This volume introduces the basic concepts of Exponential Random Graph Modeling (ERGM), gives examples of why it is used, and shows the reader how to conduct basic ERGM analyses in their own research. ERGM is a statistical approach to modeling social netwo
This volume introduces the basic concepts of Exponential Random Graph Modeling (ERGM), gives examples of why it is used, and shows the reader how to conduct basic ERGM analyses in their own research. ERGM is a statistical approach to modeling social netwoHinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Jenine K. Harris earned her doctorate in public health studies and biostatistics from Saint Louis University School of Public Health in 2008. Currently, she teaches biostatistics courses as an Associate Professor in the Brown School public health program at Washington University in St. Louis. In 2013, she authored An Introduction to Exponential Random Graph Modeling, which was published in the Sage Quantitative Applications in the Social Sciences series and is accompanied by the ergmharris R package available on the Comprehensive R Archive Network (CRAN). She is an author on more than 80 peer-reviewed publications, and developed and published the odds.n.ends R package available on the CRAN. She is the leader of R-Ladies St. Louis, which she co-founded with Chelsea West in 2017 (@rladiesstl). R-Ladies St. Louis is a local chapter of R-Ladies Global (@rladiesglobal), an organization devoted promoting gender diversity in the R community. Her recent research interests focus on improving the quality of research in public health by using reproducible research practices throughout the research process.
Inhaltsangabe
1. The Promise and Challenge of Network Approaches 2. Statistical Network Models 3. Building a Useful Exponential Random Graph Model 4. Extensions of the Basic Model for Directed Networks and Using Dyadic Attributes as Predictors 5. Conclusion and Recommendations
1. The Promise and Challenge of Network Approaches 2. Statistical Network Models 3. Building a Useful Exponential Random Graph Model 4. Extensions of the Basic Model for Directed Networks and Using Dyadic Attributes as Predictors 5. Conclusion and Recommendations
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