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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.
Office supplies for a design studio
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
In experience sampling we learned earlier
that representative customers
0:00
are interrupted several times a day to
note their experience in realtime.
0:04
You then collect hundreds or thousands of
data points.
0:10
The technique to analyze this large body
of data is as follows.
0:13
Decide on categories.
0:18
Estimate which categories of data you'll
be collecting during research.
0:20
For example, let's assume you ask your
experience sampling
0:24
participants the following question.
0:28
What frustrated you the last time you went
grocery shopping?
0:30
Categories for answers for this question
might be.
0:34
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
0:51
family who might be involved in grocery
shopping frustrations.
0:53
These could be a spouse, a roommate, or
kids.
0:57
People at the store.
1:01
Other customers, cashier, deli personnel,
produce or
1:03
dairy personnel, or other service people.
1:06
Issue.
1:10
Finding items, understanding costs, long
lines, shopping cart, and so on.
1:11
Classify data.
1:17
When data starts pouring, look at each of
the answers you've collected and
1:19
one by one classify them into the
categories you have predefined.
1:24
If you work in a team, do the first chunk
together.
1:28
This way you'll better understand how to
classify answers in a consistent manner.
1:31
For example, here's an answer you might
get.
1:37
A slow cashier combined with an elderly
person who was in
1:39
front of me on the line caused me to be
late to pick up my son from school.
1:43
This answer would be classified as
follows.
1:48
Location: at the store.
1:51
Close people: not applicable.
1:54
People at the store, other customers,
cashier.
1:57
And issue: long lines.
2:01
Get the idea?
2:03
This way you disassemble each and
2:04
every answer you collected into its
components that you can later count.
2:07
Adjust categories.
2:13
As you make progress with classification
you will realize that
2:15
some categories need to be changed,
removed and that new ones should be added.
2:19
For example, you might find that under
location it doesn't make sense to
2:25
have both in car and way to store, because
their redundant.
2:29
Or you might find there are many answers
that would
2:33
benefit from creating a separate cost
category with value such as item,
2:36
expensive, cheap compared to other store
and so on and so forth.
2:41
Clean the data as you go, you'll find that
some entries are just incomprehensible,
2:48
irrelevant, or just duplicates.
2:53
For example, the answer dogs is
incomprehensible, and the answer my
2:56
husband is just annoying, he wants to
watch sports all the time, is irrelevant.
3:01
Generate frequency charts and identify
themes.
3:07
As soon as you're done classifying the
answers,
3:11
you can easily produce frequency charts to
indicate what's happening most.
3:14
A frequency chart shows you how many times
a certain value occurs for
3:18
a particular variable.
3:23
For example, you can create a frequency
chart for
3:24
the location category that might look like
that.
3:27
These frequency charts will tell you the
story of the data you
3:31
collected in numbers.
3:35
Eye ball the data.
3:38
Another way to get a good grasp of
experiencing sampling data
3:40
is called eyeballing it.
3:44
Eyeballing the data means you just read
the answers to
3:46
the experience sampling question and just
get a feel of what answers are like and
3:48
what categories are out there.
3:54
Without any analysis, you'll be able to
reach to conclusions about what you found.
3:56
Combine that with the classification
results, and
4:01
you have analyzed the data collected in
the best way possible.
4:04
Next, we'll introduce analysis and
synthesis with an affinity diagram.
4:08
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