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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.


"I have been using Comprehensive Meta‐Analysis for the past five years and have enjoyed the many features of this software including the numerous options for fitting data into the spreadsheet for analysis of categorical and continuous data and the ease of labeling variables and study names. The built in analysis features are many and easy to apply including the test for heterogeneity and Egger’s test for publication bias. This software has allowed me to publish meta‐analyses with confidence in several peer reviewed journals including the Journal of Cardiology. Also I am often asked to give lectures on the use of meta‐analyses. My demonstrations include examples from this program. When asked by my peers for a reliable meta‐analysis package I do recommend this software."

Al Bartolucci, Ph.D. - Professor, Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama


"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