Despite the widespread use of repeated-measures data and hierarchical or multilevel data in experimental, observational, and clinical research, reporting effect sizes presents challenges to researchers unfamiliar with this topic. These challenges can start with the initial process of choosing which measure out of many is most appropriate for the data, the analysis, and the research question. Though effect size measures appropriate for repeated-measures or multilevel data have already been proposed (Cohen, 1988; Nakagawa and Cuthill, 2007; Peugh, 2010) and can be directly or indirectly obtained through common statistical software packages, existing sources rarely provide comprehensive information about these processes that are accessible to naïve researchers. This article presents a practical guide to calculating Cohen’s f 2, an effect size measure for a single variable within a multivariate, mixed-effects regression model, from the output of PROC MIXED in SAS software. This guide is intended for researchers in a wide variety of scientific fields who are unfamiliar with effect sizes, and aims to facilitate effect size reporting in analyses that are commonly performed, yet too complex for standard measures of effect size.
