Sarndal, Swensson and Wretman (1992) Model assisted survey sampling. New York, Springer Verlag
Hedayat and Sinha (1991) Design and inference in finite population sampling. New York, Wiley
S. K. Thompson (2012) Sampling, 3rd Edition. New York, Wiley
G. Nicolini; D. Marasini; G.E. Montanari; M. Pratesi; M.G. Ranalli; E. Rocco (2013). Metodi di stima in presenza di errori non campionari. Milano: Springer-Verlag Italia
Class notes and slides.
After completing the course the students should be able to:
- correctly design and analyze simple and complex sampling strategies for the study of specific phenomena;
- analyze the problems associated with the presence and statistical treatment of non-sampling errors, with particular attention to the non-response problems.
Knowledge of basic elements of probability theory and statistical inference.
Lectures and classroom exercises
Type of Assessment
The evaluation is based on two elements:
- Written exam including both practical exercises and questions on theory.
- Oral exam: the student passing the written exam is admitted to the oral exam; the oral exam concerns the theory and the interpretation and includes the presentation of a final project; at the end of the oral exam, a final mark is given.
The basic concepts in sampling theory for finite populations and the Horvitz-Thompson's estimator
Most commonly used probabilistic sampling designs: simple random sampling with and without replacment, stratified sampling, cluster sampling, two-stage sampling, systematic sampling, probability-proportional-to-size sampling, complex sampling designs
Types of non-probability sampling.
Sample Size and Sample Allocation
Estimation using known auxiliary variables: the ratio estimator, the difference estimator, the regression estimator, the post-stratified estimator, calibration and weighting estimators, estimation for domains.
Non-sampling errors; frame imperfections; unit and item nonsponse; statistical treatment of non-sampling errors.