Methods to analyze data taught in introductory statistics and econometric courses often rely on the assumption that the data are collected in a simple random sampling process. However, in practice rather complex sampling techniques are often used, such as stratification or clustering. In these cases, a weight has to be assigned to the sampled units to account for the sampling design. The course will cover methods to select random samples and weight the sampled units to infer from the single sample to the population. We will discuss the several steps in a weighting process including 1) obtaining the design weights to account for the random sample selection, 2) adjusting the design weights to compensate for nonresponse, and 3) adjusting the weights such that the sample estimates agree with known population totals. The focus of the course is on the intuition of the underlying theory and the statistical methods so that the participants can assess the appropriateness of the methods in their application by themselves. For the course, no prior knowledge on survey statistics is required, but the participants are expected to be comfortable with statistics and to have some experience with data analysis. We will do exercises in R that apply the techniques learned in the lecture. Participants will get the most out of the class if they have prior experience with R.

 

The detailed syllabus with course times and literature will be available soon.