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
Course Syllabus
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
2024
Instructors: Dr. Michael Borenstein
Instructors: Dr. Michael Borenstein
2025
Instructors: Dr. Michael Borenstein
Instructors: Dr. Michael Borenstein
Prerequisites
Familiarity with the issues of Sample Size and Power Determination is also helpful but not required.
The Statistics.com courses have helped me a lot, pushing me to the limit and making me learn much more than I expected I could. The knowledge I gained I could immediately leverage in my job … then eventually led to landing a job in my dream company – Amazon.
Karolis Urbonas
This program has been a life and work game changer for me. Within 2 weeks of taking this class, I was able to produce far more than I ever had before.
Susan Kamp
The material covered in the Analytics for Data Science Certificate will be indispensable in my work. I can’t wait to take other courses. Great work!
Stephen McAllister
I learned more in the past 6 weeks than I did taking a full semester of statistics in college, and 10 weeks of statistics in graduate school. Seriously.
Amir Aminimanizani
This is the best online course I have ever taken. Very well prepared. Covers a lot of real-life problems. Good job, thank you very much!
Elena Rose
The more courses I take at Statistics.com, the more appreciation I have for the smart approach, quality of instructors, assistants, admin and program. Well done!
Leonardo Nagata
This course greatly benefited me because I am interested in working in AI. It has given me solid foundational knowledge…After completing this last course, I feel I have gained valuable skills that will enhance my employability in Data Science, opening up diverse career opportunities.
Richard Jackson
Frequently Asked Questions
-
What is your satisfaction guarantee and how does it work?
-
Can I transfer or withdraw from a course?
-
Who are the instructors at Statistics.com?
Visit our knowledge base and learn more.
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
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.
Course Fee & Information
Enrollment
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.
Discounts
Academic affiliation? In most courses you are eligible for a discount at checkout.
New to Statistics.com? 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 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:
Chrome
- Color Enhancer (for colorblindness)
- HelperBird (for colorblindness, dyslexia, and reading difficulties)
Firefox
- Mobile Dyslexic
- Color Vision Simulation (native accessibility feature)
- Other native accessibility features instructions
Safari
- Navidys (for colorblindness, dyslexia, and reading difficulties)
- HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)