent format to make use of the data in future stages of the review. The extracted data will be presented in the review as a summary table or summary of findings table and described in the narrative.
It is recommended that you pilot your data extraction tool (especially if you will code your data) to determine if fields should be added or clarified.
For staff, researchers and PhD students it is recommended that at least two reviewers independently extract data from each study, with a clear process in place to address any discrepancies that may arise and that extraction should be piloted to ensure all reviewers are recording similar data and the template used is appropriate. Once all reviewers have finished their extraction, they meet to compare their extracted data and create a final single data set.
The data extraction of included studies should adhere to established guidelines. Information such as title, author, year, journal, research question, specific aims, conceptual framework, hypothesis, research methods or study type, and conclusions may need to be extracted from each included study and well as effects sizes, population characteristics, etc., based on the purpose of the review. It is important to carefully review the methodology to categorise studies by type in the results section of the review. Additionally, for any intended meta-analysis, raw and refined data should be extracted from each study result.
The data extraction forms can be used to produce a summary table of the key characteristics for each included study that were considered important for inclusion.