Isvoranu A. Network Psychometrics with R. A Guide...2022 by Adela-Maria Isvoranu

Author:Adela-Maria Isvoranu
Format: pdf
Tags: A systematic, innovative introduction to the field of network analysis, Network Psycho-metrics 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 modeling 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 modeling, 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. 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., R; RStudio; qgraph; network visualization techniques; layout design; practical exercises; variance-covariance matrices; network structures; degree distributions; network psychometrics; cluster detection; clustering coefficients; causal inference; Directed Acyclic Graphs; bnlearn; statistical models; Gaussian graphical models; mixed graphical models; PMRF; cross-sectional data; pruning; regularization; bootnet; estimation algorithm; point estimates; GVAR; SVAR; graphicalVAR; psychonetrics, mlVAR; resilience; sudden transitions; violations of stationarity; temporal networks; mixed data sets; data science; turtorial


Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.