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Grappling with Grouping III Social Network Analysis

David Henry David Henry

University of Illinois at Chicago Allison Dymnicki

American Institutes for Research

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Families and Communities Social Network and N ti I fl P j t

Acknowledgments

Research Group Patrick Tolan Deborah Gorman-Smith

Normative Influence Projects Fern Chertok Daneen Deptula Allison Dymnicki Michael Schoeny y Jane Jegerski Christopher Keys Kimberly Kobus

Jennifer Watling Neal Jennifer Watling Neal Zachary Neal

Michael Schoeny

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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 the responsibility of the

p y p y

authors and does not necessarily represent the official views of the funders.

• For more information visitFor more information, visit www ihrp uic eduwww.ihrp.uic.edu.

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I. Cluster Analysis

Grappling with Grouping

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 the same way that a sample mean approximates a population.

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N

k A

l i

• Widely used in community psychology research

Network Analysis

• 28 studies since 2000 in just two journals (AJCP, JCP)j j ( , )

– Search terms: “Network Analysis”

• Similar search using the term “cluster analysis” returned 14 studies.

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Community Studies Employing Network Analysis

y

p y g

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 A single case study using ego networks Ego network Fleisher & Krienert, 2004

Violence among female gang members increases before 

pregnancy and decreases afterward Qualitative Interviews Criticism practical support were significant predictors of

Levendosky et al., 2004

Criticism, practical support were significant predictors of 

mental health for battered women. Rating scales Toohey et al., 2004

Differences between nearly homeless and housed women 

on beliefs about netweok members as housing resources Rating scales Zea et al., 2004

Target‐specific factors were related to the probability of 

disclosure. Rating scales 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 Social Support in clubhouse mental health programs Rating scales

Advancing Health Practice and Policy through Collaborative Research

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Community Studies Employing Network Analysis

y

p y g

y

Study Variables Type of

Analysisy

Toro et al., 2008 Social Support in homeless adults ‐ego networks Ego network Campo et al., 2009 Convergent and discriminant validity with other measures Rating scales Latkin et al., 2009

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 t l 2010 C it C N t k S i t i

Lugue et al., 2010 Community Cancer Network Sociometric Prelow et al., 2010

Social Support buffered ecological risk effects on psychological 

distress Rating scales Haines et al., 2011

Network characteristics of an interdisciplinary collaboration 

based on multiple types of relationships Sociometric Neal et al (2011) Cohesion vs structural similarity in teacher advice networks Sociometric

Advancing Health Practice and Policy through Collaborative Research

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V i t

f N t

k M th d

Variety of Network Methods

in Community Psychology Studies

Type Number Sociometric nominations 5 Informant-based Methods 1 Ego-networks 7 Rating Scales 8 Rating Scales 8 Qualitative 1

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Outline

• Overview of methods Sociometrics

Outline

– Sociometrics – Informants

– Ego-networksEgo networks

– Dynamic • For each (-1)

– Theory/method/measures – Software

Advancing Health Practice and Policy through Collaborative Research – Strengths and limitations

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• Data source: Relationships

Network Analysis with Sociometrics

• Data source: Relationships

– “Who are your friends?” (Kobus & Henry, 2009)

– “What organizations do you belong to?” (Crowe, a o ga a o s do you be o g o (C 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 B E C 0 0 0 0 1 1 0 D 1 1 0 0 0 0 0 E 0 0 1 0 0 1 0 A C D E F

Advancing Health Practice and Policy through Collaborative Research

F 1 0 0 0 0 0 0

G 0 0 0 0 0 1 0 G

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Sociometrics: Network Measures

28 . 0 42 / 12 ) 1 (    

g g X DENSITY where X = relationships (ties) = 12 ) (g g

g = network size (# of potential relationships) = 42

A B C D E F G Σ A 0 1 0 1 0 0 0 2 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 A B E F 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 A C D F G

Advancing Health Practice and Policy through Collaborative Research

Σ 3 2 1 2 1 3 0 12

G

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Sociometrics: Networks Measures

• Mutuality Index 0.77 ) 1 ( ) 1 ( 2 2 2 2 2 2 2 2         L L g L L L M g

M = number of mutual relationships = 5 g = network size = 7

L f th td f th t t l t k 12

)

(g 2

L = sum of the outdegree of the total network = 12

L2 = sum of squares of the outdegree of the total network =22

A B C D E F G Σ A B E F 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 A C D F G 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

Advancing Health Practice and Policy through Collaborative Research

G

G 0 0 0 0 0 1 0 1

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Sociometrics: Network Measures

• Boundary Density (Hirsch, 1980)

T

Tactual = number of actual ties across subgroups = 2

083 . 0  

possible actual T T BD

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 E 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 A C D F G 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

Advancing Health Practice and Policy through Collaborative Research

G Σ 3 2 1 2 1 3 0 12

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Sociometrics: Measures of Individuals

Mean Geodesic Distance

j ji j ij D or D

where D = Distance and B = Reachability

j ij j ij B o B

In: 1.33 for F, 2.0 for D and 2.16 for G

A B C D E F G Σ A B C E F 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 C D G 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

Advancing Health Practice and Policy through Collaborative Research

G 0 0 0 0 0 1 0 1

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Sociometrics: Measures of Individuals

Sociometrics: Measures of Individuals

• Position A B E F – Member – Liaison – Isolate C D G

• Liaisons > Members or Isolates on tobacco and alcohol use (2 studies)

(2 studies)

• Members and Isolates more influenced by peer substance use than Liaisons.

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Sociometrics: Statistical Models Example

D d h t d t b t i d

Dyads have a tendency to become triads:

“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

Advancing Health Practice and Policy through Collaborative Research provide alternatives suitable for predicting network

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N

k A

l i

i h S

i

i

Network Analysis with Sociometrics

• Strengths

– Unbiased assessment of social influence – Patterns of diffusion and communication – Rich measurement and theory

Li it ti

• Limitations

– Missing nominators compromise accuracy

– Costly assessmentCostly assessment

– Complex coding and analysis – Requires bounded social space

Advancing Health Practice and Policy through Collaborative Research

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Network Analysis with Sociometrics

Software

• Stand-alone ProgramsStand alone Programs

– 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 – snasna • Excel N d XL F htt // d l d l /

Advancing Health Practice and Policy through Collaborative Research

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Network Analysis from Informants

• Data Source: “Who hangs out together?”

Informants Compilation

Informant Port Kit Tunner Port Kit Tunner

Informant 1

Port Kit Tunner Port Kit Tunner

Port 1 0 1 .5 Kit 0 5 Kit 0 .5 Informant 2 Port 1 1 • Variations Port 1 1 Kit 1

– 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.

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Informants: Software

• Cognitive Social Structures • Cognitive Social Structures – consensus aggregation

across k informant matrices

k j i, ij

R

R

ac oss o a 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 (man-chi.leung@unc.edu ) for a copy of the SCM 4.0 program and manual.

Advancing Health Practice and Policy through Collaborative Research p g

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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 – Difficult to assess directed relations

– Requires bounded social space (e.g., classrooms, schools, organizations)

Advancing Health Practice and Policy through Collaborative Research

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Ego Network Analysis

Ego Network Analysis

• Theory: Best for unbounded networks where • 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:

Net ork si e densit di ersit bo ndar densit – Network size, density, diversity, boundary density,

heterogeneity, position, behavior of network members

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Ego Networks: Measures

Ego Networks: Measures

• Heterogeneity • Heterogeneity                

e A ity Heterogene n k iA 1 2 1       n ity Heterogene iA 1

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

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Ego Networks: Examples

g

p

• Dominguez & Maya-Lariego, 2008

Ego networks of host individuals and immigrants in – Ego networks of host individuals and immigrants in

the U.S. and Spain

– Host individuals had lower centrality than did y

immigrants according to multiple measures.

T l G S ith & H 2003

• Tolan, Gorman-Smith, & Henry, 2003

– Ego network assessments of delinquent involvement in adolescent males

in adolescent males

– Network violence predicted future individual violence.

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E

N

k

Ego Networks

• Strengths

– Does not require assessment of entire network – Can provide social network and social support

i f ti

information

– Does not require bounded social space. • Limitations

• 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

N

k

S f

Ego Networks: Software

• 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 • 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

can often be conducted by hand or using Excel or SPSS.

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Dynamic Social Network Analysis

Dynamic Social Network Analysis

• Theory

– Social relationships are dynamic – Most SNA is static

– Static analysis may miss important characteristics of the social world.

• Examples • Examples

– Is “liaison” a position or a transition state?

– Changes in parent groups over the course of g p g p

intervention.

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Group 212: Pre

p

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Group 212: Session 4

p

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Group 212: Session 9

p

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Group 212: Session 14

p

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SAFE-E Group 212

0 8 0.9 1 2 5 3 0 4 0.5 0.6 0.7 0.8 1.5 2 2.5 0 0.1 0.2 0.3 0.4 0 0.5 1 # of contacts Density
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Dynamic SNA

Dynamic SNA

• Methods – Berger-Wolf method • α (Persistence) • β (turnover) • γ (membership) Software – 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 non-participants? Multiple Time Points participants? Points N Y N Y N Y Sociometrics - + + - + -Informants - + + + + -Ego-Networks + ? + - + -Dynamic SNA ? ? ? ? - +

Advancing Health Practice and Policy through Collaborative Research

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