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Network Psychometrics with R: A Guide for Behavioral and Social Scientists [Pehme köide]

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Edited by (University of Amsterdam), Edited by , Edited by , Edited by
  • Formaat: Paperback / softback, 260 pages, kõrgus x laius: 246x174 mm, kaal: 1000 g
  • Ilmumisaeg: 31-Mar-2022
  • Kirjastus: Routledge
  • ISBN-10: 0367612941
  • ISBN-13: 9780367612948
Teised raamatud teemal:
Network Psychometrics with R: A Guide for Behavioral and Social ScientistsSuurem pilt
  • Formaat: Paperback / softback, 260 pages, kõrgus x laius: 246x174 mm, kaal: 1000 g
  • Ilmumisaeg: 31-Mar-2022
  • Kirjastus: Routledge
  • ISBN-10: 0367612941
  • ISBN-13: 9780367612948
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367612948.html
Märksõnad:
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.

Arvustused

"The PsychoSystems team at the University of Amsterdam has sparked a conceptual and methodological revolution in psychology. Their network approach to mental disorders is galvanizing our field, producing an urgent need for an accessible, user-friendly text for novices as well as for experienced researchers. Network Psychometrics with R is a splendid book that fulfills this need admirably. Importantly, the authors are seasoned teachers of network analysis, accustomed to introducing the approach to beginners in the field." -- Professor Richard McNally, Harvard University, USA

"This thorough introduction into all important details of network psychometrics, by a group of authors including many of the leading scientists in the field, fills an important lacuna in the literature. It is highly recommended for widespread use in teaching and applied research." -- Professor Peter Molenaar, Pennsylvania State University, USA "The PsychoSystems team at the University of Amsterdam has sparked a conceptual and methodological revolution in psychology. Their network approach to mental disorders is galvanizing our field, producing an urgent need for an accessible, user-friendly text for novices as well as for experienced researchers. Network Psychometrics with R is a splendid book that fulfills this need admirably. Importantly, the authors are seasoned teachers of network analysis, accustomed to introducing the approach to beginners in the field." Professor Richard McNally, Harvard University, USA

"This thorough introduction into all important details of network psychometrics, by a group of authors including many of the leading scientists in the field, fills an important lacuna in the literature. It is highly recommended for widespread use in teaching and applied research." Professor Peter Molenaar, Pennsylvania State University, USA

Preface 1(6)
I Network Science in R
7(84)
1 Network Perspectives
9(20)
1.1 Introduction
9(2)
1.2 Network approaches
11(1)
1.3 Network models
12(5)
1.4 Network theories
17(2)
1.5 Network approaches, models, and theories
19(4)
1.6 Conclusion
23(1)
1.7 Exercises
23(6)
2 Short Introduction to R
29(16)
2.1 Introduction
29(1)
2.2 The R environment
29(2)
2.3 Basics of R programming
31(3)
2.4 Basic R data structures
34(2)
2.5 Functions and packages
36(2)
2.6 Advanced object structures
38(2)
2.7 Working with data in R
40(2)
2.8 Conclusion
42(1)
2.9 Exercises
43(2)
3 Descriptive Analysis of Network Structures
45(22)
3.1 Introduction
45(1)
3.2 Complex systems and network science
46(2)
3.3 From network science to network psychometrics
48(1)
3.4 Constructing networks
49(1)
3.5 Analyzing networks
50(11)
3.6 Lord of the Rings example
61(1)
3.7 Conclusion
62(1)
3.8 Exercises
63(4)
4 Constructing and Drawing Networks in qgraph
67(12)
4.1 Introduction
67(1)
4.2 qgraph functionality
67(5)
4.3 qgraph interpretation
72(3)
4.4 Saving qgraph networks
75(1)
4.5 Descriptive analysis of networks using qgraph
75(1)
4.6 Conclusion
76(1)
4.7 Exercises
77(2)
5 Association and Conditional Independence
79(12)
5.1 Introduction
79(1)
5.2 Independence and dependence
80(3)
5.3 Conditional independence
83(1)
5.4 Testing for statistical dependencies
84(2)
5.5 Where do conditional dependencies come from?
86(2)
5.6 Conclusion
88(1)
5.7 Exercises
88(3)
II Estimating Undirected Network Models
91(64)
6 Pairwise Markov Random Fields
93(18)
6.1 Introduction
93(1)
6.2 Pairwise Markov random fields
94(1)
6.3 Interpreting pairwise Markov random fields
95(7)
6.4 Estimating saturated network models
102(5)
6.5 Conclusion
107(1)
6.6 Exercises
107(4)
7 Estimating Network Structures using Model Selection
111(22)
7.1 Introduction
111(1)
7.2 Comparing multivariate statistical models
112(3)
7.3 Thresholding & pruning
115(3)
7.4 Model search
118(2)
7.5 Regularization
120(3)
7.6 Recommendations for applied researchers
123(6)
7.7 Exercises
129(4)
8 Network Stability, Comparison, and Replicability
133(22)
8.1 Introduction
133(2)
8.2 Stability and accuracy in one sample
135(5)
8.3 Analyzing and comparing multiple samples
140(10)
8.4 Conclusion
150(1)
8.5 Exercises
150(5)
III Network Models for Longitudinal Data
155(56)
9 Longitudinal Design Choices: Relating Data to Analysis
157(12)
9.1 Introduction
157(1)
9.2 Data designs
158(2)
9.3 Analysis designs
160(2)
9.4 Differences between data and analysis
162(4)
9.5 Separating contemporaneous and temporal effects
166(1)
9.6 Conclusion
167(1)
9.7 Exercises
168(1)
10 Network Estimation from Time Series and Panel Data
169(24)
10.1 Introduction
169(1)
10.2 Graphical vector auto-regression
170(1)
10.3 N = 1 estimation: personalized network models
171(5)
10.4 N > 1 estimation: multi-level estimation
176(6)
10.5 Challenges to GVAR estimation
182(6)
10.6 Conclusion
188(1)
10.7 Exercises
188(5)
11 Modeling Change in Networks
193(18)
11.1 Introduction
193(2)
11.2 Time-varying network models
195(2)
11.3 Estimating time-varying network models
197(4)
11.4 Mood measurements example
201(5)
11.5 Conclusion
206(1)
11.6 Exercises
206(5)
IV Theory and Causality
211(36)
12 Causal Inference
213(20)
12.1 Introduction
213(1)
12.2 A language for expressing causal relations
214(2)
12.3 Statistical and causal relations
216(8)
12.4 Structural causal models
224(4)
12.5 Conclusion
228(1)
12.6 Exercises
228(5)
13 Idealized Modeling of Psychological Dynamics
233(14)
13.1 Introduction
233(1)
13.2 Basics of the Ising model
234(2)
13.3 Idealized simulations of attitude dynamics
236(3)
13.4 Modeling phenomena in intelligence research
239(4)
13.5 Conclusion
243(1)
13.6 Exercises
243(4)
Index 247
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.