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Classifying correlation matrices into relatively homogeneous subgroups: A cluster analytic approach

机译：将相关矩阵分为相对均一的子组：一种聚类分析方法

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摘要

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.
机译：研究人员对将元分析技术和结构方程建模相结合以从一组研究中测试理论模型感兴趣。现有的大多数程序都基于所有相关矩阵都是齐次的假设。很少有研究解决当被分析的研究是异类的并且对于均质亚组分析的主持人变量搜索失败时下一步应该做什么。本文提出并进行聚类分析作为一种探索性工具，用于将研究分类为相对同类的组。仿真研究表明，将欧几里德距离用于原始相关系数或具有完整链接或Ward最小方差方法的U转换得分将提供令人满意的结果。

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