ANALYSIS OF REPEATED MEASURES

STA5ARM

2018

Credit points: 15

Subject outline

Repeated measures data is used commonly in many disciplines including health, psychology, economics and biology. This subject provides students with the knowledge of how to perform the appropriate statistical analysis in a repeated measures data environment by using models such as the linear mixed model, correlated random effects model and marginal model. Students will learn how to examine research questions by applying these models using the R statistical package. 

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorDavid Farchione

Available to Study Abroad StudentsYes

Subject year levelYear Level 5 - Masters

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites Must be admitted in SMDS Master of Data Science course and have completed STA4SS or equivalent. All other students require approval of the Head of Department of Mathematics and Statistics.

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsLinear mixed models: A practical guide using statistical software, 2nd ed.PrescribedWest, B., Welch, K., and Galecki, A. (2015).Roca Raton, FL: CRC Press.

Graduate capabilities & intended learning outcomes

01. Use specialised computer software to critically analyse, reflect on and summarise complex information, problems and concepts for repeated measures data.

Activities:
Online Lectures: This consists of readings from the prescribed textbook; and online video clips that (i) explain the readings and (ii) cover practical examples using the R computer package. Computer Labs: Students in this class work through practical examples using the R computer package. Online Discussion Forum: Topics covered in Online Lectures and Computer Labs are discussed by students and teachers via the asynchronous online discussion forum.

02. Demonstrate an understanding of complex repeated measures regression models by expressing a research question in the form of a regression model.

Activities:
Online Lectures: This consists of readings from the prescribed textbook; and online video clips that (i) explain the readings and (ii) cover practical examples using the R computer package. Computer Labs: Students in this class work through practical examples using the R computer package. Online Discussion Forum: Topics covered in Online Lectures and Computer Labs are discussed by students and teachers via the asynchronous online discussion forum.

03. Use advanced written communication skills to disseminate findings from analyses of repeated measures data at a level commensurate with what is appropriate in the scientific literature for a range of disciplines.

Activities:
Online Lectures: This consists of readings from the prescribed textbook; and online video clips that (i) explain the readings and (ii) cover practical examples using the R computer package. Computer Labs: Students in this class work through practical examples using the R computer package. Online Discussion Forum: Topics covered in Online Lectures and Computer Labs are discussed by students and teachers via the asynchronous online discussion forum.

04. Use advanced written communication skills to critique published analyses of repeated measures data and to justify findings that result from applying a variety of methods.

Activities:
Online Lectures: This consists of readings from the prescribed textbook; and online video clips that (i) explain the readings and (ii) cover practical examples using the R computer package. Computer Labs: Students in this class work through practical examples using the R computer package. Online Discussion Forum: Topics covered in Online Lectures and Computer Labs are discussed by students and teachers via the asynchronous online discussion forum.

Subject options

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Start date between: and    Key dates

Melbourne, 2018, Semester 1, Blended

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorDavid Farchione

Class requirements

Unscheduled Online Class Week: 10 - 22
One 2.0 hours unscheduled online class per week on any day including weekend during the day from week 10 to week 22 and delivered via online.

Computer Laboratory Week: 11 - 22
One 1.0 hours computer laboratory per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
Ten x 15 minute online quizzes (1500 word total equiv)Each quiz worth 1.5% Students can attempt each quiz a maximum of three times. Randomly assigned questions for each quiz instance. 15 02
Three written assignments, submitted online (1750 words total equiv)35 01, 02, 03, 04
2.5 hour Final Exam (2500 word equiv)50 01, 02, 03, 04