ANALYSIS OF REPEATED MEASURES

STA5ARM

2020

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.

SchoolEngineering and Mathematical Sciences

Credit points15

Subject Co-ordinatorDavid Farchione

Available to Study Abroad/Exchange StudentsYes

Subject year levelYear Level 5 - Masters

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

Prerequisites Must be admitted in the Master of Data Science (SMDS) and have passed STM4PSD or both STA4SS and STM4PM Other students require Coordinators Approval

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsN/A

Minimum credit point requirementN/A

Assumed knowledgeN/A

Readings

Linear mixed models: A practical guide using statistical software

Resource TypeBook

Resource RequirementPrescribed

AuthorWest, B., Welch, K., and Galecki, A.

Year2015

Edition/Volume2nd ed

PublisherRoca Raton, FL: CRC Press.

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Career Ready

Career-focusedNo

Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/A

Graduate capabilities & intended learning outcomes

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

Subject options

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Start date between: and    Key dates

Melbourne (Bundoora), 2020, Semester 1, Blended

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorDavid Farchione

Class requirements

Computer Laboratory Week: 11 - 22
One 1.00 h computer laboratory per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.

Unscheduled Online Class Week: 10 - 22
One 2.00 h unscheduled online class per week on any day including weekend during the day from week 10 to week 22 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle% 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.N/AN/AN/ANo15 SILO2
Three written assignments, submitted online (1750 words total equiv)N/AN/AN/ANo35 SILO1, SILO2, SILO3, SILO4
3 hour Final Exam (3000 word equiv)N/AN/AN/ANo50 SILO1, SILO2, SILO3, SILO4