Redhead Daniel, McElreath Richard, Ross Cody T
Department of Human Behavior, Ecology and Culture, Max Planck Institute for Evolutionary Anthropology.
Psychol Methods. 2024 Dec;29(6):1100-1122. doi: 10.1037/met0000519. Epub 2023 Mar 6.
Social network analysis provides an important framework for studying the causes, consequences, and structure of social ties. However, standard self-report measures-for example, as collected through the popular "name-generator" method-do not provide an impartial representation of such ties, be they transfers, interactions, or social relationships. At best, they represent perceptions filtered through the cognitive biases of respondents. Individuals may, for example, report transfers that did not really occur, or forget to mention transfers that really did. The propensity to make such reporting inaccuracies is both an individual-level and item-level characteristic-variable across members of any given group. Past research has highlighted that many network-level properties are highly sensitive to such reporting inaccuracies. However, there remains a dearth of easily deployed statistical tools that account for such biases. To address this issue, we provide a latent network model that allows researchers to jointly estimate parameters measuring both reporting biases and a latent, underlying social network. Building upon past research, we conduct several simulation experiments in which network data are subject to various reporting biases, and find that these reporting biases strongly impact fundamental network properties. These impacts are not adequately remedied using the most frequently deployed approaches for network reconstruction in the social sciences (i.e., treating either the union or the intersection of double-sampled data as the true network), but are appropriately resolved through the use of our latent network models. To make implementation of our models easier for end-users, we provide a fully documented R package, STRAND, and include a tutorial illustrating its functionality when applied to empirical food/money sharing data from a rural Colombian population. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
社会网络分析为研究社会关系的成因、后果和结构提供了一个重要框架。然而，标准的自我报告测量方法——例如，通过流行的“提名生成法”收集的数据——并不能公正地呈现这些关系，无论它们是转移、互动还是社会关系。充其量，它们代表的是经过受访者认知偏差过滤后的认知。例如，个人可能会报告并未真正发生的转移，或者忘记提及确实发生的转移。出现这种报告不准确情况的倾向既是个体层面的特征，也是项目层面的特征，在任何给定群体的成员中都是可变的。过去的研究强调，许多网络层面的属性对这种报告不准确情况非常敏感。然而，仍然缺乏易于部署的统计工具来考虑此类偏差。为了解决这个问题，我们提供了一个潜在网络模型，使研究人员能够联合估计测量报告偏差和潜在基础社会网络的参数。基于过去的研究，我们进行了几个模拟实验，其中网络数据受到各种报告偏差的影响，发现这些报告偏差强烈影响基本网络属性。使用社会科学中最常用的网络重建方法（即，将双重抽样数据的并集或交集视为真实网络）并不能充分纠正这些影响，但通过使用我们的潜在网络模型可以适当地解决这些问题。为了让终端用户更轻松地实现我们的模型，我们提供了一个有完整文档的R包STRAND，并包含一个教程，说明其应用于来自哥伦比亚农村人口的经验性食物/金钱共享数据时的功能。（PsycInfo数据库记录（c）2024美国心理学会，保留所有权利）
