sta5ma meta analysis
META ANALYSIS
STA5MA
2020
Credit points: 15
Subject outline
The literature abounds with findings that collectively may offer important new insights for the betterment of the medical, psychological and life sciences, to name just a few. This subject is designed to provide students with the ability to combine estimated measures of evidence, known as effects, from comparable studies to increase power. Estimators are introduced which are commonly found in meta-analytic research and pitfalls are discussed. On completion of the subject, the student will have an understanding of the different effects that can be collected from the literature as well as an appreciation of how effect sizes arising from data measured on different scales can be combined. Importantly, this subject also shows students how meta-regression can be used to account for study-specific covariates that cannot be adequately accounted for using random-effects models. The freely available software packages R and RevMan are used throughout the subject.
SchoolEngineering and Mathematical Sciences
Credit points15
Subject Co-ordinatorLuke Prendergast
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
Learning resources
Online learning materials
Resource TypeWeb resource
Resource RequirementPrescribed
AuthorPrendergast
Year2017
Edition/VolumeN/A
PublisherLa Trobe University
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
Subject options
Select to view your study options…
Melbourne (Bundoora), 2020, Semester 2, Blended
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorLuke Prendergast
Class requirements
Computer LaboratoryWeek: 31 - 43
One 2.00 hours computer laboratory every two weeks on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
Unscheduled Online ClassWeek: 31 - 43
One 2.00 hours unscheduled online class per week on any day including weekend from week 31 to week 43 and delivered via online.
Approximately two hours per week of videos and readings.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
| Online quizzes (750-word equivalent) There are 5 short quizzes throughout the semester. Students can attempt each quiz a maximum of three times and the best mark for that quiz taken. Randomly assigned questions for each quiz instance. | N/A | N/A | No | 10 | SILO2, SILO3 |
| Two written assignments submitted online (each 750-word equivalent) | N/A | N/A | No | 20 | SILO1, SILO2, SILO3 |
| Written Project (2250-word equivalent) Students must choose a recently published meta-analysis for appraisal/critique. The chosen paper must be pre-approved by the Subject Coordinator. Project must include replication of the presented results which will form part of the assessment. | N/A | N/A | No | 20 | SILO2, SILO3, SILO4 |
| One 2-hour final examination | N/A | N/A | No | 50 | SILO1, SILO2, SILO4 |