Using Power Tables to Compute Statistical Power in Multilevel Experimental Designs

Power computations for one-level experimental designs that assume simple random samples are greatly facilitated by power tables such as those presented in Cohen’s book about statistical power analysis. However, in education and the social sciences experimental designs have naturally nested structures and multilevel models are needed to compute the power of the test of the treatment effect correctly. Such power computations may require some programming and special routines of statistical software. Alternatively, one can use the typical power tables to compute power in nested designs. This paper provides simple formulae that define expected effect sizes and sample sizes needed to compute power in nested designs using the typical power tables. Simple examples are presented to demonstrate the usefulness of the formulae. Accessed 18,608 times on https://pareonline.net from May 20, 2009 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right