Facebook and online newspapers often offer informal, pop-up quizzes that ask a single question about politics or an event. The poll is not meant to be formal, but it provides a general idea of what the readership thinks.
Modern public opinion polling is relatively new, only eighty years old. These polls are far more sophisticated than straw polls and are carefully designed to probe what we think, want, and value. The information they gather may be relayed to politicians or newspapers, and is analyzed by statisticians and social scientists.
As the media and politicians pay more attention to the polls, an increasing number are put in the field every week. Most public opinion polls aim to be accurate, but this is not an easy task.
Political polling is a science. From design to implementation, polls are complex and require careful planning and care.
Our history is littered with examples of polling companies producing results that incorrectly predicted public opinion due to poor survey design or bad polling methods.
In , Literary Digest continued its tradition of polling citizens to determine who would win the presidential election. The magazine sent opinion cards to people who had a subscription, a phone, or a car registration.
Only some of the recipients sent back their cards. The result? Alf Landon was predicted to win Franklin D. Roosevelt won another term, but the story demonstrates the need to be scientific in conducting polls. A few years later, Thomas Dewey lost the presidential election to Harry Truman , despite polls showing Dewey far ahead and Truman destined to lose.
More recently, John Zogby, of Zogby Analytics, went public with his prediction that John Kerry would win the presidency against incumbent president George W. Bush in , only to be proven wrong on election night. These are just a few cases, but each offers a different lesson. In , pollsters did not poll up to the day of the election, relying on old numbers that did not include a late shift in voter opinion. These examples reinforce the need to use scientific methods when conducting polls, and to be cautious when reporting the results.
Polling process errors can lead to incorrect predictions. On November 3, the day after the presidential election, a jubilant Harry S. Most polling companies employ statisticians and methodologists trained in conducting polls and analyzing data. A number of criteria must be met if a poll is to be completed scientifically.
First, the methodologists identify the desired population, or group, of respondents they want to interview. For example, if the goal is to project who will win the presidency, citizens from across the United States should be interviewed. If we wish to understand how voters in Colorado will vote on a proposition, the population of respondents should only be Colorado residents.
When surveying on elections or policy matters, many polling houses will interview only respondents who have a history of voting in previous elections, because these voters are more likely to go to the polls on Election Day. Politicians are more likely to be influenced by the opinions of proven voters than of everyday citizens. Once the desired population has been identified, the researchers will begin to build a sample that is both random and representative.
A random sample consists of a limited number of people from the overall population, selected in such a way that each has an equal chance of being chosen. In the early years of polling, telephone numbers of potential respondents were arbitrarily selected from various areas to avoid regional bias. While landline phones allow polls to try to ensure randomness, the increasing use of cell phones makes this process difficult.
Cell phones, and their numbers, are portable and move with the owner. To prevent errors, polls that include known cellular numbers may screen for zip codes and other geographic indicators to prevent regional bias.
A representative sample consists of a group whose demographic distribution is similar to that of the overall population. For example, nearly 51 percent of the U. To match this demographic distribution of women, any poll intended to measure what most Americans think about an issue should survey a sample containing slightly more women than men. Pollsters try to interview a set number of citizens to create a reasonable sample of the population. This sample size will vary based on the size of the population being interviewed and the level of accuracy the pollster wishes to reach.
If the poll is trying to reveal the opinion of a state or group, such as the opinion of Wisconsin voters about changes to the education system, the sample size may vary from five hundred to one thousand respondents and produce results with relatively low error.
The sample size varies with each organization and institution due to the way the data are processed.
Gallup often interviews only five hundred respondents, while Rasmussen Reports and Pew Research often interview one thousand to fifteen hundred respondents. A larger sample makes a poll more accurate, because it will have relatively fewer unusual responses and be more representative of the actual population. Pollsters do not interview more respondents than necessary, however. Increasing the number of respondents will increase the accuracy of the poll, but once the poll has enough respondents to be representative, increases in accuracy become minor and are not cost-effective.
The margin of error is a number that states how far the poll results may be from the actual opinion of the total population of citizens. The lower the margin of error, the more predictive the poll. Large margins of error are problematic. A lower of margin of error is clearly desirable because it gives us the most precise picture of what people actually think or will do.
With many polls out there, how do you know whether a poll is a good poll and accurately predicts what a group believes? First, look for the numbers. Polling companies include the margin of error, polling dates, number of respondents, and population sampled to show their scientific reliability. Was the poll recently taken? Is the question clear and unbiased? Was the number of respondents high enough to predict the population? Is the margin of error small?
It is worth looking for this valuable information when you interpret poll results. While most polling agencies strive to create quality polls, other organizations want fast results and may prioritize immediate numbers over random and representative samples. For example, instant polling is often used by news networks to quickly assess how well candidates are performing in a debate. Ever wonder what happens behind the polls? A: A couple of them recur frequently.
The first is that it is just impossible for one thousand or fifteen hundred people in a survey sample to adequately represent a population of million adults. Truman election, where nearly all pollsters predicted a Dewey victory. The Gallup Poll also inaccurately projected a slim victory by Gerald Ford in , where he lost to Jimmy Carter by a small margin. For the U. In , Gallup interviewed no fewer than 1, U. Gallup also conducts 1, interviews per day, days out of the year, among both landline and cell phones across the U.
Though the ANES was formally established by a National Science Foundation grant in , the data are a continuation of studies going back to The study has been based at the University of Michigan since its origin and, since , has been run in partnership with Stanford University. Its principal investigators for the first four years of the partnership were Arthur Lupia and Jon Krosnick.
The consistency of the studies, which includes asking the same questions repeatedly over time, makes it very useful for academic research. As a result it is frequently cited in works of political science.
Based on one of the first comprehensive studies of election survey data what eventually became the National Election Studies , came the conclusion that most voters cast their ballots primarily on the basis of partisan identification which is often simply inherited from their parents , and that independent voters are actually the least involved in and attentive to politics.
Today, ANES data are used by numerous scholars, students, and journalists. The ANES also has a long history of innovation. In , it opened the ANES Online Commons, becoming the first large-scale academic survey to allow interested scholars and survey professionals to propose questions for future ANES surveys. The main types of polls are: opinion, benchmark, bushfire, entrance, exit, deliberative opinion, tracking, and the straw poll.
Steps to conduct a poll effectively including identifying a sample, evaluating poll questions, and selecting a question and response mode. Survey samples can be broadly divided into two types: probability samples and non-probability samples. Stratified sampling is a method of probability sampling such that sub-populations within an overall population are identified and included in the sample. Usually, a poll consists of a number of questions that the respondent answers in a set format.
An open-ended question asks the respondent to formulate his or her own answer; a closed-ended question asks the respondent to pick an answer from a given number of options. Four types of response scales for closed-ended questions are as follows:. A questionnaire is a series of questions asked to individuals to obtain statistically useful information about a given topic. When properly constructed and responsibly administered, questionnaires become a vital instrument for polling a population.
Adequate questionnaire construction is critical to the success of a poll. Inappropriate questions, incorrect ordering of questions, incorrect scaling, or bad questionnaire format can make the survey valueless, as it may not accurately reflect the views and opinions of the participants.
Pretesting among a smaller subset of target respondents is useful method of checking a questionnaire and making sure it accurately captures the intended information. Their background may affect their interpretation of the questions. Respondents should have enough information or expertise to answer the questions truthfully. The type of scale, index, or typology to be used is determined. The level of measurement used determines what can be concluded from the data.
You cannot, however, conclude what the average respondent answered. The types of questions closed, multiple-choice, open should fit the statistical data analysis techniques available and the goals of the poll. Questions and prepared responses should be unbiased and neutral as to intended outcome.
Prior previous questions may bias later questions. Also, the wording should be kept simple: no technical or specialized vocabulary.
The meaning should be clear. Ambiguous words, equivocal sentence structures and negatives may cause misunderstanding, possibly invalidating questionnaire results.
Care should be taken to ask one question at a time. The list of possible responses should be collectively exhaustive. Respondents should not find themselves without category that fits them. Additionally, possible responses should be mutually exclusive; categories should not overlap.
Writing style should be conversational, concise, accurate and appropriate to the target audience. Many respondents will not answer personal or intimate questions. For this reason, questions about age, income, marital status, etc. Thus, if the respondent refuses to answer these questions, the research questions will have already been answered.
Finally, questionnaires can be administered by research staff, by volunteers or self-administered by the respondents. Clear, detailed instructions are needed in either case, matching the needs of each audience. Questions should flow logically from one to the next, from the more general to the more specific, from the least sensitive to the most sensitive, from factual and behavioral questions to attitudinal and opinion questions, from unaided to aided questions.
Finally, according to the three stage theory, or the sandwich theory, initial questions should be screening and rapport questions. The second stage should concern the product specific questions. In the last stage demographic questions are asked. A very important tool in data analysis is the margin of error because it indicates how closely the results of the survey reflect reality.
The margin of error is a statistic used to analyze data. Margin of Error : This normal distribution curve illustrates the points of various margin of errors.
When a single, global margin of error is reported for a survey, it refers to the maximum margin of error for all reported percentages using the full sample from the survey. For example, if the true value is 50 percentage points, and the statistic has a confidence interval radius of 5 percentage points, then we say the margin of error is 5 percentage points. For example, suppose the true value is 50 people and the statistic has a confidence interval radius of 5 people.
The confidence level, the sample design for a survey, and in particular its sample size, determines the magnitude of the margin of error.
A larger sample size produces a smaller margin of error, all else remaining equal. If the exact confidence intervals are used the margin of error takes into account both sampling error and non-sampling error. If an approximate confidence interval is used then the margin of error may only take random sampling error into account.
It does not represent other potential sources of error or bias such as a non-representative sample-design, poorly phrased questions, people lying or refusing to respond, the exclusion of people who could not be contacted, or miscounts and miscalculations.
Polls typically involve taking a sample from a certain population. In the case of the Newsweek Presidential Election poll, the population of interest was the population of people who would vote. Sampling theory provides methods for calculating the probability that the poll results differ from reality by more than a certain amount simply due to chance. The margin of error is a measure of how close the results are likely to be.
The FPC, factored into the calculation of the margin of error, has the effect of narrowing the margin of error. It holds that the FPC approaches zero as the sample size approaches the population size, which has the effect of eliminating the margin of error entirely. For one thing, the margin of error as generally calculated is applicable to an individual percentage and not the difference between percentages.
The difference between two percentage estimates may not be statistically significant even when they differ by more than the reported margin of error. The survey results also usually provide strong information even when there is not a statistically significant difference. Sampling is concerned with choosing a subset of individuals from a statistical population to estimate characteristics of a whole population.
In statistics and survey methodology, sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. The three main advantages of sampling are that the cost is lower, data collection is faster, and the accuracy and quality of the data can be easily improved.
Normal Distribution Curve : The normal distribution curve can help indicate if the results of a survey are significant and what the margin of error may be. In a simple random sample SRS of a given size, all such subsets of the frame are given an equal probability. Each element has an equal probability of selection.
Furthermore, any given pair of elements has the same chance of selection as any other pair. This minimizes bias and simplifies analysis of results. In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results.
Systematic sampling relies on arranging the target population according to some ordering scheme, a random start, and then selecting elements at regular intervals through that ordered list. As long as the starting point is randomized, systematic sampling is a type of probability sampling.
It is easy to implement and the stratification can make it efficient, if the variable by which the list is ordered is correlated with the variable of interest. However, if periodicity is present and the period is a multiple or factor of the interval used, the sample is especially likely to be un representative of the overall population, decreasing its accuracy.
Another drawback of systematic sampling is that even in scenarios where it is more accurate than SRS, its theoretical properties make it difficult to quantify that accuracy. As described above, systematic sampling is an EPS method, because all elements have the same probability of selection. In this way, researchers can draw inferences about specific subgroups that may be lost in a more generalized random sample.
Additionally, since each stratum is treated as an independent population, different sampling approaches can be applied to different strata, potentially enabling researchers to use the approach best suited for each identified subgroup. Stratified sampling can increase the cost and complicate the research design.
Probability-proportional-to-size PPS is sampling in which the selection probability for each element is set to be proportional to its size measure, up to a maximum of 1. The PPS approach can improve accuracy for a given sample size by concentrating the sample on large elements that have the greatest impact on population estimates.
PPS sampling is commonly used for surveys of businesses, where element size varies greatly and auxiliary information is often available. Sampling is often clustered by geography or by time periods.
Clustering can reduce travel and administrative costs. It also means that one does not need a sampling frame listing all elements in the target population. Instead, clusters can be chosen from a cluster-level frame, with an element-level frame created only for the selected clusters.
Cluster sampling generally increases the variability of sample estimates above that of simple random sampling, depending on how the clusters differ between themselves, as compared with the within-cluster variation.
In quota sampling, the population is first segmented into mutually exclusive subgroups, just as in stratified sampling. Then judgment is used to select the subjects or units from each segment based on a specified proportion. For example, an interviewer may be told to sample females and males between the age of 45 and In quota sampling the selection of the sample is non-random.
What is the nature of their influence? What are the effects of measuring public opinion through polls? What are the techniques pollsters employ? How might those techniques sometimes lead to errors in measurement or to outright changes in public opinion?
What is the appropriate role for public opinion to play in a polity that values both democracy and republicanism? In what ways do politicians govern for us?
How can we make policy for ourselves? Norton and Company, Inc. All rights reserved.
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