Abstract The validity of survey-based reports of social relationships is a critical assumption for much social network research. Research on informant accuracy has shown that observational data and recalled behavior by informants are imperfectly correlated, which calls into question whether complex relations like friendship and advice-seeking can be accurately measured from individual reports. A class of network inference models, the Bayesian Network Accuracy Models, growing out of the pioneering work of Batchelder and Romney on inference from informant reports, provides a principled basis for inferring network structure given such error-prone data. Using these models, we can gain insight into the accuracy of informants’ self and proxy reports of social ties, and more broadly, the reliability and validity of respondents’ reports of informal social relations. While existing data does not provide a criterion validity check for inferring most relationships, other notions of validity and/or reliability can still be applied. For instance, if friendship reports are generated from a common underlying network that is perceivable (albeit imperfectly) by all actors, then random subsets of actors should produce estimates that should agree (i.e., split-half reliability). Using informant reports on friendship and advice-seeking networks from four different organizations, we show substantially higher levels of split-half reliability than can be explained by chance, suggesting that models are indeed estimating a common underlying relation. We also show that informants’ errors appear to be structured in ways that are consistent with cognitive models of social perception, with greater accuracy on average for large-scale network features rather than fine details, for own versus others’ ties, and for core–periphery structures versus bipartitions. Evidence from construct validity checks further suggests the that common networks underlying informants’ reports have properties that would be expected of true social structures. Taken together, our findings support the view that informants’ mental models of social structure, while error-prone, nevertheless reflect an underlying social reality.

中文翻译：

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关于感知社会结构的有效性

摘要 基于调查的社会关系报告的有效性是许多社会网络研究的关键假设。对线人准确性的研究表明，线人的观察数据和回忆行为不完全相关，这使得人们质疑是否可以从个人报告中准确衡量友谊和寻求建议等复杂关系。一类网络推理模型，即贝叶斯网络精度模型，源于 Batchelder 和 Romney 对线人报告推理的开创性工作，为在给定此类容易出错的数据的情况下推断网络结构提供了原则基础。使用这些模型，我们可以深入了解信息提供者的社会关系自我报告和代理报告的准确性，更广泛地说，受访者关于非正式社会关系的报告的信度和效度。虽然现有数据不提供用于推断大多数关系的标准有效性检查，但仍然可以应用其他有效性和/或可靠性概念。例如，如果友谊报告是从所有参与者都可以感知（尽管不完美）的公共基础网络生成的，那么参与者的随机子集应该产生应该一致的估计（即，分半可靠性）。使用来自四个不同组织的友谊和咨询网络的线人报告，我们显示出比偶然可以解释的更高水平的分半可靠性，这表明模型确实在估计一个共同的潜在关系。我们还表明，线人的错误似乎以与社会感知认知模型一致的方式构建，平均而言，对于大规模网络特征而不是精细细节，对于自己与他人的关系以及核心 -外围结构与双分区。结构有效性检查的证据进一步表明，作为知情人报告的基础的共同网络具有真实社会结构所预期的特性。综上所述，我们的研究结果支持这样一种观点，即信息提供者的社会结构心智模型虽然容易出错，但却反映了潜在的社会现实。以及核心 - 外围结构与双分区。结构有效性检查的证据进一步表明，作为知情人报告的基础的共同网络具有真实社会结构所预期的特性。综上所述，我们的研究结果支持这样一种观点，即信息提供者的社会结构心智模型虽然容易出错，但却反映了潜在的社会现实。以及核心 - 外围结构与双分区。结构有效性检查的证据进一步表明，作为知情人报告的基础的共同网络具有真实社会结构所预期的特性。综上所述，我们的研究结果支持这样一种观点，即信息提供者的社会结构心智模型虽然容易出错，但却反映了潜在的社会现实。