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Quantitative Applications in the Social Sciences Ser.: Longitudinal Network Models by Scott Duxbury (2022, Trade Paperback / Trade Paperback)
Rarewaves (662339)
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About this product
Product Identifiers
PublisherSAGE Publications, Incorporated
ISBN-101071857738
ISBN-139781071857731
eBay Product ID (ePID)4057247524
Product Key Features
Number of Pages160 Pages
Publication NameLongitudinal Network Models
LanguageEnglish
Publication Year2022
SubjectMethodology, Sociology / General, Statistics
TypeTextbook
Subject AreaSocial Science
AuthorScott Duxbury
SeriesQuantitative Applications in the Social Sciences Ser.
FormatTrade Paperback / Trade Paperback
Dimensions
Item Weight12 Oz
Item Length8.5 in
Item Width5.5 in
Additional Product Features
Intended AudienceCollege Audience
LCCN2023-275463
ReviewsA brilliant 'how to' for modelling dynamic network data. An exquisite balance of model intuition, assumptions and practical advice, accessible to all network / data scientists. -- Alexander John Bond This is a very timely book that provides critical skills for conducting explanatory analysis of longitudinal social network data. Both beginners, and advanced analysts can benefit from reading this book as it provides many real life examples, illustrating computational processes, interpreting results, and even furnishing R codes. For those who aspire to learn advanced topics in analyzing longitudinal social network data, this is a must-have book. -- Song Yang This book presents the state-of-art of longitudinal network analysis. It is comprehensive while staying concise, well structured, and clearly written. Definitely a moneyball in the field! -- Weihua An, A brilliant how to for modelling dynamic network data. An exquisite balance of model intuition, assumptions and practical advice, accessible to all network / data scientists., This book presents the state-of-art of longitudinal network analysis. It is comprehensive while staying concise, well structured, and clearly written. Definitely a moneyball in the field!, This is a very timely book that provides critical skills for conducting explanatory analysis of longitudinal social network data. Both beginners, and advanced analysts can benefit from reading this book as it provides many real life examples, illustrating computational processes, interpreting results, and even furnishing R codes. For those who aspire to learn advanced topics in analyzing longitudinal social network data, this is a must-have book.
Dewey Edition23
IllustratedYes
Dewey Decimal300.72
Table Of ContentChapter 1. Introduction Chapter 2: Temporal Exponential Random Graph Models Chapter 3: Stochastic Actor-oriented Models Chapter 4: Modeling Relational Event Data Chapter 5: Network Influence Models Chapter 6: Conclusion
SynopsisAlthough longitudinal social network data are increasingly collected, there are few guides on how to navigate the range of available tools for longitudinal network analysis. Author Scott Duxbury assumes that the reader is familiar with network measurement, description, and notation, and is versed in regression analysis, but is likely unfamiliar with statistical network methods. The goal of the book is to guide readers towards choosing, applying, assessing, and interpreting a longitudinal network model, and each chapter is organized with a specific data structure or research question in mind. A companion website includes data and R code to replicate the examples in the book., Although longitudinal social network data are increasingly collected, there are few guides on how to navigate the range of available tools for longitudinal network analysis. The applied social scientist is left to wonder: Which model is most appropriate for my data? How should I get started with this modeling strategy? And how do I know if my model is any good? This book answers these questions. Author Scott Duxbury assumes that the reader is familiar with network measurement, description, and notation, and is versed in regression analysis, but is likely unfamiliar with statistical network methods. The goal of the book is to guide readers towards choosing, applying, assessing, and interpreting a longitudinal network model, and each chapter is organized with a specific data structure or research question in mind. A companion website includes data and R code to replicate the examples in the book.
LC Classification NumberHM741
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