This course is a statistical methods class appropriate for second year Master’s students and PhD students. The course will be a combination of hands-on applications and general review of the theory behind different approaches to sampling and weighting. Topics covered include:
- Sample size calculations using estimation targets based on relative standard error, margin of error, and power requirements;
- Use of mathematical programming to determine sample sizes needed to achieve estimation goals for a series of subgroups and analysis variables;
- Resources for designing area probability samples;
- Methods of sample allocation for multistage samples;
- Steps in weighting, including computation of base weights, nonresponse adjustments, and uses of auxiliary data;
- Nonresponse adjustment alternatives, including weighting cell adjustments, formation of cells using regression trees, and propensity score adjustments;
- Weighting via poststratification, raking, general regression estimation, and other types of calibration.