Free Short Course Application
sponsored by Paul F. Lazarsfeld Lecture Series
Course: Social Network Analysis
This course introduces some fundamental ideas, concepts, measures, and methods of social network analysis. The focus is on the fundamentals of social network analysis, on how social network analysis is used in social scientific research, and on some quantitative techniques appropriate for the analysis of social network data. A tutorial on the software package for the analysis of social network data UCINET is also part of the course.
See Full Course Outline Below Application
Instructor: Dr. Mattias Smångs
Friday, November 9, 2018: 10:00 - 12:00 p.m. and 1:00 - 3:00 p.m.
Friday, November 16, 2018: 10:00 - 12:00 p.m. and 1:00 - 3:00 p.m.
Please note that this course takes place on consecutive Fridays. Participants must attend all sessions.
**APPLICATION DEADLINE: October 29, 2018 at noon**
Participants will be notified of their admission by Nov. 2. This course is Free.
FULL COURSE OUTLINE
WEEK ONE. Introduction to Social Network Analysis and UCINET
1.1 Social network analysis as social science
1.1.1 Attributes versus relations
1.2 What is a social network? Basic elements of social network data
1.2.1 Nodes and lines
1.2.2 Direction, value, and mode
1.3 Basic data structures
1.3.1 Graphs, matrices and lists
1.4.1 Importing data and viewing matrices in Ucinet
1.4.2 Symmetrizing, dichotomizing, recoding, unpacking
1.5 Two-mode Social Network Data
1.6.1 Visualizing network and attribute data in Netdraw
1.6.2 Properties (size, shape, and color of nodes, lines, and labels)
WEEK TWO: Basic Approaches, Concepts, and Measures
2.1 Relational or Connectionist Orientation
2.1.2 Network level cohesion (density, connectivity, distance, components, fragmentation, small-world networks)
2.1.3 Cohesive subgroups (cliques, k-plex, k-core, factions, subgroup overlap analysis)
2.1.4 Individual level cohesion (degree, betweenness, closeness)
2.2 Structural or Positional Orientation
2.2.1 Roles and positions (structural and regular equivalence, block models)