This approachable book introduces network research in R, walking you through every step of doing social network analysis. Drawing together research design, data collection and data analysis, it explains the core concepts of network analysis in a non-technical way. The book balances an easy to follow explanation of the theoretical and statistical foundations underpinning network analysis with practical guidance on key steps like data management, preparation and visualisation. With clarity and expert insight, it: * Discusses measures and techniques for analyzing social network data, including digital media * Explains a range of statistical models including QAP and ERGM, giving you the tools to approach different types of networks * Offers digital resources like practice datasets and worked examples that help you get to grips with R software

Social Networks at Work provides the latest thinking, from top-notch experts, on social networks as they apply to industrial and organizational (I/O) psychology. Each chapter provides an in-depth review along with discussions of future research and managerial implications of the social network perspective. Altogether, the volume illustrates the importance of adding a social capital perspective to the traditional human capital focus of I/O psychology. The volume is organized into two groups of chapters: the first seven chapters focus on specific network concepts (such as centrality, affect, negative ties, multiplexity, cognition, and structural holes) applied across a variety of topics. The remaining eight chapters focus on common I/O topics (such as personality, creativity, turnover, careers, person-environment fit, employment, teams, and leadership) and examine each from a network perspective, applying a variety of network concepts to the topic. This volume is suited for students and academics interested in applying a social network perspective to their work, as well as for practicing managers. Each topic area provides a useful review and guide for future research, as well as implications for managerial action.

Social Networks at Work provides the latest thinking, from top-notch experts, on social networks as they apply to industrial and organizational (I/O) psychology. Each chapter provides an in-depth review along with discussions of future research and managerial implications of the social network perspective. Altogether, the volume illustrates the importance of adding a social capital perspective to the traditional human capital focus of I/O psychology. The volume is organized into two groups of chapters: the first seven chapters focus on specific network concepts (such as centrality, affect, negative ties, multiplexity, cognition, and structural holes) applied across a variety of topics. The remaining eight chapters focus on common I/O topics (such as personality, creativity, turnover, careers, person-environment fit, employment, teams, and leadership) and examine each from a network perspective, applying a variety of network concepts to the topic. This volume is suited for students and academics interested in applying a social network perspective to their work, as well as for practicing managers. Each topic area provides a useful review and guide for future research, as well as implications for managerial action.

Designed to walk beginners through core aspects of collecting, visualizing, analyzing, and interpreting social network data, this book will get you up-to-speed on the theory and skills you need to conduct social network analysis. Using simple language and equations, the authors provide expert, clear insight into every step of the research process-including basic maths principles-without making assumptions about what you know. With a particular focus on NetDraw and UCINET, the book introduces relevant software tools step-by-step in an easy to follow way. In addition to the fundamentals of network analysis and the research process, this Second Edition focuses on: Digital data and social networks like Twitter Statistical models to use in SNA, like QAP and ERGM The structure and centrality of networks Methods for cohesive subgroups/community detection Supported by new chapter exercises, a glossary, and a fully updated companion website, this text is the perfect student-friendly introduction to social network analysis.

Social network analysis has transformed the study of organizations over the past 30 years. Rather than focus on individuals or teams, network scholars view the organization as a nexus of relationships. As its popularity increases, the scholarship is evolving in many directions including new theory on network dynamics, multilevel thinking, multiplex networks, digital networks, instrumental and affective ties, and cross-cultural considerations, in addition to advances in methods and measures, and new organizational applications. This compendium of essays points the way to contemporary issues in organizational social networks. Research in the Sociology of Organizations is an established international, peer-reviewed series that examines cutting edge theoretical, methodological and research issues in organizational studies. The series is especially concerned with specifying the unique contributions of sociological theories and research techniques to the analysis of organizations. In addition to publishing high quality original research, each volume aims to foster debate about the value of new theories and research to the growing international community of organizational studies scholars. Each volume is designed around carefully selected and interesting themes that aim to facilitate the development of new research communities and perspectives within the organizational studies field. All contributions are peer reviewed to ensure high quality, and are individually downloadable through your library website, enabling wide distribution and easy access.

Egocentric network analysis is used widely across the social sciences, especially in anthropology, political science, economics, and sociology, and is increasingly being employed in communications, informatics, and business and marketing studies. Egocentric network analysis requires a unique set of data collection and analysis skills that overlap only minimally with other network methodologies. However, until now there has been no single reference for conceptualizing, collecting, and analyzing egocentric social network data. This comprehensive guide to study design, data collection, and analysis brings together the state of knowledge with the most effective research tools to guide newcomers to this field. It is illustrated with many engaging examples and graphics and assumes no prior knowledge. Covering the entire research process in a logical sequence, from conceptualizing research questions to interpreting findings, this volume provides a solid foundation for researchers at any stage of their career to learn and apply ego network methods.

This approachable book introduces network research in R, walking you through every step of doing social network analysis. Drawing together research design, data collection and data analysis, it explains the core concepts of network analysis in a non-technical way. The book balances an easy to follow explanation of the theoretical and statistical foundations underpinning network analysis with practical guidance on key steps like data management, preparation and visualisation. With clarity and expert insight, it: * Discusses measures and techniques for analyzing social network data, including digital media * Explains a range of statistical models including QAP and ERGM, giving you the tools to approach different types of networks * Offers digital resources like practice datasets and worked examples that help you get to grips with R software

Egocentric network analysis is used widely across the social sciences, especially in anthropology, political science, economics, and sociology, and is increasingly being employed in communications, informatics, and business and marketing studies. Egocentric network analysis requires a unique set of data collection and analysis skills that overlap only minimally with other network methodologies. However, until now there has been no single reference for conceptualizing, collecting, and analyzing egocentric social network data. This comprehensive guide to study design, data collection, and analysis brings together the state of knowledge with the most effective research tools to guide newcomers to this field. It is illustrated with many engaging examples and graphics and assumes no prior knowledge. Covering the entire research process in a logical sequence, from conceptualizing research questions to interpreting findings, this volume provides a solid foundation for researchers at any stage of their career to learn and apply ego network methods.

Designed to walk beginners through core aspects of collecting, visualizing, analyzing, and interpreting social network data, this book will get you up-to-speed on the theory and skills you need to conduct social network analysis. Using simple language and equations, the authors provide expert, clear insight into every step of the research process-including basic maths principles-without making assumptions about what you know. With a particular focus on NetDraw and UCINET, the book introduces relevant software tools step-by-step in an easy to follow way. In addition to the fundamentals of network analysis and the research process, this Second Edition focuses on: Digital data and social networks like Twitter Statistical models to use in SNA, like QAP and ERGM The structure and centrality of networks Methods for cohesive subgroups/community detection Supported by new chapter exercises, a glossary, and a fully updated companion website, this text is the perfect student-friendly introduction to social network analysis.

To better understand how structure, content and meaning are interrelated, there is great potential in conceptualizing mixed structure linkages, where social relations, events, actions and text-based information intersect. This potential is all the more salient in view of the large data flows and analytical tools that researchers can draw on. However, the increasing availability of tools and data seem to outpace theory development. In response to these trends, this volume aims to advance theoretical understanding of how structure, content and meaning are dynamically intertwined, in both online and offline domains. We also explain the methodological implications of such investigations. This volume therefore responds to the need for in-depth analyses studying the theoretical and methodological implications of the assumed unity of network approaches at the intersections of structure, content and meaning. With these analyses, we show promising approaches, provoke debates in the field, and suggest potential future directions.