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Presented By: Summer Institute in Survey Research Techniques

Probability and Non-Probability Sampling Methods

Jim Lepkowski and Sunghee Lee, University of Michigan

Probability and Non-probability Sampling Methods is a sampling course that differs from traditional sampling classes. First, this class gives an equal amount of attention to both probability and non-probability sampling methods as non-probability sampling cannot be discussed meaningfully without understanding probability sampling and these two methods offer distinctive advantages and disadvantages. Second, this class will combine theoretical/conceptual parts of sampling through lectures and practical applications of different approaches through lab sessions.

The course will start with examining probability sampling techniques and their properties, including simple random selection, systematic selection, cluster sampling, stratified sampling, and probability proportionate to size selection. Issues of weighting to compensate for unequal chances of selection and variance estimation for calculating confidence intervals are also examined. Then the wide variety of non-probability sampling methods are examined, from panel-based convenience samples, to river samples, quota samples, respondent-driven samples, and other techniques. The properties of these samples are discussed, and assumptions needed to obtain estimates are examined. We will also examine these two approaches from the total survey error perspectives.

The lab sessions to be held after each class will combine R programming and group discussions on the topics that need to be considered when implementing various sampling approaches. Hands-on examples of frame preparation, sample draws, post-survey adjustments and analysis specific to design will be provided and discussed.

The course is not designed to provide the mastery of survey sampling. Rather, it provides materials that will accommodate participants to become familiar with advantages and disadvantages of the two methods and their implementation which will allow them to make informed design decisions.

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