Behavioural Surveillance Surveys - The Wisdom of Whores

In the end, the issue boils down to one
of credibility. A survey based upon nonprobability
sampling methods may produce
the same results as a probability survey,
but the results will be harder to defend against
skeptics who suspect that the findings may
reflect poor sampling rather than actual
behavior. Probability methods produce data
that can be interpreted with much greater
confidence. This should in turn translate into
a firmer basis for decision-making in designing
HIV prevention programs and in allocating
resources.
Early in the HIV epidemic, much research
was conducted on an ad hoc basis — a response
to the need for ANY information, as quickly
as possible. More recently, however, the
demand has grown for the systematic
collection of high-quality data that can be
interpreted and acted upon with greater
confidence. This demand has spurred the
development of methods to extend probability
sampling as much as possible to surveys of
population sub-groups that are difficult to
enumerate. It is acknowledged, however,
that the use of probability sampling methods
will not be feasible for some populations;
notably, those whose members do not
congregate in fixed locations and for whom
it is thus not feasible to develop a list or
sampling frame. When a sampling frame
cannot be constructed, the use of non-probability
sampling methods is the only alternative.
Guidance on how to make data collected
under these circumstances as objective and
replicable as possible is provided later in this
chapter.
Multi-stage cluster sampling
methods
In probability sampling, the key element in
the sample selection process is randomization.
This means that units and/or respondents are
randomly selected from all those included in
the sampling frame. This reduces potential
bias. Randomization can occur at various
levels; the scheme chosen will depend on
the level of error surveillance managers are
prepared to accept in their results, as well as
what is most feasible. There is almost always
a trade-off between these two elements.
In general, easier types of sampling such as
the multi-stage cluster sampling described
below carry wider margins of error than
simple random sampling from the entire
population. This means that a larger sample
size will be needed to achieve the same levels
of precision.
Where a complete list of all individuals
in the group to be sampled is available, it is
possible to select individuals randomly from
that list. However this is rarely the case.
More commonly, a list of larger units where
the individuals gather is more likely to be
available, or easy to construct. These units
are known as primary sampling units
(PSUs) or clusters. Examples might include
schools, brothels, or gay bars. If a list of all
PSUs can be compiled, a set number of PSUs
can be selected at random. Then lists of
individuals need only be compiled for the
PSUs selected, and individuals can be chosen,
preferably at random, from within the selected
PSUs. As discussed below, some variants of
multi-stage cluster sampling do not even
require that a list of elements within sample
clusters be created.
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C H A PTER 4 B EHAV I OR A L S U R V EI L L A NC E S U R V EY S