Background - Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics - page 16

• Basic descriptive statistics such as the mean and median, standard devia-

tion, and the concept of confidence intervals , which reflect how accurate
your estimates of measures such as task times, task success rates, and sub-
jective ratings actually are.

• Simple statistical tests for comparing means and analyzing relationships

between variables.

• Tipsfor presenting your data visually in the most effective way.

We use Microsoft Excel 2010 for all of the examples in this chapter (and really
in most of this topic) because it is so popular and widely available. Most of the
analyses can also be done with other readily available spreadsheet tools such as
Google Docs or OpenOffice.org.

2.1 INDEPENDENT AND DEPENDENT VARIABLES
At the broadest level, there are two types of variables in any usability study: inde-
pendent and dependent. Independent variables are the things you manipulate
or control for, such as designs you're testing or the ages of your participants.
Dependent variables are the things you measure, such as success rates, number
of errors, user satisfaction, completion times, and many more. Most of the met-
rics discussed in this topic are dependent variables.

When designing a user experience study, you should have a clear idea of what
you plan to manipulate (independent variables) and what you plan to measure
(dependent variables). The most interesting outcomes of a study are at the inter-
section of the independent and dependent variables, such as whether one design
resulted in a higher task success rate than the other.

2.2 TYPES OF DATA
Bothindependentanddependentvariablescanbemeasuredusingoneoffour
general types of data: nominal, ordinal, interval, and ratio. Each type of data has
its own unique characteristics and, most importantly, supports specific types of
analyses and statistics. When collecting and analyzing user experience data, you
should know what type of data you're dealing with and what you can and can't
do with each type.

2.2.1 Nominal Data
Nominal (also called categorical) data are simply unordered groups or catego-
ries. Without order between the categories, you can say only that they are dif-
ferent, not that one is any better than the other. For example, consider apples,
oranges, and bananas. They are just different; no one fruit is inherently better
than any other.

In user experience, nominal data might be characteristics of different types
of users, such as Windows versus Mac users, users in different geographic loca-
tions, or males vs females. These are typically independent variables that allow
you to segment data by these different groups. Nominal data also include some