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...
An EPIC Talk with ALEX HUGHES (UC Berkeley), JENNY LO (Uber) & WILL MONGE (Good Research)
Qual and quant are so divided these days—by academic discipline, language, communities of practice, job titles. In many organizations, there’s also a hierarchy based on misconceptions about research and the kind of evidence that’s suitable to “act on.” But EPIC people are leading the charge to bridge these divides, and this EPIC Talk advances that agenda.
In an interactive session, Alex Hughes, Jenny Lo, and Will Monge share core concerns shared by all researchers, arguing that this common ground establishes a basis for closer collaboration among researchers of all stripes. In breakouts and group discussions, we discuss the constraints we experience as qualitative and mixed methods researchers; vocabulary for communicating the value of ethnographic work to quantitative colleagues; and strategies for more fully and effectively integrating ethnographic work into research and business cycles.
Case Study—One of Uber’s company missions is to make carpooling more affordable and reliable for riders, and effortless for drivers. In 2014 the company launched uberPOOL to make it easy for riders to share their trip with others heading in the same direction. Fundamental to the mechanics of uberPOOL is the intelligence that matches riders for a trip, which can introduce various uncertainties into the user experience. Core to the business objective is understanding how to deliver a ‘Perfect POOL’—an ideal situation where 3 people in the vehicle are able to get in and out at the same time and location allowing for a more predictable and affordable experience. This case study argues that, for a reduced fare and a more direct route, riders are willing to forego the convenience of getting picked up at their door in exchange for waiting and walking a set amount to meet their driver.
This case study explores the integration of qualitative and quantitative research...