The Quick Count and Election Observation

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