Cochran (1977) outlines eleven steps in the planning of a survey. Good sampling methods must exist in the environment of all of these steps. These steps are (1) a statement of the survey objectives, (2) the definition of the population to be sampled, (3) the data to be collected, (4) the degree of precision required, (5) the methods of measurement, (6) the frame or the partitioning of the population into sampling units, (7) the sample selection methods, (8) the pretest, (9) the fieldwork organization, (10) the summary and analysis of the data, and (11) a review of the entire process to see what can be learned for future surveys. Mathematically, the major concerns for sample design have focused on the sample selection procedures and the associated estimation procedures that yield precise estimates. Optimization of sample designs involves obtaining the best possible precision for a fixed cost or minimizing survey costs subject to one or more constraints on the precision of estimates. Optimized designs sometimes are called efficient designs.
The mathematical presentation of sampling theory often focuses on obtaining efficient sample designs with precision measured in terms of sampling error only, although both Cochran (1977) and many earlier texts (e.g., Deming, 1950, or Hansen, Hurwitz, & Madow, 1953) discuss nonsampling errors in surveys. A more recent text by Lessler and Kalsbeek (1992) is devoted entirely to nonsampling errors in surveys, classified as frame error, nonresponse error, and measurement error.