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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.


"We perform a variety of meta‐analyses for academic, regulatory, and international clients. Each presents a different set of challenges regarding study design and outcome measurement. We have found CMA to be invaluable in this work. The ability of the software to capture a variety of data elements (study design, multiple outcomes, covariates/confounders) and present details of computations is important in the credibility of our work. The ease of use and ability to produce graphics in a variety of formats aids in preparation of the report. In many instances, we are required to replicate the results of CMA in another package (e.g., SAS). We have always found the support staff at CMA very helpful in these replications and the results of CMA have been replicated in every instance. CMA is a great tool in the scientific credibility of our meta‐analytic studies."

Donna F. Stroup, PhD, MSc - Data for Solutions, Inc.


"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