Sampling Techniques

For describing or testing hypotheses about a population, sampling a small portion of the population is often preferable to taking a census of the entire population. Taking a sample is usually less expensive and less time-consuming than taking a census and more accurate because more effort and care can be spent ensuring that the right data are gathered in the right way. Data collected appropriately can be used to make inferences about the entire population.

Sampling techniques can be categorized into nonprobability samples and probability samples. A probability sample is selected in a way such that virtually all members of a population have a nonzero probability of being included, and that probability is known or calculable. A nonprobability sample is gathered in a way that does not depend on chance. This means that it is difficult or impossible to estimate the probability that a particular unit of the population will be included. Moreover, a substantial proportion of the population is typically excluded. The quality of the sample, therefore, depends on the knowledge and skill of the researcher.

In general, probability samples are preferable to nonprobability samples because results can be generalized to the entire population using statistical techniques. Such generalization is typically invalid with nonprobability samples because the exclusion of portions of the population from sampling means the results are likely to be biased. People who volunteer to participate in a study, for example, may be different from those who do not; they may differ in age, gender, occupation, motivation, or any number of other characteristics that may be related to the study. If the study concerns attitudes or opinions, volunteer participants may have different and often stronger feelings about the issues than nonparticipants.

Nonprobability samples, however, have their advantages and uses. They are relatively easy and inexpensive to assemble. They can be valuable for exploratory research or when the researcher wants to document a range or provide particular examples rather than investigate tendencies or causal processes. Moreover, techniques have recently been developed for obtaining unbiased results from certain kinds of nonprobability samples.

Two concepts are important to sampling in general: the target population and the sampling frame. The target population is the population to which the researcher wants to generalize the findings. One important characteristic of the population is the kind of entities its members are, known as the unit of analysis. The cases in the sample correspond to this unit of analysis. Examples of a unit of analysis are the individual, the organizational department, the organization, or some geographical unit, such as the state. The unit of analysis is characterized by a set of attributes on which the researcher gathers data. These are the variables the researcher scores for each case in the sample. For example, a researcher might explore individual characteristics such as age or years of education. Usually, the target population is circumscribed by some characteristic or combination of characteristics. It may be employees of a particular firm, or there may be a geographical limitation, such as residents of a particular city. Constraints on gender, ethnicity, [Page 690]age-group, work status, or other characteristics may be specified as well. A target population, for example, might be permanent, full-time female employees of a particular company.

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