A systematic, innovative introduction to the field of network analysis, Network Psychometrics with R: A Guide for Behavioral and Social Scientists provides a comprehensive overview of and guide to both the theoretical foundations of network psychometrics as well as modelling techniques developed from this perspective.

Written by pioneers in the field, 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 analyse network structures and their stability using R. While Network Psychometrics with R 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. Additionally, while the text is written in a non-technical manner, technical content is highlighted in textboxes for the interested reader.

Network Psychometrics with R 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.

This book is accompanied by a companion website with resources for both students and lecturers.

part I|83 pages

Network Science in R

chapter Chapter 1|19 pages

Network Perspectives

ByDenny Borsboom, Angélique O. J. Cramer, Eiko I. Fried, Adela-Maria Isvoranu, Donald J. Robinaugh, Jonas Dalege, Han L. J. van der Maas

chapter Chapter 2|16 pages

Short Introduction to R

ByGabriela Lunansky, Sacha Epskamp, Adela-Maria Isvoranu

chapter Chapter 3|21 pages

Descriptive Analysis of Network Structures

ByMarie K. Deserno, Adela-Maria Isvoranu, Sacha Epskamp, Tessa F. Blanken

chapter Chapter 4|12 pages

Constructing and Drawing Networks in qgraph

ByAdela-Maria Isvoranu, Sacha Epskamp

chapter Chapter 5|11 pages

Association and Conditional Independence

ByLourens J. Waldorp, Denny Borsboom, Sacha Epskamp

part II|63 pages

Estimating Undirected Network Models

chapter Chapter 6|18 pages

Pairwise Markov Random Fields

BySacha Epskamp, Jonas M. B. Haslbeck, Adela-Maria Isvoranu, Claudia D. van Borkulo

chapter Chapter 7|22 pages

Estimating Network Structures using Model Selection

ByTessa F. Blanken, Adela-Maria Isvoranu, Sacha Epskamp

chapter Chapter 8|21 pages

Network Stability, Comparison, and Replicability

ByEiko I. Fried, Sacha Epskamp, Myrthe Veenman, Claudia D. van Borkulo

part III|55 pages

Network Models for Longitudinal Data

chapter Chapter 9|12 pages

Longitudinal Design Choices: Relating Data to Analysis

BySacha Epskamp, Ria H. A. Hoekstra, Julian Burger, Lourens J. Waldorp

chapter Chapter 10|24 pages

Network Estimation from Time Series and Panel Data

ByJulian Burger, Ria H. A. Hoekstra, Alessandra C. Mansueto, Sacha Epskamp

chapter Chapter 11|17 pages

Modeling Change in Networks

ByJonas M. B. Haslbeck, Oisín Ryan, Han L. J. van der Maas, Lourens J. Waldorp

part IV|35 pages

Theory and Causality

chapter Chapter 12|20 pages

Causal Inference

ByFabian Dablander, Riet van Bork

chapter Chapter 13|13 pages

Idealized Modeling of Psychological Dynamics

ByJonas Dalege, Jonas M. B. Haslbeck, Maarten Marsman