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) 2023 APA, all rights reserved).

中文翻译：

---

来自不可靠数据的可靠网络推理：使用 STRAND 进行潜在网络建模的教程。

社会网络分析为研究社会关系的起因、后果和结构提供了一个重要的框架。然而，标准的自我报告措施——例如，通过流行的“姓名生成器”方法收集——并不能公正地代表这种联系，无论是转移、互动还是社会关系。充其量，它们代表了通过受访者的认知偏见过滤掉的看法。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。造成此类报告不准确的倾向是个人级别和项目级别的特征变量，在任何给定组的成员之间都是可变的。过去的研究强调，许多网络级属性对此类报告不准确高度敏感。然而，仍然缺乏易于部署的统计工具来解决此类偏差。为了解决这个问题，我们提供了一个潜在的网络模型，允许研究人员共同估计测量报告偏差和潜在的潜在社交网络的参数。基于过去的研究，我们进行了几次模拟实验，其中网络数据受到各种报告偏差的影响，并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部署的网络重建方法（即，将双采样数据的并集或交集视为真实网络），这些影响没有得到充分补救，但通过使用我们的潜在问题得到了适当的解决网络模型。为了让最终用户更轻松地实施我们的模型，我们提供了一个完整记录的 R 包 STRAND，并包括一个教程，说明它在应用于来自哥伦比亚农村人口的经验食物/金钱共享数据时的功能。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。