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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
- Compare
- Categorize open-ended responses
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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
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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,
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for example, very satisfied would be 5 and
very dissatisfied would be 1.
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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
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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.
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Just like we did with our
usability test findings,
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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
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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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