A few notes about the course: the approach to survey sampling in this course will be statistical and practical. By "statistical," I mean that it's about the effective use of quantitative data and includes such issues as model building, design, estimation, and inference (thought not necessarily in that order!). By "practical," I refer to the attention that will be paid to the large and small imperfections that occur in real world surveys. It is generally impossible to implement the sampling designs exactly, substantial amounts of non-response and self-selection are inevitable, and the models employed will be, at best, approximations.
I will not be discussing "survey methodology." How to write a questionnaire, how to train interviewers, or manage a Web panel are all important skills for conducting a survey. This is largely an art (as indicated by the title of Stanley Payne's The Art of Asking Questions, still one of my favorites) and best learned by doing. In recent years, numerous studies have been done testing various hypotheses about survey methods, but this literature tends to be an ad hoc collection of results, often of limited generality, and not, despite claims to the contrary, a coherent "new science." At least, that's my opinion.
The applications that will be covered in the course come largely from surveys of U.S. elections. This reflects primarily my personal interests, but the methods and results have much wider applicability. Between campaign and media polls, exit polls, academic surveys (such as the American National Election Studies), and Internet panels, we encounter all of the common designs (simple random samples, stratified samples, one and multi-stage cluster samples, probability proportional to size, systematic, and balanced selection), estimation methods (ratio and regression estimators, post-stratification, raking, propensity scores, matching, Hierarchical and empirical Bayes), and problems (frame imperfections, nonresponse, self-selection).
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