The Quick Count and Election Observation
The Quick Count and Election Observation The Quick Count and Election Observation
THE QUICK COUNT AND ELECTION OBSERVATION Consequently, a cautious data interpretation strategy calls for re-calculating the margin of error based on the actual number of votes counted. Figure 5-8 illustrates this point. 79 As the table shows, as turnout decreases, the margin of error increases. If turnout is above 60 percent, margin of error will increase by approximately 0.02 percent for every 10 percent drop in turnout. As turnout approaches 50 percent, the increase in margin of error is much greater. A graph of the increase in margin of error corresponding to decrease in turnout is presented in Figure 5-9. 0.6 FIGURE 5-9 MARGINS OF ERROR AND TURNOUT 0.5 99% confidence level Margin of Error 0.4 0.3 0.2 95% confidence level 0.1 0 100 90 80 70 60 50 40 Turnout This chapter has laid out the broad statistical principles underlying quick counts for a general audience, and it has outlined the statistical foundations of the quick count methodology. Organizers should understand this methodology, particularly the concepts of reliability and validity, as well as why a sample must meet the criteria for randomness. This knowledge is vital to the design of effective and reliable observer forms and training programs. It also underscores the importance of preparing to retrieve data from every part of the country—even the most remote areas. Finally, this chapter also has considered the more technical matters of how sample sizes can be calculated, and how such issues as levels of confidence, margins of error and heterogeneity or homogeneity of the population shape the sample. Most observer groups seek the services of a trained statistician to construct and draw a sample and to analyze the data on election day. Civic groups must realize that the quick count is a matter of applying statistical principles to practical, unique circumstances where standard textbook assumptions may not be satisfied. For that reason, the chapter outlines what are the most common correction factors that should be taken into account when analysts consider the interpretation of the data that are successfully retrieved on election day.
CHAPTER FIVE: STATISTICAL PRINCIPLES AND QUICK COUNTS 80 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.
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CHAPTER FIVE: STATISTICAL PRINCIPLES AND QUICK COUNTS<br />
80<br />
<strong>The</strong> broad principles underlying quick counts can be understood<br />
easily by non-statisticians, <strong>and</strong> there are important reasons why<br />
key personnel in observer groups should become familiar with<br />
these principles:<br />
1. Underst<strong>and</strong>ing the importance of ensuring the robustness of quick count<br />
data will facilitate decisions about the design of the quick count <strong>and</strong><br />
help staff to develop effective observer forms <strong>and</strong> training programs.<br />
2. Staff that appreciate the relationship between a sample <strong>and</strong> a population<br />
<strong>and</strong> the centrality of the requirement of r<strong>and</strong>omness to the<br />
integrity of that relationship are motivated to build a strong volunteer<br />
network that can cover even the most remote polling stations.<br />
Groups should enlist the support of a statistician experienced in<br />
conducting quick counts to undertake the technically complex tasks<br />
of constructing a sample <strong>and</strong> analyzing quick count results.<br />
Experience with quick counts around the world underscores several<br />
points:<br />
1. <strong>The</strong> unit of analysis for a quick count is the polling station. Sampling<br />
cannot begin until an accurate <strong>and</strong> comprehensive list of polling stations—the<br />
sampling frame—is available.<br />
2. <strong>Quick</strong> counts always use probability samples (e.g., general r<strong>and</strong>om<br />
samples or stratified r<strong>and</strong>om samples) in order to produce results that<br />
are representative of the whole population.<br />
3. Observer groups undertaking quick counts are never able to retrieve<br />
100 percent of the data from the sample. Analysts must prepare for<br />
this inevitability. <strong>The</strong> solution, which can be built into the original sample<br />
design, is to oversample by the margins of the expected recovery<br />
rate.<br />
4. Analysts must also consider correction factors when designing a sample.<br />
Most important are those that take into account variations in (a)<br />
voter turnout, <strong>and</strong> (b) the number of voters in the basic unit of analysis,<br />
the polling station.
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