The Quick Count and Election Observation

THE QUICK COUNT AND ELECTION OBSERVATION
that distinguishes us as individuals, let alone for entire populations; there are
just too many possible combinations of factors to document. Fortunately, quick
count methodology does not require this. Quick counts are not concerned
with all of the things that make people different. Quick counts are only concerned
with factors that have a demonstrable impact on the distribution of
votes within the voting population.
Sample points from the relevant population must be selected at random, and
only at random, for the resulting sample to be representative of the total population.
In practice, randomness means that the probability of any single sample
point being selected from the population is exactly the same as the probability
that any other sample point will be selected. And for reasons that have
already been outlined, the law of large numbers and central limit theorem
indicate that the larger the sample drawn, the more accurately that sample
will represent the characteristics of the population.
Sample points must be
selected at random,
and only at random,
for the resulting sample
to be representative of
the total population.
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Homogeneity and Heterogeneity
Reliable samples do not require huge amounts of detailed information about
the social characteristics of the total population. However, it is essential to
know whether the population of interest is relatively diverse (heterogeneous)
or not (homogenous). Assessments of heterogeneity and homogeneity have
a significant impact on how populations can be reliably sampled.
There are several ways to examine the level of heterogeneity, or diversity, of
any population. Ethnic composition, religion and languages can impact heterogeneity.
The primary concern for quick counts, however, is not just with
the level of ethnic or religious heterogeneity in a population. The vital question
for quick counts is the question of whether that heterogeneity has a
significant impact on voting behavior. If one candidate is preferred by 80 percent
of the population, then that population is considered relatively
homogeneous, regardless of the religious, linguistic or ethnic diversity of the
population. Similarly, if the electoral race is close, with the votes evenly divided
between two or more candidates, a population is considered relatively
heterogeneous.
A common misperception is that socially diverse populations will always be
heterogeneous voting populations. However, just because populations are
socially heterogeneous, it does not follow that they will be heterogeneous
when it comes to voting. For example, India has a multiplicity of languages
and religions but is relatively homogenous when it comes to constructing a
sample of the voting population.
The greater the heterogeneity
of the voting
population, the larger
the sample has to be
in order to produce an
accurate estimate of
voting behavior.
The greater the heterogeneity of the voting population, the larger the sample
has to be in order to produce an accurate estimate of voting behavior. A comparison
of required sample sizes for three countries with very different population
sizes – Canada, the United States and Switzerland – illustrates this point.