מטא-אנליזה מקיפה

הורד גרסת ניסיון ל-10 ימים

"I've been using Comprehensive Meta‐Analysis (CMA) for about five years now and have found it to be the most user‐friendly program for conducting meta‐analyses. CMA allows researchers to conduct meta‐analyses on a wide array of data sets. Further, CMA includes an array of some of the most sophisticated publication bias analyses, allowing researchers to examine an issue that is too often overlooked in meta‐analysis. I would highly recommend CMA to any researcher conducting metaanalyses."

Christopher J Ferguson - Associate Professor Department of Behavioral Sciences, Texas A&M International University

"I have the pleasure to use your software from 2004, and thanks to that, my group had the opportunity to explore many issues in clinical oncology. I am a doctor (not a statistician) and I have to say that the software is really simple (especially v. 2.0), fast‐to‐use, and really easy to understand for anyone who wants to use it. We currently prefer CMA 2.0 to other free software given the mentioned features. Actually, I have no drawbacks to stress, I am really friendly in using your software right now."

Emilio Bria

"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