Meta Analysis 1
This course will explain meta analysis and the methods that are used to assess multiple statistical studies on the same subject and draw conclusions.
Overview
Meta-Analysis refers to statistical analyses that are used to synthesize summary data from a series of studies. In this course we will discuss the logic of meta-analysis and the way that it is being used in many fields, including medicine, education, social science, ecology, business, and others. We will also look at various controversies in meta-analysis (such as questions of mixing apples and oranges and “garbage-in-garbage-out”), and draw on recent headline-making analyses to understand real-world examples.
- Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
Students who complete this course will learn how to conduct a meta-analysis (how to compute an effect size, compute summary effects, assess heterogeneity of effects, test for differences in effect size across subgroups, and more). Participants will get hands-on experience performing analyses using Excel and Comprehensive Meta-Analysis (CMA). All participants will have free access to CMA for the duration of the course. At the conclusion of the course, students should feel comfortable using software to conduct a meta-analysis from start to finish.
- Describe the role of meta-analysis in research and setting policy
- Compute treatment effects for various types of data
- Assess and deal with heterogeneity among effect sizes
- Distinguish between and fit both fixed and random effects models
- Identify situations involving multiple subgroups and multiple outcomes
Who Should Take This Course
Researchers who plan to perform a meta-analysis, or who want to be able understand meta-analyses that have been published by others.
Our Instructors
Dr. Michael Borenstein
Dr. Hannah Rothstein
Course Syllabus
Week 0
- Readings in the course text (see “Requirements” section)
- Discussion forum with instructor
- Homework (see below)
- End of course data modeling project (see below)
- Short narrated software demos
- Supplemental readings available online
- Supplemental – Archive of prior course discussions
Week 1
Computing the Overall Effect in Meta Analysis
- What is meta analysis
- Meta analysis in various fields
- Meta analysis in medicine: Saving heart attack patients
- Meta analysis in education: Some examples
- Meta analysis in criminal justice: The “Scared Straight” jail program
- The role of meta analysis
- In planning research
- In setting policy
- Organizations for evidence-based policy
- The Cochrane Collaboration (medicine)
- The Campbell Collaboration (social science)
- Computing a treatment effect
- Focusing on treatment effects rather than p-values
- From binary data
- From continuous data
- From correlational data
- Computing an overall effect
- Weighted means
- Basic statistics
- Forest plots
- Basic issues
Week 2
Fixed vs. Random Effects in Meta Analysis
- Heterogeneity among effect sizes
- Assessing heterogeneity
- Fixed effect vs. random effects models
- Conceptual differences between these models
- Computational formulas for these models
Week 3
Differences in Treatment Effects in Meta Analysis
- Understanding differences in treatment effects
- Moderator variables
- Analysis of variance
- Meta regression
- Forest Plot
- Advanced issues
Week 4
Publication Bias and other Issues in Meta Analysis
- Publication bias
- Funnel plots
- Multiple subgroups within studies
- Multiple outcomes within studies
- Common criticisms of meta analysis
- Apples and oranges
- Garbage in, garbage out
- Discrepancies between randomized trials and meta analyses
Class Dates
2022
Instructors:
Instructors:
2023
Instructors:
Instructors:
Prerequisites
Familiarity with the issues of Sample Size and Power Determination is also helpful but not required.
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Additional Information
Homework
Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software and end of course data modeling project.
In addition to assigned readings, this course also has an end of course data modeling project, short narrated software demos, and supplemental readings available online.
Course Text
The required text for this course is Introduction to Meta-Analysis, by Borenstein, Hedges and Higgins.
Software
Class illustrations will be provided in the software program Comprehensive Meta Analysis. Please be aware that this software program is for Windows only, and will not run on a Mac OS platform.
Supplemental Information
Literacy, Accessibility, and Dyslexia
At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:
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