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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 the poll results only those who say they are likely to vote.
Sometimes researchers face situations in which they know that units differing on some characteristic have different responses to the same question. Most experts in business activity know that large and small firms react very differently to government regulations that involve a lot of reporting; large firms can absorb it more easily because they have more people who can deal with paperwork while others make the product, sell the goods, or provide the service than do small companies. Comparative politics experts agree that urban and rural individuals have different attitudes on many questions. Gender, race, and income are known to strongly affect political attitudes on some issues. When survey researchers believe that the units they want to investigate will have different views base don some characteristic, they will make sure that their sample closely reflects how the population divides on that characteristic by using a proportionate stratified sample. In this procedure, the researcher divides the population into two or more groupings (strata) on the characteristic, and then draws random samples from the units in each grouping. Thus, if they were investigating attitudes on war, in which individuals’ attitudes are known to differ significantly by gender, researchers would identify use separate lists of males and females to identify their respondents. In a proportionate stratified sample, the size of the sample drawn from each strata is adjusted so that the proportion of respondents in each grouping matches the proportion of the grouping in the population. For a study of attitudes using genders as the strata, a proportionate stratified sample would include an equal number of males and females anywhere the gender ratio is close to 50-50. In an area where 40% of the population is male, 40% of the respondents would be drawn from the list of males, and 60% from the list of females. When a subgroup’s share of the population of units is very low, researchers may