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Posts Tagged ‘data analysis’

The roots of ethnography

January 31st, 2010

In case you want to check out some of the pioneers of ethnography, this entry mentions a few of their names. Lewis Henry Morgan was one of the earliest. He lived and worked with the Iroquois, studying their kinship patterns and the influence these patterns had on their society. He tried to use research and his influence as a lawyer to protect Native Americans from exploitation. Because he was such a strong advocate, he was adopted by the Seneca Indians, and was given the name Tayadaowuhkuh , “one who bridges the gap.”

German-born Franz Boas developed a more systematic approach to ethnography. Boas promoted the idea that a culture should be understood in terms of its own beliefs and history, rather than from the context of one’s own culture. This is the key point that connects ethnography as a research method and user-centered design as a professional practice.

Polish anthropologist Bronisław Malinowski developed ethnography further, codifying the practice of participant observation, spending long periods of time in the field, and recording copious field notes.

I mention these pioneers, not because I want to emulate them or study similar issues. Kinship patterns and tangential social gradients of power do not interest me at all. Rather, I’m interested in applying research techniques that help me understand and describe behavior in context, and the unseen forces that shape that behavior on a daily basis, and these researchers blazed a trail that I and my associates are picking up for a very different purpose.

Copyright 2009, Paul Bryan, Usography Corporation (http://www.usography.com)
Linked In: http://www.linkedin.com/in/uxexperts

Copyright 2009, Paul Bryan, Usography Corporation (http://www.usography.com)

Linked In: http://www.linkedin.com/in/uxexperts

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Depth interviews in design research: Planning data analysis

August 31st, 2009

Depth interviews typically result in a mountain of data, including researcher notes, results of exercises and other research artifacts, video or audio recordings, and transcripts. Reviewing and analyzing this data is very time-consuming. You need a plan before the interviews start for what will be captured and how it will be processed prior to presenting results. As a rule of thumb, we allocate at least two hours for every hour of interviews for data review and analysis: 1 hour to review, 1 hour to analyze and formulate findings. It is easy to underestimate the time this will take. Watching a one hour video typically takes more than an hour, because you not only have to watch it through to make sure you catch all the information, but you will probably start and stop it to highlight the most significant sections, write notes, get the timecode precise for the clips you want, etc. Then you need time to formulate higher level findings, produce clips, and trim down the highlighted data for final presentation.

An important aspect of the data analysis plan is data reduction, i.e. how you will go from raw data to the first step of processed data. Usography standardizes this procedure so that all researchers use the same approach to data analysis, for comparison and corroboration purposes. Each researcher goes through the detailed transcript and notes at least once, recording notes about themes and high-level findings as we go. We then note recurring topics and place them into a table. Example data elements that we look for consistently across all participants are:

  • HHI
  • Internet IQ
  • Shopping IQ
  • Goals
  • Drivers
  • Categories
  • Business value
  • Typical purchase process
  • Barriers
  • Dropoff
  • Requested content/features
  • Competitive sites
  • Quotes
  • Method for tracking this user type with analytics

If we have previously conducted research in the area, or if we have done extensive secondary research prior to conducting primary research, we may have a preliminary coding system going into the study.  In this case, research assistants are labeling notes with these codes as they record or transcribe them. Those codes are then used to form the data summary table for each participant. For example, in cell phone research we may designate “communication enabler” as a predefined code. Research assistants type “ce” everytime a participant makes a comment that matches this theme. This is part of the analysis planning that takes place before we conduct the research, and is a big time saver. 

 

Written by Paul Bryan, Usography Corporation (www.usography.com)
Linked In: http://www.linkedin.com/in/uxexperts

Written by Paul Bryan, Usography Corporation (www.usography.com)

Linked In: http://www.linkedin.com/in/uxexperts

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