CHAPTER 13 - Clustering Estimates of Effect Magnitude

Abstract

This chapter discusses two methods for grouping estimates of effect magnitude into homogeneous classes proposed by Hedges and Olkin. One procedure decomposes a set of effect magnitude indices into disjoint or nonoverlapping classes, whereas in another procedure, the decomposition is into overlapping groups. In either case, methods are provided for determining statistical significance levels of the clusters. Both procedures are based on clustering theory for standard normal random variables. However, correlations and standardized mean differences are generally not normally distributed except in large samples. The chapter highlights the use of large sample theory for the transformation of the estimators to approximately standard normal variates. It explains the clustering procedures for arbitrary unit normal variates and discusses the application of those methods to correlations and effect sizes.