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STAT41020

Academic Year 2024/2025

Survey Sampling (STAT41020)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Assoc Professor Patrick Murphy
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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.

About this Module

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 Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Group Work Assignment: Group project Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Alternative linear conversion grade scale 40% No
30
 No
Assignment(Including Essay): Assignments Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Alternative linear conversion grade scale 40% No
40
 No
Quizzes/Short Exercises: Online and in person quizzes Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Alternative linear conversion grade scale 40% No
30
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Spring No
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/lectures.

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

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Fri 12:00 - 12:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Mon 15:00 - 15:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Mon 17:00 - 17:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Wed 16:00 - 16:50
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