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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,…mehr
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
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Autorenporträt
Stephen Borgatti is the Gatton Endowed Chair of Management at the Gatton College of Business and Economics at the University of Kentucky. He has published extensively in management journals, as well cross-disciplinary journals such as Science and Social Networks. He has published over 100 peer-reviewed articles on network analysis, garnering more than 70,000 Google Scholar citations. With Martin Everett, Steve is co-author of UCINET, a well-known software package for social network analysis, as well as founder of the annual LINKS Center workshop on social network analysis. He is also a 2-term past President of INSNA (the professional association for network researchers) and winner of their Simmel Award for lifetime achievement.
Inhaltsangabe
Chapter 1: Introduction Chapter 2: Mathematical Foundations Chapter 3: Research Design Chapter 4: Data Collection Chapter 5: Data Management Chapter 6: Multivariate Techniques Used in Network Analysis Chapter 7: Visualization Chapter 8: Local Node-Level Measures Chapter 9: Centrality Chapter 10: Group-level measures Chapter 11: Subgroups and community detection Chapter 12: Equivalence Chapter 13: Analyzing Two-mode Data Chapter 14: Introduction to Inferential Statistics for Complete Networks Chapter 15: ERGMs and SAOMs