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Stochastic Actor-Oriented Models for the Co-Evolution of Networks and Behavior: An Introduction and Tutorial

机译:随机演员导向模型,用于网络和行为的共同演变:介绍和教程

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摘要

Stochastic actor-oriented (SAO) models are a family of models for network dynamics that enable researchers to test multiple, often competing explanations for network change and estimate the extent and relative power of various influences on network evolution. SAO models for the co-evolution of network ties and actor behavior, the most comprehensive category of SAO models, examine how networks and actor attributes-their behavior, performance, or attitudes-influence each other over time. While these models have been widely used in the social sciences, and particularly in educational settings, their use in organizational scholarship has been extremely limited. This paper provides a layperson introduction to SAO models for the co-evolution of networks and behavior and the types of research questions they can address. The models and their underpinnings are explained in nonmathematical terms, and theoretical explanations are supported by a concrete, detailed example that includes step-by-step model building and hypothesis testing, alongside an R script.
机译:型号的演员型号(SAO)模型是一个用于网络动态的模型系列,使研究人员能够测试多个,通常竞争的网络变化的竞争解释,并估计各种影响对网络演变的影响和相对权力。 SAO模型为网络领带和演员行为的共同演变,最全面的SAO模型类别,检查网络和演员属性如何 - 他们的行为,性能或态度相互影响。虽然这些模型已被广泛用于社会科学,但特别是在教育环境中,它们在组织奖学金中的使用非常有限。本文提供了对网络和行为共同演变的撒岛模型的Layperson介绍以及他们可以解决的研究问题类型。这些模型及其支撑在非测量术语中解释,并由具体示例支持理论解释,详细示例包括逐步模型构建和假设检测,以及R脚本。

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