Models and Methods in Social Network Analysis, first published in 2005, presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust's Social Network Analysis: Methods and Applications, it is a collection of articles by leading methodologists reviewing advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodelling, the analysis of diffusion through networks, the analysis of affiliation or 'two-mode' networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphical techniques for exploring network data, and software for the analysis of social networks.

This new edition of The Sage Handbook of Social Network Analysis builds on the success of its predecessor, offering a comprehensive overview of social network analysis produced by leading international scholars in the field. Brand new chapters provide both significant updates to topics covered in the first edition, as well as discussing cutting edge topics that have developed since, including new chapters on: · General issues such as social categories and computational social science; · Applications in contexts such as environmental policy, gender, ethnicity, cognition and social media and digital networks; · Concepts and methods such as centrality, blockmodeling, multilevel network analysis, spatial analysis, data collection, and beyond. By providing authoritative accounts of the history, theories and methodology of various disciplines and topics, the second edition of The SAGE Handbook of Social Network Analysis is designed to provide a state-of-the-art presentation of classic and contemporary views, and to lay the foundations for the further development of the area. PART 1: GENERAL ISSUES PART 2: APPLICATIONS PART 3: CONCEPTS AND METHODS

This book provides an introduction to the theories, methods, and applications that constitute the social network perspective. Unlike more general texts, this title is designed for those current and aspiring educational researchers learning how to study, conceptualize, and analyze social networks. The author′s main intent is to encourage you to consider the social network perspective in light of your emerging research interests and evaluate how well this perspective illuminates the social complexities surrounding educational phenomena. Whether your interests lie in examining a peer′s influence on students′ achievement, the relationship between social support and teacher retention, or how the pattern of relations among parents contributes to schools′ norms, the tools introduced in this book will provide you with a slightly different take on these and other phenomena. Unlike other approaches, this perspective accounts for the importance of relationships within formal structures, and the informal patterns of interaction that emerge, sustain, or recede. Relying on diverse examples drawn from the educational research literature, this book makes explicit how the theories and methods associated with social network analysis can be used to better describe and explain the social complexities surrounding varied educational phenomena.

This volume introduces the basic concepts of Exponential Random Graph Modeling (ERGM), gives examples of why it is used, and shows the reader how to conduct basic ERGM analyses in their own research. ERGM is a statistical approach to modeling social network structure that goes beyond the descriptive methods conventionally used in social network analysis. Although it was developed to handle the inherent non-independence of network data, the results of ERGM are interpreted in similar ways to logistic regression, making this a very useful method for examining social systems. Recent advances in statistical software have helped make ERGM accessible to social scientists, but a concise guide to using ERGM has been lacking. This book fills that gap, by using examples from public health, and walking the reader through the process of ERGM model-building using R statistical software and the statnet package. An Introduction to Exponential Random Graph Modeling is a part of SAGE’s Quantitative Applications in the Social Sciences (QASS) series, which has helped countless students, instructors, and researchers learn cutting-edge quantitative techniques.

This edited volume demonstrates the potential of mixed-methods designs for the research of social networks and the utilization of social networks for other research. Mixing methods applies to the combination and integration of qualitative and quantitative methods. In social network research, mixing methods also applies to the combination of structural and actor-oriented approaches. The volume provides readers with methodological concepts to guide mixed-method network studies with precise research designs and methods to investigate social networks of various sorts. Each chapter describes the research design used and discusses the strengths of the methods for that particular field and for specific outcomes.

Despite the swift spread of social network concepts and their applications and the rising use of network analysis in social science, there is no book that provides a thorough general introduction for the serious reader. Understanding Social Networks fills that gap by explaining the big ideas that underlie the social network phenomenon. Written for those interested in this fast moving area but who are not mathematically inclined, it covers fundamental concepts, then discusses networks and their core themes in increasing order of complexity. Kadushin demystifies the concepts, theories, and findings developed by network experts. He selects material that serves as basic building blocks and examples of best practices that will allow the reader to understand and evaluate new developments as they emerge. Understanding Social Networks will be useful to social scientists who encounter social network research in their reading, students new to the network field, as well as managers, marketers, and others who constantly encounter social networks in their work.

Relationships and the pattern of relationships have a large and varied influence on both individual and group action. The fundamental distinction of social network analysis research is that relationships are of paramount importance in explaining behavior. Because of this, social network analysis offers many exciting tools and techniques for research and practice in a wide variety of medical and public health situations including organizational improvements, understanding risk behaviors, coordinating coalitions, and the delivery of health care services.This book provides an introduction to the major theories, methods, models, and findings of social network analysis research and application. In three sections, it presents a comprehensive overview of the topic; first in a survey of its historical and theoretical foundations, then in practical descriptions of the variety of methods currently in use, and finally in a discussion of its specific applications for behavior change in a public health context. Throughout, the text has been kept clear, concise, and comprehensible, with short mathematical formulas for some key indicators or concepts. Researchers and students alike will find it an invaluable resource for understanding and implementing social network analysis in their own practice.

Providing a comprehensive foundation for planning, executing, and monitoring public health research of all types, this book goes beyond traditional epidemiologic research designs to cover technology-based approaches emerging in the new public health landscape.

A thorough, practical reference on the social patterns behind health outcomes Methods in Social Epidemiology provides students and professionals with a comprehensive reference for studying the social distribution and social determinants of health. Covering the theory, models, and methods used to measure and analyze these phenomena, this book serves as both an introduction to the field and a practical manual for data collection and analysis. This new second edition has been updated to reflect the field's tremendous growth in recent years, including advancements in statistical modeling and study designs. New chapters delve into genetic methods, structural cofounding, selection bias, network methods, and more, including new discussion on qualitative data collection with disadvantaged populations. Social epidemiology studies the way society's innumerable social interactions, both past and present, yields different exposures and health outcomes between individuals within populations. This book provides a thorough, detailed overview of the field, with expert guidance toward the real-world methods that fuel the latest advances. Identify, measure, and track health patterns in the population Discover how poverty, race, and socioeconomic factors become risk factors for disease Learn qualitative data collection techniques and methods of statistical analysis Examine up-to-date models, theory, and frameworks in the social epidemiology sphere As the field continues to evolve, researchers continue to identify new disease-specific risk factors and learn more about how the social system promotes and maintains well-known exposure disparities. New technology in data science and genomics allows for more rigorous investigation and analysis, while the general thinking in the field has become more targeted and attentive to causal inference and core assumptions behind effect identification. It's an exciting time to be a part of the field, and Methods in Social Epidemiology provides a solid reference for any student, researcher, or faculty in public health.

This open access book applies insights from the network perspective in health research to explain the reproduction of health inequalities. It discusses the extant literature in this field that strongly correlates differences in social status with health behaviours and outcomes, and add to this literature by providing a coherent theoretical explanation for the causes of these health inequalities. It also shows that much research is needed on the precise factors and the social and socio-psychological mechanisms that are at play in creating and cementing social inequalities in health behaviours. While social support and social relations have received considerable attention within social and behavioural science research on health inequalities, this book considers the whole network of interpersonal relations, structures and influence mechanisms. This is the perspective of the social network analytical approach which has recently gained much attention in health research. The chapters of this book cover state-of-the-art research, open research questions, and perspectives for future research. The book provides network analyses on health inequalities from the perspective of sociology, psychology, and public health and is of interest to a wide range of scholars, students and practitioners trying to understand how health inequalities are reproduced across generations.