The Quick Count and Election Observation
The Quick Count and Election Observation
The Quick Count and Election Observation
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THE QUICK COUNT AND ELECTION OBSERVATION<br />
<strong>The</strong> conventional practice for statisticians is to rely on a confidence level of 95<br />
percent. Technically, the confidence level expresses, as a percentage, the probability<br />
with which one is certain that a sample mean will provide an accurate<br />
estimate of the population mean. Thus, a 95 percent confidence level indicates<br />
that 95 percent of all sample means will, indeed, correspond to the mean<br />
for the population. Because the consequences of inaccurate quick count results<br />
can be so serious, the st<strong>and</strong>ard practice in election observations is to design<br />
the sample with more conservative parameters, a 99 percent confidence level.<br />
CONSTRUCTING THE SAMPLE<br />
<strong>The</strong> practical business of constructing a quick count sample involves making<br />
a combination of judgements. <strong>The</strong>se include:<br />
• identifying the unit of analysis;<br />
• determining the margin of error <strong>and</strong> confidence levels;<br />
• determining the most appropriate type of r<strong>and</strong>om sample; <strong>and</strong><br />
• estimating correction factors for sample retrieval rates <strong>and</strong> non-voting.<br />
Because the consequences<br />
of inaccurate<br />
quick count results can<br />
be so serious, the st<strong>and</strong>ard<br />
practice in election<br />
observations is to<br />
design the sample with<br />
a 99 percent confidence<br />
level.<br />
65<br />
<strong>The</strong> Unit of Analysis<br />
<strong>The</strong> unit of analysis refers to the precise object that is being examined. If the<br />
goal is to generalize about an entire population, then the unit of analysis is<br />
often the individual. However, it is possible in some cases to generalize from<br />
a sample to a population by adopting a larger aggregate as the unit of analysis,<br />
such as a household or city block.<br />
With quick counts, the objective is to estimate the distribution of citizens’ votes<br />
between political parties. In a democratic election, the individual vote is secret<br />
<strong>and</strong> so the individual vote cannot be the unit of analysis. Instead, quick counts<br />
typically use the official result at an individual polling station as the unit of<br />
analysis. This is because the polling station is the smallest unit of analysis at<br />
which individual votes are aggregated <strong>and</strong> because election rules usually require<br />
that an official count take place at the polling station.<br />
<strong>Quick</strong> counts typically<br />
use the official result at<br />
an individual polling<br />
station as the unit of<br />
analysis.<br />
<strong>The</strong> Margin of Error: How Accurate Do We Need to Be?<br />
<strong>The</strong> margin of error is one of the most important pieces of information considered<br />
when constructing a sample. Expressed as a percentage, the margin<br />
of error refers to the likely range of values for any observation. <strong>The</strong> following<br />
example illustrates the concept:<br />
Results from one polling station indicate that 48 percent of votes support<br />
C<strong>and</strong>idate A. If the designed margin of error is five percent, there<br />
is good reason to be confident that the actual results for C<strong>and</strong>idate A<br />
will fall somewhere between 43 <strong>and</strong> 53 percent when all voters within<br />
the population are considered.