 |
| Detailed information |
| Pre-requisites |
(*)keine
|
| Original study plan |
Bachelor's programme Statistics 2015W |
| Objectives |
Students learn to draw probabilty samples from existing populations. They can use information on the population to choose an adequate sampling design and estimation procedure. Furthermore, students acquire the ability to look in-depth at literature in sampling theory, particularly in the "missing data" literature.
|
| Subject |
Representativeness of samples, simple random sampling, stratified random sampling, cluster sampling, two-stage random sampling, non-random sampling (quota sampling)
Horvitz-Thompson estimator, ratio and regression estimation, small area estimation
nonresponse (data imputation, weighting adjustment), variance estimation by resampling
|
| Criteria for evaluation |
Homework assignments and a final examination
|
| Methods |
Lecture by instructor
discussion of the homework assignments by the students
working with sample data in R (or Excel)
|
| Language |
German |
| Study material |
Quatember, A. (current version). Datenqualität in Stichprobenerhebungen - Stichprobenverfahren. Springer, Heidelberg.
Supplementary material:
Lohr, S. (2010). Sampling: Design and Analysis. 2nd edition, Brooks/Cole, Boston.
Särndal, C.-E., Swensson, B. and Wretman, J. (1992). Model Assisted Survey Sampling. Springer, New York.
|
| Changing subject? |
No |
| Corresponding lecture |
(*)4MSSTKV: KV Stichprobenverfahren (4 ECTS)
|
| Earlier variants |
They also cover the requirements of the curriculum (from - to)
4MSSTKV: KV Sampling Methods (2002W-2014S)
|
|
