Scoping Reviews

Role of the librarian in this stage

A librarian can advise you on data extraction for your systematic review, including: 

  • What the data extraction stage of the review entails
  • How to choose what data to extract from your included articles 
  • Finding examples in the literature of similar reviews and their completed data tables
  • Best practices for reporting your included studies and their important data in your review

 

Contact HSL About Your Review

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Ready to start a scoping review? HSL Librarians can help!

Fill out the Systematic/Scoping Review Request Form and the best-suited librarian will get back to you promptly. Our systematic/ scoping review service is only available to faculty, staff, students, and others who are affiliated with UNC Chapel Hill.

About data extraction

In this step of the scoping review, you will develop your evidence tables, which give detailed information for each study (perhaps using a PICO framework as a guide), and summary tables, which give a high-level overview of the findings of your review. You can create evidence and summary tables to describe study characteristics, results, or both. These tables will help you determine which studies, if any, are eligible for quantitative synthesis.

"In scoping reviews, the results and discussion focus on charting concepts, themes, and types of evidence available rather than strength of evidence and quantitative findings." - Chang 2018

Data extraction requires a lot of planning.  We will review some of the tools you can use for data extraction, the types of information you will want to extract, and the options available in the review software used here at UNC, Covidence.

How many people should extract data?

The Cochrane Handbook and other studies strongly suggest at least two reviewers and extractors to reduce the number of errors.

Data extraction tips

  1. Look for an existing extraction form or tool to help guide you.  Use existing scoping reviews on similar topic to identify what information to collect if you are not sure what to do.
  2. Train the review team on the extraction categories and what type of data would be expected.  A manual or guide may help your team establish standards.
  3. Pilot the extraction / coding form to ensure data extractors are recording similar data. Revise the extraction form if needed.
  4. Discuss any discrepancies in coding throughout the process.
  5. Document any changes to the process or the form.  Keep track of the decisions the team makes and the reasoning behind them.
  • Peters MDJ, Godfrey C, McInerney P, Munn Z, Tricco AC, Khalil, H. Chapter 11: Scoping Reviews (2020 version). In: Aromataris E, Munn Z (Editors). JBI Manual for Evidence Synthesis, JBI, 2020. Available from https://synthesismanual.jbi.global. doi: 10.46658/JBIMES-20-01
  • Brown SA, Upchurch SL, Acton GJ. A framework for developing a coding scheme for meta-analysis. West J Nurs Res. 2003;25(2):205-222. doi:10.1177/0193945902250038
  • Buscemi, N., Hartling, L., Vandermeer, B., Tjosvold, L., & Klassen, T. P. (2006). Single data extraction generated more errors than double data extraction in systematic reviews. Journal of Clinical Epidemiology, 59(7), 697-703. doi:10.1016/j.jclinepi.2005
  • Chang S. Scoping Reviews and Systematic Reviews: Is It an Either/Or Question? Ann Intern Med. 2018;169(7):502-503. doi:10.7326/M18-2205

Tools for data extraction

Covidence Logo

Screening & Extraction Software

Most review software tools have data extraction functionality that can save you time and effort.  Here at UNC, we use a review software called Covidence. You can see a more complete list of options in the Systematic Review Toolbox.

Covidence allows you to

Covidence's new data extraction features are detailed in the "Data Extraction in Covidence"  box at the end of this page.


Excel logo

Spreadsheet or Database Software

You can also use spreadsheet or database software to create custom extraction forms. Spreadsheet functions such as drop-down menus and range checks can speed up the process and help prevent data entry errors. Relational databases (such as Microsoft Access) can help you extract information from different categories like citation details, demographics, participant selection, intervention, outcomes, etc.


Qualtrics logo

Form or Survey Software

Survey or form tools can help you create custom forms with many different question types, such as multiple choice, drop downs, ranking, and more.  Content from these tools can often be exported to spreadsheet or database software as well.  Here at UNC we have access to the survey/form software Qualtrics.


Electronic Documents or Paper & Pencil

In the past, people often used paper and pencil to record the data they extracted from articles. Handwritten extraction is less popular now due to widespread electronic tools.  You can record extracted data in electronic tables or forms created in Microsoft Word or other word processing programs, but this process may take longer than many of our previously listed methods.  If chosen, the electronic document or paper-and-pencil extraction methods should only be used for small reviews, as larger sets of articles may become unwieldy. These methods may also be more prone to errors in data entry than some of the more automated methods.

  • Elamin MB, Flynn DN, Bassler D, et al. Choice of data extraction tools for systematic reviews depends on resources and review complexity. Journal of Clinical Epidemiology. 2009;62(5):506-510. 10.1016/j.jclinepi.2008.10.016

What should I extract?

You should plan to extract data that is relevant to answering the questions posed in your scoping review.  As mentioned previously, it may help to consult other similar scoping reviews to identify what data to collect.  You should use your key questions and your eligibility criteria as a starting point.  It can also help to think about your question in a framework such as PICO.

Helpful data may include:

  • Information about the study (author(s), year of publication, title, DOI)
  • Demographics (age, sex, ethnicity, diseases / conditions, other characteristics related to the intervention / outcome)
  • Methodology (study type, participant recruitment / selection / allocation, level of evidence, study quality)
  • Intervention (quantity, dosage, route of administration, format, duration, time frame, setting)
  • Outcomes (quantitative and / or qualitative)

If you plan to synthesize data, you will want to collect additional information such as sample sizes, effect sizes, dependent variables, reliability measures, pre-test data, post-test data, follow-up data, and statistical tests used.