While ethnography generally draws on qualitative data, it does not not mean that quantitative approaches shouldn’t be employed in the research process. Combining the two leads to a “mixed-method approach” that can take various forms: data collection and analysis can be either separated or addressed together, and each of them can be used in service of the other. Of course, this isn’t new in academic circles and corporate ethnography but there seems to be a renewed interest lately in this topic…The large data sets created by people’s activity on digital devices has indeed led to a surge of “traces” from smartphone apps, computer programs and environmental sensors. Such information is currently expected to transform how we study human behavior and culture, with, as usual, utopian hopes, dystopian fears and *critical sighs* from pundits.
Editor’s note: In the last post of the Ethnomining‘ edition, David Ayman Shamma @ayman gives a personal perspective on mixed methods. Based on the example of data produced by people of Egypt who stood up against then Egyptian president and his party in 2011, he advocates for a comprehensive approach for data analysis beyond the “Big Data vs the World” situation we seem to have reached. In doing so, his perspective complements the previous posts by showing the richness of ethnographic data in order to deepen quantitative findings.
David Ayman Shamma is a research scientist in the Internet Experiences group at Yahoo! Research for which he designs and evaluate systems for multimedia-mediated communication.
There’s a problem we face now; the so called Big Data world created an overshadowing world of numerical data analysis leaving everyone else to try to find a coined niche like “small data” or “long data” or “sideways data” or the like. The silos and fragmentation is overwhelming. But really, it’s just all data. Regardless of the its form or flavor, there are people who are experts at number crunching data and people who are experts at field work data. Unfortunately, the speed at which data science moves is attractive and that’s part of the problem; we don’t get the full picture at speed and everyone is racing to produce answers first.
A few months ago, in a conversation with a colleague, he told me “you don’t know what you don’t know, especially when it’s not there.” We were looking for a way to automatically surface a community of photographers on Flickr who didn’t annotate their photos. They didn’t use any titles or tags or any annotations what so ever. But they were clearly a strong and prolific community. If there was some way to automatically identify them, then we could help connect them.
Now, finding metrics for social engagement in unannotated data is not an impossible task when provided with some signal in the data that has some correlation, statistical or otherwise, to the effect you’re trying to surface. But in some cases, it’s just not possible. What you need is just not there; therein is a problem. In other cases, it’s much harder to surface features when you don’t know what they look like.
When you have a lot of data, finding that unexplainable prediction through algorithmic statistics becomes easier. It doesn’t explain why and it doesn’t always work.
Enter Ethnography to answer the why and find out what things might look like—surfacing findings in the age of big data. When I was invited to write a post on Ethnography Matters, I decided to illustrate this through a personally motivated example.
In the late January of 2011, the people of Egypt stood up against then President Hosni Mubarak and his National Democratic Party. They wanted employment, a fair government, and an end to the 30 year long emergency law which had removed most of their civilian rights. Undoubtedly, you read about it somewhere. At the time, my mother was in Cairo visiting her 100+ year old mother. So this left me glued to the only source of news I could find—a rather buggy Al Jazeera video stream. U.S. news agencies were slow to start some sparse coverage. Somewhere in-between, it was burning up on Twitter.
Editor’s note: This post for the April ‘Ethnomining‘ edition comes from Rachel Shadoan and Alicia Dudek. Following on the past posts about hybrid methods, this one features another interesting case study involving an on-line role-playing game. Their work correspond to a different approach, based on visualizations, than what we saw in the two previous posts.
Rachel Shadoan @RachelShadoan likes to find answers to interesting questions, and build interesting things using those answers. Currently she is answering interesting questions in the Intel Labs using a combination of data visualization, data mining, and ethnographic techniques.
Alicia Dudek @aliciadudek is a design ethnographer and user experience consultant. Her passion is finding unusual solutions to the usual problems. Currently, she is finding unusual solutions for Deloitte Digital, where she specializes in engaging stakeholders in research insights through participatory design workshops.
A few weeks into our study of Plant Wars, an online text-based fighting RPG developed by Jon Evans of Artful Dodger Software, we encountered a mystery. We had visualized the server log data that records the players’ in-game activities, and discovered a pattern as obvious as it was inexplicable: in June 2009, the top Plant Wars players began slowly shifting the time of day in which they were playing. Over a period of six months, the time that the top players started playing each day shifted by nearly six hours. We poured over the server log data, checking the processing code for errors, for time zone issues, for any possible explanation of this shift in play pattern. Using only the server log data, we came up empty-handed. What was going on?
Editor’s note: This post for the April ‘Ethnomining‘ edition comes from Fabien Girardin @fabiengirardin who describes his work with networked/sensor data at the Louvre Museum in Paris. Based on this inspiring case study, he discusses the overall process, how mixed-methods are relevant in his work, and what kind lessons he learnt doing this.
Fabien Girardin is Partner at the Near Future Laboratory, a research agency. He is active in the domains of user experience, data science and urban informatics.
At the Near Future Laboratory we like to experiment and to go in different directions from the typical technology consultancy. We thrive on the involvement of multiple practices, and bet on the unordinary when it comes to question formulation, data collection and solution creation. After completing my PhD in Computer Science, I left the bounded disciplines of academia to embrace learning and connecting to the other “fields”, the other ways of knowing and seeing the world. Along with partners Julian Bleecker, Nicolas Nova and a network of tactical scouts, we formed a technology-based practice that combines insight and analysis, design and research, and rapid prototyping to transform ideas into material form.
Over the past 5 years, I have led investigations that aim to extract knowledge from the byproducts of people’s digital activities (i.e. network data, also often called digital shadows or digital footprints). That intangible material can take the form of logs of cellular network activity, aggregated credit card transactions, real-time traffic information, user-generated content or social network updates. Over time my contributions have evolved into helping transform this type of big data into insights, products and services. Whether applied for a client or as part of our self-started initiatives, this practice requires the basic skills of a “data scientist” (data analysis, information architecture, software engineering and creativity) along with a capacity to engage at the intersections with a wide variety of professionals, from physicists and engineers to lawyers, strategists and designers. The transversal incline of investigations on network data requires understanding the different languages that shape technologies, reporting on the context of their use, and describing people’s practices. The model of inquiry blends qualitative field observations with quantitative evidence often extracted from logs.
Past projects have led us to exploit untapped data sources, uncover opportunities to transform data into insights, and materialize new services or products. Our method first contemplates datasets and techniques to approach our objectives. Then we develop tangible solutions that engage the project stakeholders in exploring different scenarios and solutions. It is through the experiences of people with knowledge of the project domain that we are able to extract possible near-future changes and opportunities.
Editor’s note: This post for the April ‘Ethnomining‘ edition comes from Rebekah Rousi, @rebekahrousi who describes how the combination of qualitative and quantitative data collection was fruitful in her analysis of elevator usage. The post highlights the lessons she uncovered using both approaches.
Rebekah is a researcher of user psychology and PhD candidate of Cognitive Science at the University of Jyväskylä, Finland. With a background in visual arts and cultural studies, she is particularly interested in the psychology of user experience, affective human-technology interactions and the mental factors of design encounters.
I don’t know who was more moved by the experience of elevator design, me or the 50 people I interviewed. A few years ago a leading elevator design and manufacturing company gave me the task of examining how people experienced and interacted with elevators. The scope included everything from hall call buttons, to cabin interior design and perception of technical design. When given the brief, the artistic director noted country specific design features (or omissions) and even mentioned that there may be observable elevator habits I would want to take note of. Then, on our bidding a corporate-academic farewell she added that I might want to consider the psychology of the surrounding architectural environment. With that, I was left with a long list of to-do’s and only one method I could think of that would be capable of incorporating so many factors – ethnography. Ethnographic inquiry provides a framework in which the researcher’s own observations and experiences of the phenomenon under study – in this case elevator users’ behaviour in relation to the elevators, other users and the surrounding architectural environment – can be combined with “insiders'” opinions and insights.
So, I undertook the study in two of Adelaide’s (Australia) tallest office buildings (see the building entrances above). I chose these buildings for several reasons: 1) they are both highrises in which elevator usage is a necessity; 2) they are both non-residential office buildings in which factors such as occupational well-being, health and safety, and socio-cultural dimensions including power relations and hierarchies come into play. In order to gauge and explain user behaviour in relation to the tangible and non-tangible dynamics of the spaces, it is necessary to study sites which are similar in purpose. Further, both buildings housed the same brand of elevators. And both had only recently undergone elevator upgrades.
The data collection consisted of two separate parts: the mini-interviews (or verbal questionnaires) which lasted two to five minutes; and the field observations. The mini-interviews comprised the following topics: background information; mental factors such as current mood and personality type loosely based on the Myers-Briggs Type Indicator (social, organised, intuitive and analytical); Likert-scale opinion rating of elevator design elements; design suggestions; preferences (elevators or stairs?); security and safety; and habits. The components I was looking at in the field observation were: waiting and operating habits; interaction with design; interpersonal interaction; and movement flow.
While ethnography generally draws on qualitative data, it does not not mean that quantitative approaches shouldn’t be employed in the research process. Combining the two leads to a “mixed-method approach” that can take various forms: data collection and analysis can be either separated or addressed together, and each of them can be used in service of the other. Of course, this isn’t new in academic circles and corporate ethnography but there seems to be a renewed interest lately in this topic.
One of the driving forces of this renewed interest is the huge amount of information produced by people, things, space and their interactions — what some have called “Big Data“. The large data sets created by people’s activity on digital devices has indeed led to a surge of “traces” from smartphone apps, computer programs and environmental sensors. Such information is currently expected to transform how we study human behavior and culture, with, as usual, utopian hopes, dystopian fears and *critical sighs* from pundits.
Although most of the work of Big Data has focused on quantitative analysis, it is interesting to observe how ethnographers relate to it. Some offer a critical perspective, but others see it as an opportunity to create innovative methodologies to benefit from this situation. See for instance the notion of “Ethnomining” described by Aipperspach et al. (2006) in their insightful paper Ethno-Mining: Integrating Numbers and Words from the Ground Up:
Ethno-mining, as the name suggests, combines techniques from ethnography and data mining. Specifically, the integration of ethnographic and data mining techniques in ethno-mining includes a blending of their perspectives (on what interpretations are valid and interesting and how they should be characterized) and their processes (what selections and transformations are applied to the data to find and validate the interpretations).