Much, if not most, social network data is derived from informant reports; past research, however, has indicated that such reports are in fact highly inaccurate representations of social interaction. In this paper, a family of hierarchical Bayesian models is developed which allows for the simultaneous inference of informant accuracy and social structure in the presence of measurement error and missing data. Posterior simulation for these models using Markov Chain Monte Carlo methods is outlined. Robustness of the models to structurally correlated error rates, implications of the Bayesian modeling framework for improved data collection strategies, and the validity of the criterion graph are also discussed. (C) 2003 Elsevier Science B.V. All rights reserved.
