You are here:

"Using Power Tables to Compute Statistical Power in Multilevel Experimental Designs"

Practical Assessment, Research & Evaluation Volume 14, Number 10, ISSN 1531-7714


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. (Contains 2 figures and 2 tables.)


Konstantopoulos, S. (2009). "Using Power Tables to Compute Statistical Power in Multilevel Experimental Designs". Practical Assessment, Research & Evaluation, 14(10),. Retrieved November 24, 2020 from .

This record was imported from ERIC on April 19, 2013. [Original Record]

ERIC is sponsored by the Institute of Education Sciences (IES) of the U.S. Department of Education.

Copyright for this record is held by the content creator. For more details see ERIC's copyright policy.