Advancing the Value of Ethnography

Tutorial: Beyond “Quant-vs-Qual”—Creating Collaborative Inquiry



Qual and quant are so divided these days—by academic discipline, language, communities of practice, job titles. Too often, quantitative research is conflated with data science (or vice versa), and data science with optimization algorithms or simply engineering. In many organizations, being “data-driven” tends to define “data” with a narrow conception of enumeration and (mis-) conceptions about the kind of evidence that is suitable to act on.

This tutorial critically examines this territory and move beyond it, empowering ethnographers to develop more interdisciplinary programs of inquiry. First the instructors review fundamentals of quantitative research and provide tools ethnographers can use to evaluate its quality and validity. Then they examine constraints and barriers to quant/qual collaboration, including time, funding, values, epistemological conflicts, organizational silos, and more. Finally, using core principles that underlie inquiry of all kinds, they discuss strategies for collaboration that unite rather than divide the current research “camps.” The sessions covers:

This tutorial was presented in full at EPIC202020. The video includes instructor presentations; discussions and breakout sessions are excluded for the privacy of the participants.


Alex Hughes researches how group identity shapes political access and how social connections shape political behavior. His research has been published in The Lancet, the Proceedings of the National Academies of Science, the Journal of Experimental Political Science, Perspectives on Politics, and field journals; the research has been supported by grants from the Bill and Melinda Gates Foundation, the National Science Foundation, USAID, and others. Dr. Hughes holds a Ph.D. in Political Science from the University of California, San Diego and is currently an Adjunct Assistant Professor at the UC Berkeley School of Information.

Jenny Lo currently leads the User Research team for the Rides Business lines at Uber. She has led many large-scale design projects and experiment projects that span across functions. Jenny specializes in the study of quantitative research and information technology in developing countries (ICTD). Jenny received her Masters of Information Management and Systems from the School of Information at University of California, Berkeley and Bachelors from Wesleyan University.

Will Monge is a researcher at Good Research, where he focuses on privacy, fairness, and model accountability by applying data science, ethnography, and UX research methods. Will is intent on bringing more people from different backgrounds and methodologies into the “data conversation”. For this purpose, Will enjoys serving as a consultant on several legal and regulatory organizations, where he helps bridge the gap between technology and policy. Will has a background in mathematics (Complutense University in Madrid), risk modeling (ICADE), and data science (University of California, Berkeley).


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