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The Interuniversity Consortium for Social and Political Research and the
Indiana University Network Science Institute will be hosting two new short
courses in July. There are still seats left for those interested in
enrolling. Courses run 9-5 from Monday-Friday and registration is through
the ICPSR summer program.
http://www.icpsr.umich.edu/icpsrweb/content/sumprog/registration.html
<http://www.icpsr.umich.edu/icpsrweb/content/sumprog/registration.html>


1. *July 18-22: Egocentric Network Analysis (Brea Perry, with Erin Pullen) *
http://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0213
<http://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0213>

2. *July 25-29: Network Analysis: Study Design and Methods (Bernice
Pescosolido and Ann McCranie)*
http://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0212
<http://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0212>


*Egocentric Network Analysis*


Egocentric social network analysis (SNA) is a methodological tool used to
understand the structure, function, and composition of network ties around
an individual. Both sociocentric (i.e. whole) network analysis and
egocentric network analysis share the basic assumption that behaviors,
beliefs, attitudes, and values of individuals are shaped through contact
and communication with others. However, these two methods are distinct in a
number of important ways:



   1. Unbounded versus bounded networks. Sociocentric SNA collects data on
   ties between all members of a socially or geographically-bounded group and
   has limited inference beyond that group. Egocentric SNA assesses
   individuals' personal community networks across any number of social
   settings using name generators, and is therefore less limited in
   theoretical and substantive scope.
   2. Focus on individual rather than group outcomes. Sociocentric SNA
   often focuses on network structures of groups as predictors of group-level
   outcomes (e.g. concentration of power, resource distribution, information
   diffusion). In contrast, egocentric SNA is concerned with how people's
   patterns of interaction shape their individual-level outcomes (e.g. health,
   voting behavior, employment opportunities).
   3. Flexibility in data collection. Because sociocentric SNA must use as
   its sampling frame a census of a particular bounded group, data collection
   is very time-consuming, expensive, and targeted to a specific set of
   research questions. In contrast, because egocentric SNA uses individuals as
   cases, potential sampling frames and data collection strategies are
   virtually limitless. Egocentric data collection tools can easily be
   incorporated into large-scale or nationally-representative surveys being
   fielded for a variety of other purposes.

While no single course could cover the entire breadth of the field, we will
examine the most fundamental methodological issues and practical concerns
that arise in egocentric network research. This course requires no prior
knowledge of egocentric SNA. We will begin with an introduction to the
foundational concepts of egocentric SNA, highlighting linkages to theories
commonly used in the social and health sciences (e.g. social capital). The
rest of the course will cover methodological considerations and statistical
techniques for egocentric SNA. In addition to covering data collection
strategies (e.g. name generators, name interpreters), measures, and
modeling in a lecture format, participants will learn to use Stata and
E-NET software packages in daily lab sessions. These sessions will
primarily focus on interactive use of Stata and E-NET in a computer lab,
providing hands-on practice exercises using a range of substantive topics.
E-NET is a free software package for egocentric network analysis and
visualization created by the developers of UCI-NET.



*Network Analysis: Study Design and Methods*


Social network analysis (SNA) focuses on relationships between social
entities. It is used widely in the social and behavioral sciences. The
social network perspective, which will be taught in this workshop, has been
developed over the last seventy years by researchers in psychology,
sociology, political science, and anthropology. New interest in this field
by physics, information science, social media studies, and biomedical
fields has spiked in the past 15 years - this approach is often referred to
as "network science." While this approach sometimes differs importantly in
scale and substantive interest, it is often used to study the exact same
problems as traditional SNA. This course will connect these two traditions
in their terminology and specific methodological approaches.


This week-long workshop covers precisely those SNA concepts and tools, and
has a special focus on how to design a network study and how to plan and
execute data collection. It will present an introduction to various
concepts, methods, and applications of social network analysis drawn from
the social and behavioral sciences. The primary focus of these methods is
the analysis of relational data measured on groups of social actors. Topics
to be discussed include a basic introduction to SNA, graphs and matrices,
basic network measures and visualization, reciprocity and transitivity,
dyadic and triadic analysis, centrality, egocentric networks, two-mode
networks (affiliations, bibliographic/scientometric analysis), cohesive
subgroups, equivalences and blockmodeling, and a brief introduction to
statistical modeling in network (ergm/p*/RSiena.)


Please note: The focus on statistical models (ergm/p*/Siena models) is
limited and introductory in this course - those are the explicit focus of
the other advanced courses in the ICPSR series. Also, this course focuses
largely on "whole" or "complete" networks in which sociometric analysis is
required. Egocentric analysis is not a primary focus of this course, but
will be a topic of discussion and inclusion when appropriate with the rest
of the course.

***
Ann McCranie ([log in to unmask])
Assistant Director of Research Administration
Indiana University Network Science Institute <http://iuni.iu.edu/>

Managing Editor, Network Science <http://journals.cambridge.org/nws> (
[log in to unmask])

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