Data Management Lab 2.0: Session 3 learning outcomes & activities

Attendance held steady with a couple of people returning and a couple missing due to other obligations. In light of my realization about most not being in a place to develop their own DMP, I adjusted the activities for session 3 to use a case study approach. Based on the pilot feedback, I tried to keep the case-based activities very narrow and focused. Students seemed more engaged with the activities and discussion, but uploading products to Box remained a problem. I’m not sure if it’s lack of familiarity with the technology – I have had to provide more support than expected there – or lack of motivation to complete the exercise. Perhaps the exercises are too demanding, too lengthy, not engaging enough, or irrelevant to their needs?

I’m hoping some of this comes out in the final evaluation. Given the poor response rate for the midpoint evaluation, I’ve set aside plenty of time at the end of session 4 to *encourage* completion during the workshop. I also brought treats to bribe them 🙂

Session 3: Learning Outcomes

  • Develop procedures for quality assurance and quality control activities
  • Describe key considerations for selecting data collection tools
  • Use best practices for coding
  • Use best practices for data entry
  • Develop a screening and cleaning protocol and/or checklist
  • Explain why automation provides better provenance than manual processes
  • Identify effective tools for automating data processing and analysis

Major activities & products

  • Data collection instrument (case study: 5 pre-assigned variables)
  • Data coding scheme (case study: 5 pre-assigned variables)
  • Data screening & cleaning checklist (case study: 5 pre-assigned variables)

Products gathered for assessment

  • Data collection instrument
  • Data coding scheme


Codebook, student questionnaire, and data from the Health Behavior in School-Aged Children study (ICPSR 34792)



By Heather L. Coates

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