ear 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 working.
When working from a list of potential respondents (that is, all units of the population of interest), a pollster or survey researcher can take the list, assign each unit on the list a number (1, 2, 3, etc), and use a random number table to pick out as many respondents as needed by matching numbers in the random number table to the numbers assigned to each individual in the list of potential respondents.
Alternately, a researcher can take the list, divide the number of units on the list by the number of respondents desired, select an initial respondent from near the top of the list, and then go down the list until reaching the respondent that is as many numbers down as is the result of dividing units by the number of respondents wanted. Thus, if there were 200 units and 50 desired for the sample, the researcher would count down 200/50 or four lines for the next respondent.
Making sure that potential respondents are part of the population can be done in either of two ways. Many populations can be identified with an existing list (voter registration lists, for example) or with a list that researchers assemble themselves (like using county records to get names and addresses of all farm owners). Survey researchers seeking to understand reactions to the job-related stresses experienced by nurses today would want lists of persons employed as nurses from hospitals, rehab centers, nursing homes, and health clinics. Once they have the list from each place employing nurses, they can make a consolidated list of all nurses in the area and then use the random selection techniques already described. If researchers can’t get accurate lists, they need to include questions that will elicit answers revealing whether the respondent is in the relevant population. Pollsters, who are always in this position because lists of registered voters are not equivalent to lists of everyone who actually votes in an upcoming election, ask the people they contact in a pre-election poll whether they are likely or unlikely to vote and include in 