The method of maximum likelihood provides a versatile way to estimate and conduct inference about moderators of effect size in meta-analytic models. The metafor package for the open-source statistical software R offers easy access to this method. We discuss inferential choices that the meta-analyst must make, and advocate the general choice of random-effects methods. We demonstrate the use of the metafor package using data from two meta-analyses that address group processes. These demonstrations illustrate two contrasting approaches to meta-analytic inference: a priori random-effects inference and conditionally random inference. The examples show that these approaches typically lead to the same conclusions when applied correctly.
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