The Quick Count and Election Observation

CHAPTER FIVE: STATISTICAL PRINCIPLES AND QUICK COUNTS
74 Selecting the Sample Points
Once the required size of the random sample is known, the sample can be
selected from the sample frame. For quick counts, polling stations (the sample
points) are selected from the complete list of polling stations (sample
frame). The simplest way to do this is to use a random computer program.
However, this task can also be accomplished without a computer. The first step
involves dividing the total number of polling stations by the desired number
Random ordering is a
technique that provides
additional assurance
that the probability of
the selection of each
point in the sample is
equal to the chances
of any other point
being selected.
of polling stations, and the second step requires determining a random starting
point. Again, the numbers from the 2001 quick count in Peru can be used
to illustrate how this is done:
On election day, the Peruvian universe consisted of 90,780 polling stations.
First, the total number of polling stations is divided by the desired
number of stations in the sample (90,780÷1,020 = 89). This indicates
that one in every 89 polling stations needs to be selected. Second, a
random starting point is selected by placing 89 slips of paper, numbered
1 to 89, in a hat, and randomly selecting a piece of paper. The
piece of paper selected contains the number 54. The 54th polling station
on the randomly ordered list is the first sample point, then every
89th polling station after that first sample point is selected. Thus the
second polling station in the sample is the 143rd polling station on
the list (54 plus 89). The procedure is repeated until the total sample
size of 1,020 is reached.
No large-scale quick
count undertaken by
any observer group
has ever been able to
deliver data from
every single data point
in the original sample.
Why does the list of polling stations have to be ordered randomly? This strategy
further protects the validity and reliability of the quick count. If the original
list is organized by size, region, or other criteria, the results of a simple draw
could be biased. Usually this is not a serious concern, but random ordering is
a technique that provides additional assurance that the probability of the selection
of each point in the sample is equal to the chances of any other point
being selected.
Correction Factors
It is sometimes necessary to make adjustments to various elements of the quick
count methodology. These adjustments apply to volunteer recruiting and training
and to more technical elements of the quick count, including sampling.
The sample calculations outlined above usually require some additional adjustment.
This is because it is assumed initially that all sample points will be identified
and that data will be delivered from each and every point. In practice, however,
no large-scale quick count undertaken by any observer group has ever been
able to deliver data from every single data point in the original sample.
In quick count situations, it is important to draw a distinction between a theoretical
sample and a practical sample. Most theoretical discussions of sampling
assume that, once a sample point is selected, data from that sample point will