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In statistics, this “idea” translates to a model, and we will have a look at what the meta-analytic model looks like.Īs we will see, the nature of the meta-analysis requires us to make a fundamental decision right away: we have to assume either a fixed-effect model or a random-effects model. Importantly, we will also discuss the “idea” behind meta-analyses. As we previously mentioned, meta-analysis comes with many “researcher degrees of freedom.” There are a myriad of choices concerning the statistical techniques and approaches we can apply, and if one method is better than the other often depends on the context.īefore we begin with our analyses in R, we therefore have to get a basic understanding of the statistical assumptions of meta-analyses, and the maths behind it.
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Here, we will focus on functions of the package allows us to tweak many details about the way effect sizes are pooled. There are many packages which allow us to pool effect sizes in R. We can assure you that the time you spent working through the previous chapters was well invested. Thorough preparation is a key ingredient of a good meta-analysis, and will be immensely helpful in the steps that are about to follow.
Comprehensive meta analysis means in one group how to#
We have already discussed various topics in this book, including the definition of research questions, guidelines for searching, selecting, and extracting study data, as well as how to prepare our effect sizes. We hope that you were able to resist the temptation of starting directly with this chapter. Fortunately, we have now reached the core part of every meta-analysis: the pooling of effect sizes. A long and winding road already lies behind us.