轮廓
同种类的
度量(数据仓库)
计算机科学
心理学
星团(航天器)
数据挖掘
人工智能
数学
组合数学
程序设计语言
作者
Bertolt Meyer,Andreas Glenz
标识
DOI:10.1177/1094428113484970
摘要
Team faultlines—hypothetical dividing lines based on member attributes that split a team into relatively homogeneous subgroups—influence team processes across contexts, as recent meta-analytic findings show. We review the available faultline measures with regard to their properties and identify several limitations, including dealing with more than two subgroups. We thus propose a new cluster-based approach, average silhouette width (ASW), that identifies the number of subgroups and subgroup membership. We then compare the measures with 1,400 simulated teams with varying properties and investigate their factor structure and their behavior under missing values. We also investigate the predictive validity of the measures with data from real work teams. Results show that different measures respond to different team features in different ways but that most of them load on two correlated factors. Taken together, the ASW measure had the most favorable attributes and was the only measure that accurately determined subgroup membership in the presence of more than two subgroups. We discuss limitations and further research opportunities pertaining to faultline measures and provide software for calculating all investigated measures at http://www.group-faultlines.org .
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