Behavioural Surveillance Surveys - The Wisdom of Whores

Calculating weights from
sampling probabilities
Once sampling probabilities have been
calculated, these are converted to sampling
weights as follows:
Where:
W i
= 1/P i
W i
= sampling weight for elements in the
ith cluster; and
P i
= probability of selection for elements in
the ith cluster.
Note, however, that when sampling weights
are applied to survey data using standard
computer software packages (e.g., EPI-INFO,
SPSS), the number of sample observations will
be inflated and will thus imply a larger sample
size than was actually realized in a survey.
As a result, statistical tests for changes in
indicators over time will be based upon
incorrect sample sizes, and misleading
conclusions as to the effects of programs
might result. For example, changes that were
not statistically significant based upon the
actual sample size will appear to be significant
based upon the weighted number of cases.
Calculating standardized weights
To compensate for this, standardized
weights are often used. Standardized
weights assign a weight to each sample
observation that reflects its relative probability
of selection in comparison with other sample
observations, but does not change the overall
survey sample size. Standardized weights (w i
’)
for sample elements in the i th cluster are
calculated as follows:
It will be noted that since each element
in a given cluster has the same probability
of selection, each will also receive the same
standardized weight. Figure 7 illustrates the
computation of standardized weights using
hypothetical survey data.
In order to make use of standardized
sampling weights during data analysis, it is
necessary to include an appropriate “weight”
variable in the survey data file to be analyzed.
The standardized weights can either be
calculated by hand or using a spreadsheet
and entered as a variable during data entry,
or alternatively the first- and second-stage
selection probabilities could be entered and
the weights calculated using appropriate
commands for the statistical package used.
What kind of bias can result
by failing to weight the data?
If characteristics of the sub-population
being measured differ from one cluster to
the next, and those differences are correlated
to the size of the cluster, then this can have
the effect of changing the point estimates
(values for indicators).
For example, imagine a situation where an
intervention was conducted to promote 100%
condom use in brothels. Although there were
30 brothels, for the sake of efficiency the
intervention focused on only the 10 largest
brothels, because those brothels were thought
to house around 75% of all sex workers. As a
result, after several months of the intervention,
80% of the women in the larger brothels were
consistently using condoms. However, in the
smaller brothels, only 30% of the women were
doing so.
w i
’ = w i
n i
/ Σw i
n i
B EHAV I OR A L S U R V EI L L A NC E SURV EY S CHAPTER 5
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