The Urban Institute
Meagan Cahill
Samantha S. Lowry
Pamela Lachman
Temple University
Caterina Roman
Carlena Orosco
University of Florida
Chris McCarty
Annual Meeting of the American Society of Criminology
November 16, 2011
This work was prepared under grant number 2007-MU-FX-001 from the Office of Juvenile Justice and Delinquency Prevention,
U.S. Department of Justice. Points of view or opinions expressed in this presentation are those of the authors and do not
necessarily represent the official position or policies of OJJDP or the U.S. Department of Justice.
Department of Criminal Justice
Temple University
URBAN INSTITUTE
Justice Policy Center
 Explore
how associations with antisocial (and
prosocial) individuals and groups influence
delinquency, gang membership and violence.
 Move
beyond the school-based social network
to explore what can be learned from
neighborhood

Estimates of gang membership in urban areas range from 14 to
30 percent; estimates suggest that minority (black and Latino)
youth are almost twice as likely to join a gang by age 17.
Although delinquent peers are a key predictor of delinquent
and violent behavior, little is known about the relationships
between delinquent peers relative to the larger social networks
of individuals

Billions spent on neighborhood-based prevention/intervention

Millions spent on deterrence-based strategies

• Target individuals who will spread deterrence message to their networks
• No certainty that message will be relayed, be paid any attention, or be
meaningful or appropriate for initial target.
 To
use ego-centric network analysis to assess the
extent individual networks are related to at-risk and
criminal behavior.
 To
use socio-centric network analysis to assess the
extent to which group networks are related to at-risk,
criminal, and group/gang behavior.


Aggregate personal networks
Method must allow for unconstrained social
context
• Any person who exerts influence on ego, good or bad

Method must also enable understanding of
influence of network and subgroup (gang, cooffenders) structures within geographic
context
One GeographicallyBounded Neighborhood
in Suburban
Washington DC
Can we predict delinquency based on position within the
neighborhood network and relationships with antisocial alters
outside of bounded neighborhood?
 Particularly important question for disadvantaged
Latino community with high residential instability
 Choose
one neighborhood with high gang
activity/involvement in predominantly Latino area
 Neighborhood
 Work
 Hold
becomes the network
closely with local community-based agencies
focus groups with youth
 Recruit
ex-gang members to help us pinpoint a
geographically bounded neighborhood that would
have meaning to youth

The overall network will be homogeneous and based
on gang affiliation
 Participation
network
in delinquency will be high across whole
 Subgroups
will exist, and will be characterized by
certain types of delinquent activities (with variation
across components)
• Examples: Gun carrying versus drug dealing versus violence.





Youth with strong ties to those who use/condone violence will
be more likely to engage in delinquent/criminal
behavior/group-based behavior
Youth with many ties to neighborhood relations will be more
likely to engage in delinquent/criminal behavior/group-based
behavior
With regard to delinquency, ties to non-peers will be just as
important as ties to peers
Larger prosocial advice networks will protect against
delinquency
In a ethnically homogenous neighborhood, homophily will be
positively related to gang membership;
Surveyed all youth in neighborhood between ages of
14 and 21
 Asked youth to elicit 20 alters (required 20)

• Please list 20 people that you hang out with or might see regularly in a typical day. Start by thinking of
the people you hang out with every day. Then, think of the people you talk to or see the most—it may
be family members, friends, neighbors, or even people you don't like.
 Asked
youth 19 key questions about alters
• How do you know alter; time hanging out with alter; how much do you
like alter?
• Commit crimes with alter?
• How violent is alter? Alter carry weapons? Alter in gang?
 One
alter pair question:
• What is the likelihood that X and X talk to each other or hang out with each other without your
involvement or independently of you? Think about any kind of interaction, even if the two don't get
along. Would you say not at all, they might, but not sure, or definitely?
• 160 surveys
administered
• 13 later thrown out
______________
147 total
Did the East Coast Blizzard of 2010
decrease turnout or increase it??
Tracking Outcome
n
%
No shows
44
5.2
2 Knock, no answer (KNA)
105
12.3
3 KNA
52
6.1
4 KNA
20
2.3
5 KNA
Invalid address/vacant/mail
returned
2
0.2
96
11.3
No youth ages 14-21
417
48.9
Refused
# Addresses of actual
participants
28
3.3
89
10.4
TOTAL ADDRESSES
853
100

Network Composition (e.g., # peers, # alters go
to for advice, # alters in neighborhood)
Personal Network – Structure (e.g., #
components)
 Whole Network – Structure, Position (e.g.,
density, betweenness)
 Acculturation variables
 Controls (based on risk factor/theoretical lit)

Delinquency Scale (0-9) (alpha = .83)
1. Avoided paying for things, like a movie, taking bus rides, or anything else
2. Tried to steal/ actually stolen things worth $100 or less
3. Damaged, destroyed property on purpose
4. Tried to steal or actually stolen money or things worth more than $100
5. Tried to steal or actually stolen a car or other motor vehicle
6. Been involved in a gang fight
7. Sold illegal drugs
8. Used a weapon or force to get things from people
9. Attacked someone with a weapon
2. Last six months …Delinquency Scale (0-9) (alpha=.82)
3. Serious delinquency (6 items from scale; alpha = .77)
4. Binary variables:
- gang/gang fight
- sold drugs
- carried a weapon
- attack someone with intent of hurting him/her
Network Composition
• Peers in network *
• Delinquent peers/delinquent alters*
• Same neighborhood friends/alters*
• Males in network
• Average age of network
*measures used in “final” regression models
Network Composition—Strength of Ties and
Homophily
• Advice network (go to for advice) *
• High frequency contact network (spends a lot of time
with)
• Likeable network (count of alters R likes a lot)
• Strength of Ties Scale using above three measures
• Count of “homophilous” alters—with regard to
nationality, ethnicity, and gender
Network Structure—Ego Network
• Number of Components in ego network*
• Network Density (ego network)
Network Structure—Whole Network
• Meagan Cahill will describe these…
 Acculturation
Measures
• Ethnic attachment/identity scale. Multigroup Ethnic
Identity Measure (MEIM) as developed by Phinney
(1992). Twelve items assessing familiarity with the
customs and traditions of his/her ethnicity; attachment
to and understanding of ethnic identity; positive
feelings about being a member of his/her ethnic group.
(α=0.89).*
• Number years in United States*
Controls
(all are used in final models):
• Age
• Male
• Latino
• Family member completed high school
• Parent/caregiver encourages youth in school
• Family cohesion scale
• Number of years lived at address


Negative Binomial Regression—scaled outcome
Logistic Regression—binary outcomes
(count measures with offset variable = count of peers)
• Final models (dropped network density, homophily, all
strength of ties measures except advice networks)
• Examined possible interactions but determined not
enough power
Predictive modeling at the ego level
Speaker: Pamela Lachman
% of Sample
Demographics
Average age
Male
Ethnicity/Nationality
Hispanic/Latino(a)
Born abroad
Either parent born abroad
Lived abroad
Language
Only Spanish
Only English
Spanish and English
Multiple
Other
17.8
66.0
76.9
36.1
84.4
49.9
5.4
14.3
69.4
7.5
3.4
% of Sample
School/Parental Status
Currently lives with parent(s)
Currently in school (<18)
Currently in school (18+)
Parent support in school
Adult in family graduated HS
Employment
Currently have a job (<18)
Currently have a job (18+)
Religion
Christian
Attends services...
At least once a month
Never
82.3
96.7
46.5
91.8
70.1
11.5
58.1
81.0
50.3
27.9
% of Sample
(N=147)
Group Activity
See lots of gang activity in neighborhood
Approached to join gang
Thought about joining gang
Pressure to join a gang
In a gang
In a gang fight
In a gang fight, not in a gang
Delinquent Behavior
Used drugs
Used drugs in last six months
Sold drugs
Sold drugs in last six months
Stolen goods more than $100
Stolen goods $100+ in last six months
Carried weapon
Carried weapon in last six months
Attacked someone to hurt or seriously injure them
Attacked someone in last six months
34.0
19.7
15.6
12.2
10.2
17.0
7.5
26.5
12.2
8.8
5.4
17.7
7.5
23.1
6.7
10.2
4.8
Alter Characteristics
% Respondents
(N = 147)
Respondents who…
% Respondents
(N = 147)
More than half of alters were
friends
78.2
Co-offend with at least one
alter
19.7
Two or more alters were
siblings
36.1
Commit violence with at least
one alter
12.9
At least one alter was a parent
40.8
Respondent would go to at
least half of alters for advice
29.3
56.5
Have at least one alter in a
gang
Respondent likes more than
half of alters a lot
8.8
47.6
Have at least one family
member in a gang
At least half of alters live in the
same neighborhood
29.9
Have at least one alter who
has been in a gang fight
17.7
More than half of alters were
born in the US
57.1
Have at least one alter who
carries a gun
16.3
More than half of alters were
born in Latin America
33.3
Have at least one alter who
sold drugs
29.3
Delinquent friends
Non peer delinquent alters
Alters live in same neigh.
Peers go to for advice not delinquent
Num. components
Years lived in US
Ethnic attachment
Age
Male
Parent-school encouragement
Family cohesion
Years at address
Non peer model
Peer model
-2.5
-2
-1.5
-1
-0.5
0
0.5
Exp(b*s.d.)
1
1.5
2
2.5
Non peer delinquent alters
Peers go to for advice not delinquent
Num. components
Years lived in US
Ethnic attachment
Age
Male
Parent-school encouragement
Family cohesion
Non peer model
Peer model
Years at address
-2
-1.5
-1
-0.5
0
Exp(b*s.d.)
0.5
1
1.5
2
Delinquent friends
Non peer delinquent alters
Years lived in US
Age
Male
Parent-school encouragement
Non peer model
Peer model
Years at address
-20
-15
-10
-5
0
5
10
Exp(b*s.d.)
15
20
25
30
 Of
all personal network measures, number of
delinquent alters is strongest predictor of
delinquency—stronger than delinquent peers
 Greater number of components/subgroups
reduces delinquent activity
 Parental/family support measures remain
significant in all alter models
 Influence of age is only significant in peer/control
models
 More acculturation (years in United States) and
more ethnic attachment increase delinquency
Collecting and exploring the data
used to create the social network
Speaker: Samantha Lowry
 Why
do we need to elicit names?
 What is the best approach?
 Roster method most commonly used
 Geographic boundaries
 How
many names to ask for?
 What types of people do you want?
 Measurement error
Please list 20 people that you hang out with or might see
regularly in a typical day. Start by thinking of the people you
hang out with every day. Then, think of the people you talk to or
see the most- it may be family members, friends, neighbors, or
even people you don't like.
• Requires 20 names
• Additional prompts to get youth thinking about other
people who fit
• Geographic limit not imposed
What is the likelihood that X and X talk to each other or hang
out with each other without your involvement or
independently of you? Think about any kind of interaction,
even if the two don't get along. Would you say not at all, they
might but not sure, or definitely?
• Attribute tie data creates “whole” network
• Used DEFINITELY to create tie/link
 Matching/Cleaning
Alter Names
• Jose Batty
• Jose Batrova
• Jose B
• Jose
• Jose Jose
• Batty
 160
unique ego names and 3,200 non-unique
alter names
 What
is true response rate for neighborhood
when not starting from a census?
 When to give up on knock-no-answers
 Respondent
burden—limiting alter Qs
 Data quality/validity:
 Delete egos (and their alter data) where some alter names
look fake?
 Handling “don’t knows” on alter tie questions
 Setting up a protocol for disagreements? For example: Is
EgoX in a gang? Five of seven say he is, ego says he isn’t.

Type of People Named
 Difficulty naming 20 alters for younger people resulting in more
connections beyond peers; smaller peer network
 Number of “out of neighborhood” connections increases with age

Strength of Ties and Closeness
 Strength of connections increases with age
 Number of strong ties increases with age

Deliquency and Co-offending
 Number of delinquent alters increases with age and are named
sooner in the alter list
 Co-offending increases with age and creates stronger ties, resulting in
being named sooner in the alter list
Measures and Age Categories
Relationship
Youngsters (14–15 years old)
Teenagers (16–18 years old)
College Aged (19–21 years old)
Young Adults (22 years old)
Alter Age
Youngsters (14–15 years old)
Teenagers (16–18 years old)
College Aged (19–21 years old)
Young Adults (22 years old)
•
•
•
•
First 5 Alters
Last 5 Alters
Friend
Friend
Friend
Friend
Friend
Friend
Friend
Friend
19
19
22
24
19
19
22
26
Mom more commonly listed in First 5; Dad not common and in Last 5
Neighbors listed mainly by Youngsters in the middle
Boyfriend/girlfriend listed in First 5 less than 50% of the time
Youngsters naming immediate family and friends; teenagers also
naming friends with lower percentages of parents/higher cousins
Measures and Age Categories
Gender
Youngsters (14–15 years old)
Teenagers (16–18 years old)
College Aged (19–21 years old)
Young Adults (22 years old)
Geographic Location
Youngsters (14–15 years old)
Teenagers (16–18 years old)
College Aged (19–21 years old)
Young Adults (22 years old)
First 5 Alters
Last 5 Alters
Male
Male
Male
Male
Male
Male
Male
Male
In Neigh.
Out Neigh.
Out Neigh.
In Neigh.
Out Neigh.
Out Neigh.
Out Neigh.
Equal
• Females naming 60% females; listing females first then males last
• Males naming 70% males; listing males first then females in the
middle and then more males
Measures and Age Categories
Strength of Ties
Youngsters (14–15 years old)
Teenagers (16–18 years old)
College Aged (19–21 years old)
Young Adults (22 years old)
Closeness
Youngsters (14–15 years old)
Teenagers (16–18 years old)
College Aged (19–21 years old)
Young Adults (22 years old)
First 5 Alters
Last 5 Alters
4.17
3.72
3.83
4.00
2.57
2.25
2.19
2.50
29.95
22.59
26.35
34.24
28.72
21.78
25.27
32.32
Measures and Age Categories
Delinquency
Youngsters (14–15 years old)
Teenagers (16–18 years old)
College Aged (19–21 years old)
Young Adults (22 years old)
Co-offending
Youngsters (14–15 years old)
Teenagers (16–18 years old)
College Aged (19–21 years old)
Young Adults (22 years old)
First 5 Alters
Last 5 Alters
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
• Delinquent alters: 26% in First 5, 25% in Last 5; average age=20
• Delinquent alters (N=203) named by egos aged 17
• Co-offenders: 30% of delinquent alters co-offended with egos; 36% in
First 5, 23% in Last 5
Individual Measures
Size (count of nodes)
Average age
Percent named as parent
...sibling
...friend
Percent who live in neighborhood
Percent Latino
Percent born abroad
Percent male
Percent who sold drugs2
...carry a weapon2
...were in a gang fight2
...use violence2
...are in a gang
Percent delinquent3
1The
Whole Network
2+ Network1
2,521
20 years
3.9
7.7
69.1
34.9
69.4
43.8
57.6
4.2
5.6
5.7
5.5
4.5
7.5
369
19 years
4.6
20.3
68.5
72.9
78.9
37.1
61.8
6.8
5.9
8.9
7.6
9.5
13.8
2+ network includes only egos or people who were named at least twice by egos.
of those deemed very likely or somewhat likely to engage in behavior listed.
3Percent of those who were very likely to participate in at least one of 5 delinquent behaviors reported.
2Percent
Examining the whole network, its
structure, and its subgroups
Speaker: Meagan Cahill
 Four
main measures:
• Density: proportion of actual ties to possible ties (network level
only)
• Degree centrality: the number of direct connections a node has
• Closeness centrality: the total distance from each node to every
other node in the network
• Betweenness centrality: how much of a bridging role each node
performs
 Of special interest for this work, esp. for policy implications
 “Bridgers” in a position to spread messages, act autonomously
 High betweenness could be associated with drug dealing
 Compare
characteristics of central players
Network Measures
Size (count of nodes)
Avg. number of nominations per node
Number of ties between nodes
Density
Density
Effective Density1
Degree
Mean Centrality (normalized)
Network centralization index (%)
Betweenness 2
Mean Centrality (normalized)
Network centralization index (%)
Closeness 3
Mean Centrality (normalized)
Network centralization index
1Effective
Whole Network
2+ Network
2,521
1.17
16,552
369
2.14
1,810
0.0026
0.33
0.01
-
0.26
2.04
1.33
5.48
0.12
9.19
0.51
6.75
34.06
436.86
35.08
347.95
density is calculated using a network size that accounts for the number of people respondents were asked to name.
2Freeman node betweenness score is reported.
3Calculated using the Valente-Foreman average of reversed distances.
Central Actors (in top 1%)
Size (count of nodes)
Avg. number of nominations
Percent who are egos
Average age
Percent named as parent
...sibling
...friend
Percent who live in neigh.
Percent Latino
Percent born abroad
Percent male
Percent who sold drugs2
...carry a weapon2
...were in a gang fight2
...use violence2
...are in a gang
Percent delinquent3
Average of centrality measure
Whole Network
2,521
1.17
5.83
20 years
3.9
7.7
69.1
34.9
69.4
43.8
57.6
4.2
5.6
5.7
5.5
4.5
7.5
-
Degree
29
5.0
93.1
17 yrs.
0.0
34.5
96.6
96.6
89.7
27.6
86.2
13.8
20.7
20.7
10.3
10.3
20.7
1.6
Between.
26
4.1
74.1
17 yrs.
0.0
29.6
88.9
85.2
81.5
22.2
85.2
16.0
14.8
23.0
3.7
11.1
25.9
4.7
Closeness
28
4.8
46.4
16 yrs.
0.0
21.4
96.4
78.6
92.9
28.6
96.4
7.7
10.7
11.5
0.0
7.1
17.9
0.14
 Community
structure: existence of coherent
subgroups within in the larger network
 Use the Newman-Girvan modularity algorithm
• Ideal case:
 All nodes within the subgroup connected to each other
 No nodes connected to others outside the subgroup
• This rarely occurs; algorithm assesses the extent to
which structure approximates this ideal
 No
a priori expectation of number of groups
 Identified
25 subgroups in whole network
 17 appeared to be ego-networks (ego and
his/her alters only)
 One catchall subgroup with 1,700+ members
 For each subgroup, examined:
• Demographics
• Cohesion, centrality measures
• Sociogram (network picture)
 Present
five plus catchall group
Subgroup Name
Subgroup Description
"Everyone
Else"
"The [Latino]
Outsiders"
"No 'Aging
Out' Here"
"The [Latino]
Insiders"
Low accult.,
older, outside
neigh.
Non-Latino,
older, delinq
Low accult.,
younger,
inside neigh.
Catchall
"A Thug in
Charge"
"A Tale of
Two
Brothers"
Brothers,
Latino, gang
younger not
members,
delinq.,
delinquency
older delinq.
Individual (node)-level measures
Size (number of nodes)
Average age
Percent Latino
Percent named as friends
1,792
19
73.5
72.0
21
23
100.0
61.9
21
24
4.8
66.7
21
18
100.0
4.8
20
23
90.0
75.0
40
21
25.0
75.0
Percent named as family
25.1
23.8
28.6
0.0
15.0
10.0
Percent born abroad
Percent male
41.1
58.5
100.0
66.7
0.0
66.7
85.7
100.0
20.0
80.0
25.0
72.5
Percent live in neigh.
35.9
19.0
52.4
95.2
55.0
35.0
Percent in gang
...gang fights
5.5
6.4
0.0
0.0
9.5
4.8
0.0
0.0
15.0
15.0
15.0
10.0
6.3
0.0
0.0
0.0
5.0
5.0
8.6
0.0
9.5
0.0
40.0
15.0
...use violence
Percent delinquent
 Network-level
models...
• Have more observations (2,521)
• Have fewer variables (restricted to alter data)
• Offer opportunity to test role of network structure
 Developed
binary logistic models
• Dependent var: overall delinquency (1=very likely to
participate in delinquent behavior)
 Included
different network structural
measures (degree, betweenness, closeness)
 Hosmer-Lemeshow test: betweenness model
was a good fit, others were not
Whole Network
Coeff.
Constant
Network structure variables
Betweenness centrality
Closeness centrality
Degree centrality
Controls
Ego
Age
Male
Latino
Born in U.S.
Live in Neighborhood
Cox & Snell R Square
†p<0.10; *p<0.05; ** p<0.01; ***p<0.001
-3.47***
S.E.
Exp(B)
(OR)
0.39

∆p
(%)
0.03

0.13
-
0.53†
0.01
0.79***
-0.16
0.18
0.69***
0.09
0.28
0.01
0.19
0.20
0.20
0.19
0.03
-
1.14
1.70
1.01
2.20
0.85
1.20
2.00
30.28%
48.44%

41.41%
Betweenness
measure not
significant
Closeness (+),
degree (-)
measures highly
significant (but
model doesn’t fit
well)
∆p (%) based on
base delinquent
level of 0.5
 Lower
than expected prevalence of delinquency and
gang membership
A
subset of Ego networks (~30%) appear tightly
bounded to their neighborhood, but most extend
beyond boundaries
 The
dominant characteristic of the majority of
factions is not related to geographic space
(neighborhood or school identity), but
neighborhoods appear to matter with regard to
delinquency (in-neighborhood node → delinquency)





Youth with strong ties to those who use/condone violence will
be more likely to engage in delinquent/criminal
behavior/group-based behavior
Youth with many ties to neighborhood relations will be more
likely to engage in delinquent/criminal behavior/group-based
behavior
With regard to delinquency, ties to nonpeers will be just as
important as ties to peers
Larger prosocial advice networks will protect against
delinquency
In an ethnically homogenous neighborhood, homophily will be
positively related to gang membership;





Involve community leaders
Develop and nurture community assets
within local practices
Identify and nurture prosocial networks that
already exist
Explore mentoring opportunities
Invest in parenting programs
 Multitude
of theory-based research questions that
can be answered with this approach
 But
future work must begin to address validity of
data on key questions and ability of Rs to report on
alters’ behavior
 Replicate
study in various neighborhoods (and with
sufficient funding to ensure “knock-no-answers” are
minimized).
 Future:
Re-interview youth to establish how networks
change and time frame for behavior change