Meta Analysis

taught by Hannah Rothstein and Michael Borenstein

Aim of Course:

Meta-Analysis refers to the statistical analyses that are used to synthesize summary data from a series of studies. If the effect size (or treatment effect) is consistent across all the studies in the synthesis, then the meta-analysis yields a combined effect that is more precise than any of the separate estimates, and also allows us to conclude that the effect is robust across the kinds of studies sampled. By contrast, if the effect size (or treatment effect) varies from one study to the next, the meta-analysis may allow us to identify the reason for the variation and report (for example) that the treatment is more effective in a particular kind of patient, or in a particular dose range.

In this online course, "Meta Analysis" 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. Participants 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). We will also discuss various controversies in meta-analysis (such as the question of mixing apples and oranges, the criticism of garbage-in-garbage-out). We will also draw on recent headline-making analyses such as the Avandia meta-analysis.

Participants will get hands-on experience in performing analyses using Excel(tm) and also using Comprehensive Meta-Analysis (CMA). All participants will have access to a free trial of CMA for the duration of the course. At the conclusion of the course, all participants should feel comfortable conducting a meta-analysis from start to finish using this or other software.

This course may be taken individually (one-off) or as part of a certificate program.
Course Program:
  • 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


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.

Meta Analysis

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.

You should be familiar with introductory statistics.  Try these self tests to check your knowledge.

Familiarity with the issues of Sample Size and Power Determination (another course) is also helpful.
Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement:
About 15 hours per week, at times of  your choosing.

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.
  4. Digital Badge - Courses evaluated by the American Council on Education have a digital badge available for successful completion of the course.  
  5. Other options - Specializations, INFORMS CAP recognition, and academic (college) credit are available for some courses
Course Text:

The required text for this course is Introduction to Meta-Analysis, by Borenstein, Hedges and Higgins.


Class illustrations will be provided in the software program Comprehensive Meta Analysis (click here for details on how to get access to a special version). Please be aware that this software program is for Windows only, and will not run on Macs.


July 17, 2020 to August 14, 2020 November 06, 2020 to December 04, 2020

Meta Analysis


July 17, 2020 to August 14, 2020 November 06, 2020 to December 04, 2020

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Email jdobbins "at" to get information on group rates. 

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