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.
School: Engineering and Mathematical Sciences (Pre 2022)
Credit points: 15
Subject Co-ordinator: Luke Prendergast
Available to Study Abroad/Exchange Students: Yes
Subject year level: Year Level 5 - Masters
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
Online learning materials
Resource Type: Web resource
Resource Requirement: Prescribed
Author: Prendergast
Year: 2017
Edition/Volume: N/A
Publisher: La Trobe University
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
Melbourne (Bundoora), 2020, Semester 2, Blended
Overview
Online enrolment: Yes
Maximum enrolment size: N/A
Subject Instance Co-ordinator: Luke 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 |