2013 Workshop‎ > ‎Modules‎ > ‎

Mini-Modules

From Monday through Thursday, we are offering a variety of mini-modules from 4:15 to 5:45 each day to supplement the major tracks.  A description of the various mini-modules can be found below. There is no need to sign up for these -- just show up. 

       
Monday
(4:15-5:45)
124 Computer problems Fagan, Borgatti, Tech Support
152 Archival Data Soltis
234 E-Net Floyd & Sterling
TBD Data Lab Various
       
Tuesday
(4:15-5:45)
124 Managing Your Research Site Sterling & Grosser
152 2-mode Data Everett
234 Visualization Fagan
TBD Data Lab Various
       
Wednesday
(4:15-5:45)
124 Online surveys Floyd & Ofem
152 Negative ties Marineau
234 Longitudinal analysis with UCINET Borgatti
TBD Data Lab Various
       
Thursday
(4:15-5:45)
124 Criticisms & Misconceptions of SNA Brass
152 CSS Data Soltis
234 NodeXL Smith
TBD Data Lab Various
       

EVERY DAY

Data Lab (Room TBD)

Instructors: Various

Data labs are an opportunity to work one-on-one with someone to solve technical problems like getting your data into UCINET. 

MONDAY 

Computer Problems (Room 124)

Instructor: Jesse Fagan & Steve Borgatti

Having trouble getting UCINET installed or running on your computer?  Installed the latest version of UCINET, but somehow the old one keeps running? This mini-module is designed to help trouble-shoot problems with the installation and/or running of UCINET on your computer.  In general, the mini-module will be driven by whatever issues participants are experiencing with the software, but if time permits (i.e., we run out of issues/problems), we will also cover some tricks for making using UCINET in a windows environment more friendly and some commonly encountered problems and their solutions.

Archival Data (Room 152)

Instructor: Scott Soltis

In this mini-module we will discuss some of the benefits of collecting data from archival sources (such as information accessible via web sites).  After a brief discussion of data scraping, we will collect two datasets using publicly available data, which we will use to explore some basic features of UCINET such as data entry, visualization, and measuring centrality.

E-Net (Room 234)

Instructors: Theresa Floyd & Chris Sterling

This module covers ego network analysis, the collection of personal network data focusing on local structure.  The first part of this module will present the basics of ego network research highlighting the differences from a whole network approach.  This will be followed by a crash course in E-Net, a computer program designed by Dr. Borgatti specifically for ego network analysis.  The E-Net application portion will cover everything from downloading the software, importing data, and data analysis. 

TUESDAY

Managing your Research Site (Room 124)

Instructors: Chris Sterling & Travis Grosser

Struggling to gain access to an organization to collect data for your project?  Having problems generating a high enough response rate?  Collecting social network data presents some unique problems, organizational decision makers are often unfamiliar with the methods and aims of our research and the relational nature of social network data requires high response rates while making it impossible to guarantee anonymity to our respondents.  These obstacles create a greater need to have organizational decision makers invested in your project, a greater need to build trust and rapport with your respondents, and a greater need to establish your identity within the organization.  This module is aimed at maximizing response rate and building a long-term relationship with your organization by discussing  several different techniques used  throughout the project life cycle including: gaining access to research sites, building trust with participants, handling the logistical issues of data collection, and feeding back results to the organization.  This module will particularly focus on conducting research inside companies and business organizations. 

2-Mode Data (Room 152)

Instructor: Martin Everett

Data which has two types of actors such that there are only connections between types and no connections within is known as 2-mode data. Examples include but are not limited to people attending events, directors and boards of companies, and authors and journals. We shall look at the issues related to data of this type. We first start with visualization seeing how the data is represented in netdraw and show how to include attribute information. We then examine the two basic approaches to data analysis namely projection and the direct method. We look at different types of projections and normalization and consider what to do with valued two-mode data. We also describe the recent dual projection approach and show how this can be applied to core-periphery and structural equivalence. We then move on to the direct methods looking at particularly at centrality and cohesive subgroups.

Visualization (Room 234)

Instructor: Jesse Fagan

In this module we will cover some of the concepts, recent advances, and various tools available related to the visualization of social network data. The first part of the course will cover visualization principles (e.g. color, position, contrast), various ways of visualizing network data, and various layouts for node-link diagrams. The second part we will use the network software Gephi to escape the hairball to create clear visualizations. We will walk through the process of creating high quality network visualizations for publication, posters, or framing on your wall. We will end with a discussion of some recently created tools for the creation of interactive web-based network visualizations.

WEDNESDAY

Online Surveys (Room 124)

Instructors: Theresa Floyd & Brandon Ofem

Want to know how to actually create an online survey and turn the resulting data into UCINET files? This module will cover the nuts and bolts of how to do that. We will discuss how to design whole network surveys using Survey Gizmo and Qualtrics survey software. We’ll also show you how to download the data & get it into UCINET. The examples covered will be specific to management research but the principles will apply to anybody who would like to collect data on a predetermined population of respondents. 

Negative Ties (Room 152)

Instructor: Josh Marineau

This module focuses on relational content and structure from the negative side of the “social ledger”—negative ties. We will discuss negative social content, negative asymmetry, and examine a sample of research studies which use direct and indirect negative ties in social network research. We will also discuss the unique methodological challenges and opportunities related to the study of negative ties. Lastly, we will walk through the process of planning and conducting a negative tie research project with a hands-on lab where we will practice analyzing negative tie data.

Longitudinal Analysis with UCINET (Room 234)

Instructor:  Steve Borgatti

A quick overview of methods for assessing network change, or predicting future ties from present-day ties.

THURSDAY

Criticisms and Misconceptions of SNA (Room 124)

Instructor: Dan Brass 

A discussion of common criticisms and misconceptions about social network research.

CSS Data (Room 152)

Instructor: Scott Soltis

Collecting individual perceptions of social networks is a rewarding, but challenging venture.  In this mini-module we will discuss cognitive social structures, methods for collecting this data, and what to do once you have a dataset.  Topics to be discussed include: Krackhardt-style CSS data, visual scales of network perceptions, CSS aggregation techniques, and measuring accuracy.

NodeXL (Room 234)

Instructor: Marc Smith

Using NodeXL, users can easily make a map of public social media conversations around topics that matter to them. Maps of the connections among the people who recently said the name of a product, brand or event can reveal key positions and clusters in the crowd. Some people who talk about a topic are more in the "center" of the graph, they may be key influential members in the population. NodeXL makes it a simple task to sort people in a population by their network location to find key people in core or bridge positions. NodeXL supports the exploration of social media with import features that pull data from personal email indexes on the desktop, Twitter, Flickr, YouTube, Facebook and WWW hyper-links. The tool allows non-programmers to quickly generate useful network statistics and metrics and create visualizations of network graphs.