The author(s) shown below used Federal funds provided by the U.S. 

Department of Justice and prepared the following final report:

Document Title: Social Networks, Delinquency, and Gang 

Membership: Using a Neighborhood Framework 

to Examine the Influence of Network 

Composition and Structure in a Latino 

Community

Author: Caterina G. Roman, Meagan Cahill, Pamela 

Lachman, Samantha Lowry, Carlena Orosco, 

Christopher McCarty, with Megan Denver and 

Juan Pedroza

Document No.: 238538

Date Received: June 2012 

Award Number: 2007-MU-FX-0001

This report has not been published by the U.S. Department of Justice. 

To provide better customer service, NCJRS has made this Federallyfunded grant final report available electronically in addition to 

traditional paper copies. 

 

Opinions or points of view expressed are those 

of the author(s) and do not necessarily reflect 

the official position or policies of the U.S. 

Department of Justice.
Social Networks, Delinquency, 

and Gang Membership:

Using a Neighborhood Framework to Examine 

the Influence of Network Composition and 

Structure in a Latino Community

Department of Criminal 

Justice

Caterina G. Roman, Temple University

Meagan Cahill, The Urban Institute

Pamela Lachman, The Urban Institute

Samantha Lowry, The Urban Institute

Carlena Orosco, Temple University

Christopher McCarty, University of Florida

with

Megan Denver, The Urban Institute

Juan Pedroza, The Urban Institute

February 2012
SOCIAL NETWORKS AND DELINQUENCY

SOCIAL NETWORKS, DELINQUENCY, AND GANG MEMBERSHIP: USING A NEIGHBORHOOD 

FRAMEWORK TO EXAMINE THE INFLUENCE OF NETWORK COMPOSITION AND STRUCTURE IN A 

LATINO COMMUNITY 

Authors: 

Caterina Gouvis Roman 

Meagan Cahill 

Pamela Lachman 

Samantha Lowry 

Carlena Orosco 

Christopher McCarty 

 With Megan Denver and Juan Pedroza 

©2012 

The Urban Institute 

2100 M Street, NW 

Washington, D.C. 20037 

(202) 833-7200 

Prepared for 

The Office of Juvenile Justice and Delinquency Prevention 

The views expressed in this report are those of the authors and should not be attributed to the Office of 

Juvenile Justice and Delinquency Prevention, Temple University, the Urban Institute, its trustees, or its 

funders. 

This research was supported by OJJDP Grant #2007-MU-FX-0001

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

i 

ACKNOWLEDGMENTS 

The authors would like to thank the many individuals and organizations that made contributions to 

this report. First and foremost, we thank the staff of Identity, Inc., a nonprofit organization in 

Maryland dedicated to helping at risk Latino youth. Diego Iriburu and Candace Kattar were 

instrumental as collaborators. They and their staff helped us choose the appropriate target 

neighborhood, provided advice on survey items and wording, and provided youth to help us recruit 

for the network survey. They also provided youth for focus groups and pilot testing of the survey. 

This study would not have been possible without their help. 

A number of researchers assisted the team in the field. From Identity, Inc., we would like to thank 

Marvin Esquivel, Michael Balderas, and Adrian Hinojosa, who helped with understanding the 

neighborhood and tirelessly canvassing to recruit participants, no matter the weather. The authors 

would also like to thank Alia Fahim of Chicanos por La Causa, in Phoenix, Arizona, for her 

willingness to share her time, local insight, and effort in trying to get a survey site started in Phoenix. 

From the Urban Institute, we would like to thank Meredith Dank, Colleen Owens, and Molly Scott 

for assisting with the data collection effort, even when the team had to go door to door in a 

snowstorm! 

We would also like to acknowledge our senior advisors who supplied advice, support, and their 

generosity of time on this project over the years and through a multitude of challenging issues: 

Christopher McCarty (also a report author), Mark Fleisher, Adele Harrell, Mark Edberg, and Scott 

Decker. We are particularly indebted to Mark Fleisher, who guided our initial hypotheses and 

introduced us to Christopher McCarty. This report would not have been possible without Mark’s 

insight and encouragement to tackle a complex method and topic and to survey a hard-to-reach 

population. 

We would also like to thank Martin Smith and Eric Lavigne for their assistance with custom 

programming of EgoNet, the software used to collect survey data for this research. While EgoNet is 

freely available, Martin and Eric were quick programmers who were happy to respond to our 

requests for changes to the software that allowed us to complete our research. 

We are indebted to the willingness of the youth in the Silver Spring community who came out in 

force even during the record-breaking blizzard snowstorms of 2009–10. We thank them 

wholeheartedly. We also thank Morris Buster and the other staff members with the Montgomery 

County Parks and Recreation Department who allowed us to use their facility to conduct surveys 

with youth. 

Finally, the authors thank Akiva Liberman for reviewing the draft report, and especially Steffie Rapp, 

our patient project manager at OJJDP, for her tireless support of this project. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

ii 

SOCIAL NETWORKS, DELINQUENCY, AND GANG MEMBERSHIP: 

USING A NEIGHBORHOOD FRAMEWORK TO EXAMINE THE INFLUENCE OF 

NETWORK COMPOSITION AND STRUCTURE IN A LATINO COMMUNITY 

Report Abstract 

This study employed a social network framework to understand the patterns of relations 

among youth in a predominantly Latino1 neighborhood and the nature of the links binding youth to 

groups and their social contexts. Three main research questions guided the study: (1) Are 

network/structural variables important predictors of delinquency and gang membership (beyond the 

traditional set of risk factors) across ego networks? (2) What are the properties and characteristics of 

the sociocentric (i.e., neighborhood-based) network as defined by overlapping egocentric networks? 

How do these characteristics relate to or influence delinquency and gang membership? and (3) How 

does an individual’s position and connectedness within the whole network relate to his/her 

probability of being involved in delinquent behavior at the individual level? In other words, we 

determined how one’s position at the sociocentric level relates to behaviors measured and modeled 

at the egocentric level and whether sociocentric level measures (e.g., centrality) improve our 

understanding of individual behavior. By examining two levels of social processes for the unit of 

analysis (individual and group relationships) through both egocentric and sociocentric network 

analysis, and extending our network analysis to include different types of relationships (e.g., friend, 

relative, neighbor), we are able to examine multiple research questions that have not yet been 

addressed in the delinquency and gang literature. 

We selected one disadvantaged Latino neighborhood in Maryland that was home to a large 

proportion of high-risk youth. With the goal of surveying all youth between the ages of 14 and 21 

living in the target neighborhood, we ultimately surveyed 147 youth. We used a network survey 

consisting of three basic parts: (1) questions about the ego, (2) the listing of 20 alters by respondents 

followed by a set of questions asked about each of those alters, and (3) an alter tie question that was 

 

1 The terms “Latino” and “Hispanic” are used interchangeably in this report. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

iii 

used to create the structural links among members of the network (i.e., Does Juan know Marvin 

without the respondent?). After overlapping the networks of each of the 147 youth, we conducted 

both egocentric and sociocentric analyses. Results from our egocentric analyses showed that youth 

are highly connected to people from the neighborhood but that networks with a larger proportion of 

in-neighborhood relations are not significantly associated with delinquency, violence, or gang 

membership. Network density was not significantly associated with delinquency, but the number of 

components (i.e., subgroups) in one’s ego network was marginally significant. This finding suggests 

that the more separate groups of relations one has, the more constraint on behavior and, hence, the 

less likely an individual is to be involved with delinquency. Egocentric analyses also revealed the 

importance of including non-peer relations when examining the effect of delinquent associations on 

delinquency: models using all relations provided a better fit to the data than those that only 

examined the delinquency of peer relations, controlling for other covariates. In addition, the effect 

of delinquency of all relations was larger in size than the measure for delinquency of just peers. 

Finally, a complex picture of risk and protective factors emerged. Separation from U.S. 

culture (lower acculturation) was a significant predictor for delinquency (but not gang membership); 

a youth who was born abroad and remained more closely connected to the Spanish language was 

less likely to be involved in delinquency. Other risk and protective factors varied in their significance 

across delinquency outcomes. 

The sociocentric analyses revealed that the density of the network has important 

implications for the selection of an appropriate intervention: networks with very low densities—as 

found in the current study—are more successful contexts for intervening. The most appropriate 

interventions, then, would not rely on pro-social, anti-delinquent messages being spread through the 

network via well-placed, influential individuals. Rather, more targeted efforts to get individuals 

involved might be required because the message might not be effectively spread throughout the 

relatively sparsely connected community 

Within the network, we examined the role of central players, and for the most part, 

supported the notion that the central role allowed for greater autonomy and led to higher levels of 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

iv 

delinquency. However, we found that central players were not necessarily more violent than 

peripheral players. In fact, many central players were less violent. 

Given that many anti-gang and anti-delinquency programs are implemented in a selected 

geographic area, one of the main concerns was whether it was valid to approximate the 

neighborhood with a complete social network. Here, all delinquency measures were significantly 

higher for the in-neighborhood network, which indicated that there are systematic differences 

between the groups of people who are neighborhood residents and those who are not. This 

supports the notion that the neighborhood influences behavior, and in this specific case, stronger 

associations with the neighborhood tend to be associated with higher levels of delinquency.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
The Influence of Social Networks on 

Delinquency and Gang Membership

CONTENTS 

Acknowledgments ..................................................................................................................... i 

Report Abstract ......................................................................................................................... ii 

Contents ..................................................................................................................................... v 

List of Tables ...........................................................................................................................vii 

List of Figures ........................................................................................................................ viii 

Executive Summary ................................................................................................................... I 

Chapter 1 Introduction .............................................................................................................. 1 

Goals and Objectives .............................................................................................................................................. 5 

Research Questions ................................................................................................................................................. 6 

Background ............................................................................................................................................................... 7 

Control Theory–The Social Bond .................................................................................................................. 8 

Differential Association and Social Learning Theory ................................................................................. 9 

Routine Activities ............................................................................................................................................ 12 

Risk Factor Framework for Delinquency and Gang Membership ......................................................... 13 

Race, Ethnicity, Acculturation, and Delinquency ...................................................................................... 16 

Social Networks, Delinquency, and Gangs ....................................................................................................... 22 

The Current Study .......................................................................................................................................... 25 

Research Hypotheses ............................................................................................................................................ 26 

Chapter 2 Design and Methodology ....................................................................................... 29 

The Site .................................................................................................................................................................... 29 

The Sample ............................................................................................................................................................. 32 

The Neighborhood as the Network ............................................................................................................ 32 

Survey Recruitment and Administration ..................................................................................................... 41 

Preparing Whole Network Data for Analysis ............................................................................................ 46 

Measures .................................................................................................................................................................. 53 

Dependent Variables ...................................................................................................................................... 53 

Personal Network Variables .......................................................................................................................... 55 

Acculturation ................................................................................................................................................... 56 

Strength of Ties and Homophily in Personal Networks .......................................................................... 57 

Ego-Level Network Structure Variables ..................................................................................................... 59 

Sociocentric Network Structure Variables .................................................................................................. 60 

Control Variables ............................................................................................................................................ 62 

Analysis Plan ........................................................................................................................................................... 63 

Egocentric Network Analysis Plan .............................................................................................................. 63 

Whole Network Analysis Plan ...................................................................................................................... 70 

Chapter 3 Findings ............................................................................................................. 76 

Egocentric Descriptive Analyses ......................................................................................................................... 76 

Respondent Characteristics ........................................................................................................................... 76 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

vi 

Relationships within Ego Networks ............................................................................................................ 80 

T-Test Analyses ............................................................................................................................................... 84 

Egocentric Predictive Analyses ........................................................................................................................... 87 

Logistic Regression Model Results (Binary Outcome Measures) ........................................................... 87 

Negative Binomial Regression Results (Scaled Outcome Measures) ..................................................... 96 

Sociocentric Descriptive Analyses .................................................................................................................... 105 

Demographics of the Whole Network ...................................................................................................... 105 

Cohesiveness and Centrality of the Networks ......................................................................................... 113 

Identifying Central Actors in the Network ............................................................................................... 119 

Identifying Community Structure within the Network .......................................................................... 125 

Predictive Models, with Network Measures .................................................................................................... 132 

Ego-Level Regression Models with Sociocentric Network Measures .................................................. 132 

Node-Level Regression Models with Sociocentric Network Measures ............................................... 141 

Chapter 4 Summary and Discussion ..................................................................................... 147 

Delinquency at the Ego-Level ........................................................................................................................... 147 

The Influence of Delinquent Alters ........................................................................................................... 148 

Network Structure across Ego Networks ................................................................................................. 151 

Ethnic Identity and Acculturation .............................................................................................................. 152 

Other Individual Characteristics ................................................................................................................. 154 

Properties of the Whole Network ..................................................................................................................... 155 

Who Is in the Network? .............................................................................................................................. 155 

Sociocentric Measures .................................................................................................................................. 157 

Neighborhood as Network? ........................................................................................................................ 159 

Community Structure within the Whole Network .................................................................................. 161 

Integrating Whole Network Positional Measures in Ego Analyses ............................................................. 162

Who Are the Central Players? ..................................................................................................................... 163 

The Influence of Centrality on Delinquency ............................................................................................ 164 

Other Findings ..................................................................................................................................................... 167 

Levels of Gang Membership ....................................................................................................................... 167 

Gang Members and Delinquency ............................................................................................................... 168 

Diversity of Network Members .................................................................................................................. 169 

Limitations ............................................................................................................................................................ 169 

Implications For Policy and Practice ................................................................................................................ 173 

Chapter 5 Conclusion and Future Research ......................................................................... 175 

Future Research ................................................................................................................................................... 176 

References ............................................................................................................................... 181 

Appendices ............................................................................................................................. 191 

Appendix A Interview Protocol 

Appendix B List of All Measures Used in Analysis 

Appendix C Full Correlation Matrix 

Appendix D Regression with Full Suite of Covariates 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

vii 

LIST OF TABLES 

Table 1. Census Characteristics for County and Survey Site ..................................................................... 31

Table 2. Questions about the Alter ............................................................................................................... 40

Table 3. Survey Yield ....................................................................................................................................... 45

Table 4. Descriptive Statistics for Dependent Variables (N = 147) ......................................................... 64

Table 5. Model Comparison––Overall Delinquency .................................................................................. 64

Table 6. Model Comparison––Recent Delinquency ................................................................................... 65

Table 7. Tests for Collinearity, Overall Delinquency (Binary Outcome) Model .................................... 70

Table 8. Descriptive Statistics, Basic Demographics .................................................................................. 78

Table 9. Nationality of Respondents, as Indicated by Place of Birth for Parents .................................. 79

Table 10. Group Activity and Delinquency ................................................................................................. 79

Table 11. Delinquent Behavior and Gang Association .............................................................................. 80

Table 12. Descriptive Statistics for Network Measures ............................................................................. 80

Table 13. Alter Characteristics ....................................................................................................................... 81

Table 14. Alter Characteristics, by Proportion Delinquent/Criminal ...................................................... 82

Table 15. Alter Behavior ................................................................................................................................. 82

Table 16. Proportion of Ego Network that is Delinquent, by Respondent Delinquency .................... 83

Table 17a. T-Test Results by Delinquency Measure ................................................................................... 84

Table 18. Separation from the United States ............................................................................................... 87

Table 19a. Binary Logistic Regression Results Predicting Overall Delinquency .................................... 91

Table 20. Negative Binomial Regression Results Predicting Overall Delinquency ............................ 100

Table 21. Negative Binomial Regression Results Predicting Recent Delinquency ............................. 101

Table 22. Negative Binomial Regression Results Predicting Serious Delinquency ............................. 104

Table 23. Demographic Characteristics of Whole Networks ................................................................. 106

Table 24. Demographic Characteristics of Networks Within and Outside the Neighborhood ........ 112

Table 25. Cohesion and Centrality Measures ............................................................................................ 113

Table 26. Correlation between Different Measures of Centrality, Whole Network ........................... 118

Table 27. Correlation between Different Measures of Centrality, 2+ Network.................................. 118

Table 28. Centrality of Networks Within and Outside the Neighborhood ......................................... 118

Table 29. Summary Demographic Characteristics and Centrality of Whole Network Members with 

High Centrality Values .......................................................................................................................... 120

Table 30. Summary Demographic Characteristics and Centrality of 2+ Network Members with High 

Centrality Values .................................................................................................................................... 122

Table 31. Demographic and Delinquency Summary Measures for Selected Girvan-Newman 

Partitions ................................................................................................................................................. 127

Table 32. Cohesion and Centrality Measures for Selected Girvan-Newman Partitions ..................... 128

Tables 33a–f. Binary Logistic Regression Results Predicting Delinquency Measures, Including 

Network Structure Variables ................................................................................................................ 134

Table 34. Negative Binomial Regression Results Predicting Overall Delinquency ............................ 140

Table 35. Binary Logistic Regression Results Predicting Overall Delinquency at Network Level, Incl. 

Betweenness Centrality ................................................................................................................................ 144

Table 36. Binary Logistic Regression Results Predicting Overall Delinquency at Network Level, Incl. 

Closeness Centrality .................................................................................................................................... 145

Table 37. Binary Logistic Regression Results Predicting Overall Delinquency at Network Level, Incl. 

Degree Centrality ........................................................................................................................................ 146

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

viii 

LIST OF FIGURES 

Figure 1. Illustration of Three Different Types of Networks ................................................................... 30

Figure 2. Sociograms of Whole Network and 2+ Network ................................................................... 111

Figure 3. Comparison of Central Nodes via Three Centrality Measures .............................................. 124

Figure 4. Ego-Network of a Highly Central Individual (node 394) ...................................................... 125

Figure 5. Star-Shaped Networks ................................................................................................................. 131

Figure 6. Dense Network with Low Centrality ........................................................................................ 131

Figure 7. A Two-Ego Network ................................................................................................................... 132

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

I 

EXECUTIVE SUMMARY 

INTRODUCTION 

This research report summarizes findings from a social network study of youth living in a 

high-risk neighborhood in suburban Maryland. The study utilized a social network framework to 

understand the patterns of relations among youth in a predominantly Latino2 neighborhood and the 

nature of the links binding youth to groups and their social contexts. In conducting this study, we 

sought to fill an important gap in the delinquency literature: How do interpersonal relationships and 

networks—beyond peer networks—shape social interaction, and, in turn, individual-level antisocial 

behavior? Do ties to the neighborhood elicit particular patterns in social networks that shape 

behavior? With recent sociological research suggesting that immigrant generational status is highly 

correlated with the commission of violence by Latino youth (Sampson, Morenoff, & Raudenbush 

2005), and that Latinos have more co-offenders than offenders of other races/ethnicities (Daly & 

Johnson 2006), it becomes critical to take a closer look at risk factors for delinquency, violence and 

gang membership within Latino immigrant communities. 

Indeed, during the past few years, there has been increasing interest in crime and 

delinquency among the Latino population (Krohn, Schmidt, Lizotte & Baldwin 2011). Not only is 

the Latino population now the largest ethnic or racial minority in the country, it is also growing at a 

faster rate than all other groups (U.S. Census Bureau 2011). Further, gang violence has been of 

particular concern in Latino communities. Although many recent initiatives give voice to best 

practices, there is astonishingly little research evidence to rely on when it comes to gang prevention 

and intervention in Latino communities. Similarly, although evidence-based practices exist for 

delinquency prevention with Latino youth, the research evidence is limited in telling us what particular 

components are most effective and why. The social network approach has emerged within 

criminology as a valuable approach for examining the complex mechanisms underlying group-based 

and delinquent behavior (Sarnecki 2001). Findings from network studies can help build a solid 

foundation for prevention and intervention (Bouchard & Spindler 2010; Haynie & Osgood 2005; 

McGloin & Piquero 2009). 

The main goals of the study are to (1) assess the extent to which individuals and networks 

are related to delinquency, violence, and gang membership across a targeted neighborhood 

 

2 The terms “Latino” and “Hispanic” are used interchangeably in this report. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

II 

comprising at-risk youth, and (2) describe characteristics of the sociocentric, or whole, network—

made up of overlapping personal networks of neighborhood youth—and their relationship to 

delinquency, violence, and gang membership. To achieve these two goals, we use personal social 

network data, collected via a culturally relevant survey, to assess the composition and influence of 

youth networks aggregated for all research participants into a complete social network (McCarty & 

Wutich 2005; McCarty & Bernard 2003). These network data are used to derive the structural 

properties that may have an influence on the commission of crime and gang membership. Our 

methodological strategy of aggregating—or overlapping—personal network data across all 

respondents allows us to assess how positional network-based variables from the whole network can 

be used in ego-level analyses to examine factors that influence the commission of crime and gang 

membership (at the individual level). And similar to the pioneering work of Jerzy Sarnecki examining 

subgroups across youth suspected of committing offenses in Stockholm, Sweden (2001), we can also 

examine how network linkages influence subgroups and the properties and behaviors characteristic 

of those subgroups. By understanding these dynamics and behaviors, we can not only add to the 

small body of knowledge on network-based risk factors, but also develop a greater understanding of 

the types of processes and programs that reinforce pro-social behavior and prevent or disrupt 

antisocial behavior. 

RESEARCH QUESTIONS 

Our work is guided by three main research questions and several subquestions, listed below: 

1) Are network/structural variables important predictors of delinquency and gang membership 

(beyond the traditional set of risk factors) across ego networks? Related questions include: 

a) Do egocentric network density, proportion of delinquent peers, and other variables 

characterizing network composition influence delinquency and gang membership? 

b) What is the influence of non-peer (i.e., non-friend) relations on delinquency and gang 

membership? Are characteristics of non-peer relations important influences on delinquency 

and gang membership? For instance, are antisocial networks that include relatives, non-peer 

neighborhood connections and other adults important to understanding the nature and 

extent of youth crime, violence, and gang membership? 

c) Does having many neighborhood-based associations (versus networks not based in the 

community) influence participation in crime and gangs at the ego level? Are youth who are 

members of neighborhood-based groups or gangs involved in more serious delinquent or 

violent behavior? 

d) How does an individual’s—in particular, a Latino’s—level of acculturation influence his 

delinquent behavior and gang membership? How important is immigrant generational status 

and language use with regard to violence and gang membership across ego networks? 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

III 

2) What are the properties and characteristics of the sociocentric (i.e., neighborhood-based) 

network as defined by overlapping egocentric networks? How do these characteristics relate to 

or influence delinquency and gang membership? Relatedly, we ask: 

a) What are the characteristics of those individuals who are the central players in the subgroups 

and in the overall network? 

b) Do dense subgroups of gangs or delinquent peer groups exist? 

c) Do variables related to acculturation or immigrant generation characterize subgroups? Or are 

the dominant characteristics of the subgroups rooted in neighborhood-based associations? 

d) How do the key characteristics that define delinquent or violent subgroups vary from 

subgroups not comprising delinquent or criminal individuals? 

3) How does an individual’s position and connectedness within the whole network relate to his/her 

probability of being involved in delinquent behavior at the individual level? In other words, how 

does one’s position at the sociocentric level relate to behaviors measured and modeled at the 

egocentric level? How do such sociocentric level measures (e.g., centrality) improve our 

understanding of individual behavior? 

By examining two levels of social processes for the unit of analysis (individual and group 

relationships) through both egocentric and sociocentric network analysis, and extending our network 

analysis to include different types of relationships (e.g., friend, relative, neighbor, etc.), we are able to 

examine multiple research questions that have not yet been addressed in the delinquency and gang 

literature. Our study draws on the research literature from a number of substantive areas related to 

antisocial behavior of individuals and groups—in particular, research on delinquency and youth 

violence, the peer context, gangs, acculturation, and social networks. Across these literatures, we 

specifically draw on three theories—social bonding/control theory, social learning theory, and 

routine activities theory—that hold central principles related to relationships and, hence, guide 

hypothesis development for this study. 

OVERALL DESIGN AND DATA COLLECTION STRATEGY 

The design and methodology of the study are based on the theoretical and analytical 

framework of the network approach. The data collection strategy involved (1) selecting an 

appropriate neighborhood to provide the framework for the whole network, (2) utilizing a censusbased method to survey all youth between the ages of 14 and 21 living in the neighborhood, and (3) 

asking the youth to complete a network-based survey. 

To determine the specific boundaries of the study area, we conducted field research that 

included focus groups, informal interviews, neighborhood ride-alongs, and examination of 

administrative and census data. The survey site consists of 853 households; over 60 percent of 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

IV 

residents are foreign born, only 55 percent have high school degrees, and per capita income is less 

than half that of the county average. 

There are three basic parts to the survey: (1) questions about the respondent, (2) the listing 

of 20 “alters” (individuals named by respondents) followed by a set of questions asked about every 

alter, and (3) the alter tie question that is used to create the structural links among members of the 

network. Given our research goals, and in order to create personal networks for each ego and then 

match up all egos and respondents into one large network, we chose to use a free recall method 

(Marin 2004) using a “name generator,” or a question that asked respondents to name by memory 

20 individuals (alters). The name generator question is critical to the study in that it defines the 

sample of network alters—or the people who are in an individual’s social network. For our study, we 

wanted to capture the networks of youth that comprise people who are important to the youth and 

might influence the youth—either positively or negatively. Alters could be friends, parents, siblings, 

or anyone else the youth knows. Our name generator question reads: 

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. 

On average, it took participants about one hour to complete a survey. The survey was 

administered at a recreation center on several Saturdays between December 2009 and March 2010. 

Respondents received $50 for participating in the survey. A total of 160 youth completed the survey 

over that period. We dropped 13 respondents found to be ineligible, giving us a final count of 147 

valid surveys. 

OVERLAPPING NETWORKS 

While preparation of the egocentric data for analysis followed a standard process of cleaning 

and recoding data, preparing the sociocentric data required a very different approach. This process 

was required to allow visualization of the whole network of connected egos and alters, and to allow 

analyses of the “whole network” data. The database contained 1603 unique ego names and 3,200 

non-unique alter names; creating a whole network required the research team to determine which of 

the 3,200 alters matched egos and other alters. 

 

3 Even though only 147 were used in the final analyses, we cleaned data for all 160 respondents in case they provided information on 

others who were eligible and were included in the survey. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

V 

After going through each name, we were able to identify just over 2,500 unique individuals. 

All of the alter data had to be summarized for each unique individual. In other words, if one 

individual was named by 10 different egos, we had to summarize the 10 separate records for that 

one person, which may or may not report the same details/attributes. Two types of data needed to 

be summarized: the attribute data (including such items as age, ethnicity, and delinquency) and the 

tie data, which indicated whether one alter was tied to another. We used EgoNet to summarize the 

alter data. For the attribute data, where conflicts in alter records existed, we used the majority answer 

or, if there was no majority, a randomly selected response for that alter. For numeric data, values 

were averaged (e.g., for age). For ego-specific variables, where different egos could have different 

answers about the same alter, EgoNet created summary measures (e.g., percent of egos who called 

this alter a friend/sibling/cousin; percent of egos who would go to this alter for advice). We used 

those data instead of the “majority rule” alter data to represent the ego-dependent alter data. 

MEASURES 

The measures described below are only those measures used in final models—for a list of all 

measures created for this study and used in preliminary analyses, please see the full report. It is 

important to emphasize that ego-level self-report measures are used to create the measures that 

reflect the behavior of alters. Hence, for the attribute measures described below, when used in 

egocentric analysis, they are based on self-report; when used in sociocentric analyses, the attribute 

measures for all nodes that are not egos are based on report by others. 

Delinquency/Criminal Behavior Variables 

1. Overall delinquency is an additive scale of the following nine items: (a) damaged, destroyed, 

or marked up someone else’s property on purpose; (b) avoided paying for things, like a 

movie, taking bus rides, or anything else; (c) tried to steal or actually stolen money or things 

worth $100 or less; (d) tried to steal or actually stolen money or things worth more than 

$100; (e) tried to steal or actually stolen a car or other motor vehicle, (f) been involved in a 

gang fight; (e) sold illegal drugs such as marijuana, crack, heroin, or methamphetamine; (f) 

used a weapon or force to get money or things from people; and (g) attacked someone with 

a weapon or with the idea of seriously hurting or killing them. 

2. Recent (past six months) delinquency is an additive scale that includes the nine items above, 

but respondents were asked whether they did that crime “in the past six months.” 

3. Serious delinquency is an additive scale that included six of the nine items listed above (items 

excluded were damaging property, minor stealing [items less than $100], and avoiding paying 

for things like a movie). 

4. Member of a street gang is a binary measure of whether the individual is in a street gang. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

VI 

5. Gang fight is a binary measure that captures ever being involved in a gang fight. 

6. Sold drugs is a binary measure capturing whether an individual has ever sold illegal drugs 

7. Carried a weapon is a binary measure for whether the individual ever carried a weapon. 

8. Attacked someone is a binary measure capturing whether the individual attacked someone 

with a weapon or with the idea of seriously hurting or killing them. 

9. Serious delinquency-binary is a binary measure capturing any behavior that could be 

regarded as delinquent or criminal. Individuals receive a score of one if they answered yes to 

any of the following five items: ever a member of a street gang, ever in a gang fight, ever 

sold illegal drugs, ever carried a weapon, or ever attacked someone with a weapon or with 

the idea of seriously hurting or killing them. 

Network Compositional Variables 

Network compositional variables provide descriptive information about alter characteristics (i.e., 

delinquency, neighborhood, closeness to the respondent, number of subgroups in personal network) 

in aggregate form. Measures used in the final analyses include: 

• Peers in network. The proportion of alters who were listed as a “friend” is used as a control 

variable. Continuous variable, values range from 0 to 1.

• Delinquent peers. The proportion of delinquent peers (friends) in the respondent’s personal 

network. A friend is defined as delinquent if the ego responded that the friend was involved in 

any of the following five behaviors: ever a member of a street gang, participated in a gang fight, 

sold illegal drugs, carried a weapon, or used violence to get what he/she wants. Binary variable, 

values are 0 or 1.

• Delinquent alters. This variable is computed the same way as “delinquent peers” but includes 

any person named in the respondent’s personal network (thus, it includes friends in addition to 

everyone else) that has been involved in any of the five delinquent behaviors listed above. Binary 

variable, values are 0 or 1.

• Same neighborhood friends. The proportion of peers (friends that are not relatives) who live in 

the same neighborhood as the respondent, as reported by the respondent. Continuous variable, 

values range from 0 to 1. 

• Same neighborhood alters. The proportion of all alters who live in the same neighborhood as 

the respondent, as reported by the respondent.4 Continuous variable, values range from 0 to 1.

• Advice support from pro-social alter is a measure capturing the proportion of alters in a 

respondent’s network to whom the respondent would go for advice and are not 

delinquent/criminal. Continuous variable, values range from 0 to 1.

Acculturation 

• Separation scale. We formed a scale composed of summing the following seven variables to 

measure the respondent’s level of “separation” from (i.e., lack of assimilation) the United States: 

(1) the place of birth for the respondent—“were you born abroad?”(yes/no); (2) the place of 

 

4 The criteria “live in neighborhood” was subject to respondent interpretation of neighborhood; we did not provide maps of the 

target area to respondents, so it is possible that there are youth who reside across the street from or near the target area but not 

technically inside the target area. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

VII 

birth of respondent’s mother—“where was your mother born?” (recoded yes, respondent’s 

mother was born abroad/no, respondent’s mother was not born abroad);(3) the place of birth of 

respondent’s father—“where was your father born?” (recoded yes, respondent’s father was born 

abroad/no, respondent’s father was not born abroad);(4) if the respondent speaks a language 

other than English (yes/no); (5) if the respondent speaks a language other than English at home 

(yes/no); (6) if the respondent speaks a language other than English with his/her friends 

(yes/no); and (7) an ordinal measure of the proportion of the respondent’s lifetime spent abroad 

ranging from 0 (respondent spent no time abroad) to 4 (respondent spent more than 78 percent 

of his/her lifetime abroad).5 A high score on this scale measures lower levels of acculturation (or 

more separation), whereas a low score on the scale measures higher levels of acculturation (less 

separation). Reliability was good. (α = 0.653). Values range from 0 to 9.

Network Structure and Positional Measures–Ego Network 

• The number of components is the number of subgroups in the respondent’s personal network. 

Components are a measure of separately maintained groups (no link exists between any two 

nodes of the different groups) within the larger personal network. The measure only consists of 

a count of groups and does not contain information on type of group. This measure is 

automatically generated in EgoNet statistics for each ego network. Sociological research has 

suggested that as the number of groups within a person’s network increase, the ego becomes 

more constrained to normative behavior (Krackhardt 1999; Krohn 1986). Continuous, values range 

from 0 to 4.

Network Structure and Positional Measures–Whole Network 

• Density represents the number of actual ties between all members across the overlapping 

network as a proportion of all possible ties (if every node was connected to every other node). 

The measure provides insight into how tightly connected the individuals in the network are. 

Higher density values indicate a network in which nodes are closely connected, and lower 

density values indicate that fewer ties are present between nodes. 

• Respondent as isolate. A dichotomous measure (isolate =1) was created to represent whether the 

respondent was an isolate in the whole network. In our study, an isolate is a respondent who was 

not named as an alter by any other respondent and did not have any alters who were also egos 

(i.e., surveyed). Binary variable, values are 0 or 1.

• Degree centrality is the most commonly used measure of centrality (Valente 2010) and 

represents the number of direct connections in a network. While degree can be calculated based 

on ties received (“in-degree”) or ties sent (“out-degree”), our network data is non-directed, so we 

use (non-directional) degree centrality, counting all direct ties in the computation of degree. 

• Betweenness centrality is the frequency a node lies on the shortest path connecting other nodes 

in the network, or the number of geodesics (shortest paths) connecting two nodes on which a 

third node lies. Betweeenness represents whether a node occupies a strategic position in the 

network. The term broker is often used to describe individuals in high betweenness positions. It 

represents a gatekeeper function. 

 

5 We made the proportion of the respondent’s lifetime spent abroad an ordinal variable based on the distribution of responses across 

the sample (i.e., one standard deviation above and below the mean [but greater than 0] were coded 3 and 1, respectively, while the 

range in between was coded 2). If the respondent spent no time abroad, the variable was coded 0. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

VIII 

• Closeness centrality measures the distance from each node to each other node in the network, 

based on both direct and indirect ties (Valente and Forman 1998). Higher closeness values 

indicate that a node is able to reach all other nodes over shorter distances. The closeness 

measure can be thought of as a sort of “degree of separation” as it effectively counts, for each 

node to every other node in the network, the number of links with other nodes that are needed 

to reach every node. The closeness measure does suffer from some complications regarding its 

calculation when isolates exist in the network (isolates cannot be reached by all other nodes, so 

the distance between them and other nodes is effectively ∞), and it is subsequently not as widely 

used as the first two centrality measures described. The network closeness centralization value is 

the average closeness value for all nodes. 

• Eigenvector centrality for a node is based “on the centrality of its neighboring nodes” (Valente 

2010, 87). This measure is useful in large network studies because it takes into account the 

overall structure of the network; it is not as susceptible to more “local” patterns, or patterns of 

closeness or centrality that may exist in small subgroups but may not have a strong role or effect 

in the larger network structure. 

Controls 

Control variables included in final egocentric analyses include: age, gender (male = 1), Latino 

(Latino = 1), number of years lived at address, family cohesion scale, parental support for education 

(binary), adult graduated from high school (binary). 

ANALYSES 

To accomplish the research objectives, two types of analyses are employed: (1) egocentric 

network analysis and (2) sociocentric analysis. Egocentric networks refer to the composition and 

pattern of the social relations of an individual (McCarty & Wutich 2005). For this study, egocentric 

analysis is used to measure whether aspects of egocentric network composition and measures of 

network structure influence delinquency and crime. Guided by learning theory, bonding theories, 

and routine activities theory, using logistic regression, we modeled the likelihood that a youth/young 

adult would be involved (yes or no) in a variety of criminal behaviors, given the network 

characteristics and the youth’s individual position and connectedness within the network, and 

controlling for important risk and protective factors identified in the criminological literature. We 

also attempted to predict the extent of involvement by modeling delinquency as a scale using 

negative binomial regression techniques. The network measures are those related to personal 

networks that characterize or quantify relations between a respondent and his/her 20 nominated 

alters. The sample for these ego-level models included only those individuals who responded to the 

survey (N = 147). 

Sociocentric networks refer to the pattern of relationships between actors within a specific 

group. In the current study, sociocentric analyses involve a wide range of analyses that include 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

IX 

description of the whole network, description of parts of the network framed using different 

characteristics, computation of network structural and positional measures, subgroup analyses, and 

predictive analysis using all nodes. 

FINDINGS 

Description of Surveyed Youth 

The majority of youth surveyed were male (66 percent), with an average age of almost 18. 

Roughly three-quarters of youth surveyed (76.9 percent) identified their ethnicity as Latino; a little 

over a third of the youth were foreign born. Even though the majority (63 percent) of respondents 

was born in the United States, 84 percent have at least one parent who was born abroad, and 69 

percent speak both Spanish and English. Fifty percent of respondents have lived abroad at some 

point in their lives. Almost half of the respondents were Guatemalan or Salvadoran, and 10 percent 

were Puerto Rican or of Caribbean descent. These characteristics of the youth surveyed generally 

reflect the characteristics of the target neighborhood. 

Overall, our sample exhibits a number of very pro-social characteristics: 97 percent of 

respondents under age 18 are still in school, and 92 percent of respondents report parental support 

for attending school (based on past or present support, depending on whether the respondent was 

still in school). The majority of respondents over 18 (58 percent) are currently employed. Just over 

50 percent attend religious services at least once a month. 

Respondents reported lower levels of gang activity and individual delinquency than we 

anticipated. Only 34 percent reported seeing lots of gang activity in the neighborhood, and only 20 

percent said they had ever been approached to join a gang. While the vast majority (75.5 percent) of 

our sample identified as being a member of a group (answering yes to whether they have a group of 

friends with whom they hang out), far fewer respondents (10 percent) reported being current or 

former gang members or having been in a gang fight at some point in their lives (17 percent). Of all 

the delinquent behaviors included as survey items, prevalence was highest for using drugs (27 

percent), followed by carrying a weapon (23 percent). 

Findings Related to Research Question 1: Are network/structural variables important predictors of 

delinquency and gang membership (beyond the traditional set of risk factors) across ego networks? 

• For ego networks, the proportion of relations (alters) who are delinquent is highly significant 

across all binary outcomes where it is included in the model—serious delinquency, carried a 

weapon, sold drugs, and attacked someone. For instance, for every additional delinquent alter in 

an ego’s network, the ego’s probability of selling drugs increases by 38 percent. If the percentage 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

X 

of delinquent members in a respondent’s network changes by 25 percent, that respondent’s 

delinquent activity increases 119 percent. We find similar results looking at the corresponding 

alter delinquency variable when we examine the proportion of alters in a gang fight or in a gang, 

respectively. Individuals who named an individual in a gang fight are 1.6 times more likely to be 

in a gang fight themselves. Thus, having delinquent/criminal relationships appears to be a key 

factor in shaping an individual’s likelihood of delinquency and gang membership. 

• Because the current study asked respondents to nominate people—both other youth and 

adults—who were influential to them, this study also had the opportunity to assess whether 

relationships beyond peer relationships are associated with delinquency and gangs. Instead of 

examining nested models, in order to compare models, we examined effect sizes and model fit 

between sets of negative binomial models. Two sets of models were created for each dependent 

variable: one set that included the proportion of peer delinquency and one set that included the 

proportion of all alters (not just peers) who were delinquent/criminal. The results from these 

comparison models showed that although both models fit the data well, the “all alters” models 

fit significantly better, and the effect of the variable measuring the proportion of 

delinquent/criminal alters on delinquency was slightly larger in each model than the effect of the 

peer delinquency term. 

• We also assessed the strength of pro-social ties by including a variable that measured the 

proportion of one’s network comprising individuals to whom the respondent would go for 

advice and who were not delinquent/criminal. This measure was found to be significant 

(approaching significance at p = 0.05) in only one model—the model predicting “attack 

someone with intent to harm.” In this model, every additional person in a network to whom the 

respondent would go for advice and who is not delinquent/criminal reduces the probability of 

attacking someone with a weapon by 7 percent. Note that we tested other measures of “strength 

of ties” in preliminary models (such as time spent with others), but these measures were not 

found to be significant. 

• The proportion of one’s network comprising individuals residing in the target neighborhood 

(i.e., the respondent’s neighborhood) is not significantly associated with any of our delinquency 

outcomes. 

• The number of components was a significant predictor of delinquency in the negative binomial 

models for all three scaled criminal behavior outcomes and in two of our binary outcomes 

(serious delinquency and weapon carrying). In both the peer and the alter models of overall 

delinquency, for instance, one additional component decreases the expected count of 

delinquency by about one-quarter. 

• Our composite variable measuring separation from U.S. culture is a significant predictor of 

delinquency and serious delinquency in the negative binomial models. A one-unit decrease in the 

separation scale (read as increasing acculturation) translates into an 18 percent increase in the 

expected count of delinquency and an 11 percent decrease in expected count of serious 

delinquency. Note that a measure for ethnic identity (Phinney 1992) was not significant in any 

original models and hence, dropped from final models. 

• Control variables varied in significance across models. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

XI 

Findings Related to Research Question 2: What are the properties and characteristics of the 

sociocentric (i.e., neighborhood-based) network as defined by overlapping egocentric networks? 

How do these characteristics relate to or influence delinquency and gang membership? 

In order to more fully assess the effects of network structure on delinquent behavior, we 

explored two different networks developed from our data: one comprising all egos and all alters 

named by any ego in the survey (the “whole network” with 2,521 members) and one comprising all 

egos and those alters who were named by at least two egos (the “2+ network” with 369 members). 

Eleven percent of the whole network members were also 2+ network members. The comparison of 

results for these two networks allowed greater insight into the mechanisms through which network 

structure can influence behavior for individuals within those networks. 

• When we examined the in-neighborhood and out-of-neighborhood status for members of each 

of the sociocentric networks, we found that approximately one-third of the members of the 

whole network live in the neighborhood while nearly three-quarters of the members of the 2+ 

network live in the neighborhood. This finding was not surprising—it is more likely that those 

named by more than one ego are peers who live nearby. It also suggests that the core social 

network of youths is neighborhood based, as the individuals who were named most frequently 

do indeed reside in the neighborhood. 

• In addition to comparing the whole network and the 2+ network, the sociocentric analyses 

looked at the neighborhood as a network, as one of the main goals of the research was to 

explore the accuracy and utility of defining a social network using geographic boundaries. The 

in-neighborhood group is significantly smaller of the two, at nearly half the size of the out-ofneighborhood group, and has significantly different characteristics: more parents and siblings 

were named by in-neighborhood nodes; more friends were named by out-of-neighborhood 

nodes. More in-nodes are Latino while more out-nodes were born abroad. In addition, the innodes have significantly higher levels of delinquency, for every measure examined. 

These patterns indicate that there are systematic differences between the groups of people who 

are neighborhood residents and those who are not. The differences also support the notion that 

neighborhood context is associated with behavior, and in this specific case, more associations 

with the neighborhood tend to be associated with higher levels of delinquency. Because we lack 

longitudinal data, we are unable to determine whether this is because of self-selection—where 

new residents tend to be similar to those who already live there, perpetuating the prevailing 

attitudes and norms in the area (in this case, to support certain levels of delinquency)—or 

because of influence—where new residents are eventually influenced by the attitudes and norms 

of the neighborhood. 

• In addition to dividing up the whole network into in- and out-of-neighborhood networks, we 

also divided the whole network up into subgroups, here called “partitions” using the GirvanNewman iterative algorithm. The optimum number of partitions in the whole network was 25, 

but one large faction comprised more than 65 percent of whole network members. Additional 

analyses not discussed in this report did not reveal any obvious reason why this partition was not 

divided into smaller subgroups. In addition, 17 of the partitions were actually ego networks—

they included about 21 members: an ego and that ego’s 20 alters. While these were interesting to 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

XII 

explore descriptively, the partitioning did not provide us with a large amount of additional 

insight regarding network embeddedness of subgroups or how or why subgroups might be 

formed. 

• At the whole network level, we considered the most central players to be those who had 

centrality scores on any of the three centrality measures that were in the top 1 percent. We found 

that central players were actually younger than the average whole network member by three to 

four years—a large difference. In addition, a much higher percentage of central actors were 

named as siblings and friends compared to all members in the whole network, and central actors 

were likely to be born in the United States. More than three-quarters (and for degree centrality, 

nearly 100 percent) of all central players live in the neighborhood. Finally, delinquency levels 

were significantly higher for central players: one-fifth to one-quarter of central actors was 

delinquent while only 8 percent of the whole network was. 

Findings Related to Research Question 3: How does an individual’s position and connectedness 

within the whole network relate to his/her propensity to be involved in delinquent behavior at the 

individual level? In other words, how does one’s position at the sociocentric level relate to behaviors 

measured and modeled at the egocentric level? 

• To examine the contribution of betweenness centrality and isolate position to delinquency and 

related outcomes, we re-ran the same basic logistic and negative binomial regression models 

from the ego-level analyses and added the two variables to each model. The isolate variable is 

not statistically significant in any of the models, and betweenness centrality approaches 

significance at the p < 0.05 level only in the model predicting drug selling. The model should be 

interpreted with caution however, because the effect size is basically zero, and no change of any 

real magnitude is predicted to take place based on changes in betweenness centrality. 

• We also conducted binary logistic regression analyses using all members of the whole network 

(n = 2,521) to predict overall delinquency. The model was restricted by the availability of data 

for all alters. We tested each of the three centrality measures in separate models, with all other 

predictors the same in each model. We found, surprisingly, that betweenness centrality was the 

only centrality measure that was not significant; both degree and closeness centrality were 

significant for the whole network. Degree, or the number of direct ties an individual has, was 

associated with decreases in delinquency while closeness was associated with higher levels of 

delinquency. 

• In the whole network regressions, living in the neighborhood also was significantly associated 

with higher levels of delinquency, (in contrast to our finding at the ego level that peers or alters 

living in the neighborhood did not significantly influence delinquency). 

CONCLUSION AND FUTURE RESEARCH 

The current study provides a much-needed quantitative examination of the network context 

of youth living in disadvantaged minority communities. It bears emphasizing that the core 

explanatory concepts in the criminological field refer to social relationships, but few studies examine 

the social relationship beyond simply the characteristics of individuals. A social network framework 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

XIII 

provides a theoretically grounded backdrop relevant to the exploration of micro-level social 

interaction and relations. 

Furthermore, collecting network data on youth within a targeted geographic area for all 

important social relations provided the opportunity to examine how network structure and 

composition influence aspects of delinquency and related antisocial behavior beyond what has 

previously been studied in the extant literature. For instance, this study provided us the opportunity 

to examine how neighborhood-based relations may influence the delinquency of individuals or the 

formation of subgroups across the neighborhood. Results indicated youth are very connected to 

people from the neighborhood: for 30 percent of respondents, at least half of their alters lived in the 

neighborhood. The majority of peers nominated across respondents resided in the same 

neighborhood as the respondent, and were not necessarily school-based friends. Although our study 

did not find that having a larger proportion of in-neighborhood relations was significantly related to 

delinquency or other antisocial outcomes at the ego level, the measure was significant in regression 

models that included all nodes in the whole network. These findings have implications for how 

network studies collect data from youth. The reliance on peer-based networks to study the processes 

and risk factors related to delinquency overlooks the importance of non-peer relations as highlighted 

in our findings. We are able to better examine differential associations among youth simply because 

of the variety of associations studied, and can additionally utilize information about the strength of 

ties to assess the quality of associations. In addition, the findings from this study have shown that 

network content alone provides an incomplete picture: an understanding of network structure is also 

important for advancing delinquency research. 

Future Research 

As social network-based research becomes more commonplace in criminology, researchers 

can continue to articulate a vast range of testable hypotheses related to how social relations shape 

criminal behavior. We recognize that the current study is only one small step among many new 

network-based approaches that can help shed insight into the processes and dynamics that shape 

and reinforce (or buffer against) delinquent and criminal behavior. Below, we provide a few 

suggestions for future research. 

First and foremost, research that attempts to replicate these findings in other Latino 

neighborhoods, as well as in neighborhoods comprising youth from different minority groups, 

would provide insight on the reliability of our findings. The findings from this study regarding the 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

XIV 

“typically studied” risk factors were mostly in line with past research. However, we examined a set of 

network compositional and structural measures that have not, to our knowledge, been examined in 

the study of delinquency. It will be important for future research to shed light on how these findings 

might vary (or not) across different communities. Second, as the cross-sectional nature of this study 

limited our ability to infer causal relationships, longitudinal research would provide the basis to 

obtain more insight on the particular aspects of relationships that influence behavior as well as how 

one’s position in a network shapes opportunities to engage in crime. Specifically, longitudinal 

research in this field will help advance the debate on selection versus influence. Also, research has 

suggested that peer networks are rather fleeting; longitudinal research can inform hypotheses related 

to how changes in relationships impact behavior or influence the criminal career (or gang career), as 

very little is known about how pro-social or antisocial relationships influence desistance. 

Our findings support the idea that widening the framework beyond schools for social 

network analyses seeking to inform delinquent, criminal, or gang-related behavior would yield 

substantial benefits for research, policy, and practice. Certainly self-report data collected on 

networks needs further exploration of their validity (and specifically with regard to using overlapping 

networks), but the point to be made is that exploration of neighborhood-based networks or more 

broadly, networks not bounded by a specific type of relation, will provide the opportunity to more 

directly examine core constructs and hypotheses from social learning, bonding, and routine activities 

theories as well as to test integrated models combining aspects of these theories—as many recent 

studies have suggested. But, analyses that fail to recognize the importance of non-peer relationships 

could suffer from bias and model misspecification. 

Although we have demonstrated that the data collection strategy we devised can be 

successful—at least on a small scale—future research should attempt to replicate this study using 

neighborhoods with large numbers of youth (or perhaps more resources) that would enable 

recruitment of larger samples of youth. Research conducted simultaneously in more than one 

neighborhood would also enable some ego-level analyses to be conducted on a combined sample, 

and at the same time, buoy reliability. Solid planning and valid data on youth populations will enable 

future studies to make important gains. 

It is also important to set aside resources to ensure that serious delinquents and gang 

members are recruited and represented in a study of this sort. In addition, future network studies 

seeking to understand delinquency would benefit from adding a qualitative component to the data 

collection effort, to explore with respondents the structure of their network, the key players, and the 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

XV 

relationships between network members. Having respondents reflect on network graphics would 

allow researchers to explore additional hypotheses and follow up on any puzzling or unanticipated 

findings. 

Larger sample sizes and a more comprehensive set of data on alters would also provide new 

opportunities to examine how non-peers influence youth behavior. This includes testing whether 

relations other than peers can be protective factors in a youth’s life or whether, if these non-peers 

are delinquent or criminal, their negative behavior is more influential than such behavior among 

peers. Large samples and data collection techniques using different alter nomination strategies would 

provide new opportunities to test the tenets of current integrated theoretical models related to 

familial relations. The level of bonding to adults is important in understanding how adult role 

models facilitate maintenance of criminal careers or help delinquent youth desist from crime and 

gangs. 

Although this study uncovered only a handful of delinquent peer groups and gang members, 

the data collection technique and analyses performed lend themselves easily to gang research. In 

gang communities, this type of study could help develop knowledge about delinquent and crew 

subgroups. Much research on gang violence has been conducted Los Angeles and Chicago, where 

gangs tend to have structure and organization. As such, current gang prevention, intervention, and 

suppression strategies, as well as drug market elimination, are often developed from efforts that 

target the behaviors of organized gangs, leaving little understanding of less organized criminal 

groups. Network research aimed at understanding how loosely based “crew” networks evolve and 

change over time, and how aspects of networks are related to changes in criminal behavior, would 

greatly assist intervention efforts. 

We conclude with the suggestion that experts in network analyses and network programs 

continue to devise ways to make network research more practitioner-friendly. One of the ultimate 

goals of our research is to determine which particular network-based questions or network routines 

(in analyses) are the most salient for understanding who (i.e., which individual or group of 

individuals) should be targeted for different types of prevention and intervention efforts. The 

methods used here are not feasible or practical for most practitioners. But a short risk and network 

structure assessment instrument (based on our longer instrument) could make such considerations 

more accessible to practitioners. In this way, they could realistically use social network data to 

inform the development of an appropriate intervention strategy for individuals and/or entire 

neighborhoods. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

1 

CHAPTER 1 

INTRODUCTION 

This research report summarizes findings from a social network study of youth living in a 

high-risk neighborhood in suburban Maryland. The study utilized a social network framework to 

understand the patterns of relations among youth in a predominantly Latino6 neighborhood and the 

nature of the links binding youth to groups and their social contexts. Social network data for youth 

age 14–21 and residing in the selected neighborhood were collected via an extensive computer-based 

survey. Using these data, we reconstructed a social network of the youth and used that network to 

explore a number of questions related to social ties, delinquency, gang membership, and 

acculturation. 

In conducting this study, we sought to fill an important gap in the delinquency literature: 

How do interpersonal relationships and networks—beyond peer networks—shape social interaction 

and, in turn, individual-level antisocial behavior? Do ties to the neighborhood elicit particular 

patterns in social networks that shape behavior? With recent sociological research suggesting that 

immigrant generational status is highly correlated with the commission of violence by Latino youth 

(Sampson, Morenoff, & Raudenbush 2005), and that Latinos have more co-offenders than offenders 

of other races/ethnicities (Daly & Johnson 2006), it becomes critical to take a closer look at risk 

factors for delinquency, violence, and gang membership within Latino immigrant communities. 

Indeed, during the past few years, there has been increasing interest in crime and 

delinquency among the Latino population (Krohn, Schmidt, Lizotte, & Baldwin 2011). Not only is 

the Latino population now the largest ethnic or racial minority in the country, it is also growing at a 

faster rate than all other groups (U.S. Census Bureau 2011). Furthermore, gang violence has been of 

 

6 The terms “Latino” and “Hispanic” are used interchangeably in this report. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

2 

particular concern in Latino communities, and federal, state, and local governments are spending 

hundreds of thousands of dollars annually to augment anti-gang programs and develop new 

suppression-focused tactics. Although many of the recent initiatives give voice to best practices, 

there is astonishingly little research evidence to rely on when it comes to gang prevention and 

intervention in Latino communities. Similarly, although evidence-based practices exist for 

delinquency prevention with Latino youth, the research evidence is limited in telling us what particular 

components are most effective and why. Many of the limitations are due to the dearth in knowledge 

about risk factors that pertain specifically to Latino youth and about possible differences in risk and 

protective factors across different subpopulations of Latino youth. 

Hence, research is needed that takes a closer look at predictors of delinquency and related 

antisocial outcomes for Latino youth, in addition to research that critically examines the 

characteristics and predictors of group-based delinquency and delinquent/criminal associations. 

Similarly, gang experts have voiced the opinion in recent years that gang prevention and intervention 

strategies must include an understanding of the norms, beliefs, and practices that reinforce groupbased criminal behavior (Fleisher & Kreinert 2004; Kennedy 2006; Klein 2006a; Vigil 2007). The 

social network approach has emerged within criminology as a valuable approach to examine the 

complex mechanisms underlying group-based and delinquent behavior, in that it overcomes many 

shortcomings of extant delinquency and gang research (Sarnecki 2001). Furthermore, findings from 

network studies can help build a solid foundation for prevention and intervention (Bouchard & 

Spindler 2010; Haynie & Osgood 2005; McGloin & Piquero 2009). 

Social network analysis refers to both a perspective for examining social relations and a 

methodological technique for analyzing those relations. The technique has been used in fields 

ranging from sociology and anthropology to social psychology and criminology. The perspective of 

social network analysis is that relations between actors create patterns and, eventually, structures that 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

3 

in turn shape the behavior of individuals (Marsden 1990; Wasserman & Faust 1994). Social network 

data describe the contacts, ties, and attachments that one individual has to another. By examining 

these data, and recreating the social networks of each individual, researchers can reconstruct the 

patterns of interaction and social structures that influence behavior (see, for example, Carley, Lee, & 

Karckhardt 2002; Gould 1996; Granovetter 1973; Krebs 2002; Lin 1999; Neaigus 1998; Ryan & 

Gross 1943; and Valente & Rogers 1995). Krohn (1986) was the first to suggest that a social 

network approach was important for understanding delinquency. He articulated that the language of 

the network approach is consistent with key tenets of social control theory in that network 

characteristics such as multiplexity and density can result in more or less constraint on delinquent 

behavior. 

Given the gaps in the delinquency and gang literature, we set out to study the social 

networks of disadvantaged high-risk Latino youth within a neighborhood setting. Our intent was to 

structure a study where we could unravel the types of relations between individuals and the 

association these relations have with different types of behaviors—whether positive or negative—

within a specific neighborhood and cultural setting. As will be described in more detail in the 

methods section, we chose a disadvantaged neighborhood, based on research and input from 

neighborhood informants, where violent crime and gang activity were prevalent and youth were at 

risk of joining gangs. Focusing on high-risk youth living in one neighborhood, the study examines 

both group properties—the patterns of relationships that make up the neighborhood’s social 

network—and individual properties—the specific characteristics of individuals in the network and 

their personal relationships with other network members. 

A study using a neighborhood-based social network framework can examine how individuals 

and their relations are embedded (or not embedded) in neighborhood networks, including 

delinquent groups and gang networks. Social network analyses in this context can help provide more 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

4 

precise knowledge of the mechanics of individual and group-based behavior. Such knowledge can 

provide insight into possible strategies related to both how to target prevention and intervention 

efforts in a culturally competent way and who should be the target of those efforts. More 

specifically, a social network framework using data collected from youth living in a specific 

neighborhood allows one to move beyond descriptions of contextual factors that might be related to 

delinquency and crime, and provides insight into how relationships and structural position within 

and across individuals and groups in the 

neighborhood might influence behaviors 

across the specific context studied. This 

framework is potentially very important for 

advancing delinquency prevention and 

intervention efforts in that the neighborhood 

is a primary context for socialization (Bursik 

& Grasmick 1993). Peer relationships and 

other connections are those that, at least in 

part, are developed around geographic 

contexts. And it is the neighborhood or 

“community” that has been the testing 

grounds for effective delinquency prevention 

and intervention. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

5 

GOALS AND OBJECTIVES 

The main goals of the study are to (1) assess the extent to which individuals and networks 

are related to delinquency, violence, and gang membership across a targeted neighborhood 

comprised of at-risk youth; and (2) describe characteristics of the sociocentric, or whole, network—

made up of overlapping personal networks of neighborhood youth—and their relationship to 

delinquency, violence, and gang membership. To achieve these two goals, we use personal social 

network data, collected via a culturally relevant survey, to assess the composition and influence of 

youth networks aggregated for all research participants into a complete social network (McCarty & 

Wutich 2005; McCarty & Bernard 2003). These network data are used to derive the structural 

properties that may have an influence on the commission of crime and gang membership. Our 

methodological strategy of aggregating—or overlapping—personal network data across all 

respondents allows us to assess how positional network-based variables from the whole network can 

be used in ego-level analyses to examine factors that influence the commission of crime and gang 

membership (at the individual level). And similar to the pioneering work of Jerzy Sarnecki examining 

subgroups across youth suspected of committing offenses in Stockholm, Sweden (2001), we can also 

examine how network linkages influence subgroups and the properties and behaviors characteristic 

of those subgroups. By understanding these dynamics and behaviors, we can not only add to the 

small body of knowledge on network-based risk factors, but also develop a greater understanding of 

the types of processes and programs that reinforce pro-social behavior and prevent or disrupt 

antisocial behavior. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

6 

RESEARCH QUESTIONS 

Our work is guided by three main research questions and several sub-questions, listed below: 

1) Are network/structural variables important predictors of delinquency and gang membership 

(beyond the traditional set of risk factors) across ego networks? Related questions include: 

a) Do egocentric network density, proportion of delinquent peers, and other variables 

characterizing network composition influence delinquency and gang membership? 

b) What is the influence of non-peer (i.e., non-friend) relations on delinquency and gang 

membership? Are characteristics of non-peer relations important influences on delinquency 

and gang membership? For instance, are antisocial networks that include relatives, non-peer 

neighborhood connections and other adults important to understanding the nature and 

extent of youth crime, violence and gang membership? 

c) Does having many neighborhood-based associations (versus networks not based in the 

community) influence participation in crime and gangs at the ego-level? Are youth who are 

members of neighborhood-based groups or gangs involved in more serious delinquent or 

violent behavior? 

d) How does an individual’s—in particular, a Latino’s—level of acculturation influence his 

delinquent behavior and gang membership? How important is immigrant generational status 

and language use with regard to violence and gang membership across ego networks? 

2) What are the properties and characteristics of the sociocentric (i.e., neighborhood-based) 

network as defined by overlapping egocentric networks? How do these characteristics relate to 

or influence delinquency and gang membership? Relatedly, we ask: 

a) What are the characteristics of those individuals who are the central players in the subgroups 

and in the overall network? 

b) Do dense subgroups of gangs or delinquent peer groups exist? 

c) Do variables related to acculturation or immigrant generation characterize subgroups? Or are 

the dominant characteristics of the subgroups rooted in neighborhood-based associations? 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

7 

d) How do the key characteristics that define delinquent or violent subgroups vary from 

subgroups that are not comprised of delinquent or criminal individuals? 

3) How does an individual’s position and connectedness within the whole network relate to his/her 

probability of being involved in delinquent behavior at the individual level? In other words, how 

does one’s position at the sociocentric level relate to behaviors measured and modeled at the 

egocentric level? How do such sociocentric level measures (e.g., centrality) improve our 

understanding of individual behavior? 

By examining two levels of social processes for the unit of analysis (individual and group 

relationships) through both egocentric and sociocentric network analysis, and extending our network 

analysis to include different types of relationships (e.g., friend, relative, neighbor, etc.), we are able to 

examine multiple research questions that have not yet been addressed in the delinquency and gang 

literature. A social network framework “facilitates the testing of structural hypotheses about patterns 

of relations, their properties and their effect on social and individual behavior” (Papachristos 2006, 

100), and provides a rigorous methodological foundation for examining acculturation and cultural 

context (Bernard, Killworth, Evans, McCarty, & Shelley 1988). 

BACKGROUND 

Our study draws on the research literature from a number of substantive areas related to 

antisocial behavior of individuals and groups—in particular, research on delinquency and youth 

violence, the peer context, gangs, acculturation, and social networks. Across these literatures, we 

specifically draw on three theories—social bonding/control theory, social learning theory, and 

routine activities theory—that hold central principles related to relationships and, hence, guide 

hypothesis development for this study. Below, we briefly discuss these three theories, draw out the 

connections to network research, then highlight the important risk factors involved in delinquency 

and gang research. We conclude by addressing the social network literature relevant to our project. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

8 

Control Theory–The Social Bond 

Social bonding and control theories do not ask why people commit crime, but instead focus 

on why people do not commit crime. Hirschi’s social bonding theory (1969) posits that delinquent or 

criminal behavior begins when an individual’s bond to society is weak or broken. Youth develop 

attachments to aspects of the social world—these attachments could be to people or institutions. 

Hirschi describes four components of bonds that are important for social control: attachment, 

commitment, involvement, and beliefs. Hirschi hypothesized that these elements of the social bond 

work to build a stake in conformity and thus limit involvement in normatively unconventional 

activities. Social networks are a natural fit within bonding theory: networks are “bonds” or linkages 

to others, and social network data collection can capture the strength of attachments to and 

involvement with people (both pro-social and antisocial others) in an individual’s personal network 

as well as across a variety of types of networks—such as school, neighborhood, church, sports, etc. 

The strength of ties, or attachment, can be emotional or based on some descriptive characteristic, 

such as family-based ties or time spent with an individual. It is important to note, however, that 

Hirschi hypothesized that any bonds, even bonds to delinquent friends, would have a negative 

influence on delinquency. Hirschi’s theory also suggests that delinquents generally lack any type of 

bonds, even bonds to delinquent others. Research has not supported these assertions (Giordano, 

Cernkovich & Pugh 1986; Kandel & Davies 1991; Simons, Simons, & Wallace 2004). Furthermore, 

empirical research testing bonding theory does not consistently support Hirschi’s predictions for the 

different elements of the bond, and, not surprisingly, researchers have concluded that the theory is 

stronger when its concepts are applied in conjunction with other theories such as social learning 

theory (Shoemaker 2009). 

In later years, Hirschi, working with Gottfredson, melded together aspects of bonding 

theory to develop the general theory of low self-control (Gottfredson & Hirschi 1990). Gottfredson 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

9 

and Hirschi maintain that crime at all ages can be attributed to low self-control. They argue that 

once formed in childhood, an individual’s level of self-control remains constant throughout his 

adulthood. Parental socialization—involving the correction of misbehavior combined with parental 

“warmth” (Hay & Forrest 2006)—and linkages to pro-social others during the childhood years— 

the main socialization period—are extremely important. These strong connections to pro-social 

networks at a young age help individuals to develop sufficient levels of self-control. Empirical 

research has confirmed that parental socialization (Hay 2001; Hay & Forrest 2006; Perrone, Sullivan, 

Pratt, & Margaryan 2004; Polakowsky 1994) and school socialization (Turner, Piquero, & Pratt 2005) 

are significant for developing self-control. The development of individual social-control, then, 

strongly relies on the influence of social networks. 

Other relevant research has shown that low self-control is associated with deviant peer 

relationships. For instance, the results of Chapple’s (2005) examination of data collected on youth as 

part of the National Longitudinal Survey of Youth suggest that youth with low self-control are 

rejected by pro-social peers and, in turn, seek deviant peers, resulting in higher levels of delinquency 

for those with low self-control. Essentially, Chapple found that deviant peer association had a 

significant relationship with crime, controlling for levels of self-control after establishing that selfcontrol accounted for 11 percent of the variance in peer rejection. Other research, however, suggests 

that delinquency is mainly brought on by association with deviant peers—that the causal flow is 

from association with delinquent peers to delinquency (Kandel & Davies 1991; Warr 1993), an 

assertion that Hirschi and Gottfredson do not support. 

Differential Association and Social Learning Theory 

The basic assumption of social learning theory, and differential association theory before it 

(Sutherland 1947), is that people are first indoctrinated into deviant behavior by differential 

association with deviant peers and other significant relations in one’s life. Essentially, for 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

10 

Sutherland’s theory, intimate social networks of individuals are the source of crime and delinquency. 

The frequency, duration, priority, and intensity of human relations are important facets of 

relationships that condition intimacy, and influence the likelihood of learning. Social learning theory 

(Burgess and Akers 1966; Akers 1998) extends differential association theory in that it goes beyond 

the concept of learned definitions to incorporate and more fully explain the mechanisms that lead 

individuals to continue delinquency or desist from it. Differential reinforcement becomes a key facet 

of social learning theory, in that individuals learn how to obtain rewards and avoid punishment by 

reference to the actual or anticipated consequences of given behaviors (Akers 1985). 

The most commonly measured social learning variable is differential peer association (some 

studies use the terms “deviant peer bonding” or “delinquent opportunities”) (Akers and Jensen 

2006). Research consistently shows that delinquent peer influence has a strong positive relationship 

with delinquency, particularly during adolescence (Shoemaker 2005; Warr 2002; Battin-Pearson, 

Thornberry, Hawkins, & Krohn 1998; Lahey, Gordon, Loeber, Stouthamer-Loeber, & Farrington 

1999; Maxson, Whitlock, & Klein 1998). In addition, having antisocial friends obstructs the agingout process and facilitates the maintenance of delinquent and criminal careers (War 2002). Experts 

have stated that across research studies, association with delinquent peers is one of the strongest, 

most consistent predictors of delinquency (Battin et al. 1998; Loeber & Dishion 1987; Warr 2002). 

Furthermore, longitudinal studies suggest that although there may be some effect of selection of 

friends, selection is not the sole mechanism, but that learning plays a role, as delinquent behavior is 

amplified after association with delinquent peers (Chesney-Lind & Shelden 2004; Haynie 2001; Liu 

2003). 

Social learning theory suggests that, in addition to delinquent peers, any delinquent or 

criminal association in a youth’s life will provide the opportunity to learn and reinforce criminal 

behavior. In other words, a parent’s or family member’s participation in criminal behavior can 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

11 

influence a child (Ardelt & Day 2002; McCord 1991; Rowe & Farrington 1997), with some studies 

finding that having delinquent same-sex older siblings is a particularly strong risk factor for 

delinquency (Ardelt & Day 2002). Bullock and Dishion (2002) found that sibling collusion (the 

process of forming sibling coalitions that promote delinquency and undermine parenting) uniquely 

predicted delinquency even above delinquent peer association. Furthermore, it is important to note 

that even controlling for a genetic component, familial associations have been found to be related to 

youth delinquency (Mednick, Gabrielli, & Hutchings 1984), suggesting that learning delinquent or 

criminal behavior, especially from family members, acts a causal mechanism for increased 

delinquency or criminal behavior on the part of the learner. 

Similar to differential association theory, the strength of ties is an important consideration in 

social learning theory. The strength of ties become particularly relevant for familial relationships in 

that families are considered the primary group within which potentially strong relationships are 

developed and socialization takes place. Few studies, however, explicitly examine the contribution of 

different types of social relationships to delinquency or the influence of varying levels of intimacy or 

closeness on delinquency. It is more common for studies to incorporate basic measures of 

attachment to parents, based on bonding theory. Some studies that incorporate measures of both 

social learning and social bonding/control theories have found, for the most part, that the social 

learning variables of peer association, definitions favorable to law violation, and differential 

reinforcement have stronger effects than the social bonding variables (Akers & Cochran 1985; Akers 

& Lee 1999; Jensen 2003). Research also has shown that the influence of deviant peers can be 

magnified by lack of bonds to or involvement with parents or pro-social adults, such as teachers or 

coaches (Steinberg 1987; Warr 1993). A number of studies have shown that strong, positive parental 

influence can counteract the influence of delinquent peers (Warr 2002). In fact, some researchers 

consider attachment to parents as directly reducing delinquency. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

12 

Routine Activities 

Routine activities theory falls under the umbrella of opportunity theories and hence is 

focused on the situation. The theory has little interest in individual socialization and learning from 

connected individuals. The theory is still relevant to social networks, however, in that research has 

shown that the routine activities of youth often include a large portion of unstructured and 

unsupervised time where youth are simply hanging out with other youth (Felson & Boba 2010; 

Osgood, Wilson, O’Malley, Bachman, & Johnston 1996), during which time they are learning and/or 

reinforcing different types of behavior—whether pro-social, deviant, or criminal (Osgood et al. 

1996; Dishion 2000). Over the past decade, researchers expanding routine activities theory have 

suggested that it is important to account for the social context of routine activities in that differential 

social relations guide routine activities, in part, and that motivation is contingent on social 

environments (Bernburg & Thorlindsson 2001). Hence, neighborhood associations become 

important: youth spend an extraordinary amount of time hanging out in their own neighborhoods, 

and this may be particularly true of youth living in disadvantaged neighborhoods (Rankin & Quane 

2000). Although the social capital school of thought might suggest that neighborhood ties are 

protective factors, research had found that local ties can confer antisocial influences in 

disadvantaged neighborhoods (Chung & Steinberg 2006). Research by Mennis and Harris (2011) 

also suggests that peer contagion has a strong neighborhood component, in that aggregate measures 

representing delinquent youth living nearby other delinquent youth have a strong positive effect on 

recidivism. 

Similarly, Osgood and Anderson (2004) found that unstructured socializing with peers had 

both individual and contextual effects, explaining a great deal of the variation in delinquency rates 

across neighborhoods. In addition, monitoring by parents had a strong contextual effect on 

unstructured socializing, echoing one of the tenets of the general theory of low self-control. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

13 

Research by Dishion (2000; Dishion, McCord, & Poulin 1999) that set out to integrate social 

learning theory and routine activities theory suggests that time spent with delinquent peers becomes 

a type of “deviancy training” in that peers reinforce each other’s deviant attitudes and actions by 

laughing or accepting antisocial comments and behavior. His research found that reinforcement 

through such reactions was associated with increased substance use, delinquency, and violence. 

Risk Factor Framework for Delinquency and Gang Membership 

In addition to the three criminological orientations related to social networks (control 

theories, social learning theory, and routine activities theory), it is important to consider a risk factor 

framework. For our particular study, a risk factor framework highlights the importance of 

neighborhood risk factors and provides the foundation for examining causal relationships without 

being encumbered by testing any specific theory or theories, thus allowing for a broader assessment 

of the mechanisms that could support prevention and intervention (Farrington 1995). Studies like 

ours that set out to examine predictors of delinquency in hope of informing policy will need to 

control for a standard set of risk factors. With regard to this study, because our outcomes extend 

beyond delinquency to include gang membership, we also provide a discussion of the predictors that 

have particular salience related to gang membership. 

Indeed, the factors associated with involvement in delinquency and youth violence have long 

been a focus of criminological research, with past studies identifying a multitude of risk factors 

stemming from behavioral, institutional, environmental and demographic characteristics (Saner & 

Ellickson 1996). Risk factors related to social networks include peer association, school-based risk 

factors, and family-based factors. The strongest risk factors include a lack of attachment to academic 

institutions and the family, parental attitudes favorable to violence, and reduced parental 

involvement with their children. Deviant peer association, which emerged from previous research 

(Farrington 1995; Hawkins, Herrenkohl, Farrington, Brewer, Catalano, and Hirschi 1998) and stems 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

14 

from the social learning perspective, is also a strong risk factor. Exposure to neighborhood violence, 

weapons and drugs, and exposure to networks that value violence and the use of weapons and drugs, 

similarly put youth at risk for involvement in delinquency. 

Research has consistently shown that those at risk for delinquency are also at risk for joining 

a gang. Some researchers suggest that although each risk factor by itself may place an adolescent at 

risk for delinquency, the more risk factors that are present, the more likely the individual will join a 

gang (Howell & Egley Jr. 2005). With regard to neighborhood context, research has shown that 

youth from neighborhoods with a greater availability of drugs, as well as neighborhoods in which 

many young people are in trouble, have greater odds of joining a gang than those from areas without 

such issues (Hill, Howell, Hawkins, & Battin-Pearson 1999). On the other hand, recent area-level 

research examining concentrations of gang members finds that gang membership is less likely in 

social contexts characterized by residentially unstable population or changing structural conditions 

(Katz & Schnebly 2011), such as in areas undergoing gentrification. 

 Klein (2006) attests that what differentiates predictors of delinquency from predictors of 

gang membership is that only a subset of delinquency predictors are relevant to gang membership. 

Looking across a number of gang studies, Klein found that the following constructs were common 

predictors of gang membership: 

• early involvement in delinquent activities 

• early self-concept as a delinquent 

• absence of helpful adults outside the family 

• exposure to stressful life events 

• family members in a gang or in serious legal trouble 

• lower family supervision or parental monitoring 

• delinquent friends and friends who endorse violence and violent forms of conflict 

resolution 

• enjoyment from just hanging around the neighborhood with friends 

• lower commitment to school, lower expectations for higher education 

• higher levels of disrespect for police and other officials 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

15 

In addition to a specification of risk factors that may differentiate gang youth from merely 

delinquent youth, research has found that gang members account for a disproportionate share of the 

crime problem (Dukes, Martinez, & Stein 1997; Esbensen & Huizinga 1993; Gordon, Lahey, Kawai, 

Loeber, Stouthamer-Loeber & Farrington 2004; Thornberry 1998), and that active gang membership 

facilitates participation in delinquency and violence such that individuals are much more involved in 

delinquency and violence during periods of gang membership than when they are not active gang 

members (Thornberry 1998).Thornberry’s longitudinal study of Rochester youth found that gangs 

appeared qualitatively different from other delinquent peer groups. The study showed that at all 

eight waves, gang members reported committing violent offenses at significantly higher levels than 

did the non-gang members who associated regularly with delinquent peer groups. The same study 

also found that although only one-third of the sample consisted of gang members, they accounted 

for 70 percent of drug-selling behavior in the sample. 

Research from the Seattle Social Development Project (Battin, Hill, Hawkins, et al. 1998) 

found that individuals who were gang members always had the highest rates of delinquency and 

substance use. Across 11 measures of delinquency and substance use measures, rates for gang 

members were significantly higher on 9 measures than those for youth with delinquent peers (but 

not in a gang). Even after controlling for the effects of delinquent peers and previous delinquency, 

the authors found that gang membership significantly predicted delinquency. These findings provide 

some support to social learning theory’s assertion that behavior is the result of association with a 

delinquent group. Thornberry refers to the causal model as the “facilitation” model (Thornberry 

1998, 159–60) but does not necessarily suggest that the mechanism at work is learning. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

16 

Race, Ethnicity, Acculturation, and Delinquency 

Race and Ethnicity as Risk Factors 

Race and ethnicity add to the complexity of the risk factor literature; race and ethnicity are 

each risk factors, and racial and ethnic differences exist among risk factors associated with 

delinquency. For example, several studies have shown that school behavior and school factors (e.g., 

low GPA and dropping out of school) better predicted delinquent behaviors of African American 

youth than white American youth (Joseph 1996; Voelkl, Welte, & Wieczorek 1999), while peer 

influence has been more strongly related to substance abuse among white American adolescents 

than among African American or Latino adolescents (Johnson & Hoffman 2000; Marsiglia, Kulis, & 

Hecht 2001). 

Race and ethnicity do not uniformly predict delinquency for African American and Latino 

youth. For example, ethnic identity emerged as a predictor of attitudes toward fighting among 

African American youth but not among Latino youth, even after accounting for parental control and 

negative peer behavior (Arbona, Jackson, McCoy, & Blakely 1999). French, Seidman, Allen, and 

Aber (2000) surveyed adolescents in their last year of junior high school and one year later at the end 

of their first year of senior high school. They found that African American and Latino youth 

responded differently to similar experiences, suggesting that ethnic identity may mean very different 

things for these two groups. 

Latino Ethnicity and Acculturation 

In addition to exploring differences between African American and Latino youth, 

researchers have investigated important intra-ethnic differences among Latino subgroups. Latino 

“ethnicity” in the United States applies to a broad range of populations with roots in numerous 

countries, their connection or migration to the United States, and their level of integration into 

mainstream American society. Intra-ethnic differences among Latinos can prove instructive when 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

17 

examining the links between risk factors and delinquency or health outcomes. Chief among the 

concepts employed to explore such intra-ethnic differences stands “acculturation.” Acculturation 

refers to a multifaceted process by which individuals of one culture adopt the language, values, 

customs, identity, and behaviors of another, usually more dominant, culture (Gil, Vega, & Dimas 

1994; Gil, Wagner, & Vega 2000). Acculturation has been operationalized in many different ways, 

and most often as a single dimension or variable. More recently, however, researchers have made a 

strong case for examining acculturation as a complex, latent variable that has multiple components. 

With regard to Latinos, acculturation has been operationalized as language use (Gil, Vega, & Dimas 

1994), a composite of language use and place of birth (Amaro, Whitaker, Coffman, & Heeren 1990), 

and cultural orientation (Vega, Koury, Zimmerman, Gil, & Warheit 1995). 

A small but growing body of research evidence suggests that the longer Latinos live in the 

United States, the more likely they are to participate in at-risk and high-risk behaviors. The 

phenomenon has gained general recognition as the “immigrant paradox,” named after the 

unexpected results of heightened resilience among less acculturated or first-generation youth 

compared to their more acculturated or second-generation counterparts. Specifically, greater levels 

of acculturation among Latino youth have been associated with increased rates of smoking, drinking, 

and substance abuse; lower rates of family formation; and increased rates of dependence on 

government assistance programs (Amaro et. al. 1990; Gil, Vega, & Dimas 1994; Gil, Wagner, & 

Vega 2000; Hayes-Bautista 1989). 

In general, it appears that being less acculturated or being a recent immigrant functions as a 

protective factor in communities with a sizeable co-ethnic population, especially in the Southwest, 

Miami, and Chicago. Buriel, Calzada, and Vasquez (1982) found that among 81 Mexican American 

male adolescents in Los Angeles, third-generation individuals had lower expectations for themselves 

and higher rates of delinquency than those from first and second generations. Similarly, in their 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

18 

examination of 6th and 7th grade students in a Southwestern city (n = 1,170), Fridrich and Flannery 

(1995) found that after employing measures to account for parental monitoring, acculturation, and 

delinquent behavior, Mexican American acculturated individuals reported higher levels of delinquent 

behavior than those who were recent immigrants, Caucasian, or less acculturated by choice. 

Additionally, those falling into the recent immigrant category reported higher levels of parental 

monitoring than other groups. Coatsworth, Maldonado-Molina, Pantin, and Szapocznik (2005) 

found that youth who had assimilated had significantly higher scores on problem behavior scales, in 

addition to lower scores on the parental monitoring measure. Conversely, those considered 

bicultural had more familial resources and increased parental monitoring, as well as the most 

adaptive patterns. A study using data from the Project on Human Development in Chicago 

Neighborhoods (Sampson et al. 2005) found that third-generation Latinos were more likely to be 

involved in violence relative to first- and second-generation immigrants. These findings extend to 

substance abuse outcomes as well. Most studies have also found that greater levels of acculturation 

are associated with an increase in substance abuse (Amaro et al. 1990; Gil, Wagner, & Vega 2001; 

Martinez 2006; McQueen, Getz, & Bray 2003). 

Race, Ethnicity, Acculturation, and Gangs 

In contrast with the above studies, Lopez and Brummett (2003) examined acculturation 

within the specific context of Mexican gangs in Los Angeles and found that Latino gang members 

were less acculturated than non-gang members. The study employed a bicultural classification scale, 

and it challenges the broader literature on acculturation and delinquency. The study found that gang 

members, compared to non-gang members, associated more with Mexicans, had more Mexican 

friends, and were less accepting of non-Hispanic whites. The authors hypothesized that gang 

membership for Mexican Americans in Los Angeles is a variation of “reaction formation,” or 

moving away from things American and toward original immigrant identities. Under this model, 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

19 

gang membership is just one manifestation of this reaction, and the joining of gangs as a way to 

embrace Latino culture is sometimes referred to as “choloization” (Lopez & Brummett 2003). Gang 

membership redirects resentment resulting from marginality and culture conflict (ibid, p.629). A 

recent study documented a similar phenomenon in the Southwest: utilizing a large sample of 

Mexican-American adolescents residing in low-income barrios in the Southwest, Miller, Barnes, and 

Hartley (2009) found that individuals who were “more assimilated into mainstream ‘Anglo’ culture” 

were less likely to report membership in a gang (p. 14), with ethnic marginalization mediating 

roughly 30 percent of the acculturation effects on likelihood of gang membership (p. 18). Related 

research on acculturation stress may explain why these gang membership findings contrast with the 

broader literature on delinquency and substance abuse. 

Studies that measured the concept of acculturation stress have generally found that stress 

can yield increased deviance and substance abuse (Gil, Wagner, & Vega 2000). Studies of 

acculturation stress or conflict may explain why less acculturated Latino youth in the studies above 

reported a higher likelihood of gang membership. Coatsworth and colleagues (2005) studied Latino 

youth of various nationalities in Miami, and found that moderate or in-between acculturation types 

scored lower on general adaptation measures than youth who identified closely with their parents’ 

culture (“Hispanicism”) or mainstream culture (“Americanism”). Youth with high scores on 

measures of both Hispanicism and Americanism fared well compared to in-between acculturated 

youth who did not identify strongly with either. Vega and colleagues (1993) studied delinquency 

among Cuban youth in Miami and found an association between acculturation conflict and low selfesteem. They concluded that weakened family protective factors allow acculturation stress to play a 

strong role in promoting delinquency among less acculturated youth. Although neither study directly 

examined gang membership, their findings suggest that, unlike delinquency or substance abuse 

outcomes, gang membership may function as an outlet for Latino youth who experience 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

20 

acculturation stress or conflict and, at the same time, feel no strong attachment to their parents’ 

culture or mainstream American culture. 

Other research examines race-ethnic differences in gang membership, while empirically 

testing a “marginalization” theory that predicts gang involvement both globally and differentially by 

race-ethnicity. While not specifically focused on Latino populations like research discussed above, 

more global theories of marginalization dovetail nicely with that work; for instance, Lopez and 

Brummet (2003) discuss the process of joining a gang in response, in part, to marginalization of 

Latinos from mainstream American culture. Vigil’s marginalization theory (1988, 2002) posits a 

series of macrohistorical and macrostructural forces that lead to economic insecurity and loss of 

informal and formal social control, and supposes that these influences interact to produce a 

cumulative effect of marginalization. Once marginalized, segregation and discrimination create a lack 

of connection to or involvement in society. Marginality is linked to difficulty in establishing a selfidentity (Malec 2006; Vigil 1988). Gang membership, then, would represent an adaptation to 

marginalization and a group that provides socialization and a self-identity. Vigil developed the theory 

while working with Hispanic youth, and others have applied the theory to African American, 

Salvadoran, and Vietnamese populations (Vigil 2002). 

Freng and Esbensen (2007) criticize Vigil’s theory for its failure to articulate the nature and 

relative strength of each of the key factors and how they could lead to gang membership. Using 

cross-sectional data from the National Evaluation of G.R.E.A.T. (a sample of 8th graders in 11 

sites), they found significant differences in current gang membership by race-ethnicity. For instance, 

the ecological stress variables did not predict gang membership for African Americans and Latinos 

but did for white Americans. Also, neutralization (justifying delinquent behavior because it seems 

warranted if, for instance, someone starts a fight with you, or you are just protecting yourself) 

predicted current gang membership for African Americans and white Americans, but not for 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

21 

Latinos. More important, only four of the multiple marginality constructs tested (i.e., ethnic identity, 

parental attachment, parental monitoring, and limited educational opportunities) manage to perform 

equally across the three racial-ethnic groups examined—but each proved nonsignificant across the 

groups. Only attitudes toward the police and street socialization (with regard to current gang 

membership) were significant marginalization constructs for Latinos. 

Freng and Esbensen suggest that specific factors may differ for gang membership across 

racial-ethnic groups, and that these factors lead to the recommendation of implementing different 

programs at various stages of gang involvement, particularly at the prevention stage. Race-specific 

programming may be warranted, a finding that could be extended to include tailored prevention 

programming for different groups of Latinos (e.g., less acculturated versus more acculturated, first 

generation versus later generations, large co-ethnic cohort versus small co-ethnic cohorts). 

A more recent study used the longitudinal Rochester Youth Development Survey to test 

some of the tenets of Vigil’s theory (Krohn et al. 2011) across different racial and ethnic subgroups 

and found that some predictors of joining a gang varied among racial-ethnic groups and hence, not 

all of Vigil’s hypotheses were supported. It is also important to note that the authors found that 

speaking Spanish at home dramatically reduced the odds of gang membership by more than 70 

percent—it did so by reducing the impact of risky time with close friends. Krohn and colleagues 

interpreted the finding as support for theories suggesting that youth more acculturated to the 

dominant American culture have negative outcomes with regard to gangs, but also suggested that, 

because some studies have found that higher levels of acculturation reduced chances of gang 

membership (Lopez & Brummett 2003; Miller et al. 2009), further research is needed that examines 

and compares different Latino subgroups. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

22 

SOCIAL NETWORKS, DELINQUENCY, AND GANGS 

Historically, the use of social network analysis within the field of criminology has been 

limited. However, within the last decade there has been a growing interest in applying the 

methodology to the study of delinquency and gangs (McGloin & Kirk 2010). Because a strategic 

arena within social network research involves the study of social influence, the study of peer 

influence on delinquency within a social network frame becomes quite obvious. As discussed earlier, 

while not always explicit, social networks play a large role in a number of criminological theories—

social control, learning theories, routine activities—and also others such as social disorganization 

and collective efficacy (Bursik & Grasmick 1993; Sampson, Raudenbush, & Earls 1997). As such, 

researchers—though not necessarily criminologists—have used social network analysis to examine 

individual antisocial outcomes such as aggressive behavior and illegal drug use. 

For instance, social network analysis has been used to study peer relations with regard to 

drug use (Ennett & Bauman 1993; Henry & Kobus 2007; Williams & Latkin 2007), smoking (Abel, 

Plumridge, & Graham 2002; Mercken, Snijders, Steglich, Vartiainen, & de Vries 2010; Pearson & 

Michell 2000), and aggression (see for example, Cairns, Cairns, Neckerman, Gest, & Gariepy 1988; 

and Xu, Farver, Schwartz, & Chang 2004). Most of those researchers, however, come from the 

fields of developmental psychology or public health. In their assessment of drug use among 

adolescents, Henry and Kobus (2007) found that liaisons between groups (or individuals who link 

groups together) in a social network were more at risk for tobacco use than isolates (who are 

connected to no or few other members) or actual network members, which may be attributed to the 

fact that liaisons are more likely to interact with multiple groups at once and, as a result, may have a 

greater exposure to smoking. A Baltimore study of adult current heroin and cocaine users found 

that, using a scale of network attributes (drug-using ties, support for drug use, and connection to 

daily users) the odds of reporting current drug use for individuals with stronger drug influences was 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

23 

8.5 times higher than for individuals with weaker drug influences (Williams & Latkin 2007). A 1988 

study of aggression in 4th and 7th graders (Cairns et al. 1988) found that aggressive youth tended to 

be friends with other aggressive youth and although aggressive youth were less popular than control 

subjects in the overall social network, they were equally often identified as being nuclear members of 

social clusters. 

Within criminology, there are only a handful of network studies based on self-report that 

examine delinquency outcomes. In one of the first studies of its kind, Haynie (2001) used data from 

the National Longitudinal Study of Adolescent Health (Add Health) to map out the delinquent 

context of adolescent friendship networks. Haynie, situating her research in differential association 

and social learning theories, posited that the proportion of delinquent friends in the network, as well 

as the absolute level of delinquency of those peers, will be predictors of delinquency. She found that 

the proportion of delinquent friends is strongly associated with delinquency, controlling for prior 

delinquency, but that the absolute level of peer delinquency is not a statistically significant predictor 

of delinquency when proportion of delinquent friends is in the model. 

Haynie’s later study using Add Health data (2002) examined the influence on delinquency of 

important network structural variables, such as density, centrality, and popularity. Initial findings 

suggested that structural characteristics of friendship networks were unassociated with delinquency 

when peer delinquency is accounted for, but Haynie then examined interaction effects between peer 

delinquency and each network characteristic. She found that each of the three network structural 

characteristics condition the delinquency-peer association. In a more recent social network study 

examining selection effects versus socialization, Haynie and Osgood (2005) found that delinquency 

is causally influenced by peer socialization, with adolescents affecting one another regardless of the 

strength of their attachments, or the amount of time spent together. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

24 

Other network studies that assess delinquency using social network methods are often set 

within European countries and approach the research questions in a variety of ways—not necessarily 

to examine structural effects of networks on delinquency outcomes. In their assessment of networks 

and delinquency among a sample of high school–age students, Baerveldt, Völker, and Van Roessem 

(2008) found that delinquent students preferred to establish friendships with other delinquent 

students, with such delinquent networks becoming more closely connected over time. Weerman and 

Bijleveld (2007) found that although friendship networks consisted of both delinquent and 

nondelinquent peers, friends and best friends from the same delinquency category (nondelinquent, 

minor delinquent, serious delinquent) are nominated more often than other students. Additionally, it 

was established that minor delinquent boys were more popular than nondelinquent or seriously 

delinquent individuals, in addition to exhibiting more central positions in the network. 

Dijkstra and colleagues (2010) examined network dynamics for weapon carrying among a 

sample of middle- and high school–age boys in a predominantly Hispanic area of New Jersey. Those 

who carried weapons had an increased number of best friend nominations received, yet gave fewer 

best friend nominations, and the authors concluded that weapon carrying is deemed popular, with 

those actually doing the weapon carrying being more selective in terms of whom they choose to be 

in their network. It was also revealed that adolescents chose to model their weapon-carrying 

behavior to resemble that of their friends, alluding to the fact that networks are extremely influential 

in terms of peer behavior, specifically as it relates to weapon carrying. 

 Recently, researchers have begun to use network analysis in the study of gangs (Fleisher 

2002, 2006; Papachristos 2005; McGloin 2004). The studies can take either an egocentric approach, 

considering impacts at the individual level (Fleisher 2002), or a sociocentric approach (McGloin 

2005; Papachristos 2005), where the analysis spans multiple groups (i.e., gangs) (McGloin 2005; 

Papachristos 2005) or takes place within one gang or criminal network (Natarajan 2006). The 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

25 

research most relevant to our study is Fleisher’s (2002) study of women in gangs in Champaign, 

Illinois. Fleisher used network methods, in combination with participant observation, to create what 

he called a “more nuanced analysis” of adolescent gang life in a specific ecological niche. His 

research highlighted the importance of egocentric (individual-level) networks, as he showed that 

ego-gang networks of female gang members were socially flexible in that they included male and 

female gang members and male and female non-gang members. Papachristos’s network analysis of 

gang homicide in Chicago (2005) is also important to the current study in that he found that 

violence was a contagious process sustained by the group nature of gang activity and driven by 

norms supporting violence that defined both the group and individual identities. His research 

findings support the notion that the structural processes of the group can greatly influence 

behavior—in this case, how individual and group responses to threats can result in homicide. 

The Current Study 

We sought to advance both the substantive and methodological literature pertaining to 

delinquency and other high-risk behaviors in a number of ways: (1) by collecting in-depth network 

data from high-risk youth within one neighborhood to examine how neighborhoods influence 

relationships and, in turn, delinquency and group-based behavior; (2) by going beyond the typical 

school-based network found in most network studies of delinquent behavior to include all important 

social relations in a youth’s life; (3) by creating a whole network through overlapping egocentric 

(individual) networks in order to provide insight into group dynamics; (4) by examining how 

structural characteristics (such as centrality) created from the sociocentric (or overlapped) network 

might influence individual- and group-level behavior; and (5) by paying close attention to how 

dimensions of culture, ethnic identity, and acculturation might relate to delinquent behavior and 

gang membership. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

26 

RESEARCH HYPOTHESES 

Below, we first outline egocentric (individual-level) hypotheses as derived from the goals and 

objectives of the current study, and then set forth our sociocentric network–level hypotheses related 

to research that will be conducted using overlapping networks. The relevant research question with 

which each hypothesis is associated is denoted in parentheses. 

(1) The strength of ties—as defined by the amount of time one spends with delinquent 

others or whether one would go to a delinquent relation for advice—will be significant 

predictors related to delinquency and gang membership (Q1a). 

(2) Individuals who are brokers across the whole network will be more likely to be involved 

in selling drugs (in particular) and other delinquent behavior (Q1a, Q3a). 

(3) The number of subgroups within a personal network (i.e., components) will influence 

delinquency in that as the number of subgroups increases, behavior is more constrained 

to the norm. Hence, as the number of subgroups increases, the odds of being delinquent 

will decrease (Q1a). 

(4) Associations with delinquent and/or gang youth will influence a youth’s participation in 

delinquency and gangs, but the effect of associations with all relations that are 

delinquent/criminal (i.e., delinquent peers and non-peers) will be larger than that of just 

including delinquent peers (Q1b). 

(5) Having many ties to youth living within the same neighborhood will increase an 

individual’s likelihood of being in a gang, selling drugs, and other delinquent behavior 

(Q1c). 

(6) Higher levels of acculturation will be associated with delinquency and drug dealing, while 

lower levels of acculturation, and, more specifically, a separation from American culture, 

will be associated with gang membership. In addition, high levels of ethnic identity and 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

27 

cultural cohesiveness will act as a protective factor from all antisocial behavior, including 

gang membership (Q1d). 

Whole Network Hypotheses 

The hypotheses below are divided into those related to the search for community structure, 

or the existence of distinct, coherent subgroups (here called factions or Newman-Girvan 

subgroups), and those related to individual position within the overall structure. 

(7) Within the whole network, we will be able to identify a small set of central network 

members who are well connected. 

(a) The central players will have high levels of acculturation and will be identified more 

frequently than others as individuals to whom others go to for advice (Q2a). 

(b) Central individuals will tend to be more delinquent, as more powerful or central 

individuals will feel less pressure to conform to the norm whereas less central players 

will be more constrained by the behaviors of those around them (Q2a, Q3a).7

(c) Most central players will live in the neighborhood; within a neighborhood-based 

network, presence in the neighborhood and strength of ties to others in the 

neighborhood are important to maintaining a central position (Q2a). 

(8) Within the whole network, we will be able to identify distinct clusters, or subgroups, of 

individuals. 

 

7 It is important to note the distinction between the two hypotheses stated above. On the surface, hypotheses 3 and 7b appear to

contradict each other; one (3) suggests that those connected to more groups will find their behavior constrained, thus participating 

in fewer delinquent behaviors, while the other (7b) suggests that those who are central to the network, and presumably have ties to a 

number of different nodes from which they derive their central positions, will be less constrained to act pro-socially and will actually 

demonstrate higher levels of delinquency. Actually, the two hypotheses are talking about two very different concepts: hypothesis 3 

refers to a network characteristic akin to degree, but regarding ties between nodes in different groups (it is not enough to be tied to 

several people in the same group). Despite ties to nodes in different groups, a person in this position may not be central in any of 

the groups, and juggling relationships across a number of groups may actually prevent the individual from increasing his centrality in 

any one group. Hypothesis 7, on the other hand, regards an individual’s centrality and is only concerned with connections between 

nodes, regardless of whether the nodes are in the same group or in different groups. Thus if a person is central, connected to many 

different individuals, he may not feel reliant on any one node for acceptance or friendship and may feel more emboldened to act

independently with fewer constraints on his behavior. In this way, centrality can actually increase one’s likelihood of being 

delinquent. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

28 

(a) The subgroups will be homophilous8 in terms of demographics (Q2b, c, d).

(b) The networks for groups comprising mostly individuals with lower levels of 

acculturation (those who are foreign born and Latino) will be dense and have fewer 

“external” ties to individuals outside the group. These groups will also have lower 

levels of delinquency (Q2c, d). 

(c) The subgroups that have more delinquent members will be denser with one or a few 

central players. Those in more loosely connected groups are more constrained by the 

behaviors of others and are thus be less likely to be delinquent (Q2b, c, d). 

(d) Subgroups that have more delinquent or violent members will also have more ties to 

individuals in the neighborhood (Q2c, d). 

The following chapter discusses the design of the study, the data collection methods, and 

measures used in the analyses, and then presents the analysis plan. 

 

8 Homophilous literally means “love of the same,” but it is commonly used to refer to the phenomenon where similar people tend to 

gravitate toward each other and spend more time together. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

29 

CHAPTER 2 

DESIGN AND METHODOLOGY 

The design and methodology of the study are based on the theoretical and analytical 

framework of the network approach. A central hallmark of network analysis is its emphasis on 

relationships. The network framework is designed to capture how the structure of relations 

influences the dynamic individual and group processes that both shape and are shaped by criminal 

behavior. For our particular study, because we planned to conduct both egocentric analysis and 

sociocentric analysis using overlapping networks, we required a data collection strategy that allowed 

for the wide range of analytic possibilities. Figure 1 graphically illustrates the difference between 

three different networks and the sampling techniques that yield data for different types of analyses 

(and answer different research questions). Network data can be collected in various ways, and each 

method yields its own set of data that then provide unique management and analysis requirements 

(Valente, 2010). Our intention in collecting egocentric data for use in whole network analysis via 

overlapping networks required a specific method, described in more detail below. We first describe 

the research site and sample, and then describe the data collection strategy, followed by a detailed 

description of data preparation for whole network analysis and measures. We conclude the chapter 

with a description of the analysis plan for both the egocentric and sociocentric analyses. 

THE SITE 

We chose a small neighborhood in Montgomery County, Maryland, just north of the District 

of Columbia. Montgomery County was chosen as the research site for several reasons: evidence of a 

variety of active Latino groups/gangs existed, the research team had familiarity with the 

neighborhoods and a general understanding of the nature of gang/groups in the county, and the 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

30 

research team had already built a solid relationship with a community organization (Identity, Inc.,) 

that works closely with high-risk and gang-involved Latino youth. In addition, Identity, Inc., 

reported that gang activity and gang structure in the county were greatly influenced by level of 

acculturation. Staff from Identity, Inc., shared with us a community assessment they conducted 

2006, providing further evidence of the high-risk nature of youth in target site. 

Because we intended to choose one well-defined neighborhood, we conducted focus groups 

and spent time in the field to learn about perceptions of neighborhood boundaries, youth 

attachments, gangs, and crews. We examined crime data, parcel data, and census data for areas in 

Figure 1. Illustration of Three Different Types of Networks; (a) Egocentric, (b)Whole Network via 

Surveying All Individuals, (c) Whole Network via Linking/Overlapping Ego Networks 

a b 

c 

Nodes with squares inside indicate survey respondents; gray nodes indicate that data on these nodes are not based on 

self-report, but on report by those surveyed (or, in the case of figure 1-c, average/majority of responses for nodes). 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

31 

suburban Maryland that were predominantly Latino neighborhoods. We conducted informal, 

unstructured interviews with neighborhood leaders and youth informants. We drove around the 

neighborhoods with our informants to gain an understanding of neighborhood boundaries and 

whether there were common definitions of what constituted a “neighborhood” for youth. Data 

from the unstructured fieldwork and focus groups were used to revise the research design, finalize 

the sampling process, inform study protocols, and develop survey recruitment procedures. We also 

used the fieldwork to explore whether highly secretive gangs (if identified) would be amenable to 

being subjects of this research. 

The target area selected is residential and mostly comprises low-rise apartment buildings and 

a few single-family homes. There are a park and an elementary school within the target area and a 

recreation center just outside the boundaries. The area is approximately one mile from a major 

interstate, has public bus stops within the boundaries, and is located a few miles from a metro 

Table 1. Census Characteristics for County and Survey Site

Montgomery 

County, Md. Survey Site 

Male 47.8% 51.0% 

Population under 18 years 25.3% 28.9% 

Population over 65 years 10.2% 5.2% 

Population 14–21 8.9% 15.9% 

Black 15.0% 17.9% 

Hispanic/Latino 11.5% 59.0% 

Female-headed households with children under 18 14.4% 23.8% 

Speaks English “not well” or “not at all,” ages 5–17 0.8% 3.7% 

Speaks English “not well” or “not at all,” ages 18–65 5.2% 26.9% 

Foreign born 26.7% 62.3% 

New resident since 1995 47.3% 61.5% 

High school degree or higher 90.3% 55.2% 

Unemployed 2.2% 5.9% 

Below poverty level 5.4% 17.5% 

Per capita income (1999) $35,684 $14,100 

Renter-occupied units 30.3% 74.0% 

Vacant units 3.0% 2.2% 

Large (6+) households 4.2% 13.2% 

Source: U.S. Census, 2000. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

32 

station. Table 1 provides U.S. Census-based characteristics (from 2000) for the target area and for 

the county in which the site is located. As can be seen in the table, the survey site consists of 

households where over 60 percent of residents are foreign born, only 55 percent have high school 

degrees, and per capita income is less than half that of the county average. Other important 

characteristics to note are higher than average residential instability, a large population of youth, and 

a large percentage of large households. 

 

THE SAMPLE 

The Neighborhood as the Network 

The neighborhood constitutes the framework for the group “network.” The particular 

neighborhood was chosen because it is a disadvantaged, high-crime neighborhood that has variation 

on characteristics important to the goals of our study: respondent country of birth, age, participation 

in criminal behavior, and gang membership. For a number of reasons, the sample was purposely 

designed to be a census of the neighborhood youth. First, crime prevention and intervention 

programs, particularly anti-gang programs, are most often neighborhood-based with the expectation 

that a program will target youth “in the neighborhood.” Neighborhood-based programming is based 

on the idea that one can address issues that are rooted in the socioeconomic structure of the 

neighborhood and the associated problems that are coupled with disadvantage. Second, scholars 

know little about how neighborhood context and contacts influence networks and behavior, yet 

entire bodies of research have suggested that neighborhoods influence outcomes with regard to 

criminal behavior (for example, social disorganization theory and research, and other opportunity 

theories). Third, network studies that concentrate only on school networks or friendship networks 

are missing important links that shape behavior (see full discussion on this below in the whole 

network methods section). Hence, we determined that an innovative way to understand networks 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

33 

and to learn the most we could from our study would be to carve out the framework for the 

network as a bounded neighborhood. 

The fact that this is not a random sample of the youth population has analytical advantages 

for exploring the relationship of social networks with delinquency and gang membership. Since all 

respondents are members of “the neighborhood,” we are able to examine how personal networks of 

all respondents may be linked together or overlap, yielding insight into the connections within and 

beyond the neighborhood that might influence delinquent behavior in ways that have not been 

explored. For instance, we can examine the influence of relationships that are formed beyond peer 

networks, which may be rooted in the community or in other contextual aspects of the social 

environment. In addition, we can examine how position within the neighborhood network, as well 

as the personal characteristics of the respondents (egos) and relations (alters), influence the 

likelihood that an ego is involved in delinquent or violent behavior. 

To develop our sample, using a geographic information system, we overlaid parcel/address 

data on the specified neighborhood boundary with the intent of surveying all youth between the 

ages of 14 and 21 living in the defined area. There were 853 addresses within the target area. Our 

study did not conduct a census of households before outreach and recruitment for the survey 

administration began. Given a specific amount of resources, we chose to increase the canvassing and 

recruiting time per staff member, as opposed to using funds to launch a separate effort to conduct a 

detailed census of households. Furthermore, as one of the aims of our study was to create a whole 

network of all the respondents in order to gauge the structure of subgroups within the 

neighborhood network, we would be able to obtain data on the characteristics and behaviors of 

neighborhood-based individuals who were not surveyed. Given the likelihood that more than one 

respondent listed the same youth who was not surveyed, we can use those multiple data points for 

the same individual to verify the characteristics and behaviors of the nonrespondents. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

34 

A key facet for our study is that we will use all relations named across all respondents as the 

basis for our whole network analysis (described in detail in later sections). This forms the basis for 

the whole network analysis—we use the data for all alters across all respondents and examine the 

structure of groups and subgroups that appear across all relations, regardless of whether they live in 

the target neighborhood or out of the target neighborhood, or whether they are peers or not. 

The Survey Protocol: Egocentric Data 

The survey protocol was developed to be primarily a network-related survey that would 

allow us to create a whole network from overlapping personal networks. There are three basic parts 

to the survey: (1) questions about the ego, (2) the listing of 20 alters by respondents followed by a 

set of questions asked about every alter, and (3) the alter tie question that is used to create the 

structural links among members of the network. The first part of the survey resembles any other 

youth risk survey, and includes questions in a number of risk domains, guided by the criminological 

literature reviewed in Chapter 1. The protocol can be found in Appendix A. Key domains included 

on the survey are listed below: 

• respondent demographics 

• acculturation and ethnic identity 

• attachment to institutions (school, employment, church/religion, sports, community) 

• family bonding and family encouragement in school 

• reasons for hanging out with friends 

• at-risk and criminal behavior; gang membership 

Items were derived from a few key sources or previous criminological studies. We relied on 

the Rochester Youth Development Study for the delinquency items, and borrowed many items from 

Maxson’s U.S. Department of Education–funded study “School-based Protection of Youth At Risk 

for Joining Gangs” (Maxson & Whitlock 2004) and Fleisher’s network study of girls in gangs in 

Champaign, Illinois. But it should be noted that since there have been few network studies of 

delinquency, and none that utilize our data collection method, many of the survey questions were 

developed specifically for this study, with input from a core team of senior advisors. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

35 

The Survey Protocol: Collecting Alter Data 

The second section of the survey begins by requiring the respondent to name 20 people that 

he/she knows and with whom he/she interacts. This is where personal network data collection 

diverges from typical surveys used in social science research. A standard method for collecting 

network data from youth—even peer data—has not arisen from the delinquency (or substance 

abuse) literature (Kirke 1996). The roster, or list, method, however, is probably the most common 

way to construct personal networks among youth. This method is typically done in school-based or 

other settings where the whole network of interest (e.g., a classroom, grade, or whole school) is well 

defined and all members are known. 

Given our research goals, and in order to create personal networks for each ego and then to 

match up all egos and respondents into one large network, we chose instead to use a free recall 

method (Marin 2004) using a name generator, or a question that asked respondents to name by 

memory 20 individuals. We used the name generator to elicit from egos the names of people they 

know (alters). The name generator question is critical to the study in that it defines the sample of 

network alters—or the people who are in an individual’s social network. For our study, we wanted 

to capture the networks of youth that comprise people who are important to the youth and might 

influence the youth—either positively or negatively. The people elicited could be friends, parents, 

siblings and other relatives, even teachers, coaches, and people met just hanging out in the 

neighborhood. We worked closely with delinquency and gang experts, as well as social network 

experts, to create a meaningful question that would reduce measurement error in the production of 

personal networks. Our name generator question reads: 

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. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

36 

We required that respondents listed a specific number of people (20). This method of requiring a set 

number of alters is specifically used to ensure that respondents are thinking comprehensively about 

the people in their lives and will be less likely to leave anyone important out, and it avoids missing 

data and bias issues that can arise when egos are not asked to name a specific number of alters 

(McCarty, Killworth, & Rennell 2007). In addition, previous research has shown that if egos are not 

required to list a specific number of alters, they are more likely than not to exclude important 

individuals because they simply forget about them (Brewer 2000; Brewer & Webster 1999; Marin 

2004). Also note that a geographic limit was not imposed. 

We have not found any examples of delinquency or gang research that have used this 

method of social network analysis collection, and very few studies in other fields have even 

employed it. As mentioned above, most network research involving youth uses the roster method. 

In this type of network data collection, the survey instrument includes a roster or list of all 

individuals in the network. Respondents are asked to nominate a certain number of individuals from 

the list who fit specified criteria (e.g., “your five best friends” or “ten people who pick on you”) and 

then answer questions about those selected individuals. A benefit of this method is that the 

“boundaries”—or complete membership of the whole network—are known, and researchers don’t 

have to spend time determining whether respondent A is talking about, for instance, the same James 

as respondent B. The roster thus prevents any ambiguities as to who is being described in the alter 

questions. Response rates and the extent of missing data are also easier to estimate when the size of 

the whole network is known. 

On the other hand, many youth have friends or contacts who do not attend the same 

school—they may be on the same extramural sports team or know each other through their family 

or their neighborhood. These individuals who do not attend the same school as respondents may be 

influential in a respondent’s life but would not be included in the survey analysis. This exclusion of 

possible influential individuals in a youth’s life could lead to biased or misleading results when 

analyses are performed on the data. This is an especially relevant consideration for research on 

delinquency and gangs in particular, where older individuals and individuals not in school are likely 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

37 

to have strong influences on the delinquent behaviors of youth in school—those influences would 

be missed were a delinquency study to exclude anyone not in the same school (class, grade) as the 

respondent. 

Another drawback is that when youth are provided with a list of names from which to 

choose alters, they may pick the individuals that they see first on the list, potentially excluding 

individuals who may be more influential from their responses. In addition, the roster method 

typically asks students to nominate relatively few individuals who form (part of) their social 

network—typically 10 or fewer individuals. Again, delinquency and gang studies present unique 

challenges than studies on other phenomena; youth may be strongly influenced by people that they 

would not call their best friends (e.g., older siblings/family members who are in gangs), and if they 

are in a gang, they may spend a lot of time with, and follow directions or advice from, individuals 

that they don’t necessarily like. In these cases, asking youth to name just a handful of their closest 

friends at school would exclude a large part of their social networks that influences their behavior. 

The Add Health data, an extensive dataset that includes network data collected via the roster 

method in multiple schools, has been used in a large number of social network analyses on youth 

behavior by different researchers. The availability of these data are part of what make the roster 

method one of the most common used in youth-focused social network analyses (for examples, see 

Haynie 2002; Haynie & Osgood 2005; Kreager 2004; and Young 2010). The method is popular, 

however, even among researchers not using Add Health data (for examples, see Faris & Felmlee 

2011; Kreager, Rullison, & Moody 2011; and Young, Barnes, Meldrum, & Weerman 2011). 

Secondary data sources can also be used to create networks that can then be analyzed using 

social network analysis techniques. In delinquency studies, these sources are often arrest or court 

records for a specific jurisdiction. These data can provide such information as who is co-offending 

together (if arrested together or listed together as suspects for a specific crime) or which individuals 

are in the same gang (if police or other agencies are tracking gang membership). These data are often 

restrictive in their utility in social network analyses, as they rely on a third party (the police or courts) 

to record co-offending behaviors, and they typically connect just two people (dyads) instead of 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

38 

connecting multiple people together into a full network. Nonetheless, if readily available, such 

records make social network analysis more accessible because they don’t require extensive primary 

data collection. Schaefer’s (2011) study of co-offending in Maricopa County, Arizona, relied on 

court records and offender home addresses to study neighborhood effects on delinquency and 

offending patterns. 

For several reasons, we chose not to use the roster method or secondary data (e.g., court 

records or Add Health) that were already collected. First, we were explicitly interested in the 

neighborhood network—not a school-based network—because we were interested in exploring the 

efficacy of place-based anti-delinquency programming. Second, given our expectation that adults and 

other individuals not in school could strongly influence youth behavior, we did not want to restrict 

youths’ networks to classmates or schoolmates; we wanted them to tell us about anyone—peers, 

siblings, family members, neighbors—who was influential in their lives. We also did not want to 

restrict their networks only to those with whom they were friends; youth may spend a large amount 

of time with fellow gang members that they do not like but who nonetheless influence their 

behavior. 

In addition, we did not want respondents to name only individuals who fit one category (e.g., 

were bullies, were bullied by respondent) as was done in Add Health and other existing social 

network data sets (Faris & Felmlee 2011). Finally, instead of looking only at personal ties, where 

dyadic data may have been sufficient, we wanted to create a “whole network”—connecting everyone 

who took the survey (egos) and everyone who was named in the survey (alters) in order to get an 

overall picture of social ties in the area. Therefore, we needed data on how each individual’s alters 

were connected, and we needed more than just a handful of alters from each ego. 

The Survey Protocol: Identifying 20 Alters and Connecting Them 

Respondents could name any person as an alter, regardless of whether he/she lived in the 

same neighborhood (i.e., within the target area). Previous network research estimating general 

personal network sizes (Zheng, Salganik, & Gelman 2006) suggests that respondents would be able 

to list at least 20 people, although this type of research, and particularly research not using the roster 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

39 

method, has rarely been conducted with youth. While we did have some concern that youth would 

have trouble reaching 20 alters, McCarty, Killworth, and colleagues’ work (2007) demonstrated that 

most network measures stabilized between 10 and 20 alters; that is, asking alters to name more than 

20 alters did not significantly change any of the standard network measures, while dipping below 10, 

or even 15, could cause some instability in network measures. We therefore chose 20 so youth could 

name all required alters yet we could most accurately model network properties using the alter data. 

We found that most youth were able to name 20 individuals with no problem, and only a handful 

required some prompting or help brainstorming for additional alters.9 This indicates that for the age 

range of respondents (14–21), 20 is an appropriate number of alters. 

After the respondent lists the full names of the 20 people, he/she is asked a series of 19 

questions about the characteristics and behaviors of those alters and their relationship to the 

respondent. The questions are listed below in Table 2. The last part of the survey consists of one 

question and is used to obtain the structural data by asking the respondent whether the people 

(alters) that were named in his/her network know each other. The structural or “alter tie” question 

is: 

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? 

With regard to collecting the structural data, respondent burden can be a serious issue 

(McCarty, Killworth, et al. 2007). Not only are we asking the youth to report answers to 19 

questions for each of the 20 names generated (19 multiplied by 20), but we are asking youth to 

evaluate alter-alter ties among of network of 20 individuals, which requires 190 evaluations. 

 

9 If respondents had difficulty completing the list or submitted partial names, survey staff would brainstorm with the youth to think 

of additional people (reiterating that it was not limited to friends or peers) and encourage more complete names. For example, if a 

nickname was provided for a first name, staff would suggest the respondent move the nickname to the next page (where nicknames 

or alternate names are asked) and put the person’s first name instead. Some respondents also left out last names when initially

entering the alter list; when this occurred, staff would remind youth that the information is confidential and that people outside the 

research team would never have access to this list or their other responses. In some cases, youth could not remember or did not

know last names; when this occurred, staff encouraged initials or partial last names (when possible) and detailed descriptions of the 

alter (e.g., tattoos, distinctive features, nicknames) to help link or identify the alter later. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

40 

Finalizing the Survey and Preparing for Administration 

We conducted a number of pilot tests over a two-month period using at-risk Latino youth 

who lived near (not in) the target area. To ensure that youth would be willing to report on their own 

possible gang involvement and that of peers, we recruited youth currently known to be in a gang. 

During the pilot testing, we tried different data collection methods and made notes on respondents’ 

comments on the wording of questions, language of the survey, and their comfort in answering the 

questions, especially the more sensitive questions about delinquent behavior. After all changes had 

been made to the protocol, the survey was programmed into a social network software program 

called EgoNet. EgoNet is a free software program designed by one of the project’s expert 

consultants, Christopher McCarty, that allows you to collect data from respondents about 

Table 2. Questions about the Alter 

1. What are $$’s nicknames or other names that friends and family use to refer to __________? 

2. How old is __________? 

3. Is ___________ male or female? 

4. Can you name one thing to describe __________ so that we can tell the difference between this 

__________ and another __________? 

5. Who is ______________? (relationship) 

6. Does ______________ live in your neighborhood? 

7. How did you meet ______________? 

8. Is ______________of Hispanic, Latino, or Spanish origin or descent? 

9. What country was ______________ born in? 

10. How much time do you spend each week hanging out with ______________? 

11. How much do you like ______________? 

12. If you needed some information or advice about something, is ______________ someone you could 

go to? 

13. How likely is it that ______________ carries a gun (including in his/her car)? 

14. Has ______________ ever sold illegal drugs such as marijuana, cocaine, or crack? 

15. How likely is it that ______________ has been in a gang fight over the last year? 

16. Would you consider ______________ to be in a gang? 

17. Would you be willing to tell me what name _________’s gang goes by? If so, what is it? 

18. Have you ever in your life committed a crime with ______________? Please think of any crime that 

you know is against the law. 

19. How likely is ______________ to use violence to get what he/she wants? 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

41 

interaction with network members (alters). EgoNet was originally designed to collect and analyze 

information on personal networks, and was not set up to analyze whole networks. However, at the 

time our study began, Dr. McCarty was developing functionality in EgoNet to allow for analysis of 

whole networks. We worked closely with Dr. McCarty and his programmers to develop the 

functionality that would allow the program to compare names of alters and egos across all 

respondents and match names together to form an overlapping network. 

The survey was developed in English for EgoNet and translated into Spanish for a paper 

version of the survey for staff interviews with youth who could not take the electronic English 

version. All respondents took the survey in English when possible, although a bilingual speaker was 

available to conduct interviews in Spanish when necessary. 

Survey Recruitment and Administration 

For survey administration, we sought a site that could accommodate multiple survey-takers 

at one time (on separate computers), that had flexible hours, and that was easily accessible to youth. 

We also required the location to be neutral in terms of gang or group affiliation. Based on these 

requirements, we selected a county-operated recreation center that was directly across the street 

from the target neighborhood. The location was a neutral and safe location for respondents; it was 

convenient (within walking distance of homes) for youth; it was well known by residents; and it was 

large, housing various activities and programs throughout the day, and therefore not raising unusual 

attention to youth respondents, since youth are constantly coming in and out of the building. The 

facility worked with the survey staff to accommodate our space and time needs. 

Two training sessions were held for staff involved in recruiting and surveying youth. Staff 

were provided training on outreach strategies, the door-to-door recruitment process, determining 

eligibility of potential respondents, consenting youth and parents, and responding to questions or 

concerns. The training session also included a review of the study’s goals, the potential risks and 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

42 

benefits to respondents, and safety precautions in the field. In addition, staff members were trained 

to administer the survey. Perhaps most important, the training emphasized that the project aimed to 

conduct a census of youth residing within the target neighborhood boundaries. Conducting a census 

instead of using a sampling plan meant that careful record had to be made of which residential units 

were visited, on which day and at what time, and what the result of each visit was. This information 

was crucial to estimating not only how many youth in the target age range lived in the neighborhood 

but also how likely it was that we could recruit youth with additional outreach or canvassing efforts. 

In addition to formal training, staff members also received project identification badges and 

t-shirts to clearly identify them as survey staff while in the field. Staff dressed casually (e.g., jeans and 

project t-shirts) so they would not be perceived as government or immigration service employees. 

This was a notable concern in this particular community, since many immigrants reside in the target 

area and the local police department had recently indicated that it would begin asking residents for 

identification. 

The survey recruitment process involved several strategies. First, outreach efforts took place 

both before and during the survey period. Bilingual mailings were sent to all eligible households 

(defined as those located within the community boundaries); bilingual flyers were distributed 

throughout the neighborhood and signs were posted in the local community center (where the 

survey was administered); a toll-free number was designated for the study, where English- and 

Spanish-speaking project managers were available to answer questions from parents, youth, or other 

community members and schedule survey appointments; and information about the study and the 

survey was circulated by word of mouth by our community partner. 

In addition to these outreach strategies, door-to-door recruitment took place from 

November 2009 to March 2010. The recruitment teams of two persons each were made up of one 

staff member from both the research team and the community organization. In addition to the age 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

43 

and organizational diversity of research agency and community organization teams, these teams were 

diverse in gender, ethnicity, and Spanish-speaking ability. Having Spanish-speaking recruiters proved 

crucial for this project, as the vast majority of residents (especially parents) in the community spoke 

Spanish and many of the households did not have any English-speaking residents. 

Recruitment teams were given specific areas within the neighborhood within which to 

canvass, and the teams documented recruitment results at each household (e.g., knock but no 

answer, no youth living at the address, appointment(s) made, requests for returning to the address or 

scheduling appointments, refusals, and any relevant notes). To increase the likelihood of reaching all 

the eligible youth in the neighborhood, recruitment teams would often ask neighbors whether 

nearby households not answering knocks had eligible youth living there. Recruiters would also ask 

those answering the door but requesting a return visit for their telephone contact information. 

Recruitment teams attempted to knock on each non-answering household door at least four times 

throughout the data collection period. Although all-day recruiting on Saturdays was the most 

popular day and time for multiple teams to recruit, teams of two also went out into the community 

on weekday nights to reach those who were unavailable or working on weekends and increase the 

response rate (see the Response Rate section below for more information on these results). 

The Survey Process and Procedures 

While several pairs of survey staff were engaged in Saturday door-to-door recruitment, other 

team members simultaneously set up a survey room in the community recreation center. When 

recruiting door to door on Saturdays, teams could direct youth immediately to the community center 

throughout the day. When a potential survey participant would arrive at the community center, 

eligibility was verified. Youth were asked to fill out a contact information card (also called a locator 

form); from that form, the person’s address and age were used to determine eligibility. If both 

requirements were met, any youth under age 18 were then required to have parental consent before 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

44 

continuing. After parental consent was obtained10 (or for those age 18 or over), the youth consent 

was then administered. Both the youth and parental consent described the purpose of the project, 

the voluntary nature of the survey, the process for keeping all respondents and responses protected 

and confidential, the potential risks and benefits of their participation, the $50 incentive each 

respondent received for taking the survey, and the time commitment involved in participating. 

The surveys were conducted on encrypted, password-protected laptops using EgoNet. 

Youth were spread out in the room and seated at different tables when possible; as much as 

possible, the laptop screens were also positioned so they were not visible to other youth in the 

room. The research team had at least 20 laptops ready for use each survey day; on a few occasions, 

the room was almost filled to capacity. 

After a youth had consented and was ready to begin the survey, a researcher explained the 

computer administration process to the youth and sat with her/him as she completed two practice 

questions. The staff member also explained at that time that there were several “stop points” that 

were used throughout the survey, and that respondents should ask staff at any point if they had 

questions or were confused by anything in the survey. The stop points were built into the survey to 

provide additional confidentiality to respondents; every respondent was required to raise his hand a 

minimum number of times during the survey, so if an individual had a personal question for a staff 

member regarding the survey, it would not be out of the ordinary or draw attention to raise his hand 

for assistance. 

On average, it took participants about one hour to complete a survey, although some 

respondents took up to two hours, mostly based on English language speaking ability. As soon as a 

respondent completed the survey, he was provided with a $50 Visa gift card, signed a receipt, and 

 

10 Parental consent was most often obtained in person by recruitment staff going door to door in the neighborhood; the forms were

transferred to the survey location so consent could be confirmed. In other cases, parents came into the center or survey staff spoke 

with parents over the phone to confirm consent. Over-the-phone consent was approved by the Urban Institute’s IRB after we had 

difficulties obtaining in-person parental consent. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

45 

left the room. The respondent’s survey was immediately copied onto a jump drive, and the survey 

file on the laptop was destroyed. The survey jump drive was always kept on a research staff person 

or in a secure locked file box; upon transfer back to the evaluation team’s office, the jump drive was 

also kept in a locked cabinet when not in use. 

The survey was administered at the recreation center on several Saturdays between 

December 2009 and March 2010. A total of 160 youth completed the survey over that period. After 

cleaning the data, research staff found 10 youth who lived at least one or two streets outside the 

specified target area and 3 youth who were outside the targeted ages; we dropped those ineligible 

individuals, giving us a final count of 147 valid surveys. 

Response Rate. It is not possible to calculate a true response rate because, as stated earlier, our study 

did not conduct a census of households before outreach and tracking before survey administration 

began. Using 2000 Census data, the study team estimated that there were roughly 440 youth within 

the target area. This is a very rough estimate in that the Census data were old, residential mobility is 

very high, and Census data do not provide a specific estimate for our target age group. The survey 

yield is shown in Table 3. As mentioned earlier, the research team initially identified 853 addresses 

within the community. Of these homes, approximately 49 percent (n = 417) did not have any youth. 

Table 3. Survey Yield 

Tracking Outcome N (%) 

Number of addresses in target area 853 

No shows 44 (5.2) 

Two knocks, no answer 105 (12.3) 

At least three knocks, no answer 74 (8.6) 

Invalid address/vacant 96 (11.3) 

No youth age 14–21 417 (48.9) 

Refused 28 (3.3) 

Number of addresses that yielded participants 89 (10.4) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

46 

Preparing Whole Network Data for Analysis 

As mentioned briefly in earlier sections, whole network analysis is very different from ego 

analysis. Whole network analyses for the current study are conducted using data from overlapping 

egocentric network data. The process involves taking all the data for egos and alters and determining 

for whom we have multiple data points, and then supplying a “true” value on that characteristic or 

behavior for that node in the social network. Essentially, the alters become nodes in the whole 

network and hence will have their own position and structural characteristics across the whole 

network. 

While preparation of the egocentric data for analysis followed a standard process of cleaning 

and recoding data, preparing the sociocentric data required a very different approach. This process, 

described below, was required to allow visualization of the whole network of connected egos and 

alters, and to allow analyses of the “whole network” data. The database contained 16011 unique ego 

names and 3,200 non-unique alter names; creating a whole network required the research team to 

determine which of the 3,200 alters matched egos and other alters. 

The first step in this process was to gather data on respondents and egos from a variety of 

sources. Throughout the data collection process, respondents were asked to fill out various forms, 

such as parental consent forms, youth consent forms, and contact information cards (locator forms). 

The consent forms were required as part of the human subjects protection efforts of the project, but 

they were also useful in providing additional information on respondents, such as additional last 

names that may be in use in their household (e.g., if the parent that signed the form had a different 

last name), siblings in the household (all youth in one household could use one parental consent 

form), and correct full names of the youth and actual birthdate (instead of age). 

The locator forms were used by the research team to verify eligibility when youth came in to 

take the survey; youth were required to list their name, age, and address. In addition, planning for 

the possibility of replications of the work in the same area, we asked for a parent name if the youth 

 

11 Although only 147 were used in the final analyses, we cleaned data for all 160 respondents in case they provided information on 

others who were eligible and were included in the survey. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

47 

was under 18 and the name of someone who “usually knows how to get in touch with you” so we 

could locate respondents for a possible second survey. Youth were allowed to list anyone on the 

locator form, and that gave us an additional individual who was connected to the respondent, even if 

they did not list that person as an alter in the actual survey. Finally, the survey team also recorded 

the date and time each individual took the survey along with where in the room each respondent sat. 

This allowed us to determine during the cleaning process which youth had come in together and 

near whom each respondent sat while taking the survey. 

The survey itself also contained a number of items useful in helping the research team make 

matches of two individuals with the same or similar names. For each alter, respondents had to give 

us the individual’s full name (to the best of their ability), nickname(s), a brief physical description 

(e.g., tall or blonde hair), age, sex, relationship to respondent, whether he/she lived in the 

neighborhood, and his/her ethnicity. These questions, together with information gathered from the 

various consent and check-in forms, allowed the research team to make informed decisions on 

matching individuals. 

While the survey software used for the project, EgoNet, was useful in collecting social 

network data, its utility for the name cleaning/matching process was limited. We therefore exported 

the alter names and demographics to an Excel spreadsheet, joined in information from the forms 

that had been entered into an electronic spreadsheet, and conducted the name matching outside 

EgoNet. Two members of the research team were responsible for making all decisions on name 

matching. This ensured that the decisions made were as consistent as possible, and having two 

individuals review different data sources both made the process faster and guaranteed that two 

people had agreed on whether a match should be made. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

48 

The actual matching process was an 

iterative one and started by cleaning names 

that were misspelled and running frequencies 

on those names. The first pass through the 

data sought those individuals who occurred 

most frequently and were likely to have many 

matches. We considered first, last, and 

nicknames separately: for each name 

appearing in the dataset, we checked other 

similar first names, last names, and nicknames 

for possible matches. When we found two 

names that might be a match, we checked 

ages, physical descriptions, and other alter data 

to determine how likely it was that the two 

individuals were indeed the same person. 

When we determined that two (or more) alter 

records were the same person, those two (or more) records were given the same ID number. 

We encountered a number of different challenges during the name-matching process. First, 

spelling was not consistent across many alters, even those that we determined were the same person. 

While the actual spelling of the name was irrelevant in terms of the research, in order to run 

frequencies to assist us with the matching, we had to do some basic corrections of typos and 

misspellings; otherwise the frequency was no more helpful than the original list of names. Second, 

many individuals in our survey had similar names. For males, Jose, Carlos, Alex, Luis, and Juan were 

the most popular, with each appearing more than 30 times (Jose appeared 90 times in the name list). 

For females, Maria, Ana, Jennifer, Jessica, and Rosa were the most common, each appearing more 

than 15 times (Maria appeared 29 times in the name list). These common names, especially if listed 

with no last name or an equally common last name, were difficult to confidently match with other 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

49 

alters as the same individual. For these and other very common names, we relied heavily on 

nicknames, ages, and physical descriptions. We also had a large number of alters where incomplete 

names were given; most of the incomplete alters had a first name and last initial although some listed 

only a nickname as the full name. In these cases, we also had to rely on alternate sources of data to 

make match decisions. Finally, we had to contend with inconsistent reports of alter characteristics. 

For example, Juan Ramos might appear in the names list twice but his age might be listed as 14 and 

16 by two different egos. In these cases, we had to use other data sources and our best judgment as 

to whether they were the same person or not (e.g., we might assume Juan’s older brother who 

named him would be a better judge of Juan’s age than a cousin or friend). 

Inconsistent reports of alters posed a challenge beyond simply matching names, however. 

After going through each name in the database, we were able to identify just over 2,500 unique 

individuals. Before moving to visualization of the whole network and analysis of the data, all of the 

alter data had to be summarized for each unique individual. In other words, if one individual was 

named by 10 different egos, we had to summarize the 10 separate records for that one person, 

which may or may not report the same details. Two types of data needed to be summarized: the 

attribute data, including such items as age, ethnicity, and delinquency, and the tie data, which 

indicated whether one alter was tied to another. 

We used EgoNet to summarize the alter attribute data; the software could read in the IDs 

for each unique individual that were created during the name matching process and summarize the 

data based on those IDs. For these data, where conflicts in alter records existed, the software would 

take the majority answer; if no majority answer existed, it would use a value for the variable from 

one randomly selected record. In addition, if this process was necessary across multiple variables for 

one individual, EgoNet would take answers from different egos for each variable, so that one ego 

didn’t supply all of the answers about the individual. For numeric data, the software would average 

the values (e.g., for age). 

Some alter variables were ego-specific, meaning that we would expect different egos to have 

different answers about the same alter, such as the relationship between the ego and alter and 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

50 

whether the ego and alter had committed any crimes together. EgoNet can create summary 

measures for each possible item response value by ego (e.g., percent of egos who called this alter a 

friend/sibling/cousin; percent of egos who would go to this alter for advice). We used those data 

instead of the “majority rule” alter data to represent the ego-dependent alter data. Finally, using the 

alter measures, we created an alter delinquency index consistent with the ego-level delinquency index 

described in the above section. This allowed all egos and alters to be analyzed together using 

consistent measures. 

Next, we had to resolve conflicts within the alter tie question. This question asks egos to 

report, for each alter, that person’s likelihood of talking with all other alters that person named. In 

many cases, two egos named the same two alters, and we thus have multiple reports on whether 

alters are connected to each other. Two egos may differ on whether they think two alters will speak 

with each other, and those differences needed resolution. EgoNet can handle this process, and the 

software offers some summarization options for creating the whole network. First, it allows you to 

choose whether you want a network containing only those individuals who appeared at least twice in 

the dataset (e.g., were named by at least two egos), whether you always include egos (regardless of 

whether they were named by someone else as an ego), and how alter discrepancies should be 

resolved. The most conservative way to resolve conflicts is to require that at least one ego says the 

alters should be tied and none say they should not be tied (the minimum number of ties); the most 

liberal way to resolve conflicts is to require that at least one ego says the alters should be tied and 

disregard any that say they should not be tied (the maximum number of ties). For the present study, 

we used the “majority” criterion to resolve conflicts, where the majority answer was used. As with 

the attribute data, if no majority existed, no tie would be made. If the individual was an ego (i.e., if 

he/she also took the survey), his report on whether he/she is tied to another individual overrides 

others’ reports. 

Our data contained individuals who were named by up to 11 respondents, but 30 percent of 

egos were not named by any other respondents. The dataset contained 369 people, or about 15 

percent of individuals, who were named more than twice; nearly 90 percent of the individuals were 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

51 

named only once. This high number of individuals named only once stems partially from our design 

that allowed respondents to name any individual in his/her life, instead of limiting it to peers or 

schoolmates. The design resulted in many respondents naming siblings or parents—individuals that 

may only be influential in their lives, and not the lives of their peers in the neighborhood. This could 

also partially be a result of our inability to match individual names as the same person due to lack of 

complete information on an alter. While some analyses employ only nodes who were named more 

than once, we chose to compare results for the network that includes all network members and one 

that includes those named two or more times. We decided to explore the network with all members, 

despite their being less connected than others, for two main reasons: first, we would lose a 

significant number of network members if we imposed stricter criteria (dropping from 2,521 to 369); 

and second, our theoretical design backs inclusion of everyone who was named, as we are seeking a 

greater understanding of who influences whom; without all members, we could not fully assess that 

question. 

Missing Data. With any survey data collection effort, there is likely to be missing data in the form of 

items that were not completely filled out by respondents who took the survey but also in the form 

of missing respondents. Understanding the extent and nature of these missing respondents was of 

utmost importance to this research given our goal of conducting a census of youth in the target area. 

Due to the unique nature of the data collected, we were able to both estimate the number of missing 

respondents and collect data on those individuals via others who did take the survey. 

Because we are using overlapping personal networks to create a whole network, in many 

cases more than one respondent provided evaluations of a single individual’s behavior—whether or 

not that individual also took the survey. Missing respondents for this project are considered to be 

those youth age 14–21 who live in the target area but were never interviewed. Usually, the problems 

associated with sampling and missing data in whole network analysis stem from the inability of 

researchers to interview or observe network members (McCarty, Molina, Aguilar, & Rota 2007). But 

missing data of this type (missing respondents) is less problematic for the present study than it is for 

a typical whole network study because we have data on missing respondents provided via actual 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

52 

respondents. In addition, in many cases, we have multiple evaluations of those “missing 

respondents.” Therefore, we can not only assess the number of individuals who we should have 

interviewed (because they were in our target age and lived in the neighborhood) but in many cases 

we also have multiple informants providing information on those individuals. We view the values 

attributed to those “missing but reported on” individuals as much less likely to have respondent bias 

than if only one person reported on him/her. 

We estimated the number of youth who live in the target area but were not interviewed by 

examining alter names provided by all egos as living in the target neighborhood and being between 

the ages of 14 and 21 and then checking those names against the list of actual respondents. 

Individuals who were not in the list of respondents were counted as missing respondents. We 

estimate that about 330 youth were in the target age group and lived within the target area but were 

not interviewed. But, as noted above, we do not view this information as missing—we have 

information on these youth from actual respondents. 

As further support for the accuracy of the size of the missing respondent pool, we estimated 

the total size of the target population at about 440 youth using census data; given that we 

interviewed 147 youth, that would mean we had missed 293 eligible youth. Our estimate that we 

missed 330 respondents is thus an accurate estimate of the actual number of missed respondents. 

Also keep in mind that the criteria “live in neighborhood” was subject to respondent interpretation 

of neighborhood; we did not provide maps of the target area to respondents, so it is possible that 

there are youth who reside across the street from or near the target area but not technically inside 

the target area. Therefore, our estimates should be considered just that—our best guesses at the 

number of respondents we missed. 

After the name cleaning, matching, and resolution of alter attribute and tie data were 

complete, we were able to create a whole network. We exported the final dataset from EgoNet, 

made any needed adjustments or edits in another statistical software package, and then imported the 

data into UCINet. The next section describes the analysis plan for both the egocentric analyses and 

the whole network, or sociocentric, analyses. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

53 

MEASURES12

Dependent Variables 

For the ego network analyses, we focused on a number of outcome variables that measure 

different types of delinquent and criminal behavior, as well as gang membership. We created two 

additive delinquency scales that capture the various survey items that represent delinquent and criminal 

behavior. The first scale is based on whether the respondent “ever” committed the offense; the 

second scale was based on whether the respondent committed the offense “in the past six months.” 

The nine items included in the scales are: 

1. Avoided paying for things, like a movie, taking bus rides, or anything else 

2. Tried to steal or actually stolen money or things worth $100 or less 

3. Damaged, destroyed, or marked up someone else’s 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 such as marijuana, crack, heroin, or methamphetamine 

8. Used a weapon or force to get money or things from people 

9. Attacked someone with a weapon or with the idea of seriously hurting or killing them 

The “ever” and “recent” overall delinquency scale scores range from zero to nine. Reliability was 

high for both scales (overall delinquency scale: α = 0.83; six months prior delinquency scale: 

α = 0.82). We also created a “serious delinquency” additive scale that excluded three items, related to 

damaging property and minor stealing (avoiding paying for things like a movie and stealing items 

worth less than $100). Reliability was good (α = 0.77). 

We also developed a binary serious delinquency measure to capture any behavior that could be 

regarded as delinquent or criminal. The respondent received a score of one if they answered yes to 

any of the following five items: 

1. Ever a member of a street gang 

2. Ever in a gang fight 

3. Ever sold illegal drugs such as marijuana, crack, heroin, or methamphetamine 

4. Ever carried a weapon such as a gun or knife 

 

12 Detailed descriptions of all measures can be found in Appendix C. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

54 

5. Ever attacked someone with a weapon or with the idea of seriously hurting or killing 

them 

These items were chosen in part because they mirror the items that are available to measure the 

criminality or delinquency of alters—the 20 individuals nominated by each respondent. Note that 

these elements of crime are on the more serious end of the spectrum for youth than other 

delinquency measures available for egos, but for the sake of consistency with alter data, we use the 

term delinquency. 

We also examined five important delinquent/criminal outcomes individually in binary form 

(yes/no): 

1. Ever a member of a street gang 

2. Ever in a gang fight 

3. Ever sold illegal drugs such as marijuana, crack, heroin, or methamphetamine 

4. Ever carried a weapon such as a gun or knife 

5. Ever attacked someone with a weapon or with the idea of seriously hurting or killing 

them 

With the exception of “member of a street gang,” each outcome above was measured using 

one survey item that directly asked whether the respondent had participated in that behavior. For 

“member of street gang,” respondents either answered yes to “have you ever been a member of a 

street gang?” or answered yes to being a “member of a group,”13 and then to “is that group a street 

gang?” In summary, the dependent variables that were created for the ego-level analyses include the 

following: 

• Scale measures: 

o “Ever” overall delinquency: sum of nine binary “ever” delinquency measures; 

ranges from zero to nine. 

o “Recent” overall delinquency: sum of nine binary “last six months” delinquency 

measures; ranges from zero to nine. 

o “Ever” serious delinquency: sum of seven binary “ever” delinquency measures 

(excludes property damage and petty theft); ranges from zero to six. 

• Binary measures 

 

13 We asked respondents if they could think of a group of friends that they regularly hung out with, and then asked whether they 

considered that group a street gang. This was done so we could develop different criteria for considering someone to be in a gang 

beyond simple self-reported involvement. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

55 

o Overall (serious) delinquency: equals one if yes to any of five delinquency 

measures. 

o Individual delinquency: individual binary measures for each of the five 

delinquent behaviors. 

The next sections discuss different types of independent variables, including control and predictor 

variables. 

Personal Network Variables 

We created specific measures to examine the characteristics of each respondent’s personal 

network. These variables are referred to as network compositional variables; essentially, the measures 

provide descriptive information about alter characteristics in aggregate form. Because we wanted to 

examine any differences between variables that only include peer alters (or, alters named as friends) 

versus all types of alter relationships (mother, father, sibling, teacher, aunt, etc.), we created a series 

of variables first using only alters designated as friends14 by the respondents, and then created similar 

variables using any type of relation. Only examining the influence of peer networks, as opposed to 

individuals’ entire social network, can be limited in that it ignores the role of other important or 

influential persons in one’s life (Keisner, Kerr, & Stattin 2004). Our definition of peer strictly uses 

classification as a friend and does not include siblings or cousins. The following are the network 

compositional variables created for use in the egocentric analysis: 

• Peers in network. The proportion of alters who were listed as a friend is used as a 

control variable. Continuous variable, values range from 0 to 1.

• Delinquent peers. The proportion of delinquent peers (friends) in the respondent’s 

personal network. A friend is defined as delinquent if the ego responded that the friend 

was involved in any of the following five behaviors: ever a member of a street gang, 

participated in a gang fight, sold illegal drugs, carried a weapon, or used violence to get 

what he/she wants. Binary variable, values are 0 or 1.

• Delinquent alters. This variable is computed the same way as “delinquent peers,” but 

includes any person named in the respondent’s personal network (thus, it includes 

friends) that has been involved in any of the five delinquent behaviors listed above. 

Binary variable, values are 0 or 1.

 

14 Respondents were specifically asked to name the relationship they had with their alters—friend was one option. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

56 

• Same neighborhood friends. The proportion of peers (friends) who live in the same 

neighborhood as the respondent, as reported by the respondent. Continuous variable, values 

range from 0 to 1. 

• Same neighborhood alters. The proportion of all alters who live in the same 

neighborhood as the respondent, as reported by the respondent.15 Continuous variable, 

values range from 0 to 1.

• Males in network. The proportion of all alters who were males was used as a control in 

the original analyses. Continuous variable, values range from 0 to 1.

• Average age of network. The average age of all alters in each personal network was 

computed and used as a control in the original analyses. Continuous variable, values range 

from 13 to 37.

Acculturation 

We use two measures to assess the level of acculturation of respondents. 

• Ethnic identity scale. We used the Multigroup Ethnic Identity Measure (MEIM) as 

developed by Phinney (1992). This scale includes 12 items assessing the respondent’s 

familiarity with the customs and traditions of his/her ethnicity; the respondent’s 

attachment to and understanding of ethnic identity; and the respondent’s positive 

feelings about being a member of his/her ethnic group. Internal reliability is high 

(α = 0.892). Interval variable, values range from 20 to 60.

• Separation scale. We formed a scale composed of summing the following seven variables 

to measure the respondent’s level of “separation” from (i.e., lack of assimilation) the 

United States: (1) the place of birth for the respondent—“were you born 

abroad?”(yes/no); (2) the place of birth of respondent’s mother—“where was your 

mother born?” (recoded yes, respondent’s mother was born abroad/no, respondent’s 

mother was not born abroad);(3) the place of birth of respondent’s father—“where was 

your father born?” (recoded yes, respondent’s father was born abroad/no, respondent’s 

father was not born abroad);(4) if the respondent speaks a language other than English 

(yes/no); (5) if the respondent speaks a language other than English at home (yes/no); 

(6) if the respondent speaks a language other than English with his/her friends (yes/no); 

and (7) an ordinal measure of the proportion of the respondent’s lifetime spent abroad 

ranging from 0 (respondent spent no time abroad) to 4 (respondent spent more than 78 

percent of his/her lifetime abroad).16 A high score on this scale measures lower levels of 

acculturation (or more separation), whereas a low score on the scale measures higher 

levels of acculturation (less separation). Reliability was good. (α = 0.653). Interval variable, 

values range from 0 to 9.

 

15 The criteria “live in neighborhood” was subject to respondent interpretation of neighborhood; we did not provide maps of the 

target area to respondents, so it is possible that there are youth who reside across the street from or near the target area but not 

technically inside the target area. 

16 We made the proportion of the respondent’s lifetime spent abroad an ordinal variable based on the distribution of responses across 

the sample (i.e., one standard deviation above and below the mean (but greater than 0) were coded 3 and 1, respectively, while the 

range in between was coded 2). If the respondent spent no time abroad, the variable was coded 0. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

57 

Strength of Ties and Homophily in Personal Networks 

Because a key component of this study is assessing how relations influence an individuals’ 

network, it is important to examine the strength of ties among and across network relationships. Tie 

strength is a quantifiable property that characterizes specific properties of links between two 

individuals or nodes. Granovetter (1973) asserts that tie strength generally includes four properties: 

amount of time spent with someone; emotional intensity of the relationship, intimacy (e.g., friend vs. 

best friend), and whether reciprocal services are provided or the relationship itself is reciprocated. 

Other theorists and researchers have suggested that plausible indicators of tie strength also include 

emotional support or advice given and/or received (Lin, Ye, & Ensel 1985; Matthews, White, Soper, 

& van Bergen 1998; Wellman 1982), duration of relationship (Marsden & Campbell 1984), and 

contextual factors such as social and demographic homogeneity and shared affiliations (Alba & 

Kadushin 1976). However, few quantitative studies exist (let alone within criminology itself) that 

include measures of tie strength (Matthews et al. 1998; Petroczi, Nepusz, & Bazsó 2007). As 

reviewed earlier, some criminological theories, such as bonding and learning theories, support the 

idea that strength of attachment will influence delinquent outcomes. 

Our survey included three questions that tap into the strength of ties between egos and 

alters. We asked each ego to report on (1) how much time he/she spends with each alter per week, 

(2) how much does he/she like each alter, and (3) whether he/she would go to alter for advice. 

Based on responses to these questions, we developed several measures pertaining to strength of ties. 

The measures were used to assess each ego’s attachment to his/her entire personal network (see 

Appendix C for a complete list of variables and associated definitions): 

• High frequency of contact with network is a measure of the proportion of alters in the 

respondent’s network with whom the respondent said they spent “a whole lot of time” 

during the week. Values for this measure ranged from 0 percent (respondent does not 

spend a lot of time with any alters) to 100 percent (respondent spends a lot of time with 

all alters).Continuous variable, values range from 0 to 1.

• Likable network relationships is a measure of the proportion of alters in the respondent’s 

network that the respondent likes a lot. Values for this measure ranged from 0 percent 

(respondent does not like any alters a lot) to 100 percent (respondent likes all alters a 

lot). Continuous variable, values range from 0 to 1.

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

58 

• Advice support is a measure capturing the proportion of alters in a respondent’s network 

to whom the respondent would go for advice. Values for this measure ranged from 0 

percent (respondent would not go to any alters for advice) to 100 percent (respondent 

would go to all alters for advice). Continuous variable, values range from 0 to 1.

Because the strength of tie measures described above do not take into account whether a 

respondent is close to a delinquent/criminal relation versus a nondelinquent/criminal relation, it is 

likely that these measures are not particularly relevant with regard to antisocial outcomes. Hence, for 

each of the measures above, we created variables that measured a respondent’s closeness (on each 

type of strength of tie measure) to delinquent alters and to nondelinquent alters. For instance, if half 

of an ego’s network was delinquent/criminal, then we only summed the responses on “go to for 

advice” for those delinquent alters. We did the same for nondelinquent alters because, as articulated 

by Sutherland in differential association theory, we wanted to examine the possibility that the 

strength of attachment to delinquent alters matters differently than strength of attachment to 

nondelinquent (or pro-social) alters. 

We also created a number of new measures at the alter level that would capture similarity 

between ego and alter on certain characteristic. Similarity between two nodes in networks is referred 

to as “homophily,” and the literature on adolescents suggests homophily can represent an aspect of 

attachment or closeness in such that distance in terms of social characteristics translates into 

network distance (McPherson, Smith-Lovin, & Cook 2001). Research has shown that youth are 

more likely to be friends with someone who is similar to themselves with regard to age, gender, or 

race/ethnicity, but the tendency for homophilous friendships decreases as the closeness of 

friendship decreases and also decreases with age (Joyner & Kao 2000; Kao & Joyner 2004). The 

tendency for friendship homophily in youth also varies by race/ethnicity (Kao & Joyner 2004). 

Some research suggests that homophily in race and ethnicity create the strongest divides in personal 

environments (i.e., friendship groups are divided based on race and ethnicity), followed by age, 

religion, education, occupation, and gender (McPherson, Smith-Lovin, & Cook 2001). 

We first created a set of homophily measures for ego-alter links based on four 

characteristics: age, gender, ethnicity and nationality. For age homophily, we considered the ego and 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

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SOCIAL NETWORKS AND DELINQUENCY

59 

the alter to be similar if the alter was within (plus or minus) one year of the age of the respondents. 

Using this information, we constructed a binary variable where age homophily = 1. For ethnic 

homophily, an alter scored a 1 if he/she was the same ethnicity (response to item: “do you consider 

yourself to be Hispanic or Latino”) as the ego. For nationality homophily, an alter scored a 1 if the 

country of nationality of the alter was the same as the birthplace of ego’s mother or father, or if ego 

and alter were both born in the United States. Ego and alter must be the same sex for alter to score 

a 1 on gender homophily. After these alter-level homophily variables were created, we created a 

summed homophily scale for each respondent’s alter; the alter was given a score ranging from 0 

(does not match the respondent on any of the four homophily measures) to 4 (is approximately the 

same age, same ethnicity, same nationality, and same gender as the respondent). We then summed 

across all 20 alters for each ego to create the final homophily scale. Essentially, we used the alterlevel homophily variables to create one final network-related homophily scale that incorporates the 

various characteristics across all alters. 

Ego-Level Network Structure Variables 

Two variables were created to measure features of a respondent’s personal network 

structure, as well their relationship to the whole network. The measures were calculated using 

EgoNet. The network structure variables used for this work are as follows: 

• The number of components is the number of subgroups in the respondent’s personal 

network. Components are a measure of separately maintained groups (no link exists 

between any two nodes of the different groups) within the larger personal network. The 

measure only consists of a count of groups and does not contain information on type of 

group. This measure is automatically generated in EgoNet statistics for each ego 

network. Sociological research has suggested that as the number of groups within a 

person’s network increase, the ego becomes more constrained to normative behavior 

(Krackhardt 1999; Krohn 1986). Continuous, values range from 0 to 4.

• Personal network density is a measure of the connections among alters within the 

respondent’s personal network, and uses only those connections where the respondent 

said his/her alters “definitely” knew each other. EgoNet calculated a mean density 

measure of the respondent’s personal network, using the number of these “definite” 

connections between alters. Continuous variable, values range from 0 to 19.

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

60 

Sociocentric Network Structure Variables 

The sociocentric level measures used for this work focus on measuring two main network 

characteristics: cohesion and centrality. Here, cohesion of the network is measured using overall 

network density. Density, also calculated for each egocentric network and described above, measures 

the overall intensity of the connected actors. Another cohesion measure, calculated for use in the 

regression analyses, was respondent as isolate. This measure captures those nodes that have little to 

no connection to other nodes in the network. Centrality measures can be calculated at both the 

whole network level, when they are referred to as “centralization” measures, or at the individual 

level, when they are referred to as simply centrality measures. Centralization indicates whether ties 

are primarily concentrated on a small number of nodes (hierarchical) or are spread evenly across 

members (decentralized) (Valente 2010). The individual centrality measures (described below) are 

used to calculate the overall network centralization measure; there are thus several different types of 

centralization, depending on which centrality measure is used in its calculation. 

The following describe the cohesion and centrality measures employed for this research. All 

formulas provided below are from the UCINet software documentation. The first set of measures 

describes individual position within the network and the second set describes whole network 

measures. 

• Measures of Individual Position in Network 

o Respondent as isolate. A dichotomous measure (isolate = 1) was created to 

represent whether the respondent was an isolate in the whole network. In our 

study, an isolate is a respondent who was not named as an alter by any other 

respondent and did not have/list any alters who were also egos (i.e., surveyed).

Binary variable, values are 0 or 1.

o Degree centrality is the most commonly used measure of centrality (Valente 

2010), and represents the number of direct connections in a network. While 

degree can be calculated based on ties received (“in-degree”) or ties sent (“outdegree”), our network data is nondirected, so we will simply use (nondirectional) 

degree centrality, counting all direct ties in the computation of degree. 

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SOCIAL NETWORKS AND DELINQUENCY

61 

o Betweenness centrality is described by Valente (2010) as “akin to bridging and 

centrality combined” (p. 87), with an emphasis not on degree but on total 

distance between nodes—using both direct and indirect ties. Specifically, 

betweenness is “the frequency a node lies on the shortest path connecting other 

nodes in the network” (p. 87), or the number of geodesics (shortest paths) 

connecting two nodes on which a third node lies. 

Description of betweenness centrality formula: “Let bjk be the proportion of all 

geodesics linking vertex j and vertex k which pass through vertex i. The 

betweenness of vertex i is the sum of all bjk where i, j and k are distinct” 

(Borgatti, Everett, & Freeman 1999). 

o Closeness centrality measures the distance from each node to each other node in 

the network, based on both direct and indirect ties (Valente & Forman 1998). 

Higher closeness values indicate that a node is able to reach all other nodes over 

shorter distances. The closeness measure can be thought of as a sort of “degree 

of separation” as it effectively counts the number intermediate nodes that are 

needed to make a connection between two nodes. This is done for each node to 

every other node in the network. The closeness measure does suffer from some 

complications regarding its calculation when isolates exist in the network (isolates 

cannot be reached by all other nodes, so the distance between them and other 

nodes is effectively ∞), and it is subsequently not as widely used as the first two 

centrality measures described. 

Closeness centrality formula, using Valente-Foreman reversed distances: 

෍ 1

ሻܽ݊ܿ݁ݐݏ݀݅	݅ܿݏ݀݁݋݁ܩሺ–	ሻ݇ݎ݋ݓݐ݊݁	݄݁ݐ	݂݋	ݎ݁ݐ݅ܽ݉݁ܦሺ

The sum is computed for the shortest possible connections between all node 

pairs. The geodesic distance is the shortest distance between two vertices or 

nodes. 

o Eigenvector centrality for a node is based “on the centrality of its neighboring 

nodes” (Valente 2010, 87). This measure is useful in large network studies 

because it takes into account the overall structure of the network; it is not as 

susceptible to more “local” patterns, or patterns of closeness or centrality that 

may exist in small subgroups but may not have a strong role or effect in the 

larger network structure. 

Explanation of the eigenvector centrality formula is beyond the scope of this report; please see the 

UCINet documentation for the exact formula. 

• Network-Level Measures (computed or either ego network or whole network) 

o Density represents the number of actual ties between all members as a 

proportion of all possible ties (if every node was connected to every other node). 

The measure provides insight into how tightly connected the individuals in the 

network are. Higher density values indicate a network in which nodes are closely 

connected and lower density values indicate that fewer ties are present between 

nodes. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

62 

Density formula: 

ݏ݅݁ݐ	݈ܽݑݐܽܿ ൬

	100 ∗ ൰ݏ݅݁ݐ	ܾ݈݅݁ݏݏ݋݌

o Degree centralization is the network-level measure based on degree centrality. It 

is similar to standard deviation measures from descriptive statistics (Knoke & 

Yang 2008) and provides an overall measure of degree for the whole network—

however it is defined—instead of just for individual network members. 

Degree centralization formula (individual-level degree centrality is simply a count): 

൫∑൫ܿ௠௔௫ െ ܿሺݒ௜ሻ൯൯

ܿ௠௔௫ ௣௢௦௦௜௕௟௘

	∗ 	100

where ܿሺݒ௜ሻ is the centrality of vertex ݒ ௜and ܿ௠௔௫ is the maximum degree 

centrality in the network and ܿ௠௔௫ ௣௢௦௦௜௕௟௘ is the maximum possible degree 

centrality for the network. The measure, then, is expressed as a percentage of the 

maximum possible degree centrality. 

o Betweenness centralization is the network-level measure based on betweenness 

centrality. It is calculated using the same formula as used for degree 

centralization. 

o Closeness centralization is the network-level measure based on closeness 

centrality. The network closeness centralization value is the average closeness 

value for all nodes (Borgatti, Everett, & Freeman 1999). 

o Eigenvector centralization is the network-level measure based on eigenvector 

centrality. Please see UCINet documentation (Borgatti, Everett, & Freeman 

1999) for a full description of its computation. 

Control Variables 

We included the following ego-level independent variables in our initial models as control 

variables: 

• Family cohesion. We used the cohesion subscale of the Family Relationship 

Characteristics scale (Tolan, Gorman-Smith, Huesmann, & Zelli 1997), developed to 

help predict risk of antisocial behavior. The 10 items included in this scale address how 

close the respondent feels to his/her family; whether the respondent communicates well 

with family members; and whether the respondent’s values and views are similar to 

his/her family members. We also included an additional item, “Compared to most 

families, would you say yours was very close to each other, somewhat close, not very 

close, or not close at all?” from Cheryl Maxson’s Family Scale (Maxson & Whitlock 

2004). The 11 items had high internal reliability (α = 0.898). 

• Parental support for education. A dichotomous measure (school support = 1) was 

created to capture the response to the question: “Does a parent or guardian regularly 

insist that you go to school and do well?” Binary variable, values are 0 or 1.

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SOCIAL NETWORKS AND DELINQUENCY

63 

• Religiosity. This measure represents the response to one item: how often he/she attends 

religious services (never = 0, seldom = 1, once a month = 2, almost every week = 3, 

every week or more = 4). A higher score signifies greater religiosity. Interval variable, values 

range from 0 to 4.

We also controlled for key demographics in each of our models, including the respondent’s 

age (continuous variable), gender (male = 1) and ethnicity (Latino/Hispanic = 1). In addition, we 

controlled for the amount of time the respondent had lived at his or her current address, as well as 

whether the respondent’s parent/guardian/adult in household had graduated from high school 

(yes = 1). 

 The next section describes the analysis process used for the study, discussing both ego-level 

and network-level analyses. 

ANALYSIS PLAN 

To accomplish the research objectives, two types of analyses are employed: (1) egocentric 

network analysis and (2) sociocentric analysis. As described earlier, egocentric networks refer to the 

composition and pattern of the social relations of an individual (McCarty & Wutich 2005). 

Sociocentric networks refer to the pattern of relationships between actors within a specific group. 

These two types of network analyses allow us to provide not only a picture of how personal 

networks influence delinquency and crime, but also provide a comprehensive picture of subgroup 

formation and group attachments and network structure across the larger network of neighborhood 

and non-neighborhood affiliations. 

Egocentric Network Analysis Plan 

Egocentric network analysis is ideal for understanding the way the social environment of a 

particular group member impacts norms, attitudes, and behaviors. For this study, egocentric analysis 

is used to measure whether aspects of egocentric network composition—the characteristics of the 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

64 

network members, such as the proportion of the personal network that is delinquent—and measures 

of network structure influence delinquency and crime. Essentially, these measures provide insight 

into the individual-level and network properties that reinforce or deter criminal behavior over and 

above characteristics of the individuals themselves. 

We relied on the software EgoNet for data collection, cleaning, and new variable creation. 

EgoNet outputs several files. One of the files includes a flat file that combines information about 

the respondent, summaries of information about the network members (compositional data) and 

Table 4. Descriptive Statistics for Dependent Variables (N = 147) 

Min Max Mean S.D 

Overall delinquency scale 0 9 1.64 2.21 

Recent delinquency scale 0 9 0.76 1.58 

Serious delinquency scale 0 6 0.84 1.41 

Binary delinquency measure (violence) 0 1 0.37 0.49 

Carried a weapon 0 1 0.23 0.42 

Sold illegal drugs 0 1 0.09 0.28 

Attacked someone with a weapon 0 1 0.1 0.3 

Ever involved in a gang fight 0 1 0.17 0.38 

Ever in a gang 0 1 0.1 0.3 

Table 5. Model Comparison––Overall Delinquency 

Goodness of Fit–Negative Binomial Regression, Overall Delinquency 

 Sig. df Value/df 

Deviance 129.824 131 0.991 

Scaled Deviance 129.824 131 

Pearson Chi-Square 111.417 131 0.851 

Scaled Pearson Chi-Square 111.417 131 

Log Likelihood -225.915 

Goodness of Fit–Poisson Regression, Overall Delinquency 

 Sig. df Value/df 

Deviance 277.945 131 2.122 

Scaled Deviance 277.945 131 

Pearson Chi-Square 265.01 131 2.023 

Scaled Pearson Chi-Square 265.01 131 

Log Likelihood -246.415 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

65 

summaries of the ties between network members (structural data). Additional files include adjacency 

matrices that help to create network visualizations. EgoNet output files were then analyzed in SAS 

and SPSS. 

After we created our dependent variables, we examined the distribution of each scale to 

determine what type of regression model would be appropriate. Descriptive statistics for the 

dependent variable can be found in Table 4. Based on the skew in our scaled outcome measures, the 

means for our delinquency scales, which were 1.64 (overall delinquency over lifetime), 0.76 

(delinquency in the last six months) and 0.84 (serious delinquency over lifetime) and the large 

percentage of respondents who reported no delinquency (48.3 percent), we determined that the data 

violated the normality assumption of OLS, and that a negative binomial regression model (designed 

to handle many zero values and high skewness) would be more appropriate. To confirm that 

negative binomial regression was the best fit for our model, we used the GENLIN procedure in 

SPSS to compare the goodness of fit between the negative binomial regression model and the 

Poisson regression model using both the overall delinquency and recent delinquency scales as 

Table 6. Model Comparison––Recent Delinquency

Goodness of Fit–Negative Binomial Regression, Recent Delinquency 

 Sig. Df Value/df 

Deviance 118.651 131 0.906 

Scaled Deviance 118.651 131 

Pearson Chi-Square 132.331 131 1.01 

Scaled Pearson Chi-Square 132.331 131 

Log Likelihood -149.07 

Goodness of Fit–Poisson Regression, Recent Delinquency 

 Sig. Df Value/df 

Deviance 206.442 131 1.576 

Scaled Deviance 206.442 131 

Pearson Chi-Square 243.725 131 1.86 

Scaled Pearson Chi-Square 243.725 131 

Log Likelihood -164.599 

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SOCIAL NETWORKS AND DELINQUENCY

66 

dependent variables. We used a basic set of predictor variables in the models testing fit.17 The fit 

statistics are shown in Tables 5 and 6. 

In reviewing the goodness of fit statistics for both models, we focused primarily on the value 

divided by the degrees of freedom and log likelihood for both models. Based on the lower value of 

the deviance statistic and the lower log likelihood values for the negative binomial regressions, we 

determined that those models had better fit than the Poisson models, and were most appropriate for 

our dataset. Hence, we selected negative binomial regression models for use in predicting the scaled 

delinquency outcomes (modeled with SPSS). For the binary outcomes (a) been in gang, (b) gang 

fight, (c) sold drugs, (d) carried a weapon, and (e) attacked someone, we developed logistic 

regression models in SPSS. 

Because we have a relatively small sample of youth, as well as a wide age range, we wanted to 

limit the number of predictors used in final regression models. We thus examined correlations 

among all variables created (and described in the preceding sections). The full correlation matrix can 

be found in Appendix B. For the purposes of the following discussion, all correlations are significant 

at the p < 0.05 level. First we examined the correlations between the network compositional 

variables and the delinquency measures. The number of components in the respondent’s personal 

network was negatively correlated (-0.18) to overall delinquency (binary variable). The measure of 

betweenness within a respondent’s personal network was positively correlated to whether she had 

carried a weapon (0.16). The respondent’s personal network density and isolate measures were not 

significantly correlated to any delinquency variable. 

 

17 The following predictor variables were entered into the models to compare negative binomial and Poisson distributions: the 

proportion of delinquent alters; the proportion of alters who live in the respondent’s neighborhood; the proportion of alters to 

whom the respondent goes for advice and are not delinquent; the number of components in the respondent’s personal network; 

whether the respondent is an isolate; the respondent’s betweenness centrality; the respondent’s age, gender, and ethnicity; the

proportion of friends in the respondent’s network; if the respondent has an adult family member who graduated from high school;

if the respondent’s parent(s) support him/her in school; the respondent’s level of family cohesion; the respondent’s separation from 

U.S. culture; and the number of years the respondent has lived at his/her current address. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

67 

We then examined the correlations between our strength of ties/homophily measures and 

our delinquency measures. None of the overall tie strength or homophily measures was significantly 

correlated to any delinquency variable, with the exception of being in a gang, which was positively 

correlated (0.26) to the proportion of alters whom the respondent spends a lot of time with. All 

delinquent tie strength measures (e.g., proportion of alters whom respondent goes to for advice and 

are delinquent, etc.) were positively correlated to all three delinquency scales (0.39 to 0.46). The 

measure of closeness between the respondent and nondelinquent alters (as measured by the whether 

the respondent goes to the alters for advice) were negatively correlated to the delinquency scales (-

0.19 to -0.23). The homophily index for delinquent alters in the respondent’s network was positively 

correlated to the three delinquency scales (0.4 to 0.5). 

Looking at the basic demographic variables we planned to enter as control measures in the 

regression models for our delinquency scales, we noted several significant correlations. Age was 

negatively correlated to recent delinquency (-0.18), and gender was positively correlated to overall 

delinquency and serious delinquency (0.23), suggesting that respondents who were older and male 

were more likely to have higher scores on the delinquency scales. 

Because our correlation analyses showed that our strength of ties and homophily measures 

had either insignificant or small significant correlations to our delinquency measures, we ran a series 

of regression models to see if any of the strength of tie/homophily measures were significant 

predictors. We first ran regressions testing a full suite of variables including the key strength of ties 

variables. We determined that only one of the strength of tie variables should be included in the final 

model, and that the strength of ties measure should capture connection to delinquent alters versus 

nondelinquent alters. As we pared down our key variables and ran additional regressions using all 

dependent variables (including binary measures), it also became clear that our measures of ethnic 

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SOCIAL NETWORKS AND DELINQUENCY

68 

identity, religiosity, and personal network density18 should be dropped from the final analytical 

model. Appendix D contains the output of the regression with all variables before the final models 

were developed. 

We tested these variables in our model with the overall delinquency scale as a dependent 

variable, running the models with and without a measure of the proportion of the respondents’ 

alters or peers who are delinquent. The measure of the strength of a respondent’s ties to 

nondelinquent alters or peers was not significant in any models, and the measure of the respondent’s 

attachment to delinquent alters or peers controlled for the effects of the proportion of delinquent 

alters or peers in the respondent’s network, so neither variable was significant. However, because the 

proportion of alters to whom the respondent goes for advice and who are not delinquent was 

negatively correlated to the three delinquency scales and the binary delinquency variables (-0.19 to 

-0.23) we determined that this would be the most appropriate strength of tie measure to include in 

our models. 

With regard to the network structure and composition measures, we determined that the 

proportion of friends in a respondent’s network would be the best control variable to include in our 

models, particularly because of the missing data imputation that was required for this variable.19

While the proportion of male friends was found to be significant in prior research (Haynie 2002), it 

was not a significant predictor in our sample and we deemed it an unnecessary control variable, 

particularly because we were including gender and proportion of friends in network as control 

variables in our models. 

 

18 We determined that personal network density was similar to the number of components (which was already in our models), and 

that betweenness could more accurately measure the respondent’s connection to the whole network. 

19 Of the 2,940 alters in the dataset, 22 percent (N = 647) were missing age data. Five respondents did not report on the ages for any 

of their alters. To address this problem of missingness, we imputed the whole network mean age based on the alter’s relationship (e.g., 

the mean age for all alters who are listed as friends in the network) for alters who did not have age data. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

69 

Final Models for Ego Analysis 

As discussed above, a number of nonsignificant variables were dropped from final models, 

both to improve fit and to avoid over-fitting the models given our small sample size. Upon 

determining the predictor and control variables we would include in our final models, we then tested 

our independent variables on one of our binary dependent variables—if the respondent exhibited 

any delinquency in his/her lifetime—to determine if multicollinearity was an issue in our regression 

models. Based on the tolerance and VIFs in the model as shown in Table 7,20 we determined that 

collinearity is not present in our models. 

The variables included in Table 7 are the full set of variables that are used, in different 

combinations, in the predictive models for each of the different dependent variables, using only the 

egos as observation (N = 147). The results of those models are presented below in the Findings 

section. It should also be noted that, where possible, additional models are developed using the 

much more limited alter data. These models include all members of the whole network and thus 

have the advantage of using a larger number of observations for the estimation procedures 

(N = 2,521). The models, however, suffer from far more limited availability of data—we are 

restricted to only the variables that were collected for alters. These models are thus considered very 

preliminary and as such were not subjected to the extensive exploration and testing that were 

conducted for the ego-level models. Nonetheless, they do indicate a future direction for research 

and point to a potential utility of our methods of network data collection for providing sample sizes 

not limited to those who responded to the survey. 

 

20 We ran collinearity tests on all our delinquency models, and found no tolerance levels higher than 1 and no VIF levels higher than 

2. For parsimony, we show only one model (overall delinquency, binary outcome) in this report. 

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SOCIAL NETWORKS AND DELINQUENCY

70 

Table 7. Tests for Collinearity, Overall Delinquency (Binary Outcome) Model

Tolerance VIF 

Alter variables 

 Proportion delinquent alters 0.71 1.417 

 Proportion alters live in same 

neighborhood 0.86 1.157 

 Proportion go to for advice (not 

delinq.) 0.75 1.328 

Network structure variables 

 Number of components 0.93 1.072 

 Isolate 0.88 1.133 

 Betweenness centrality 0.82 1.216 

Controls 

 Age 0.86 1.166 

 Male 0.86 1.161 

 Latino 0.71 1.418 

 Proportion of peers in network 0.81 1.238 

 Family member completed high 

school 0.80 1.245 

 Parent-school encouragement 0.85 1.178 

 Family cohesion 0.74 1.344 

 Separation from U.S. culture 0.64 1.568 

 Num. years at address 0.85 1.174 

 

Whole Network Analysis Plan 

Over the last two decades, despite a recent explosion in the research of social networks, only 

a limited number of studies have reported the use of sociocentric (“whole network”) analysis (for 

example, see Abbasi, Uddin, & Hossain 2011; and Hirdes & Scott 1998) and very few of those 

focused on youth behaviors, delinquency, or criminal behavior (for example, see Faris & Felmlee 

2011; McGloin 2005; and Natarajan 2006). Many studies that consider the effects of the whole 

network actually conduct their analyses using dyads or triads, instead of conducting analyses with all 

network members together (for example, see Baerveldt, van Duijn, Vermeij, & van Hemert 2004; 

Faust 2008; and Goodreau, Kitts, & Morris 2009). In addition, most of the analyses on true whole 

networks (as opposed to dyads or triads) employed descriptive methods for investigations of the 

whole network as opposed to developing predictive models, but for good reason. The nature of 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

71 

whole network data—matrices indicating connections between all included individuals—create 

difficulties in performing traditional predictive modeling, which is more easily done when 

conducting egocentric or dyadic data analysis. The present study, then, is unique in its exploration 

not only of personal networks and how an individual’s connections to different types of people 

influence his/her behavior, but also how everyone is connected to a larger network, and how 

different elements of the network (e.g., subgroups) and one’s position within the whole network and 

the network elements can influence the pro-social and delinquent or criminal behavior of 

individuals. 

Sociocentric network analysis is accomplished using UCINet, a program that can be used to 

conduct numerous different analysis routines on social network data, and NetDraw, a program that 

creates network visualizations, but also has some analytic capabilities similar to that provided by 

UCINet. EgoNet is capable of producing some basic social network measures and can create some 

limited network visualizations, but its main utility is for data collection, not analysis or visualization. 

We conduct identical analyses on the whole network (which includes everyone who was 

named any number of times) and the network comprised of only those named two or more times. 

We refer to this second network as the “2+ network.” 

Describing the Whole Network 

We first provide descriptive information on the characteristics of the members of the whole 

network, including demographic characteristics and delinquent or pro-social behavior. These are 

different than the descriptive characteristics that are first provided in the egocentric analysis findings 

(see Findings section) as they provide information on all members of the network—egos and 

alters—whereas the egocentric analysis focuses only on the characteristics and behavior of the egos. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

72 

Computing Network Structural Measures 

We then compute a number of network structural measures used to describe the form of the 

network, including the frequency of connections between actors and the degree of hierarchy in the 

network. After considering the characteristics of the whole network, we look at individual positions 

within the network, with an emphasis on identifying those individuals with the highest potential for 

the diffusion of ideas (e.g., nonviolence messages) or for influence on other network members. 

Work on identifying such nodes has typically focused on individual measures of centrality. Centrality 

analysis can provide a picture of how knowledge is communicated across members by identifying 

those nodes with a disproportionately high number of links with other nodes (whether direct or 

indirect). A number of different measures of centrality have been developed and our investigation of 

nodal positions will thus entail a multifaceted centrality analysis employing some of the most 

popular centrality measures among researchers. The centrality measures, described in the Measures 

section, are calculated at both the network level (centralization) and the individual level (centrality), 

and include 

• density: proportion of actual ties to possible ties (network level only); 

• isolate: network members not connected to other nodes; 

• degree centrality: the number of direct connections a node has; 

• betweenness centrality: how much of a bridging role each node performs; 

• closeness centrality: the total distance from each node to every other node in the 

network; and 

• eigenvector centrality: the importance of a member in a network based on centrality of 

the node’s connections. 

While we are exploring centrality through four different measures, the theoretical importance 

of the betweenness centrality measure for this work should be emphasized. Valente and Fujimoto 

(2010) suggest that nodes in central positions but with fewer direct ties (i.e., lower degree 

centrality)—sometimes called “bridges”—may more efficiently spread messages throughout the 

network because they have fewer individuals to convince of their message. They also suggest that 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

73 

such individuals might be more “receptive to behavior change and more likely to be persuaded by 

targeted communications” (212). Kadushin (2002) also explores the roles of those in such bridging 

positions, describing them as “structurally autonomous” and able to play nodes off of each other, 

thus gaining power and control over other members or components of a network. Betweenness 

centrality is one way to measure the “bridging” between different nodes. 

While receptivity to behavior changes, structural autonomy, and control in a network 

certainly depend on personal characteristics, individuals with high betweenness may hold the key to 

most effectively spreading messages throughout a network. In this sense, betweenness becomes a 

very important measure for the policy implications present in this study in that identifying those 

individuals who are not only central but could also convincingly and efficiently spread nonviolence 

or pro-social messages through a network would be of utmost importance to decreasing and 

preventing gang involvement and delinquent behavior (Objective 5). As described earlier in the 

Measures section of this report, we also use betweenness centrality as a predictor of delinquency 

(and specifically, drug dealing) in egocentric analysis. It is not a stretch to hypothesize that those 

individuals who are very well connected (and closely connected with regard to social distance) to a 

lot of other individuals, would make good (i.e., successful) drug dealers. 

As part of the centrality analysis, we will also be able to determine the importance of cultural 

characteristics across members of the network. Here, we can explore the characteristics of the most 

central players (as determined through any one of the above measures), including their ethnicity and 

whether they are foreign born. We will also examine the levels of delinquency of the most central 

players to determine how they might be influencing those around them, and how far their reach of 

influence within the network travels. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

74 

Community Structure of the Network 

After sufficiently describing the properties of the two networks, we will investigate, using 

two social network analysis methods, the existence of subgroups, or subcomponents, within the 

larger network. There are numerous ways to examine the “community structure” of whole networks, 

or the division of the network into smaller, cohesive groups, but we will focus our efforts on one 

common method: Newman-Girvan modularity. This is considered a top-down approach as it starts 

with the whole network itself and then focuses on areas of especially high density within the overall 

network structure (Hanneman & Riddle 2005). 

The Newman-Girvan modularity algorithm is based on the idea that subgroups within a 

whole network can be completely inward-looking; that is, all nodes within the subgroup could be 

connected to each other and none would be connected to nodes outside the subgroup. While this is 

rarely, if ever, the case in practice, the algorithm attempts to divide nodes into a set of partitions that 

most closely replicates this “ideal” faction structure, and then assesses the degree to which the 

structure approximates that ideal structure (Hanneman & Riddle 2005). The Newman-Girvan 

routine offers a goodness-of-fit measure to assess the number of partitions into which a network 

should be divided. It should be noted that though we expect that while we will be able to coherently 

divide the whole network into subgroups—especially those based on gangs or delinquent 

behaviors—there is no a priori expectation of the number of subgroups that we expect to find. 

Rather, this portion of the whole network analysis involves exploring the community structure of 

the whole network to understand how delinquent and nondelinquent youth cluster within a larger 

social network. 

As part of the community structure analysis of the whole network, we calculate standard 

measures for each subgroup that were calculated at the level of the whole network, including size, 

density, and centrality measures. We also explore the demographic characteristics of the subgroups 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

75 

and the levels of delinquency among members of different subgroups in order to expand our 

knowledge of the characteristics upon which youth in the whole network cluster. Finally, we 

examine ties between different subgroups (via ties between two nodes in different subgroups). 

The next chapter presents the findings of the different analyses organized according to the 

three different research questions. We first present the descriptive analyses for the egocentric 

analyses (based on ego networks), followed by the predictor modeling aimed to answer research 

question 1. We then present the results of the sociocentric analysis directed at answering research 

question 2. Last, we present the results of the egocentric analyses utilizing the measures created from 

the whole network. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

76 

CHAPTER 3 

FINDINGS 

EGOCENTRIC DESCRIPTIVE ANALYSES 

Respondent Characteristics 

As described earlier, our data collection strategy was designed as a census of all youth, both 

males and females, between the ages of 14 and 21 who reside in the designated target area. Table 8

below provides a detailed description of the 147 survey respondents. The majority of youth surveyed 

were male (66 percent), with an average age of almost 18. Roughly three-quarters of youth surveyed 

(76.9 percent) identified their ethnicity as Latino; a little over a third of the youth were foreign born. 

Even though the majority (63 percent) of respondents was born in United States, 84 percent have at 

least one parent who was born abroad, and 69 percent speak both Spanish and English. Fifty 

percent of respondents have lived abroad at some point in their lives. These characteristics of the 

youth surveyed generally reflect the characteristics of the target neighborhood (Table 1). 

Parents’ place of birth (Table 9) provides information on nationality for the surveyed youth. 

Table 9 shows that almost half of the respondents were Guatemalan or Salvadoran, and 10 percent 

were Puerto Rican or of Caribbean descent. 

 Table 10 provides the descriptive characteristics on delinquency and group activity. Overall, 

our sample exhibits a number of very pro-social characteristics: 97 percent of respondents under 18 

are still in school, and 92 percent of respondents report parental support for attending school (based 

on past or present support, depending on whether the respondent was still in school). The majority 

of respondents over 18 (58 percent) are currently employed. Just over 50 percent attend religious 

services at least once a month. 

Given that we were targeting a high-crime area, respondents reported lower levels of gang 

activity and individual delinquency than we anticipated. Only 34 percent reported seeing lots of gang 

activity in the neighborhood, and only 20 percent said they had ever been approached to join a gang. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

77 

While the vast majority (75.5 percent) of our sample identified as being a member of a group 

(answering yes to whether they have a group of friends with whom they hang out), far fewer 

respondents (10 percent) reported being current or former gang members or having been in a gang 

fight at some point in their lives (17 percent). Of all the delinquent behaviors included as survey 

items, prevalence was highest for using drugs (27 percent), followed by carrying a weapon (23 

percent). 

Table 11 provides a picture of the differences among respondents who have been gang 

members, in a gang fight, or are in a group. Not surprisingly, reported delinquency is significantly 

higher among respondents who have been in a gang or in a gang fight, while the respondents who 

are members of a group have delinquency rates that are similar to the overall sample. 

We also examined the distribution of a series of network composition measures to determine 

the structure of the respondents’ personal networks. Key measures include the number of 

components (i.e., groups) within respondents’ personal networks; ties between alters (all alters, 

delinquent alters and nondelinquent alters) in respondents’ personal networks; respondent’s 

betweenness centrality to the whole network; and betweenness centrality within respondents’ 

personal networks (across delinquent and nondelinquent alters) (Table 12). The average number of 

network components (number of subgroups within personal network) was just over 1, but it ranged 

from 0 to 4. With a mean density of 0.04, the statistics for the density measure indicate that 

individual-level networks are not very dense.21 The measure of betweenness centrality calculated 

from whole network represents the “brokering” position across the whole (overlapped) network. 

 

21 Density is sometimes listed as a proportion on a scale of 0 to 1 and sometimes reported as a percent, ranging from 0 to 100. We 

report all density measures on a 0–1 scale. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

78 

Table 8. Descriptive Statistics, Basic Demographics 

% Respondents 

(N = 147) 

Demographics 

Average age 17.8 

Male 66.0 

Ethnicity/Nationality 

Hispanic/Latino(a) 76.9 

Born abroad 36.1 

Either parent board abroad 84.4 

Lived abroad 49.9 

Separation from U.S. culture (mean) 4.16 

Ethnic attachment (mean) 45.62 

Language 

Only Spanish 5.4 

Only English 14.3 

Spanish and English 69.4 

Multiple 7.5 

Other 3.4 

School/Parental Status 

Currently living with parent(s) 82.3 

Currently in school (under 18) 96.7 

Currently in school (18 or over) 46.5 

Parent support in school 91.8 

Adult in family graduated high school 70.1 

Family cohesion (mean) 44.61 

Employment 

Currently have a job (under 18) 11.5 

Currently have a job (18 or over) 58.1 

Religion 

Christian 81.0 

Attends services 

At least once a month 50.3 

Never 27.9 

 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

79 

Table 9. Nationality of Respondents, as Indicated by Place of Birth for Parents 

Respondents’ nationality (N = 147) 

Mother born in... 

(%) 

Father born in... 

(%) 

United States 13.6 13.6 

Mexico 4.8 4.1 

El Salvador 33.3 36.7 

Guatemala 15.6 14.3 

Honduras 4.1 2.7 

Nicaragua 3.4 4.1 

Puerto Rico, the Dominican 

Republic, or other Caribbean country 10.2 10.2 

Bolivia 0.7 0.7 

Columbia 1.4 0.0 

Other South American country 1.4 2.0 

Other country not listed 11.6 10.9 

Don’t know 0.0 0.7 

Mother/father born in same country = 84%

Table 10. Group Activity and Delinquency

Group Activity % 

See lots of gang activity in neighborhood 34.0 

Approached to join gang 19.7 

Thought about joining gang 15.6 

Pressure to join a gang 12.2 

In a group 75.5 

Thinks of group as gang 5.4 

In a gang 10.2 

In a gang fight 17.0 

In a gang fight but not in a gang 7.5 

Delinquent Behavior22

Used drugs 26.5 

Used drugs in last six months 12.2 

Sold drugs 8.8 

Sold drugs in last six months 5.4 

Stolen goods more than $100 17.7 

Stolen goods +$100 in last six months 7.5 

Carried weapon 23.1 

Carried weapon in last six months 6.7 

Attacked someone with idea of hurting or seriously injuring them 10.2 

Attacked someone in last six months 4.8 

 

22 The percentage of respondents who report recent delinquent activity is based on the percentage of the total sample, not the 

number of respondents who reported delinquent activity over the course of their lifetime.

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

80 

Table 11. Delinquent Behavior and Gang Association 

% 

Respondents

(N = 147) 

In a Gang 

(N = 15) 

In a Gang 

Fight 

(N = 25) 

In a 

Group 

(N = 111) 

Used drugs 26.5 66.7** 60.0** 29.7 

Sold drugs 8.8 40.0** 40.0** 10.8** 

Stolen goods more than $100 17.7 53.3** 44.0** 18.9 

Carried weapon 23.1 60.0** 60.0** 27.9** 

Attacked someone with idea of 

hurting or seriously injuring 

them 

10.2 33.3** 32.0** 12.6** 

** p < 0.05 

Significance levels represent significant differences between the respondents in the specified 

category (e.g., in a gang, in a gang fight, or in a group) compared to those who are not. 

 

Table 12. Descriptive Statistics for Network Measures 

Network Measures (N = 147) Min Max Mean S.D 

Number of components in personal network 0.00 4.00 1.07 0.70 

Proportion of ties in personal network (density) 0.00 19.00 4.19 4.24 

Betweenness centrality of ego/alter nodes, 

calculated from whole network 0.00 295,446.81 389,15.55 55,935.56

 

Relationships within Ego Networks 

Table 13 summarizes the characteristics of the alters in respondents’ personal networks, 

including the relationship and similarities between the respondent and the alters. The statistics 

provide some interesting insight on who is important in the lives of the surveyed youth. Prior 

literature focuses almost exclusively on the importance of peers in influencing youth behavior. And 

not surprisingly, three-quarters of the respondents named peers (friends) for half of their alters. 

Although the term peers is often used to include siblings or cousins who close in age, for the 

purposes of understanding the influence of the different types and categories of relationships, we 

exclude siblings and other young relatives from the definition of peers. 

It is interesting to note, however, that when given the opportunity to name the 20 people 

most involved in their lives, 91.8 percent of youth surveyed named at least one individual who could 

be considered something other than a friend. Forty-one percent of respondents named at least one 

parent. We thought this percentage would be higher, but it could be a function of the phrasing we 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

81 

Table 13. Alter Characteristics 

Respondents’ Alter Characteristics 

% Respondents 

(N = 147) 

More than half of alters were friends 78.2 

Two or more alters were siblings 36.1 

At least one alter was a parent 40.8 

Respondent would go to at least half of alters for advice 56.5 

Respondent likes more than half of alters a lot 47.6 

At least half of alters live in the same neighborhood 29.9 

More than half of alters were born in the United States 57.1 

More than half of alters were born in Latin America 33.3 

used for the name generator. We asked respondents to think first of people they “hang out” with, 

and respondents might not have considered their parents to be people they “hang out” with, even if 

their parent is influential in their lives. 

Not surprisingly, respondents are not equally close with all their alters. Only roughly half 

(56.5 percent) would go to at least half of their alters for advice and just under half (47.6 percent) 

indicated that they like more than half of their alters “a lot.” That means there are a large number of 

alters for each respondent that he/she doesn’t like a lot or to whom he/she would not go for advice. 

Almost one-third of respondents (29.9 percent) indicated that at least half of their alters lived within 

the target neighborhood. While not necessarily surprising, this supports our idea that school-based 

network analyses may be missing key individual’s in a youth’s life. The nearby high school that most 

school-age youth in the neighborhood attend is several miles away and draws teenagers from a large 

area. This gives youth the opportunity to develop friendship networks with individuals who do not 

necessarily live close, yet at least one-third of individuals spend most of the their time with others 

from within the neighborhood. Whether this is out of convenience or for another reason, we cannot 

judge, but regardless, it does demonstrate the utility of the “neighborhood as network” design. Table 

14 below shows the breakdown of relations listed across categories of respondents (all respondents, 

delinquent and not delinquent). We did not incorporate t-test results into these tables due to the 

small number of alters in certain categories (e.g., aunt/uncle, grandparent, etc.), which tended to 

inflate the significance levels, but we want to point out possibly interesting differences among 

groups. Delinquent youth were more likely to list a cousin than nondelinquent respondents. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

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82 

Table 14. Alter Characteristics, by Proportion Delinquent/Criminal23

 % Respondents % Delinquent % Not Delinquent 

(N = 147) (N = 55) (N = 92) 

All peers 8.2 5.5 9.8 

No peers 0.7 0.0 1.1 

At least half family 13.6 12.7 14.1 

Peers/immediate family only 17.7 10.9 21.7 

Peers/family only 65.3 70.9 62.0 

At least one cousin 60.5 72.7 53.3 

At least one aunt/uncle 32.0 7.3 30.4 

At least one grandparent 10.9 34.5 13.0 

At least one teacher 8.8 5.5 10.9 

Nondelinquents were more likely to list an aunt or uncle, but were less likely to mention a 

grandparent. It could be that delinquent youth are more likely living with a grandparent as opposed 

to a parent, indicating the absence of a parent or both parents in the youth’s life. Nondelinquent 

youth were also twice as likely to mention a teacher as delinquent youth. 

In addition to examining the types of relations in respondents’ personal networks, we looked 

at the behavior among respondents’ alters (shown in Table 15 below), as reported by the 

respondent. There are a number of notable differences between the behavior of respondents’ alters 

and respondents’ behavior themselves (all as reported by the respondents). While only 9 percent of 

respondents said they sold drugs, 29 percent said at least one of their alters sold drugs. While only 10 

Table 15. Alter Behavior 

 % Respondents 

(N = 147) 

Respondents who... 

Co-offend with at least one alter 19.7 

Commit violence with at least one alter 12.9 

Have at least one alter in a gang 29.3 

Have at least one family member in a gang 8.8 

Have at least one alter who has been in a gang fight 17.7 

Have at least one alter who carries a gun 16.3 

Have at least one alter who sold drugs 29.3 

 

23 We did not incorporate t-test results into these tables due to the small number of alters in certain categories (e.g., aunt/uncle, 

grandparent, etc.), which tended to inflate the significance levels. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

83 

Table 16. Proportion of Ego Network that is Delinquent, by Respondent Delinquency

Proportion of 

respondent’s alters who 

exhibit delinquent 

behavior 

# Respondents 

(N = 147) 

# Delinquent 

Respondents 

(N = 55) 

# Nondelinquent 

Respondents 

(N = 92) 

0 % 88 17 71 

5 % 23 13 10 

10 % 10 5 5 

15 % 5 3 2 

20 % 7 6 1 

25 % 4 3 1 

30 % 3 3 0 

35 % 1 1 0 

40 % 2 1 1 

45 % 2 1 1 

90 % 1 1 0 

100 % 1 1 0 

percent of respondents reported being in a gang, 29 percent said at least one of their alters was in a 

gang. 

In order to better understand which respondents included delinquent alters in their 

networks, we stratified the sample by self-reported delinquent behavior (delinquent meaning that the 

individual participated in at least one of the five main delinquent behaviors). We then examined the 

proportion of alters who committed at least one type of delinquent behavior, as reported by the 

respondent (ego). Table 16 reveals that 71 youth (77 percent) who were not delinquent also did not 

report any delinquent alters. At the other extreme, only one delinquent respondent reported that 

his/her network consisted of all others who were delinquent. The table indicates that there is likely 

little over-exaggeration of alter characteristics in that most delinquent respondents only report a very 

small portion of their personal network to be delinquent. Essentially, 96 percent of the 55 

delinquent respondents report that less than half their alters were delinquent, while 17 (31 percent) 

of respondents who were delinquent reported no delinquent or violent behavior for their alters. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

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84 

T-Test Analyses 

We conducted a series of t-tests (Table 17a–g) to establish any significant differences in 

means for our final independent measures across key delinquency variables: overall delinquency (as a 

binary outcome), carrying a weapon, selling drugs, being in a gang fight and being in a gang. These 

tables provide a first look at how our independent variables might vary in importance across the 

different delinquency and crime outcomes. The t-tests compare the mean of each independent 

variable (e.g., gender, birthplace, family support) across the delinquent and nondelinquent groups. 

The tables thus report, for example, the percentage of delinquent respondents who are male and 

whether that figure is significantly different from the percentage of nondelinquent respondents who 

are male. 

Consistent with the research literature, significantly more male respondents reported overall 

delinquency and weapon carrying. Significantly more respondents who were delinquent or who used 

drugs were also born in the United States, hinting that higher levels of acculturation might be 

associated with negative outcomes. Similarly, respondents who reported overall delinquency, 

carrying a weapon, or being in a gang fight had significantly lower levels of separation from the 

United States (were more acculturated), than respondents not reporting these behaviors, and 

respondents who reported overall delinquency had significantly lower levels of family cohesion. 

Table 17a. T-Test Results by Delinquency Measure 

 % Delinquent % Not Delinquent 

(N = 55) (N = 92) 

Male** 83.6 55.4 

Born abroad** 21.8 44.6 

Either parent born abroad 81.8 85.9 

Parent support in school 90.9 92.4 

Adult in family graduated 70.9 69.6 

High ranking on scales: 

Family cohesion** 10.9 23.9 

Ethnic attachment 14.5 17.4 

Religiosity 7.3 6.5 

Separation from U.S.** 14.5 35.9 

** p < 0.05 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

85 

Table 17b. 

 % Carried Weapon % Did Not Carry Weapon 

(N = 34) (N = 114) 

Male** 91.2 58.4 

Born abroad 76.5 39.8 

Either parent born abroad 82.4 85 

Parent support in school 94.1 91.2 

Adult in family graduated 76.5 68.1 

High ranking on scales: 

Family cohesion 14.7 20.4 

Ethnic attachment 17.6 15.9 

Religiosity 5.9 7.1 

Separation from U.S. 17.6 31 

** p < 0.05 

Table 17c. 

 % Used Drugs % Did Not Use Drugs 

(N = 39) (N = 108) 

Male 74.4 63 

Born abroad** 17.9 42.6 

Either parent born abroad 82.1 85.2 

Parent support in school 92.3 91.7 

Adult in family graduated 71.8 69.4 

High ranking on scales: 

Family cohesion 10.3 22.2 

Ethnic attachment 17.9 15.7 

Religiosity 5.1 7.4 

Separation from U.S.** 15.4 32.4 

** p < 0.05 

Table 17d. 

 % Sold Drugs % Did Not Sell Drugs 

(N = 13) (N = 134) 

Male 84.6 64.2 

Born abroad 23.1 37.3 

Either parent born abroad 61.5 86.6 

Parent support in school 76.9 93.3 

Adult in family graduated 69.2 70.1 

High ranking on scales: 

Family cohesion 15.4 19.4 

Ethnic attachment 15.4 16.4 

Religiosity 7.7 6.7 

Separation from U.S. 15.4 29.1 

** p < 0.05 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

86 

Table 17e. 

 % Attacked with Intent % Did Not Attack with Intent 

(N = 15) (N = 132)

Male** 86.7 63.6 

Born abroad 80 62.1 

Either parent born abroad 73.3 85.6 

Parent support in school 86.7 92.4 

Adult in family graduated 80 68.9 

High ranking on scales: 

Family cohesion 13.3 19.7 

Ethnic attachment 6.7 17.4 

Religiosity 6.7 6.8 

Separation from U.S. 13.3 29.5 

** p < 0.05 

Table 17f. 

 % In a Gang Fight % Not in a Gang Fight 

(N = 25) (N = 122) 

Male 76 63.9 

Born abroad 24 38.5 

Either parent born abroad 80 85.2 

Parent support in school 84 93.4 

Adult in family graduated 68 70.5 

High ranking on scales: 

Family cohesion 12 20.5 

Ethnic attachment 8 18 

Religiosity 8 6.6 

Separation from U.S.** 12 31.1 

** p < 0.05 

Table 17g. 

 % In a gang % Not in a Gang 

(N = 15) (N = 132) 

Male 60.0 66.7 

Born abroad 20.0 37.9 

Either parent born abroad 86.7 84.1 

Parent support in school 80.0 93.2 

Adult in family graduated 73.3 69.7 

High ranking on scales: 

Family cohesion 13.3 19.7 

Ethnic attachment 13.3 16.7 

Religiosity 6.7 6.8 

Separation from U.S.** 6.7 30.3 

** p < 0.05 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

87 

Because it is apparent that respondents’ country of birth (whether U.S.-born or not) and 

level of acculturation are important—our measure of “separation from the U.S. culture” was 

significant in four of the six t-test analyses—we explored the level of separation from U.S. culture 

across our sample (Table 18) to obtain some insight on what types of individuals might have high or 

low separation. We examine delinquency, age, ethnicity, and gender for the different separation 

levels. Categories of “low” and “high” are defined as one standard deviation below and above the 

mean, respectively. None of the differences shown below are statistically significant, meaning that 

the level of a respondent’s separation from the United States did not vary significantly based on 

delinquency, age, ethnicity, or gender. This is important context for the regression analyses discussed 

later on in this chapter. 

Table 18. Separation from the United States 

 % Low Separation % Medium Separation % High Separation

(N = 16) (N = 94) (N = 37) 

Delinquency scale 1.94 1.92 .90 

Age 17.81 17.48 18.46 

Percent Hispanic 48.06 45.71 44.46 

Percent male 75.00 63.30 68.30 

** p < 0.05 

EGOCENTRIC PREDICTIVE ANALYSES 

Logistic Regression Model Results (Binary Outcome Measures) 

In this section we describe the findings from the logistic regression models developed for six 

binary variables: serious delinquency, carried a weapon, sold drugs, attacked someone with the intent 

to harm, been in a gang fight, and been in a gang. Discussion of the change in the dependent 

variables is not as straightforward for logistic regression results as it is for other types of regression 

models. The coefficients that are produced by the model are actually in log form. In order to 

describe change in a unit scale that is sensible, we use both the odds of the outcome of interest (i.e., 

the dependent variable) for each of the models, or the odds ratio (OR). The OR is calculated by 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

88 

exponentiating the regression coefficient; in the tables below, it is also identified as Exp(B). We also 

convert the odds ratio into probability terms and discuss the probability that, given a certain 

magnitude of change in an independent variable, the dependent variable would equal one (in other 

words, that the respondent would be delinquent). 

While discussing results in terms of probabilities makes a model’s outcomes easier to 

understand, the conversion from ORs to probabilities is not a straightforward process. The 

relationship between odds ratios and probabilities differ depending on the size of the probability 

(i.e., the relationship is not linear).24 We thus compute the hypothetical change given an (arbitrarily) 

selected baseline probability of the dependent variable; here, we use the “centered” case (Liberman 

2005), which is 0.5 (the center of the range for a binary variable). In other words, we assume that at 

baseline, respondents have a 50 percent chance of being delinquent. We then use this as the starting 

figure from which to calculate change in probability given changes in the respondent’s other 

characteristics (i.e., given changes in the independent variables). We illustrate the probability of 

delinquency for this centered case given a one-unit change in each independent variable. As part of 

the conversion from ORs to probabilities, we also computed the risk ratios (RRs); these allow results 

to be discussed in terms of how many more times likely an event (here, delinquency) is to occur. The 

RRs provide an alternative way of considering the impacts of the independent variables on the 

dependent variable. The results of the binary logistic regression models and the conversion from 

ORs to probabilities for all six dependent variables are provided in Table 19a through 19f.25

Of the alter variables measuring composition of individual ego networks, the only variable 

that is significant is the proportion of delinquent alters, and it is significant across all four 

 

24 While calculating probabilities from odds ratios is not always straightforward, and there are a number of ways to handle the 

conversion, a full explication of those methods is beyond the scope of this report. We follow Liberman’s (2005) suggestions on 

calculating changing probabilities using the centered case. See Liberman (2005) for a full explanation of these methods. 

25 It should be noted that while we report the Cox and Snell R2 values with the binary logistic results, these goodness-of-fit statistics 

are quite low compared to typical linear regression results (Hosmer & Lemeshow 2000). The low value should not be taken as a sign 

of poor fit for the models. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

89 

delinquency models where it is included. Its effect is strongest in the model for selling drugs, where 

an increase of one additional delinquent alter to an ego’s network increases the ego’s probability of 

selling drugs by 38 percent. For the “gang fight” and “in a gang” models, we use corresponding alter 

variables instead of the general delinquent alter variable (“proportion of alters in a gang fight” and 

“proportion of alters in a gang,” respectively). These versions of the alter delinquency variable are 

also significant; those who named an individual in a gang fight or in a gang are more than 1.5 times 

more likely to be in a gang fight or a gang themselves (1.59 and 1.57 times more likely, respectively). 

The number of components in a respondent’s network is a significant predictor of serious 

delinquency (p < 0.10) and carrying a weapon (p < 0.10); one additional group in a respondent’s 

network decreases his odds of serious delinquency and carrying a weapon by 1.43 times and 1.47 

times, respectively. The gender variable (male) was significant across all models except in the one 

predicting gang membership. In some cases, the variable had a rather large impact on the probability 

of a delinquent outcome. Being male had the biggest impact on selling drugs; males are more than 

14 times more likely than females to sell drugs. The large effect size is not surprising, as our sample 

comprised a larger number of males and males tend to participate in more delinquent behaviors than 

do females at all ages. What is surprising is that being male is not associated with gang membership. 

At the outset of the modeling effort, we were interested in the relationship between 

acculturation and participation in delinquent behaviors. The binary logistic analysis revealed that 

separation from U.S. culture (lower level of acculturation) is highly significant in the serious 

delinquency model but not significant for the other outcomes. For serious delinquency, the model 

indicates that increasing one’s acculturation (read as a one-unit decrease in the separation scale) 

results in an 18 percent increase in the probability of delinquency. Parent-school encouragement was 

significant in four models, and had a large negative effect on the delinquency measure in those three 

models: drug selling, being in a gang fight, and being in a gang. Those with parents who encouraged 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

90 

them in school were nearly one-ninth as likely to sell drugs, and one-third as likely to be in a gang or 

a gang fight. 

Having a family member who completed high school was only significant in the drug-selling 

model, but it did have a large negative effect (decreasing the likelihood of selling drugs by 1/3). 

Finally, residential stability (as measured by the number of years spent at one’s current address) 

appears to be a risk factor for most antisocial outcomes—one additional year at the same address 

increases the probability of selling drugs by more than 6 percent and increases the probability of 

being in a gang fight or in a gang by 5 and 6 percent, respectively. This variable can be interpreted as 

a partial test of the validity of both our use of the neighborhood as a network and our selection of 

the specific neighborhood where we conducted the survey. We expected that additional time in the 

neighborhood would result in an individual becoming more connected to others within the 

neighborhood, and therefore, more likely to spend time with delinquent others, and more likely to 

become delinquent him/herself. 

Across the binary dependent variables, we compared the results of the bivariate correlation 

analyses (Appendix C) to the logistic regression model results, and noted a few findings that did not 

carry over from the bivariate correlations into the regressions. While the proportion of alters who 

live in the same neighborhood as the respondent is positively correlated to the respondent’s serious 

delinquency and whether he carries a weapon (p < 0.05), these correlations are not reflected in the 

regression models. The proportion of nondelinquent alters to whom the respondent would go for 

advice is negatively correlated to the respondent’s serious delinquency (p < 0.01), carrying a weapon 

(p < 0.05), selling drugs (p < 0.10), and being in a gang fight (p < 0.10), but these correlations were 

also not reflected in the regression models. A negative correlation between age and being in a gang 

was not reflected in the regression model. The proportion of peers in the respondent’s network is 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

91 

negatively correlated with selling drugs and attacking someone with the intent to harm (p < 0.10), 

but these relationships are not significant in the regression models. 

Table 19a. Binary Logistic Regression Results Predicting Overall Delinquency 

 Overall Delinquency 

Coeff. S.E.

Exp(B) 

(OR)2 p RR3 Δp4

Δp 

(%)5

Constant 1.54 2.56 4.66 0.55

Alter variables 

Proportion delinquent alters1 6.61** 2.51 1.39 0.01 1.18 0.08 17.97%

Proportion alters live in same neighb.1 0.93 0.95 1.05 0.33

Proportion go to for advice (not delinq.)1 -0.11 0.88 0.99 0.90

Personal network structure variables 

Number of components -0.71† 0.40 0.49 0.07 0.70 0.18 -29.85%

Acculturation 

Separation from U.S. culture -0.34* 0.13 0.71 0.01 0.84 0.08 -15.55%

Controls 

Age 0.05 0.09 1.05 0.61

Male 2.01** 0.59 7.46 0.00 2.73 0.46 173.19%

Latino 1.04 0.68 2.83 0.12

Proportion of peers in network1 -0.44 1.04 0.98 0.67

Family member completed HS -0.38 0.51 0.69 0.46

Parent-school encouragement -0.24 0.86 0.79 0.79

Family cohesion -0.08* 0.04 0.93 0.03 0.96 0.02 -3.92%

No. years at address 0.06 0.04 1.06 0.12

Goodness-of-fit measures 

Cox & Snell R Square 0.33

Cox & Snell R Square (model with peer vars) 0.32

† p < 0.10; * p < 0.05; ** p < 0.01; ***p < 0.001 1 Proportion variables range from 0.00 to 1.00.

2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of 

proportion of alters. Each ego has 20 alters; 1 alter out of 20 is 5 percent. 3 RR is calculated by taking the square root of the OR. 4 The change in p is the difference between the hypothetical high and low probabilities, calculated using the RR and 

a hypothetical initial probability of delinquency of 0.5. 

5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable 

and a hypothetical initial probability of 0.5. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

92 

Table 19b. Binary Logistic Regression Results Predicting Weapon Carrying

 Carried a Weapon 

Coeff. S.E.

Exp(B) 

(OR)2 p RR3 Δp4

Δp 

(%)5

Constant -2.90 2.95 0.06 0.33

Alter variables 

Proportion delinquent alters1 7.26 ** 2.17 1.44 0.00 1.20 0.09 19.89%

Proportion alters live in same neighb.1 1.30 1.12 1.07 0.24

Proportion go to for advice (not delinq.)1 0.35 1.02 1.02 0.74

Personal network structure variables 

Number of components -0.78 † 0.44 0.46 0.08 0.68 0.19 -32.12%

Acculturation 

Separation from U.S. culture -0.08 0.15 0.93 0.63

Controls 

Age 0.03 0.11 1.03 0.80

Male 3.34 ** 1.10 28.16 0.00 5.31 0.68 430.69%

Latino -0.25 0.75 0.78 0.74

Proportion of peers in network1 -0.75 1.17 0.96 0.52

Family member completed HS -0.17 0.61 0.84 0.78

Parent-school encouragement 0.39 1.14 1.48 0.73

Family cohesion -0.04 0.04 0.96 0.30

No. years at address 0.06 0.04 1.06 0.14

Goodness-of-fit measures 

Cox & Snell R Square 0.29 

Cox & Snell R Square (model with peer vars) 0.26 

† p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 

1 Proportion variables range from 0.00 to 1.00. 

2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of 

proportion of alters. Each ego has 20 alters; 1 alter out of 20 is 5 percent. 3 RR is calculated by taking the square root of the OR. 4 The change in p is the difference between the hypothetical high and low probabilities, calculated using the RR and 

a hypothetical initial probability of delinquency of 0.5. 

5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable 

and a hypothetical initial probability of 0.5. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

93 

Table 19c. Binary Logistic Regression Results Predicting Drug Selling

 Sold Drugs 

Coeff. S.E.

Exp(B) 

(OR)2 p RR3 Δp4

Δp 

(%)5

Constant -7.51 5.88 0.00 0.20

Alter variables 

Proportion delinquent alters1 12.80** 3.80 1.90 0.00 1.38 0.16 37.70%

Proportion alters live in same neighb.1 -1.57 2.25 0.92 0.49

Proportion go to for advice (not delinq.)1 0.48 2.14 1.02 0.82

Personal network structure variables 

Number of components 0.46 0.74 1.58 0.53

Acculturation 

Separation from U.S. culture -0.47 0.37 0.62 0.20

Controls 

Age 0.17 0.21 1.19 0.42

Male 5.36* 2.54 211.90 0.04 14.56 0.87 1355.60%

Latino -0.11 1.34 0.90 0.94

Proportion of peers in network1 -0.30 2.28 0.99 0.90

Family member completed HS -2.09† 1.25 0.12 0.10 0.35 0.48 -64.88%

Parent-school encouragement -4.35* 1.82 0.01 0.02 0.11 0.80 -88.63%

Family cohesion 0.03 0.07 1.04 0.62

No. years at address 0.14* 0.07 1.15 0.04 1.07 0.04 7.36%

Goodness-of-fit measures 

Cox & Snell R Square 0.27 

Cox & Snell R Square (model with peer vars) 0.25 

† p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 

1 Proportion variables range from 0.00 to 1.00. 

2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of 

proportion of alters. Each ego has 20 alters; 1 alter out of 20 is 5 percent. 3 RR is calculated by taking the square root of the OR. 4 The change in p is the difference between the hypothetical high and low probabilities, calculated using the RR and a 

hypothetical initial probability of delinquency of 0.5. 5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable and 

a hypothetical initial probability of 0.5. 

 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

94 

Table 19d. Binary Logistic Regression Results Predicting Attacking Someone

 Attacked Someone with Intent to Harm 

Coeff. S.E.

Exp(B) 

(OR)2 p RR3 Δp4

Δp 

(%)5

Constant -4.65 3.92 0.01 0.24

Alter variables 

Proportion delinquent alters1 5.18* 2.13 1.30 0.02 1.14 0.06 13.83%

Proportion alters live in same neighb.1 -0.87 1.50 0.96 0.57

Proportion go to for advice (not delinq.)1 -3.05† 1.58 0.86 0.05 0.93 0.04 -7.35%

Personal network structure variables 

Number of components -0.06 0.53 0.94 0.90

Acculturation 

Separation from U.S. culture -0.08 0.20 0.93 0.71

Controls 

Age 0.12 0.15 1.13 0.40

Male 2.59† 1.35 13.36 0.06 3.65 0.57 265.46%

Latino 0.27 0.98 1.31 0.78

Proportion of peers in network1 -1.24 1.49 0.94 0.41

Family member completed HS -0.32 0.86 0.72 0.71

Parent-school encouragement -0.51 1.16 0.60 0.66

Family cohesion 0.02 0.05 1.02 0.71

No. years at address -0.03 0.06 0.97 0.62

Goodness-of-fit measures 

Cox & Snell R Square 0.17 

Cox & Snell R Square (model with peer vars) 0.14 

† p < 0.10; *p < 0.05; ** p < 0.01; ***p < 0.001 

1 Proportion variables range from 0.00 to 1.00. 

2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of 

proportion of alters. Each ego has 20 alters; 1 alter out of 20 is 5 percent. 

3 RR is calculated by taking the square root of the OR. 

4 The change in p is the difference between the hypothetical high and low probabilities, calculated using the RR and a 

hypothetical initial probability of delinquency of 0.5. 5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable and 

a hypothetical initial probability of 0.5. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

95 

Table 19e. Binary Logistic Regression Results Predicting Participation in Gang Fight

 Been in a Gang Fight 

Coeff. S.E.

Exp(B) 

(OR)2 p RR3 Δp4

Δp 

(%)5

Constant 3.64 3.09 38.04 0.24

Alter variables 

Proportion of alters in a gang fight1 18.45** 6.01 2.52 0.00 1.59 0.23 58.60%

Proportion alters live in same neighb.1 -0.13 1.17 0.99 0.92

Proportion go to for advice (not delinq.)1 0.57 1.15 1.03 0.62

Personal network structure variables 

Number of components -0.41 0.47 0.66 0.39

Acculturation 

Separation from U.S. culture -0.28 0.18 0.76 0.12

Controls 

Age -0.17 0.13 0.84 0.17

Male 1.51* 0.77 4.52 0.05 2.13 0.36 112.65%

Latino 0.83 0.90 2.29 0.36

Proportion of peers in network1 0.67 1.43 1.03 0.64

Family member completed HS -0.86 0.65 0.42 0.18

Parent-school encouragement -2.13* 0.99 0.12 0.03 0.34 0.49 -65.60%

Family cohesion -0.04 0.04 0.96 0.28

No. years at address 0.09* 0.04 1.10 0.04 1.05 0.02 4.66%

Goodness-of-fit measures 

Cox & Snell R Square 0.23 

Cox & Snell R Square (model with peer vars) 0.23 

† p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 

1 Proportion variables range from 0.00 to 1.00. 

2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of 

proportion of alters. Each ego has 20 alters; 1 alter out of 20 is 5 percent. 3 RR is calculated by taking the square root of the OR. 4 The change in p is the difference between the hypothetical high and low probabilities, calculated using the RR and a 

hypothetical initial probability of delinquency of 0.5. 5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable and 

a hypothetical initial probability of 0.5. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

96 

Table 19f. Binary Logistic Regression Results Predicting Gang Membership

 Been in a Gang 

Coeff. S.E.

Exp(B) 

(OR)2 p RR3 Δp4

Δp 

(%)5

Constant -1.54 4.17 0.22 0.71 

Alter variables 

Proportion of alters in a gang1 17.98** 5.22 2.46 0.00 1.57 0.22 56.75%

Proportion alters live in same neighb.1 -0.79 1.64 0.96 0.63 

Proportion go to for advice (not delinq.)1 2.03 1.68 1.11 0.23 

Personal network structure variables 

Number of components -0.79 0.71 0.46 0.27 

Acculturation 

Separation from U.S. culture -0.29 0.24 0.75 0.23 

Controls 

Age -0.01 0.17 0.99 0.95 

Male -0.03 0.85 0.97 0.97 

Latino 2.27 1.61 9.72 0.16 

Proportion of peers in network1 3.37 2.57 1.18 0.19 

Family member completed HS -0.86 0.95 0.42 0.37 

Parent-school encouragement -2.28* 1.14 0.10 0.05 0.32 0.52 -68.02%

Family cohesion -0.08 0.06 0.93 0.18 

No. years at address 0.12* 0.06 1.12 0.05 1.06 0.03 5.92%

Goodness-of-fit measures 

Cox & Snell R Square 0.24 

Cox & Snell R Square (model with peer vars) 0.22 

† p < 0.10; *p < 0.05; ** p < 0.01; *** p < 0.001 

1 Proportion variables range from 0.00 to 1.00. 

2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of 

proportion of alters. Each ego has 20 alters; 1 alter out of 20 is 5 percent. 3 RR is calculated by taking the square root of the OR. 4 The change in p is the difference between the hypothetical high and low probabilities, calculated using the RR and a 

hypothetical initial probability of delinquency of 0.5. 5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable and 

a hypothetical initial probability of 0.5. 

Negative Binomial Regression Results (Scaled Outcome Measures) 

We also examined predictors of delinquency using our three scaled outcomes (overall 

delinquency, delinquency in past six months, and serious delinquency over one’s lifetime) as scales 

capture more information than binary measures. The results of the negative binomial models are 

presented in Table 20 through Table 22. As discussed in the Analysis Plan section, the models 

include all variables deemed appropriate for final inclusion. Table 20 through Table 22 each include 

two models: Model 1 includes peer-related alter variables (which consider only alters named as 

friends), network variables, and control variables. Model 2 employs all alter variables instead of the 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

97 

peer-only variables. These sets of models are shown side by side as a way to compare the fit of the 

models and determine whether we gain any important information by considering complete personal 

networks (with all alters) as opposed to only peer networks (with only friends) as they relate to 

delinquency and gang membership. As discussed in the literature review, almost all network analyses 

examining self-reported delinquency have focused solely on peer networks (and many have just used 

school networks). We utilized the Akaike Information Criterion (AIC) to compare the fit of the 

models. A smaller AIC signifies a better fit, with the criteria for a significant difference being a 

difference of 2.5 or more (Hilbe 2007) for sample sizes around 200. 

In addition to providing the coefficients from the regression models, the tables include the 

exponentiated coefficients (exp(B)) for ease of interpretation. We can express a measure’s effect in 

terms of the percent change in the dependent variable as ([exp(B)–1] ∗ 100). For the binary logistic 

regression analyses, we interpret the odds ratio; for the negative binomial regression analyses, we 

calculate the percent change in delinquent activity. The percent change is interpreted differently 

depending on the type of predictor variable (proportion vs. count). To discuss the magnitude of the 

effects of the proportion variables (e.g., proportion of delinquent alters in a respondent’s personal 

network) on the delinquency measures, we exponentiate the coefficient multiplied by any given 

meaningful percentage increase in the proportion of the predictor variable, and then subtract one. 

For example, if we are reporting on the change in delinquent activity based on a change in an alter’s 

delinquent network of 25 percent (that is, the respondent increases the percent of delinquent alters 

in his network by 25 percent), we calculate (exp(B*0.25)-1) * 100. For independent variables that 

represent a change in count (e.g., number of components), we simply calculate (exp(B)-1) * 100 to 

represent the percent change in delinquent activity that would occur given a one-unit increase in the 

variable. A positive value for the coefficient (before exponentiating) represents an increase in 

delinquency, and a negative relationship represents a decrease in delinquency. 

Model 1 in Table 20 shows the influence of delinquent peers on respondents’ delinquent 

activity. If the percentage of delinquent members in a respondent’s network changes by 25 percent 

(calculated by dividing the number of delinquent peers by the total number of peers in a 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

98 

respondent’s network), that respondent’s delinquent activity increases 119 percent ((exp(3.129*0.25)-

1)) * 100); if half of the peers in the respondent’s network are delinquent, the respondent’s 

delinquent activity increases by nearly 400 percent ((exp(3.129*0.5)-1) * 100). Model 1 also shows 

that for every additional subgroup the respondent adds to her personal network (i.e., a one-unit 

increase in the number of components), the respondent’s delinquent activity decreases 26 percent 

((exp(-0.306)-1) * 100) (p < 0.05). Male respondents exhibit 232 percent ((exp(1.18)-1) * 100) more 

delinquent activity than female respondents. Having parental support for attending school reduces 

delinquent activity by 49 percent (p < 0.10). Likewise, higher levels of the respondent’s family 

cohesion decrease a respondent’s delinquent activity by 3 percent (i.e., every additional unit of family 

cohesion represents a 3 percent decrease in delinquent activity (p < 0.05)). Increasing acculturation 

(read as a one-unit decrease in the separation scale) corresponds to an 18 percent increase in the 

respondent’s delinquent activity (p < 0.01). Finally, for every additional year a respondent lives at 

his/her current address (and, therefore, in the target neighborhood), the respondent exhibits a 4 

percent increase in delinquency (p < 0.01). Housing stability is associated with a slight increase in 

delinquency but, as with the binary logistic models, this effect is likely tied to more to the 

neighborhood context than to housing stability itself. In other words, stability alone is not likely to 

be associated with increased delinquency, but stability in a negative environment—which, in many 

ways, the target area is—can have the opposite affect and be associated with increased delinquency. 

Model 2 shows the influence of all alters in predicting respondents’ delinquent activity. 

There are very few differences between the results of Model 1 and 2, although Model 2 has a slightly 

better fit in that the AIC for Model 2 is 481.24 compared to an AIC of 485.22 for Model 1. A 

comparison of the results of Models 1 and 2 indicates that the coefficient for the proportion of 

delinquent peers, compared to the proportion of delinquent alters, is the same. The difference, 

however, is marginal when used in calculating percent change of the dependent variable: a 25 

percent change in delinquent alters would result in a 36.7 percent increase in delinquency whether 

the peer or alter variable is used. In Model 2, as well (using alter (non-peer) variables), parental 

support for attending schools is not a significant predictor of overall delinquent activity, and its size 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

99 

is lower than that found in the peer model. The other slight change in the results is that the number 

of components is less significant in Model 2 (at the p < 0.10 level) than it is in Model 1 (at the 

p < 0.05 level); the size of the predictor in both models is almost equal, however. For every 

additional component the respondent has within his/her personal network, then, the respondent’s 

delinquent activity decreases 26 percent (p = 0.05) in the peer variable model and 25 percent 

(p = 0.05) in the alter variable model. Because these two models are so similar, we cannot make a 

statement about which network measures—peer or alter—best describe the influence of social 

networks of youth on delinquency in our study. 

It is also worthwhile to note that a number of significant relationships found in the bivariate 

correlations did not remain significant in the regression models. Bivariate analyses (Appendix C) 

showed a negative correlation between the proportion of nondelinquent alters to whom the 

respondent would go for advice and the overall delinquency scale (p < 0.05); however, this variables 

was not significant in our regression models. 

Table 21 presents the results of the models examining recent (last six months) delinquency. 

Comparing the two models reveals that there are almost no differences between the models 

(although the alter model has a slightly better fit). What is notable is that the effect for the variable 

“alter delinquency” (in Model 2) is larger than the effect for “peer delinquency” (in Model 1) (they 

were effectively equal in the model of overall delinquency). If delinquency within a respondent’s 

network increases by 25 percent, that respondent’s recent delinquent activity increases by 90 percent 

((exp(2.560*0.25)-1) * 100); if delinquent peers increase by 50 percent, the respondent’s delinquent 

activity increases 260 percent ((exp(2.560*0.50)-1) * 100). When a respondent’s alter network 

increases in delinquency by 25 percent, her delinquent activity increases 97 percent 

((exp(2.720*0.25)-1) * 100); if delinquent alters increase by 50 percent, her delinquent activity 

increases 290 percent. Because the effects are different, having information on relationships other 

than peers may be very important for understanding delinquency. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

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SOCIAL NETWORKS AND DELINQUENCY

100 

Table 20. Negative Binomial Regression Results Predicting Overall Delinquency 

 Model 1: Peer Variables Model 2: Alter Variables 

Coeff. S.E. Exp(B) p Coeff. S.E. Exp(B) p 

Intercept 2.00† 1.14 7.42 0.08 0.77 1.11 2.16 0.49

Peer variables 

Proportion delinquent friends 3.13*** 0.81 22.86 0.00

Proportion friends in same neighb. -0.22 0.39 0.80 0.57

Proportion go to for advice (not 

delinq.) -0.12 0.43 0.89 0.79

Alter variables 

Proportion delinquent alters 3.13*** 0.67 22.79 0.00

Proportion alters in same neighb. 0.46 0.45 1.58 0.32

Proportion go to for advice (not 

delinq.) -0.04 0.48 0.96 0.93

Network structure variables 

No. of components -0.31* 0.15 0.74 0.04 -0.29† 0.15 0.75 0.05

Acculturation 

Separation from U.S. culture -0.20** 0.06 0.82 0.00 -0.20** 0.06 0.82 0.00

Controls 

Age -0.01 0.04 0.99 0.79 0.01 0.04 1.01 0.75

Male 1.18*** 0.27 3.26 0.00 1.26*** 0.27 3.52 0.00

Latino 0.46 0.31 1.58 0.15 0.43 0.31 1.54 0.16

Proportion of peers in network 0.55 0.53 1.73 0.30 0.85 0.54 2.34 0.12

Family member completed HS -0.29 0.27 0.75 0.29 -0.37 0.27 0.69 0.18

Parent-school encouragement -0.68† 0.39 0.51 0.08 -0.45 0.39 0.64 0.24

Family cohesion -0.03* 0.01 0.97 0.02 -0.03* 0.01 0.97 0.02

No. years at address 0.05** 0.01 1.05 0.00 0.04** 0.01 1.04 0.01

Goodness of fit 

Log likelihood -227.61 -225.62 

Deviance value/df 1.09 1.98 

Pearson chi-square value/df 0.97 0.97 

Likelihood ratio chi-square 51.90 55.88 

AIC 485.22 481.24 

† p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001

Note: Proportion variables range from 0.00 to 1.00. 

For recent delinquency, the alter model has a better fit than the peer model, and in addition, 

some of the variables that were not significant in the overall alter delinquency model were significant 

in the recent delinquency model. Age is marginally significant in the recent delinquency model: for 

every year older a respondent is, she exhibits 7 percent fewer recent delinquent acts (marginally 

significant at p = 0.06). Parent support for school was not significant in the alter model for overall

delinquency, but in the recent delinquency alter model, having parental support for attending school is 

associated with a statistically significant decrease in the respondent’s recent delinquent activity (by 60 

percent; p = 0.01). 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

101 

Table 21. Negative Binomial Regression Results Predicting Recent Delinquency

 Model 1: Peer Variables Model 2: Alter Variables 

Coeff. S.E. Exp(B) p Coeff. S.E. Exp(B) p 

Intercept 3.13† 1.62 22.96 0.05 1.74† 1.75 5.72 0.05

Peer variables 

Proportion delinquent friends 2.56** 0.97 12.94 0.01 

Proportion friends in same neighb. -0.36 0.52 0.70 0.49

Proportion go to for advice (not 

delinq.) -0.64 0.50 0.53 0.20 

Alter variables 

Proportion delinquent alters 2.72** 0.84 15.17 0.01

Proportion alters in same neighb. 0.60 0.61 1.81 0.49

Proportion go to for advice (not 

delinq.) -0.55 0.56 0.57 0.20

Network structure variables 

No. of components -0.37† 0.20 0.69 0.06 -0.37† 0.20 0.69 0.06

Acculturation 

Separation from U.S. culture -0.12 0.08 0.89 0.14 -0.12 0.08 0.88 0.14

Controls 

Age -0.11† 0.06 0.90 0.06 -0.08† 0.06 0.93 0.06

Male 1.15** 0.34 3.14 0.00 1.26** 0.35 3.52 0.00

Latino 0.07 0.38 1.08 0.85 0.04 0.37 1.04 0.85

Proportion of peers in network 0.41 0.73 1.50 0.58 0.76 0.75 2.13 0.58

Family member completed HS 0.04 0.35 1.04 0.91 -0.10 0.35 0.91 0.91

Parent-school encouragement -1.15** 0.44 0.32 0.01 -0.93** 0.44 0.40 0.01

Family cohesion -0.03 0.02 0.97 0.21 -0.03 0.02 0.97 0.21

No. years at address 0.07** 0.02 1.08 0.00 0.07** 0.02 1.07 0.00

Goodness of fit 

Log likelihood -151.49 -149.85 

Deviance value/df 0.82 0.83 

Pearson chi-square value/df 0.91 0.89 

Likelihood ratio chi-square 35.36 38.65 

AIC 332.98 329.69 

† p < 0.10; *p < 0.05; ** p < 0.01; ***p < 0.001

Note: Proportion variables range from 0.00 to 1.00. 

 

While a respondent’s family cohesion and separation from the United States are significant 

predictors of overall delinquency, they were not significant predictors of recent delinquency. While 

there were some differences between the predictors of overall delinquency and recent delinquency, 

one key network-based variable—the number of components in the respondent’s network—was a 

(marginally) significant predictor of recent delinquency, as it was in the overall delinquency model. 

The effects of the number of components on recent delinquency, controlling for all variables, was 

slightly larger than its effect on overall delinquency: in Model 2, each additional component 

represents a 31 percent decrease in recent delinquent activity (p=0.06). 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

102 

There were fewer notable differences between the bivariate analyses (shown in the 

correlation matrix in Appendix C) and the recent delinquency model results (compared to the overall 

delinquency bivariate and multivariate analyses). As is the case with overall delinquency, having 

associates to whom you can go for advice is associated with lower delinquency (p < 0.05), but this 

relationship was not reflected in the regression model. While the model for recent delinquency 

offered some support for exploring the impacts of inclusive alter networks instead of just peer 

networks, the models are again very similar and overall, the use of neither method appears to offer 

significant improvement over the other in predicting delinquency. 

Table 22 presents the results for the models examining the scale of serious delinquency. 

Similar to the models for recent delinquency, the alter model (Model 2) has a slightly better fit and 

the alter delinquency measure has a larger effect than the peer delinquency measure. When 

delinquency in a respondent’s alter network increases by 25 percent delinquent, that respondent’s 

serious delinquent activity increases significantly by 170 percent ((exp(3.980*0.25)-1) * 100); if 

network delinquency increases by 50 percent, the respondent’s delinquency increases by 631 percent 

(exp(3.98*0.5)). It is important to note that, based on these figures, the existence of delinquent alters 

in a personal network is a much stronger predictor of serious delinquency than overall delinquency or 

recent delinquency. Other important findings for the model of serious delinquency are that the 

“number of components” variable and the measure of separation from United States culture are 

significant. Consistent with the model of overall delinquency, every additional group represents a 34 

percent decrease in serious delinquent activity (p < 0.05). Also consistent with overall delinquency 

models, higher levels of separation from the United States (lower acculturation) decrease a 

respondent’s delinquent activity by 20 percent (p < 0.05). It is also worth noting the importance of 

some of the control variables—having a close family member (i.e., parent or guardian) complete 

high school reduces the respondent’s serious delinquent activity by 51 percent (p < 0.05). The only 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

103 

other outcome where this variable was significant was for selling drugs (see Table 19 above). The 

slightly larger effect sizes for the alter delinquency variables suggest that in modeling serious 

delinquency, there is a benefit to including wider networks beyond simple peer relationships.

Having parental encouragement in school also reduces serious delinquent activity by 58 

percent (p < 0.05). Over the six models of delinquent behavior, this measure was significant in five, 

but followed an interesting pattern. When peer delinquent variables were used in the model, the 

parental encouragement variable was strongly significant. When the alter variables were used in the 

model, the parental encouragement variable either dropped out of significance (e.g., in overall 

delinquency model) or dropped in both magnitude and significance. So, the variable is less important 

in the model when youths’ full networks are reported on—not just their peers. While additional 

investigation into this effect is beyond the scope of this report, an exploration of the parental 

relationship and its effect on delinquency, it is something worth an additional look in future efforts. 

In examining the bivariate analyses (shown in the correlation matrix in Appendix C) and the 

serious delinquency models, three relationships were notable. As was the case with overall and 

recent delinquency, the proportion of nondelinquent alters to whom the respondent would go for 

advice is negatively correlated to the respondent’s recent delinquency (p < 0.05), but this correlation 

was not reflected in the regression model. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

104 

Table 22. Negative Binomial Regression Results Predicting Serious Delinquency

 Model 1: Peer Variables Model 2: Alter Variables 

Coeff. S.E. Exp(B) p Coeff. S.E. Exp(B) p 

Intercept 1.42 1.36 4.13 0.30 -0.13 1.30 0.88 0.92

Peer variables 

Proportion delinquent friends 3.86*** 1.00 47.66 0.00

Proportion friends in same neighb. -0.06 0.45 0.95 0.90

Proportion go to for advice (not 

delinq.) -0.33 0.45 0.72 0.46

Alter variables 

Proportion delinquent alters 3.98*** 0.76 53.53 0.00

Proportion alters in same neighb. 0.23 0.52 1.26 0.66

Proportion go to for advice (not 

delinq.) -0.28 0.50 0.76 0.58

Network structure variables 

No. of components -0.46* 0.18 0.63 0.01 -0.42* 0.18 0.66 0.02

Acculturation 

Separation from U.S. culture -0.22** 0.08 0.80 0.00 -0.22** 0.08 0.80 0.01

Controls 

Age 0.00 0.05 1.00 0.98 0.04 0.05 1.04 0.45

Male 1.66*** 0.29 5.27 0.00 1.85*** 0.32 6.36 0.00

Latino 0.36 0.36 1.43 0.32 0.33 0.33 1.39 0.32

Proportion of peers in network -0.16 0.62 0.85 0.80 0.19 0.60 1.21 0.75

Family member completed HS -0.59† 0.32 0.55 0.07 -0.71* 0.31 0.49 0.02

Parent-school encouragement -1.07** 0.39 0.35 0.01 -0.82* 0.37 0.44 0.03

Family cohesion -0.02 0.02 0.98 0.16 -0.02 0.02 0.98 0.23

No. years at address 0.05** 0.02 1.05 0.01 0.04* 0.02 1.04 0.01

Goodness of fit 

Log likelihood -152.08 -149.49 

Deviance value/df 0.88 0.92 

Pearson chi-square value/df 0.86 0.93 

Likelihood ratio chi-square 63.09 68.27 

AIC 345.80 328.98 

† p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001

Note: Proportion variables range from 0.00 to 1.00. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

105 

SOCIOCENTRIC DESCRIPTIVE ANALYSES 

Demographics of the Whole Network 

 The first step in the whole network analysis was to explore the characteristics of the 

individuals who compose the whole network after overlapping the ego and alter data. We first 

“sliced” the network in two different ways as a method to compare characteristics of the 

neighborhood-based network—one “full” slice of the whole network comprised all 2,521 unique 

individuals who were named by any ego, and the other slice involved defining the network so it 

would comprise only those individuals who were egos or an alter who was named at least twice. 

Approximately 11 percent of the whole network was named at least twice; the “2+ network” has 369 

members. We expect that many of the individuals who were named only once are older individuals 

(parents or other family members) who may influence one person’s network but not multiple 

youths’ networks; if this is true, the 2+ network should comprise more peers. We also examined the 

descriptive statistics by age group—network members were classified into four distinct age groups 

(where age was known): kids are under age 14, youth are the target ages—14–21 years, young adults 

are age 22–30, and adults are over 30 years. We used these groups to determine whether patterns of 

demographics and delinquent behavior differed by broad age categories.26 Table 23 provides 

demographic information for both networks. 

 

26 We did not provide each measure by age group in the table but note interesting patterns where present in the text. Full statistics by 

age group are available from the authors on request. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

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SOCIAL NETWORKS AND DELINQUENCY

106 

Table 23. Demographic Characteristics of Whole Networks 

Individual Measures Whole Network 2+ Network1

Size (count of nodes) 2,521 369 

Average age 20 years 19 years 

Percent named as parent 3.9 4.6 

Percent named as sibling 7.7 20.3 

Percent named as friends 69.1 68.5 

Percent who live in neighborhood 34.9 72.9 

Percent Latino 69.4 78.9 

Percent born abroad 43.8 37.1 

Percent male 57.6 61.8 

Percent who sold drugs2 4.2 6.8 

Percent who carry a weapon2 5.6 5.9 

Percent in gang fights2 5.7 8.9 

Percent who use violence2 5.5 7.6 

Percent in gang 4.5 9.5 

Percent delinquent3 7.5 13.8 1 The 2+ network includes only egos or people who were named at least twice by egos. 

2 Percent of those deemed very likely or somewhat likely to engage in behavior listed. 

3 Percent of those who were very likely to participate in at least one of five delinquent behaviors reported. 

Age of Network Members 

The average age of the two networks was of interest because it could demonstrate that youth 

would name individuals not in their peer group, lending support to our decision not to conduct a 

school-based survey. Indeed, we found the average ages to be surprisingly high, considering that our 

respondents were between the ages of 14 and 21; the whole network had an average age of 20 years 

and the 2+ network average age was just slightly lower at 19 years. This was higher than the average 

age for egos alone, which was about 18 years. 

The most common ages for the two networks were slightly different, however: the mode of 

the whole network was 17 while the mode of the 2+ network was 20. Of those in the whole 

network, ages ranged from newborn to 78 years of age; the range of ages in the 2+ network was 

slightly smaller, from 7 years old to 68 years old. While the average ages are slightly higher than we 

might have expected, and the age ranges for each network are very wide, the majority of individuals 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

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SOCIAL NETWORKS AND DELINQUENCY

107 

actually did fall into a much narrower age range, with over 70 percent of the whole network 

members and over 80 percent of the 2+ network members between the ages of 14 and 21 years. 

This suggests that the individuals who were named twice are likely peers (or closer in age to the 

respondents), as we had expected prior to conducting the analysis. 

Just about half of the members in both networks were school-age, or between the ages of 13 

and 18. Fully half of the individuals in both networks, then, were over age 18, indicating that schoolbased surveys about delinquent behaviors may be missing a significant portion of the social 

networks that influence youth. Below, we will discuss how levels of delinquency vary by age to 

determine whether the older individuals are more likely to be acting as protective factors or negative 

factors in a youth’s life. 

Relationship to Egos 

 The alter questions about type of relationship were ego-specific, meaning that different egos 

would respond differently about the same alter on the question. As described in an earlier section, 

because EgoNet outputs the summary statistics for every relationship, we can calculate percentages 

for types of relationships that would be inclusive for the node (or individual). Table 24 reports on 

the percentages of individuals who were named as a parent, sibling, or friend (the three most 

common relationship categories), but one individual could show up in more than one category; the 

categories of relationship as reported in this table, therefore, are not mutually exclusive. 

One interesting characteristic of the 2+ network is the fact that many more of its members 

were named as siblings than in the whole network; this could be a result of having siblings from the 

same family take the survey (once one individual took the survey, it was much more likely that 

his/her siblings would also take the survey) so all siblings would be included in the network as egos 

regardless of how many times they were named as alters, or it could be that the siblings are part of 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

108 

the same social networks, and so are named by multiple egos. Also interesting is that the number of 

siblings in the whole network seems relatively low. 

The relationship variables also reveal that very similar proportions of both networks were 

named as parents and/or as peers; while our analyses revealed that the average age of the 2+ 

network is lower, suggesting that it probably contains more peer relationships, the proportions of 

parents and peers in each network, at least, are relatively similar (3.9 and 4.6, respectively, for parents 

and 69.1 and 68.5, respectively, for peers). In addition, a very small proportion of individuals were 

named as parents, but this is not surprising given the way the whole network is calculated when egos 

most likely only have a maximum of four parents to list as possible alters out of 20. 

Geography of the Network 

 One measure with dramatically different proportions was the “live in neighborhood” 

variable; only approximately 30 percent of the members of the whole network live in the 

neighborhood while nearly three-quarters of the members of the 2+ network live in the 

neighborhood. While not wholly unexpected—it is more likely that those who are named by (and 

thus known by) more than one ego are peers who live nearby than individuals who live farther 

afield—it does suggest that the core social network for the youth who are most well-connected or 

well-known in the area are neighborhood based, another sign of support for the design of the 

survey. This could have implications for the influence of the individuals in the neighborhood versus 

those from outside the neighborhood; such a finding warrants further investigation, which will be 

discussed below. In order to more fully explore those who live in the neighborhood versus those 

who live outside the neighborhood, we divide nodes into those two categories and look at their 

characteristics separately, below. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

109 

Other Demographic Characteristics 

 For each network, the majority of members were Latino, with the 2+ network having a 

slightly higher proportion of Latino members at nearly 80 percent, very similar to the percentages 

for egos only. Members of the 2+ network were born abroad at a similar rate to egos, but members 

of the whole network were slightly more likely than the egos and the 2+ network to have been born 

abroad. At the network level, gender appeared divided similarly to egos only, where slightly less than 

two-thirds of the egos were male in both networks. But more interesting patterns emerged when we 

looked at gender by age group for each network. Among adults in the whole network, the gender 

pattern reversed, with slightly more adult females (54 percent) than adult males. In the 2+ network, 

the gender ratio (about 2:1) was similar to the egos across all age groups. This indicates that when 

egos named adults, they were more likely to name women as individuals that they saw frequently or 

spent time with, but when they nominated peers, egos were more likely to nominate males. This 

pattern likely did not hold for the 2+ network because again, there were fewer adults in the 2+ 

network. If youth named mothers, aunts, or other adult females they were likely only influential for 

one individual in the network, and not several. 

Delinquency/Criminality of Network Members 

 We explored the delinquency of the network members by looking at the five main 

delinquency measures individually (weapon use, sold drugs, in gang fight, in gang, and use 

violence)27 and together via the overall delinquency measure.28 While not shown in Table 23, for 

these measures, we also considered delinquency for different age groups in the data, described 

above; we wanted to assess whether older individuals might be acting as risk or protective factors 

based on their levels of delinquency. 

 

27 As mentioned in the measures section of the report, we use the term delinquency and criminality interchangeably in that many of 

the alters are not under 18. 

28 An individual is considered delinquent if he/she was very likely to participate in at least one of the five delinquency measures. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

110 

 The 2+ network had higher levels of delinquency than did the whole network on every 

measure; only weapon use was similar across the two networks.29 The difference was greatest for 

percent in a gang; only 4.5 percent of the whole network was in a gang, but nearly 10 percent of the 

2+ network was. Ten percent is a relatively small number of individuals, but it is an indication that 

gang members who were named in the network tended to be named more than once, as opposed to 

being named as influential for just one ego. In addition, among the delinquency measures, “in gang” 

was highest for the 2+ network, but “gang fight” was highest for the whole network. Overall, nearly 

14 percent of the 2+ network was delinquent, while just over 7 percent of the whole network was 

delinquent. This means that 14 percent of members in the 2+ network were judged to be very likely

to engage in one of the delinquent behaviors of interest. This is lower than the rate of delinquency 

for the egos only (20 percent of egos were delinquent), indicating that on the whole, egos were 

attributing more delinquent behavior to themselves than to members of their networks. Overall, the 

levels of delinquency found among network members for both networks were relatively low, given 

our local knowledge of the neighborhood. 

Figure 2 provides a picture of both networks, combining each node’s delinquency status and 

in- versus out-of-neighborhood status. From these figures, we can see that both networks have 

clearly defined and large central clusters of members, and that the delinquent members (the light 

colored nodes) tend to be located in this central cluster. The pictures also reveal, not surprisingly, 

that the in-neighborhood nodes appear to be more centrally located within the network while the 

out-of-network members appear more peripheral and seem to have fewer connections. Finally, the 

2+ network has a large number of isolates, nodes who are not connected to anyone else, likely the 

result of dropping a large number of nodes. 

 

29 Because the 2+ network is a subset of the whole network, we cannot perform t-tests to statistically compare the differences 

between the two networks. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

111 

Figure 2. Sociograms of Whole Network and 2+ Network, Displaying Node Delinquency and “InNeighborhood” Nodes 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

112 

The Neighborhood Network 

Because one of the main goals of the research was to explore the accuracy and utility of 

defining a social network using geographic boundaries, we also investigated the characteristics of 

those individuals residing in and outside the neighborhood. Those characteristics are presented in 

Table 24. The in-neighborhood group is significantly smaller, at nearly half the size of the out-ofneighborhood group. We performed t-tests to determine whether the two groups were significantly 

different, and every measure was significant. Other than for average age, for most measures, the 

differences between the two networks are stark; in-neighborhood nodes were more likely to be 

parents and siblings; out-of-neighborhood nodes were more likely to be friends. More in-nodes are 

Latino while more out-nodes were born abroad. In addition, the table demonstrates clearly that the 

in-nodes have significantly higher levels of delinquency for every measure examined. Table 24 

indicates that there are likely some systematic differences between the two groups that are 

manifested in higher levels of delinquency for in-neighborhood nodes. 

Table 24. Demographic Characteristics of Networks Within and Outside 

the Neighborhood 

Individual Measures Live in Neigh. Live out of Neigh. 

Size (count of nodes) 879 1,642 

Average age 21 years 20 years 

Percent named as parent 8.9 1.2 

Percent named as sibling 15.0 3.9 

Percent named as friends 59.0 74.4 

Percent who live in neighborhood 100.0 0.0 

Percent Latino 76.5 65.6 

Percent born abroad 51.3 61.4 

Percent male 62.3 55.1 

Percent who sold drugs1 8.4 3.8 

Percent who carry a weapon1 9.7 4.8 

Percent in gang fights1 10.3 5.0 

Percent who use violence1 8.8 5.5 

Percent in gang 7.1 3.1 

Percent delinquent2 11.5 5.4 1 Percent of those deemed very likely or somewhat likely to engage in behavior 

listed. 

2 Percent of those who were very likely to participate in at least one of five 

delinquent behaviors reported. 

Note: All differences between networks significant at p < 0.05 level. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

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113 

Cohesiveness and Centrality of the Networks 

 As discussed in the analysis plan above, there are many different network measures that can 

be employed—with different results—to identify both how cohesive and centralized a network is 

and how central individual members are, based on such things as ties to others, distance to all nodes 

in the network, and centrality of nodes an individual is connected to. We employ one cohesion 

measure (density) and four centrality measures for this work, to compare the findings across 

measures and to provide a comprehensive understanding of connections between nodes in the two 

networks under study. Table 25 provides the cohesion and centrality measures for each network.30

Table 25. Cohesion and Centrality Measures

Network Measures Whole Network 2+ Network 

Size (count of nodes) 2,521 369 

Avg. number of nominations per node 1.17 2.14 

Number of ties between nodes 16,552 1,810 

Density 

Density 0.0026 0.01 

Effective density1 0.33 - 

Degree centrality 

Mean 6.57 4.91 

Mean, normalized degree 0.26 1.33 

Network centralization index (%) 2.04 5.48 

Betweenness centrality2

Mean 3,860.93 341.14 

Mean, normalized betweenness 0.12 0.51 

Network centralization index (%) 9.19 6.75 

Closeness centrality3

Mean 6.13 5.26 

Mean, normalized closeness 34.06 35.08 

Network centralization index 436.86 347.95 

Eigenvector centrality 

Mean 0.004 0.02 

Mean, normalized eigenvector 0.63 3.01 

Network centralization index (%) 42.58 41.06 1 Effective density is calculated using a network size that accounts for the number of 

people respondents were asked to name. 2 Freeman node betweenness score is reported. 

3 Calculated using the Valente-Foreman average of reversed distances. 

 

30 Centralization measures are calculated at the network level, while centrality measures are calculated at the individual (node) level. 

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Cohesion 

As noted earlier, the whole network contains 2,521 nodes while the 2+ network has 369 

nodes. The whole network contains far more ties (because it contains far more nodes) than the 2+ 

network. Density is the most basic and commonly used measure of cohesiveness and is simply the 

proportion of actual ties relative to all possible ties; it ranges from 0 to 1. As the size of the network 

increases, density typically decreases, and we can see that effect with the whole network and the 2+ 

network used here. The whole network has a density of effectively 0 while the 2+ network has a 

very slightly higher density at 0.01. Both densities, then, are very low, indicating loosely connected 

networks with sparse ties. Especially in the case of the whole network, this finding is not very 

surprising; respondents’ family members who were included in the whole network were unlikely to 

be tied to anyone but the ego naming them, and thus there are many nodes with only one tie. 

 For networks based on nomination data, Valente (2010) suggested a modified version of 

density that takes into account the size of the personal networks used to make the whole network. 

In other words, the density measure is adjusted for the number of alters each ego was asked to 

name. In this case, each ego named 20 individuals. Valente’s “effective density” measure, taking into 

account the 20 alters for each ego, resulted in a higher density for the whole network of 0.33, 

indicating that about one-third of the possible ties actually existed. While still relatively low, this 

density value is similar to those reported by Kreager, Rullison, and Moody (2011) and Snijders and 

Baerveldt (2003) in their studies of delinquent groups, and higher than the density of Natarajan’s 

(2006) drug distribution network. Because the 2+ network does not include all members of an ego’s 

personal network, with a size of 20, but instead only those alters that were named by two or more 

individuals, the effective density is not appropriate for that network. 

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Degree Centrality 

 Like density, degree—the number of direct ties between nodes—is another very standard 

network measure that is commonly reported in whole network studies and is used as a measure of 

network structure in predictive models. Table 25 reports the mean degree, or the average degree for 

each member of the network; this is calculated at the node level. While the raw mean degree is 

higher for the whole network, the normalized degree is adjusted for the size of the network, allowing 

that measure to be compared across networks. The normalized degree is higher for the 2+ network, 

indicating that members in that network have more direct ties to other nodes. This result is not 

surprising; everyone in that network was either an ego (with an automatic 20 ties) or was nominated 

at least twice. 

As described above, the centralization index is akin to a standard deviation of the centrality 

measures, and lower centralization values indicate a less centralized network while higher values 

indicate a more centralized network (Hanneman & Riddle 2005; Knoke & Yang 2008; Valente 

2010). Even though the degree centralization index for the 2+ network is more than twice as large as 

that of the whole network, the values for both networks are extremely low, indicating that neither is 

especially centralized. 

Betweenness Centrality 

 We calculated betweenness centrality using the Freeman node betweenness method. The 

normalized betweenness measure for the whole network is more than four times the value for the 

2+ network, but both values are very small. The standard deviations for both measures are high 

relative to the normalized mean values (0.59 and 1.13, respectively), suggesting that there is a lot of 

variation of betweenness scores among actors in both networks. The betweenness centralization 

values, however, are very low, indicating that the level of centralization in both networks overall are 

very low. These low levels of betweenness centralization for both networks suggest that there are 

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SOCIAL NETWORKS AND DELINQUENCY

116 

few members who have high betweenness scores and rather that most members have similar, low 

betweenness scores. This may be an indication that there are few people who act as “bridges” or 

brokers (e.g., of information, drugs) between groups within each network. 

These low values for betweenness centralization are surprising given the low degree values; 

those values suggested that a small number of network members were directly connected to a large 

number of nodes. Given the degree scores, we expected to find a small number of central players 

connected to many nodes and many nodes connected to very few other nodes—in other words, a 

loosely structured network with high betweenness centrality. The first two measures of centrality, 

however, suggest that both networks have similarly low levels of centralization, low levels of ties 

between members, and few individuals to act as brokers or “go-betweens” for different subsections 

of each network. 

Closeness Centrality 

 We next examined closeness centrality of both networks. Closeness is a measure of the 

distance between each node and every other node.31 The normalized closeness of both networks is 

very similar at around 35, even while the closeness centralization index is much higher for the whole 

network. This may be a function of the whole network being significantly larger than the 2+ 

network, so a node must go farther in order to reach every other node. On the whole, these 

measures indicate that the number of nominations received does not appear to have a great effect on 

the levels of closeness for each actor; those with at least two nominations have virtually the same 

levels of closeness to other actors as those with only a single nomination. 

Eigenvector Centrality 

 The 2+ network has a much higher mean eigenvector centrality than the whole network, 

even while the values themselves are relatively low. The standard deviations for the mean 

 

31 We used Valente and Foreman’s (1998) reverse distances measure to calculate closeness. 

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eigenvector centrality measures for both networks are high (2.75 and 6.72, respectively), indicating 

that there is a high amount of variability in individual eigenvector measures, and in turn, that 

centrality is not evenly distributed across network members but is instead high for some members 

and low for others. This finding is supported by the eigenvector centralization value for each 

network, which for both networks is just over 40 percent. The centralization value suggests a 

substantial amount of centralization in both networks. The individual- and network-level values 

together support the finding that the networks are both centralized. 

We also, however, examined the eigenvalues for each factor in each network. The 

eigenvalues of the first factor are extremely low, explaining just 2.5 percent and 0.6 percent of the 

variability in the whole and 2+ networks, respectively. Combined with the pattern of eigenvalues 

across each factor, we conclude that the eigenvector centrality measure should not be regarded as 

accurate for these data. Therefore, although the eigenvector centrality measure suggested the highest 

levels of centrality of the all four measures examined, we do not consider it to have produced useful 

findings in this situation. In addition, it produced measures of centrality that were much higher than 

what we found with the other three centrality measures, further calling into question the validity of 

the measure as applied to these specific networks. 

Similarity of the Centrality Measures 

We ran correlations of the four different centrality measures to assess how similar the 

measures were across individuals. Table 26 and Table 27 provide the results of the correlation 

analysis. The results indicate that the measures are not very similar; in other words, they are likely 

measuring very different structural characteristics of the networks. Degree centrality is the most 

closely related to every other measure (its correlation with betweenness is the highest of all 

comparisons for both networks), but even the degree correlation coefficients are relatively small. 

Thus, the use of multiple centrality measures is warranted, and these tables reiterate the importance 

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SOCIAL NETWORKS AND DELINQUENCY

118 

of selecting the correct centrality measure for the construct to be studied. While not shown, we also 

investigated the correlations of the centrality measures with the five delinquency measures of 

interest; the correlations, while significant, were all extremely low (i.e., less than 0.01). 

Table 26. Correlation between Different Measures of Centrality, Whole Network 

Degree Betweenness Closeness Eigenvector

Degree 1.00 0.51** 0.03 0.47** 

Betweenness 0.51** 1.00 0.13** 0.34** 

Closeness 0.03 0.13** 1.00 0.14** 

Eigenvector 0.47** 0.34** 0.14** 1.00 

Table 27. Correlation between Different Measures of Centrality, 2+ Network 

Degree Betweenness Closeness Eigenvector 

Degree 1.00** 0.63** 0.41** 0.80** 

Betweenness 0.63** 1.00** 0.36** 0.47** 

Closeness 0.41** 0.36** 1.00** 0.41** 

Eigenvector 0.80** 0.47** 0.41** 1.00** 

Table 28. Centrality of Networks Within and Outside the Neighborhood 

Network Measures Live in Neighb. Live out of Neighb.

Size (count of nodes) 879 1,642

Average number of nominations 1.33 1.08

Number of ties between nodes 4,682 4,246

Density

Density 0.0061 0.0016

Effective density1 - -

Degree centrality

Mean 5.33 2.59

Mean, normalized degree 0.61 0.16

Network centralization index (%) 3.27 1.12

Betweenness centrality2

Mean 874.22 6.07

Mean, normalized betweenness 0.23 0.00

Network centralization index (%) 8.79 0.03

Closeness centrality3

Mean 3.98 0.03

Mean, normalized closeness 26.56 0.45

Network centralization index (%) 366.28 11.71 1 Effective density is calculated using a network size that accounts for the number of people 

respondents were asked to name.

2 Freeman node betweenness score is reported.

3 Calculated using the Valente-Foreman average of reversed distances.

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Cohesion and Centrality of the Neighborhood Network 

 Table 28 presents the network structural measures for the neighborhood networks (inneighborhood vs. out of neighborhood). Despite having only about half of the nodes of the outnetwork, the in-network has more ties, and, accordingly has a higher level of density as well. As was 

the case with the whole networks presented above, however, the density measures overall are very 

low, indicating loose structures for both in- and out-networks. The in-network also demonstrates 

higher levels of centrality based on all three measures employed (eigenvector centrality was excluded 

based on the results of the whole network analysis above). The centrality of the in-network may 

contribute to the levels of delinquency observed; consequently the “live in neighborhood” variable 

will be included in the predictive models presented below, allowing a more sophisticated test of its 

relationship to delinquency. 

Identifying Central Actors in the Network 

The four main centrality measures examined—degree, betweenness, closeness, and 

eigenvector—produced pictures of the two networks under study as loosely connected nodes with 

few ties between them and few central players either playing a brokering or bridging role between 

different subgroups in the data. Despite the fact that the networks overall have low levels of 

centrality, we can still identify specific actors who are the most central in both networks based on 

the four centrality measures. For this portion of the analysis, central players in the whole network 

were those who were in the top 1 percent for at least one of the centrality measures; for the smaller, 

2+ network, we examined the top 5 percent of individuals for at least one of the centrality measures. 

Table 29 presents a comparison of summary statistics for the whole network for those central 

players as identified using the degree, betweenness, and closeness measures. 

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Table 29. Summary Demographic Characteristics and Centrality of Whole Network 

Members with High Centrality Values 

Central Actors1 Whole Network Degree Between. Closeness

Size (count of nodes) 2,521 29 26 28 

Avg. number of nominations 1.17 5.0 4.1 4.8 

Percent who are egos 5.83 93.1 74.1 46.4 

Average age 20 years 17 yrs. 17 yrs. 16 yrs. 

Percent named as parent 3.9 0.0 0.0 0.0 

Percent named as sibling 7.7 34.5 29.6 21.4 

Percent named as friend 69.1 96.6 88.9 96.4 

Percent in neigh. 34.9 96.6 85.2 78.6 

Percent Latino 69.4 89.7 81.5 92.9 

Percent born abroad 43.8 27.6 22.2 28.6 

Percent male 57.6 86.2 85.2 96.4 

Percent who sold drugs2 4.2 13.8 16.0 7.7 

Percent who carry a weapon2 5.6 20.7 14.8 10.7 

Percent in gang fights2 5.7 20.7 23.0 11.5 

Percent who use violence2 5.5 10.3 3.7 0.0 

Percent in gang 4.5 10.3 11.1 7.1 

Percent delinquent3 7.5 20.7 25.9 17.9 

Average of centrality measure - 1.6 4.7 0.14 1 For the whole network, we used the top 1 percent of the centrality values. 

2 Percent of those deemed very likely or somewhat likely to engage in behavior listed. 

3 Percent of those who were very likely to participate in at least one of 5 delinquent behaviors 

reported. 

More of the high degree individuals were egos, an intuitive finding because egos are 

automatically connected directly to 20 individuals (their alters); far fewer of those with high 

closeness values, however, were egos (46.4 percent and 68.4 percent for the whole and 2+ networks 

respectively). These findings indicate that the closeness centrality measure may be less biased given 

the structure of our data, where not everyone was a survey respondent and egos (who did complete 

the survey) by definition have at least 20 direct connections. Instead, closeness measures the 

“distance” between each node and every other node. Those who can reach more nodes over a 

shorter “distance” have lower closeness values (or are closer to each other). Therefore, while egos 

have more direct connections (higher degree) on average than non-egos, they aren’t necessarily more 

closely connected to others in the network, with easy access (or short distance) to many other nodes 

in the network. 

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The average age of the node groups varies as well, with the central players actually younger 

than the average whole network by three to four years. Not surprisingly, no central players were 

named as someone’s parent (in other words, none of the central players was an ego’s parent) but a 

much higher percent of central actors were named as siblings and friends than in the whole network. 

Central actors are also more likely to be male and Latino but not as likely to have been born abroad 

than less central members of the whole network; the central actors thus demonstrate higher levels of 

acculturation than noncentral actors. Central actors are also more likely to live in the neighborhood. 

Delinquency among the central players appeared much higher than for members of the 

whole network on average, with most measures of delinquency significantly higher for central actors 

than for the whole network. Central players who have high betweenness scores are the most likely to 

sell drugs, participate in gang fights, and be considered delinquent using our overall delinquency 

scale. Less central, or “between” actors are not as involved in delinquent behaviors. At the same 

time, those with high betweenness scores were also less likely than noncentral members to use 

violence to get what they need. This seeming contradiction in results will be explored in more detail 

in the discussion section. 

Table 30 provides the same summary statistics for the 2+ network. As with central actors in 

the whole network, those in the 2+ network are more likely to be egos, and are a few years younger 

than the average 2+ network member. Also similar to the whole network’s central players is the 

higher frequency with which central players were named as siblings and friends. In the 2+ network, 

central actors were also more likely to be Latino and male, and less likely to have been born abroad. 

These findings on acculturation support the hypothesis that individuals with higher levels of 

acculturation are more likely to be delinquent than those who have lower levels of acculturation. 

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Delinquency among the 2+ networks central actors was higher than among noncentral network 

members, but the difference with the average network member was not nearly as dramatic as it was 

with the whole network’s central actors. About one-fifth of central actors participate in some kind of 

delinquent activities. No central players, however, use violence; this is similar to the whole network 

findings where the use of violence was very low among central players.

It is also useful to consider the location, or position, of the central nodes within the whole 

network as determined via the three centrality measures employed here. Figure 3 presents the whole 

network with the central nodes highlighted—the central nodes for each measure are larger and 

shaded a light color. Those nodes that had high degree values and high betweenness values were 

similarly located; with a central cluster at the center of the whole network, and some central 

Table 30. Summary Demographic Characteristics and Centrality of 2+ Network Members 

with High Centrality Values 

Central Actors1 2+ Network 

High 

Degree

High 

Between.

High 

Closeness 

Size (count of nodes) 369 19 19 19 

Avg. number of nominations 2.1 6.3 5.2 6.2 

Percent who are egos 39.8 78.9 78.9 68.4 

Average age 19 years 16 yrs. 16 yrs. 16 yrs. 

Percent named as parent 4.6 0.0 0.0 0.0 

Percent named as sibling 20.3 21.1 31.6 21.1 

Percent named as friend 68.5 100.0 94.7 100.0 

Percent in neigh. 72.9 94.7 94.7 94.7 

Percent Latino 78.9 94.7 89.5 94.7 

Percent born abroad 37.1 26.3 36.8 26.3 

Percent male 61.8 100.0 89.5 100.0 

Percent who sold drugs2 6.8 10.5 11.1 11.1 

Percent who carry a weapon2 5.9 15.8 10.5 15.8 

Percent in gang fights2 8.9 21.1 16.7 16.7 

Percent who use violence2 7.6 0.0 0.0 0.0 

Percent in gang 9.5 5.3 10.5 5.3 

Percent delinquent3 13.8 21.1 21.1 21.1 

Average of centrality measure - 5.3 4.6 57.1 1 For the whole network, we used the top 1 percent of the centrality values. 

2 Percent of those deemed very likely or somewhat likely to engage in behavior listed. 

3 Percent of those who were very likely to participate in at least one of five delinquent behaviors 

reported. 

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SOCIAL NETWORKS AND DELINQUENCY

123 

members in less-central clusters. Those more distant nodes in the degree and betweenness 

sociograms, however, were not the same nodes, representing some differences between the two 

measures. Perhaps most interesting, however, is the level of clustering demonstrated among nodes 

with high closeness values. This isn’t surprising; the nodes with high levels of closeness have the 

shortest connections to all other nodes in the network, so it is intuitive that they are central in the 

sociogram. The interesting point is that all three measures indicate some type of centrality, yet nodes 

that are high on closeness are the ones that do appear to be the most central nodes.32

To explore the centrality and delinquency connection in more depth, we present a small 

subgroup of network members in Figure 4 as an example. This group of individuals was chosen 

simply by selecting one node with high values on all three centrality measures (we selected node 394) 

and all of the nodes to whom node 394 is connected. The sociogram includes all ties between all 

nodes in the diagram (not just those between 394 and other nodes). The most central actor, 394, is a 

Latino male, age 16, who lives in the neighborhood. He was not a survey respondent but was named 

by 11 egos. He is also not delinquent, but a number of the other central actors are, including actors 

who named node 394. In addition, node 394 appears to be playing a brokering role between two 

distinct clusters of individuals, as reflected by his high betweenness value. Several other highly 

central actors are included here by virtue of their connection to node 394. These individuals are not 

simply central within their own group of connections but are central in the larger network, and are 

connected to other central individuals. These individuals, then, form almost a central subnetwork 

within the larger whole network, and are not simply operating as lone central individuals. 

 

32 One note of caution: the sociogram is not drawn in a way that indicates anything about the true centrality of the nodes. The 

program used for visualization, NetDraw, uses an algorithm to determine the best layout of nodes for any given network, but nodes 

can be moved manually and the algorithm does not take into account any node attributes (e.g., what the node’s centrality measures 

are) but simply positions the nodes into a visually coherent layout. That said, the nodes that are at the center of the network are 

those that have the most ties to other nodes, and the sociograms shown in Figure 3 are the same but for the changing colors to 

indicate the central nodes for each measure. 

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Figure 3. Comparison of Central Nodes via Three Centrality Measures 

While this subgroup of network members was arbitrarily chosen to provide an example of node 

centrality, the ability to divide a large network, such as we are using here, into smaller, coherent 

groups has utility for more closely examining the different characteristics of the network, how 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

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SOCIAL NETWORKS AND DELINQUENCY

125 

Figure 4. Ego-Network of a Highly Central Individual (node 394) 

groups are related to one another, and how nodes are nested within the network. The next section 

describes this process and its results. 

Identifying Community Structure within the Network 

While there are several methods that can be used to divide networks into different, coherent 

groups based on a number of different criteria,33 we used Girvan and Newman’s methods as 

implemented in UCINet. Girvan and Newman (2002), in discussing the division of nodes in a 

network into different subgroups, described the subgroups that make up a whole network as 

“communities.” While not used in the same way as the word “communities” is traditionally defined, 

we use the word here as Girvan and Newman did. The technical term used for the subgroups 

identified by the algorithm is “partition.” We assessed the community structure of the whole 

network only. 

 

33 Nodes can be classified into groups based on attributes and ties, or simply ties. We use ties only to classify nodes. 

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126 

The Girvan-Newman (G-N) iterative algorithm identified 25 partitions in the whole 

network. The networks had range of sizes, from 20 members to 1,792 members. Several (17) of the 

partitions appeared to be simply ego networks (i.e., networks surrounding a survey respondent and 

including only or mostly alters she named) but they did display differences in centrality and in 

delinquency levels. For each partition, we calculated the same demographic and delinquency 

measures as presented for the overall whole network above (Table 23). We also examined the three 

main centrality measures34 identified above for each faction. Of the 25 factions, we selected six to 

present in the text that shed light on the research questions guiding this study. To simplify the 

discussion of the factions, we named each faction based on its characteristics. The demographic and 

delinquency measures for the six factions are presented in Table 31. 

The first faction is included simply to demonstrate the different sizes and forms of partitions 

that are created through the G-N algorithm. This one, termed Everyone Else appears to be a 

catchall of nodes that were not included in other partitions. This partition is by far the largest, with 

over 1,700 nodes. Its characteristics echo those of the whole network. The second partition is 

termed The [Latino] Outsiders. Members of this group have low levels of acculturation; all are 

Latino, and all were born abroad. The average age of the group is slightly higher than for the whole 

network at 23 years, and less than 20 percent live in the neighborhood. None of the members is 

delinquent, supporting the hypothesis that lower levels of acculturation are associated with lower 

levels of delinquency. 

 

34 We excluded the fourth measure, eigenvector centrality, based on the results from the whole network analysis. 

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127 

The partition termed No “Aging Out” Here has one of the highest average ages of all 

factions at 24 years old, but also demonstrates a moderate level of delinquency; its members do not 

appear to be “aging out” of delinquent behaviors as they get older. In addition, few are Latino and 

none were born abroad. The [Latino] Insiders is similar to The Outsiders except that its members 

are, on average, a few years younger, and most live inside the neighborhood instead of outside the 

neighborhood. Similarly, most members of the partition named A Thug in Charge are Latino (90 

percent) and male (80 percent). This faction, however, has the highest level of delinquency of all 

factions, with 40 percent of its members having participated in at least one delinquent behavior and 

Table 31. Demographic and Delinquency Summary Measures for Selected Girvan-Newman Partitions

Measures Subgroups

Subgroup Name Everyone 

Else

The [Latino] 

Outsiders

No “Aging 

Out” Here

The [Latino] 

Insiders

A Thug in 

Charge

A Tale of 

Two 

Brothers

Subgroup Description Catchall 

Low 

acculturation, 

older, outside 

neighborhood

NonLatino, 

older, 

delinquency

Low 

acculturation, 

younger, 

inside 

neighborhood

Latino, 

gang 

members, 

delinquency 

Brothers, 

younger not 

delinquent, 

older 

delinquent 

Individual (node)-level measures

Size (number of nodes) 1,792 21 21 21 20 40

Average age 19 23 24 18 23 21

Percent Latino 73.5 100.0 4.8 100.0 90.0 25.0

Percent named as friends 72.0 61.9 66.7 4.8 75.0 75.0

Percent named as family 

members 25.1 23.8 28.6 0.0 15.0 10.0

Percent born abroad 41.1 100.0 0.0 85.7 20.0 25.0

Percent male 58.5 66.7 66.7 100.0 80.0 72.5

Percent who live in 

neighborhood 35.9 19.0 52.4 95.2 55.0 35.0

Percent in gang 5.5 0.0 9.5 0.0 15.0 15.0

Percent in gang fights1 6.4 0.0 4.8 0.0 15.0 10.0

Percent who use 

violence1 6.3 0.0 0.0 0.0 5.0 5.0

Percent delinquent2 8.6 0.0 9.5 0.0 40.0 15.0

1 Percent of those deemed very likely or somewhat likely to be in gang fight/use violence.

2 Percent of those who were very likely to participate in at least one of five delinquent behaviors.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

128 

fully 15 percent in a gang. Finally, A Tale of Two Brothers is a partition made up of two adjoining 

networks, one surrounding an older brother and one surrounding a younger brother. These 

adjoining networks are connected by the brothers and by a handful of others who are associated 

with both brothers. This partition has a moderate amount of delinquency, most of which is found 

on the older brother’s side of the network. In addition, this network has a relatively small number of 

Latino members (only a quarter are Latino) and a small number of people who live in the 

neighborhood. 

Table 32. Cohesion and Centrality Measures for Selected Girvan-Newman Partitions 

Measures Subgroups 

Subgroup Name Everyone 

Else 

The 

[Latino] 

Outsiders 

No “Aging 

Out” Here 

The 

[Latino] 

Insiders 

A Thug in 

Charge 

A Tale of 

Two 

Brothers 

Subgroup Centrality 

Description 

Low 

connectedness 

Low density 

but highly 

centralized, 

star shaped 

network 

Average 

connectedness 

Extremely 

dense, no 

central 

players, flat 

structure 

Low density 

but highly 

centralized, 

star shaped 

network 

Two 

connected 

networks, 

several 

central 

actors 

Network-level measures 

Number of ties 11,994 40 206 420 38 164 

Size (number of nodes) 1,792 21 21 21 20 40 

Density 

Density 0.004 0.1 0.5 1.0 0.1 0.1 

Effective density1 0.3 0.1 0.5 1.0 0.1 0.2 

Degree centraliz. 

Mean 6.7 1.9 9.8 20.0 1.9 4.1 

Normalized mean 0.4 9.5 49.1 100.0 10.0 10.5 

Centralization index 2.9 95.0 53.0 0.0 94.7 52.3 

Betweenness centraliz.2

Mean 5,411.3 9.1 5.1 0.0 8.6 24.6 

Normalized mean 0.3 4.8 2.7 0.0 5.0 3.3 

Centralization index 18.1 100.0 15.4 0.0 100.0 71.0 

Closeness centraliz.3

Mean 12.0 1.1 1.5 1.0 1.1 1.7 

Normalized mean 66.4 54.8 74.2 100.0 55.0 58.0 

Centralization index 281.4 100.0 56.3 0.0 100.0 92.0 1 Effective density is calculated using a network size that accounts for the number of people respondents 

were asked to name. 

2 Freeman node betweenness score is reported. 

3 Calculated using the Valente-Foreman average of reversed distances. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

129 

While these statistics point to similarities across the networks in terms of the characteristics 

of people, an examination of each faction’s network structure reveals that structure and node 

characteristics do not always match up in the same way. Table 32 provides summary cohesion and 

centrality measures for each of the six selected partitions. The table also provides a brief description 

of the centrality of each partition. The Outsiders and A Thug in Charge provide classic examples of 

star-shaped networks, where one central actor (the ego) is connected to all other nodes, but the 

other nodes are only connected to the ego, and none to each other. This kind of network is highly 

centralized—there is only one central figure—but it is not very dense because, except for the central 

figure, nodes are connected only to one other node. Figure 5 provides the sociograms for these two 

networks, displaying the egos, delinquent nodes, and in- versus out-of-neighborhood status. These 

two networks, despite having very similar densities and centrality measures, display very different 

delinquency characteristics. A Thug in Charge, as the name suggests, is centered on one delinquent 

individual, a 19-year-old Latino male who is gang involved. No members of The Outsiders, on the 

other hand, are delinquent; the central player is also a 19-year-old Latino male, but one who is 

unlikely to use violence or to participate in delinquent behaviors. He also spends less time with 

others from the neighborhood than the individual at the center of “Thug.” The sociograms in Figure 

5 demonstrate the hierarchical nature of these networks, their one-node centrality, and the variations 

in delinquency across the network structures. 

The [Latino] Insiders partition, while similar demographically and delinquency-wise to The 

[Latino] Outsiders, reveals a very different network structure, one where nodes are highly connected 

and ties are very dense. In fact, this faction has the maximum density possible, meaning that all 

nodes are connected to all other nodes. The structure is thus very flat, without an obvious hierarchy 

or central figure. The mean degree is also the highest possible, while betweenness and closeness are 

extremely low; there are no nodes that are on more paths between nodes, or could reach a node 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

130 

over a shorter distance, than any other node. The sociogram for this partition is shown in Figure 6. 

The last partition highlighted here is A Tale of Two Brothers. This partition is larger than 

the others highlighted because it contains two connected ego networks. The partition has a 

moderate level of overall density, but because it has a few very central actors (each brother and a 

handful of others connected to both brothers), the partition’s centrality measures reveal a 

hierarchical network structure. The centrality measures of this partition are more similar to those of 

the star-networks than those of the dense, flat network. In fact, the sociogram for this partition, in 

Figure 7, reveals that one brother’s network is dense and well-connected while the other brother’s 

network is akin to the star-shaped network. The faction thus displays multiple types of structures 

within one network. In addition, the sociogram reveals that the older brother, with higher levels of 

delinquency, also spends more time with individuals from within the neighborhood while the 

younger brother is more outwardly focused in his social network. Finally, the sociogram identifies 

the four central nodes connected to both brothers and the two brothers themselves (the two egos in 

the sociogram) as central actors. 

 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

131 

Figure 5. Star-Shaped Networks 

Figure 6. Dense Network with Low Centrality 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

132 

Figure 7. A Two-Ego Network 

PREDICTIVE MODELS, WITH NETWORK MEASURES 

Ego-Level Regression Models with Sociocentric Network Measures 

And last, we turn to our third research question that asks whether the addition of whole 

network positional measures are important predictors of delinquency and related outcomes. Thus, 

we ran the same regression models (both logistic regression and negative binomial regression 

models) as specified in the ego-level findings sections but included two additional network structure 

variables: the respondent’s betweenness centrality to the whole network (which assesses the 

respondent’s “brokerage” across all egos and alters in the network) and whether the respondent is an 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

133 

“isolate” in the whole network, which means that the respondent is not named by anyone else in the 

network and his/her alters are not egos (survey respondents). 

Tables 33(a–f) provide the results of the binary logistic models for each of the individual 

delinquency measures and Table 34 provides the negative binomial results for the overall 

delinquency scale measure.35 While the Cox & Snell R2 figures are relatively low, this is typical for 

binary logistic regression models (Hosmer & Lemeshow 2000). The figures are useful, however, for 

comparison purposes with the previous models that did not include network measures, and such a 

comparison reveals no change in the Cox & Snell R2 figures. Therefore, the network structure 

variables do not appear to be adding explanatory power to the overall models. 

At the individual variable level, betweenness centrality was a significant predictor of only one 

dependent variable—selling drugs. For every additional unit of betweenness centrality to the entire 

network, the respondent’s odds of selling drugs increased one time. However, in the bivariate 

analyses, we saw correlations that were not reflected in the binary logistic regressions. The 

respondent’s betweenness centrality to the whole network was positively correlated to the 

respondent’s overall delinquency, carrying a weapon, and being in a gang fight (p < 0.10), but was 

not significant in either regression model. The variables also are not significant predictors in the 

models for recent delinquency and serious delinquency (not shown). We also tested the use of the 

closeness centrality measure and the degree centrality measure, and found neither that differences in 

the overall explanatory power of the model from the models without network measures, nor did we 

find either measure to be significant in for any of the delinquency measures. 

 

 

35 As mentioned earlier, the Cox & Snell R2 figures are provided but should be interpreted with caution. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

134 

Tables 33a–f. Binary Logistic Regression Results Predicting Delinquency Measures, Including 

Network Structure Variables 

Table 33a. Binary Logistic Regression Results Predicting Overall Delinquency, incl. Network Measures. 

 Overall delinquency 

Coeff. S.E.

Exp(B) 

(OR)2 p RR3 Δp4

Δp 

(%)5

Constant 2.77 2.68 15.94 0.301 

Alter variables 

Proportion delinquent alters1 7.72** 2.70 1.47 0.00 1.21 0.10 21.30%

Proportion alters live in same neighb.1 1.28 0.99 1.07 0.20 

Proportion go to for advice (not delinq.)1 0.06 0.90 1.00 0.95 

Personal network structure variables 

Number of components -0.83* 0.42 0.43 0.05 0.66 0.21 -34.06%

Isolate 0.79 0.68 2.21 0.24 

Betweenness centrality 0.00 0.00 1.00 0.17 

Acculturation 

Separation from U.S. culture -0.39** 0.14 0.68 0.01 0.82 0.10 -17.51%

Controls 

Age 0.00 0.10 1.00 0.98 

Male 2.20*** 0.62 8.98 0.00 3.00 0.50 199.67%

Latino 1.16† 0.70 3.20 0.10 1.79 0.28 78.87%

Proportion of peers in network1 -0.24 1.07 0.99 0.82 

Family member completed HS -0.48 0.52 0.62 0.36 

Parent-school encouragement -0.17 0.88 0.84 0.84 

Family cohesion -0.09* 0.04 0.91 0.01 0.96 0.02 -4.50%

No. years at address 0.07† 0.04 1.07 0.06 1.04 0.02 3.56%

Goodness-of-fit measures 

Cox & Snell R Square 0.34

Cox & Snell R Square (model with peer vars) 0.34

† p < 0.10; * p < 0.05; **p < 0.01; *** p < 0.001 1 Proportion variables range from 0.00 to 1.00.

2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of 

proportion of alters. Each ego has 20 alters; 1 alter out of 20 is 5 percent. 3 RR is calculated by dividing the high probability by the low probability, given the OR for the variable and an initial 

probability of 0.05 (the center of the possible range for the dependent variable). 4 The change in p is the difference between the high and low probabilities given the OR for the variable and a 

hypothetical probability of 0.5. 

5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable 

and a hypothetical probability of 0.5. 

 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

135 

Table 33b. Binary Logistic Regression Results Predicting Carrying a Weapon, incl. Network Measures 

 Carried a Weapon 

Coeff. S.E. 

Exp(B) 

(OR)2 p RR3 Δp4

Δp 

(%)5

Constant -2.30 3.07 0.10 0.45 

Alter variables 

Proportion delinquent alters1 7.64** 2.28 1.47 0.00 1.21 0.21 21.06%

Proportion alters live in same neighb.1 1.52 1.17 1.08 0.19 

Proportion go to for advice (not delinq.)1 0.52 1.04 1.03 0.61 

Personal network structure variables 

Number of components -0.77† 0.46 0.46 0.09 0.68 0.32 -32.09%

Isolate 1.22 0.76 3.38 0.11 

Betweenness centrality 0.00 0.00 1.00 0.86 

Acculturation 

Separation from U.S. culture -0.09 0.16 0.91 0.55 

Controls 

Age -0.01 0.11 0.99 0.95 

Male 3.38** 1.13 29.25 0.00 5.41 4.41 440.87%

Latino -0.19 0.76 0.82 0.80 

Proportion of peers in network1 -0.68 1.20 0.97 0.57 

Family member completed HS -0.28 0.63 0.76 0.66 

Parent-school encouragement 0.49 1.17 1.64 0.67 

Family cohesion -0.05 0.04 0.95 0.20 

No. years at address 0.06 0.04 1.07 0.12 

Goodness-of-fit measures 

Cox & Snell R Square 0.30 

Cox & Snell R Square (model with peer vars) 0.27 

† p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 

1 Proportion variables range from 0.00 to 1.00. 

2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of 

proportion of alters. Each ego has 20 alters; 1 alter out of 20 is 5 percent. 3 RR is calculated by dividing the high probability by the low probability, given the OR for the variable and an 

initial probability of 0.05 (the center of the possible range for the dependent variable). 4 The change in p is the difference between the high and low probabilities given the OR for the variable and a 

hypothetical probability of 0.5. 

5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable 

and a hypothetical probability of 0.5. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

136 

Table 33c. Binary Logistic Regression Results Predicting Drug Selling, incl. Network Measures 

 Sold Drugs 

Coeff. S.E.

Exp(B) 

(OR)2 p RR3 Δp4

Δp 

(%)5

Constant -15.14† 8.00 0.00 0.06 

Alter variables 

Proportion delinquent alters1 16.30** 5.51 2.26 0.00 1.50 0.20 50.30%

Proportion alters live in same neighb.1 -4.25 3.01 0.81 0.16 

Proportion go to for advice (not delinq.)1 -0.50 2.62 0.98 0.85 

Personal network structure variables 

Number of components 0.34 0.82 1.40 0.68 

Isolate -0.33 1.48 0.72 0.82 

Betweenness centrality 0.00† 0.00 1.00 0.07 1.00 0.00 0.00% 

Acculturation 

Separation from U.S. culture -0.34 0.44 0.71 0.45 

Controls 

Age 0.56 0.35 1.75 0.11 

Male 4.95† 2.59 141.60 0.06 11.90 0.84 1089.95%

Latino -1.04 1.78 0.35 0.56 

Proportion of peers in network1 -1.12 2.66 0.95 0.67 

Family member completed HS -2.88† 1.66 0.06 0.08 0.24 0.62 -76.30%

Parent-school encouragement -4.91* 2.06 0.01 0.02 0.09 0.84 -91.42%

Family cohesion 0.09 0.09 1.09 0.29 

No. years at address 0.16† 0.08 1.17 0.06 1.08 0.04 8.06% 

Goodness-of-fit measures 

Cox & Snell R Square 0.29 

Cox & Snell R Square (model with peer vars) 0.27 

† p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001 

1 Proportion variables range from 0.00 to 1.00. 

2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of

proportion of alters. Each ego has 20 alters; 1 alter out of 20 is 5 percent. 3 RR is calculated by dividing the high probability by the low probability, given the OR for the variable and an 

initial probability of 0.05 (the center of the possible range for the dependent variable). 4 The change in p is the difference between the high and low probabilities given the OR for the variable and a 

hypothetical probability of 0.5. 

5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable 

and a hypothetical probability of 0.5. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

137 

Table 33d. Binary Logistic Regression Results Predicting Attacking Someone, incl. Network Measures 

 Attacked Someone with Intent 

 Coeff. S.E. Exp(B) 

(OR)2 p RR3 Δp4 Δp 

(%)5

Constant -3.46 4.27 0.03 0.42 

Alter variables 

Proportion delinquent alters1 5.13 ** 2.27 1.29 0.02 1.14 0.06 13.68%

Proportion alters live in same neighb.1 -0.22 1.61 0.99 0.89 

Proportion go to for advice (not delinq.)1 -3.05 † 1.58 0.86 0.05 0.93 0.04 -7.33%

Personal network structure variables 

Number of components -0.24 0.60 0.79 0.70 

Isolate -0.74 1.05 0.48 0.48 

Betweenness centrality 0.00 0.00 1.00 0.15 

Acculturation 

Separation from U.S. culture -0.13 0.22 0.88 0.54 

Controls 

Age 0.07 0.17 1.07 0.69 

Male 3.12 † 1.60 22.65 0.05 4.76 0.65 375.88%

Latino 0.53 1.04 1.70 0.61 

Proportion of peers in network1 -1.19 1.55 0.94 0.44 

Family member completed HS -0.42 0.86 0.66 0.63 

Parent-school encouragement -0.33 1.22 0.72 0.79 

Family cohesion 0.01 0.05 1.01 0.82 

No. years at address -0.03 0.06 0.97 0.62 

Goodness-of-fit measures 

Cox & Snell R Square 0.18 

Cox & Snell R Square (model with peer 

 variables) 0.15 

† p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001 

1 Proportion variables range from 0.00 to 1.00. 

2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead 

of proportion of alters. Each ego has 20 alters; 1 alter out of 20 is 5 percent. 3 RR is calculated by dividing the high probability by the low probability, given the OR for the variable and an 

initial probability of 0.05 (the center of the possible range for the dependent variable). 4 The change in p is the difference between the high and low probabilities given the OR for the variable and a 

hypothetical probability of 0.5. 

5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable 

and a hypothetical probability of 0.5. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

138 

Table 33e. Binary Logistic Regression Results Predicting Being in a Gang Fight, incl. Network 

Measures

 Been in a Gang Fight 

Coeff. S.E.

Exp(B) 

(OR)2 p RR3 Δp4

Δp 

(%)5

Constant 3.30 3.19 27.11 0.30 

Alter variables 

Proportion of alters in a gang fight1 18.3** 6.05 2.50 0.00 1.58 0.22 58.01%

Proportion alters live in same neighb.1 -0.3 1.23 0.99 0.81 

Proportion go to for advice (not delinq.)1 0.57 1.16 1.03 0.62 

Personal network structure variables 

Number of components -0.43 0.49 0.65 0.38 

Isolate -1.13 1.16 0.32 0.33 

Betweenness centrality 0.00 0.00 1.00 0.92 

Acculturation 

Separation from U.S. culture -0.28 0.19 0.76 0.14 

Controls 

Age -0.15 0.13 0.86 0.25 

Male 1.59* 0.80 4.90 0.05 2.21 0.38 121.44%

Latino 0.77 0.92 2.16 0.40 

Proportion of peers in network1 0.66 1.46 1.03 0.65 

Family member completed HS -0.82 0.65 0.44 0.21 

Parent-school encouragement -2.20* 1.00 0.11 0.03 0.33 0.50 -66.71%

Family cohesion -0.04 0.04 0.96 0.35 

No. years at address 0.09* 0.04 1.09 0.04 1.05 0.02 4.60%

Goodness-of-fit measures 

Cox & Snell R Square 0.24 

Cox & Snell R Square (model with peer vars) 0.24 

† p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001 

1 Proportion variables range from 0.00 to 1.00. 

2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of 

proportion of alters. Each ego has 20 alters; 1 alter out of 20 is 5 percent. 3 RR is calculated by dividing the high probability by the low probability, given the OR for the variable and an 

initial probability of 0.05 (the center of the possible range for the dependent variable). 4 The change in p is the difference between the high and low probabilities given the OR for the variable and a 

hypothetical probability of 0.5. 

5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable 

and a hypothetical probability of 0.5. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

139 

Table 33f. Binary Logistic Regression Results Predicting Gang Membership, incl. Network Measures 

 Gang Member 

Coeff. S.E.

Exp(B) 

(OR)2 p RR3 Δp4

Δp 

(%)5

Constant 0.25 4.43 1.28 0.96 

Alter variables 

Proportion of alters in a gang1 21.19 ** 6.11 2.88 0.00 1.70 0.26 69.85%

Proportion alters live in same neighb.1 -0.42 1.70 0.98 0.81 

Proportion go to for advice (not delinq.)1 2.63 1.80 1.14 0.14 

Personal network structure variables 

Number of components -0.96 0.72 0.38 0.18 

Isolate 1.49 1.20 4.41 0.22 

Betweenness centrality 0.00 0.00 1.00 0.39 

Acculturation 

Separation from U.S. culture -0.35 0.25 0.71 0.17 

Controls 

Age -0.10 0.18 0.91 0.60 

Male -0.02 0.91 0.98 0.98 

Latino 2.31 1.62 10.10 0.15 

Proportion of peers in network1 3.97 2.74 1.22 0.15 

Family member completed HS -1.13 1.01 0.32 0.27 

Parent-school encouragement -2.38 * 1.17 0.09 0.04 0.30 0.53 -69.55%

Family cohesion -0.09 0.06 0.91 0.12 

No. years at address 0.13 * 0.06 1.13 0.03 1.06 0.03 6.45%

Goodness-of-fit measures 

Cox & Snell R Square 0.25 

Cox & Snell R Square (model with peer vars) 0.22 

† p <0.10; *p < 0.05; **p < 0.01; ***p < 0.001 

1Proportion variables range from 0.00 to 1.00. 

2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of 

proportion of alters. Each ego has 20 alters; 1 alter out of 20 is 5 percent. 3 RR is calculated by dividing the high probability by the low probability, given the OR for the variable and an 

initial probability of 0.05 (the center of the possible range for the dependent variable). 4 The change in p is the difference between the high and low probabilities given the OR for the variable and a 

hypothetical probability of 0.5. 

5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable 

and a hypothetical probability of 0.5. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

140 

Table 34. Negative Binomial Regression Results Predicting Overall Delinquency

 Model 1: Peer Variables Model 2: Alter Variables 

Coeff. S.E. Exp(B) p Coeff. S.E. Exp(B) p

Intercept 2.02 † 1.16 7.55 0.08 1.89 1.21 6.61 0.12

Peer variables 

Proportion delinquent friends1 3.21 *** 0.87 24.69 0.00 

Proportion friends in same neighb. 1 -0.22 0.41 0.80 0.58 

Proportion go to for advice (not delinq.)1 -0.12 0.43 0.89 0.78 

Alter variables 

Proportion delinquent alters1 3.27 *** 0.72 26.33 0.00

Proportion alters in same neighb.1 0.50 0.46 1.65 0.28

Proportion go to for advice (not delinq.)1 -0.05 0.48 0.95 0.91

Network structure variables 

No. of components -0.32 * 0.15 0.73 0.04 -0.32 * 0.15 0.73 0.04

Isolate 0.00 0.34 1.00 1.00 0.05 0.35 1.05 0.89

Between. centrality 0.00 0.00 1.00 0.87 0.00 0.00 1.00 0.46

Acculturation 

Separation from U.S. culture -0.20 ** 0.06 0.82 0.00 -0.20 ** 0.06 0.82 0.00

Controls 

Age -0.01 0.04 0.99 0.77 0.01 0.04 1.01 0.89

Male 1.20 *** 0.27 3.32 0.00 1.31 *** 0.28 3.71 0.00

Latino 0.47 0.31 1.60 0.13 0.46 0.30 1.58 0.13

Proportion of peers in network1 0.59 0.55 1.81 0.28 0.99 0.56 2.69 0.08

Family member completed HS -0.29 0.27 0.75 0.29 -0.37 0.27 0.69 0.17

Parent-school encouragement -0.66 † 0.40 0.52 0.10 -0.41 0.40 0.67 0.31

Family cohesion -0.04 0.02 0.97 0.02 -0.04 * 0.02 0.97 0.02

No. years at address 0.05 ** 0.02 1.05 0.00 0.05 ** 0.02 1.05 0.00

Goodness of fit 

Log likelihood -227.815 -225.915 

Deviance value/df 1.02 0.991 

Pearson chi-square value/df 0.895 0.851 

Likelihood ratio chi-square 59.25 63.051 

† p <0.10; *p < 0.05; **p < 0.01; ***p < 0.001

1Proportion variables range from 0.00 to 1.00. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Node-Level Regression Models with Sociocentric Network Measures 

In order to more fully answer our third research question on the effects of network 

structural measures on delinquency, we developed two sets of binary logistic models at the network 

level: one predicting the binary delinquency outcome for members of the whole network, and one 

for members of the 2+ network. While these models benefited from larger sample sizes than the ego 

models (2,521 and 369 network members compared to 147 egos), they were restricted by the 

amount of data that were collected on alters (17 items about each alter). Only a handful of the same 

predictors could be used in the whole network model, but we did include network centrality 

measures to examine more broadly the effects of a person’s position in the network on whether 

he/she was delinquent. We created three models of serious delinquency (binary), each including a 

different centrality measure, for each of the two networks. We did not include all measures in one 

model because of multicollinearity issues among the centrality measures. 

The results of the models are presented in Table 35 to 37. The tables are presented in the 

same format as the binary logistic models developed at the ego level: the results include the risk 

ratios for all significant predictors and the hypothetical change in probability assuming a 50 percent 

initial delinquency level. All probability calculations, including change in probability, are based on 

this initial 50 percent level. While the goodness of fit for all six models is extremely low, this is 

typical for binary logistic regression models, so the significance of certain predictors therefore still 

warrants mention (Hosmer & Lemeshow 2000). Because the Cox and Snell R2

s were very low, we 

also examined the Hosmer-Lemeshow test. That test indicated that the betweenness model was a 

good fit (the Hosmer-Lemeshow test was not rejected) but that the models including closeness and 

degree did not have good fits. This is interesting because the betweenness measure was not 

significant while the closeness and degree terms were. This may be an indication that, as we found in 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

142 

the ego-level models, the addition of network structural variables does not improve explanatory 

power of our models. 

In all of the whole network models, being an ego was significant, but this was not the case in 

the 2+ network models. Being an ego in the whole network is associated with a higher probability of 

delinquency. Age, however, follows the opposite pattern: it is significantly negative, and only in the 

2+ models. The effect of age on delinquency is also smaller than the effect of being an ego. The 

“live in neighborhood” term is significant across all six models, and increases the probability of 

delinquency for members of both networks, although the effect is much larger in the 2+ network: 

individuals residing in the neighborhood are over two times more likely to be delinquent than those 

not in the neighborhood. 

Of the centrality measures, only two are significant, and in the whole network only; no 

centrality measures are significant in the 2+ network, and betweenness centrality is not significant 

for either network. At the whole network level, closeness centrality is highly significant and has a 

large positive effect, increasing one’s probability of delinquency. Given that we were unable to 

control for other measures (such as we used in the ego-level models) and that the models have 

extremely low goodness of fit levels, though, we are hesitant to give the large effect size much 

weight. Degree centrality is significantly negative in the whole network model, indicating that 

individuals who are more directly connected to many others have a lower probability of being 

delinquent. This supports the hypothesis that individuals who are connected to many groups may 

find their behavior more restricted, and may therefore have a lower probability of being delinquent. 

While these models are certainly restricted in terms of the availability of measures pertaining to 

network members, they do shed light on the effects of centrality measures. The very different results 

obtained for the three different centrality measures, while all else was held the same, is further 

support for the notion that centrality cannot be treated as one single construct, and that different 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

143 

measures of centrality are appropriate given different research hypotheses. The significance of the 

live in neighborhood term also lends weight to our selection of neighborhood, and suggests that 

there is something about having stronger ties within the neighborhood that is associated with higher 

levels of delinquency. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
144 

Table 35. Binary Logistic Regression Results Predicting Overall Delinquency at Network Level, Incl. Betweenness C

Whole Network 2+ Network 

Coeff. S.E.

Exp(B) 

(OR)2 p RR3 Δp4

Δp 

(%)5 Coeff. S.E.

Exp(B) 

(OR)2 p RR3 

Constant -3.47*** 0.39 0.03 0.00 -2.24* 1.12 0.11 0.05 Network structure variables

Betweenness centrality 0.13 0.09 1.14 0.17 -0.04 0.12 0.96 0.75Closeness centrality - - - - - - - - Degree centrality - - - - - - - -

Controls 

Ego 0.53† 0.28 1.70 0.06 1.30 0.13 30.28% 0.17 0.34 1.18 0.63Age 0.01 0.01 1.01 0.58 -0.10* 0.05 0.91 0.04 0.9Male 0.79*** 0.19 2.20 0.00 1.48 0.19 48.44% 0.81* 0.38 2.25 0.03 1.5Latino -0.16 0.20 0.85 0.41 -0.31 0.38 0.74 0.42Born in U.S. 0.18 0.20 1.20 0.36 0.52 0.36 1.69 0.15Live in neighborhood 0.69*** 0.19 2.00 0.00 1.41 0.17 41.41% 1.72** 0.65 5.59 0.01 2.3

Goodness-of-fit measure 

Cox & Snell R Square 0.03 0.09 

† p < 0.10; * p < 0.05; ** p <0.01; *** p <0.001 1 Proportion variables range from 0.00 to 1.00.2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of proportion of alters. Each 

1 alter out of 20 is 5 percent. 

3 RR is calculated by taking the square root of the OR.4 The change in p is the difference between the hypothetical high and low probabilities, calculated using the RR and a hypothetical initial pr

delinquency of 0.5. 

5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable and a hypothetical initial p

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

145 

Table 36. Binary Logistic Regression Results Predicting Overall Delinquency at Network Level, Incl. Closeness Cent

Whole Network 2+ Network 

Coeff. S.E. Exp(B) (OR)2 p RR3 Δp4 Δp (%)5 Coeff. S.E. Exp(B) (OR)2 p RR3 

Constant -4.49*** 0.48 0.01 0.00 -2.96* 1.20 0.05 0.01Network structure variables 

Betweenness centrality - - - - - - - -Closeness centrality 8.96*** 2.45 7,820.32 0.00 88.41 0.98 8741.13% 0.01 0.01 1.01 0.10Degree centrality - - - - - - - -

Controls 

Ego 0.71** 0.25 2.04 0.01 1.43 0.18 42.69% 0.26 0.34 1.30 0.45Age 0.01 0.01 1.01 0.33 -0.08† 0.05 0.93 0.09 0.96 Male 0.79*** 0.19 2.21 0.00 1.49 0.20 48.59% 0.73† 0.37 2.08 0.05 1.44 Latino -0.26 0.20 0.77 0.20 -0.46 0.39 0.63 0.24Born in U.S. 0.13 0.20 1.14 0.52 0.47 0.37 1.61 0.19Live in neighborhood 0.70*** 0.19 2.01 0.00 1.42 0.17 41.62% 1.71** 0.65 5.51 0.01 2.35 

Goodness-of-fit measure 

Cox & Snell R Square 0.04 0.09 

† p < 0.10; * p < 0.05; ** p < 0.01; *** p <0.001 1 Proportion variables range from 0.00 to 1.00.2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of proportion of alters. Each 

1 alter out of 20 is 5 percent. 

3 RR is calculated by taking the square root of the OR.4 The change in p is the difference between the hypothetical high and low probabilities, calculated using the RR and a hypothetical initial pr

delinquency of 0.5. 

5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable and a hypothetical initial p

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

146 

Table 37. Binary Logistic Regression Results Predicting Overall Delinquency at Network Level, Incl. Degree Central

Whole Network 2+ Network 

Coeff. S.E.

Exp(B) 

(OR)2 p RR3 Δp4

Δp 

(%)5 Coeff. S.E.

Exp(B) 

(OR)2 p RR3 

Constant -3.39*** 0.40 0.03 0.00 -2.23* 1.13 0.11 0.05Network structure variables 

Betweenness centrality - - - - - - --Closeness centrality - - - - - - --Degree centrality -0.82* 0.37 0.44 0.03 0.66 0.20 -33.60% -0.02 0.11 0.98 0.84

Controls 

Ego 1.25*** 0.36 3.50 0.00 1.87 0.30 87.01% 0.16 0.34 1.17 0.65Age 0.01 0.01 1.01 0.54 -0.10* 0.05 0.91 0.04 0.95 Male 0.84*** 0.19 2.32 0.00 1.52 0.21 52.35% 0.80* 0.38 2.23 0.03 1.49 Latino -0.12 0.20 0.89 0.56 -0.30 0.39 0.74 0.44Born in U.S. 0.21 0.20 1.23 0.30 0.52 0.36 1.69 0.15Live in neighborhood 0.76*** 0.19 2.14 0.00 1.46 0.19 46.37% 1.72** 0.65 5.58 0.01 2.36 

Goodness-of-fit measure 

Cox & Snell R Square 0.03 0.09 

† p < 0.10; * p <0.05; ** p <0.01; *** p <0.001 1 Proportion variables range from 0.00 to 1.00.2 The OR, or Exp(B) for the proportion variables is multiplied by 0.05 to put it in scale of count of alters, instead of proportion of alters. Each 

1 alter out of 20 is 5 percent. 

3 RR is calculated by taking the square root of the OR.4 The change in p is the difference between the hypothetical high and low probabilities, calculated using the RR and a hypothetical initial pr

delinquency of 0.5. 

5 The change in p (%) is the percent change between the high and low probabilities given the OR for the variable and a hypothetical initial p

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
CHAPTER 4 

SUMMARY AND DISCUSSION 

This study set out to examine the importance of social networks in understanding delinquent 

and criminal behavior of youth living in a high-risk neighborhood. We used innovative network 

methods to overlap personal networks—creating a “whole” network— and to understand how the 

structure and composition of subgroups, as well as an individual’s position within the neighborhood 

network, might be important factors shaping delinquency and criminal behavior. In addition to 

conducting egocentric analyses aimed at identifying key predictors of delinquency and criminal 

behavior, we used the whole network to explore the features of the neighborhood network via 

sociocentric measures and techniques. Combining egocentric and sociocentric analyses within one 

study provided greater insight into the potential importance of neighborhoods as the frame for 

learning and socialization with regard to delinquency, or as frameworks for prevention and 

intervention efforts. Below we briefly review and discuss the findings following the research 

questions laid out in the introduction of this report. 

DELINQUENCY AT THE EGO-LEVEL 

Research Question 1: Are network/structural variables important predictors of delinquency and gang membership 

(beyond the traditional set of risk factors) across ego networks? 

To answer this research question, we relied on a risk factor framework to model the 

likelihood that a youth/young adult would be involved in a variety of criminal behaviors, given the 

network characteristics and the youth’s individual position and connectedness within the network, 

and controlling for important risk and protective factors identified in the criminological literature. 

We attempted to predict the extent of involvement by modeling delinquency both as a binary 

measure (delinquent/not delinquent) and as a scale (number of delinquent acts). The network 

measures are those measures related to personal networks that characterize or quantify relations 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

148 

between a respondent and his/her 20 nominated alters. The sample for these ego-level models 

included only those 147 individuals who responded to the survey. 

The Influence of Delinquent Alters 

Not surprisingly, for ego networks, the proportion of relations (alters) who are delinquent is 

highly significant across all binary outcomes where it is included in the model: serious delinquency, 

carried a weapon, sold drugs, and attacked someone. For instance, for every additional delinquent 

alter in an ego’s network, the ego’s probability of selling drugs increases by 38 percent. We find 

similar results when we examine the proportion of alters in a gang fight or in a gang, respectively. 

Individuals who named an individual in a gang fight are 1.6 times more likely to be in a gang fight 

themselves. Thus, having delinquent/criminal relationships appears to be a key factor in shaping an 

individual’s likelihood of delinquency and gang membership. 

The Youth Network, Beyond Friends 

Because the current study asked respondents to nominate people—both other youth and 

adults—who were influential to them, this study also had the opportunity to assess whether 

relationships beyond those with peers are associated with delinquency and gangs. Instead of 

examining nested models, in order to compare models, we examined effect sizes and model fit 

between sets of negative binomial models. Two sets of models were created for each dependent 

variable: one set that included the proportion of peer delinquency and one set that included alter

delinquency (see Table 20, Table 21, and Table 22). The results from these comparison models 

showed that although both models fit the data well, the “all alters” models fit significantly better and 

the effect of the variable measuring the proportion of delinquent/criminal alters on delinquency was 

slightly larger in each model than the effect of the peer delinquency term. This finding is important, 

and one that we hope to expand upon with future analyses and larger sample sizes, because the 

overwhelming majority of network-based studies on delinquency focus only on peer networks. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

149 

Within high-risk areas rife with intergenerational gangs, assessing the importance of all 

relationships to delinquency and gang membership becomes particularly salient. In our study, over 

40 percent of respondents named at least one parent as an alter, 61 percent named at least one aunt 

or uncle, and one-third of the respondents named at least two siblings as alters. Larger sample sizes 

would enable future research to examine whether characteristics of these family relations (e.g., 

whether the respondent would go to the family member for advice, or whether the family member is 

delinquent or criminal) serve as protective or risk factors over and above relationships with nonrelative peers. These avenues have not been explored—mostly because studies of this kind (i.e., 

widening the network to all types of relations) have not been conducted before. Typically, 

researchers would use networks to examine peer relationships but then examine parental 

relationships and institutional attachments (as suggested by bonding theory) through the typical 

survey items based on self-report, not as attributes of network relations. 

Future research could more appropriately test the tenets of bonding theory related to familial 

relations via network-based surveys and resultant measures. The findings from our study that nonpeer criminal influences may be just as important or influential with regard to delinquency as peer 

influences will hopefully advance research in this field by widening our focus to go beyond simply 

studying peers within a network framework. There are unlimited possibilities for research in this 

vein. A social network framework could be used to examine how adult role models facilitate 

maintenance of or help delinquent youth desist from crime or leave gangs. Although this study did 

not explicitly ask respondents to include their parents or caregivers future research could set the 

network frame to cast a wider net over specifically pro-social relations. 

Replication of this research with larger sample sizes would also provide the opportunity to 

explore the influence of teachers on the delinquent behaviors of those who name teachers as 

alters—almost 10 percent of respondents named a teacher in our study. Research has suggested that 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

150 

teachers acting as positive role models within and outside the classroom setting can reduce the 

delinquent behaviors of students (Gottfredson 1987). 

Neighborhood Ties 

Because research has shown that delinquency and gang membership are often rooted in the 

neighborhood, we also hypothesized that the likelihood that egos are involved in delinquent 

behavior would increase with an increase in relationships from within the neighborhood (regardless 

of whether relations were delinquent/criminal themselves). “Proportion of alters who live in the 

neighborhood” was included in all delinquency models but was not significant in any of the ego 

models. The results, therefore, did not support this hypothesis. It is likely that sample size could be 

an issue; fortunately, we also examined this hypothesis using the whole overlapping network and 

found that in-neighborhood relations were significantly associated with delinquency and criminal 

behavior. This finding will be discussed in more detail in the appropriate section. 

The Strength of Ties 

We also assessed the strength of prosocial ties by including a variable that measured the 

proportion of one’s network that comprised individuals to whom the respondent would go for 

advice and who were not delinquent/criminal. This measure was found to be significant 

(approaching significance at p = 0.05) in the model predicting “attack someone with intent to harm.” 

In this model, every additional person in a network to whom the respondent would go for advice 

and who is not delinquent/criminal reduces the probability of attacking someone with a weapon by 

7 percent. It is possible that pro-social advice networks are important with regard to a violent 

outcome (as the other outcomes capture different, less violent behavior) in that having “influential” 

pro-social relationships puts a damper on a person’s willingness to be involved in violent crime. It is 

also worth noting that we used a variety of measures to capture aspects of tie strength and 

homophily, but no other measure was found to be significant in initial models (and hence, they were 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

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SOCIAL NETWORKS AND DELINQUENCY

151 

dropped from the final models). Additional research, perhaps using qualitative methods, to dissect 

whether pro-social advice networks and role models (and other aspects of ties to pro-social 

individuals) can influence behavior would be an important step in further understanding how certain 

relationships or characteristics of those relationships help constrain behavior. 

Network Structure across Ego Networks 

One of the key network structure variables incorporated in the final ego regression models 

was the number of components that exist in a personal network. The number of components is a 

network measure that captures the number of subgroups within an ego network that are not 

connected to other subgroups (i.e., no links exist between any two nodes across components). We 

hypothesized, following extant network research on constraint and normative behavior (see for 

example, Krackhardt 1999), that the more “separate” groups of relations one has, the more 

constraint, and hence, the less likely to be involved in delinquent behavior. This hypothesis was 

confirmed in the negative binomial models for all three scaled criminal behavior outcomes and with 

regard to two of our binary outcomes (serious delinquency and weapon carrying). In both the peer 

and the alter models of overall delinquency, one additional component decreases the expected count 

of delinquency by about one-quarter (p = 0.04 and p = 0.05). For both the recent and serious 

delinquency measures, an increase by one component decreases the expected count of delinquency 

by about one-third (p = 0.06 for recent delinquency; p = 0.01 and 0.02 for serious delinquency), and 

by 30 percent and 32 percent for the binary measures of serious delinquency and carrying a weapon, 

respectively (logistic regression models). 

We know of no published delinquency research that has tested this important hypothesis—

that the number of components may be an important protective factor against delinquency. 

Mangino (2009) examined a somewhat similar construct that he called bridges—where an individual 

is positioned as a bridge between two or more subgroups in a friendship network. He found that 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

152 

being a bridge was associated with reductions in delinquency, and concluded that an individual’s 

association with multiple groups placed constraints on behavior. Our finding of the importance of 

components in networks is worthy of further exploration as it could have important implications for 

prevention and intervention. Specifically, this finding suggests that prevention practitioners might 

find utility in facilitating new pro-social networks for youth that allow them to step beyond their 

current friends and other networks. Encouraging a new hobby, or new youth group affiliation, or 

having a local organization start a new club (that has pro-social supervision) could assist in 

expanding youth networks away from existing delinquent or at-risk groups. 

Regardless, future network-based delinquency research should be sure to include a measure 

of components alongside measures of density and centrality. Past research may have overlooked 

including a components measure, because the focus has generally been on density of a network, a 

construct somewhat related to the number of components. A few studies have examined network 

density as a predictor variable and, for the most part, have found that as density increases, 

delinquency decreases (see Haynie 2002). However, in the current study, network density was not a 

significant predictor of any of our outcomes, nor was the interaction term for density and delinquent 

or criminal relations. (As indicated earlier, these variables were dropped from the final models.) 

Ethnic Identity and Acculturation 

 In addition to the network measures, we were also interested in measuring ethnic identity 

and acculturation within our sample. The literature generally has been mixed in findings on 

acculturation; it is often found to be a risk factor for delinquency, but a protective factor with regard 

to gang membership. In addition, new research has suggested that the inclusion of measures of 

ethnic identity in survey research can help shed more light on the relationship between culture and 

antisocial behaviors. Interestingly, our findings revealed that ethnic identity (as measured using the 

Multigroup Ethnic Identity Measure (Phinney 1992) was not a significant predictor in any of our 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

153 

binary delinquency variables. Our composite variable measuring separation from U.S. culture (which 

captures place of birth of respondents and of parents and languages spoken at home, with parents, 

and with friends), however, is a significant predictor of delinquency and serious delinquency in the 

negative binomial models. A one-unit decrease in the separation scale (read as increasing 

acculturation) translates into an 18 percent increase in the expected count of delinquency and an 11 

percent decrease in expected count of serious delinquency. 

These findings confirm the general literature on acculturation and delinquency. In the 

current study, separation was not found to be a significant protective or risk factor for gang 

membership or participation in a gang fight. This null finding could be the result of the small sample 

size; we suggest that further research is needed here—additional research breaking down the 

components of separation would be an important addition to the criminal justice field. Are these 

“separated” youth simply not hanging around delinquent youth because they have few friends or 

relationships when they first immigrate to the United States? Or do recent immigrants have different 

norms, morals, or belief systems than individuals who have been in the United States for a number 

of years? 

It would be presumptuous to suggest that programming within Latino communities could 

benefit from supporting the maintenance of use of the Spanish language. But these findings could 

be touching on some underlying issues related to parental bonding or acculturation stress; Martinez 

(2006) suggested that understanding these phenomena is important for understanding the criminal 

behavior of Latino youth. This type of exploration could help uncover the facets of acculturation 

and separation that are relevant with regard to delinquency and crime. Martinez suggests that 

intervention efforts not be targeted to acculturation processes, but to family bonding and parenting. 

These implications may be directly relevant to our findings, given that, for some outcomes, family 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

154 

cohesion and parent encouragement for school were found to be protective factors (see Table 19a 

and Table 20). 

Other Individual Characteristics 

Being male is also a serious risk factor in our models for all delinquent outcomes save for 

being in a gang. Boys are 14 times more likely to sell drugs than girls, 3.7 times more likely to attack 

someone with a weapon, and twice as likely to participate in a gang fight. It is not clear why being 

male was not a significant factor for gang membership when it was a predictor for the other 

outcomes. Our respondents included the same percentage of girls in a gang as boys in a gang. It 

could be that recruitment for the survey reached subgroups of girls who were connected through the 

same gang—a gang that was known to have many (or all) girl members (although responses to openended survey data do not bear this out) or it could be that the gangs that exist in the target area 

simply do not cater predominantly to males. Perhaps our findings represent an underlying 

characteristic of gangs in the target area—girls are just as likely as boys to self-identify as being part 

of gangs, but are much less likely to be involved in gang fights. 

Another interesting finding, although not central to the current study’s research questions, is 

that a number of other variables used as controls varied in significance across the different 

outcomes. This suggests that risk and protective factors may act differently for different outcomes. 

Null findings for some of these variables in extant research should not move researchers and 

practitioners away from buoying all sorts of supports in the lives of children and youth. For instance, 

we found that having an adult in the household encouraging a youth to do well in school was highly 

significant in negative binomial models predicting recent (past six months) delinquency, but not 

significant in negative binomial models predicting overall delinquency or serious delinquency. 

Because the implications for prevention are relatively simple and inexpensive to implement—

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

155 

teaching parents and caregivers the importance of encouraging their children in school could 

possibly return many benefits—continued research in this area is warranted. 

PROPERTIES OF THE WHOLE NETWORK 

Research Question 2: What are the properties and characteristics of the sociocentric (i.e., neighborhood-based) network 

as defined by overlapping egocentric networks? How do these characteristics relate to or influence delinquency and gang 

membership? 

In order to more fully assess the effects of network structure on delinquent behavior, we 

explored two different networks developed from our data: one comprising all egos and all alters 

named by any ego in the survey (the “whole network” with 2,521 members) and one comprising all 

egos and those alters who were named by at least two egos (the “2+ network” with 369 members). 

Eleven percent of the whole network members were also 2+ network members. The comparison of 

results for these two networks allowed greater insight into the mechanisms through which network 

structure can influence behavior for individuals within those networks. 

Who Is in the Network? 

We first considered the characteristics of the two sociocentric networks, including the 

demographic characteristics of network members and the relationships (e.g., sibling, cousin, friend) 

between members of each of the two networks.36 Not surprisingly, about 70 percent of network 

members in each of the networks were named as friends by at least one person. There were a 

significant number of alter members with non-peer relationships to egos, however, and while we 

expected that the 2+ network would consist of a greater number of peers (because more youth 

would be named more than once), we actually found that the percentage who were parents was 

similar to that in the whole network (about 4 percent) and the percentage named as siblings was 

much higher in the 2+ network—at 20 percent. As further evidence that these networks include 

important non-peers, the average ages of the whole and 2+ networks were relatively high, at 20 years 

and 19 years, respectively—higher than the typical high school age range. Because non-peers appear 

 

36 Remember that these categories are not mutually exclusive; someone can be named as a friend by one respondent and as a sibling

by another respondent, thus being counted in two categories. The percentages, then, do not add to 100. 

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SOCIAL NETWORKS AND DELINQUENCY

156 

frequently among all alters, capturing these relationships in social network analyses of delinquent 

behavior is key. 

Digging deeper into alter relationships by age and gender revealed some noteworthy 

patterns. While the majority of both networks were male (at about 60 percent in each network), 

when we looked at gender by age in the whole network, we found slightly more adult females (54 

percent) than adult males. Across the whole network, then, when egos named adults, they were 

more likely to name women. When they nominated peers, egos were more likely to nominate males. 

This could be a result of the common trend of young adult males being incarcerated at higher rates 

than females, thus leaving females as the adult role models in many disadvantaged communities. 

While our data cannot confirm that male incarceration rates are the underlying cause of the 

gender-age trends in our data, this findings warrants further investigation in future research. In 

addition, prevention and intervention programs that can tap into local strengths—the presence of 

positive role models, for instance—should be aware that fewer male adults are seen as role models 

in the neighborhood. More effort may be needed to recruit the help of positive male role models 

who are in the neighborhood, or who are from other areas but can still connect with the youth in the 

neighborhood. The suggestion that programs try to combat the detrimental effects of missing male 

role models is by no means new or groundbreaking, but it is consistent with findings from a large 

body of previous research on mentoring and provides further support for the role of mentoring, and 

specifically males as mentors, in this specific neighborhood. 

As discussed earlier, acculturation and ethnic identity were two key constructs that we 

sought to measure in relation to delinquent behavior. The findings regarding acculturation’s effects 

on delinquency were minimal at the ego level. At the whole network level, however, we found that a 

significant number of network members were Latino (although fewer than we expected prior to 

launching the survey), but far fewer were born abroad. The network members, therefore, likely had 

overall high levels of acculturation.37

 

37 At the alter level, we lacked the data on the acculturation scale used at the ego-level, so we are only able to use born abroad as a 

proxy for acculturation at the alter level. 

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157 

Finally, we looked at delinquency among the whole network. On the whole, we found that 

egos were attributing more delinquent behavior to themselves than to members of their networks—

the overall levels of delinquency among alters were relatively low and were lower than those 

reported for egos alone. Because these whole networks include more adults, such as parents, aunts, 

uncles, and teachers, it is not surprising that the overall delinquency levels are low. We found that 

delinquency was slightly higher in the 2+ network, which contained more young people. We also 

found that gang members who were named in the network tended to be named more than once, as 

opposed to being named as influential for just one ego. The few gang members in the network, 

therefore, appear to be relatively central figures. In addition, among the delinquency measures, “in 

gang” was highest for the 2+ network, while it was lowest for the whole network. Even so, the level 

of overall delinquency in the 2+ network was only about 14 percent, which is fairly low given our 

selected neighborhood. Again, this might be an indication that we were unable to interview the more 

delinquent youth in the neighborhood, or that the levels of delinquency among residents were 

actually lower than we initially believed. 

Sociocentric Measures 

Many of the sociocentric network measures are the same as those used at the ego level, but 

are calculated at the sociocentric level instead of for each personal network. In addition, many of the 

findings at the whole network level echo the ego-level findings. As with the personal networks, we 

considered the density of the two sociocentric networks and found both to have very low levels of 

density, and to have very similar levels of density. We also presented Valente’s (2010) effective 

density measure, which takes into account the number of alters named by each ego (20). These 

density measures were higher, around 0.33 for the whole network. While still relatively low, this 

density value is similar to densities reported in previous work on delinquency and criminal networks 

(Kreager, Rullison, & Moody 2011; Natarajan 2006; Snijders & Baerveldt 2003). The very low 

densities found in the current study suggest that interventions may be more successful there than in 

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SOCIAL NETWORKS AND DELINQUENCY

158 

a denser network—it might be easier for an individual to leave a delinquent group if it is only loosely 

organized, for instance. 

On the other hand, networks with sparse connections mean that individuals are freer to act 

independently (Natarajan 2006); as such, it may be harder to use the network structure to influence 

the behaviors of its members. In this case, the most appropriate interventions would not rely on prosocial, anti-delinquent messages being spread through the network via well placed, influential 

individuals. The low levels of density also suggest that prevention and intervention strategies that 

cast a wide net in the area, attempting to reach all youth who need services or assistance, might not 

be as successful; more targeted efforts to get individuals involved might be required because, again, 

the message—for instance, that violence will bring law enforcement attention, or that services are 

available—might not be effectively spread throughout the relatively sparsely connected community. 

The density of the network, therefore, can have important implications for the selection of an 

appropriate intervention. 

We also explored four measures of centralization at the sociocentric level: degree, 

betweenness, closeness, and eigenvalue centrality. Initial tests showed that eigenvalue centrality was 

not appropriate for our data, so we did not continue to use that measure in the analyses. We 

examined the bivariate correlations between all centrality measures and found that degree centrality 

was the most closely related to the other two measures, but that even those correlations were 

relatively low. All of the measures assess the hierarchical nature of the networks and whether there 

are very central players within the networks. The low correlations between the measures, however, 

indicate that the three measures explored here do capture different types of centrality, as 

hypothesized. That said, the centralization values for all three measures were low, indicating that 

there is little hierarchy or centrality across either sociocentric networks. Based on our centralization 

analysis, the networks have relatively flat structures and few people enjoy central positions across the 

whole network—to be sure, subgroups within the network have central figures (discussed in the next 

section) but there are no clear players with strong influence across the larger networks. 

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Policy suggestions from this finding dovetail nicely with those stemming from the low 

network density findings: there are few to no central figures who can quickly spread pro-social 

messages across the network, and extensive efforts to target different, unconnected groups would be 

required in this kind of social setting. From a law enforcement perspective, removing central figures 

would be implausible and likely have little effect on the overall delinquency of the neighborhood’s 

social network (as it might were the network dense and hierarchical). As suggested earlier, though, 

one benefit to this type of social structure is that it may make interventions more successful; without 

strong leaders enforcing antisocial group norms, the behaviors and attitudes of youth in the area 

might be more malleable. Police and/or practitioners might find it easier to convince youth to desist 

from their delinquent or criminal behaviors and redirect their energies into pro-social activities. 

Neighborhood as Network? 

One of the main concerns with our selected methods was whether it would prove useful to 

approximate a complete social network using a selected neighborhood—in other words, could we 

use the neighborhood to define our sample and get a coherent, connected network of individuals? 

We were able to test this issue because of two study design features: (1) all respondents lived in the 

small, defined neighborhood and (2) one of the alter questions asked whether the individual resided 

in the neighborhood. Using this information, we could discern who was from within the 

neighborhood and who was from outside the neighborhood. This is important in terms of policy 

implications because many anti-gang and anti-delinquency programs are geographically based; they 

are implemented for a selected geographic area only. 

As discussed above, for the ego-level models, we did not find the “in-neighborhood” 

proportion of alters to be a significant predictor for any of the delinquent outcomes. However, in 

order to more fully explore the effects of neighborhood context, we also considered the inneighborhood and out-of-neighborhood status for members of each of the sociocentric networks. 

Approximately one-third of the members of the whole network live in the neighborhood while 

nearly three-quarters of the members of the 2+ network live in the neighborhood. This finding is 

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SOCIAL NETWORKS AND DELINQUENCY

160 

not surprising—it is more likely that those named by more than one ego are peers who live nearby. 

It also suggests that the core social network of youths is neighborhood based, as the individuals who 

were named most frequently do indeed reside in the neighborhood. 

We also considered differences in demographic and delinquency characteristics and cohesion 

and centrality measures among individuals who live in the neighborhood and those who live outside. 

After splitting the whole network into “in-neighborhood” (879 members) and “out-ofneighborhood” networks (1,642 members), we found significant differences on every measure. 

Notably, all delinquency measures are significantly higher for the in-neighborhood network, with 

11.5 percent of in-neighborhood individuals exhibiting delinquent behavior compared to only 5.4 

percent of those outside the neighborhood. Examination of the three centrality measures reveal that 

the in-neighborhood network is more centralized than the out-of-neighborhood network, with the 

differences in some cases relatively large (e.g., the “in” network degree centrality is more than three 

times larger than that of the “out” network). Members of the “in” network have more ties on 

average—and more direct ties—to other members and are more tightly connected than are members 

of the “out” network. 

These patterns indicate that there are systematic differences between the groups of people 

who are neighborhood residents and those who are not. The differences also support the notion that 

neighborhood context is associated with behavior, and in this specific case, stronger associations 

with the neighborhood tend to be associated with higher levels of delinquency. Because we lack 

longitudinal data, we are unable to determine whether this is because of self-selection—where new 

residents tend to be similar to those who already live there, perpetuating the prevailing attitudes and 

norms in the area (in this case, to support certain levels of delinquency)—or because of influence—

where new residents are eventually influenced by the attitudes and norms of the neighborhood 

(Johnston & Pattie 2011). But either way, the data and models tested at the whole network level 

support the idea that having more associations with residents of the neighborhood (i.e., having more 

friends or contacts in the neighborhood) is associated with higher levels of delinquency for youth. 

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Our findings on the neighborhood network suggest that geographically based (or 

neighborhood-based) interventions may be appropriate as long as they take into account the social 

structure of at-risk and delinquent youth in the neighborhood. If influence is the main mechanism 

through which new residents become delinquent (or become more delinquent) then reaching those 

who are not yet deeply embedded in the neighborhood’s social norms and attitudes can help stem 

the spread of delinquency in the area. Any intervention, then, would need to be careful to tailor 

outreach to those residents who are not well-connected within the neighborhood but could benefit 

from services as well. Partnering with programs in nearby areas, including sharing information on 

program participants across different programs—such as those in nearby neighborhoods where an 

individual may also have connections and ties—can help neighborhood-based approaches to 

succeed. 

In addition, because of the strong association found related to the neighborhood context, 

programs that seek to broaden youth horizons and explore the vast array of opportunities that exist 

outside of their neighborhood social structure would likely be successful. These types of programs—

like employment skills programs (e.g., Job Corps) or recreational activities that remove them from 

the negative effects of the neighborhood—can open youths’ eyes to pro-social opportunities. Youth 

who reside in negative neighborhood contexts can benefit from spending time participating in prosocial activities outside of the physical boundaries of their wider neighborhood, and these 

experiences can help build the protective factors for individual youth that will prevent them from 

becoming delinquent, or will help them to cease any existing delinquent behavior. 

Community Structure within the Whole Network 

In addition to dividing up the whole network into in- and out-of-neighborhood networks, 

we also divided the whole network up into subgroups, here called “partitions,” using the GirvanNewman iterative algorithm. The optimum number of partitions in the whole network was 25, but 

one large faction was comprised of more than 65 percent of whole network members. Additional 

analyses not discussed in this report did not reveal any obvious reason why this partition was not 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

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SOCIAL NETWORKS AND DELINQUENCY

162 

divided into smaller subgroups. This is a finding we will explore in more detail in the future. In 

addition, 17 of the partitions were actually ego networks—they comprised about 21 members that 

included one ego and that ego’s 20 alters. While these were interesting to explore descriptively, the 

partitioning did not provide us with a large amount of additional insight regarding network 

embeddedness of subgroups or how or why subgroups might be formed. Only seven factions were 

some combination of network members beyond just ego networks—several egos and alters 

together. We chose five factions plus the large catch-all faction to explore in more depth. The 

factions were chosen based on their varied centrality patterns within the partitions and their 

different delinquency levels to showcase an array of different subgroups that can exist within the 

whole network. 

The exercise of describing the factions, however, highlighted the fact that even within a 

whole network, the landscape of ties, density, centrality, and delinquency can vary greatly within 

different parts of the network. In other words, one-size-fits-all approaches aren’t likely to be as 

successful as those that consider the unique nature of the specific population that is being targeted 

together with the overall context within which that population is located. For instance, an at-risk 

Latino group of youth with low levels of both acculturation and delinquency that is embedded in a 

larger network of more delinquent youth from the same neighborhood would not respond as well to 

an intervention program that might be successful with the delinquent, acculturated youth. 

INTEGRATING WHOLE NETWORK POSITIONAL MEASURES IN EGO ANALYSES 

Research Question 3: How does an individual’s position and connectedness within the whole network relate to his/her 

propensity to be involved in delinquent behavior at the individual level? In other words, how does one’s position at the 

sociocentric level relate to behaviors measured and modeled at the egocentric level? 

The network technique of overlapping ego networks to develop a whole network provides a 

unique opportunity to assess the contribution of whole network structural measures to delinquency 

and other antisocial behaviors. One element of the whole network that we investigated in detail was 

the position of individuals in the network, and how that position influences delinquency. We created 

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SOCIAL NETWORKS AND DELINQUENCY

163 

a number of measures derived from one’s position within the whole network, including isolates and 

degree, closeness, betweenness, and eigenvector centrality. We first explored the characteristics of 

the central players, then used the measures in predictive models at the ego and alter levels. 

Who Are the Central Players? 

 At the whole network level we considered the most central players to be those who had 

centrality scores on any of the three centrality measures that were in the top 1 percent. For the 2+ 

network, we looked at the top 5 percent of central members. We found, despite our previous 

analysis indicating that the centrality measures were not highly correlated, that there was a great deal 

of overlap in the groups: those who were central actors based on degree centrality tended to be high 

on the other two centrality measures as well. 

We found that central players were actually younger than the average whole network 

member by three to four years—a large difference. In addition, a much higher percent of central 

actors were named as siblings and friends compared to all members in the whole network. 

Therefore, in order to get the most central, and thus influential, players involved in any prevention 

or intervention program, getting an individual’s whole family involved is key. While this supports 

previous (non-social network) research that suggests that parent involvement in a youth’s life 

decreases delinquent behavior, the network perspective provides additional insight. We would 

suggest that siblings also have an important role to play. Having both older and younger siblings 

involved can help prevent the spread of delinquent or criminal behavior to a younger generation, 

provide an important familial support structure for youth in prevention and intervention programs, 

and help to further spread pro-social messages throughout the network structure. 

We also found that the central players in both networks were more likely than those in the 

whole network to be Latino, but fewer were born abroad. In addition, more than three-quarters (and 

for degree centrality, nearly 100 percent) of all central players live in the neighborhood. Finally, 

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164 

delinquency levels were significantly higher for central players; one-fifth to one-quarter of central 

actors were delinquent while only 8 percent of the whole network was. We explore the relationship 

between centrality and delinquency in the next section. 

The Influence of Centrality on Delinquency 

As mentioned earlier, having a central role—regardless of the definition of centrality used—

may allow a person greater autonomy to act as he wishes; he may have less dependence on any one 

individual for his needs (e.g., friendship or companionship, economic support, etc.), allowing him 

the freedom to act as he wishes, including participating in delinquent acts. In this sense, the higher 

levels of delinquency observed among central players is expected. Previous research has identified 

central players as more delinquent, especially when drug use and selling are examined (Baron & 

Tindall 1993; Lee 2004; McGloin & Shermer 2009), and, for the most part, the current work 

supported the earlier findings from previous research. 

There are several possible explanations for this pattern. Those who have high betweenness 

scores are sources of connection for other nodes; in other words, they frequently lie on the paths 

between two other nodes. In this way, they can often operate as go-betweens or brokers between 

two nodes or groups. Indeed, Lee’s (2004) work found drug sellers to be at the center of networks, 

which were typically comprised of drug users. These high betweenness players may also participate 

in gang fights more often than less central (or less “between”) actors, because their centrality may 

make them targets for competition; they may have to defend their position within the group 

(whether a gang or other group) through fighting or other means. In addition, according to the 

“code of the street” where respect is of utmost importance, such central figures might not be able to 

overlook any sign of disrespect from others, no matter how small the slight (Anderson 1999). They 

might therefore find themselves involved in more altercations to “save face” or preserve their 

reputation and the respect others have for them than they would otherwise undertake. 

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For the predictive models at the ego level, the only network measures we included were 

betweenness centrality and “ego as isolate,” because these measures have hypothesized relationships 

with delinquency and gang membership. To examine the contribution of betweenness centrality and 

isolate position to delinquency and related outcomes, we re-ran the same basic logistic and negative 

binomial regression models from the ego-level analyses and added the two variables to each model. 

The isolate variable is not statistically significant in any of the models, and betweenness centrality 

approaches significance at the p < 0.05 level only in the model predicting drug selling. The model 

should be interpreted with caution however, because the effect size is basically zero, and no change 

of any real magnitude is predicted to take place based on changes in betweenness centrality. With a 

larger sample, it is likely that the coefficient would be significant and have a large effect size; hence, 

we believe it is a noteworthy finding. 

It is sensible that betweenness—a measure of brokering—is associated with drug dealing 

because dealers would have many connections, and connections to people who are not connected 

otherwise. Because selling drugs typically requires getting drugs from a distributor and then having a 

customer base to buy the product (therefore acting as a connection between distributors and 

buyers), having many connections between others would facilitate drug selling. In other words, the 

drug dealer is in a good position to reach many other people and their, perhaps unconnected, 

groups. Essentially, a node with high betweenness has great influence over what flows (or does not 

flow) across the network. However, because these data are cross sectional, we are limited in knowing 

whether a dealer becomes a broker once he starts selling drugs, or he finds his calling because he 

already is an a position across a whole network that provides ample clients. 

We also conducted binary logistic regression analyses using all members of the whole 

network to predict overall delinquency. The model was restricted by the availability of data for all 

alters; the data were not nearly as rich or extensive as those available at the ego level. We did, 

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SOCIAL NETWORKS AND DELINQUENCY

166 

however, include measures of centrality in the models. This provided an additional test of the 

relationship between centrality and delinquency across all network members, not just egos, who we 

knew were more central on average than non-egos. We tested each of the three centrality measures 

in separate models, with all other predictors the same in each model. We found that living in the 

neighborhood was significantly associated with higher levels of delinquency; in contrast to our 

finding at the ego level that peers or alters living in the neighborhood did not significantly affect 

delinquency. We also found, surprisingly, that betweenness centrality is the only centrality measure 

that is not significant; both degree and closeness centrality were significant for the whole network. 

Degree, or the number of direct ties an individual has, is associated with decreases in delinquency 

while closeness was associated with higher levels of delinquency. While these models are more of an 

exercise in modeling at the whole network level (because we knew at the outset that we lacked 

sufficient data to replicate the ego-level models), they did further demonstrate the distinctness of the 

centrality measures and the different results that can be obtained depending on the centrality 

measure employed. 

While the descriptive and regression analyses focused on overall delinquency, we also find 

that a smaller proportion of central actors are violent than in the whole network or 2+ network. The 

finding contradicts previous work in this area that suggests, as described above, that central players 

tend to be more delinquent than peripheral players (Baron & Tindall 1993; McGloin & Shermer 

2009). Some very recent work, however (Faris & Felmlee 2011), has suggested that there is a 

nonlinear relationship between social status and aggression. There is significant support in the 

literature for the idea that central individuals are likely to be considered popular, and in order to 

achieve and maintain that status, they use both “antagonistic” (aggressive) and positive behaviors. 

Faris and Felmlee suggested, however, that once youth reach the “pinnacle” of the social structure, 

the need for aggression decreases. This lowered aggression is related to the fact that once at the peak 

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167 

of the social structure, there is no higher status to achieve, and those few at the peak thus lose 

motivation for their aggressive behavior and the frequency of such behavior is greatly reduced. 

One alternative explanation to aid in understanding the results here—that “bridgers,” or 

highly “between” individuals are less violent—might be to consider the function of connections to 

many nodes. The high betweenness individual may feel freer to operate with a certain level of 

autonomy, able to resist becoming beholden to any one individual because there are many others 

available to serve a purpose or fill a need. In this situation, violence may be needed less because one 

can just move on to another individual, instead of resorting to violence. While we are unable to test 

this hypothesis given our dataset, it is one finding that certainly warrants further investigation. 

OTHER FINDINGS 

There are other interesting findings that are not directly related to our key research 

questions, but are important to summarize in this summary report. 

Levels of Gang Membership 

One obvious and important finding is that only a small proportion of the youth interviewed 

were involved in gangs (current or past). Only 10 percent indicated they were either currently or had 

ever been involved in gangs (another 8 percent, however, had been involved in gang fights). When 

we set out to choose a target neighborhood, we examined a variety of data and talked with 

community leaders and current and former gang members to determine the particular 

neighborhoods that had high levels of gang involvement and high-risk youth. Newspaper stories 

verified a number of gang-related incidents in and around the target area. It is possible that youth 

were not honest in reporting their gang status or that the words and phrases chosen did not resonate 

with youth in that they considered themselves to be in a group, but did not view the group as a gang. 

We feel that this latter possibility, however, is unlikely because we held two focus groups to collect 

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168 

information on phrases and terminology related to gangs and street crime used by youth in the area. 

We then pretested the survey protocol with at least 10 youth. Youth do report gang activity in their 

neighborhood—when asked whether there “is a lot of gang activity in the neighborhood,” 34 

percent said yes, with another 33 percent saying they don’t know, and 50 percent said there “was a 

lot of talk about gangs in the neighborhood.” It is possible that there was an underlying bias in who 

completed the survey; that our survey respondents were telling the truth, and the youth living in the 

neighborhood who were not surveyed were more gang-involved. But given that we know gang status 

for all the neighborhood-based alters for every respondent and (most of who were not surveyed), it 

is not likely that we are too far off in estimating the prevalence of gang membership across 

neighborhood youth. Across the 2,521 youth in the whole network, the prevalence of gang 

membership (current or past) is 4.5 percent (N = 113). 

Gang Members and Delinquency 

Although we did not have a large proportion of “gang” youth in our sample, another 

interesting finding has to do with the relationship between gang membership and criminal 

behavior—a topic widely studied in criminology. T-tests revealed that youth self-identifying as 

currently or ever in a gang were three times more likely to carry a weapon, use drugs, or attack 

someone with a weapon, and four times more likely to sell drugs (differences are statistically 

significant) than the average youth in our study. These findings are in line with the extant literature 

that has shown that youth associated with gangs experience an amplification in their offending, or 

what Thornberry (2003) calls the facilitation effect. Because we do not have longitudinal data or data 

that would allow us to determine whether crimes reported were committed while a youth was in a 

gang, we did not conduct additional analyses to examine amplification or facilitation. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

169 

Diversity of Network Members 

And lastly, an unexpected finding was the extent of diversity in the surveyed population by 

race and ethnicity as well as the diversity within ego networks and subgroups identified by statistical 

routines. It is important to remember that populations that appear homogenous on the outside may 

indeed be very different with regard to a number of factors such as language use, religious affiliation, 

value systems, and country of birth. In our study, ethnicity (based on country of origin for self or 

earlier generation) varied greatly in the small community and youth networks contained a wide 

variety of relations with regard to ethnicity and nativity. In particular, even the few delinquent 

subgroups identified through network clustering routines included a mix of individuals with regard 

to whether they were born in the United States or abroad.38 We did not find any strong results 

indicating that homophily was an important feature of youth relations with regard to delinquency or 

gangs. Youth were just as likely to nominate a person of another ethnicity or country of birth/parent 

country of birth. 

LIMITATIONS 

Although we hope that we have broken new ground with regard to an innovative data 

collection method that provides data that can be used to form overlapping networks, we note some 

important limitations of our research. Overlapping ego networks to create whole networks for 

analysis is a new technique in the social network field. The method brings with it some limitations 

(as is typical of any data collection strategy). In the current study, we do not know the level of 

delinquency/criminal behavior of alters in a network because we do not have self-report data from 

all alters listed. First, involvement in criminal activities for alters is based on reports from others. For 

some individuals we have multiple reports (from various egos) on an individual’s behavior, providing 

 

38 We did not collect information on the place of birth of alters; we only have data on whether they were born in the United States or 

abroad, so we cannot state whether individuals in subgroups represented different nationalities. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

170 

some ability to corroborate or verify the information. However, we do not ask the respondents to 

report on a full set of characteristics on their alters, and as a result, we are unable to capture the level 

of delinquency. We assume that relations do not know everything about their friends/contacts, and 

we would not be able to rely on others’ reports to gain a comprehensive picture of someone else’s 

criminal activity (nor would it be valid to rely on the ego to report on alter attitudes). In addition, the 

respondent burden would be too heavy if they were required to complete a full list of delinquent 

acts for each of their 20 alters. Similarly, it was not our intent to interview every alter listed by each 

of the targeted youth. It would be virtually impossible to reach all alters for an interview, regardless 

of resources—many lived far away or in another country. Yet, we purposely chose the data 

collection method and associated analytical techniques because we believed it would provide us the 

greatest capability to answer our research questions. 

Relatedly, it is important to mention underreporting bias as a potential problem. Research 

has shown that youth are likely to underreport their criminal behavior, and given the nature of our 

network questions (naming friends and important relationships and then describing their behavior) 

our respondents may purposely underreport the criminal behavior of people important to them. A 

similar potential problem is that research has shown that youth will tend to ascribe their own 

characteristics to those of their friends. However, the frequency distribution of criminal behavior 

across alters for all of the egos suggests that youth are not readily under-reporting or overexaggerating alter criminal involvement. Although 31 percent of delinquent egos reported no 

delinquent or violent behavior for their alters, the remaining two-thirds exhibited typical variation in 

the proportion of alters who egos reported as having engaged in delinquent/criminal behavior. In 

other words, the distribution did not raise any red flags (see Table 16). At the outset of our study, we 

had intended to collect criminal records as a means of validating self-reported information. 

However, once we determined that the study design would include asking respondents to reveal 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

171 

names of individuals and gangs and any criminal behavior associated with the individuals nominated, 

we knew we would not engender the trust of the respondents if we additionally asked youth to 

consent to access criminal records or other law enforcement data. Given the likelihood that few 

youth would want to complete the survey if we told them we were accessing arrest records or talking 

to the police, let alone be truthful during administration, we did not collect arrest records (and 

intentionally told the youth we would not be doing so). Even if we had arrest records or other law 

enforcement data on gang participation, we would have been only able to validate the criminal 

behavior of the survey respondents—not the behavior of the alters (unless the alter was an ego). 

Another limitation is that this study may have low generalizability. This study involves one 

network of youth and their relations in one disadvantaged, predominantly Latino community in 

Maryland. Although specific knowledge gained about these youth and subgroups might not directly 

generalize to other jurisdictions, we believe that findings regarding problem youth behavior do. 

Many of the findings related to risk and protective factors mirrored those in the extant literature, 

lending credence to the methods used for data collection and analysis. Similarly, the findings related 

to network content and structure, where addressed in previous literature, also were in line with 

extant research. Even when we examined hypotheses that were new with regard to delinquency, the 

findings, for the most part, were in the expected direction following past network theory and 

research. 

The cross sectional nature of the study can also be viewed as a limitation. Much of the past 

and current research on peer effects has attempted to separate peer influence from selection. The 

research presented here was not intended to address this issue. Longitudinal data is needed to 

contribute to research in that area. However, endogeneity becomes a methodological issue for the 

current study: we cannot claim causality for any of our findings. For instance, where we found that a 

youth’s betweenness position in an ego network is associated with selling drugs, we cannot address 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

172 

whether he/she moved into a position of higher betweenness after he began selling drugs, or whether 

the nature of his/her high betweenness contributed to his choice (or motivation) to sell drugs. 

Undoubtedly, future research of this kind that sets out to collect data at more than one point in time 

to understand the dynamics of networks processes over time, and how these dynamics contribute to 

delinquency and gang membership, would be a solid contribution to the field. 

Finally, as touched upon above, because of the high respondent burden in survey data 

collection in this study, we had to make difficult decisions about the content of the survey. Adding 

one alter question results in 20 additional questions for each respondent, and as a result, we only 

included 19 alter questions, each selected to directly address our hypotheses. We also attempted to 

keep the ego questions to a minimum, and are aware that that effort resulted in the exclusion of 

some important constructs. For instance, questions regarding self-control or unsupervised time spent 

with peers were not included. Similarly, as mentioned above, we did not ask egos to report on alter 

attitudes or beliefs, and thus did not ask egos to report on their own attitudes and beliefs about 

violence. Some researchers may view the omission of measures of norms and attitudes as a concern; 

we understand this concern, but chose to rely on behaviors, not only because the research method 

chosen does not support collecting reliable information on attitudes and norms, but because past 

research has shown that peer behavior has stronger effects on delinquency than do peer attitudes 

and beliefs (Warr & Stafford 1991). 

There were also questions we did not include because we thought youth (in some cases the 

youngest youth), would not necessarily know the answer or want to report the answer; these 

included questions regarding socioeconomic status, immigrant status (i.e., legal versus illegal) and 

home address (or cross streets) for each alter. A related point is that although we purposely surveyed 

a wide age range of youth (between 14 and 21), this wide range, coupled with our small sample size, 

makes it difficult to utilize a subset of typical control variables, such as school status, attachment to 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

173 

or involvement in school or school-related activities, or employment status, because many youth 

were past school age or not of an age where they would be likely to be employed. 

We believe these limitations are outweighed by the accomplishments achieved through this 

study. It is typical that research incorporating new data collection methods and network techniques 

begins small (in this case, in one site), also testing the efficacy of the method in light of the research 

questions set forth. 

IMPLICATIONS FOR POLICY AND PRACTICE 

We have demonstrated that it is possible to collect extensive social network data on sensitive 

topics from a significant number of youth, and that such work can yield interesting and informative 

findings with significant implications for policy and practice. Although we have interwoven a 

discussion of implications throughout the sections above, we believe it is worthwhile to highlight a 

few below. 

• Neighborhoods shape relations. First and foremost, neighborhoods shape relations. Because 

there may be something particular about neighborhood relations that is associated with 

delinquency (as shown in whole network regression models), it is important for practice to 

continue to focus on neighborhood-based programs as a means of intervention and prevention. 

However, programs that bring delinquent youth together should take care to help youth 

establish new positive relationships that can be sustained. In addition, neighborhood-based 

efforts to change the mindset of youth and modify attitudes that support violence or gun 

carrying via public health model programs like Chicago CeaseFire can help address the culture 

(or subculture) of violence within a neighborhood setting. Programs that allow youth to have 

experiences beyond the boundaries of the neighborhood can open their eyes to new pro-social 

opportunities and provide possible avenues for developing positive relationships outside the 

community. 

• Families should be sources of prevention and intervention. The presence of siblings in the 

networks present spoke to the importance of peer-aged family support. Including the siblings of 

delinquent and at-risk youth could increase chances of success for prevention and intervention 

efforts. Similarly, strengthening the relationship between youth and their parents/caregivers 

should provide benefits for youth in numerous ways. Special attention should be given to youth 

who have parents that are incarcerated or have been involved in gangs and the roles these adults 

play in their children’s lives. 

• Adult role models should be sources for prevention and intervention. Because one of our key 

findings suggests that the delinquency/criminality of non-peer alters is associated with 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

174 

delinquency, it is important for prevention and intervention programs to address the bonds and 

relationships that youth have with people other than peers. It might be difficult to break up a 

group of delinquent peers but easier to find or nurture a mentor or new role models for youth. 

In fact, research has shown that mentoring programs lead to a reduction in delinquency. The 

findings from this study add to the growing body of research suggesting strong positive 

relationships matter for youth. To increase the probability of making a difference in the lives of 

youth, mentor relationships would need to be sustained long enough so that a youth would 

consider the mentor an important, influential person, someone the youth would turn to for 

advice, as advice networks appear to be a protective factor when it comes to aggression. 

• Pro-social peer networks can be fortified and leveraged. Programs should use strong outreach to 

appeal to the peer networks of youth already affiliated with existing programs and make an 

effort to help fortify pro-social peer networks and encourage pro-social activities. Programs that 

mix delinquent youth with nondelinquent youth could attempt to explicitly reinforce the 

importance of pro-social behavior and help detach youth from antisocial peers. Our study did 

not find any strong influences on delinquency related to the amount of time spent with peers or 

with regard to how close or whether someone was liked; thus, it is possible that practitioners 

conducting peer outreach can engage a wide swath of peers when recruiting youth for their 

programming and services. 

• Network structure can help determine the most appropriate interventions. Perverse effects can 

result from even the most well-intentioned programs if neighborhood context and social 

structure of the targeted area are not understood at the outset. An understanding of youth 

networks can inform practitioners of the most appropriate interventions, such as whether prosocial messages can be effectively and efficiently spread throughout a network, or if removing 

key individuals can successfully disrupt a social network’s negative behaviors. 

• Acculturation’s role in delinquency is unclear. Our findings supported prior work demonstrating 

the link between acculturation and certain types of delinquency. But, given prior work in this 

area and our findings on the protective roles of family and other pro-social adults, we reiterate 

Martinez’s suggestion that family bonding and parenting should be the focus on intervention 

effects—not acculturation itself—but we also suggest that making these interventions culturally 

appropriate is of utmost importance to ensuring community participation and success. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

175 

CHAPTER 5 

CONCLUSION AND FUTURE RESEARCH 

The current study provides a much-needed quantitative examination of the network context 

of youth living in disadvantaged minority communities. It bears emphasizing that the core 

explanatory concepts in the criminological field refer to social relationships, but few studies examine 

the social relationship beyond simply the characteristics of individuals. A social network framework 

provides a theoretically grounded backdrop relevant to the exploration of micro-level social 

interaction and relations. 

Furthermore, collecting network data on youth within a targeted geographic area for all 

important social relations provided the opportunity to examine how network structure and 

composition influence aspects of delinquency and related antisocial behavior beyond what has 

previously been studied in the extant literature. For instance, this study provided us the opportunity 

to examine how neighborhood-based relations may influence the delinquency of individuals or the 

formation of subgroups across the neighborhood. Results indicated that youth are very connected to 

people from the neighborhood—for 30 percent of respondents, at least half of their alters lived in 

the neighborhood. The majority of peers nominated across respondents resided in the same 

neighborhood as the respondent, and were not necessarily school-based friends. Although our study 

did not find that having a larger proportion of in-neighborhood relations was significantly related to 

delinquency or other antisocial outcomes at the ego level, the measure was significant in regression 

models that included all nodes in the whole network. 

These findings have implications for how network studies collect data from youth. The 

reliance on peer-based networks to study the processes and risk factors related to delinquency 

overlooks the importance of non-peer relations as highlighted in our findings. We are able to better 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

176 

examine differential associations among youth simply because of the variety of associations studied, 

and can additionally utilize information about the strength of ties to assess the quality of 

associations. In addition, the findings from this study have shown that network content alone 

provides an incomplete picture—an understanding of network structure is also important for 

advancing delinquency research. 

FUTURE RESEARCH 

As social network-based research becomes more commonplace in criminology, researchers 

can continue to articulate a vast range of testable hypotheses related to how social relations shape 

criminal behavior. We recognize that the current study is only one small step among many new 

network-based approaches that can help shed insight into the processes and dynamics that shape 

and reinforce (or buffer against) delinquent and criminal behavior. Below, we provide a few 

suggestions for future research. 

First and foremost, research that attempts to replicate these findings in other Latino 

neighborhoods, as well as in neighborhoods comprising youth from different minority groups, 

would provide insight on the reliability of our findings. The findings from this study regarding the 

“typically studied” risk factors were mostly in line with past research. However, we examined a set of 

network compositional and structural measures that have not, to our knowledge, been examined in 

the study of delinquency. It will be important for future research to shed light on how these findings 

might vary (or not) across different communities. Second, as the cross-sectional nature of this study 

limited our ability to infer causal relationships, longitudinal research would provide the basis to 

obtain more insight on the particular aspects of relationships that influence behavior as well as how 

one’s position in a network shapes opportunities to engage in crime. Specifically, longitudinal 

research in this field will help advance the debate on selection versus influence. Also, because 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

177 

research has suggested that peer networks are rather fleeting, longitudinal research can inform 

hypotheses related to how changes in relationships impact behavior or influence the criminal career 

(or gang career), as very little is known about how pro-social or antisocial relationships influence 

desistance. We admit that tracking high risk youth over time can be very resource intensive and high 

response rates might be difficult to achieve, but the success would yield a wealth of data that could 

be used to support a wide range of critical questions that could advance the field. When relying on 

self-report data, our findings support the idea that widening the framework beyond schools for 

social network analyses seeking to inform delinquent, criminal, or gang-related behavior would yield 

substantial benefits for research, policy and practice. Certainly self-report data collected on networks 

needs further exploration of their validity (and specifically with regard to using overlapping 

networks), but the point to be made is that exploration of neighborhood-based networks or more 

broadly, networks not bounded by a specific type of relation, will provide the opportunity to more 

directly examine core constructs and hypotheses from social learning, bonding, and routine activities 

theories as well as to test integrated models combining aspects of these theories—as many recent 

studies have suggested. 

While school networks are obviously very important because youth spend a significant 

portion of their lives in school, the inclusion of non-peers in an analysis of a youth’s network sheds 

more light on the array of influences shaping an individual’s behavior. School-based surveys have 

clear advantages—the network is easily “closed” (all network members are known at the outset and 

can be included in analysis), and data coding and cleaning can be much easier. In addition, basic 

logistics of conducting the data collection efforts can be easier in a school-based setting, where 

youth can be considered a “captive audience,” more likely to take the survey. But, analyses that fail 

to recognize the importance of other, non-peer relationships could suffer from bias and model 

misspecification. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

178 

Admittedly, conducting longitudinal studies of this sort would require ample resources to 

ensure adequate samples. When we set out with our study, we did not know the exactly how many 

youth between the ages of 14 and 21 lived in our study site. We began with finite resources and 

hoped to reach a full “census” of youth to be surveyed. Although we have demonstrated that the 

data collection strategy we devised can be successful, at least on a small scale, future research should 

attempt to replicate this study using neighborhoods with large numbers of youth (or perhaps more 

resources) that would enable recruitment of larger samples of youth. Research conducted 

simultaneously in more than one neighborhood would also enable some ego-level analyses to be 

conducted on a combined sample, and at the same time, buoy reliability. These types of studies are 

resource intensive, but solid planning and valid data on youth populations will enable future studies 

to make important gains. 

It is also important to set aside resources to ensure that serious delinquents and gang 

members are recruited and represented in a study of this sort. In addition, future network studies 

seeking to understand delinquency would benefit from adding a qualitative component to the data 

collection effort, to explore with respondents the structure of their network, the key players, and the 

relationships between network members. Having respondents reflect on personal network graphics 

would allow researchers to explore additional hypotheses and follow up on any puzzling or 

unanticipated findings. 

Larger sample sizes and a more comprehensive set of data on alters would also provide new 

opportunities to examine how non-peers influence youth behavior. This includes testing whether 

relations other than peers can be protective factors in a youth’s life or whether, if these non-peers 

are delinquent or criminal, their negative behavior is more influential than such behavior among 

peers. Large samples and data collection methods using different alter nomination strategies would 

provide new opportunities to test the tenets of current integrated theoretical models as related to 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

179 

familial relations. The level of bonding to adults is also important in understanding how adult role 

models facilitate maintenance of criminal careers or help delinquent youth desist from crime or leave 

gangs. Finally, looking in more depth at different types of peer relationships, such as cross-gender 

friendships and their role as protective or risk factors, will provide a more nuanced view of how 

delinquent peers influence others’ behavior, and in turn offer possible strategies for prevention and 

intervention. 

Although this study uncovered only a handful of delinquent peer groups and gang members, 

the data collection technique and analyses performed lend themselves easily to gang research. The 

field of gang research is wide open for network analyses using self-reported data. In gang 

communities, this type of study could help develop knowledge about delinquent and crew 

subgroups. The majority of research on gang violence in the past 20 years has been conducted in 

areas such as Los Angeles and Chicago, where gangs tend to have structure and organization. As 

such, most current gang prevention, intervention, and suppression strategies, as well as drug market 

elimination, are developed from efforts that target the behaviors of organized gangs, leaving little 

room for investigation of how and why less organized criminal groups develop and thrive. 

Essentially, without precise knowledge of the mechanics of group behavior within the local context, 

we do not know how to develop strategies to modify norms and behavior. Network research aimed 

at understanding how loosely based “crew” networks evolve and change over time and how aspects 

of networks are related to changes in criminal behavior, would greatly assist intervention efforts. 

Finally, we would like to conclude with the suggestion that experts in network analyses and 

network programs continue to devise ways to make network research more practitioner-friendly. 

One of the ultimate goals of our research, after multiple replications using the same survey 

instrument, is to determine which particular network-based questions or network routines (in 

analyses) are the most salient for understanding who (i.e., which individual or group of individuals) 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

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180 

should be targeted for different types of prevention and intervention efforts. We recognize that the 

extensive data collection process we undertook as part of this study is not feasible or practical for 

most practitioners. But a short risk and network structure assessment instrument (based on our 

longer instrument) could make such considerations more accessible to practitioners. In this way, 

they could realistically use social network data to inform the development of an appropriate 

intervention strategy for individuals and/or entire neighborhoods. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
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REFERENCES 

Abbasi, A., Uddin, S., & Hossain, L. (2011). Socioeconomic analysis of patient-centric networks: 

Effects of patients and hospitals’ characteristics and network structure on hospitalization costs. The 

European Journal of Health Economics (in press). 

Abel, G., Plumridge, L., & Graham, P. (2002). Peers, networks or relationships: Strategies for 

understanding social dynamics as determinants of smoking behaviour. Drugs: Education, Prevention and 

Policy, 9, 325-338. 

Akers, R. L. (1989). A social behaviorist's perspective on integration of theories of crime and 

deviance. In S. Messner, M. D. Krohn, & A. Liska (Eds.), Theoretical integration in the study of deviance 

and crime: Problems and prospects (23-36). Albany, NY: SUNY Press. 

Akers, R. L. (1998). Social learning and social structure: A general theory of crime and deviance. Boston: 

Northeastern University Press. 

 

Akers, R. L., & Lee, G. (1999). Age, social learning, and social bonding in adolescent substance use. 

Deviant Behavior, 20, 1-25. 

Amaro, H., Whitaker, R., Coffman, G., & Heeren, T. (1990). Acculturation and marijuana and 

cocaine use: Findings from HHANES 1980-1984. American Journal of Public Health, 80 (Suppl.), 54-60. 

Anderson, E. (1999). Code of the street: Decency, violence, and the moral life of the inner city. New York: W.W. 

Norton and Company. 

Arbona, C., Jackson, R. H., McCoy, A. & Blakely, C. (1999). Ethnic identity as a predictor of 

attitudes of adolescents toward fighting. The Journal of Early Adolescence, 19: 323-340. 

Ardelt, M., & Day, L. (2002). Parents, siblings, and peers: Close social relationships and adolescent 

deviance. Journal of Early Adolescence, 22 (3), 310-349. 

Baerveldt, C., Van Duijn, M., Vermeij, L., & Van Hemert, D. (2004). Ethnic boundaries and 

personal choice: Assessing the influence of individual inclinations to choose intra-ethnic 

relationships on pupils’ networks. Social Networks, 26, 55-74.

Battin, S. R., Hill, K. G., Abbott, R. D., Catalano, R. F., & Hawkins, J. D. (1998). The contribution 

of gang membership to delinquency beyond delinquent friends. Criminology, 36(1), 93-115. 

Battin-Pearson, S. R., Thornberry, T. P., Hawkins, J. D., & Krohn, M. D. (1998). Gang membership, 

delinquent peers, and delinquent behavior (Bulletin). NCJ 171119. 

Bernard, H. R., Killworth, P. D., Evans, M., McCarty, C., & Shelley, G. (1988). Studying social 

relations cross-culturally. Ethnology, 27, 155-179. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

182 

Bernburg, J. G., & Thorlindsson, T. (2001). Routine activities in social context: A closer look at the 

role of opportunity in deviant behavior. Justice Quarterly, 18(3), 543-567. 

Borgatti, S. P., M. G. Everett, and L. C. Freeman. 1999. UCINET 5.0 Version 1.00. Natick: Analytic 

Technologies. 

Bouchard, M., & Spindler, A. (2010). Groups, gangs, and delinquency: Does organization matter? 

Journal of Criminal Justice, 38(5), 921-934. 

Brewer, D. D. (2000). Forgetting in the recall-based elicitation of personal and social networks. Social 

Networks, 22, 29-43. 

Brewer, D. D., & Webster, C. M. (1999). Forgetting of friends and its effects on measuring 

friendship networks. Social Networks, 21, 361-373. 

Bullock, B. M., & Dishion, T. J. (2002). Sibling collusion and problem behavior in early adolescence: 

Toward a process model for family mutuality. Journal of International Child Psychology, 30, 143–153. 

Burgess, R., & Akers, R. L. (1966). A differential association-reinforcement theory of criminal 

Behavior. Social Problems, 14, 363-383. 

Buriel, R., Calzada, S., & Vasquez, R. (1982). The relationship of traditional Mexican American 

culture to adjustment and delinquency among three generations of Mexican American male 

adolescents. Hispanic Journal of Behavioral Sciences, 4, 41-55. 

Bursik, Jr., R. J., & Grasmick, H.G. (1993). Neighborhoods and crime: The dimensions of effective community 

control. New York: Lexington. 

Cairns, R. B., Cairns, B. D., Neckerman, H. J., Gest, S. D., & Gariépy, J. (1988). Social networks and 

aggressive behavior: Peer support or peer rejection? Developmental Psychology, 24(6), 815-823. 

Carley, K. M., Lee, J-S., & Krackhardt, D. (2002). Destabilizing networks. Connections, 24(3), 79-92. 

Carlsson, Y., & Decker, S. H. (2005). Gang and youth violence prevention and intervention: 

Contrasting the experience of the Scandinavian welfare state with the United States. In S. H. Decker 

and F. Weerman (Eds.), European street gangs and troublesome youth groups. Walnut Creek, Calif.: Alta 

Mira Press. 

Chapple, C. L. (2005). Self-control, peer relations, and delinquency. Justice Quarterly, 22, 89–106. 

Coatsworth, J. D., Maldonado-Molina, M., Pantin, H., & Szapocznik, J. (2005). A person-centered 

and ecological investigation of acculturation strategies in Hispanic immigrant youth. Journal of 

Community Psychology, 33(2), 157-174. 

 

Chung, H. L., & Steinberg, L. (2006). Relations between neighborhood factors, parenting behaviors, 

peer deviance, and delinquency among serious juvenile offenders. Developmental Psychology, 42(2), 319-

331. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

183 

Daly, R. M., & Johnson, F. (2006). Social networks of co-offending among adult probationers. Paper 

presented at the Annual Meeting of the American Society of Criminology. Los Angeles, CA. 

November, 2006. 

Dishion, T. J. (2000). Cross-setting consistency in early adolescent psychopathology: Deviant 

friendships and problem behavior sequelae. Journal of Personality, 68, 1109–1127. 

Dishion, T. J., McCord, J., & Poulin, F. (1999). When interventions harm: Peer groups and problem 

behavior. American Psychologist, 54(9), 755-764. 

Dukes, R. L., Martinez, R. O., & Stein, J. A. (1997). Precursors and consequences of membership in 

youth gangs. Youth & Society, 29(2), 139-165. 

Ennett, S. T., & Bauman, K. E. (1993). Peer group structure and adolescent cigarette smoking: A 

social network analysis. Journal of Health and Social Behavior, 34(3), 226-236. 

Faris, R., & Felmlee, D. (2011). Status struggles: Network centrality and gender segregation in sameand cross-gender aggression. American Sociological Review, 76(1), 48-73. 

Farrington, D. P. (1995). The development of offending and antisocial behavior from childhood: 

Key findings from the Cambridge Study in Delinquent Development. Journal of Child Psychology and 

Psychiatry, 36, 929–964. 

Faust, K. (2008). Triadic configurations in limited choice sociometric networks: Empirical and 

theoretical results. Social Networks, 30, 273-282. 

Felson, M., & Boba, R. (2010). Crime and everyday life. (4th Edition). Thousand Oakes. Sage. 

Fleisher, M. S. (2002). Doing field research on diverse gangs: Interpreting youth gangs as social 

networks. In C. R. Huff (Ed.), Gangs of American III. Sage, Thousand Oaks, California. 

Fleisher, M. S. (2006). Youth gang social dynamics and social network analysis: Applying degree 

centrality measures to assess the nature of gang boundaries. In J. F. Short Jr. and L. A. Hughes 

(Eds.), Studying youth gangs. AltaMira Press, Lanham, Maryland. 

Fleisher, M. S., & Krienert, J. L. (2004). Life course events, social networks, and the 

emergence of violence among female dang members. Journal of Community Psychology, 32(5), 607-632. 

Freng, A., & Esbensen, F-A. (2007). Race and gang affiliation: An examination of multiple 

marginality. Justice Quarterly, 24(4), 600-628. 

Fridrich, A. H., & Flannery, D. J. (1995). The effects of ethnicity and acculturation on early 

adolescent delinquency. Journal of Child and Family Studies, 4, 69-87. 

Gil, A. G., Vega, W.A., & Dimas, J. M. (1994). Acculturative stress and personal adjustment among 

Hispanic adolescent boys. Journal of Community Psychology, 22(1), 43-54. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

184 

Gil, A., Wagner, E., & Vega, W. (2000). Acculturation, familism and alcohol use among Latino 

adolescent males: Longitudinal relations. Journal of Community Psychology, 28(4), 443-458. 

Giordano, P., Cenkovich, S., & Pugh, M. D. (1986). Friendships and delinquency. American Journal of 

Sociology, 91, 1170-1202. 

Goodreau, S. M., Kitts, J. A., & Morris, M. (2009). Birds of a feather, or friend of a friend? Using 

exponential random graph models to investigate adolescent social networks. Demography, 46(1), 103-

125. 

Gordon, R. A., Lahey, B. B., Kawai, E., Loeber, R., Stouthamer-Loeber, M., & Farrington, D. P. 

(2004). Antisocial behavior and youth gang membership: Selection and socialization. Criminology,

42(1), 55-87. 

Gottfredson, G. D. (1987). Peer group interventions to reduce the risk of delinquent behavior: A 

selective review and a new evaluation. Criminology, 25,671-714 

Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Palo Alto, CA: Stanford University 

Press. 

Gould, D. (1996). The funder’s guide to successful media investments for social change. Larchmont, NY: 

Douglas Gould & Company.

Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. Riverside, CA: University 

of California, Riverside.

Hawkins, J. D., T. Herrenkohl, D. P. Farrington, D. Brewer, R. F. Catalano, and T. W. Harachi. 

1998. A review of predictors of youth violence. In Serious and violent juvenile offenders: Risk factors and 

successful interventions, edited by R. Loeber and D. P. Farrington, 106-46.Thousand Oaks, CA: Sage.

Hayes-Bautista, D. E. (1989). Identifying ‘Hispanic’ populations: The influence of research 

methodology upon public policy. American Journal of Public Health, 73, 353-356. 

Haynie, D. L. (2001). Delinquent peers revisited: Does network structure matter? The American 

Journal of Sociology, 106(4), 1013-1057. 

Haynie, D. L. (2002). Friendship networks and adolescent delinquency: The relative nature of peer 

delinquency. Journal of Quantitative Criminology, 18(2), 99-134 

Haynie, D. L., & Osgood, D.W. (2005). Reconsidering peers and delinquency: How do peers matter? 

Social Forces, 84(2), 1109-1130. 

Henry, D. B., & Kobus, K. (2007). Early adolescent social networks and substance use. Journal of 

Early Adolescence, 27(3), 346-362. 

Hilbe, J. M. (2007). Negative binomial regression. Cambridge University Press, 2011. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

185 

Hill, K., Howell, J. C., Hawkins, J. D., & Battin-Pearson, S. R. (1999). Childhood risk factors for 

adolescent gang membership: Results from the Seattle Social Development Project. Journal of Research 

in Crime and Delinquency, 36(3), 300-322. 

Hirdes, J. P., & Scott, K. A. (1998). Social relations in a chronic care hospital: A whole network 

study of patients, family and employees. Social Networks, 20, 119-133. 

Hirschi, T. (1969). Causes of delinquency. Berkeley: University of California Press. 

Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression, 2nd ed. New York: John Wiley and 

Sons. 

Howell, J. C., & Egley, Jr., A. (2005). Moving risk factors into developmental theories of gang 

membership. Youth Violence and Juvenile Justice, 3(4), 334-354. 

Jensen, G. F. (2003). Gender variations in delinquency: Self-images, beliefs and peers as mediating 

mechanisms. In R. L. Akers and G. F. Jensen (Eds.), Social learning and the explanation of crime: New 

directions for a new century. New Brunswick, NJ: Transaction Publishers. 

Johnson, R. A., & Hoffmann, J. P. (2000). Adolescent cigarette smoking in U.S. racial/ethnic 

subgroups. Journal of Health and Social Behavior, 41, 392–407. 

Johnston, R., & Pattie, C. (2011).Social networks, geography and neighbourhood effects. In J. Scott and P. 

Carrington (Eds.), The SAGE handbook of social network analysis. Thousand Oaks, CA: Sage. 

Joseph, J. (1996). School factors and delinquency: A study of African American youths. Journal of 

Black Studies, 26(3), 340. 

Kadushin, C. (2002). The motivational foundation of social networks. Social Networks, 24(1), 79–91. 

Kandel, D. B., & Davies, M. (1991). Friendship networks, intimacy, and illicit drug use in young 

adulthood: A comparison of two competing theories. Criminology, 29,441-70. 

Keisner, J., Kerr, M., & Stattin, H. (2004). Very important persons in adolescence: Going beyond inschool, single friendships in the study of peer homophily. Journal of Adolescence, 27(5), 545-560. 

Kennedy, D. (2006). Old wine in new bottles: Policing and the lessons of pulling levers. In D. L. 

Weisburd and A. A. Braga (Eds.), Police innovation: Contrasting perspectives. New York: Cambridge 

University Press. 

Kirke, D. M. (1996). Collecting peer data and delineating peer networks in a complete network. 

Social Networks, 18, 333-346. 

Klein, M. W. (1971). Street gangs and street workers. Englewood Cliffs, N.J.: Prentice-Hall. 

Klein, M. W. (1995). The American street gang. New York: Oxford University Press. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

186 

Klein, M. W. (2006). Peer effects in naturally occurring groups. In K. A. Dodge, T. J. Dishion, and J. 

E. Lansford (Eds.), Deviant peer influences in programs for youth. Guilford Press, New York. 

Knoke, D., & Yang, S. (2008). Social network analysis. Los Angeles: Sage. 

Kreager, D. (2004). Strangers in the halls: Isolation and delinquency in school networks. 

Social Forces, 83(1), 351-390. 

Kreager, D. A., Rullison, K., & Moody, J. (2011). Delinquency and the structure of adolescent peer 

groups. Criminology, 49(1), 95-127. 

Krebs, V. E. (2002). Mapping networks of terrorist cells. Connections, 24(3), 43-52. 

Lahey, B. B., Gordon, R. A., Loeber, R., Stouthamer-Loeber, M., & Farrington, D. P. (1999). Boys 

who join gangs: A prospective study of predictors of first gang entry. Journal of Abnormal Child 

Psychology, 27(4), 261-276. 

Lee, J. S. (2004). Generating networks of illegal drug users using large samples of ego-network data. 

intelligence and security informatics, proceedings. Lecture Notes in Computer Science. Vol. 3073. 

Berlin: Springer-Verlag. 

Lin, N., Ye, X., & Ensel, W. M. (1999). Social support and depressed mood: A structural analysis. 

Journal of Health and Social Behavior, 40(4), 344-359. 

Liu, R. X. (2003). The moderating effects of internal and perceived external sanction threats on the 

relationship between deviant peer associations and criminal offending. Western Criminology Review, 4, 

191–202. 

Loeber, R., & Dishion, T. J. (1987). Antisocial and delinquent youth: Methods for their early 

identification. In J. D. Burchard & S. N. Burchard (Eds.), Prevention of delinquent behavior, 10, 75–89. 

Newbury Park, CA: Sage. 

Lopez, D. A., & Brummett, P.O. (2003). Gang membership and acculturation: ARSMA-II and 

choloization. Crime & Delinquency, 49(4),627-642. 

Marin, A. (2004). Are respondents more likely to list alters with certain characteristics? Implications 

for name generator data. Social Networks, 26, 289-307. 

Marsden, P. V. (1990). Network data and measurement. Annual Review of Sociology, 16, 435-463. 

Marsden, P. V., & Campbell, K. E. (1984). Measuring tie strength. Social Forces, 63(2), 482-501. 

Marsiglia, F. F., Kulis, S., & Hecht, M. L. (2001). Ethnic labels and ethnic identity as predictors of 

drug use among middle school students in the Southwest. Journal of Research on Adolescence, 11(1), 21–

48. 

Mathews, K. M., White, M. C., Soper, B., & von Bergen, C. W. 1998.Association of indicators and 

predictors of tie-strength. Psychological Reports, 83, 1459-1469. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

187 

Martinez, Jr., C. R. (2006). Effects of differential family acculturation on Latino adolescent substance 

use. Family Relations, 55(3), 306-317. 

Maxson, C., & Whitlock, M. L. (2002). Joining the gang: Gender differences in risk factors for gang 

membership. In C. R. Huff (Ed.), Gangs in America. 3d ed. Thousand Oaks, CA: Sage. 

Maxson, C. L., Whitlock, M. L., & Klein, M. W. (1998). Vulnerability to street gang membership: 

Implications for practice. The Social Service Review, 72(1), 70-91. 

McCarty, C., & Bernard, H. R. (2003). Social network analysis. In K. Christesen and D. Levinson 

(Eds.), Encyclopedia of community: From the village to the virtual world. Thousand Oaks, CA: Sage. 

McCarty, C., Killworth, P. D., & Rennell, J. (2007). Impact of methods for reducing respondent 

burden on personal network structural measures. Social Networks, 29, 300-315. 

McCarty, C., Molina, J. L., Aguilar, C., & Rota, L. (2007). A comparison of social network mapping 

and personal network visualization. Field Methods, 19, 145-162. 

McCarty, C., & Wutich, A. (2005). Conceptual and empirical arguments for including or excluding 

ego from structural analyses of personal networks. Connections, 26, 80–86. 

McCord, J. (1991). Family relationships, juvenile delinquency, and adult criminality. Criminology. 

29(3), 397-417. 

McGloin, J. M. (2004). Associations among criminal gang members as a defining factor of organization and as a 

predictor of criminal behavior: The gang landscape of Newark, New Jersey. Ann Arbor, MI: UMI. 

McGloin, J. M. (2005). Street gangs and interventions: Innovative problem solving with network 

analysis. COPS innovations: A closer look. Washington, D.C.: U.S. Department of Justice, Office of 

Community Oriented Policing Services. 

McGloin, J. M., & Piquero, A. R. (2009). On the relationship between co-offending network 

redundancy and offending versatility. Journal of Research in Crime and Delinquency, 47(1), 63-90. 

McQueen, A., Getz, J. G., & Bray, J. H. (2003). Acculturation, substance use, and deviant behavior: 

Examining detachment and family conflict as mediators. Child Development, 74, 1737-1750. 

Mednick, S. A., Gabrielli, W. F., & Hutchings, B. (1984). Genetic influences in criminal convictions: 

Evidence from an adoption cohort. Science, 224, 891–894. 

Meldrum, R. C., Young, J. T. N., & Weerman, F. M. (2009). Reconsidering the effect of self-control 

and delinquent peers: Implications of measurement for theoretical significance. Journal of Research in 

Crime and Delinquency, 46, 353-376. 

Mennis, J., & Harris, P. (2011). Contagion and repeat offending among urban juvenile delinquents. 

Journal of Adolescence, 34, 951-963. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

188 

Mercken, L., Snijders, T. A. B., Steglich, C., Vartiainen, E., & de Vries, H. (2010). Dynamics of 

adolescent friendship networks and smoking behavior. Social Networks, 32, 72-81. 

Miller, H. V., Barnes, J. C., & Hartley, R. (2009). Reconsidering Hispanic gang membership and 

acculturation in a multivariate framework. Crime and Delinquency. Advance online publication. 

Natarajan, M. (2006). Understanding the structure of a large heroin distribution network: A 

quantitative analysis of qualitative data. Journal of Quantitative Criminology, 22(2), 171.–92. 

Neaigus, A. (1998). The network approach and interventions to prevent HIV among injection drug 

users. Public Health Report, 113, 140-150. 

Osgood, D. W., & Anderson, A. L. (2004). Unstructured socializing and rates of delinquency. 

Criminology, 42, 519–550. 

Osgood, D. W., Wilson, J. K., O’Malley, P. M., Bachman, J. G., & Johnston, L. D. (1996). Routine 

activities and individual deviant behavior. American Sociological Review, 61, 635-55. 

Papachristos, A. V. (2005). Interpreting inkblots: Deciphering and doing something about modern 

street gangs. Criminology and Public Policy, 4(3), 643-652. 

Pearson, M., & Michell, L. (2000). Smoke rings: Social network analysis of friendship groups, 

smoking, and drug-taking. Drugs: Education, Prevention and Policy, 7(1), 21-37. 

Perrone, D., Sullivan, C. J., Pratt, T. C., & Margaryan, S. (2004). Parental efficacy, self-control, and 

delinquency: A test of a general theory of crime on a nationally representative sample of youth. 

International Journal of Offender Therapy and Comparative Criminology, 48, 298-312. 

Phinney, J. (1992). The Multigroup Ethnic Identity Measure: A new scale for use with diverse 

groups. Journal of Adolescent Research, 7, 156-176. 

Rankin, B. H., & Quane, J. M. (2000). Neighborhood poverty and the social isolation of inner city 

African American families. Social Forces, 79, 139-164. 

Rowe, D. C., & Farrington, D. P. (1997). The familial transmission of criminal convictions. 

Criminology, 35, 177–201.

Ryan, B., & Gross, N. C. (1943). The diffusion of hybrid seed corn in two Iowa communities. Rural 

Sociology, 8, 15-24. 

Sampson, R. J., Morenoff, J. D., & Raudenbush, S. W. (2005). Social anatomy of racial and ethnic 

disparities in violence. American Journal of Public Health, 95(2), 224–232. 

Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A 

multilevel study of collective efficacy. Science, 277, 918-924. 

Saner, H., & Ellickson, P. (1996). Concurrent Risk Factors for Adolescent Violence. Journal of 

Adolescent Health, 19(2), 94-103. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

189 

Schaefer, D. R. (2011). Youth co-offending networks: An investigation of social and spatial effects. 

Social Networks. In Press. 

Shoemaker, D. J. (2005). Theories of delinquency: An examination of explanations of delinquent behavior (5th 

ed.). New York: Oxford. 

Shoemaker, D. J. (2009). Juvenile delinquency. New York: Rowman & Littlefield. 

Simons, R. L., Simons, L. C., & Wallace, L. E. (2004). Families, delinquency, and crime: Linking society’s 

most basic institution to antisocial behavior. Los Angeles: Roxbury. 

Sutherland, E. H. (1947). Principles of criminology . 4th ed. Philadelphia, PA: J.B. Lippincott. 

Thornberry, T. P. (1998). Membership in youth gangs and involvement in serious and violent 

offending. In R. Loeber and D. P. Farrington (Eds.), Serious & violent juvenile offenders. Sage, Thousand 

Oaks, California. 

Thornberry, T. P., Lizotte, A. J., Krohn, M. D., Smith, C. A., & Porter, P. K. (2003). Causes and 

consequences of delinquency: Findings from the Rochester youth development study. In Taking stock 

of delinquency: Longitudinal research in the social and behavioral sciences, An interdisciplinary Series (11-46). 

Springer US. 

Tita, G. K., Riley, J., & Greenwood, P. (2003). From Boston to Boyle Heights: The process and 

prospectus of a pulling levers strategy in a Los Angeles barrio. In Scott H. Decker (Ed.), Policing 

Gangs and Youth Violence. Belmont, Calif.: Wadsworth. 

Turner, M. G., Piquero, A. R., & Pratt, T. C. (2005). The school context as a source of self-control. 

Journal of Criminal Justice, 33(4), 327-339. 

Valente, T. W. (2010). Social networks and health: Models, methods, and applications. New York: Oxford 

University Press. 

Valente, T. W. & Foreman, R. K. (1998). Integration and radiality: Measuring the extent of an 

individual’s connectedness and reachability in a network. Social Networks, 20(1), 89–105. 

Valente, T. W,. & Fujimoto, K. (2010). Bridging: Locating critical connectors in a network. Social 

Networks, 32, 212-220. 

Valente, T. W., & Rogers, E. M. (1995). The origins and development of the diffusion of 

innovations paradigm as an example of scientific growth. Science Communication, 16(3), 242–73. 

Vega, W. A., Koury, E. L., Zimmerman, R. S., Gil, A. G., & Warheit, G. J. (1995). Culture conflict 

and problem behaviors of Latino adolescents in home and school settings. Journal of Community 

Psychology, 23, 167-179. 

Vega, W., Zimmerman, R. S., Warheit, G. J., Apospori, E., & Gil, A. (1993). Risk factors for early 

adolescent drug use in four ethnic and racial groups. American Journal of Public Health, 83, 185-189. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
SOCIAL NETWORKS AND DELINQUENCY

190 

Vigil, J. D. (1988). Barrio gangs: Street life and identity in southern California. Austin, TX: University of 

Texas Press. 

Vigil, J. D. (2002). A rainbow of gangs: Street cultures in a mega-city. Austin, TX: University of Texas Press. 

Voelkl, K. E., Welte, J. W., & Wieczorek, W. E. (1999). Schooling and delinquency among white and 

African American adolescents. Urban Education, 34(1), 69-88. 

Warr, M. (1993). Age, peers, and delinquency. Criminology, 31, 17-40. 

Warr, M. (2002). Companions in crime: The social aspects of criminal conduct. New York: Cambridge 

University Press. 

Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge, MA: 

Cambridge University Press. 

Weerman, F. M., & Bijleveld, C. C. J. H. (2007). Birds of different feathers: School networks of 

serious delinquent, minor delinquent and non-delinquent boys and girls. European Journal of 

Criminology, 4(4), 357-383. 

Williams, C., & Latkin, C. (2007). Neighborhood socioeconomic status, personal network attributes, 

and use of heroin and cocaine. American Journal of Preventive Medicine, 32(6), 203-210. 

Xu, Y., Farver, J. M., Schwartz, D., & Chang, L. (2004). Social networks and aggressive behaviour in 

Chinese children. International Journal of Behavioral Development, 28(5), 401-410. 

Young, J. T. N. (2010). How do they ‘end up together’? A social network analysis of self-control, 

homophily, and adolescent relationships. Journal of Quantitative Criminology (online first). 

Young, J. T. N., Barnes, J. C., Meldrum, R. C., & Weerman, F. M. (2011). Assessing and explaining 

misperceptions of peer delinquency. Criminology, 49(2), 599-630. 

Zheng, T., Salganik, M., & Gelman, A. (2006). How many people do you know in prison: Using 

overdispersion in count data to estimate social structure in networks. Journal of the American Statistical 

Association, 101(474), 409-423. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
APPENDICES 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Appendix A. 

Norms and Networks of Latino Youth 

Youth Survey Instrument 

 

 

Ego:Intro 

Welcome to the Youth Survey! 

This survey can take up to two hours and includes detailed questions about your friends, people 

you hang out with or just know on the street, and other people who might be important to you. 

We will ask about any past or current involvement with general crime, your education, and your 

relationships with family and other people you name. 

You will receive a $50 gift card for completing this survey. 

Each question will be asked in this box in bold. There may also be directions in this box to help 

guide you. Please read all questions and directions carefully. In order to answer the question, you 

will either type your answer in a box below this one or click the dot next to the response that best 

answers the question. You will only be able to select one response for each question if there are 

categories listed. Once you answer the question, the NEXT button will appear and allow you to 

move to the next question. 

Let's try an example. 

Did you read the directions above?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:Example 

Great Job! 

Let's try one more! 

What is your favorite color?

Type your answer in the box below and then click NEXT. 

 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

 

Ego:Nicknames 

You are doing great! Now let's get started. 

The first section is going to ask questions about you and where you live. If there are any words 

or questions that you do not understand, please raise your hand and the interviewer will assist 

you. Remember, all your answers will be kept private and you don't have to answer any 

questions that you don't want to. If there is a question that you do not want to answer, please 

raise your hand and the interviewer will help you skip the question. 

Here is the first question: What are your nicknames or other names that friends and family 

call you?

 

Ego:Birthdate

What is your birth date or date of birth?

NOTE: Enter date as MONTH DAY, YEAR 

For example, if you were born on January 1, 2000, type it in the box using the full month, day, 

and year. 

 

Ego:Gender

Are you male or female?

Possible selections: 

• Male (value=1) 

• Female (value=0) 

 

Ego:LivingArrangement 

WHO do you currently live with? 

If you live with more than one group below, please select "other" and specify all those people 

you live with in the next question. 

Possible selections: 

• One or both parents (value=1) 

• Other relative (value=2) 

• Boyfriend or girlfriend (value=3) 

• Friend(s) (value=4) 

• Other (value=5) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Ego:LivingArrangementOther

Please specify who you live with.

 

Ego:LivingPlace

WHERE do you currently live?

Possible selections: 

• At a parent's home (value=1) 

• At a relative's home (other than parent) (value=2) 

• At a friend's home (value=3) 

• At the home of my boyfriend or girlfriend (value=4) 

• At a shelter (value=5) 

• At a hotel/motel/rooming house (value=6) 

• On the street (value=7) 

• Someplace else (value=8) 

 

Ego:LivingPlaceOther

Please specify where you live.

 

Ego:SupportSelf

Do you now support yourself? 

For example, are you responsible for paying your own rent, buying your own food, and paying 

other bills? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:OnlyAddress

Have you lived at your current address your entire life?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

 

Ego:TimeatAddress 

How long have you lived at your current address?

Please type the number of years below. 

Ego:LivedBefore

In what city did you live before you lived at your current address?

 

Ego:LivedUS

Is that in the United States? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:BeforeNeighborhood

Was that house in your current neighborhood?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:BeforeBoundary

Where was your previous home located? For example, what is the name of the most 

important location that would help us determine where it is, such as the intersection of two 

streets, the nearest park, or a name that everyone calls that neighborhood?

 

Ego:Neighborhood

In what neighborhood do you currently spend MOST of your time? 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

 

Ego:NeighStreet

Where is that? What is the name of the most important location that will help us determine 

where it is, such as the intersection of two streets or a park? 

 

Ego:HelpText

You are doing a great job! 

This is the first checkpoint, so please raise your hand and an interviewer will come and check the 

progress of your survey. 

Please do not move past this screen without the assistance of an interviewer.

 

Ego:LastYear

Were you in school at any point during the LAST SCHOOL YEAR?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:CurrentSchool

Are you currently in school?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:CurrentGrade

What grade or year are you in?

 

Ego:HighSchool 

Have you graduated from high school or earned a GED?

Possible selections: 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

• Graduated from High School (value=1) 

• Earned a GED (value=2) 

• Neither graduated or earned a GED (value=3) 

 

Ego:LastGrade 

What was the last grade in school that you completed?

 

Ego:CurrentHeld 

Have you ever had to repeat a grade in school, for any reason?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

Ego:AdultGraduate

Did any adult in your immediate family (mother, father, either grandparent, or older 

brother or sister) graduate from high school?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:ParentSupported

Did a parent or guardian regularly insist that you go to school and do well?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:ParentSupports 

Does a parent or guardian regularly insist that you go to school and do well?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:SchoolSports 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

The next few questions are going to ask you about the sorts of things you like to do and the kinds 

of groups you belong to. 

In the LAST SCHOOL YEAR were you on any athletic or sports teams at SCHOOL?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:SchoolSportSpecify

What sport(s)?

Possible selections: 

• Football (value=1) 

• Cheerleading (value=2) 

• Basketball (value=3) 

• Soccer (value=4) 

• Baseball (value=5) 

• Volleyball (value=6) 

• Track and Field (value=7) 

• Multiple sports team (value=8) 

• Other (value=9) 

 

Ego:SchoolSportMultiple 

Please specify which types of sports.

 

Ego:SchoolSportOther 

Please specify which type(s) of sports.

 

Ego:SchoolClubs 

In the LAST SCHOOL YEAR, have you participated in any SCHOOL groups, clubs, or 

activities?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

 

Ego:SchoolClubsSpecify 

Which SCHOOL groups, clubs, or activities have you participated in during the last school 

year?

Possible selections: 

• Band, chorus, or theatre value=1) 

• Dance club (value=2) 

• Government (value=3) 

• Math, science, art, or chess club (value=4) 

• Tutoring or homework help (value=5) 

• Cultural or language club (value=6) 

• Religious (value=7) 

• Multiple groups (value=8) 

• Other (value=9) 

 

Ego:SchoolClubsMultiple 

Please list which SCHOOL groups or clubs you have participate in during the last school 

year. 

 

Ego:SchoolClubsOther 

Please specify any SCHOOL groups that you participated in the last school year.

Possible selections: 

• Yes (value=1) 

• No (value=0) 

• Refused (value=98) 

Ego:CommGroups 

In the PAST YEAR, did you participate in any COMMUNITY activities, such as groups at 

the Y, Boys and Girls clubs, or hobby clubs? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:CommGroupsSpecify 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Which COMMUNITY activities have you participated in during the past year? 

Possible selections: 

• Dance groups or clubs (value=1) 

• Sports team(s) (value=2) 

• Band, chorus, or theatre (value=3) 

• Community center programs (value=4) 

• Cultural or language groups (value=5) 

• Girls or boys clubs (value=6) 

• Multiple groups (value=7) 

• Other (value=8) 

 

Ego:CommGroupMultiple 

Please specify which community groups or activities you participated in during the past 

year.

 

Ego:CommGroupOther 

Please specify which community groups you participated in during the past year.

 

Ego:CommSports 

In the PAST YEAR, did you participate in any COMMUNITY sports, such as organized 

sports or pickup games? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

Ego:CommSportSpecify 

What sport(s)?

Possible selections: 

• Football (value=1) 

• Basketball (value=2) 

• Soccer (value=3) 

• Baseball (value=4) 

• Volleyball (value=5) 

• Track and (value=6) 

• Cheerleading (value=7) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

• Multiple teams (value=8) 

• Other (value=9) 

 

Ego:CommSportMultiple 

Please specify which community sports you participated in during the past year.

 

Ego:CommSportOther 

Please specify which community sport you participated in during the past year.

 

Ego:Volunteer 

Did you participate in any volunteer work during the PAST YEAR?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:FamilyCare 

Do you help your family regularly take care of a brother, sister, or cousin?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:JobSchool 

Do you currently have a job?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:JobSummer 

Did you have a job this past SUMMER?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

 

Ego:YourReligion 

What is your religion?

Possible selections: 

• Christian (e.g., Catholic, Pentecostal) (value=1) 

• Jewish (value=2) 

• Buddhist (value=3) 

• Muslim (value=4) 

• Mormon (value=5) 

• Jehovah's Witness (value=6) 

• Other (value=7) 

• None (value=8) 

 

Ego:YourReligionOther 

Please specify.

 

Ego:Worship 

In the PAST YEAR, how often have YOU usually gone to religious services? 

Possible selections: 

• At least once a week (value=1) 

• Almost every week (value=2) 

• About once a month (value=3) 

• Seldom (value=4) 

• Never (value=5) 

 

Ego:WorshipParents1 

When you attend religious services, how often do you usually go with your PARENTS or 

OTHER RELATIVES? 

Possible selections: 

• Always (value=1) 

• Sometimes (value=2) 

• Never (value=3) 

 

Ego:WorshipParents2 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

When you attend religious services, how often do you usually go with your PARENTS or 

OTHER RELATIVES? 

Possible selections: 

• Always (value=1) 

• Sometimes (value=2) 

• Never (value=3) 

 

Ego:WorshipParents3 

When you attend religious services, how often do you usually go with your PARENTS or 

OTHER RELATIVES? 

Possible selections: 

• Always (value=1) 

• Sometimes (value=2) 

• Never (value=3) 

 

Ego:WorshipParents4

When you attend religious services, how often do you usually go with your PARENTS or 

OTHER RELATIVES? 

Possible selections: 

• Always (value=1) 

• Sometimes (value=2) 

• Never (value=3) 

 

Ego:WorshipSchool1 

When you attend religious services, how often do you see any of your FRIENDS FROM 

SCHOOL there? 

Possible selections: 

• Always (value=1) 

• Sometimes (value=2) 

• Never (value=3) 

 

Ego:WorshipSchool2 

When you attend religious services, how often do you see any of your FRIENDS FROM 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

SCHOOL there? 

Possible selections: 

• Always (value=1) 

• Sometimes (value=2) 

• Never (value=3) 

 

Ego:WorshipSchool3 

When you attend religious services, how often do you see any of your FRIENDS FROM 

SCHOOL there? 

Possible selections: 

• Always (value=1) 

• Sometimes (value=2) 

• Never (value=3) 

 

Ego:WorshipSchool4 

When you attend religious services, how often do you see any of your FRIENDS FROM 

SCHOOL there? 

Possible selections: 

• Always (value=1) 

• Sometimes (value=2) 

• Never (value=3) 

 

Ego:FamilyKnows 

This next section asks questions about your family. Your family can be who you define as family 

and whatever family means to you. 

Read each statement and indicate how much you agree or disagree with that statement about 

your family. 

My family knows what I mean when I say something.

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Ego:FamilyView 

My family and I have the same views about what is right and wrong.

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

Ego:FamilyFeel 

I am able to let others in my family know how I really feel.

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:FamilySuccess 

My family and I have the same views about being successful.

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:FamilyTalk

I'm available when others in my family want to talk to me.

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

 

Ego:FamilyListen 

I listen to what my other family members have to say, even when I disagree.

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:FamilyHelp 

My family members ask each other for help.

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:FamilyTime 

My family members like to spend free time with each other.

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:FamilyClose 

My family members feel very close to each other.

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:FamilyActivities 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

We can easily think of things to do together as a family.

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:FamilyCompared 

Compared to most families, would you say yours was very close to each other, somewhat 

close, not very close, or not close at all. 

Possible selections: 

• Very close (value=1) 

• Somewhat close (value=2) 

• Not very close (value=3) 

• Not close at all (value=4) 

 

Ego:CommHaveEntertain 

Now, please think about the neighborhood you LIVE IN and tell me whether each statement is 

true or false. 

There are entertainment places in my neighborhood such as movie theatres and shopping 

malls.

Possible selections: 

• True (value=1) 

• False (value=0) 

 

Ego:CommUseEntertain 

I use entertainment places in my neighborhood such as movie theatres and shopping malls.

Possible selections: 

• True (value=1) 

• False (value=0) 

 

Ego:CommHaveRec

There are recreational facilities available in my neighborhood such as gyms. 

Possible selections: 

• True (value=1) 

• False (value=0) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Ego:CommUseRec

I use the recreational facilities available in my neighborhood such as gyms.

Possible selections: 

• True (value=1) 

• False (value=0) 

 

Ego:Ethnicity

This section asks about you and your family's race and cultural background. Remember to let 

the interviewer know if there are any words or questions that you do not understand. First you 

will asked about your Latino descent and then about your race. 

Are you of Hispanic, Latino/a, or Spanish origin or descent?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:Race 

And do you consider yourself to be White, Black/African American, or some other race?

Possible selections: 

• White (value=1) 

• Black/African American (value=2) 

• Other Race (value=3) 

 

Ego:RaceOther 

Please specify.

 

Ego:MotherBorn 

What country was your mother born in?

 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Title: Ego:FatherBorn 

What country was your father born in?

 

Ego:BornUS 

Were YOU born in the United States?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

Ego:TimeOutsideUs 

Have YOU lived in ANOTHER country? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:TimeinUS 

How many years have YOU lived in the United States? 

 

Ego:TimeinUSforeign 

How many years have YOU lived in the United States? 

 

Ego:WhyUS 

What is the main reason why YOU came to the United States? 

Possible selections: 

• For work (value=1) 

• To attend school (value=2) 

• To be with family (value=3) 

• Was studying abroad (value=4) 

• Other reason (value=98) 

 

Ego:WhyUSOther 

Please specify.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Ego:WhyUSforeign 

What is the main reason why YOU came to the United States? 

Possible selections: 

• Work (value=1) 

• School (value=2) 

• Family (value=3) 

• Other (value=4) 

 

Ego:WhyUSforeignOther 

Please specify.

 

Ego:Speak 

What different languages do you speak? 

Possible selections: 

• Only Spanish (value=1) 

• Only English (value=2) 

• Both Spanish and English (value=3) 

• Other (one language) (value=4) 

• Other (multiple languages) (value=5) 

 

Ego:SpeakOtherOne 

Please specify.

 

Ego:SpeakOtherMult 

Please specify. 

 

Ego:SpeakMultiple

Do you speak one better than the other? 

NOTE:If the languages you speak are not included, please specify the relationship in the OTHER 

category. 

Possible selections: 

• Spanish better than English (value=1) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

• Both Spanish and English equally (value=2) 

• English better than Spanish (value=3) 

• Other combination of languages (value=4) 

 

Ego:SpeakMultipleSpec 

Do you speak one better than the other? 

NOTE:If the languages you speak are not included, please specify the relationship in the OTHER 

category. 

Possible selections: 

• Spanish better than English (value=1) 

• Both Spanish and English equally (value=2) 

• English better than Spanish (value=3) 

• Other (value=4) 

 

Ego:SpeakMultiOther 

Please specify. 

 

Ego:LanguageHome1 

What languages do you usually speak at HOME? 

Possible selections: 

• Spanish (value=1) 

• English (value=2) 

• Both Spanish and English (value=4) 

• Other (value=3) 

 

Ego:LanguageHOther1 

Please specify. 

Ego:LanguageHome2 

What languages do you usually speak at HOME? 

Possible selections: 

• Spanish (value=1) 

• English (value=2) 

• Both Spanish and English (value=4) 

• Other (value=3) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

 

Ego:LanguageHOther2 

Please specify. 

 

Ego:LanguageFriends1 

What languages do you usually speak with your FRIENDS? 

Possible selections: 

• Spanish (value=1) 

• English (value=2) 

• Both Spanish and English (value=3) 

• Other (value=4) 

 

Ego:LanguageFOther1 

Please specify. 

 

Ego:LanguageFriends2 

What languages do you usually speak with your FRIENDS? 

Possible selections: 

• Spanish (value=1) 

• English (value=2) 

• Both Spanish and English (value=3) 

• Other (value=4) 

 

Ego:LanguageFOther2

Please specify.

 

Ego:Accept1 

For the next set of questions, please indicate how much you agree or disagree with each 

statement about race and culture. 

I have spent time trying to find out more about my own ethnic group, such as its history, 

traditions, and customs. 

Possible selections: 

• Strongly Agree (value=1) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:Accept2 

I am active in organizations or social groups that include mostly members of my own 

ethnic group. 

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:Accept3 

I have a clear sense of my ethnic background and what it means for me.

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:Accept4 

I think a lot about how my life will be affected by my ethnic group membership. 

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:Accept5 

I am happy that I am a member of the ethnic group I belong to. 

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

• Strongly Disagree (value=5) 

Ego:Accept6 

I have a strong sense of belonging to my own ethnic group. 

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:Accept7 

I understand pretty well what my ethnic group membership means to me.

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:Accept8 

In order to learn more about my ethnic background, I have often talked to other people 

about my ethnic group. 

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:Accept9 

I have a lot of pride in my ethnic group. 

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Ego:Accept10 

I participate in cultural practices of my own ethnic group, such as special food, music, or 

customs. 

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:Accept11 

I feel strong attachment toward my own ethnic group. 

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:Accept12 

I feel good about my cultural or ethnic background. 

Possible selections: 

• Strongly Agree (value=1) 

• Agree (value=2) 

• Neither Agree or Disagree (value=3) 

• Disagree (value=4) 

• Strongly Disagree (value=5) 

 

Ego:TalkGangs 

In these surveys, we want to understand the different experiences you may have had at home, in 

your school, and in your neighborhood. This next section asks questions about how much you 

think your neighborhood is 

affected by gangs. 

For instance: 

Do people TALK about gangs in your neighborhood? 

Possible selections: 

• Mostly Yes (value=1) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

• Mostly No (value=0) 

• Don't Know (value=97) 

Ego:GangActivity 

Is there a lot of gang ACTIVITY around your neighborhood? 

Possible selections: 

• Mostly Yes (value=1) 

• Mostly No (value=0) 

• Don't Know (value=97) 

Ego:GangsClose 

Are there gang RIVALRIES in your neighborhood? 

Possible selections: 

• Mostly Yes (value=1) 

• Mostly No (value=0) 

• Don't Know (value=97) 

 

Ego:PressureGang 

Is there PRESSURE on neighborhood youth to JOIN gangs in your neighborhood? 

Possible selections: 

• Mostly Yes (value=1) 

• Mostly No (value=0) 

• Don't Know (value=97) 

 

Ego:ImportantGang 

Among the youth in the neighborhood, how important is it to be a MEMBER of a gang? 

Possible selections: 

• Very important (value=3) 

• Somewhat important (value=2) 

• Not important at all (value=1) 

• Don't Know (value=97) 

 

Ego:NeighborsGang 

Are any of the people living on your STREET members of a gang? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

• Don't Know (value=97) 

 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Ego:GanginNeigh 

Do gang members HANG OUT on your street? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

• Don't Know (value=97) 

 

Ego:ApproachGang 

Since you have been living in this area, have you ever been approached to join a gang? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

Ego:ApproachResponse 

How did you respond? 

 

Ego:ThoughtGang 

Have you ever thought about joining a gang? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:HurtGang 

Are you afraid that someone will hurt you if you don't join a gang? 

Possible selections: 

• Often Afraid (value=3) 

• Sometimes Afraid (value=2) 

• Never Afraid (value=1) 

 

Ego:Group 

The next set of questions are going to ask you about groups of friends and the reasons why 

people hang out in groups or gangs. 

Is there a group of friends that you hang around with a lot? 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

It can be any group or even an undefined or informal group of people. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:StreetGang

Do you consider that GROUP to be a street gang? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:PartGang 

Have YOU EVER been a MEMBER of a street gang? 

Possible selections: - Yes (value=1) 

- No (value=0) 

 

Ego:StillGang 

Are you still? 

Possible selections: - Yes (value=1) 

- No (value=0) 

 

Ego:Affiliation 

What other names do you use to refer to this group other than "gang"? 

For example, do you just call it a gang or do you use clique, organization, nation, etc. List all 

names that are commonly used. 

 

Ego:AgeGang1 

How old were you when you first began hanging out with your gang? 

 

Ego:FormerAgeGang 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Why did you "quit" being a gang member? 

Please indicate the proper term below as well if, for example, you call it "deactivate" and not 

"quit". 

 

Ego:StillHangGang 

Do you still hang out with members of your former gang?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

• Refused (value=98) 

Ego:GangNameCG 

Would you be willing to tell us the name of your gang? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:GangNameSpecCG 

What is it? 

 

Ego:NumberGangCG 

How many people are in your gang? 

 

Ego:GangGirlsCG 

How many of these are girls? 

 

Ego:GangYoungCG 

How old is the YOUNGEST member of your gang? 

 

Ego:GangOldCG 

How old is the OLDEST member of your gang? 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

 

Ego:GangTimeCG

How long has your gang been around? 

 

Ego:GangTerritoryCG 

Does the gang have a territory it claims as its own? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:GangStreetsCG 

Can you give me the cross streets or names of the streets where the gang hangs out? 

 

Ego:HelpTextCG 

You are doing a great job! 

This is another checkpoint, so please raise your hand and an interviewer will come and check 

your progress with the survey. 

Please do not move past this screen without the assistance of an interviewer. 

 

Ego:GangNeighborCG 

Do most of the members live in your neighborhood? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:GangClaimCG 

What happens when a member goes to prison, do they still claim your gang? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

• It Depends (value=2) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

• No Members in Prison (value=3) 

Ego:GangPrisonCGy 

Are you still in touch with them while they are in prison? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

• It Depends (value=2) 

• Not Applicable (value=96) 

 

Ego:GangPrisonCGn 

Are you still in touch with them while they are in prison? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

• It Depends (value=2) 

• Not Applicable (value=96) 

 

Ego:GangPrisonCGd 

Are you still in touch with them while they are in prison? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

• It Depends (value=2) 

• Not Applicable (value=96) 

 

Ego:GangReturnCG 

What happens when a member of your gang who was in prison comes back to the 

neighborhood? Are they still in the gang? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

• It Depends (value=2) 

• No members in prison (value=3) 

 

Ego:GangNameFG 

Would you be willing to tell us the name of the gang you were in? 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

Ego:GangNameSpecFG 

What was it? 

 

Ego:NumberGangFG 

How many people were in the gang? 

 

Ego:GangGirlsFG 

How many of those were girls? 

 

Ego:GangYoungFG 

How old was the YOUNGEST member of the gang? 

 

Ego:GangOldFG 

How old was the OLDEST member of the gang? 

 

Ego:GangTimeFG 

How long has that gang been around? 

 

Ego:GangTerritoryFG 

When you were in the gang, did the gang have a territory it claimed as its own?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

• Refused (value=98) 

 

 

Ego:GangStreetsFG 

Can you give me the cross streets or names of the streets where the gang hung out? 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

 

Ego:HelpTextFG 

You are doing a great job! 

This is another checkpoint, so please raise your hand and an interviewer will come and check 

your progress with the survey. 

Please do not move past this screen without the assistance of an interviewer. 

 

Ego:GangNeighborFG 

When you were in the gang, did most of the members live in your neighborhood? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:GangClaimFG 

When you were in the gang, what happened when a member went to prison, did they still 

claim the gang? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:GangPrisonFG 

Were you still in touch with them while they were in prison? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

• Not Applicable (value=96) 

 

Ego:GangReturnFG 

What happened when a member of the gang who was in prison came back to the 

neighborhood? Were they still in the gang? 

Possible selections: 

• Yes (value=1) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

• No (value=0) 

• Not Applicable (value=96) 

Ego:ReasonFriends 

Text: Now, please think about the group of friends that you are MOST involved with. Can you 

think of one? 

This can be an informal group of friends. If more than one comes to mind, please choose one. 

There are lots of reasons young people HANG OUT WITH THEIR FRIENDS. Considering 

YOUR GROUP OF FRIENDS, what are the REALLY important reasons you chose your 

friends? 

 

Title: Ego:ReasonGang 

Now, please think about your gang. 

There are lots of reasons young people JOIN GANGS. Considering YOUR GANG, what 

are the REALLY important reasons you chose your gang? 

 

Ego:ReasonFormerGang 

Text: Now, think about your former gang. 

There are lots of reasons young people JOIN GANGS. Considering YOUR FORMER 

GANG, what are the REALLY important reasons you chose that gang? 

 

Ego:WhyFriendFriend 

Next you will be given a list of other reasons why some people hang out with their friends. 

Please indicate if any of them were important to you for hanging with your friends. 

It is OK if you already included some of them in your previous answer. 

Did you select your group of friends, to make friends?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyFriendCG 

Next you will be given a list of other reasons why some people join gangs. Please indicate if any 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

of them were important to you for when selecting your gang. 

It is OK if you already included some of them in your previous answer. 

Did you select your gang, to make friends?

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyFriendFG 

I'm now going to read you a list of other reasons why some people join gangs and I'd like 

you to tell me if any of them were important to you for joining your former gang. 

OK. Listen to each and then tell me if it is important for you.

To make friends.

Possible selections: 

• Yes (value=1) 

• No (value=0) 

• Refused (value=98) 

 

Ego:WhyRep 

To get a reputation.

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyFillup 

To fill up empty time. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhySupport 

For support and loyalty. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

 

Eo:WhyImportant 

To feel important. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyNoticed 

To be noticed. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyBelong 

To feel like you belong to something. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyAvoid 

To avoid home. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyTrouble 

To keep out of trouble. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyProtect 

For protection.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

Ego:WhyExcite 

For excitement. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyIllegal 

To get away with illegal activities (such as stealing or breaking into cars). 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyGroup 

To participate in group activities. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyOwn 

To have a territory of your own.

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyRespect 

To get your parents' respect. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyMember 

Because someone in your family was in the group. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

Ego:WhyMeet 

To meet guys/girls easily. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyProud 

Because the group is one you can feel proud of. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyMoney 

To get money or other things. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyFriend 

Because a friend was in the group. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyFamily 

To get what you don't get from your family. 

For example, to get support or respect. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Ego:WhyLike 

To be with other people like you. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyCool 

Because the things they do are cool. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WhyPressure 

Because I did NOT have a choice. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:DamagedProperty 

Our purpose is to understand your experiences and activities. We want you to remember that we 

will NOT hare this information with anyone (including people from Identity and the CYOC) and 

we will not judge you. Everything that you put in this survey will be kept a secret. 

For this next set of questions, please indicate if you have EVER done any of these things. 

Have you ever damaged, destroyed, or marked up someone else's property on purpose? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:DamagedPropertyRecent 

In the PAST 6 MONTHS, have you damaged, destroyed, or marked up someone else's 

property on purpose? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

 

Ego:Stolen 

Have you ever avoided PAYING for things, like a movie, taking bus rides, or anything else? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:StolenRecent 

In the LAST 6 MONTHS, have you avoided PAYING for things, like a movie, taking bus 

rides, or anything else? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:Stolen2 

Have you ever tried to STEAL or actually stolen money or things worth $100 or less? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:Stolen2recent 

In the LAST 6 MONTHS, have you tried to STEAL or actually stolen money or things 

worth $100 or less? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:Stolen3 

Have you ever tried to STEAL or actually stolen money or things worth more than $100? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Ego:Stolen3recent 

In the LAST 6 MONTHS, have you tried to STEAL or actually stolen money or things 

worth more than $100? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:Stolen4 

Have you ever tried to <u>STEAL</u> or actually stolen a car or other motor vehicle? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:Stolen4recent 

In the LAST 6 MONTHS, have you tried to STEAL or actually stolen a car or other motor 

vehicle? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:GangFight 

Have you ever been involved in a gang FIGHT? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:GangFightRecent 

In the LAST 6 MONTHS, have you been involved in a gang FIGHT? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:UsedDrugs 

Have you ever USED illegal drugs such as marijuana, crack, heroine, or 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

methamphetamine? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

Ego:UsedDrugsRecent 

In the LAST 6 MONTHS, have you USED illegal drugs such as marijuana, crack, heroine, 

or methamphetamine? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:SoldDrugs 

Have you ever SOLD illegal drugs such as marijuana, crack, heroine, or 

methamphetamine? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:SoldDrugsRecent 

In the LAST 6 MONTHS, have you SOLD illegal drugs such as marijuana, crack, heroine, 

or methamphetamine? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:Weapon 

Have you ever carried a WEAPON such as a gun or knife? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:WeaponRecent 

How many times in the LAST 6 MONTHS have you used a weapon or force to get money 

or things from people? 

Possible selections: 

• 1 time (value=2) 

• 2 or 3 times (value=3) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

• 4 or more times (value=4) 

• Never (value=1) 

 

Ego:WeaponTimes 

How many times in your LIFETIME? 

Possible selections: 

• 1 time (value=2) 

• 2 or 3 times (value=3) 

• 4 or more times (value=4) 

• Never (value=1) 

 

Ego:WeaponArrest 

Have you ever been arrested for this offense? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Ego:AttackRecent 

How many times in the LAST 6 MONTHS have you ATTACKED someone with a 

WEAPON or with the idea of SERIOUSLY hurting or killing them? 

Possible selections: 

• 1 time (value=2) 

• 2 or 3 times (value=3) 

• 4 or more times (value=4) 

• Never (value=1) 

 

Ego:AttackTimes 

How many times in your LIFETIME? 

Possible selections: 

• 1 time (value=2) 

• 2 or 3 times (value=3) 

• 4 or more times (value=4) 

• Never (value=1) 

 

Ego:AttackArrest 

Have you ever been arrested for this offense? 

Possible selections: 

• Yes (value=1) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

• No (value=0) 

 

Ego:AskInterviewer 

Great job! 

You are now ready to start the next portion of the survey. Please raise your hand and an 

interviewer will come and assist you with the instructions. 

Please do not move past this screen without the assistance of an interviewer. 

 

Questions of type: Alter Prompt 

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. 

NOTE: Please use each person's full name and if you do not know a person's full name, include 

the first initial of his/her last name or a nickname that will let us know that John and John are 

two different people. For example, 

 John Smith 

 John S 

 John Smitty 

DO NOT MOVE PAST THIS SCREEN. Raise your hand and the interviewer will check your 

progress once you have entered 20 names. 

 

(1) 

(2) 

(3) 

(4) 

(5) 

(6) 

(7) 

(8) 

(9) 

(10) 

(11) 

(12) 

(13) 

(14) 

(15) 

(16) 

(17) 

(18) 

(19) 

(20) 

Questions of type: Alter 

 

Alter:Intro 

The next portion of the survey will ask a set of about 15 questions for each of the people you just 

listed. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

If you have any questions along the way, please raise your hand and an interviewer will assist 

you. 

Please click NEXT to begin. 

[Note: the following questions were asked for each of the 20 alters entered by the 

respondent.] 

 

Alter:Nicknames 

What are ______________’s nicknames or other names that friends and family use to refer 

to ________? 

 

Alter:Age 

How old is ______________? 

 

Alter:Gender

Is ______________ male or female? 

Possible selections: 

• Male (value=1) 

• Female (value=0) 

 

Alter:Describe 

Can you name one thing to describe ______________ so that we can tell the difference 

between this person and another person with the same name? 

For example, I have two friends named Sally, but one of my friends, Sally, has pink hair and the 

other one has her nose pierced. Another example would be if one person has crazy hair or if 

another is really tall. 

 

Alter:Relationship 

Who is ______________ 

Possible selections: 

• Mom (value=1) 

• Dad (value=2) 

• Sibling (brother, sister, step-brother, step-sister, or half-sibling) (value=3) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

• Aunt or Uncle (value=3) 

• Grandparent (value=4) 

• Cousin (value=5) 

• Friend (value=6) 

• Neighbor (value=7) 

• Teacher (value=8) 

• Boyfriend/Girlfriend (value=9) 

• Husband/Wife (value=10) 

• Other (value=11) 

 

Alter:RelationshipOther 

Please specify. 

 

Alter:LiveInNeighborhood 

Does ______________ live in your neighborhood? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Alter:Met 

How did you meet ______________? 

Possible selections: 

• Relative (value=1) 

• At school (value=2) 

• Hanging out (value=3) 

• Through a friend (value=4) 

• Through a relative (value=5) 

• Church (value=6) 

• Neighbor (value=7) 

• At a party (value=8) 

• Work (value=9) 

• Other (value=10) 

 

Alter:MetOther 

Please specify. 

 

Alter:Ethnicity 

Is ______________of Hispanic, Latino, or Spanish origin or descent? 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Possible selections: 

• No (value=0) 

• Hispanic (value=1) 

• Latino/a (value=2) 

• Spanish (value=3) 

• Other (value=4) 

Alter:EthnicityOther 

Please specify. 

 

Alter:Birthplace 

What country was ______________ born in? 

Possible selections: 

• United States (value=1) 

• Mexico (value=2) 

• El Salvador (value=3) 

• Guatemala (value=4) 

• Honduras (value=5) 

• Nicaragua (value=6) 

• Other Central American country (value=7) 

• Puerto Rico, the Dominican Republic, or other Caribbean country (value=8) 

• Peru (value=9) 

• Bolivia (value=10) 

• Colombia (value=11) 

• Other South American country (value=12) 

• Other country not listed (value=13) 

• Don't Know (value=97) 

Alter:BirthplaceCentral 

Please specify. 

 

Alter:BirthplaceSouth 

Please specify. 

 

Alter:Birthplaceother 

Please specify. 

 

Alter:TimeSpent 

How much time do you spend each week hanging out with ______________? 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

Possible selections: 

• A whole lot (value=1) 

• Some (value=2) 

• Not any (value=3) 

 

Alter:PosRelationship 

How much do you like ______________? 

Possible selections: 

• A whole lot (value=1) 

• Some (value=2) 

• Not at all (value=3) 

 

Alter:Advice 

If you needed some information or advice about something, is ______________ someone 

you could go to? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Alter:CarriesGun 

The next set of questions asks you about the different experiences ______________ may have 

had with getting into trouble. Our purpose is to understand these activities, and we want you to 

remember that we will NOT share this information with anyone. Everything that you tell me 

about ______________ will be kept a secret. 

How likely is it that ______________ carries a gun (including in his/her car)? 

Possible selections: 

• Very likely (value=1) 

• Somewhat likely (value=2) 

• Not at all likely (value=3) 

• Don't Know (value=97) 

 

Alter:SoldDrugs 

Has ______________ EVER sold illegal drugs such as marijuana, cocaine, or crack? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

• Don't Know (value=97) 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

 

Alter:GangFight 

How likely is it that ______________ How likely is it that $$ has been in a gang fight over 

the LAST YEAR? 

Possible selections: 

• Very likely (value=1) 

• Somewhat likely (value=2) 

• Not at all likely (value=3) 

• Don't Know (value=97) 

 

Alter:GangTitle 

Would you consider ______________ to be in a gang? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Alter:GangName 

Would you be willing to tell me what name ______________ 's gang goes by? 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Alter:GangName2 

What is it? 

 

Alter:AlterEgo 

Have YOU ever in your life committed a crime WITH ______________? Please think of 

any crime that you know is against the law. 

Possible selections: 

• Yes (value=1) 

• No (value=0) 

 

Alter:Violence 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix A 

How likely is ______________ to use violence to get what he/she wants? 

Possible selections: 

• Very likely (value=1) 

• Somewhat likely (value=2) 

• Not at all likely (value=3) 

• Don't Know (value=97) 

Questions of type: Alter Pair 

 

Alter Pair:IndependentContact 

[Note: this question was asked for all possible combinations of alters entered by the 

respondent.] 

What is the likelihood that ______________ and ______________ 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? 

Possible selections: 

• Not at all (value=1) 

• They might, but not sure (value=2) 

• Definitely (value=3) 

• Don't know (value=4) 

 

 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix B. Variable Definitions 

Variable Definition Used in 

Final 

Models? 

Dependent Variables 

Ego delinquency scale Delinquency additive scale: (ever in lifetime) sum of 

damaged property, stolen goods (four levels), been in 

a gang fight, sold drugs, used a weapon, attacked with 

intent to harm (α=.829). 

Yes 

Ego 6 mos delinquency 

 scale 

Delinquency additive scale for activity over last 6 

months (α=.824). 

Yes 

Ego serious delinquency 

 scale 

Serious delinquency additive scale: (ever in lifetime) 

sum of 2 levels of stolen goods, been in gang fight, 

sold drugs, used a weapon, attacked with intent to 

harm (α=.770). 

Yes 

Ego delinquency binary Respondent is either in a gang, in a gang fight, sold 

drugs, carried a weapon or attacked someone with 

intent to harm. 

Yes 

Ego carry weapon Respondent has carried a weapon. Yes 

Ego sold drugs Respondent has sold illegal drugs. Yes 

Ego attack Respondent has attacked someone with the intent to 

seriously harm. 

Yes 

Ego gang fight Respondent has been in a gang fight. Yes 

Ego gang member Respondent identifies as ever being a member of a 

gang or as being part of a group that is a street gang. 

Yes 

Independent Variables 

Peer delinquency Proportion of delinquent friends in the respondent’s 

personal network (delinquent is defined as either being 

a member of a street gang, being in a gang fight, 

selling illegal drugs, carrying a weapon, or using 

violence to get what they want). 

Yes 

Alter delinquency Proportion of delinquent alters in the respondent’s 

personal network (delinquent is defined as either being 

a member of a street gang, being in a gang fight, 

selling illegal drugs, carrying a weapon, or using 

violence to get what they want). 

Yes 

Peer in neighborhood Proportion of friends in the respondent's personal 

network who live in the same neighborhood as the 

respondent. 

Yes 

Alter in neighborhood Proportion of alters in the respondent's personal 

network who live in the same neighborhood as the 

respondent. 

Yes 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix B. Variable Definitions 

Variable Definition Used in 

Final 

Models? 

Peers/friends in network Proportion of alters in the respondent's personal 

network who are friends. 

Yes 

Alter male friends Proportion of respondent's alters who are male 

friends. 

No 

Average alter age Average age of the alters in the respondent's personal 

network. 

No 

Ethnicity scale Additive measure of the following 12 items that are 

part of the Multigroup Ethnic Identity Measure 

(MEIM) as developed by Phinney (1992) (α=.89): 

• I have spent time trying to find out more 

about my own ethnic group, such as its 

history, traditions, and customs. 

• I am active in organizations or social groups 

that include mostly members of my own 

ethnic group. 

• I have a clear sense of my ethnic background 

and what it means for me. 

• I think a lot about how my life will be affected 

by my ethnic group membership. 

• I am happy that I am a member of the ethnic 

group I belong to. 

• I have a strong sense of belonging to my own 

ethnic group. 

• I understand pretty well what my ethnic group 

membership means to me. 

• In order to learn more about my ethnic 

background, I have often talked to other 

people about my ethnic group. 

• I have a lot of pride in my ethnic group. 

• I participate in cultural practices of my own 

ethnic group, such as special food, music, or 

customs. 

• I feel strong attachment toward my own ethnic 

group. 

• I feel good about my cultural or ethnic 

background. 

No 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix B. Variable Definitions 

Variable Definition Used in 

Final 

Models? 

Ego separation scale Additive scale of respondent being born abroad, 

respondent parents' being born abroad, proportion of 

lifetime respondent lived abroad, respondent speaks 

non-English language, respondent speaks non-English 

language with friends, respondent speaks non-English 

language at home (α=.65). 

Yes 

Alters lot of time spent Proportion of alters respondent spends a lot of time 

with. 

No 

Alters like a lot Proportion of alters respondent likes a lot. No 

Alters advice support Proportion of alters respondent would go to for 

advice. 

No 

Alters time spent 

delinquent 

Proportion of alters respondent spends a lot of time 

with and are delinquent. 

No 

Alters advice support not 

delinquent 

Proportion of alters respondent would go to for 

advice and are not delinquent. 

Yes 

Alters advice support 

delinquent 

Proportion of alters respondent would go to for 

advice and are delinquent. 

No 

Homophily index Additive scale of four alter-level variables (aggregated 

across all 20 alters): number of respondents' alters 

who are born in the same country; number of 

respondents' alters who are of the same ethnicity; 

number of respondents' alters who are of the same age 

(+/- one year); and number of respondents' alters who 

are of the same gender. The scale can range from 0 to 

80. 

No 

Num components The number of subgroups in the respondent’s 

personal network. Components are a measure of 

separately maintained groups (no link exists between 

any two nodes of the different groups) within the 

larger personal network. 

Yes 

Ego is isolate Respondent is/is not identified as an alter by another 

ego in the network and none of the respondent's alters 

are named by another ego. 

Yes 

W_betweenness Betweenness centrality is calculated using overlapping 

networks of all ego data. The measure is calculated by 

taking every pair in the overlapping whole network 

and counting how many times a node can interrupt 

the shortest paths (geodesic distance) between the two 

nodes of the pair. For standardization, the 

denominator is (n-1)(n-2)/2. 

Yes 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix B. Variable Definitions 

Variable Definition Used in 

Final 

Models? 

Ego network density Number of alters in respondent's network who are 

connected to one another (export from EgoNet). 

No 

Family cohesion scale Additive measure of the following 11 items related to 

closeness of family; similarity of views and values 

(α=.90): 

• My family knows what I mean when I say 

something. 

• My family and I have the same views about 

what is right and wrong. 

• I am able to let others in my family know how 

I really feel. 

• My family and I have the same views about 

being successful. 

• I'm available when others in my family want to 

talk to me. 

• I listen to what my other family members have 

to say, even when I disagree. 

• My family members ask each other for help. 

• My family members like to spend free time 

with each other. 

• My family members feel very close to each 

other. 

• We can easily think of things to do together as 

a family. 

• Compared to most families, would you say 

yours was very close to each other, somewhat 

close, not very close, or not close at all? 

Yes 

Parent support in 

education 

Response to survey item: Did a parent or guardian 

regularly insist that you go to school and do well? 

(binary: yes=1) 

Yes 

Ego religiosity Response to survey item: In the past year, how often 

have you usually gone to religious services? Response 

categories are: at least once a week (4); almost every 

week (3); about once a month (2); seldom (1); and 

never (0) 

No 

Ego age Age of respondent as a continuous variable Yes 

Ego male Gender of respondent (binary: male=1) Yes 

Ego Latino Ethnicity of respondent (binary: Latino/not Latino) Yes 

Ego time at address Number of years respondent has lived at his/her 

current address as a continuous variable 

Yes 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix B. Variable Definitions 

Variable Definition Used in 

Final 

Models? 

Family member completed 

HS 

Response to survey item: Did any adult in your 

immediate family (mother, father, either grandparent, 

or older brother or sister) graduate from high school? 

(yes=1) 

Yes 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix C 

Appendix C. Table C1. Correlation Coefficients of Variables (N=147) 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 

1 1 2 .71*** 1 3 .91*** .68*** 1 4 .79*** .50*** .77*** 1 5 .68*** .36*** .72*** .71*** 1 6 .63*** .63*** .74*** .40*** .40*** 1 7 .52*** .42*** .66*** .44*** .40*** .45*** 1 8 .62*** .46*** .71*** .59*** .40*** .50*** .33*** 1 9 .52*** .31*** .57*** .44*** .30** .37*** .26** .69*** 1 

10 .41*** .30** .46*** .36*** .39*** .44*** .31** .41*** .42*** 1 11 .46*** .42*** .52*** .37*** .42*** .47*** .37*** .46*** .43*** .93*** 1 12 -.09 -.04 -.05 -.12 -.10 .04 -.01 .001 -.06 -.13 -.11 1 13 .13 .10 .10 .14 .16 .05 .02 .05 .05 .19* .20* -.85*** 1 14 -.03 -.09 -.12 -.07 -.11 -.16 -.19* -.07 -.03 -.25** -.30** .09 -.11 1 15 .15 .09 .12 .17* .13 .01 .05 .09 .05 -.04 -.09 -.14 .09 .67*** 16 -.12 -.12 -.07 -.09 -.07 .05 .01 -.11 -.08 -.05 -.05 .08 -.03 -.48*** -17 .05 .05 .04 -.04 .09 .05 .02 -.06 -.02 .09 .06 -.6 .10 -.10 18 0.19* -.10 -.18* -.19* -.13 -.13 -.10 -.09 -.05 -.20* -.20* -.01 .07 .05 19 .05 .08 .07 .05 .10 .07 .02 .15 .26** .26** .25** -.18* .19* -.15 20 -.07 -.08 -.09 -.09 -.08 -.02 -.05 -.02 .10 .05 .03 .02 .03 -.10 21 -.03 -.03 -.06 -.11 -.02 -.004 -.14 .01 .07 .10 .09 -.05 .07 .06 22 .39*** .41*** .44*** .31** .39*** .39*** .33*** .44*** .40*** .82*** .93*** -.12 .21* -.29** 23 -.19* -.19* -.23** -.23** -.17* -.16 -.26** -.15 -.08 -.24** -.29** -.01 -.003 .17* 24 .40*** .40*** .43*** .30** .38*** .40*** .31** .40*** .37*** .86*** .95*** -.11 .19* -.29** 25 -.18* -.17* -.23** -.22** -.17* -.15 -.25** -.15 -.07 -.21** -.24** .03 .004 .24** 26 .02 -.03 -.01 .10 .08 -.10 .01 -.08 -.05 -.06 -.07 -.06 .01 .13 27 -.12 -.07 -.14 -.18* -.15 .004 -.07 -.12 -.13 -.09 -.11 -.03 .08 -.04 28 -.06 -.01 -.02 .01 .05 .01 -.01 -.13 -.01 -.08 -.08 .17* -.13 -.04 29 .15 .07 .11 .08 .16 .15 -.09 .15 .03 .07 .10 -.11 .16 .11 30 -.09 .03 -.03 .04 -.04 .04 .08 -.03 -.09 -.01 -.01 -.13 .20* -.13 31 .10 -.07 -.07 -.18* -.05 -.02 -.05 -.13 -.11 -.07 -.13 -.04 .07 .04 32 -.07 -.19* -.12 -.03 .05 -.17* -.06 -.13 -.15 -.02 -.11 -.08 -.01 .15 33 -.03 -.02 -.11 -.05 .01 -.13 -.13 -.15 -.05 -.14 -.15 .07 -.03 .02 34 -.06 -.18* -.05 -.02 -.06 -.02 -.08 -.14 -.08 -.18* -.23** .03 -.01 .05 35 .23** .13 .23** .29** .29** .12 .15 .10 -.04 .06 .01 -.12 .05 .16* 36 .01 -.02 -.004 .02 -.04 -.06 -.03 .08 .13 -.06 -.06 -.05 .10 .08 37 .17* .14 .13 .10 .08 .15 -.08 .08 .09 -.08 -.08 -.02 .09 .18* 38 .0009 .02 -.02 .01 .08 -.01 .07 -.02 .02 .21* .22** .10 -.08 -.07 

1.Ego delinquency scale 2.Ego 6mosdlelin scale 3.Ego serious delinquency 4.Ego delinquency (binary) 5.Ego carry weapon 6. Ego sold drugs 7. Ego attack binary 8. Ego gang fight 

9. Ego gang total 10.Peer delinquency (proportion) 11. Alter delinquency (propor) 12.Peer lives in neighbrhd (propor) 13. Alter lives in neighbrhd (prop) 14. Prp. alters that are friends 15. Prp. alters that are male 16. Average age of alters 

17.Ethnicty scale 18.Separation scale 19. Prp lot of time spent w/alters 20.Prp alters R likes a lot 21. Prp of alters go to for advice 22. prp lot of time w/delinq alters 

23.Prp go to for advice not delinq. 24. Prp go to for advice delinquent 25.Prp peers advice not delinq 26.Homophily index 27. Number of components 28. Ego is Isolate 29. W_Betweenness 30. Ego network density 

31.Family sc32. Parent su33.Religious34. Ego age 35. Ego gend36. Ego ethn37.Time lived 38. Adult in f

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix C 

Appendix C. Table C1. (Continued) Correlation Coefficients of Variables (N=147) 

20 21 22 23 24 25 26 27 28 29 30 31 32 33 

1 2 3 4 5 6 7 8 9 

10 11 12 13 14 15 16 17 18 19 20 1 21 .64*** 1 22 .07 .12 1 23 .61*** .92*** -.26** 1 24 .07 .17* .92*** -.22** 1 25 .53*** .83*** -.22** .90*** -.19* 1 26 .06 .02 -.28 .06 -.10 .11 1 27 .24** .09 -.08 .12 -.08 .10 -.11 1 28 -.002 -.08 -.09 -.05 -.08 -.02 -.06 .02 1 29 -.07 -.01 .11 -.03 .06 -.02 .08 -.03 -.24** 1 30 .24** .19* .05 .18* .01 .19* .15 .20 .01 -.17* 1 31 .24** .27** -.06 .31** -.10 .25** .18* -.01 .14 -.04 .21** 1 32 .04 .07 -.13 .10 -.09 .08 .24** .07 -.02 .10 .06 .26** 1 33 .11 .09 -.11 .15 -.16 .17* .07 .002 .07 -.01 -.03 .14 .06 1 34 -.05 .02 -.30** .12 -.26** .10 -.10 -.06 .15 -.21* -.02 .03 -.01 -.08 35 -.23** -.19* .01 -.18* -.03 -.24** .04 .03 .01 .18* .06 .08 .15 -.04 36 .07 .07 .01 .08 -.02 .08 -.17* .01 -.05 .03 .05 -.09 -.11 -.14 37 .07 .10 -.06 .13 -.08 .13 .21** .01 -.05 .16 .06 .20* .16* .13 38 .13 .03 .19* -.05 .20* -.01 .13 -.02 .06 -.06 .21* .17 .08 .08 

1.Ego delinquency scale 2.Ego 6mosdlelin scale 3.Ego serious delinquency 4.Ego delinquency (binary) 5.Ego carry weapon 6. Ego sold drugs 7. Ego attack binary 8. Ego gang fight 

9. Ego gang total 10.Peer delinquency (proportion) 11. Alter delinquency (propor) 12.Peer lives in neighbrhd (propor) 13. Alter lives in neighbrhd (prop) 14. Prp. alters that are friends 15. Prp. alters that are male 16. Average age of alters 

17.Ethnicty scale 18.Separation scale 19. Prp lot of time spent w/alters 20.Prp alters R likes a lot 21. Prp of alters go to for advice 22. prp lot of time w/delinq alters 

23.Prp go to for advice not delinq. 24. Prp go to for advice delinquent 25.Prp peers advice not delinq 26.Homophily index 27. Number of components 28. Ego is Isolate 29. W_Betweenness 30. Ego network density 

31.Family sc32. Parent su33.Religious34. Ego age 35. Ego gend36. Ego ethn37.Time lived 38. Adult in f

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix D. Regression with Full Suite of Covariates 

Negative Binomial Regression Results Predicting Overall Delinquency (N=147) 

 Original Model

 Coeff. S.E. Exp(B) p = 

Intercept 1.121 1.3060 3.069 .390 

Alter variables 

 Proportion delinquent alters 5.941* 3.0197 380.213 .049 

 Proportion alters live in same 

neighborhood 

.484 .4757 1.623 .309 

 Proportion friends .383 .9281 1.467 .680 

 Proportion male friends .621 .9024 1.861 .491 

Average age of alters -.032 .0407 0.969 .437 

Amount of time spent with delinquent 

alters 

-4.637 3.0784 0.010 .132 

 Proportion alters liked very much 

(delinq.) 

5.515† 3.0612 248.318 .072 

 Proportion alters go to for advice 

(delinq.) 

-3.444 2.1691 0.032 .112 

 Homophily index for delinquent alters -.019 .0494 0.981 .701 

Network structure variables 

 Num. components -.303† .1583 0.738 .055 

 Is ego isolate -.121 .3579 0.886 .736 

 Betweenness .000 .0000 1.000 .332 

 Ego network density -.032 .0270 0.968 .236 

Acculturation 

 Separation from U.S. culture -.224*** .0630 0.799 .000 

 Ethnic attachment .029† .0170 1.030 .085 

Control variables 

Age .000 .0509 1.000 .997 

 Male 1.186*** .3171 3.275 .000 

 Latino .618† .3224 1.855 .055 

 Family member completed high school -.361 .2809 0.697 .199 

 Parent-school encouragement -.405 .3876 0.667 .296 

 Family cohesion -.052* .0202 0.949 .010 

 Num. years at address .053** .0156 1.054 .001 

 Religiosity .121 .2518 1.128 .631 

Goodness of fit 

Log likelihood 221.782

Deviance value/df 0.988

Pearson chi-square value/df 0.832

Likelihood ratio chi-square 71.316

†p < .10; *p < .05; ** p < .01; ***p < .001 

*Proportion variables range from .00 to 1.00 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.