Experience Sampling Classification

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 0:38 are home, way to store, in car, at parking lot, at store, or away from store. 0:43 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