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Dissertation Subject Guide

including systematic reviews, literature reviews and scoping reviews

What is data extraction?

Data extraction can be challenging. It requires you to go back to your chosen articles and highlighting the relevant information that will answer your research question. Normally this involves extracting the data related to your chosen framework and its components e.g. PICO, PEO etc. To standardise and improve the validity of the results, it's crucial to create a data extraction form or table (Bettany-Saltikov, 2012, p. 96).

Points to consider when creating data extraction form/table

Data extraction is the process of extracting the relevant pieces of information from the studies you have assessed for eligibility in your review and organizing the information in a way that will help you synthesize the studies and draw conclusions.

 

Top Tips for designing your data extraction table and column options
  • the study's author - first named author and year of publication
  • article title
  • journal title - full text? or abstract only?
  • study characteristics  - 
  • study design
  • where/when study conducted
  • inclusion/exclusion criteria
  • number of participants (including dropouts)
  • participants demographics e.g. age, sex, socio-economic status, ethnicity, co-morbidities etc.
  • interventions and comparators
  • study outcomes
  • analyses
  • for certain study designs (RCTs etc.) extract baseline participant data
  • additional notes

(Information adapted from Boland, 2017, p.97)

Boland, A. (2017) Doing a systematic review : a student’s guide. 2nd edn. Thousand Oaks: SAGE Publications. READ CHAPTER 6

 

Here's some examples of data extraction forms that can be adapted for your own purpose:

Rationale for data collection forms for Systematic Reviews
JBI template source of evidence details, characteristics and results extraction instrument for Scoping Reviews
If you're unsure, have a look at existing systematic reviews/literature reviews/scoping reviews on your topic to identify what information to collect. Look at their extraction tables/forms for ideas.
Here's an example of a data extraction table:

(adapted from 8 elements of person-centred care of older people in primary healthcare: a systematic review with thematic analysis)

 

Table 3: Characteristics of chosen research papers

Author, Country

Research Design

Aim of Research

Sample Size

Main findings

Sarkisian et al. (2020)

USA

Qualitative study; focus group

Compares older adult & GP expectations of appointments

n=49 older adult

n= 11 GPs

Reasons for appointments

Physical function, cognitive function, social function, pain

Older adult expressed that they felt like numbers not people, not involved in decision-making

GP stared at computer throughout conversation with no eye contact

Bastiaens et al. (2021)

Belgium

Qualitative study; interviews

Explores the views of older adult (aged 70 and over) on their involvement in primary healthcare in 11 European Countries

n=406 older adults (aged between 70 and 96)

Older adults want to be involved in their care and decision-making.

The study stressed the importance of good communication, interest in their problems, clear information, being reliable and supportive

Data Extraction Tools

You may have read or seen some of the data extraction software available (e.g. Covidence). Unfortunately, we currently do not have any licences for this software at University of Dundee.

Alternative options include creating a table, form manually that suits your review - e.g. above example was created in MS Word.

Tool Benefits Limitations
Spreadsheets (Excel, Google Sheets)
  • Free options available
  • Easy to learn and use (i.e., extractors will be able to begin quickly compared to using other software)
  • Easy to customize extraction fields
  • Manually review, find, and resolve discrepancies
  • Increase in potential bias if all extractors are using or have access to the same file (e.g., issues with blinding data extracted)
  • Potential for more errors and less accuracy due to manual data entry and review
Electronic Docs (Word, Google Docs)
  • Free options available
  • Easy to learn and use (i.e., extractors will be able to begin quickly compared to using other software)
  • Easy to customize extraction fields
  • Manually review, find, and resolve discrepancies
  • Increase in potential bias if all extractors are using or have access to the same file (e.g., issues with blinding data extracted)
  • Potential for more errors and less accuracy due to manual data entry and review
Cochrane RevMan
  • Free
  • Capabilities to write the entire review using this software
  • Steeper learning curve to learn new software

For more information, videos etc about Cochrane RevMan, see Cochrane Training: RevMan

Information taken from Systematic Reviews: Step 7: Extract Data from Included Studies