Network psychometrics with R: a guide for behavioral and social scientists
Gespeichert in:
| Weitere Verfasser: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Abingdon, Oxon ; New York
Routledge
2022
|
| Ausgabe: | First edition |
| Schriftenreihe: | Research methods and statistics
|
| Schlagworte: | |
| Online-Zugang: | Ausführliche Beschreibung Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben |
| Beschreibung: | 250 Seiten Illustrationen, Diagramme 25 cm |
| ISBN: | 9780367612948 0367612941 9780367628765 |
| DOI: | 10.4324/9781003111238 |
Internformat
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Datensatz im Suchindex
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| adam_text | Contents Preface 1 I Network Science in R 7 1 Network Perspectives 1.1 Introduction................................................................................................ 1.2 Network approaches ................................................................................. 1.3 Network models.......................................................................................... 1.4 Network theories....................................................................................... 1.5 Network approaches, models, and theories............................................... 1.6 Conclusion ................................................................................................ 1.7 Exercises .................................................................................................... 9 9 11 12 17 19 23 23 2 Short Introduction to R 2.1 Introduction................................................................................................. 2.2 The R environment.................................................................................... 2.3 Basics of R programming........................................................................... 2.4 Basic R data structures.............................................................................. 2.5 Functions and packages.............................................................................. 2.6 Advanced object structures........................................................................ 2.7 Working with data in
R.............................................................................. 2.8 Conclusion ................................................................................................ 2.9 Exercises........................................................... 29 29 29 31 34 36 38 40 42 43 3 Descriptive Analysis of Network Structures 3.1 Introduction................................................................................................. 3.2 Complex systems and network science..................................................... 3.3 From network science to network psychometrics...................................... 3.4 Constructing networks.............................................................................. 3.5 Analyzing networks.................................................................................... 3.6 Lord of the Rings example........................................................................ 3.7 Conclusion ................................................................................................ 3.8 Exercises .................................................................................................... 45 45 46 48 49 50 61 62 63 4 Constructing and Drawing Networks in qgraph 4,1 Introduction................................................................................................ 4.2 qgraph functionality ................................................................................. 61 67 67
4.3 4.4 4.5 4.6 4.7 5 qgraph interpretation................................................................................... Saving qgraph networks ............................................................................ Descriptive analysis of networks using qgraph.......................................... Conclusion ................................................................................................... Exercises...................................................................................................... 72 75 75 76 77 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 dependenciescome 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............................................................ 1θ2 6.5 Conclusion ................................................................................................ 107 6.6 Exercises....................................................................................................... 107 7 Estimating Network Structures using Model Selection 111 7.1 Introduction.................................................................................................... Hl 7.2 Comparing multivariate statistical models.................................................. 112 7.3 Thresholding pruning .............................................................................. 115 7.4 Model search............................................................................................. H8 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 9.3 9.4 9.5 9.6 9.7 Data designs................................................................................................ 158 Analysis designs ........................................................................................... 160 Differences between data and analysis.......................................................... 162 Separating contemporaneous and temporal effects....................................... 166 Conclusion ................................................................................................ 167 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-vary ing 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 Theory and Causality IV 12 12.1 12.2 12.3 12.4 12.5 12.6 211 Causal Inference 213 Introduction................................................................................................... 213 A language for expressing causal relations.................................................. 214 Statistical and causal relations .................................................................... 216 Structural causal models..............................................................................224 Conclusion ................................................................................................... 228 Exercises ...................................................................................................... 228 13Idealized 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
|
| adam_txt |
Contents Preface 1 I Network Science in R 7 1 Network Perspectives 1.1 Introduction. 1.2 Network approaches . 1.3 Network models. 1.4 Network theories. 1.5 Network approaches, models, and theories. 1.6 Conclusion . 1.7 Exercises . 9 9 11 12 17 19 23 23 2 Short Introduction to R 2.1 Introduction. 2.2 The R environment. 2.3 Basics of R programming. 2.4 Basic R data structures. 2.5 Functions and packages. 2.6 Advanced object structures. 2.7 Working with data in
R. 2.8 Conclusion . 2.9 Exercises. 29 29 29 31 34 36 38 40 42 43 3 Descriptive Analysis of Network Structures 3.1 Introduction. 3.2 Complex systems and network science. 3.3 From network science to network psychometrics. 3.4 Constructing networks. 3.5 Analyzing networks. 3.6 Lord of the Rings example. 3.7 Conclusion . 3.8 Exercises . 45 45 46 48 49 50 61 62 63 4 Constructing and Drawing Networks in qgraph 4,1 Introduction. 4.2 qgraph functionality . 61 67 67
4.3 4.4 4.5 4.6 4.7 5 qgraph interpretation. Saving qgraph networks . Descriptive analysis of networks using qgraph. Conclusion . Exercises. 72 75 75 76 77 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 dependenciescome 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. 1θ2 6.5 Conclusion . 107 6.6 Exercises. 107 7 Estimating Network Structures using Model Selection 111 7.1 Introduction. Hl 7.2 Comparing multivariate statistical models. 112 7.3 Thresholding pruning . 115 7.4 Model search. H8 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 9.3 9.4 9.5 9.6 9.7 Data designs. 158 Analysis designs . 160 Differences between data and analysis. 162 Separating contemporaneous and temporal effects. 166 Conclusion . 167 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-vary ing 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 Theory and Causality IV 12 12.1 12.2 12.3 12.4 12.5 12.6 211 Causal Inference 213 Introduction. 213 A language for expressing causal relations. 214 Statistical and causal relations . 216 Structural causal models.224 Conclusion . 228 Exercises . 228 13Idealized 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 |
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| spelling | Network psychometrics with R a guide for behavioral and social scientists edited by: Adela-Maria Isvoranu, Sacha Epskamp, Lourens J. Waldorp, Denny Borsboom First edition Abingdon, Oxon ; New York Routledge 2022 250 Seiten Illustrationen, Diagramme 25 cm txt rdacontent n rdamedia nc rdacarrier Research methods and statistics Literaturangaben Psychometrie (DE-588)4176236-8 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content R Programm (DE-588)4705956-4 s Psychometrie (DE-588)4176236-8 s DE-604 Isvoranu, Adela-Maria 1992- (DE-588)1261316371 edt Epskamp, Sacha (DE-588)1261333012 edt Waldorp, Lourens J. (DE-588)1261331834 edt Borsboom, Denny 1973- (DE-588)1037995759 edt Erscheint auch als Online-Ausgabe 978-1-003-11123-8 X:Taylor&Francis https://doi.org/10.4324/9781003111238 Ausführliche Beschreibung Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033620802&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Network psychometrics with R a guide for behavioral and social scientists Psychometrie (DE-588)4176236-8 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4176236-8 (DE-588)4705956-4 (DE-588)4143413-4 |
| title | Network psychometrics with R a guide for behavioral and social scientists |
| title_auth | Network psychometrics with R a guide for behavioral and social scientists |
| title_exact_search | Network psychometrics with R a guide for behavioral and social scientists |
| title_exact_search_txtP | Network psychometrics with R a guide for behavioral and social scientists |
| title_full | Network psychometrics with R a guide for behavioral and social scientists edited by: Adela-Maria Isvoranu, Sacha Epskamp, Lourens J. Waldorp, Denny Borsboom |
| title_fullStr | Network psychometrics with R a guide for behavioral and social scientists edited by: Adela-Maria Isvoranu, Sacha Epskamp, Lourens J. Waldorp, Denny Borsboom |
| title_full_unstemmed | Network psychometrics with R a guide for behavioral and social scientists edited by: Adela-Maria Isvoranu, Sacha Epskamp, Lourens J. Waldorp, Denny Borsboom |
| title_short | Network psychometrics with R |
| title_sort | network psychometrics with r a guide for behavioral and social scientists |
| title_sub | a guide for behavioral and social scientists |
| topic | Psychometrie (DE-588)4176236-8 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Psychometrie R Programm Aufsatzsammlung |
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