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...
As a team of researchers was asked by a French home-improvement retailer to redefine their strategy, they designed and carried out an ethnographic and quantitative research to identify new business opportunities. But no sooner had they set foot in field, they were struck not only by the richness and complexity of such ordinary activities to the point they asked themselves if these practices were even measurable? Scaling from ethnography to quantitative research was not as seamless as they expected, they had to find their way to deal with two sets of data that belong to different scales if not ontological worlds. Are these two scales really strictly separated? Can't there be a way to combine them and to make them coincide? Based on the study of DIYing practices, this case study presents an attempt to integrate ethnographic and quantitative research and the challenge of resolving the scale differences between two methodologies. From turning DIYers into numbers and vice-versa, it explores the implications...
This paper argues that ethnographers can gain increased agency in data-driven corporate environments by increasing their quantitative literacy: their ability to create, understand, and strategically use quantitative data to shape organizations. Drawing on the author's experience conducting strategic user research at a technology company, the paper explores how the ability to engage with quantitative data can increase ethnographers’ independence and autonomy within organizations, and can also up-level the role and value of qualitative research. The paper also explores how a deep familiarity with quantitative data can enable ethnographers to imbue quantitative data itself with new forms of agency, and can ultimately give ethnographers the tools to change institutions from within. With a greater understanding of how quantitative data is made and used, ethnographers can ensure that data is collected in representative ways, point out the limitations of existing metrics, and argue for new ways of measuring and...
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...
by ERIN B. TAYLOR, Canela Consulting & Holland FinTech
Evidence produced within quantitative disciplines like economics and finance carries an aura of gospel. The numbers, models, and forecasts we see in economic reports and market analyses in the news and reports seem certain, authoritative, and unarguable.
Built on large data sets that are analyzed with widely accepted theories and tools, economic and financial evidence have become hugely influential in governance and business—so much so that more qualitative approaches have been sidelined.
Even political economy, the original economics, has been pushed away in favor of what’s now called ‘evidence-based decision-making’. The presumption is that numerical data is the only solid information, and that the analytical tools used in economic and market analysis are reliable.
Of course, as we know now, this faith in economic evidence can be dangerous. As markets crashed around the world during the global financial crisis of 2007–2008, confidence in all kinds of quantitative...
by TYE RATTENBURY (Salesforce) & DAWN NAFUS (Intel)
As EPIC2018 program co-chairs, we developed the conference theme Evidence to explore how evidence is created, used, and abused. We’ll consider the core types of evidence ethnographers make and use through participant observation, cultural analysis, filmmaking, interviewing, digital and mobile techniques, and other essential methods, as well as new approaches in interdisciplinary and cross-functional teams.1
We’ve also made a special invitation to data scientists to join us in Honolulu to advance the intersection of computational and ethnographic approaches. Why?
One of us is a data scientist (Tye) and the other an ethnographer (Dawn), both working in industry. We regularly see data science and ethnography conceptualized as polar ends of a research spectrum—one as a crunching of colossal data sets, the other as a slow simmer of experiential immersion. Unfortunately, we also see occasional professional stereotyping. A naïve view of “crunching” can make it seem...
by WILLIAM O. BEEMAN, Department of Anthropology, University of Minnesota
In today’s rapidly changing, highly competitive world, product design requires swift translation of human needs and desires into technical specifications for the development of devices and services that meet those needs (Salvador et al. 2013). This calls for a complex integration of qualitative and quantitative data. But despite some notable successes, product design failures are today both extensive and expensive (Anonymous 2015), consuming enormous amounts of time and human labor. Any improvement to the process of product design would be of great public benefit.
Many “smart systems” approaches to the product design process address the problems inherent in this process with limited success. In fact, existing “smart systems” are not very smart. There is an extensive literature available to product designers and engineers addressing lapses in strategies for the successful integration of qualitative and quantitative factors in the design process. It...
by KATHY BAXTER, Google
EPIC2014 Workshop by Anna Avrekh, Kathy Baxter, & Bob Evans
At the EPIC 2013 Keynote, Tricia Wang observed that, if you are not working with “Big Data,” the implication is that your data are “small.” Although the number of data points or participants may not be in the millions or ever thousands, the data we gather is actually far richer. As our community knows, web analytics or logs can tell us WHAT people are doing but never WHY. We may attempt to infer it based on what we see but unless we ask our users why they are doing something that we have recorded (with or without their knowledge), we can never know for sure.
Later in the conference, I hosted a Salon on “Big Data” with discussants Jens Riegelsberger (Google) and Todd Cherkasky (SapientNitro). The interest in the salon far exceeded the space available. One key theme that emerged was a desire to learn how to incorporate “Big Data” into their work. Few of the participants had the means to pull logs and do deep statistical analysis...
TRICIA WANG Good morning, I am really excited to be here for my first EPIC conference. There are just so many amazing people in the audience as I look at you guys, and so many of you guys I've been following on blogs and Twitter and especially Natalie Hanson’s anthrodesign listserv. I can’t wait to talk to you guys all afterwards. Just as a reminder, I don’t know if Simon already said it, but if you’re tweeting or instragramming—use the conference hashtag EPIC 2013. If throughout the talk you have any questions, or if anything resonates with you, this is my Twitter and Instagram handle.For over twelve centuries in Ancient Greece in consulting oracles, a person who could predict the future was a part of everyday Hellenistic life. People—poor, wealthy, slave and free—asked oracles for them to answer important life questions such as should I get married, or will I come back from war alive, or questions related to business matters. Should I invest in this voyage? There were questions related to political affairs like should...
KEN ANDERSON, DAWN NAFUS, TYE RATTENBURY and RYAN AIPPERSPACH
Field research holds a special place for those who conduct it. It is also our anchor for relevance in the corporation. This paper explores the authors’ experiences with “ethno-mining”, a way of joining data base mining and ethnography. Since 2004 we have been using a variety of sensing and behavioral tracking technologies in conducting field research. We will present the main characteristics of doing ethno-mining, compare ethno-mining to other field research technologies, highlight the strengths of ethno-mining in co-creating data with participants and conclude by noting how the representations have opened new conversations and discourses inside the corporation. In this way, these new opportunities to collect sometimes counterintuitive data contributes to the research itself as well as the ongoing process of constructing oneself as relevant....
DONNA K. FLYNN, TRACEY LOVEJOY, DAVID SIEGEL and SUSAN DRAY
In many companies, numbers equal authority. Quantitative data is often viewed as more definitive than qualitative data, while its shortcomings are overlooked. Many of us have worked to marry quantitative with qualitative methods inside organizations to present a fuller view of the people for whom we develop. One area of research that increasingly needs to blend quantitative and qualitative methods is user segmentations. Our software technology product team has been using a segmentation based on quantitative data since 2005. One outcome of this effort has been the development of an algorithm–based “typing” tool intended to be used as a standard tool in recruiting for all segmentation-focused research. We learned that the algorithm was an indecipherable black box, its inner workings opaque even to those who owned it internally. This case study looks at how qualitative research came up against the impenetrable authority of a quantitative segmentation and its associated...