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.
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.
Adela-Maria Isvoranu is postdoctoral researcher of psychology at the University of Amsterdam. Sacha Epskamp is assistant professor of psychological methods at the University of Amsterdam. Lourens J. Waldorp is associate professor of psychology at the University of Amsterdam. Denny Borsboom is professor of psychology at the University of Amsterdam.
Inhaltsangabe
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; II: Estimating Undirected Network Models 6 Pairwise Markov Random Fields 7 Estimating Network Structures using Model Selection 8 Network Stability, Comparison, and Replicability; 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; IV: Theory and Causality 12 Causal Inference 13 Idealized Modeling of Psychological Dynamics
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; II: Estimating Undirected Network Models 6 Pairwise Markov Random Fields 7 Estimating Network Structures using Model Selection 8 Network Stability, Comparison, and Replicability; 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; IV: Theory and Causality 12 Causal Inference 13 Idealized Modeling of Psychological Dynamics
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