Sample surveys theory and methods

Course objectives

Learning goals The primary goal of the course on “Sample Surveys” is that student should learn the main problems and methods in sampling from finite populations. They should be able to formalize and plan the whole process of data collection and analysis in observational studies. In more detail, students should be able to plan a sample survey, to choose a sampling design, to plan the data collection, as well as to analyze real data and to estimate quantities of interest. Knowledge and understanding After attending the course the students know and understand the main methodologies in planning a sample survey, as well as in dealing with non-sampling sources of error, such as nonresponses and missing values, measurement errors, list imperfections. Furthermore, students should be able to analyze real data and to estimate quantities of interests, such as means and proportions. Applying knowledge and understanding At the end of the course the students are able to formalize and plan the whole process of data collection and analysis in observational studies. They should be able to manage the most important (i) sampling designs and (ii) point and interval estimators, as well as the main methodologies to deal with missing values, measurement errors, list imperfections. Moreover, they should be able to apply the methods to the data and to interpret the results. Making judgements Students develop critical skills through the application of sampling and estimation methodologies to a wide range of contexts. They also develop the critical sense through the comparison of different solutions and the analysis of results. Communication skills Students, through their study, should acquire the technical-scientific language of the discipline, to be used in their activity. Learning skills Students who pass the exam have learned a method of analysis to be used in the data collection and analysis from finite populations."

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PIER LUIGI CONTI Lecturers' profile

Program - Frequency - Exams

Course program
- Basic aspects on variable probability sampling designs - Inclusion probabilities: properties and approximations - Some relevant examples: simple random sampling, stratified random sampling, single-stage cluster sampling, two-stage sampling, systematic sampling, ppswr, ppswor, Midzuno-Lahiri sampling design - Theory of statistical inference in random sampling under fixed-population approach: "flat" likelihood. - Horvitz-Thompson estimator and its properties. The problem of variance estimation: exact and approximate solutions. - Sampling designs with pre-fixed inclusion probabilities: Poisson, Bernoulli, Pareto, Sampford, Conditional Poisson, sampling designs. - Balanced sampling designs and cube sampling algorithm - Calibration estimators, with applications to post-stratification and estimation in contingency tables. IPF algorithm. - Variance estimation by linearization technique. - Resampling based on pseudo-populations: general aspects and applications to variance estimation. - Non-sampling errors: general aspects - Frame imperfections. Dual frame sampling. - Measurement errors models. Effects of measurement errors. - Non-responses: general aspects. Methodologies to prevent non-responses. Methodologies to data weight. - Nonrespondents sampling: the Hansen-Hurwitz approach in a modern perspective. - Randomized response techniques - Estimation of response probabilities via homogeneous response groups - Superpopulation models: basic aspects - Design-based, model-based, model-assisted approaches to inference for superpopulation parameters - Ignorability of sampling designs and consequences of non-ignorability: the emerging of model-assisted inference. - Weighted log-likelihood (pseudo-log-likelihood) - Regression analysis for survey data. GREG estimator - Statistical inference in contingency tables for survey data.
Prerequisites
An elementary course in "Statistical Inference" + an elementary course in "Sampling Techniques"
Books
Lectures notes
Frequency
The course consists of 72 hours (9 credits) Lectures are held in classroom, in traditional mode.
Exam mode
Written report + written or oral examination
PIER LUIGI CONTI Lecturers' profile

Program - Frequency - Exams

Course program
- Basic aspects on variable probability sampling designs - Inclusion probabilities: properties and approximations - Some relevant examples: simple random sampling, stratified random sampling, single-stage cluster sampling, two-stage sampling, systematic sampling, ppswr, ppswor, Midzuno-Lahiri sampling design - Theory of statistical inference in random sampling under fixed-population approach: "flat" likelihood. - Horvitz-Thompson estimator and its properties. The problem of variance estimation: exact and approximate solutions. - Sampling designs with pre-fixed inclusion probabilities: Poisson, Bernoulli, Pareto, Sampford, Conditional Poisson, sampling designs. - Balanced sampling designs and cube sampling algorithm - Calibration estimators, with applications to post-stratification and estimation in contingency tables. IPF algorithm. - Variance estimation by linearization technique. - Resampling based on pseudo-populations: general aspects and applications to variance estimation. - Non-sampling errors: general aspects - Frame imperfections. Dual frame sampling. - Measurement errors models. Effects of measurement errors. - Non-responses: general aspects. Methodologies to prevent non-responses. Methodologies to data weight. - Nonrespondents sampling: the Hansen-Hurwitz approach in a modern perspective. - Randomized response techniques - Estimation of response probabilities via homogeneous response groups - Superpopulation models: basic aspects - Design-based, model-based, model-assisted approaches to inference for superpopulation parameters - Ignorability of sampling designs and consequences of non-ignorability: the emerging of model-assisted inference. - Weighted log-likelihood (pseudo-log-likelihood) - Regression analysis for survey data. GREG estimator - Statistical inference in contingency tables for survey data.
Prerequisites
An elementary course in "Statistical Inference" + an elementary course in "Sampling Techniques"
Books
Lectures notes
Frequency
The course consists of 72 hours (9 credits) Lectures are held in classroom, in traditional mode.
Exam mode
Written report + written or oral examination
  • Lesson code1018630
  • Academic year2024/2025
  • CourseStatistical Sciences
  • CurriculumDemografico sociale
  • Year1st year
  • Semester2nd semester
  • SSDSECS-S/01
  • CFU9
  • Subject areaStatistico