How Is Public Opinion Measured?


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. Mitt Romney’s campaign polls are only a recent example of problems stemming from polling methods. 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 1936, 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 55.4 percent of the popular vote; in the end, he received only 38 percent.Arthur Evans, “Predict Landon Electoral Vote to be 315 to 350,” Chicago Tribune, 18 October 1936. 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 1948 presidential election to Harry Truman, despite polls showing Dewey far ahead and Truman destined to lose (Figure). 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 2004, only to be proven wrong on election night. These are just a few cases, but each offers a different lesson. In 1948, 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. Zogby’s polls did not represent likely voters and incorrectly predicted who would vote and for whom. These examples reinforce the need to use scientific methods when conducting polls, and to be cautious when reporting the results.

Photo shows Harry S. Truman displaying a newspaper whose headline states “Dewey Defeats Truman.”
Polling process errors can lead to incorrect predictions. On November 3, the day after the 1948 presidential election, a jubilant Harry S. Truman triumphantly displays the inaccurate headline of the Chicago Daily Tribune announcing Thomas Dewey’s supposed victory (credit: David Erickson/Flickr).

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.S. population is female.United States Census Bureau. 2012. “Age and Sex Composition in the United States: 2012.” United States Census Bureau. (February 17, 2016). 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. For a poll to predict what Americans think nationally, such as about the White House’s policy on greenhouse gases, the sample size should be larger.

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.Rasmussen Reports. 2015. “Daily Presidential Tracking Poll.” Rasmussen Reports. September 27, 2015. (February 17, 2016); Pew Research Center. 2015. “Sampling.” Pew Research Center. (February 17, 2016). Academic organizations, like the American National Election Studies, have interviews with over twenty-five-hundred respondents.American National Election Studies Data Center. 2016. (February 17, 2016). 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.Michael W. Link and Robert W. Oldendick. 1997. “Good” Polls / “Bad” Polls—How Can You Tell? Ten Tips for Consumers of Survey Research.” South Carolina Policy Forum. (February 17, 2016); Pew Research Center. 2015. “Sampling.” Pew Research Center. (February 17, 2016).

When the sample represents the actual population, the poll’s accuracy will be reflected in a lower margin of error. 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. For example, if a poll that claims Hillary Clinton is likely to win 30 percent of the vote in the 2016 New York Democratic primary has a margin of error of +/-6, it tells us that Clinton may receive as little as 24 percent of the vote (30 – 6) or as much as 36 percent (30 + 6). 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.

The Ins and Outs of Polls

Ever wonder what happens behind the polls? To find out, we posed a few questions to Scott Keeter, Director of Survey Research at Pew Research Center.

Q: What are some of the most common misconceptions about polling?

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 250 million adults. But of course it is possible. Random sampling, which has been well understood for the past several decades, makes it possible. If you don’t trust small random samples, then ask your doctor to take all of your blood the next time you need a diagnostic test.

The second misconception is that it is possible to get any result we want from a poll if we are willing to manipulate the wording sufficiently. While it is true that question wording can influence responses, it is not true that a poll can get any result it sets out to get. People aren’t stupid. They can tell if a question is highly biased and they won’t react well to it. Perhaps more important, the public can read the questions and know whether they are being loaded with words and phrases intended to push a respondent in a particular direction. That’s why it’s important to always look at the wording and the sequencing of questions in any poll.

Q: How does your organization choose polling topics?

A: We choose our topics in several ways. Most importantly, we keep up with developments in politics and public policy, and try to make our polls reflect relevant issues. Much of our research is driven by the news cycle and topics that we see arising in the near future.

We also have a number of projects that we do regularly to provide a look at long-term trends in public opinion. For example, we’ve been asking a series of questions about political values since 1987, which has helped to document the rise of political polarization in the public. Another is a large (thirty-five thousand interviews) study of religious beliefs, behaviors, and affiliations among Americans. We released the first of these in 2007, and a second in 2015.

Finally, we try to seize opportunities to make larger contributions on weighty issues when they arise. When the United States was on the verge of a big debate on immigration reform in 2006, we undertook a major survey of Americans’ attitudes about immigration and immigrants. In 2007, we conducted the first-ever nationally representative survey of Muslim Americans.

Q: What is the average number of polls you oversee in a week?

A: It depends a lot on the news cycle and the needs of our research groups. We almost always have a survey in progress, but sometimes there are two or three going on at once. At other times, we are more focused on analyzing data already collected or planning for future surveys.

Q: Have you placed a poll in the field and had results that really surprised you?

A: It’s rare to be surprised because we’ve learned a lot over the years about how people respond to questions. But here are some findings that jumped out to some of us in the past:

1) In 2012, we conducted a survey of people who said their religion is “nothing in particular.” We asked them if they are “looking for a religion that would be right” for them, based on the expectation that many people without an affiliation—but who had not said they were atheists or agnostic—might be trying to find a religion that fit. Only 10 percent said that they were looking for the right religion.

2) We—and many others—were surprised that public opinion about Muslims became more favorable after the 9/11 terrorist attacks. It’s possible that President Bush’s strong appeal to people not to blame Muslims in general for the attack had an effect on opinions.

3) It’s also surprising that basic public attitudes about gun control (whether pro or anti) barely move after highly publicized mass shootings.

Were you surprised by the results Scott Keeter reported in response to the interviewer’s final question? Why or why not? Conduct some research online to discover what degree plans or work experience would help a student find a job in a polling organization.

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