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Analyzing User Surveys3:12 with Anya Mezak
In this video, we will analyze the results of your user surveys.
Tool used in the video demo
Make meaning of your data
- Get rid of bad data
- Calculate the means
- Categorize open ended responses
In addition to writing good survey questions, 0:00 how do you make sure that your results will be meaningful? 0:03 First, you'll need to calculate the proper sample size. 0:07 SurveyMonkey has a useful sample size calculator to do this. 0:11 The link is in the teacher's notes for your reference. 0:15 Here is the SurveyMonkey sample size calculator. 0:19 The survey that we designed in the last section was meant to represent the people 0:25 who have at least saved a custom T-shirt design, all 1000 of them. 0:30 In order to be 95% confident in our results, as shown right here, 0:35 and if you feel comfortable with a margin of error of 5%, 0:40 Then your sample size should be at least 278 people. 0:46 This means you should have at least 278 people complete your survey. 0:51 Once your data is in, take a few simple steps to make meaning of your data. 0:57 First, get rid of bad data. 1:03 If your survey provides an incentive, 1:06 some people may provide bogus responses just to get that incentive. 1:08 You'll need to discard responses from those participants. 1:13 Common red flags are nonsensical open-ended responses, patterning, 1:16 which can look like providing the same answers to all questions, or 1:21 unrealistically fast survey completion. 1:26 I've provided a link to a source describing the behaviors to watch for. 1:29 Second, calculate the means. 1:34 Take all the Likert scale questions, assign a numerical value to each option, 1:36 for example, very satisfied would be 5 and very dissatisfied would be 1. 1:42 With that in mind, you'll be able to calculate a mean for each question. 1:47 Make comparisons. 1:51 Sometimes, it could be hard to know if a satisfaction score of four, for 1:53 example, is good, or not. 1:57 This is where it helps to start tracking your data over time so 1:59 that seeing the scores go up and down begins to have meaning. 2:03 If you have data from a similar service, 2:07 comparing those scores can be useful as well. 2:09 Four, categorize open ended responses. 2:12 Just like we did with our usability test findings, 2:16 group similar responses together until you see a pattern. 2:19 You can use an automated text analysis tool to help you do this at scale. 2:23 That's all for our discussion about surveys, and 2:28 also completes our course on evaluating design. 2:32 We've covered a wide range of topics. 2:35 To understand qualitative methods, 2:38 we created our very own usability study for Amazon.com. 2:41 For our quantitative methods lesson, we learned about the basics of AB studies and 2:46 then went on to create our own user survey. 2:52 Following this course, I hope you feel equipped to take a critical eye to your 2:55 designs and to be able to evaluate what you and your team have come up with. 3:00 I've provided a link to a list of other great UX 3:04 research resources if you want to learn more about this topic. 3:07 Good luck. 3:11
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