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Meta Analysis 1

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.


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. Michael Borenstein

Dr. Michael Borenstein is the Director of Biostat, a company that develops statistical software. Dr. Borenstein is the primary developer of Power And Precision, a computer program for sample size calculation, and of Comprehensive Meta Analysis, a computer program for meta analysis and systematic reviews. He served as Director of Biostatistics at Long Island Jewish Medical Center (1982-2002) and as Associate Professor at Albert Einstein College of Medicine (1992-2002). Dr. Borenstein has served on a number of review groups and advisory panels for the National Institutes of Health, including SBIR review groups (1994-2002) and as a member of the NIMH Data Safety Monitoring Board (1997-2001). He is an active member of the statistical advisory groups of the Cochrane and Campbell Collaborations.

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


07/19/2024 to 08/16/2024
Instructors: Dr. Michael Borenstein
11/15/2024 to 12/13/2024
Instructors: Dr. Michael Borenstein


07/18/2025 to 09/19/2025
Instructors: Dr. Michael Borenstein
11/21/2025 to 12/19/2025
Instructors: Dr. Michael Borenstein


Familiarity with the issues of Sample Size and Power Determination is also helpful but not required.

Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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Meta Analysis 1

Additional Information

Organization of 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 Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.


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.


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.

Course Fee & Information

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to  Click here for a special introductory discount code.

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, 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:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)


There is no additional information for this course.

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Meta Analysis 1