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

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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.