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Sample Surveys
Last Updated: 2026-06-03 00:07:32
Abstract
The course introduces the main ideas and methods of finite-population sampling and estimation. It begins with an overview of basic sampling designs (simple random, stratified and cluster sampling). Building on these foundations, the course develops randomization-based and model-assisted inference for finite populations. It also addresses quality aspects of survey implementation and estimation.
Objective
Upon successful completion of the course, participants will be able to: 1. distinguish between finite-population and classical statistical inference; 2. compare and evaluate the designs: simple random, stratified, and cluster sampling designs with respect to their their strengths, limitations, and fields of application; 3. use R to analyze sample survey data; 4. identify and address issues related to missing and erroneous data; 5. critically assess the quality of survey estimates within the total survey error framework.
Content
Basic sampling designs and methods (simple random sampling, stratified sampling, and cluster sampling), randomization inference, unequal probability sampling (Horvitz-Thompson), variance estimation, model-assisted and model-based approach (ratio and regression estimator), total survey quality (data preparation, outlier detection, and methods for handling incomplete survey data)
Resources
Literature
Wu, C. and Thompson, M. E. (2020). Sampling Theory and Practice, Cham: Springer Verlag. URL: https://link.springer.com/book/10.1007/978-3-030-44246-0
General Information
- Language
- English
- Levels
- MSC , WBZ
- Frequency
- Every two years
Examination
- Type
- ungraded semester performance
Registration & Places
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Sample Surveys
Block course
Mon 16.11.26 08:15 - 12:00
Mon 23.11.26 08:15 - 12:00
Mon 30.11.26 08:15 - 12:00
Mon 07.12.26 08:15 - 12:00
Mon 14.12.26 08:15 - 12:00
Final Examination: Mon 11.01.27
|
No time listed | 17.5 h semesterly |
Offered In
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Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
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