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

목차 (Table of Contents)

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