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A Process View of Missing Data


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These are the slides for the Faculty Fellow short talk on October 27, 2016, at the Alan Turing Institute. In this talk, I summarise my approach to missing data analysis, and explain how my work fits into an interdisciplinary context. I will add a link to the YouTube recording once it has been uploaded.

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A Process View of Missing Data

  1. 1. partially joint work with Henry Potts and Katarzyna Stawarz (Turing Small Group), partially with the Help4Mood Consortium A Process View of Missing Data Maria Wolters Reader in Design Informatics & Faculty Fellow University of Edinburgh @mariawolters
  2. 2. Overview ❖ Motivation: Help4Mood ❖ Example: Activity Trackers ❖ Collaborating across the Turing
  3. 3. Missing Data ❖ informally: observations that we would like to be there, or that should be there, but that are not ❖ Statistical treatment differs according to whether missing data are ❖ completely random (MCAR) ❖ predictable from existing data (MAR) ❖ not predictable from existing data (MNAR)
  4. 4. Goal ❖ To investigate why data goes missing (aka data generation processes) ❖ qualitatively for deeper understanding ❖ quantitatively to feed into data analysis and visualisation
  5. 5. How I Work ❖ Stage 1: map relevant aspects of data generation and use, from multi disciplinary perspective ❖ Stage 2: drastically reduce complexity in collaboration with statisticians and data scientists to create an interesting model that converges
  6. 6. The Appropriation of Help4Mood, or: Why Data Generation Processes?
  7. 7. Depression is a change relative to an individual baseline
  8. 8. Help4Mood: Supporting People with Depression • daily monitoring • of activity using actigraph • of mood, thought patterns & psycho- motor symptoms using talking head GUI • weekly one-page reports to clinicians Maria K. Wolters, Juan Martínez-Miranda, Soraya Estevez, Helen F. Hastie, Colin Matheson (2013). Managing Data in Help4Mood AMSYS ICST DOI: 10.4108/trans.amsys. 01-06.2013.e2
  9. 9. Pilot Randomised Controlled Trial ❖ Participants with Major Depressive Disorder (SCID diagnosed) ❖ Use Help4Mood for 4 weeks every day ❖ Background measures include demographics and attitudes to computers ❖ (Pre/Post measures to establish change) ❖ Qualitative interviews at intake and debriefing for those randomized to Help4Mood
  10. 10. Usage Patterns during pilot RCT ❖ 18 in Romania, 7 in Scotland, 2 in Spain (EU Project) ❖ 14 treatment as usual (age 42 years +/- 10), 13 Help4Mood (age 35 +/- 12) ❖ None formally tracked or measured their mood before, but some used introspection ❖ Half used it regularly, but that was not daily; instead, it was 2-3 times per week. Why? 
 Appropriation: Users tweak technology to fit their needs, departing from initial design
 cf Dix, Alan (2007): Designing for Appropriation. In Proc. BCS HCI Group, (pp. 27-30)
  11. 11. Participants Used Help4Mood to Cope With and Make Sense of Their Illness The monitoring part helped me understand some things [. . .] sometimes I did not realize how I felt that day, how happy I was or how active I was. The system helped me observe these things and also control them. (RO14, female, 20–29)
  12. 12. The missing data (quantitative) alerted us to an appropriation process that we were able to describe and understand through qualitative work and that then changed how we would have designed Help4Mood II (had we gotten funding …)
  13. 13. Activity Tracking: Example of Stage 1 From Summer Small Group work
  14. 14. TRACKER when who job (e.g. nurses) allergies wrist anatomy forgetting to wear to bringto charge worried well techy motivated for change not tracking during lazy days device breaks no longer holds charge lost / stolen what swimming weightlifting team sports no Internet style / fashion stigma self-report effort not synching properly if there is a need e.g.,Rooksby, J., Rost, M., Morrison, A., and Chalmers, M. C., (2014). Personal tracking as lived informatics. In Proc. CHI ’14 (pp. 1163–1172) Lazar, A., Koehler, C., Tanenbaum, J., & Nguyen, D. H. (2015). Why we use and abandon smart devices. In Proc. UbiComp ’15 (pp. 635–646).
  15. 15. TRACKER when who forgetting is a function of - user characteristics - illness - external stress fit with identity device what detailed user modeldevice model connectivity model fit with activities effort required to track activity fit with ulterior need (why tracking?) All except for ulterior need and identity amenable to formal / quantitative modelling from appropriate discipline.
  16. 16.
  17. 17. Working Across the Turing Institute
  18. 18. Formal Collaborations ❖ Richard Dobson, Jacky Pallas, and the RADAR CNS team with Mirco Musolesi, UCL Fellow (role: user centred design). Intel funding ❖ Jon Crowcroft and his Turing PhD students, looking at data generation processes in wearables. Revise and resubmit, for use of HAT data
  19. 19. Paper And Grant Writing Groups ❖ Scott Hale, Faculty Fellow, OII, YouthNet summer internship programme work ❖ Henry Potts, UCL (Farr; no Turing affiliation) ❖ Farr/Turing working group on health care data
  20. 20. Come talk to me! ❖ Turing Calendar: availability ❖ / @mariawolters ❖ Setting up Turing Azure RStudio server collaboration space ❖ Preferred coffee: espresso macchiato / cortado