Longitudinal Network Models

This book provides guidance on navigating the range of available tools for longitudinal network analysis, covering model selection, application, assessment, and interpretation. It assumes familiarity with network measurement, description, and notation and regression analysis and is organized with a specific data structure or research question in mind.

Format: Paperback / softback
Length: 160 pages
Publication date: 02 February 2023
Publisher: SAGE Publications Inc

Even though longitudinal social network data are being collected in greater numbers, there are still few resources available to help researchers navigate the wide array of tools available for longitudinal network analysis. As a result, applied social scientists are left with a multitude of questions. Which model is most suitable for my data? How should I begin implementing this modeling strategy? And how can I determine whether my model is effective? This book aims to provide answers to these pressing concerns. Author Scott Duxbury assumes that the reader has a basic understanding of network measurement, description, and notation, as well as experience with regression analysis, but may be less familiar with statistical network methods. The primary objective of the book is to guide readers through the process of selecting, applying, assessing, and interpreting a longitudinal network model, with each chapter carefully structured to address a specific data structure or research question. Additionally, a companion website is provided, featuring data and R code that allows readers to replicate the examples discussed in the book.

The book begins by introducing the fundamental concepts and principles of longitudinal network analysis. It covers topics such as network measurement, network description, and network notation, providing readers with a solid foundation for understanding the data they are working with. The author then delves into the various types of longitudinal network models available, including structural equation models, latent variable models, and network growth models. Each model is discussed in detail, including its advantages, disadvantages, and appropriate applications.

To assist readers in applying longitudinal network models, the book provides step-by-step instructions and examples. It covers topics such as data preparation, model fitting, and interpretation, using real-world data sets to illustrate the process. The author emphasizes the importance of considering the research question, data characteristics, and available resources when choosing and implementing a longitudinal network model.

Furthermore, the book includes a comprehensive discussion of assessing the quality of longitudinal network models. It covers topics such as model fit, statistical significance, and practical considerations such as sample size and power analysis. The author provides guidelines and tools for evaluating the performance of different models, helping researchers make informed decisions about their analysis.

In conclusion, this book is a valuable resource for researchers interested in longitudinal network analysis. It provides a comprehensive and practical guide to selecting, applying, assessing, and interpreting longitudinal network models. With its clear and concise writing style, extensive examples, and companion website, the book is accessible to researchers with varying levels of experience in social network analysis. Whether you are a novice or an experienced researcher, this book will help you unlock the potential of longitudinal network data and advance your understanding of social networks.

Weight: 210g
Dimension: 215 x 139 (mm)
ISBN-13: 9781071857731