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2.2 Research hypotheses 31

Typical independent variables related to users include age, gender, computer ex-
perience, professional domain, education, culture, motivation, mood, and disabilities.
Using age as an example, we know that human capabilities change during their life
span. Children have a physically smaller build and shorter attention span. Their read-
ing skills, typing skills, and cognitive capabilities are all limited compared to typical
computer users between ages 20 and 55. At the other end of the scale, senior citizens
experience deterioration in cognitive, physical, and sensory capabilities. As a re-
sult, users in different age groups interact differently with computers and computer-
related devices. Most computer applications are designed by people between 20 and
50 years of age who have little or no knowledge or experience in the interaction style
or challenges faced by the younger and older user groups (Chisnell, 2007). In order
to understand the gap created by age differences, a number of studies have been
conducted to compare the interaction styles of users in different age groups (Zajicek,
2006; Zajicek and Jonsson, 2006).
Typical independent variables related to the context of use of technologies in-
clude both physical factors, such as environmental noise, lighting, temperature,
vibration, users' status (e.g., seated, walking or jogging) (Price et al., 2006), and
social factors, such as the number of people surrounding the user and their relation
to the user.

2.2.4 TYPICAL DEPENDENT VARIABLES IN HCI RESEARCH
Dependent variables frequently measured can be categorized into five groups: ef-
ficiency, accuracy, subjective satisfaction, ease of learning and retention rate, and
physical or cognitive demand.
Efficiency describes how fast a task can be completed. Typical measures include
time to complete a task and speed (e.g., words per minute, number of targets selected
per minute)
Accuracy describes the states in which the system or the user makes errors. The
most frequently used accuracy measure is error rate. Numerous metrics to measure
error rate have been proposed for various interaction tasks, such as the “minimum
string distance” proposed for text entry tasks (Soukoreff and Mackenzie, 2003). In
HCI studies, efficiency and accuracy are not isolated but are highly related factors.
There is usually a trade-off between efficiency and accuracy, meaning that, when the
other factors are the same, achieving a higher speed will result in more errors and
ensuring fewer errors will lower the speed. Consequently, any investigation that only
measures one of the two factors misses a critical side of the picture.
Subjective satisfaction describes the user's perceived satisfaction with the inter-
action experience. The data is normally collected using Likert scale ratings (e.g.,
numeric scales from 1 to 5) through questionnaires.
Ease of learning and retention rate describe how quickly and how easily an indi-
vidual can learn to use a new application or complete a new task and how long they
retain the learned skills (Feng et al., 2005). This category is less studied than the previ-
ous three categories but is highly important for the adoption of information technology.