![]() ![]() This tutorial is designed to facilitate the calculation and reporting of effect sizes for single variables within mixed-effects multiple regression models, and is relevant for analyses of repeated-measures or hierarchical/multilevel data that are common in experimental psychology, observational research, and clinical or intervention studies.Įffect sizes are an important complement to null hypothesis significance testing (e.g., p-values), in that they offer a measure of practical significance in terms of the magnitude of the effect, and are independent of sample size. ![]() ![]() Two examples of calculating Cohen’s f 2 for different research questions are shown, using data from a longitudinal cohort study of smoking development in adolescents. In this guide, we illustrate how to extract Cohen’s f 2 for two variables within a mixed-effects regression model using PROC MIXED in SAS ® software. Unfortunately, this measure is often not readily accessible from commonly used software for repeated-measures or hierarchical data analysis. One relatively uncommon, but very informative, standardized measure of effect size is Cohen’s f 2, which allows an evaluation of local effect size, i.e., one variable’s effect size within the context of a multivariate regression model. Reporting effect sizes in scientific articles is increasingly widespread and encouraged by journals however, choosing an effect size for analyses such as mixed-effects regression modeling and hierarchical linear modeling can be difficult. 2 Institute for Health Research and Policy, University of Illinois at Chicago, Chicago, IL, USA.1 Psychology Department, Wesleyan University, Middletown, CT, USA. ![]()
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