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page ranked second and the one ranked third. It could be that the participant
really loved one page and hated all three of the others.
The most common way to analyze ordinal data is by looking at frequencies.
For example, you might report that 40% of the users rated the site as excellent,
30% as good, 20% percent as fair, and 10% as poor. Calculating an average rank-
ing may be tempting but it's statistically meaningless.
2.2.3 Interval Data
Interval data are continuous data where differences between the values are mean-
ingful, but there is no natural zero point. An example of interval data familiar to
most of us is temperature. Defining 0° Celsius or 32° Fahrenheit based on when
water freezes is completely arbitrary. The freezing point of water does not mean
the absence of heat; it only identifies a meaningful point on the scale of tem-
peratures.Butthedifferencesbetweenthevaluesaremeaningful:thedistance
from 10° to 20° is the same as the distance from 20° to 30° (using either scale).
Dates are another common example of interval data.
In usability, the System Usability Scale (SUS) is one example of interval data.
SUS (described in detail in Chapter 6) is based on self-reported data from a
series of questions about the overall usability of any system. Scores range from 0
to 100, with a higher SUS score indicating better usability. The distance between
each point along the scale is meaningful in the sense that it represents an incre-
mental increase or decrease in perceived usability.
Interval data allow you to calculate a wide range of descriptive statistics
(including averages and standard deviation). There are also many inferential sta-
tistics that can be used to generalize about a larger population. Interval data pro-
vide many more possibilities for analysis than either nominal or ordinal data.
Much of this chapter will review statistics that can be used with interval data.
One of the debates you can get into with people who collect and analyze sub-
jective ratings is whether you must treat the data as purely ordinal or if you can
treat it as being interval. Consider these two rating scales:
o
Poor
o
Fair
o
Good
o
Excellent
Poor
o
o
o
o
Excellent
At first glance, you might say those two scales are the same, but the difference
in presentation makes them different. Putting explicit labels on items in the first
scale makes the data ordinal. Leaving the intervening labels off in the second
scale and only labeling the end points make the data more “interval-like,” which
is why most subjective rating scales only label the ends, or “anchors,” and not
every data point. Consider a slightly different version of the second scale:
Poor
o o o o o o o o o
Excellent
Presenting it that way, with 9 points along the scale, makes it even more
obvious that the data can be treated as if it were interval data. The reasonable
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