About Step 7: Data Extraction & Charting
| Average time (hours) to complete | |||||||
| 88 | |||||||
In Step 7, you will skim the full text of included articles to collect information about the studies in a table format (extract data), to summarize the studies and make them easier to compare. You will:
- Make sure you have collected the full text of any included articles.
- Choose the pieces of information you want to collect from each study.
- Choose a method for collecting the data.
- Create the data extraction table.
- Test the data collection table (optional).
- Collect (extract) the data.
- Review the data collected for any errors.
For accuracy, two or more people should extract data from each study. This process can be done by hand or by using a computer program.
If you reach the data extraction step and choose to exclude articles for any reason, update the number of included and excluded studies in your PRISMA flow diagram.
detailed information and to submit a request form.
A librarian can advise you on data extraction and charting for your review, including:
- What the data extraction stage of the review entails
- Finding examples in the literature of similar reviews and their completed data tables
- How to choose what data to extract from your included articles
- How to create a randomized sample of citations for a pilot test
- Export specific data elements from the included studies like title, authors, publication date, citation, & DOI to a Google Sheet for you to use in your data extraction.
- Best practices for reporting your included studies and their important data in your review
About data extraction (charting)
In this step of the systematic or scoping review, you will develop your evidence tables, which give detailed information for each study (perhaps using a PICO or PCC 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.
Data extraction (charting) requires a lot of planning. We will review some of the tools you can use for data extraction (charting), and the types of information you will want to extract
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. The librarian usually does not help with the data extraction but may assist in preparing for the data extraction such as creating spreadsheets, etc.
Select a tool
There are benefits and limitations to each method of data extraction. You will want to consider:
- The cost of the software / tool
- Shareability / versioning
- Existing versus custom data extraction forms
- The data entry process
- Interrater reliability
For example, in Covidence you may spend more time building your data extraction form, but save time later in the extraction process as Covidence can automatically highlight discrepancies for review and resolution between different extractors. Excel may require less time investment to create an extraction form, but it may take longer for you to match and compare data between extractors. More in-depth comparison of the benefits and limitations of each extraction tool can be found in the table below.
| Tool | Benefits | Limitations |
|---|---|---|
| Review Software (Covidence) |
|
|
| Spreadsheets (Excel, Google Sheets) |
|
|
|
Cochrane Revman |
|
|
| Survey or Form Software (Poll Everywhere, Qualtrics, etc.) |
|
|
| Electronic documents (Word, Google Docs) |
|
|
Data Extraction Templates/Examples
In a recent article by Pollock et al (2023), a recommendation of what items should be collected in the data extraction phase. They recommend creating 2 tables; the first table includes basic information about the article (which they call the Guidance sheet) and then the 2nd table includes more detailed information.
- Pollock, D., Peters, M. D. J., Khalil, H., McInerney, P., Alexander, L., Tricco, A. C., Evans, C., de Moraes, É. B., Godfrey, C. M., Pieper, D., Saran, A., Stern, C., & Munn, Z. (2023). Recommendations for the extraction, analysis, and presentation of results in scoping reviews. JBI evidence synthesis, 21(3), 520–532. https://doi.org/10.11124/JBIES-22-00123
- Sample Tables in Google Sheets. Download it to your computer.
Other templates:
- JBI Manual for Evidence Synthesis, Chapter 11: Scoping Reviews: Appendix 11.1 - JBI template source of evidence details, characteristics and results extraction instrument
JBI's recommended scoping review data extraction instrument for study details, characteristics and results extraction.
- Results of individual sources of evidence: figure
An example data extraction table from the PRISMA-ScR.
Your protocol should include a plan for how you will present your results.
Your PCC inclusion criteria will assist you in choosing how the data should be mapped most appropriately, but you can refine this toward the end of the review, when you have a better picture of the sort of data available in your included studies.
The results of a scoping review may be presented in your final paper in a variety of ways, including:
- tables and charts, featuring distribution of studies by year or period of publication, countries of origin, area of intervention (clinical, policy, educational, etc.) and research methods; and/or
- in a descriptive format that aligns with the review objective/s and scope.
The latest guidance (Pollock et al. 2023) encourages 'creative approaches...to convey results to the reader in an understandable way' such as word clouds, honeycombs, heat maps, tree graphs, iconography, waffle charts and interactive resources.
Note: If you present your data in a table/chart, also include a narrative summary to explain how the results relate to your review objectives and questions.
- Recommendations for the extraction, analysis, and presentation of results in scoping reviews
Pollock et al. 2023, JBI Evidence Synthesis, 21(3): 520–532.
JBI advise (11.3.8.1 Search results) results can be classified under main conceptual categories, such as:
- intervention type
- population (and sample size, if it is the case)
- duration of intervention
- aims
- methodology adopted
- intervention type
- key findings (evidence established)
- gaps in the research
'For each category reported, a clear explanation should be provided.'
Joanna Briggs Institute also has a template for data collection and extraction for systematic reviews in section 12.2.9.
- Aromataris E, Munn Z, (editors) (2020). JBI Manual for Evidence Synthesis. JBI. Available from: https://synthesismanual.jbi.global. https://doi.org/10.46658/JBIMES-20-01
Cochrane Manual Handbook for Systematic Reviews includes a chapter on data collection and extraction in section 5-3.
- Li T, Higgins JPT, Deeks JJ (editors) (20230. Chapter 5: Collecting data. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane. Available from https://training.cochrane.org/handbook/current/chapter-05#section-5-3
You should plan to extract data that is relevant to 
answering the question posed in your systematic review.
It may help to consult other similar systematic reviews to identify what data to collect or to think about your question in a framework such as PICO.
Helpful data for an intervention question may include:
- Information about the article
(author(s), year of publication, title, DOI) - Information about the study
(study type, participant recruitment / selection / allocation, level of evidence, study quality) - Patient demographics
(age, sex, ethnicity, diseases / conditions, other characteristics related to the intervention / outcome) - 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.
Extraction templates and approaches should be determined by the needs of the specific review. For example, if you are extracting qualitative data, you will want to extract data such as theoretical framework, data collection method, or role of the researcher and their potential bias.