Sample size and definition

When any group of people is compared with any other and the sample size of the smaller group is about 100, a difference between the two groups on a given question will be insignificant (i.e., attributable to chance or error) unless the poll finds it to be greater than 14 percentage points. If the smaller group is larger than 100, the allowance decreases approximately as follows: for a group comprising 200 cases, allow 10 percentage points; for 400 cases, allow 7 percentage points; for 800, allow 5; for 1,000, allow 4; for 2,000, allow 3. Thus, if a national sample survey shows that 27 percent of a representative sample of college students favour a volunteer army while 35 percent of adults who are not in college do and there are only 200 students in the sample, the difference between the two groups may well be insignificant. If the difference were greater than 10 percentage points, then it would be much more likely that the opinions of college students really do differ from those of other adults. Similar allowances have to be made when election polls are interpreted. The larger the sample and the larger the difference between the number of preferences expressed for each candidate, the greater the certainty with which the election result can be predicted. (Of course, these guidelines presuppose that the samples are properly selected; hence, they do not apply to “self-selected” polls or to polls that fail to prevent a single person from making more than one response.)

Errors in defining the sampling framework can also lead to errors. For example, in 1936 the journal Literary Digest mailed more than 10 million political questionnaires to American citizens and received more than 2,500 responses; nevertheless, it incorrectly predicted the outcome of the 1936 American presidential election, which was won by Democratic candidate Franklin Delano Roosevelt. The Digest drew its sample from telephone books and automobile registration lists, both of which tended to overrepresent the affluent, who were more likely to vote Republican.

Phrasing of questions

Variations larger than those due to chance may be caused by the way the questions are worded. Consider one poll asking “Are you in favour of or opposed to increasing government aid to higher education?” while another poll asks “Are you in favour of the president’s recommendation that government aid to higher education be increased?”; the second question is likely to receive many more affirmative answers than the first if the president is popular. Similarly, the distribution of replies will often vary if an alternative is stated, as in “Are you in favour of increasing government aid to higher education, or do you think enough tax money is being spent on higher education now?” It is probable that this question would receive fewer affirmative responses than the question that does not mention the opposing point of view. As a rule, relatively slight differences in wording cause significant variations in response only when the opinions people hold are not firm. In such cases, therefore, survey researchers may try to control for variation by asking the same question frequently over a period of years.

Questionnaire construction, as with sampling, requires a high degree of skill. The questions must be clear to people of varying educational levels and backgrounds, they must not embarrass respondents, they must be arranged in a logical order, and so on. Even experienced researchers find it necessary to pretest their questionnaires, usually by interviewing a small group of respondents with preliminary questions.

Poll questions may be of the “forced-choice” or “free-answer” type. In the former, a respondent is asked to reply “yes” or “no”—an approach that is particularly effective when asking questions about behaviour. Or a respondent may be asked to choose from a list of alternatives arranged as a scale (e.g., from “strongly agree” to “strongly disagree”); this format was developed by the American psychometrician L.L. Thurstone and the American social scientist Rensis Likert. Even in forced-choice questionnaires, however, respondents often reply “don’t know” or prefer an alternative that the researcher had not listed in advance. A free-answer question—for instance, “What do you think are the most important problems facing the country today?”—allows respondents to state their opinions in their own words.

Interviewing

Interviewing is another potential source of error. Inexperienced interviewers may bias their respondents’ answers by asking questions in inappropriate ways. They may even alienate or antagonize some respondents so that they refuse to complete the interview. Interviewers also sometimes fail to record the replies to free-answer questions accurately, or they are not sufficiently persistent in locating designated respondents. Most large polling organizations give interviewers special training before sending them out on surveys. Organizations may also contract with an interviewing service that provides trained and experienced interviewers.

Tabulation

Tabulation is usually done by computer. To simplify this process, most questionnaires are “precoded,” which is to say that numbers appear beside each question and each possible response. The answers given by respondents can thus be translated rapidly into a numerical form for analysis. In the case of free-answer questions, responses must usually be grouped into categories, each of which is also assigned a number and then coded. How the categories are defined may make a large difference in the way the results are presented. If a respondent mentions narcotics addiction as a major problem facing the country, for instance, this answer might be coded as a health problem or a crime problem, or it might be grouped with other replies dealing with drug abuse or alcoholism.