Bayesian Analysis of Dyadic Data
Paramjit Singh Gill, Tim B. Swartz · American Journal of Mathematical and Management Sciences · 2007
SYNOPTIC ABSTRACTThis 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.