Background - Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics - page 40

• Minimize the number of segments in each bar. More than three seg-

ments per bar can make it difficult to interpret. Combine segments as
appropriate.

• Whenpossible,makeuseofcolor-codingconventionsthatyouraudi-

ence is likely to be familiar with. For many U.S. audiences, green is good,
yellow is marginal, and red is bad. Playing off of these conventions can
be helpful, as in the good example in Figure 2.15 , but don't rely solely
on them.

2.8 SUMMARY
In a nutshell, this chapter is about knowing your data. The better you know your
data, the more likely you are to answer your research questions clearly. The fol-
lowing are some of the key takeaways from this chapter.

1. When analyzing your results, it's critical to know your data. The specific
type of data you have will dictate what statistics you can (and can't)
perform.
2. Nominal data are categorical, such as binary task success or males and
females. Nominal data are usually expressed as frequencies or percent-
ages. χ 2 tests can be used when you want to learn whether the frequency
distribution is random or there is some underlying significance to the
distribution pattern.
3. Ordinal data are rank orders, such as a severity ranking of usability
issues. Ordinal data are also analyzed using frequencies, and the distri-
bution patterns can be analyzed with a χ 2 test.
4. Interval data are continuous data where the intervals between each
point are meaningful but without a natural zero. The SUS score is one
example. Interval data can be described by means, standard deviations,
and confidence intervals. Means can be compared to each other for the
same set of users (paired samples t test) or across different users (inde-
pendent samples t test). ANOVA can be used to compare more than two
sets of data. Relationships between variables can be examined through
correlations.
5. Ratio data are the same as interval but with a natural zero. One example
is completion times. Essentially, the same statistics that apply to interval
data also apply to ratio data.
6. Any time you can calculate a mean, you can also calculate a confidence
interval for that mean. Displaying confidence intervals on graphs of
means helps the viewer understand the accuracy of the data and to see
quickly any differences between means.
7. When presenting your data graphically, use the appropriate types of
graphs. Use bar graphs for categorical data and line graphs for continu-
ous data. Use pie charts or stacked bar graphs when data sum to 100%.