Background: Cluster randomized experiments that assign intact groups such as schools or school districts to treatment conditions are increasingly common in educational research. Such experiments are inherently multilevel designs whose sensitivity (statistical power and precision of estimates) depends on the variance decomposition across levels. This variance decomposition is usually summarized by the intraclass correlation structure and, if covariates are used, the effectiveness of the covariates in explaining variation at each level of the design. Objectives: This paper provides a compilation of school and district level intraclass correlation values of academic achievement and related covariate effectiveness based on state longitudinal data systems. These values are designed to be used for planning group-randomized experiments in education. The use of these values to compute statistical power and plan 2 and 3 level group randomized experiments is illustrated. Research Design: We fit several hierarchical linear models to state data by grade and subject to estimate intraclass correlations and covariate effectiveness. We then compare our average of state estimates with the national work by Hedges and Hedberg (2007). [This article was published in "Educational Review," v37 n6 p445-489 Dec 2013, see EJ1034857.]