This practical course will cover the process of probability sampling and weighting for (social science) survey data. This includes a) sampling techniques, b) methods of weighting survey data, including design weighting, nonresponse weighting, and adjustment weighting, and c) methods of parameter estimation for complex sample survey data. Please note that the course will only provide a short excursus to non-probability sampling.

 

The course is more applied than theoretical, but you are expected to be comfortable with statistics and have some experience with survey data analysis. For each topic, you will do exercises in Stata to apply the techniques learned in the lectures. Prior knowledge and experience in how to use Stata for survey data analysis is needed. R-scripts with solutions to the exercises will be made available as well (but not discussed in more detail).

 

Organizational structure of the course

A typical course day will consist of two thematic clusters. Each cluster will start with a lecture of around 1 hour. After that lecture, you will work on practical exercises using Stata (sometimes individually, sometimes in small groups). The lecturer will provide and discuss a solution for each exercise in-depth afterward. During the last half of the final course day, the lecturer will provide guidance for your own ongoing or planned survey sampling and weighting projects.

You will find the course useful if:

By the end of the course, you will:

Software and hardware requirements

You will need to bring a laptop computer to successfully participate in this course.

 

You should have the following software installed on your computer before the course starts:

Stata (at least 17) packages: gsample, unique, distinct, survwgt, fre, coefplot, moremata, estout

 

GESIS will provide you with short-term licenses for Stata for the duration of the course if needed.