The Quick Count and Election Observation
The Quick Count and Election Observation The Quick Count and Election Observation
THE QUICK COUNT AND ELECTION OBSERVATION The size of the variance determines the precise shape of the actual distribution. The key point for quick count purposes is that any dataset that conforms to the normal distribution curve has exactly the same standard properties. These are: 68.3 percent of all observed values will fall within one standard deviation of the mean, 95.4 percent of all results will fall within two standard deviations of the mean and 99.7 percent of all results will fall within three standard deviations of the mean. Not all datasets will conform to this exact pattern. If there is a lot of variance within the data, the curve will be relatively flat. If there is little variation, the curve will appear more peaked. 69 The distance from the mean, expressed as standard deviations, can also be referred to as Z scores or critical values. Most standard statistics textbooks contain a table of Z values for the normal distribution and analysts do not have to calculate Z values each time they confront a data set. Significantly, if data have a 95 percent confidence interval (95 percent of all sample means will include the population mean), then it is clear that the results will fall within 1.96 standard deviations of the mean. Similarly, a 99 percent confidence level indicates that 99 percent of all results (for which the sample mean will include the population mean) fall within 2.58 standard deviations from the mean. In these cases, the values 1.96 and 2.58 represent the critical values, or Z values, for the confidence levels 95 percent and 99 percent, respectively. Calculating the margin of error requires relying on the standard deviation and Z values. The standard deviation and Z values, in turn, involve measures of central tendency, measures of dispersion and confidence levels. As Figure 5-3 shows, margins of error vary with confidence levels and with sample sizes. In general, the higher the confidence level, the higher the margin of error. The larger the sample size, the lower the margin of error. Decisions about what margin of error can be tolerated with a quick count will directly impact calculations to determine the required minimum sample size. Decisions about what margin of error can be tolerated with a quick count will directly impact calculations to determine the required minimum sample size. 12 FIGURE 5-3: MARGINS OF ERROR AND SAMPLE SIZES Margin of Error 10 8 6 4 2 95% confidence level 99% confidence level 0 100 250 500 750 1,000 1,500 2,000 3,000 5,000 10,000 Sample Size (individual voters)
CHAPTER FIVE: STATISTICAL PRINCIPLES AND QUICK COUNTS 70 Types of Samples There are two basic types of samples: probability samples and non-probability samples. Probability samples comply with the principles of randomness and are, therefore, representative of total populations. Quick counts always use probability samples. Non-probability samples do not select sample points randomly, and the extent to which they are representative of the wider population is not known. Nonprobability samples are useful under some circumstances. They are inexpensive and easier to construct and conduct than probability samples. The cases in the sample are chosen on the basis of how easy or comfortable they are to study. For example, a television reporter stands outside a ballpark and asks fans whether they enjoyed a baseball game. The strategy provides quick and interesting footage for broadcast, but it does not provide reliable information about the total population inside the ballpark. For quick count purposes, the fatal limitation of non-probability samples is that they are not reliable for generalizing to the population. The data they produce, therefore, are not reliable estimates of population characteristics. If, for example, a quick count sample were constructed entirely from polling stations in the capital city, the results would almost certainly be different from those coming from a sample of polling stations in rural areas. People drawing on raw data at convenient, easily accessible, locations are not using data that are representative of the population as a whole. Quick counts must always use probability samples to produce results that are representative of a defined population. Can statisticians design quick count samples that combine the benefits of the probability sample with the benefits of a nonprobability sample? Absolutely not. Either a sample is a probability sample, in which case it conforms completely to the principles of randomness, or it is a non-probability sample. Combining elements of both sampling techniques will produce a nonprobability sample. Consequently, the temptation to substitute “convenient” polling stations for inaccessible ones must be avoided. Sample points cannot be substituted for each other because that strategy violates the premise of randomness – that each sample point has exactly the same probability of being selected. Instead, quick count organizers must find a way to get trained observers to, and information from, the precise polling stations contained in the sample, even if they are in remote areas. Collecting and reporting data from a more convenient polling station can compromise the reliability and validity of the entire quick count. Quick counts must always use probability samples to produce results that are representative of a defined population. There are several types of probability
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CHAPTER FIVE: STATISTICAL PRINCIPLES AND QUICK COUNTS<br />
70 Types of Samples<br />
<strong>The</strong>re are two basic types of samples: probability samples <strong>and</strong> non-probability<br />
samples. Probability samples comply with the principles of r<strong>and</strong>omness <strong>and</strong><br />
are, therefore, representative of total populations. <strong>Quick</strong> counts always use<br />
probability samples.<br />
Non-probability samples do not select sample points r<strong>and</strong>omly, <strong>and</strong> the extent<br />
to which they are representative of the wider population is not known. Nonprobability<br />
samples are useful under some circumstances. <strong>The</strong>y are inexpensive<br />
<strong>and</strong> easier to construct <strong>and</strong> conduct than probability samples. <strong>The</strong> cases in<br />
the sample are chosen on the basis of how easy or comfortable they are to<br />
study. For example, a television reporter st<strong>and</strong>s outside a ballpark <strong>and</strong> asks<br />
fans whether they enjoyed a baseball game. <strong>The</strong> strategy provides quick <strong>and</strong><br />
interesting footage for broadcast, but it does not provide reliable information<br />
about the total population inside the ballpark.<br />
For quick count purposes, the fatal limitation of non-probability samples is<br />
that they are not reliable for generalizing to the population. <strong>The</strong> data they<br />
produce, therefore, are not reliable estimates of population characteristics. If,<br />
for example, a quick count sample were constructed entirely from polling stations<br />
in the capital city, the results would almost certainly be different from<br />
those coming from a sample of polling stations in rural areas. People drawing<br />
on raw data at convenient, easily accessible, locations are not using data that<br />
are representative of the population as a whole.<br />
<strong>Quick</strong> counts must<br />
always use probability<br />
samples to produce<br />
results that are representative<br />
of a defined<br />
population.<br />
Can statisticians design quick count samples that combine the<br />
benefits of the probability sample with the benefits of a nonprobability<br />
sample?<br />
Absolutely not. Either a sample is a probability sample, in which case it conforms<br />
completely to the principles of r<strong>and</strong>omness, or it is a non-probability<br />
sample. Combining elements of both sampling techniques will produce a nonprobability<br />
sample. Consequently, the temptation to substitute “convenient”<br />
polling stations for inaccessible ones must be avoided. Sample points cannot<br />
be substituted for each other because that strategy violates the premise of r<strong>and</strong>omness<br />
– that each sample point has exactly the same probability of being<br />
selected. Instead, quick count organizers must find a way to get trained observers<br />
to, <strong>and</strong> information from, the precise polling stations contained in the sample,<br />
even if they are in remote areas. Collecting <strong>and</strong> reporting data from a more<br />
convenient polling station can compromise the reliability <strong>and</strong> validity of the<br />
entire quick count.<br />
<strong>Quick</strong> counts must always use probability samples to produce results that are<br />
representative of a defined population. <strong>The</strong>re are several types of probability