Skip to Main Content

Evidence Synthesis & Systematic Review Research

This guide provides an introduction to evidence synthesis research methods.

Why Do Data Extraction?

Data extraction from the final set of studies included in the review allows for the completion of several review-related tasks (Adams, 2023):

  • obtain the information to assess risk of bias and applicability
  • structure information to help summarize studies
  • get quantitative data for meta-analysis (if conducting)

 

The Cochrane Handbook (Li et al., 2019) recommends that "more than one person extract data from every report to minimize errors and reduce introduction of potential biases by review authors." Also recommended is that "it is preferable that data extractors are from complementary disciplines, for example a methodologist and a topic area specialist."

 

References:

  • Adams, G.P. (2023, March 19). Selecting and using data extraction tools for systematic reviews [Seminar presentation]. Medical Library Association, online. https://www.mlanet.org/professional-development/medlib-ed/
  • Li T, Higgins JPT, Deeks JJ. Chapter 5: Collecting data [last updated October 2019]. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.5. Cochrane, 2024. Available from www.training.cochrane.org/handbook

 

 

Data Extraction Tools

Data extraction tools can be quite simple or complex. The form used will be as long or short as necessary to extract and code the information pulled from the studies included in the review.

Simple (selected)

  • Word or Docs or Pages
  • Excel or Sheets or Numbers

Complex (selected)

  • Covidence
  • DistillerSR
  • EPPI Reviewer
  • PICO Portal
  • SRDR+

 

Data extraction can be simple or complex according to the requirements of the project. The examples presented here show excerpted data summaries or descriptions of the data extracted as their corresponding data extraction forms were not available.

 

Below, the excerpt of a summary table for employability assessment instruments in the field of engineering (Zahn et al., 2025) shows several of the data elements that were extracted from the included studies (description of employability assessment, indicators of employability).

https://d2jv02qf7xgjwx.cloudfront.net/accounts/53280/images/zahn_2025-markup.png

 

Below the description of extracted data elements is extensive for a study of covid vaccine efficacy in immunocompromised patients (Lee et al., 2022) and includes study characteristic elements; participant elements; intervention elements; and outcome elements.

image of data extraction section of manuscript with four noted categories of extracted data elements highlighted.

 

 

References:

  • Lee, A. R. Y. B., Wong, S. Y., Chai, L. Y. A., Lee, S. C., Lee, M. X., Muthiah, M. D., Tay, S. H., Teo, C. B., Tan, B. K. J., Chan, Y. H., Sundar, R., & Soon, Y. Y. (2022). Efficacy of covid-19 vaccines in immunocompromised patients: systematic review and meta-analysis. BMJ (Online), 376, e068632–e068632. https://doi.org/10.1136/bmj-2021-068632
  • Zhan, S., Chapman, E., Valentine, A., & Male, S. (2025). Enhancing the measurement of employability in engineering: insights from a systematic literature review. European Journal of Engineering Education, 1–20. https://doi.org/10.1080/03043797.2025.2502473