|Writing in Political Science||Department of Political Science
Surveys and Polls
We tend to think of polls as devices for anticipating election results or finding out about public attitudes on particular questions that have become the topic of strong political controversy, and of surveys as devices for asking about a wider range of questions that may or may not include queries about individuals’ attitudes on political questions. Though distinct in the number and focus of questions, polls and surveys use many of the same basic research methods. Both surveys and polls involve contacting some people, asking them questions, and using their responses to make judgments about how everyone involved (all registered voters, all “likely voters,” all retired people, all parents with school-age children, all homeless people, all nurses, etc) lives, experiences various events, or thinks about one or more public questions. Pollsters and survey researchers both need to address two problems: finding the right people to ask and figuring out the exact questions to ask.
Polls usually focus on individuals; surveys can focus on many types of units. A survey might treat individuals as the unit, but it could as easily use towns, schools, businesses, particular road intersections, or households as the unit. Except when dealing with a relatively small number of units (the 50 state governments in the USA for example), polls and surveys are based on interviewing some people or the people in some other sort of unit and then extrapolating from what those people (the sample) say to what the wider population believes. Samples are usually fairly small. While the US Census Bureau asked one of every ten households to fill out a longer set of questions about housing and other material resources available to them during the 2000 census, the typical opinion poll taken before a US presidential election is based on answers from 1500-2000 people. You may have noticed charts reporting survey results in USA Today specifying (in real small type somewhere near the bottom of the graphic) that the results reporting some trend in national public opinion are based on asking 400-500 people.
Relying on asking some units and then extrapolating to say how all units thing about or would react to something requires paying careful attention to methods of selecting the respondents (the individuals or the members of units who are asked the questions) to be sure that those selected are representative of the whole population. Most of the time, pollsters and survey researchers want to do probability sampling, a method based on running a random sample, one in which every unit of the population has an equal chance of being included in the sample of units to be asked. Random samples should be less vulnerable to biased selection; they definitely have the advantage of allowing use of probability theory to calculate how likely it is that the pattern of answers in the sample will match the pattern of answers they would get if they could ask the whole population. Statisticians report this calculation as the error rate, and want it to be quite small. A 50% error rate says that the poll or survey has only 1 in 2 odds of reflecting the true opinions of the whole population. Pollsters will admit that an election is “too close to call” if the difference in support for competing candidates among the likely voters responding to their poll is less than, equal to, or (to err on the safe side) not much more than the error rate.
Securing a properly random set of respondents involves two distinct steps – developing a selection rule that will choose potential respondents at random and making sure the randomly-selected persons are part of the population of interest. Random sample rules are easy to construct (though sometimes hard to carry out).
In a phone poll, researchers can dial every xth number in the residential section of the phonebook or use automated random dialers.
In face-to-face surveys they could approach every xth person who passes a particular point while the survey researchers are workin