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For example, consider the data shown in Figure 8.1 from Chadwick-Dias,
McNulty, and Tullis (2003), which shows z scores of performance for two itera-
tions of a prototype. This research studied the effects of age on performance in
using a website. Study 1 was a baseline study. Based on their observations of the
participants in Study 1, especially the problems encountered by the older par-
ticipants, they made changes to the prototype and then conducted Study 2 with
a new group of participants. The z scores were equal-weighted combinations of
task time and task completion rate.
It's important to understand that the z -score transformations were done using
the full set of data from Study 1 and Study 2 combined. They were then plotted
appropriately to indicate from which study each z score was derived. The key
finding was that the performance z scores for Study 2 were significantly higher
than the performance z scores for Study 1; the effect was the same regardless of
age (as reflected by the fact that the two lines are parallel to each other). If the
z -score transformations had been done separately for Study 1 and Study 2, the
results would have been meaningless because the means for Study 1 and Study
2 would both have been forced to 0 by the transformations.
Figure 8.1 Data showing performance z scores from two studies of a prototype with participants over a
wide range of ages. The performance z score was an equal-weighted combination of task time and task
completion rate. Changes were made to the prototype between Study 1 and Study 2. The performance z
scores were significantly better in Study 2, regardless of the participant's age. Adapted from Chadwick-
Dias et al. (2003); used with permission.
8.1.4 Using Single Usability Metric
Jeff Sauro and Erika Kindlund (2005) developed a quantitative model for
combining usability metrics into a single usability score. Their focus is on task
completion, task time, error counts per task, and post-task satisfaction rating
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