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Systematic Review Guide

Data extraction in a systematic review is the process of systematically collecting and recording key information from the included studies. This typically includes study characteristics, methods, participants, interventions, outcomes, and results, to enable synthesis and analysis of the evidence.


Why is it important to extract data?

  • Ensures accuracy and consistency by collecting all relevant information systematically from each study.
  • Provides the structured data needed for narrative or quantitative analysis.
  • Allows others to see exactly what data were used and how conclusions were drawn.
  • Highlights where evidence is incomplete or inconsistent.
  • Reduces bias – standardised extraction by at least two reviewers minimises selective reporting or errors.
  • Provides the details needed to appraise study risk of bias or methodological quality.

How do I extract data?

  1. Decide which data to extract
  2. Develop a data extraction form
    1. Use or adapt an existing template to make it easier
  3. Pilot the form
    1. Test on a few studies to ensure it captures all relevant data.
  4. Assign reviewers
    1. Ideally, two independent reviewers should extract data to reduce errors
  5. Extract the data
    1. Use a tool like Covidence or MS Forms
  6. Check for accuracy
    1. Resolve discrepancies between the reviewers via consensus or a third reviewer
  7. Prepare for synthesis
    1. Organise the extracted data in tables or spreadsheets for easy comparison.

Example
Robertson, C., et al. (2014). Data extraction form: qualitative review. 
 

Recommended resources:

Monash Health Library Covidence Data Extraction Template

Covidence. (2022). Introduction to Extraction with Covidence.