An Analysis of Race, Locus of Control, and Resilience
Chapter 1: Introduction to the Study
Introduction
Recidivism is the return of an individual to prison or jail because he or she
violated the rules of probation or parole, or received new charges (Pew, 2011). Between
2005 and 2010, 16.1% of those released from prison in the United States accounted for
roughly 48.4% of the 1.2 million arrests during that time (Durose, Cooper, & Snyder,
2014). Addressing recidivism is difficult because it is not a one-solution-fits-all issue. It
is an intractable problem that involves various aspects of the legal system ranging from
the criminal justice system, to state and local governments. In this mixed methods study,
I examined the different contributors to recidivism for African American, Caucasian, and
Hispanic ex-offenders through interviewing ex-offenders and having them explain what
challenges they face that may contribute to recidivism. In addition, I administered the
Connor-Davidson Resilience scale (CD-RISC) and the multidimensional locus of control
scales (Connor & Davidson, 2003; Levenson, 1974). Results from the study may offer
some understanding of the recidivism rates in the United States which ultimately may
bring about social change. By providing information regarding the reasons surrounding
recidivism, this study may help address the challenges offenders face, and assist them in
making a successful transition back into society, thereby reducing the recidivism rate
overall.
In this chapter, I review the possible contributors to recidivism by looking at the
background of the issues. In the problem statement, I explain why recidivism is a
problem that warrants studying. Next, I describe the research methodology used and
discuss the research questions and the hypotheses of the study. I then outline the
theoretical framework and describe the theories that served as the basis of the study.
Subsequently, I identify the nature of the study and its variables, and provide definitions
of key terms. The assumptions section includes descriptions of components of the study
that I took to be true, but that cannot be demonstrated. The limitations and delimitations
sections include discussions of sample size, participant issues, and potential study
concerns. Finally, in the significance and summary sections, I discuss what I hope to
contribute to the problem that I researched, and then offer a conclusion to Chapter 1 and
an introduction to Chapter 2.
Background
Understanding recidivism requires understanding that the social barriers (e.g.
education, unemployment, and addiction) that inmates and ex-offenders face are many
and complex. One must look beyond certain offenders committing more crimes than
others. Multiple reasons should be considered when examining recidivism. For example,
Golembeski and Fullilove (2008) examined the social barriers that African Americans
and Hispanics face (e.g. limited job skills, disability), and how these issues remain
unaddressed within the criminal justice system. The impact of stricter sentencing, the
“war on crime,” and social barriers continue to affect individuals negatively before,
during, and after imprisonment (Day, 2007).
There are racial differences in the recidivism rates. McGovern, Demuth, and
Jacoby (2009) found there was an increased risk of recidivism among minorities,
especially for African Americans. Previous researchers have focused on recidivism;
however, few have concentrated on discovering if there were racial differences in
resilience and locus of control. Resilience is the internal strength that helps individuals
get through difficult or stressful situations (Connor & Davidson, 2003). Locus of control
is one’s personal beliefs about actions or behavior and consequences (Ryon & Gleason,
2014). In this study, I aimed to identify if racial differences exist in resiliency and locus
of control in order to better understand recidivism among minorities.
It is important to know if racial differences exist in the areas of resilience and
locus of control so that appropriate assistance in the form of preventative community and
social programs may be developed to reduce recidivism (Drake, Aos, & Miller, 2009). I
thus aimed to address the gap in the literature by discovering if there are racial
differences that impact recidivism that are internal (i.e., resilience, locus of control) in
nature.
Problem Statement
Criminal recidivism is a problem of growing concern in the United States, both
legally and socially. The plight of minorities in the criminal justice system continues to
be inadequately addressed (Mahmood, 2004). Although previous researchers have
demonstrated that minorities are incarcerated at higher rates and are more likely to return
to prison than Caucasians, understanding why remains unclear. McGovern, Demuth, and
Jacoby (2009) found of men aged 18-24, African Americans (26.8%) and Hispanics
(29.4%) are imprisoned more often than Caucasians (20.6%); African Americans,
Hispanics, and Caucasians make up roughly 12.6%, 16.3%, and 63.7% of the total
population respectively (United States Census Bureau, 2011). Also, African Americans
and Hispanics are imprisoned more often for drug trafficking and possession crimes
(McGovern et al., 2009). Both groups received higher sentences for these crimes when
compared to Caucasians charged with the same crimes: 63.9 months, 58.0 months, and
52.8 months respectively (McGovern et al., 2009). Also, 70.9% of African Americans
and 60.6% of Hispanics are rearrested, compared to 58.5% of Caucasians (McGovern et
al., 2009).
Interestingly, recidivism rates increase each year the offender is released (Langan
& Levin, 2002). By the third year of release, 67.5% of ex-offenders are rearrested, 46.9%
reconvicted, and 25.4% receive new convictions (Langan & Levin, 2002). Again, African
Americans have higher rates of recidivism than Hispanics or Caucasians (Langan &
Levin, 2002). It is unclear what is occurring in the first 3 years of release that contributes
to increased rates of recidivism. Researchers have reported that preventative types of
programs (e.g., education, rehabilitation) cost less and have an enormous impact on the
recidivism rate (Drake, Aos, & Miller, 2009). For example, in the state of Washington,
offering a general education program to inmates reduced the rate of recidivism by
roughly 8.3%, according to findings from 17 evidence-based studies (Drake et al., 2009).
Use of education and other programs may help to lower the costs of incarceration and
reduce recidivism rates overall. Thus, my aim was to discover if any racial differences
exist concerning resilience and locus of control that may contribute to recidivism, and to
offer alternative solutions to increase the offenders’ chances of success by understanding
the challenges they face once released from prison.
Nature of the Study
In this study, I used a mixed-methods approach. Quantitative analysis helped me
identify predictors of resilience and locus of control. Qualitative analysis aligned with the
resilience theory and the social disorganization theory. I used these theories to understand
the depth of barriers parolees face and how these barriers impact their lives (Fletcher &
Sarkar, 2013; Kingston et al., 2009).
Variables
Participants in this mixed methods study were parolees recruited from a parole
office in Texas who had previous experience being under supervision. The independent
variables for this study were the race of the participants: African American, Caucasian,
and Hispanic. These variables are important because previous researchers have reported
racial differences in recidivism; therefore, I aimed to discover if these same racial groups
have differences concerning resilience and locus of control (McGovern et al., 2009). The
total sample size (n = 126) was obtained using G*Power analysis with the following
criteria: a MANOVA with 3 racial groups and 4 scales, medium effect size (n = .063),
alpha of .05, and power of .80 for three racial groups. The dependent variables were
scores from the Connor-Davidson Resilience scale (CD-RISC) and the multidimensional
locus of control scales using the total scores of the CD-RISC and the scores from the
subscales internality, powerful others, and chance for the multidimensional locus of
control scales (Connor & Davidson, 2003; Levenson, 1974). I used these variables to
discover if any racial differences exist concerning resilience and locus of control that may
impact recidivism. I also conducted qualitative interviews with randomly selected
participants from the same sample group (n = 12). In the interviews, I explored the
challenges that parolees believed contributed to their recidivism and resiliency.
Research Questions and Hypothesis
I developed several research questions (RQs) and hypotheses for this project that
were focused on resilience, racial differences, locus of control, and recidivism:
Quantitative RQ: What racial differences are evident in resilience and locus of control
scores among parolees?
H0: There are no racial differences in resilience and locus of control scores among
parolees.
H1: There are racial differences in resilience and locus of control scores among
parolees.
Analyses: A 3 (African American x Hispanic x Caucasian) x 4 (CD-RISC,
internality, powerful others, and chance) MANOVA using the CD-RISC scale scores and
the locus of control subscale scores as the dependent variables.
Qualitative RQ: How do parolees explain their recidivism?
In Chapter 3, I offer a more in-depth discussion is found within existing literature
and in further detail in chapter 3.
Purpose
The purpose of this project was to discover if there are any racial differences in
resilience and locus of control that may contribute to or impact recidivism. I also aimed
to understand the challenges parolees face after release from jail or prison that may
contribute to recidivism. I used the social disorganization theory to understand the
relationship between environment (e.g., neighborhood) and criminality (see Kingston,
Huizinga, & Elliott, 2009). By understanding these challenges, it may become more
apparent why newly released offenders, especially African Americans, recidivate within
the first 3 years at higher rates than Caucasian or Hispanic offenders (Langan & Levin,
2002). Addressing these challenges will help to create positive social change by creating
programs and changing existing policies to help reduce recidivism within the criminal
justice system.
Theoretical Frameworks
The theoretical frameworks for this study were resilience theory (Fletcher &
Sarkar, 2013) and social disorganization theory (Kingston et al., 2009). I used both to
understand how environment and barriers (e.g., lack of education, poverty) influence
individuals either positively or negatively. In the future, these theories may help a
multidisciplinary team identify how best to address social barriers and lack of programs
(e.g., education, addiction) by looking at disadvantaged neighborhoods. Working with
community leaders and police to set up programs may lessen future crime and increase
community involvement of its residents. Using a multidisciplinary approach may break
the cycle of recidivism.
Resiliency Theory
Connor and Zhang (2006) defined resiliency as the individual characteristics that
allow one to achieve homeostasis or harmony and strength. Homeostasis (e.g., balance)
weakens because of stressors that control or influence adaptability and coping ability
(Connor & Zhang, 2006). The length of exposure to stressors determines if homeostasis is
returned to or altered (Connor & Zhang, 2006). Altered homeostasis results in the
individual’s ability to be resilient (e.g., reintegrative process), reduced homeostasis, or
dysfunction (Connor & Zhang, 2006). In Chapter 2, I discuss this theory further.
Social Disorganization Theory
Social disorganization theory helps researchers understand the link between
poverty-stricken neighborhoods, criminal activity, and resident instability (Kubrin et al.,
2007). This instability results in higher levels of residents moving in and out of the
neighborhood (Kubrin et al., 2007). These areas typically consist of minorities and lack
opportunities such as jobs and education (Kubrin et al., 2007). Also, ex-offenders usually
return to these same environments that help to create the cycle of criminality and
recidivism (Kubrin et al., 2007). I offer further discussion of this theory in Chapter 2.
Definitions
I use several terms throughout this study that need further clarification.
Resilience: Those individual characteristics that push one to persevere in times of trouble
(Connor & Davidson, 2003). Resilience is constantly in flux; it is forever changing and is
influenced by characteristics such as age, gender, and culture (Connor & Davidson,
2003). In its basic understanding, resilience defines how well one adapts during times of
stress (Campbell-Sills & Stein, 2007).
Recidivism: The return to jail or prison while on supervision (e.g., probation,
parole) within the first 3 years of release. An individual could recidivate because of
probation or parole violations, getting new charges, or being convicted of a new crime
(Pew, 2011).
Locus of control: The belief that one’s behavior determines consequences (Ryon
& Gleason, 2014).
Assumptions
In this project, I assumed that participants answered questions truthfully and
without force. Participants were reminded that answers are confidential and accessible by
me only. I also reminded them that participation was voluntary, and that they could
withdraw at any time during the study. I also assumed that participants were previously
on probation or parole. This assumption ensured that participants could give an accurate
account of their experiences while under supervision.
Limitations
One limitation is that the results cannot be generalized for all parolees. For the
qualitative study, the overall sample size was suitable (n = 12); however, the sample size
was small for each racial group (n = 4). Therefore, the results may not be representative
of all parolees who recidivate. The information gained can be used to further understand
the challenges parolees face racially and socially. The stigma of being labeled as
exoffenders or parolees may limit successful reintegration back into the community.
Therefore, by addressing the limitations (e.g., employment, housing) associated with such
labels may help in developing programs that may decrease the recidivism rate overall.
A homogeneous assumption is that there are marked limits to making claims about entire
groups according to their racial positionality. Also, participants’ experiences, or
challenges, within the criminal justice system may vary (Teti et al., 2012).
Finally, participants’ ages may be a limitation. In this study, the participant’s
current age was taken into account during the administration of the resilience and locus
of control scales. I did not consider the participant’s age at first arrest. Age of first arrest
could help show a correlation with future recidivism and needs consideration when
researching this issue. Therefore, participants may not have been representative of the
age of most offenders (i.e., they may have been older than most offenders). However, my
focus was on participants’ perception of resilience, locus of control, and recidivism.
Delimitations
I delimited this study to include participants that were previously on probation or
parole at least once. I focused on those with prior supervision so that participants could
give an honest account of their experiences and challenges. Participants were limited to
those with drug charges because they have the highest recidivism rates within the
criminal justice system (Langan & Levin, 2002). Also, I limited the age of participants to
18 and over. Participants must also identify as African American (Black), Hispanic, and
Caucasian for this study. I focused on adult parolees in the aforementioned racial groups
to discover if any racial differences exist among resilience, locus of control, and
recidivism. Finally, participants must have been able to read, write, and speak in English
to complete the questionnaires and the interview portion of the study. The
results of this study could be generalized to other states with higher recidivism rates
among ex-offenders, specifically minorities, with drug charges who are on supervision.
The findings of this study may help to give insight to the challenges offenders face that
may contribute to their increased recidivism rates, specifically within the first 3 years of
their release.
Significance
This project increases understanding of ex-offenders and the barriers that
contribute to the United States’ recidivism problem. It also provides information to those
who work with parolees, as well as the public, on how to address the challenges that
exoffenders face when they return to their communities, and it offers insight as to why
exoffenders return to the criminal justice system so soon after their release. Further, this
study provides insight into how to use a multidisciplinary approach to address barriers
and understand predictors of resiliency. This study thus provides insight into the
challenges of ex-offenders, having a positive impact on the community and the criminal
justice system thereby, creating social change in the form of state and government policy
reform.
Summary
In this chapter I gave an overview of the issues surrounding recidivism. The focus
of this study was to understand if there are any racial differences in resilience and locus
of control that may contribute to or impact recidivism. I also examined the complexity of
recidivism by looking at various challenges ex-offenders face during probation and
parole. In Chapter 2, I examine various factors that impact recidivism and go into further
detail about the theoretical frameworks, resiliency theory and social disorganization
theory. These theories may offer added insight into the possible contributors of
recidivism and provide greater understanding of racial differences among offenders who
have difficulty staying out of the prison system.
Chapter 2: Literature Review
Introduction
Recidivism is a problem that has continued to plague the United States over the
last several decades. As the U.S. justice system has become more punitive in nature,
especially for non-violent drug offenses, the increasing number of individuals housed
within the criminal justice system continues to be a problem (The Sentencing Project,
2013). Unfortunately, this problem has negatively impacted racial minorities the most
(McGovern, Demuth, and Jacoby, 2009).
In order to understand recidivism, it is important to understand that the problem is
not simple. The criminal justice system is complex, and its problems are confounded by
many factors. In this chapter, I review how the prison industrial complex and race play a
role in the recidivism problem. I also discuss the social disorganization and resiliency
theories that I used to understand factors and issues contributing to recidivism in African
Americans. Lastly, I present possible solutions and programs to help reduce recidivism.
Literature Search
In this chapter, I discuss the current peer-reviewed research on the topics of
recidivism and the challenges those within the criminal justice system face, social
disorganization theory, restorative justice, retributive justice, and resiliency theory. Most
of the literature reviewed was published from 2010 to 2014. However, some of the
literature is older, with publication dates ranging from 1999 to 2005, because of its
significance. Furthermore, most are peer-reviewed articles, and some are from state
and/or government agencies such as the Texas Department of Criminal Justice. Specific
search terms I used include recidivism, racial and recidivism, resilience, hardiness,
resiliency theory, social disorganization, social disorganization theory, restorative
justice and retributive justice. I began scholarly research using Walden Library’s EBSCO
host to access SAGE, ProQuest, and PsycARTICLES databases. Additional literature
research was done using Internet resources such as Google Scholar, which led me to
other primary sources of information on the topics mentioned above.
Literature Reviewed Concepts
Recidivism
Recidivism is a complex problem in the United States’ criminal justice system.
As I noted in Chapter 1, it is a complex issue that comprises state, local, and federal
components. Pew (2011) reported that in 2008, for every 100 people, one was
incarcerated. Pew (2011) also reported that in 2009, one adult out of 31 was under
supervision (i.e., probation, parole) or in prison. This increase in incarceration has cost
over $52 billion; a 305% increase in both state and federal spending (Pew, 2011). For
example, McGovern et al. (2009) found that African Americans and Hispanics were
incarcerated more frequently for drug-related charges (e.g., trafficking, possession).
African Americans (63.9 months) and Hispanics (58.0 months) also receive longer
sentences than Caucasians (52.8 months) when charged with the same crimes (McGovern
et al., 2009). In addition, African Americans were found to have the highest re-arrest (i.e.,
recidivism) rates overall (McGovern et al., 2009). McGovern et al. (2009) found
recidivism rates were 70.9% for African Americans, 60.6% for Hispanics, and 58.5% for
Caucasians.
Although crime rates across the nation are declining, the recidivism rate
nationally remains at approximately 40% (Pew, 2011). In order to understand the
recidivism data reported, one must understand what contributes to the recidivism rate.
Pew (2011) indicated that the recidivism rate is comprised of the number of offenders
that are released then rearrested, brought back to custody, or reconvicted in a specified
timeframe, usually 3 years. An offender is considered to have recidivated and is returned
to prison if they are convicted of a new crime. Also, recidivism occurs if probation or
parole is revoked due to a technical violation such as missing curfew or failing a drug test
(Pew, 2011). For example, from 1999 to 2002 and from 2004 to 2007 there was an 11.9%
increase in offenders released from prison (Pew, 2011). Research also showed there was
a 17.7% decrease in offenders returned to prison due to technical violations (Pew, 2011).
However, these data are misleading. As previously discussed, the national recidivism rate
has remained steady at approximately 40% (Pew, 2011). California plays a significant
role in how the national recidivism rates are produced because of the large prison
population it has. Therefore, when removing California from data analysis, the national
rate from 1999 to 2002 was 39.7% and 38.5% from 2004 to 2007 (Pew, 2011). Each
state has its recidivism problem. This problem is impacted by policy, legislators,
probation and parole officers, and the criminal justice system (e.g., judges, lawyers,
prosecutors) overall. How each state handles newly released offenders will have a
positive or negative impact on their recidivism rate (Pew, 2011). For example, if a state
releases more low-risk offenders (e.g., non-violent), they are more likely to have fewer
re-arrests and lower recidivism rates than their counterparts in other states (Pew, 2011).
These recidivism statistics are misleading because low-risk offenders are less likely to
commit additional crimes once released. For example, in 2004 Oklahoma had a
recidivism rate of 26.4% (Pew, 2011). Oklahoma’s low recidivism rate was because
lowrisk offenders were incarcerated instead of placed in programs or on supervision
(Pew, 2011). Therefore, simply looking at Oklahoma’s recidivism rate and assuming its
system is working would be premature.
One of the greatest impacts on recidivism rates is technical violations (Pew,
2011). Technical violations are violations of the rules that must be followed (e.g., curfew,
employment) as a condition of probation or parole (Pew, 2011). The less time an offender
has on probation or parole, the less likely they will have their supervision revoked and
return to prison due to a technical violation (Pew, 2011). Technical violations are variable
and are determined by a number of factors (Pew, 2011). The probation or parole officers’
caseload, rule compliance, and the relationship with the offender influence the frequency
of technical violations (Pew, 2011).
Another impact on states’ recidivism rates is laws and policies that affect
sentencing and parole. For example, a state that has the truth in sentencing law in place
has fewer offenders on probation/parole because they had to serve 85% of their sentence
(Pew, 2011). This law helped Arizona in 2004 to have one of the lowest recidivism rates,
at 11.5% (Pew, 2011). However, some policies (e.g., mandatory minimum sentencing)
have negatively impacted both the release and recidivism rates. For example, in 2004
Missouri had a 54.4% recidivism rate with 40.3% revocation due to technical violations
(Pew, 2011). Although this number has decreased to 36.4% in 2009, Missouri’s inmate
population has been steady (Pew, 2011).
Langan and Levin (2002) conducted a 3-year study on recidivism rates for
offenders released from 15 states across the United States. They found that 67.5% of
offenders were rearrested, 46.9% committed a new crime, and 25.4% were resentenced
(Langan & Levin, 2002). Also, 51.8% had new sentences or were re-incarcerated due to
technical violations (Langan & Levin, 2002). In addition, approximately 29.9% of
offenders recidivated (e.g., rearrested) within the first six months of their release (Langan
& Levin, 2002). This number almost doubles with each year added. In one year of release
44.1% recidivated, within 2 years 59.2%, and within 3 years 67.5% recidivated (Langan
& Levin, 2002). These numbers continued to increase steadily for reconviction and new
sentences for offenders. Within 3 years, 46.9% of offenders were reconvicted, and 25.4%
received new sentences (Langan & Levin, 2002).
Researchers have reported that offenders released from prison in one of 15 states
committed new crimes in one of the same 15 states (Langan & Levin, 2002). It is unclear
why these states had such a large number of offenders committing new crimes. Officials
and researchers believed that the states’ close proximity to each other played a role in the
migration of offenders once released (Langan & Levin, 2002).
Most of the crimes recommitted by offenders upon release were property offenses
such as robbery (70.2%) and burglary (74.0%), to (70.2%) selling/possessing illegal
weapons (Langan & Levin, 2002). Interestingly, these crimes are most often committed
to obtain money, and offer some insight into the problems that offenders face in society
once they are released from prison (Langan & Levin, 2002).
Another type of crime that newly released offenders commit, and that contribute
to recidivism, is drug offenses. For example, Langan and Levin (2002) reported that
66.7% of offenders released in their study were rearrested within the first 3 years.
Approximately 47.0% were reconvicted, 25.2% received new prison sentences, and
49.2% were re-incarcerated due to technical violations (Langan & Levin, 2002). In
addition, the number of prior arrests serves as a useful indicator of how soon offenders
will recidivate and be rearrested. For example, an offender with one previous arrest will
have a 20.6% rearrest rate in 1 year (Langan & Levin, 2002). Recidivism increases to
40.6% rearrest rate within 3 years (Langan & Levin, 2002). Therefore, the more prior
arrests the offender has, the more likely he or she is to recidivate within the first 3 years
of release (Langan & Levin, 2002).
Beginning in the Nixon era, officials thought that giving offenders harsher or
longer sentences would have a positive impact on crime, therefore reducing the
recidivism rate. However, Langan and Levin (2002) found that that recidivism rates
remained relatively unchanged. For prison sentences of 6 months or less, the recidivism
rate was 66.0% (Langan & Levin, 2002). Also, offenders serving 7 to 12 months, the
recidivism rate was 64.8% (Langan & Levin, 2002). In addition, for offenders sentenced
to 13-18 months and 19-24 months, recidivism rates were 64.2% and 65.4% respectively
(Langan & Levin, 2002). Even those serving 61 months or longer had a recidivism rate of
54.2% within 3 years (Langan & Levin, 2002).
Locus of Control
Locus of control was initially defined by psychologist Julian Rotter in 1966 as the
belief that one’s behavior determines consequences (Ryon & Gleason, 2014). It was
initially conceptualized as a unidimensional construct. However, over the years it has
become dichotomized and divided into internal and external dimensions (Ryon &
Gleason, 2014). Therefore, researchers now believe that individuals have either internal
or external locus of controls (Ryon & Gleason, 2014). Locus of control is a continuum
for each person that can result in an individual having more control at one time and less
in another (Ryon & Gleason, 2014). Thus, locus of control is fluid and can change
depending on the situation (Huntley, Palmer, & Wakeling, 2012).
Individuals with a more internal locus of control are thought to believe that things
occur or happen to them due to their behavior and actions (Ryon & Gleason, 2014).
Those with a more external locus of control believe “powerful others, fate, or chance
determine events” (Ryon & Gleason, 2014, p. 121). Stress and how the individual
handles stressful events has been linked to locus of control (Ryon & Gleason, 2014).
Those who believe they have more control over their lives, experience less stress when
compared to those who believe they have less control over their life events (Ryon &
Gleason, 2014). Research has indicated that those with more external locus of control had
more support, and those with more internal locus of control had more ability to deal with
stress (Ryon & Gleason, 2014). Research also suggested that those with lower internal
locus of control were less capable of dealing with stress (Ryon & Gleason, 2014). Lower
internal locus of control may decrease one’s ability to seek help when trying to work
through stressful events (Ryon & Gleason, 2014).
Those who believe things happen to them due to their own ability are thought by
researchers to have internal locus of control (Goodman & Leggett, 2007). Those with
external locus of control believe things occur due to outside influences beyond their
control, such as luck or happenstance (Goodman & Leggett, 2007). Researchers have
demonstrated that offenders have a higher external locus of control than non-offenders
(Goodman & Leggett, 2007). In addition, researchers have reported that criminal activity
is influenced by locus of control (Goodman & Leggett, 2007). Goodman and Leggett
(2007) found that offenders who committed more violent types of crime had higher
external locus of control. For example, adolescents with conduct disorders were found to
have higher external locus of control (Goodman & Leggett, 2007).
Rotter (1966) believed that those with external locus of control are the result of
maladaptive behaviors (Huntley et al., 2012). He believed that individuals having
problems failed to understand that their adverse circumstances are the result of their
negative behavior (Huntley et al., 2012). Those with an internal locus of control have
more adaptive behaviors because positive consequences support the individual’s beliefs
and behaviors causing them to be repeated (Huntley et al., 2012). Also, one’s ability to
solve problems and self-esteem are related to locus of control in both non-offenders and
offenders (Huntley et al., 2012). For example, researchers have reported that those with
low self-esteem and poor problem-solving skills are more influenced by others and
shared commonalities of external locus of control (Huntley et al., 2012). Therefore, one’s
sense of control is thought to be a part of causal reasoning, where control over outcomes
involve the ability to judge and analyze the correlation between the individual, the
behavior, and the consequence (Kormanik & Rocco, 2009). Thus, the present study
aimed to discover if there were racial differences in locus of control that contributed to or
impacted recidivism.
Prison Industrial Complex
In order to understand recidivism, one must understand how the prison industrial
complex has contributed to the incarceration problem today. The privatization of prisons
is used to describe the prison industrial complex. The prison industrial complex is the
overlapping relationship of government and industries that influence policy (e.g.,
policing, prison) to address systemic (i.e., economic, social, political) problems (Herzing,
2005). The increasing need for prisons is perceived to bring a positive change in criminal
activity. The prison population has increased at an alarming rate. In 1980, nearly 319,598
people were incarcerated (Pollock, Hogan, Lambert, Ross, & Sundt, 2012). In 2009,
almost 1,613,740 people were incarcerated, earning the United States the title of being
the world’s leader in imprisonment (Pollock et al., 2012).
The goal of prisons was to rehabilitate offenders so that they could become
productive citizens upon their release (Pollock et al., 2012). This goal was also supported
by the public; however, there was also the perception that society should be “tough on
crime.” This point of view was lobbied for by politicians and policy makers and was
typically acceptable to society (Pollock et al., 2012). To be “soft” on crime was
paramount to political suicide. Therefore, because of harsher laws and stricter sentencing,
the need for more prisons have grown into a multibillion-dollar political giant (Pollock et
al., 2012).
Privatization of prisons began in the 1980s and has increased by approximately
1600% from the 1990 to 2009 (ACLU, 2011). Private prisons contain about 6% state,
16% federal, and nearly half of detained immigrants (ACLU, 2012). The justification for
building and maintaining the prison industrial complex was the idea that it would save
money which was not true (ACLU, 2012). For example, despite evidence that showed
private prisons cost more money than public ones, private prisons are still contracted to
house almost 5,000 more offenders in Arizona (ACLU, 2012). However, this proposal
continued to be lobbied for primarily by state officials and lawmakers who had
conflicting interests with the companies who own these facilities.
Tougher laws such as mandatory minimum sentencing, truth in sentencing, and
three strikes laws have had a negative impact on the prison system (ACLU, 2012). These
laws resulted in the need for mass incarceration and the prison industrial complex
(ACLU, 2012). Stricter laws also served to increase profits for prison companies like
Corrections Corporation of American (CCA) and the GEO Group by increasing the
demand to house more offenders (ACLU, 2012). In addition, CCA and the GEO Group
influenced policy by teaming up with the American Legislative Exchange Council
(ALEC). ALEC is also responsible for the creation and implementation of these same
harsh sentencing laws in over 27 states (ACLU, 2012).
Private prison companies receive their money from the United States government
in the form of taxpayer dollars (ACLU, 2012). With the help of ALEC, the Private
Correctional Facilities Act was created (ACLU, 2012). This act allowed governmental
agencies to obtain private sector contracts for services previously conducted by
correctional institutions (ACLU, 2012). Furthermore, the act resulted in a revolving door
in the criminal justice system (ACLU, 2012). Privately owned facilities can now house
offenders from any state, without the original states’ authorization, and outsource prison
labor (ACLU, 2012).
Privately owned prisons have been associated with increased violence and higher
staff turnover (ACLU, 2012). Also, private facilities are less inclined to focus on
rehabilitation and more on the housing of offenders (ACLU, 2012). For example, the
more crimes committed resulted in more individuals that would need to be incarcerated,
thus increasing recidivism and the need for more prisons (ACLU, 2012). Therefore, it
could be argued that private prison facilities are more focused on ensuring their own
viability, and not solving the growing incarceration problem in the United States (Mason,
2012).
As mentioned earlier, lawmakers and private prison companies have relationships
that contributed to the growing prison industrial complex. For example, private prison
companies like CCA and GEO, fund many state and federal legislators (ACLU, 2102). In
addition, private prison companies are huge lobbyist for federal agencies, such as the
Federal Bureau of Prisons, the House of Representatives, and Homeland Security
(ACLU, 2012). For example, CCA had 199 lobbyists in more than 30 states from 2003 to
2011; GEO had 72 lobbyists in 17 states during the same period (ACLU, 2012). Also,
CCA, GEO, and Cornell have contributed to political campaigns and have their own
Political Action Committees or PACs (ACLU, 2012).
Restorative and Retributive Justice
As crime and punishment changed focus, concern for the victims of crime
increased. Bloom (1999) estimated that 83% of the nation would be a victim of a violent
crime at some point in their life (p. 259). As a result, Bloom (1999) indicated many
victims would exhibit psychological symptoms such as depression and anxiety. In order
for these victims to be healed, restoration (i.e., autonomy) must occur (Bloom, 1999).
Currently, the criminal justice system is offender-focused, with little focus on the
victim which often leads to victim revictimization (Bloom, 1999). The criminal justice
system fails to change criminal behavior that would make offenders more responsible;
therefore, a cycle of criminality is developed (Bloom, 1999). In addition, Bloom (1999)
indicated that the criminal justice system was focused on obtaining retribution by
attempting to answer the following questions: “1) What laws were broken? 2) Who
“done” it? and 3) What punishment do they deserve?” (p. 260). Bloom (1999) further
indicated that crime meant law breaking, law breaking meant a violation of the state, and
the victim was therefore the state (p. 260). In this basic premise, the victim was not
identified as a person per say, but an entity and retribution, or “blame and pain” must be
achieved (Bloom, 1999, p. 260).
With restorative justice, the focus was on healing relationships and the needs of
the victim, community, and offender (Bloom, 1999). Bloom (1999) argued that there was
a fundamental human need for “confession, remorse, atonement, restitution and
forgiveness as essential components of human healing” (p. 260). Thus, restorative justice
helped the offender acknowledge that he or she has done wrong, take responsibility for
their actions while society takes responsibility for both the victim and offender (Bloom,
1999).
Arguably, justice was focused on fairness and wrongdoing (e.g., retribution)
where the offender was punished or compensation was given to the victim (Strelan,
Feather, & McKee, 2011). The notion of retribution was not a new one; it has been the
norm in Western society and continues to be supported by our criminal justice system
(Strelan et al., 2011). However, there was growing evidence that a more prosocial
response (e.g., forgiveness) was being considered by society (Strelan et al., 2011).
Forgiveness was associated more with the moral values of the individual as well as debt
cancelation, but could also work in conjunction with our criminal justice system (Strelan
et al., 2011).
At its most basic level, retributive justice was punishing someone for doing wrong
(Strelan et al., 2011). But, there are quite a few differences in how society felt about how
someone should be punished (Strelan et al., 2011). The absolute penal (retributive) view
stated that the crimes against society required an action that brought balance and justice
by the punishment of wrongdoers (Strelan et al., 2011). This view suggested that the
punishment that was meted out was appropriate to the crime committed (Strelan et al.,
2011). In other words, the violator gets what he or she deserved (Strelan et al., 2011).
Strelan et al. (2011) further explained that punishment was a rightful and moral response
to breaking societal rules.
More utilitarian (retributive) theories (e.g., relative penal theories) suggested that
justice did not consider morality when dealing with offenders (Strelan et al., 2011). Thus,
punishment should focus on the possibility of future criminal acts or behavior (Strelan et
al., 2011). The primary goal of retributive theory focuses on deterrence of future criminal
behavior, protecting society via incarceration, and rehabilitation (Strelan et al., 2011).
However, restorative justice focused on repairing relationships between victims,
offenders, and society, not punishment (Strelan et al., 2011). Strelan et al. (2011) found
that although restorative justice was not punishment focused, it was an overall goal of
justice. In addition, restorative justice also has aspects of forgiveness in that restorative
justice looked beyond the crime and considered both the victim and the offender (Strelan
et al., 2011).
It was also suggested that people are less inclined to offer forgiveness depending
on the overall goal of justice (Strelan et al., 2011). For example, if justice goals are
intended to punish, forgiveness was less likely to be considered (Strelan et al., 2011). If
punishment was the focus of justice to protect society and act as a deterrent, or it was
believed the offender received what he or she deserved, forgiveness was likely to be
considered (Strelan et al., 2011). However, Strelan et al. (2011) found the opposite was
true if the goal of justice was inclusive (e.g., restorative) in nature. Rehabilitation and
restoring victim/offender/community relationships increased forgiveness tendencies
(Strelan et al., 2011). Thus, individuals who supported retributive justice are less likely to
offer forgiveness (Strelan et al., 2011). Conversely, people who favored restorative
justice are more apt to offer forgiveness (Strelan et al., 2011).
Restorative justice has become an alternative means to incarceration by reducing
violations and recidivism by deterring others from committing criminal actions (Wenzel,
Okimoto, & Cameron, 2012). Restorative justice was not initially developed as an
alternative means for justice (Wenzel et al., 2012). It was an actual critique of the
Westernized legal system (Wenzel et al., 2012). However, restorative justice became a
social movement that led to an alternative form of justice (Wenzel et al., 2012). Also,
restorative justice modeled the original critique of the United States’ court system where
the victim and offender are given control over their conflict (Wenzel et al., 2012). The
current model of restorative justice allowed the offender and victim to work together to
repair the harm caused by the offender (Wenzel et al., 2012). The offender acknowledged
their offense, offered an apology and showed remorse, which led to the victim offering
forgiveness (Wenzel et al., 2012).
The measure of success of restorative justice was its effect on recidivism because
restorative justice tried to restore justice and offer moral repair to the victim and society
(Wenzel et al., 2012). Wenzel et al. (2012) indicated that understanding victims’ feelings
towards justice restoration would offer more support, and understanding towards
restorative justice practices. In addition, those who have empathy for the offender or can
identify with the offender are more likely to be proponents of restorative justice (Wenzel
et al., 2012).
In the United States, the criminal justice system was arguably focused on
retributive justice (Wenzel et al., 2012). Also, most psychological research has been
retributive focused where justice was balanced by punishing offenders for doing wrong or
just deserts (Wenzel et al., 2012). Conceptually, social psychologists described retributive
justice as the violation of laws that led to the offender deserving punishment to restore
balance (Wenzel et al., 2012). Furthermore, restorative justice deemed that offender
suffering was an appropriate response to restore justice when criminal actions have
occurred (Wenzel et al., 2012). Thus, punishment must convey a message against
negative behavior, and it did not consider that the offender necessarily understood said
message; only that justice was restored (Wenzel et al., 2012).
Wenzel et al. (2012) also indicated that retributive justice removed the power
from the offender via punishment and restored power to those issuing punishment (e.g.,
victim, community). When power needed to be restored, Wenzel et al. (2012) argued that
victims would be more in favor of retributive justice. Also, cultures that focused more on
honor or revenge seeking would also support retributive justice (Wenzel et al., 2012). For
example, when individuals thought a power/status imbalance occurred, they were more
likely to restore balance with retributive justice, such as revenge or punishment (Wenzel
et al., 2012). Wenzel et al. (2012) indicated that individuals are more likely to be in favor
of restorative justice when there was a transgression against values, and when values
needed to be restored. Thus, when restorative or retributive justice was applied depended
on the perception that the individual or society has regarding the crime (Wenzel et al.,
2012).
There are four types of restorative justice: victim-offender mediation, group
conferencing, circles, and “other” (Umbreit, Vos, & Coates, 2006). Most required
inperson meetings between victims and offenders, with a third party as mediator. Circles
often have more community involvement and “other” types of restorative justice included
reparative boards and community type programs (Umbreit et al., 2006). The requirement
was acknowledging what occurred, how the crime impacted the victim and reaching an
agreement between all parties for reparation (Umbreit et al., 2006). These forms of
restorative justice could occur at any time during the criminal justice process (Umbreit et
al., 2006).
The original purpose of involving the victims was to allow younger offenders to
see firsthand how their actions affected others and hopefully reduced their chances of
recidivism (Umbreit et al., 2006). Researchers have indicated that are several reasons
why victims wanted to participate in victim-offender mediation (Umbreit et al., 2006).
Reasons such as understanding why a crime was committed, and letting the offender
know how their crime impacted the victim, led to participation (Umbreit et al., 2006).
Additional reasons for offender participation included repaying the victim, to put the
experience behind them, and to gain favor with the court (Umbreit et al., 2006).
Umbreit et al. (2006) found that some racial differences existed. Caucasian
victims are more likely to participate if the offender was Caucasian. Additional reasons
for participation included if there was a misdemeanor crime and if the victim was
associated with an institution (Umbreit et al., 2006). Also, mediation was more likely to
occur with property offenses (Umbreit et al., 1006). Personal crimes are less likely to
have mediation (Umbreit et al., 2006). However, the longer it took to reach mediation,
the more likely for victims to participate (Umbreit et al., 2006).
Regarding participant satisfaction, those participating in victim-offender
mediation and group conferencing have a higher satisfaction with both the process and
the criminal justice system overall (Umbreit et al., 2006). Circles such as, talking,
healing, and sentencing have mixed reviews. For example, a community circle working
with sex offenders was found to have positive satisfaction (Umbreit et al., 2006).
However, there are concerns regarding privacy, problems working with possible family
and friends, and conflicts due to religion (Umbreit et al., 2006). Finally, “other”
programs, such as the Vermont Reparative Probation program, have minimal victim
participation (Umbreit et al., 2006); however, those who participated were satisfied
(Umbreit et al., 2006).
Often, restorative justice attempted to divert offenders from the criminal justice
system (Umbreit et al., 2006). For example, a study of a victim-offender mediation in the
United Kingdom found nearly 60% of offenders are diverted from prosecution (Umbreit
et al., 2006, p. 8). In the United States, a North Carolina mediation program diverted
approximately two-thirds of their offenders from prosecution (Umbreit et al., 2006). With
group conferencing, there are no changes in diverting offenders (Umbreit et al., 2006).
Also, with circles, researchers have indicated that one program diverted over 100 people
in a 10-year period (Umbreit et al., 2006).
As indicated previously, a measure of the effectiveness of a restorative justice
program was if recidivism decreased. Umbreit et al. (2006) studied several
victimoffender programs and found mixed results with most programs having little or no
change to recidivism rates. Research has indicated that youth who participated in
victimoffender programs have lower recidivism rates, nearly 32%, and have less serious
charges if they did recidivate (Umbreit et al., 2006). Also, there are mixed results for
group conferencing and recidivism. Recidivism rates are lower for those who committed
violent crimes (Umbreit et al., 2006).
In a 2-year study of a circle program with 65 participants, there was an 80%
decrease in recidivism (Umbreit et al., 2006). Also, in a 10-year study of a sex offender
program, there was a recidivism rate of 2% (Umbreit et al., 2006). Furthermore, in a
1year follow-up study of “other” typed programs, there was a significant decrease in
recidivism; however, data were not provided (Umbreit et al., 2006).
The cost of restorative justice programs varies. For example, in California it cost
approximately $250 per case, but in Missouri, it ranged from $232 to $338 (Umbreit et
al., 2006). Also, how much time was spent on a case also impacted its cost (Umbreit et
al., 2006. Researchers have indicated that it required less time to process a mediated case
(Umbreit et al., 2006). Offering circles impacted the cost of going through the criminal
justice system process, saving approximately $6.2 to $15.9 million dollars (Umbreit et
al., 2006).
Researchers, as well as policy makers, are considering restorative justice as a
plausible alternative to the criminal justice system (Latimer, Dowden, & Muise, 2005).
Other countries such as Canada, England, and Japan, including the United States, are
considering more restorative forms of justice (Latimer et al., 2005). Restorative justice
assumed there was a “violation of people and relationships rather than merely a violation
of law” (Latimer et al., 2005, p. 128). Therefore, to achieve justice reparation must occur.
Reparation was accomplished by the offender accepting responsibility for their actions by
meeting with the victim to discuss repairing the wrongdoing (Latimer et al., 2005).
However, a stipulation of restorative justice was that participation was voluntary
(Latimer et al., 2005).
Restorative justice could happen at any point in the criminal justice process.
Latimer et al. (2005) identified five entry points for offenders: (pre-charge) police,
(postcharge) crown, (pre-sentence) courts, (post-sentence) corrections, and (pre-
revocation) parole (p. 129). Restorative justice was thought to be beneficial for the victim
because it offered vindication and healing (Latimer et al., 2005). By forming
relationships between the victim, offender, and the community, healing occurs (Latimer
et al., 2005). Also, when comparing 13 treatment programs, victim satisfaction was
higher for those who participated in a restorative justice program (Latimer et al., 2005).
However, mixed results was found among offenders. Overall offender satisfaction was
higher for those within the program; however, Latimer et al. (2005) found that one of the
13 test programs did not find any statistical significance for offender satisfaction.
One way to ensure offender compliance was with restitution agreements (Latimer
et al., 2005). Having to pay victims increased the likelihood of offenders being complaint
and taking responsibility for their negative behavior (Latimer et al., 2005). Researchers
reported that those who must pay restitution have much higher compliance with
restorative justice programs (Latimer et al., 2005). Also, researchers have indicated an
overall decrease in recidivism with restorative justice programs compared to offenders
who did not participate in these types of programs (Latimer et al., 2005). However,
Latimer et al. (2005) also found that restorative justice was not appropriate to deal with
long term criminal or delinquent behavior. Problems such as, substance abuse, antisocial
attitudes, and crime-ridden communities are not the focus of restorative justice (Latimer
et al., 2005). These issues need to be addressed in greater detail to truly impact and
reduce recidivism (Latimer et al., 2005).
There was limited data regarding the education and training of facilitators offering
restorative justice programs (Latimer et al., 2005). Latimer et al. (2005) indicated this
was important to know because facilitators have a significant impact on the outcome.
Also, there was minimal information about offenders' criminal history, offense type, and
relationship status between the victims and offenders (Latimer et al., 2005). Thus, more
information is needed to understand the real effectiveness of restorative justice programs
in future studies (Latimer et al., 2005).
Although there have been reports of overall satisfaction with restorative justice
programs such as victim-offender mediation, there was little data provided regarding
attrition. Researchers have indicated that attrition rates vary from approximately 4% to
65% with face-to-face intervention (Bonta, Wallace-Capretta, Rooney, & McAnoy,
2002). Variable attrition rates caused doubt, allowing for questions to arise concerning
the necessity of these types of programs (Bonta et al., 2002). Bonta et al. (2002) also
indicated that most studies evaluating restorative justice programs only measured the
frequency of meetings, the satisfaction of participants, and how many restitution
agreements were reached. Furthermore, research has indicated that there was not a
precise definition of what a restorative justice program was because there was a lack of
consensus (Bonta et al., 2002).
After reviewing nearly 30 studies, Bonta et al. (2002) found that recidivism rates
only decreased by approximately 3% overall. Adult programs were more effective in
lowering recidivism than youth programs (Bonta et al., 2002). Research has indicated that
programs that have restitution have lower recidivism rates, approximately 8%, as well
(Bonta et al., 2002). Also, victim-offender meetings (VORP) have slight increases in
recidivism, approximately 2% (Bonta et al., 2002). Bonta et al. (2002) found that
restorative justice programs are used for a diversion from incarceration; however, these
programs are not appropriate for all offenders. Offenders who committed crimes such as,
sexual assault, drug charges or other violence are not eligible (Bonta et al., 2002).
Furthermore, offenders accepted into the program must plead guilty, take responsibility
for their crimes, and make restitution (i.e., amends) to their victims (Bonta et al., 2002).
The offender must complete a sentencing plan that was developed by the victim (Bonta et
al., 2002). Sentencing plans such as treatment or restitution are submitted to the court and
if accepted, are given to the program's staff to ensure offender completion (Bonta et al.,
2002).
Of the 297 offenders referred to restorative justice programs, approximately 174
or 58.6% received approval (Bonta et al., 2002). Nearly 91.4% have a six-month
sentence, 43.7% are first time offenders, and 28.2% are less likely to commit violent
crimes (Bonta et al., 2002). Also, approximately 17.8% of referrals for the program are
from the prosecutor; however, when referrals are made, almost 83.3% are accepted
(Bonta et al., 2002). Only 69.3% of offenders referred by the defense are accepted (Bonta
et al., 2002). Bonta et al. (2002) found that the prosecutor had a significant influence on
who was accepted into the program. Of the 99 cases accepted by the judge, the prosecutor
recommended 57 for the restorative justice program (Bonta et al., 2002). Only 9.9% of
the offenders in the program have a lower risk of recidivating (Bonta et al., 2002). Nearly
50.5% of offenders have a medium risk, and 39.6% have a high risk to recidivate (Bonta
et al., 2002, p. 327). Although acceptance of some offenders for the restorative justice
program occurred, some are still incarcerated (Bonta et al., 2002). Incarceration called
into question the program's intent on restoration and diversion (Bonta et al., 2002).
Research has indicated that approximately 55.6% of offenders who received prison time
have also committed crimes against an individual (Bonta et al., 2002). Furthermore, those
within the restorative justice program have a history of having more technical (i.e.,
probation) violations (Bonta et al., 2002).
Bonta et al. (2002) also found that the types of crimes offenders committed
influenced recommendations for restorative justice programs. Recommendations for the
program came from business employees (41.5%), individuals (29.8%), and private
business owners (16.5%). Overall monetary losses ranged from approximately $20 to
over $20, 000 (Bonta et al., 2002). Additionally, 4.9% of victims had physical injuries,
and 22.2% of victims expressed having psychological injuries (Bonta et al., 2002). Also,
56.4% of offenders had to give restitution, ranging from $200 to $42,000 (Bonta et al.,
2002). Almost 69% of offenders received community service, ranging from 50 to over
800 hours of service (Bonta et al., 2002, p. 328). Last, approximately 96.7% of the
offenders within the program received a recommendation for counseling or some form of
treatment (Bonta et al., 2002). Bonta et al. (2002) found that offender risk profiles
indicated that many suffered from drug and alcohol, employment, and family/relationship
problems.
Thus, Bonta et al. (2002) found that participants in their study did have lower
recidivism rates, ranging from 9% to 31%. However, offenders who did nor did not
received treatment, and offenders who were incarceration or were in the restorative
justice programs, had no impact on recidivism rates (Bonta et al., 2002). Therefore, the
aim of this study was to provide insight into the challenges that ex-offenders face and to
determine if there are any racial differences in resiliency and locus of control that
impacted or contributed to recidivism.
Racial Implications
The impact of mass incarceration and recidivism have mostly affected African
Americans and Hispanics (Golembeski & Fullilove, 2005). In 2003, 1 in 8 men
incarcerated was African American, compared to 1 in 27 men for Hispanics, and 1 in 63
men for Caucasians (Golembeski & Fullilove, 2005). Also, Golembeski and Fullilove
(2005) found that there was a correlation between socioeconomic status and
imprisonment. For example, in 2002, almost 80% of those incarcerated did not have
enough money to hire an attorney. Also, in a 1991 study conducted by the US
Department of Justice found that nearly 65% of those incarcerated have limited
education, 53% are poor, and almost 50% are unemployed (Golembeski & Fullilove,
2005). Furthermore, regarding recidivism, African Americans are dealt with more harshly
than their Caucasian counterparts (Langan & Levin, 2002). For example, African
Americans have higher rearrest rates than Caucasians, 72.9%, and 62.7% respectively
(Langan & Levin, 2002). African Americans are reconvicted more frequently than
Caucasians, 51.1% and 43.3% respectively (Langan & Levin, 2002). Also, 28.5% of
African Americans received new sentences, compared to only 22.6% of Caucasians
(Langan & Levin, 2002). Furthermore, African Americans are returned to prison more
frequently than Caucasians, with overall rates of 52.2% and 49.9% respectively (Langan
& Levin, 2002). Overall incarceration rates are higher for Hispanics when compared to
Caucasians. Rearrests, reconvictions, and return to prison rates were 71.4% and 64.6%,
50.7% and 43.9%, 57.3% and 51.9% respectively (Langan & Levin, 2002).
African Americans are incarcerated six times more frequently than Caucasians
(Massoglia, Firebaugh, and Warner, 2013). Increasing incarceration negatively affected
families, communities, earning potential, and has far-reaching political implications. For
example, due to incarceration many offenders have lost or severely limited, their voting
rights, which impacted elections both on a state and federal level (Massoglia et al., 2013).
Limited voting rights created a political shifting of not only power but also, money that
shifted from poor inner-city minority neighborhoods to more affluent Caucasian
communities (Golembeski & Fullilove, 2005).
The housing of inmates also became a part of the politico-socioeconomic scheme.
Usually, prisons are placed in mostly rural Caucasian areas or communities. The
prisoners (e.g., mostly minorities) are then considered residents of these communities,
which determines how the government allocated funding and grants (Golembeski &
Fullilove, 2005). The shift in money decreased the number of subsidies meant for
innercity/urban neighborhoods thereby, giving them to prison communities (Golembeski
& Fullilove, 2005). Also, because inmates received minimal pay or in some cases no pay
at all, these figures helped lower the average income reported to the federal government
(Golembeski & Fullilove, 2005). Reducing the average reported income enabled these
communities to receive federal housing (Golembeski & Fullilove, 2005). Last, these
figures also helped to rezone political boundaries because inmates contributed to
increasing the political power in these communities (Golembeski & Fullilove, 2005).
Thus, minorities, particularly African Americans, lost not only economically, but
politically as well (Golembeski & Fullilove, 2005). Minority communities lost because
they lacked programs, did not have the money to improve their communities and lacked
the power to help offenders returning to those neighborhoods (Golembeski & Fullilove,
2005).
Racial differences in arrests for drug offenses are vast. Due to the War on Drugs,
arrests from 1985 to 1989 increased for African Americans by 100% compared to only
27% of Caucasians (Kubrin, Squires, and Stewart, 2007). The Bureau of Justice Statistics
found that in 2003, 1 in every 3 African American man would likely be incarcerated
during his lifetime (The Sentencing Project, 2012). The rates decreased for other races,
with only 1 in 6 for Hispanics, and 1 in 17 for Caucasians (The Sentencing Project,
2012). Research has indicated that of the offenders incarcerated in state prisons during
2011, 38% (581,300) were African American, compared to 23% (349,900) of Hispanics,
and 35% (516,200) of Caucasians (The Sentencing Project, 2012).
Thus, the negative impact of incarceration and recidivism on African Americans
went beyond the offender. It affected families and communities, helped to break up
relationships and created an accepted normalcy of incarceration that is passed on to
youth. Mahmood (2004) found that mass incarceration negatively impacted the social
conditions that caused criminal activity as evidenced by the increasing rates of women
and children that are also imprisoned.
Contributors of Recidivism
One area that has contributed to the recidivism problem in the United States was
the failure of successfully reintegrating offenders back into society (Hass & Saxon,
2012). Failing reintegration includes providing adequate programs while in prison and
upon release, socioeconomic problems such as jobs, housing, and adequate support (Hass
& Saxon, 2012). Approximately one-third of offenders released are returned to prison due
to technical violations while on probation or parole because of strict rules (Hass & Saxon,
2012). With limited programs for rehabilitation during incarceration, the multiple
problems offenders faced before prison (e.g., substance abuse, education), remained upon
their release (Kubrin et al., 2007). These unaddressed problems have contributed to
offenders' inability to follow the strict rules that are imposed during supervision (Kubrin
et al., 2007). For example, in a 2005 study conducted on 676 Texas offenders, researchers
have reported that nearly 80% used substances and only 21% received treatment while
imprisoned (Kubrin et al., 2007).
Having the stigma of being a convicted felon also contributed to recidivism
because it limited what the ex-offender could and could not do in society. Laws and
agendas such as the War on Drugs that placed stricter restrictions on individuals, even for
first-time offenses, also contributed to the dilemma (Kubrin et al., 2007). For example,
depending on the state, many felons are restricted from receiving public assistance (i.e.,
welfare) for the rest of their lives. They also cannot receive financial aid to attend
college. These restrictions severely limit what these individuals can do upon reentering
society.
Social and family support. Another problem that offenders faced upon
reentering society was a lack of social support. Golembeski and Fullilove (2005) found
that many offenders left prison with no support from their families or their communities
thus, increasing the likelihood of committing a crime to survive. Furthermore, due to this
lack of support, many offenders became homeless (Golembeski & Fullilove, 2005). For
example, in New York, federal law gave power to the Public Housing Authority to
restrict housing to those who have criminal records (Golembeski & Fullilove, 2005).
Arguably, adequate social support reduced the chance of ex-offenders from
committing an opportunistic crime by limiting the need to associate with peers that are
criminals (Cobbina, Huebner, & Berg, 2012). Having a strong social network was
thought to encourage ex-offenders to become positive members of society (Cobbina et
al., 2012). However, Cobbina et al. (2012) found that having a strong social network has
an opposite effect on female ex-offenders. Those who were in relationships with men
were found to increase their criminal behavior (Cobbina et al., 2012).
Another strong influence on ex-offenders were their peers. Researchers have
indicated that peers influenced everything from romantic partners to criminal activity
(Cobbina et al., 2012). Cobbina et al. (2012) found that for men who committed low-
level crimes, social networks (e.g., intimate partner, family) lowered the risk of
recidivism. Also, those who had peers committing criminal activity were found to
influence the exoffenders’ recidivism (Cobbina et al., 2012). For example, male offenders
seek their peers for social support (e.g., gangs), which increased peer pressure and could
increase criminal behavior (Cobbina et al., 2012).
Social support also included the offenders’ family. Incarceration affected not only
the offender but also the family by breaking up the family unit and hurting the family
economically. Having strong family support during incarceration decreased the
offenders’ likelihood of recidivism upon their release (Martinez & Christian, 2009). Also,
research has indicated that strong family support reduced the risk of post-release
depression (Martinez & Christian, 2009). Furthermore, family support influenced how
successfully the offender reentered society and, therefore, stayed out of prison (Martinez
& Christian, 2009).
It is also important to understand that family support means different things to
different people. Support ranges from housing to information. For example, offenders
identified support as giving information and advice (Martinez & Christian, 2009). Also,
providing instrumental support, such as occasional transportation or money, by family
members was beneficial to ex-offenders (Martinez & Christian, 2009). This form of
assistance allowed family members to help ex-offenders without feeling like they were
responsible for them, therefore, enabling them to find their way. For example, offenders
who engage in more dangerous criminal acts repeatedly was found to have less social
support (Cobbina et al., 2012). Families were less inclined to continue to be involved
with the more dangerous offender (Cobbina et al., 2012).
Social support was severely lacking for those offenders who went through the
criminal justice system as children (i.e., juveniles) and left as adults. These individuals
went in with limited education and job skills and returned with these same deficiencies
(Inderbitzin, 2009). Many returned to the same negative environment they were removed
from which often included poverty, crime and a lack of social support (Inderbitzen,
2009). Also, it was more problematic for offenders to reenter society for several reasons
(Inderbitzen, 2009). Age (e.g., youth), limited education, lack of job or social skills, and
temptations within the neighborhood influenced re-entry (Inderbitzen, 2009).
Furthermore, incarceration did not reform them. For example, Inderbitzen (2009) found
that incarceration taught one subject how to be a more successful drug dealer upon his
release.
Many young offenders were worse off after incarceration due to the familiarity of
living in a structured system (Inderbitzen, 2009). Upon their release, many juvenile
offenders had idle time and lacked the skills to become successful in society (Inderbitzen,
2009). Low-level offenders (i.e., street offenders) had a harder time with reintegration
because they are not a part of mainstream society (Inderbitzen, 2009). Thus, this lack of
support such as education, family, and community, along with a lack of skill continued
the cycle of recidivism (Inderbitzen, 2009).
Neighborhood. Many offenders, particularly African American and Hispanic
offenders came from disadvantaged communities. For example, in a Chicago study,
researchers have indicated that offenders released back into the community returned to
seven specific counties (Massoglia, Firebaugh, & Warner, 2013). These counties are
known to be highly impoverished (Massoglia et al., 2013). Also, there is the perception
that reentry neighborhoods impacted recidivism (Massoglia et al., 2013). Although there
was not a causal relationship, there are known factors that offenders tended to be
minorities who are from disadvantaged neighborhoods (Massoglia et al., 2013). Also,
many offenders returned to these same neighborhoods that lacked opportunities but
provided significant social and familial ties (Massoglia et al., 2013).
Additionally, there are racial disparities in housing and neighborhood quality or
attainment for the public and offenders. For example, Massoglia et al. (2013) found that
African Americans, regardless of SES, did not achieve the same neighborhood quality as
Caucasians. This difference in neighborhood attainment meant a definite disadvantage to
those who lived in places that lacked economic growth, opportunity and have problems
with crime. Furthermore, offenders received limited or no help upon their release from
prison and often must rely on themselves or their families who are also lacking resources
(Massoglia et al., 2013).
Researchers have indicated that Caucasian offenders tended to live in the most
advantageous neighborhoods, Hispanics lived in the middle, and African Americans lived
in the most disadvantaged areas (Massoglia et al., 2013). Also, Caucasians have the most
personal achievements such as education and job opportunities; however, African
Americans were found to be poorer and relied on public housing (Massoglia et al., 2013).
Massoglia et al. (2013) also concluded that imprisonment had little consequence with
regards to neighborhood attainment for African Americans and Hispanics because they
were already living in more disadvantaged areas than their Caucasian counterparts.
However, imprisonment significantly affected neighborhood achievement for Caucasian
offenders, causing them to live in less affluent areas (Massoglia et al., 2013). Decreased
affluence was due to incarceration and not the individual's offense (Massoglia et al.,
2013). Furthermore, research has indicated that only 1 in 5 Caucasian subjects returned to
their former neighborhood upon release, suggesting there was a causal effect between
incarceration and disadvantaged neighborhoods (Massoglia et al., 2013). Upon their
release, the offender moved to a more disadvantaged neighborhood (Massoglia et al.,
2013). This move was particularly in the case of Caucasian offenders who lived in less
affluent areas upon their release; however, minorities returned to similarly disadvantaged
environments (Massoglia et al., 2013). Thus, there are significant racial inequalities with
regards to offender reentry.
Stahler et al. (2013) defined spatial contagion as the distance the offender was
from other deviant peers. Research has indicated that high spatial contagion, or having a
closer proximity to deviant peers, played a significant role in the likelihood of recidivism
with ex-offenders (Stahler et al., 2013). High spatial contagion has been particularly true
of younger offenders and those who were previously incarcerated for drug offenses and
violence (Stahler et al., 2013). However, research has not indicated that there was an
association between the economic disadvantages of a neighborhood and recidivism
(Stahler et al., 2013). There was an increased chance of recidivism for offenders who
were young and African American men who were involved with drugs (Stahler et al.,
2013). These findings indicated that this could be due to "hot spot policing" where there
was increased police interaction (Stahler et al., 2013). "Hot spot policing" in these areas
increased the likelihood of recidivism due to spatial contagion (Stahler et al., 2013).
Barriers. Although the prison population continued to rise, funding for
rehabilitative programs has significantly decreased thus, limiting the successful reentry of
offenders back into society upon their release (Hass & Saxon, 2012). The lack of
appropriate skills such as education and employment are barriers that negatively impact
how successful offenders are in society (Hass & Saxon, 2012). Also, offenders are
limited in the type of employment they could have. For example, offenders could not
legally work with children or the elderly, nor could they work as barbers, plumbers, or
real estate professionals (Hass & Saxon, 2012). Thus, the higher paying jobs, even those
for skilled laborers, are not available to ex-offenders due to their convictions (Hass &
Saxon, 2012).
Ex-offenders lacked the same rights as other citizens that limited their ability to
reenter society successfully. Limitations are placed on where the offender could live,
what type of employment they could have, and their voting rights. They remained, in a
sense, institutionalized even after their release (Hass & Saxon, 2012). These limitations
often resulted in homelessness, and with fewer options, led to criminal activity to survive.
Hass and Saxon (2012) found that offenders received instructions on what they had to do
once they reentered society, but not on how they are supposed to accomplish the tasks.
For example, offenders knew that they were expected to get a job, housing, and treatment
(Hass & Saxon, 2012). However, they were not instructed on how to accomplish these
tasks (Hass & Saxon, 2012). Thus, remaining sober, as well as other responsibilities,
posed a challenge for success (Hass & Saxon, 2012).
The lack of opportunities such as employment played a role in the racial
differences in recidivism (Bellair & Kowalski, 2011). Researchers have indicated that
there was a link between joblessness and criminality, as well as an inverse relationship
between lower educated males with higher than average wages and crime (Bellair &
Kowalski, 2011). However, the attitudes of employers also impacted if an offender could
receive an opportunity for employment. For example, researchers have indicated that in
Los Angeles nearly 21% of companies surveyed would consider employing a convicted
felon (Bellair & Kowalski, 2011). Thirty-six percent of employers considered hiring
offenders depending on the crime, and almost 42.6% would not (Bellair & Kowalski,
2011).
Additionally, of those offenders paroled, approximately 80.8% of Caucasian
offenders lived in neighborhoods that had a 0% to 10% unemployment (Bellair &
Kowalski, 2011). Bellair and Kowalski (2011) found that African American parolees
have higher recidivism rates than their Caucasian counterparts because of limited job
opportunities. Also, those industries (i.e., manufacturing) that are more likely to hire
individuals with criminal records are diminishing (Bellair & Kowalski, 2011). Therefore,
limited employment opportunities of offenders increased the likelihood of recidivism
(Bellair & Kowalski, 2011).
Theoretical Foundations
Social Disorganization Theory
The social disorganization theory could be used to understand how disadvantaged
neighborhoods influenced or impacted residents' behavior in a negative fashion. Kubrin et
al. (2007) found that the theory could be used to understand criminal behavior in
disadvantaged environments. When compared to advantaged environments,
disadvantaged environments had higher poverty rates, racial heterogeneity, and
(residential) instability that led to social disorganization (Kubrin et al., 2007).
Researchers have indicated that socially disorganized neighborhoods, due to their
economic and social disadvantages, have less (informal) social control than socially
organized neighborhoods (Kubrin et al., 2007). Also, crime rates are influenced by
residents living in the neighborhood (Kubrin et al., 2007).
Research has indicated that neighborhood type did have a direct impact on
criminal activity (Kubrin et al., 2007). Furthermore, previous research has indicated that
neighborhood type influenced victimization and offender rates (Kubrin et al., 2007).
Because disadvantaged (i.e., disorganized) neighborhoods often have fewer services for
ex-offenders, it was difficult for them to succeed and not recidivate (Kubrin et al., 2007).
Research has indicated that socially disadvantaged neighborhoods did not always
have higher crime rates (Kingston, Huizinga, & Elliott, 2009). However, Kingston et al.
(2009) studied neighborhood social structure and its complications beyond disadvantage
and personal influence. They also studied how areas vary by the opportunities (e.g.,
positive, negative) they have available (Kingston et al., 2009). For example, areas that
lacked social control (e.g., urban, inner-city neighborhoods) could have more
opportunities for criminal activity that exposed residents to these opportunities, especially
youth (Kingston et al., 2009).
Social control was composed of three levels—intimate, parochial (e.g., schools,
church), and public (e.g., community, agencies). For example, neighborhoods that have
strong private and parochial social networks have more access to public resources that
offer more opportunities to help residents within the community (Kingston et al., 2009).
Also, neighborhoods with higher levels of social control have higher levels of collective
efficacy or solidarity (Kingston et al., 2009). A high level of collective efficacy resulted
in trust among residents, which was good for the community (Kingston et al., 2009). The
higher the level of collective efficacy, the lower the level of criminality especially among
young adults or youth (Kingston et al., 2009).
Socially disadvantaged neighborhoods are believed to have limited social controls
(e.g., education, health), and the inability to create strong prosocial networks (Kingston et
al., 2009). These strong prosocial networks helped to decrease delinquency and criminal
behavior (Kingston et al., 2009). For example, increases in single-parent households
resulted in frequent moving and reduce the ability to create collective efficacy within the
neighborhood, thereby limiting the capacity to create strong prosocial networks
(Kingston et al., 2009). Additionally, neighborhoods with more single-parent families
have fewer adults available to monitor and build healthy trusting relationships within the
community (Kingston et al., 2009).
Poverty also has a significant impact on communities and the residents within
them. Poverty limited the ability to create social networks and relationships within the
community (Kingston et al., 2009). Due to limited resources, poverty reduced
opportunities for individuals to succeed (Kingston et al., 2009). Because poverty reduced
personal success, it increased the opportunity for delinquent and criminal behavior
(Kingston et al., 2009). Therefore, disadvantaged neighborhoods lack the proper
resources that protect against criminal behavior resulting in larger numbers of
delinquency (Kingston et al., 2009).
Previous research on social disorganization has focused on macro-level factors
that impacted urban neighborhoods, such as poverty and transience (Kurlychek, Krohn,
Dong, Hall, & Lizotte, 2012). Macro-level factors negatively affected residents and social
control (Kurlychek et al., 2012). Research has indicated that poverty, instability
(residential), and racial heterogeneity negatively influenced social controls (Kurlychek et
al., 2012). Researchers have also reported that there was a link between high criminal
behavior and violence in these types of neighborhoods (Kurlychek et al., 2012).
Kurlychek et al. (2012) conducted a study to determine if there were protective factors
that influenced individuals living in disadvantaged neighborhoods. Their study was
inconclusive; some neighborhood-level factors (e.g., school, peer) did lower the risk of
violence but, not significantly (Kurlychek et al., 2012). Thus, understanding the impact of
disadvantaged neighborhoods on the individual is complex and consists of many
variables (e.g., peers, resources) that influenced criminality.
Martinez, Rosenfeld, and Mares (2008) conducted research to determine if
criminal activity was influenced by factors (e.g., instability, poverty) from the social
disorganization theory. Researchers have reported that in urban Chicago neighborhoods
that were poor, unstable and had population heterogeneity, higher rates of delinquency
and drug abuse existed (Martinez et al., 2008). For example, in the 1980s, the loss of
jobs, specifically manufacturing, in urban neighborhoods increased poverty and helped
contribute to chronic unemployment (Martinez et al., 2008). Also, these urban
communities became isolated and more crime-riddled with the addition of drug activity,
specifically with the introduction of crack during this period (Martinez et al., 2008).
Selling crack then became the new form of employment for youth within the inner-city
(Martinez et al., 2008). Unfortunately, this increased addiction, as well as the crime
within these neighborhoods, and made it almost impossible to remove this drug
(Martinez et al., 2008).
Researchers have reported that because these types of communities’ lack social
control (e.g., private, parochial, and public), they fit the model of social disorganization
(Martinez et al., 2008). However, this could not be used to link drug activity and social
disorganization because more than one type of social control was affected (Martinez et
al., 2008). Martinez et al. (2008) found that social disorganization had more to do with
spatial relationships or how the neighborhood was set up to be systematically
disadvantaged overall.
There was a link between the components of social disorganization theory,
specifically poverty (e.g., socioeconomic deprivation), population heterogeneity and
aggravated assault and robbery (Martinez et al., 2008). The main factors of the social
disorganization theory do not have a link between violence and drug activity (Martinez et
al., 2008). However, instability does impact crime, specifically assault, robbery, and
marginally drug activity (Martinez et al., 2008). Thus, socially disadvantaged
neighborhoods have many factors that contributed to offense type and crime. The
social disorganization theory has been used to define factors or conditions that
contributed to criminal activity (Kubrin & Weitzer, 2003). Social disorganization was the
lack of a community to address and solve problems (Kubrin & Weitzer, 2003). The lack
of social control, coupled with poverty, racial heterogeneity, and resident mobility helped
to lessen positive behavior thereby increasing the potential for crime (Kubrin & Weitzer,
2003). Also, research has indicated that social ties and informal controls impacted
neighborhood criminal activity (Kubrin & Weitzer, 2003). Informal controls are those
things in which the residents do to lessen criminal activity (e.g., monitoring,
involvement) whereas social ties are the connectedness the individual has within the
neighborhood (Kubrin & Weitzer, 2003). Research has indicated that those with strong
social ties and control have a positive impact on crime rates in their neighborhoods
(Kubrin & Weitzer, 2003). However, this was not a simple task to attain and was not
always possible. Researchers have reported that social ties could improve or cause
problems for social organization depending on the individuals involved (Kubrin &
Weitzer, 2003).
Another impact on socially disorganized neighborhoods was collective efficacy or
the communities’ ability to control the actions of others; however, research has indicated
that collective efficacy also negatively affected crime rates, particularly violence (Kubrin
& Weitzer, 2003). For example, although residents were involved in their neighborhoods,
they are at high risk of becoming victims of violence. Kubrin and Weitzer (2003) found
that social ties have limited impact on decreasing crime.
Researchers have indicated that if more people became involved in socially
disadvantaged neighborhoods, the criminal activity would reduce (Kubrin & Weitzer,
2003). Also, Kubrin and Weitzer (2003) found that it was expected that disadvantaged
communities would be crime-riddled and was also more accepted by its residents (Kubrin
& Weitzer, 2003). Acceptance of crime could be true because residents realized that there
are limited opportunities for success, and limited resources to prevent criminal behavior
(Kubrin & Weitzer, 2003). Furthermore, Kubrin and Weitzer (2003) found that
residential fear, cynicism, and the "street code" impacted behavior within disadvantaged
neighborhoods, therefore, limiting the ability of residents to speak out against crime.
Formal social control (e.g., legal, police) has a positive and adverse impact on socially
disadvantaged neighborhoods. Policing these neighborhoods minimally affected criminal
activity; however, it also caused tension with residents because they become targets of
policing, regardless if they were engaged in criminal activity or not (Kubrin &
Weitzer, 2003). Being targeted caused resentment from both the police and residents.
Kubrin and Weitzer (2003) found that the police had the perception that individuals
living in high-crime neighborhoods deserved to be victims, therefore responding less
quickly as they would to more prominent (i.e., rich) areas. This attitude, coupled with
"harassing behavior" caused increased tension and resentment for all parties involved.
Furthermore, if residents do not trust the police, they would be less willing to help and
become involved. Police mistrust created “street justice” in which residents take the law
into their hands as a form of formal control (Kubrin & Weitzer, 2003). Thus, there are no
simple solutions to address the issues of those living in socially disorganized
neighborhoods.
Resiliency Theory
Resilience has varying meanings. Resilience could mean how individual acts
during stress or how an individual thrives despite adverse conditions (Smith et al., 2008).
Smith et al. (2003) found that resilience has included the terms thriving and adapting as
key components. Researchers have identified six measures of resilience: protective
factors; stress-coping ability; central protective resources of health adjustment; resilient
coping behavior; and resilient personality characteristics (Smith et al., 2008). Smith
et al. (2008) found that previous research focused more on individual characteristics of
coping ability and not broader reasons that impacted resiliency (e.g., health resources,
protective factors). Existing measures such as the Resilience scale and the Connor-
Davidson Resilience scale focused on individual traits (Smith et al., 2008). Smith et al.
(2008) created the Brief Resilience Scale (BRS) to determine if resilience was the ability
to recover from a stressful event because of resources or good outcomes. Their measure
differed from others because it only measured one’s ability to come back or remain
intact after a stressful event (Smith et al., 2008). Overall, the BRS was helpful in
identifying an individual’s recovering ability because of existing illness or stress and
helped to identify resources that improved resilience (Smith et al., 2008).
Individual and community resilience worked together. Greene and Greene (2009)
found that a systemic approach that combined both individual (i.e., personal) and external
(i.e., community) factors created a symbiotic relationship that fosters resilience. In other
words, both work together and rely on each other for success on a micro and macro
system level (Greene & Greene, 2009). Both working together helped decrease adversity
and increased the success of recovery or resiliency (Greene & Greene, 2009). Increasing
doubt in the environment caused increased stress, and that also created a psychological
change (Greene & Greene, 2009). For some, a psychological change increased danger
and created a need for increased coping ability (Greene & Greene, 2009).
Greene and Greene (2009) found that several terms were identified as personal
resilience. Risk was defined as the increased chance of a negative outcome. Vulnerability
was how susceptible to threat(s) a person was (Greene & Greene, 2009). Protective
factors are qualities that gave an individual an increased chance when negative situations
occurred (Greene & Greene, 2009). Also, resilience was an adaptive pattern over time
despite adversity (Greene & Greene, 2009). Some researchers have reported that
resilience occurred only when needed such as, in stress, trouble, or macro-level (e.g.
environment, government) changes (Greene & Greene, 2009). However, some
researchers have reported that resilience occurred over an individual’s entire lifetime
because it was forever changing (Greene & Greene, 2009).
Teti et al. (2012) found that research was conducted on the resilience of poor,
urban, African American men noting that these individuals face unique circumstances.
For example, many African American men are poor, uneducated, unemployed, or have
been (prior or currently) in the criminal justice system (Teti et al., 2012). In 2008,
researchers reported that African American men are twice as likely to lack employment
than Caucasian men (Teti et al., 2012). Additionally, research has indicated that from
2000 to 2008, African American men are 6.5 times more likely to be imprisoned than
Caucasians (Teti et al., 2012). Researchers have also indicated that African American
men were 3.3 times more likely to be imprisoned when compared to all races (Teti et al.,
2012). According to the Centers for Disease Control (CDC), African American men have
the highest mortality rates in the United States (Teti et al., 2012).
Incarceration was a socially complex problem that not only involved the
individual but systems, which included education, employment, law and policy makers
(Teti et al., 2012). However, despite the challenges, African Americans are resilient even
though researchers have not focused on understanding this specific population (Teti et al.,
2012). Teti et al. (2012) found the importance of studying resilience among African
Americans to a culturally and empirically researched model that considered the unique
stressors and proactive factors of African American life overall. Teti et al. (2012) also
found four specific stressors for the participants in their study: racial micro-aggressions,
incarceration, unemployment, and surviving street life. Despite these stressors, five forms
of resilience were identified: perseverance, commitment, reflecting and refocusing to
address difficulties, creating a supportive environment, and support from religion or
spirituality (Teti et al., 2012, p. 529).
Although there was no single definition of resilience, adversity and success have
been identified as components of resilience. Teti et al. (2012) found that threats and
successes are generalized, and does not occur for every person. The stressors or
misfortune one faced depended on many causes such as, age, socioeconomic status
(SES), and race or ethnicity. Also, previous research has reported that life or severe
stressors are rare events (Teti et al., 2012). Rare events are not the case for the subjects of
this study (i.e., poor, African American) because their stressors were constant (Teti et al.,
2012). Teti et al. (2012) found that constant stressors (e.g., poverty, racism) lacked
adequate research using current models of resilience because such models viewed
stressors as single events. Although African Americans are resilient, these individuals
faced systemic stressors that would challenge their resiliency (Teti et al., 2012).
Communities and individuals must work together to try to change the odds, not simply
beat them (Teti et al., 2012).
Connor and Zhang (2006) found that previous research on resilience attempted to
discover what made individuals survive or successfully overcome misfortune. Through
its definition, resilience tried to define individual strengths that helped a person during
stressful events (Connor & Zhang, 2006). Resilience are the unique characteristics that
are impacted by time, age, gender, and culture (Connor & Zhang, 2006). Other
characteristics identified by Connor and Zhang (2006) included patience, humor, faith,
and altruism.
The resiliency theory proposes that each person has characteristics that helped
him or her gain harmony and strength (Connor & Zhang, 2006). Connor and Zhang
(2006) indicated that studies were conducted to determine how individuals reacted to
stress unconsciously and consciously by using a resiliency model. This model indicated
that individuals start in homeostasis where there was balance or adaptation in the present
(Connor & Zhang, 2006). Depending on stressors, the individual’s coping ability would
determine how he or she would adapt to stressors (Connor & Zhang, 2006). A
reintegration process occurred because of continued stress (Connor & Zhang, 2006).
Reintegration created four possibilities: disruption or growing potential; reintegration or
homeostasis; recovery due to loss or decreased homeostasis; or dysfunctional state or
destructive behaviors (Connor & Zhang, 2006, p. 6-7). Connor and Zhang (2006) found
that there are specific determinants of resilience because of a biological or physiological
response that changed depending on stress. Other determinants are genetic, temperament
or protective factors such as intelligence, family or social support, and environment
(Connor & Zhang, 2006). More studies about determinants could help to determine how
resilience differed among individuals.
Risk assessments helped to determine the likelihood an individual was to
recidivate. Many measures are used to predict and prevent the possibility of recidivism
(Lee, 2013). Because many offenders are minorities, understanding cultural differences
could help identify risk more readily and create suitable interventions (Lee, 2013). Lee
(2013) found that risk assessment should be confirmed cross-culturally and until it was
confirmed, it was inappropriate to apply to different racial groups.
Risk factors that influenced or impacted juvenile delinquency included groups
such as peers, family, and environment or community (Lee, 2013). Lee (2013) found that
a positive correlation existed between risk factors and the likelihood of delinquency.
Also, protective factors or things that reduced the effect of risk factors (e.g., family,
community) impacted recidivism positively (Lee, 2013). However, risk and protective
factors could not predict delinquency (Lee, 2013).
The risk and resiliency checkup (RRC) identified risk and protective factors to
determine resiliency after the removal of protective factors (Lee, 2013). However, this
measure was not as informative for Hispanics or other ethnic youths (Lee, 2013). Lee
(2013) studied the invariance of the RRC with African American, Caucasian, and
Hispanic youth offenders. Lee (2013) found that the measure was valid for all three
ethnic groups; however, risk and protective factors did not reflect resiliency (Lee, 2013).
Furthermore, it was unclear the effect that risk and protective factors have on recidivism,
or that protective factors have on risk factors (Lee, 2013).
Research conducted in 2007 found that roughly 93% of inmates were male, 36%
African American, 32% Caucasian, and 20% Hispanic (Maschi, Gibson, Zgoba, &
Morgen, 2011). Sixteen percent were aged 18 to 24, and 10% were over age 55 (Maschi
et al., 2011). There was an increased concern in correctional facilities because they failed
to meet the needs (i.e., age-related, psychological) of these offenders (Maschi et al.,
2011). Research has indicated that younger inmates aged 18 to 24, are still dealing with a
severe developmental stage in an environment that was contained or restricted (Maschi et
al., 2011). Also, older adults faced age-related changes that prisons are not addressing
(Maschi et al., 2011). Furthermore, grave threats to inmates are consistent with current
trauma and stress. For example, nearly 93% of offenders are either a victim or witnessed
physical or sexual assault (Maschi et al., 2011). This continued stress resulted in about
65% of offenders having symptoms of or diagnosed with posttraumatic stress disorder
(Maschi et al., 2011).
Research has indicated that nearly 20% of offenders are victimized at some point
during their lives (Maschi et al., 2011). Roughly 40% of offenders experienced out of
home placement and 25% witnessed substance abuse by their parents during their
childhood (Maschi et al., 2011). For example, research conducted in 1998 indicated that
96% of youth within the criminal justice system are witnesses to violent acts (Maschi et
al., 2011). Around 44% witnessed the physical assault of a family member, and 21% are
victimized (physically) by a relative (Maschi et al., 2011). However, researchers have
indicated that long-term exposure to stress and violence caused negative coping skills
that led to criminal behavior (Maschi et al., 2012). Also, traumatic grief due to death or
loss was a stressor discovered with offenders (Maschi et al., 2011). Researchers have
indicated that this form of grief correlated to recidivism (Maschi et al., 2011).
Maschi et al. (2011) found that younger offenders are more likely to report physical
violence, whereas older offenders are more apt to report sexual violence. Age group
differences determined there was a need for more specific interventions in prison
populations (Maschi et al., 2011). Also, youth risk factors for delinquency included age,
impulsivity, neurological problems, negative peer groups, and exposure to violence
(Mowder, Cummings, & McKinney, 2010). Protective factors included future orientation,
social support, and relationships (Mowder et al., 2010). These factors could help identify
risks for delinquency as well as prevention (Mowder et al., 2010).
There are three types of resilience: positive outcomes in lieu of negative
environment; competent functioning during acute or chronic stress; and recovery from
traumatic events (Mowder et al., 2010). Mowder et al. (2010) conducted research on male
and female juvenile offenders to determine resiliency levels using the Resiliency Scales
for Children and Adolescents (RSCA). Each group was assigned into clusters. Custer 1
participants consisted of mostly young females that had lower levels of resiliency and had
the highest levels of rule-breaking behavior (Mowder et al., 2010). Cluster 2 participants
consisted of mostly males and had average-ranged scores. Cluster 3 had the highest
number of minorities. Subjects in this group were more disruptive, broke the rules, and
were isolated more often (Mowder et al., 2010). These types of behaviors decreased
protective factors thus, lowering resilience (Mowder et al., 2010). Custer 4 consisted of
older subjects with the least time imprisoned, fewer violations, and had average levels of
resilience (Mowder et al., 2010). Mowder et al. (2010) suggested that despite having
average resiliency, the subjects in Cluster 4 could be the most resilient of all cluster
groups. Cluster 4 participants were the most resilient because they spent less time
exposed to the criminal justice system and were older (Mowder et al., 2010).
Researchers have indicated that individuals could face, at a minimum, one
“potentially” traumatic event during a lifespan (Fletcher & Sarkar, 2013). Traumatic
events could vary because of how each person reacted to stress (Fletcher & Sarkar,
2013). Reaction differences were studied to determine the differences between people
and how they handled stress (Fletcher & Sarkar, 2013). Fletcher and Sarkar (2013) found
that prior research was conducted to determine why some (young) people thrive in
stressful environments. Some researchers have indicated that some thrive because of
individual characteristics such as temperament or self-esteem or other protective factors
(Fletcher & Sarkar, 2013). However, research has changed its focus from protective
factors to gleaning information on how individuals overcome (Fletcher & Sarkar, 2013).
Arguably, one problem with resiliency research was that it lacked a precise definition or
conceptualization (Smith et al., 2008). Resiliency definitions varied from personal
qualities to overcoming misfortune and coping skills, to recovery during stressful
situations (Fletcher & Sarkar, 2013; Smith et al., 2008). For example, Greene and Greene
(2009) found that (personal) resilience consisted of risk (e.g., poverty), vulnerability
(e.g., SES), and protective factors (e.g., parents, social support) that allowed an
individual to adapt over time despite personal challenges. Also, to define resilience, it
requires a clear understanding of negative circumstances (e.g., hardship) that people face
daily or at various times in their lives (Fletcher & Sarkar, 2013). Furthermore, there must
be a positive adaptation or “internal well-being” to become resilient (Fletcher & Sarkar,
2013, p. 14). Positive adaptation meant when facing trouble, an individual could change
(Fletcher & Sarkar, 2013). However, some researchers have indicated that this concept
was a Western view that stressed the importance of what the individual must do (e.g.,
individualism), which did not consider other cultures with more collectivistic points of
view (Fletcher & Sarkar, 2013).
Conceptualizing resilience was unclear because some researchers have indicated
that it was a trait or ability that allowed one to adapt to changing situations (Fletcher &
Sarkar, 2013). Conceptualization included using a concept called ego resilience or traits
that showed individual strength or flexibility because of demands (Fletcher & Sarkar,
2013). Conceptualization also included psychological resilience or the changes to a
person’s personality that occurred because of life events over time (Fletcher & Sarkar,
2013). The different forms of resilience helped to increase understanding and how each
was conceptualized. However, what was also gleaned regarding resilience was if a
situation changed, it also changed resilience (Fletcher & Sarkar, 2013).
Connor and Davidson (2003) defined resilience as individual characteristics that
allowed one to succeed in times of trouble. Resilience was not stagnant; it varied with
age, gender, and culture (Connor & Davidson, 2003). Researchers have reported that to
understand changing resilience, it required using the resiliency model (Connor &
Davidson, 2003). The resiliency model started with homeostasis or balance in the
individual (Connor & Davidson, 2003). This balance could be hindered because of
stressors, and how one dealt with stressors decided if homeostasis would return.
Therefore, four results could occur; disruption or increased resilience and homeostasis;
return of starting homeostasis; decrease homeostasis due to loss; and dysfunction due to
poor coping skills (Connor & Davidson, 2003). Also, Connor and Davidson (2003) found
that there was not an acceptable tool that measured resilience simply. The
ConnorDavidson Resilience scale (CD-RISC) was created as an empirically sound
measure of resilience (Connor & Davidson, 2003). The CD-RISC was found to be a valid
and straightforward measure of resilience with varying cultures and conditions (Connor
&
Davidson, 2003).
Researchers have indicated that every person could be resilient and adapt
(Masten, 2001). The ability to adapt created strong development to occur; however, if
adaptation was limited in times of trouble, developmental problems could happen
(Masten, 2001). Masten (2001) identified two models of resilience. Variable-focused
resilience measured the amount of risk and protective qualities an individual had when
faced with negative outcomes (Masten, 2001). Person-focused (resilience) compared
different groups of people to identify what specific characteristics made certain people
more resilient than others (Masten, 2001).
One problem with the variable-focused model was that it sometimes failed to
identify patterns across groups that could be at risk or needed help (Masten, 2001).
Variable-focused research has found that parenting styles, intellect, SES, and positive
self-regard correlated with how well one adapted (Masten, 2001). However, these same
variables were also identified as risk factors with negative results during high stress or
adverse situations (Masten, 2001). It was unclear of the long-term effects of high stress or
adversity on adaptive behaviors (Masten, 2001). Long-term effects depended on the
intactness or strength of risk factors (Masten, 2001).
Person-focused research has found that there are distinct differences within
highrisk groups. For example, research has indicated that the more resilient group of
participants had better parenting and cognitive skills, as well as, higher self-regard
(Masten, 2001). Researchers have reported that even within the high-risk group, a
resilient subgroup was found to have resilient factors (Masten, 2001). However, one issue
with this and similar studies was that it failed to identify low-risk groups and it neglected
to determine if individuals (i.e., children, adults) from low-risk groups would be as
resilient without having high-risk factors (Masten, 2001).
Richardson (2002) found that there are three waves of Resiliency Inquiry, which
came from a phenomenological approach and was used to described the characteristics of
young survivors in high-stress situations. The first wave aimed to discover characteristics
of those who had thrived during misfortune (Richardson, 2002). The second wave aimed
to define specific resilient qualities (Richardson, 2002). These waves helped to define
resilience as an individual’s coping ability when faced with adversity or change
(Richardson, 2002). Last, the third wave contributed to the conceptualization of
resilience. This conceptualization included reintegration and motivation when faced with
trouble (Richardson, 2002).
The first wave of resiliency inquiry changed its focus from identifying risk factors
to identifying individual strengths that help people in times of trouble (Richardson,
2002). Prior research has identified various measures and traits (e.g., social relationships,
coping skills) that many resilient individuals have. Also, previous research has indicated
that around 50% to 70% of at-risk children became well-adapted (e.g., caring, confident)
adults (Richardson, 2002).
The second wave aimed to determine how people gained resilient qualities
(Richardson, 2002). This focus helped to define resiliency as a set of stages that included
(biopsychosocial) homeostasis, interactions, disruptions, and reintegration (Richardson,
2002). Resiliency started with adapting to life’s challenges (Richardson, 2002).
Resiliency also included biopsychosocial homeostasis because this was when the
individual adapted to internal or external stressors either physically, spiritually, or
mentally (Richardson, 2002). Richardson (2002) found that individuals battled chronic
stress because of ineffective adapting or coping skills to handle life’s problems. These
challenges offered opportunities for learning and growth which fostered successful
reintegration or a place of homeostasis (Richardson, 2002). Last, the third wave of
resiliency included reintegration or opportunities for spiritual growth (Richardson, 2002).
The resilience theory posited that each person has within them intrinsic features such as,
wisdom and strength that allowed them to be resilient (Richardson, 2002).
Payne (2011) found that to understand the plight of African American men and
resilience, it required an understanding of their challenges in the form of street life. Street
life was individualized, personal, and often needed for survival (Payne, 2011). It was
ideology or thought processes and behavior that included legal and illegal activity
(Payne, 2011). For example, an individual may work and be involved in dealing drugs or
violence (Payne, 2011). Payne (2011) found that involvement in street life was “a choice
made for right or for wrong, a consequence of being overwhelmed by personal and
economic strife” (p. 428). How strongly or deeply the person’s involvement in street life
was depended on many factors. Race, SES, gender, location, and development stage
determined the involvement in street life (Payne, 2011). Payne (2011) found that the
current definition of resilience needed to be reconceptualization to include African
American men involved in street life. Also, Payne (2011) found the "site of resilience”
theory that described how African American men found meaning and accomplishment, as
well as, how they survived despite misfortune.
Previous research has reported that resilience dichotomized individuals,
suggesting that some people are resilient while some are not (Payne, 2011). This
dichotomy was found when research was conducted on African American boys (Payne,
2011). The argument was that a both-and, rather than an either-or approach needed
consideration (Payne, 2011). For example, no one was either good or bad; each person
was both good and bad, depending on the circumstances (Payne, 2011). Also, Payne
(2011) found that traditional models of resilience were ineffective; they failed to
recognize the fluidity of behavior. Therefore, surviving street life was resilience in itself;
what is “good” for some may be “bad” for others (Payne, 2011).
Researchers have indicated that daily stress decreased with age; however, there
are mixed findings on age and how one dealt with stress (Diehl & Hay, 2010). Older
adults reacted less to stress than their younger counterparts because of decreased
physiological reactivity (Diehl & Hay, 2010, p. 1133). Researchers have indicated that
around 50% of the days reported by adults was stressful (Diehl & Hay, 2010). Moreover,
researchers have indicated that older adults recovered from stressful situations (e.g.,
emotional) faster than younger ones (Diehl & Hay, 2010).
To understand age and stress, Diehl and Hay (2010) analyzed how self-concept
influenced resilience. Self-concept differentiation determined how one’s perceptions
differ, depending on social roles and circumstances (Diehl & Hay, 2010). For example,
researchers have indicated that older adults with low self-concept have lower levels of
psychological well-being, and increased negative psychological well-being than younger
adults (Diehl & Hay, 2010). Diehl and Hay (2010) also found that when subjects reported
more stress, they have a greater negative affect as well. Furthermore, individuals with a
more positive self-concept have a more negative affect that varied daily (Diehl & Hay,
2010). Their findings also indicated that self-concept was not related to how one reacted
to increased stress (Diehl & Hay, 2010).
Regarding personal control, researchers have indicated that the more (realized)
control an individual has, the lower their negative affect (Diehl & Hay, 2010). Therefore,
personal control acted as a buffer for stress (Diehl & Hay, 2010). Also, younger adults
reported more negative affect regarding decreased personal control than older adults
(Diehl & Hay, 2010). This negative affect could be due to younger adults having less life
experience to develop coping skills that tend to develop over time (Diehl & Hay, 2010).
However, no matter the age, decreased (perceived) personal control correlated with less
well-being and lowered response to stress (Diehl & Hay, 2010). Last, Diehl and Hay
(2010) found that age was a resilience factor and not a risk factor. This finding indicated
that older adults are not at risk to the effects of stress as they aged (Diehl & Hay, 2010).
Resilience could be used to help understand how an individual overcame stress or
misfortune throughout the lifespan (Windle, Bennett, & Noyes, 2011). Windle et al.
(2011) defined resilience as one’s ability to negotiate and deal with varying amounts of
stress and trauma. Internal and external factors helped the individual to adapt and
overcome misfortune (Windle et al., 2011). Windle et al. (2011) found that there was a
need for stringent resilience measures in research. Valid measures meant using measures
appropriately and ensuring that they measured what they claimed to measure (Windle et
al., 2011). For example, researchers had indicated that when (unpublished) invalidated
measures are used in clinical trials on individuals with schizophrenia, around 40%
reported that treatment was effective (Windle et al., 2011). Also, despite there being
several measures for resilience, there was no preference because of a lack of robust
evidence to guide selection (Windle et al., 2011). Therefore, selection and administration
of measures were used arbitrarily, and often wrong (Windle et al., 2011).
Researchers have also compared 15 resilience measures to determine their quality
(Windle et al., 2011). Of the 15, the top three were the CD-RISC, the Resilience Scale for
Adults (RSA), and the Brief Resilience scale (Windle et al., 2011). Also, only five
measures reflected resilience complexly; The Child and Youth Resilience Measure
(CYRM), RSA, the Resilience Scale of the California Healthy Kids, Survey the READ,
and the (YR: ADS) Youth Resiliency: Assessing Developmental Strengths (Windle et al.,
2011).
Researchers have indicated that resilience was the ability to adapt to stress in a
positive way (Campbell-Sills & Stein, 2007). The ability to adapt positively was
paramount to understanding resilience (Campbell-Sills & Stein, 2007). Also, resilience
was comprised of several factors such as internal (i.e., genetic, biological, and
psychological) and external or environmental (Campbell-Sills & Stein, 2007). The
effectiveness of the CD-RISC on individuals with childhood maltreatment was also
researched (Campbell-Sills & Stein, 2007). Additional research was conducted on people
with high and low scores on the CD-RISC to determine their degree of psychiatric
symptoms (Campbell-Sills & Stein, 2007). Researchers have reported that subjects who
saw themselves as more resilient on the CD-RISC did not present with higher levels of
psychological symptoms (Campbell-Sills & Stein, 2007). Therefore, researchers have
indicated the CD-RISC was a valid measure to help clinicians determine who was more
resilient (i.e., adapting) after stressful events (Campbell-Sills & Stein, 2007). However,
the individual’s present state of mind influenced responses; thus, more research was
needed (Campbell-Sills & Stein, 2007).
Although some African Americans faced many challenges such as violence and
poverty, some could be more resilient than others facing similar circumstances (Brown,
2008). Researchers have attempted to identify (protective) factors that made African
Americans more resilient. Brown (2008) found that racial socialization and social support
helped African Americans to be resilient. Racial socialization was “behaviors,
communications, and interactions between parents and children that address how African
Americans ought to feel about their cultural heritage and how they should respond to the
racial hostility or conform in American society” (Brown, 2008, p. 33). Racial
socialization included information and actions that helped one to understand racial status
and social relationships (Brown, 2008). Information about racial socialization could come
from family members or peers; however, researchers have reported that racial
socialization came from parents (Brown, 2008). Also, racial socialization could impact an
individual’s psychologically (e.g., self-esteem) to educational success (Brown, 2008).
Researchers have also indicated that positive racial socialization, where information
instilled pride and cultural education, increased positive results academically and
psychologically (Brown, 2008).
Social support included family, churches, and “play” family members that are
described as people that are close but are not blood-related (Brown, 2008). The people
who made up social networks for African Americans were a part of their culture that
helped individuals deal with adversity (Brown, 2008). Brown (2008) found that social
networks acted as a buffer when dealing with stress. For example, the extended family
gave support in the form of advice or information (Brown, 2008). Also, individuals with
extended families dealt better with daily stress (Brown, 2008). Furthermore, the role of
the church played an important part in the lives of African Americans. Researchers have
reported that church acted as a role model for children and was also a place for
inspiration and healing (Brown, 2008). Thus, social networks helped African Americans
develop coping skills, which increased resiliency (Brown, 2008).
Additional research was conducted to determine the effects of racial socialization
and social support on African Americans. Research has indicated that racial socialization
and social support positively correlated with an individual’s perception of resiliency
(Brown, 2008). Positive racial messages such as cultural pride, also influenced an
individual’s perception of resiliency (Brown, 2008). Also, social support from
nonimmediate family members or a specific person influenced an individual’s resiliency
(Brown, 2008). Thus, receiving positive messages and support from individuals within
the community (e.g., extend family, church) helped to foster coping and resiliency
(Brown, 2008).
Eschleman, Bowling, and Alarcon (2010) found that hardiness was described as
those characteristics that made one resistant to the effects of stress while adapting and
coping positively with environmental demands. Hardy people felt they were in control of
what occurred in their lives, and saw misfortune as a challenge (Eschleman et al., 2010).
Hardiness consisted of commitment, control, and challenge (Eschleman et al., 2010).
Commitment was how entrenched the individual was in one’s life (Eschleman et al.,
2010). Commitment also represented social support, and it gave the individual a sense of
purpose that was an important facet of hardiness (Eschleman et al., 2010).
Control, a component of hardiness, posited that an individual influenced what occurred
in their life (Eschleman et al., 2010). Control prepared the individual on how they might
deal with stress; therefore, the less control a person felt, the greater the stress
(Eschleman et al., 2010).
Eschleman et al. (2010) defined stressors as circumstances that caused adaptation
and could cause illness. Stressors included events such as divorce or day-to-day events
(Eschleman et al., 2010). Strains were the effects of stress on the individual’s well-being
such as physical or psychological illness (Eschleman et al., 2010). Research has indicated
that hardy (i.e., resilient) people could be inherently protected from environmental stress
(Eschleman et al., 2010). For example, hardy individuals who work in high-stress
environments with high demands felt stress differently than those who were less hardy
(Eschleman et al., 2010).
Another aspect of hardiness was social support. Researchers have indicated that
having strong social support decreased the effects of stress, therefore increasing hardiness
(Eschleman et al., 2010). Because hardy individuals have strong social networks (e.g.,
family, work), they are more prone to receive support overall (Eschleman et al., 2010).
Also, researchers have reported that hardy individuals have active coping skills. Active
coping skills are those skills that changed how stress was viewed by changing stressors
into “benign experiences” (Eschleman et al., 2010, p. 282). Hardy individuals performed
well under stress, therefore, limiting any adverse effects of stress (Eschleman et al.,
2010).
Finally, Eschleman et al. (2010) found that hardiness, commitment, control, and
challenge did act as buffers to stress. Also, researchers have reported that there was a
correlation between hardiness and social support (Eschleman et al., 2010). As mentioned
before, research has indicated that hardy people have more coping skills (Eschleman et
al., 2010). Furthermore, hardy individuals have more control over their lives and
environment, which could cause them to face stressors more readily (Eschleman et al.,
2010). Thus, the aim of the present study was to determine if there were any racial
differences in resilience (i.e., hardiness) and locus of control that contributed to or
impacted recidivism. I also examined the complexity of recidivism by analyzing the
various challenges ex-offenders faced while on parole.
Solutions
Restorative reentry programs and policies helped the community successfully
reintegrate offenders into society (Hass & Saxon, 2011). Creating these types of
programs and policies helped meet the needs of victims and offenders by creating a
pathway to success (Hass & Saxon, 2011). Researchers have reported that these types of
programs needed four main parts: (1) focus on community; 2) reparation; (3) decreased
social stigma; and (4) citizenship with fewer restrictions (Hass & Saxon, 2011). Focusing
on community resources instead of strict supervision (e.g., parole, probation) could
increase social capital by allowing offenders to reintegrate into society successfully (Hass
& Saxon, 2011).
Community-focused type programs helped offenders feel connected and accepted
which fostered success (Hass & Saxon, 2011). Community-focused programs helped
change the identity of the offender, to one that was more positive and allowed them to
embrace their new role in the community (Hass & Saxon, 2011).
Reparation was another important part of restorative reentry programs because it
restored the community after a criminal act was committed (Hass & Saxon, 2011). For
reparations to be successful, they must be voluntary (Hass & Saxon, 2011). Hass and
Saxon (2011) found that punitive reparations caused fear in offenders because of the
strict conditions of their release. Also, reparations were viewed more as a punishment or
perceived as the offender owed a debt (Hass & Saxon, 2011). Reparations could cause
resentment and reduced the success of offenders reintegrating into the community.
Last, restorative reentry programs allowed offenders to become citizens once again (Hass
& Saxon, 2011). Allowing offenders their rights and privileges as citizens could help
them reintegrate into society more successfully (Hass & Saxon, 2011). Continued labels
such as convict or criminal, as well as restrictions, limited the success of reentry into
society and contributed to recidivism (Hass & Saxon, 2011). However, Hass and Saxon
(2011) found that restorative programs were designed to help offenders return to society,
not necessarily be accepted by society.
Hass and Saxon (2011) found that restorative reentry programs lacked
comprehensive plans supported by research. Also, current programs focused on a variety
of topics ranging from the overall program to sentencing (Hass & Saxon, 2011). Some
programs focused more on the relationship between the criminal justice system and
community before the release and parole of offenders (Hass & Saxon, 2011). However,
researchers have indicated that offenders wanted to be more self-reliant while atoning for
their crimes within the community (Hass & Saxon, 2011). This focus on self-reliance was
more of a strength-based approach where returning to society helped the offender rebuild
skills that they were lacking or were of concern (Hass & Saxon, 2011). A strength-based
approach could help society be more accepting of offenders by removing the negative
stigma of “criminal,” increasing offenders’ positive perception of themselves, and
fostering positive behavior changes (Hass & Saxon, 2011).
To further encourage the success of offenders, community reparative boards
served to monitor offenders’ progress within the reentry program (Hass & Saxon, 2011).
The board was responsible for creating a plan for offenders once released, while also
serving as a form of social control in place of criminal justice agencies (Hass & Saxon,
2011). Furthermore, these boards put offenders in contact with community leaders and
helped foster positive relationships and accountability (Hass & Saxon, 2011). The
label of convict or offender acted as a barrier to successful reentry into society because
of the stigma associated with it (Malott & Fromader, 2010). This stigma significantly
decreased resources such as treatment or employment that offenders needed to decrease
their chance of recidivating (Malott & Fromader, 2010). Researchers have identified
several areas in which programs needed to focus on that helped decrease offenders’
chances to recidivate: counseling, education, and work (Malott & Fromader, 2010). For
example, Family Life Education programs aided in changing the attitudes of offenders
for them to have better familial and social relationships (Malott & Fromader, 2010).
Research has indicated that developing healthy relationships with family lowered
recidivism (Malott & Fromader, 2010). Also, researchers have reported that employment
decreased criminality (Malott & Fromader, 2010). Malott and Fromader (2010) found
that offenders recidivated less when they went through drug courts rather than criminal
courts. Recidivism decreased because drug courts offered rehabilitative services that
traditional court did not (Malott & Fromader, 2010).
Offenders who can work and earn wages were less likely to recidivated because
they increased their economic opportunities (Malott & Fromader, 2010). However,
imprisonment decreased the offenders’ opportunity to be successful because of the
negative stigma of being labeled “criminal” (Malott & Fromader, 2010). The label of
“criminal” limited the types of jobs and the earning potential of the offender (Malott &
Fromader, 2010). Also, without economic opportunities, this increased unemployment
and the likelihood of recidivism (Malott & Fromader, 2010). For example, in comparing
offenders to non-offenders for the same job, research has indicated that offenders earned
roughly 7% less (Malott & Fromader, 2010).
In a study conducted by Malott and Fromader (2010), they found that most of the
participants in their study felt they would receive emotional support from their families
upon their release. Also, participants determined the need for support in combating stress
and how to appropriately communicate their needs (Malott & Fromader, 2010).
Participants also perceived that their criminal record could pose a barrier to getting
employment upon their release (Malott & Fromader, 2010). Researchers have reported
that imprisonment resulted in offenders having limited job opportunities that could
contribute to their likelihood of recidivating (Malott & Fromader, 2010). Malott and
Fromader (2010) found that less than 50% of their participants believed they would find a
job upon their release (Malott & Fromader, 2010). Also, participants felt that having a job
decreased their chances of recidivism (Malott & Fromader, 2010). Malott and Fromader
(2010) found that there were mixed findings regarding counseling, rehabilitation, and
caseworkers effect on reducing recidivism (Malott & Fromader, 2010). However, the
ineffectiveness of caseworkers reducing recidivism could be related to the adverse
attitudes towards caseworkers overall (Malott & Fromader, 2010). Although Malott and
Fromader (2010) had a small sample size, their findings are in line with previous
research. Having resources (e.g., jobs, rehab) reduced the likelihood of recidivism
(Malott & Fromader, 2010).
Racial impact statements are tools that tell lawmakers how present laws or
policies impacted minorities in the criminal justice system (The Sentencing Project, n.d.).
The statements provided statistical data about the possible racial disparity of a policy
before its implementation (The Sentencing Project, n.d.). Statistical data did not
guarantee that policies would not be carried out if they affected minorities. The policy’s
greater purpose was to reduce disparities (The Sentencing Project, n.d.).
Every state or agency does not use racial impact statements. Currently, around 21
states and the District of Columbia used sentencing commissions that examined racial
implications (The Sentencing Project, n.d.). Also, budget and fiscal agencies (i.e., state
legislative analysis) also used racial impact statements (The Sentencing Project, n.d.).
The Department of Corrections, consisting of state and federal systems, used racial
impact statements to forecast racial data of prison populations (The Sentencing Project,
n.d.). Although few states used racial impact statements, it was increasing in use. For
example, in 2008, Iowa passed a bill that required data analysis of sentencing and parole
by race (The Sentencing Project, n.d.). Connecticut also required racial impact statements
to be used for bills to determine how they affected offenders (The Sentencing Project,
n.d.). In a study conducted in 2009, researchers have indicated that both Texas and
Oregon imposed the use of racial impact statements with sentencing laws (The
Sentencing Project, n.d.).
Although research has indicated that racial impact statements are useful, some
argued that there should not be a racial component when implementing public policy
(The Sentencing Project, n.d.). The goal of racial impact statements was to use the
information to help create policies that are practical and equitable (The Sentencing
Project, n.d.).
Offenders with felonies find themselves with limited rights. With the changes and
growth of the United States’ criminal justice system, creating stricter laws further
disenfranchised offenders, limiting their voice socially and politically (The Sentencing
Project, 2013). Currently, 48 states and the District of Columbia, do not allow those
imprisoned with felonies to vote (The Sentencing Project, 2013). Ex-offenders are
authorized to vote only in the states of Maine and Vermont (The Sentencing Project,
2013). Paroled individuals cannot vote in 35 states whereas 31 states do not allow those
on probation to vote (The Sentencing Project, 2013). Seven states forbid voting rights
based on crime and others allowed voting after a waiting period that could take 2 years or
longer (The Sentencing Project, 2013). Since there are no universal checks and balances
for voting rights, each state was responsible for deciding which offender could vote (The
Sentencing Project, 2013). However, many ex-offenders did not to take advantage of the
restoration process because it was too challenging and complex (The Sentencing Project,
2013).
Nearly 5.85 million people within the United States have lost their right to vote
(The Sentencing Project, 2013). Research has indicated that most of those without voting
rights are African American, approximately 7.7% (The Sentencing Project, 2013).
However, among non-Black offenders (i.e., Caucasian, Hispanic), only 1.8% have lost
their rights to vote (The Sentencing Project, 2013). Also, researchers have reported that
the states with the highest disenfranchisement rates are Florida (23%), Kentucky (22%),
and Virginia (20%). Furthermore, researchers have indicated that around 40% of African
American men would lose their voting rights (The Sentencing Project, 2013). Although
roughly 2.6 million people have paid their debt to society (i.e., time served), nearly 45%
of those that are disenfranchised have no voting rights (The Sentencing Project, 2013).
Some states have changed their policies. For example, in 2003 Alabama allowed some
individuals with felonies to vote after they have completed their sentences (The
Sentencing Project, 2013). In 2013, Delaware removed the 5-year waiting period
allowing some ex-offenders with felonies to vote after they were released (The
Sentencing Project, 2013). Also in 2013, Virginia restored the voting rights of nonviolent
offenders without the need to apply to vote (The Sentencing Project, 2013).
The Texas legislature created new policies (e.g., HB1711, HB2161) regarding the
reentry of offenders (TDCJ, 2012b). For example, in 2008, 1 in 22 people in Texas was
under supervision as a condition of the Texas criminal justice system (TDCJ, 2012b).
Nearly 651,000 offenders made up the Texas criminal justice system, making Texas the
state with the largest number of offenders in the United States (TDCJ, 2012b). The
creation of HB1711 addressed the reentry process by creating a Reentry Task Force
(RTF) to meet the growing needs of offenders (TDCJ, 2012b). The RTF was a
multidisciplined approach that increased from eight agencies to twenty-two and included
agencies ranging from the Texas Board of Pardons and Paroles to the Urban County
Commissioner (TDCJ, 2012b). The purpose of the RTF was to provide help (e.g.,
housing, drug treatment) to offenders after release, and to work with existing reentry
programs to meet the needs of offenders in urban and rural communities (TDCJ, 2012b).
Providing satisfactory housing to offenders was a challenge because of limits placed by
local housing authorities making it almost impossible for those with criminal records to
live (TDCJ, 2012b). Housing limitations led to offenders being homeless and increased
the likelihood of recidivism (TDCJ, 2012b). One recommendation that addressed the
housing issue was to offer tax incentives to rental companies who rented to offenders
(TDCJ, 2012b).
To address the employment challenges of offenders, Texas started Project Rio
which helped provide job training (TDCJ, 2012b). However, in 2011, the 81st legislative
session removed the program (TDCJ, 2012b). Also, the occupational code limited the
types of jobs offenders could have (TDCJ, 2012b). Research has indicated that
employment decreased recidivism by providing stability and increasing self-esteem
(TDCJ, 2012b). One recommendation to increase job opportunities for offenders was to
remove some of the limits placed by the occupational code (TDCJ, 2012b). Another
recommendation was to provide incentives (i.e., tax) to companies that employed
offenders (TDCJ, 2012b).
Research has indicated that there was a lack of psychological services (e.g.,
mental health, addictions) for offenders transitioning back into society (TDCJ, 2012b).
Without proper treatment, these individuals are at risk to recidivate (TDCJ, 2012b). One
recommendation to address those needs was to fund evidence-based treatment programs
(TDCJ, 2012b). Also, the TDCJ (2012b) determined there remained a need for better
communication regarding who needed treatment or additional services. Also, to combat
the cost of treatment, creating ways to expand Medicaid could increase the number of
offenders who received treatment with current state funding (TDCJ, 2012b).
Another area of support for offenders was teaching basic life skills such as decision-
making skills, not only while imprisoned but upon their release (TDCJ, 2012b).
Allowing community services to partner with agencies that supported offenders, gave
offenders greater access to needed services (TDCJ, 2012b). Also, helping offenders gain
pro-social support (e.g., family, community) upon their release could assist them to
transition back into the community more successfully (TDCJ, 2012b). People involved
in pro-social support could act as mentors that encouraged offenders to become
productive citizens within the community. This collaboration could foster positive
attitudes and decrease criminal behavior (TDCJ, 2012b).
Other ways to assist reentry programs to become more efficient could be to
improve how multiple agencies received information (TDCJ, 2012b). For example,
information may be duplicated or not received between agencies, which reduced their
effectiveness (TDCJ, 2012b). Also, it could be useful to cross-train employees in multiple
areas to better serve offenders who are reentering society (TDCJ, 2012b).
Although other agencies were willing to aid, agencies were hesitant because of
existing problems (TDCJ, 2012b). For example, the Social Security Administration
(SSA) offered assistance in obtaining social security cards for offenders; however,
because of the multitude of incorrect information, the SSA declined to provide further
help (TDCJ, 2012b). Also, the Veterans Administration (VA) used to give help by
identifying veterans within the criminal justice system (TDCJ, 2012b). However, because
of policy changes, it was the offenders’ responsibility to inform the criminal justice
system of their veteran status (TDCJ, 2012b). Once identified, the VA assisted in locating
resources for offenders upon their release (TDCJ, 2012b).
The Texas Department of Criminal Justice-Texas Correctional Office on
Offenders with Medical or Mental Impairments (TDCJ-TCOOMMI) conducted a 3-year
study that compared the recidivism rates of those in and out of the program. Research has
indicated that the recidivism rates were lower for those in TCOOMMI (13.9%) than those
released with supervision (22.6%), discharged from prison (23.3%), and those discharged
from state jail (31.1%) (TDCJ, 2012a). Also, research has indicated that the recidivism
rates for those on probation (17.6%) and parole (4.2%) decreased (TDCJ, 2012a).
Research have reported that after 1-year, rates for probation were 12.6% and 2.9% for
those paroled (TDCJ, 2012a).
The TCOOMMI program focused on dedicating resources for offenders identified
as high risk (TDCJ, 2012a). Through the identification of offenders who needed the
Medically Recommended Intensive Supervision (MRIS), there was a 5.2% decline in
offenders going to probation and parole boards, and a 7.7% decline in supervision than
the previous year (TDCJ, 2012a).
Although there are more offenders identified by the mental health authority
within the Texas CJS, 278 more cases in 2012, there are 113 fewer offenders that needed
probation (TDCJ, 2012a). However, this increase in overall cases revealed that more
work needed to occur regarding diversion programs (TDCJ, 2012a).
Pew (2011) found that policy and lawmakers wanted to impose changes in the
criminal justice system that addressed recidivism and public safety. Research has
indicated that several states have made changes in four areas: staff and program costs,
operating efficiencies, sentencing and release policies, and recidivism reduction strategies
(Pew, 2011).
Thirty-two states had placed hiring freezes or removed staff to cut costs, and 22
states had removed much-needed programs (Pew, 2011). Limited the space in prisons,
closing prisons, and making prisons more energy efficient were ways some state officials
were reducing costs and spending that increased prison efficiency (Pew, 2011).
Lawmakers were examining existing policies on the sentencing of offenders (Pew, 2011).
State officials were also examining the criminal offenses and changing policies that could
lower the number of offenders imprisoned by offering alternatives (Pew, 2011). Last,
state officials were analyzing ways that improved policies, as well as working with other
agencies to help stop the cyclical nature of recidivism (Pew, 2011).
Research has indicated that to reduce recidivism, creating and using programs that
used evidence-based practices (EBP) helped lower recidivism by 50% (Pew, 2011). For
example, Arizona had a 31% decrease in recidivism (i.e., new convictions) by using EBP
and policy changes (Pew, 2011). Using data from 2004, Pew (2011) found that if states
lowered recidivism by 10%, this could result in roughly $635 million savings in the first
year. The objective was using EBP not only in prisons but in every area that affected the
offender; from supervision to the community (Pew, 2011).
Another way to reduce recidivism was by focusing on offender needs, while also
setting goals (i.e., performance) for prisons that rewarded improvement (Pew, 2011).
Focusing on the needs and prison goals could help change offenders and prison culture
(Pew, 2011). For example, Arizona, California, Illinois, and South Carolina have all
created an incentive program to lower recidivism while offenders are on probation (Pew,
2011). Arizona offered up to a 40% refund “to counties that cut revocations to prisons”
(Pew, 2011, p. 28).
Properly preparing and planning for offenders’ release from prison also reduced
recidivism (Pew, 2011). Planning should start as soon as the offender entered prison by
using assessment tools that identified the needs of the offender (Pew, 2011). Identifying
needs allowed case managers to offer help and resources not only while imprisoned but,
upon their release (Pew, 2011). Case managers helped offenders successfully reenter
society and reduced recidivism by giving offenders proper supervision and placement in
programs that addressed their needs (Pew, 2011).
Research has indicated that another way that reduced recidivism was to impose
progressive sanctions (Pew, 2011). Progressive sanctions created accountability but also
allowed the offender to succeed instead of immediately sending them back to prison for
technical violations (Pew, 2011). However, if offenders were violated, the punishment
should be appropriate to the crime (Pew, 2011).
Last, creating an incentive or rewards system for offenders could reduce
recidivism by creating motivation for them to keep trying to succeed and follow the rules
(Pew, 2011). Incentives encouraged positive behavior, especially among low-risk
offenders (Pew, 2011). Also, using incentives such as earned-time credits could decrease
offenders’ probation time (Pew, 2011). Using these types of incentives could help reduce
the number of cases that probation and parole officers had, allowing them to concentrate
on newly released or higher risk offenders (Pew, 2011).
Drake et al. (2009) found that many evidence-based practices aimed at reducing
crime so that lawmakers could make cost-effective decisions on which programs to
establish. Both adult and juvenile programs were examined for their effectiveness in
reducing recidivism, prevention, as well as taxpayer costs (Drake et al., 2009). Drake et
al. (2009) found that programs that lowered recidivism rates also resulted in less victim
and taxpayer funds going towards the criminal justice system.
Drake et al. (2009) examined program costs per person, as well as the long-term
savings because of crime reduction. For adults, one of the most effective programs that
reduced crime was the Intensive Supervision: Treatment-oriented programs (Drake et al.,
2009). After examining 11 similar studies, this programs reduced recidivism by 17.9%,
reduced crime costs to victims by roughly $16,239, and saved taxpayers more than
$10,235 (Drake et al., 2009). Also, the program costs around $7,356 per person but,
would save $19,118 (per person) in long-term crime reduction (Drake et al., 2009).
Another cost-effective program was cognitive-behavioral therapy (CBT) in prison
or communities. Drake et al. (2009) examined 25 programs and found that CBT reduced
recidivism by 6.4%, reduced crime costs to victims by $10,234, and saved taxpayers
$5,235 (p. 184). CBT cost $107 per person and saved $15,361 (per person) in long-term
costs (Drake et al., 2009). However, not all programs reduced crime or recidivism. Drake
et al. (2009) examined 11 jail diversion programs for mentally ill offenders and found
that those programs increased crime by 5.3%. Also, those programs cost victims nearly
$4,831 and taxpayers $3,045 with no long-term cost savings or crime reduction benefits
(Drake et al., 2009). Some programs do not reduce recidivism nor lowered costs to
victims and taxpayers; however, long-term savings on imprisonment or court costs,
existed (Drake et al., 2009). For example, using electronic monitoring to offset jail time
had long–term savings (Drake et al., 2009). Drake et al. (2009) found that jail costs, in
Washington state, was around $2,227 and to monitor offenders the cost was $1301 (per
person), thus, leaving a deficit of $-926 but, long-term it also saved $926.
Around 18 states had started the Justice Reinvestment Initiative (JRI) to find ways
that decreased the growing prison population and costs (Austin et al., n.d.). The JRI not
only addressed imprisonment and public safety but determined ways to put money back
into disadvantaged minority communities that were most affected (Austin et al., n.d.).
Unfortunately, the JRI had no effect on lowering the prison population nor did it reinvest
money back into minority communities (Austin et al., n.d.).
Austin et al. (n.d.) found that one problem with the JRI was it had become a term
with unclear meaning. Also, the JRI focused on legislation that did nothing to decrease
admissions or sentencing within the criminal justice system (Austin et al., n.d.).
Furthermore, reinvestment money was seized by other agencies (i.e., law enforcement)
and not the communities that needed it (Austin et al., n.d.).
For JRIs to take effect in the criminal justice system, state and local governments
needed to create more efficient policies (Austin et al., n.d.). The policies should reduce
unnecessary arrests (i.e., drug crimes), remove unnecessary pretrial detention, and
reclassify some drug and other crimes (Austin et al., n.d.) Additional suggestions for
policymakers are to remove mandatory minimum sentences which impact minorities,
eliminate revocations to prison for probation and parole violations, and require racial
impact statements (Austin et al., n.d., p. 17). These policies would significantly impact
the entry and reentry issues within the criminal justice system. Also, reducing the length
of stay, even by months, decreased the prison population (Austin et al., n.d.). For
example, the average duration of stay in prison was 29 months (Austin et al., n.d.). In
reducing the length of stay by roughly 8 months, the overall population would decrease
by more than 400,000 (Austin et al., n.d.). Research has indicated that length of stay cost
each state around $10 billion each year (Austin et al., n.d.).
Although some states’ legislators have attempted to change laws to address mass
incarceration, they lacked aggression to truly impact (i.e., lower) the prison population
(Austin et al., n.d.). State legislators that have attempted to address sentencing reform
have concentrated on policies such as mandatory sentencing, especially concerning drug
offenses (Mauer, 2011). For example, New York’s 1973 Rockefeller Drug Laws that
worked in conjunction with the “War on Drugs” was one major change at sentencing
reform (Mauer, 2011). Also, the focus has been on more offenders receiving parole and
changing strict policies that caused offenders to have their probation revoked (Mauer,
2011). Changes have occurred for nonviolent offenders in states such as Colorado and
Kentucky, who increased parole eligibility by 2 months (Mauer, 2011).
Parolees faced a particular challenge because of strict rules and policies. Many
returned to jail or prison because of technical violations based solely on the parole
officers’ decision (Mauer, 2011). Also, many agencies use graduated sanctions that give
clear consequences for behavior and do not depend on the parole officer’s discretion to
address the problem of technical violations (Mauer, 2011).
At the federal level, fewer changes occurred because nearly 13% of offenders
make up the federal prison population (Mauer, 2011). However, changes on a federal
level could impact state policies garnered national attention and offered (financial)
incentives (Mauer, 2011). Mauer (2011) found that in 1994, of the $30 billion meant to
address federal crime, $8 billion was for building more state prisons. States could not
receive this money without enforcing the “truth in sentencing” policies (Mauer, 2011).
There are many barriers to reforming the United States’ criminal justice system. It was
difficult getting lawmakers and politicians to fight for changes because they feared being
labeled as “soft on crime” (Mauer, 2011). Also, some felt that focusing on changing the
system, meant one was fighting for the offenders (Mauer, 2011). Fighting for offenders
was perceived negatively, especially when considering the victims (Mauer, 2011). Also,
most politicians who have fought for changes have been House Democrats that were
African American (Mauer, 2011). Mauer (2011) found that the last outspoken Caucasian
representative to speak about these issues was Senator Paul Simon (IL) in the 1990s.
Mauer (2011) found that the main barrier to reforming the United States’ criminal
justice system was changing the attitudes of policy makers so they could be more
receptive to change. In other words, alter the focus of the debate from offenders and jail,
to how to make better and safer communities (Mauer, 2011). Also, focusing on race
caused division and lacked support to make effective changes (Mauer, 2011).
Researchers have indicated that this was true, especially for Caucasians (Mauer, 2011).
Removing the racial focus was challenging; however, getting conversations started
needed to occur not only by African Americans but everyone (Mauer, 2011). Thus, the
aim of the present study was to determine if there were any racial differences in resilience
(i.e., hardiness) and locus of control that contributed to or impacted recidivism. This
study examined the complexity of recidivism by analyzing the various challenges
exoffenders faced during parole.
Methodology
The methods used for this mixed methods study was a sequential explanatory
design where quantitative data collection occurred first (see Creswell, 2009). Quantitative
data collection occurred by giving the Connor-Davidson Resilience scale (CD-RISC) and
the multidimensional locus of control scales (Connor & Davidson, 2003; Levenson,
1974). Data gained from these questionnaires was analyzed.
Qualitative data collection consisted of interviews of randomly selected
participants focusing on their lives and experiences in the criminal justice system. Once
data were collected, interpretation of both the quantitative and qualitative studies was
analyzed. A further detailed explanation occurs in Chapter 3.
Summary
There are many challenges offenders faced that contributed to recidivism.
Because of stricter policies, legislation, and rigid rules during supervision, recidivism
continues to be a concern. Solutions remain unclear and may take a multidisciplinary
approach to alleviate the problem.
This study filled the gap in research by determining if there were racial
differences among paroled offenders concerning resilience and locus of control, and how
their experiences and opportunities differ racially. Racial differences could help identify
specific barriers and needs not only in the criminal justice system, but the community as
well. Addressing these barriers and needs could help offenders become more successful
and resilient, so the revolving door stays closed.
Chapter 3 discusses the methodology (i.e., mixed methods) of the study, the
measures used (e.g., CD-RISC, multidimensional locus of control scales), as well as the
participant criterion and selection. Finally, Chapter 3 discusses my role, potential issues
within the study and how they are addressed, as well as the protection of participants and
data collection.
Chapter 3: Research Method
Introduction
It is unclear if there are any associations between race, resilience, locus of control,
and recidivism among former drug offenders. The purpose of this mixed methods study
was to determine if there are racial differences among former offenders regarding
resilience and locus of control that may contribute to or affect recidivism. The study
consisted of two parts. For the quantitative portion, I used the Connor-Davidson
Resiliency scale (CD-RISC) and the multidimensional locus of control scales, using the
total scores of the CD-RISC and the scores from the subscales internality, powerful
others, and chance for the multidimensional locus of control scales (Connor & Davidson,
2003; Levenson, 1974). The qualitative part consisted of participant interviews to gain an
understanding of resilience and the challenges that may contribute to recidivism.
Together, both portions of the study provided information on how individuals perceive
resilience, locus of control, and the challenges faced by ex-offenders.
In this chapter I explain the methodology I used for participant selection, the
study design, and my role as researcher. I also discuss quantitative and qualitative data
collection and analysis techniques, and the research questions I designed to give focus to
the study.
Setting
The present study took place at a parole office where I recruited and interviewed
participants. This setting was relevant to the study because it targeted ex-offenders who
were currently on probation or parole and had prior experience being under supervision.
It was emphasized to participants that neither this study nor I had any association with the
criminal justice system, the probation/parole office, or its employees. Additionally,
obtaining participants who met the criterion for the study (i.e., drug offenders) was more
readily available in this particular setting. Furthermore, the probation/parole office
offered a wide variety of racial participants because of the number of ex-offenders who
were currently under supervision and thus, obtaining help from probation/parole officers
or administration for guidance made seeking appropriate participants easier.
Research Design
There have been numerous studies done regarding recidivism and resilience.
However, no researchers have attempted to use a mixed-methods study to determine if
there are racial differences in resilience and recidivism. In this study, I used a sequential
explanatory design consisting first of quantitative data collection and second, of
qualitative data collection (see Creswell, 2009). The quantitative study consisted of
MANOVA statistical interpretations to determine if there were racial differences among
offenders using the total scores of the CD-RISC and the scores from the subscales
internality, powerful others, and chance for the multidimensional locus of control scales
(Connor & Davidson, 2003; Levenson, 1974). A randomly selected convenience sample
of 126 male parolees (N = 126) participated in this portion of the study.
The qualitative aspect of the study consisted of interviewing parolees. Their
answers helped me understand the challenges that contribute to recidivism. I used a
randomly selected sample of 12 parolees (n = 12) who volunteered for the quantitative
portion of the study.
Role of Researcher
My role was to obtain informed consent from the participants’ parole/probation
office, as well as the parolees. In addition, I administered the questionnaires, performed
interviews, and collected, analyzed, interpreted, and reported all data results. I had no
prior association with any participants, and did not currently work in the field of
psychology with ex-offenders.
Additionally, researcher bias was limited because the subjects volunteered
randomly without my prior knowledge. I informed study participants that they could
withdraw at any time during the study in order to address potential power relationships or
ethical concerns. Furthermore, I informed the participants that I had no affiliation with
the probation/parole office, its employees, or the criminal justice system.
Methodology
Participant Selection
The participants in the study were previously recidivated male parolees separated
into three racial groups: African American, Caucasian, and Hispanic. These racial groups
were the focus of the study to determine if there are any racial differences regarding
resilience and locus of control. These variables are important because McGovern et al.
(2009) found racial differences in recidivism. Therefore, I aimed to discover if these
same racial groups have differences in resilience and locus of control as well. Since this
study was focused on adults, the age requirement for participants was 18 or older. In
addition, each participant must have had prior drug offenses and have been on probation
or parole. This requirement was also a focus because it allowed participants to give
information regarding their experiences (e.g., recidivism) while previously on
probation/parole.
The total sample size (N = 126) was obtained using G*Power analysis with the
following criteria: a MANOVA with 3 racial groups and 4 scales, medium effect size (n
= .063), alpha of .05, and power of .80 for three racial groups. For the qualitative study, I
interviewed 12 participants (n = 12). Saturation was obtained from the sample size of the
qualitative portion of the study where redundancy or common themes had been
identified. Finally, prior to the administration of the questionnaire and interview, I
obtained verbal (i.e., implied) informed consent.
Recruitment
Recruitment of participants occurred through flyers placed within the parole
office indicating the dates of the study. Again, criteria must be met for study
participation. I was on-site to administer surveys and conduct interviews to interested
participants in individual sessions. Those participating in the survey and are interested in
being interviewed was rescheduled at a later date. In addition, created and assigned an
alphanumeric code prior to handing out questionnaires. These same numbers were used
to match participants for the qualitative portion of the study. Compensation for
participants, in the form of a $5 gift card, was discussed with parole supervisor before the
study was conducted.
Protection of Participants
Participant identification occurred by using an alphanumeric code that was
created to ensure confidentiality. All data obtained was secured and accessed only by me.
I obtained written informed consent prior to the study. I reminded participants that they
are free to withdraw from the study at any time without penalty. All data obtained from
the study participants such as tapes, transcripts, surveys, or questionnaires, was secured
and accessible only to me. Five years after the study, I will destroy all data. The study
was approved by Walden University’s Institutional Review Board 05-16-16-0137974 and
parole officials prior to its conduction.
Research Questions
This project increased understanding of the barriers that may contribute to the
United States’ recidivism problem. The study provided information on how to better
address the challenges parolees face when returning to their communities. Also, the study
provided a greater understanding of why parolees are re-incarcerated so soon after their
release. By using a multidisciplinary approach to combating barriers and understanding
predictors of resilience, this study could have a positive impact on parolees, the
community, and the criminal justice system. Hopefully, a multidisciplinary approach will
ultimately create social change in the form of state and government policy reform.
Quantitative RQ: What racial differences are evident in resilience and locus of control
scores among parolees?
H0: There are no racial differences in resilience and locus of control scores among
parolees.
H1: There are racial differences in resilience and locus of control scores among
parolees.
Qualitative RQ: How do parolees explain their recidivism?
The participants in this study did not need any special training or classes. The
study took approximately seven weeks to conduct due to the number of participants and
the activities within the parole facility. Each group was separated racially (e.g., African
American, Caucasian, and Hispanic) for both aspects of the study.
Qualitative Components
Upon completion of the CD-RISC and the multidimensional locus of control
scales, four participants from each racial group was interviewed and recorded via audio
(Connor & Davidson, 2003; Levenson, 1974). Verbal (implied) informed consent was
obtained prior to the interview. Participants was selected from the quantitative portion of
the study by asking if they would be interested in being interviewed. Each participant
received instructions that they were to answer each open-ended question honestly. The
interview allows greater understanding of their experiences while on probation or parole,
challenges they face, and possible contributors to recidivism. Each participant was
identified by an alphanumeric ID code. The ID code was the same used in the
quantitative part of the study. Finally, content validity was established by transcribing the
interview responses verbatim to identify significant themes or differences among
participant responses.
Quantitative Components
The participants of the quantitative portion of the study answered the CD-RISC
and the multidimensional locus of control scales; the total scores of the CD-RISC and the
scores from the subscales internality, powerful others, and chance for the
multidimensional locus of control scales was used (see Connor & Davidson, 2003;
Levenson, 1974). Participants consisted of 42 individuals, in three different racial group,
totaling 126 participants overall. The reason for the study and informed consent was
explained prior to the administration of the questionnaires. Again, an alphanumeric code
that I had created, was assigned to ensure confidentiality.
Demographic data. Information obtained for each participant consisted of
general demographic information such as race, gender, age, marital status, and
socioeconomic class. This information was accessible only to me. I will destroy all data 5
years after the study's completion.
Quantitative Instruments. The Connor-Davidson Resilience scale is a 25-item
self-report questionnaire used to determine resiliency (Connor & Davidson, 2003). The
CD-RISC is also used to show the results of psychological treatment with or without
medication, stress management, and certain changes in the brain (CD-RISC, n.d.). The
answers are on a Likert scale ranging from not true at all (scored as 0) to true nearly all
the time (scored as 4). Scores range from 0 to 100, and higher scores suggest greater
resilience (Connor & Davidson, 2003). For example, the CD-RISC may ask questions
such as when you are stressed, do you have access to help, or do challenges or adversity
make you work harder? The questionnaire takes approximately 5-10 minutes to complete.
I obtained permission to use the CD-RISC from the developers.
Connor and Davidson’s (2003) original study of the U.S. general population (n =
577) had an internal consistency (Cronbach’s alpha) of 0.89 (p. 79). Validity of the same
study sample (e.g., US general population) was 80.7 (CD-RISC, n.d.). The CD-RISC is
used in a variety of settings and conditions. Validity was consistent in settings and
conditions such as primary care (71.8), psychiatric outpatient (68.0), generalized anxiety
(62.4), and PTSD (47.8 and 52.8).
The multidimensional locus of control scales is a 24 item self-report questionnaire
used to measure locus of control using three scales—internality, powerful others, and
chance (Levenson, 1974). Each scale consists of eight questions, using a Likert score
ranging from strongly disagree (scored as -3) to strongly agree (scored as +3). Total
scores for each scale ranging from 0 to 48 (Levenson, 1974). Higher scores indicate what
area of locus of control the individual has either external (e.g., chance, powerful others)
or internal (Levenson, 1974). For example, the multidimensional locus of control scales
may ask questions such as, are you able to solve problems effectively, or do you believe
life is made up of circumstances? The questionnaire takes approximately 10 to 15
minutes to complete. I obtained permission to use the multidimensional locus of control
scales from the developer.
The multidimensional locus of control scales measure was found to have a
moderately high internal consistency and correlated well with Rotter’s original locus of
control I-E scale (Levenson, 1974). Kuder-Richardson reliabilities for the internal (I)
scale was r = .64, r = .77 for powerful others (P scale), and the chance (C) scale was r =
.78 (Levenson, 1974). Split-half reliabilities for the IPC scales were as follows: r
= .62, .66, and .64 consecutively (Levenson, 1974). Test-retest reliabilities for the IPC
scales after one-week were rs = .64, .74, and .78 (Levenson, 1974).
Data Analysis
Data collection and analysis of this study was a mixed-methods approach.
Quantitative analysis helped identify differences and predictors of resilience among
offenders by race. Qualitative analysis aligned with the resilience theory and the social
disorganization theory. These theories helped in understanding the depth of the barriers
these individuals face and how they have impacted their lives (Fletcher & Sarkar, 2013
and Kingston et al., 2009). As previously indicated, the study aimed to answer the
following research questions:
Quantitative RQ: What racial differences are evident in resilience and locus of
control scores among parolees?
H0: There are no racial differences in resilience and locus of control scores
among parolees.
H1: There are racial differences in resilience and locus of control scores among
parolees.
Analyses: A 3 (African American x Hispanic x Caucasian) x 4 (CD-RISC,
internality, powerful others, and chance) MANOVA using the CD-RISC scale scores and
the locus of control subscale scores as the dependent variables.
The study began with the quantitative portion and consisted of administering the
CD-RISC and the multidimensional locus of control scales questionnaires. A MANOVA
determined any significant differences in resilience and locus of control among the three
racial groups for the study. SPSS for Windows (current version) was the software product
used for statistical analysis.
The last phase of the study consisted of the qualitative interview of participants
individually, focusing on possible barriers to resiliency and recidivism. The interview
was recorded and transcribed verbatim. Transcripts were reviewed and coded (e.g.,
alphanumeric) in order to identify significant themes or differences between the racial
groups.
Threats to Validity
Threats to validity were minimal for this study. Possible threats to external
validity was testing reactivity, where participants did not act or respond as they normally
would due to study participation. To address this, I instructed participants to answer all
questions honestly and that there are no correct or incorrect answers. In addition, the
overall generalizability of the qualitative portion of the study could not be ensured due to
the small sample size (n = 12). However, data collection, analysis, and results allowed for
a possible correlation or noticeable differences found among racial groups regarding
resiliency and recidivism.
Threats to internal validity resulted from participant history and testing that could
influence the outcome of the study. Events that I was not aware of could influence how
the participant responds to the research questions. I advised participants that no response
is “too bad” or “too good” to guarantee selection for the secondary study. In addition,
possible experimental mortality could be a problem where there was a loss of participants
at any point in the study. To address this, I ensured that each participant was aware that
they could withdraw from the study at any time.
Issues of Trustworthiness
Credibility occurred twofold. The quantitative study had a moderate sample size
(N = 126) with three racial groups to compare results. The qualitative study had an
adequate (n = 12) sample size where participants were interviewed to allow for different
racial perspectives to be given (e.g., triangulation). There was limited member checking
due to the time constraints of the study. However, the interviews were taped to ensure the
accuracy of participant answers.
Transferability was accomplished by allowing participants to give a thick
description. A thick description allowed for greater detail about participant experiences
within the criminal justice system and the context of how or why they became involved
in the criminal justice system. Also, participant living situations (e.g., SES), and
educational backgrounds were included. These descriptions were for each racial group in
the study.
Having audit trails that explained in detail how data were collected and kept to
ensure dependability. Providing a copy of the interview questions within the appendix of
the proposal showed the accuracy of data analysis. Results included racial group analysis.
In addition, triangulation was accomplished via the interview process by asking
openended questions. Open-ended questions allowed each participant, by racial group, to
give their personal accounts of their experiences within the criminal justice system.
In order to address conformability, review of data and overall findings ensured clarity
and understanding. For the quantitative portion of the study, the results using the total
scores from the CD-RISC and the multidimensional locus of control scales provided data
for each racial group and individual (Connor & Davidson, 2003; Levenson, 1974). For
the qualitative study, the interview questions along with the verbatim answers provided
data given by each participant and by racial group. Similarities among and within racial
groups was reviewed and audited. Keeping an open mind and allowing participants to
express their experiences within the criminal justice system addressed reflexivity.
Reflexivity occurred without research bias.
Ethical Procedures
Ethical procedures start with procuring an agreement from administrators within
the parole board/office allowing the research study to occur. A copy was provided to the
Institutional Review Board (IRB) application. In addition, the treatment of human
participants was in conjunction with the American Psychological Association’s Code of
Conduct Standard 8 for research and publication (APA, 2010). I provided all
documentation and participant informed consent to the IRB, along with the appropriate
approval numbers.
With regards to recruitment, ethical concerns were making sure that participation
was voluntary and that they met the requirements for participation. Recruitment included
working with parole officers to provide notification via flyers prior to the study, and
creating an alphanumeric system accessible only by me. Taking these measures ensured
the confidentiality of the participants.
Ethical concerns surrounding data collection was ensuring that participants would
answer both instruments (e.g., CD-RISC, multidimensional locus of control scales) and
the interview questions truthfully. To reduce the risk of early withdrawal from the study,
participants received information about the instruments and a copy of the interview
questions. In addition, an approximate length of time was provided for how long each
study would take. Incentives was discussed with parole officers regarding
appropriateness to ensure participation. I reminded participants that they could withdraw
from the study at any time without reprimand.
Only I had access to the information that the participants provided. Information
included questionnaire answers, audio recorded interviews, and the alphanumeric coding
of participants used in the final reporting of data collection. All information remained
confidential and was stored in a locked cabinet accessible by me only. All data removal
will occur after 5 years.
Other ethical concerns were the power differential in working with offenders or
parolees. To address ethical concerns, I helped create an environment where participants
can speak freely and ask questions as needed. I ensured that participants knew that any
information obtained would only be accessible by me. Lastly, incentives were used where
appropriate and with prior approval from administration.
Summary
This study consisted of both a quantitative and qualitative data analysis and
collection. The focus of the study was to determine if there were any racial differences
among parolees with regards to resilience and locus of control. Data interpretation
occurred to determine if racial differences exist. A more detailed discussion of the results
occurs in Chapters 4 and 5.
Chapter 4: Results
Introduction
The purpose of this mixed methods study was to determine if there are racial
differences among former offenders regarding resilience and locus of control that may
contribute to or affect recidivism. The study consisted of two parts. In the quantitative
portion I used the Connor-Davidson Resiliency scale (CD-RISC) and the
multidimensional locus of control scales using the total scores of the CD-RISC and the
scores from the subscales internality, powerful others, and chance for the
multidimensional locus of control scales (Connor & Davidson, 2003; Levenson, 1974).
The qualitative part consisted of participant interviews to gain an understanding of
resilience and the challenges that may contribute to recidivism. Together, both portions of
the study provide information on how individuals perceive resilience, locus of control,
and the challenges faced by ex-offenders.
Setting
I conducted this study at a parole office where I recruited and interviewed
participants using flyers posted within the facility. This setting was appropriate for the
study because it is frequented by ex-offenders who are currently on parole and have prior
experience being under supervision.
Demographics
Information obtained for each participant consisted of general demographic data
such as race, gender, age, marital status, and socioeconomic class. The participants of the
study were previously recidivated male parolees separated into three racial groups:
African American, Caucasian, and Hispanic age 18 and older.
Data Collection
The quantitative portion of the study contained a sample of 126 (N = 126)
participants, divided equally by racial groups (Hispanic, Caucasian, and African
American), and I used 4 scales from the CD-RISC and multidimensional locus of control
scales. The qualitative portion of the study contained a sample of 12 participants (n = 12)
divided equally by racial group. Alpha-numeric coding was assigned to each participant
and cross-referenced for participants again for the qualitative portion of the study.
Participants completed the CD-RISC and multidimensional locus of control
scales, and I coded with each alphanumerically. I asked participants how they identified
racially, and then coded them appropriately. No other information was collected. Upon
completion, I asked participants if they were interested in participating in an interview,
and, if they were interested, I set a later time to conduct the interview. Participants who
agreed to be interviewed were asked to keep their alphanumeric code, which I also kept. I
verified the alphanumeric before the interview was conducted.
Variations from the original data collection plan consisted of not collecting age or
conviction information; however, the written consent did specify the requirement of a
drug conviction, and I thus assumed that participants carried a drug conviction. I
conducted data collection at the parole office with some participants who were first-time
parolees. This did not mean that they had not previously recidivated or were on probation
or parole in another county at another time. The recidivism requirement was also stated in
the informed consent, and I assumed that their answers were truthful. I recruited
participants by posting announcements throughout the entire parole office facility in
which scheduled parole meetings and re-entry drug offender classes were held. Both of
the parole and re-entry offices worked together and were a requirement for parole
conditions. Finally, NVivo was not used to analyze the qualitative results of this study. I
reviewed, documented, and scored each audio recording.
Data Analysis
Quantitative Results
I conducted a 3 (African American x Hispanic x Caucasian) x 4 (CD-RISC,
internality, powerful others, and chance) MANOVA using the CD-RISC scale scores and
the locus of control subscale scores as the dependent variables. Participants were at least
18-years-old, identified racially as African American, Caucasian, or Hispanic, and had
been previously on parole. I carried out preliminary assumption testing was to check for
the following with no major violations found: normality, multicollinearity, linearity,
homogeneity of variance-covariance matrices, and univariate and multivariate outliers
(see Pallant, 2013). There was no statistically significant difference between race and the
combined dependent variables, F (8, 240) = .803, p = .60; Wilks’ Lambda = .95; partial
eta squared = .03. Therefore, the null hypothesis is retained; there were no racial
differences in resilience and locus of control scores among the parolees.
Qualitative Results
I conducted interviews with each of the 12 participants (4 from each racial group)
to glean information regarding how parolees explain their recidivism. The interview
questions asked about socioeconomic status with regards to the neighborhood before and
after incarceration, education, employment, housing, family support, and training.
Additional questions asked about the parolees’ experiences in trying to get a job, housing,
and challenges experienced while on parole (see Table 1).
Table 1
Demographic Data of Participants
PARTICIPANT RACE SES EDUCATION EMPLOYMENT
B4XONEH
C2VWBC4
African American
Caucasian
Poor
Poor
HS Diploma
GED
Unemployed
Employed
B8CP6OP African American Middle GED Unemployed
AX379DT Hispanic Middle GED Employed
C4QKMUR Caucasian Middle GED Unemployed
ALPHWKS Hispanic Middle GED Employed
CHTNCQS Caucasian Middle GED Unemployed
CIRHM7U Caucasian Upper-Middle HS Diploma Employed
APHFTCI Hispanic Middle Diploma Employed
A3N8BDA Hispanic Poor GED Employed
BQ9BIEZ African American Poor GED Employed
B1D3EIX African American Middle HS Diploma Unemployed
With regards to socioeconomic class, 60% (n = 6) of respondents came from
middle-class environments, 30% (n = 4) came from poor settings, while only 10% (n =
2) came from upper-middle-class environments. In talking about the links between
gentrification and racism, one Hispanic participant who was transferred to Texas due to
the interstate compact said:
So, a lot of us have moved out and there was a lot of discrimination and I mean, it
seemed like we just ended up having a big target on our back for any particular
reason. Cause they knew the development was coming, they found very which
way that they could get us out of there.
Fifty percent of respondents (n = 6) said there were job opportunities in their
neighborhood, while almost 45% (n = 5) of respondents said there were no job
opportunities in their neighborhood. Around 5% (n = 1) of respondents identified as
disabled and was therefore unable to work. Responding to a question about the difficulty
of securing work while on parole, one Hispanic participant said, “Um, not really. It was
just ah, ah basically go…uh, just the workforce more or less and uh, if you want to go to
TCC, you can apply for TCC. That's it.”
Of those respondents who said there were job opportunities, approximately 95%
(n = 5) stated that they were qualified for the jobs available, while roughly 5% (n = 1)
indicated that they were unqualified for the jobs. A Caucasian participant said, “Uh, I
mean, I'm sure I qualify for a lot, but I'm not sure. I didn't really look for a job.” Most of
the jobs available were for skilled workers (e.g., construction, carpentry) and fast-food
restaurants. Responding to a question about the types of employment available while on
parole, a Caucasian participant said, “Um, I'm a carpenter. I found work as a, working at
a sheet metal company.”
Around 55% (n = 7) of respondents were currently employed, while
approximately 45% (n = 5) were currently unemployed. Of those who were unemployed,
almost 40% (n = 2) were disabled. Sixty percent (n = 3) of unemployed respondents
indicated that their job search has been good with no challenges to date. Around 75% (n
= 2) of those seeking employment have help or employment potential from a prior
employer or a friend who can offer employment. A Caucasian participant who indicated
that he had a good experience searching for employment said:
Yes. Very well. I've got a guaranteed job working at Wendy's. A friend of mine
on Facebook told me, he's the assistance manager at Wendy's. I got a job, but I'm
going to look for something in carpeting because that's what I do. But, if it don't
work out, I will go flip a burger.
Regarding education, approximately 70% (n = 8) had a GED, and around 30% (n
= 4) graduated from high school. Of those with a GED, approximately 25% (n = 2)
received a skilled trades certification (e.g. air conditioner, mechanical) while they were
incarcerated. Responding to a question about certifications offered during incarceration,
one African American participant said, “I have a GED and I have a Microsoft
certification specialist skills trades, and mechanical trades.”
During their incarceration, approximately 50% (n = 6) of respondents received
some training, while 50% (n = 6) did not. Of those who did not receive training during
their incarceration, around 50% (n = 3) indicated that training was not offered, while the
other 50% (n = 3) cited reasons for not receiving training as being a veteran, not being in
the facility long, or they were not interested. A Caucasian participant said, “I spent 4 1/2
years in school,” indicating how he spent his time while incarcerated.
Of those respondents who did receive training during incarceration,
approximately 50% (n = 3) received training such as cooking or custodial work. Around
40% (n = 2) trained in some type of skilled working (e.g., brick masonry, AC technician),
while approximately 10% (n = 1) received training in technology (i.e., computer
software). However, roughly 95% (n = 5) were unable to utilize the skills they were
trained for, while only 5% (n = 1) of respondents said they were working in a job that
utilized their training. Of those not utilizing their training, approximately 45% (n = 2) of
respondents stated that jobs were unavailable, 45% (n = 2) cited they were unable to
utilize their training due to being recently released, and roughly 10% (n = 1) had an
unknown reason for not using their training. Responding to a question about utilizing
training received during incarceration, one African American participant said:
I got a uh custodial technicians training. No I wasn't. I couldn’t find employment
when I got out and then I went in another direction and I tried to find…well, I.
This last time I got out, I just worked in another field. I got the trade while I was
in prison, but I worked in another field when I got out.
Eighty percent (n = 10) of respondents indicated that they received some sort of
assistance prior to their release from prison, while roughly 20% (n = 2) said they did not.
Assistance was identified as receiving documents such as photo identification, social
security cards, birth certificates, and community resource information to find clothing
and local food pantries. Responding to a question about assistance received prior to being
released from prison, one African American participant said:
Yes ma'am. Um, re-entry lady that was on the unit, she gave me uh, things for
food pantry, peer support groups, NA/AA, clothing, uh she gave me a packet for
that. And uh, they was providing me with uh my birth certificate and my Texas
ID.
Approximately 80% (n = 10) of respondents were released to new neighborhoods,
while approximately 20% (n = 2) of respondents were discharged to the same
neighborhoods before their incarceration. Approximately 45% (n = 5) identified their new
neighborhood as being poor, 45% (n = 5) identified their new neighborhood as being
middle class, 5% (n = 1) identified their neighborhood as being upper middle class, while
5% (n = 1) was unknown, stating that the neighborhood was “more populated.” An
African American participant who was paroled to a new neighborhood said, “No, a
different one, a different neighborhood. Well, it's alright, it's pretty good. It's like poor,
but it's not drug infested.”
With regards to job opportunities in the release neighborhood, approximately 95%
(n = 8) of respondents said there were job opportunities, while roughly 5% (n = 1) were
unsure of job opportunities as of yet. Approximately 90% (n = 7) of respondents felt they
were trained for the jobs in their new neighborhood, while approximately 10% (n = 2) did
not identify as being trained due to being a retired veteran or disabled. Responding to a
question about the availability of jobs in their release neighborhood, one Caucasian
participant said:
They say there is. I haven’t had time. I’m on a monitor so, I haven’t been able to
get out and move around yet, but I mean, I see there’s work going on, so I’m sure
there is. People coming and going on, there’s jobs available.
Approximately 90% (n = 10) of respondents said they had family support during and after
their incarceration. Responding to a question about family support, one Caucasian
participant said, “Absolutely. They've been supportive of anything I needed and since
being released, they've been my support team working with parole to meet all my
obligations.”
Approximately 10% (n = 2) of respondents did not have any support both during or after
their incarceration. Approximately 55% (n = 6) of respondents received both financial
and emotional support from their families and loved ones. Approximately 15% (n = 2)
received only financial assistance such as commissary assistance, or money to assist after
being paroled, 15% (n = 2) received only emotional support such as words of
encouragement, and 15% (n = 2) did not get any family support. One Caucasian
respondent who did not receive family support said, “They've got their own lives, they're
living their own lives. Plus they, when I was incarcerated, they weren't even aware, they
didn't even know.”
One hundred percent (n = 12) of respondents had housing. Approximately 75% (n
= 7) of respondents were living with family members, 10% (n = 2) rented their homes,
5% (n = 1) lived in a halfway house, 5% (n = 1) lived in a senior living facility, and 5%
(n = 1) of respondents lived in a residence. One African American participant said he
lived in a “House. Just a plain house,” but did not clarify if it was with family, alone,
rented, or a halfway home. Approximately 20% (n = 2) of respondents found it
challenging to find housing after their incarceration, while 80% (n = 10) had not tried to
obtain housing because they were living with family, in a halfway house, or thought it
was too soon for them to try to look for adequate shelter. An African American
participant recounted his housing experience saying:
Well, you know uh, being having uh, being a ex-felon a lot of places don't want to
uh you know you can't find housing, but I was able to get uh, on housing and so
that's what I'm doing now. Housing assistance program.
Seventy-five percent (n = 9) of respondents said their parole experience had been
good or wonderful. Approximately 25% (n = 3) of respondents identified their parole
experience as being challenging with reasons cited as being strict parole officers or
limited ability to work due to an ankle monitor. One African American participant
summed up his experience by saying:
I don't like it. Cause they want me to do so many classes and uh, I feel like I don't
need all them classes. At the same time, uh, even after I get a job they still want
me to put those classes before my job and I feel like it’s unfair if I'm working.
Like I told them, I don't mind doing peer support or NA class because I have a
known drug problem though I was just selling drugs. So like I told, I asked them,
I don't mind doing one or two of those every other week, but as far as me trying
to complete 4 classes a week, plus work, plus pay child support, plus pay all my
fees, plus take care of home, this and that, the third, I feel it’s unfair and there's
no way out of it and they already told me it's ugly if I don't comply.
Finally, approximately 45% (n = 5) of respondents identified various challenges
encountered upon their release such as restarting in society and limitations due to ankle
monitoring. Approximately 25% (n = 3) identified finding employment as being a
challenge. Approximately 20% (n = 2) of respondents said they did not have any
challenges, while 5% (n = 1) identified financial difficulties as being a challenge, with
another 5% (n = 1) identifying transportation as being a challenge. Responding to a
question about the challenges encountered while on parole, one African American
respondent said:
For the people that was out here, I'm not moving fast enough for them. It's kind of
pressure on me. Because I know how to get it other ways and it seems like when I
got it the other way nobody tripped. But now that I'm doing it the right way it's
taking longer and people are starting to get antsy, and uh, uh, it's, it's kind, kind of
overbearing at times, but I'm surviving.
Evidence of Trustworthiness
Credibility occurred twofold. The quantitative study had a moderate sample size
(N = 126) with three racial groups to compare results. The qualitative study had an
adequate (n = 12) sample size that were interviewed to allow for different racial
perspectives to be given (e.g., triangulation). There were limited member checking due to
the time constraints of the study. However, the interviews were taped to ensure the
accuracy of participant answers.
There were no changes made regarding transferability. Participants were allowed
to give a thick description, which provides greater detail about participant experiences
within the criminal justice system. However, participants did not indicate how or why
they became involved in the criminal justice system. Also, participant living situations
(e.g., SES), and educational backgrounds were given. These descriptions are for each
racial group in the study.
There were no changes to dependability. Audit trails that explain in detail how
data were collected and kept ensure dependability. Also, providing a copy of the
instrument and interview questions within the appendix of the proposal shows the
accuracy of data analysis.
There were no changes to confirmability. Review of data and overall findings
occurred to ensure clarity and understanding. For the quantitative portion of the study, the
results using the total scores from the CD-RISC and the multidimensional locus of
control scales provided data for each racial group and individual (Connor & Davidson,
2003; Levenson, 1974). For the qualitative study, the interview questions along with the
verbatim answers given by each participant were provided. Similarities among and within
racial groups were reviewed and audited. Keeping an open mind and allowing
participants to express their experiences within the criminal justice system addressed
reflexivity.
Summary
Chapter 4 gave the results of the mixed-methods study regarding resilience, locus
of control, and potential reasons for recidivism from the parolees’ perspective. Although
there were no statistically significant differences in resilience and multidimensional locus
of control scores racially, information obtained during the interviews suggest that
although parolees may feel optimistic about their release, they still face challenges with
regards to employment, financing, housing, and education. Thus, these challenges offer a
glimpse into the reality of parole and how difficult these challenges may become in the
future. A more detailed exploration of the study findings is discussed in Chapter 5.
Chapter 5: Discussion, Conclusions, and Recommendations
Introduction
The purpose of this mixed methods study was to determine if there were racial
differences among former offenders regarding resilience and locus of control that may
contribute to or affect recidivism. The study consisted of two parts. In the quantitative
portion, I used the Connor-Davidson Resiliency scale (CD-RISC) and the
multidimensional locus of control scales using the total scores of the CD-RISC and the
scores from the subscales internality, powerful others, and chance for the
multidimensional locus of control scales (Connor & Davidson, 2003; Levenson, 1974).
The qualitative part consisted of participant interviews to gain an understanding of
resilience and the challenges that may contribute to recidivism. Together, both portions of
the study provided information on how individuals perceive resilience, locus of control,
and the challenges faced by ex-offenders.
Summary of Key Findings
The quantitative part of the study showed that there were no racial differences in
how Caucasians, African Americans, and Hispanic ex-offenders viewed their resiliency.
The same results were seen for locus of control; there were no significant racial
differences in the overall multidimensional locus of control scales results. However, in
the qualitative portion of the study, there were some noticeable trends expressed by the
participants. Many of the participants reported having a GED and being employed;
however, there were some racial differences. Of the four African American participants,
only one was employed. Half of the Caucasian participants were employed, and all the
Hispanic participants were currently employed. Around 60% (n = 6) of the participants
came from middle-class environments; however, half of the African American
participants came from poor neighborhoods. Roughly 80% (n = 10) of the participants
were placed in new neighborhoods upon their release from prison, with 90% (n = 10) of
those participants being placed in neighborhoods described as poor or middle-class. With
regards to family support, around 90% (n = 10) of participants did have family support,
with approximately 55% (n = 6) receiving emotional assistance, financial assistance, or
both to aid in their transition from prison.
Only half of the participants indicated that they received some training (e.g.,
education, trade) while incarcerated. Approximately 50% (n = 3) of the training received
was for custodial work or cooking. Only one participant received some type of training to
work with Microsoft Office products, and two participants received some skilled trades
training (e.g., masonry, HVAC). Finally, 75% (n = 9) of participants described their
parole experience as positive at this point. Challenges cited now while being on parole
centered on employment, restarting their lives, financial strain, and transportation.
Interpretation of Findings
The qualitative findings extend the knowledge in the discipline by providing more
information regarding the challenges that may impact and contribute to recidivism.
Although 55% (n = 7) of the participants were employed, there were clear differences
racially, with regards to African American participants. Previous researchers have shown
that African American men were twice as likely to lack employment than Caucasian men
(Teti et al., 2012). These findings were confirmed in this study, with approximately 75%
of African American participants being unemployed, while 50% of Caucasian men were
unemployed. The findings also showed that half the African American participants come
from poor neighborhoods, have limited education, and limited skills for viable
employment. As one participant indicated, the challenges he faced was finding a job
while trying to balance the demands of his family and the requirements of parole.
Additionally, many of the participants were paroled to different neighborhoods, with half
being classified as poor, where limited jobs existed. Kubrin et al. (2007) indicated that
ex-offenders usually return to these same environments that help to create the cycle of
criminality and recidivism.
All of the participants in the qualitative study were a part of a re-entry program in
which drug offender classes were required as a condition of parole. While none of the
participants indicated that they received drug offender or substance abuse classes during
their incarceration, some effort was being made upon their release. However, the
challenge remains; substance use and problems surrounding addiction may contribute to
offenders' inability to follow the strict rules that are imposed during supervision (Kubrin
et al., 2007). Additionally, the stigma of being an offender may cause further challenges
while parolees are trying to reintegrate back into society with roadblocks surrounding
housing, employment, and education (Kubrin et al., 2007). As one participant indicated, it
was challenging finding accommodation in a retirement home due to his past criminal
record. Also, the importance of family or social support is backed up in the literature and
this study’s findings. Most of the participants had good social or family support, which
research shows reduces the risk of recidivism (Martinez & Christian, 2009).
Finally, others factors that may impact the trajectory of recidivism are social
barriers. Hass and Saxon (2012) indicated that the lack of appropriate skills such as
education and employment are barriers that negatively impact how successful offenders
are in society. These obstacles were also reported by participants, with the majority
having only GEDs and limited employable skills beyond that of fast-food and custodial
work. Furthermore, offenders are limited in the type of employment they can have due to
a lack of education, training, and their criminal records, which further places a barrier in
their socioeconomic attainment overall (Hass & Saxon, 2012).
Limitations
Although the findings offer some insight as to how parolees view their
experiences and challenges, there were some limitations. One limitation is that the
qualitative findings are not generalizable to all parolees because of the small sample size
(n = 12). Also, the CD-RISC and multidimensional locus of control scale scores showed
a snapshot in time and may not represent a significant portion of parolees. As time and
challenges increase, resilience and locus of control may decline. As previous longitudinal
studies showed, recidivism is most likely to occur within the first 3 years of release,
especially for African American parolees (McGovern et al., 2009).
Another limitation of the qualitative study is that the participants may not have
revealed their true experiences on parole. Although most described their experiences as
being good, some felt that their experiences were challenging due to the strictness of the
parole officer, familial or social pressure, and limited autonomy due to ankle monitoring.
These findings again are not generalizable to all parolees, who may express their
experiences differently as more challenges occur during their parole time.
Recommendations
Future researchers should focus on longitudinal examinations of parolees’
resilience and locus of control. As I noted in the previous section, resilience and locus of
control are variable and may change as more challenges are encountered. It would be
appropriate to garner additional information about these factors throughout the parole
experience at certain intervals (e.g., 6 months, 12 months). Furthermore, future research
could focus on how a combination of factors such as race, education, family support, and
skill impact parolees’ success or failure over time.
Implications
This study leads to positive social change at an individual, organizational, and
societal level. Data obtained from the qualitative study can help parolees better
understand how certain factors (e.g., lack of education, employment) may negatively
impact their success on parole. At the organizational level, probation or parole officers
and others working with parolees can learn how factors such as lack of skill, education,
family support, neighborhood, and socioeconomic status can impact a parolee’s success
while on supervision. Being aware of these factors can help create programs to assist
parolees to overcome some of these challenges, thus potentially impacting the recidivism
rate. Last, on a societal level, understanding the challenges parolees face will help create
positive social change by assisting with the implementation of programs that focus on
education, employment, and housing to help support parolees, thereby increasing their
ability to successfully reintegrate back into society and decreasing their chance of
recidivism overall.
Conclusion
Many factors impact an individual’s success or failure while on parole. Racial
disparities, education, socioeconomic status, skill, and family support all play a role in
how successful an individual is or is not while under supervision. By understanding the
immense challenges parolees face, those working with this population can better assist
them in meeting these difficulties head on. By working together, organizations and
society can create change by offering support and a second chance that may finally
impact the recidivism rate.
