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Exploratory network analysis of large social science questionnaires
"... There are now many large surveys of individuals that include questions covering a wide range of behaviours. We investigate longitudinal data from the Add Health survey of adolescents in the US. We describe how structural inference for (dynamic) Bayesian networks can be used to explore relationships ..."
Abstract

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There are now many large surveys of individuals that include questions covering a wide range of behaviours. We investigate longitudinal data from the Add Health survey of adolescents in the US. We describe how structural inference for (dynamic) Bayesian networks can be used to explore relationships between variables in such data and present this information in an interpretable format for subjectmatter practitioners. Surveys such as this often have a large samplesize, which, whilst increasing the precision of inference, may mean that the posterior distribution over Bayesian networks (or graphs) is concentrated on disparate graphs. In such situations, the standard MC3 sampler converges very slowly to the posterior distribution. Instead, we use a Gibbs sampler (1), which moves more freely through graph space. We present and discuss the resulting Bayesian network, focusing on depression, and provide estimates of how different variables affect the probability of depression via the overall probabilistic structure given by the Bayesian network. 1