The Quick Count and Election Observation

CHAPTER FIVE: STATISTICAL PRINCIPLES AND QUICK COUNTS
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The broad principles underlying quick counts can be understood
easily by non-statisticians, and there are important reasons why
key personnel in observer groups should become familiar with
these principles:
1. Understanding the importance of ensuring the robustness of quick count
data will facilitate decisions about the design of the quick count and
help staff to develop effective observer forms and training programs.
2. Staff that appreciate the relationship between a sample and a population
and the centrality of the requirement of randomness to the
integrity of that relationship are motivated to build a strong volunteer
network that can cover even the most remote polling stations.
Groups should enlist the support of a statistician experienced in
conducting quick counts to undertake the technically complex tasks
of constructing a sample and analyzing quick count results.
Experience with quick counts around the world underscores several
points:
1. The unit of analysis for a quick count is the polling station. Sampling
cannot begin until an accurate and comprehensive list of polling stations—the
sampling frame—is available.
2. Quick counts always use probability samples (e.g., general random
samples or stratified random samples) in order to produce results that
are representative of the whole population.
3. Observer groups undertaking quick counts are never able to retrieve
100 percent of the data from the sample. Analysts must prepare for
this inevitability. The solution, which can be built into the original sample
design, is to oversample by the margins of the expected recovery
rate.
4. Analysts must also consider correction factors when designing a sample.
Most important are those that take into account variations in (a)
voter turnout, and (b) the number of voters in the basic unit of analysis,
the polling station.