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Opinion polls From Wikipedia, the free encyclopedia
Potential for inaccuracy
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., theBradley 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 due to a non-response bias. 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 techniques for adjusting 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 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.
Wording of questions
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 does lumping some candidates in an "other" category or vice versa. 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:
- asking enough questions to allow all aspects of an issue to be covered and to control effects due to the form of the question (such as positive or negative wording), the adequacy of the number being established quantitatively with psychometric measures such as reliability coefficients, and
- analyzing the results with psychometric techniques which synthesize the answers into a few reliable scores and detect ineffective questions.
These controls are not widely used in the polling industry.[why?]
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 Digestin 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 are typically excluded from polling samples. There is concern that, 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, with varying degrees of success. Studies of mobile phone users by the Pew Research Center in the US, in 2007, 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, there is concern that polling only landlines is no longer representative of the general population. In 2003, only 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, in 2008, there was a clear tendency for polls which included mobile phones in their samples to show a much larger lead for Obama, than polls that did not.
The potential sources of bias are:
- Some households use cellphones only and have no landline. This tends to include minorities and younger voters; and occurs more frequently in metropolitan areas. Men are more likely to be cellphone-only compared to women.
- Some people may not be contactable by landline from Monday to Friday and may be contactable only by cellphone.
- Some people use their landlines only to access the Internet, and answer calls only to their cellphones.
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:
- It is difficult to get co-operation from cellphone users, because in many parts of the US, users are charged for both outgoing and incoming calls. That means that pollsters have had to offer financial compensation to gain co-operation.
- US federal law prohibits the use of automated dialling devices to call cellphones (Telephone Consumer Protection Act of 1991). Numbers therefore have to be dialled by hand, which is more time-consuming and expensive for pollsters.
An oft-quoted example of opinion polls succumbing to errors occurred during the UK General Election of 1992. Despite the polling organizations using different methodologies, virtually all the polls taken before 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:
- Late swing
- Voters who changed their minds shortly before voting tended to favour the Conservatives, so the error was not as great as it first appeared.
- Nonresponse bias
- Conservative voters were less likely to participate in surveys than in the past and were thus under-represented.
- The Shy Tory Factor
- The Conservatives had suffered a sustained period of unpopularity as a result of economic difficulties and a series of minor scandals, leading to a spiral of silence in which some Conservative supporters were reluctant to disclose their sincere intentions to pollsters.
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 election campaigns.
A widely publicized 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 of1970 and 1992, and Labour's victory in 1974. However, their figures at other elections have been generally accurate.
Effect on voters
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 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; William Safire reported that the term 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 ortactical 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 candidateStephen 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.
In addition, Mark Pickup in Cameron Anderson and Laura Stephenson's "Voting Behaviour in Canada" outlines three additional "behavioural" responses that voters may exhibit when faced with polling data.
The first is known as a "cue taking" effect which holds that poll data is used as a "proxy" for information about the candidates or parties. Cue taking is "based on the psychological phenomenon of using heuristics to simplify a complex decision" (243).
The second, first described by Petty and Cacioppo (1996) is known as "cognitive response" theory. This theory asserts that a voter's response to a poll may not line with their initial conception of the electoral reality. In response, the voter is likely to generate a "mental list" in which they create reasons for a party's loss or gain in the polls. This can reinforce or change their opinion of the candidate and thus affect voting behaviour.
Third, the final possibility is a "behavioural response" which is similar to a cognitive response. The only salient difference is that a voter will go and seek new information to form their "mental list," thus becoming more informed of the election. This may then affect voting behaviour.
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 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.
Effect on politicians
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Starting in the 1980s, tracking polls and related technologies began having a notable impact on U.S. political leaders. According to Douglas Bailey, a Republican who had helped run Gerald Ford's 1976 presidential campaign, "It's no longer necessary for a political candidate to guess what an audience thinks. He can [find out] with a nightly tracking poll. So it's no longer likely that political leaders are going to lead. Instead, they're going to follow."
Some jurisdictions over the world restrict the publication of the results of opinion polls, especially during the period around an election, in order to prevent the possibly erroneous results from affecting voters' decisions. For instance, in Canada, it is prohibited to publish the results of opinion surveys that would identify specific political parties or candidates in the final three days before a poll closes.
However, most western democratic nations don't support the entire prohibition of the publication of pre-election opinion polls; most of them have no regulation and some only prohibit it in the final days or hours until the relevant poll closes. A survey by Canada's Royal Commission on Electoral Reform reported that the prohibition period of publication of the survey results largely differed in different countries. Out of the 20 countries examined, 3 prohibit the publication during the entire period of campaigns, while others prohibit it for a shorter term such as the polling period or the final 48 hours before a poll closes. In India, Election commission has prohibited it before 48 hours. Recently, a there was some echo regarding complete ban on opinion polls whereas some political parties supported and opposed it. Even many civil society organizations and activists were divided on this issue. Famous activist and Psephologist Yogendra Yadav argued its importance, on the other hand, another article was written by Ravi Nitesh, founder of Mission Bhartiyam regarding itsdisadvantages and loopholes.