ITA | ENG
B029809 - DESIGN AND ANALYSIS OF SAMPLE SURVEYS
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Academic Year 2020-21
Coorte 2019 - Second Cycle Degree in STATISTICS AND DATA SCIENCE
Course year
Second year - First Semester
Belonging Department
Statistics, IT and its applications "G. Parenti" (DISIA)
Course Type
Single education field course
Scientific Area
SECS-S/01 - STATISTICS
Credits
9
Teaching Hours
72
Teaching Term
14/09/2020 ⇒ 09/12/2020
Attendance required
No
Type of Evaluation
Final Grade
Course Content
show
Course program
show
Lectureship
Mutuality
Course teached as:
B029809 - DESIGN AND ANALYSIS OF SAMPLE SURVEYS
Second Cycle Degree in STATISTICS AND DATA SCIENCE
Curriculum STATISTICA UFFICIALE
B029809 - DESIGN AND ANALYSIS OF SAMPLE SURVEYS
Second Cycle Degree in STATISTICS AND DATA SCIENCE
Curriculum STATISTICA UFFICIALE
Teaching Language
English
Course Content
The overall aim of this course is to provide participants with the knowledge required to project and realize a sample survey with attention to the most common sources of non-sampling errors.
Suggested readings (Search our library's catalogue)
Yves Tillé (2020) Sampling and Estimation from Finite Populations, John Wiley & Sons Ltd
Paul S. Levy, Stanley Lemeshow (2008) Sampling of Populations 4th Edition, JOHN WILEY & SONS
Steven G. Heeringa, Brady T. West, Patricia A. Berglund (2010) Applied Survey Data Analysis, Chapman & Hall/CRC
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.
Paul S. Levy, Stanley Lemeshow (2008) Sampling of Populations 4th Edition, JOHN WILEY & SONS
Steven G. Heeringa, Brady T. West, Patricia A. Berglund (2010) Applied Survey Data Analysis, Chapman & Hall/CRC
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.
Learning Objectives
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.
- 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.
Prerequisites
Preliminary teachings: "Inferenza statistica e metodi computazionali" and "Probabilità e matematica per la statistica"
Teaching Methods
Lectures and classrooom exercises
Type of Assessment
The evaluation is based on three elements:
- Written exam including both practical exercises and questions on theory;
- Project based on the analyses of data
- Oral exam: the student who received a sufficient evaluation in both the written exam and project will be admitted to the oral exam; the oral exam covers the theory and interprertation and includes the discussion of the project; at the end of the oral exam, a final mark will be given
- Written exam including both practical exercises and questions on theory;
- Project based on the analyses of data
- Oral exam: the student who received a sufficient evaluation in both the written exam and project will be admitted to the oral exam; the oral exam covers the theory and interprertation and includes the discussion of the project; at the end of the oral exam, a final mark will be given
Course program
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.
Designing samples for repeated surveys.
Variance estimation for complex sampling design.
Linear and generalized linear models with data from complex surveys.
Missing data: assumptions and treatment. Multiple imputation.
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.
Designing samples for repeated surveys.
Variance estimation for complex sampling design.
Linear and generalized linear models with data from complex surveys.
Missing data: assumptions and treatment. Multiple imputation.