Schätzung komplexer Social Relations Model-Daten mittels Likelihood- und Bayes-Methoden

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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) resulting in a number of different SRM parameter estimates. Based on our own work showing how to estimate the SRM parameters with a likelihood and a Bayesian estimator, the planned project aims to extend the SRM to more complex data situations and to develop and implement likelihood and Bayesian methods for the analysis of this complex data. For instance, we intend to develop and implement likelihood and Bayesian methods for multivariate round-robin data and for longitudinal round-robin data. Altogether we believe that the planned extensions will give applied researchers the opportunity to investigate a number of new and interesting research questions using adequate statistical approaches.