Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Exploratory network analysis of large social science questionnaires

Tools
- Tools
+ Tools

Goudie, Robert J. B., Mukherjee, Sach and Griffiths, Frances (2011) Exploratory network analysis of large social science questionnaires. In: Proceedings of the 8th Bayesian Modelling Applications Workshop, Barcelona, Spain, 14th July 2011. Published in: The 8th Bayesian Modelling Applications Workshop pp. 34-42.

Research output not available from this repository.

Request-a-Copy directly from author or use local Library Get it For Me service.

Official URL: http://abnms.org/uai2011-apps-workshop/proceedings...

Request Changes to record.

Abstract

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 subject-matter practitioners. Surveys such as this often have a large sample-size, 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 MC 3 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.

Item Type: Conference Item (Paper)
Subjects: H Social Sciences > HA Statistics
R Medicine > R Medicine (General)
Divisions: Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences
Faculty of Science, Engineering and Medicine > Science > Statistics
Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences > Social Science & Systems in Health (SSSH)
Library of Congress Subject Headings (LCSH): Questionnaires, Social sciences -- Network analysis
Journal or Publication Title: The 8th Bayesian Modelling Applications Workshop
Official Date: 2011
Dates:
DateEvent
2011Published
Page Range: pp. 34-42
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Conference Paper Type: Paper
Title of Event: Proceedings of the 8th Bayesian Modelling Applications Workshop
Type of Event: Workshop
Location of Event: Barcelona, Spain
Date(s) of Event: 14th July 2011

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item
twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us