Longitudinal Network Models
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Go to College Publishing WebsiteDescription
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 for the book at https://study.sagepub.com/researchmethods/qass/duxbury-longitudinal-network-models includes data and R code to replicate the examples in the book.
Contents
Chapter 1. Introduction
Chapter 1. Introduction
Chapter 2: Temporal Exponential Random Graph Models
Chapter 2: Temporal Exponential Random Graph Models
Chapter 3: Stochastic Actor-oriented Models
Chapter 3: Stochastic Actor-oriented Models
Chapter 4: Modeling Relational Event Data
Chapter 4: Modeling Relational Event Data
Chapter 5: Network Influence Models
Chapter 5: Network Influence Models
Chapter 6: Conclusion
Chapter 6: Conclusion
Description
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 for the book at https://study.sagepub.com/researchmethods/qass/duxbury-longitudinal-network-models includes data and R code to replicate the examples in the book.
Contents
Chapter 1. Introduction
Chapter 1. Introduction
Chapter 2: Temporal Exponential Random Graph Models
Chapter 2: Temporal Exponential Random Graph Models
Chapter 3: Stochastic Actor-oriented Models
Chapter 3: Stochastic Actor-oriented Models
Chapter 4: Modeling Relational Event Data
Chapter 4: Modeling Relational Event Data
Chapter 5: Network Influence Models
Chapter 5: Network Influence Models
Chapter 6: Conclusion
Chapter 6: Conclusion
Reviews
November 2022 | 160 pages | Sage US
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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 for the book at https://study.sagepub.com/researchmethods/qass/duxbury-longitudinal-network-models includes data and R code to replicate the examples in the book.
Table Of Contents:
- Chapter 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