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

循环设计被用于许多不同的心理学科，以调查人际感知，判断和行为的原因和后果。在这个设计中，每个参与者被要求判断所有其他参与者关于一个人际变量，参与者也被所有参与者判断关于这个变量。然后使用社会关系模型（SRM）对数据进行分析。在建议的项目中，SRM三元判断的扩展被认为是。这些判断收集在一个扩展的循环设计中，参与者被要求判断他们不属于的所有二分体的变量。然后，可以使用三元SRM来分析所得到的三元数据，以调查例如个人可以多准确地评估社会结构。基于我们自己的研究，展示了如何估计SRM参数，我们将推导出似然和贝叶斯方法来分析三元SRM数据，并在R中实现。此外，还将进行一项小组研究，以复制社会结构感知准确性的研究结果，并调查开放性问题，如人格特质对三元感知的影响。总之，我们相信，计划的发展将使应用研究人员有机会调查一些新的和有趣的研究问题，使用适当的统计方法。

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.