Longitudinal network models:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Los Angeles ; London ; New Delhi ; Singapore ; Washington DC
SAGE
[2023]
|
| Schriftenreihe: | Quantitative applications in the social sciences
192 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xv, 140 Seiten Illustrationen, Diagramme |
| ISBN: | 9781071857731 |
Internformat
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Datensatz im Suchindex
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CONTENTS Series Editor’s Introduction xi Acknowledgments xiii About the Author xv 1. Introduction What This Book Covers What This Book Does Not Cover The Path Ahead What Are Longitudinal Network Data? Modeling Capabilities Why Not Standard Regression? Theoretical Issues Summary Accompanying Website X 2 2 2 3 6 8 10 13 13 2. Temporal Exponential Random Graph Models 14 What Are Network Panel Data? 14 The Cross-Sectional ERGM 16 Temporal Exponential Random Graph Models (TERGM) 18 The Intuition 19 Assumptions 20 Model Specification 20 Example 2.1. A TERGM Analysis of Friendship Formation Among Dutch College Students 24 Other Modeling Considerations 39 Conclusion 42 3. Stochastic Actor-Oriented Models 43 The Stochastic Actor-Oriented Model (SAOM) 43 The Intuition 45 Assumptions 46 Model Specification 47 Example 3.1. An Actor-Oriented Analysis of Friendship Forma tion Among Dutch College Students 49 Other Modeling Considerations 59 Model Extensions 63 Selecting a Network Panel Model 63 Conclusion 67
4. Modeling Relational Event Data What Are Relational Event Data? The Relational Event Model (REM) The Intuition Assumptions Model Specification Example 4.1. Illegal Drug Trade on the Dark Web Other Modeling Considerations Model Extensions An Alternative: The Dynamic Network Actor Model When to Use REM? Conclusion 5. Network Influence Models What Do Network Influence Data Look Like? The Temporal Network Autocorrelation Model (TNAM) The Intuition Assumptions Model Specification Example 5.1. A Network Influence Model of Adolescent Smoking Behavior Other Modeling Considerations Coevolution Models: SAOM for Behavioral and Network Change The Intuition Assumptions Model Specification Example 5.2. A Coevolution Influence Model of Adolescent Drink ing Behavior An Alternative Approach: Simulating Network Diffusion How Should We Think About Network Influence? Conclusion 68 69 69 71 72 73 75 81 85 87 90 91 92 93 94 95 96 97 99 102 105 108 109 110 111 118 119 121 6. Conclusion Missing Data Measurement Error Interpretation Causal Inference Models for Relational Event Data Unobserved Heterogeneity in Network Panel Models Scalability Conclusion 122 122 123 124 125 126 126 127 128 References 129 Index 138 |
| adam_txt |
CONTENTS Series Editor’s Introduction xi Acknowledgments xiii About the Author xv 1. Introduction What This Book Covers What This Book Does Not Cover The Path Ahead What Are Longitudinal Network Data? Modeling Capabilities Why Not Standard Regression? Theoretical Issues Summary Accompanying Website X 2 2 2 3 6 8 10 13 13 2. Temporal Exponential Random Graph Models 14 What Are Network Panel Data? 14 The Cross-Sectional ERGM 16 Temporal Exponential Random Graph Models (TERGM) 18 The Intuition 19 Assumptions 20 Model Specification 20 Example 2.1. A TERGM Analysis of Friendship Formation Among Dutch College Students 24 Other Modeling Considerations 39 Conclusion 42 3. Stochastic Actor-Oriented Models 43 The Stochastic Actor-Oriented Model (SAOM) 43 The Intuition 45 Assumptions 46 Model Specification 47 Example 3.1. An Actor-Oriented Analysis of Friendship Forma tion Among Dutch College Students 49 Other Modeling Considerations 59 Model Extensions 63 Selecting a Network Panel Model 63 Conclusion 67
4. Modeling Relational Event Data What Are Relational Event Data? The Relational Event Model (REM) The Intuition Assumptions Model Specification Example 4.1. Illegal Drug Trade on the Dark Web Other Modeling Considerations Model Extensions An Alternative: The Dynamic Network Actor Model When to Use REM? Conclusion 5. Network Influence Models What Do Network Influence Data Look Like? The Temporal Network Autocorrelation Model (TNAM) The Intuition Assumptions Model Specification Example 5.1. A Network Influence Model of Adolescent Smoking Behavior Other Modeling Considerations Coevolution Models: SAOM for Behavioral and Network Change The Intuition Assumptions Model Specification Example 5.2. A Coevolution Influence Model of Adolescent Drink ing Behavior An Alternative Approach: Simulating Network Diffusion How Should We Think About Network Influence? Conclusion 68 69 69 71 72 73 75 81 85 87 90 91 92 93 94 95 96 97 99 102 105 108 109 110 111 118 119 121 6. Conclusion Missing Data Measurement Error Interpretation Causal Inference Models for Relational Event Data Unobserved Heterogeneity in Network Panel Models Scalability Conclusion 122 122 123 124 125 126 126 127 128 References 129 Index 138 |
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| illustrated | Illustrated |
| index_date | 2024-07-03T21:38:01Z |
| indexdate | 2024-10-10T18:00:31Z |
| institution | BVB |
| isbn | 9781071857731 |
| language | English |
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| physical | xv, 140 Seiten Illustrationen, Diagramme |
| publishDate | 2023 |
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| series | Quantitative applications in the social sciences |
| series2 | Quantitative applications in the social sciences |
| spelling | Duxbury, Scott Verfasser (DE-588)1280549890 aut Longitudinal network models Scott Duxbury Los Angeles ; London ; New Delhi ; Singapore ; Washington DC SAGE [2023] © 2023 xv, 140 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Quantitative applications in the social sciences 192 Längsschnittuntersuchung (DE-588)4034036-3 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Netzwerkmodell (DE-588)4131643-5 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 s Längsschnittuntersuchung (DE-588)4034036-3 s Netzwerkmodell (DE-588)4131643-5 s DE-604 Quantitative applications in the social sciences 192 (DE-604)BV000005102 192 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034107235&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Duxbury, Scott Longitudinal network models Quantitative applications in the social sciences Längsschnittuntersuchung (DE-588)4034036-3 gnd Sozialwissenschaften (DE-588)4055916-6 gnd Netzwerkmodell (DE-588)4131643-5 gnd |
| subject_GND | (DE-588)4034036-3 (DE-588)4055916-6 (DE-588)4131643-5 |
| title | Longitudinal network models |
| title_auth | Longitudinal network models |
| title_exact_search | Longitudinal network models |
| title_exact_search_txtP | Longitudinal network models |
| title_full | Longitudinal network models Scott Duxbury |
| title_fullStr | Longitudinal network models Scott Duxbury |
| title_full_unstemmed | Longitudinal network models Scott Duxbury |
| title_short | Longitudinal network models |
| title_sort | longitudinal network models |
| topic | Längsschnittuntersuchung (DE-588)4034036-3 gnd Sozialwissenschaften (DE-588)4055916-6 gnd Netzwerkmodell (DE-588)4131643-5 gnd |
| topic_facet | Längsschnittuntersuchung Sozialwissenschaften Netzwerkmodell |
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