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Survey Methods

Objectives

This course aims to provide the students with knowledge on survey methodology, with particular emphasis on probabilistic sampling. The course covers the main forms of sampling design: simple random sampling, stratified sampling, cluster sampling and two-stage sampling.
The selection of sampling units with equal and unequal probabilities is covered, in particular selection with probabilities proportional to size. An introduction to ratio and domain estimation is also presented as well as introductory topics on using auxiliary information.  With this course students should be able to select an appropriate sampling design for any particular survey, to determine the appropriate sample size, to choose the right estimators and to produce measures of precision of the estimation.  Through a research project students should gain an understanding on sampling methodologies.

General characterization

Code

200089

Credits

7.5

Responsible teacher

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

Perfect knowledge of descriptive statistics and inductive statistics;

Bibliography

·     William G. Cochran (1977) Sampling Techniques, 3-rd Ed. New York: Wiley, ISBN 0-471-16240-X

·     Carl-Erik Särndal, Bengt Swensson, Jan Wretman (1992) Model Assisted Survey Sampling. New York: Springer Verlag, ISBN 0-387-97528-4

·     Carl-Erik Särndal, Sixten Lundström (2005) Estimation in Surveys with Nonresponse. Chichester: Wiley, ISBN 0-470-01133-5

·     Coelho P, Pereira L, Pinheiro J and Xufre P (2016), Sondagens, Livraria escolar Editora.

 

Teaching method

  1. Introduction
  2. Simple random sampling
  3. Stratified random sampling
  4. Post-stratification
  5. Complex designs: cluster sampling and two-stage sampling
  6. Ratio and domain estimation
  7. Using auxiliary infomation

Evaluation method

Intermediate test, final exam, courses participation

Subject matter

  1. Introduction
  2. Simple random sampling
  3. Stratified random sampling
  4. Post-stratification
  5. Complex designs: cluster sampling and two-stage sampling
  6. Ratio and domain estimation
  7. Using auxiliary infomation