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Experience Sampling Classification
4:13 with Tomer SharonHow to interpret the data from experience sampling
Evans, W. (2013). Introduction to Design Studio Method. TLCLabs.
Klocek, S. (2011). Better together; the practice of successful creative collaboration. Cooper Journal.
Gothelf, J. (2013). Lean UX. O’Reilly: Sebastopol, CA.
Lindstrom, J. (2011). Design Studios: The Good, the Bad, and the Science. UX Booth.
Warfel, T.Z. (2012). The Design Studio Method. Agile UX NYC 2012.
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The KJ Technique: A group process for establishing priorities, by Jared Spool.
Creating an Affinity Diagram Sophie Brenny and Freek de Bruijn
Using Affinity Diagrams Arizona Public Health Training Center
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In experience sampling we learned earlier that representative customers
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are interrupted several times a day to note their experience in realtime.
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You then collect hundreds or thousands of data points.
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The technique to analyze this large body of data is as follows.
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Decide on categories.
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Estimate which categories of data you'll be collecting during research.
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For example, let's assume you ask your experience sampling
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participants the following question.
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What frustrated you the last time you went grocery shopping?
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Categories for answers for this question might be.
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Location, which refers to where the frustration took place and the options
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are home, way to store, in car, at parking lot, at store, or away from store.
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Close people are the friends and
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family who might be involved in grocery shopping frustrations.
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These could be a spouse, a roommate, or kids.
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People at the store.
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Other customers, cashier, deli personnel, produce or
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dairy personnel, or other service people.
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Issue.
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Finding items, understanding costs, long lines, shopping cart, and so on.
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Classify data.
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When data starts pouring, look at each of the answers you've collected and
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one by one classify them into the categories you have predefined.
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If you work in a team, do the first chunk together.
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This way you'll better understand how to classify answers in a consistent manner.
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For example, here's an answer you might get.
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A slow cashier combined with an elderly person who was in
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front of me on the line caused me to be late to pick up my son from school.
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This answer would be classified as follows.
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Location: at the store.
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Close people: not applicable.
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People at the store, other customers, cashier.
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And issue: long lines.
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Get the idea?
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This way you disassemble each and
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every answer you collected into its components that you can later count.
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Adjust categories.
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As you make progress with classification you will realize that
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some categories need to be changed, removed and that new ones should be added.
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For example, you might find that under location it doesn't make sense to
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have both in car and way to store, because their redundant.
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Or you might find there are many answers that would
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benefit from creating a separate cost category with value such as item,
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expensive, cheap compared to other store and so on and so forth.
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Clean the data as you go, you'll find that some entries are just incomprehensible,
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irrelevant, or just duplicates.
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For example, the answer dogs is incomprehensible, and the answer my
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husband is just annoying, he wants to watch sports all the time, is irrelevant.
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Generate frequency charts and identify themes.
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As soon as you're done classifying the answers,
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you can easily produce frequency charts to indicate what's happening most.
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A frequency chart shows you how many times a certain value occurs for
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a particular variable.
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For example, you can create a frequency chart for
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the location category that might look like that.
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These frequency charts will tell you the story of the data you
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collected in numbers.
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Eye ball the data.
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Another way to get a good grasp of experiencing sampling data
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is called eyeballing it.
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Eyeballing the data means you just read the answers to
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the experience sampling question and just get a feel of what answers are like and
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what categories are out there.
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Without any analysis, you'll be able to reach to conclusions about what you found.
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Combine that with the classification results, and
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you have analyzed the data collected in the best way possible.
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Next, we'll introduce analysis and synthesis with an affinity diagram.
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