Latent Reciprocal Engagement and Accuracy Variables in Social Relations Structural Equation Modeling

The social relations model (SRM) is the standard approach for analyzing dyadic data stemming from round-robin designs. The model can be used to estimate correlation-coefficients that reflect the overall reciprocity or accuracy of judgements for individual and dyads on the sample- or population level. Within the social relations structural equation modeling framework and on the statistical grounding of stochastic measurement and classical test theory, we show how the multiple indicator SRM can be modified to capture inter-individual and inter-dyadic differences in reciprocal engagement or inter-individual differences in reciprocal accuracy. All models are illustrated on an open-access round-robin data set containing measures of mimicry, liking, and meta-liking (the belief to be liked). Results suggest that people who engage more strongly in reciprocal mimicry are liked more after an interaction with someone and that overestimating one’s own popularity is strongly associated with being liked less. Further applications, advantages and limitations of the models are discussed.

社会关系模型（Social Relations Model, SRM）是分析源自循环设计的二元数据的标准研究方法。该模型可用于估算相关系数，以反映样本或总体层面上，个体与二元组的判断所具备的整体互惠性与准确性。在社会关系结构方程建模框架下，并基于随机测量与经典测验理论的统计学基础，本文阐述了如何对多指标社会关系模型进行修正，以捕捉互惠参与过程中的个体间与二元组间差异，或是互惠准确性层面的个体间差异。所有模型均通过一份开放获取的循环数据集进行演示，该数据集包含模仿行为、喜爱程度与元喜爱（即认为自己被喜爱的信念）的测量指标。研究结果显示，在互动后，在互惠模仿中参与程度更高的个体更易被他人喜爱；而高估自身受欢迎程度的个体则往往更不被他人喜爱，二者存在强关联。本文还讨论了该模型的其他应用场景、优势与局限性。