Intraclass Correlations and Covariate Outcome Correlations for Planning Two- and Three-Level Cluster-Randomized Experiments in Education

Larry V. Hedges and Eric Hedberg ()

Evaluation Review, 2013, vol. 37, issue 6, 445-489

Abstract: 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 (ICC) structure and, if covariates are used, the effectiveness of the covariates in explaining variation at each level of the design. Objectives: This article provides a compilation of school- and district-level ICC 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 two- and three-level group-randomized experiments is illustrated. Research Design: We fit several hierarchical linear models to state data by grade and subject to estimate ICCs and covariate effectiveness. The total sample size is over 4.8 million students. We then compare our average of state estimates with the national work by Hedges and Hedberg.

Keywords: education; content area; methodological development; content area (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:evarev:v:37:y:2013:i:6:p:445-489

DOI: 10.1177/0193841X14529126

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