Introduction To Sampling Methods

Good sample design is an essential component of surveys and analytic studies. Hence, this chapter contains methods for obtaining data from a representative subset (sample) of a population and makes inferences about the characteristics of the population. Other aspects of data collection (e.g., questionnaire design) are discussed in 6.1.

Sometimes data from a census are available to describe events in a population; no sampling is required and hence no information is lost, as can occur when selecting only a subset of the population. More frequently, data are available from only a subset of the population, and that subset may or may not have been selected by formal sampling methods. For example, data from outbreak investigations or routinely collected data from hospitals or client records (e.g., case reports) may be viewed as arising from a sample of the population, although no formal sampling is used. As will become apparent, there are fewer problems in extrapolating from data obtained by formal planned sampling than from data whose collection was unplanned.

There are two reasons why an epidemiologist would take a planned sample of a population. One is to describe the characteristics (i.e., frequency and/or distribution of disease or production levels) of a population. Examples might include selecting a sample of dairy cows to estimate the extent of subclinical mastitis in a population and selecting a sample of the dog population to estimate the percentage vaccinated against diseases such as rabies. Descriptive studies such as these are called surveys. The process of collating and reporting information from planned surveys, routinely collected data, or outbreak investigations is termed descriptive epidemiology (see Chapter 4).

The second reason for taking a planned sample is to assess specific associations (e.g., test hypotheses) between events and/or factors in the population. Examples would be a sample designed to look for associations between the type of milking equipment and milking procedures and the level of mastitis in the herd, or a study designed to test the hypothesis that certain phenotypes of dogs are more susceptible to bone cancer than others. Studies such as these are analytic studies, and the process of collating, analyzing, and interpreting the information is termed analytical epidemiology (see Chapter 6). In practice, the differences between these types of observational studies often become nebulous. For example, it is not uncommon to do some hypothesis testing using data from surveys. Nonetheless, since the main emphasis of surveys differs from hypothesis testing, the distinction is maintained to simplify and add order to the description of the underlying sampling strategies.

Whether the study is a survey or an analytic study, how the study members are obtained from the population (i.e., the method of sampling) will determine the precision and nature of extrapolations from the sample to the population. Planning the sampling strategy is a major component of survey design. Although sampling per se is only a small part of the design of an analytic study, its central importance is indicated by the fact that the three common types of analytic studies are named on the basis of the sample selection strategy.

Further details on sampling are available in a number of texts (Snedecor and Cochran 1980; Cochran 1977; Levy and Lemeshow 1980; Leech and Sellers 1979; Schwabe et al. 1977). An excellent manual on sampling in livestock disease surveys is provided by Cannon and Roe (1982).