Team Composition Revisited: Expanding the Team Member Attribute Alignment Approach to Consider Patterns of More Than Two Attributes
扩展了团队构成的属性对齐方法，从两个属性扩展到三个及以上，提出了几何、物理和代数三种对齐方式，并用合成数据和实地数据比较了它们，为研究者选择合适方法提供了实用指南。
The attribute alignment approach to team composition allows researchers to assess variation in team member attributes, which occurs simultaneously within and across individual team members. This approach facilitates the development of theory testing the proposition that individual members are themselves complex systems comprised of multiple attributes and that the configuration of those attributes affects team-level processes and outcomes. Here, we expand this approach, originally developed for two attributes, by describing three ways researchers may capture the alignment of three or more team member attributes: (a) a geometric approach, (b) a physical approach accentuating ideal alignment, and (c) an algebraic approach accentuating the direction (as opposed to magnitude) of alignment. We also provide examples of the research questions each could answer and compare the methods empirically using a synthetic dataset assessing 100 teams of three to seven members across four attributes. Then, we provide a practical guide to selecting an appropriate method when considering team-member attribute patterns by answering several common questions regarding applying attribute alignment. Finally, we provide code ( https://github.com/kjem514/Attribute-Alignment-Code ) and apply this approach to a field data set in our appendices.