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Educational and Psychological Measurement
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Classifying Correlation Matrices Into Relatively Homogeneous Subgroups: A Cluster Analytic Approach

Mike W.-L. Cheung

University of Hong Kong, mikewlcheung{at}hku.hk

Wai Chan

Chinese University of Hong Kong

Researchers are becoming interested in combining meta-analytic techniques and structural equation modeling to test theoretical models from a pool of studies. Most existing procedures are based on the assumption that all correlation matrices are homogeneous. Few studies have addressed what the next step should be when studies being analyzed are heterogeneous and the search for moderator variables for homogeneous subgroup analysis fails. Cluster analysis is proposed and evaluated in this article as an exploratory tool to classify studies into relatively homogeneous groups. Simulation studies indicate that using Euclidean distance on raw correlation coefficients or U-transformed scores with the complete linkage or Ward’s minimum-variance methods will provide satisfactory results.

Key Words: meta-analysis • structural equation modeling • cluster analysis • moderator searching

Educational and Psychological Measurement, Vol. 65, No. 6, 954-979 (2005)
DOI: 10.1177/0013164404273946
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Home page
 Journal of Cross-Cultural PsychologyHome page
M. W.-L. Cheung, K. Leung, and K. Au
Evaluating Multilevel Models in Cross-Cultural Research: An Illustration With Social Axioms
Journal of Cross-Cultural Psychology, September 1, 2006; 37(5): 522 - 541.
[Abstract] [PDF]