The analysis of social networks

  • A. James O’Malley
  • Peter V. Marsden


Many questions about the social organization of medicine and health services involve interdependencies among social actors that may be depicted by networks of relationships. Social network studies have been pursued for some time in social science disciplines, where numerous descriptive methods for analyzing them have been proposed. More recently, interest in the analysis of social network data has grown among statisticians, who have developed more elaborate models and methods for fitting them to network data. This article reviews fundamentals of, and recent innovations in, social network analysis using a physician influence network as an example. After introducing forms of network data, basic network statistics, and common descriptive measures, it describes two distinct types of statistical models for network data: individual-outcome models in which networks enter the construction of explanatory variables, and relational models in which the network itself is a multivariate dependent variable. Complexities in estimating both types of models arise due to the complex correlation structures among outcome measures.


Correlation Exponential random graph model Latent-space model Network autocorrelation model Social relationship Social network 



We thank Nancy Keating for allowing us to re-analyze the data from the physician influence network. Research for the paper was supported by NIH grants R01 AG024448-02, P01 AG031093, and Robert Wood Johnson Foundation Award #58729.


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© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of Health Care PolicyHarvard Medical SchoolBostonMAUSA
  2. 2.Department of SociologyHarvard UniversityCambridgeUSA

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