AI 中文总结
本文推广社会关系模型（SRM），结合双重抽样与多层次结构方程模型，用MCMC方法估计，推导跨集群网络，以尼加拉瓜农村社区社会支持网络数据验证。
AI 中文摘要
我们考虑分析不同集群内个体间关联的二元网络数据，其中定向关联的存在由二元组中的每个个体报告，且集群层面的网络是研究关注对象。本文提出了社会关系模型（Social Relations Model, SRM）的一种推广形式，该模型在个体和集群层面均包含行动者、搭档及二元组效应。该模型额外使用关联的“双重抽样”来估计测量模型，以调整并量化报告者效应的程度。该模型可视为一种多层次结构方程模型，具有多个交叉分类随机效应，可在贝叶斯软件中使用马尔可夫链蒙特卡洛（Markov chain Monte Carlo, MCMC）方法进行估计。基于该多层次SRM的参数估计，我们随后提出了两种推导跨集群网络的替代方法，这些方法基于对跨集群关联强度的预测。我们使用尼加拉瓜农村社区的社会支持网络数据来说明本文方法，其中个体对与其他家庭个体双向支持交换的报告被用于推导跨家庭网络。
英文摘要
We consider the analysis of dyadic network data on ties between individuals in different clusters where the presence of a directed tie is reported by each individual in a dyad and the cluster-level network is of interest. A generalisation of the Social Relations Model (SRM) is proposed which includes actor, partner and dyad effects at the individual and cluster levels. The model additionally uses ``double-sampling'' of ties to estimate a measurement model which adjusts for and quantifies the extent of reporter effects. The model can be viewed as a type of multilevel structural equation model, with multiple cross-classified random effects, which can be estimated using Markov chain Monte Carlo (MCMC) methods in Bayesian software. Using parameter estimates from this multilevel SRM, we then propose two alternative ways of deriving the between-cluster network that are based on predictions of the strength of between-cluster ties. Our approach is illustrated using data on social support networks in a rural community in Nicaragua where individual reports of bidirectional exchanges of support with individuals from other households are used to derive the between-household network.
