Hu Bo, Templin Jonathan, Hoffman Lesa
Department of Applied Psychology, Ningbo University, Ningbo, China.
Department of Psychological and Quantitative Foundations, University of Iowa, Iowa City, IA, United States.
Front Psychol. 2022 Apr 7;13:860837. doi: 10.3389/fpsyg.2022.860837. eCollection 2022.
In the current paper, we propose a latent interdependence approach to modeling psychometric data in social networks. The idea of latent interdependence is adopted from social relations models (SRMs), which formulate a mutual-rating process by both dyad members' characteristics. Under the framework of the latent interdependence approach, we introduce two psychometric models: The first model includes the main effects of both rating-sender and rating-receiver, and the second model includes a latent distance effect to assess the influence from the dissimilarity between the latent characteristics of both sides. The latent distance effect is quantified by the Euclidean distance between both sides' trait scores. Both models use Bayesian estimation Markov chain Monte Carlo. How accurately model parameters were estimated was evaluated in a simulation study. Parameter recovery results showed that all parameters were accurately recovered under most of the conditions investigated. As expected, the accuracy of model estimation was significantly improved as network size grew. Also, through analyzing empirical data, we showed how to use the estimates of model parameters to predict the latent weight of connections among group members and rebuild either a univariate or multivariate network at a latent trait level. Finally, we discuss issues regarding model comparison and offer suggestions for future studies.
在当前论文中，我们提出一种潜在相互依存方法来对社交网络中的心理测量数据进行建模。潜在相互依存的概念源自社会关系模型（SRMs），该模型通过二元组成员双方的特征来构建相互评级过程。在潜在相互依存方法的框架下，我们引入了两种心理测量模型：第一个模型包含评级发送者和评级接收者的主效应，第二个模型包含一个潜在距离效应，以评估双方潜在特征差异的影响。潜在距离效应通过双方特质分数之间的欧几里得距离来量化。两个模型均使用贝叶斯估计和马尔可夫链蒙特卡罗方法。在一项模拟研究中评估了模型参数估计的准确程度。参数恢复结果表明，在大多数所研究的条件下，所有参数均被准确恢复。正如预期的那样，随着网络规模的增大，模型估计的准确性显著提高。此外，通过分析实证数据，我们展示了如何使用模型参数估计来预测组成员之间连接的潜在权重，并在潜在特质水平上重建单变量或多变量网络。最后，我们讨论了关于模型比较的问题，并为未来研究提供了建议。
