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Systematic Review: 4. Extract/Manage data

Analysis - Step-by step

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Apply criteria

Assess quality

Extract/Manage Data

Case Study

 

(Please note: Case study is under construction)

Extract/Manage data

Once the final list of articles for inclusion is determined, it is time to extract any relevant data.   This requires careful planning to ensure it is managed appropriately (and in line with any ethics requirements).  Some considerations might include:

  • Only extract data you need to answer the review question
  • Use a consistent approach to summarising the data and apply this each study
  • Make clear any abbreviations
  • Carefully convert all data to the same units
  • Name columns in a way that is meaningful to others
  • Keep content brief

       Alexandria tutorial -  Finding, extracting and reporting data relevant to the review question   (for Monash Staff and students only)

 

There are various tools available for the extraction stage of your systematic review.  Elamin et al. (2009) evaluate a range of tools that might be useful.

Some analysis packages that can also be used to manage extracted data include:

 

     NVIVO (qualitative and unstructured data)
    SPSS (statistics, some qualitative functionality)
    PSPP (open-source version of SPSS)
     STATA (data analysis and statistical software)
    JASP (open-source graphical statistics interface)

     QARI (data extraction templates)

Managing your data

As with any data collection and reporting process, it is worthwhile keeping in mind basic data management principles:

 

  • Back up work regularly and in multiple, different physical locations
  • Use filenames and systems that timecode your entries and updates, to avoid different collaborators working on different versions.  (Using an electronic lab notebook such as LabArchives can be a useful way around this).

    LabArchives - Cloud-based platform that is designed to manage, organise, store and share research findings

 

Monash also provides advice and guidelines relating to managing research data.

 

You should also consider making your data open (fully or partially):

Open access data might even be a requirement, for some systematic review organisations and publications.  There are many benefits to publishing and sharing your data.  You can learn more through the Australian National Data Service (ANDS).


 

          Exercise 9 - Data Extraction