Classifying Correlation Matrices Into Relatively Homogeneous Subgroups: A Cluster Analytic Approach  

Oleh:

Cheung, Mike W.-L. ; Chan, Wai

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Educational and Psychological Measurement vol. 65 no. 06 (Dec. 2005), page 954-979.
Topik: meta-analysis; structural equation modeling; cluster analysis; moderator searching
Fulltext: 954.pdf (150.95KB)

Isi artikel

(PKPM) 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. Fewstudies 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.

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