A User’s Guide to Network Analysis in R
A User’s Guide to Network Analysis in R

Author: Douglas Luke

Publisher: Springer

Published: 2015-12-14

Total Pages: 241

ISBN-13: 3319238833

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Presenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, data collection and management, network description, visualization, and building and testing statistical models of networks. As with all of the books in the Use R! series, each chapter contains extensive R code and detailed visualizations of datasets. Appendices will describe the R network packages and the datasets used in the book. An R package developed specifically for the book, available to readers on GitHub, contains relevant code and real-world network datasets as well.

Statistical Analysis of Network Data with R
Statistical Analysis of Network Data with R

Author: Eric D. Kolaczyk

Publisher: Springer

Published: 2014-05-22

Total Pages: 214

ISBN-13: 1493909835

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Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).

Doing Meta-Analysis with R
Doing Meta-Analysis with R

Author: Mathias Harrer

Publisher: CRC Press

Published: 2021-09-15

Total Pages: 500

ISBN-13: 1000435636

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Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book

Conducting Personal Network Research
Conducting Personal Network Research

Author: Christopher McCarty

Publisher: Guilford Publications

Published: 2019-02-22

Total Pages: 293

ISBN-13: 1462538436

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Written at an introductory level, and featuring engaging case examples, this book reviews the theory and practice of personal and egocentric network research. This approach offers powerful tools for capturing the impact of overlapping, changing social relationships and contexts on individuals' attitudes and behavior. The authors provide solid guidance on the formulation of research questions; research design; data collection, including decisions about survey modes and sampling frames; the measurement of network composition and structure, including the use of name generators; and statistical modeling, from basic regression techniques to more advanced multilevel and dynamic models. Ethical issues in personal network research are addressed. User-friendly features include boxes on major published studies, end-of-chapter suggestions for further reading, and an appendix describing the main software programs used in the field.

Practical Graph Mining with R
Practical Graph Mining with R

Author: Nagiza F. Samatova

Publisher: CRC Press

Published: 2013-07-15

Total Pages: 495

ISBN-13: 1439860858

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Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or cluste

Analyzing Social Networks Using R
Analyzing Social Networks Using R

Author: Stephen P. Borgatti

Publisher: Sage Publications Limited

Published: 2022-05-14

Total Pages: 472

ISBN-13: 9781529722482

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This approachable book introduces network research in R, walking you through every step of doing social network analysis. Drawing together research design, data collection and data analysis, it explains the core concepts of network analysis in a non-technical way. The book balances an easy to follow explanation of the theoretical and statistical foundations underpinning network analysis with practical guidance on key steps like data management, preparation and visualisation. With clarity and expert insight, it: Discusses a range of statistical models including QAP and ERGM, giving you the tools to approach different types of networks Provides a fully integrated discussion of digital data and networks like Twitter, sociolab and Amazon Offers digital resources like practice datasets and worked examples that help you get to grips with R software

Data Science in Education Using R
Data Science in Education Using R

Author: Ryan A. Estrellado

Publisher: Routledge

Published: 2020-10-26

Total Pages: 315

ISBN-13: 1000200906

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Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.

Qualitative Comparative Analysis Using R
Qualitative Comparative Analysis Using R

Author: Ioana-Elena Oana

Publisher: Cambridge University Press

Published: 2021-10-28

Total Pages: 249

ISBN-13: 1316518728

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"This book offers a hands-on introduction and teaching resource for students, users, and teachers of Qualitative Comparative Analysis (QCA; Ragin, 1987, 2000, 2008b). Given its superior ability to model certain aspects of complexity, QCA has made inroads into virtually every social science discipline and beyond. Software solutions for QCA have also been developing at a fast pace. This book seeks to reduce the time and effort required when we first encounter the logic of not just a new method but also new software. It offers a genuinely simple, intuitive, and hands-on resource for implementing the state-of-the-art protocol of QCA using R, the most advanced software environment for QCA. Our book has an applied and practical focus"--

Social Network Analysis
Social Network Analysis

Author: Song Yang

Publisher: SAGE Publications, Incorporated

Published: 2016-12-02

Total Pages: 0

ISBN-13: 9781483325217

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Social Network Analysis: Methods and Examples by Song Yang, Franziska B. Keller, and Lu Zheng prepares social science students to conduct their own social network analysis (SNA) by covering basic methodological tools along with illustrative examples from various fields. This innovative book takes a conceptual rather than a mathematical approach as it discusses the connection between what SNA methods have to offer and how those methods are used in research design, data collection, and analysis. Four substantive applications chapters provide examples from politics, work and organizations, mental and physical health, and crime and terrorism studies.

Urban Network Analysis
Urban Network Analysis

Author: Andres Sevtsuk

Publisher:

Published: 2018-08-20

Total Pages:

ISBN-13: 9780692172773

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Reference and user guide for the Urban Network Analysis plugin for Rhinoceros 3D software, along with case study applications.