Conti Pie luigi, Marella Daniela (2012) Campionamento da popolazioni finite;
teaching material provided by the teacher during the course.
Cicchitelli G., Herzel A., Montanari G. E (1992) Il campionamento statistico;
Sharon L. Lohr (1999) Sampling: design and analysis;
Jelke Bethlehem (2009) Applied Survey Methods;
Frosini B. V., Montinaro M., Niccolini G. (2011) Campionamenti da popolazioni finite;
Nicolini G., Marasini D., Montanari G.E., Pratesi M., Ranalli M.G., Rocco E. (2013) Metodi di stima in presenza di errori campionari.
After completing the course the student should be able to:
- be responsible for planning and implementation of a statistical survey
- take the key decisions relating to the choice of a sampling design
- evaluate alternatives available for estimating population parameters
- assess the quality and reliability of the information contained in the results of sample surveys published by the media and by public and/or private specialist publications.
Elements of descriptive and inferencial statistics
Lectures and classroom exercises
The course uses in part online materials and resources
Type of Assessment
The examination consists of a written and an oral test.
An introduction to survey sampling: defining a survey, sample vs census survey, survey objective, target population, sampling frame, sampling and non-sampling errors, bias and variability, probability sampling vs non-probability sampling.
Basic ideas in estimation from probability sampling: framework for probability sampling, sampling design, sampling selection algorithms, inclusion probabilities, the notion of a statistic, the sample membership indicators, estimators and their basic properties, estimation of sampling error, confidence intervals, design-based inference vs model-based inference.
Probability sampling designs: Simple random sampling, stratified sampling, cluster sampling, systematic sampling, two-stage sampling, Bernoulli Sampling, Poisson sampling, unequal probability sampling.
The class of mean linear estimators, the Horvitz-Thompson estimator and the Hansen-Hurwitz estimator
The use of the auxiliary information in the estimation process: the difference estimator, the regression estimator, the ratio estimator, calibration estimators
Some considerations on continuing surveys and on the nonresponse problem and nonresponse correction techniques.