Skip to main content

Data Management in Large-Scale Education Research

19 Appendix

For summary purposes, this appendix provides a high-level overview of some of the most common activities that occur in each phase of the research data management life cycle.

DMP (Chapter 5)

  • Review oversight requirements.
  • Create data sources catalog.
  • Create data management plan.

Planning (Chapter 6)

  • Develop style guide.
  • Choose storage locations.
    • Build directory structures for electronic data and physical folder structures for paper data.
  • Develop a data management working group (DMWG).
    • Schedule planning meetings and keep meeting notes.
      • Review checklists.
      • Develop workflows.
  • Assign roles and responsibilities.
  • Initiate any necessary processes (e.g., data request, Institutional Review Board application).

Document (Chapter 8)

  • Create research protocol.
  • Create data collection timeline.
  • Create SOPs.
    • ID schema
    • Consent process
    • Inclusion/exclusion criteria
    • Data collection processes
    • Data entry procedures
  • Create data dictionaries.
  • Start data cleaning plans.

Create instruments (Chapters 10 and 11)

  • Create participant tracking database.
  • Create data collection instruments.
  • Develop consent forms and include data sharing language.

Collect data (Chapter 11)

  • Implement quality assurance procedures in the field.

Track data (Chapter 10)

  • Track incoming data daily in a participant tracking database.

Capture data (Chapter 12)

  • Capture original paper and electronic data.
  • Capture external data.

Store (Chapter 13)

  • Store all raw data and documents for project.

Clean and Validate (Chapter 14)

  • Clean data.
  • Validate data (e.g., create a codebook to check for errors).
  • Store clean data.
  • Create participant flow diagram.

Version (Chapter 14)

  • Version finalized data if errors are found and update changelog.

Prepare to archive (Chapter 15)

  • Prepare paper and electronic data for long-term storage.
  • Update data inventory with new project datasets.
  • Develop an internal data reuse process.
  • Prepare data and documentation for open sharing (more information in Chapter 16).

Sharing (Chapter 16)

  • Share data and documentation in an open repository, using controlled access as needed.