A fundamental feature of many sample surveys is a probability sample of subjects. Probability sampling requires rigorous application of mathematical principles to the selection process. Methods of Survey Sampling is a moderately advanced course in applied statistics, with an emphasis on the practical problems of sample design, which provides students with an understanding of principles and practice in skills required to select subjects and analyze sample data. Topics covered include stratified, clustered, systematic, and multi-stage sample designs, unequal probabilities and probabilities proportional to size, area probability sampling, ratio means, sampling errors, frame problems, cost factors, and practical designs and procedures. Emphasis is on practical considerations rather than on theoretical derivations, although understanding of principles requires review of statistical results for sample surveys. The course includes an exercise that integrates the different techniques into a comprehensive sample design.
Why take this course?
- To understand the basic ideas, concepts and principles of probability sampling from an applied perspective
- To be able to identify and appropriately apply sampling techniques to survey design problems
- To be able to compute the sample size for a variety of sample designs
- To understand and be able to assess the impact of the sample design on survey estimates
- To learn how to design and select a probability sample involving complex sampling techniques in a survey project, and receive expert feedback on a sampling report