STATS 7057 - Sampling Theory & Practice
North Terrace Campus - Semester 2 - 2015
General Course Information
Course Code STATS 7057 Course Sampling Theory & Practice Coordinating Unit Statistics Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites STATS 2107 or (MATHS 2201 and MATHS 2202) Assumed Knowledge Experience with the statistical package R such as would be obtained from STATS 1005 or STATS 2107 Course Description Sample surveys are an important source of statistical data. A great many published statistics on demographic, economic, political and health related characteristics are based on survey data. Simple random sampling is a well known method of sampling but, for reasons of efficiency and practical constraints, methods such as stratified sampling and cluster sampling are typically used by statistical authorities such as the Australian Bureau of Statistics and by market research organisations. This course is concerned with the design of sample surveys and the statistical analysis of data collected from such surveys.
Topics covered are: experiments and surveys, steps in planning a survey; randomisation approach to sampling and estimation, sampling distribution of estimator, expected values, variances, generalisation of probability sampling; prediction approach, inadequacies of approach, decomposition of population total, concomitant variables; regression through the origin, estimation by least squares, ratio estimation, variance formulae; balance and robustness; best fit sample; stratified sampling, estimation, allocation, construction of strata, stratification on size variables, post-stratification; two-stage sampling, estimation, allocation, cluster sampling.
Course Coordinator: Andrew Metcalfe
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
Students who successfully complete the course should:
1. understand the principles underlying sampling as a means of making inferences about a population,
2. understand the difference between randomization theory and model based analysis,
3. understand the concepts of bias and sampling variability and stragies for reducing these,
4. be able to analyse data from multi-stage surveys,
5. have an appreciation of the practical issues arising in sampling studies.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. all The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 1,3,5 An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 1,5 Skills of a high order in interpersonal understanding, teamwork and communication. 5 A proficiency in the appropriate use of contemporary technologies. all A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. all A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. 1,5 An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 1,5
1. Lohr SL, Sampling Design and Analysis (2e), Brooks/Cole 2010
2. Scheaffer RL, Mendenhall W, Ott RL, Gerow KG Elementary Survey Sampling (7e), Brooks/Cole 2012
3. Barnett V, Sample Survey Principles and Methods (3e), Wiley 2002
4. Kish L, Survey Sampling, Wiley 1995
5. Lumley T, Complex Surveys: a Guide to Analysis using R, Wiley 2010
Online LearningThe course is suppported by MyUni, and the site will include: lecture slide shows; practicals and dicussions; assignment papers; and past exam papers; and links to additional resources.
Learning & Teaching Activities
Learning & Teaching Modes
Two lectures per week are supplemented with alternating tutorials and practicals. The tutorials will usually be based on experimental data and you should bring a hand calculator. The practicals are PC based, using R. The tutorials and practicals are an essential component of the course and include some topics that are not explicitly covered in lectures.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload Lectures 24 72 Tutorials 5 15 Practicals 5 15 Presentation 1 14 Assignments 4 40 Total 156
Learning Activities Summary
Introduction to sampling (1)
Simple random sampling (2)
Stratified sampling (4)
Ratio and regression estimators (3)
Estimation of population size (2)
Sampling with unequal probabilities (2)
Cluster sampling (2)
Multi-stage sampling (4)
Categorical data analysis (2)
Snowball sampling (1)
Small Group Discovery Experience
In small groups of 3 or 4: preparation of a presentation on a practical application of sampling to be given to the entire class. The application can be taken from a media report or published research work in any discipline.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Item Distributed Due Weighting Tutorial/practical weeks 1-11 in class !0% Assignment 1 week 2 week 4 4% Assignment 2 week 5 week 7 4% Assignment 3 week 7 week 9 4% Assignment 4 week 9 week 11 4% Presentation week 2 week 12 4% Examination 70%
Assessment Related RequirementsAn aggregate score of at least 50% is the pass standard.
Item Set Due Weight Tutorial/practical weeks 1-11 at the end
of the class
10% Assignment 1 week 2 week 4 4% Assignment 2 week 5 week 7 4% Assignment 3 week 7 week 9 4% Assignment 4 week 9 week 11 4% Presentation week 2 week 12
4% Examination 70%
1. Assignments are to be submitted to the designated hand-in boxes in the School of Mathematical Sciences with a signed cover sheet attached.
2. Late assignments will only be accepted under exceptional circumstances.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
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