**History of the Utah Colleges Exit Poll****Why a Sample Survey?****What is a Statistic?****Why a Random Sample?****What kinds of surveys are there?****How do we judge the quality of a sample survey?****Is there any protection to the public against bad statistics?****How and why do we predict voter turnout?****What is non-response and how can it affect survey accuracy?****What is a margin of error?****Further information**

**History of the Utah Colleges Exit Poll**

Coming soon!

What is the purpose of a sample survey? We use surveys any time we want to gather information about any large group, when it would be too costly or cumbersome to interview every individual. Sampling means that we choose part of a population to represent the whole. (A population is the collection of units from which we want information. In the case of the Exit Poll, our population is voters in Utah who actually vote.) It is important to try to select a sample that will be representative of the population with regard to the information we are trying to find. In a sample survey, we randomly select individuals to question, and use their answers to make inferences about how the population at large thinks or feels.

The results of surveys can be used for diverse purposes: they help us understand the demographics of an area, they help determine government policy, they determine which television or radio programs are broadcast (and when they are broadcast), and they help advertisers decide where and when to place advertisements. These are only a few of the many uses for surveys.

Some people may wonder why we bother to conduct an exit poll. After all, the election winners will be known when the votes are counted. So why bother to estimate who will win? Actually, our exit poll collects more information than just the election winners. We ask questions about the current political climate, environmental issues, government policies, etc., that can be very useful to better understand the political process and why people vote the way they do.

A statistic is the summary of numerical values for the characteristic we are measuring in the sample. In our exit poll, one of the statistics we measure is the proportion of people in our sample who voted for a certain candidate. This statistic estimates the actual proportion of voters in the population who voted for a certain candidate. The actual proportion is called the parameter.

The best way to choose a sample is by some process of random sampling. This means that we allow impersonal chance to do the choosing. This method prevents favoritism by the sampler and self-selection by the respondents. In fact, random sampling gives each and every individual in the population a known chance to be a part of the sample. There are many ways to do this. We used computer software to randomly select the sampling units in the exit poll.

**What
kinds of surveys are there? **

There are many different kinds of sampling methods. Methods of sampling can be classified into two groups, according to how the sample is selected: probability sampling or non-probability sampling.

In probability sampling, researchers control the chance (or probability) an individual in the population being selected for the sample. In the simplest kind of probability sampling, a Simple Random Sample (SRS), every set of individuals of the same size has a known chance of being selected. For the exit poll, we're interested in asking questions of individual voters. For an SRS, we would randomly select voters from a list of all registered voters in Utah.

In another kind of probability sample, a stratified sample, we first divide our population into different groups, or strata, based on a predetermined characteristic. For example, at a university, students might be grouped into strata according to their major. Then, we select a certain number of individuals from each stratum. The exit poll uses a stratified sample. Each of the large, densely populated counties in the state, as well as any county that had a participating school in it, was included. The counties in the rural portion of the state were grouped into strata based on the percentage of voters who voted Democratic in the last election (2000). We then took a probability sample of counties from each stratum.

A third, commonly used kind of probability sample, is called a multistage sample. In a multistage sample, we choose the sample in stages. For example, a nationwide exit poll might first choose a sample of states (say, ten states). Then, from each of the ten states, they'd choose a sample of counties. Within each county, they choose a sample of polling places. Finally, at each polling place they choose a sample of voters to fill out their survey. The Utah Colleges Exit Poll uses both stratified sampling and multistage sampling to ultimately select the individuals to interview.

There are other ways of selecting samples as well. These samples are called non-probability, or non-scientific samples--in part because no randomization takes place when the individuals are selected to participate in a survey. Sometimes the individuals are self-selected; the individuals volunteer themselves to answer a survey. Examples of this kind of survey are web surveys, or 1-900 number phone surveys, where you can call in and respond if you desire. These surveys are called voluntary response surveys.

Another non-probability survey is called a convenience sample. In this kind of sample, individuals are chosen because they are easily accessible. An example of this might be an exit poll where the pollster chose to interview the first twenty voters at the first polling place she came to. Mall-intercept samples are similar to convenience samples; in these samples, interviewers ask question of mall shoppers.

Finally, in quota sampling, interviewers simply administer the survey until they have reached a certain goal or quota with regard to race, gender, age, etc. These non-probability surveys are all limited because they don't accurately represent the whole population. That's why most survey companies choose to use probability sampling.

**How
do we judge the quality of a sample survey? **

The quality of a statistic is based on several factors, among them are:

- The sampling method
- The sample size
- The margin of error
- Who is included in the sample
- How the questions that are asked are worded
- How the questions are ordered
- When the poll is conducted
- What is in the news during the polling

A good statistic will be based on good information, so the pollster will not feel the need to hide his information from the reader.

When selecting a sample, some type of probability sampling should be used. This means that everyone in the population has a chance of being selected for the sample. Call-in and or internet surveys never meet this requirement.

When choosing an appropriate size for a sample, the general rule is, the bigger the better. The more people surveyed, the more accurate the statistic will be.

It is important to know what population the statistic applies to. When we are told that 47% of all people prefer vanilla ice cream to chocolate, it is important to know whether that 47% is for all the people in the United States, or only those living in Utah, or just the people in Provo.

A very important quality of a sample survey is its margin of error. The margin of error should always be available for a good statistic. If it is not given, it may be too large to be trustworthy, or the sample was not randomly selected. These problems undermine the validity of the statistic.

**
Is there any protection to the public against bad statistics? **

Unfortunately, the answer is no. Some people use unethical methods to create a statistic that says what they want it to say. Your best bet is to be an informed reader and to always carefully question and analyze each statistic that you encounter.

**
How and why do we predict voter turnout? **

For each of the counties in our sample, we estimate how many voters will vote on election day. This is for both statistical and practical purposes. For instance, we use these estimates for choosing which voting precincts, or more specifically, which polling places to include in our sample. (A precinct is a small geographic division within a county, created to manage the voting process in a county. One or more precincts is assigned to vote at each polling place, so all the voters do not overrun a single voting station.) We designed our sample so that polling places with more expected voters are more likely to appear in the sample.

To predict the voter turnout of each county, we used statistical methods to predict voter turnout based on turnouts from past elections. Next, we estimate the number of voters who will come to the selected polling places within those counties. Again, we used past data to find the proportion of voters in that county who voted at these polling places and distributed the county turnout according to those percentages.

Just how close are our estimates? We won't know until Election Day!

**
What is non-response and how can it affect survey accuracy? **

Simply put, the rate of non-response is the proportion of people who refuse to participate in the survey when asked. Because we use probability sampling in the exit poll, we identify certain voters as the sample, and these individuals are the people we ask to participate in the poll. If they refuse to do so, we do not know how they would have responded, and so our results may be inaccurate. For example, in the exit poll, if everyone who refuses to participate votes for the same candidate, our results will show a lower proportion for that candidate than actually occurred, because we were not able to gather data from the non-respondents.

We take surveys to estimate some measurement of the entire group on the basis of a smaller sample of individuals within that group. We are estimating this value because we don't know the true value. No matter how good a survey is, you will rarely get an estimate that perfectly matches the actual results. That is the nature of statistics. As a result, we want to calculate an interval that we are reasonably certain will contain the true value. Statisticians use the margin of error to calculate this interval.

Say that a political poll gives a certain candidate 55% of the popular vote with a margin of error of ± 3%. What this means is that the poll is reasonably certain that the actual percent of popular vote is between 52% and 58%. When the resulting intervals for both candidates overlaps it is difficult to say for certain who is really ahead.

The lower the margin of error, the better the survey. There is only one way to be 100% sure that your results are correct, and that is to take a census. As an informed reader, be suspicious of any survey that either omits the margin of error entirely or has an unusually high margin of error.

**
For more further information regarding how we obtained our sample, **click
here

**For more information regarding survey research methods in general visit:**

The Survey Research Methods Section of the American Statistical Association