Multilevel Network Analysis for the Social Sciences -

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Résumé :
This volume provides new insights into the functioning of organizational, managerial and market societies. Multilevel analysis and social network analysis are described and the authors show how they can be combined in developing the theory, methods and empirical applications of the social sciences. This book maps out the development of multilevel reasoning and shows how it can explain behavior, through two different ways of contextualizing it. First, by identifying levels of influence on behavior and different aggregations of actors and behavior, and complex interactions between context and behavior. Second, by identifying different levels as truly different systems of agency: such levels of agency can be examined separately and jointly since the link between them is affiliation of members of one level to collective actors at the superior level. It is by combining these approaches that this work offers new insights. New case studies and datasets that explore new avenues of theorizing and new applications of methodology are presented. This book will be useful as a reference work for all social scientists, economists and historians who use network analyses and multilevel statistical analyses. Philosophers interested in the philosophy of science or epistemology will also find this book valuable. ?

Sommaire:
1: Introduction: Tom A.B. Snijders, Emmanuel Lazega, and Mark Tranmer.- Part I: THEORY: 2: The Multiple Flavours of Multilevel Issues for Networks: Tom A.B. Snijders.- 3: Neo-structural sociology, social change and dynamics of multilevel structures in the organizational society: Emmanuel Lazega.- PART II: METHODS: 4: Multilevel Modeling of Social Network and Relational Data: Marijtje A.J. van Duijn.- 5: Multilevel Models for Multilevel Network Dependencies: Mark Tranmer.- 6: Exponential random graph models for multilevel networks: Peng Wang, Garry Robins, Philippa Pattison, Emmanuel Lazega.- 7: Social selection models for multilevel networks: Peng Wang, Garry Robins, Philippa Pattison, Emmanuel Lazega, Marie-Th?r?se Jourda.- 8: Blockmodeling of multilevel network data: Ale? ?iberna.- 9: Correspondence Analysis of Multilevel Networks: Mengxiao? Zhu, Stanley Wasserman.- 10: Multi-Level Network Analysis from The Duality of Cases and Variables: Ronald L. Breiger.- 11: Multi-splitting large networks for multi-level analysis: Christophe Prieur.- PART III: APPLICATIONS: 12: Experience sampling across organizational levels: Paola Zappa and Alessandro Lomi.- 13: Social integration of gifted students among their classmates: Miranda Jessica Lubbers.- 14:The impact of network position and team structure on individual outcomes: using a multilevel model with an autocorrelation component to predict individual (employee) job satisfaction: Filip Agneessens, Johan Koskinen.- 15: Small fish in big ponds: The structure of international fisheries agreements: James Hollway, Johan Koskinen.- 16: Dynamic interlocks and multilevel analysis in European corporate elites: Fran?ois-Xavier Dudouet and Antoine Vion.- 17: Comparing fields of sciences: the network of collaborations of physicists and philosophers: Elisa Bellotti.- 18: Connecting and dissecting collective agency in community: applying a multi-level relational approach to the study of community infrastructure and collective action in two Manchester neighbourhoods: Beth Carley.- 19: Multilevel alignments between interactions and relations over different time frames: Implications for group innovativeness: Eric Quintane.- 20: A Multilevel network study of the Biotech industry in France: Alvaro Pina-Stranger, Ana Maria Falconi, Emmanuel Lazega.- 21: Economic exchange vs. social exchange: A Multilevel approach of a trade fair for TV programs in Africa: Guillaume Favre, Julien Brailly, Josiane Chatellet, Emmanuel Lazega.- 22?: Exponential Random Graph Models for a MultiLevel Network ? A Case Study of the Audiovisual Market in Eastern Europe: Julien Brailly, Guillaume Favre, Josiane Chatellet, Emmanuel Lazega.