Are you struggling to design your social network research? Are you looking for a book that covers more than social network analysis?
If so, this is the book for you! With straight-forward guidance on research design and data collection, as well as social network analysis, this book takes you start to finish through the whole process of doing network research. Open the book and you′ll find practical, ′how to′ advice and worked examples relevant to Ph D students and researchers from across the social and behavioural sciences.
The book covers:
- Fundamental network concepts and theories
- Research questions and study design
- Social systems and data structures
- Network observation and measurement
- Methods for data collection
- Ethical issues for social network research
- Network visualization
- Methods for social network analysis
- Drawing conclusions from social network results
This is a perfect guide for all students and researchers looking to do empirical social network research.
Cuprins
The difference with social networks research
Fundamental network concepts and ideas
Thinking about networks: Research questions and study design
Social systems and data structures: relational ties and actor attributes
Network observation and measurement
The empirical context of network data collection
Ethical issues for social networks research
Network visualization: What it can and cannot do
A review of social network analytic methods
Drawing conclusions: Inference, generalization, causality and other weighty matters
Despre autor
Garry Robins is Professor in the Melbourne School of Psychological Sciences at the University of Melbourne. He has won research awards from the Psychometric Society and the American Psychological Association, and is a past winner of the Freeman Award for the scientific study of social structure. He is co-editor of the journal Network Science, a member of the Board of the International Network for Social Network Analysis, and former editor of the Journal of Social Structure. His research has been centred on the development of exponential random graph models for social networks, as well as a wide range of empirical and applied social network studies from cattle herding to criminal networks, from drug-sharing to environmental management, from little data to Big Data.
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