Network inference, error, and informant (in)accuracy: a Bayesian approach

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 simultaneou...

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Bibliographic Details
Published in:Social networks 2003-05, Vol.25 (2), p.103-140
Main Author: Butts, Carter T.
Format: Article
Language:English
Subjects:
Accuracy
Bayesian method
Data collection
Data collection strategies
Error of Measurement
Hierarchical Bayesian models
History, theory and methodology
Inference
Informant accuracy
Mathematical methods
Mathematical Models
Mathematics
Measurement
Measurement error
Methodology
Methodology (Data Collection)
Network Analysis
Network inference
Networks
Social Networks
Sociology
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Summary: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.
ISSN:0378-8733
1879-2111
DOI:10.1016/S0378-8733(02)00038-2