An opinion poll is a survey of public opinion from a particular sample. Opinion polls are usually designed to represent the opinions of a population by conducting a series of questions and then extrapolating generalities in ratio or within confidence intervals.
The first known example of an opinion poll was a local straw poll conducted by The Harrisburg Pennsylvanian in 1824, showing Andrew Jackson leading John Quincy Adams by 335 votes to 169 in the contest for the United States Presidency. Such straw votes gradually became more popular, but they remained local, usually city-wide phenomena. In 1916, the Literary Digest embarked on a national survey (partly as a circulation-raising exercise) and correctly predicted Woodrow Wilson's election as president. Mailing out millions of postcards and simply counting the returns, the Digest correctly called the following four presidential elections.
In 1936 however the Digest came unstuck. Its 2.3 million "voters" constituted a huge sample; however they were generally more affluent Americans who tended to have Republican sympathies. The Literary Digest was ignorant of this new bias. The week before election day, it reported that Alf Landon was far more popular than Franklin D. Roosevelt. At the same time, George Gallup conducted a far smaller, but more scientifically-based survey, in which he polled a demographically representative sample. Gallup correctly predicted Roosevelt's landslide victory. The Literary Digest soon went out of business, while polling started to take off.
Elmo Roper was another American pioneer in political forecasting using scientific polls. He predicted the reelection of President Franklin D. Roosevelt three times, in 1936, 1940, and 1944. Louis Harris had been in the field of public opinion since 1947 when he joined the Elmo Roper firm then later became partner.
Gallup launched a subsidiary in the United Kingdom, where it correctly predicted Labour's victory in the 1945 general election, in contrast with virtually all other commentators, who expected a victory for the Conservative Party, led by Winston Churchill.
By the 1950s, various types of polling had spread to most democracies. In Iraq, surveys conducted soon after the 2003 war have aimed to measure the true feelings of Iraqi citizens to Saddam Hussein, post-war conditions, and the presence of US forces.
Opinion polls for many years were maintained through telecommunications or in person-to-person contact. Methods and techniques vary, though they are widely accepted in most areas. Verbal, ballot, and processed types can be conducted efficiently, contrasted with other types of surveys, systematics, and complicated matrices beyond previous orthodox procedures. Opinion polling developed into popular applications through popular thought, although response rates for some surveys declined. Also, the following has also led to differentiating results: Some polling organizations, such as and Angus Reid Strategies, YouGov, Rate Your Politician LLP and Zogby use Internet surveys, where a sample is drawn from a large panel of volunteers, and the results are weighed to reflect the demographics of the population of interest. This is in contrast to popular web polls that draw on whomever wishes to participate, rather than a scientific sample of the population, and are therefore not generally considered professional.
A tracking poll is a poll repeated at intervals generally averaged over a trailing window. For example, a weekly tracking poll uses the data from the past week and discards older data.
A key benefit of tracking polls is that the trend of a tracking poll (the change over time) corrects for bias: regardless of whether a poll consistently over or underestimates opinion, the trend correctly reflects increases or decreases.
A caution is that estimating the trend is more difficult and error-prone than estimating the level – intuitively, if one estimates the change, the difference between two numbers X and Y, then one has to contend with the error in both X and Y – it is not enough to simply take the difference, as the change may be random noise. For details, see t-test. A rough guide is that if the change in measurement falls outside the margin of error, it is worth attention.
Polls based on samples of populations are subject to sampling error which reflects the effects of chance and uncertainty in the sampling process. The uncertainty is often expressed as a margin of error. The margin of error is usually defined as the radius of a confidence interval for a particular statistic from a survey. One example is the percent of people who prefer product A versus product B. 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. If the statistic is a percentage, this maximum margin of error can be calculated as the radius of the confidence interval for a reported percentage of 50%. Others suggest that a poll with a random sample of 1,000 people has margin of sampling error of 3% for the estimated percentage of the whole population. A 3% margin of error means that if the same procedure is used a large number of times, 95% of the time the true population average will be within the 95% confidence interval of the sample estimate plus or minus 3%. The margin of error can be reduced by using a larger sample, however if a pollster wishes to reduce the margin of error to 1% they would need a sample of around 10,000 people. In practice pollsters need to balance the cost of a large sample against the reduction in sampling error and a sample size of around 500–1,000 is a typical compromise for political polls. (Note that to get complete responses it may be necessary to include thousands of additional participators.) Another way to reduce the margin of error is to rely on poll averages. This makes the assumption that the procedure is similar enough between many different polls and uses the sample size of each poll to create a polling average. An example of a polling average can be found here: 2008 Presidential Election polling average. Another source of error stems from faulty demographic models by pollsters who weigh their samples by particular variables such as party identification in an election. For example, if you assume that the breakdown of the US population by party identification has not changed since the previous presidential election, you may underestimate a victory or a defeat of a particular party candidate that saw a surge or decline in its party registration relative to the previous presidential election cycle.
Over time, a number of theories and mechanisms have been offered to explain erroneous polling results. Some of these reflect errors on the part of the pollsters; many of them are statistical in nature. Others blame the respondents for not giving candid answers (e.g., the Bradley effect, the Shy Tory Factor); these can be more controversial.
Since some people do not answer calls from strangers, or refuse to answer the poll, poll samples may not be representative samples from a population. Because of this selection bias, the characteristics of those who agree to be interviewed may be markedly different from those who decline. That is, the actual sample is a biased version of the universe the pollster wants to analyze. In these cases, bias introduces new errors, one way or the other, that are in addition to errors caused by sample size. Error due to bias does not become smaller with larger sample sizes, because taking a larger sample size simply repeats the same mistake on a larger scale. If the people who refuse to answer, or are never reached, have the same characteristics as the people who do answer, then the final results should be unbiased. If the people who do not answer have different opinions then there is bias in the results. In terms of election polls, studies suggest that bias effects are small, but each polling firm has its own formulas on how to adjust weights to minimize selection bias.
Survey results may be affected by response bias, where the answers given by respondents do not reflect their true beliefs. This may be deliberately engineered by unscrupulous pollsters in order to generate a certain result or please their clients, but more often is a result of the detailed wording or ordering of questions (see below). Respondents may deliberately try to manipulate the outcome of a poll by e.g. advocating a more extreme position than they actually hold in order to boost their side of the argument or give rapid and ill-considered answers in order to hasten the end of their questioning. Respondents may also feel under social pressure not to give an unpopular answer. For example, respondents might be unwilling to admit to unpopular attitudes like racism or sexism, and thus polls might not reflect the true incidence of these attitudes in the population. In American political parlance, this a phenomenon is often referred to as the Bradley Effect. If the results of surveys are widely publicized this effect may be magnified - a phenomenon commonly referred to as the spiral of silence.
It is well established that the wording of the questions, the order in which they are asked and the number and form of alternative answers offered can influence results of polls. For instance, the public is more likely to indicate support for a person who is described by the operator as one of the "leading candidates". This support itself overrides subtle bias for one candidate, as is lumping some candidates in an "other" category or vice versa. 21st century Polling arms variate in complexity due to these circumstances. Thus comparisons between polls often boil down to the wording of the question. On some issues, question wording can result in quite pronounced differences between surveys. This can also, however, be a result of legitimately conflicted feelings or evolving attitudes, rather than a poorly constructed survey.
A common technique to control for this bias is to rotate the order in which questions are asked. Many pollsters also split-sample. This involves having two different versions of a question, with each version presented to half the respondents.
The most effective controls, used by attitude researchers, are:
These controls are not widely used in the polling industry.
Another source of error is the use of samples that are not representative of the population as a consequence of the methodology used, as was the experience of the Literary Digest in 1936. For example, telephone sampling has a built-in error because in many times and places, those with telephones have generally been richer than those without.
In some places many people have only mobile telephones. Because pollsters cannot call mobile phones (it is unlawful in the United States to make unsolicited calls to phones where the phone's owner may be charged simply for taking a call), these individuals will never be included in the polling sample. If the subset of the population without cell phones differs markedly from the rest of the population, these differences can skew the results of the poll. Polling organizations have developed many weighting techniques to help overcome these deficiencies, to varying degrees of success. Studies of mobile phone users by the Pew Research Center in the US concluded that "cell-only respondents are different from landline respondents in important ways, (but) they were neither numerous enough nor different enough on the questions we examined to produce a significant change in overall general population survey estimates when included with the landline samples and weighted according to US Census parameters on basic demographic characteristics."
This issue was first identified in 2004, but came to prominence only during the 2008 US presidential election. In previous elections, the proportion of the general population using cell phones was small, but as this proportion has increased, the worry is that polling only landlines is no longer representative of the general population. In 2003, a 2.9% of households were wireless (cellphones only) compared to 12.8 in 2006. This results in "coverage error". Many polling organisations select their sample by dialling random telephone numbers; however, there is a clear tendency for polls which included mobile phones in their sample to show a much larger lead for Obama than polls that did not.
The potential sources of bias are:
Some polling companies have attempted to get around that problem by including a "cellphone supplement". There are a number of problems with including cellphones in a telephone poll:
An oft-quoted example of opinion polls succumbing to errors was the UK General Election of 1992. Despite the polling organizations using different methodologies virtually all the polls in the lead up to the vote, and to a lesser extent exit polls taken on voting day, showed a lead for the opposition Labour party but the actual vote gave a clear victory to the ruling Conservative party.
In their deliberations after this embarrassment the pollsters advanced several ideas to account for their errors, including:
The relative importance of these factors was, and remains, a matter of controversy, but since then the polling organizations have adjusted their methodologies and have achieved more accurate results in subsequent elections.
In Australia the most notable companies are:
In Canada the most notable companies are:
In Egypt, the most notable polling organization is
In Jordan the dominant organization is:
In Iran, some notable polling organisations include:
In Nigeria the most notable polling organization is:
In South Africa the most notable company is:
In the Ukraine, the most notable pollsters are:
In the United Kingdom, the most notable pollsters are:
In the United States, some notable companies include:
All the major television networks, alone or in conjunction with the largest newspapers or magazines, in virtually every country with elections, operate their own versions of polling operations, in collaboration or independently through various applications. One of the applications can be found on Facebook 
Several organizations try to monitor the behavior of polling firms and the use of polling and statistical data, including the Pew Research Center and, in Canada, the Laurier Institute for the Study of Public Opinion and Policy.
The best-known failure of opinion polling to date in the United States was the prediction that Thomas Dewey would defeat Harry S. Truman in the 1948 US presidential election. Major polling organizations, including Gallup and Roper, indicated a landslide victory for Dewey.
In the United Kingdom, most polls failed to predict the Conservative election victories of 1970 and 1992, and Labour's victory in 1974. However, their figures at other elections have been generally accurate.
By providing information about voting intentions, opinion polls can sometimes influence the behavior of electors, and in his book The Broken Compass, Peter Hitchens asserts that opinion polls are actually a device for influencing public opinion. The various theories about how this happens can be split up into two groups: bandwagon/underdog effects, and strategic ("tactical") voting.
A bandwagon effect occurs when the poll prompts voters to back the candidate shown to be winning in the poll. The idea that voters are susceptible to such effects is old, stemming at least from 1884; reported that it was first used in a political cartoon in the magazine Puck in that year. It has also remained persistent in spite of a lack of empirical corroboration until the late 20th century. George Gallup spent much effort in vain trying to discredit this theory in his time by presenting empirical research. A recent meta-study of scientific research on this topic indicates that from the 1980s onward the Bandwagon effect is found more often by researchers.
The opposite of the bandwagon effect is the underdog effect. It is often mentioned in the media. This occurs when people vote, out of sympathy, for the party perceived to be "losing" the elections. There is less empirical evidence for the existence of this effect than there is for the existence of the bandwagon effect.
The second category of theories on how polls directly affect voting is called strategic or tactical voting. This theory is based on the idea that voters view the act of voting as a means of selecting a government. Thus they will sometimes not choose the candidate they prefer on ground of ideology or sympathy, but another, less-preferred, candidate from strategic considerations. An example can be found in the United Kingdom general election, 1997. As he was then a Cabinet Minister, Michael Portillo's constituency of Enfield Southgate was believed to be a safe seat but opinion polls showed the Labour candidate Stephen Twigg steadily gaining support, which may have prompted undecided voters or supporters of other parties to support Twigg in order to remove Portillo. Another example is the boomerang effect where the likely supporters of the candidate shown to be winning feel that chances are slim and that their vote is not required, thus allowing another candidate to win.
These effects indicate how opinion polls can directly affect political choices of the electorate. But directly or indirectly, other effects can be surveyed and analyzed on on all political parties. The form of media framing and party ideology shifts must also be taken under consideration. Opinion polling in some instances is a measure of cognitive bias, which is variably considered and handled appropriately in its various applications.