Network psychometrics with R : a guide for behavioral and social scientists

Table of Contents: Part I: Network Science in R 1. Network Perspectives 2. Short Introduction to R 3. Descriptive Analysis of Network Structures 4. Constructing and Drawing Networks in qgraph 5. Association and Conditional Independence Part II: Estimating Undirected Network Models 6. Pairwise Markov Random Fields 7. Estimating Network Structures using Model Selection 8. Network Stability, Comparison, and Replicability Part III: Network Models for Longitudinal Data 9. Longitudinal Design Choices: Relating Data to Analysis 10. Network Estimation from Time Series and Panel Data 11.. Modeling Change in Networks Part IV: Theory and Causality 12 Causal Inference 13. Idealized Modeling of Psychological Dynamics

The systematic, innovative introduction to the field of network analysis, this book provides a comprehensive overview of and guide to both the theoretical foundations of network psychometrics as well as modelling techniques developed from this perspective. This textbook showcases cutting-edge methods in an easily accessible format, accompanied by problem sets and code. After working through this book, readers will be able to understand the theoretical foundations behind network modelling, infer network topology, and estimate network parameters from different sources of data. This book features an introduction on the statistical programming language R that guides readers on how to analyze network structures and their stability using R. It is written in the context of social and behavioral science, the methods introduced in this book are widely applicable to data sets from related fields of study. This book is ideal for instructors and students of undergraduate and graduate level courses and workshops in the field of network psychometrics as well as established researchers looking to master new methods.