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Data extraction is the manual process of transcribing relevant information from each of the studies selected in the Select and Screen phase of your review (i.e. your 'included studies') to a standard form or template.

The information captured during this process will include:

  • data about the included studies -- author, year, title, intervention and comparator, participants, study design etc.
  • data specific to your review -- outcomes data, measurement tool or instrument, results data such as participants lost to follow-up etc.

Note: Throughout the data extraction process, any significant decisions made by the extractors or the review team should be documented, along with the reasoning.

  1. Data points - The data that you extract must be decided upon in advance and should be detailed in your review's protocol. For information on possible data points, see Data points and templates below.
  2. Choose an electronic data extraction tool - See Data extraction tools below.
  3. Develop and pilot a data extraction form - See Data points and templates below.
  4. Select data extractors - Determine which members of the review team will extract data (the 'data extractors'). To reduce bias and error, at least two data extractors should work independently to extract data from each study. This is called 'dual data extraction' and results in two sets of data for each included study. For more information on selecting and training data extractors, see the Cochrane Handbook. 
  5. Extract data from each included study - Take care to extract data consistently in the data extraction form, and avoid data that is not needed in the analysis or synthesis.
  6. Consensus - Once the extractors have finished data extraction, any discrepancies between the two sets of data are reviewed and discussed to reach consensus. If necessary or desirable, a third member of the review team may moderate.
  7. Store extracted data - Ensure that the extracted data is stored in a secure and stable location that can be accessed for the duration of the review and into the future, if the review is updated.

As per the PRISMA 2020 Checklist, your systematic review must present:

  • Characteristics of each included study (Item #17)
  • Results of each included study, including key statistics for all outcomes (Item #19)

For more detailed guidance on which data to extract, see Table 5.3.a of the Cochrane Handbook, linked below.

Cochrane - Checklist of items to consider in data collection

Tip: If you are unsure, consider checking other systematic reviews on your topic to see which data they extracted.

Data extraction for meta-analysis

Planning to conduct a meta-analysis? See the below guide by Pedder et al. (2016), and tips from the Centre for Evidence-Based Medicine (CEBM) at the University of Oxford.

Pedder et al. (2016) - DECiMAL guide

CEBM - Data extraction for meta-analysis

Covidence

Monash Health employees and researchers can access Covidence through the library. Request a Covidence workspace here. As well as selecting and screening studies, Covidence can be used for data extraction. The video below provides a brief introduction to extracting data in Covidence. Covidence provides more information in their free eBook, A Practical Guide: Data Extraction for Intervention Systematic Reviews.

A Practical Guide: Data Extraction for Intervention Systematic Reviews

SPSS Statistics

SPSS software can be used to analyse and manage quantitative data. SPSS is available at designated Monash Health Library PCs in Clayton, Dandenong, and Casey. See the IBM Documentation for SPSS Support.

 PSPP is a free alternative to SPSS. Please note that the Library does not provide support for PSPP. 

Learn more

NVivo

NVivo software can be used to analyse and manage qualitative data. NVivo is available at designated Monash Health Library PCs in Clayton, Dandenong, and Casey. NVivo Transcription services are available via a ‘pay-as-you-go’ model to be paid by your Department. Search the NVivo Knowledge Base for support.

 Dedoose, Atlas.ti, and MAXQDA are paid alternatives to NVivo. Taguette is a free alternative. Please note that the Library does not provide support for these products

Systematic Review Data Repository (SRDR+)

SRDR+ is a free platform for extracting, archiving, and sharing data generated from systematic reviews.With SRDR+ you can build data extraction forms, extract data, export, and collaborate with members of your team. The below video provides an introduction to creating an extraction template in SRDR+.

RevMan

RevMan is the preferred tool for creating Cochrane Reviews. RevMan should be available for non-Cochrane reviews, for a fee, from Q1 2023 onwards. For RevMan support, see the RevMan Knowledge Base.

Colandr

Colandr is a free, open access platform for conducting reviews. Data extraction within Colandr is powered by natural language processing to speed up the process. See the Setting Up Data Extraction guidance for more information.

Excel or Google Sheets

Using Excel or Google Sheets for data extraction can be a simple, yet effective method. Excel and Google Sheets can be customised and are freely available. You can also use and adapt existing templates, such as this one available from Texas A&M University Library.

Developing an extraction form

You don't need to start from scratch. Existing data extraction forms and templates are available in many data extraction tools such as Covidence. Note: Existing forms and templates must be adapted to better fit your specific review and the data that you plan to extract. 

Our Covidence Extraction Template Guide provides practical tips and guidance on developing and adapting a data extraction template to meet the needs of your review.

Covidence Extraction Template Guide

Templates are also available online in various formats -- examples are linked below.

Piloting the extraction form

Before beginning data extraction, first pilot the form to ensure that the data extractors are recording similar data for every field. If there are any discrepancies, revise the form before proceeding to data extraction.

This step avoids wasted time later in the review, as making changes to the form after data extraction has begun will compromise the data already collected; and it takes time to resolve discrepancies between extracted data. Piloting the form is also important for training the data extractors.

Qualitative systematic reviews require different data to be extracted, such as:

  • theoretical framework
  • cultural setting
  • data collection method used in the study
  • the authors' potential biases
  • findings - in the form of e.g. quotations from participants or themes and subthemes identified by the authors

The types of data you extract will be determined by the methodological approach you have taken.

More information:

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