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Kickstart your research with this practical, bestselling guide to doing social network analysis. Get to grips with the mathematical foundations and learn how to use software tools such as NeTDraw and UCINET to reach your research goals. Supporting you step-by-step through the entire research process, from design and data collection to coding, visualisation, and analysis, the book also offers: . Case studies and examples using real data . Exercises drawn from the authors' decades of teaching experience . Online access to datasets, worked examples and a software manual to help…mehr
Kickstart your research with this practical, bestselling guide to doing social network analysis. Get to grips with the mathematical foundations and learn how to use software tools such as NeTDraw and UCINET to reach your research goals.
Supporting you step-by-step through the entire research process, from design and data collection to coding, visualisation, and analysis, the book also offers:
. Case studies and examples using real data . Exercises drawn from the authors' decades of teaching experience . Online access to datasets, worked examples and a software manual to help you practice your skills.
Whether you are new to social network analysis or an experienced researcher, this approachable book is your technical toolbox and research companion all in one.
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Autorenporträt
Stephen P. Borgatti, PhD 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 as cross-disciplinary journals such as Science and Social Networks. He has published over 100 peer-reviewed articles on network analysis, garnering more than 80,000 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 two-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
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
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
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
Rezensionen
This is an ideal textbook for teaching and learning social network analysis, as it strikes a good balance between scope and depth, theoretical rationales and technical details, data collection and data analysis. It s a joy to read! Keming Yang 20231128
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