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Neighborhood Crime, Depression, and Social Disorganization Theory

Permanent Link: http://ufdc.ufl.edu/UFE0021223/00001

Material Information

Title: Neighborhood Crime, Depression, and Social Disorganization Theory
Physical Description: 1 online resource (60 p.)
Language: english
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: community, crime, depression, disorganization, illinois, mental, neighbhorhood, ross, social, strain
Criminology, Law and Society -- Dissertations, Academic -- UF
Genre: Criminology, Law, and Society thesis, M.A.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The amount of literature exploring the relationship between community crime, depression, and social disorganization is severely limited. Nonetheless, previous research on depression and crime, respectively, finds that areas low in collective efficacy and social cohesion are higher in residential depression and crime commission. The present study surveys attitudes on individual depression levels, neighborhood disorder/dissatisfaction, economic disadvantage, strain, and criminal involvement in order to determine the associational relationship between social disorganization, crime, and depression. Household income, gender, employment status, and race, among other sociodemographic variables, are included as control measures in analyses. Limitations and future research questions are addressed.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis: Thesis (M.A.)--University of Florida, 2008.
Local: Adviser: Akers, Ronald L.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0021223:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021223/00001

Material Information

Title: Neighborhood Crime, Depression, and Social Disorganization Theory
Physical Description: 1 online resource (60 p.)
Language: english
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: community, crime, depression, disorganization, illinois, mental, neighbhorhood, ross, social, strain
Criminology, Law and Society -- Dissertations, Academic -- UF
Genre: Criminology, Law, and Society thesis, M.A.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The amount of literature exploring the relationship between community crime, depression, and social disorganization is severely limited. Nonetheless, previous research on depression and crime, respectively, finds that areas low in collective efficacy and social cohesion are higher in residential depression and crime commission. The present study surveys attitudes on individual depression levels, neighborhood disorder/dissatisfaction, economic disadvantage, strain, and criminal involvement in order to determine the associational relationship between social disorganization, crime, and depression. Household income, gender, employment status, and race, among other sociodemographic variables, are included as control measures in analyses. Limitations and future research questions are addressed.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis: Thesis (M.A.)--University of Florida, 2008.
Local: Adviser: Akers, Ronald L.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0021223:00001


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a63d08fe7d29cbd5f833344dca496c18950fe80d







NEIGHBORHOOD CRIME, DEPRESSION, AND SOCIAL DISORGANIZATION THEORY


By

BILLY D. HOLCOMBE
















A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF ARTS

UNIVERSITY OF FLORIDA

2008



































O 2008 Billy D. Holcombe





































This document is dedicated to my strong support system and to the selfless individuals who came
before me and fought to make my academic opportunities a reality.









ACKNOWLEDGMENTS

I owe an undeniable amount of thanks to my thesis supervisory committee, Dr. Ron

Akers, Dr. Alex Piquero, and Dr. Jodi Lane. Without their continued patience, support, and most

importantly guidance, I would not be able to explore and contribute to an area which fulfills me.

I would like to thank Dr. Lane for never settling for mediocrity and for mentoring me during my

undergraduate and graduate career. I would like to thank Dr. Piquero for his willingness to

answer any question, no matter how simple it may seem. I would like to thank Dr. Akers,

committee chair, for his continued perseverance and for his thoughtful words, genuine interest,

and fostering my interest in academia. Dr. Akers, who is quite omniscient, had the ability to

answer questions before I could even ask them. I am forever grateful for and indebted to these

individuals for their sacrifice and encouragement.

I also owe a great deal of thanks to my family and parents, Earnest and Sharon

Holcombe, for always supporting my creativity and continually reminding me that education is

the key to my success. Included in this family are Progressive Black Men, Inc., my graduate

cohort, and dear friend Erika Jones, who all offered listening ears when I felt all sanity was

leaving me. I dedicate this document to them.

I would also like to recognize my lifelong mentors, Dr. Terry Mills and Dr. Stephanie

Evans, who unarguably challenged me personally and professionally and were never too busy to

have hour-long chats with me. They have forever left a positive impact on my life.












TABLE OF CONTENTS


page

ACKNOWLEDGMENTS .............. ...............4.....


LIST OF TABLES .........__.. ..... .__. ...............6....


LIST OF FIGURES .............. ...............8.....


AB S TRAC T ......_ ................. ............_........9


CHAPTER


1 LITERATURE REVIEW AND THEORETICAL FRAMEWORK ................. ................. 10


Introducti on ................. .... ... ..._.._ ... ..._._. .............1
Social Disorganization Theory: An Overview ........._..._.._ ........._..._......11_._.....
General Strain Theory: An Overview................. .. .................1
Exploring the Social Disorganization-Crime Relationship .............. ...............15....
Exploring the Social Disorganization-Depression Relationship .............. .....................1
Exploring the Crime-Depression Relationship ...._.__... ..... ..___.. .....__... ..........2

2 DATA AND METHODOLOGY .............. ...............23....


Sam ple ................. ...............23...
Dependent Variables............... ...............2
Independent Variables .............. ...............24....
Control Variables............... ...............2

Analytic Plan .............. ...............26....

3 RE SULT S .............. ...............32....


4 CONCLUSIONS .............. ...............45....


Discussion ................. .... ...._. ... ..... ..__... .. ...........4
Limitations and Implications for Future Research .............. ...............47....

APPENDIX: PARAMETER ESTIMATES FOR OLS REGRESSION MODELS............._._... ...50


LIST OF REFERENCES ........._._.... ...............55..._.........


BIOGRAPHICAL SKETCH .............. ...............60....










LIST OF TABLES


Table page

2-1 Descriptive Statistics ................. ...............30........... ...

2-2 Ross-Mirowsky Perceived Neighborhood Disorder Scale Reliability ............... .... ...........31

3-1 Bivariate Correlations Output ................. ...............38......__._....

3-2 OLS Statistical Model Summary using Depression as the Dependent Variable ...............39

3-3 Statistical ANOVA Summary using Depression as the Dependent Variable....................39

3-4 Statistical Collinearity Summary using Depression as the Dependent Variable ...............40

3-5 OLS Statistical Model Summary using Self-Reported Criminal Activity as the
Dependent Variable .............. ...............40....

3-6 Statistical ANOVA Summary using Self-Reported Criminal Activity as the
Dependent Variable .............. ...............41....

3-7 Statistical Collinearity Summary using Self-Reported Criminal Activity as the
Dependent Variable .............. ...............41....

3-8 OLS Statistical Model Summary using Trouble with the law/Done Something Illegal
as the Dependent Variable .............. ...............42....

3-9 Statistical ANOVA Summary using Trouble with the law/Done Something Illegal as
the Dependent Variable............... ...............42

3-10 Statistical Collinearity Summary using Trouble with the law/Done Something Illegal
as the Dependent Variable .............. ...............43....

3-11 Statistical Model Summary using Minor Law Violation as the Dependent Variable........43

3-12 Statistical ANOVA Summary using Minor Law Violation as the Dependent Variable....44

3-13 Statistical Collinearity Summary using Minor Law Violation as the Dependent
Variable............... ...............44

A-1 Perceived neighborhood disorder and economic disadvantage effects on depression
(parameter estimates for OLS regression) .............. ...............51....

A-2 Perceived neighborhood disorder and economic disadvantage effects on self-reported
criminal activity (parameter estimates for OLS regression) ........._..._.._ ............_........52

A-3 Perceived neighborhood disorder and economic disadvantage effects on trouble with
the law/done something illegal (parameter estimates for OLS regression)............_..._.. ....53










A-4 Perceived neighborhood disorder and economic disadvantage on minor law violation
(parameter estimates for OLS regression) .............. ...............54....










LIST OF FIGURES

Figure page

2-1 Theoretical M odel ........... ..... ._ ...............29...









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Arts

NEIGHBORHOOD CRIME, DEPRESSION, AND SOCIAL DISORGANIZATION THEORY

By

Billy D. Holcombe

May 2008

Chair: Ronald L. Akers
Major: Criminology, Law, and Society

The amount of literature exploring the relationship between community crime,

depression, and social disorganization is severely limited. Nonetheless, previous research on

depression and crime, respectively, finds that areas low in collective efficacy and social cohesion

are higher in residential depression and crime commission. The present study surveys attitudes

on individual depression levels, neighborhood disorder/dissati sfaction, economic disadvantage,

strain, and criminal involvement in order to determine the associational relationship between

social disorganization, crime, and depression. Household income, gender, employment status,

and race, among other sociodemographic variables, are included as control measures in analyses.

Limitations and future research questions are addressed.









CHAPTER 1
LITERATURE REVIEW AND THEORETICAL FRAMEWORK

Introduction

Susan Smith. This name will forever resonate in the minds of Americans as a cold,

sadistic killer. Suffering from post-partum depression, she drowned her two young children in a

South Carolina river. Though the severity of her crime may seem extreme, Smith was rumored

to have depression. But was she suffering from clinical depression and if so what did that have

to do with her crime? Similar questions can be asked in response to such media reported cases.

There are as yet no well-supported answers to such questions. Social science research has

explored the relationship between depression and crime in relation to social factors (such as

exposure to crime and Einancial strain). As this point it is not clear that depression does

predispose one to commit crime, and it is not clear that depression is a high risk factor for crime.

Explanations of the association between crime and depression are limited and more research is

needed.

This research seeks to address social factors which may link criminality to depression.

Due to limited research in the Hield, questions still remain about the relationship between social

disorganization, crime, and depression. Are crime and depression related? Is social

disorganization related to crime and depression, respectively? Do the negative effects of social

disorganization, such as strain, explain the assumed relationship between crime and depression?

Using social disorganization theory as a guide, this research seeks to bring an additional

understanding of the relationship of crime to the violent ideations and aggressions some

depressed individuals experience. Specific attention will be given to individual and community-

level variables which may buffer, motivate, or contribute to depressive criminality. Based on

social disorganization and strain theory, I expect the following to occur: crime and depression










will be positively related to one another, and I expect both to be positively related to social

disorganization.

Social Disorganization Theory: An Overview

Social disorganization theory has its roots in the twentieth century Chicago school of

sociology. Since its inception, proponents of social disorganization theory have focused on

neighborhood conditions of disadvantage, but the concept itself is defined as the inability of

residents to maintain social control and order and deal with community problems. Social

disorganization is the result of a decrease in the effect of social rules over the behavior of group

members (Thomas and Znaniecki, 1920). Kubrin and Weitzer (2003) note that social

disorganization is the result of a community being unable to resolve chronic issues. The

prediction is that when social disorganization persists, residential strife, deviance, and crime

occur.

Social disorganization results when there is an overabundance of disorder relative to

order. Neighborhood characteristics of disorder include social traits (i.e., noise, population

heterogeneity, high population density, broken families, and high residential mobility) in

addition to physical traits (abandoned/decrepit buildings, graffiti, and litter) (Gottfredson &

Hirschi, 1990; Ross & Mirowsky, 1999). Skogan and Maxfield (1981) use the term perceived

neighborhood disorder to denote negative conditions, whether criminal or non-criminal, which

residents assume to be cues of social disorder.

Shaw & McKay (1942), credited for advancing social disorganization theory during its

early years, provided three relevant theoretical constructs. Social order, they contended, required

the following: community members to supervise and control teenage groups, local friendship

networks to form, and residential participation in formal/voluntary organizations. Before

elaborating on these constructs, it is first necessary to provide a historical sketch of Shaw &










McKay's theory. Their study, which assessed community problems in Chicago, included

measures of crime, delinquency, truancy, and mental disorder. They found that areas with social

problems also had low education levels, families on welfare, poor community organization, and

low values for rental properties. What effect do Shaw & McKay's social order requirements

have on reducing social order problems and minimizing levels of social disorganization?

Sampson & Groves (1989) argue that teenage group behavior, assumed to be unstable

and prone to delinquency, can be positively shaped by residents in cohesive communities.

Delinquency is primarily an act which occurs in group settings (Thrasher, 1963). Faris (1955)

found that "the family normally derives important support in its tasks of guiding the behavior of

children from the friends and neighbors, the primary group outside of the family itself. In an

integrated community, these outside persons also by example reinforce the teachings of the

parents. Furthermore, they constitute an important source of supervision, for children soon learnt

that any misbehavior which is observed by neighbors is likely to be reported to parents" (p. 393).

In this sense, responsibility falls not only on the parents/guardians of the teenagers, but is further

extended to all members in the community. Order, then, is maintained by all members.

Secondly, local friendship networks must be established in order for community

satisfaction to exist. When these networks are created, community members are more motivated

to shield their neighbors. Faris (1955) argued that "persons involved in homogenous and unified

social groups experience a satisfying confidence in the structure of mutual affection in the

group" (p. 88). Furthermore, this construct lends support to the notion of residents assuming

responsibility for what occurs in their communities and preventing victimization from outsiders.

Sampson & Groves (1989) contend that participation in formal or voluntary organizations is key

to preserving community social order. Examples of such organizations, in modern-day society,










include local police task forces or community "Crime Watch" programs. Partnerships between

these institutions, it is assumed, will positively contribute to residential safety. Previous research

(Shaw & McKay, 1942; Sampson & Groves, 1989) noted that when links between community

institutions are weak, a community's ability to defend its interests is compromised. In essence,

voluntary organizations/associations and social networks may act as buffers that counteract risk

factors for crime and increase cohesiveness among concerned residents seeking to improve their

communities.

What can explain the variances in disorganization levels between communities? What

factors impede social order? Sampson & Groves found four factors associated with varying

levels of social disorganization: socioeconomic status, residential mobility, family disruption,

and urbanization. Silver (2000), in his study of violence among the mentally ill, had similar

findings--social disorganization accounted for the effect of community-level socioeconomic

status, residential stability, and family disruption (relative to personal and property

victimization). Communities with residents of low economic status often lack money and

resources, which can cause elevated levels of stress and strain. There is a strong association

between neighborhood disadvantage and low-income individuals (Ross, 2000; Beardman et al.,

2001). Additionally, low socioeconomic status communities are less equipped to control and

supervise youth groups, compared to high socioeconomic status communities (Sampson &

Groves, 1989). Having high levels of residential mobility can disrupt a community's network of

social support and relations (Sampson & Groves, 1989; Ross, Reynolds, & Geis, 2000).

According to social disorganization theory, "low residential turnover increases the likelihood that

neighbors will know each other, share values, and exert informal social control (Shaw & McKay,

1942). Bursik (1988) purports that families are less likely to form and participate in community-









based organizations if they plan to relocate at the first opportunity. Family disruption, or

deviation from the ideal two-parent household structure, acts to decrease informal social controls

and reduces guardianship in the community. As cities expand and become more industrialized,

urbanization threatens to weaken local friendship networks and disengage residents from

participating in local affairs (similar to that of residential mobility). Compared to the

conventional community structure, where "there is a network of cooperation between the

children who play together and their parents, such a social structure is lacking in the most urban

regions" (Faris, 1955, p. 416).

Previous literature characterizes community social order as a mix of friendship and

loyalty among residents. Kasarda & Janowitz (1974) call this the systemic model, whereby a

community is viewed as a unit of formal and informal ties rooted in family life and socialization.

An element central to understanding the systemic model is the notion of collective efficacy.

Collective efficacy is the term used to denote a "willingness to intervene on behalf of the

common good" (Sampson et al., 1997, p. 918). Collective efficacy, in short, is the sense of the

residents that they can trust one another to intervene and exercise informal social control over

trouble and problems that may occur in the neighborhood, and contribute to the community's

upkeep and preservation of social order (Sampson et al., 1997; Sampson & Raudenbush, 1999;

Wells et al., 2006).

General Strain Theory: An Overview

Strain is the result of negative life events occur or aversive relationships between

individuals. Exogenous sources of strain include elements of social disorganization such as

negative neighborhood conditions, violent victimization, and stressful life experiences. Agnew

(1992), credited for addressing strain and delinquency in his general strain theory (GST), cites

three causal factors of strain: the failure to achieve positively valued stimuli or goals; the










removal of positively valued stimuli or goals; presence of negative stimuli. GST lays the

foundation for a social disorganization-crime-depression relationship through its three main

tenets: exposure to strain increases delinquency; when negative factors are present (given

exposure to strain), delinquent responses are more likely to occur; delinquent responses are more

likely to occur when strain exposure leads to anger. Mazerolle et al. (2003) contend that

individuals may seek adaptive mechanisms such as crime and delinquency to cope with anger.

Hoffmann & Miller (1998), on the other hand, argue that high self-efficacy, high self-esteem,

and modeling positive peer behavior serve as positive coping strategies. Individuals with high

self-efficacy attribute their successes and failures to personal attributes rather than external

agents, such as the federal government. High self-esteem, it is argued, acts a preventive shield to

limit engagement in delinquent activities and increase one's resilience to strain. Modeling

positive peer behavior is instrumental, they contend, because peers can remind one another of

adverse consequences (as a result of delinquent behavior) and peers can serve as role models.

Exploring the Social Disorganization-Crime Relationship

As previously noted, factors such as presence of graffiti in public places and low

socioeconomic status can increase levels of stress and contribute to social disorganization.

Social disorganization scholars point to the preventive and central effects of social support and

collective efficacy in exploring the motivating factors of criminality. Social support, which is

generally defined as informal ties with one's neighbors, is crucial in mitigating the strain

associated with disadvantage. Cullen (1994) stated that "the more social support in a person's

social network, the less crime will occur" (p. 540). Silver (2000) contends that anger, substance

abuse and lack of social support (which are all indicative of social disorder) are proximate risk



i Silver & Teasdale (2005) found that individuals with substance abuse disorder also exhibited the highest rates of
violence.










factors for violence. Furthermore, having strong social support was found to buffer residential

fear of crime and distrust (Ross & Jang, 2000). When fear of crime is widespread in

communities, residents are less likely to engage in programs and activities which reduce disorder

and criminality. Without the mediating effects of collective efficacy, crime is fostered.

Collective efficacy has often been cited to help explain differential crime rates across

communities (Sampson et al., 1997). The term evokes a feeling of responsibility among

residents to intervene in community affairs through informal measures. Such efforts include

street surveillance and direct intervention in problems (Kubrin & Weitzer, 2003). Based on

previous research, the qualitative effects of informal control were found to be significant.

Residential interventions may be effective for delinquent groups/perpetrators since they do not

carry the same negative stigmas as do more formalized means (such as police intervention or

incarceration). In fact, collective efficacy was found to be more instrumental in crime prevention

than was intervention by authorities--neighborhoods with disadvantage levels are less likely to

secure law enforcement resources and protection (Sampson, 1997; Kubrin & Weitzer, 2003).

A considerable volume of literature has been devoted to exploring the effects of

disorganization on neighborhood crime. Social disorganization theory notes that local ties and

informal control limit the effects of economic disadvantage, residential instability, and other

factors on neighborhood crime (Kubrin & Weitzer, 2003). Previous studies suggest that crime

rates are related to neighborhood ties, informal social control, and residential interaction (Warner

2 Silver (2002), in a similar study, examined the role of social relationships in mediating victimization among the
mentally ill. Silver posits that because the mentally ill are more likely to have unstable, conflicted relationships with
neighbors, they are at a greater risk of being victimized. This finding suggests a shielding or deterrent value of
social support.

3 Taylor (1996) posits that neighborhood attachment levels will decrease when residents view disorder or crime. He
notes an opposite effect when residential exposure to crime is low.

4 Osgood & Chambers (2003) found residential instability to be associated with increased rates of rape, simple and
aggravated assault, weapons violations, and the overall crime index.









& Rountree, 1998; Rountree & Warner, 1999; Veysey & Messner, 1999; Bellair, 1997; Elliot et

al., 1996; Sampson et al., 1997; Hirschfield & Bowers, 1997). In their case study, Morenoff,

Sampson, & Raudenbush (2001) looked at predictive factors of neighborhood homicide in

Chicago for the 1996 1998 period. To gage actual and perceived neighborhood violence rates,

the authors used onfcial data from the 1990 Census and responses from a 1995 survey

distributed to local residents (n= 8,872). Their findings provided support for a link between

collective efficacy and criminality. For example, 72% of neighborhoods having high levels of

collective efficacy experienced low levels of homicide. Additionally, 75% of areas defined as

homicide "hot spots" had low levels of collective efficacy. This study found that concentrated

disadvantage and low collective efficacy, independent of one another, predict increased homicide

levels. Nevertheless, involvement in local organizations or voluntary associations increased

levels of collective efficacy in achieving social control and residential cohesion. Nash and

Bowen (1999) assessed social capital, demographics, and residential perception of crime among

a national sample of middle and high school adolescents using the School Success Profile (SSP)

instrument (n= 1,796) to determine factors which influence perception of crime. Nash and

Bowen hypothesized that when participants perceived high levels of crime, they were also less

likely to see their peers as involved in prosocial activities. Results from the study showed that

for middle and high school students, a significant relationship was found between perceived

informal social control and perceived neighborhood crime, even after control variables were

added'. Additionally, as perception of informal social control increased, participants perceived

less crime in the neighborhood. Perceived informal social control had a negative effect on





5Control variables included race, sex, and socioeconomic status.










perceived neighborhood crime. Furthermore, as perceptions of high neighborhood

socioeconomic status' increased, lower neighborhood crime levels were perceived. Nash and

Bowen purport that "at the macro level, community organization and political action are needed

to reduce neighborhood crime and its correlates, such as poverty and economic dislocation"

(p. 183). At the micro level, "social workers can collaborate with adolescents and their families

to reduce exposure to crime even in communities with high crime rates" (p. 183). Collective

efficacy of community agencies and social agents can minimize neighborhood crime rates.

Paxton et al. (2004), in a similar study, found that among adolescents, poverty, discrimination,

and low education/employment opportunities contribute to the risk for witnessing and

experiencing violence.

Exploring the Social Disorganization-Depression Relationship

Social disorganization theory has been used to explain the neighborhood context of

depression and other mental disorders (Silver, Mulvey, & Swanson, 2002). Hiday (1997)

suggests that "neurobiological factors may be the origin of severe of mental illness, but social

factors affect its course, manifestations, and connections to violence" (p. 412). Exposure to

stressful events shapes depressive problems among adolescents (Colten & Gore, 1991; Compass

and Wagner, 1991). Structural and psychological factors which affect individual depression

include living in an urban area, having disadvantaged economic status, experiencing stressful life

events, and having weak social relationships (De Coster, 2003).





6 Perceived neighborhood crime has a significantly negative effect on the perceived prosocial behavior of
adolescents.

SBursik & Grasmick (1993) posit that areas with low socioeconomic status also have greater rates of residential
mobility and ethnic heterogeneity. These factors lead to weak social control systems and high rates of crime and
delinquency.









Ross (2000) examined the assumed di sadvantage-depression relationship among Illinois

residents using the Community, Crime, and Health (CCH) survey (n= 2,482) Previous studies,

she noted, found depression levels to be higher among individuals with low education, low

income, and the unemployed. Based on this, she hypothesized that residents of disadvantaged

neighborhoods exhibited higher rates of depression (compared to more "advantaged"

neighborhoods) and neighborhood disorder further accounted for the association between

depression and disadvantage. Physical malaise (inclusive of having trouble sleeping and/or

concentrating) and feelings of sadness were indicative of depression in this study. Respondents

provided answers to questions assessing prior criminality and community satisfaction. After

recoding responses, Ross found that poverty and single-mother households were the only

indicators of neighborhood disadvantage significantly associated with depression. As a result,

individuals who live in these disadvantaged neighborhoods are more likely to have poor mental

health. Additionally, Ross found that residents who drink heavily or engage in illegal activities

had significantly higher levels of depression than those who did not. Sociodemographic

variables not found to be associated with depression include the following: education level in

neighborhood, homeownership, racial/ethnic composition, and residential stability.

As previously noted, perceived neighborhood disorder has a strong effect on social

cohesion. Subsequently, due to strain and a lack of social support, depression may ensue. An

inverse relationship exists between social cohesion and depression. Depression is lowest when

people in a neighborhood know each other (Aneshensel & Sucoff, 1996). Empirical research

lends support to a disorganization-depression relationship. Latkin & Curry (2003) noted a strong


SDepression is also high among residents who are minorities, women, and individuals who are unmarried.

9 Perceived neighborhood disorder has a stronger association to an individual's mental health status than does
official measures of neighborhood characteristics (Hadley-Ives et al., 2000).









association existed between perceived neighborhood decay and depression (even after

controlling for baseline depressive symptoms). In a study of adolescent students, Stevenson

(1989) found a relationship between low levels of depressive symptoms and strong feelings of

neighborhood safety. Aneshensel & Sucoff (1996), in a similar study of adolescents, found that

the more threatening a neighborhood is perceived to be, the more common the symptoms of

anxiety and depression. On the other hand, students who identified their neighborhoods as

having low social capital also reported high levels of depressive symptoms. Silver (2000)

pointed to low socioeconomic status to explain the social disorganization-depression

relationship, finding that "environmental adversity" facilitates mental disordersio. He adds that

the inability of some mentally ill individuals to function in a normal setting reduces the

likelihood of forming long-lasting, extensive social networks.

Exploring the Crime-Depression Relationship

Research on crime and depression is severely limited in number. Of the literature

available, specific attention is focused on individual/community criminal law violations and

depression levels. Rosenbaum (1991) found that in intimate partner homicides, 75% of the

perpetrators were depressed. De Coster (2003) proposed a link between law violation and

depression. De Coster contends that there are three sequential mechanisms which lead to law

violation and depression. First, social-structural positions, including living in economically

disadvantaged neighborhoods, which may expose youths to stressful life events, can lead to law

violation and symptoms of depression. In turn, deviant responses to stressful life events shape






"' The study also finds that patients discharged into disadvantaged neighborhoods were significantly more likely to
engage in violence than a comparative group discharged into less disadvantaged neighborhoods.










social relationships with conventional norms and deviance. Relationships formed may

"ultimately influence law violation and depression" (p. 130). Paxton et al. (2004) found that

exposure to crime is linked to anxiety and depression, perhaps suggesting a causal relationship.

De Coster & Heimer (2001) found that delinquent adolescents were more likely to become

depressed in early adulthood, noted by a significant effect of delinquency on adulthood

depression.

Though few studies have directly explored the relationship between crime and

depression, several studies (Dollfus et al., 1993; Sax et al., 1996; Lancon et al., 2001) found that

depressive symptoms are also components of schizophrenia, though the frequency of depressive

symptoms varies based on the stage of schizophrenia. Swanson et al. (1990), using data from the

Epidemiological Catchment Area' s five study sites, analyzed the relationship between violence

and psychiatric disorder (n= 10,059). Based on the relationship between depression and

schizophrenia, their results add support to a crime-depression hypothesis, finding that those with

schizophrenia were more violent than those without. Individuals with schizophrenia alone, for

example, were found to be more violent than those without (8% violence rate compared to 2%

violence rate)12. When substance abuse, (Ross, 2000), was factored in, the violence rate among

schizophrenics jumped to 30%. Though the causative effects of schizophrenia cannot be

deduced from this study, it does show the compounding effect of substance abuse on

schizophrenic criminality. In a cross-sectional study, Steadman et al. (1998) looked at the effect

substance abuse has on the relationship between mental disorder and violence. Their study


11 These social relationships shape elements of role taking, including reflected appraisals, modes of deviance, and
reactions to deviance.

12 Though 92% of schizophrenic participants in Swanson et al. (1990) were presumably non-violent, the percentage
of schizophrenics who engage in crime was still slightly greater than participants in the control group. In a related
study, Silver and Teasdale (2005) found mental disorders and violence to be significantly related.









measured rates of violence among individuals age 18 to 40 during their first year of discharge

from a mental institution (n = 1,136)13. Their results showed substance abuse significantly raised

the prevalence rate of violence in mental disorder populations. Based on the relationship

between the underlying components of depression and schizophrenia, it is plausible to assume

that substance abuse may incite aggressive or violent behavior in depressed individuals.

In recent years, a small body of research has examined the relationship between social

disorganization, crime, and depression. Previous violence, substance abuse, psychopathy,

relationship instability, personality disorder, anger, and lack of social support were all found to

increase the chance of an individual engaging in violence or being victimized (Silver, 2000).

Silver (2002), in a study of psychiatric patients from the Western Psychiatric Institute and Clinic

(WPIC) in Pittsburgh, noted that discharged patients were more likely to engage in violence than

those without a psychotic diagnosisl4. Additionally, even after controlling for individual and

community-level correlates, individuals with psychotic diagnoses were more likely to be victims

of violence (compared to non-psychotic individuals).


















13 Researchers gathered data from self-report surveys, informants, and official police reports. The study monitored
violence to others every ten weeks following discharge from one of three mental institutions.

14 Ross (2000) found that individuals who engage in criminal activities are more depressed than those who do not.









CHAPTER 2
DATA AND METHODOLOGY

Sample

This research study is a secondary analysis of data taken from Ross & Britt' s

Community, Crime, and Health (CCH) survey (1995, 1998). Data were retrieved using the

University of Michigan' s Inter-University Consortium for Political and Social Research (ICPSR)

database. The Community, Crime, and Health survey was administered to adults aged 18 and

older in select Illinois households (n= 2, 482) to determine the relationship between

neighborhood disadvantage and physical/mental health. Participants were contacted via

telephone by a random-digit dialing method which increases the rate of contacting home phone

numbers (as opposed to disconnected or business phone numbers). Up to 10 calls were initially

made to select and contact a respondent; up to a maximum of ten follow-up calls were made to

complete the interview. The adult in the household with the most recent birthday was selected as

the respondent. This survey had a 73.1% response rate. Demographic information for

respondents is as follows: 1) English speaking adults; 2) minimum age of 18 (maximum of 92);

3) mean age is 42 years; 4) mean education is 13.8 years (indicating some degree of advanced

training or higher education); 5) mean income is $48,274, 6) 41% of respondents are male; 7)

84. 1% of respondents are White; 8) 53.3% of respondents are married. Descriptive statistics for

all variables (with recorded variable names in parentheses) are shown in Table 2-1.

Dependent Variables

The two dependent variables used for analysis purposes are "depression"

(DEPRES SION) and self-reported crime" (CRIMINALACTIVITY).

DEPRESSION represents summed responses to Ross' (2000) 7-item modification of the

Center for Epidemiological Studies' Depression (CES-D) scale. Previous literature defines










depression as continued feelings of despair and lethargy that goes beyond the ordinary and

temporary feelings of melancholy or the "blues". To gage depression levels, respondents were

asked "On how many days in the past week have you: 1) had trouble getting to sleep or staying

asleep?; 2) felt you just couldn't get going?; 3) had trouble keeping your mind on what you were

doing?; 4) felt that everything was an effort?; 5) felt sad?; 6) felt lonely?; 7) felt you couldn't

shake the blues?". Responses were coded from 0 (No depression) to 7 (High depression). For

analysis purposes, "No Coded Response", "Don't Know", and "Refused/Missing" responses

were recorded as "System Missing" for all 7 items. The resident depression scale has an alpha

reliability of .783.

CRIMINALACTIVITY represents summed self-report measures of criminal activity

using measures of self-reported crime. To gage respondent criminality, respondents were asked

"In the past twelve months, have you 1) done anything that would have gotten you in trouble if

the police had been around/done anything illegal?; 2) been caught in a minor violation of the

law?; 3) been arrested?; 4) been in jail for more than 24 hours?". Responses were recorded as "O"

(No) and "1" (Yes). For analysis purposes, "No Coded Response", "Don't Know", and

"Refused/Missing" responses were recorded as "System Missing" for all 4 items. These activities

form an additive scale indicative of increasing trouble with the law. The scale has a low alpha

reliability of .330. Nonetheless, I have retained this measure of criminal activities.

Interpretation of findings must keep this low scale reliability in mind. The self-reported criminal

activity scale has an alpha reliability of .330.

Independent Variables

Independent measures of interest deal with a variety of factors comprising neighborhood

disorganization. For analysis purposes, disorganization is broken down into two variables,









perceived neighborhood disorganization (PERCEIVEDDISOR) and reported financial

disadvantage (ECONDIS).

PERCEIVEDDISOR represents summed responses to the 15-item Ross-Mirowsky

Perceived Neighborhood Disorder Scale (1999) (see Table 2-2). Neighborhood disorder "refers

to conditions and activities, both maj or and minor, criminal and non-criminal, that residents

perceive to be signs of the breakdown of social order (Ross, 2000, p.181). To gage perceived

disorder levels, respondents were asked to answer the following 15 items: 1) "There is a lot of

graffiti in my neighborhood"; 2) "My neighborhood is noisy"; 3) "Vandalism is common in my

neighborhood"' 4) "There are a lot of abandoned buildings in my neighborhood"; 5) "My

neighborhood is clean"; 6) "People in my neighborhood take good care of their houses and

apartments"; 7) "There are too many people hanging around on the streets near my home"; 8)

"There is a lot of crime in my neighborhood"; 9) "There is too much drug use in my

neighborhood"; 10) "There is too much alcohol use in my neighborhood"; 11) "I'm always

having trouble with my neighbors"; 12) "In my neighborhood, people watch out for each other";

13) "The police protection in my neighborhood is adequate"; 14) "The people in my

neighborhood trust one another"; 15) "My neighborhood is safe". Items 1-4 are indicators of

physical disorder. Items 5-6 are indicators of physical order. Items 7-11 are indicators of social

disorder. Items 12-15 are indicators of social order. Collectively, items 1-4 and 7-11 form an

index variable of disorder (DISORDER). Items 5-6 and 12-15 form an index variable of order

(ORDER). Disorder items are coded in Likert scale form as strongly disagree (1); disagree (2);

agree (3); strongly agree (4). Order items are reverse coded in Likert scale form as strongly

agree (1); agree (2); disagree (3); strongly disagree (4). For analysis purposes, "No Coded

Response", "Don't Know", and "Refused/Missing" responses were recorded as "System Missing"









for all 3 items. The Ross-Mirowsky scale has a high alpha reliability of .919 based on the

original sample of 2,482 respondents (see Table 2-2).

ECONDIS represents summed responses to a 3-item economic disadvantage scale used to

represent financial strain. Respondents were asked "In the past twelve months have you: 1) not

had enough money to buy food and other household necessities?; 2) not had enough money to

pay for medical care?; 3) not had enough money to pay for bills?". Responses were coded as

very often (1); fairly often (2); not very often (3); never (4). For analysis purposes, "No Coded

Response", "Don't Know", and "Refused/Missing" responses were recorded as "System Missing"

for all 3 items. The financial strain scale has an alpha reliability of .809.

Control Variables

Several demographic control variables were selected as control variables for regression

models. These variables include gender (GENDER), recorded as "O" for male and "1" for

female; respondent employment status (EMPLOY), coded as "O" for full-time employment and

"1" for non full-time employment; spouse employment status (SPOUSEEMP) coded as "O" for

full-time employment and "1" for non full-time employment; racial background (RACE) coded

as "O" for White and "1" for non-White; annual family household income (in dollars) in 1997

(INCOME) coded as "O" for $30,000 or more and "1" for less than $30,000; respondent is of

Hispanic/Spanish origin (HISPANICS) coded as "O" for no or "1" for yes. For analysis

purposes, "Something Else", "No Coded Response", "Don't Know", and "Refused/Missing"

responses were recorded as "System Missing" for all 6 items.

Analytic Plan

The obj ective of this research is to examine the relationships between social

disorganization, crime, and depression. The assumption is that both criminal behavior and

depression are responses to strain (unmeasured in this study) resulting from living in socially









disorganized neighborhoods (see Figure 2-1). Due to the limitations of the data set the causal

implications of this underlying assumption or in Figure 2-1 are not tested. Rather the present

analysis simply examines the bivariate and multivariate relationships between measures of crime

and depression as dependent variables and measures of social disorganization as independent

variables. Present analyses use perceived neighborhood disorder (PERCEIVEDDISOR) and

economic disadvantage (ECONDIS) as indicators of social disorganization. Using the dependent

variable for self-reported criminal activity (CRIMINALACTIVITY) and independent variable

for perceived neighborhood disorder, (PERCEIVEDDISOR), which include items assessing

perceptions of crime and deviance in the neighborhood, may present confounding or tautological

analysis issues, but the perception of crime is not the same as reporting one's own offenses and

the measure is necessary to explore the relationship between crime and depression. SPSS

statistical software will be used to perform all analyses. Due to the multiple category

composition of dependent and independent variables used in analyses, crosstabular examinations

will not be performed. Nonetheless, bivariate correlation analyses will be conducted on the

dependent (depression (DEPRESSION), self-reported criminal activity

(CRIMINALACTIVITY)) and independent (economic disadvantage (ECONDIS), and perceived

neighborhood disorder (PERCEIVEDDISOR)) variables to address the proposed associational

relationship between variables. To address the proposed relationships between social

disorganization and crime and social disorganization and depression, respectively, multivariate

OLS regression analyses will be performed to determine the relationship between the selected

dependent variable and independent variables and to test for multicollinearity. Demographic

control variables will also be introduced into the analyses. That is models will be run first with

depression as the dependent and perceived neighborhood disorder and economic disadvantage as









the independent variables; then with self-reported criminal activity as the dependent variable and

perceived neighborhood disorder and economic disadvantage as the independent variables. To

address the low reliability of the self-reported criminal activity scale (CRIMINALACTIVITY),

separate models will be run with indicators of self-reported criminal activity (ILLEGRALREC,

MINVIOLREC, ARRESTEREC, and JAILREC, respectively) as the dependent variables and

neighborhood disorder and economic disadvantage as the independent variables. Control

variables will then be added as a block to each regression analysis. If the main and net effects of

social disorganization on depression and crime are in the expected direction of higher crime and

depression associated with higher social disorganization and are greater than the main and net

effects of depression and crime on each other, the main hypothesis of the study will be

supported. Also, the expected relationship between crime and depression is positive; higher

levels of depression are expected to be associated with higher criminal activity. If not, then

appropriate conclusions about the theoretical model and future research will be drawn.










+


Self-
Reported
Criminal
Activity


Figure 2-1. Theoretical Model


Depression









Table 2-1. Descriptive Variable Statistics
Min. Max. Mean SD Alpha
Dependent Variables
Depression Scale s (DEPRESSION)- # days .783
Had trouble sleeping (SLEEPREC) 0 7 1.03 2.003
Had trouble getting going (GETGOREC) 0 7 0.70 1.589
Had trouble keeping focused (MINDREC) 0 7 0.65 1.575
Felt everything was an effort (EFFORTREC) 0 7 0.51 1.483
Felt sad (SADREC) 0 7 0.71 1.496
Felt lonely (LONELYREC) 0 7 0.48 1.416
Felt could not shake blues (BLUESREC) 0 7 0.29 1.083
Criminal Activity Scale (CRIMT~'NALACTIVITY)- "O" no; "1"yjes .330
Done Something Illegal/ to Cause Trouble (ILLEGREC) 0 1 0.03 0.171
Caught in Minor Violation (MINVIOLREC) 0 1 0.02 0.146
Been Arrested (ARRESTEREC) 0 1 0.00 0.057
Been In Jail More Than 24 Hours (JAILREC) 0 1 0.00 0.000
Independent Variables
Perceived Neighborhood Disorder Scale (PERCEIVEDDISOR) .9 19
Graffiti in neighborhood (GRAFFITIREC) 1 4 1.56 0.609
Neighborhood is noisy (NOISYREC) 1 4 1.80 0.700
Neighborhood vandalism common (VANDALSREC) 1 4 1.67 0.616
Abandoned buildings present (ADBANBLDREC) 1 4 1.51 0.574
Too much alcohol use (ALCOHOLUSREC) 1 4 1.90 0.715
Too much hanging out on streets (HANGOUTREC) 1 4 1.70 0.699
Too much drug use (DRUGUSEREC) 1 4 1.80 0.723
A lot of crime in neighborhood (CRIMEREC) 1 4 1.68 0.643
Neighborhood is clean (CLEANREC) 1 4 1.68 0.562
Neighbors take care of homes (CAREHOMREC) 1 4 1.64 0.562
Neighborhood is safe (SAFEREC) 1 4 1.68 0.632
Watch out for One Another (WATCHOUREC) 1 4 1.77 0.645
Adequate Police Protection (POLICEREC) 1 4 1.83 0.639
Trust Neighbors (NEITRUSREC) 1 4 1.77 0.614
Economic Disadvantage Scale (ECONDIS)- "1 SD; "4"S .0
Trouble Buying Food (STRNFOOREC) 1 4 3.78 0.558
Trouble Paying for Medical Care (STRNMEDREC) 1 4 3.81 0.552
Trouble Paying Bills (STRNBILLREC) 1 4 3.58 0.750
Control Variables
Respondent Gender (Female = 1) (GENDER) 0 1 0.60 0.491
Hispanic/Spanish Ethnicity (Yes = 1) (HISPANICS) 0 1 0.01 0.109
Spouse Employ (Non Full-time = 1) (SPOUSEEMP) 0 1 0.39 0.489
Respondent Race (Non-White = 1) (RACE) 0 1 0.34 0.474

SA score of 1 indicates "strongly disagree", 2 indicates "disagree", 3 indicates "agree" and 4 indicates "strongly
agree". This is a reverse coding so that 1 equals lowest economic disadvantage and 4 equals highest economic
disadvantage. For perceived neighborhood disorder items have been direct coded or reversed coded as needed so
that 1 equals lowest neighborhood disorder and 4 equals highest neighborhood disorder.










Table 2-1 Continued.


Min. Max. Mean SD


Alpha


Control Variables
Respondent Employ (Non Full-time = 1) (EMPLOY) 0
Total Household Income (< than $30,000 = 1) (INCOME) 0


0.44 0.496
0.34 0.474


Table 2-2. Ross-Mirowsky Perceived Neighborhood Disorder Scale Reliability
N %
Valid 1332 53.7
Excluded 1150 46.3
Total 2482 100.0
Total Items 15
Alpha Reliability .919









CHAPTER 3
RESULTS

The first section of analysis examines the relationship between dependent and

independent variables. Bivariate correlation analysis (using pairwise deletion) was conducted to

determine the strength of association between depression, self-reported criminal activity,

perceived neighborhood disorder, and economic disadvantage. Based on previous studies (Ross,

2000), the perceived neighborhood disorder and economic disadvantage scales, as independent

variables, were used as indicators of social disorganization. Correlation analysis yielded several

important results, though some were non-signifieant. A positive relationship was found between

self-reported criminal activity and economic disadvantage (p< .01), which is consistent with

previous literature showing and the expectations from the theoretical framework of this study. A

positive relationship also existed between economic disadvantage and perceived neighborhood

disorder (p<.01), which is further supported by the literature. Alternately, the correlation

analysis yielded several results which are not in the expected direction. Correlation analysis

showed a negative, rather than the expected positive, relationship between economic

disadvantage and depression. Additionally, a negative relationship was found between self-

reported criminal activity and depression (p<.01). Though not significant, negative relationships

were found between the following: self-reported criminal activity and perceived neighborhood

disorder; depression and perceived neighborhood disorder. These findings offer partial support

for the theoretical model, but do not support social disorganization theory, which expects a

positive relationship between social disorganization and crime and social disorganization and

depression. Furthermore, while research on crime and depression in the past has shown a positive

relationship between the two variables, the Eindings here are that, contrary to expectations, the

two are negatively related. Results for correlation analysis are shown in Table 3-1. All findings









are reported with the caveat that criminal activity is positively and significantly related to

economic disadvantage (as expected), but negatively related to perceived neighborhood disorder

(though this finding was not significant).

The next section of the analysis examines the effects of social disorganization indicators

(neighborhood disorder and economic disadvantage) on depression and criminal activity levels,

respectively. It is important to note that a considerable number of participants (N= 685)

participants did not answer questions gaging household income. This finding may alter the

overall results in regression analysis. Multiple regression analyses were performed for each

dependent variable (depression, self-reported criminal activity, trouble with the law/illegal

activity, minor law violation, ever been arrested, and been jailed for more than 24 hours,

respectively) using the same independent and control variables: economic disadvantage,

perceived neighborhood disorder, gender, respondent employment status, spouse employment

status, respondent race, total household income, and Hispanic/Spanish ethnicity. Despite dummy

coding all control variables, an assumed statistical error message indicating that the variable

HISPANICS was a constant, resulted in SPSS removing the variable indicating Hispanic/Spanish

ethnicity from all regression analyses. This finding remained even after replacing the recorded

variable HISPANICS with the original variable (HISPANI2) from the CCH dataset. Similarly,

the same error occurred when ARRESTEREC and JAILREC, respectively, were used as

dependent variables, thus eliminating the dependent variables from regression analyses. This

may possibly suggest an error in entering variable data into the dataset. Additionally, although

the Ross-Mirowsky scale has a high reliability, nearly half of participant responses (n = 2,482)

were removed during scale reliability analysis. After running a frequency analysis, it was

determined that the responses removed were recorded as system-missing due to "no coded",










"system missing", or "don't know" responses. These are fewer missing cases than found when

listwise deletion is used, but even with pairwise deletion the number of system missing cases is

large and findings must be viewed with this in mind.

The first OLS multiple regression analysis estimated the effects of perceived

neighborhood disorder and economic disadvantage on resident depression. Model 1 shows

perceived neighborhood disorder and economic disadvantage account for 1.2% of the variation

in depression levels. ANOVA results suggest that findings are likely to be obtained by chance

(F= .639, p=.530). Additionally, the model parameter showed that as perceived neighborhood

disorder and economic disadvantage decreased, respectively, depression levels increased, thus a

negative (though not significant) relationship existed between the dependent variable

(depression) and the independent variables (perceived neighborhood disorder and economic

disadvantage). This finding does not support this study's hypothesis nor a large volume of

research on social disorganization theory. Model 2 introduced control variables in the analysis.

Independent and control variables present account for 5.7% of the variation in depression levels.

ANOVA results for Model 2 were similar to Model 1, suggesting a strong likelihood that

outcomes may be obtained by chance (F= .852, p= .549). Model parameter results indicated that

as economic disadvantage and perceived neighborhood disorder decreased, respectively,

depression levels increased. The control variable gender (GENDER) was significant in the

model (p < .05). Based on relatively low eigenvalues and high condition indexes for the

analysis, collinearity issues may also be present. Results are presented in Tables 3-2 through 3-4

and parameter estimates are presented in Table A-1.

The second OLS regression estimated the effects of perceived neighborhood disorder and

economic disadvantage on self-reported criminal activity. Model 1 shows perceived









neighborhood disorder and economic disadvantage accounted for 5.3% of the variation in self-

reported criminal activity levels. ANOVA results suggest outcome results may likely be

obtained by chance (F= 2.905, p = .059). Model parameter results indicated that as perceived

neighborhood disorder and economic disadvantage, respectively, decreased, self-reported

criminal activity levels increased. These findings are not consistent with previous literature,

which predicts a positive relationship between the dependent variable and independent variables.

Independent variable economic disadvantage (ECONDIS) was significant in the model (p < .05).

Model 2 introduced control variables into the regression analysis. Independent and control

variables accounted for 12.2% of variation in self-reported criminal activity levels. As with

other models, a low F-value indicated that outcomes are likely to be obtained by chance (F=

1.964, p= .068). Model 2, in essence, did not increase the predictability of the regression

analysis. Model parameter outcomes showed that as perceived neighborhood disorder and

economic disadvantage levels decreased, respectively, self-reported criminal activity levels

increased, which does not support previous literature. As with Model 1, economic disadvantage

was significant in Model 2 and in the expected direction as well. Independent variable economic

disadvantage (ECONDIS) and control variable spouse employment status (SPOUSEEMP) were

significant in the model (p < .05 for both outcomes). A low eigenvalue and high condition index

values for Model 1 and Model 2 (2nd and 7th dimensions, respectively) are indicative of

multicollinearity. Results are presented in Tables 3-5 through 3-7 and parameter estimates are

presented in Table A-2.

The third regression analysis estimated the effects of perceived neighborhood disorder

and economic disadvantage on having trouble with the law/doing something illegal.

ILLEGREC, which represents having trouble with the law, is included the scale variable









CRIMINALACTIVITY. Due to the low alpha reliability of the scale, ILLEGREC was entered

to indicate an individual having committed an illegal act or having any punitive interaction with

the law. Model 1 shows perceived neighborhood disorder and economic disadvantage accounted

for 7.6% of variation in trouble with the law levels. Significant ANOVA results indicate that it

is unlikely that Model 1 outcomes were obtained by chance (F= 4.29, p < .05). Model parameter

results indicated that as economic disadvantage levels decrease, trouble with the law/illegal

activity levels will increase, which contradicts literature on social disorganization theory. This

contrasts with the findings in Table 3-1 in which economic disadvantage is positively related to

levels of criminal activity when measured as a scale. Alternately, as perceived neighborhood

disorder levels increased, trouble with the law/illegal activity levels increased, again as

contrasted with the direction of relationship between perceived neighborhood disorder and the

scale of criminal activity. These differences may reflect problems with the low reliability of the

scale. Independent variable perceived neighborhood disorder was significant in the model

(p < .01). Model 2 showed perceived neighborhood disorder and economic disadvantage

account for 13.6% of variation in trouble with the law/illegal activity outcomes. As with Model

1, significant ANOVA results indicate a strong likelihood that outcomes were not obtained by

chance. Model parameter results showed that as economic disadvantage levels increased, trouble

with the law/illegal activity levels increased, which is consistent with previous literature and the

expectations of the theoretical model. Similarly, consistent with previous literature and social

disorganization theory, as perceived neighborhood disorder levels increased, trouble with the

law/illegal activity still increased. After control variables were entered, economic disadvantage

was still significant (p < .05). As with previous analyses, issues of multicollinearity are present.










Results are presented in Tables 3-8 through 3-10 and parameter estimates are presented in Table

A-3.

Final OLS multiple regression analysis estimated the effects of perceived neighborhood

disorder and economic disadvantage on minor law violations. Model 1 shows perceived

neighborhood disorder and economic disadvantage account for 2.5% of the variation in minor

law violation levels. ANOVA results suggest that findings are likely to be obtained by chance

(F= 1.323, p=.271). Additionally, the model parameter showed that as perceived neighborhood

disorder and economic disadvantage increased, respectively, minor law violation levels

increased, which supports this paper' s hypothesis. Perceived neighborhood disorder was found

to be significant at the .05 level. Model 2 introduced control variables in the analysis.

Independent and control variables present account for 9.9% of the variation in depression levels.

ANOVA results for Model 2 were similar to Model 1, suggesting a strong likelihood that

outcomes may be obtained by chance (F= 1.552, p= .159). Model parameter results indicated

that as economic disadvantage and perceived neighborhood disorder increased, respectively,

minor law violation levels increased. There were no significant findings when independent and

control variables were introduced into analyses. Based on relatively low eigenvalues and high

condition indexes for the analysis, collinearity issues may also be present. Results are presented

in Tables 3-11 through 3-13 and parameter estimates are presented in Table A-4.










Table 3-1. Bivariate Correlations Output


Economic
Disadvantage


Self-Reported
Criminal Activity
.075**
.006
1331


Perceived Neighborhood
Disorder
.096**
.000
1331


Depression


Economic
Disadvantage


Self-Reported
Criminal
Activity

Perceived
Neighborhood
Disorder


Pearson Correlation
Sig. (2-tailed)





Pearson Correlation
Sig. (2-tailed)


Pearson Correlation
Sig. (2-tailed)


-.332**
.000
1331

-.080**
.004
1332


1331


.075**
.006
1331

.096**
.000
1331

-.332**
.000
1331


1

2482

-.012
.650
1332


-.012
.650
1332


-.047
.086
1332


1332

-.047
.086
1332


Depression


-.080**
.004
1332


1332













Table 3-2. OLS Statistical Model Summary using Depression as the Dependent Variablec
Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change
1 .110a .012 -.007 .63150 .012
2 .238b .057 -.010 .63245 .045
a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder
b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status, Respondent Race,
Respondent Employment Status, Total Household Income, Gender
c. Dependent Variable: Depression

Table 3-3. Statistical ANOVA Summary using Depression as the Dependent Variablec
Model Sum of Squares Df Mean Square F Sig.
1 Regression .510 2 .255 .639 .530a
Residual 41.475 104 .399
w Total 41.984 106
2 Regression 2.386 7 .341 .852 .547b
Residual 39.599 99 .400
Total 41.984 106

a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder
b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status,
Respondent Race, Respondent Employment Status, Total Household Income, Gender
c. Dependent Variable: Depression










Table 3-4. Statistical Collinearity Summary using Depression as the Dependent Variablea
Model Dimension Eigenvalue Condition Index


.002


42.663


8
a. Dependent Variable: Depression


.002


57.913


Table 3-5. OLS Statistical Model Summary using Self-Reported Criminal Activity as the Dependent Variablec
Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change
1 .230 .053a .035 .07635 .053
2 .349 122b .060 .07535 .069

a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder
b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status, Respondent Race,
Respondent Employment Status, Total Household Income, Gender
c. Dependent Variable: Self-Reported Criminal Activity










Table 3-6. Statistical ANOVA Summary using Self-Reported Criminal Activity as the Dependent Variablec
Model Sum of Squares Df Mean Square F Sig.
1 Regression .034 2 .017 2.905 .059a
Residual .606 104 .006
Total .640 106
2 Regression .078 7 .011 1.964 .068b
Residual .562 99 .006
Total .640 106
a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder
b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status, Respondent Race,
Respondent Employment Status, Total Household Income, Gender
c. Dependent Variable: Self-Reported Criminal Activity


Table 3-7. Statistical Collinearity Summary using Self-Reported Criminal Activity as the Dependent Variablea
Model Dimension Eigenvalue Condition Index


3 .002 42.663









8 .002 57.913
a. Dependent Variable: Self-Reported Criminal Activity










Table 3-8. OLS Statistical Model Summary using Trouble with the law/Done Something Illegal as the Dependent Variablec
Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change
1 .276 .076a .059 .204 .076
2 .368 .136b .074 .206 .059
a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder
b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status, Respondent Race,
Respondent Employment Status, Total Household Income, Gender
c. Dependent Variable: Trouble with the law/Done Something Illegal

Table 3-9. Statistical ANOVA Summary using Trouble with the law/Done Something Illegal as the Dependent Variablec
Model Sum of Squares Df Mean Square F Sig.
1 Regression .364 2 .182 4.296 .016a
Residual 4.403 104 .042
Total 4.766 106
2 Regression .646 7 .092 2.219 .039b
Residual 4.120 99 .042
Total 4.766 106

a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder
b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status,
Respondent Race, Respondent Employment Status, Total Household Income, Gender
c. Dependent Variable: Trouble with the law/Done Something Illegal










Table 3-10. Statistical Collinearity Summary using Trouble with the law/Done Something Illegal as the Dependent Variablea


Model


Dimension


Eigenvalue


Condition Index


42.663


.002


8 .002 57.914
a. Dependent Variable: Trouble with the law/Done Something Illegal

Table 3-11. Statistical Model Summary using Minor Law Violation as the Dependent Variablec
Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change
1 .158 .025a .006 .190 .025
2 .314 .099b .035 .187 .074

a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder
b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status, Respondent Race,
Respondent Employment Status, Total Household Income, Gender
c. Dependent Variable: Minor Law Violation










Table 3-12. Statistical ANOVA Summary using Minor Law Violation as the Dependent Variablec
Model Sum of Squares Df Mean Square F
1 Regression .096 2 .048 1.323


Sig.
.271a


.159b


Residual
Total
2 Regression
Residual
Total


3.755 104
3.850 106
.381 7
3.470 99
3.850 106


.036

.054
.035


1.552


a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder
b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status, Respondent Race,
Respondent Employment Status, Total Household Income, Gender
c. Dependent Variable: Minor Law Violation


Table 3-13. Statistical Collinearity
Model Dimension
1--


Summary using Minor Law Violation as the Dependent Variablea
Eigenvalue Condition Index


3 .002


42.663


8 .002
a. Dependent Variable: Minor Law Violation


57.913









CHAPTER 4
CONCLUSIONS

Discussion

This study's main obj ective was to determine whether an associational relationship

existed between the following: (1) social disorganization and crime, (2) social disorganization

and depression, and (3) crime and depression (as a result of strain caused by social

disorganization). Results of the correlation analyses indicate relationships between depression

and economic disadvantage; depression and self-reported criminal activity; economic

disadvantage and self-reported criminal activity; economic disadvantage and perceived

neighborhood disorder. Though strain and its interaction effects were not measured, economic

disadvantage (proposed to influence strain levels) was the only indicator of social

disorganization found to be a significant predictor of both self-reported criminal activity and

depression. Based on correlation analysis, there appears to be a negative relationship between

self-reported criminal activity and depression. This finding was not expected and contradicts

previous studies which found positive, significant relationships between the two variables

(Paxton et al., 2004). One possible explanation for this finding is that although pairwise deletion

techniques were used, a substantial amount of responses were deleted from correlation analyses,

which would alter the results. Another explanation might be that individuals who are depressed

are less likely to leave their homes (than non-depressed individuals), which would limit their

participation in outside criminal activities.

Though causality cannot be determined these findings still advance research on

community-level crime and depression. First, economic disadvantage was significant in some

models of criminal behavior while perceived neighborhood disorder was not and vice versa

depending on which measure of criminal behavior was used, the scale of criminal activity or









individual items measuring criminal behavior. Secondly, strain experienced as a result of social

disorganization may not have a strong effect on depression (as was proposed). Perceived

neighborhood disorder and economic disadvantage, as indicators of social disorganization, held

no significant ability to predict depression. Though entered as control variables, future research

may consider the direct effects of gender and spouse employment status on depression and

self-reported crime.

In summation, the analyses show mixed support for the hypothesized relationships

between crime, depression, and social disorganization. Based on regression analyses, social

disorganization was associated with self-reported criminal activity (a significant relationship was

found between economic disadvantage and self-reported criminal activity) and having trouble

with the law (a significant relationship was found between economic disadvantage and self-

reported criminal activity). Based on the Eindings, there was no significant association found

between social disorganization and depression. Contrary to expectations, correlation analyses

found a significant negative relationship between depression and self-reported criminal activity.

Nonetheless, a strong positive association was found between economic disadvantage and self-

reported criminal activity in some of the analyses. The relationship between crime and

depression (explained by the effects of social disorganization) cannot be determined based on the

present analyses, as analyses did not examine the interaction effects of crime and depression.

Nonetheless, social disorganization only held marginal predictive validity on criminal activity

(R2 = .053) and depression (R2 = .012) when entered in Model 1 (Table 3-5) of regression

analyses. This indicates that a third, unknown variable, may account for a greater percentage of

variation in the dependent variables. This unknown variable may also explain the main and net









effects of crime and depression on one another (relative to the effects of social disorganization

on crime and depression).

Limitations and Implications for Future Research

First, attrition presented major issues when running analyses. For example, reliability

analysis shows that nearly half of participant responses to Ross-Mirowsky scale were eliminated

due to "system missing" data. All findings, then, must be viewed with this caveat. Eliminating a

large set of responses will skew all correlation, regression, and ANOVA analysis. While this

paper does not explore the effect of "system missing" responses in the Ross-Mirowsky Perceived

Neighborhood Disorder scale, one possible result is that the relationships reported between

dependent and independent variables (depression, self-reported criminal activity, perceived

neighborhood disorder, and economic disadvantage, respectively) are inaccurate. In this sense,

the negative relationship reported between perceived neighborhood disorder and depression is

incorrectly signed (meaning the relationships between the variables may be positive, as opposed

to being reported negative, or vice-versa), as are the relationships between economic

disadvantage and depression; self-reported criminal activity and depression; self-reported

criminal activity and perceived neighborhood disorder. The assumed wrong sign direction

reported in correlation analyses may be a function of the large attrition rate of the Ross-

Mirowsky scale. As noted, these findings offer at best mixed support for expectations from

theory and the previous literature which finds positive relationships between these variables,

respectively. Similarly, the self-reported criminal activity scale, which has a low alpha

reliability, may severely skew the findings in this study and help explain the assumed wrong sign

direction of the analytic results. Secondly, interview questions did not allow respondents to

include motivations or influences for committing crime or becoming depressed. Because the

Community, Crime, and Health survey was cross-sectional, the following is not clear: (1)









attrition rate, (2) dates interviews were conducted, and (3) the long-term or interaction effects

between social disorganization, crime, and depression. This study's research design did not

address buffering or coping strategies, such as collective efficacy, that may deter or limit

engagement in criminal activities and/or depressive symptoms. Third, the Eindings in this study

do not support the proposed positive relationship between crime and depression. Statistical

errors, which may appear in the dataset and/or analyses, might help explain this contradiction.

Thus, future studies must examine this dataset to determine if similar Eindings occur.

Additionally, the research design did not control for respondent ages, which may influence

perceptions or awareness of disorder. Based on collinearity diagnostics, the severe possibility of

multicollinearity cannot be discounted. Future research must address these issues and determine

if the present study's results are replicated and generalized. Finally, one cannot exclude the

effects) that eliminating Hispanic respondents from regression and ANOVA analyses had on

this study's outcome. Individuals of Hispanic ethnicity may have different responses to and

perceptions of economic disadvantage and perceived neighborhood disorder which this paper

does not consider. Likewise, due to the fact many respondents did not answer questions gaging

family income, one can expect the overall findings in this paper to be skewed. By eliminating

the dummy variable from analyses, one must view this study's findings with caution.

Findings and conclusions must be viewed in light of the limitations of the study. The

present study addressed the relationship between perceived neighborhood disorder and economic

disadvantage (as independent variables) on dependent variables, yet, there may be indicators or

examples of social disorganization the study does not take into account. Another issue not

discussed in the current study is the "type" or "category" of crime one may commit as a result of

economic disadvantage. Deviant responses to Einancial strain may presumably range from a










petty crime such as shoplifting to a more serious infraction such as aggravated robbery. Finally,

the study faced the same problem that all secondary data analyses of not always finding the

measures to relate directly to the concepts and variables found in the literature and included in

the theoretical framework.

Ultimately, it is clear that in order to develop effective policies and social programs,

future studies must examine the detrimental effects of social disorganization on communities.

Additionally, applying other criminological theories, such as Social Learning Theory and

Differential Association Theory, may be helpful in determining the mechanisms used to cope

with strain and depression, and deducing the motivations behind criminal engagement. Using

social learning theory as a theoretical framework, for example, longitudinal studies on

adolescents might examine the predictive effects that learning deviant responses has on crime

commission. Likewise, cross-sectional studies may examine the effect that associating with

delinquent peers has on future crime commission and depression.

Based on the Eindings in this paper and issues with the dataset used, several suggestions

can be offered for future research on this subject. First, future researchers should design a

longitudinal study which explores the long-term effects of strain and/or social disorganization on

crime and depression. Secondly, future studies should increase the survey distribution and

sample size to areas outside of one state. In doing so, research can gage the effects of social

disorganization in different tracts and may also limit the potential for large attrition rates.

Finally, future studies should consult other studies on this subj ect and create scales which

accurately detail the variables of interest (i.e., depression, criminal activity, Einancial

strain/di advantagee. The present study used occurrences such as lack of sleep and happiness to

represent depression, which may be problematic and subj ect to criticism.









APPENDIX
PARAMETER ESTIMATES FOR OLS REGRESSION MODELS










Table A-1. Perceived neighborhood disorder and economic disadvantage effects on depression (parameter estimates for OLS
regression)


Model


Beta t


Error
1.226
.196
.317
1.285
.203
.322
.147
.135
.334
.171
.146


1 (Constant)
Perceived neighborhood disorder
Economic disadvantage
2 (Constant)
Perceived neighborhood disorder
Economic disadvantage
Gender
Spouse employment status
Respondent race
Total household income
Respondent employment status


1.689
-.190
-.231
1.336
-.177
-.185
.305
.070
.099
-.019
-.065


1.377 .171
-.096 -.971 .334
-.072 -.730 .467
1.039 .301
-.089 -.872 .385
-.058 -.572 .568
.242 2.078 .040
.056 .518 .606
.030 .295 .768
-.012 -. 114 .909
-.052 -.443 .659










Table A-2. Perceived neighborhood disorder and economic disadvantage effects on self-reported criminal activity (parameter
estimates for OLS regression)
Model B Std. Beta t Sig.
Error
1 (Constant) .295 .148 1.991 .049
Perceived neighborhood disorder -.020 .038 -.052 -.534 .595
Economic disadvantage -.057 .024 -.233 -2.406 .018
2 (Constant) .333 .153 2.175 .032
Perceived neighborhood disorder -.025 .038 -.064 -.660 .511
Economic disadvantage -.056 .024 -.231 -2.337 .021
Gender -.010 .017 -.065 -.579 .564
Spouse employment status -.033 .016 -.210 -2.020 .046
Respondent race .036 .040 .087 .895 .373
Total household income -.007 .020 -.037 -.364 .717
Respondent employment status -.009 .017 -.057 -.506 .614










Perceived neighborhood disorder and economic disadvantage effects on depression (parameter estimates for OLS regression)
Table A-3. Perceived neighborhood disorder and economic disadvantage effects on trouble with the law/done something illegal
(parameter estimates for OLS regression)
Model B Std. Beta t Sig.
Error
1 (Constant) 1.440 .400 3.604 .000
Perceived neighborhood disorder .175 .064 .262 2.740 .007
Economic disadvantage -.060 .103 -.055 -.578 .564
2 (Constant) 1.667 .415 3.557 .001
Perceived neighborhood disorder .025 .065 .245 2.504 .014
Economic disadvantage .056 .104 -.066 -.688 .493
Gender .010 .047 .094 .845 .400
Spouse employment status .033 .044 .173 1.675 .097
Respondent race -.036 .108 -.180 -1.861 .066
Total household income .007 .055 .010 .095 .925
Respondent employment status .009 .047 -.067 -.598 .551










Table A-4. Perceived neighborhood disorder and economic disadvantage effects on minor law violation (parameter estimates for OLS
regression)
Model B Std. Beta t Sig.
Error
1 (Constant) 1.379 .369 3.737 .000
Perceived neighborhood disorder .053 .059 .146 1.484 .007
Economic disadvantage .142 .095 .088 .900 .564
2 (Constant) 1.194 .380 3.138 .002
Perceived neighborhood disorder .062 .095 .178 1.811 .073
Economic disadvantage .173 .060 .103 1.034 .304
Gender .001 .043 .001 .012 .991
Spouse employment status .057 .040 .150 1.427 .157
Respondent race .058 .099 .058 .581 .559
Total household income .024 .051 .050 .482 .631
Respondent employment status .063 .043 .167 1.466 .146









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BIOGRAPHICAL SKETCH

Billy D. Holcombe is a native of Fort Lauderdale, FL and graduated from Nova High

School in 2001. He received a B.A. in criminology with a minor in sociology from the

University of Florida in 2005. During his collegiate tenure, he focused on academic excellence

and student leadership, having been inducted into Golden Key International Honour Society,

Florida Blue Key, and the University of Florida Hall of Fame. Billy anticipates gaining field

experience in community crime and psychiatric care before pursuing a Ph.D. in clinical

psychology.





PAGE 1

1 NEIGHBORHOOD CRIME, DEPRESSION, AND SOCIAL DISOR GANIZATION THEORY By BILLY D. HOLCOMBE A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORI DA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS UNIVERSITY OF FLORIDA 2008

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2 2008 Billy D. Holcombe

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3 This document is dedicated to my strong support sy stem and to the selfless individuals who came before me and fought to make my academic opportunities a reality.

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4 ACKNOWLEDGMENTS I owe an undeniable amount of thanks to my thesis supervisory committee, Dr. Ron Akers, Dr. Alex Piquero, and Dr. Jodi Lane. Without their conti nued patience, support, and most importantly guidance, I would not be able to expl ore and contribute to an area which fulfills me. I would like to thank Dr. Lane for never settling for mediocrity and for mentoring me during my undergraduate and graduate career. I would like to thank Dr. Piquero for his willingness to answer any question, no matter how simple it may seem. I would like to thank Dr. Akers, committee chair, for his continued perseverance and for his thoughtful words, genuine interest, and fostering my interest in academia. Dr. Akers, who is quite omniscient, had the ability to answer questions before I could ev en ask them. I am forever grat eful for and indebted to these individuals for their sacrifice and encouragement. I also owe a great deal of thanks to my family and parents, Earnest and Sharon Holcombe, for always supporting my creativity a nd continually reminding me that education is the key to my success. Included in this family are Progressive Black Men, Inc., my graduate cohort, and dear friend Erika Jones, who all o ffered listening ears when I felt all sanity was leaving me. I dedicate this document to them. I would also like to recognize my lifelong mentors, Dr. Terry Mills and Dr. Stephanie Evans, who unarguably challenged me personally and professionally and we re never too busy to have hour-long chats with me. They have forever left a positive impact on my life.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........6 LIST OF FIGURES.........................................................................................................................8 ABSTRACT.....................................................................................................................................9 CHAP TER 1 LITERATURE REVIEW AND TH EORETICAL FRAMEWORK ...................................... 10 Introduction................................................................................................................... ..........10 Social Disorganization Theory: An Overview....................................................................... 11 General Strain Theory: An Overview..................................................................................... 14 Exploring the Social Disorgan ization-Crim e Relationship.................................................... 15 Exploring the Social Disorganiz ation-Depression Relationship ............................................ 18 Exploring the Crime-De pression Relationship .......................................................................20 2 DATA AND METHODOLOGY........................................................................................... 23 Sample....................................................................................................................................23 Dependent Variables............................................................................................................ ...23 Independent Variables............................................................................................................24 Control Variables....................................................................................................................26 Analytic Plan..........................................................................................................................26 3 RESULTS...............................................................................................................................32 4 CONCLUSIONS.................................................................................................................... 45 Discussion...............................................................................................................................45 Limitations and Implicati ons for Future Research ................................................................. 47 APPENDIX: PARAMETER ESTIMATES FOR OLS REGRESSION MODELS...................... 50 LIST OF REFERENCES...............................................................................................................55 BIOGRAPHICAL SKETCH.........................................................................................................60

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6 LIST OF TABLES Table page 2-1 Descriptive Statistics..................................................................................................... .....30 2-2 Ross-Mirowsky Perceived Neighbor hood Disorder Scale Reliability............................... 31 3-1 Bivariate Correlations Output............................................................................................38 3-2 OLS Statistical Model Summary using Depression as the Dependent Variable ............... 39 3-3 Statistical ANOVA Summary using De pression as the Dependent Variable .................... 39 3-4 Statistical Collinearity Summary using Depression as the Dependent Variable............... 40 3-5 OLS Statistical Model Summary using Self -Reported Criminal Activity as the Dependent Variable........................................................................................................... 40 3-6 Statistical ANOVA Summary using Se lf -Reported Criminal Activity as the Dependent Variable........................................................................................................... 41 3-7 Statistical Collinearity Summary using Self-Reported Criminal Activity as the Dependent Variable ........................................................................................................... 41 3-8 OLS Statistical Model Summary using Tr ouble with the law/Done Som ething Illegal as the Dependent Variable.................................................................................................42 3-9 Statistical ANOVA Summar y using Trouble with the law/D one Something Illegal as the Dependent Variable......................................................................................................42 3-10 Statistical Collinearity Summary using Tr ouble with the law/Done Som ething Illegal as the Dependent Variable.................................................................................................43 3-11 Statistical Model Summary using Minor Law Violation as the Dependent Variable ........ 43 3-12 Statistical ANOVA Summar y using Minor Law Violation as the Dependent Variable .... 44 3-13 Statistical Collinearity Summary using Minor La w Violation as the Dependent Variable..............................................................................................................................44 A-1 Perceived neighborhood disorder and ec onom ic disadvantage effects on depression (parameter estimates for OLS regression)......................................................................... 51 A-2 Perceived neighborhood disorder and economic disadvantage eff ects on self-reported crim inal activity (parameter estimates for OLS regression).............................................. 52 A-3 Perceived neighborhood disorder and ec onom ic disadvantage effects on trouble with the law/done something illegal (param eter estimates for OLS regression)....................... 53

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7 A-4 Perceived neighborhood disorder and econom ic disadvantage on minor law violation (parameter estimates for OLS regression)......................................................................... 54

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8 LIST OF FIGURES Figure page 2-1 Theoretical Model.......................................................................................................... ....29

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9 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Arts NEIGHBORHOOD CRIME, DEPRESSION, AND SOCIAL DISOR GANIZATION THEORY By Billy D. Holcombe May 2008 Chair: Ronald L. Akers Major: Criminology, Law, and Society The amount of literature exploring the relationship between community crime, depression, and social disorganiz ation is severely limited. None theless, previous research on depression and crime, respectively, finds that areas low in collective efficacy and social cohesion are higher in residential depr ession and crime commission. The present study surv eys attitudes on individual depression levels neighborhood disorder/dissatisf action, economic disadvantage, strain, and criminal involvement in order to determine the associational relationship between social disorganization, crime, and depression. Household income, gender, employment status, and race, among other sociodemographic variables, ar e included as control measures in analyses. Limitations and future resear ch questions are addressed.

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10 CHAPTER 1 LITERATURE REVIEW AND THEORETICAL FRAMEWORK Introduction Susan Smith. This name will forever resonate in the minds of Americans as a cold, sadistic killer. Suffering from post-partum depression, she drowned her two young children in a South Carolina river. Though the severity of her crime may s eem extreme, Smith was rumored to have depression. But was she suffering from c linical depression and if so what did that have to do with her crime? Similar questions can be asked in response to such media reported cases. There are as yet no well-supported answers to su ch questions. Social science research has explored the relationship between depression and crime in relati on to social factors (such as exposure to crime and financial strain). As this point it is not clear that depression does predispose one to commit crime, and it is not clear that depression is a high risk factor for crime. Explanations of the association between crime an d depression are limited and more research is needed. This research seeks to address social factors which may link criminality to depression. Due to limited research in the field, questions st ill remain about the rela tionship between social disorganization, crime, and depression. Are crime and depression related? Is social disorganization related to crime and depression, re spectively? Do the negative effects of social disorganization, such as strain, explain the assumed relationship be tween crime and depression? Using social disorganization theory as a guide, this research seeks to bring an additional understanding of the relationship of crime to the violent ideations and aggressions some depressed individuals expe rience. Specific attention will be given to indivi dual and communitylevel variables which may buffer, motivate, or co ntribute to depressive criminality. Based on social disorganization an d strain theory, I expect the following to occur: crime and depression

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11 will be positively related to one another, and I expect both to be positively related to social disorganization. Social Disorganization Theory: An Overview Social disorganization theory has its root s in the twentieth cent ury Chicago school of sociology. Since its inception, pr oponents of social disorganization theory have focused on neighborhood conditions of disadvant age, but the concept itself is defined as the inability of residents to maintain social co ntrol and order and deal with community problems. Social disorganization is the re sult of a decrease in the effect of social rules over the behavior of group members (Thomas and Znaniecki, 1920). Kubr in and Weitzer (2003) note that social disorganization is the result of a community being unable to resolve chronic issues. The prediction is that when social disorganization persists, residential stri fe, deviance, and crime occur. Social disorganization results when there is an overabundance of disorder relative to order. Neighborhood characteristic s of disorder include social traits (i.e., noise, population heterogeneity, high population density, broken families, and high residential mobility) in addition to physical traits (abandoned/decrepit buildings, graffiti, and litter) (Gottfredson & Hirschi, 1990; Ross & Mirowsky, 1999). Skogan and Maxfield (1981) use the term perceived neighborhood disorder to denote negative conditions, whether criminal or non-criminal, which residents assume to be cues of social disorder. Shaw & McKay (1942), credited for advanci ng social disorganiza tion theory during its early years, provided three relevant theoretical constructs. Social order, they contended, required the following: community members to supervise and control teenage gr oups, local friendship networks to form, and resident ial participation in formal/voluntary organizations. Before elaborating on these constructs, it is first necessary to provide a historical sketch of Shaw &

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12 McKays theory. Their study, which assesse d community problems in Chicago, included measures of crime, delinquency, truancy, and mental disorder. They found that areas with social problems also had low education levels, families on welfare, poor community organization, and low values for rental properties. What eff ect do Shaw & McKays social order requirements have on reducing social order pr oblems and minimizing levels of social disorganization? Sampson & Groves (1989) argue that teenag e group behavior, assumed to be unstable and prone to delinquency, can be positively shaped by residents in cohesive communities. Delinquency is primarily an act which occurs in group settings (Thrashe r, 1963). Faris (1955) found that the family normally derives important support in its tasks of gu iding the behavior of children from the friends and neighbors, the prim ary group outside of the family itself. In an integrated community, these outside persons al so by example reinforce the teachings of the parents. Furthermore, they constitute an importa nt source of supervision, for children soon learnt that any misbehavior which is observed by neighbors is likely to be reported to parents (p. 393). In this sense, responsibility fall s not only on the parents/guardians of the teenagers, but is further extended to all members in the community. Orde r, then, is maintained by all members. Secondly, local friendship networks must be established in order for community satisfaction to exist. When these networks ar e created, community members are more motivated to shield their neighbors. Fari s (1955) argued that persons in volved in homogenous and unified social groups experience a sati sfying confidence in the structur e of mutual affection in the group (p. 88). Furthermore, this construct lends support to the notion of residents assuming responsibility for what occurs in their communitie s and preventing victimization from outsiders. Sampson & Groves (1989) contend th at participation in formal or voluntary organizations is key to preserving community social order. Examples of such organizations, in modern-day society,

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13 include local police task forces or community Crime Watch pr ograms. Partnerships between these institutions, it is assumed, will positively contri bute to residential safety. Previous research (Shaw & McKay, 1942; Sampson & Groves, 1989) not ed that when links between community institutions are weak, a community s ability to defend its interest s is compromised. In essence, voluntary organizations/associations and social networks may act as buffers that counteract risk factors for crime and increase cohesiveness among concerned residents seeking to improve their communities. What can explain the variances in disorgan ization levels between communities? What factors impede social order? Sampson & Groves found four factors asso ciated with varying levels of social disorganization: socioeconomic status, residential mobility, family disruption, and urbanization. Silver (2000), in his study of violence among th e mentally ill, had similar findings--social disorganization accounted for th e effect of community-level socioeconomic status, residential stability, and family di sruption (relative to personal and property victimization). Communities with residents of low economic status often lack money and resources, which can cause elevated levels of stress and strain. There is a strong association between neighborhood disadvantage and low-income individuals (Ross, 2000; Beardman et al., 2001). Additionally, low socioeconomic status communities are less equipped to control and supervise youth groups, compared to high so cioeconomic status communities (Sampson & Groves, 1989). Having high levels of residential mobility can disrupt a communitys network of social support and relations (Sampson & Groves, 1989; Ross, Reynolds, & Geis, 2000). According to social disorganization theory, low residential turnover incr eases the likelihood that neighbors will know each other, share values, and exert informal social control (Shaw & McKay, 1942). Bursik (1988) purports that families are le ss likely to form and participate in community-

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14 based organizations if they plan to relocate at the first opportu nity. Family disruption, or deviation from the ideal two-parent household structure, acts to d ecrease informal social controls and reduces guardianship in the community. As cities expand and become more industrialized, urbanization threatens to weaken local friends hip networks and dise ngage residents from participating in local affairs (similar to that of residential mobility). Compared to the conventional community structure, where there is a network of cooperation between the children who play together and their parents, such a social structur e is lacking in the most urban regions (Faris, 1955, p. 416). Previous literature characterizes community social order as a mix of friendship and loyalty among residents. Kasarda & Janowitz (1 974) call this the systemic model, whereby a community is viewed as a unit of formal and inform al ties rooted in family life and socialization. An element central to understanding the systemic model is the notion of collective efficacy. Collective efficacy is the term used to denot e a willingness to intervene on behalf of the common good (Sampson et al., 1997, p. 918). Collect ive efficacy, in short, is the sense of the residents that they can trust one another to in tervene and exercise info rmal social control over trouble and problems that may occur in the neighborhood, and contribute to the communitys upkeep and preservation of social order (S ampson et al., 1997; Sampson & Raudenbush, 1999; Wells et al., 2006). General Strain Theory: An Overview Strain is the result of negative life even ts occur or aversive relationships between individuals. Exogenous sources of strain include elements of so cial disorganization such as negative neighborhood conditions, violent victimiza tion, and stressful life experiences. Agnew (1992), credited for addressing strain and delinquency in his genera l strain theory (GST), cites three causal factors of strain: the failure to achieve positively valued stimuli or goals; the

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15 removal of positively valued stimuli or goals; presence of negative stimuli. GST lays the foundation for a social disorganization-crimedepression relationship through its three main tenets: exposure to strain increases delinquenc y; when negative fact ors are present (given exposure to strain), delinquent responses are more likely to occur; delinquent responses are more likely to occur when strain exposure leads to an ger. Mazerolle et al (2003) contend that individuals may seek adaptive mechanisms such as crime and delinquency to cope with anger. Hoffmann & Miller (1998), on the other hand, argue that high self-efficacy, high self-esteem, and modeling positive peer behavior serve as positive coping strategies. Individuals with high self-efficacy attribute their succe sses and failures to personal attr ibutes rather than external agents, such as the federal government. High self-esteem, it is argued, acts a preventive shield to limit engagement in delinquent activities and increas e ones resilience to strain. Modeling positive peer behavior is instrumental, they co ntend, because peers can remind one another of adverse consequences (as a resu lt of delinquent behavior) and pe ers can serve as role models. Exploring the Social Disorganization-Crime Relationship As previously noted, factors such as pres ence of graffiti in public places and low socioeconomic status can increase levels of st ress and contribute to so cial disorganization. Social disorganization scholars point to the preventive and central effects of social support and collective efficacy in exploring the motivating fact ors of criminality. Social support, which is generally defined as informal ties with ones neighbors, is crucial in mitigating the strain associated with disadvantage. Cullen (1994) stat ed that the more soci al support in a persons social network, the less crime will occur (p. 540). Silver (2000) contends that anger, substance abuse1, and lack of social support (whi ch are all indicative of social disorder) are proximate risk 1 Silver & Teasdale (2005) found that individuals with substance abuse disorder also exhibited the highest rates of violence.

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16 factors for violence2. Furthermore, having strong social support was found to buffer residential fear of crime and distrust (Ross & Jang, 2000). When fear of crime3 is widespread in communities, residents are less likely to engage in programs and activities which reduce disorder and criminality. Without the mediating effect s of collective efficacy, crime is fostered. Collective efficacy has often been cited to help explain differential crime rates across communities (Sampson et al., 1997). The term evokes a feeling of responsibility among residents to intervene in community affairs thro ugh informal measures. Such efforts include street surveillance and direct intervention in problems (Kubr in & Weitzer, 2003). Based on previous research, the qualitativ e effects of informal control were found to be significant. Residential interventions may be effective for delinquent groups/perpetr ators since they do not carry the same negative stigmas as do more form alized means (such as police intervention or incarceration). In fact, collectiv e efficacy was found to be more in strumental in crime prevention than was intervention by author ities--neighborhoods with disadvantage levels are less likely to secure law enforcement resources and prot ection (Sampson, 1997; K ubrin & Weitzer, 2003). A considerable volume of literature has been devoted to exploring the effects of disorganization on neighborhood crime. Social disorganization theo ry notes that local ties and informal control limit the effects of econom ic disadvantage, resi dential instability4, and other factors on neighborhood crime (Kubrin & Weitzer, 2003). Previous studies suggest that crime rates are related to neig hborhood ties, informal soci al control, and resident ial interaction (Warner 2 Silver (2002), in a similar study, examined the role of social relationships in mediating victimization among the mentally ill. Silver posits that because the mentally ill are more likely to have unstable, conflicted relationships with neighbors, they are at a greater risk of being victimized. This finding suggests a shielding or deterrent value of social support. 3 Taylor (1996) posits that neighborhood attachment levels w ill decrease when residents view disorder or crime. He notes an opposite effect when residential exposure to crime is low. 4 Osgood & Chambers (2003) found residential instability to be associated with increased rates of rape, simple and aggravated assault, weapons viola tions, and the overall crime index.

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17 & Rountree, 1998; Rountree & Warner, 1999; Veysey & Messner, 1999; Bella ir, 1997; Elliot et al., 1996; Sampson et al., 1997; Hirschfield & Bowers, 1997). In their case study, Morenoff, Sampson, & Raudenbush (2001) looked at pred ictive factors of neighborhood homicide in Chicago for the 1996 1998 period. To gage act ual and perceived neig hborhood violence rates, the authors used official data from the 1990 Census and responses from a 1995 survey distributed to local resident s (n= 8,872). Their findings pr ovided support for a link between collective efficacy and criminality. For exampl e, 72% of neighborhoods having high levels of collective efficacy experienced low levels of hom icide. Additionally, 75% of areas defined as homicide hot spots had low le vels of collective efficacy. Th is study found that concentrated disadvantage and low collective efficacy, independen t of one another, predict increased homicide levels. Nevertheless, involvement in local or ganizations or voluntary associations increased levels of collective efficacy in achieving soci al control and resident ial cohesion. Nash and Bowen (1999) assessed social cap ital, demographics, and reside ntial perception of crime among a national sample of middle and high school ad olescents using the School Success Profile (SSP) instrument (n= 1,796) to determine factors whic h influence perception of crime. Nash and Bowen hypothesized that when participants perceive d high levels of crime, they were also less likely to see their peers as invol ved in prosocial activities. Resu lts from the study showed that for middle and high school stude nts, a significant relationship was found between perceived informal social control and perceived neighborh ood crime, even after control variables were added5. Additionally, as perception of informal soci al control increased, participants perceived less crime in the neighborhood. Perceived inform al social control had a negative effect on 5Control variables included race, sex, and socioeconomic status.

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18 perceived neighborhood crime6. Furthermore, as perceptions of high neighborhood socioeconomic status7 increased, lower neighborhood crime le vels were perceived. Nash and Bowen purport that at the macro level, community organization and political action are needed to reduce neighborhood crime and it s correlates, such as poverty and economic dislocation (p. 183). At the micro level, social workers can collaborate with adolescents and their families to reduce exposure to crime even in communities with high crime rates (p. 183). Collective efficacy of community agencies and social agents can minimize neighborhood crime rates. Paxton et al. (2004), in a similar study, found that among adolescents, poverty, discrimination, and low education/employment opportunities co ntribute to the risk for witnessing and experiencing violence. Exploring the Social Disorganization-Depression Relationship Social disorganization theory has been used to explain the neighborhood context of depression and other mental disorders (Silv er, Mulvey, & Swanson, 2002). Hiday (1997) suggests that neurobiological factors may be the or igin of severe of ment al illness, but social factors affect its course, manifestations, and connections to violence (p. 412). Exposure to stressful events shapes depressive problems among adolescents (Colten & Gore, 1991; Compass and Wagner, 1991). Structural and psychological factors which affect individual depression include living in an urban area, having disadvantaged economic st atus, experiencing stressful life events, and having weak social relationships (De Coster, 2003). 6 Perceived neighborhood crime has a significantly negative effect on the perceived prosocial behavior of adolescents. 7 Bursik & Grasmick (1993) posit that areas with low socioeconomic status al so have greater rates of residential mobility and ethnic heterogeneity. These factors lead to weak social control systems and high rates of crime and delinquency.

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19 Ross (2000) examined the assumed disadva ntage-depression relationship among Illinois residents using the Community, Crime, and Health (CCH) survey (n= 2,482)8. Previous studies, she noted, found depression levels to be highe r among individuals with low education, low income, and the unemployed. Based on this, she hypothesized that reside nts of disadvantaged neighborhoods exhibited higher rates of depression (compared to more advantaged neighborhoods) and neighborhood disorder further accounted for the association between depression and disadvantage. Physical malaise (inclusive of having trouble sleeping and/or concentrating) and feelings of sadness were indicative of depr ession in this study. Respondents provided answers to questions assessing prior criminality and community satisfaction. After recoding responses, Ross found that poverty a nd single-mother households were the only indicators of neighborhood disadvantage significantly associated with depression. As a result, individuals who live in these di sadvantaged neighborhoods are more likely to have poor mental health. Additionally, Ross found that residents who drink heavily or engage in illegal activities had significantly higher levels of depressi on than those who did not. Sociodemographic variables not found to be associ ated with depression include th e following: education level in neighborhood, homeownership, racial/ethnic co mposition, and residential stability. As previously noted, perceived neighborhood disorder has a str ong effect on social cohesion9. Subsequently, due to strain and a lack of social support, depression may ensue. An inverse relationship exists between social cohesion and depressi on. Depression is lowest when people in a neighborhood know each other (Anesh ensel & Sucoff, 1996). Empirical research lends support to a disorganizati on-depression relationship. Latkin & Curry (2003) noted a strong 8 Depression is also high among residents who are minorities, women, and individuals who are unmarried. 9 Perceived neighborhood disorder has a stronger association to an individuals mental health status than does official measures of neighborhood char acteristics (Hadley-Ives et al., 2000).

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20 association existed between perceived nei ghborhood decay and depression (even after controlling for baseline depressive symptoms). In a study of adolescent students, Stevenson (1989) found a relationship between low levels of depressive symptoms a nd strong feelings of neighborhood safety. Aneshensel & Sucoff (1996), in a similar study of adolescents, found that the more threatening a neighborhood is perceived to be, the more common the symptoms of anxiety and depression. On the other hand, students who id entified their neighborhoods as having low social capital also re ported high levels of depressive symptoms. Silver (2000) pointed to low socioeconomic status to e xplain the social diso rganization-depression relationship, finding that environmental ad versity facilitates mental disorders10. He adds that the inability of some mentally ill individuals to function in a normal setting reduces the likelihood of forming long-lasti ng, extensive social networks. Exploring the Crime-Depression Relationship Research on crime and depression is severe ly limited in number. Of the literature available, specific attention is focused on indi vidual/community criminal law violations and depression levels. Rosenbaum (1991) found that in intimate partner homicides, 75% of the perpetrators were depressed. De Coster ( 2003) proposed a link betw een law violation and depression. De Coster contends that there are three sequential mechanisms which lead to law violation and depression. First, social-str uctural positions, including living in economically disadvantaged neighborhoods, which may expose youths to stressful life events, can lead to law violation and symptoms of depre ssion. In turn, deviant responses to stressful life events shape 10 The study also finds that patients discharged into disadvantaged neighborhoods were significantly more likely to engage in violence than a comparative group discharged into less disadvantaged neighborhoods.

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21 social relationships with conventional nor ms and deviance. Relationships formed11 may ultimately influence law violat ion and depression (p. 130). Pa xton et al. (2004) found that exposure to crime is linked to anxiety and de pression, perhaps suggesting a causal relationship. De Coster & Heimer (2001) found that delinquent adolescents were more likely to become depressed in early adulthood, noted by a si gnificant effect of de linquency on adulthood depression. Though few studies have directly explor ed the relationship between crime and depression, several studies (Dollf us et al., 1993; Sax et al., 1996; Lancon et al., 2001) found that depressive symptoms are also components of sc hizophrenia, though the fre quency of depressive symptoms varies based on the stag e of schizophrenia. Swanson et al. (1990), using data from the Epidemiological Catchment Areas five study sites, analyzed the relationship between violence and psychiatric disorder (n= 10,059). Based on the relations hip between depression and schizophrenia, their results add support to a crime-depression hypot hesis, finding that those with schizophrenia were more violent than those with out. Individuals with schizophrenia alone, for example, were found to be more violent than t hose without (8% violence rate compared to 2% violence rate)12. When substance abuse, (Ross, 2000), was factored in, the violence rate among schizophrenics jumped to 30%. Though the cau sative effects of schi zophrenia cannot be deduced from this study, it does show th e compounding effect of substance abuse on schizophrenic criminality. In a cross-sectional study, Steadman et al. (199 8) looked at the effect substance abuse has on the relationship between mental disorder and violence. Their study 11 These social relationships shape elements of role taki ng, including reflected appraisals, modes of deviance, and reactions to deviance. 12 Though 92% of schizophrenic participants in Swanson et al. (1990) were presumably non-violent, the percentage of schizophrenics who engage in crime was still slightly greater than participants in the control group. In a related study, Silver and Teasdale (2005) found mental disorders and violence to be significantly related.

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22 measured rates of violence among individuals age 18 to 40 during their first year of discharge from a mental institution (n = 1,136)13. Their results showed substa nce abuse significantly raised the prevalence rate of violence in mental disorder populat ions. Based on the relationship between the underlying components of depression and schizophrenia, it is plausible to assume that substance abuse may incite aggressive or violent behavior in depressed individuals. In recent years, a small body of research has examined the relationship between social disorganization, crime, and depression. Prev ious violence, substance abuse, psychopathy, relationship instability, personality disorder, anger, and lack of social support were all found to increase the chance of an indivi dual engaging in violence or be ing victimized (Silver, 2000). Silver (2002), in a study of psyc hiatric patients from the Western Psychiatric Institute and Clinic (WPIC) in Pittsburgh, noted that discharged patient s were more likely to engage in violence than those without a psychotic diagnosis14. Additionally, even after controlling for individual and community-level correlates, individuals with psycho tic diagnoses were more likely to be victims of violence (compared to non-psychotic individuals). 13 Researchers gathered data from self-report surveys, inform ants, and official police reports. The study monitored violence to others every ten weeks following disc harge from one of three mental institutions. 14 Ross (2000) found that individuals who engage in crimin al activities are more depressed than those who do not.

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23 CHAPTER 2 DATA AND METHODOLOGY Sample This research study is a secondary anal ysis of data taken from Ross & Britts Community, Crime, and Health (CCH) survey (1995, 1998). Data were retrieved using the University of Michigans Inter-University Cons ortium for Political and Social Research (ICPSR) database. The Community, Crime, and Health su rvey was administered to adults aged 18 and older in select Illinois households (n= 2, 482) to determine the relationship between neighborhood disadvantage and physical/mental health. Participants were contacted via telephone by a random-digit dialing method which increases the rate of contacting home phone numbers (as opposed to disconnected or business phone numbers). Up to 10 calls were initially made to select and contact a respondent; up to a maximum of ten follow-up calls were made to complete the interview. The adult in the househol d with the most recent birthday was selected as the respondent. This survey had a 73.1% response rate. Demographic information for respondents is as follows: 1) E nglish speaking adults; 2) minimu m age of 18 (maximum of 92); 3) mean age is 42 years; 4) mean education is 13.8 years (indicating some degree of advanced training or higher education); 5) mean income is $48,274, 6) 41% of respondents are male; 7) 84.1% of respondents are White; 8) 53.3% of respondents ar e married. Descri ptive sta tistics for all variables (with re coded variable names in parent heses) are shown in Table 2-1. Dependent Variables The two dependent variables used fo r analysis purposes are depression (DEPRESSION) and self-reported crime (CRIMINALACTIVITY). DEPRESSION represents summed responses to Ross (2000) 7-item modification of the Center for Epidemiological Stud ies Depression (CES-D) scale. Previous literature defines

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24 depression as continued feelings of despair and lethargy that goes beyond the ordinary and temporary feelings of melancholy or the blues. To gage de pression levels, respondents were asked On how many days in the past week have you: 1) had trouble gettin g to sleep or staying asleep?; 2) felt you just couldn t get going?; 3) had trouble k eeping your mind on what you were doing?; 4) felt that everything wa s an effort?; 5) felt sad?; 6) felt lonely?; 7) felt you couldnt shake the blues?. Responses were coded from 0 (No depression) to 7 (High depression). For analysis purposes, No Coded Response, Dont Know, and Refused/Missing responses were recoded as System Missing for all 7 items. The resident depression scale has an alpha reliability of .783. CRIMINALACTIVITY represents summed se lf-report measures of criminal activity using measures of self-reported crime. To gage respondent criminality, respondents were asked In the past twelve months, have you 1) done anything that would have gotten you in trouble if the police had been around/done anything illegal?; 2) been caught in a minor violation of the law?; 3) been arrested?; 4) been in jail for more than 24 hours?. Responses were recoded as (No) and (Yes). For analysis purpos es, No Coded Response, Dont Know, and Refused/Missing responses were recoded as S ystem Missing for all 4 items. These activities form an additive scale indicative of increasing tr ouble with the law. The scale has a low alpha reliability of .330. Nonetheless, I have retained this measure of criminal activities. Interpretation of findings must keep this low scal e reliability in mind. The self-reported criminal activity scale has an alpha reliability of .330. Independent Variables Independent measures of inte rest deal with a variety of factors comprising neighborhood disorganization. For analysis purposes, disorg anization is broken down into two variables,

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25 perceived neighborhood disorganization (PE RCEIVEDDISOR) and reported financial disadvantage (ECONDIS). PERCEIVEDDISOR represents summed res ponses to the 15-item Ross-Mirowsky Perceived Neighborhood Disorder Scale (1999) (s ee Table 2-2). Neighborhood disorder refers to conditions and activities, both major and mi nor, criminal and non-criminal, that residents perceive to be signs of the breakdown of social order (Ross, 2000, p.181). To gage perceived disorder levels, respondents were asked to answer the following 15 items: 1) There is a lot of graffiti in my neighborhood; 2) My neighborhood is noisy; 3) Vandalism is common in my neighborhood 4) There are a lot of aba ndoned buildings in my neighborhood; 5) My neighborhood is clean; 6) People in my neighborhood take good care of their houses and apartments; 7) There are too many people hang ing around on the streets near my home; 8) There is a lot of crime in my neighborhood ; 9) There is too much drug use in my neighborhood; 10) There is too much alcoho l use in my neighborhood; 11) Im always having trouble with my neighbors; 12) In my neighborhood, people watch out for each other; 13) The police protection in my neighborhood is adequate; 14) The people in my neighborhood trust one another; 15) My neighborhood is safe. Items 1-4 are indicators of physical disorder. Items 5-6 are i ndicators of physical order. Items 7-11 are indicators of social disorder. Items 12-15 are indicato rs of social order. Collectiv ely, items 1-4 and 7-11 form an index variable of disorder (DISORDER). Items 5-6 and 12-15 form an index variable of order (ORDER). Disorder items are coded in Likert scale form as strongly disa gree (1); disagree (2); agree (3); strongly agree (4). Order items are reverse coded in Likert scale form as strongly agree (1); agree (2); disagree (3); strongly disagree (4). For analysis purposes, No Coded Response, Dont Know, and Refused/Missing responses were recoded as System Missing

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26 for all 3 items. The Ross-Mirowsky scale ha s a high alpha reliability of .919 based on the original sample of 2,482 respondents (see Table 2-2). ECONDIS represents summed responses to a 3-item economic disadvantage scale used to represent financial strain. Respondents were asked In the past twelve months have you: 1) not had enough money to buy food and other household necessities?; 2) not had enough money to pay for medical care?; 3) not ha d enough money to pay for bills?. Responses were coded as very often (1); fairly often (2); not very often (3); never (4). For an alysis purposes, No Coded Response, Dont Know, and Refused/Missing responses were recoded as System Missing for all 3 items. The financial strain scale has an alpha reliability of .809. Control Variables Several demographic control variables were se lected as control variables for regression models. These variables include gender (GE NDER), recoded as for male and for female; respondent employment status (EMPLOY) coded as for full-time employment and for non full-time employment; spouse employme nt status (SPOUSEEMP) coded as for full-time employment and for non full-time employment; racial background (RACE) coded as for White and for non-White; annual family household income (in dollars) in 1997 (INCOME) coded as for $30,000 or more and for less than $30,000; respondent is of Hispanic/Spanish origin (HISPANICS) coded as for no or for yes. For analysis purposes, Something Else, No Coded Respons e, Dont Know, and Refused/Missing responses were recoded as System Missing for all 6 items. Analytic Plan The objective of this research is to examine the relationships between social disorganization, crime, and depression. The a ssumption is that both criminal behavior and depression are responses to strain (unmeasured in this study) resu lting from living in socially

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27 disorganized neighborhoods (see Figure 2-1). Due to the limitations of th e data set the causal implications of this underlying a ssumption or in Figure 2-1 are not tested. Rather the present analysis simply examines the bivariate and multi variate relationships between measures of crime and depression as dependent variables and measures of social disorganization as independent variables. Present analyses use perceived neigh borhood disorder (PERCEIVEDDISOR) and economic disadvantage (ECONDIS) as indicators of social disorganization. Using the dependent variable for self-reported criminal activity (CRIMINALACTIVITY) and independent variable for perceived neighborhood disorder, (PERCE IVEDDISOR), which include items assessing perceptions of crime and deviance in the neighborhood, may pres ent confounding or tautological analysis issues, but the percepti on of crime is not the same as reporting one's own offenses and the measure is necessary to explore the rela tionship between crime and depression. SPSS statistical software will be used to perform all analyses. Due to the multiple category composition of dependent and independent variables used in analyses, crosstabular examinations will not be performed. Nonetheless, bivariat e correlation analyses will be conducted on the dependent (depression (DEPRESSION), self-reported criminal activity (CRIMINALACTIVITY)) and indepe ndent (economic disadvantage (ECONDIS), and perceived neighborhood disorder (PERCEIVE DDISOR)) variables to address the proposed associational relationship between variables. To addre ss the proposed relationships between social disorganization and crime and social disorgan ization and depression, respectively, multivariate OLS regression analyses will be performed to de termine the relationship between the selected dependent variable and independent variables an d to test for multicollinearity. Demographic control variables will also be introduced into the analyses. That is models will be run first with depression as the dependent and perceived neig hborhood disorder and ec onomic disadvantage as

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28 the independent variables; then with self-reported criminal activity as the dependent variable and perceived neighborhood disorder an d economic disadvantage as the independent variables. To address the low reliability of the self-reported criminal activ ity scale (CRIMINALACTIVITY), separate models will be run with indicators of self-reported criminal activity (ILLEGRALREC, MINVIOLREC, ARRESTEREC, and JAILREC, resp ectively) as the dependent variables and neighborhood disorder and economic disadvantage as the independent variables. Control variables will then be added as a block to each regression analysis. If the main and net effects of social disorganization on depres sion and crime are in the expected direction of higher crime and depression associated with higher social disorganization and are greater than the main and net effects of depression and crime on each other, the main hypothesis of the study will be supported. Also, the expected re lationship between crime and de pression is positive; higher levels of depression are expected to be associated with higher criminal activity. If not, then appropriate conclusions about the theoretical model and future research will be drawn.

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29 + + + Figure 2-1. Theoretical Model Depression Social Disorganization Strain (unmeasured) SelfReported Criminal Activity Perceived Neighborhood Disorder Economic Disadvantage

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30 Table 2-1. Descriptive Variable Statistics Min. Max. Mean SD Alpha Dependent Variables Depression Scale s (DEPRESSION)# days .783 Had trouble sleeping (SLEEPREC) 0 7 1.03 2.003 Had trouble getting going (GETGOREC) 0 7 0.70 1.589 Had trouble keeping focused (MINDREC) 0 7 0.65 1.575 Felt everything was an effort (EFFORTREC) 0 7 0.51 1.483 Felt sad (SADREC) 0 7 0.71 1.496 Felt lonely (LONELYREC) 0 7 0.48 1.416 Felt could not shake blues (BLUESREC) 0 7 0.29 1.083 Criminal Activity Scale (CRIMINALACTIVITY)- no;yes .330 Done Something Illegal/ to Cause Trouble (ILLEGREC) 0 1 0.03 0.171 Caught in Minor Violati on (MINVIOLREC) 0 1 0.02 0.146 Been Arrested (ARRESTEREC) 0 1 0.00 0.057 Been In Jail More Than 24 Hours (JAILREC) 0 1 0.00 0.000 Independent Variables Perceived Neighborhood Disorder Scale (PERCEIVEDDISOR) .919 Graffiti in neighborhood (GRAFFITIREC) 1 4 1.56 0.609 Neighborhood is noisy (NOISYREC) 1 4 1.80 0.700 Neighborhood vandalism co mmon (VANDALSREC) 1 4 1.67 0.616 Abandoned buildings present (ADBANBLDREC) 1 4 1.51 0.574 Too much alcohol use (A LCOHOLUSREC) 1 4 1.90 0.715 Too much hanging out on st reets (HANGOUTREC) 1 4 1.70 0.699 Too much drug use (DRUGUSEREC) 1 4 1.80 0.723 A lot of crime in neighborhood (CRIMEREC) 1 4 1.68 0.643 Neighborhood is clean (CLEANREC) 1 4 1.68 0.562 Neighbors take care of homes (CAREHOMREC) 1 4 1.64 0.562 Neighborhood is safe (SAFEREC) 1 4 1.68 0.632 Watch out for One Another (WATCHOUREC) 1 4 1.77 0.645 Adequate Police Protection (POLICEREC) 1 4 1.83 0.639 Trust Neighbors (NEITRUSREC) 1 4 1.77 0.614 Economic Disadvantage Scale (ECONDIS)- SD;SA1 .809 Trouble Buying Food (STRNFOOREC) 1 4 3.78 0.558 Trouble Paying for Medical Care (STRNMEDREC) 1 4 3.81 0.552 Trouble Paying Bills (STRNBILLREC) 1 4 3.58 0.750 Control Variables Respondent Gender (Female = 1) (GENDER) 0 1 0.60 0.491 Hispanic/Spanish Ethnicity (Yes = 1) (HISPANICS) 0 1 0.01 0.109 Spouse Employ (Non Full-time = 1) (SPOUSEEMP) 0 1 0.39 0.489 Respondent Race (Non-White = 1) (RACE) 0 1 0.34 0.474 1 A score of 1 indicates strongly di sagree, 2 indicates disagree, 3 indi cates agree and 4 indicates strongly agree. This is a reverse coding so that 1 equals lowest economic disadvantage and 4 equals highest economic disadvantage. For perceived neighborhood disorder items have been direct coded or reversed coded as needed so that 1 equals lowest neighborhood disorder and 4 equals highest neighborhood disorder.

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31 Table 2-1 Continued. Min. Max. Mean SD Alpha Control Variables Respondent Employ (Non Full-time = 1) (EMPLOY) 0 1 0.44 0.496 Total Household Income ( than $30,000 = 1) (INCOME) 0 1 0.34 0.474 Table 2-2. Ross-Mirowsky Perceived Neighborhood Disorder Scale Reliability N % Valid 1332 53.7 Excluded 1150 46.3 Total 2482 100.0 Total Items 15 Alpha Reliability .919

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32 CHAPTER 3 RESULTS The first section of analysis examines the relationship between dependent and independent variables. Bivariate correlation analysis (using pairwise deletion) was conducted to determine the strength of a ssociation between depression, se lf-reported criminal activity, perceived neighborhood disorder, an d economic disadvantage. Based on previous studies (Ross, 2000), the perceived neighborhood disorder and ec onomic disadvantage scales, as independent variables, were used as indicator s of social disorganization. Co rrelation analysis yielded several important results, though some were non-significa nt. A positive relationship was found between self-reported criminal activ ity and economic disadvantage (p .01), which is consistent with previous literature showing and the expectations from the theore tical framework of this study. A positive relationship also existed between ec onomic disadvantage and perceived neighborhood disorder (p .01), which is further supported by the li terature. Alternately, the correlation analysis yielded several results which are not in the expected direction. Correlation analysis showed a negative, rather than the expected positive, relationship between economic disadvantage and depression. Additionally, a negative relationship wa s found between selfreported criminal activity and depression (p .01). Though not significant, negative relationships were found between the following: self-reported criminal activity a nd perceived neighborhood disorder; depression and percei ved neighborhood disorder. These findings offer partial support for the theoretical model, but do not support so cial disorganization th eory, which expects a positive relationship between social disorganizat ion and crime and social disorganization and depression. Furthermore, while research on crime and depression in the past has shown a positive relationship between the two variables the findings here are that, contrary to expectations, the two are negatively related. Results for correlation analysis are s hown in Table 3-1. All findings

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33 are reported with the caveat that criminal activ ity is positively and significantly related to economic disadvantage (as expected ), but negatively related to perceived neighborhood disorder (though this finding was not significant). The next section of the analysis examines th e effects of social diso rganization indicators (neighborhood disorder and economic disadvantage) on depression and criminal activity levels, respectively. It is important to note that a considerable num ber of participants (N= 685) participants did not answer questions gaging household income This finding may alter the overall results in regression analysis. Multiple regression analyses were performed for each dependent variable (depression, self-reported crimin al activity, trouble w ith the law/illegal activity, minor law violation, ever been arrested, and been jailed for more than 24 hours, respectively) using the same independent a nd control variables: economic disadvantage, perceived neighborhood disorder, gender, respon dent employment status, spouse employment status, respondent race, total household income, and Hispanic/Spanish ethnicity. Despite dummy coding all control variables, an assumed statistical error message indicating that the variable HISPANICS was a constant, resulted in SPSS removing the variable indicating Hispanic/Spanish ethnicity from all regression analyses. This fi nding remained even afte r replacing the recoded variable HISPANICS with the original variable (HISPANI2) from the CCH dataset. Similarly, the same error occurred when ARRESTEREC and JAILREC, respectively, were used as dependent variables, thus eliminating the depend ent variables from regression analyses. This may possibly suggest an error in entering variable data into the dataset. Additionally, although the Ross-Mirowsky scale has a high reliability, nearly half of pa rticipant responses (n = 2,482) were removed during scale reliability analysis After running a frequency analysis, it was determined that the responses removed were recoded as system-missing due to no coded,

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34 system missing, or dont know responses. Th ese are fewer missing cases than found when listwise deletion is used, but even with pairwise deletion the number of system missing cases is large and findings must be viewed with this in mind. The first OLS multiple regression analysis estimated the effects of perceived neighborhood disorder and economic disadvantag e on resident depressi on. Model 1 shows perceived neighborhood disorder an d economic disadvantage account for 1.2% of the variation in depression levels. ANOVA results suggest that findings are likely to be obtained by chance (F= .639, p=.530). Additionally, the model parame ter showed that as perceived neighborhood disorder and economic disadvantage decreased, re spectively, depression leve ls increased, thus a negative (though not significant) relationshi p existed between the dependent variable (depression) and the independent variables (perceived neighborhood disorder and economic disadvantage). This finding does not support this studys hypothesis nor a large volume of research on social disorganization theory. Model 2 introduced contro l variables in the analysis. Independent and control variables present account for 5.7% of the va riation in depression levels. ANOVA results for Model 2 were similar to Model 1, suggesting a strong likelihood that outcomes may be obtained by chance (F= .852, p= .549) Model parameter results indicated that as economic disadvantage and perceived ne ighborhood disorder de creased, respectively, depression levels increased. The control vari able gender (GENDER) wa s significant in the model (p .05). Based on relatively low eigenvalu es and high condition indexes for the analysis, collinearity issues may also be present. Results are pr esented in Tables 3-2 through 3-4 and parameter estimates are presented in Table A-1. The second OLS regression estimated the eff ects of perceived neighborhood disorder and economic disadvantage on self-reported crim inal activity. Model 1 shows perceived

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35 neighborhood disorder and economic disadvantage accounted for 5.3% of the variation in selfreported criminal activity levels. ANOVA resu lts suggest outcome results may likely be obtained by chance (F= 2.905, p = .059). Model parame ter results indicated that as perceived neighborhood disorder and economic disadvantag e, respectively, decreased, self-reported criminal activity levels increased. These finding s are not consistent with previous literature, which predicts a positive relations hip between the dependent variable and independent variables. Independent variable economic disadvantage (ECONDIS) was significant in the model (p .05). Model 2 introduced control variables into the regression analysis. Independent and control variables accounted for 12.2% of va riation in self-reported criminal activity levels. As with other models, a low F-value indicated that ou tcomes are likely to be obtained by chance (F= 1.964, p= .068). Model 2, in essence, did not in crease the predictability of the regression analysis. Model parameter outcomes showed that as perceived neighborhood disorder and economic disadvantage levels decreased, respec tively, self-reported criminal activity levels increased, which does not support previous literature. As with Model 1, economic disadvantage was significant in Model 2 and in the expected di rection as well. Independent variable economic disadvantage (ECONDIS) and control variable spouse employment status (SPOUSEEMP) were significant in the model (p .05 for both outcomes). A low eigenvalue and high condition index values for Model 1 and Model 2 (2nd and 7th dimensions, respectively) are indicative of multicollinearity. Results are presented in Tables 3-5 through 3-7 and parameter estimates are presented in Table A-2. The third regression analysis estimated th e effects of perceive d neighborhood disorder and economic disadvantage on having trouble with the law/doing something illegal. ILLEGREC, which represents having trouble with the law, is included the scale variable

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36 CRIMINALACTIVITY. Due to the low alpha relia bility of the scale, ILLEGREC was entered to indicate an individual having committed an illegal act or having any punitive interaction with the law. Model 1 shows perceived neighborhood disorder and economic disadvantage accounted for 7.6% of variation in trouble with the law leve ls. Significant ANOVA resu lts indicate that it is unlikely that Model 1 outcome s were obtained by chance (F= 4.29, p .05). Model parameter results indicated that as econom ic disadvantage levels decrease trouble with the law/illegal activity levels will increase, which contradicts literature on social disorganization theory. This contrasts with the findings in Table 3-1 in wh ich economic disadvantage is positively related to levels of criminal activity when measured as a scale. Alternately, as perceived neighborhood disorder levels increased, troubl e with the law/illegal activity levels increased, again as contrasted with the direction of relationship between perceive d neighborhood disorder and the scale of criminal activity. These differences may reflect problems with the low reliability of the scale. Independent variable perceived neighborhood disorder wa s significant in the model (p .01). Model 2 showed perceived neighbor hood disorder and economic disadvantage account for 13.6% of variation in tr ouble with the law/illegal activ ity outcomes. As with Model 1, significant ANOVA results indicate a strong likelihood that outcomes were not obtained by chance. Model parameter results showed that as economic disadva ntage levels increased, trouble with the law/illegal activity levels increased, whic h is consistent with previous literature and the expectations of the theoretical model. Similarl y, consistent with previ ous literature and social disorganization theory, as perc eived neighborhood disorder levels increased, trouble with the law/illegal activity still increased. After cont rol variables were entered, economic disadvantage was still significant (p .05). As with previous analyses, issu es of multicollinear ity are present.

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37 Results are presented in Tables 3-8 through 3-10 and parameter es timates are presented in Table A-3. Final OLS multiple regression analysis estim ated the effects of perceived neighborhood disorder and economic disadvantage on minor law violations. Model 1 shows perceived neighborhood disorder and economic disadvantage account for 2.5% of the variation in minor law violation levels. ANOVA result s suggest that findings are like ly to be obtained by chance (F= 1.323, p=.271). Additionally, the model parame ter showed that as perceived neighborhood disorder and economic disadvantage increased, respectively, minor law violation levels increased, which supports this papers hypothesi s. Perceived neighbor hood disorder was found to be significant at the .05 le vel. Model 2 introdu ced control variables in the analysis. Independent and control variables present account for 9.9% of the va riation in depression levels. ANOVA results for Model 2 were similar to Model 1, suggesting a strong likelihood that outcomes may be obtained by chance (F= 1.552, p= .159). Model parameter results indicated that as economic disadvantage and perceive d neighborhood disorder increased, respectively, minor law violation levels incr eased. There were no significant findings when independent and control variables were introduced into analyses. Based on rela tively low eigenvalues and high condition indexes for the analysis, collinearity issues may also be present. Results are presented in Tables 3-11 through 3-13 and parameter estimates are presented in Table A-4.

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38Table 3-1. Bivariate Correlations Output Economic Disadvantage Self-Reported Criminal Activity Perceived Neighborhood Disorder Depression Economic Pearson Correlation Disadvantage Sig. (2-tailed) N 1 1331 .075** .006 1331 .096** .000 1331 -.332** .000 1331 Self-Reported Pearson Correlation Criminal Sig. (2-tailed) Activity N .075** .006 1331 1 2482 -.012 .650 1332 -.080** .004 1332 Perceived Pearson Correlation Neighborhood Sig. (2-tailed) Disorder N .096** .000 1331 -.012 .650 1332 1 1332 -.047 .086 1332 Depression Pearson Correlation Sig. (2-tailed) N -.332** .000 1331 -.080** .004 1332 -.047 .086 1332 1 1332

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39 Table 3-2. OLS Statistical Model Summary using Depression as the Dependent Variablec Model R R Square Adjusted R SquareStd. Error of the EstimateR Square Change 1 .110a .012 -.007 .63150 .012 2 .238b .057 -.010 .63245 .045 a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status, Respondent Race, Respondent Employment Status, Total Household Income, Gender c. Dependent Variable: Depression Table 3-3. Statistical ANOVA Summary using Depression as the Dependent Variablec Model Sum of Squares Df Mean Square F Sig. 1 Regression Residual Total .510 41.475 41.984 2 104 106 .255 .399 .639 .530a 2 Regression Residual Total 2.386 39.599 41.984 7 99 106 .341 .400 .852 .547b a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder b. Predictors: (Constant), Economic Disadvantage, Perc eived Neighborhood Disorder, Spouse Employment Status, Respondent Race, Respondent Employment Status, Total Household Income, Gender c. Dependent Variable: Depression

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40 Table 3-4. Statistical Collinearity Summary using Depression as the Dependent Variablea Model Dimension Eigenvalue Condition Index 1 ----3 .002 42.663 2 --------------8 .002 57.913 a. Dependent Variable: Depression Table 3-5. OLS Statistical Model Summary using Self-R eported Criminal Activity as the Dependent Variablec Model R R Square Adjusted R SquareStd. Error of the EstimateR Square Change 1 2 .230 .349 .053a .122b .035 .060 .07635 .07535 .053 .069 a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status, Respondent Race, Respondent Employment Status, Total Household Income, Gender c. Dependent Variable: Self-Re ported Criminal Activity

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41 Table 3-6. Statistical ANOVA Summary using Self-Reported Criminal Activity as the Dependent Variablec Model Sum of Squares Df Mean SquareF Sig. 1 Regression Residual Total .034 .606 .640 2 104 106 .017 .006 2.905 .059a 2 Regression Residual Total .078 .562 .640 7 99 106 .011 .006 1.964.068b a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status, Respondent Race, Respondent Employment Status, Total Household Income, Gender c. Dependent Variable: Self-Re ported Criminal Activity Table 3-7. Statistical Collinearity Summary using Self -Reported Criminal Activity as the Dependent Variablea Model Dimension Eigenvalue Condition Index 1 ----3 .002 42.663 2 --------------8 .002 57.913 a. Dependent Variable: Self-Re ported Criminal Activity

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42 Table 3-8. OLS Statistical Model Summary using Trouble with the law/Done Something Illega l as the Dependent Variablec Model R R Square Adjusted R SquareStd. Error of the EstimateR Square Change 1 2 .276 .368 .076a .136b .059 .074 .204 .206 .076 .059 a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status, Respondent Race, Respondent Employment Status, Total Household Income, Gender c. Dependent Variable: Trouble with the law/Done Something Illegal Table 3-9. Statistical ANOVA Summary usi ng Trouble with the law/Done Something Illegal as the Dependent Variablec Model Sum of Squares Df Mean Square F Sig. 1 Regression Residual Total .364 4.403 4.766 2 104 106 .182 .042 4.296 .016a 2 Regression Residual Total .646 4.120 4.766 7 99 106 .092 .042 2.219 .039b a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder b. Predictors: (Constant), Economic Disadvantage, Perc eived Neighborhood Disorder, Spous e Employment Status, Respondent Race, Respondent Employment Status, Total Household Income, Gender c. Dependent Variable: Trouble wi th the law/Done Something Illegal

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43Table 3-10. Statistical Collinearity Summary using Trouble with the law/Done Something Illega l as the Dependent Variablea Model Dimension Eigenvalue Condition Index 1 ----3 .002 42.663 2 --------------8 .002 57.914 a. Dependent Variable: Trouble with the law/Done Something Illegal Table 3-11. Statistical Model Summary using Mi nor Law Violation as the Dependent Variablec Model R R Square Adjusted R SquareStd. Error of the EstimateR Square Change 1 2 .158 .314 .025a .099b .006 .035 .190 .187 .025 .074 a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status, Respondent Race, Respondent Employment Status, Total Household Income, Gender c. Dependent Variable : Minor Law Violation

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44 Table 3-12. Statistical ANOVA Summary using Minor Law Violati on as the Dependent Variablec Model Sum of Squares Df Mean Square F Sig. 1 Regression Residual Total .096 3.755 3.850 2 104 106 .048 .036 1.323 .271a 2 Regression Residual Total .381 3.470 3.850 7 99 106 .054 .035 1.552 .159b a. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder b. Predictors: (Constant), Economic Disadvantage, Perceived Neighborhood Disorder, Spouse Employment Status, Respondent Race, Respondent Employment Status, Total Household Income, Gender c. Dependent Variable : Minor Law Violation Table 3-13. Statistical Collinearity Summary using Minor Law Violation as the Dependent Variablea Model Dimension EigenvalueCondition Index 1 ----3 .002 42.663 2 --------------8 .002 57.913 a. Dependent Variable: Mi nor Law Violation

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45 CHAPTER 4 CONCLUSIONS Discussion This studys main objective was to determ ine whether an associational relationship existed between the following: (1) social disorganization and crime, (2) social disorganization and depression, and (3) crime and depression (a s a result of strain caused by social disorganization). Results of the correlation an alyses indicate relationships between depression and economic disadvantage; depression and self-reported criminal activity; economic disadvantage and self-reported criminal ac tivity; economic disadva ntage and perceived neighborhood disorder. Though strain and its interaction effect s were not measured, economic disadvantage (proposed to influence strain levels) was the only indicator of social disorganization found to be a significant predictor of both self-reported criminal activity and depression. Based on correlation an alysis, there appears to be a negative relationship between self-reported criminal activity and depression. This findi ng was not expected and contradicts previous studies which found positive, significant relationships between the two variables (Paxton et al., 2004). One possible explanation for this finding is that although pairwise deletion techniques were used, a substant ial amount of responses were de leted from correlation analyses, which would alter the results. A nother explanation might be that individuals who are depressed are less likely to leave their homes (than nondepressed individuals), which would limit their participation in outside criminal activities. Though causality cannot be determ ined these findings still advance research on community-level crime and depression. First, economic disadvantage was significant in some models of criminal behavior while perceived neighborhood disorder was not and vice versa depending on which measure of criminal behavior was used, the scale of criminal activity or

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46 individual items measuring criminal behavior. Secondly, strain ex perienced as a result of social disorganization may not have a strong effect on depression (as was proposed). Perceived neighborhood disorder and economic disadvantage, as indicators of social disorganization, held no significant ability to predict depression. Though entered as control variables, future research may consider the direct effects of gender and spouse employment stat us on depression and self-reported crime. In summation, the analyses show mixed support for the hypothesized relationships between crime, depression, and social disorgan ization. Based on regression analyses, social disorganization was associated with self-reported criminal activity (a significant relationship was found between economic disadvantage and self-re ported criminal activity) and having trouble with the law (a significant relationship was found between economic disadvantage and selfreported criminal activity). Based on the findings, there was no signifi cant association found between social disorganization and depression. Contrary to e xpectations, correlation analyses found a significant negative relationship between depression and self -reported criminal activity. Nonetheless, a strong positive association wa s found between economic disadvantage and selfreported criminal activity in some of the analyses. The relationship between crime and depression (explained by the effect s of social disorganization) ca nnot be determined based on the present analyses, as analyses did not examine th e interaction effects of crime and depression. Nonetheless, social disorganization only held ma rginal predictive validity on criminal activity (R2 = .053) and depression (R2 = .012) when entered in Model 1 (Table 3-5) of regression analyses. This indicates that a third, unknown variable, may account for a greater percentage of variation in the dependent variab les. This unknown variable may also explain the main and net

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47 effects of crime and depression on one another (rel ative to the effects of social disorganization on crime and depression). Limitations and Implications for Future Research First, attrition presented m ajor issues when running analyses. For example, reliability analysis shows that nearly half of participant responses to Ross-Mirowsky scale were eliminated due to system missing data. All findings, then, must be viewed w ith this caveat. Eliminating a large set of responses will skew all correla tion, regression, and ANOVA an alysis. While this paper does not explore the effect of system mi ssing responses in the Ross-Mirowsky Perceived Neighborhood Disorder scale, one possible result is that the relationships reported between dependent and independent variables (depressi on, self-reported crimin al activity, perceived neighborhood disorder, and economic disadvantage, respectively) are inaccura te. In this sense, the negative relationship reported between perc eived neighborhood disorder and depression is incorrectly signed (meaning the relationships betw een the variables may be positive, as opposed to being reported negative, or vice-versa), as are the relations hips between economic disadvantage and depression; se lf-reported criminal activity a nd depression; self-reported criminal activity and percei ved neighborhood disorder. The assumed wrong sign direction reported in correlation analyses may be a functio n of the large attrition rate of the RossMirowsky scale. As noted, these findings offe r at best mixed support for expectations from theory and the previous literature which finds positive relationships between these variables, respectively. Similarly, the self-reported criminal activity scale, which has a low alpha reliability, may severely skew the findings in th is study and help explain the assumed wrong sign direction of the analytic resu lts. Secondly, interview questions did not allow respondents to include motivations or influences for committing crime or becoming depressed. Because the Community, Crime, and Health survey was cro ss-sectional, the follo wing is not clear: (1)

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48 attrition rate, (2) dates interviews were conducted, and (3) th e long-term or interaction effects between social disorganization, crime, and depression. This studys re search design did not address buffering or coping strategies, such as collective efficacy, that may deter or limit engagement in criminal activities and/or depre ssive symptoms. Third, the findings in this study do not support the proposed positive relationship between crim e and depression. Statistical errors, which may appear in the dataset and/or an alyses, might help explain this contradiction. Thus, future studies must examine this datase t to determine if similar findings occur. Additionally, the research design did not cont rol for respondent ages, which may influence perceptions or awareness of disorder. Based on collinearity diagnos tics, the severe possibility of multicollinearity cannot be discounted. Future re search must address these issues and determine if the present studys results ar e replicated and generalized. Finally, one cannot exclude the effect(s) that eliminating Hi spanic respondents from regre ssion and ANOVA analyses had on this studys outcome. Individuals of Hispanic ethnicity may have diffe rent responses to and perceptions of economic disadva ntage and perceived neighborhood disorder which this paper does not consider. Likewise, due to the fact many respondents did not an swer questions gaging family income, one can expect the overall findings in this paper to be skewed. By eliminating the dummy variable from analyses, one must view this studys findings with caution. Findings and conclusions must be viewed in light of the limitations of the study. The present study addressed the re lationship between perceived neighborhood disorder and economic disadvantage (as independent variab les) on dependent variables, ye t, there may be indicators or examples of social disorganization the study does not take into account Another issue not discussed in the current study is the type or cat egory of crime one may commit as a result of economic disadvantage. Deviant responses to fi nancial strain may pres umably range from a

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49 petty crime such as shoplifting to a more serious infraction such as aggravated robbery. Finally, the study faced the same problem that all seconda ry data analyses of not always finding the measures to relate directly to the concepts a nd variables found in the lit erature and included in the theoretical framework. Ultimately, it is clear that in order to develop effective policies and social programs, future studies must examine the detrimental effects of social disorganization on communities. Additionally, applying other crim inological theories, such as Social Learning Theory and Differential Association Theory, may be helpful in determining the mechanisms used to cope with strain and depression, and deducing the mo tivations behind criminal engagement. Using social learning theory as a theoretical fram ework, for example, longitudinal studies on adolescents might examine the predictive effects that learning deviant responses has on crime commission. Likewise, cross-sectional studies may examine the effect that associating with delinquent peers has on future crime commission and depression. Based on the findings in this paper and issues with the dataset use d, several suggestions can be offered for future research on this subjec t. First, future rese archers should design a longitudinal study which explores the long-term effects of strain a nd/or social disorganization on crime and depression. Secondly, future studies should increase the su rvey distribution and sample size to areas outside of one state. In do ing so, research can gage the effects of social disorganization in different tract s and may also limit the potential for large attrition rates. Finally, future studies should consult other st udies on this subject a nd create scales which accurately detail the variables of interest (i.e., depression, criminal activity, financial strain/disadvantage). The present study used occurrences such as lack of sleep and happiness to represent depression, which may be problematic and subject to criticism.

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50 APPENDIX PARAMETER ESTIMATES FOR OLS REGRESSION MODELS

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51Table A-1. Perceived neighborhood disorder and economic disadvantage effects on depression (parameter estimates for OLS regression) Model B Std. Error Beta t Sig. 1 (Constant) Perceived neighborhood disorder Economic disadvantage 1.689 1.226 -.190 .196 -.231 .317 -.096 -.072 1.377 -.971 -.730 .171 .334 .467 2 (Constant) Perceived neighborhood disorder Economic disadvantage Gender Spouse employment status Respondent race Total household income Respondent employment status 1.336 1.285 -.177 .203 -.185 .322 .305 .147 .070 .135 .099 .334 -.019 .171 -.065 .146 -.089 -.058 .242 .056 .030 -.012 -.052 1.039 -.872 -.572 2.078 .518 .295 -.114 -.443 .301 .385 .568 .040 .606 .768 .909 .659

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52Table A-2. Perceived neighborhood disorder and economic disadvantage effects on se lf-reported criminal activity (parameter estimates for OLS regression) Model B Std. Error Beta t Sig. 1 (Constant) Perceived neighborhood disorder Economic disadvantage .295 .148 -.020 .038 -.057 .024 -.052 -.233 1.991 -.534 -2.406 .049 .595 .018 2 (Constant) Perceived neighborhood disorder Economic disadvantage Gender Spouse employment status Respondent race Total household income Respondent employment status .333 .153 -.025 .038 -.056 .024 -.010 .017 -.033 .016 .036 .040 -.007 .020 -.009 .017 -.064 -.231 -.065 -.210 .087 -.037 -.057 2.175 -.660 -2.337 -.579 -2.020 .895 -.364 -.506 .032 .511 .021 .564 .046 .373 .717 .614

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53Perceived neighborhood disorder and economic disadvantage effects on depression (par ameter estimates for OLS regression) Table A-3. Perceived neighborhood disorder and economic disadvantage effects on trouble with the law/don e something illegal (parameter estimates for OLS regression) Model B Std. Error Beta t Sig. 1 (Constant) Perceived neighborhood disorder Economic disadvantage 1.440 .400 .175 .064 -.060 .103 .262 -.055 3.604 2.740 -.578 .000 .007 .564 2 (Constant) Perceived neighborhood disorder Economic disadvantage Gender Spouse employment status Respondent race Total household income Respondent employment status 1.667 .415 .025 .065 .056 .104 .010 .047 .033 .044 -.036 .108 .007 .055 .009 .047 .245 -.066 .094 .173 -.180 .010 -.067 3.557 2.504 -.688 .845 1.675 -1.861 .095 -.598 .001 .014 .493 .400 .097 .066 .925 .551

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54Table A-4. Perceived neighborhood di sorder and economic disadvantage effects on minor law violation (parameter estimates for OL S regression) Model B Std. Error Beta t Sig. 1 (Constant) Perceived neighborhood disorder Economic disadvantage 1.379 .369 .053 .059 .142 .095 .146 .088 3.737 1.484 .900 .000 .007 .564 2 (Constant) Perceived neighborhood disorder Economic disadvantage Gender Spouse employment status Respondent race Total household income Respondent employment status 1.194 .380 .062 .095 .173 .060 .001 .043 .057 .040 .058 .099 .024 .051 .063 .043 .178 .103 .001 .150 .058 .050 .167 3.138 1.811 1.034 .012 1.427 .581 .482 1.466 .002 .073 .304 .991 .157 .559 .631 .146

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55 LIST OF REFERENCES Agnew, R. ( 1992). Foundation for a General St rain Theory of Crime and Delinquency. Criminology V30: 47-87. Aneshensel, C., and Sucoff, C. (1996). The Neighborhood Context of Adolescent Mental Health. Journal of Health and Social Behavior V37: 293-310. Baum, A., Garofalo, J., and Yali, A. 1999. Soci oeconomic Status and Chronic Stress. Does Stress Account for SES Effects on Health? Annals of the New York Academy of Sciences V896: 131-144. Beardman, J., Finch, B., Ellison, C., Williams, D., and Jackson, J. (2001). Neighborhood Disadvantage, Stress, and Drug Use Among Adults. Journal of Health and Social Behavior V42: 151-165. Bellair, P. (1997). Social Interaction and Co mmunity Crime: Examining the Importance of Neighbor Networks. Criminology V 35(4): 677-703. Bursik, R. (1988). Social Disorganization and Theories of Crime and Delinquency: Problems and Prospects. Criminology V26(4): 519-551. Bursik, R., and Grasmick, H. (1993). Neighborhoods and Crime: The Dimensions of Effective Community Control Lexington Books, NY, NY. Colten, M., and Gore, S. (1991). Introduction: Adolescent Stress, Social Relationships, and Mental Health. In Colte n, M. and Gore, S. (eds.), Adolescent Stress: Causes and Consequences (Social Institutions and Social Change) Aldine de Gruyter, NY, NY. Compass, B. and Wagner, B. (1991). Psychological Stress During Adolescence: Intrapersonal And Interpersonal Processes. In Colten, M. and Gore, S. (eds.), Adolescent Stress: Causes and Consequences (Social Institutions and Social Change) Aldine de Gruyter, NY, NY. Cullen, F. (1994). Social Support as an Organi zing Concept for Criminology: Presidential Address to the Academy of Criminal Justice Sciences. Justice Quarterly V11(4): 527-559. De Coster, S. (2003). Delinquency and Depression: A Gendered Role-Taking and Social Learning Perspective. In Akers, R. L. and Jensen, G. F. (eds.), Social Learning Theory and the Explanations of Crime: A New Guid e for the New Century (Advances in Criminological Theory) Transaction Books, New Brunswick, NJ. De Coster, S., and Heimer, K. (2001). The Relationship Between Law Violation and Depression: An Interactionist Analysis. Criminology V39(4): 799-836.

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60 BIOGRAPHICAL SKETCH Billy D. Holcom be is a native of Fort Lauderdale, FL and graduated from Nova High School in 2001. He received a B.A. in criminology with a minor in sociology from the University of Florida in 2005. During his colleg iate tenure, he focused on academic excellence and student leadership, having been inducted in to Golden Key International Honour Society, Florida Blue Key, and the University of Florida Hall of Fame. Billy anticipates gaining field experience in community crime and psychiatric care before pursuing a Ph.D. in clinical psychology.