STATS 7008 - Statistics Topic D
North Terrace Campus - Semester 2 - 2021
General Course Information
Course Code STATS 7008 Course Statistics Topic D Coordinating Unit School of Mathematical Sciences Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Y Course Description Please contact the School of Mathematical Sciences for further details, or view course information on the School of Mathematical Sciences web site at http://www.maths.adelaide.edu.au
Course Coordinator: Dr Sharon Lee
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesIn 2021 STATS 7008 will be offered online through the AMSI ACE Network as the course
Categorical Data Analysis
A link to the subject guide can be found here:
Students will need to enrol in the course via the following link: https://www.tfaforms.com/4900937 in addition to enrolling in the course through MyAdelaide. A quiz is available to check your background knowledge here: https://rhed.amsi.org.au/wp-content/uploads/sites/73/2021/04/categorical-data-analysis-quiz.pdf
Note that the course will start on July 19 and end on October 29
The lecturer for the course will be Professor Eric Beh. Lectures will be held via Zoom.
The following is a summary of the course from the above subject guide:
Categorical data abounds in all disciplines as researchers and analysts search for
ways of analysing data collected from surveys or questionnaires. Undergraduate
courses only provide a cursory glance at how categorical data can be analysed. In
this course we will examine some of the core contributions to categorical data
analysis with a focus on measures of association, categorical data visualisation and
modelling categorical data.
The course will include the following topics
• Visualisation of categorical data
• History and development of contingency tables
• Pearson’s chi-squared statistic and related measures
• Features, and variations of the odds ratio for single and stratified data
• Reciprocal averaging and singular value decomposition
• Correspondence analysis
• Modelling categorical data
On successful completion of the course, students will be able to:
1. Gain a deeper understanding of the analysis of categorical data
2. Explore more deeply the issue concerned with Pearson’s chi-squared statistic and related measures of association that reflect symmetric and asymmetric association
3. Apply new statistical tools to numerically and visually analyse multiple categorical variables
4. Apply a variety of correspondence analysis techniques
5. Model categorical data using association models and log-linear models
6. Apply their skills to real-life data using R
7. Undertake basic research skills concerned with categorical data analysis
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 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
all Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
all Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
all Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Required ResourcesStudents are not required to purchase any reference book for this course. Instead lecture notes will be provided. Published articles in commonly available, high-profile, journals will also be made available to students for additional insight and context of the weekly topics; they will need to access this material through their own library (full bibliographic information of each article will be provided)
Students will need to have access to R/Rstudio. They can be freely downloaded from the internet.
Learning & Teaching Activities
Learning & Teaching Modes
No information currently available.
No information currently available.
Learning Activities SummaryWeek by week topic overview:
Week 1: An overview of categorical data, its history and standard techniques
Week 2: Analysis of a single categorical variable – goodness-of-fit tests
Week 3: The contingency table, chi-squared statistic & related measures
Week 4: Measures of symmetric association for 2x2 contingency tables
Week 5: Measures of symmetric association for IxJ contingency tables
Week 6: Measures of asymmetric association for IxJ contingency tables
Week 7: Scaling categorical data – reciprocal averaging & canonical correlation analysis
Week 8: Simple correspondence analysis
Week 9: Non-symmetric correspondence analysis
Week 10: Multiple correspondence analysis
Week 11: Models of correlation and association
Week 12: Log-linear models
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.
Assessment Percent of final mark Written assignments (3) 45 Final exam 55
The due dates for the assignments are as follows:
Assignment 1: due August 20
Assignment 2: due September 17
Assignment 3: due October 29
Assessment Related RequirementsA final aggregate score of at least 50% is required to pass the course.
No information currently available.
SubmissionAll written assignments are to be submitted to the lecturer with a signed cover sheet attached. There will be a maximum two week turn-around time on assignments for feedback to students.
Late assignments will not be accepted, but students may be excused from an assignment for medical or compassionate reasons. In such cases, documentation is required and the lecturer must be notified as soon as possible before the fact.
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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