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


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




PublisherLa Trobe University


Chapter/article titleN/A



Other descriptionN/A

Source locationN/A

Career Ready


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. Present clear, well-structured arguments that validate the combination of effect sizes from several studies that may differ in several aspects, including with respect to moderators and data measurement scales.
02. Describe summary output from meta-analyses in a manner which can be understood by scientists with weak statistical backgrounds, but with knowledge from the discipline for which the research question is of interest.
03. Demonstrate an expert understanding of the use of statistical software packages by carrying out valid analyses of real meta-data and providing clear interpretations of all computer output.
04. Critique and summarise published meta-analyses, clearly highlighting weaknesses and flaws that undermine the key findings.

Subject options

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Start date between: and    Key dates

Melbourne (Bundoora), 2021, Semester 2, Blended


Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorLuke Prendergast

Class requirements

Computer LaboratoryWeek: 30 - 42
One 2.00 hours computer laboratory every two weeks on weekdays during the day from week 30 to week 42 and delivered via face-to-face.

Unscheduled Online ClassWeek: 30 - 42
One 2.00 hours unscheduled online class per week on any day including weekend during the day from week 30 to week 42 and delivered via online.
Approximately two hours per week of videos and readings.


Assessment elementCommentsCategoryContributionHurdle% 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.


Two written assignments submitted online (each 750-word equivalent)


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.


one 2-hr final examination