Grappling with Grouping III Social Network Analysis

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Grappling with Grouping III
Social Network Analysis
David Henry
University of Illinois at Chicago
Allison Dymnicki
American Institutes for Research
Advancing Health Practice and Policy through Collaborative Research
Acknowledgments
Families and Communities
Research Group
Patrick Tolan
Deborah Gorman-Smith
Michael Schoeny
Social Network and
N
Normative
ti IInfluence
fl
P
Projects
j t
Fern Chertok
Daneen Deptula
Allison Dymnicki
y
Jane Jegerski
Christopher Keys
Kimberly Kobus
Jennifer Watling Neal
Zachary Neal
Michael Schoeny
Advancing Health Practice and Policy through Collaborative Research
Acknowledgments
• This work was supported by grants from the
Centers for Disease Control and Prevention and the
National Institute of Justice. The content of this
presentation is solely
p
y the responsibility
p
y of the
authors and does not necessarily represent the
official views of the funders.
• For more information
information, visit www.ihrp.uic.edu.
www ihrp uic edu
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Grappling with Grouping
I. Cluster Analysis
II. Clustering methods for binary variables
III. Social Network Analysis
Central theme: Clustering approximates uniqueness in
the same way that a sample mean approximates a
population.
Advancing Health Practice and Policy through Collaborative Research
N
Network
kA
Analysis
l i
• Widely used in community psychology research
• 28 studies since 2000 in jjust two jjournals ((AJCP,, JCP))
– Search terms: “Network Analysis”
• Similar search using the term “cluster analysis”
returned 14 studies.
Advancing Health Practice and Policy through Collaborative Research
Community
y Studies Employing
p y g Network Analysis
y
Study
Variables
Type of Analysis
Swindle et al., 2000
Positive and negative social transactions in networks of HIV+ persons
Rating scales
Hirsch et al., 2002
Differences in strength of ties by race
Ego network
Ying, 2002
Social network composition of Taiwanese graduate students Rating scales
Langhout, 2003
Rating scales
Zea et al., 2004
A single case study using ego networks
Violence among female gang members increases before pregnancy and decreases afterward
Criticism practical support were significant predictors of
Criticism, practical support were significant predictors of mental health for battered women.
Differences between nearly homeless and housed women on beliefs about netweok members as housing resources
Target‐specific factors were related to the probability of disclosure.
Chia, 2006
Sociometric nominations in a work organization
Sociometric
Knowlton & Latkin, 2007
Ego networks
Ego network
Dominguez & Maya‐Lariego, 2008
Ego network support characteristics
Ego network
Pernice Duca 2008
Pernice‐Duca, 2008
Social Support in clubhouse mental health programs
Social Support in clubhouse mental health programs
Rating scales
Rating scales
Fleisher & Krienert, 2004
Levendosky et al., 2004
Toohey et al., 2004
Ego network
Qualitative Interviews
Rating scales
Rating scales
Advancing Health Practice and Policy through Collaborative Research
Community
y Studies Employing
p y g Network Analysis
y
Study
Variables
Type of
Analysis
y
Toro et al., 2008
Social Support in homeless adults ‐ ego networks
Campo et al., 2009
Latkin et al., 2009
Convergent and discriminant validity with other measures
Rating scales
Network drug use contributed to perceptions of neighborhood disorder.
Ego network
Neal, 2009
Density, centrality, and relational aggression
Informant
Trotter & Allen, 2009
Ego networks, qualitative analysis
Ego network
Crowe, 2010
Personal and Organizational Community Networks
Sociometric
L
Lugue et al., 2010
t l 2010
Haines et al., 2011
Community Cancer Network
C
it C
N t
k
S i
Sociometric
ti
Social Support buffered ecological risk effects on psychological distress
Rating scales
Network characteristics of an interdisciplinary collaboration based on multiple types of relationships
Sociometric
Neal et al (2011)
Neal et al. (2011)
Cohesion vs structural similarity in teacher advice networks
Cohesion vs. structural similarity in teacher advice networks
Prelow et al., 2010
Ego network
Sociometric
Advancing Health Practice and Policy through Collaborative Research
Variety
V
i t off Network
N t
kM
Methods
th d
in Community Psychology Studies
Type
Number
Sociometric nominations
5
Informant-based Methods
1
Ego-networks
7
Rating Scales
8
Qualitative
1
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Outline
• Overview of methods
– Sociometrics
– Informants
– Ego-networks
Ego networks
– Dynamic
• For each (-1)
– Theory/method/measures
– Software
– Strengths and limitations
Advancing Health Practice and Policy through Collaborative Research
Network Analysis with Sociometrics
• Data source: Relationships
– “Who are your friends?” (Kobus & Henry, 2009)
– “What
a o
organizations
ga a o s do you be
belong
o g to?”
o (C
(Crowe,
o e,
2010)
• Analysis: Matrix and Graph Representations
A
B
C
D
E
F
G
A
0
1
0
1
0
0
0
B
1
0
0
1
0
0
0
C
0
0
0
0
1
1
0
D
1
1
0
0
0
0
0
E
0
0
1
0
0
1
0
F
1
0
0
0
0
0
0
G
0
0
0
0
0
1
0
B
E
F
C
A
D
G
Advancing
Health
Practice
Policy
through
CollaborativeResearch
Research
Advancing
Health
Practice
andand
Policy
Through
Collaborative
Sociometrics: Network Measures
DENSITY 
X
g (g
( g  1)
 12 / 42  0.28
where
X = relationships (ties) = 12
g = network size (# of potential relationships) = 42
A B C
D E
F
G
Σ
A
0
1
0
1
0
0
0
2
B
1
0
0
1
0
0
0
2
C
0
0
0
0
1
1
0
2
D
1
1
0
0
0
0
0
2
E
0
0
1
0
0
1
0
2
F
1
0
0
0
0
0
0
1
G
0
0
0
0
0
1
0
1
Σ
3
2
1
2
1
3
0
12
B
E
F
C
A
D
G
Advancing
Health
Practice
Policy
through
CollaborativeResearch
Research
Advancing
Health
Practice
andand
Policy
Through
Collaborative
Sociometrics: Networks Measures
2
2
g
M
L
2
(

1
)

 L2
2
 
 0.77
2
2
L( g  1)  L  L2
• Mutuality Index
M = number of mutual relationships = 5
g = network size = 7
L = sum off the
th outdegree
td
off the
th total
t t l network
t
k = 12
L2 = sum of squares of the outdegree of the total network =22
A B C
D E
F
G
Σ
A
0
1
0
1
0
0
0
2
B
1
0
0
1
0
0
0
2
C
0
0
0
0
1
1
0
2
D
1
1
0
0
0
0
0
2
E
0
0
1
0
0
1
0
2
F
1
0
0
0
0
0
0
1
G
0
0
0
0
0
1
0
1
Σ
3
2
1
2
1
3
0
12
B
E
F
C
A
D
G
Advancing Health Practice and Policy through Collaborative Research
Sociometrics: Network Measures
• Boundary Density (Hirsch, 1980)
Tactual

BD 
 0.083
Tpossible
Tactual = number of actual ties across subgroups = 2
Tpossible = number of possible ties across subgroups = 24
A B C
D E
F
G
Σ
A
0
1
0
1
0
0
0
2
B
1
0
0
1
0
0
0
2
C
0
0
0
0
1
1
0
2
D
1
1
0
0
0
0
0
2
E
0
0
1
0
0
1
0
2
F
1
0
0
0
0
0
0
1
G
0
0
0
0
0
1
0
1
Σ
3
2
1
2
1
3
0
12
B
E
F
C
A
D
G
Advancing Health Practice and Policy through Collaborative Research
Sociometrics: Measures of Individuals
D
B
ij
Mean Geodesic Distance
j
ij
j
oor
D
B
ji
j
ij
j
where D = Distance and B = Reachability
In: 1.33 for F, 2.0 for D and 2.16 for G
A B C
D E
F
G
Σ
A
0
1
0
1
0
0
0
2
B
1
0
0
1
0
0
0
2
C
0
0
0
0
1
1
0
2
D
1
1
0
0
0
0
0
2
E
0
0
1
0
0
1
0
2
F
1
0
0
0
0
0
0
1
G
0
0
0
0
0
1
0
1
Σ
3
2
1
2
1
3
0
12
B
E
F
C
A
D
G
Advancing Health Practice and Policy through Collaborative Research
Sociometrics: Measures of Individuals
B
E
• Position
– Member
– Liaison
– Isolate
F
C
A
D
G
• Liaisons > Members or Isolates on tobacco and alcohol use
(2 studies)
• Members and Isolates more influenced by peer substance
use than Liaisons.
Advancing Health Practice and Policy through Collaborative Research
Sociometrics: Statistical Models Example
D d h
Dyads
have a ttendency
d
tto b
become ttriads:
i d
“birds of a feather? or “friends of friends?”
We can model the likelihood of triad closure, but the
chance models are complex
Random graph models and double permutation tests
provide alternatives suitable for predicting network
structures or individual ties.
Advancing Health Practice and Policy through Collaborative Research
N
Network
kA
Analysis
l i with
i h Sociometrics
S i
i
• Strengths
– Unbiased assessment of social influence
– Patterns of diffusion and communication
– Rich measurement and theory
• Limitations
Li it ti
– Missing nominators compromise accuracy
– Costly assessment
– Complex coding and analysis
– Requires
q
bounded social space
p
Advancing Health Practice and Policy through Collaborative Research
Network Analysis with Sociometrics
Software
•
Stand alone Programs
Stand-alone
– UCINET (http://www.analytictech.com/ucinet/)
– Krackplot (Freeware – visualization software)
(http://www.andrew.cmu.edu/user/krack/krackplot.sh
http://www andrew cmu edu/user/krack/krackplot sh
tml)
•
R (http://www.r-project.org/)
– iGraph
– sna
•
Excel
– NodeXL:
N d XL Freeware
F
http://nodexl.codeplex.com/
htt // d l d l
/
Advancing Health Practice and Policy through Collaborative Research
Network Analysis from Informants
• Data Source: “Who hangs out together?”
Informants
Informant
1
Port
Port
Compilation
Kit
Tunner
1
0
Kit
0
Port
Kit
1
Tunner
.5
.5
5
Informant
2
Port
Kit
1
1
1
• Variations
– Cognitive Social Structures (Krakhardt, 1987)
– Social Cognitive Mapping (Cairns et al., 1985)
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Informants: Examples
• Cairns, Leung, Buchannan, and Cairns (1995) used
social cognitive mapping to study the fluidity, reliability,
and interrelations of social networks of 4th and 7th
graders over a 3-week period.
• Neal (2009) used Cognitive Social Structures to study
the influence of centrality and density on relational
aggression in a sample of 3rd through 8th grade
children.
Advancing Health Practice and Policy through Collaborative Research
Informants: Software
• Cognitive Social Structures
R ij  R i, j
– consensus aggregation
k
across
ac
oss k informant
o a matrices
a ces
can be done in Excel or R.
– See Krackhardt (1987) for specific instructions.

• Social Cognitive Mapping
– Contact Man-Chi Leung, Ph.D. at UNC ([email protected] ) for a copy of the SCM 4.0
program
p
g
and manual.
Advancing Health Practice and Policy through Collaborative Research
Network Analysis from Informants
• Strengths:
– Provides valid estimates of network ties with
comparatively few informants.
– Missing data does not decrease accuracy
– Economical to administer
• Limitations
– Difficult to assess directed relations
– Requires bounded social space (e.g., classrooms,
g
)
schools,, organizations)
Advancing Health Practice and Policy through Collaborative Research
Ego Network Analysis
• Theory: Best for unbounded networks where
saturation is not possible
• Data Source:
– Prompts for different social functions
– Demographics, relationships, frequency
– Behavior of network members
• Analysis:
– Network
Net ork size,
si e densit
density, diversity,
di ersit boundary
bo ndar densit
density,
heterogeneity, position, behavior of network
members
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Ego Networks: Measures
• Heterogeneity
 n  Ak  2 
  
e
HeterogeneityiA  1   1   


n




where A = a categorical attribute (e.g., gender, race)
Ak = number of individuals with the attribute
e = number of individuals with valid data on A
n = total number of traits of A in the ego network
Advancing Health Practice and Policy through Collaborative Research
Ego
g Networks: Examples
p
• Dominguez & Maya-Lariego, 2008
– Ego networks of host individuals and immigrants in
the U.S. and Spain
– Host individuals had lower centralityy than did
immigrants according to multiple measures.
• T
Tolan,
l
G
Gorman-Smith,
S ith & H
Henry, 2003
– Ego network assessments of delinquent involvement
in adolescent males
– Network violence predicted future individual violence.
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E
Ego
Networks
N
k
• Strengths
– Does not require assessment of entire network
– Can provide social network and social support
i f
information
ti
– Does not require bounded social space.
• Limitations
– Possible bias in the direction of the individual’s
behavior
– Ego is central by definition, so meaning of position
and centrality are problematic.
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E
Ego
Networks:
N
k S
Software
f
• Like informant-based network data, ego networks
populate matrices and graphs of the type we have
been discussing.
• Ego network data can be visualized in Krackplot and
other programs and analyzed using any software
program you would use to analyze sociometric data.
• Because ego networks tend to be smaller than
networks derived from sociometric studies, analyses
can often be conducted by hand or using Excel or
SPSS.
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Dynamic Social Network Analysis
• Theory
– Social relationships are dynamic
– Most SNA is static
– Static analysis may miss important characteristics of
the social world.
• Examples
– Is “liaison” a position or a transition state?
– Changes
g in p
parent g
groups
p over the course of
intervention.
Advancing Health Practice and Policy through Collaborative Research
Group
p 212: Pre
Advancing Health Practice and Policy through Collaborative Research
Group
p 212: Session 4
Advancing Health Practice and Policy through Collaborative Research
Group
p 212: Session 9
Advancing Health Practice and Policy through Collaborative Research
Group
p 212: Session 14
Advancing Health Practice and Policy through Collaborative Research
SAFE-E Group 212
3
25
2.5
2
1.5
1
0.5
0
1
0.9
08
0.8
0.7
0.6
0.5
04
0.4
0.3
0.2
0.1
0
# of contacts
Density
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Dynamic SNA
• Methods
– Berger-Wolf method
• α (Persistence)
• β (turnover)
• γ (membership)
– Software
• tnet package in R does analysis of time-stamped
ties - http://toreopsahl.com/tnet/
p
p
• DNA (Discourse Network Analyzer)
http://www.philipleifeld.de/
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Summary
Saturation
Possible?
Absentees or
nonparticipants?
Multiple
Time
Points
N
Y
N
Y
N
Y
Sociometrics
-
+
+
-
+
-
Informants
-
+
+
+
+
-
Ego-Networks
+
?
+
-
+
-
Dynamic
y
SNA
?
?
?
?
-
+
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