Transcript Slide 1

POPULATION RESEARCH SEMINAR SERIES
Sponsored by the Statistics and Survey Methods Core of the U54 Partnership
Personal Social Network Analysis
and its Applications
Rosalyn Negrón, PhD
UMass Boston
Social Network Analysis
SNA is the study of the relationships and the
pattern of relationships between social
actors.
Examples of actors
• Organizations
• Positions within an
organization
• School children
• Nations
• Tweets
• Animals
Examples of relationships (or ties)
• Knowing by face and name
• Asking for advice
• Sharing needles
• Exchanging emails
• Trading
• Sitting next to each other
• Re-tweets
Definitions
• Most social network
measures start from a
representation of nodes and
edges.
– Nodes can be individuals
or other social actors like
organizations or
positions in
organizations
– Edges are the
relationships between
social actors (e.g.,
communication,
friendship choices,
advice, trust, influence,
exchange, and so on)
Graph
Adjacency matrix
This image is a graphic representation of
the following network
V:= {1,2,3,4,5,6}
E:= {{1,2},{1,5},{2,3},{2,5},{3,4},{4,5},{4,6}}
Nodes are often termed “vertices”
Describing Social Networks
•
•
•
•
Composition
Structure
Refers to the attributes of
members of a social network
Measurement of ties between
actors.
Measures include:
average strength of ties
average age of network
members
percent of network that is
female
percent of network that is of
a particular nationality
Measures include:
• centrality
• cliques
• components
• density
Two types of Social Networks
• Sociocentric (Whole) networks:
– a defined group
• Egocentric (Personal) networks:
– pattern of relationships that surround one
individual (ego)
Sociocentric network data collection
• Observed data:
–
–
–
–
selected contacts from a roster
e-mail transactions
telephone calls
attendance at events
• Ask network members to evaluate tie:
– Yes / No
– Scale of 0 to 5, how well do you know, how
close are you)
• Construct matrix representing ties between
network members
sociocentric network data from your class
class network
tie strength
sociocentric network data from your class
class network
Structurally equivalent
Maximizes network
fragmentation
Most degree &
between central
Personal Network Analysis
(aka – egocentric network analysis)
• Select sample of respondents
• Ask respondent questions about themselves
• Elicit network members (alters) from
respondents
• Ask respondent questions about alters
• Ask respondent about relationship between
alters (this is rarely done)
Analyzing Egocentric Network Data
Network Composition: refers to the attributes of
members of a personal network and the nature of
their relationship to the ego.
Personal Network Composition
Friends
Co-workers
Family
Personal Network Composition
Analyzing Egocentric Network Data
Network Structure: refers to the patterns that
emerge from the relationships between different
actors in a network.
Personal Network Structure
Computer-Assisted Personal
Network Data Collection
Existing programs:
Egonet - http://sourceforge.net/projects/egonet/
Egoweb - http://www.rand.org/methods/egoweb.html
VennMaker - http://www.vennmaker.com/?lang=en
Egonet
Includes four modules:
1. For collecting data about ego
2. For eliciting names of alters
3. For collecting data about alters
4. For evaluating ties between alters
Study creation - Ego questions
Study creation - Alter elicitation
questions
Study creation - Questions about alters
Study creation - Alter pair questions
Example output data file
Example of adjacency matrix
Katherine_Lamb
Cathy_Eastin
Joshua_Lamb
Rod_EastinRebecca_Eastin
Donna_Lamb
Rebecca_Lamb
David_Lamb
Phil_Lamb Jennifer_Paigan
Michael_Paigan
Jonathan_Paigan
Arriana_Paigan
Evelyn_Hamilton
Chester_Hamilton
Byron_Hamilton
Amy__Hamilton
Christa_Ham
Cathy_Eastin
0
1
1
1
1
1
1
0
1
0
0
0
1
1
1
1
1
Joshua_Lamb
1
0
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
Rod_Eastin
1
1
0
1
0
0
1
1
0
0
0
0
1
1
1
1
1
Rebecca_Eastin
1
1
1
0
1
1
1
0
0
0
0
0
1
1
1
1
1
Donna_Lamb
1
1
0
1
0
0
1
1
1
1
1
1
0
0
0
0
0
Rebecca_Lamb
1
1
0
1
0
0
1
1
1
1
1
1
0
0
0
0
0
David_Lamb
1
1
1
1
1
1
0
1
1
1
1
1
0
0
0
0
0
Phil_Lamb
0
1
1
0
1
1
1
0
1
1
1
1
0
0
0
0
0
Jennifer_Paigan
1
1
0
0
1
1
1
1
0
1
1
1
0
0
0
0
0
Michael_Paigan
0
1
0
0
1
1
1
1
1
0
1
1
0
0
0
0
0
Jonathan_Paigan
0
1
0
0
1
1
1
1
1
1
0
1
0
0
0
0
0
Arriana_Paigan
0
1
0
0
1
1
1
1
1
1
1
0
0
0
0
0
0
Evelyn_Hamilton
1
0
1
1
0
0
0
0
0
0
0
0
0
1
1
1
1
Chester_Hamilton 1
0
1
1
0
0
0
0
0
0
0
0
1
0
1
1
1
Byron_Hamilton
1
0
1
1
0
0
0
0
0
0
0
0
1
1
0
1
1
Amy__Hamilton
1
0
1
1
0
0
0
0
0
0
0
0
1
1
1
0
1
Christa_Hamilton
1
0
1
1
0
0
0
0
0
0
0
0
1
1
1
1
0
Micah_Hamilton
1
0
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
Charlie_Davis
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
Jan_Davis
1
1
1
1
0
0
0
0
0
0
0
0
1
1
0
1
1
Emily_Davis
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
Christina_Davis
1
0
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
Ian_Davis
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
Marian_Duntley
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
Jim_Duntley
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
Fran_Guyett
1
0
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
Scott_Guyett
1
0
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
Remy_Guyett
0
0
0
1
0
0
0
0
0
0
0
0
1
1
1
1
1
Francis_Lamb
1
1
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
Dot_Wilson
1
1
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
Francis_Rupprecht 1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Doug_Rupprecht
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Judy_Rivera
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Kelly_Anderson
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Craig_Anderson
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Allison_Veal
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Gene_Veal
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Savannah_Veal
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Lauren_Veal
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Sophie_Veal
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Amelia_Veal
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Claudio_Galarsa
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Crystal_Galarsa
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Crystal_Ewell
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Rachel_Criss
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
Some Uses of Egocentric Network Data
• Enables researchers to identify people who play
key roles within people’s networks
• Explore perceptions of people’s social
environment
• Understand a person’s embeddedness or
isolation within her social network.
• Having individuals evaluate the ties between a
large number of alters (ie., 50 alters rather than
5 alters), allows researchers to account for
greater structural variability. For example, more
likely to capture weak ties.
Centrality
Degree Centrality:
• This structural measure is an indicator of
network activity.
• The more degree central a network is, the more
directly interconnected alters are to each other.
• Identifying the most degree central alter is
important because degree central persons can
potentially influence the perceptions and
behaviors of the ego.
Centrality, cont.
Most Degree Central Alter
for Black respondents
for White respondents
Partner
4%
Other
28%
Daughter
8%
Brother
13%
Friend
4%
Co-w orker
0%
Other
23%
Daughter
1%
Brother
0%
Friend
16%
Other family
43%
Partner
22%
Co-w orker
10%
Other family
28%
Centrality, cont.
Betweeness Centrality:
• a measure of information control.
• A highly between central person acts as a bridge
between alters, and thus potentially controls information.
• low mean betweeness scores indicate a highly cohesive
network, with less bridging.
• networks that circulate redundant information because of
their homogeneity and cohesiveness may reinforce likeminded perceptions and behaviors, which can either
strengthen or weaken ethnic identity.
Betweeness Centrality
Most Between
Central Alter
Area of High
Overall Betweenness
Betweeness Centrality
Normalized Mean Betweenness
White
2.1
Black
1.6
0
0.5
1
1.5
2
2.5
P = .05
Betweeness Centrality
Normalized Betweenness Value
for Highest Point Central Alter
27.9
White
Black
15.1
0
5
10
15
20
25
30
P = .001
Other Structural Measures
Isolates and Components
A network with many components implies a
compartmentalized network.
A network with many isolates may be
associated with geographic movement, social
seclusion, or lack of network cohesiveness.
Isolates and Components
People not connected
to anyone else
Visualization
• Visualizes both structural and
compositional network information
• Facilitates exploration & analysis
• Can be used as an interviewing tool
Visualization Tools
• NetDraw: 2-D; contains a number of tools for
visualizing both structural and attributional network
information.
• Visione: 2-D; enables the creation and transformation
of visuals; creates high resolution images
• Egonet: 2-D; visualizations built-in to data collection
module; fast and convenient; visualizes both structural
and attributional network information.
Visualization Tools
• 2-D
•Facilitates the
identification of
clusters
Multi-dimensional Scaling
Using Visualizations
• Identify key informants
• Tool for Rapid Assessment Procedures (RAP)
• Aide in recall
• Enable respondents to better understand what
may seem like abstract sets of relationships
(“Hmm…I never thought about it that way.”)
• Mental health research
Identifying Key Informants and RAP
Aide in recall
Ah-ha Moments
Ah-ha Moments
Mental Health Research
Attend AA
meetings
together
Father’s side
of family
Co-workers
and friends
Distant relatives
Past “hard-core” drinking buddies
Distribution of alters who smoke
Visualization of the networks of two sisters
Label = Country of origin, Size = Closeness, Color = Skin color, Shape =
Smoking Status
• Mercedes is a 19-year-old second
generation Gambian woman in Barcelona
• Laura is a 22-year-old second generation
Gambian woman in Barcelona
• She is Muslim and lives with her parents
and 8 brothers and sisters
• She is Muslim and lives with her parents
and 8 brothers and sisters
• She goes to school, works and stays
home caring for her siblings. She does
not smoke or drink.
• She works, but does not like to stay
home. She smokes and drinks and goes
to parties on weekends.
Source: Christopher McCarty & Jose Luis Molina
POPULATION RESEARCH SEMINAR SERIES
Sponsored by the Statistics and Survey Methods Core of the U54 Partnership
Questions? Comments?
Send us an email!
[email protected]