Correcting a Significance Test for Clustering

Larry V. Hedges
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Larry V. Hedges: Northwestern University

Journal of Educational and Behavioral Statistics, 2007, vol. 32, issue 2, 151-179

Abstract: A common mistake in analysis of cluster randomized trials is to ignore the effect of clustering and analyze the data as if each treatment group were a simple random sample. This typically leads to an overstatement of the precision of results and anticonservative conclusions about precision and statistical significance of treatment effects. This article gives a simple correction to the t statistic that would be computed if clustering were (incorrectly) ignored. The correction is a multiplicative factor depending on the total sample size, the cluster size, and the intraclass correlation Ï . The corrected t statistic has Student’s t distribution with reduced degrees of freedom. The corrected statistic reduces to the t statistic computed by ignoring clustering when Ï = 0. It reduces to the t statistic computed using cluster means when Ï = 1. If 0

Keywords: cluster-randomized trials; significance tests; intraclass correlations; multilevel models (search for similar items in EconPapers)
Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:32:y:2007:i:2:p:151-179

DOI: 10.3102/1076998606298040

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