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Systematic Reviews: Data Extraction

Interested in writing a systematic review? This guide is designed to help you navigate the process.

What Data to Collect

Determining what information to collect during the coding phase of a systematic review is of critical importance and should be developed in the early planning stages. It is important to have a realistic and clear understanding of the information that will be extracted from each of the articles. Overlooking important information in the initial data extraction phase can weaken the quality of the review and may require extra time to return to each article to gather this information. Conversely, attempting to extract too much information can be tedious and may actually obscure the key purposes for the review. For these reasons, it is imperative to have a clearly defined scope and objectives for the review. Typically, the data collected will include the following elements:

  • Study methods (study design, statistical analysis, etc.)

  • Participants (setting, geographic location, demographic information, etc.)

  • Intervention (description of how the study is testing/observing the research questions, etc.)

  • Outcomes (dependent variables in the study, including measurement tool or instrument)

  • Results (summary data for study participants/groups, both significant/non-significant findings, etc.)

PRISMA Protocol Checklist

How to Collect Data

Prior to extracting data from articles that meet the eligibility criteria, study authors should develop a concise codebook that easily and clearly identifies all the data that should be collected. This codebook can include brief explanations and other instructions to ensure consistency in data extraction.

It is helpful to conduct a pilot coding session to ensure that all coding variables are clearly defined and that all pertinent data is being captured. During this pilot session, all data extractors should test this codebook on a smaller sample of articles to see if the data is collected consistently and without confusion. Modifications to the codebook can then be made prior to completing data extraction from all of the eligible articles.

Data should be extracted using some type of data collection form. Typically, this is an electronic form (e.g., Google Form, Qualtics Survey, Microsoft Access), but paper forms can also be used. Electronic forms have the advantage of allowing easier data manipulation and analysis. Additionally, various data extraction software tools are available to effectively manage extracted data from multiple reviewers.

Cochrane Good Practice Data Extraction Form

Data Extraction Tools

  • Covidence – a web-based software platform for conducting systematic reviews, which includes support for collaborative title and abstract screening, full-text review, risk-of-bias assessment and data extraction. Full access to this system normally requires a paid subscription but is free for authors of Cochrane Reviews. A free trial for non-Cochrane review authors is also available.

  • DistillerSR – a web-based software application for undertaking bibliographic record screening and data extraction. It has a number of management features to track progress, assess interrater reliability and export data for further analysis. Reduced pricing for Cochrane and Campbell reviews is available.

  • EPPI-Reviewer – web-based software designed to support all stages of the systematic review process, including reference management, screening, risk of bias assessment, data extraction and synthesis. The system is free to use for Cochrane and Campbell reviews, otherwise it requires a paid subscription. A free trial is available.

  • Systematic Review Data Repository (SRDR) – created by the Brown University Evidence-based Practice Center and the Agency for Healthcare Research and Quality, SRDR is a freely accessible collaborative, web-based repository of systematic review data. This resource serves as both an archive and data extraction tool and is shared among organizations and individuals producing systematic reviews worldwide, enabling the creation of a central database of systematic review data which may be critiqued, updated, and augmented on an ongoing basis.