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Comprehensive Meta-Analysis

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Comprehensive Meta-Analysis (CMA) is a powerful computer program for meta-analysis. The program combines ease of use with a wide array of computational options and sophisticated graphics.


"My experience with Comprehensive Meta‐analysis is highly positive. The spreadsheet interfaces for data entry are very intuitive. They handle an astonishing wide array of indices and formats for effect sizes, including the possibility of entering them in the spreadsheet by copy‐and‐paste. The exploratory phase is easily performed thanks to a variety of facilities, as cumulative meta‐analysis, sensitivity analysis by the “one‐study‐removed” procedure, and subgroup analysis, among others. It covers all basic fixed and random effect models and provides high‐quality graphical outputs for reports. The tutorial and the manual are really helpful and friendly. In short, Comprehensive Meta‐analysis is my favorite tool both for performing and teaching meta‐analysis."

Professor Juan Botella - Autonomous University of Madrid


"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