In experience sampling we learned earlier that representative customers

are interrupted several times a day to note their experience in realtime.

You then collect hundreds or thousands of data points.

The technique to analyze this large body of data is as follows.

Decide on categories.

Estimate which categories of data you'll be collecting during research.

For example, let's assume you ask your experience sampling

participants the following question.

What frustrated you the last time you went grocery shopping?

Categories for answers for this question might be.

Location, which refers to where the frustration took place and the options

are home, way to store, in car, at parking lot, at store, or away from store.

Close people are the friends and

family who might be involved in grocery shopping frustrations.

These could be a spouse, a roommate, or kids.

People at the store.

Other customers, cashier, deli personnel, produce or

dairy personnel, or other service people.

Issue.

Finding items, understanding costs, long lines, shopping cart, and so on.

Classify data.

When data starts pouring, look at each of the answers you've collected and

one by one classify them into the categories you have predefined.

If you work in a team, do the first chunk together.

This way you'll better understand how to classify answers in a consistent manner.

For example, here's an answer you might get.

A slow cashier combined with an elderly person who was in

front of me on the line caused me to be late to pick up my son from school.

This answer would be classified as follows.

Location: at the store.

Close people: not applicable.

People at the store, other customers, cashier.

And issue: long lines.

Get the idea?

This way you disassemble each and

every answer you collected into its components that you can later count.

Adjust categories.

As you make progress with classification you will realize that

some categories need to be changed, removed and that new ones should be added.

For example, you might find that under location it doesn't make sense to

have both in car and way to store, because their redundant.

Or you might find there are many answers that would

benefit from creating a separate cost category with value such as item,

expensive, cheap compared to other store and so on and so forth.

Clean the data as you go, you'll find that some entries are just incomprehensible,

irrelevant, or just duplicates.

For example, the answer dogs is incomprehensible, and the answer my

husband is just annoying, he wants to watch sports all the time, is irrelevant.

Generate frequency charts and identify themes.

As soon as you're done classifying the answers,

you can easily produce frequency charts to indicate what's happening most.

A frequency chart shows you how many times a certain value occurs for

a particular variable.

For example, you can create a frequency chart for

the location category that might look like that.

These frequency charts will tell you the story of the data you

collected in numbers.

Eye ball the data.

Another way to get a good grasp of experiencing sampling data

is called eyeballing it.

Eyeballing the data means you just read the answers to

the experience sampling question and just get a feel of what answers are like and

what categories are out there.

Without any analysis, you'll be able to reach to conclusions about what you found.

Combine that with the classification results, and

you have analyzed the data collected in the best way possible.

Next, we'll introduce analysis and synthesis with an affinity diagram.