论文标题
二元互惠与协变量的函数
Dyadic Reciprocity as a Function of Covariates
论文作者
论文摘要
二元相互作用中的互惠很普遍，并且是跨学科的感兴趣的话题。在某些情况下，在某些类型的二元组中，互惠可能会或多或少地普遍存在。为了回应研究人员对估计二元互惠的兴趣，这是协变量的函数，本文提出了扩展到多层社会关系模型的扩展。结果变量被认为是二项式比例，正如观察性和档案研究中通常遇到的那样。该方法借鉴了多层次建模的原理，以实现二元组之间不同的随机拦截和斜率。相应的方差函数允许计算二元相互相关性。建模方法可能可能与社交网络分析领域的其他统计模型集成在一起。
Reciprocity in dyadic interactions is common and a topic of interest across disciplines. In some cases, reciprocity may be expected to be more or less prevalent among certain kinds of dyads. In response to interest among researchers in estimating dyadic reciprocity as a function of covariates, this paper proposes an extension to the multilevel Social Relations Model. The outcome variable is assumed to be a binomial proportion, as is commonly encountered in observational and archival research. The approach draws on principles of multilevel modeling to implement random intercepts and slopes that vary among dyads. The corresponding variance function permits the computation of a dyadic reciprocity correlation. The modeling approach can potentially be integrated with other statistical models in the field of social network analysis.
