"One of the hardest things for non‐statisticians conducting meta‐analyses is to figure out how to combine data when the data are in different forms. Using continuous outcome as an example, one study might report before‐and‐after scores, and another might report change scores. Comprehensive Meta‐Analysis allows one to take data in any form and seamlessly converts it so that all the data can be included, or tells the meta‐analyst what additional information is necessary to complete the process. This one aspect of the program can save hours of time for non‐statisticians who are not used to converting data from one format to another."

Ian Shrier - McGill University, Canada

"The program is a perfect companion to Borenstein et al's book since it allowed my students to try the concepts discussed in the book. We have done most of the computations by hand first and then checked our answers with CMA. This gave the students 'a feel' for meta‐analysis and made them realise that the method is not just about feeding some abstract numbers into a black box and getting a meaningless number at the end. Instead, using the book and the program together the students learned the maths behind the computations and the meaning of the final results. I found the help manual especially user‐friendly and ready for classroom use. My students were able to get most of the exercises done at home such that we had the time to discuss the answers and their implications in class."

Dr. Karina De Santis (PhD) - Lecturer in Statistics and Research Methods, Jacobs University, Bremen gGmbH School of Humanities and Social Sciences, Bremen, Germany