The Quick Count and Election Observation

CHAPTER SEVEN: COLLECTING AND ANALYZING QUICK COUNT DATA
114 sidered to have stabilized when the addition of new information from observers
has no discernible, or material, effect on the results that have already been
accumulated. In practice, this means that analysts watch the data findings
This “point of stability”
is an important
concept. Analysts
watch the data findings
until the basic
results, the distributions
across key variables,
do not change.
evolve until the basic results, the distributions across the key variables, do not
change. To establish a point of stability, analysts have to plan regular “takes”
of the data, regular intervals at which additional pieces of the accumulating
data are downloaded from the quick count database and analyzed.
There is no hard and fast rule about precisely what these intervals should be or
how regularly these data takes should be timed. One of two criteria are usually
used. The frequency of the data takes might be set according to timed
intervals: Take 1(T1) might be 30 minutes after the polls have closed, T2 might
be one hour after they have closed, T3 after one hour and a half later, and so
on. Alternatively, the intervals for the data takes might be established according
to the number of completed cases in the evolving dataset. So T1 might be
analyzed after there are 100 cases in the dataset, T2 after 200 cases, and so on.
The usual procedure is for T1 to be early, perhaps after the first fifty sample
points have arrived. The T1 data take serves two purposes: It provides an initial
check on whether all the computer hardware and software are handling
the data satisfactorily, and it provides benchmark data. The data from T2 are
usually used to conduct initial data sweeps, to scan the data for unusual variations.
Then, data from T3 through to Tn, are used to investigate in greater
detail the origins, and possible causes, of these variations. At issue are a number
of key questions. What is the scope of the problems? Are the problems
randomly distributed or not? If the problems are not randomly distributed,
then in what ways can the distributions be said to be non-random? And, does
the non-random distribution of problems work to the material benefit of any
party competing in the election?
Does the non-random distribution of a problem necessarily
mean that fraud has taken place?
No, not necessarily. Consider the previous example once again. The fact that
the indelible ink did not show up in 35 percent of the cases and that all of those
cases are concentrated in, say, the capital city, may only mean that there was
an administrative error in the distribution of materials and that administrative
error was only made by the official working on the materials for the capital
region. An election report should certainly draw attention to the evidence indicating
the scope and location of such a problem. But that evidence, by itself, is
insufficient grounds for drawing the conclusion that fraud has taken place. What
is required is a further investigation of the reasons for why the indelible ink was
missing from the materials for capital city region polling stations. Additionally,
if the election is expected to be close (or a sub-national election in the area is
quite competitive), the missing ink could warrant added vigilance against illegal,
multiple voting.