Bayesian Analysis of Dyadic Data

SYNOPTIC ABSTRACT

This paper considers the Bayesian analysis of dyadic data with particular emphasis on applications in social psychology. Various existing models are extended and unified under a class of models where a single value is elicited to complete the prior specification. Certain situations which have sometimes been problematic (e.g. incomplete data, non-standard covariates, missing data, unbalanced data) are easily handled under the proposed class of Bayesian models. Inference is straightforward using software that is based on Markov chain Monte Carlo methods. Examples are provided which highlight the variety of data sets that can be entertained and the ease in which they can now be analyzed.

Key words and Phrases:

• Bayesian analysis
• incomplete data
• Markov chain Monte Carlo
• missing data
• social relations model
• WinBUGS