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Health Sciences - Systematic Reviews & Meta-Analyses: Step 5: Data extraction

LibGuide on Systematic Reviews and Meta-Analyses

Data Extraction Elements

  • Consider your research question components and objectives
  • Consider your study eligibility (inclusion / exclusion) criteria
  • Study characteristics - This information, or a summarized version will form a preliminary table summarizing all included studies, titled as (or similar to): TABLE 1 - Study Characteristics. Examples of characteristics included:
  • Full citation 
  • Location
  • Duration
  • Objectives
  • Intervention / object of study
  • Study Design and Methodology
  • Outcome Measures
  • Results

Data Extraction Planning

Develop and pilot test a form for data extraction to:

  • Ensure that you are including a prompt to pull information you will need to describe the studies and for data synthesis/analysis
  • Test and ensure inter-rater reliability in extracting data from studies

Systematic reviews and meta-analyses: a step-by-step guide

Step 5

Data extraction

Once you have identified all studies to be included in the systematic review, the next step is to extract and analyze the data contained in those studies. For a qualitative (non-meta-analysis) systematic review, you will create Summary of Findings tables and Bias/Evidence Quality figures. A meta-analysis requires pooling of data and specialised statistical analysis.

The data extraction should be based on the previously defined interventions and outcomes established during the research question, inclusion/exclusion criteria, and search protocol development. If those stages have been done properly, it should not be too difficult to identify the data elements that need to be extracted from each included study.

Create a data extraction form that will be filled in for each included study. Use a software program that will allow you to create a form/questionnaire/survey and then create statistics, tables, and figures out of that data. There are a variety of these available including Microsoft Access/Excel, Qualtrics, REDCap, Google Forms/Sheets, etc.

At a minimum, Data Extraction includes: Study Characteristics; with particular detail related to characteristics, Outcome Measures of interest, and Results that you may use in data synthesis.

For Cochrane's Handbook on Preparing for Data Extraction click here.

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Subject Guide

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Mlungisi Dlamini
University of Johannesburg Library, Doornfontein Campus

Subject Guide

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Dorcas Rathaba
University of Johannesburg Library, Doornfontein Campus
Skype Contact: dikomorathaba