Self-Reported Metrics - Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics - page 123

6.1 IMPORTANCE OF SELF-REPORTED DATA
Self-reported data give you the most important information about users' percep-
tion of the system and their interaction with it. At an emotional level, the data
may tell you something about how the users feel about the system. In many situ-
ations, these kinds of reactions are the main thing that you care about. Even if it
takes users forever to perform something with a system, if the experience makes
them happy, that may be the only thing that matters.

Your goal is to make the users think of your product first. For example, when
deciding what travel-planning website to use for an upcoming vacation, users
are more likely to think of the site that they liked the last time they used it.
They're much less likely to remember how long the process was or that it took
more mouse-clicks than it should have. That's why users' subjective reactions
to a website, product, or store may be the best predictor of their likelihood to
return or make a purchase in the future.

6.2 RATING SCALES
One of the most common ways to capture self-reported data in a UX study is
with some type of rating scales. Two of the classic approaches to rating scales are
the Likert scale and the semantic differential scale.

6.2.1 Likert Scales
A typical item in a Likert scale is a statement to which respondents rate their
level of agreement. The statement may be positive (e.g., “The terminology used
in this interface is clear”) or negative (e.g., “I found the navigation options con-
fusing”). Usually a five-point scale of agreement like the following is used:

1. Strongly disagree
2. Disagree
3. Neither agree nor disagree
4. Agree
5.

Strongly agree

In the original version of the scale, Likert (1932) provided “anchor terms”
for each point on the scale, such as Agree, and did not use numbers. Some
people prefer to use a seven-point scale, but it gets a bit more difficult to
come up with descriptive terms for each point as you get to higher numbers.
This is one reason many researchers have dropped the intervening labels and
just label the two ends (or anchor points) and perhaps the middle, or neutral,
point. Many variations on Likert scales are still used today, but most Likert-scale
purists would say that the two main characteristics of a Likert scale are (1) it
expresses a degree of agreement with a statement and (2) it uses an odd num-
ber of response options, thus allowing a neutral response. By convention, the
“Strongly Agree” end of a Likert scale is generally shown on the right when pre-
sented horizontally.