Ethnographic Analytics for Anthropological Studies: Adding Value to Ethnography Through IT-based Methods

January 22nd, 2013, by § 4 Comments

Ethnographic analytics? What’s that? In short, ethnographic analytics takes advantage of today’s technology to benefit anthropological studies, and is a great example of how science and technology can come together to help us understand and explain much about society and our human condition overall. I suggest that, using the computing power of software tools and techniques, it is possible to construct a set of useful indicators or analytics to complement the five human senses for ethnographic investigation.

Where did the idea of ethnographic analytics originate? How have ethnographic analytics been used and with what results? How can you incorporate them in your work? These are all questions I will address in the following short example of a recent study application in which ethnography and IT-based analytics complemented one another to produce insights about organizational innovation. In this blog, I will focus on one indicator that I have found very useful: an emotion indicator called the Positivity Index.

Over the past three decades, I believe it has been readily apparent that computing has entered our daily lives and especially the business world in the physical forms of desktops, laptops, tablets and smartphones. These devices are tied together with an invisible infrastructure powered by the internet, and now the “cloud,” using software applications to help us do our work, connect with others around the world, and manage many of our daily activities. Two of my colleagues, Julia Gluesing, an anthropologist and also my wife, along with Jim Danowski, a communication professor, and I thought that this new extensive information technology infrastructure could be tapped as resource to help study the diffusion of innovations in globally networked corporations. The result of our collaboration was a five year National Science Foundation (NSF) grant titled: “Accelerating the Diffusion of Innovations: A Digital Diffusion Dashboard Methodology for Global Networked Organizations” (NSF 2010). This mixed methods study provided a very real demonstration of how IT-based methods can complement and extend conventional ethnographic methods. For more detail about the study see the chapter “Being There: the Power of Technology-based Methods” in the new book Advancing Ethnography in Corporate Environments: Challenges and Emerging Opportunities, edited by Brigitte Jordan, which was recently released in 2012.

Overall, we used three software tools, Linguistic Inquiry and Word Count (LIWC), WORDij and Condor, to create a set of seven diffusion indicators or analytics that provided us guidance in selecting a sample of workers and managers for ethnographic interviews and shadowing to explore the context of engineering sub-teams who were working to deliver an innovation for a new vehicle. Working with our sponsors, the company’s legal team, and two university IRBs, we were able to collect 45,000 emails exchanged by a global innovation team working in the early stages of an automotive product innovation. With that data one of the indicators we computed was a weekly “emotion” analytic, which we called the Positivity Index, for the engineering sub-teams using the Linguistic Inquiry and Word Count software (LIWC).

Specifically, we divided the LIWC category percent “posemo” by the category percent ”negemo” to compute the Positivity Index analytic. The “posemo” category contains 407 word or word stems like: “benefit, cool, excit*, great, opportun* etc. The “negemo” category contains 499 word or word stems like: awful, damag*, miss, lose, risk* etc. For example, at the beginning of one project an electrical sub-team had a high Positivity Index about an idea they had using the words “excited potential”, “significant benefit” etc. However, after a few weeks of email exchanges with the transmission group, the Positivity Index plummeted when the combined team realized they would “miss” their deadline, and “risk” not meeting their cost targets. A listing of the LIWC 64 standard categories is available here. Research by Marcial Losada (1999) indicates that a 2.9:1 (positive to negative) ratio is needed for a healthy social system. This is referred to as the “Losada Line.”

If the positivity ratio is above 2.9:1, individuals and business teams flourish, and if it is below 2.9:1, they languish (Fredrickson and Losada 2005). High-performance teams have a positive ratio of 5.6:1 and low-performance teams ratio of 0.4:1. Moreover, there appears to be an upper limit of 11.6:1 where it is possible to have too high a positivity ratio, creating the likelihood that the team will flounder because it does not consider or ignores negative input.

We used the Positive Index to create a graph of scores over time to provide us with an initial sense of each sub-team’s progress in forming and working as a team. Some sub-teams had quite an emotional roller coaster, while the emotion in others did not oscillate nearly as much.

The graphs provided us with a handy, easily understood analytic to explore with the teams to gain a deeper understanding of the context surrounding their work. In another case, rather than email, we used meeting minutes to assess sub-team performance using emotion and found that a Positivity Index derived from minutes also provided a reliable indicator of the health of a team. Over the years, I have calculated the Positivity Index on interviews, newspaper reviews of products, letters, web sites and host of other texts. I have consistently found that it gave me an initial assessment to guide subsequent ethnographic interviews and observations. I have had some false negative readings on occasion, however. In one instance, the texts of plant safety reports described that there were “no deaths or fatalities”. In this case, the Positivity Index gave an inaccurate negative reading of the emotion; “no deaths or fatalities” for the monthly report actually was quite positive. Also, sometimes there is not enough text to generate a percent “negemo,” or negative emotion, to compute a ratio. These outliers have been few, and I now routinely calculate the Positivity Index on my textual research data.

You can try out the Positivity Index using the LIWC software for free. Note: the LIWC website does use an older engine and users will get a slight difference in the results between the Tryonline and the full LIWC version.

The web page will ask you to identify your gender and age, then paste in your text. The words in the text will be counted and a percentage calculated for 7 of the 64 LIWC categories, including self-references (“I,” “me,” “my”), social words, positive emotions, negative emotions, overall cognitive words, articles (“a,” “an,” “the”), and big words (more than six letters). LIWC does provide you with the ability to customize the dictionary with your own vocabulary as well.

My research experience has made me a fan of text analytics that can augment and enhance ethnographic methods with speed and accuracy using the natural language of participants in a very systematic manner. And best of all, the analytics, like the Positivity Index to measure the emotional content of text, are reusable and repeatable.

References:

Fredrickson, Barbara L., and Marcial F. Losada
2005 Positive Affect and the Complex Dynamics of Human Flourishing. American Psychologist 60(7):678–686.

Losada, Marcial
1999 The Complex Dynamics of High Performance Teams. Mathematical and Computer Modeling 30(9–10):179–192.

National Science Foundation (NSF)
2010 Award Abstract No. 0527487. DHB: Accelerating the Diffusion of Innovations: A Digital Diffusion Dashboard Methodology for Global Networked Organizations. Available at http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0527487. Accessed January 15, 2013.

Riopelle, Ken
2012 Being There: The Power of Technology-based Methods. In Advancing Ethnography in Corporate Environments: Challenges and Emerging Opportunities. Brigitte Jordan, ed. pp. 38-55. Walnut Creek: Left Coast Press, Inc.

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§ 4 Responses to Ethnographic Analytics for Anthropological Studies: Adding Value to Ethnography Through IT-based Methods

  • Ken Riopelle says:

    A special thanks goes to Chad R. Maxwell who posed “The idea of Ethnographic Analytics” in his Chapter titled: Accelerated Pattern Recognition, Ethnography and the Era of Big Data. In Advancing Ethnography in Corporate Environments: Challenges and Emerging Opportunities. Brigitte Jordan, ed. pp. 175-192. Walnut Creek: Left Coast Press, Inc. 2012.

  • […] analytics have been proposed as useful for understanding the human condition. In his post on Ethnographic Analytics, Ken Riopelle states that using an emotional indicator that he calls the Positivity Index can […]

  • Cesa Padilla says:

    I’m sorry, but, how do this index and software packages enhance ethnography? It seems to me that your methodology is completely opposite to the ethnographic enterprise: to understand human action and culture from the ground. Quantitative analysis could be part of an ethnography, but is not the core of it.

    • Ken Riopelle says:

      Cesa, I totally agree that quantitative analysis “could be part of an ethnography, but is not the core of it.” Here is how I think the text analysis tools can complement and support the core ethnographic methods. Different software packages, such as LIWC and WORDij enhance ethnographic fieldwork by helping the ethnographer discover patters in the text data. Looking for patterns and making sense of your field notes, interviews, newsletters, news stories etc., in my mind, is something that ethnographers routinely do. Software can help understand the data collected from the ground up and offer a pattern recognition for the ethnographer to consider. Such a pattern may corroborate your insight or contradict it. Again, text analysis can be performed many different ways to understand human action and culture from the ground up. Give it a try on some of your own data and see if it helps finding any patterns you may have missed.

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My research experience has made me a fan of text analytics that can augment and enhance ethnographic methods with speed and accuracy using the natural language of participants in a very systematic manner.

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