Upon completion of this chapter, the student should be able to:  Define sampling.
 Document the historical connection between sampling and political polling.
 Describe and illustrate each of the following types of nonprobability sampling:
 reliance on available subject sampling,
 purposive (judgmental) sampling,
 quota sampling, and
 snowball sampling.
 Describe the role of informants in nonprobability sampling and provide advice for how to select them.
 Describe the logic of probability sampling, and include heterogeneity and representativeness in your response.
 List two advantages of probability sampling over nonprobability sampling.
 Define an EPSEM sample.
 Define each of the following terms and explain its role in probability sampling:
 element,
 population,
 study population,
 sampling unit,
 sampling frame, and
 parameter
 Differentiate a parameter from a statistic.
 Define sampling error and show how confidence levels and confidence intervals are used in interpreting sampling errors.
 Using probability sampling theory, describe the sampling distribution.
 Explain how to interpret a standard error in terms of the normal distribution.
 Explain why largescale samples tend to make use of probability sampling.
 Restate the cautions regarding making generalizations from sampling frames to populations.
 Describe simple random sampling and list two reasons why it is seldom used.
 Summarize the steps in using a table of random numbers.
 Describe systematic sampling and employ the concepts of sampling interval, sampling ratio, and periodicity in the description.
 Link stratified sampling with the principle of heterogeneity and describe how this strategy is executed.
 Identify the major advantage of multistage cluster sampling and describe how this procedure is executed.
 Present guidelines for balancing the number of clusters and the cluster size in multistage cluster sampling.
 Explain why a researcher might use probability proportionate to size sampling and explain the logic behind this strategy.
 Outline the rationale for disproportionate sampling and weighting and note the dangers in using these strategies.
