The Quick Count and Election Observation

THE QUICK COUNT AND ELECTION OBSERVATION
samples, and each can provide accurate representations of the population by
relying on different methods. The two most common types of probability samples
are the general random sample and the stratified random sample.
General Random Samples
In the general random sample, units of analysis are randomly selected one at
a time from the entire population. This gives each unit in a population an equal
chance of being included in the sample. However, for every unit of analysis to
have an equal chance of being included in the sample, there must be an accurate
list of all possible units of analysis.
Statisticians refer to the list of all members of a population as a sampling frame.
In the case of a quick count, the unit of analysis is the polling station; therefore,
the sampling for a quick count can only begin when an accurate and
comprehensive list of all the polling stations is available.
The sampling for a
quick count can only
begin when an accurate
and comprehensive
list of all the
polling stations is
available.
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Stratified Random Samples
The stratified random sample applies the same principles of randomness as
the general random sample. However, the sample frames from which the sample
points are selected consist of pre-determined, and mutually exclusive, strata
of the total population. For example:
The goal of a project is to use a sample of 1000 students to generalize
about a university population of 20,000 students, half of whom are
undergraduate students and half of whom are graduate students. While
the general random sample approach simply randomly selects 1000
sample points out of the total list of 20,000 students, the stratified
sample approach follows two steps. First, it divides the list of all students
into two groups (strata), one including all undergraduate students
and the other including all graduate students. Next, it selects 500 cases
from strata 1 (undergraduates) and another 500 cases from strata 2
(graduates).
In the stratified approach, the selection of each case still satisfies the criteria of
randomness: the probability of the selection of each case within each strata is
exactly the same (in the above example, 1 in 20). However, the practice of stratifying
means that the end result will produce a total sample that exactly reflects
the distribution of cases in the population as a whole. In effect, the stratification
procedure predetermines the distribution of cases across the strata.
Stratification may be useful in another way. Some observer groups do not have
the resources to conduct a nation-wide observation. In that case, the observer
group might want to limit its observation to a particular strata of the country,
perhaps the capital city, or a coastal region. In these instances, with a ran-
The practice of stratifying
means that the
end result will produce
a total sample that
exactly reflects the
distribution of cases in
the population as
a whole.