STATS 3003 - Sampling Theory and Practice III

North Terrace Campus - Semester 2 - 2015

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.

  • General Course Information
    Course Details
    Course Code STATS 3003
    Course Sampling Theory and Practice III
    Coordinating Unit Statistics
    Term Semester 2
    Level Undergraduate
    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
    Assessment ongoing assessment 30%, exam 70%
    Course Staff

    Course Coordinator: Andrew Metcalfe

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    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 analsis,
    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
  • Learning Resources
    Required Resources
    Recommended Resources
    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 Learning
    The course is supported by MyUni, and the site will include: lecture notes; lecture slide shows; practicals and discussions; 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
    Lecture Outline

    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)
    Non-response (1)
    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.
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment item Distributed Due Weighting
    Tutorial/practical weeks 1-11 in class 10%
    Assignment 1 week 2 week 4 5%
    Assignment 2 week 5 week 7 5%
    Assignment 3 week 7 week 9 5%
    Assignment 4 week 9 week 11 5%
    Presentation week 2 week 12 5%
    Examination 65%
    Assessment Related Requirements
    An aggregate score of at least 50% is the pass standard.
    Assessment Detail
    Item Set Due Weight
    Tute/prac weeks 1-11 in week 10%
    Assignment 1 week 2 week 4 5%
    Assignment 2 week 5 week 7 5%
    Assignment 3 week 7 week 9 5%
    Assignment 4 week 9 week 11 5%
    Presentation week 2 week 12 5%
    Examination 65%
    1. Assignments 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.
    Course Grading

    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.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.