ABSTRACT
As with measurement in a research design, sampling is an often-overlooked but critical aspect of good research. In this chapter, you learn the reasons for sampling, common sampling terminology, how to construct and interpret probability and nonprobability samples, guidelines for determining the appropriate sample size, and how to evaluate the adequacy of samples. We discuss four common probability sampling designs: simple random sampling, systematic sampling, stratified random sampling, and cluster sampling. We discuss the applications of each type of sampling, including benefits and limitations. The nonprobability sampling techniques discussed include convenience sampling, purposive sampling, quota sampling, and snowball sampling. The chapter then discusses the sample size needed, the associated sampling errors, the accuracy of the sample, and the response rate of different samples. As with measurement, sampling is important because research-based policy decisions depend upon appropriate sampling. While intuition suggests that a bigger sample is better, in the real world, a bigger sample comes with greater expense, and if the sample is poorly conceived, then incorrect decisions may be made or resources wasted.
