1. Social Network Analysis: A Practical Introduction for Beginners
Jonathan Rubright, MS
Steve Fifield, PhD
Delaware Education Research and Development Center
EERS 19 April 2010

2. SNA: What is it?
"the mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities." (Valdis Krebs, 2002)
Nodes
People, Organizations . . .
Connected by Ties
Relationship existence and strength
Form analyzable Patterns
Social network analysis helps reveal real network outside organizational charts, and can identify boundaries of flow and connection.

3. Sociogram example

4. Before and After
What changed? What remains similar?
2006
2008

5. 2006 2008 Yellow=Clinician Green=Clinician/Scientist Blue=Scientist
White=NR

6. Common Issues & Applications
Formal vs. informal organizational structures & processes
Key individuals & subgroups
Information flow & supportiveness
Changing practices & changing relationships

7. Data Collection Considerations
Unbounded network data (name-generator)
Social networks reported by individuals
Bounded network data (roster approach)
Network boundaries with complete list of members

8. Unbounded Network data
Which people in the neighborhood would you trust to look after your child?
At work, in neighborhood – how define?

9. Bounded network data
Which of these people in the neighborhood would you trust to look after your child?
Name
Trust to look after?
Susan
Yes / No
Jill
Tommy
Billy
Bounded networks not able to draw from outside members, yet often produce more connected networks.

10. Data Setup
Most familiar datasets have a set of cases with variables on each case…
Name
Sex
Age
Role
Jack M 22
Admin
Jill F 35
Director
Andre 68
Sandra 42
Financial

11. Data Setup 2
Most SNA datasets are matrices with nodes in both rows and columns
Cells represent relationships between nodes
Jack
Jill
Andre
Sandra . 1

12. Methodological Considerations
Missing data can seriously distort network
Limiting number of network choices truncates the network
Informant fatigue
Limitations of self report
Inconsistent definition of relations
Dynamic nature of networks

13. Data Collection Example
Complete network data
Have you met this person before this session at EERS?
Data organization, management, analysis

14. Connectedness
Identifying characteristics of individual actors based on their relationships with others in the network
Examples: indegree, outdegree,betweenness
Identifying characteristics of subgroups in the network
Identifying characteristics of the network as a whole
Density

15. Questions and Comments?
Questions you might like to answer using social network analysis?

16. Thank you! Jonathan Rubright, rubright@udel.edu
Steve Fifield,

17. Bibliography of Resources
Books
Websites

18. 2006

19. 2008

20. 2006 Yellow=Clinician Green=Clinician/Scientist Blue=Scientist
White=NR 20

21. 2008 Yellow=Clinician Green=Clinician/Scientist Blue=Scientist
White=NR

22. SPSS Code Example
*Extract data from master data file for anonymous collaborative network analysis.
*Research colloborator before?
*First I will load the main Excel dataset into SPSS.
GET DATA
/TYPE=XLS
/FILE='C:\Documents and Settings\rubright\Desktop\data_clean_ impute.xls'
/SHEET=name 'Sheet1'
/CELLRANGE=full
/READNAMES=on
/ASSUMEDSTRWIDTH=32767.
DATASET NAME DataSet1 WINDOW=FRONT.
*I'll rename the variables names for easier use in UCINet.
RENAME VARIABLE (John_before=John).
RENAME VARIABLE (Steve_before=Steve).
*Finally, I will save the updated dataset as an Excel file named before_anon.xls, only variables usable in UCINet.
SAVE TRANSLATE OUTFILE='C:\Documents and Settings\rubright\Desktop\before_nr_anon.xls'
/VERSION=8
/MAP
/REPLACE
/FIELDNAMES
/CELLS=VALUES
/KEEP= ID
John
Steve.