Domestic Violence and Demographic Variables as Predictors of Resiliency Among Young Adults.

Chapter 1: Introduction to the Study

Introduction

Domestic violence is a detrimental, widespread phenomenon that impairs family

bonding relationships with children. This impairment can often result in emotional pain

that inevitably leads to physical abuse and poor developmental processes (Foshie et al.,

2016; Margolin & Gordis, 2000). Domestic violence affects a large portion of the

adolescent population in America, yet it is problematically difficult to determine the

exact extent of its devastating effects. Exemplifying this issue, the U.S. Department of

Justice Bureau of Justice Statistics (DJBJS; 2000) proposed that domestic violence can be

perpetrated among current or former spouses, parents, or nonmarital partners in the home

where children live, but failed to mention how domestic violence influences the behavior

of adolescents who have witnessed the overwhelming violent events. On the other hand,

the U.S. Department of Justice (2000; Jan, Adeline, Angela, Danielle, & Deborah, 2015)

and the National Crime Victimization Survey (1999) reported that domestic violence

affects not only the direct victims but also witnesses to the event.

Because of these issues with measuring the influences of domestic violence,

estimates of the instances of domestic violence are difficult to determine. Specifically, the

precise incidence of domestic violence in American adolescents’ responses is nebulous

(Compton, 2010). There are several reasons for the lack of clarity in the data: (a) not all

incidences of domestic violence affecting adolescents are reported; (b) even on surveys

that purport to investigate this national epidemic crisis, there are no social scientists

whose sole duty is to gather information on this topic; and (c) scholars disagree on the

definition of domestic violence and its effects on adolescents’ resilience (Foshee et al.,

2016).

Nevertheless, research indicates that the impact of domestic violence on

adolescents’ emotional response is one of the prevalent social diseases in America.

Despite issues which may exclude many victims of domestic violence from being

acknowledged, Compton (2010), Giles (1998), and Carlson (2003) revealed an estimated

900,000 victims of violence with at least 160,000 fatal bodily injuries and hospitalization

among women and adolescents. Moreover, the results of this study indicated that

domestic violence is not limited to one socio-cultural or socio-economic background,

although it may vary along several lines, including race, gender, and age (see Bureau of

Justice Statistics, 2000; Jan et al., 2015). FBI homicide data also confirmed that domestic

violence has a significant effect on adolescents (Compton, 2010). Moreover, the effects

and outcomes of exposure to domestic violence can translate to individuals’ experiences

as young adults (Widom & Wilson, 2015). Therefore, it is clear that domestic violence is

an issue of epidemic proportions that may affect young adults’ resilience. These effects

are evidenced firsthand in many foster homes and treatment facilities (Carney, Buttell, &

Dutton, 2007; Compton, 2010).

Background

After many years of research on domestic violence, it appears that this social

travesty is increasing within American society (Beverly et al., 2015; Creswell, 2008).

Furthermore, poor definitions, poor research surveys, and poor methodologies in research

about domestic violence have opened doors for criticism (Brookmeyer, Fanti, & Henrich,

2006; Karol, Jeffrey, & Cory, 2015). Frustratingly, current data cannot even accurately

estimate domestic violence victims. For these reasons, Allen and Allen (2006) and Joyce,

Barros, Cafferky, and Johannes (2015) suggested that an estimated 3.3 million children

will be exposed to parental violence. English, Edleson, and Herrick (2007) and Vasquez-

Salgado, Greenfield, and Burgos-Cienfuegos (2015) suggested that most research

provides generalized findings based on old data and outdated views of domestic violence

rather than identifying specific issues affecting present day American families. Because

of the neglect in this area, child maltreatment, child abuse, neglect, sexual abuse, physical

abuse, and other forms of violence between family members continue to proliferate

(Allen & Allen, 2006; Monique, 2015). Moreover, this violence can be passed on

between generations (Widom & Wilson, 2015).

Thus, there existed a significant challenge to the accuracy of data that predicted

young adults’ resilience. The proliferation of domestic violence and the culture which

allows it to perpetuate have not only affected law makers’ and practitioners’ views, but

have also misled the public opinion about domestic violence and its victims (Karol et al.,

2015). Further study was necessary to eliminate this continuing problem (Karol et al.,

2015). Specifically, there was a gap in the literature regarding the factors that can predict

resilience among young adults who were exposed to domestic violence.

Research showed that negative outcomes as a result of risk factors, such as

domestic violence, included self-harming tendencies, anger and frustrations, fighting,

alcohol and drug abuse, juvenile delinquency issues, suicide, depression, and spending

time in juvenile detention/treatment facilities (Beam, Gil-Rivas, Greenberger, & Chen,

2012; Plancherel & Bolognini, 2012; Storksen, Roysamb, Moum, & Tambs, 2010). Once

the individuals are removed from detrimental home situations, however, their resilience

may increase (Kassis, Artz, Moldenhauer, Geczy, & Rossiter, 2015). Potential factors

that may predict resilience development included age (Ali, Naylor, Croot, & O’Cathain,

2015; Garthe, Sullivan, & Kliewer, 2014; Jan et al., 2015); gender (Black et al., 2015;

Dietz et al., 2014); ethnicity (Danquah, Wasserman, Meininger, & Bergstrom, 2010;

Flores, Cicchetti, & Rogosch, 2014; Holland & Gill, 2010; McKnown-Johnson & Rhodes

2013; Zautra, Hall, & Murry, 2010); individual emotional and physical abilities

(Monique, 2015); types of exposures (Dawn & Lynda, 2015); frequency of domestic

violence exposure (Johnson & Easterling, 2015); and when the exposures occurrences

likely stopped by either removal from the social environment or by intervention agencies

or groups that intervened to assist the individual from further emotional and physical

harm from such violent environment (Fossie et al., 2016). Research regarding the

influence of domestic violence on resilience was conflicted, suggesting it might either

improve resilience (Anderson, Renner, & Danis, 2012) or lessen it (Kassis, Artz, &

Moldenhauer, 2013). Further research was required to understand whether and how the

variables in conjunction predict resilience among adolescents after being removed from

the domestic violence situation.

Problem Statement

Domestic Violence

Domestic violence affects many adolescents regardless of ethnicity, age, gender,

race, or socio-economic background (Hamberger, 1994; Monique, 2015). Recent reports

on the cause of family violence suggest that poor home management, economic situation,

unemployment, parental education, and relationship status contribute to domestic

violence (Bradley & Corwyn, 2002; Graham-Bermann, 1994). The effects may continue

into young adulthood, as Carlson (2000) and Widom and Wilson (2015) proposed that the

domestic violence cycle potentially perpetuates itself among prior victims. Specifically,

Storksen et al. (2005) found that as child develops within a domestic violence situation,

he or she will begin to see the abusive relationship between his/her parents and may

demonstrate emotional withdrawal. Storksen et al. reported that this developmental

problem may lead the child, and later the young adult, to engage in self-harming

behaviors, such as anger and frustration.

In the long term, domestic violence, particularly affects those exposed to domestic

violence within the adolescent stage of development. Adolescents who are exposed to

domestic violence are reported to have emotional and development deficits (Allen &

Allen, 2006; Carlson, 2003; Johnson & Easterling, 2015). According to Kupersmidt

(1998) and Olszewski-Kubilius, Young Lee, and Thomson (2014), the adolescent stage

has been characterized as the time for developing self-awareness and independence,

especially from age 10-17 years. Regardless of variable age differences, negative

interaction and poor bonding relationships between parents and the child aged 10-17

promotes poor cognition and mediated trauma, thereby hindering the ability to set goals

toward self-actualization (Maureen, Dawn, & Lynda, 2015; McElliskem, 2006).

Therefore, when domestic violence occurs, the child could get stuck without

improvements in maturation that may translate to their young adulthood. Moreover, when

the same child experiences threats, emotional instability, maltreatment, and neglect,

he/she may begin to use those aforementioned traumatic experiences to perpetrate

aggression, not typically correlated with the child’s age, gender, or ethnicity (Carlson,

2000; Johnson & Easterling, 2015). In sum, domestic violence experiences affect

children’s growth, delay age progression and maturation, create speech impairment, and

lead to poor resilience (Bliststein, 2005). These effects may have a long term effect,

which requires additional investigation of factors that might influence resilience among

young adults.

In prior research, age, gender, and culture have been preliminarily examined for

their effect on the extent of damage done by domestic violence. Grotberg (2010) posited

that in spite of age, culture, and gender differences, generally adolescents who are

exposed to domestic violence exhibit poor emotions, learning disabilities, self-harming,

low self-esteem, and aggression. They can also exhibit symptoms of chronic depression

and posttraumatic stress disorder (PTSD), leading to mental health disorders (Berk 2000;

Lanza & Taylor, 2010). However, individuals’ responses to external adversities may

differ slightly from each other given the individual’s age, culture, and gender (Fergus &

Zimmerman, 2005). These factors may influence why some young adults exposed to

domestic violence are more resilient than others (Fergus & Zimmerman, 2005). Kliewer

and Murrelle (2007) and Jan et al. (2015) proposed that adolescents’ cultural response to

domestic violence occurs in different emotional adjustments given their upbringing.

Therefore, domestic violence in general is a negative phenomenon on personal growth

regardless of gender, culture, and age (Berk 2000; Jan et al., 2015; Lanza & Taylor, 2010;

Whipps, 2009); however, environmental factors can determine the extent of these

developments.

Resilience

Within the field of psychology, resilience is presented as an individual’s ability to

cope with social, environmental stress in spite of adversity (Lanza & Taylor, 2010;

Ungar, 2004a). Resilience research has gained much attention among scholars despite

differing opinions that resilience can be based on individual’s temperament and family

upbringing (Margolin & Gordis, 2000; Olszewski-Kubilius et al., 2014). Within the

treatment facility setting, studies have shown that adolescents have the ability to cope

with external predicaments that can be incorporated in therapy (Ali et al., 2015; Masten,

2009). Masten (2001) proposed that “programs will be most effective when they tap into

these basic but powerful systems” (p. 235), yet current models of resilience continue to

be contested in the literature (Ungar, Ghazinour, & Richter, 2013).

Adolescence is considered the most vulnerable stage for the development of

resilience (Margolin & Gordis, 2000). Fergus and Zimmerman (2005) reported that

adolescents’ resilience development is achieved when the treatment plan is focused on

strength-based treatment, wherein the therapist investigates areas of focus for particular

individuals. This strength-based criterion should also consider the individual’s age,

gender, and culture to achieve a positive therapeutic outcome as these can be reinforced

through positive interactions and rapport with the practitioner or therapist (Fergus &

Zimmerman, 2005). A predictive model for resilience would help to inform such

intervention for individuals previously exposed to domestic violence.

Domestic Violence Treatment and Resilience

Unfortunately, the strength-based focus of research has not yet reached treatment

for victims of domestic violence. This is a problem in both protective supervision and

among the general population. According to the Child Welfare System (2011), a large

number of American minors under protective supervision are domestic violence victims,

yet many studies do not attempt to determine whether and how resilience is developed for

these victims, and if demographic variables might influence the development of

protective factors and resilience. In other populations, researchers have determined that

exposure to domestic violence increased resilience (Anderson et al., 2012). In addition,

demographic factors that may influence resilience development included age (Ali et al.,

2015; Garthe et al., 2014; Jan et al., 2015); gender (Black et al., 2015; Dietz et al., 2014);

and ethnicity (Danquah et al., 2010; Flores et al., 2014; Holland & Gill, 2010;

McKnown-Johnson & Rhodes 2013; Zautra et al., 2010). It is not known whether and

how the variables in conjunction predict resilience among young adults after being

removed from the domestic violence situation.

The discussion of factors influencing resilience among young adults exposed to

domestic violence is essential because Family and Youth Protective Services, Youth

Probation Departments, and the Juvenile Courts have traditionally responded to

adolescent victims by providing crisis intervention programs (Flores et al., 2014).

However, lack of current research data describing the factors that predict the

development of resilience among young adults who were victims of domestic violence

results in little or no information to generate appropriate treatment programs that will

help victims set personal goals for the future. Based on these issues, a significant gap

existed in the literature concerning the variables that influence the development of

resilience among young adults.

Purpose of the Study

The purpose of this quantitative study was to examine whether domestic violence,

age, gender, ethnicity, individual emotional and physical abilities, types of domestic

violence exposure, and frequency of domestic violence exposure can adequately predict

resilience among young adults in the Midsouth region of the United States. Resilience,

the criterion variable, was defined as the individual’s ability to bounce back and set

personal goals for future functioning. In this study, I applied Bronfrenbrenner’s

ecological systems theory to examine domestic violence predictor variables experienced

in the mesosystem (home) in the population. The criterion and predictor variables in this

study were: (a) resilience (criterion variable), (b) domestic violence, (c) age, (d), gender,

(e) ethnicity, (f) individual emotional and physical abilities, (g) types of domestic

violence exposure, and (h) frequency of domestic violence exposure based on

individual’s exposure experiences.

I aimed to create an initial, exploratory, predictive model for resilience among

young adults based on variables including exposure to domestic violence, age, gender,

ethnicity, individual emotional and physical abilities, types of domestic violence

exposure, and frequency of domestic violence exposure. The results provided from this

study were expected to enhance understanding of the demographic factors that influence

adolescents’ behavioral responses and be used to devise appropriate treatment plans that

will help adolescents recover from poor home environment experiences (see Garthe et al.,

2014). While not a focused purpose of this study, the results may potentially provide a

rationale for improved treatment methodologies among young adults.

Research Question and Hypotheses

I developed the following research question to guides this study:

Do domestic violence, age, gender, ethnicity, individual emotional and physical

abilities, types of domestic violence exposure, and frequency of domestic violence

exposure, when in linear combination, significantly predicts resilience as

measured by the Resilience Scale among young adults in the Midsouth region of

the United States?

: Domestic violence, age, gender, ethnicity, individual emotional and

physical abilities, types of domestic violence exposure, and frequency of

domestic violence exposure, when in linear combination, do not

significantly predict resilience as measured by the Resilience Scale among

young adults in the Midsouth region of the United States.

: Domestic violence, age, gender, ethnicity, individual emotional and

physical abilities, types of domestic violence exposure, and frequency of

domestic violence exposure, when in linear combination, significantly

predicts resilience as measured by the Resilience Scale among young

adults in the Midsouth region of the United States.

Theoretical Framework

The theoretical framework for this study was the socioecological theory advanced

by Bronfrenbrenner (see Bradley & Corwyn, 2002; Gunlicks-Stoessel, Mufson, Jekal, &

Turner, 2010), which looks at an individual’s development within the context of the

system of relationships that form behaviors within an individual’s environment. Key

concepts of this theory include microsystem, mesosystem, and exosystem (Bradley &

Corwin, 2002). To study a person’s developmental process, not only the immediate

environment (mesosystem) but also the larger environmental interactions (exosystem) are

investigated (Gunlicks-Stoessel et al., 2010).

Bronfenbrenner’s theory posits that complex layers of a social environment affect

developmental responses and behaviors (Bliststein, 2005; Bornstein, Hahn, & Haynes,

2010). This assertion infers that the interaction between factors in the person’s biological

stages (microsystem), the immediate family/community environment (mesosystem), and

the societal landscape can trigger certain developmental issues (exosystem). In this study,

I expected that the social environment mesosystem (IV), characterized by (a) exposure to

violence and (b) the individual’s age, gender, and ethnicity, would influence resilience

(domestic violence). For example, studies utilizing Bronfenbrenner’s model, including

Archer and Brown (1988); Bornstein et al. (2010); Garthe et al., (2014); Carlson (2000,

2003); and Middlebrooks and Audage (2008) reported that (a) witnessing poor parental

modeling; (b) witnessing family altercations such as physical fights, arguments and

name-calling between family members; and (c) parents choosing a lover over the children

in the home can become onset for low self-esteem, emotional neglect, maltreatment,

physical abuse, emotional abuse, and aggression. Thus, I used the following key

variables: exposure to domestic violence, age, gender, ethnicity, individual emotional and

physical abilities, types of domestic violence exposure, and frequency of domestic

violence exposure to determine young adults’ ability to predict resilience in this study.

Nature of the Study

In this study, I used a quantitative methodology based on postpositive worldviews

that suggest objective and measurable outcomes, which are required to advance

knowledge (Creswell, 2009; Dietz et al., 2014) and test hypotheses. A step-wise multiple

linear regression design was employed. A multiple linear regression is appropriate for

analysis of joint and separate influences of two or more predictor variables on the

criterion variable, generating a linear equation (Dietz et al., 2014). The linear model

produces a standardized regression coefficient, indicating the relative importance of the

corresponding predictor variables to determine the predicted value of the criterion

variable (Dietz et al., 2014). Multiple linear regression was appropriate because I sought

to predict a quantitative outcome variable (resilience) or criterion variable based on

several potentially predictive independent or predictor variables. The step-wise method

for inserting the variables into the equation was used in order to create a reduced

predictive model that uses only the significant variables.

I drew the sample of young adults included in this study from mentees in the Big

Brother Big Sister (BBBS) community organization in the Midsouth region of the United

States. The BBBS organization connects children and young adults with mentors from

around the county to help them become motivated to self-actualize (BBBS, 2017). This

program accepts children and young adults aged 10 to 26 years from the general

population, although this study only included young adults who were between the ages of

18 and 25. Selecting eligible participants from BBBS helped ensure variability in the

criterion variable, resilience, and resulted in a more accurate predictive equation.

The variables that I investigated as potential predictors of resilience included

exposure to domestic violence, age, gender, ethnicity, individual emotional and physical

abilities, types of domestic violence exposure, and frequency of domestic violence

exposure. Resilience was measured by the Resilience Scale, developed by Wagnild and

Young (1993). Exposure to domestic violence, individual emotional and physical

abilities, types of domestic violence exposure, and frequency of domestic violence

exposure were measured using the Children’s Exposure to Domestic Violence (CEDV)

instrument (Edleson et al., 2007; Margolin & Gordis, 2000; Tilton-Weaver, 2014). The

variables of age, gender, and ethnicity were assessed using a demographic questionnaire.

In this study, I used the term other in association with the variable of culture, the

participants' self-identified culture groups. Some participants may not have considered

themselves members of any of the ethnic groups listed on the questionnaire. Another may

have also been selected by respondents who viewed themselves as mixed race.

Definitions

The main operational definitions required for this study include: adolescence,

domestic violence, adolescents’ aggression, resilience, age, culture, gender, and ethnicity.

The definitions of these terms are as follows:

Age: Part of the duration of an organism or a thing between the beginnings of a

being at any given time aiming at maturation (Bogat, Levendosky, & von Eye, 2005;

Hankin & Mermelstein, 2010).

Aggression: A behavior derived from accumulated violent observations that defile

resilience, leading to frustration as a method of responding to threats (Jacobson &

Mufson, 2010; Whipps, 2009). According to Lippold, Greenberg, and Collins (2013),

aggression is an obstructive behavior “displayed by self-expressive drive to mastery” (p.

156).

Domestic violence: A pattern of behavior by a family member to intimidate, harm,

or dominate their victims (Dutton, 1994; Henrichs, Bogaerts, Sijtsema, & Van Mierlo,

2015).

Ethnicity: The individual’s identity and awareness of belonging to a particular

cultural group (Dietz et al., 2014). This distinction enables an individual to maintain

identity and the characteristics of that group (Black et al., 2015; Buka, Stichik,

Birdthishtle, & Earls, 2001).

Gender: Beyond biological differences, gender develops self-identity depending

on designations of male/female and influences an individual’s ability to respond to the

environmental issues based on society’s expectations (Black et al., 2015; Bradley &

Corwyn, 2002).

Resilience: The strength-based approach or the ability to bounce back and take

control of an individual’s destiny (Jaffee, Caspi, Moffitt, Polo-Tomas, & Taylor, 2007).

Assumptions

I assumed that resilience is a normal function of human experience, wherein

humans adapt particular strategies to cope with “serious threats to adaptation or

development” (see Greenberg, and Collins, 2013; Masten, 2001, p. 228). Therefore, I

assumed that developing a predictive model for resilience was potentially useful for

treatment of young adults who have been exposed to adverse situations to determine the

extent of development in resilience that is required for particular young adults. I also

assumed that appropriate screening of young adults’ experiences in home violence was

necessary to obtain information on the individual’s strength and weakness (Bradley &

Corwyn, 2002; Henrichs et al., 2015). To ascertain the family dynamics, I proposed that

an analysis would reveal the young adults’ involvement in domestic violence and in the

process facilitates placement (see Delfos, 2003; Kernberg, Ritvo, & Keable, 2012).

Lastly, I assumed if the local agencies and administrators understand the impacts of an

individual’s home experiences of the individual’s story, this consideration would

minimize behavioral reenactment and eliminate future recidivism because of resilience

development. These assumptions were verified by surveying young adults in BBBS in the

southwest region of Texas.

Scope, Limitations, and Delimitations

Scope

I designed this study to explore whether or not selected predictor variables can

adequately predict resiliency among young adults. Specifically, I focused on developing a

predictive model to explore the predictive relationship between young adults’ resilience

and several potential predictive variables: (a) domestic violence; (b) age; (c) gender and

ethnicity; (d) individual emotional and physical abilities (Monique, 2015); (e) types of

exposures (Dawn & Lynda, 2015); and (f) frequency of domestic violence exposure

(Johnson & Easterling, 2015). In this study, I constructed a variable-focused model of

resilience, rather than the person-focused model (Black et al., 2015; Masten, 2001). The

outcome of this process established a predictive model for resilience which may be useful

for increasing the efficacy of treatment based on the individual’s history of domestic

violence, age, gender, and ethnicity.

Limitations and Delimitations

The main limitation of this study was the small sample size of young adults drawn

from within a limited geographic region. The population and resulting sample resides in

the Midsouth region of the United States and does not represent the population impacted

by domestic violence in other regions of the country. Consequently, the results obtained

from this study may not be used to generalize the U.S. population and only to the

Midsouth region. Additionally, the study population consisted of Hispanic, African

American, and Caucasian young adults. Also included in the study are some participants

who may not have wanted to identify themselves as members of a particular race or are

neutral in terms of their racial identity. Therefore, it was appropriate to include other as

an option on the questionnaire. Despite these limitations, I expected that the results could

benefit organizations in other regions of the United States who are also working with

young adults affected by domestic violence.

Significance

Significance for Researchers

The significance of this study was based in potentially developing a predictive

model for resiliency by examining exposure to domestic violence, age, gender, ethnicity,

individual emotional and physical abilities, types of domestic violence exposure, and

frequency of domestic violence exposure among young adults. Previous studies of

resilience have produced contradictory results of resilience prediction, and no resilience

studies have focused specifically on young adults exposed to domestic violence after they

have been removed from the domestic violence situation (Kassis et al., 2013; Masten,

2001; Van Wyk, 2011). This study may represent a turn to an asset-based domestic

violence research, wherein researchers emphasize potential assets that young adults

exposed to domestic violence develop. This change in domestic violence research would

be consistent with the asset-based approach in resilience research (Damant et al., 2010;

DeForge, Belcher, O’Rourke & Lindsey, 2005; D’Imperio et al., 2000; Resnick,

Bearman, Blum, & Bauman, 1997; Rutter, 1985).

Significance for Practice

Developing a predictive resilience model can have beneficial effects for treatment

of individuals affected by domestic violence (Hopf, 2010). It is imperative to provide

education on the issues affecting young adults’ coping skills and resilience formation.

The awareness I attempted to develop in this study will make way for social justice

advocates in the best interest of adolescents who have been victims of domestic violence.

The development of awareness will also promote plans for appropriate psychological

evaluation based on young adults’ perception of values and worldviews for their

behaviors before placed in state or private treatment facilities. These suggestions are

significant for therapists and direct care-workers who desire, awareness of young adults’

ordeals in order to acquire therapeutic tools for individual resilience formation (Delfos,

2003; Mowder, Cummings, & McKinney, 2010).

Significance for Social Change

The first step toward social change as a result of this study is the potential

contribution this research may provide in illuminating factors that potentially predict

resiliency among young adults. This new knowledge may assist treatment providers in

determining the potential negative effects of domestic violence and provide a first step in

understanding how to treat adolescents exposed to domestic violence. The project also

was a part of integrating domestic violence awareness within the treatment community.

The outcome could help intake departments, therapists, direct-care workers, and facility

administrators make appropriate assessments of each individual who is a victim of

domestic violence and the impacts this home situation has created on young adults’

resilience response.

When therapists and direct care workers do not understand the individual’s

experiences and worldviews, young adults are likely to be judged based on the presented

behaviors (Daro, 1988). Such harsh judgment, not only delays the implementation of

treatment plans, but also triggers anger, self-defense, aggression, and loss of hope for

resilience (Prince-Embury & Steer, 2010). Therefore, to create social change, community

leaders, including practitioners such as therapists, Child Protective Services, and

treatment facilities, need to collaborate and organize treatment plans based on the

individual needs which consider age, culture, ethnicity, and gender (Daro, 1988; Prince-

Embury & Steer, 2010). This process would help therapists and stakeholders understand

the individuals’ emotions and psychological needs of the individual family member

temperaments (Delfos, 2010). When these key issues are in place, a collaborative effort

between the community leaders and policy makers will become consistent to advocate for

social change in the best interest of these young adults in the community (Gelles, 1997;

Wardle, 1999).

Summary

In this chapter, I proposed that domestic violence is a multifaceted issue that

cannot be easily resolved due to the field’s misconception of ideas and lack of forensic

exploration of the cause due to outdated literature. I also stated that poor home

environments and demographic factors may influence resilience development. Therefore,

I undertook a study that would attempt to create a predictive model for resilience

development as experienced by a sample of young adults. In order to understand the

phenomenon, I examined Bronfrenbrenner’s ecological systems theory, which explains

the relationship between domestic violence experienced in the mesosystem (home) and

its effect on young adults’ emotional development (Bureau, 2009; Edleson et al., 2007;

Edleson, Shin & Armendariz, 2008; Kleinman, Adams, Kashdan, & Riskind, 2013). If a

predictive model is developed, it could have broad significance among policy makers,

therapists, and stakeholders in the region as inevitable tasks, to implement guidance that

will effect changes in placement procedures that aim to eliminate poor resilience. In

Chapter 2, I will provide a literature review and more detailed information on the social

and psychological constructs underlying this study.

Chapter 2: Literature Review

Introduction

The purpose of this quantitative study was to examine whether domestic violence,

age, gender, ethnicity, individual emotional and physical abilities, types of domestic

violence exposure, and frequency of domestic violence exposure can adequately predict

resilience among young adults in the Midsouth region of the United States. In this

chapter, I will examine the influence of an individual’s age, gender, and ethnicity on

domestic violence among young adults in the Midsouth region of the United States.

Through the literature review, I clarify the gap that necessitated this research.

The databases I used in this study included: ERIC, EBSCOhost, PsychINFO,

Mental Measurement Yearbook database, and Academic Search Premier. I accessed these

databases through the online library of Walden University. Other literature I used in this

study included published books and scholarly journal articles to explore the impact of

domestic violence on adolescents’ resilience responses. In this study, I examined some

key topics relevant to this topic: (a) the theoretical framework of Bronfenbrenner’s theory

and nature vs. nurture; (b) an overview of domestic violence, adolescents, and the social

environment; (c) domestic violence impacts on adolescents; (d) resilience overview; (e)

adolescents’ resilience adaptation; and (f) factors that promote resilience.

Theoretical Framework: Bronfenbrenner’s Theory

Bronfenbrenner’s (2009) theory investigated the development of a person who

develops behavioral responses to relationships and context within a social environment.

Bronfenbrenner proposed that complex layers within social environment can affect age

and gender developmental processes as well as behavioral responses despite an

individual’s ethnicity (see Berk, 2000; Lanza & Taylor, 2010). Researchers indicated that

the environment not only contributes to a child’s behavior, but also affects the bioecology

that aids in successful child development (Children’s Health Fund, 2011; Ekstrom &

Young, 2009). In the theory of the bioecological system, Bronfenbrenner posited that

interaction between immediate families, the environment, and human biology are

landscapes for human development (Ekstrom & Young, 2009). Thus, child development

should not be limited to biological trends alone, but should also include the immediate

environment and the child’s interaction within the larger system that forms the

individual’s attitude and behaviors (Addison, 2008; Edwall, 2012). The layers of

interaction that form a child’s behavioral responses, according to Bronfenbrenner (2009)

included the microsystem, the mesosystem, the exosystem, and the chronosystem.

Microsystem

For Bronfenbrenner, a microsystem is one of the layers that creates a person’s

structured interaction with the immediate environment and conditions the individual’s

behaviors (Berk, 2000; Hjemdal, Vogel, Solem, Hagen, & Stiles, 2011). As the individual

develops interaction within the system, complex alliances are formed with the multiple

presences of outside groups, such as peers, extended-family, neighborhood, church,

school, or the childcare environments (Bronfenbrenner, 2009). These outside stimuli

result in dual means of interaction (Bronfenbrenner, 2009). Bronfenbrenner examined

these two types of interaction and classified them as (a) from the child and (b) toward the

child systems of interaction (Berk, 2007). The from the child scenario exists when a

child’s behavior, not meeting family, cultural expectations, causes friction in the home

(Edwall, 2012). For example, a child may use certain words or responses that are

inappropriate to the values. On the other hand, a toward the child scenario is when the

child’s behaviors initiate parents’ consequences of such behaviors (Berk, 2007; Edwall,

2012; Walker, Kerns, Lyon, Bruns, & Cosgrove, 2010). The consequences lead the child

to question their cultural difference to peers, a frustration that may result in negative

responses towards others. This bidirectional influence, according to Bronfenbrenner

(2009), shows how the environment interaction can quickly condition a child’s response

toward others with whom he/she comes in contact.

Mesosystem

The mesosystem is the trust-building structured interaction, connecting the child

and outer groups (Berk, 2000). Teachers, schools, churches, and neighborhoods condition

a child to build trust and develop resilience, and the absence of this structured system can

cause a delay in the development of these behaviors (Ungar, 2011). Ungar’s (2011) study

also revealed that environmental connections affect adolescents and may condition

adolescents to the external stimuli, responses because they are aware of the repeated

accounts of specific violence surrounding their social environment to which the exposure

to those violence exposures could trigger violence responses. Development theorists

assert that as adolescents become used to making their own decisions, community

mentors and parental relationships teach the skills needed to master life challenges

against adversity (Bornstein et al., 2010; Solberg, Carlstrom, Howard, & Jones, 2007).

Bronfenbrenner confirmed adolescents’ connections between positive mentors and

parental bonding relationships, promote motivation toward resilience adaptation (Hurt,

Malmud, Brodsky, & Giannetta, 2001).

Arundale (2004), Hurt et al. (2001), and Wan-Yi (2010) confirmed that higher

rates of co-occurrence between interfamilial and community violence exposure does

eventually exist in most of the adolescent’s environment, following Bronfenbrenner’s

theory; however, positive mentors and parents’ bonding relationships can generate

positive interactions that replace negative experiences and support adolescents’ self-

actualization. Rossman, Hughes, and Rosenberg (2000) and Garthe et al. (2014) clarified

that poor environmental connections not only expose adolescents’ to develop high risks

behavior, but also compromise the individual’s resilience efforts generated by lack of

trust for establishing relationships with others. Therefore, poor environmental

experiences, whether within the family or community, have equal negative effects on

adolescents’ moral life development (Patterson, 2002; Wan-Yi, 2010). In summary,

adolescents’ positive interaction with adults in the family or within the social

environment motivates adolescents to establish trust, positive thinking, and

empowerment to negate resentment for aggression.

Exosystem

The exosystem is the layer of interaction involved in a child’s failure to function

directly or participate actively in the daily decision-making process (Patterson, 2002;

Wan-Yi, 2010). The child’s isolation creates resentment, anger, and despair, all of which

make the child act out without giving second thoughts for his/her actions (Patterson,

2002). When a child is left out or forced to comply with family dictating the child’s

emotional cultivation, he/she may not assume responsibilities appropriately (Berk, 2000;

Goodrum, Jones, Kincaid, Cuellar, & Parent, 2012). Additionally, leaving a child out of

important decision-making processes may result in negative behaviors (Berk, 2000). For

example, a community-based or family issue where a child is denied a voice can become

grounds to feel out of control of his or her environment, which could trigger low self-

esteem, disconnection, impaired thinking, and poor goal setting.

On the other hand, according to Kaufman et al. (2000) when a child is included in

the family discussion of decisions, the child feels empowered to establish relationships

with the environment instead of living in isolation—which sends negative messages that

the environment is unsafe. Consequently, the child’s thinking ability is suppressed and

repressed, resulting in impulsivity, hyperactivity, and aggression (Kaufman et al., 2000).

The child’s id-ego and superego-ego build defense mechanisms against the social system

(Johnson et al., 2002).

Chronosystem

Bronfenbrenner’s chronosystem refers to interaction changes that occur within a

child’s social system (Larson & McQuiston, 2011). Bronfenbrenner explained that the

child’s environment encompasses the dimension of time and experiences for responses

and suggests that environmental development crisis inevitably and frequently occurs

(Garthe et al., 2014; Henderson, 1995). For example, parents fighting, separation, and

divorce are emotional conflicts that could impair a child’s developmental process to

exhibit anger, frustration, blame, low self- esteem, and poor resilience into adulthood

functioning (De Los Reyes, Thomas, Goodman, & Kundey, 2013; Pinel, 2003). However,

as the child becomes older, he/she will begin to process how the environmental changes

may influence his/her future functioning (Carlson, 2003; Gunlicks-Stoessel et al., 2010)

and correlated aggressors.

In summary, Bronfenbrenner’s theory presents the five systems of interaction to

explain the challenges in a child’s development and its potential impacts on emotional

functioning that could become a nursing ground for domestic violence. Therefore, I used

the Bronfenbrenner’s theory as the theoretical framework that provides explanatory value

for this study. Another important influence of Bronfenbrenner’s theory was drawn from

the combination of the simultaneous influences of biological factors (nature) and

environmental factors (nurture).

Nature vs. Nurture

In the field of psychology, the concept of nature vs. nurture is one of the terms

used extensively in child maturation dimensions (Dietz et al., 2014; Zuckerbrot, Cheung,

Jenson, & Stein, 2007;). Bronfenbrenner’s (2009) theory indicated that environmental

effect and genetics are the component parts from which a child could derive a sustaining

system for development, and both genetics and environment can trigger an individual’s

emotional responses (Berk, 2000, 2007; Goodrum et al., 2012). While the chronosystem

examines the role of environment and the dimension of time relating to a child’s

experiences in the social system (Feldman, 2003; Gunlicks-Stoessel et al., 2010), genetic

researchers contend that biology and the social environment impact human emotional

development. According to Jacobson and Mufson (2010), and Ridley (2003), a child’s

biological and social environment play significant roles in the child’s ongoing emotional

development.

Bronfenbrenner argued that in spite of human biology, the interaction between a

child and the social environments can become complex (Berk, 2000, 2007; Sorbello,

Eccleston, Ward & Jones, 2012), thereby resulting in varied responses. Given the nature

of poor environmental nurturing, Bronfenbrenner proposed that when a child grows up in

a violent environment, delinquency is inevitable. Other researchers indicated that as a

result of a violent environment, poor behavioral choices and other psychopathological

dilemma will begin to unfold (Plomin & Spinath, 2004; Williams & Steinberg, 2011).

In summary, the social environment and human biology can trigger certain

behavioral responses, particularly when those experiences are particularly traumatic, as is

the case in domestic violence scenarios. Certainly, parents or adult role modeling are

instruments for redirection that would affect future functioning in spite of vulnerability,

helplessness, and hopelessness of experiences. However, this modeling may not be

available for those children who are exposed to domestic violence, such as the

participants I examined in this study.

Overview of Domestic Violence

Domestic violence has many different names: family violence, intimate partner

abuse, wife beating, battering, child abuse, or family member abuse (Kumar, Steer, &

Gulab, 2010; Rodriguez, Bauer, McLoughlin, & Grumbach, 1999; Straus & Smith,

1999). According to the American Psychiatric Association (APA; 2008), violent acts

toward other family members can result in physical, emotional, and psychological

consequences, especially in adolescent children living in the home. Although current and

former intimate partners perpetrate many incidences of domestic violence, there is no

“typical” victim—it is prevalent in all socioeconomic families, regardless of gender and

with no specific cultural limitation (Bureau, 2009; Prince-Embury, 2010; Rodriguez et

al., 1999). In many reported domestic violence incidences, adolescents, children, and

female partners are more likely to experience bodily injuries, self-harming, and other

psychological and emotional hurt (Carlson, 2000; Rodriguez et al., 1999). Over time,

cognitive impairment among abused adolescents has increased, evidenced in multiple

studies on early childhood trauma and in the intrafamiliar violent exposures (Bureau,

2009; Margolin & Gordis, 2000; Straus, Gelles, & Steinmetz, 1980; Tilton-Weaver,

2014).

The experiences of domestic violence and abuse differ for different couples, but

there are some similarities in the experience, according to the research. In abusive

situations, domestic abuse often occurs between partners after arguments become hostile

(APA, 2008; Lippold, Reenberg, & Collins, 2013). In these instances, physical abuse is

as a result of poor impulse control includes punching, slapping, pushing, choking, or

bodily injury on the victim that could lead to physical and emotional pain (Tilton-

Weaver, 2014). Alternatively, some abusers only engage in verbal assaults. Thompson,

Saltzman, and Johnson (2003) reported that verbal assault causes more mental and

emotional damages as opposed to physical assault, which causes physical harm. Although

verbal attacks may not leave physical marks, they may result in feelings of inadequacy,

low self-esteem, and later, aggression for self-defense (Thompson et al., 2003). It is also

noteworthy that when a partner abuses a parent, the child is emotionally affected (Boydell

& Ferguson, 2012; Thompson et al., 2003). The consequence may be that the child will

use the learned behavior to develop social responses and other psychopathology against

the associated environment. These secondary effects are troubling due to the prevalence

of domestic violence; I will review these effects in the next section.

Domestic Violence Statistics

Researchers estimated that approximately 14 million American adolescents are

affected by domestic violence annually, and over 3.3 million of adolescents have

witnessed violence in their homes each year (APA, 2008; Bureau, 2010; Thompson et al.,

2003). These numbers were derived from national surveys, which were not designed to

measure individual adolescent’s age, ethnicity, and gender for resilience. Kaufman et al.

(2000), Thrash, Cassidy, Maruskin, and Elliot (2010) and Thompson et al. (2003)

reported that 33.2% of Canadian and 40.2% of U.S. adolescents have witnessed multiple

domestic violence events in their own homes; unfortunately, the estimate again did not

report types of exposure. The oversights and gaps in the domestic violence statistics have

limited the field’s understanding of domestic violence’s impact. Perhaps resulting from

this lack of awareness, the U.S. Census Bureau’s recent poll estimated that domestic

violence has increased from 10% to 30% over the past 50 years from 1951 through 2010

(Allen & Allen, 2000; U.S. Census Bureau, 2010).

It is interesting to note that the effect of adolescents’ exposure to domestic

violence not only affects the lives and responsorial behaviors of adolescents who are

abused, but also impacts the negative development upon which they express emotions in

their future functioning. According to Graham-Bermann and Edleson (2001), it is

estimated that approximately 10 million adolescents are more likely to be exposed and or

witnessing domestic violent acts each year. Prior studies documented the negative long-

term effects of adolescent exposure to domestic violence which is carried on to adulthood

development, thereby causing certain emotional deficits in terms of making personal

decisions in preparation for transition to adulthood life (Fodor, 2010; O’Shea, Spence, &

Donovan, 2013). Although prior research has also stated that not all adolescents exposed

to domestic violence have the tendency to become aggressive or non resilient, however,

the study mentioned some of the deficits that might occur whether the individual is

resilient or not. These effects include but not limited to self-blame, shame, low self-

esteem, anxiety, depression, and PTSD. Giving these effects as maturity continues, it is

also very important to know that adolescents may also exhibit significant anger emotions

as well as behavioral, cognitive, and social problems (Evans, Davies, & DiLillo, 2008;

Fowler & Chanmugam, 2007). In addition to these symptoms and characteristics in

behavioral responses, it is also reported that adolescents living in homes where domestic

violence is frequently observed have a greater risk of becoming abuse themselves in part

because the observed behavior often and always dictates the individual information

processing, cognition, and the physical abilities associated with internal locus of control

to respond to the external stimuli.

Contextual Emphasis in the Associations Between Domestic Violence and Early

Adulthood Mental Health

The current consensus among researchers is that individual’s responses to

domestic violence exposure may differ from each other (Fergusson et al., 2005). It has

also been reported that exposure to domestic violence associated with increased rate of

significant symptoms of fear, self-blame, speech deficits, anger emotions, anxiety, and

depression for the witness victims (Gao et al., 2010; Lindhorst & Beadnell, 2011; Straus

& Mickey, 2012). However, the frequency and severity of the exposure is also significant

as its negative consequences may as well depend upon the extent to which the victimized

parent expresses the aftermath effects of the event such as symptoms of depression and

anxiety and also whether or not the parent has coping skills and or supported by other

family members or even the children in the home (Howell, 2011; Renner & Boel-Studt,

2013). Holmes (2011) used secondary data analysis from the National Survey of Child

and Adolescent Well-Being (NSCAW) to determine that domestic violence witnessed

between parents associated significantly especially with poor mental health outcomes.

Holmes further suggested that major depressive episodes, heavy alcohol use, and other

substance abusive related behaviors may affect the coping skills of children and

adolescents aggressive behavior in the long term (Blackwell et al., 2015; Lang,

Blackwell, Harmer, Davison, & Holmes, 2012; Marshalll, Tilton-Weaver, & Stattin,

2013). Moreover, coping activities on the part of parents in domestic violence situations

such as smoking, partying, and children abandonment can also associate with children

and adolescents externalizing and internalizing problems that can be carried on to

adulthood even after controlling for numerous covariates (Ashford, van Lier,

Timmermans, Cuijpers, & Koot, 2008; Graham-Bermann & Edleson, 2001; MacMillan &

Wathen, 2014; Tjaden & Thoennes, 2014).

Although a significant direct association exists between parent’s exposure to

domestic violence and the impacts it creates in children and adolescents’ emotional

responses and behavioral distress (Yoo & Huang, 2012 ), it is also worthy to note that the

effect of domestic violence on children and adolescents may emerge in poor physical

abilities associated with helplessness and hopelessness mediated by parental poor coping

skills/distress (Renner & Boel-Studt, 2013 ), substance abuse (Skeer, McCormick,

Normand, Buka, & Gilman, 2009; Skeer et al., 2011 ), family dynamics and structures,

and low income (Turner, Finkelhor, Hamby, & Shattuck, 2013). All of these factors may

contribute to the type of resiliency children and adolescent may display as maturity

progresses. However, no study existed that conjoined the confluence of variables,

including exposure to domestic violence, age, gender, ethnicity, individual emotional and

physical abilities, types of domestic violence exposure, and frequency of domestic

violence exposure, that might contribute to or detract from resilience.

There is yet to be disclosed in previous studies the form of analysis by

incorporating scientific sound data to promulgate evidenced based detail findings in

regards to this social phenomenon (Howell, 2011). This is because most studies used

cross-sectional rather than longitudinal formation of research, which makes it very

difficult to adequately control the range of potential emphasis in relating to the public

their outcome assessment to help treatment facilities who work with this population to

draft the appropriate treatment plan based on the individual young adults experiences

growing up in the violent homes. As a result, there are needs among scholars to plunge

into research in this area with prepared motivation to help the communities, society, law

makers, and treatment facilities to determine young adults’ behavioral issues and also to

ascertain whether within this population if gender differences impacts way they respond

to domestic violence also become aware of the percentile rate among those who may

perceive themselves as resilient (Halford, Farrugia, Lizzio, & Wilson, 2010; MacMillan

& Wathen, 2014).

According to Feder and MacMillan (2012), Halford et al. (2010), MacMillan and

Wathen (2014) and Tjaden and Thoennes (2000), the comorbid rate of adolescents home

violence exposure is between 45 and 70% giving the percentile rate of the survey

collected from a national database in 2009. These bodies of studies have reported

substantial negative impacts that this type of exposure contributes to individual responses

to domestic violence in adolescents in preparation to transition to adulthood. In spite of

this body of literature, several gaps in these research literatures require significant

attention to address the cultural implications that influence how the individuals may

respond to this social phenomenon.

Second, the studies conducted in this area concentrated on adolescents still living

with their mothers in battered women’s shelters (Gilbert, El-Bassel, Chang, Wu, & Roy,

2012). Researchers have also failed to obtain information from adolescent who have been

considered as runaway children and are left to fetch on their own exposing them to re-

victimization. Thus, I sought to gather information to analyze young adults in response to

their domestic violence experiences and the impact of these experiences in their

responses to the external stimuli and their quest to emotional transition to adulthood. I

believe this formation in the present study will clarify the ongoing effects of domestic

violence on adolescent transition to adulthood. It is the intention of this study to identify

the effects of domestic violence on adolescents with respect to the relationship with

resilience. Lastly, the present study examines whether exposure to domestic violence,

community violence exposure and substance abuse have differential effects on how

young adults perceive themselves as resilient in their transition to adulthood.

According to Carlson (2004), over 4 million adolescents living in violent homes

have experienced physical and emotional abuse, leaving children with emotional scars

that more often resulted in aggressive behaviors than resilience. In the late 90s, studies

mentioned the impacts of environmental exposure on adolescents’ violence (Polivka,

Lovell, & Smith, 1998). It was confirmed by the 1999 National Child Abuse and Neglect

Data Systems (1999) that approximately 826,000 children (12 out of every 1,000) are

victims of family and community violence. These environmental influences may lead to

problematic adolescent behaviors due to low resilience or the lack thereof (Lee, Hankin

& Mermelstein, 2010; Patterson, 2002). The relation between these factors stems from

the Bronfenbrenner’s theory of the microsystem, which revealed that poor behaviors are

consequences of harsh environments, as they lead to poor decision-making to compensate

for hurtful feelings and anger against the authority figures. The section below examines

the influence of domestic violence on adolescents.

Domestic Violence’s Impact on Young Adults

According to Bronfrenbrenner’s ecological system, human behavior centers on

environmental impacts that generate responses (Underwood, Tallbott, Mosholder & von

Dresen, 2008). Multiple studies of youth within residential facility schools found that

behavioral responses combined with family violence histories preclude physical,

emotional, and sexual abuse (Underwood et al., 2008) and other psychiatric issues

(O’Donohue & Ferguson, 2006). However, Fodor (2010), O’Shea, Spence, and Donovan

(2013), and Thompson et al. (2003) noted environmental upbringing may link to different

emotional responses. Domestic violence may have cognitive, emotional, behavioral, and

social effects on young adults.

Cognitive Effects

Young adults exposed to violence are potentially prone to experience life-trauma

followed by vulnerability, helplessness, extreme hopelessness, and self-worthlessness

(Clauzade, 2009; Sorbello, Eccleston, Ward, & Jones, 2012). Consequently, poor

cognition has been reported as a predominant issue among adolescents exposed to

domestic violence with little or no self-control. According to Pervin and John (2001) and

Ridley (2003), parent-to-child violence was associated with chronic abuse, maltreatment,

cognitive deficits such as low IQ, poor oratory skills, poor nutrition, attention, poor

memory, poor visual-motor integration skills and resilience deficit. A number of studies

reported that adolescents’ poor cognitive skills linked to truancy, poor academic, peer

pressure, aggression, and low self-esteem associated with poor home upbringing (Hurt et

al., 2001; Kliewer & Murrelle, 2007; Williams & Steinberg, 2011; Zimmerman &

Arunkumar, 1994). Studies have also noted that a lacking upbringing links to attention

regulation deficits, poor language skills, lack of self-control, hyperactive disorder, poor

information process, and aggression. In sum, poor home upbringing and violence

exposure can deter adolescents’ resilience and their abilities to organize, recall, and

encode information process and the ability of expression (Fodor, 2010; Lazarus, 2000;

Miller et al., 1999). Additionally, exposure to violence can lead to emotional effects.

Emotional Effects

Emotional effects of domestic violence are characterized as a negative exposure

that almost always carries consequences, particularly when such experiences become

frequent in the home (Job, Bernecker, & Dweck, 2012; Kliewer & Murrelle, 2007).

Overstreet and Braun (2000) confirmed that intrafamilial violence often leads to anger,

agitation, withdrawal, isolation, depression, poor problem solving, self-harming and

negative self-concepts. Researchers reported that witnessing and proximity to violence, as

well as being a victim, could increase anxiety and depressive symptoms that would

change adolescents’ worldview about their social environment (Grigorenko & Sternberg,

2003; Johnson et al., 2002; Löckenhoff, Reed, & Maresca, 2012). Thus, whether this

violence is related to immediate family or to community types of violence, adolescents’

resilience development can be compromised. This effect results in other psychological

consequences, such as PTSD, poor concentration, sleep disorder, sudden startling, anger,

tantrum, delinquency, poor task accomplishment, low self-esteem, uninspired, and

intrusive raising thoughts (Alvord & Grados, 2005; Flowers, Hastings, & Kelley, 2000;

Guillaumin, 2002; Pang, 2010a).

Young adults with emotional disorders have often been stigmatized (Boydell &

Ferguson, 2012; Grotberg, 1999) because of their involvement in high risk behaviors and

poor lifestyle. These behaviors, however, should lead researchers and clinicians to

question not the person, but the person’s development process (Alvord & Grados, 2005).

The answer to this question may lie within the unresolved family issues to which

responses are exhibited (Carlson, 2000; Zautra, Hall, & Murray, 2010). Moreover, these

emotional influences can lead to behavioral and social consequences.

Behavioral and Social Effects

It is evident that early childhood development and exposure to violence can

initiate social and behavioral deficits (Zautra et al., 2010). For example, when parental

tactics to teach a child, family values, self-control, and responsibility become ineffective,

it could result in self-defense mechanisms (Calrson, 2000). Negative self-defense

mechanisms not only interfere with the child’s efforts to attain personal goals, but also

hinder information processing to regulate emotions and to understanding of human life,

values, properties, and the society’s expectations (Feldman, 2003; Lock, 2000). The

emotional deficits, irritation and anger characteristic of the adolescent stage can

progressively develop into false identity and low resilience (Kaufman et al., 2000; Linley,

2003).

One study that confirmed the behavioral influence of domestic violence was

conducted in a large number of (140) adolescent participants comprised of experimental

and control groups to which a number of psychological tools were administered,

including the Millon Clinical Multiaxial Inventory (MCMI; Dutton, 1994; Dutton &

Starzomski, 1994), the Minnesota Multiphasic Personality Inventory (MMPI; Dutton, van

Ginkle, & Starzomski,1995), Borderline Personality Organization (Straus & Gelles,

1990), and the Conflict Tactics Scale (Saunders, 1996) to identify triggering factors for

poor behaviors. From a treatment perspective, the experimental outcome revealed that

adolescent borderline personality organization was found in 11 to 15 percent of the

participants due to home violence (Dutton & Golant, 1995; Nastasi & Bernstein, 1994).

In summary, studies on adolescents’ emotional behaviors have long-term

traumatic consequences triggered by poor environment and intra-familial violence for

poor impulse control (Feldman 2003; Johnson et al., 2002; Lippold, Greenberg, &

Collins, 2013). These influences have long term effects for individuals exposed to

domestic violence in their homes, and may alter the way that individuals behave,

according to Bronfenbrenner’s theory. The confluence of these effects are often felt when

a child who has been exposed to domestic violence is placed within a foster home or

treatment facility. Moreover, these effects will continue to influence the individuals’

behaviors as a young adult (Lipton et al., 2013).

Because abuse reports have been exposed in many states’ residential treatment

centers (Cancio & Johnson, 2007; Kernberg et al., 2012; Phillips, Leathers, & Erkanli,

2009), JCAHO has established minimal requirements and expectations for Residential

Treatment Centers (RTC; Cancio & Johnson, 2007; McGuffin, 1991). In 1999, following

policies developed in the 1960s, 1970s, and 1992 for foster home and treatment facilities

to meet adolescents’ psychological and emotional needs, the U.S. Surgeon General, in

collaboration with JCAHO, required each RTC to provide admission policy that will

grant positive impact for resilience formation (Black et al., 2015; Chen & Ma, 2007) The

Surgeon General and the JCAHO set limitations on admission policies that disallow

unqualified residential treatments centers for operation. This decision was based on

Werner’s 1982 research on adolescents’ resilience on the Hawaiian island of Kauai,

which continues to set the standard for many professional organizations today. The

following examines Werner’s adolescent resilience overview adopted by the Surgeon

General and the JCAHO.

Prior to the 1980 act, dependent minors lived in many different foster homes and

in different neighborhoods because of aggression and acting out behaviors because of

exposure to domestic violence which led to poor academic achievements (Black et al.,

2015; Pelton, 2008). In 1993, Californian foster parents reported that most children

attended an average of 9 different schools by age 18 and demonstrated significant poor

academic achievement, truancy, poor self-esteem, anger, and shame (Kaufman et al.,

2000; Kernberg et al., 2012). The children’s educational discontinuity as reviewed by

Kaufman suggests that there are multiple negative emotional consequences to a child’s

removal from one home/facility to another. Kaufman also expressed that inadequate

adolescents’ academic record transfer and loss of credits may contribute to poor school

performance and lack of resilience for success.

For these reasons, 70% of minors in foster homes/facilities have significant

domestic violence, abuse histories and other behaviors that led to mental health problems

promulgated by the observed domestic violence and poor home environment (Finlay,

Darlington & Nicastro, 2001; Mowder et al., 2010). Cancio and Johnson (2007) noted

that appropriate psychological academic evaluation as proscribed by the individuals with

disabilities act (IDEA) can improve the individual’s academic success, rather than

leaving the child to dwell in the vicarious impacts of domestic violence. While we

continue to see the lack of this accommodation in some chartered schools in the Southern

region of the United States, only 15% graduate from high school, which is below the

American taxpayers’ expectations (Cancio & Johnson, 2007; Kleinman et al., 2013).

In recent years, domestic violence has been reported as a major contributor to the

over 6% annual increase in child dependency in the United States (Lloyd & Emery, 1994;

Mowder et al., 2010). According to Greene, Coles, and Johnson (1994), the 1985 and

1996 reports suggested that over 2.9 million adolescents’ dependency increased. In 2005,

a report was submitted to the house committee who were assigned to investigate the truth

of this claim. It was found that both foster homes and treatment facilities population have

decreased 37% between 1994 and 2000 in both academics and admissions (Chambers,

2008; Kumar et al., 2010). Based on analysis of the timeline of these reported estimates,

American society is not necessarily declining in child dependency cases and treatment

facility and foster homes are not necessarily declining.

According to Yehuda’s (2004) report, it was observed that there are many

treatment facilities and foster home care services being built in Bexar County (Texas),

California/Los Angeles, Orange County, Colorado, Michigan State, and New York. The

U.S. Advisory Board on Child Abuse and Neglect (USABCAN, 2010) argued that the

decline in minor placement or the decreasing number of foster homes and treatment

facilities is misleading, leading society to pay minimal attention to the cause of

adolescents’ removal from home, emotional stress, and psychological needs as a result of

domestic abuse. Moreover, this decrease may influence the policymakers’ views of the

importance of this social phenomenon (Howell, 2008; Lanza & Taylor, 2010).

In summary, as of 2008, research shows that about 650 nongovernmental

residential programs have been built nationwide with certain program methodologies to

deliver treatment plans that would address adolescents’ psychological and emotions

needs in their quest for resilience (Karol et al., 2015; Martin & Pear, 2007). These

statistics indicate that, in terms of decline in adolescents’ dependency and placement,

nothing has changed (Monique, 2015; Phillips et al., 2009). To gain a more accurate

measurement, the field must reconsider the approach from which these studies were

conducted, examines the methods, and verify whether researchers focused mainly on

government facilities rather than including non-profit or privately owned operated

facilities and the policies that affect private and non-profit program agencies.

Policy Impacts on Facility Programs and Methodologies

After the policy review in the 1980s, foster homes/facilities, quality program

methodologies have varied greatly given the federal status of limitation for adolescents’

behavioral modification intervention reinforcements (Cancio & Johnson, 2007; Martin &

Pear, 2007; Monique, 2015). For example, the southern region of the United States is one

of the regions in the United States that house 133 out risk adolescents, male and female

residents that other private and foster homes would not accept because of the limitation of

operation and licensures. The Southern region of the U.S. facility is licensed to use

approved protocol to administer psycho-emotional treatment to individuals exposed to

domestic violence and abuse. Thus, the behavioral modification process is based on the

regulatory act that compels the facility to use certain methodologies to affect therapy

procedures. These methodologies include the use of point accumulation by each resident

at the end of each clinical hour activity to reward good behaviors, while restrictions

including point deductions, status reversal, treatment extension, and physical restraint are

employed by the facility policy to confront and modify behaviors (Henrichs et al., 2015;

McGuffin, 1991; Phillips et al., 2009).

These methods may not be used in other facilities because of individual facility

state regulations. However, critics have considered this method of operation unequivocal

to behavior modification as it does not implement criteria for resilience. Again, instances

of abuse have been reported in most residential treatment facilities and foster homes,

which prompted the JCAHO to investigate these reported incidences (Kernberg et al.,

2012; Underwood et al., 2008). In most cases, JCAHO finds that the use of consequences

as therapeutic models for behavior modification is successful as long as the methods are

supervised by licensed therapists, medical personnel, and quality complaint

administrators to prevent flashbacks to the individual’s past abuse violent experiences

(Cancio & Johnson, 2007; Chen & Ma, 2007; Monique, 2015). These behavioral

modification programs may facilitate the development of resilience among adolescents

who have been exposed to domestic violence.

Resilience Historic Overview

Resilience Defined

Resilience is an individual’s ability to bounce back regardless of the individual’s

environmental circumstances (Daining, 2005; Hopf, 2010; Jaffe, 1998). Resilience also

refers to the ability to seek or acquire personal and cultural resources that create a

meaningful interaction with others. Most academics agree that resilience refers to an

individual’s skill at continuing normal developments despite environmental adversities

(Deal, 2000, 2002; Siemieniuk, Krentz, Gish, Jessica, & Gill, 2010). Many researchers

concluded that exposure to trauma can create an instinct to certain responses for survival

(Dietz et al., 2014; Ungar et al., 2008). In much of the literature, it has been demonstrated

that resilience emerges when individual efforts are threatened (Gish, Jessica, & Gill,

2010; Masten, Best, & Garmezy, 1990).

The Emergence of Resilience in Treatment Facilities

Prior to resilience research, some studies devoted extensive work to adolescents’

behaviors as opposed to focusing on protective factors to explain catastrophic life event

responses to stages of adulthood transitions. These researchers failed to explain how the

individual’s traits and temperament led to poor resilience (Cicchetti & Rogosch, 1997;

Fredrickson, Tugade, Waugh, & Larkin, 2003; Garmezy & Streitman, 1974). From

Rutter’s, Bronfrenbrenner’s and Werner’s findings, the term “resilience” has developed

into a major theoretical research framework of topics that investigates the environment in

terms of its adverse effects on human adversaries (Werner & Smith, 2001; Yoon et al.

2015). Arguments have surfaced, however, disputing Rutter’s, Bronfrenbrenner’s, and

Werner’s findings that children living with schizophrenic parents show low resilience

compared to adolescents living with healthy parents because of the parents’ poor abilities

to ,care for these adolescents’ basic needs (Luthar, 1999; Thrash, Cassidy, Maruskin, &

Elliot, 2010). Despite this controversy, some adolescents have reported adjustment to

adversity and success in setting personal goals for future achievements (Davies, Winter,

& Cicchetti, 2006).

In sum, Werner and Rutter’s network of research has provided awareness about

adolescents’ behaviors. This awareness educates practitioners about the individual’s drive

toward self-fulfilling (Benard, 1991; Damant et al. 2010; Werner & Smith, 1992). The

studies also pay greater attention to cultural and an individual value, which indicates that

resilience, can be understood as a social construct associated with an individual’s

worldview or the ability to navigate social norms successfully or compress negative

emotions as the individual begins to become aware of the social environment (Garmezy,

1991; Masten & Powell, 2003; Mowder et al., 2010; Williams & Steinberg, 2011).

Resilience among Young Adults

Scholars have credited Garmezy (1973) and Ungar et al. (2008) as first to study

adolescents’ resilience, although their study methods were based on epidemiology that

focused on reasons some adolescents’ are more resilient than others, which also relate to

how much resilient those individuals may carry along to adulthood functioning. Garmezy

proposed that some protective factors, such as rewards, praise, recognition, goal setting,

and self-esteem, are criteria for resilience development. Conversely, poor environment

and low self-esteem could lead to self-indulgence and poor behavior imitations (Black et

al., 2015; Henrichs et al., 2015; Nastasi & Bernstein, 1998).

Unlike Garmezy, Werner, an American developmental psychologist, investigated

the impact of social environment on adolescents’ resilience and their ability to maintain

such traits in adulthood life. In Hawaii where this research was conducted, Werner found

that, depending on the individual’s temperament; at-risk adolescents can make good

choices (Werner, 2004) that adolescents may be bound to fail in their quest to achieve

adulthood functioning requirements in the society. This finding was based on the

longitudinal study of 698 participants in the Hawaiian island of Kauai, where

reproductive factors for resilience included not only domestic violence, but also

premature birth, unstable household, parental substance abuse, exposure to intimate

partner violence (IPV), and mental health (Black et al., 2015; Masten & Powell, 2003;

Yoon et al. 2015). In continuation, Werner revealed that some adolescents exposed to

high environment risks may have no delinquent behaviors or mental/physical health

problems compared to those exposed to fewer risk factors as transition to adulthood

continues to occur (Prince-Embury & Steer, 2010; Werner, 1982). Werner and her

colleagues identified a number of protective factors that helped the studied population

thrive, which include strong bonding relationships, aunts, babysitters, teachers, religion,

adult role models, and community group organizations for the individual’s development

in the absence of biological parents (Prince-Embury & Steer, 2010; Werner, 1982).

Similarly, Masten and Powell (2003) found that one third of all high-risk adolescents

exhibited resilience and developed into caring, competent and confident young adults,

despite poor developmental experiences. Additionally, Rutter (1980) found that although

high-risk adolescents were more likely to develop behavioral issues that tend to militate

against their transitioning preparation to adulthood than were the general population;

however the majority of those adolescents have shown resiliency and have developed into

competent, healthy productive adults. Rutter also found that even though most

adolescents were living in disadvantaged homes, they spent more time with positive peers

and were able to develop strong personalities, positive thinking, and goal setting

behaviors that motivate problem solving skills and efficacy towards self-actualization.

Coburn and Nelson (1989), Geary (1988), and Werner and Smith (1987)

confirmed that the presence of role modeling adults, good health, teachers, goal-setting

and aspiration to excel in life can play important roles in adolescents’ protective factors

for adulthood transitioning. Thus, the outcomes suggested that good physical health,

positive thinking and communication with role model adults can foster adolescents’

abilities to handle different life challenges and make commitments for a long term goal

and success. These findings complemented Werner’s observation that adult role modeling

can foster neurological development that increases the immune system against diseases

and better choice-making processes for smooth transitions (Mowder et al., 2010; Sorbello

et al., 2012). Additionally, heredity and family bonding imprint meaningful interaction

towards building quality and healthy relationships consistent for resilience development

from childhood to adolescence; therefore paving ways upon which adulthood transition

could be made possible without counter-interact with other associated negative militating

community factors (Bonem et al. 2008; Burgos-Cienfuegos, 2015; Rutter 1980, 1984,

1985; Williams & Steinberg, 2011).

In sum, resilience formation is closely associated with adolescents’ self-

awareness, self-appraisal, and the perception of the social environment for the

development of emotional stability towards successful young adults. Adolescents’

protective factors have become the focus of many studies because of the exploration of

asset-based evidence that reinforces resilient intervention plan in the quest to empower

adolescents for successful adulthood transitions (Damant et al., 2010; DeForge et al.,

2005; D’Imperio et al., 2000; Resnick et al., 1997; Rutter, 1985). Interventions in

treatment facilities and previously developed resilience among these adolescents that are

preparing to transition to adulthood may potentially help to mitigate the harmful effects

that upbringing may cause in achieving this tendency. The following section discusses

the development of resilience in treatment facility environments.

Resilience in Treatment Facility Environments

The literature indicated that the accumulation of adolescent’ daily risk factors can

lead to mental health problems (Burgos-Cienfuegos, 2015). Similarly, studies have

emphasized that adolescents’ risk factors are related to the increase rates of self-harming

tendencies, anger and frustrations, fighting, alcohol and drug abuse, juvenile delinquency

issues, suicide, depression, and spending time in the juvenile detention/treatment

facilities (Beam, Gil-Rivas, Greenberger, & Chen, 2012; Plancherel & Bolognini, 2012;

Storksen, Roysamb, Moum, & Tambs, 2010). Alternatively, these risk factors among

adolescents may lead to the development of resilience as young adults (Karol et al.,

2015). Interventions in treatment facilities may assist in the development of these positive

behaviors.

Dutton and Starzomski (1994) conducted a study that involved 78 self-referred

and court-referred male and female adolescents undergoing treatment for aggression, low

self-esteem, and depression in a treatment facility setting. The results showed that over

65% of the entire sampled population had clinically significant self-blame from domestic

violence, while 35% reported borderline personality disorder due to poor environment

upbringing. These findings confirmed the studies of Hamberger (1994) and Starzomski

and Nussbaum (2000), which stated that borderline personality disorder is found in

adolescents who lacked parental bonding and adult role models. Additionally, the poor

lifestyle developed by adolescents in a poor home environment can accumulate to low

self-esteem (Goodrum et al., 2012; Kesner, Julian & McKenry, 1997). Thus, the

individuals who enter treatment facilities frequently suffer from behavioral issues due to

their previous circumstances.

However, researchers have demonstrated that adolescents who spend more time

with adults can develop self-control, good feelings, and self-expression, leading to an

improved worldview (Fantuzzo et al 1997), Lepore & Greenberg 2002). Studies have also

shown that expressive writing increases adolescent’ verbal skills to express what

happened to them in their individual homes, improve self-esteem, social network, values,

goal achievements, characteristics, and respect (Garthe et al., 2014; Lepore & Greenberg,

2002). Hamberger and Hastings (1991) examined these characteristics among adolescents

admitted in a treatment facility and confirmed that adolescents that spend more time with

adults are motivated to stay on tasks. Hamberger and Hastings revealed that 75% of

adolescents who lacked adult role models develop psychopathology associated with

antisocial behaviors and borderline personality disorders while the other 25%

problematically ignore negativity and have problems opening up in therapy. These

findings were further supported by later researchers (Goodrum et al., 2012; Schwartz et

al., 2014).

Adolescents and Young Adults Exposure to Domestic Violence

The ongoing effect of domestic violence exposure on adolescents is yet to be

understood in the context of what we should know or how significance the impact of

domestic violence may affect a child. A child who has been exposed to domestic violence

often may exhibit changes in behaviors and some changes in the dynamics of maturation,

especially around the puberty period (Schwartz et al., 2014), that may carry on following

the transition to adulthood. Since adolescents’ mental health or health in general may

disrupt the developmental process, failure to recognize the challenge could lead to

resilience decline, therefore causing some emotional deficits limiting a complete

transition from adolescents to young adulthood. However, concentrating on young adult

mental health and behavior outcomes in a simple paradigm provides a better prediction

and the understanding about adult life trajectories than outcome assessment as observed

in adolescents, which allows adolescents to perceive themselves as more matured than

others. Fergusson, Boden, and Horwood (2008) proposed in their large-scale population-

based prospective studies using diagnostic measures to address the question whether

adolescents exposed to domestic violence predicts young adult behavioral response

outcome. Prior to this study, no previous researchers examined this relationship.

Consequences of Domestic Violence Exposure

Amato and Sobolewski (2001); Lieberman, Chu, Van Horn, and Harris (2011);

and Nayak, Lown, Bond, and Greenfield (2012) have suggested that in spite of other

studies in this area, growing up in a home characterized by domestic violence and its

concomitant experiences, such as child-case hearing at the court, custody battle, and

divorce, may also contribute to greater psychological distress with decreased self-

esteem, loss of hope, life-role, and independence, resilience formation, and other

emotional well-being in young adulthood. The outcome was largely reported in cross

sectional designed studies. Other evidence suggested that domestic violence effects on

adolescents can manifest in aggression, stagnant growth, substance abuse, low self-

esteem, runaway, self-harming, demoralize potentials, emotional withdrawal, attention

problems, poor school achievements, poor decision making, procrastination tendencies,

low resilience, and psychiatric symptoms (Beam et al., 2012; Plancherel & Bolognini,

2012). Consequently, these negative transparencies may be carried into adulthood and

affect the young adults’ broader external stimuli responses (Lieberman, Chu, Van Horn,

& Harris, 2011).

Cisler et al.’s (2012) longitudinal study of the effects of domestic violence on

adolescents was found to be one of the many reasons they are admitted to treatment

facilities as transition to adulthood could become a lifetime struggle. In 2012,

longitudinal studies were conducted on adolescents who made a successful transition to

adulthood. A national survey among adolescents ages 12–17 (N = 3,614 at Wave 1) to

investigate whether exposure to domestic violence at Wave 1 was associated with PTSD,

depression, excessive alcohol use, and delinquency (Cisler et al., 2012). The longitudinal

study also examined Wave 2 and 3 a year later respectively. The finding was that

exposure to domestic violence at Wave 1 associated with adolescents’ depression,

delinquent acts and binge drinking at Wave 3 (Cisler et al., 2012). The same research

group also conducted another study using the another data set in their investigation and

found that there is associations between adolescents’ who have experienced traumatic

events such as: verbal abuse, physical abuse, emotional abuse, sexual assault, witnessing

parental conflicts or community violence at Wave 1; while substance behaviors was

consistent among the participants group at Wave 2 after 15 months of the study. By

aggregating all types of victimization adolescents may have been through all together to

report their findings, it appears that adolescents’ victims have difficulties transitioning to

adult life after exposure to domestic violence. Consequently, the findings generally stated

that after examined the entire studies conducted in this area; there are few research that

have used a longitudinal design to investigate the outcome of domestic violence on

adolescents.

None of the reported studies utilize the longitudinal design to examine fully the

factors that influence resilience development after domestic violence exposure.

Therefore, it is necessary to gain more data regarding the experiences of the young adults

after exposure to gain an idea of the factors that influence their resilience development. In

addition, while these studies examined the effect of domestic violence on adolescents by

utilizing self-report or mother report scales oftentimes found in Child Behavior Check-

list (CBCL) as evidenced in Howell (2011) and Lamers-Winkelman, Willemen, and

Visser (2012), this design limited the core value of information what we should know

apropos to this present study which sought to utilize structured clinical surveys to assess

the variables that may influence resilience development.

Exploring Gender Differences and the Exposure of Domestic Violence

The impact of domestic violence may differ by gender. It is worthy to note that

many studies have controlled the significance of gender to report their findings.

Consequently, very few studies have examined gender differences directly and have

reported different outcome that appeared different from other studies. Even when some

studies that included gender differences failed to report their findings on what was

measured (gender differences) or the significance thereof (Kitzmann et al., 2003; Lamers-

Winkelman et al., 2012; Wolfe, Crooks, Lee, McIntyre-Smith, & Jaffe, 2003). All these

gaps create misconceptions leading to underreporting what we should know about the

impact of domestic violence exposed children, adolescent as the population makes their

transition to young adults’ life. Two recent studies examined gender differences in the

context of adolescent exposure to domestic violence. Skeer et al.’s (2011) study was

based on longitudinal data from the Project on Human Development in Chicago

Neighborhoods (PHDCN) that included 1,421 young adults aged 18–22 (in Wave 3 of

this study) and adolescents who were 10–17 years when domestic violence in the family

was assessed (Wave 1) using substance abuse and dependence as the main dependent

variable. According to Skeer et al., males living in families where domestic violence is

frequent was found not significant to become at-risk for either substance use or other

symptoms, such as anxiety, aggression, or depression. In the same study, it was reported

that unlike their male counterpart females, living in domestic violent homes was

associated with conduct disorder, explosive anger, verbal aggression, fighting, substance

use and emotional dependence (Skeer et al., 2011). On the other hand, Begle et al. (2011)

conducted a U.S. national study following this study with a representative sample of

3,614 adolescents aged 12–17 years of age and 3,536 young adults to investigate the

significance of gender differences outcomes following witnessing domestic violence in

the home. It was found that males are more likely to be affected than females (Begle et

al., 2011), and as a result males are more likely to be admitted to treatment facilities than

their female gender. The differences of opinion in these studies depict that there is a need

to explore gender differences in young adults’ outcomes of domestic violence as children

and this present study is designed to provide detailed scientific findings to add to the

body of knowledge about the influence of domestic violence exposure and demographic

variables on resilience.

Environmental Factors’ Influence on Adolescents’ Resilience Development

The following section identifies environmental and demographic influences that

may promote resilience in adolescents and young adults. For example, male and female

African American adolescents’ values may be different from Hispanics’ and Caucasians’

in terms of a treatment plan based on age, environment, and cultural norms considering

also the environmental issues that may present. Lloyd and Emery (1994) and Dutton and

Starzomski (1993) proposed that adolescents’ culture and gender are pivotal to

understanding resilience development, especially as they make another transitional step

toward adulthood, thus, connecting the individual’s age, gender, and culture when

making treatment plans may be essential for understanding and predicting adolescents’

emotional responses to make some self-appraisal in preparation to adulthood functioning.

Starzomski (1993) noted that an adequate treatment plan based on age, gender, and

cultural identity can inspire motivation toward change in lifestyle that influences

resilience. As evidenced in Abbot and Hall (2006); Allen and Allen (2006); Brown

(1997); Rohr (1990); Straus and Smith (1999); Tolman and Bennett (1990); and Waltz,

Babcock, Jacobson, and Gottman (2006), adolescents’ age, gender, culture, events, life

styles, community connectedness, and family bonding relationships are all supportive

dynamic factors that promote resilience and self-betterment to become stabilized and

rational young adults in the society. The following examines the aforementioned resilient

factors.

Some researchers have found that people that are exposed to a variety of

environmental risk factors have significant mental health issues (Black et al., 2015;

Delfos, 2010). Researchers have also demonstrated that adolescents exposed to these

environmental forces have risk factors, including poor self-thought processes, decreased

interest in education achievements, low self-esteem and empathy (Holland, Benson,

Orloswki, Fredison, & Defenburg, 2015), followed by other environmental risk factors

such as bullying in the playground or during lunch time that results in conflicts with

peers. These underage exposures can become the internal locus of control issues they go

through due to poor intervention process performed or low support trends from either

parents’ caregivers or teachers and may carry the same unresolved feelings to adulthood

living (Black et al., 2015; Goodrum et al., 2012). The developed disordered thinking in

childhood and adolescence will continue on to a young adults’ mental and physical well-

being.

Researchers have found that the above mentioned risk factors are significantly

related to poor behavioral response in the adolescents’ attempts to build defensive

mechanisms that often times hidden leading to significant mental health problems (Black

et al., 2015; Karol et al., 2015). Alternatively, these factors can work to promote

resilience. Included below are reviews of literature examining how life event, family,

community, and facility or school factors can influence resilience.

Life Event Factors

Based on Rutter’s (1987, 1990) and other similar studies, it is clear that

unexpected life events can pose psychological and emotional threats to how people

respond to circumstances (Brookmeyer et al., 2006; Carroll, 2006; Edwall, 2012; Irby,

2001; Spaccarelli, Sandler, & Roosa, 1994; Wolfe et al., 2003). Following these numbers

of studies, the National Self-Report Database noted that adolescents’ poor life

experiences impact behaviors compared to those not exposed to those experiences

(Patricia & Nancy, 2000). Other similar studies proposed that an individual’s

environment and experiences can also become factors to either accept poor

environmental dictates or become resilient. Bliststein (2005); McElliskem (2006); Huang,

Tajima, and Whitney (2003); Van Breda (2001); and Children’s Health Fund (2011)

supported that adolescents ages 15-17 are more affected by the environmental dictates

than those aged 10-14. According to this study, the 15-17 year old adolescents exhibit

high-risk behaviors with little adjustment compared to 10-14 years old; however,

Bornstein et al. (2010), Carlson (2000), and Elliott (1994) reported that regardless of age,

high-risk choices can be observed in every age generated by the individual’s experiences,

encouraged by negative influence to conform in one’s personal life.

Family Factors

Chapter 1 of this study introduced that family values and dynamics can determine

a child’s propensity towards aggressiveness or resilience. Thus, family dynamism in this

study is understood as those values agreed upon as a limited set of responses and

behavior expected of the whole in events that display characteristics and attitudes of the

entire membership. These values include relationships, emotion management,

communication, discipline and respect to the outer groups (Brookmeyer et al., 2005; De

Los Reyes et al., 2013; Kennedy & Minami, 1993; Myers et al., 1993). The relationship

ordained by family members teaches social responsibilities and expectations that not only

unify members through common beliefs, but also provide evidence of what makes them

different from others. In a family where this relationship is lacking, it demoralizes a child

and produce chaos that projects, individualized responses, isolation, lack of discipline and

social accountability (Carroll, 2006; Garthe et al., 2014; Gerard & Buehler, 1999;

Waldon et al., 2001).

A child growing in this family eventually exhibits poor matriculation in his/her

social environment. This modeling behavior occurs not only within the conventional

nuclear families, but also within extended families that are not interested in promoting

adolescent’s mental health (Ungar, 2004). Flores et al. (2014) noted that recent studies

have beginning to investigate the effects of domestic violence, community violence,

divorce, child maltreatment and abuse to predict resilience processes. According to Flores

et al., resilience depends on how individual child is raised and supported given the

protective factors from the immediate family and extended family.

Rodgers and Rose (2002) also found that poor parental skills oftentimes resulted

in adolescents’ negative responses to external issues and poor cognition. Furthermore,

family dynamics are linked to adolescents’ attitude, defense mechanisms (Czarnetzki,

2003; Demo & Acock, 1996; Dishion & McMahon, 1998; Emery & Forehand, 1994),

and certain regressive behaviors like eating and sleeping disorders, somatic complaints,

emotional disorders, poor concentration, generalized anxiety and other psychopathology.

Therefore, family dynamics paint a truer picture of a child’s development than diagnosis

of negative behavioral responses (Callie, 2003; Garthe et al., 2014).

Community Factors

Community relationships are necessary for development of resilience because

negative influences can occur when there is a breakdown in family communication

dynamics; as has been demonstrated, every child or adolescent needs adult support to

develop resilient protective factors (Dondero, 1997; Wolkow & Ferguson, 2001).

Rodgers and Rose (1994) clarified that these relationships are particularly lacking in

families with single working parents. Without the presence of coaching adults or nuclear

family, these minors are prone to negative environmental trauma. Thus, for instance, in a

community where drugs and alcohol are the community culture, adolescents growing up

in that community can be quickly influenced to engage in similar behavior (Dietz et al.,

2014; Zimmerson et al., 2002). Conversely, a number of studies found that the presence

of coaching adults through sports involvement, community activities and volunteerism

enhanced self-esteem, intelligence, problem solving, task orientation, achievements,

respect for others, and goal actualization (De Los Reyes et al., 2013; Garthe et al., 2014;

Grossman & Tierney, 1998; O’Donohue & Ferguson, 2006).

However, some have argued that the cause of poor behaviors is a combination of

poor family dynamics and community violence. Olchowski, Foster, and Webster-Stratton

(2007) found that there was no relationship between community factors in adolescents’

manifestation of poor behaviors, depression, anxiety, and stress symptoms. Although

certain emotional symptoms may exist regardless of community’s morals, an individual

may apply immediate family values to make choices and set personal goals (Jacobson &

Mufson, 2010; Underwood et al., 2008). All the same, community mentorship and

support have proved effective in the adolescents’ decision making process. To this end,

Stoiber and Good (1998) agreed that adolescents can derive resilience from community

adult role modeling and mentorship to deter aggression as evidenced in

Bronfrenbrenner’s ecological systems (Thomas, Goodman, & Kundey, 2013).

Facility or School Factors

Another factor that may lead to the facility or school influencing resilience is the

intervention available at a facility. Whether a child is enrolled in the public school or in

an RTC, the environment can become the center for building behavior/emotional

development (Olchowski et al., 2007). Although most RTCs are classified as therapeutic

boarding schools or emotional growth boarding schools (Bohensky & Maru, 2011;

Gallant & Lafreniere, 2003), the system implements behavior modifications that provide

structured routines with specialized supervision programs that facilitate therapy and

educational formation to improve self-control (Callie, 2003; Howard & Johnson, 2000b).

The system also provides a safe environment that helps adolescents develop a sense of

purpose, goal achievement, sense of community, responsibility and emotional

connectedness that impact moral values for social skills, college preparatory, problem-

solving, social competence, and self-respect. Although resilience may not occur in the

classroom alone, peer interaction during recreation has proven to increase sharing

individual experiences, encouragement, self- discipline and efficacy for resilience

building (Gableet al., 2004; Thomas et al., 2013). In sum, facility school system provides

adolescents modeling behavior through staff members and positive peer interaction that

encourage personal values and strength for goal attainment.

Grotberg (2010) noted that the most reliable predictors of resilience are not

demographic, but rather the individual’s internal locus of control. In spite of demographic

differences, high-risk individuals that are unable to manifest resiliency may perceived

their social environment as random and uncontrollable (Lanza & Taylor, 2010).

Cultivating the sense of control over the environment among troubled adolescents may

therefore increase resilience (Grotberg, 2010). However, the perception that an

adolescent has over their self-efficacy in this area may be determined by demographic

variables, although this aspect of the literature has not been examined. The following

section reviews what literature has been published regarding demographic factors and

their influence on development of resilience.

Demographic Factors’ Influence on Young Adults’ Resilience Development

As yet, many studies on domestic violence experiences have only focused on risk

behaviors and environmental factors regarding the development of resilience, rather than

emphasizing demographic factors (Bondü & Krahé, 2014; Kelly, 2000; Tedeschi &

Kilmer, 2005). Carlson (2004) suggested that in spite of adolescents’ low resilience to

external adversaries, not all adolescents from violent homes exhibit poor behavioral

responses. These differential responses may be tied to demographic factors. Some

demographic factors that researchers have determined may influence the development of

resilience include age, gender, and ethnicity. Age, gender, and ethnicity variables may

provide the asset-base to determine individual’s experiences with resilience formation

(Edleson et al., 2007; Edleson et al., 2008; Osthoff 2002).

Age and Resilience

Age is the duration of an organism or a thing between the beginnings of a being at

any given time aiming at maturation (Bogat, Levendosky & von Eye, 2005). According

to Löckenhoff, Reed, and Maresca (2012) and Olchowski et al. (2007), an individual’s

age is significant in research to determine how each individual may view child

abuse/domestic violence. Studies related to the influence of age on the development of

resilience have been fairly consistent in showing that the later an individual is exposed to

a risk factor, or the earlier an intervention is made, the more likely it is that the individual

will develop resilience (Ali et al., 2015; Garthe et al., 2014; Jan et al., 2015).

For example, Ali et al. (2015) determined that children who were exposed to

domestic violence early have lesser chance to acquire resilience compared to those who

were totally removed from the violent environment early on in their lives. On the other

hand, Garthe et al. (2014) determined through logistical regression analysis of 132

children living in a foster care family who had once been exposed to domestic violence

that resilience could be maximized when early interventions were made (Garthe et al.,

2014). Garthe et al. concluded that children that were removed from violent environment

early met criteria for resilience when compared to a population of children who were left

in a violent environment unattended

Another retrospective study conducted by Jan et al. (2015) focused on 122 adult

male and females who were exposed to community violence, family violence and abuse

as children. Jan et al. used logistical regression analysis to analyze the data, and found

that children exposed to aggression in their early age were almost twice as likely to

engage in aggression and poor behaviors and were less likely to evidence resilience. Jan

et al. further determined that because these children had no supportive system, and lived

in a harsh environment without early removal, they developed ego-resiliency and ego-

control and defensive mechanism as the best emotional attachment options to meet their

daily survival needs. However, research on the influence of age on the development of

resilience remains sparse, and requires further validation. Another demographic variable

that may have a relation to the development of resilience is gender.

Gender and Resilience

Many studies have reported that gender has significant effects on a child’s coping

skills (Bornstein et al., 2010). For instance, Dadds, Atkinson, Turner, Blums, and

Lendich (1999) used neurological development to propose that adolescent males may

respond differently compare to female counterparts of the same culture. Regarding males’

coping mechanisms, Dietz et al. (2014) reported that adolescent males and females

function differently when it comes to environmental difficulties, including their

temperament and instinct acquired through family or nuclear family cultural values and

tradition dynamics. Specifically, Dietz et al. found that adolescent males tend to make

more use of adaptive coping skills than were females. According to the study, when

males become overwhelmed, their awareness of the problem becomes evident in their

mind and they are ready for a fight (emotionally and physically), thus they tend to cope

by externalizing their ego-control and become focused and directive in their actions

(Dietz et al., 2014). At same time, adolescent males can become distracted at the pressure

of the militating event and can become self-instructors by taking initiatives to calm

themselves down and develop a sense of wholeness and dominance with a strategic

avenue to extricate themselves from the presented problem (Dietz et al., 2014). Jacobson

and Mufson (2010) conducted a similar study with 120 male adolescents to investigate

gender differences in community violence responses. The regression method apropos to

this study found that adolescent males have more aggressive instinct for self defense and

are more likely to fight only when it is beneficial to their masculine ego-dominance

(Jacobson & Mufson, 2010).

Adolescent females have different ways to cope with their environmental

militating forces, including seeking immediate help for support and using community

social resources as sustaining system to achieve redemption (Black et al., 2015). While

adolescent male chose to use physical recreational activities and aggressive reactions

such as sport and fighting to cope with environmental adversities, adolescent females

tend to seek more support from the community and friends than males (Black et al.,

2015). Although these differences in coping mechanisms have been demonstrated, review

of the literature revealed no studies that examined the influence of gender on the

development of resilience. Thus, gender was included as a variable in the proposed study.

Ethnicity and Resilience

The most commonly studied demographic variable that may influence resilience

is ethnicity. Although Kegler et al. (2011) reported that in spite of cultural norms no

single culture is superior to other culture; studies have confirmed that African American

and Hispanic youths have improved strategies for developing resilience than do

Caucasian youth (Holland & Gill, 2010; McKnown-Johnson, & Rhodes 2013; Zautra et

al., 2010). For instance, African-American adolescents are reported to have a driven

instinct for resilience through enhancing interpersonal skills and self-confidence toward

goal attainment with the presence of community member adult role models in connection

with community norm (Zautra et al., 2010).

Alternatively, cultural and ethnic factors may exacerbate or influence the role of

gender in resilience development, depending on the agency assigned to women among

the specific group with which the adolescent identifies (Compton, 2010).

Grotberg (2010) examined the role of ethnicity in adolescent resilience in other

countries, using the International Resilience Research Project to investigate what made

some adolescents resilient despite harsh ethnic expectations. The analyses included 1,200

families from 22 countries in 27 sites with children 12-17 years of age to report their

findings. The study identified that there are some cultural/ethnic differences in

development apropos to adolescent resilience formation. Twenty-two different countries

were investigated, and the results indicated that ethnicity plays a significant role in an

individual’s response to social issues. However, these findings were questioned by some

scholars, who argued that the studies’ insufficient data between ethnic groups and

similarities pose problems for their conclusion (Daining, 2011). Additional studies also

examined the impacts of ethnicity, age, life style, life events, and biological make-up as

factors for resilience formation (Mowder et al., 2010; Zimmerman & Arunkumar, 1994).

Gender Issues and Young Adults’ Resiliency

Prior studies reported that gender issue on resilience has significant effect on

adolescents coping strategies (Daining, 2011; Grotberg, 2010). Coping strategies in this

context can be categorized into two identifiable types such as: maladaptive and adaptive

strategies (Bornstein et al., 2010). Male young adults tend to exercise their masculinity to

address their emotional issues and are more likely to make more use of adaptive coping

strategies that focus on the immediate problem solving than their female counter parts.

On the same note, male strategies are extern

alized and more often exercise a direct action

that includes but not limited to distractive and positive self-instruction momentum to

manipulate their internal locus of control (

Holland & Gill, 2010; McKnown-Johnson &

Rhodes 2013; Zautra et al., 2010

Recent studies also show that males are easily to rush

into resolving issues than taking the time to think about their actions. On the other hand

they act before they think which in most cases ends up in legal consequences unlike their

female gender. On the same study, the authors reported that there is evidence that proves

that girls cope with daily stressors more than their male counterparts by seeking social

support, meeting coaches with whom they can confined on to express their concerns and

are more likely than males to utilize social resources within their reach to cope with

issues (Frydenberg & Lewis, 1993). The conclusion of the differences between genders

submitted that young adult males unlike their female counterparts are more likely to use

physical recreation such as sports, yelling, punching whole on the wall, high breathing

techniques to cope with adversity (Frydenberg & Lewis, 1993).

In spite being under stress, young adult females have been found to use resilience

factors such as using emotional techniques such as crying, befriending other strong peers to

seek emotional support more often than boys (

Daining, 2011; Grotberg, 2010

). More also,

in another study

surveys were administered to 1,109 male and 1163 young adults females

(N = 2492) in 2004 to assess self-perception of resilience and associated protective

factors. Female young adults are found to be more likely to report self-expression,

empathy, personal issues, help-seeking, and goals oriented for future and aspirations.

However, this study did not report cultural implication that may present in their

conclusion.

The studies mentioned above focused on young adults, however few studies

have examined age and gender differences in resilience in treatment facilities in the United

States.

In the current study, the effects of gender, ethnicity, and age on young adults’

resilience were examined and reported. The gender differences in individual young

adults’ characteristics and protective factors deserve further investigation, in view of their

potential implications considering also cultural differences for mental health prevention

and adaptation to problem solving skills (Danquah et al., 2010; Flores et al., 2014;

Mowder et al., 2010).

Resilience and Age Among Young Adults

Age is the duration of an organism between the beginning of a being and a given

time (Bogat, Levendosky, & von Eye, 2005). Social psychologists have considered age a

process of development to which individuals respond differently. For instance, Dadds et

al. (1999) used neurological development to propose that young adult males respond

differently compare female counterparts of the same culture (Alvord & Grados, 2005)

based on age, attitude, maturity and body language. As each gender matures through

biopsychosocial neuroendocrine responses associated with different ages, young adult’s

thought process, behaviors and lifestyle continue to struggle to form and to conform to

cultural norms and society’s expectations, that could become struggles for transitional

process to adulthood (Rutter, Pickles, Murray, & Eaves, 2001). Females tend to show

significant maturity than males; however, upon victimization, females may begin to

regress in thought process in spite of age (Danquah et al., 2010; Flores et al., 2014).

In male young adults on the other hand, in spite of age male may use aggression

to display show defiant attitude. Male young adults always and often respond to life

struggles and threats with caution but at the same time try to communicate that they are

matured enough to take care of themselves while they still lack sense of self-identity.

Often time both male and female young adults may utilize their age as methods to push

people away and therefore refusing help from a role model coach. This response

however, is indication of their life experiences that allowed them to grow over time

suggesting they know it all (Carlson, 2003; Kelly, 2000; Vitopoulos, Peterson-Badali &

Skilling, 2012). In sum, age significantly influences behavior and can be used to address

resiliency formation in therapeutic setting. As yet, though, many studies on young adults’

domestic violence experiences have only focused on risk behaviors, rather than

emphasizing age demographics considering also cultural implication (Kelly, 2000;

Tedeschi & Kilmer, 2005).

The influence of culture on resilience in general. One element that may explain

the influence that ethnicity could have on resilience is cultural differences in raising

children (Danquah et al., 2010; Flores et al., 2014). Although little has been said about

cultural implications on young adults’ resilience, Kliewer and Murrelle (2007) and

Starzomski and Nussbaum (2000) noted that regardless of ethnic differences,

adolescents’ risk reduction can be fostered by utilizing community norms common to

adolescents for risk minimization (Masten, 2009). Resilience and cultural differences

have been advocated within the field of social work, counseling, and psychology to

understand the individual client’s internal locus of control (Jaffe, 1998). Similarly,

Danquah et al. (2010) used predictive regression analysis and determined that several

factors influenced the development of resilience at different ages, including family

dynamics, development functions, number of people in the family, personal resources,

and relationship to each family member as predicting factor for resiliency in the child.

Damant et al. (2010), Daining (2011), and Grotberg (2010) confirmed that

different cultures have different ways of responding to adversity. For instance, Daining et

al. (2011) insisted that Hispanic adolescents have a higher sense of self-control in

adversity compare to other cultures. This trait suggests that Hispanic adolescents who in

contact with their community norms have higher self-worth and resilience. Furthermore,

Kliewer and Murrelle (2007) found that African American and Hispanic teens living in a

supportive community developed a strong bound and sense of cultural pride to associate

within the community. Ungar, Shorey, Cornelius, and Bell (2008) reviewed a number of

studies examining the influence of race, cultural values and individual tradition in

predicting resiliency among African Americans, Hispanic and Caucasians. Ungar et al.

concluded that racial identity buffered against the influence of stress and led African

American, Hispanic and Caucasians youth to competently address adversity within their

environments.

Phinney (1996) indicated that limitations in most studies, especially those that

may not have appropriate knowledge of the society and culture, adversely affected the

definition of the cultural differences between socio-cultural groups. Thus, generalizing

different cultural variables into one component (a unit of analysis) is misleading

compared to specific discussions about whether a particular cultural/ethnic group

perceives itself as an individualistic community or as a collective community. Phinney

(1996) argued that adequate ethnic knowledge between cultural groups may provide a

detailed determination whether one culture is more independent— that is, culturally

separated from family affiliations or more prone to self-acquisition than generosity, or

perceives self-fulfillment as more important than interpersonal harmony (Daining, 2011;

Grotberg, 2010). In most countries where researchers disregard the differences between

ethnic groups, assumptions about universality can become a problem as a unit of analysis

rather than the differences along which individuals and groups and how they vary from

each other (Grotberg, 1995; Phinney, 1996). Thus, the present study attempted to avoid

universalizing assumptions about culture, despite using ethnicity as a demographic

variable in the potential model.

Domestic Violence and Resilience

Previous researchers primarily suggested that domestic violence has negative

cognitive, emotional, and behavioral and social effects for individuals (Job et al., 2012;

Löckenhoff et al., 2012; Sorbello et al., 2012; Williams & Steinberg, 2011). Regarding

resilience, researchers have turned their focus to asset-based evidence, which suggests

that prior experiences may have provided young adults with protective factors that

allowed them to maintain resilience despite adversity (Damant et al., 2010; DeForge et

al., 2005; D’Imperio et al., 2000; Resnick et al., 1997; Rutter, 1985). However, review of

the research revealed no discussion of a predictive model of resilience among young

adults. The experiences with domestic violence may have influenced the resilience

development of young adults (Anderson et al., 2012; Kassis et al., 2013), and

demographic factors may similarly influence the results.

Some research suggested that domestic violence might promote resilience, acting

as a protective factor against further negative influences. Anderson et al. (2012)

conducted a mixed methods study of 37 women who had previously been in a domestic

violence relationship. Anderson et al. assessed whether psychological, sexual, and

physical abuse influenced posttraumatic stress disorder and resilience. Results of Pearson

correlations suggested that earlier exposure to abuse correlated with increased

posttraumatic stress symptoms, and higher education levels correlated with decreased

posttraumatic stress. On a range of 0—100, participants in the sample had a high average

resilience score (74.97), and higher scores in resilience correlated with lesser

posttraumatic stress symptoms. Thus, Anderson et al.’s results suggested that a strengths-

based view of domestic violence victims might emphasize resilience as a correlation with

domestic violence exposure.

Alternatively, exposure to domestic violence might reduce resilience. Among a

sample of middle-school students from Austria, Germany, Slovenia, and Spain (N =

5,149), Kassis et al. (2013) determined through multiple linear regression that negative

experiences, such as domestic violence, poor parenting, and alcohol and drug abuse,

reduced resilience to domestic violence. Related to the present study, Kassis et al. found

that structural variables, including gender and socioeconomic status, did not influence

resilience development. However, Kassis et al.’s findings contradicted a significant body

of literature regarding demographics’ influence on resilience, including age (Ali et al.,

2015; Garthe et al., 2014; Jan et al., 2015); gender (Black et al., 2015; Dietz et al., 2014);

and ethnicity (Danquah et al., 2010; Flores et al., 2014; Holland & Gill, 2010;

McKnown-Johnson & Rhodes, 2013; Zautra et al., 2010). In fact, in a follow up analysis

on the same data, Kassis et al. (2015) determined that reliable gender differences existed

in protective and risk factors between the genders. Additionally, Kassis et al. determined

that modifying the home environment could influence resilience status within the sample.

Together, the findings of these studies suggested that additional research should be

conducted on demographics, domestic violence, and resilience.

Summary

In summary, studies have found that a variety of factors contribute to

development of low or high resilience (Callie, 2003; Gableet al., 2004; Garmezy, 1994).

Understanding resilience within adolescent mental health treatment facilities is important,

as risk factors among adolescents increase rates of self-harming tendencies, anger and

frustrations, fighting, alcohol and drug abuse, juvenile delinquency issues, suicide,

depression, and spending time in juvenile detention/treatment facilities (Beam et al.,

2012; Plancherel & Bolognini, 2012; Storksen et al., 2010). Identifying factors that could

predict resilience within this population could help practitioners to develop targeted

treatments for addressing the influence of protective factors, or addressing factors which

negatively predicted resilience. As yet, the research was limited with regards to predictive

models for resilience among young adults. The lacking investigation of the predictive

value of demographic variables and domestic violence on resilience constituted a

significant gap in the literature.

Demographic factors that might influence resilience development included age

(Ali et al., 2015; Garthe et al., 2014; Jan et al., 2015); gender (Black et al., 2015; Dietz et

al., 2014); and ethnicity (Danquah et al., 2010; Flores et al., 2014; Holland & Gill, 2010;

McKnown-Johnson et al., 2013; Zautra et al., 2010). Extensive research revealed one

study that contradicted the suggested influence of demographic influences on resilience

(Kassis et al., 2013), but the researchers had focused on adolescents who remained in the

domestic violence situation, and explored only gender, socioeconomic status, and

migration status. Still, the research was lacking in developing the predictive value of

these demographic variables in resilience development, particularly with regards to age

and gender. These predictive factors are worthy of consideration in future study to

explore resilience. Additionally, factors within the mesosystem related to domestic

violence, such as exposure to domestic violence, individual emotional and physical

abilities, types of domestic violence exposure, and frequency of domestic violence

exposure might also influence resilience development.

The research regarding the influence of domestic violence on resilience was

conflicted. In a sample of women exposed to domestic violence, Anderson et al. (2012)

demonstrated that resilience within domestic violence survivors was high. Conversely,

Kassis et al. (2013) determined that negative family experiences, including domestic

violence, negatively predicted resilience within a population of adolescents who

remained in the domestic violence situation. In the proposed study, a sample of young

adults was studied. These young adults were pulled from the BBBS organization. Given

the contested nature of resilience among domestic violence victims, it is essential to

further understand the predictive value of domestic violence for resilience within this

particular population.

Based on the gaps in the literature, it was unclear whether age, gender, ethnicity,

exposure to domestic violence, individual emotional and physical abilities, types of

domestic violence exposure, and frequency of domestic violence exposure predicted

resilience among young adults. The purpose of this quantitative study was to examine

whether domestic violence, age, gender, ethnicity, individual emotional and physical

abilities, types of domestic violence exposure, and frequency of domestic violence

exposure can adequately predict resilience among young adults in the Midsouth region of

the United States. In Chapter 3 of this study, I will explore the methodology used to

pursue this purpose.

Chapter 3: Research Method

Introduction

The purpose of this quantitative study was to explore whether the predictor

variables of domestic violence, age, gender, ethnicity, individual emotional abilities,

individual physical abilities, types of domestic violence exposure, and frequency of

domestic violence exposure can adequately predict resilience among young adults in the

Midsouth region of the United States. I measured the predictor variable, domestic

violence, using the CEDV scale; I measured the criterion variable, resilience, with the

Resilience Scale. I investigated the following research question: Do domestic violence,

age, gender, ethnicity, individual emotional and physical abilities, types of domestic

violence exposure, and frequency of domestic violence exposure, when in linear

combination, significantly predicts resilience as measured by the Resilience Scale among

young adults in the Midsouth region of the United States?

In this chapter, I address three main topics, which include (a) the research design

and rationale, (b) the methodology, (c) and the threats to validity. In the research design

and rationale section, I identify the variables, identify how the design is connected to the

research question, explain the time and resource constraints, and show how the design

aligns with existing knowledge in the discipline. The methodology section includes the

population (including the sample size), recruitment procedures, participation, data

collection, and the instrumentation and operationalization of constructs. In the validity

section, I address the applicable internal, external, and statistical conclusion threats in the

study. I also address the ethical concerns. Finally, this chapter concludes with a concise

summary, which is a reiteration of the major components of this chapter.

Research Design and Rationale

I grouded this study in a postpositivist worldview; I employed a quantitative

approach to address the research question. This study qualified for the postpositivist

approach because I tested an extant theory by examining the relationships between

variables (Creswell, 2009; Pang, 2010a, 2010b). I used psychometrically sound

instruments that yielded numbered data suitable for statistical analysis to measure the

variables and constructs in this study. I determined the aforementioned criteria fit a

quantitative paradigm as defined by Creswell (2009). Furthermore, quantitative inquiry is

nomothetic becuase quantitative researchers apply and infer their findings in an abstract

and global manner (Boydell & Ferguson, 2012; Smith, Flowers, & Larkin, 2009). In this

study, I collected and analyzed data from a group of individuals and generalized the

findings to a much broader population (the midsouthern United States). I analyzed the

effect the independent variables (age, gender, ethnicity, individual emotional and

physical abilities, types of domestic violence exposure, and frequency of domestic

violence exposure) had on the dependent variable (resilience).

The qualitative approach was not appropriate for this study. Qualitative studies

are idiographic in nature becuase researchers use this approach to gain individual

perspectives of specific phenomena (Creswell, 2009; Pang, 2010a, 2010b). Furthermore,

researchers do not collect qualitative data using psychometrically sound instruments;

variables are not components of this inquiry methodology (Creswell, 2009). The

quantitative approach was appropriate for the purpose of this study, in which I aimed to

understand the nomothetic relationships between variables. To examine the relationships

between the independent and dependent variables, I employed step-wise multiple linear

regression.

Methodology

Population

The population of interest were young adults, between the ages of 18 and 25. This

study took place in the BBBS community organization in southwest Texas. Specifically,

the participants in this study were mentees participating in BBBS. The BBBS

organization provides mentoring services to children and young adults to help them

succeed (Herrera, Grossman, Kauh, & McMaken, 2011). This program accepts children and

young adults ages 10–26 from the general population (Herrera, et al), although this study

only included young adults who were between 18–25.

Sampling and Sampling Procedures

I chose convenience sampling as the sampling method for this study. Convenience

sampling is appropriate when it is not feasible to collect a random sample from the entire

population of interest (Creswell, 2009). Because not all members of the population had

an equal chance of selection for the study, the study sample was a convenience sample. In

this study, I used a sample of 118 participants, who were all young adults between the

ages of 18–25. Participants in this study included men and women who were ethnically

diverse (i.e., African American, Hispanic, and Caucasian). Also, participants had to

affirm they could read at a fifth grade reading level.

To determine an appropriate sample size for the study, I conducted a power

analysis using G*Power software (see Faul, Erdfelder, Buchner, & Lang, 2014). I

conducted an a priori power analysis for a multliple linear regression with 10 predictor

variables; I used a medium effect size (f

= .15), an alpha level of .05, and a power level

of .80. The results indicated a minumum sample size of 118 participants to achieve

empirical validity. Increasing the sample size to 172 (assuming a medium effect size and

alpha level of .05) increased the power level to .95. Power refers to the probability of

correctly rejecting the null hypothesis (Creswell, 2009). In most disciplines, a generally

accepted power level is .80 and as the sample size increases, power also increases

(Creswell, 2009). Therefore, I sought between 118 and 172 participants for this study (see

Figure 1).

Figure 1. Power analysis for linear regression with six predictors, an alpha level of .05,

and medium effect size, as a function of sample size.

Recruiting Participants and Data Collection

To begin, I secured Walden Institutional Review Board (IRB) approval to conduct

the study using the standards and procedures set forth by Walden University. The initial

research step entailed contacting the director at the BBBS community center study site

via phone calls and face-to-face meetings. The purpose of this initial contact was to (a)

introduce the study; (b) discuss the possibility of recruiting persons within the

organization to participate in the study; and (c) gain an understanding of this community

organization’s policies, which I had to follow. The initial contact allowed me to provide

the community center director with information on the purpose and procedures of the

study. Then, I familiarized myself with the community center requirements regarding

ethical precautions. I wrote a letter of agreement for the organization stipulating the

nature of the research, which granted me permission to recruit potential participants and

conduct survey-based research in the community center. The form had spaces for

signatures and dates from the community center’s director and me.

To recruit participants from the community center, I worked with the directors to

identify and arrange an appropriate method and time for me to distribute a recruitment

flyer (see Appendix A). The BBBS community organization’s policy did not permit

posting flyers in the building, so I identified other arrangements. Specifically, I attended

one of the community center’s meetings to present information about the study. The flyer

included an invitation to attend an optional information meeting and to contact me so I

could explain the research and answer any questions.

The community center directors approved times and places for me to conduct the

optional initial information meetings. No designated representatives were required to

conduct the meetings. At the information meetings, I distributed and explained the

informed consent form (see Appendix B). If the attendees elected to participate, I asked

them to read and sign the document at their leisure. I asked them to bring the form with

them when they came to the scheduled data collection meeting.

I scheduled a suitable date, time, and location to conduct the survey collection

after securing the necessary approvals. At this meeting, I collected the informed consent

form, administered the Resiliency Scale (see Appendix B), and administered the CEDV

(see Appendix C). I greeted participants as they arrived, collected each participant’s

informed consent form, and issued them an identification number. Blank copies of the

consent form were also available if a participant arrived without a signed form or did not

attend the initial information meeting. Individuals who were not willing to sign a consent

form were not allowed to participate in the study.

I began data collection by welcoming the participants, briefly explaining the

research study, and explaining my expectations for the participants. I reminded the

participants the information they provided would be held in confidence: their personal

identity and answers would not be linked together and they would not be reported

individually. The participants could withdraw from the study at any point without

repercussions. Following the verbal instructions, when I specifically asked the

participants not to write their names on the two instruments, I distributed the instruments.

Only I administered the instruments; no organization staff participated. The community

center had private rooms and had access to an auditorium where most of the

organization’s events take place. The data collection did not interfere with any scheduled

activities because each participant had his or her BBBS meetings outside the

organization’s general program, which is open to the public residing within the county.

The BBBS community center’s policy stipulated the staff members were not allowed to

help in any way during the data collection. It was my sole responsibility to administer the

surveys, collect the data, and store the data securely.

I told the participants to turn their documents face down and leave them on the

table when they completed the survey. I personally thanked each participant as they left

the room. After all instruments were completed, I collected and placed these items in a

sealed envelope and stored the data in a locked file cabinet, accessible only to me prior

to, during, and after data analysis. I ensured confidentiality by using only the participants’

identification numbers instead of their names.

Informed Consent

I informed the individuals participating in this study that participation was strictly

voluntary. As previously noted, I used an informed consent form (see Appendix B) to

adhere to this requirement. Before attending a scheduled meeting to administer the

sruvey, I provided the participants with a copy of the written informed consent form at

one of two preliminary informational meetings. After an individual contacted me and

stated their desire to participate in the research but did not attend one of the meetings, I

provided them with a copy of the informed consent form via the U.S. Mail. Blank forms

were available on the day I collected data. I required participant signatures prior to a

participant’s participation in the study.

The informed consent form specified the study was voluntary and participants

were free to withdraw at any time. The consent document also included background

information about the study and how long the questionnaire and instrument would take to

complete. Potential participants were informed of the risks and benefits of the study. My

contact information was available if they had any questions about the study.

Instrumentation and Operationalization of Constructs

Children Exposed to Domestic Violence

In this study, I used the CEDV to assess young adults exposed to domestic

violence as children living in America (Edleson et al., 2007; Margolin & Gordis, 2000;

Tilton-Weaver, 2014). The CEDV is a self-administered instrument designed to collect

data on childhood domestic violence experiences. Children and adults can complete the

instrument (e.g., Makhubela, 2012). Specifically, Makhubela (2012) used the CEDV on a

sample of adolescents and young adults, including undergraduate students ages 18–20.

The authors who created this tool intended to gather an understanding of young adults’

exposure to domestic violence and their ability to set goals for future functioning. I

obtained permission to use the instrument from the authors. This survey appears in

Appendix D.

The CEDV authors identified local domestic violence shelter organizations that

provide services to women and children who have been domestically abused. The authors

gave presentations to key staff members at each agency, inviting them to participate in

the scale development project. As a result, four organizations representing five domestic

violence shelters for battered women and children were invited to participate in the

research. Individual agency staff members were asked to identify the guardians of

potential participants. The potential participants were children between 10 and 17 years

of age residing at the community shelter. This designation constituted domestic violence

presence in the home from which these children were coping (Delaney-Black, Covington,

& Sokol, 2006; Mowder, Cummings, & McKinney, 2010; Prince-Embury & Steer,

2010). Three psychometricians and facilities officials examined interrelated themes from

the domestic violence perspective and concluded the results were accurate in terms of age

and situations that were appropriate for the study constructs (Kernic, Monary-Erensdorff,

Koepsell, & Holt, 2005; Kumar, Steer, & Gulab, 2010).

In this study, coefficients measured by Cronbach’s alpha demonstrated a strong

relative internal consistency between α =.74 and α =.76. The results indicate high

reliability to establish a strong test-retest reliability (r =.67) with interrater reliability

results (r =.81) among children’s exposure to domestic violence (Richters & Martinez,

1990; Van Wyk, 2011). This exposure affected behavior, school performance, and future

functioning (Delaney-Black, Covington, & Sokol, 2006; Richter & Martinez, 1993).

Researchers have also demonstrated the reliability of the CEDV using a sample of

adolescents and young adults (including individuals as old as 20 years) with a Cronbach’s

alpha of .85.

In this section, validity refers to a tool’s ability to accurately measure what it

purports to measure. Four residential treatment organizations, representing five domestic

violence exposure shelters were recruited to participate in the study to accurately obtain

the resilience constructs (Edleson, Shin, & Johnson, 2007; Hopf, 2010; Kernic, Monary-

Erensdorff, Koepsell, & Holt, 2005; Leong & Austin, 2006; Pallant, 2009). Participants

from these shelters consisted of 65 children and adolescents. Among those recruited were

children between the ages of 10 and 16 years old. I gave the guardians of the children a

list detailing the instructions, expectations, confidentiality, risks, benefits, and purpose of

the study (Edleson et al., 2007; Kleinman, Adams, Kashdan, & Riskind, 2013).

The CEDV consists of six subscales that measure: violence, exposure to violence

at home, exposure to violence in the community, involvement in violence, risk factors,

and other victimization (see Appendix C). I used the exposure to violence at home

subscale to measure the overall level of domestic violence. Specifically, this subscale

represented the theoretical implications of a microsystem, representing family violence. I

used the violence subscale to measure the frequency of domestic violence exposure. I

used the exposure to violence in the community subscale to measure types of violence

exposure. Finally, I used the other victimization subscale to measure emotional and

physical abilities.

The exposure to violence at home subscale is comprised of Questions 1–10 on the

CEDV. Each item has two parts. The first part requires a yes or no response. If the

individual answered “no”, I moved to the next question. If the individual answered “yes”,

I addressed the second part of the question by selecting as many of the five choices

available. A total score is derived by summing the total number of choices selected on the

second part of Questions 1–10. The total score can range from 0–50. Higher scores

indicate a higher level of exposure to violence in the home. This scale yielded a

quantitative score, or interval data. An example question from the scale is, “How often

has your mom’s partner hurt, or tried to hurt, a pet in your home on purpose?”

Demographic Information

Embedded within Part III of the CEDV instrument (Items 38–40; Appendix C) are

three items measuring the demographic variables of participants. Specifically, the items

ask the participants their age, gender, and ethnicity. This information serves as potential

predictor variables.

Resilience Scale

The Resilience Scale is a 25-item Likert scale instrument that measures resilience,

the dependent variable in this study. The resilience measures five characteristics of

resilience: self-reliance, purposeful life, equanimity, perseverance, and existential

aloneness. Wagnild and Young (1991) derived these five invariant essences from a

qualitative inquiry. The purpose of their study was to explore resilience through two

targeted populations: women who successfully moved on with their lives after a

significant loss (i.e., loss of spouse, health, or employment) and women whose spouses

suffered from Alzheimer’s’ disease. I obtained permission to use the instrument from the

authors. The instrument is in Appendix D.

Internal consistency reliability coefficients, measured by Cronbach’s alpha, have

ranged from .85 to .95 across a diverse sample. Examples include: healthy adults (.92), a

depressed population (.92), sheltered battered women (.94), mothers with pre-school

children (.85), elderly Korean Women (.95), and military wives (.86; Humphreys, 2003;

Lee et al., 2007; Monteith & Ford-Gilboe, 2002; Schachman, Lee, & Lederman, 2004;

Wagnild, 2009; Wagnild & Young, 1993)

Validity refers to the ability of a measurement to accurately measure what it

purports to measure (Wagnild, 2009). As mentioned earlier, reserachers developed the

Resilience Scale from themes derived from a qualitative study (Wagnild & Young, 1993).

The researchers identified five interrelated components that constituted resilience. Two

psychometricians and nurse practitioners reviewed the interrelated themes and

determined the instrument accurately depicted resilience constructs.

Convergent validity refers to the degree of correlation between measures of the

same trait. Convergent validity exists if the observed correlation coefficients are high.

Conversely, discriminant validity refers to the degree in which two measures differ in

measuring a specific trait. Discriminant validity exists when the a reasearcher observes

low correlation coefficients. To assess convergent and discriminant validity, I used items

from the Health Promoting Lifestyle Profile (HPLP), a psychometrically sound

assessment of health promoting behaviors. The HPLP has six subscales: stress

management, health responsibility, nutrition, exercise, self-actualization, and

interpersonal support. I used a sample of 776 middle-aged to older adults in the analyses.

I expected higher correlations (convergent validity) between the Resilience Scale

responses and the corresponding HPLP domains. I expected lower correlations between

Resilience Scale domains, and the exercise and nutrition domains of the HPLP. The

analyses supported acceptable convergent and discriminant properties. Table 1 depicts

the results of these analyses.

Table 1

Correlation Coefficients between the RS and HPLP Domains

Scale

Self-

Actualization

Health

Responsibility

Nutrition

Exercise

Inter-

Personal

Support

Stress

Management

RS 0.62 0.37 0.33 0.26 0.49 0.46

Concurrent validity refers to the degree to which scores on a new measure (i.e.,

Reslience Scale) are related to scores from a criterion measure administered at the same

time. I assessed concurrent validity using the Life Satisfaction Survey, the Philadelphia

Geriatric Center Morale Scale, and the Beck Depression Inventory. I hypothesized a

positive correlation would be found between the Resliency Scale, Life Satisfaction

Survey, and the Philadelphia Geriatric Center Morale Scale. I hypothesized a negative

correlation would be found between the Resliency Scale and the Beck Depression

Inventory. These hypotheses were supported. Table 2 depicts the correlation coefficients

of these analyses.

Table 2

Correlation Coefficients

Life Satisfaction Morale Depression

Resilience Scale 0.37 0.32 -0.41

For each item, the participants used a 7-point Likert scale, ranging from 1

(strongly disagree) to 7 (strongly agree). Summing the scores for each of the 25-item

responses derives a total score, or interval data. The total score can range from 25–175.

Higher scores indicate a higher level of resilience. All items were worded positively,

therefore, there was no reverse scoring of any items. An example item on the instrument

was, “I usually take things in stride.”

Data Analysis Plan

I entered and analyzed data using SPSS version 22 (IBM, 2012). First, I screened

the data for missing values. I removed any participants who did not complete the entire

demographic questionnaire and research instrument. Likewise, I examined the

demographic and instrument calculated scores for outliers. When I identified an outlier, I

removed it. For the purposes of this study, I defined outliers as values larger than 3.29

standard deviations from the mean (Stevens, 2009).

After cleaning the data, I conducted the analysis using multiple linear regression

to pursue the previously stated research question and associated hypotheses:

Research Question: Do domestic violence, age, gender, ethnicity, individual

emotional and physical abilities, types of domestic violence exposure, and

frequency of domestic violence exposure, when in linear combination,

significantly predicts resilience, as measured by the Resilience Scale among

young adults in the Midsouth region of the United States?

Null Hypothesis (H

): Domestic violence, age, gender, ethnicity, individual

emotional and physical abilities, types of domestic violence exposure, and

frequency of domestic violence exposure, when in linear combination do not

significantly predict resilience, as measured by the Resilience Scale among young

adults in the Midsouth region of the United States.

Alternative Hypothesis (H

): Domestic violence, age, gender, ethnicity, individual

emotional and physical abilities, types of domestic violence exposure, and

frequency of domestic violence exposure, when in linear combination

significantly predicts resilience, as measured by the Resilience Scale among

young adults in the Midsouth region of the United States.

I conducted stepwise multiple linear regression to test the null hypothesis and

answer the research question and hypothesis. Because I wanted to predict a quantitative

outcome variable, multiple linear regression was appropriate (Creswell, 2009; Oransky,

Hahn, & Stover, 2013). Resilience was based on a set of ten predictor variables: domestic

violence experienced at home, gender, age, three categories of ethnicity, emotional

abilities, physical abilities, types of domestic violence exposure, and frequency of

domestic violence exposure. I considered a factorial ANOVA, but I determined it would

be an inappropriate technique for developing exploratory predictive models.

The resiliency scale, the CEDV, and age yield participant data on an interval, or a

continuous, level of measurement. Gender and ethnicity are categorical, or nominal,

variables. To use these two variables as predictors, I transformed them into dummy

variables suitable for use in multiple linear regression. Gender was a dichotomous

variable, coded as 0 (male), and 1 (female). The ethnicity variable consisted of four

ethnic groups or categories: African American, Caucasian, Hispanic, and other. Table 3

presents how I coded the dummy variables for ethnicity.

Table 3

Dummy Coding for the Ethnicity Variable

Dummy Variable 1 Dummy Variable 2 Dummy Variable 3

African American 0 0 0

Hispanic 1 0 0

White 0 1 0

Other 0 0 1

I used stepwise multiple linear regression. The stepwise method accounts for all

the independent (predictor) variables to determine the best final predictive equation. I

entered variables into the model one at a time, starting with the variable with the most-

predictive power (highest correlation). I added or subtracted subsequent variables from

the equation. This entering-or-removing process continued until I entered or discarded all

the potential predictor variables, resulting in the optimum prediction equation based on R,

multiple correlation. I evaluated variables based on what each added to the prediction of

the dependent variable that was different from the predictability provided by the other

predictors (Tabachnick & Fidell, 2012). I used the default values in SPSS for entry (p =

.05) and removal (p = .10). I used the F test to assess whether the resting set of

independent variables collectively predicted the dependent variable. After each step in the

regression analysis, I examined the R

, the multiple coefficient of determination, to assess

the additional predictive power each additional variable added to the model. I reported

the final R

value and used to to indicate how much variance in the dependent variable

was accounted for by the set of independent variables. I included a t-test in the analysis to

determine the significance of each predictor variable’s beta coefficients.

I assessed the assumptions underlying multiple linear regression: linearity,

homoscedasticity and absence of multicollinearity. I explain this in detail in Chapter 4.

Linearity refers to the existence of a straight line relationship between the predictor

variables and the criterion variable. Homoscedasticity refers to the idea that scores are

normally distributed around the regression line. I assessed linearity and homoscedasticity

by examining scatter plots. The absence of multicollinearity means the predictor variables

are not strongly related to one another, which I assessed using variance inflation factors

(VIF). If a VIF value is greater than 10, this finding indicates multicollinearity (Stevens,

2009).

Threats to Validity

External Validity

In this research, I studied a defined population; therefore, the results of this study

may not be generalized to other populations within or outside the target organization.

However, the results can provide baseline data for future research. Results from this

study may allow speculative inferences for other similar organizations, but such

inferences must be viewed as tentative. Future findings must be validated through

appropriate research protocols.

It is conceivable that the findings from this study may carry unintended weight. It

was my responsibility to exercise caution when reporting findings or making evaluative

statements about the results. In addition to answering the stated research question, I also

considered the social, political, and human implications of this study when I reported the

findings. Furthermore, accepted professional ethical principles, such as those set forth by

the American Psychological Association (2010), guided but did not restrict my right to

view and use the appropriate reporting standards in the best interests of my conscience

and for the benefit of the scientific community.

Internal Validity

Statistical conclusion validity. Threats to statistical conclusion validity

constitute conditions that impair the likelihood of accuracy in detecting an empirical

relationship (Leong & Austin, 2006). Failure to address these threats may compromise

the results and lead either a Type I or a Type II error. These threats include sample size,

reliability of the instruments, and violations of the assumptions regarding multiple linear

regression. I address these threats, as they pertained to this study, as follows.

Sample size. I used the appropriate sample size, as previously described, to

ensure the study had enough power to detect a significant predictive relationship, if one

existed in the population. I addressed this threat by conducting a power analysis to

determine the ideal sample size.

Reliability of instruments. Invalid or unreliable instruments are a threat to

internal research validity. Although I previously described the instruments were as being

valid and reliable, I calculated Cronbach’s alpha for the CEDV and Resilience Scale to

determine their reliability as it pertained to this sample. A coefficient of .70 or higher

indicates acceptable reliability (Pallant, 2009). I compared the obtained coefficients to

this standard.

Regression assumptions. The results of the multiple linear regression can be

affected by violations of the assumptions underlying the procedure. These assumptions

include multicollinearity, outliers, normality, linearity, homoscedasticity, and

independence of the residuals. Multicollinearity refers to a high degree of correlation

between each of the independent variables. This means the correlation between any two

of the independent variables should not exceed .70 (Pallant, 2009). I examined this

assumption through a review of collinearity diagnostics produced from the SPSS

procedure. Collinearity diagnostics indicate problematic correlations that may not be

apparent in a correlation matrix. I examined the VIF indicators. If a VIF value is higher

than 10, the results may be problematic (Pallant, 2009). When I identified

multicollinearity, I retained the predictor(s) with the highest correlation for the analysis

and removed or combined the other predictor(s), when appropriate.

I assessed outliers, normality, linearity, homoscedasticity, and independence of

the residuals using a normal probability plot of the regression standardized residual and

the scatterplot created in the regression SPSS procedure (Pallant, 2009). I made

appropriate data corrections (i.e., dummy variable transformations and deletion of

outliers) prior to inferential statistical testing.

Ethical Procedures

Avoiding coercion and preserving human rights are universal precautions

researchers take when using human participants in a research study (IRB Forum, 2008). I

accepted the responsibility to maintain ethical research procedures and eliminate

potentially harmful emotional reactions, physical harm, or psychological effects

experienced by the participants. Although psychological harm is difficult to define, I

identified and removed any suspected harmful effects that may have been caused by

participating in the study. Furthermore, ethical standards dictate before obtaining consent

from participants, it is the researcher’s obligation to inform the participants of the

research purpose, potential benefits, and anything that may affect participants’

willingness to participate. Reserachers must also answer any participants’ questions to

assure participant comprehension. I designed the research protocol for this study to

address these standards. Before consent was obtained from the participants, I secured the

necessary approvals from the previously identified organization to conduct research in

that setting (see Appendix E; IRB Forum, 2008).

Because this research contains no manipulated independent variable(s) and used

survey research methodology, there was minimal risk for the voluntary participants.

Additionally, if participants experienced any psychological or emotional distress during

the study, I provided them contact information for appropriate counseling services.

I obtained approval from the Walden University IRB prior to collecting any data.

As required by the IRB, I removed all the participant identification information, such as

names, prior to the data analysis to ensure participants’ confidentiality, as I promised

during participant recruiting.

I placed the collected data in a sealed envelope and stored it in a locked file

cabinet before removing it for analysis. Subsequently, I kept electronic data (e.g., SPSS

data files) on my personal computer in a password protected folder. I will destroy all

electronic and paper data five years after the study was completed.

Summary

In this chapter, I outlined the design for the research study, which was

correlational and quantitative in nature. I discussed the characteristics of the sample, the

description of the study site, and the procedures for sampling and data collection. I

provided the psychometric properties, including internal reliability, convergent validity,

and concurrent validity of the instruments used in the study. Each of instruments have

been shown to have strong reliability and validity, and were therefore appropriate for use

in the study. In this chapter, I delineated the data analysis, and included rationale and

procedures for using stepwise linear regression to address the research question. I

considered potential threats to internal and external validity, and provided several

strategies to combat these threats. I also included the protocol for ethical considerations

for the protection of human subjects; I took every step necessary to protect participants

from undue risk.

Chapter 4: Results

Introduction

The purpose of this study was to explore whether domestic violence, age, gender,

ethnicity, individual emotional abilities, physical abilities, types of domestic violence

exposure, and frequency of domestic violence exposure could, individually or in linear

combination, adequately predict resiliencey among young adults in the Midsouth region

of the United States. The specific research question was:

Do domestic violence, age, gender, and ethnicity, individual emotional and

physical abilities, types of domestic violence exposure, and frequency of domestic

violence exposure, when in linear combination, significantly predicts resilience,

as measured by the resiliency scale among young adults in the Midsouth region of

the United States?

: Domestic violence, age, gender, ethnicity, individual emotional and physical

abilities, types of domestic violence exposure, and frequency of domestic violence

exposure, when in linear combination, do not significantly predict resilience as

measured by the RS among young adults in the Midsouth region of the United

States.

: Domestic violence, age, gender, ethnicity, individual emotional and physical

abilities, types of domestic violence exposure, and frequency of domestic violence

exposure, when in linear combination, significantly predicts resilience as

measured by the RS among young adults in the Midsouth region of the United

States.

This chapter contains the results of the data analysis I conducted to address the

research question and hypotheses. In this chapter, I describe the data collection and

demographic characteristics of the sample. Then, I present the results of the data analysis.

This chapter will conclude with a summary.

Data Collection

I collected the data for this study in June 2017. A total of 118 young adults from

BBBS completed the survey. No participants were excluded because of missing data and

I did not identify any outliers in the data. Table 4 displays descriptive statistics for the

categorical demographic variables. The age range of the participants was 18–22 years (M

= 19.99, SD = 1.42). The sample was approximately split between men (n = 60, 50.8%)

and women (n = 58, 49.2%). The largest proportion of participants indicated their

ethnicity as Black (n = 35, 29.7%).

Table 4

Descriptive Statistics for Categorical Demographic Variables

Variable n %

Gender

Male 60 50.8

Female 58 49.2

Ethnicity

White 34 28.8

Black 35 29.7

Latino 32 27.1

Other 17 14.4

In Table 5, I display the descriptive statistics for the summated scores

representing domestic violence, frequency of domestic violence exposure, types of

domestic violence, emotional abilities, physical abilities, and resilience. Specifically, I

used the exposure to violence at home subscale of the CEDV instrument to measure the

overall level of domestic violence. I used the violence subscale of the CEDV to measure

the frequency of domestic violence exposure. I used the exposure to violence in the

community subscale of the CEDV to measure the types of violence exposure (i.e., degree

of community violence exposure). I measured participant’s emotional and physical

abilities using the other victimization subscale of the CEDV. I measured resilience using

the overall score on the resiliency sclae. I computed the summated scores by summing

the responses to the items corresponding to each variable.

Table 5

Descriptive Statistics for Summated Scores

Variable M SD

Domestic violence 25.32 5.16

Frequency of domestic violence exposure

16.58 4.47

Type of domestic violence exposure 15.58 3.69

Emotional and physical abilities 6.69 2.77

Resilience 128.54 30.86

Results

To answer the research question and hypotheses, I conducted a stepwise multiple

linear regression. Reslience, domestic violence, emotional and physical abilities, types of

domestic violence exposure, and frequency of domestic violence exposure yielded data

on an interval, or continuous, level of measurement. Gender and ethnicity were

categorical, or nominal, variables. To use these two variables as predictors, I transformed

them into dummy variables. Gender was coded as 0 (male) and 1 (female). Ethnicity

consisted of four categories: Black, White, Latino, and other. I dummy-coded these

variables and Black served as the reference category. Becuase there was no logic for

doing otherwise, I used the default values in SPSS for stepwise predictor entry (p = .05)

and removal (p = .10) in the regression equation.

I assessed the assumptions of linearity, homoscedasticity, and absence of

multicollinearity prior to analysis. Linearity means there is a straight line relationship

between the predictor variables and the criterion variable (Leong & Austin, 2006; Pallant,

2009). Homoscedasticity means scores are normally distributed about the regression line

Leong, et al). I assessed linearity and homoscedasticity by examining scatter plots (see

Figure 2, Figure 3). The data did not strongly deviate from the normal line (Figure 2), and

the data were approximately evenly distributed around zero (Figure 3). Therefore, the

assumptions of linearity and homoscedasticity were met. The absence of multicollinearity

means the predictor variables are not strongly correlated with each other, which assessed

using VIF (Plancherel & Bolognini, 2012). Stevens (2009) noted VIF values over 10

suggest the presence of multicollinearity. In the present study, the VIF values were below

10, so this assumption was met.

Figure 2. Normal P-P scatter plot for stepwise multiple linear regression.

Figure 3. Scatter plot of residuals versus predicted values for stepwise multiple linear

regression.

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Using the stepwise algorithm, I arrived at the final model in one step. In the final

model, I only included the frequency of domestic violence exposure. I excluded the other

predictor variables: domestic violence, age, gender, ethnicity, emotional and physical

abilities, and types of domestic violence exposure. The final model determined by the

stepwise algorithm was significant: F(1, 116) = 22.25, p < .001, R

= .16. This indicated

the final model, consisting of one predictor, significantly predicted resilience in this

sample. Therefore, the H

was rejected. The R

value indicated the final model accounted

for 16% of the variability in resilience. I present the results of the regression model in

Table 6. Frequency of domestic violence exposure was a significant negative predictor (B

= -2.77, p < .001), indicating participants who scored higher on frequency of domestic

violence exposure tended to have lower resilience scores.

Table 6

Final Stepwise Multiple Linear Regression Model Predicting Resilience

Predictor B Std.

Error Beta t Sig. VIF

Frequency of domestic

violence exposure

-2.77 0.59 -0.40 -4.72 < .001 1.00

Note. F(1, 116) = 22.25, p < .001, R

= .16.

Summary

This chapter contained the results of the data analysis I conducted to address the

research question. The research question asked the following: do domestic violence, age,

gender, and ethnicity, individual emotional and physical abilities, types of domestic

violence exposure, and frequency of domestic violence exposure, when in linear

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combination, significantly predicts resilience as measured by the resiliency scale among

young adults in the Midsouth region of the United States? I conducted a stepwise

multiple linear regression analysis to address this question. The results of the analysis

were significant, so the H

was rejected. The final model determined by the stepwise

algorithm included one predictor: frequency of domestic violence exposure. This was a

significant negative predictor, indicating participants with higher frequency of domestic

violence exposure tended to have lower resilience. Chapter 5 contains a discussion of

these findings in relation to previous literature. I also discuss the implications and

directions for future research in Chapter 5.

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Chapter 5: Conclusions, Summary, and Recommendations

Introduction

Domestic violence has significant negative influences on the 1.5 million young

adults in the United States who witness and experience it each year (Childhood Domestic

Violence Association, 2014). However, resilience allows these youths to grow into

caring, competent, and confident young adults (Black et al., 2015; Masten & Powell,

2003; Rutter, 1980; Werner, 2004). Therefore, it is beneficial for researchers to

investigate factors that predict resilience. Previous researchers have suggested exposure

to domestic violence may increase resilience (Anderson et al., 2012). In addition,

demographic factors that may influence resilience levels include age (Ali et al., 2015;

Garthe et al., 2014; Jan et al., 2015); gender (Black et al., 2015; Dietz et al., 2014); and

ethnicity (Danquah et al., 2010; Flores et al., 2014; Holland & Gill, 2010; McKnown-

Johnson & Rhodes, 2013; Zautra et al., 2010). Research had not been completed

regarding these factors in conjunction and how they interact to predict resilience among

young adults.

The purpose of this study was to explore whether domestic violence, age, gender,

ethnicity, individual emotional and physical abilities, types of domestic violence

exposure, and frequency of domestic violence exposure could adequately predict

resilience among young adults in the Midsouth region of the United States. In this study,

I examined responses to the CEDV instrument from a sample of 118 participants who

were members of BBBS in southwest Texas. The stepwise multiple linear regression

revealed frequency of domestic violence exposure predicted resilience. Specifically, the

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more frequently participants experienced domestic violence, the less likely they were to

have a high resiliency score. Chapter 5 includes an interpretation of the findings in the

study and the study limitations. The chapter also includes a discussion of the

recommendations for further research and the implications of the findings. Lastly, I

dicuss the conclusions along with social change implications.

Interpretation of the Findings

Young adults exposed to domestic violence often experience negative social,

emotional, and cognitive long-term outcomes (Job et al., 2012; Löckenhoff et al., 2012;

Sorbello et al., 2012; Williams & Steinberg, 2011). The adverse effects associated with

domestic violence are consistent with Bronfenbrenner’s ecological theory. Domestic

violence occurs in the mesosystem and fundamentally shapes the individuals’ subsequent

interactions in the mesosystem and exosystem (Holland et al., 2015), continuing as the

affected individuals reach adulthood (Black et al., 2015; Goodrum et al., 2012).

Conversely, resilience forms when access to mesosystem or exosystem resources

counteract negative microsystem factors; for example, this might include close

relationships with extended families, friends, or role models (De Los Reyes et al., 2013;

Dondero, 1997; Garthe et al., 2014; Grossman & Tierney, 1998; O’Donohue & Ferguson,

2006; Wolkow & Ferguson, 2001).

The results of this study indicated that among the sample of 118 participants, only

one of the multiple predictor variables significantly predicted resilience: frequency of

domestic violence exposure. Frequency of domestic violence exposure was a significant

negative predictor (B = -2.77, p < .001) and predicted 16% of the variance in resilience

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scores. The frequency of domestic violence exposure negatively predicted resilience. The

more frequently participants experienced domestic violence, the less likely they were to

have high resilence scores. I examined the frequency of domestic violence exposure

using the violence subscale of the CEDV, which asks participants to identify, on a scale

ranging from never to always, how frequently they experience various violent situations.

The results indicated different types of domestic violence did not predict resilience;

moreover, I did not identify a relationship between resilience and any of the demographic

predictor variables.

The results of the present study were inconsistent with some results in the

literature. For example, Anderson et al. (2012) determined a sample of women exposed to

domestic violence had higher than average resilience scores. Anderson et al. (2012) did

not examine the frequency of exposure to domestic violence as a specific variable;

however, the results of Anderson et al.’s (2012) study suggested those who experienced

domestic violence had higher resilience scores. These data were inconsistent with the

findings of the present study, which indicated more exposure to domestic violence

resulted in lower resilience scores.

Many researchers have focused on young adults’ psychological and behavioral

responses; however, researchers should focus their attention on young adults’ social

environments and the frequency of exposure to predict if resilience will occur (Widom &

Wilson, 2015). The results of this study related to frequency of domestic violence are

consistent with studies indicating domestic violence exposure in the young adults’

environments negatively influences resilience development. For example, Kassis et al.

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(2013) determined, through multiple linear regression, that negative experiences such as

domestic violence, poor parenting, and alcohol/drug abuse reduced resilience among a

sample of middle-school students from Austria, Germany, Slovenia, and Spain (N =

5,149). Similarly, researchers have noted negative family environments deter resilience

development (Carroll, 2006; Garthe et al., 2014; Gerard & Buehler, 1999; Waldon et al.,

2001). Flores et al. (2014) noted the family environment and the protective factors within

a microsystem were determinants of resilience development. Thus, the findings of this

study supported the findings of previous researchers regarding frequent domestic

violence exposure in the mesosystem. This exposure adversely affected the participants’

abilities to form resilience.

The finding that there was no relationship between demographic variables and

resilience development was mixed in relation to how it corresponded to recent literature.

Similar to the findings of the present study, Kassis et al. (2013) found no relation

between demographic variables and resilience development, instead emphasizing the

importance of structural variables. However, previous researchers supported the influence

of demographics on resilience formation, including age (Ali et al., 2015; Garthe et al.,

2014; Jan et al., 2015); gender (Black et al., 2015; Dietz et al., 2014); and ethnicity

(Danquah et al., 2010; Flores et al., 2014; Holland & Gill, 2010; McKnown-Johnson &

Rhodes, 2013; Zautra et al., 2010).

The findings of the present study did not indicate a predictive relationship

between gender and resilience development. Some reserachers determined gender

influenced resilience development (Black et al., 2015; Dietz et al., 2014). However, the

106

findings from the present study did not support that claim. Previous researchers indicated

males were more likely than females to display adverse outcomes from domestic violence

exposure, such as aggression instead of resilience (Hughes, 2015; Rosenbaum &

O’Leary, 2010; Wexler, 2015). Furthermore, there was an indication that male young

adults were more likely than females to experience physical violence directed toward

themselves, which researchers suggested may influence their responses to domestic

violence exposure (Margolin & Gordis, 2015; Miller, Handal, Gilner & Cross, 2015;

Schwarz & Getter, 2015; Straus et al., 2014; Widom, 2014). However, the results of the

present study were consistent with the results found by other researchers who did not

determine a relationship between gender and resilience formation. For example, Solberg

et al. (2007) found no significant differences between young adult males and young adult

females with respect to resilience formation.

Regarding ethnicity and resilience, this study included a roughly equal

representation of Black (N = 35), Latino (N = 32), and White (N = 34) participants, and a

significant representation of other (N = 17) participants, whereas other studies on

ethnicity included a sample that represented the demographic breakdown in the United

States (Danquah et al., 2010; Flores et al., 2014; Holland & Gill, 2010; McKnown-

Johnson & Rhodes 2013; Zautra et al., 2010). The participants’ ethnic breakdowns

reflected the BBBS convenience sample in southwest Texas. Surveying larger

proportions of ethnic minorities might provide more representation; however, the results

may not be representative of the population of the United States.

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The results of the present study seemed to contradict the idea that age is a

significant predictor. The results related to frequency of domestic violence exposure and

resilience may further illuminate previous results regarding the relationship between age,

resilience, and domestic violence. Ali et al. (2015) and Rosenthal and Wilson (2003)

determined the earlier in life participants were exposed to domestic violence, the less

likely they were to form resilience. Garthe et al. (2014) found early removal from a

domestic violence situation increased the likelihood that a youth would develop

resilience. This finding could have been the result of early and prolonged exposure to

domestic violence, which would result in greater perceived frequency of domestic

violence incidences because the youth would have been exposed to such behavior

throughout his or her childhood. Therefore, the findings of this study further explain

previous correlations between age of exposure, domestic violence, and a lack of

resilience formation.

The results of the present study indicated that young adults exposed to domestic

violence were statistically prone to exhibiting low resiliency and had tendencies to utilize

aggressive methods to respond to external threatening issues and this can also be based

on age, and culture of the individual.

However, because of the limited sample size of this study, one cannot make

generalizations regarding over or under representation of domestic violence about one

ethnicity. From this study findings, it is shown that young adults, especially the African

American participants, reported low resilience compared to White participants. It is not

clear why Hispanic participants had higher resilience levels than their counterparts. This

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could be a result of cultural differences, such as how each culture perceives the term

domestic violence, and the values associated with the term and beliefs (Coie & Dodge,

2014; Edleson, Shin, & Johnson Armendariz, 2008).

Limitations of the Study

The main limitation of this study was the small sample size of young adults from

a limited geographic region. Individuals in the population and the resulting sample

resided in the Midsouth region of the United states and did not represent the population

of individuals affected by domestic violence in other regions of the country.

Consequently, I cannot use the results obtained from this study to generalize the U.S.

population, only the Midsouth region.

In this study, I focused on violence young adults primarily observe at home and

within their social environments. As previously noted, influences beyond the

microsystem, including the exosystem and the mesosystem may also influence resilience

development. These might include mentorships and coaching (De Los Reyes et al., 2013;

Dondero, 1997; Garthe et al., 2014; Grossman & Tierney, 1998; O’Donohue & Ferguson,

2006; Wolkow & Ferguson, 2001) or the removal of an individual from a domestic

violence situation (Garthe et al., 2014). One limitation of the study involved microsystem

factors as the primary focus of the chosen variant.

I used the CEDV tool to gather data for this study. The CEDV is an established

tool for assessing domestic violence exposure (Edleson, et al). However, the results of

this study are limited to the accuracy of the tool.

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Recommendations

The results of this study significantly contributed to the body of scholarly data by

identifying a predictor variable for resilience; however, additional research is required to

develop a model with a higher predictive value. Although the findings of the present

study were significant (R

= .16), it is important for future researchers to seek out

conjunctive variables to more accurately predict resiliency. From the findings of this

study, I believe scholars will find it helpful to continue studies on the effects of frequent

domestic violence exposure on young adults’ resilience. Specifically, I recommend a

more granular look, using the CEDV, to see which specific behaviors on the violence

subscale most influence resilience scores. Future researchers should seek alternative

variables within an ecosystem, such as mentors, coaches, and child protective service

interventions, to see how these resources influence resilience development.

Researchers should consider further study on this topic, including the insights of

future findings, this study did not provide because of limitations and sample size. Until

the time of this study, researchers frequently employed homogenous demographic

samples. Many researchers conducted studies using only people who identified as women

or identified as a specific ethnicity (Black et al., 2015). In this study, I focused on

participants of all genders and a wide range of ethnicities to gain a better understanding

of individual resilience formation and adaptation. I recommend researchers continue to

gain a wide range of experiences from varied samples. I also recommend replicating the

present study with a national sample to test the generalizability of the findings.

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Furthermore, longitudinal studies may help researchers better understand the long-term

predictors of resilience as young adults exposed to domestic violence enter adulthood.

Implications

Although frequent exposure to domestic violence can reduce resilience, previous

researchers indicated exposure to a community, role models, and early removal from a

domestic violence situation can increase an individual’s resilience (Ali et al., 2015;

Garthe et al., 2014; Rosenthal & Wilson, 2003). Therefore, the results of the present

study, in conjunction with previous research, indicate the necessity for further research.

Also, practitioners, lawmakers, social leaders, and support staff in schools to identify

domestic violence situations and to intervene with positive support, such as BBBS, or in

extreme circumstances, to remove the child from the home.

Researchers have shown if violence happens once, it usually reoccurres with a

greater consequence (Ali et al., 2015; Beam, Gil-Rivas, Greenberger, & Chen, 2012;

Garthe et al., 2014; Jan et al., 2015; Plancherel & Bolognini, 2012; Storksen, Roysamb,

Moum, & Tambs, 2010). The results of the present study indicated frequent exposure to

domestic violence predicted decreased resilience. Therefore, it is important to put a crisis

plan in place for the caregiver and the victims. A crisis plan should consist of a safe plan

that includes community organizations, lawmakers, policies, friends, and relatives to help

reinforce the safety plan in the best interest of the young adult. To deal with young

adults’ emotional abuse and home life experiences requires adequate intervention

programs such as counselling, role modeling adults, adequate accomodation,

transportation, and financial support. However, to promote adequate resources and to

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execute the safety plan, local and governmental agencies must establish collaborative

responses to produce a positive response outcome.

For therapists, facility workers, and lawmakers, this finding indicates an

awareness of the effects of frequent domestic violence exposure as a priority for the

development of youth in American society. Therapists and community agencies must

support trauma-informed education. Community agencies must develop asset-based

research and information that will influence policy and practices affecting young adults

exposed to domestic abuse. Social agencies and their employees must be able to establish

basic education for parents, teachers, and counselors to be more aware of the influence of

frequent adult violent behaviors in the home. Similarly, case workers in government and

private agencies might use the findings from this study as a tool to advocate for

lawmakers to acknowledge and expedite laws protect parents and families in the best

interests of children, namely by reducing the frequency of domestic violence experiences.

The results further imply the need for widescale programs to support youths who

have been exposed to frequent domestic violence. These programs may help youths

improve coping skills based on their previous exposures to violence. The providers

working with this population must have adequate resources to train volunteers in this

issue and promote the message of hope despite the negative effects of violence on young

adults’ transitions to adulthood. Individuals who have been exposed to domestic violence

need technical assistance, encouragement, and guidance related to resilience adaptation

for future functioning.

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The results of this study provide practitioners, lawmakers, and social leaders with

vital information about the effects of domestic violence on young adults They also

provide future researchers and clinicians with a significant opportunity to better

understand the issues that affect young adults’ responses to resiliency. Researchers have

focused on asset-based evidence, which suggests prior experiences may have provided

young adults with protective factors that allowed them to maintain resilience despite

adversity (Damant et al., 2010; DeForge, Belcher, O’Rourke, & Lindsey, 2005;

D’Imperio et al., 2000; Resnick, Bearman, Blum, & Bauman, 1997; Rutter, 1985).

However, the present study indicates researchers must acknowledge the significant

influence on resiliency that frequent exposure to domestic violence may cause. For

individuals with high domestic violence exposure, researchers and clinicians should focus

on and empathize with the adverse effects such exposure may have had on the person’s

ability to form a resilient response. Lastly, these findings can be used as a baseline for

data collections regarding young adults’ experiences with domestic violence. This

exposure can allow behavior modification toward self-actualization.

Implications for Social Change

Young adults who are exposed to frequent domestic violence may not have the

ability to use effective methods to resolve issues, therefore continuing the cycle of abuse.

Among young adults exposed to domestic violence, microsystem situations have

conditioned their information processing system to respond to threats with violence rather

than using ignoring tatics or walking away. For example, if young adults have witnessed

physical, financial, and emotional abuse by one of their parents or have seen a parent

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slapping the other parent, throwing things or objects to the wall, choking, stabbing, or

shooting in the home, they are more likely to use learned behaviors to respond to threats

(Edleson et al., 2007; Horn, Williams, & Scott, 2002; Lesch & Ursula, 2000; Pagelow,

1990; Solberg et al., 2007).

When a young adult is exposed to a negative experience such as domestic

violence as a child, the individual can either develop resilience or have an adverse

reaction. Adverse reactions include negative social, cognitive, and behavioral issues,

which young adults may carry into adulthood (Ali et al., 2015; Garthe et al., 2014; Jan et

al., 2015). The results of the present study implied frequent exposure to violence affects

young adults adversely. Carlson (2006) reported high rates of poverty are attributed to

direct exposure to family violence and community violence. Similarly, Horowitz,

Margolin, and Gordis (2000) indicated 50% of children exposed to domestic violence met

the criteria for PTSD. These researchers suggested there is a need for community social

supports and available resources to minimize the effects of living in violent homes and

neighborhoods. Exposure to domestic violence directly affects young adults in the short-

term, but also indirectly affects young adult’s development and behaviors in the long-

term (Horowitz et al., 2000). The present study supported this assertion by demonstrating

frequent exposure to domestic violence predicted low resilience.

To foster resilience, practitioners must understand predictive factors that influence

resilience development. To effect social change, individuals must seek methods to reduce

the frequency of domestic violence exposure. Individuals can accomplish this through

interventions to remove the child from the home (Garthe et al., 2014) or providing

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alternative community support (Ali et al., 2015). Through these interventions, individuals

may avoid the long-term effects of domestic violence exposure, including mental illness,

aggression, and low self-esteem (Clauzade, 2009; Sorbello et al., 2012; Williams &

Steinberg, 2011).

Conclusion

Previous researchers focused on adolescent behavior, rather than the microsystem

surrounding the adolescents. They also focused on predictive factors that increased or

decreased resilience, provided an incomplete view of the influence of domestic violence

on youth’s development of resilience (Beam, Gil-Rivas, Greenberger, & Chen, 2012;

Edleson et al., 2007; Edleson, Shin, & Johnson Armendariz, 2008; LaLiberte et al., 2010;

Plancherel & Bolognini, 2012; Shin & Edleson, 2007; Storksen, Roysamb, Moum, &

Tambs, 2010). I designed the present study to gain insight into specific factors that would

predict resilience. Specifically, the purpose of this study was to explore whether domestic

violence, age, gender, ethnicity, individual emotional and physical abilities, types of

domestic violence exposure, and frequency of domestic violence exposure could

adequately predict resilience among young adults in the Midsouth region of the United

States.

I performed a multiple linear regression of the responses to the CEDV from a

sample of 118 participants, who were members of BBBS in southwest Texas. The results

revealed that the frequency of domestic violence exposure negatively predicted

resilience, indicating participants with higher frequency of domestic violence exposure

tended to have lower resilience, compared to participants with fewer exposures. This

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finding suggested continued, frequent exposure to domestic violence significantly

influenced an individual’s ability to develop resiliency. Results indicate the need for

continued vigilance from researchers, clinicians, child protective services, and lawmakers

to reduce children’s frequency of exposure to domestic violence. Furthermore, the results

indicated the need for further examination of environmental protective factors that may

affect resilience development, according to Bronfenbrenner’s ecological model. By

discovering factors that predict resilience, whether positive or negative, stakeholders can

target interventions and develop policies to eradicate the harmful social and

psychological effects of domestic violence on children.

Domestic Violence and Demographic Variables as Predictors of Resiliency Among Young Adults.

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