Social Network Analysis

Monday, June 6, 2016 - Friday, June 10, 2016 8:30 AM - 12:30 PM
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Course Description

This course will lay the groundwork behind social network analysis (SNA) from a conceptual and computational perspective. The ways SNA differs from other analytic perspectives requires unique strategies for data collection, storage, descriptive and statistical analysis. The course will address each of these by sampling from a range of the most commonly used analytic concepts, and demonstrate their implementation (primarily in R). Those concepts will be presented around two organizing principles: (1) the two primary theoretical frameworks researchers employ to capture different reasons we think networks 'matter'; and (2) how each class of measures can be applied across different units of analysis: individuals, groups and 'whole' networks. While by no means exhaustive, this introduction will provide the tools with which participants will be equipped to dig deeper into SNA on their own.

This course is eligible for an EPIC scholarship. Visit the scholarship application page for more information.


Course Objectives

By the end of the course, participants will be able to:

  1. Understand the primary frameworks (theoretical and analytic) upon which SNA is developed.
  2. Have a grasp of the primary strategies for gathering and storing social network data.
  3. Compute and interpret several of the primary classes of SNA measures, as applied to individuals, small groups and 'whole' networks.
  4. Describe some of the most commonly observed patterns in empirical networks.
  5. Run descriptive and statistical analyses in R for these network patterns on 'real world' network data examples.

Prerequisites

A computer with R installed and internet access.


Course Reading List
  1. Butts CT. Social Network Analysis: A Methodological Introduction. Asian Journal of Social Psychology 2008; 11:13-41.
  2. Marsden PV. Survey Methods for Network Data. In: Scott J, Carrington PJ, editors. The Sage Handbook of Social Network Analysis: Sage; 2011. p. 370-388.
  3. Bender-deMoll S, McFarland DA. The Art and Science of Dynamic Network Visualization. Journal of Social Structure 2006;7(2).
  4. Rivera, MT, Soderstrom SB, Uzzi B. Dynamics of Dyads in Social Networks: Assortative, Relational, and Proximity Mechanisms. Annual Review of Sociology 2010;36: 91-115.
  5. Torfs P, Brauer C. A (very) Short Introduction to R. 2012. (http://bit.ly/R-intro)

Instructor(s)


jimi adams, PhD

The focus of jimi adams' research is on how networks promote or constrain the spread of things like diseases and ideas through a population. Increasingly, this work focuses on how interdisciplinary scientific fields are arranged and evolve through time. Previously, this has involved examining patterns that contribute to HIV/AIDS transmission and prevention in the US and sub-Saharan Africa. Dr. adams’ previous SNA-related teaching includes a conceptually oriented survey course for undergraduates, a graduate seminar on social network data collection methods in health research, and SNA (both for graduate students and for 4 previous years at EPIC) - each drawing students from a wide-range of disciplinary backgrounds.



Course Fee

Registration is $850.00


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Location

Hammer 316

Hammer Health Sciences Building
701 West 168th Street
New York, NY 10032

Click here for directions


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