"I'm a graduate student at NC State University, and recently I tried out CMA for my lab. For the record, it is awesome. My lab, Meta World, is specifically focused on meta‐analysis, so we've used a lot of programs both good and bad. CMA is by far the best we've tried in terms of ease of use. It is simple enough for a first time meta‐analyst to use, but the best part is the diversity of options for the more advanced user. It is amazingly easy to switch effects models and other aspects, to the point that it's fun to fiddle around with things to see the difference they make. The options for output are everything you'd need for publication, and they look a good sight better than what I would have come up with without CMA. It is the only software that had all the functionality I needed for my thesis, and without it I would be stuck writing my own code."

Jennifer London - Doctoral Student in I/O Psychology, NC State University

"Given that publications report a wide range of values from analyses (e.g., means and standard deviations, r, F, t values, eta squared, partial eta squared, etc.), it can be extremely difficult to compute effect sizes that take each of these factors into consideration. This can make the process of a metaanalysis more time consuming that it necessarily has to be. I found one useful and time‐saving aspect of Comprehensive Meta‐Analysis is that it allowed me to enter effect size data from articles in a number of formats. Upon running the analysis, the programme would compute standardised effect sizes for each study (even though I might have used around 10 different types of data entry), as well as an overall effect size. Furthermore, even though I had over 50 moderators to assess, CMA made it simple to test each moderator, whilst offering the option to test moderators according to other specific study characteristics. This meant I could delve deeper into my data to see what was really going on. For these more sophisticated methods, the programme also reports the information required to compute additional statistics, such as tau squared within and between studies (enabling me to compute the R squared statistic), which are not provided by some other programmes but are commonly reported in published meta‐analyses."

Natalie Taylor, PhD - Researcher, Health and Social Psychology Group, Institute of Psychological Sciences, University of Leeds, Leeds