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 About the Instructor
Dr. Michael Borenstein, one of the authors of Introduction to Meta-Analysis, is widely recognized for his ability to make statistical concepts accessible to researchers as well as to statisticians. He has lectured widely on meta-analysis, including at the NIH, CDC, and FDA.
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Who Should Attend
This workshop is intended for researchers, clinicians, educators, statisticians, graduate students, and anyone with an interest in understanding or performing meta-analyses. Our approach is primarily conceptual rather than mathematical, and participants with only one or two statistics classes in their background should be able to fully understand the materials.
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Software Testimonials
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Comprehensive Meta-Analysis (CMA) is one of those rare tools in the research arsenal that does what it is purports to and makes both the process and output understandable. I have used CMA for seven years in my own meta-analytic work and in EBP workshops and courses. The ease of learning the program application and the resulting output make for a wonderful data analysis and learning tool.
Chad Nye, PhD, Executive Director, University of Central Florida, Center for Autism & Related Disabilities, Orlando, FL
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Common Mistakes in Meta-Analysis and How to Avoid Them
Researchers often use the I2 index to quantify the dispersion of effect sizes in a meta-analysis. Some suggest that I2 values of 25%, 50%, and 75%, correspond to small, moderate, and large amounts of heterogeneity...
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About the Workshop
The workshop is based on the books shown here. Learn to understand a meta-analysis, to perform a meta-analysis, and to avoid common mistakes in meta-analysis. The lectures will focus primarily on concepts and practical applications rather than formulas. You will learn to understand a published meta-analysis. You will also learn how to perform simple meta-analyses and more advanced analyses using the software Comprehensive Meta-Analysis.
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Online Workshop Testimonial
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As a professor of statistics, I was unsure whether to take the initial online course in meta-analysis, thinking that it might either be too dense and jargon-filled to be interesting (given Dr. Borenstein’s impressive credentials and knowledge base) or too basic (given that I was already teaching a course). As I finish the series now, however, I am awestruck by the quality of this offering. Dr. Borenstein is quite simply a master teacher, so he offers complex material in a completely comprehensible form, the best of both worlds. I picked up details on the statistics, the use of CMA, and the theory behind the statistics, but I also (hopefully) will model the style and clarity of his teaching style in my own work in the future. I look forward to the intermediate and advanced sessions.
Constance J. Dalenberg, PhD, Alliant International University
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