The Anthropometrics of Disability - Designing Accessible Communities

predictive validity before they can be used with confidence.
6. INCORPORATING BEHAVIORAL AND SOCIAL FACTORS
Several of the participants at the conference argued that social science research
methods are needed to understand many anthropometric issues. In particular, such
research can help us understand the perspective of people with disabilities, how
social and cultural factors influence anthropometrics, and the differences between
groups. These specific recommendations were made on how such research could
benefit the field:
• Consider the perspectives of people with disabilities. Not only is it important
to insure that the critical issues of concern to the population with disabilities
are addressed in anthropometric studies, it is also important, given limited
resources, to avoid devoting resources those issues that are not perceived to
be problematic to the target population. Thus, studies that would uncover
priorities for giving research attention to specific issues would be very
valuable.
• Anthropometric research should consider the role that social and cultural
factors play in successful task performance. This includes understanding the
role of age, education, ethnic origin and other such variables.
• Utilize psychophysical scales in combination with observable measures of
performance. Typical outcome measures are complete/incomplete, maximum
reach or comfortable reach. More sophisticated measures a re needed.
Scales that evaluate difficulty, satisfaction, acceptance and other
psychological measures can help us to understand how people evaluate
demands that the environment makes on human abilities. Special emphasis
should be given to the psychological evaluation of effort during manual
exertions. This seems to be a major knowledge gap.
• Validate the findings of laboratory studies through field research. This could
include the use of 1.) psychophysical methods to rate successful performance
and 2.) ethnographic research studies to understand how individuals interpret
tasks and decide on the level of performance they will strive to attain.
Predictive validity would be strengthened if personal perceptions were
studied along with observed performance. Using data fusion methods to
handle multiple data inputs like this would help to justify code requirements.
• Set design criteria based on subjective response rather than purely
observation of behavior. In particular, it is important to consider acceptable
of levels of effort, independence and productivity. Simple ability to complete
a task is not sufficient for understanding whether it can be done
satisfactorily. Ideally, the criteria for successful performance should be
determined by the target population.
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