The Center for Integrated Latent Variable Research (CILVR)

presents the ONLINE short course:

INTRODUCTION TO SOCIAL NETWORK ANALYSIS

December 10-11, 2021 (Friday-Saturday)

taught by

Tracy Sweet, University of Maryland

SHORT COURSE DESCRIPTION

Social networks are defined by a set of relationships among a group of individuals and are common in any discipline in which individuals interact. Examples include friendship among a group of people, collaborations among employees in an organization and co-authorship among researchers in a field.  Analyzing how and why individuals interact can help researchers better understand both the system as a whole as well as how these interactions impact systemic change.

The purpose of this two-day course is to introduce methods for analyzing social network data, focusing on the types of network data common in the social sciences.  Although some exploratory and descriptive methods in social network analysis will be covered, the focus of this course is to teach participates how to fit and interpret social network models. 

This course is targeted for participants interested in learning about social network models and is appropriate for researchers at any stage in their career, students included. We recommend that participants be familiar with R and fitting statistical models although previous experience with social network analysis is not necessary.

The course will begin with a brief introduction to R for participants who are new to that software. We will then cover descriptive methods and practice visualizing/exploring networks in R.  Much of the course will focus on social network models, social selection models in particular.  We will also cover multilevel social network models and discuss goodness of fit.  Throughout the course, we will incorporate hands-on practice, analyzing real world data and fitting models in R.

After completing the course, participants will have an understanding of quantitative methods available for analyzing social networks as well as the current state of model capabilities.  They will be able to analyze their own network data using R code created during the course; they will be able to both fit and interpret a variety of social network models; and they will have an understanding of model assessment and goodness of fit.

TOPICS

  • Introduction to R
  • Introduction to Social Network Analysis
  • Descriptive statistics and community detection
  • Introduction to Social Network Models

TARGET AUDIENCE

Graduate students, faculty, research professionals who are interested in social networks.

REQUISITE KNOWLEDGE

It is assumed that participants have knowledge of general and generalized linear models, especially logistic regression. It is recommended that participants be familiar with R or other command line software.

SOFTWARE

Empirical examples and hands-on exercises for this workshop will be done using the open source software R.  It is recommended that participants attend the course having downloaded the most recent version of R on their laptop.

DATES AND TIMES

December 10-11, 2021 (Friday-Saturday)

10:00am-5:00pm Eastern Standard Time (UTC-5)

Instructor will determine timing of lunch break, as well as morning and afternoon breaks.

REGISTRATION RATES

Professional: $345

Full-time student*: $195

*Full-time students must submit student status proof at https://go.umd.edu/CILVR-STUDENT for prompt processing of the registration.

Free for registered HDQM Department faculty and degree-seeking students, although you must register through the internal link. 

HOW TO REGISTER

One-time Registration:

- For professional and full-time student participants, please register using this link: https://go.umd.edu/SNA-2021  

- Full-time students must also submit the student status proof at https://go.umd.edu/CILVR-STUDENT for prompt processing of the registration. Note that it may take 2-3 business days for your registration to be processed.

Bundle Registration:

- Participants who wish to register for multiple CILVR short courses in 2021-2022 as a bundle and obtain ONE receipt for the bundle registrations could submit the request at https://go.umd.edu/CILVR-BUNDLE

HDQM Registration:

- HDQM department registrants can register using the following registration form: https://go.umd.edu/SNA-2021-HDQM  

LOCATION AND PLATFORM

This workshop will be delivered entirely online via the video conferencing software Zoom (https://zoom.us/). 

Within a limited time, the video recordings of the short course will become available for both synchronous and asynchronous participants on Vimeo (https://vimeo.com/). The videos will be available for six months from the first date of the short course.

IMPORTANT COURSE DETAILS

Format: Participants will receive a personalized login code to use on their own computer to access a reliable live-stream of the short course over Zoom, showing the instructor as well as the handouts.

Materials: Participants will receive electronic copies of the short course materials, as well as any other relevant materials or information.

Timing/access: Participants may choose to watch the stream synchronously, or may elect to watch a recording of the short course asynchronously, or both. Recordings will be available to participants for six months from the start of the short course. This is especially useful for on-line participants in different time zones who may choose to watch at some later time than (but within six months of) the actual short course time. (Asynchronous participation does not include real-time chat with other on-line participants, although a visual record of prior chats will be viewable).

Technical support: Participants are responsible for installing the conferencing software Zoom on their own electronic devices and for obtaining a Zoom account that allows the participant to join Zoom meetings and webinars hosted by external organizations. Participants are assumed to be able to secure a reliable computer, internet browser, and Wi-Fi connection. Challenges at the user end must be resolved by the user. Fortunately, because the short course is recorded, users experiencing technical challenges can still “catch up” by watching the recordings to which they will have access.

Content support: During the lecture, real-time content support for on-line participants is mostly limited to real-time chat with the on-line (Zoom) participant community and any quantitative methodology doctoral students who might also be participating. Participants may have direct interactions with the instructor in some format during the practice sessions. On-line participants may e-mail the instructor for further content support that cannot be addressed in real-time.

REFUND POLICY

Full refund if cancellation occurs at least 10 business days prior to the workshop date; 50% refund if within 10 days of the first day of the course.

QUESTIONS?

For any questions, please contact Ms. Yishan Ding: sna.cilvr@gmail.com

ABOUT THE INSTRUCTOR

Dr. Tracy Sweet is an Associate Professor of Measurement, Statistics, and Evaluation (EDMS) in the Department of Human Development and Quantitative Methodology at the University of Maryland. Dr. Sweet has taught several workshops on social network models and recently taught an advanced graduate course on social network models. She has also taught a number of graduate-level quantitative methods courses on general linear models, multilevel modeling, Bayesian inference, and statistical consulting. Her research interests include developing statistical models for social networks as well as data mining methods for multilevel social network data. Dr. Sweet has published methodological papers in prominent journals such as Journal of Educational and Behavioral Statistics and Social Networks, and application papers in American Educational Research JournalSociology of Education, and Educational Psychologist. Her research has been funded by the Institute of Educational Sciences (IES). She currently serves as an Associate Editor for Journal of Educational and Behavioral Statistics.  Before joining the EDMS faculty in the fall of 2013, Dr. Sweet worked as a postdoctoral researcher at Carnegie Mellon University where she received her Ph.D. in 2012. She may be reached at tsweet@umd.edu.