Aggregated Relational Data (ARD) contain summary information about individual social networks and are widely used to estimate social network characteristics and the size of populations of interest. Although a variety of ARD estimators exist, practitioners currently lack guidance on how to evaluate whether a selected model adequately fits the data. We introduce a diagnostic framework for ARD models that provides a systematic, reproducible process for assessing covariate structure, distributional assumptions, and correlation. The diagnostics are based on point estimates, using either maximum likelihood or maximum a posteriori optimization, which allows quick evaluation without requiring repeated Bayesian model fitting. Through simulation studies and applications to large ARD datasets, we show that the proposed workflow identifies common sources of model misfit and helps researchers select an appropriate model that adequately explains the data.

翻译：聚合关系数据（Aggregated Relational Data, ARD）包含个体社交网络的汇总信息，被广泛用于估计社交网络特征及目标群体的规模。尽管存在多种ARD估计方法，实践者目前仍缺乏指导来评估所选模型是否充分拟合数据。本文提出一个ARD模型诊断框架，该框架通过系统化、可复现的流程来评估协变量结构、分布假设及相关性。这些诊断基于点估计（采用极大似然或最大后验优化方法），无需重复进行贝叶斯模型拟合即可实现快速评估。通过模拟研究及对大规模ARD数据集的应用分析，我们证明所提出的工作流程能够识别模型失配的常见来源，并帮助研究者选择能够充分解释数据的合适模型。