Meta-analytic pooling of intraclass correlation coefficient estimates

Intraclass correlation coefficient (ICC) estimates are necessary for several statistical techniques. Researchers need accurate ICC estimates when conducting prospective power analyses for clustered data scenarios. In addition, meta-analysts require reasonable ICC values when adjusting effect size estimates to account for clustered primary study data or to correct for psychometric artifacts when using the ICC as a reliability measure. The validity of these analyses hinges on the accuracy of the ICC estimate. Beyond these secondary analyses, ICC estimates have been used as the focal outcome of meta-analysis itself to obtain a pooled measure of agreement, reliability, or the influence of a cluster's effect. This study evaluates how well meta-analytically pooled ICC estimates recover the population ICC parameter value when using different ICC variance formulas as the inverse variance weights used in the pooling. We found that the variance formula that uses a normalizing transformation performs best across most conditions.