1 00:00:00,600 --> 00:00:04,870 In experience sampling we learned earlier that representative customers 2 00:00:04,870 --> 00:00:10,140 are interrupted several times a day to note their experience in realtime. 3 00:00:10,140 --> 00:00:13,500 You then collect hundreds or thousands of data points. 4 00:00:13,500 --> 00:00:18,560 The technique to analyze this large body of data is as follows. 5 00:00:18,560 --> 00:00:20,840 Decide on categories. 6 00:00:20,840 --> 00:00:24,940 Estimate which categories of data you'll be collecting during research. 7 00:00:24,940 --> 00:00:28,320 For example, let's assume you ask your experience sampling 8 00:00:28,320 --> 00:00:30,710 participants the following question. 9 00:00:30,710 --> 00:00:34,800 What frustrated you the last time you went grocery shopping? 10 00:00:34,800 --> 00:00:38,390 Categories for answers for this question might be. 11 00:00:38,390 --> 00:00:43,060 Location, which refers to where the frustration took place and the options 12 00:00:43,060 --> 00:00:50,231 are home, way to store, in car, at parking lot, at store, or away from store. 13 00:00:51,250 --> 00:00:53,160 Close people are the friends and 14 00:00:53,160 --> 00:00:57,050 family who might be involved in grocery shopping frustrations. 15 00:00:57,050 --> 00:01:01,230 These could be a spouse, a roommate, or kids. 16 00:01:01,230 --> 00:01:03,040 People at the store. 17 00:01:03,040 --> 00:01:06,780 Other customers, cashier, deli personnel, produce or 18 00:01:06,780 --> 00:01:10,380 dairy personnel, or other service people. 19 00:01:10,380 --> 00:01:11,320 Issue. 20 00:01:11,320 --> 00:01:16,410 Finding items, understanding costs, long lines, shopping cart, and so on. 21 00:01:17,910 --> 00:01:19,620 Classify data. 22 00:01:19,620 --> 00:01:24,110 When data starts pouring, look at each of the answers you've collected and 23 00:01:24,110 --> 00:01:28,670 one by one classify them into the categories you have predefined. 24 00:01:28,670 --> 00:01:31,980 If you work in a team, do the first chunk together. 25 00:01:31,980 --> 00:01:37,360 This way you'll better understand how to classify answers in a consistent manner. 26 00:01:37,360 --> 00:01:39,920 For example, here's an answer you might get. 27 00:01:39,920 --> 00:01:43,850 A slow cashier combined with an elderly person who was in 28 00:01:43,850 --> 00:01:48,950 front of me on the line caused me to be late to pick up my son from school. 29 00:01:48,950 --> 00:01:51,815 This answer would be classified as follows. 30 00:01:51,815 --> 00:01:54,016 Location: at the store. 31 00:01:54,016 --> 00:01:57,240 Close people: not applicable. 32 00:01:57,240 --> 00:02:01,147 People at the store, other customers, cashier. 33 00:02:01,147 --> 00:02:03,180 And issue: long lines. 34 00:02:03,180 --> 00:02:04,640 Get the idea? 35 00:02:04,640 --> 00:02:07,300 This way you disassemble each and 36 00:02:07,300 --> 00:02:11,600 every answer you collected into its components that you can later count. 37 00:02:13,690 --> 00:02:15,720 Adjust categories. 38 00:02:15,720 --> 00:02:19,230 As you make progress with classification you will realize that 39 00:02:19,230 --> 00:02:25,030 some categories need to be changed, removed and that new ones should be added. 40 00:02:25,030 --> 00:02:29,444 For example, you might find that under location it doesn't make sense to 41 00:02:29,444 --> 00:02:33,870 have both in car and way to store, because their redundant. 42 00:02:33,870 --> 00:02:36,810 Or you might find there are many answers that would 43 00:02:36,810 --> 00:02:41,960 benefit from creating a separate cost category with value such as item, 44 00:02:41,960 --> 00:02:46,720 expensive, cheap compared to other store and so on and so forth. 45 00:02:48,310 --> 00:02:53,860 Clean the data as you go, you'll find that some entries are just incomprehensible, 46 00:02:53,860 --> 00:02:56,130 irrelevant, or just duplicates. 47 00:02:56,130 --> 00:03:01,240 For example, the answer dogs is incomprehensible, and the answer my 48 00:03:01,240 --> 00:03:05,440 husband is just annoying, he wants to watch sports all the time, is irrelevant. 49 00:03:07,210 --> 00:03:11,120 Generate frequency charts and identify themes. 50 00:03:11,120 --> 00:03:14,010 As soon as you're done classifying the answers, 51 00:03:14,010 --> 00:03:18,760 you can easily produce frequency charts to indicate what's happening most. 52 00:03:18,760 --> 00:03:23,020 A frequency chart shows you how many times a certain value occurs for 53 00:03:23,020 --> 00:03:24,850 a particular variable. 54 00:03:24,850 --> 00:03:27,960 For example, you can create a frequency chart for 55 00:03:27,960 --> 00:03:30,590 the location category that might look like that. 56 00:03:31,690 --> 00:03:35,380 These frequency charts will tell you the story of the data you 57 00:03:35,380 --> 00:03:36,970 collected in numbers. 58 00:03:38,900 --> 00:03:40,680 Eye ball the data. 59 00:03:40,680 --> 00:03:44,010 Another way to get a good grasp of experiencing sampling data 60 00:03:44,010 --> 00:03:46,090 is called eyeballing it. 61 00:03:46,090 --> 00:03:48,970 Eyeballing the data means you just read the answers to 62 00:03:48,970 --> 00:03:54,340 the experience sampling question and just get a feel of what answers are like and 63 00:03:54,340 --> 00:03:56,590 what categories are out there. 64 00:03:56,590 --> 00:04:01,730 Without any analysis, you'll be able to reach to conclusions about what you found. 65 00:04:01,730 --> 00:04:04,600 Combine that with the classification results, and 66 00:04:04,600 --> 00:04:08,440 you have analyzed the data collected in the best way possible. 67 00:04:08,440 --> 00:04:12,770 Next, we'll introduce analysis and synthesis with an affinity diagram.