Social influence occurs when an individual’s outcome is affected by another individual’s actions. Current approaches in psychology that seek to examine social influence have focused on settings where individuals are nested in predefined groups and do not interact across groups. Such study designs permit using standard estimation methods such as multilevel models for estimating treatment effects but restrict social influence to originate only from individuals within the same group. In more general settings, such as social networks where an individual is free to interact with any other individual, the absence of discernible clusters or scientifically meaningful groups precludes existing estimation methods. In this article, we introduce a new class of methods for assessing social influence in social networks in the context of randomized experiments in psychology. Our proposal builds on the potential outcomes framework from the causal inference literature. In particular, we exploit the concept of (treatment) interference, which occurs between individuals when one individual’s outcome is affected by other individuals’ treatments. Estimation proceeds using randomization-based approaches that are established in other disciplines and guarantee valid inference by construction. We compared the proposed methods with standard methods empirically using Monte Carlo simulation studies. We illustrated the method using publicly available data from an experiment assessing the effects of an anticonflict intervention among students’ peer networks. The R scripts used to implement the proposed methods in the simulation studies and the applied example are freely available online. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

中文翻译：

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使用潜在结果估计社交网络中的社会影响力。

当一个人的结果受到另一个人的行为影响时，就会产生社会影响。当前寻求检验社会影响的心理学方法侧重于个人嵌套在预定义群体中并且不跨群体互动的环境。此类研究设计允许使用标准估计方法（例如多级模型）来估计治疗效果，但将社会影响限制为仅来自同一组内的个体。在更一般的环境中，例如个人可以自由与任何其他个人互动的社交网络，缺乏可辨别的集群或具有科学意义的群体排除了现有的估计方法。在本文中，我们介绍了一类新的方法，用于在心理学随机实验的背景下评估社交网络中的社会影响。我们的建议建立在因果推理文献中的潜在结果框架之上。特别是，我们利用了（治疗）干扰的概念，当一个人的结果受到其他人的治疗影响时，这种情况就会发生在个体之间。估计使用其他学科中建立的基于随机化的方法进行，并保证通过构造进行有效推理。我们使用蒙特卡洛模拟研究根据经验将提出的方法与标准方法进行了比较。我们使用来自一项实验的公开可用数据来说明该方法，该实验评估了学生同伴网络中反冲突干预的效果。用于在模拟研究和应用示例中实施所提出方法的 R 脚本可在线免费获得。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）