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***** To join INSNA, visit http://www.insna.org ***** As the principal outlet for the Public Policy Section of the American Political Science Association and for the Policy Studies Organization, the Policy Studies Journal (PSJ) is the premier channel for theoretically and empirically grounded research on policy process and analysis. PSJ is co-edited by Peter deLeon and Chris Weible (University of Colorado Denver) This is a request for abstracts for a special issue on statistical models of political networks that will be directed by guest editors, Mark Lubell (University of California Davis), John Scholz (Florida State University), Ramiro Berardo (University of Arizona), and Garry Robins (The University of Melbourne). The anticipated publication for this special issue is 2012. A workshop for the contributors may accompany this special issue. Send your questions and abstracts (approximately 300 words) to Mark Lubell ([log in to unmask]) by September 1, 2010. The main goal is to demonstrate to the general audience of policy scholars how statistical models of policy networks can be used to test core hypotheses of policy theories. Network concepts are central parts of many different policy theories (advocacy coalitions, issue networks, and social capital are three prominent examples), and cutting-edge research in policy networks aims to use statistical models to empirically represent these policy theory concepts. The primary task of this enterprise is to translate policy theory into the empirical realm of network analysis, which can help identify how network structures evolve in different settings and how these structures affect other variables including individual behavior. Statistical models of networks are useful for testing hypotheses that seek to uncover the inner workings of these complex processes. The majority of published research on policy networks relies on a combination of network visualization techniques and descriptive statistics. Statistical models complement descriptive network analysis by providing a framework for making inferences about the social processes underlying network formation. Most statistical models are based on (explicit or implicit) probability distributions of graphs that describe how links are formed between nodes and/or the structural patterns present among links. Some directly model the network structure; others use simulated null distributions against which to compare observed network structure. Other statistical models examine diffusion, influence and other processes on a network; or infer latent classes of nodes with similar structural roles or positions. Some approaches model cross-sectional, and others longitudinal, network data. The magnitude and direction of the empirically estimated parameters are linked to policy theory hypotheses. This special issue seeks to be a one-stop shop for policy scientists interested in applying these models, including some of the underlying methodological intuitions and interpretation. The first article reviews how policy theory and statistical models of networks have converged in policy research. The second article summarizes the basic idea of statistical models of networks for readers more familiar with traditional multivariate analysis, and introduces some of the most commonly used statistical models in policy research, including Exponential Random Graph Models and Stochastic Actor Oriented Models. The remaining articles are solicited from the research community, and will include applications of network models that test policy theory in different substantive policy domains. US policy, comparative, and international policy applications are all within the scope of this solicitation. Garry Robins University of Melbourne, Australia _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.