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Statistical Network Analysis for Analyzing Policy Networks
Author(s) -
Robins Garry,
Lewis Jenny M.,
Wang Peng
Publication year - 2012
Publication title -
policy studies journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 69
eISSN - 1541-0072
pISSN - 0190-292X
DOI - 10.1111/j.1541-0072.2012.00458.x
Subject(s) - exponential random graph models , computer science , conceptualization , statistical inference , inference , social network analysis , statistical model , network analysis , key (lock) , data science , data mining , management science , econometrics , random graph , graph , machine learning , artificial intelligence , theoretical computer science , economics , mathematics , computer security , statistics , engineering , world wide web , electrical engineering , social media
To analyze social network data using standard statistical approaches is to risk incorrect inference. The dependencies among observations implied in a network conceptualization undermine standard assumptions of the usual general linear models. One of the most quickly expanding areas of social and policy network methodology is the development of statistical modeling approaches that can accommodate such dependent data. In this article, we review three network statistical methods commonly used in the current literature: quadratic assignment procedures, exponential random graph models (ERGMs), and stochastic actor‐oriented models. We focus most attention on ERGMs by providing an illustrative example of a model for a strategic information network within a local government. We draw inferences about the structural role played by individuals recognized as key innovators and conclude that such an approach has much to offer in analyzing the policy process.