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Advanced sampling - 2024

Advanced sampling methods

Course Leader

Rosanna Verde

Target Group

Junior or senior staff of methodology divisions using sample survey techniques in the production of statistics.

Entry Qualifications

Good knowledge of basic sampling techniques and survey methodology.


The objective of the course is to introduce participants to some advanced techniques of survey sampling and to the treatment of non-response, variance estimation, calibration and sample coordination. Some of the theory will be illustrated by examples and some exercises.

In addition to theoretical aspects, the course provides the use of R software that will enable participants to better understand the practical applications of these sampling techniques and successfully implement them in practice.


  • Probabilistc and non-probabilistic sampling
  • Sampling Methods for Rare Populations
  • Stratified Random Sampling 
  • Systematic Sampling 
  • Cluster Sampling 
  • Adaptative Sampling 
  • Simulating a sampling process using R.
  • Resampling methods
  • Introduction to the R software (lecture + practicals) 
  • Sample coordination techniques (lecture + practicals)

Expected Outcome

At the end of the course participants are expected to have a deeper understanding of the techniques they have learned and be able to successfully apply the new methodology in their daily practice.

Training Methods

The course is based mainly on lectures and practical exercises on PC using the R software.

Required Reading 

  • Särndall C.E., Swensson B. and Wretman J. (1992). Model Assisted Survey Sampling. PART I: Principles of Estimation for Finite Populations and Important Sampling Designs (Chapters 1 to 5)

  • Thompson, S. K. (2012). Sampling. 3rd Edition. Wiley series in probability and statistics (Chapters 1 to 6, 11, 12, 23, 24, 26)                                                               

ISBN 978-0-470-40231-3

Suggested Reading

  • Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050-1059.

  • Thompson, S. K. (1991). Stratified adaptive cluster sampling. Biometrika, 78(2), 389-397

  • Roger Vaughan, 2017: Oversampling in Health Surveys: Why, When, and How? American Journal of Public Health 107, 1214_1215,

Advanced reading

  • Fuhg, J.N., Fau, A. & Nackenhorst, U. State-of-the-Art and Comparative Review of Adaptive Sampling Methods for Kriging. Arch Computat Methods Eng 28, 2689-2747 (2021).

Required Preparation

Knowledge of Basic Statistics and Statistical Inference


Rosanna Verde (Independent expert)


Practical Information





Application  via National Contact Point


3 days

Cologne, Germany

ICON-INSTITUT Public Sector GmbH

Deadline: 23.09.2024