Overview
Activity trackers, instrumented environments, and other kinds of electronic monitors offer new possibilities and new challenges for ethnographic research. They provide a trace of what goes on when the researcher isn’t there, and can help research participants reflect on their lives in a new way. In the right contexts, sensor data can help bridge the gap between ethnographic and data science approaches. At the same time, sensors can be challenging to set up, and occasionally mislead if the context is poorly understood.
This tutorial will help you determine when and how to use sensor data in an ethnographic research practice. We’ll talk about some of the practical pitfalls to watch out for, when you do and don’t need a data scientist, and some of the trickier aspects of inviting research participants to reflect on the data collected about them. Participants will learn how to:
- Assess sensors for maximum research value
- Ensure the research setup is feasible
- Wrangle data just enough for participants to reflect on it (which is not about producing fancy visualizations)
- Develop interviewing approaches and handle privacy considerations when sensors are in the mix
This will be an interactive forum, with plenty of discussions and hands-on data exploration. The tutorial will not assume any prior knowledge about data, math, or sensors. We will be looking at data ethnographically, not statistically.
Participants will have the opportunity to do a short and easy self-tracking exercise beforehand, so that you can work with data that comes from a context you know well. We’ll also provide some example data to play with.
The video includes only the presentation portions of the tutorial.
Background Readings
- Anderson, Ken, Dawn Nafus, Tye Rattenbury, and Ryan Aipperspach. “Numbers Have Qualities too: Experiences with Ethno‐Mining.” 2009 EPIC Proceedings.
- Evans, Bob. “Paco – Applying Computational Methods to Scale Qualitative Methods.” 2016 EPIC Proceedings.
- Forlano, Laura. 2017. “Maintaining, Repairing and Caring for the Multiple Subject.”
- Mehta, Rajiv, Nafus, Dawn. 2016. Atlas of Caregiving Pilot Study.
- Nimmi Rangaswamy, Saurabh Srivastava, Tejasvin Srinivasan & Priyanka Sharma. 2016. “Hey, the water cooler sent you a joke!”: ‘Smart’, Pervasive and Persuasive Ethnography.
- Ruckenstein, Minna. “Visualized and Interacted Life: Personal Analytics and Engagements with Data Doubles.” Societies 4.1 (2014): 68-84.
Dawn Nafus is a Senior Research Scientist at Intel Corporation, where she conducts anthropological research for new product innovation. Her ethnographic research has been primarily on experiences of time, data literacy, self-tracking and wearables. Most recently, she has been working on instrumentation and data interpretation for community-based environmental health projects. Her work takes place in the US and Europe. She is the editor of Quantified: Biosensing Technologies in Everyday Life and co-author of Self-Tracking. She holds a PhD from University of Cambridge.
