Estimation of triadic social relations model data using Likelihood- and Bayesian methods

循环法设计在许多不同的心理学学科中被用来调查人际感知、判断和行为的原因和结果。在这个设计中，每个参与者都被要求根据一个人际变量来判断所有其他参与者，所有参与者也都要根据这个变量来判断参与者。然后使用社会关系模型(SRM)对数据进行分析。在拟议的项目中，考虑了SRM的扩展以用于三元判断。这些判断是在扩展的循环设计中收集的，在该设计中，参与者被要求判断他们不属于的所有二元组的变量。然后，可以使用三元SRM分析得到的三元数据，以调查例如，个人评估社会结构的准确性。在我们关于如何估计SRM参数的研究的基础上，我们将推导出似然和贝叶斯方法来分析三元SRM数据并将其应用于R。此外，还将进行一项小群体研究，以复制关于社会结构知觉准确性的结果，但也将调查诸如人格特征对三元知觉的影响等开放问题。总之，我们相信，计划中的发展将使应用研究人员有机会利用适当的统计方法调查一些新的和有趣的研究问题。

The round-robin design is used in a number of different psychological disciplines to investigate the causes and consequences of interpersonal perceptions, judgments and behavior. In this design, every participant is asked to judge all other participants concerning an interpersonal variable and the participant is also judged by all participants concerning this variable. The data is then analyzed with the Social Relations Model (SRM). In the proposed project, an extension of the SRM for triadic judgments is considered. These judgments are collected in an extended round-robin design in which participants are asked to judge the variable for all dyads to which they do not belong. The resulting triadic data can then be analyzed with the triadic SRM, to investigate, for example, how accurately individuals can assess social structures. Building on our own research showing how to estimate the SRM parameters, we will derive likelihood and Bayesian methods to analyze triadic SRM data and implement them in R. In addition, a small-group study will be conducted to replicate findings on the accuracy social structure perceptions, but also to investigate open questions such as the influence of personality traits on triadic perceptions. Altogether we believe that the planned developments will give applied researchers the opportunity to investigate a number of new and interesting research questions using adequate statistical approaches.