a process view of missing data
TRANSCRIPT
partially joint work with Henry Potts and Katarzyna Stawarz (Turing Small Group), partially with the Help4Mood Consortium
A Process View of Missing Data
Maria WoltersReader in Design Informatics & Faculty FellowUniversity of Edinburgh
[email protected]@mariawolters
http://www.slideshare.net/mariawolters
Overview
❖ Motivation: Help4Mood
❖ Example: Activity Trackers
❖ Collaborating across the Turing
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)
Goal
❖ To investigate why data goes missing (aka data generation processes)
❖ qualitatively for deeper understanding
❖ quantitatively to feed into data analysis and visualisation
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
The Appropriation of Help4Mood, or: Why Data Generation Processes?
depressioncomix.tumblr.com
Depression is a change
relative to an individual baseline
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
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
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 designcf Dix, Alan (2007): Designing for Appropriation. In Proc. BCS HCI Group, (pp. 27-30)
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)
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 …)
Activity Tracking: Example of Stage 1
From Summer Small Group work
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
whatswimming
weightlifting
team sports
no Internetstyle / fashion
stigmaself-report
effort
not synchingproperly
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).
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.
http://thoughtstipsandtales.com/2014/11/06/fitbit-fun-ten-months-later/
http://thoughtstipsandtales.com/2015/03/05/fabulous-fitbit-accessory-to-keep-the-clasp-from-opening/
Working Across the Turing Institute
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
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
Come talk to me!
❖ Turing Calendar: mariawolters.net/calendar/availability
❖ [email protected] / @mariawolters
❖ Setting up Turing Azure RStudio server collaboration space
❖ Preferred coffee: espresso macchiato / cortado