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.

School: Engineering and Mathematical Sciences (Pre 2022)

Credit points: 15

Subject Co-ordinator: David Farchione

Available to Study Abroad/Exchange Students: Yes

Subject year level: Year Level 5 - Masters

Exchange Students: No

Available as Elective: No

Learning Activities: N/A

Capstone subject: No

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-requisites: N/A

Incompatible subjects: N/A

Equivalent subjects: N/A

Quota Management Strategy: N/A

Quota-conditions or rules: N/A

Special conditions: N/A

Minimum credit point requirement: N/A

Assumed knowledge: N/A

Learning resources

Linear mixed models: A practical guide using statistical software

Resource Type: Book

Resource Requirement: Prescribed

Author: West, B., Welch, K., and Galecki, A.

Year: 2015

Edition/Volume: 2nd ed

Publisher: Roca Raton, FL: CRC Press.

ISBN: N/A

Chapter/article title: N/A

Chapter/issue: N/A

URL: N/A

Other description: N/A

Source location: N/A

Career Ready

Career-focused: No

Work-based learning: No

Self sourced or Uni sourced: N/A

Entire subject or partial subject: N/A

Total hours/days required: N/A

Location of WBL activity (region): N/A

WBL addtional requirements: N/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.

Melbourne (Bundoora), 2020, Semester 1, Blended

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: David Farchione

Class requirements

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

Unscheduled Online ClassWeek: 10 - 22
One 2.00 hours 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 element	Comments	Category	Contribution	Hurdle	%	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/A	N/A	N/A	No	15	SILO2
Three written assignments, submitted online (1750 words total equiv)	N/A	N/A	N/A	No	35	SILO1, SILO2, SILO3, SILO4
3 hour Final Exam (3000 word equiv)	N/A	N/A	N/A	No	50	SILO1, SILO2, SILO3, SILO4