STAT41020 Survey Sampling

Academic Year 2022/2023

This is an introductory course in survey sampling covering the main types of sampling and sampling errors.
Lecture topics will include planning a survey, non-sampling errors and sampling errors and non-response adjustments. Different types of sampling will be discussed: simple random sampling, stratified sampling, ratio estimation, cluster sampling and systematic sampling. This will include in each case, the advantages and disadvantages of each type of sampling, how to choose sample sizes and how to construct confidence intervals for estimates as well as cost considerations. Time permitting, special topics such as randomized response, estimating population size- capture/recapture methods will be discussed. The module will include computer laboratories using R software and tutorials. Typewritten notes for the course will be provided on Brightspace.

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Curricular information is subject to change

Learning Outcomes:

On successful completion of this module students should have developed skills in questionnaire design and be able to differentiate between sampling and non-sampling errors. Students should be able to propose an appropriate sampling scheme to address a wide range of research questions.

Students should have knowledge of the main types of sampling schemes, the advantages and disadvantages of each, how to choose sample size and be able to produce estimates and confidence intervals. In addition students should be able to explain the connection between the methods used here and those used in an introductory statistics course.
Students should be able to able to conduct analyses from each sampling scheme using the software R.

Indicative Module Content:

Keywords.
Questionnaire design; Simple random sampling; Stratified sampling; Cluster sampling; Ratio estimation; Systematic sampling; Sample size calculations; Capture-recapture; Non-response adjustments.

Student Effort Hours: 
Student Effort Type Hours
Lectures

24

Tutorial

5

Computer Aided Lab

4

Specified Learning Activities

30

Autonomous Student Learning

48

Total

111

Approaches to Teaching and Learning:
Lectures, tutorials, computer based laboratories, inquiry and problem-based learning 
Requirements, Exclusions and Recommendations
Learning Requirements:

A knowledge of statistical inference to the level of Inferential Statistics STAT20100 is required. A knowledge of calculus and linear algebra to the level of First Science is required.

Learning Recommendations:

A knowledge of probability to the level of Probability Theory STAT20110 is desirable.


Module Requisites and Incompatibles
Equivalents:
Survey Sampling (STAT30020)


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Combination of Group Projects, assignments and in class quizes. Throughout the Trimester n/a Alternative linear conversion grade scale 40% No

100


Carry forward of passed components
No
 
Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Assignments will be graded and feedback on each assignment will be provided for each student individually. Group feedback will be provided in tutorials.

Scheaffer, Mendenhall, Ott and Gerow. Elementary Survey Sampling. Cengage Learning; 7 edition (February 18, 2011)

Barnett V. (1991) Sample Survey Principles and Methods. Arnold

Lohr S. (1999) Sampling design and analysis. Duxbury Press

Rao Poduri S.R.S. (2000) Sampling Methodologies with Applications. Chapman & Hall.

Thompson Steven K. (2012). Sampling. Wiley.

Lumley T (2010). Complex Surveys. Wiley.

Supplementary Reading:

Huff D. (1954) How to lie with statistics. Penguin Books.

Tanur J. Editor (1978). Statistics: A guide to the unknown. Holden-Day.
Name Role
Assoc Professor Patrick Murphy Lecturer / Co-Lecturer