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Longitudinal and Correlated Data (POPH90123)
Graduate courseworkPoints: 12.5Online
Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 1
Melbourne School of Population and Global Health
OR
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Email: Contact Stop 1
Future Students:
- Further Information: MSPGH Website
- Email: Enquiry Form
Overview
Availability | Semester 1 - Online |
---|---|
Fees | Look up fees |
This subject covers statistical models for longitudinal and correlated data. Beginning with models based on normal distributions, the concept of hierarchical data structures is developed. Numerical and analytical examples are used to demonstrate the inadequacy of standard statistical methods, including the limitations of the repeated-measures analysis of variance. Extensions to non-normal data using generalised estimating equations (GEE’s) and generalised mixed linear models (GLMM’s) are explored using the R and Stata statistical software packages.
Intended learning outcomes
- Recognise the existence of correlated or hierarchical data structures, and describe the limitations of standard methods in these settings.
- Develop and analytically describe appropriate models for longitudinal and correlated data based on subject matter considerations.
- Be proficient at using statistical software packages (Stata and R) to fit models and perform computations for longitudinal data analyses, and to correctly interpret results.
- Express the results of statistical analyses of longitudinal data in language suitable for communication to medical investigators or publication in biomedical or epidemiological journal articles.
Generic skills
- Independent problem solving;
- facility with abstract reasoning;
- clarity of written expression;
- sound communication of technical concepts.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
POPH90014 | Epidemiology 1 | Semester 1 (Online) |
12.5 |
Code | Name | Teaching period | Credit Points |
---|---|---|---|
POPH90148 | Probability and Distribution Theory |
Semester 2 (Online)
Semester 1 (Online)
|
12.5 |
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90100 | Inference Methods in Biostatistics | March (Dual-Delivery - Parkville) |
12.5 |
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90102 | Linear Regression | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90099 | Categorical Data: Models and Methods | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Description | Timing | Percentage |
---|---|---|
Five short assignments (400 words each) (8% each). Due in weeks 3, 5, 7, 9 and 11 respectively.
| Throughout the teaching period | 40% |
Major Assignment #1
| Week 8 | 30% |
Major Assignment #2
| During the examination period | 30% |
Last updated: 3 November 2022
Dates & times
- Semester 1 - Online
Coordinator John Holmes Mode of delivery Online Contact hours None Total time commitment 170 hours Teaching period 1 March 2021 to 30 May 2021 Last self-enrol date 12 March 2021 Census date 31 March 2021 Last date to withdraw without fail 7 May 2021 Assessment period ends 25 June 2021 Semester 1 contact information
Melbourne School of Population and Global Health
OR
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Email: Contact Stop 1
Future Students:
- Further Information: MSPGH Website
- Email: Enquiry Form
Time commitment details
170 hours
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
None
Recommended Text:
Fitzmaurice G, Laird N, Ware J. Applied Longitudinal Analysis. 2nd ed. John Wiley and Sons, 2011. (ISBN 9780470380277)Resources Provided to Students: Printed course notes and assignment material by mail, email, and online interaction facilities.
Special Computer Requirements: Stata statistical software
- Subject notes
This subject is delivered online via our partners in the Biostatistics Collaboration of Australia (www.bca.edu.au). It is not generally available in the Master of Public Health nor in any program outside the MSPGH.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Graduate Diploma in Biostatistics Course Master of Biostatistics - Links to additional information
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 3 November 2022