STATS 4103 - Sampling Theory and Practice - Honours

North Terrace Campus - Semester 2 - 2017

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 4103
    Course Sampling Theory and Practice - Honours
    Coordinating Unit School of Mathematical Sciences
    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
    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 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 analysis; 
    3. Understand the concepts of bias and sampling variability and strategies 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)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    all
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    all
  • Learning Resources
    Required Resources
    None. 
    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 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.
    Workload

    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)
    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
    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 Requirements
    An aggregate score of at least 50% is required to pass this course. 
    Assessment Detail
    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
    in class
    4%
    Examination 70%
    Submission
    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.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M11 (Honours Mark Scheme)
    GradeGrade reflects following criteria for allocation of gradeReported on Official Transcript
    Fail A mark between 1-49 F
    Third Class A mark between 50-59 3
    Second Class Div B A mark between 60-69 2B
    Second Class Div A A mark between 70-79 2A
    First Class A mark between 80-100 1
    Result Pending An interim result RP
    Continuing Continuing CN

    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 (http://www.adelaide.edu.au/policies/101/) 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.

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  • Policies & Guidelines
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