Behavioural Surveillance Surveys - The Wisdom of Whores

equired. In this case, the pros and cons of
using larger sample sizes which are designed
to measure statistically significant changes
each year vs. smaller sample sizes which may
only register statistically significant change
over the long term should be debated and
well understood ahead of time. If only
changes of larger magnitude are deemed
worth measuring, it may be worth considering
cutting the frequency of BSS.
There are other trade-offs, too, in setting
the magnitude of change parameter at a high
or low levels. Parameters of significance and
power included in the sample size calculation
will determine that the sample size is large
enough to detect the selected level of change
with the confidence desired. But the smaller
the sample size, the higher the probability that
an indicator estimated from that sample does
not represent the true value of the indicator
for the whole universe from which the sample
is drawn. To compensate for this uncertainty,
it is common practice to calculate confidence
intervals around an estimate. The confidence
level represents a range for the likely value
of the estimate of an indicator from a survey.
A 95% confidence interval means you can be
95 percent sure that the total population value
for an indicator lies within the specified range
around the value measured in the sample
population. For a given level of power and
significance, smaller sample sizes will yield
wider confidence intervals around an estimate.
The wider the confidence interval, the less
precise the estimate of the true population
value will be. More information on confidence
intervals is given in Chapter 7.
Where specific program targets are not
available, it is recommended that sample size
calculations use a “generic” target of 10-15
percentage points of detectable change.
This magnitude of change generally produces
sample sizes that are within the resource
levels available for data collection for most
programs, while producing results in a range
narrow enough to be meaningful.
Determining starting or baseline levels of
indicators (P 1
)
Another challenge concerns the choice of a
starting value for an indicator being monitored;
that is, P 1
. Ideally, this choice would be based
on information available from other surveys
that have been conducted in the study setting.
Where such information is unavailable,
an informed guess will have to be made.
In choosing a value for P 1
, the recommended
course of action is to err toward assigning P 1
a value of .50. The reason for this is that the
variances of indicators measured as proportions
are maximized as they approach .50. Thus,
erring toward .50 provides a measure of
insurance that the sample size chosen will be
sufficient to satisfy the measurement objectives
of the survey even if the estimate of P 1
used
is wrong. The safest course would, of course,
be to choose P 1
=.5 for all indicators. However,
this would result in samples that are much
larger than is needed in the event that the
actual value of P1 is very different from .50.
Thus, the recommended approach is to
make the best guess based upon available
information, and err toward .50 in selecting
values of P 1
.
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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