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Electronic Theses, Treatises and Dissertations
The Graduate School
2007
Peer- Driven Justice: Development and
Validation of the Teen Court Peer Influence
Scale (Tcpis)
Kenneth Scott Smith
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THE FLORIDA STATE UNIVERSITY
COLLEGE OF SOCIAL WORK
PEER- DRIVEN JUSTICE: DEVELOPMENT AND VALIDATION OF THE TEEN COURT
PEER INFLUENCE SCALE (TCPIS)
By
KENNETH SCOTT SMITH
A dissertation submitted to the
College of Social Work
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Degree Awarded:
Summer Semester, 2007
The members of the Committee approve the dissertation of Kenneth Scott Smith defended on
May 23, 2007.
__________________________________
Darcy C. Siebert
Professor Directing Dissertation
__________________________________
Daniel P. Mears
Outside Committee Member
__________________________________
C. Aaron McNeece
Committee Member
Approved:
_____________________________________
C. Aaron McNeece, Dean, College of Social Work
The Office of Graduate Studies has verified and approved the above named committee members.
ii
This work is dedicated to my wonderful, supportive, and loving wife, Betsy Smith.
She has kept me focused and has remained my strength throughout.
To my parents, Ken and Liz Smith, who have given me their unconditional love, guidance, and
support, without you I would not be who I am today.
To my daughter, Addison, with hard work, all of your dreams can come true. You are a blessing
and inspiration to your mother and me.
To all of you, I love you so very much for all that you have given me!
iii
ACKNOWLEDGEMENT
To my wife, my best friend, and to whom I owe everything. You have blessed me with
your love and your support. Words cannot express my love for you and our daughter. Just
looking at both of you gives me hope, shows me God’s grace, and inspires me to make a
difference. I by no means could have achieved what I have without you. Just know that I love
and adore you more every passing minute of the day.
I would like to acknowledge the support and guidance from my cohort. Alicia, Nina, and
Jill have kept me sane throughout this whole process. I have grown as an individual because of
my experiences with them. They have pushed me when I wanted to give up and they have made
this life-changing event more memorable than they could imagine. I will never forget you and all
that you have done for me. I love each of you dearly: Give me a ‘B’ and set me free!
I would also like to thank, Dr. Darcy Siebert. I have grown as an academic through your
guidance, support, encouragement, and patience (YIKES). Your humor and candor is quite
refreshing and you have always kept me grounded when I at times was a little off. Dr. McNeece,
you were the first person that really took time to get to know me. You have given so much to our
profession and to me and I have been honored to learn from you. I would also like to thank Dr.
Daniel Mears, who helped put this whole experience into perspective and who made
considerable contributions to this dissertation. To the many colleagues and professors at the
College of Social Work, who have provoked me to become more than just another person in our
field with a Ph.D. I am eternally grateful!
iv
TABLE OF CONTENTS
List of Tables ……………………………………..…………………………………………….vii
List of Figures …………………………….……………….…………………………………...viii
Abstract ……………………………………………………..……………………………………ix
1. AN EVALUATION OF TEEN COURTS: A CALL FOR A THEORETICAL ANALYSIS ...1
Teen Courts ……………………………………………………………………………….1
Gaps in Teen Court Literature ……………………………………………………………8
Theoretical Foundations of Teen Courts ………………………………………………….9
Future Considerations in Positive Peer Influence Research …………………………….13
Implications ……………………………………………………………………………...14
2. ITEM DEVELOPMENT AND EXPLORATORY FACTOR ANALYSIS ………………….17
Theory …………………………………………………………………………………...17
Item Construction ………………………………………………………………………..18
Sampling and Data Collection …………………………………………………………..19
Data Analysis ……………………………………………………………………………21
Findings …………………………………………………………………………………25
Limitations ………………………………………………………………………………29
Theoretical Implications ……………………..………………………………………….30
Research Implications ………………………………….……………………………….31
Policy Implications ...……………………………………………………………………32
3. ASSESSING THE THEORETICAL MODEL AND VALIDITY OF THE TCPIS …………33
Methodology …………………………………………………………………………….33
Sampling and Data Collection …………………………………………………………..35
Measures ………………………………………………………………………………...36
Data Analysis ……………………………………………………………………………38
Findings …………………………………………………………………………………40
Validity ………………………………………………………………………………….45
v
Limitations ………………………………………………………………………………46
Implications and Discussion …………………………………………………………….46
Conclusions ……………………………………………………………………………...49
4. ASSESSING THE PREDICTIVE VALIDITY OF THE TCPIS …………………………….50
TCPIS ……………………………………………………………………………..…….50
Predictive Validity ………………………………………………………………………51
Outcome Measures and Hypotheses ……………………………………………………………..51
Sample and Data Collection ……………………...……………………………………...52
Data Analysis ……………………………………………………………………………53
Findings …………………………………………………………………………………55
Limitations ………………………………………………………………………………57
Future Directions ………………………………………………………………………..58
Social Work Practice …………..………………………………………………………...58
Conclusions ……………………………………………………………………………...59
5. FUTURE RESEARCH EFFORTS……………………………………………………………61
Subsequent Teen Court Research ……………………………………………………….62
Expanding the TCPIS …………………………………………………………………...63
Social Work’s Responsibility ………………………………………………….………..64
APPENDICES …………………………………………………………………………………..65
REFERENCES ………………………………………………………………………………….68
BIOGRAPHICAL SKETCH ……………………………………………………………………79
vi
LIST OF TABLES
Table 1: Empirical Articles: Secondary Data Analysis Design ………..………………..………5
Table 1.2: Empirical Articles: Quasi- Experimental Design ……………….…..…………..……6
Table 2.1: Standard Deviation Comparison from All Three Samples …….……..……..………22
Table 2.2: TCPIS Interitem Correlations ………...……………………….…..…………...……23
Table 2.3: Assessing Representativeness Using FDJJ Demographic Data …..…….…………...25
Table 2.4: Model 1 with Varimax Rotation …………………………………..…..……………..27
Table 2.5: Model 2 with Direct Oblimin Rotation …………………………..….………………28
Table 3.1: Model Comparisons ……………………………………………..…..……………….41
Table 3.2: TCPIS Items Categorized by Factor …………………………………………………43
Table 4.1: Regression Analyses Results …………………………………..…………………….57
vii
LIST OF FIGURES
Figure 3.1: Structural model for the TCPIS CFA ……………..……...….…………..………….34
Figure 3.2: Path Diagram for Final Model ………………………………………………………42
viii
ABSTRACT
Criminological and sociological theories suggest that peers play a significant role in
reinforcing delinquent behavior. Numerous studies discuss the negative impact of peer influence
variables. Unfortunately, little attention is provided to the protective factors of positive peers.
One program that utilizes positive peer influence to redirect delinquent behavior so that
participants will not reenter the juvenile justice system is the teen court model. To date, teen
court research has not evaluated the impact of positive peer influence or determined whether
positive peer influence even exists within these courts. This research not only addresses gaps in
criminological, sociological, and teen court theory, it fills a research void concerning the
operations of teen courts and their potential impacts. The results of this study also establish a
foundation for examining how a neglected but potentially critical dimension of youth’s
relationships, positive peers, may both reduce and deter delinquency.
This research reports the development and validation of the Teen Court Peer Influence
Scale (TCPIS). The TCPIS was disseminated to 404 participants in six teen courts in the state of
Florida. The sample was systematically split to form two groups. The first group was used for the
exploratory factor analysis (EFA) and the second for the confirmatory factor analysis (CFA).
Results from these analyses support that positive peer influence is operationalized by three latent
constructs, three correlations between factors, and fourteen observed variables. The domains
focus on developing positive cognitions, positive identity development, and modeling. The
theoretical model derived by the EFA and CFA was a good fit to the data with all fit indices
meeting appropriate standards. The reliability for the TCPIS was .88 and the scale showed
promising construct, concurrent, and predictive validity using delinquency measures. The results
from this study fill conceptual and empirical gaps in teen court research by identifying
theoretical mechanisms of positive peer influence. The TCPIS can now be utilized in future teen
court research to determine whether specific strategies used by these courts are effective.
ix
CHAPTER ONE
AN EVALUATION OF TEEN COURTS: A CALL FOR A THEORETICAL ANALYSIS
The reduction of juvenile crime will continue to be a national priority with drug abuse
and loitering charges dramatically increasing by 145% and 81% in 2002, respectively (Dick,
Pence, Jones, & Geertsen, 2004). Youth accounted for 11.8% of all drug related crimes in 1994;
ten years later, there has been little reduction to this statistic (Department of Justice,
2004).Within the last two years charges for youth increased 24% from 2004 to 2005 with youth
accounting for 48.6% of persons charged for arson (Federal Bureau of Investigation, 2006).
The costs to rehabilitate youth are also increasing. Washington State Institute for Public
Policy (2002) reported that juvenile justice spending has increased nationally, exceeding $100
million annually in additional costs from 1975 to present. By 2020, the youth population in the
U.S. is expected to rise to 44 million (11% increase), so federal agencies, states, and juvenile
justice officials are preparing for a corresponding spike in reported adolescent behavior problems
(Hannan, 2001). These statistics support that youth crime remains stable, costly, and problems
with youth crime may accelerate in the near future, suggesting that research should begin to
identify evidence-based strategies that reduce delinquent behaviors. One program that has shown
to be a viable option in reducing delinquent behavior is the teen court model.
This study addresses gaps in criminological, sociological, and teen court research
regarding positive peer influence through measurement development. Research is limited as to
what types of positive influence exist and how it is measured. Similar gaps exist within teen
courts. Teen courts operate on the notion that positive peer influence is the driving force behind
youth outcomes with no evidence to support these claims. The outcomes from this study identify
three types of positive peer influence, which are significantly related to youth outcomes. This
study lays the foundation for examining positive peer influence, not only in the context of teen
courts, but also in other realms that include youth. The following provides an overview of teen
court programs, current research, theoretical foundations, and gaps in teen court and positive
peer influence literature.
Teen Courts
Teen courts present “an alternative sentencing option for first-time non-violent juvenile
offenders wherein they are sentenced by a jury of their peers” (Williamson County, 2006, p.1).
The teen court concept has become a popular diversion program through high levels of parent,
1
teacher, and youth satisfaction (Butts, 2002; Butts & Buck, 2000). For example, the Attorney
General of Illinois is currently encouraging every county in the state to develop a teen court
program (National Youth Court Center, 2006). There are 102 counties in Illinois, and over 95
such courts are operating in twenty of them. Teen courts are increasing in their recognition and
have grown from 80 sites nationally in 1993 to more than a 1,000 teen courts in the United States
serving over 100,000 youth a year (Butts et al., 2000; Rasmussen, 2004; Godwin, 2000).
Teen Court Models
The teen court program follows four models: adult judge, youth judge, youth tribunal,
and peer jury (Butts, 2002). The adult judge model, which is most commonly used, consists of an
adult volunteer who supervises a youth jury and enforces sentencing (Forgays, Demilio, &
Schuster, 2004). The youth judge model allows a peer rather than adult to supervise the court
proceedings. The youth tribunal model employs a youth panel of three peer judges that determine
appropriate sanctions for the defendant (Butts, 2002). Finally, the peer jury consists of
adolescents who ask questions of the defendant and determine the appropriate sanction (Godwin,
1996).
Sixty percent of teen courts utilize the adult judge model, 22% use teen jury, 7% tribunal,
and 7% youth judge with 4% using a combination of models (Butts et al., 2000). Each county
and state also varies in the laws that govern the use and development of teen courts. Some states
have laws that set procedures and eligibility for teen court, and others are more active in
determining funding, case eligibility, confidentiality, and sanctions (Butts, 2002). Although
states differ in the number of teen courts and developmental policies, teen courts have specific
requirements.
Teen Court Process
Teen courts admit status offenders from ages 10 to 18 and defendants range from fifth
grade to twelfth grade (Butts, 2002). A youth who meets the first-time offender requirement
receives a referral from the arresting police officer or county prosecutor to teen court (Minor,
Wells, Soderstrom, Bingham, & Williamson, 1999). The youth attends an intake interview where
the youth is required to admit guilt to the delinquent act. Admission into teen court also requires
parental consent (Butts, 2002). If the teen or parent does not agree to these stipulations, the youth
returns to traditional processing via juvenile court. Once the youth and family agree to the
adolescent receiving their disposition, the teen court process begins.
2
Sanctions vary from program to program. However, community service hours are the
most commonly used within the teen court system (McNeece, Falconer, Bryant, & Shader,
1996). Other sanctions that teen courts enforce are apology letters, essays, monetary
compensation, jury duty, restitution, and classes focusing on issues like drugs and alcohol
(Acker, Hendrix, & Hogan, 2001). Teen court participants who do not complete the teen court
sanction are returned to juvenile court.
Teen Court Offenders
The population within teen courts is diverse with a wide array of demographic factors. In
one study, 60.7% of the sample was male with a median age of 15.95 (Minor et al., 1999). Racial
composition in another study showed a breakdown of 74% White, 21% African American, 4%
Hispanic, 1% mixed, and 2% Asian with a median age of 13.71 and 63% were males
(Rasmussen, 2004). Other studies have a higher proportion of females at 38.7% (Garrison, 2001)
and 47.5% (Weisz, Lott, & Thai, 2002). In addition, a recent study reported a large (68%)
population of Hispanics in teen courts (Harrison, Maupin, & Mays, 2001). Variations in
demographics are highly dependent on the geographical region. Some counties that participate in
teen court research may have a population that is younger in age and contain greater number of
Hispanics or males. For example, a sample from Indiana may be quite different from a sample in
southern New Mexico.
Teen Court Offenses
Misdemeanors are the primary infractions processed by teen courts, which would include
shoplifting, possession of drugs or alcohol, fighting, and vandalism (Harrison et al., 2001). A
national survey of teen courts reported that theft or shoplifting is most common, minor assault is
next, followed by disorderly conduct, alcohol possession, and vandalism (Butts, 2002). Among
344 teen court participants, 27.9% were charged with petty theft, 12.2% battery, 8.1% possession
of marijuana, 7.3% criminal mischief, and 7% possession of alcohol (Smith & Siebert, 2007, “in
progress”). In this same study, 19.2% had more than one charge, 4.7% more than 2, and 2% had
four offenses. McNeece (1996) and colleagues found that referrals to teen court consisted of
petty theft (65.6%), criminal mischief (10%), and battery (8.9%). There is little research about
the effects of the severity of charge and the number of charges on the outcomes of youth in teen
courts.
3
Teen Court Research
In order to assess the research on teen courts, this study employed a systematic research
synthesis (SRS). Databases included Psych Info, Social Service Abstracts, ERIC, Applied Social
Sciences Index and Abstracts, and Sociological Abstracts. Articles were obtained from 19952005 that contained the key words: teen courts, peer courts, youth courts, effectiveness,
treatments, and outcomes. Articles were flagged and reviewed for their empirical and theoretical
assessment of teen courts. The SRS identified twenty-four articles, nine were empirical, and
fifteen articles were conceptual. The conceptual articles were reviewed to identify the theoretical
foundations of teen courts. The empirical articles were reviewed to determine if teen court
research has assessed teen court theory. Results of the empirical articles are reported in Table 1
and Table 1.2.
Teen court research has found that teen courts produce a recidivism rate of 13% over
twelve months (Weisz et al., 2002), 12.6% within five months of completion (LoGalbo &
Callahan, 2001), and 6% to 9% within a six-month follow-up (Butts, 2002). Recidivism rates
refer to the percentage of individuals who relapse into criminal behavior. This rate is defined by
adolescents returning to court for a new offense and receiving a sanction for this crime (McKean
& Ransford, 2004). A Florida study concluded that 19% of participants from 2002 to 2004
recidivated after one year follow-up (Smith et al., 2007, “in progress”). This teen court
recidivism rate in Florida is lower than the annual juvenile recidivism rate of 47% (Florida
Department of Juvenile Justice [FDJJ], 2005); however, the evidence from these studies is
skeptical because results are rarely based on well-designed quasi-experimental designs that
include appropriate comparison groups.
Teen courts have a completion rate ranging from 63.3% to 81% (Forgays & Demilio,
2005; Garrison, 2001; McNeece et al., 1996). Completion rates refer to the percent of
participants that successfully complete all requirements set by the court. Further, defendants and
their parents report high levels of satisfaction (Weisz et al., 2002), and preliminary data suggest
teen courts may be effective with second-time offenders (Forgays et al., 2005). Although these
results are encouraging, there are numerous conceptual and empirical gaps in the literature.
4
Table 1: Empirical Articles: Secondary Data Analysis Design
Study/ Teen
court type
N
Participants
Measurement
Assessed
theory
Significant Findings
McNeece
117
Mean age = 14.5
Perception about
NO
8% recidivated; 75% completion rate
84% White
teen court
(1992)
55% Male
questionnaire
18% did not complete; 85% agreed
(1996)
T.C. Type: Adult
strongly with program; increased
judge
knowledge (1995-96)
Minor et al.
234
Sample gathered
None
NO
32% recidivated within one year. Prior
(1999)
between years 1994 to
offense and certain previous sanctions were
T.C. Type: Adult
1997 (60.7 % Male,
associated with a greater likelihood of
judge
Mean age = 15.95)
recidivism
NO
63% Male, (Mean age =
OSR scale
(2004)
13.71, SD = 1.99)
implemented to
T.C. Type: Adult
74% White, 21%
classify offenses in
judge
African American, 4%
archival data
Rasmussen, A.
648
Recidivism rates: 12% after one year, 19%
after 2 years
Hispanic, 1% Mixed, &
2% Asian
Garrison, A. H.
106
(2001)
55 in 2000)
measured through
were rearrested (3 months); 6 months
reentry into
(2.8%), 9 months (5.6%), 12 months
(9.8%); 63.3% success rate
58% White; 38.7%
juvenile justice
judge
African American
system
Harrison et al.
62% Male
Recidivism:
38% Female
measured through
478
Of the 71 youths discharged, only 11.2%
Recidivism:
T.C. Type: Adult
(2001)
NO
Juveniles (51 in 1999,
reentry into
T.C. Type: Adult
68% Hispanic, 27%
juvenile justice
judge type
White, 2% African
system
American, 3% Other,
45% Both Parents
5
32.3% recidivated, shoplifting a correlate
NO
for not completing, males more likely to
recidivate
Table 1.2: Empirical Articles: Quasi-experimental Design
Study/ Teen
court type
N
Participants
Design/
Variables
Measurement
Assessed
theory
Significant Findings
Butts et al.
532
Four study sites, 40%
R O1 X O2
Self-administered
NO
Teen court offenders less
Female, 60% Male
O3
questionnaires
likely to reoffend then
(youth and parent
comparison in two of the
attitudes)
four sites
(2002)
R O1
O2
T.C. Type:
Gender, Age, SES,
Used
Family Dynamics,
DV: adolescent
all four
and Race for each site
perceptions,
Recidivism rates
court types
provided
recidivism
(monthly): 1. 6%, 2. 9%,
stratified random
3. 9%, 4. 9% compared
sample of comparison
to comparison group
group
23% , 28%, 28%, 15%
respectively
93 Males, 84 Females
NR O1 X O2
Davis Empathy
Control group:
NR O1
Scale: 28
the attitudes and beliefs of
statements
either the defendant or the
IV: teen court
Attitudes to
volunteer.
T.C. Type:
DV: Attitudes,
Authority Scale:
Adult judge
empathy,
17 statements/
type
satisfaction,
Weisz et al.
(2002)
177
O2
136 high school civic
students
NO
Did not significantly affect
The re-offense rate for the
defendants was 13%
The Just World
just world
Scale: 20 questions
Parents reported high
levels of satisfaction,
6
Teen Court
Defendants: more
Satisfaction
alienated from
Surveys: 29 items
institutional authority
Table 1.2 continued
Study/
Teen
court type
N
Participants
Design/
Variables
Measurement Assessed
theory
Forgays et
27
12 – 17 years
R O1 X O2
Northwest
old (Mean age =
R O1
Youth Services
al. (2005)
O2
15.42, SD =
1.45)
T.C. Type:
Adult judge
81% completed their sentence,
12.5% reoffend
Intake and
IV:
Assessment
Teen court offenders were more
Teen court
Record and
likely to complete, Less likely to
Education form
reoffend
Effective with second time offenders
85% White
type
NO
Significant Findings
15% Other
DV: age,
65% Male
gender, type of
Harter self
35% Female
crime, sentence
perception
random
completion,
profile for
selection of
and recidivism
adolescents
comparison
(.74 to .92)
group
LoGalbo et
al. (2001)
111
juvenile
NR O1 X O2
Five parts:
offenders
NR O1
1. K.T.: (22
O2
NO
Recidivism rate of 12.6% (5 months
after initial involvement)
questions on
T.C. Type:
Comparison
Adult Judge
group: non-
Type
offenders
pertinent laws
Non-reoffender attitudes improved,
IV: Teen court
and procedures)
Feelings toward police officer
DV:
2. S.F.T. (4
declined within teen court group
recidivism
point Likert)
more so than comparison
3. T.F.T.:
Control V:
pretest general
Dem.,
feelings toward
recidivism,
teen court; and
Knowledge about legal system did
attitudes, self-
feeling “sorry”
not increase attitude toward teen
feelings,
for offense
court
knowledge
4. Feeling
Age influenced recidivism
thermometer
5. Demographic
question
Notes: Sample size: N; Teen court type: T.C. Type; Demographics: Dem.; Independent variable: IV; Dependent variable: DV;
Knowledge Test: K.T.; Self –feeling test: S.F.T.; Teen court feeling test: T.F.T.
7
Gaps in Teen Court Literature
In evaluating how teen courts may reduce delinquency, more rigorous outcome
evaluations are required, so, too, are studies that assist in clarifying the theoretical foundations of
these courts in order to enhance program design and implementation. Among the nine empirical
articles that were reviewed, not one article addressed theoretical aspects of teen courts.
Shoemaker, Tankard, & Lasorsa (2004) add that there is an abundance of research without a
theoretical purpose. Most studies solely focus on recidivism data, which does not provide
evidence regarding the mechanisms that influenced one defendant to commit a subsequent crime
and another to stay out of trouble. Theoretical constructs are ambiguous within teen court
literature, and the application of theory to teen court research may assist in providing evidence as
to how the teen courts can influence youth outcomes.
The transition from adolescence to adulthood is a phase of life “when uncertainty and
self-doubt is greatest and when … it is hardly surprising that peers play an unusually important
role” in the maturation process of youth (Coleman, 1980, p. 409). The influence of peers on the
prevalence of delinquency is well-documented (Shaw & McKay, 1942; Newcomb, 1950;
Simmel, 1955; Sutherland & Cressey, 1978; Hirschi, 2005). Literature emphasizes the causal
mechanisms of peer influence and “researchers … are now examining under which condition are
adolescents most likely to be influenced” (Deptula & Cohen, 2004, p. 84). Historically, the focus
has been solely on the negative impacts of youth with small movements that utilize the impact of
a valuable population, positive peers. The use of positive peers to redirect negative behavior is
not a new concept with the National Research Council (2002) reporting on community programs
that promote youth development. Positive peer culture, an intervention developed by Vorath and
Brendtro (1985), also mobilizes the peer group to become a more productive unit. It is
understandable why teen courts are developed on this notion that positive peer influence can
motivate an individual towards a more positive way of life.
Although teen courts are developed to utilize positive peer influence, teen court research
has not described, measured, or evaluated this construct nor does it provide a clear description of
how positive peer influence is operationalized, leaving the following question: Does positive
peer influence work? Before conclusions about the positive effects of peer influence in teen
courts are prematurely drawn, an evaluation of peer influence is necessary to ensure that the
program’s purpose and causal mechanisms are well understood.
8
Theoretical Foundations of Teen Courts
Literature reports that teen courts employ four theoretical perspectives: social control
theory, labeling theory, rational choice theory, and social learning theory (Dick et al., 2004).
These theories, along with social influence theory (not mentioned in teen court literature) will be
discussed here to determine their relevance in assessing peer influence within teen courts.
Content will provide a brief theoretical description of each and reasons for inclusion or exclusion
from this study.
Social Control Theory
Social control theory focuses on regulating human behavior through compliance and
conformity to the rules of society, including the influence of family, friends, and values
(Onwudiwe, 2004; Dick et al., 2004). Social control theory claims that delinquents fail to form
social bonds to society that involve commitment, beliefs, attachment, and involvement
(Wiatrowski, Griswold, & Roberts, 1981). Attachment refers to social bonds to parents, peers,
and conventional institutions (Costello & Vowell, 1999). Involvement refers to youth or parents
participating in activities like work, homework, hobbies, and sports (Paternoster & Brame,
1997). Other elements of control theory suggest that commitment to long-term goals and a belief
in the moral fiber of the law are essential in controlling criminal behavior (Hirschi, 2005).
Research concludes that peers provide the most influential forms of control (Agnew,
1993; Aseltine, 1995; Benda, 1994). However, social control theory does not explain how an
individual becomes attached, committed, or involved in a peer group. Similarly, the evolution
from one set of beliefs about peers to the next is unclear. There are specific peer mechanisms that
are missing from this theory that explain how peers impact the attachment, involvement,
commitment, and beliefs of a youth. Previous research fails to illustrate social control variables’
predictive value in relation with peer influence and delinquency (Erickson & Empey, 1965;
Hindelang, 1973; Elliott & Voss, 1974; Haynie & Osgood, 2005).
Labeling Theory
Concepts of labeling theory originated in the works of Tannebaum (1938). In his work,
negative reactions towards adolescent behaviors from parents, teachers, and other authority
figures reinforce future acts of delinquency (Gottfredson & Hirischi, 1990). The argument is that
the labeling of youth by certain groups in authority positions can have an adverse effect on
adolescents and promote secondary acts of delinquent behavior (Lemert, 1967; Klein, 1969). If
9
adolescents perceive themselves to be delinquents, they may begin to see themselves in that
context, which reinforces future acts of delinquency (Harris, Welsh, & Butler, 2000).
There is no empirical evidence of the application of labeling theory within teen court
research. The first-time offender status of the participants may be an influential component
causing this empirical gap. Labeling processes require repeated exposure and pressure from
authority figures to produce change. Given that participants are first time offenders, it is likely
that exposure to authority figures is minimal (Patrick & Marsh, 2005). It is also probable that
since peers and not authority figures administer teen courts, the prevalence of labeling theory
within teen courts is diminished.
The goal behind this theory is the prevention of stereotypes such that adolescents refrain
from negative self-concepts from authority figures. Nevertheless, peers provide a source of
identity development through processes not explained in this perspective. If peers provide the
opportunity to commit delinquent acts and reinforce this behavior, the labeling by institutions or
authority figures may be less influential. Keeping adolescents from entering an environment that
reinforces their self-concept does not eliminate or address the primary source of this identity, and
research concludes that peers can be the primary catalyst of this developmental process (Vorath
& Brendtro, 1985; Warr, 2002). It is also unclear how labeling theory integrates with peer
influence variables.
Rational Choice Theory
Criminal behavior can be attributable to a persons’ assessment of the consequences of
that behavior (Dick et al., 2004). A concern expressed within the literature is that this theory
assumes adolescents are rational and that the delinquent act was premeditated and not impulsive
(Akers & Lee, 1996). It becomes difficult to determine whether the adolescent consciously chose
to perform the behavior without the influence of peers. The concept of rationality and its impact
on juvenile decision-making are “very vague and categorical assertions that all persons or all
behavior are rational are indefensible” (Akers, 1990, p. 662). Measurement issues arise because:
The notion of rationality is intolerably vague, and empirically applicable
definitions of rational behavior are bound to be arbitrary…if rational behavior is
defined as simply goal-oriented behavior… then virtually all of human behavior is
rational and the rational-irrational distinction has no real consequences (Gibbs,
1989, p. 394).
10
Numerous studies have discovered that an adolescent performs a delinquent act because his or
her friends were doing it, and not on the notion that the behavior was rationally justifiable
(Shannon, 1991).
Social Learning Theory
Socialization is a process filled with multiple learning mechanisms (Sutherland, 1947).
Members of a peer group either internalize or displace information through either
communication of beliefs or observation of behaviors (Akers, La Greca, Cochran, & Sellers,
1989). The learning process of criminal behavior through social learning theory presents four
principal mechanisms:
(1) Differential association (direct and indirect interaction with others), (2)
differential reinforcement (instrumental learning through rewards and punishers),
(3) imitation (observational learning), and (4) cognitive definitions (attitudes) that
are favorable or unfavorable, functioning as discriminative (cue) stimuli, for the
behavior (Akers et al., 1996, p. 318).
The premise of teen courts is that the developmental stage of adolescents concentrates heavily on
social bonds and that close contact with negative peers creates problems in the adolescent’s life
(Weisz et al., 2002). Adolescents continue to relate to this negative peer group until internal or
external factors reinforce an alternative way of behaving. Social learning theory should be an
influential theory in subsequent research on teen courts given that it captures the foundational
concept utilized by these courts. However, the generality of social learning theory “makes it
difficult to test and evaluate” and researchers have difficulty identifying sources of reinforcement
(Warr, 2002, p. 78). Given theses gaps in social learning theory, social influence theory is
introduced, which may provide a more parsimonious structure for evaluating positive peer
influence, whereas social learning theory provides a broader assessment.
Social Influence Theory
Peer influence embeds itself within the concept of social influence, which states
“influence occurs when people continually compare themselves with others to ascertain whether
or not their own behavior is appropriate” (Maxwell, 2002, p. 267). Peer influence refers to
pressure by external social groups to conform to specific group norms. In 1955, social scientists
conceptually defined peer influence by two domains: normative and informative influence
(Deutsch & Gerard). Normative influence refers to the change in behavior due to the anticipation
of others’ expectations and beliefs, and that an individual will comply with group norms in fear
11
of isolation (Maxwell, 2002). Informative influence states that individuals alter behaviors
through information conveyed by others (Leventhal, 1997).
Given the breadth of possible informational influences, modern literature has further
separated informational influence into two domains: peer pressure, and modeling (Leventhal,
1997). Normative influence combines with peer pressure and modeling to make up the latent
variable, peer influence. Modeling, similar to imitation in social learning theory, is the process
by which an individual alters his or her behavior through observing someone engaging in a
behavior. Peer pressure refers to the communication, verbal and nonverbal stimuli, by peers that
aim to redirect one’s behavior (Maxwell, 2002).
Social influence theory and social learning theory provide the best conceptual
justification for teen courts. However, these theories are very inattentive to the various ways in
which peers influence youth behavior with teen courts utilizing peer behaviors that extend well
beyond these criminological theories. For instance, social learning theory variables are very
vague with little description as to what types of interactions and reinforcement are beneficial.
Participants in teen court interact with peers in various ways through problem-solving, legal
discussion, process groups, and in a courtroom setting. More specifically, the theories lack any
detail of how a youth changes his/her beliefs or behaviors based on peer influence. A youth
interacts with positive peers and than what happens? The influence by peers is much more
detailed than these theories suggest. These theories are very broad and do not systematically
support teen courts and the types of positive peer influence that exist within these courts.
Even though social learning and social influence theory offer a theoretical foundation for
positive peer influence, no current measures of peer influence are applicable to teen court
settings. For instance, measures of peer influence generally address questions about negative
influences, and are usually about friends’ behaviors (Jussim & Osgood, 1989; Matsueda &
Anderson, 1998; Haynie, 2002). Teen courts consist of positive peer volunteers, not friends of
the participants, who perform specific court tasks that are not consistent with current measures of
peer influence. Typical peer influence measures are not capable of capturing the positive
attributes of peer processes and court specific procedures within this setting. Teen court methods
are unique and require measurement items that capture this exclusivity. Thus, the first step in
determining the impact that positive peer influence has on youth would be the development and
validation of a positive peer influence scale.
12
Future Considerations in Positive Peer Influence Research
Several limitations in peer influence research should be highlighted. First, research on
peer influence generally evaluates the negative effects peers have on psychological and
behavioral aspects of youth, whereas there is little focus on the effects of positive peer influence
(Leventhal, 1997; Maxwell, 2000). If youth are going to alter the negative behavioral patterns
established by negative peer influence, positive peers must change prior beliefs through positive
peer influence. For example, adolescents (N=891) who spent more time with peers who
presented positive peer interactions produce higher parent and teacher ratings of behavior
change, greater improvements in social problem solving, and demonstrated a reduction in
aggressive behaviors (Conduct Problems Prevention Research Group, 1999). This suggests that
the measurement of positive influence may be a significant research domain (Vorath et al., 1985;
Vitaro, Tremblay, Kerr, Pagani, & Bukowski, 1997).
In addition, measurement issues arise when evaluating the perceptions about the peer
influence of friends due to selection effects (Aseltine, 1995; Krohn, Lizotte, Thornberry, Smith,
& McDowall, 1996; Kandel, 1996; Matsueda et al., 1998; Haynie, 2002). Selection refers to the
influence of personal attributes of others on individual behavior, meaning that a persons’ age or
race may be just as influential as other forms of peer influence. This skews the outcomes of peer
influence research because respondents’ perceptions about their friends can contain aspects of
normative influence or social selection. Teen courts provide an opportunity to control for
selection effects because the participants are positive peers, not friends, and not always from the
subculture of delinquents that the adolescent would normally select as group members.
Current measures of peer influence tend to focus on negative influences and tend to
combine negative influences with positive influences. Does the respondent perceive the behavior
or influence as negative, or do we assume that just because the behavior is socially unacceptable
that the youth perceives it to be negative? It becomes complicated, at best, to determine whether
the construct being measured is from negative influence, positive influence, or selection, thus
confounding the explained variance. Teen courts allow the disentanglement of negative and
positive peer influence by only using positive peers to impact a youth’s behavior in a positive
direction.
Another concern is same-source bias. This phenomenon asserts that the perception about
a friend’s behavior is inflated “because people tend to project his or her own attitudes and
13
behavior onto their friends” (Haynie et al., 2005, p. 1111). The assessment of peer influence
through the behaviors of friends is a limitation that can also overestimate the effects of peer
influence (Kandel, 1996). Research can begin to control for this bias by not asking participants
questions about their friends’ behavior.
Finally, peer influence researchers tend to study grade level students rather than youth
who are at risk of entering into the juvenile justice system (Warr, 2002). Studies compiled by the
National Institute of Mental Health (2000) to identify risk and protective factors of delinquency
illustrate that middle and high school students are different from individuals who are already
status or habitual offenders. Outcomes from studies that rely on a population of grade school
students may not be generalizable to individuals within the juvenile justice system. The impact
of negative peer influence on delinquency may not be relevant to those who already exhibit
positive behaviors and have not established a negative behavior pattern. Peers that are more
positive may skew the impact of peer influence. A sample from teen court offers a solution
because participants are at-risk youth and provide a population that is generalizable to youth
whom may have preexisting delinquent behavior.
Implications
The repository of available measures that assess positive peer influence is limited. Peer
influence research places a great emphasis on assessing the negative effects of youth with
minimal consideration about the protective factors of positive peers. Teen courts attempt to
utilize this valuable population. However, no research is available that evaluates the impact of
positive peers. This empirical gap is created because there is a lack of measurement tools that
can address positive peer influence. More importantly, there is no evidence that positive peer
influence is even present within these courts.
Existing research on teen courts does not identify positive peer influence variables, and it
is not apparent which peer influence theory, if any, is at work within these courts. Teen courts
just assume that positive peer influence is the driving force within this program. Constructs from
social influence theory, not typically mentioned in teen court literature, may provide the structure
for evaluating positive peer influence. It is not clear whether social learning theory or social
influence theory provides a basis for developing a measure of peer influence, supporting the
significance of a study that addresses these conceptual and empirical gaps.
14
Therefore, the development of a measure that captures this construct is a prerequisite
before any conclusions can be made about the presence and impact of positive peer influence.
Literature supports this movement such that “future research should expand the list of theoretical
mediators and moderators and, more important, isolate and directly measure the underlying
theoretical mechanisms” of peer influence (Jaccard, Blanton, & Dodge, 2005, p.145). Scholars
also conclude that studies that include the measurement of peer relationships in the juvenile
justice system are of the utmost necessity (Osgood & Briddell, 2004).
Social Work
The Code of Ethics within the social work profession states that “social workers pursue
social change, particularly with and on behalf of vulnerable and oppressed individuals and
groups of people,” and at the same time “should monitor and evaluate policies, the
implementation of programs, and practice interventions” (National Association of Social
Workers, 2005, p. 1). Individuals who are at risk of entering a life of crime, especially children,
are of concern to the social work profession. Evaluating programs that serve disenphranchised
populations should also be of interest. This evaluation includes assessing programs empirically,
theoretically, and developing outcome measures so that programs may evolve to become more
effective.
The social work profession is in the mist of transitioning from its traditional focus,
practice wisdom, towards a focus on evidence-based practice (Gibbs & Gambrill, 2002). In a
time of fiscal accountability, the movement towards evidenced-based practice is mimicked
within the juvenile justice system with an emphasis on empirically driven programs. Evidencebased practice has proven to be cost-effective and beneficial in making programmatic changes
that will reinforce effective and efficacious service delivery. Gibbs et al. (2002) offer that
“evidence-based practice (EBP) is the conscientious, explicit, and judicious use of current best
evidence in making decisions about the care of clients” (p. 452). Thus, the responsibility of
social work professionals, their movement towards accountability, and the movement within the
justice system provides a prism for reinforcing social works’ dedication toward evidence-based
practice.
Teen courts may be effective, and they are widely prevalent, and for these reasons bear
greater scrutiny. To assume these preliminary results based on recidivism would be ill informed.
The reliance on recidivism as the sole outcome is not sufficient when considering that the term is
15
defined in various ways, it is considered a outcome of policing, and if one individual does not
reoffend in one county does not mean he or she did not get arrested in another.
Programs become effective through evaluating the theoretical premise behind the
program and making advancements with this knowledge. The development of a scale that can
assess the theoretical processes of teen courts will allow teen courts the ability to improve upon
existing procedures and develop additional processes to assist youth. It is our professional
responsibility as social workers to ensure that our clients receive rigorously tested treatments. It
is also our responsibility to increase the knowledge base in our area of concentration. Social
work should remain a stable force in program evaluation and measurement development. If there
are no measures that can be used to evaluate the programs accessed by our clients, then there is
no real accountability.
16
CHAPTER TWO
ITEM DEVELOPMENT AND EXPLORATORY FACTOR ANALYSIS
Government agencies and officials are currently looking for innovative approaches to
reduce the number of youth entering the juvenile justice system (Hannan, 2001). Given that
misdemeanors are on the rise in the adolescent population, the need for a program that addresses
these concerns is imperative (Federal Bureau of Investigation, 2006). Research has documented
that teen courts are an effective program that redirects first-time offenders from entering the
juvenile justice system by exposing youth to positive peer influence (Butts, 2002). Currently,
research has not yet evaluated the impact of positive peer influence within this population
(Smith, 2007, “in progress”). This empirical gap is in part due to the absence of a measure that
captures this construct.
In an attempt to fill gaps in criminological and teen court theory, this study developed
and validated a scale that measures positive peer influence. The development of the Teen Court
Peer Influence Scale (TCPIS) proceeded in multiple steps, beginning with clearly describing the
construct of interest (DeVellis, 2003). This study conceptually defines positive peer influence as
the process by which peers communicate behaviors to explicitly or implicitly direct behavior in a
positive way (Leventhal, 1997). This chapter addresses the methods used to develop and validate
the TCPIS using qualitative methods to develop items, expert interviews to assess content and
face validity, and conducting an exploratory factor analysis to determine the structure of the
TCPIS. The purpose of this chapter is to determine what types of positive peer influence are
present and what these findings mean for criminological and teen court theory. This analysis
resulted in four domains that operationalize positive peer influence.
Theory
Conceptual articles identify social learning theory as the guiding peer influence theory
within teen courts. This theory has received no attention in teen court research and the presence
of this theory in teen court procedures remains ambiguous. Both the lack of proper instruments
and the absence of research on positive peer influence suggest that the development of a scale
that operationalizes positive peer influence within teen courts would be valuable. With the
absence of empirical evidence assessing the theoretical foundations of teen courts, this study
identified two possible theories, social learning theory and social influence theory, which may be
present in teen court processes and useful in developing items for the TCPIS.
17
Social learning theory suggests that adolescents learn deviant behavior through four
mechanisms: differential reinforcement, imitation, cognitive definitions, and differential
association (Akers et al., 1996). Imitation refers to the adolescent performing behaviors observed
by his/her peers. Cognitive definitions are attitudes that are favorable or unfavorable towards
certain behaviors. Differential reinforcement is process by which adolescents learn through
reward and punishment. Differential association is the direct and indirect interaction with peers.
The existent literature also identifies that social influence theory, a more specific set of
concepts embedded within social learning theory, may provide a more parsimonious structure
with three variables: peer pressure, modeling, and normative influence (Maxwell, 2002). Peer
pressure is an attitude in which “people your own age encourage you to do something or to keep
from doing something else, no matter if you personally want to or not” (Brown, Clasen, &
Eicher, 1986, p. 522). Normative influence refers to adolescents changing their behavior because
of their peers’ expectations and beliefs about that behavior (Leventhal, 1997). Modeling follows
the same line of thought as imitation; adolescents will begin to mirror their peers’ behavior.
Both of these theories provide a useful perspective for developing items; however, due to
the lack of empirical evidence, it is premature to develop items based on any one theory of peer
influence.
Item Construction
To properly operationalize positive peer influence for a population of teen court
participants, four observations of teen courts and five key informant interviews (three staff and
two teen court participants) were conducted to assess the kinds of peer influence utilized by teen
courts and experienced by defendants. These observations and interviews suggested that teen
court participants believed that observing the positive peer volunteers and their behaviors was a
beneficial experience. The participant’s interactions with the positive peers provided an
opportunity for the youth to begin to develop positive behaviors and attitudes. These behaviors
consisted of appropriate communication and problem solving. This is consistent with imitation
and modeling from social learning theory and social influence theory. The positive peers also
provide sanctions and feedback to the participants. This process is similar to the peer influence
mechanism, peer pressure, mentioned in social influence theory and differential reinforcement in
social learning theory.
Teen court participants in the interviews expressed a change in beliefs about the law,
18
their future, their goals, and their behaviors due to their interactions with the positive peers.
Interviews also alluded to the future behavior and aspirations of the teen court participants. This
qualitative approach suggests that social learning theory and social influence theory may be
influential within the development of the TCPIS. However, it remained unclear as to which
theoretical constructs were more influential within teen courts during this qualitative process,
thus items were developed based on all of these themes.
These qualitative methods, combined with the review of peer influence literature,
supported the development of twenty-seven items that make up the preliminary structure of the
TCPIS. The TCPIS utilizes the following response options: 1= strongly disagree to 7 = strongly
agree. Seven response options were chosen to include a neutral midpoint “neither agree nor
disagree,” and to have a large set of possible answers to obtain more variability (DeVellis, 2003).
The neural midpoint is important because youth may (1) not have experienced positive peer
influence, and (2) the positive peer influence experience may not have been beneficial or
adverse.
The scores of the TCPIS items sum to represent a level of peer influence; high scores
indicate positive perceptions of positive peer influence, and low scores indicate negative
perceptions about positive peer influence. For instance, a total score of 108 suggests that, on
average, an individual is in neither agreement nor disagreement about the value of positive peer
influence (response # 4 * 27 items). Any youth who scores above this threshold (> 108), agrees
that the positive peer influence experience was useful or a significant component of teen courts.
More specifically, the youth found that the positive peer influence mechanisms utilized by the
court influenced the participant cognitively and behaviorally.
Six experts in the field, three directors of teen courts, and three academics who have
conducted research in the area reviewed each item. Each expert assessed the content and face
validity of the TCPIS, and reported scores for each item ranging from 1= not all appropriate to 5
= good fit as to whether the items relate to positive peer influence. Items required a score of three
or above to be “good items” (Springer, Abell, & Nugent, 2002) and all items received a score of
three or higher.
Sampling and Data Collection
The next step in the developmental process was to disseminate the TCPIS to a sample of
teen court participants to assess the psychometric properties of the scale, including the factor
19
structure (Crocker & Algina, 1986). The TCPIS survey was administered anonymously to a
nonprobability sample of teen court participants from September 2006 to February 2007. Six
teen court sites in Florida and 404 individuals participated in this study. Every teen court site in
the state of Florida had an equal opportunity to participate. Teen courts were contacted and
notified of the research study, its purpose, and potential for involvement. Teen courts either
opted to participate or declined the opportunity due to insufficient staff or resources.
Participating court sites were scattered geographically across Florida, with court sites in
the panhandle, near Tampa, and in southern Florida. This allowed for a wide range of
demographics to be included. Each participating court utilized the same teen court model, adult
judge, so no systematic error was introduced.
Consent and assent were obtained from both the parents and participants at intake
(Appendix A & B). Parents and the teen court participants were notified of the purpose of the
study that participation was voluntary, and that choosing not to be involved in the study would
not hurt their court outcome. The survey was completed at an exit interview occurring
immediately after the teen court experience. Participants were given credit for two community
service hours for their participation. This study received approval from the Human Subjects
Committee of the Internal Review Board at Florida State University (Appendix C), and consent
from the participating teen court sites.
Selection Effects
A major concern in peer influence research is the presence of selection bias. Selection
bias refers to the process by which individuals select peers based on personal attributes or
physical characteristics (Kandel, 1996). This factor can overestimate the effect of positive peer
influence, meaning that the variance explained by the model may be from the TCPIS and from
selection (Cox & Cox, 1998). This study utilizes three strategies to limit this bias. First, selection
effects deal with friendship formation, and the peer volunteers within this study are not friends of
the participant. Second, the period for observations of personal characteristics in the court
process is minimal, whereas selection effects tend to require long periods of time (Deptula et al.,
2001, Warr, 2002; Snyder, Horsch, & Childs, 1997; Brendgen, Bowen, Rondeau, & Vitaro,
1999; Newcomb, Bukowski, & Bagwell, 1999). However, the passage of time within this study
is long enough to capture other aspects of peer influence, so minor effects of selection could be
present. Third, this study administers a question that elicits a response about the race of the
20
volunteers and whether it influenced the teen court participants. The correlation of this item to
the TCPIS will illustrate the impact of racial selection within this study. This study hypothesizes
that the race of the peer volunteers will not be significant, illustrating minimal selection effects.
Additional Measures
The demographics portion of the measurement battery solicited information about the
participant’s age, gender, race, parental income, grade level, letter grade average, and the impact
of the positive peer volunteer’s race. The reasoning for the inclusion of these items is that age,
race, and gender remain significantly related to an adolescent’s outcomes after controlling for
numerous predictors of delinquency (Haynie et al., 2005). The annual parental income will assist
in determining the impact of socio-economic status on positive peer influence. Lastly, this study
includes the participants perceived school grade average because research offers that an
individual’s performance in school affects the impact of peer influence variables (Warr, 2002).
Data Analysis
The data were inspected for missing values and scores out of the specified range of
responses using Statistical Package for the Social Sciences (SPSS) 14.0. The sample was split
into two groups by randomly choosing a starting point for selection and selecting every other
case for assignment into Group 1. The remaining cases were placed into Group 2, and each group
had 202 cases. Sample group 1 was utilized to address the psychometrics of the TCPIS and
evaluate the structure of the scale through a comprehensive exploratory factor analysis (EFA).
Sample group 2 was utilized to examine the theoretical model and relationships using
confirmatory factor analysis with structural equation modeling (LISREL 8.80), and is described
in the next chapter. The full sample (N=404), group1, and group 2 samples were compared on
demographic variables to examine comparability and potential for systematic error introduced by
the sample split. Table 2.1 provides the outcomes of this comparison and the standard deviations
of the demographic variables from each sample are quite similar suggesting the samples are
comparable and no systematic error is present.
21
Table 2.1: Standard Deviation Comparisons from All Three Samples
Demographics
Age
Gender
Race
Household Income
Grade Level
Grade Average
Race Importance
Complete sample (N=404)
Sample A (N=202)
Sample B (N=202)
1.60
.49
1.16
2.21
1.76
.824
.25
1.63
.49
1.03
2.16
1.74
.79
.27
1.57
.49
1.32
2.26
1.79
.85
.23
SPSS 14.0 was used for the EFA and the reliability analysis. This study anticipated that
items would have strong factor loadings above 0.40, and that the TCPIS would have strong
internal consistency above 0.70. This study utilized listwise deletion for missing values because
the sample size was large enough and there were so few missing responses. Only 2.5% of the
respondents had missing data. Descriptive statistics and frequencies confirmed that the data met
all statistical assumptions. Skewed or kurtotic items were also identified through this method,
and items #3 (3.567), item #5 (2.560), and #6 (2.214) fell above the 2.0 threshold of acceptable
standards of residuals (Kline, 2005; Curran, West, & Finch, 1996), thus deleted from the TCPIS.
Assumptions
The assumptions for an EFA include the presence of intercorrelations, the absence of
multicollinearity, and that the items are measuring a common factor (s) (Field, 2000). The first
assumption was tested through the Bartlett’s test of Sphericity. This test calculates the
derterminate of the sums of products and cross-products from which the intercorrelation matrix
is derived (Pedhazur & Schmelkin, 1991). This states that if the correlations are at zero then
there are no factors to extract. The Bartlett’s test should be significant (p < .05) illustrating that
the correlations are larger than zero (McDonald, 1999). This test resulted in a p-value of .000
offering that there are no violations to this assumption. In addition, each item should be at least
correlated with one other item (<.30) within the TCPIS (Thompson, 2004). Each item has a
correlation of .3 or higher with at least on other item.
Multicollinearity refers to a dramatic increase in the magnitude of the coefficient’s
standard error due to the intercorrelations reaching a critical level (Tate, 1998). Multicollinearity
can be assessed through inspecting the intercorrelation between the items, and any items that
22
have correlations above .70 are problematic and should be deleted (Pedhazur et al., 1991). Table
2.2 offers that there are no items with correlations above .70. Given that there are 343
intercorrelations, the possibility of correlations being significant at the .05 level due to error or
chance increases (Pedhazur et al., 1991). A Bonferroni correction was used and the p-value of
.05 was divided by the number of interitem correlations being tested resulting in a corrected pvalue of .000145. The outcomes from Table 2.2 utilize this correction. The last assumption states
that variables within the TCPIS share a common factor. This is assessed through the KaiserMeyer-Olkin Measure of Sampling Adequacy (KMO). The KMO statistic should be as close to
1.0 as possible with a minimal standard of .5 (Field, 2000). The KMO was .91 supporting a
presence of a common factor.
Table 2.2: TCPIS Interitem Correlations (N=202)
Item
1
2
3
4
5
6
7
8
9
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
1
.21
.23
.37*
.08
.23
. 35*
.33*
-.07
. 18
.12
.22
.25
.19
.26
.28
.30*
.25
.11
.05
.28*
.35*
.29*
.32*
-.21
.26
.19
1
.25
.23
-.02
.20
.17
.13
-.01
.12
.05
.13
.30*
.23
.11
.15
.13
.18
.06
.15
.23
.31*
.20
.26
-.10
.23
.26
1
.56*
.27*
.43*
.19
.45*
-.10
.38*
.27*
.40*
.31*
.30*
.31*
.44*
.29*
.33*
.37*
.24
.39*
.43*
.37*
.39*
-.21
.19
.41*
1
.30*
.52*
.30*
.48*
-.03
.35*
.28*
.40*
.46*
.28*
.39*
.41*
.33*
.45*
.26
.23
.40*
.46*
.44*
.52*
-.40*
.29*
.39*
1
.22
.12
.26
-.11
.19
.06
.18
.10
.03
.10
.15
.11
.20
.36*
.16
.19
.22
.17
.14
-.12
.08
.20
1
.40*
.46*
-.04
.41*
.31*
.37*
.41*
.40*
.40*
.37*
.35*
.49*
.30*
.16
.38*
.49*
.36*
.48*
-.28*
.32*
.59*
1
.44*
-.09
.44*
.36*
.25
.27*
.43*
.43*
.28*
.45*
.50*
.05
.19
.39*
.41*
.28*
.44*
-.23
.43*
.27*
1
-.03
.48*
.29*
.35*
.35*
.28*
.46*
.34*
.32*
.43*
.34*
.30*
.46*
.43*
.50*
.52*
-.37*
.26*
.37*
1
. 18
.12
.22
.25
.19
.26
.28*
.30*
.25
-.02
-.03
-.03
.06
-.10
-.02
-.02
-.07
.10
Bonferroni correction *p < .000145
23
Table 2.2 continued (N=202)
Item
10
11
12
13
14
15
16
17
18
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
1
.33*
.38*
.25
.41*
.39*
. 41*
.40*
.45*
.23
.31*
.46*
.46*
.25
.39*
-.37*
.41*
.43*
1
.25
.32*
.53*
.40*
.42*
.40*
.34*
.12
.19
.34*
.34*
.14
.29*
-.18
.36*
.31*
1
.55*
.32*
.44*
.44*
.36*
.35*
.41*
.32*
.31*
.46*
.28*
.43*
-.28*
.30*
.37*
1
.36*
.47*
.38*
.43*
.48*
.36*
.31*
.37*
.59*
.30*
.39*
-.27*
.21
.46*
1
.51*
.44*
.53*
.42*
.17
.17
.35*
.50*
.16
.37*
-.21
.36*
.36*
1
.47*
.59*
.51*
.29*
.21
.44*
.45*
.31*
.44*
-.32*
.28
.36*
1
.25
.49*
.30*
.33*
.48*
.51*
.35*
.50*
-.33*
.54*
.46*
1
.63*
.19
.24
.37*
.55*
.33*
.49*
-.33*
.40*
.37*
1
.23
.31*
.46*
.46*
.25
.39*
-.37*
.41*
.43*
Bonferroni correction *p < .000145
Table 2.2 continued (N=202)
Item
19
20
21
22
23
24
25
26
27
19
20
21
22
23
24
25
26
27
1
.22
.31*
.29*
.19
.27*
.13
.33*
-.25
1
.45*
.34*
.21
.26
-.26
.30*
.26
1
.61*
.37*
.45*
-.32*
.44*
.42*
1
.44*
.58*
-.29*
.46*
.53*
1
.55*
-.42*
.29*
.31*
1
-.40*
.51*
.44*
1
-.32*
-.23
1
.36*
1
Bonferroni correction *p < .000145
Findings
Sample
The teen court sample was representative of first-time status offenders entering the
Florida Department of Juvenile Justice (Table 2.3; FDJJ, 2006). Residential programs within
Table 2.3 refer to interventions where the youth generally commits to treatment for six months to
a year whereas nonresidential programs are prevention programs or outpatient programs.
24
Participants were 59.4% White, 28.2% African American, 9.9% Hispanic, .5% Asian, and 2.0 %
Other. Respondents ranged in age from 10 to 18 (M= 15.3) and were 56.4 % male and 43.6%
female. Participants were, on average, in the ninth grade and reported a ‘B’ in school
performance. The household income per year averaged $35,001-$45,000 and 92% of youth
stated that the race of the peer volunteers was not important.
Table 2.3: Assessing Representativeness Using FDJJ Demographic Data (N=27,315)
Program type
Gender
Race
Age (Mean)
Residential
48% male
52% female
56% White
25% Black
13% Hispanic
6% Other
15
Nonresidential
54% male
46% female
58% White
26% Black
14% Hispanic
2% Other
14
Reliability
After removal of the skewed items, the remaining items (N= 24) yielded a reliability
coefficient of .89. None of the items increased the reliability if they were deleted. The Guttman
Split- Half Coefficient was .89. A final reliability coefficient will be reported in the next chapter,
after the final structure is confirmed.
Exploratory Factor Analysis
Two extraction methods were used by this study to determine the factor structure,
principle component analysis (PCA), and principal axis factoring (PAF). Eigenvalues above one
and an inspection of Scree plots were used to determine the number of factors. Factors represent
latent constructs of positive peer influence with the items that load to the specific factor
operationalizing the latent variables.
A PCA conducts an unrotated analysis that maximizes the variance explained by the
factors (Tate, 1998). This means that the first extraction attempts to extract as much variance as
possible, with the second factor extracting from the remaining variance, and so on (Thompson,
2004). This extraction method resulted in five factors with 57.7% of variance explained.
However, the fourth and fifth factor only had two items and the items loaded weakly on each
25
factor. This evidence suggested further analysis using a PAF and various rotations.
The PCA includes both unique variance as well as error, whereas a PAF uses the
variability in an item that it has in common with the other items, commonalities (Pedhazur et al.,
1991). These coefficients represent the proportion of each item’s variance explained by the latent
variable or the squared multiple correlation of an item with all other items as an estimate of the
communality (DeVellis, 2003). Using the commonalities allows a more robust assessment of
structure, where the PAC reduces the amount of data to the principal components explaining the
most variance. The PAF achieves this structure through multiple iterations and rotations so that
two sets of estimates converge supporting a more parsimonious factor structure (Thompson,
2004).
The analysis began by utilizing all twenty-four items with a PAF extraction and an
orthogonal rotation (Varimax). Items were deleted if they did not load onto any specific factor
(loading < .30) and if they crossloaded. Crossloading refers to an item that loads comparably on
more than one factor, suggesting that the item does not fit to either factor well. If an item loads
strongly onto one factor (> .40) and not onto other factors, the item mathematically hangs with
the items within that domain. This means that all the items together form the latent variable or
factor.
Items 2, 9, 20, 26, and 27 did not load onto any particular factor. Items 10, 16, 22, 25, and
possibly 7 and 18 were cross loading items. Items 7 and 18 were left in the model because they
loaded strongly onto one factor. Table 2.4 reports the factor loadings for Model 1 with a Varimax
rotation. The EFA suggested that fifteen items loaded onto three factors explaining 47.3% of the
variance. The assessment is not complete once a factor structure is determined. A conceptual
inspection of the factors and items is required. Just because items hang together does not mean
they collectively make conceptual sense. The factor structure and items were inspected to
determine if the item-factor fit made conceptual sense.
Conceptually, items in factor 1 collectively addressed how positive peers can change a
youth’s cognitions to become more positive. Factor 2 focused on peers changing the youth’s
identity to become more prosocial. The last factor focused on how observing behaviors of
positive peers changes the perceptions of the teen court participants. When considering this
model, the factor structure suggests that researchers should consider evaluating the affects that
positive peers have on a youth’s cognitions, identity, and observations.
26
Table 2.4: Model 1 with Varimax Rotation (N= 202)
1
Items
TCPIS14
TCPIS15
TCPIS17
TCPIS11
TCPIS7**
TCPIS18**
TCPIS8
TCPIS4
TCPIS23
TCPIS24
TCPIS21
TCPIS1
TCPIS12
TCPIS13
tcpisnew19
.72
.58
.66
.57
.55
.54
Factors
2
3
.44
.44
.63
.53
.66
.64
.43
.42
.61
.56
.55
Note: **Crossloading items; Table does not include loadings below .40
However, peer influence literature reports that peer influence variables tend to overlap
and correlate (Warr, 2002), an oblique or Direct Oblimin rotation was employed next with all
twenty-four items and a PAF extraction. This process revealed that items 9, 16, 22, 25, and 27
did not load onto any particular factor, and that item 2 crossloaded. These items were also
problematic in the previous analysis, so they were eliminated. Model 2 suggests that seventeen
items loaded onto four factors (Table 2.5). The first factor was similar to factor 2 in the first EFA
results, as the items reflected positive identity change. Factor 2 was similar to factor 1 in the
previous results and focused more changing or positive cognitions. Factor 3 remained the same
as in the previous analysis, with three items focusing on modeling behaviors. The difference
between the Varimax results, other than the rotation, is the presence of a fourth factor. Not as
many items were flagged as problematic and deleted, leaving a fourth factor. However, this
factor has conceptual problems. Items 20 and 21 deal with peer pressure, and item 10 states
“Observing the peer volunteers in Teen Court makes me think about my future.” This weak
factor must be considered for deletion in subsequent analyses.
27
Table 2.5: Model 2 with Direct Oblimin Rotation (N= 202)
Factors
1
Items
TCPIS23
TCPIS24
TCPIS4
TCPIS1
TCPIS8
TCPIS14
TCPIS17
TCPIS11
TCPIS15
TCPIS7
TCPIS12
TCPIS13
tcpisnew19
TCPIS20
TCPIS21
TCPIS26**
TCPIS10**
2
3
4
.70
.62
.52
.51
.51
-.79
-.60
-.58
-.54
-.47
.49
.55
.49
.59
.53
.36
.42
Note: ** Conceptually problematic items; Table does not include loadings below .40
There are differences between the models. After inspecting both models empirically and
conceptually, Model 2 provides the best fit to the data. Model 1 contained two cross loading
items, that when deleted, the three-factor structure fell apart. The model also had conceptual
problems with items not hanging well with factors or other items within that factor. This suggests
that Model 1 is instable and the structure of the model is problematic. Model 2 did not have any
cross loading items offering that the model provided a better fit. There were too many
inconsistencies within the results of Model 1. Moreover, Model 1 is orthogonal and numerous
studies suggest that peer influence variables are reciprocal. The strong intercorrelations of items
and factors offered that the factors should be correlated supporting that Model 2 provided the
best fit.
Selection Effects
Using Model 2 from the EFA, selection effects were evaluated (N=202) by examining the
relationships between the importance of the race of the peer volunteer variable and the summated
TCPIS score. A Mann-Whitney Test was conducted because the variable used for determining
selection effects produced standardized residuals of 3.4 (skewed) and 9.6 (kurtotic) above the
28
threshold for this study of 2.5 (Kline, 2005), violating the parametric assumption of normality.
This test was also conducted because the relationships contain a dichotomous and continuous
variable. The test for Model 2 produced a statistic of 950.00 with a p-value of .22. This outcome
was not significant, offering that selection effects were minimal during this phase of the study.
The final model confirmed by the CFA will be assessed for selection effects in the next chapter.
Limitations
Two criticisms reported by literature regarding the use of factor analysis and the
interpretation of results are that “factors, by themselves, are neither things nor causes; they are
mathematical abstractions” (Gould, 1996, p. 285). The statement, factors are not things, points to
whether researchers can actually measure aspects of perceived reality and whether these
perceptions are similar across individuals. Does the presence of four factors mean that there is
truly peer influence present or is there something else that is immeasurable at this time? The
second criticism is that factor analyses utilize correlation matrices and causality cannot be
derived from a correlation. A correlation suggests that there is an association present but it does
add clarity as to why this relationship exists, eliminating causal interpretations.
The ambiguity of findings and the inaccuracy of research will always be present. These
fallible aspects of current research technology are lessoned with the use of additional statistical
procedures (confirmatory factor analysis) in attempt to triangulate information while ruling out
other potential influences like selection effects. The development of the TCPIS was also based
on theory strengthening the methodology and results of the EFA.
The replication of this study will also assist in determining whether the TCPIS retains its
factor structure in each additional sample, or if there are discrepancies present. If results from
study to study are similar, it suggests that the structure of the TCPIS is more reliable not a
scientific fact. There is always the possibility that a study will produce a two or three factor
structure, falsifying this study’s claim about positive peer influence. However, this is the case in
every study; the purpose is to provide some clarity and information to a realm of scientific
inquiry that is limited while understanding the boundaries of interpretation and science.
Theoretical Implications
Given these limitations, the results from the EFA suggest that positive peer influence is
operationalized by four factors and the preliminary reliability coefficient for this structure was α
= .90. This structure mirrors the four-factor solution in social learning theory (SLT). An
29
interesting outcome is that Model 2 is conceptually different from SLT, focusing on change
whereas SLT does not place a great emphasis on this component. For example, in SLT, cognitive
definitions are conceptually defined as attitudes that are favorable or unfavorable towards certain
behaviors (“I enjoy getting into trouble or I like being positive”). This construct does not offer
how a youth can begin to alter from favorable to unfavorable. The items from the TCPIS address
change within the youth, with an adolescent having a negative self- concept and moving to a
more positive thought process with questions like “I will be able to make better choices in my
life because of the peer volunteers.” This question addresses change within the youth from a
perception that they made bad choices in the past to a cognitive definition that they could begin
to make better choices in the future. SLT constructs do not address this transformation.
Another conceptual difference is that SLT addresses behaviors in the form of
interactions, peer pressure, and reinforcement. These constructs neglect to address the types of
behaviors that influence youth and the outcomes from observing these behaviors. For example,
the item “If I started acting like a peer volunteer, my life would be more positive” from the
TCPIS provides a baseline behavior, that the youth were exhibiting negative behaviors in the
past. The item also provides a behavior that if conducted, would alter this negative behavior
pattern (acting like the peer volunteers). It then moves to address that there would be a positive
change in the youth’s life if the behavior was performed on a regular basis. The questions from
the TCPIS are quite different from the questions typically used in social learning theory and
social influence theory research. Research questions within this body of literature usually focus
on how many delinquent friends the youth have (interactions), and what types of pressure from
friends influenced them to perform negative behaviors (peer pressure; reinforcement) (Brown et
al., 1986; Akers et al., 1996; Cox et al., 1998; Matsueda et al., 1998; Warr, 2002; Haynie et al.,
2005).
The items within the TCPIS provide a description of thought processes, behaviors, and
outcomes. For instance, items in Model 2 focus more on altering the participants’ cognitive
definitions and behaviors from negative to positive. The TCPIS addresses change and provides
descriptive factors whereas criminological theory is ambiguous with constructs not pointing to
how a youth alters his/her perceptions through peer influence. This is an advantage of the TCPIS.
More importantly, the conceptual makeup and factor structure of both social learning
theory and social influence theory are different from the results found in the EFA. There are
30
similarities like modeling and cognitive definitions found in both. However, the difference is that
in Model 2, peer pressure from social influence theory, replaced the differential reinforcement
construct in social learning theory. Peer pressure may be viewed as a type of reinforcement thus
providing more detail to a vague construct. In addition, the differential association construct
found in social learning theory is not present. Instead, a new construct emerged that focuses on
behaviors.
These differences may suggest that positive peer influence functions differently than
previously expected. Criminological theories that have been repeatedly used to assess the affects
of peer influence, mostly used in the evaluation of negative influence, may be inappropriate for
the evaluation of positive peer influence. Conceptual literature and research should not assume
that one theory covers both positive and negative aspects of peer influence.
Policy Implications
The results from the EFA inform teen court practice, policy, development, and
implementation. Teen courts are developed and operated based on a treatment manual developed
in 1996 by Godwin. This manual does not discuss the presence of positive peer influence nor
does it highlight how teen courts can utilize positive peer influence. There was no information
about what types of peer influence are present in teen courts when Godwin disseminated the
manual. The results from this chapter suggest that there are specific types of peer influence
present in teen courts and that this information should be considered when developing future teen
court programs. Additionally, existing teen courts should evaluate whether they address these
types of peer influence and if not should expand their procedures to incorporate the factors
identified within this study.
Teen courts vary from site to site, model to model, and there should be consistency across
court sites. Programs should adhere to evidence-based strategies identified by research that
uncovers theoretical mechanisms within teen court. Teen courts need to establish a universal set
of procedures that address positive peer influence variables so that they can be implemented the
same way at each site nation wide. Teen courts might become more effective if they emphasize
certain types of peer influence, especially if these influences are found to exert a significant
effect on future acts of delinquency.
31
Research Implications
Subsequent research should consider the predictive validity of the TCPIS. If the TCPIS is
related to positive outcomes of youth, the above policy implications become more important.
Scores on the TCPIS need to be significantly associated with outcome variables before teen
courts can address whether they have strategies that focus on positive peer influence variables.
Policy decisions should be informed by evidence and not speculation. Moreover, there needs to
be research conducted on the various types of models and their relationship with positive peer
influence.
The results from this chapter are preliminary and future research should assess the TCPIS
further using a confirmatory factor analysis (CFA) to determine the theoretical model. The
validity of the scale and the final reliability of the TCPIS have not been established. These issues
are addressed in the next chapter.
32
CHAPTER THREE
ASSESSING THE THEORETICAL MODEL AND VALIDITY OF THE TCPIS
Teen courts are diversion programs that attempt to redirect first-time status offenders
from a life of crime. The number of teen courts has increased by 92% since 1993, with over a
1,000 programs currently providing services to first-time status offenders (Butts, 2002). This
program has advanced with minimal consideration of the theoretical influences driving these
courts (Smith, 2007, “in progress”). Research solely focuses on recidivism data and has been
unable to identify whether the foundational principle of these courts, positive peer influence, is
even present. The Teen Court Peer Influence Scale (TCPIS) was developed to measure positive
peer influence within teen courts in an attempt to fill these empirical and conceptual gaps. An
exploratory factor analysis (EFA) concluded that the TCPIS contains four domains, with
correlations between factors that operationalize positive peer influence. The next step in the
developmental process is to evaluate the model identified by the EFA.
The results from an EFA provide preliminary information about the factor structure, and
allow the researcher to examine both statistical and conceptual meanings (Kiefer, 1999). A
confirmatory factor analysis (CFA) using structural equation modeling (LISREL 8.80) is the next
step after the researcher determines the likely number of factors present. The CFA provides an
assessment of the theoretical model and relationships (Henson et al., 2000) whereas an EFA
assumes that factors are either correlated or uncorrelated, so optimizing the model fit is difficult
(Thompson, 2004). The literature also offers that the CFA provides a more robust evaluation of
the factor structure of the TCPIS (Joreskkog, 1993). Finally, a CFA addresses theory
development and assesses the model parameters (MacCallum & Austin, 2000), which is the
primary goal of this chapter. The process of conducting a CFA includes specifying the
hypothesized factor model, estimating, and evaluating the model, considering possible revisions,
and presenting model results (Kaplan, 2000).
Methodology
Figure 3.1 represents the relationships suggested by the principal axis factor extraction
for the oblique rotation (Oblimin) from the EFA. The identification of the model also includes
testing whether the model contains sufficient information based on the t-rule or degrees of
freedom (Tate, 1998). The model can be overidentified, exactly identified, just identified, or
underidentified. The test of the overall fit requires that the model be overidentified in that the
33
degrees of freedom, p (p+1)/2 – t, must be equal to or greater than zero (Pedhazur et al., 1991).
The degrees of freedom for Model 1 are 113. This study will begin by assessing the model fit for
the Oblimin model to determine the best fit to the data.
Factor 1
Factor 2
Factor 3
Factor 4
Figure 3.1: Structural Model for the TCPIS CFA
Model Estimation & Fit Indices
The models derived by the EFA results will be tested through the maximum likelihood
method (MLE). The asymptotically distribution-free approach is used with nonnormal data and
some literature suggests that the sample size must be larger than 1,000 due to analytic constraints
(Thompson, 2004). This study will use MLE upon determining normality. If the sample size is
large, the assumptions are met, and the model is specified (EFA), the MLE method will provide
unbiased and efficient parameter estimates and standard errors (Yuan & Butler, 1998). This
method utilizes various fit indices to determine the model fit.
The chi-square statistic (χ2) assesses the overall fit. The null for this statistic is that the
population covariance matrix is equal to the residual matrix (Kaplan, 2000). The goal is to fail to
reject this null hypothesis (> .05); a large chi-square statistic indicates a better fit (Kline, 2005).
The chi-square statistic is sensitive to sample size, thus making it very difficult to obtain a nonsignificant p-value, so other fit indices are applied to assess the model fit (Thompson, 2004). The
chi-square degrees of freedom ratio (χ2/df) is utilized to assess model fit and should produce a
figure below 3.0 (Nunnally & Bernstein, 1994; DeVellis, 2003).
Other fit indices used in this analysis include the normal fit index (NFI), comparative fit
index (CFI), root-mean-square error of approximation (RMSEA), adjusted goodness of fit
34
(AGFI), global fit indices (GFI), and non-normed fit indices (NNFI) (Kline, 2005). The NFI
compares the χ2 for the hypothesized model versus the χ2 for the baseline model (Bentler &
Bonnet, 1980). This figure should exceed .90 to indicate a good model fit. The CFI and NNFI
function similar to the NFI in that LISREL compares the hypothesized model to a null or
baseline model, which assumes independence of observations and produces a statistic that
determines model fit (Osterlind, 2006). These indices should also be larger than .90 for a good
model fit. The RMSEA addresses the covariance residuals within the model fit. This fit index
determines the amount of error produced by the model and should be as close to zero as possible
(< .50 = proffered; < .7 acceptable) (Thompson, 2004). The AGFI produces a statistic that
measures how the specified model fits the data compared to no model at all and should be larger
than .90 (Nunnally et al., 1994). Ideally, the residual covariance matrix should not contain
standardized residuals larger than 2.5 or 3.0 (Kline, 2005). Models that contain large residuals
indicate that the hypothesized model does not fit the data as well as one would like (Kaplan,
2000). Other cues indicating model problems are if the R2 is above one and there are negative
error variances (Tate, 1998). These outcomes are not statistically probable and there are likely
problems with the data or data entry (Kaplan, 2000).
Model Revisions
The LISREL output will suggest model revisions in the form of new paths, correlations
vis a vis factors, items, and error variances. Modifications to the model will be implemented if
the reduction in the chi-square statistic is reduced by ten or more and is supported by theory
(Bentler et al., 1980; Nunnally et al., 1994; Kline, 2005). Given that peer influence variables are
known to correlate, it is possible that the modification indices will suggest the correlation of
error terms for the items. These terms will be considered if the error relationships remain in the
domain structure specified by the EFA (Bentler et al., 1980). It is not defensible to improve the
model by correlating nontheoretically related items. Models should only contain revisions in
“cases in which the researcher can articulate a persuasive rationale as to why the modification is
theoretically and practically defensible” (Thompson, 2000c, p. 272).
Sampling and Data Collection
Data were collected from teen court participants ages 10 to 18 who attended teen court
from September 2006 to February of 2007 at six teen court sites in the state of Florida. Consent
and assent were obtained at intake and the TCPIS was administered at exit interview.
35
Participants were given credit for two community service hours for participating, and the sample
was split to form two groups through a systematic probability sampling technique. The first
sample was used for the EFA, and the second sample (phase 2) was used for the CFA, with six
teen court sites and 202 individuals participating in phase two of this study.
Measures
To evaluate the validity of the TCPIS, additional measures were included to explore the
TCPIS’s theoretical and empirical relationships with constructs related to positive peer influence
as well as demographic variables. The Youth Self-administered Questionnaire (YSQ) developed
by Butts (2002) was used as a basis for construct validity and concurrent validity (knowninstruments). A systematic literature review suggested that this scale was the only instrument
utilized in teen court research that would be appropriate for the goals of this study. Other teen
court studies, for example, implement knowledge tests, satisfaction surveys, and attitudes
towards authority measures. These scales would not provide the necessary theoretical link that is
required for construct validity.
Construct validity is assessed by determining the empirical relationships between the
TCPIS and theoretically related constructs (Messek, 1995; Garrison, 2006). These relationships
generate two measurable properties, convergent and discriminant validity. Convergent
relationships reflect a strong empirical correlation supporting the theoretical association whereas
the discriminant variable will have a weak correlation because the constructs are theoretically
unrelated (DeVellis, 2003). After confirming the TCPIS structure through the CFA, this study
assessed construct validity by examining a Pearson Product-Moment Correlation (Pearson’s r)
between the summated TCPIS score and the variables selected to examine convergent and
discriminate validity. This process will determine whether the TCPIS converged with intended
theoretical constructs, which will support the validity of the scale. Concurrent validity is
established when the correlation between the scale in question (TCPIS) and a scale that has
already demonstrated validity (YSQ) within the same population is strong above .50 (Royse,
1999).
Youth Self-administered Questionnaire (YSQ)
The YSQ originally contained fifty-two items with a four response format ranging from
1= strongly disagree to 4= strongly agree. The YSQ consisted of five domains that measure
perceptions about teen court satisfaction (α= .88), prosocial attitudes (α= .83), prosocial bonds
36
(α= .79), delinquent peer association (α= .76), and sense of responsibility (α= .80) (Butts, 2002).
The original study reported that nine items did not load onto any of the above domains.
The factor structure and reliabilities from all studies are sample dependent, so the results
from the YSQ in the present study may differ. For this reason, an exploratory factor analysis
(EFA) and reliability analysis were conducted to determine the psychometric properties and
factor structure of the YSQ. The original 52-item scale was shortened to 27 items through the
EFA, a conceptual assessment of items, and reliability results. The original five factors remained
intact although revisions required the deletion of a few items and the addition of several new
items to the domains. The domains in the YSQ sum to form the YSQ global score, as the authors
originally intended. This score was used to examine concurrent validity, and this study
anticipates that the TCPIS would have a strong correlation with the YSQ global score. Subscale
scores were used to examine other kinds of validity.
Prosocial Bonds (YSQ)
The “Prosocial Bonds” domain of the YSQ was used to investigate discriminant validity.
The original structure of this domain contained ten items that focused on the bonds between
youth and their parents and schoolteachers; e.g., “My parents are proud of me.” The new EFA
analysis suggested that this domain contains five original items and that five of the original items
were eliminated because they loaded onto more than one factor and compromised factor
reliability. The new five-item domain was reliable (α= .80), similar to the coefficient in Butts
(2002) analysis. The current study hypothesizes that the TCPIS will have a low correlation with
this domain. More importantly, lower than the correlation with the variable used for convergent
validity because this domain addresses relationships with teachers and family whereas the TCPIS
deals with peer relationships.
Positive Perceptions of Teen Court (YSQ)
The “Positive Perceptions of Teen Court” domain of the YSQ was used to examine
convergent validity. The original structure of this domain consisted of eight items that addressed
the participants’ perception about the teen court experience; e.g., “I was treated fairly by the teen
court.” The current EFA suggested that this domain retain five original items and delete three
items due to weak loadings and cross loading. The reliability coefficient for this domain is
acceptable at .79 but lower than .88 in the previous study. This reduction may be due to the
reduction of items, as an increase in items can increase the reliability coefficient (Osterlind,
37
2006). The current study hypothesized that the TCPIS global score will have a strong correlation
with this domain illustrating convergent validity. The correlation will be strong because items on
the TCPIS address positive peer influence and a youth’s perception of teen court includes their
perception about positive peer influence.
Household Income (Demographics)
This study utilized the household income variable to assess the TCPIS’s discriminant
validity, by hypothesizing that the TCPIS should have an insignificant relationship (p-value <
.05) with household income.
Demographics
The demographics portion of the measurement battery requested information about the
participant’s age, gender, race, parental income, grade level, letter grade average, and the impact
of the positive peer volunteer’s race. Demographic variables were assessed for their impact on
the summated score of the TCPIS using a Pearson’s r for correlations between the TCPIS and the
age of the participants. A Mann-Whitney Test was used for comparisons between the TCPIS and
the race of the teen court participant because this item violated the parametric assumption of
normality with a standardized residual of 6.79 (kurtotic). An ANOVA was conducted to
determine differences between the TCPIS across household income, grade level, and grade
average. This sample’s characteristics were also compared with demographic data from the
Florida Department of Juvenile Justice for representativeness.
Data Analysis
The data from this sample (N= 202) were inspected for missing data, scores out of the
specified range of responses, and outliers. The list-wise deletion method was used to handle
missing data due to the low number of missing items and large sample size. This study utilized a
cutoff point that no less than seventy percent of the TCPIS needed to be completed to be
considered a part of the sample (Bloom, Fischer, & Orme, 1999). No participants were flagged
by this criterion. This study also assessed whether questions with missing data were significantly
different from those with no missing responses (Heppner & Heppner, 2004). There were no
differences between these groups (.060; p-value > .05). Teen court directors reported that no
individuals opted out of the study, so the response rate was 100%.
Although some researchers suggest that descriptive statistics should not produce a
skewness or kurtosis value below -3 or above 3 (Kline, 2005; Curran et al., 1996), this study
38
used a more conservative threshold of -2 or 2 for skewness and kurtosis data (Pedhazur et al.,
1991). Histograms and descriptive statistics were utilized to examine problematic items and
errors in data entry. There were no data entry errors, and items 3, 5, and 6 were once again
identified as skewed and kurtotic, falling above the specified range, and were deleted from the
analysis.
Assumptions
The assumptions for a CFA include the requirement of continuous variables, multivariate
normality, and independence (Pedhazur et al., 1991). The TCPIS scores are continuous, and the
design of this study ensures that observations are independent, meaning that scores are not
collected at pretest and posttest. Scores are exclusive in that one individual has only one set of
scores thus the independence assumption is fulfilled (Shadish & Cook, 2002). If there is not
univariate normality, there cannot be multivariate normality (Howell, 2002), and descriptive
statistics, histograms, and scatterplots support that the data are normal.
An ANOVA was used to assess the impact of demographic variables on scores from the
TCPIS. The assumptions for an ANOVA are that the equation contains a continuous dependent
variable, there is independence of scores, there is normality, and there is homogeneity of
variance (Pedhazur et al., 1991). The TCPIS contains continuous variables, and there are no
repeated measures within this research design supporting that there is no violation to the
independence assumption. Normality was assessed using descriptive statistics, histograms, and
scatterplots. Homogeneity of variance means that group variances are equal and may be
determine through a Levene’s test. This test should be non-significant with a p-value about alpha
at .05 (Kline, 2005).
A nonparametric test was used for demographic items that violated the normality
assumption. Descriptive statistics determined that the race importance variable was skewed
producing standardized residuals of -3.93 (skewness) and 13.61 (kurtosis). The race of the teen
court participant was also skewed at 2.5 and kurtosis at 6.78. These results were above the
specified threshold of 2.5 used by this study (Kline, 2005). A Mann-Whitney Test was used to
evaluate the differences across groups for the race importance variable and a Kruskall-Wallis
Test for the race of the teen courts participant.
39
Findings
Sample
Among the 202 participants respondents were 58.7% White, 21.9% African American,
13.4% Hispanic, and 1.5% Asian. The mean age for respondents was 15.4, and the majority of
participants were male (54.7%). The average grade level reported was ninth with a ‘B’ grade
average. The mean household income per year was $35,001-$45,000. The teen court sample was
representative of first-time status offenders entering the juvenile justice system in Florida. Firsttime status offenders in the state (N= 27,315) are on average 54% male, 58% White, 26%
African American, 14% Hispanic, and 2% Other (FDJJ, 2006). Youth are on average fourteen
years of age. This sample is similar to the group 1 sample used for the EFA with youth
presenting the following demographics: 59.4% White, 28.2% African American, 9.9% Hispanic,
.5% Asian, 2.0 % other, and on average were 15 years old. This suggests that there was no
systematic error present from randomly splitting the sample.
Model Estimation & Fit Indices
The CFA analysis was an iterative process. The maximum likelihood method was utilized
to assess the measurement model’s “goodness of fit” with the data. The four-factor model with
seventeen observed variables was entered into LISREL 8.80. This model produced a χ2 statistic
of 228.84 with the χ2/df of 2.0, NFI at .95, CFI at .97, and NNFI at .97. The RMSEA (.070),
AGFI (.84), and GFI (.88) were below acceptable standards. The modification indices and
residuals were inspected to determine potential problems with the model and target model
revisions. The outcomes of this evaluation suggest that item 20 and 21 seemed problematic with
residuals above 2.5. Items 26 and 24 had similar issues with residuals at 3.13. Another way to
assess if items are problematic is if they are mentioned repeatedly in the modification indices or
correlated error variances. Items 10, 20, 21, 26, 23, and 24 remained problematic. A review of
the outcomes from the EFA and a conceptual inspection of items within factor 4 suggested that
item 10 and 26 do not conceptually match with items 20 and 21. The evidence from the initial
EFA (i.e., that these items loaded weakly on the fourth factor in the oblique model and were not
present in the orthogonal model), along with the CFA findings supported the deletion of items 10
and 26 from the model.
The deletion of these items produced another concern, as the fourth factor had two items,
and the remaining items together produced large residuals and had an intercorrelation above .60.
40
The remaining items, 20 and 21, seemed to measure the same construct. These results required a
decision to either (1) delete both the items thus deleting the factor, or (2) delete the factor and
move one item to another domain if there was a conceptual match. Item 21 deals with positive
behavior change, similar to the items in factor one and in the Varimax rotation, the item loaded
onto that domain. Item 21 was placed into the positive behavior domain, deleting the fourth
factor, and the model fit was assessed.
Model Revisions
The model fit improved with these revisions. The final path diagram for this model is
illustrated in Figure 3.3. The revised model contains three factors with fourteen observed
variables. This model produced a χ2 value of 130, df =74 (p < .00). This outcome rejected the null
hypothesis that this model had a good fit. Given that the χ2 statistic is vulnerable to sample size
additional fit indices were inspected. The χ2/df was below 2.0 at 1.7. The RMSEA of .05 had
improved from the previous results. The AGFI (.88) and GFI (.92) increased to acceptable
standards. Other fit indices were excellent, with the NFI at .96, the NNFI at .98, and the CFI at
.98. All residuals were below 3.0. The LISREL program suggested correlating the error
variances between item 8 and 7, and between 24 and 23, but because these did not make sense
conceptually, no adjustment was made. Table 3.1 offers a comparison between Model 1 (the
original model examined in the CFA) and the final model (Model 2) after all revisions was made.
Model 2 was the best fit both statistically and conceptually. The final path diagram is presented
in Figure 3.2. Table 3.2 provides a list of the final fourteen items and their related factor.
Table 3.1: Model Comparisons (N=202)
Fit Indices
Model 1
Degrees of Freedom (df)
Chi-square (χ2)
Chi-square/df ratio (χ2/df)
Root-mean-square error of approximation (RMSEA)
Normal fit index (NFI)
Non-normed fit indices (NNFI)
Comparative fit index (CFI)
Global fit indices (GFI)
Adjusted goodness of fit (AGFI)
113
228.84
2.0
.07
.95
.97
.97
.88
.84
41
Model 2
74
130
1.7
.05
.96
.98
.98
.92
.88
Figure 3.2: Path Diagram for Final Model (N=202)
42
Table 3.2: TCPIS Items Categorized by Factor
Factor 1 (positive cognitions)
Item 1: Peer volunteers can change my attitudes about authority figures
Item 4: I will be able to make better choices in my life because of the peer volunteers
Item 8: Peer volunteers can change my beliefs about criminal behavior
Item 21: Having my peers judge my behaviors can motivate me to complete the teen court program
Item 23: Pressure from my peers in Teen court will keep me from being rearrested
Item 24: Having peers judge my behaviors can make me a better person
Factor 2 (positive identity)
Item 7: Peer volunteers can change my beliefs about my friends
Item 11: I want to be a peer volunteer at teen court
Item 14: Having peer volunteers in Teen Court makes me interested in the law
Item 15: If I started acting like a peer volunteer, my life would be more positive
Item 17: My friends should be more like the peer volunteers
Factor 3 (modeling)
Item 12: Peer volunteers are positive role models
Item 13: I can learn a lot from watching the peer volunteers in teen court.
Item 19: Peer volunteers in teen court are clueless
Reliability
A reliability analysis was conducted on the final model derived by the CFA. Factor one,
addressed positive cognitions, with six items that produced a coefficient alpha of .81. The second
factor addressed positive identity development with five items that were reliable at α = .81. The
last factor addressed modeling with only three items that produced an alpha of .70. This low
reliability coefficient may be due to the small number of items within this domain. Future
revisions to the TCPIS should include additional items within this domain. The global coefficient
for the TCPIS was .88, thus providing preliminary support for the use of a global TCPIS
summary score representing positive peer influence.
Standard Error of Measurement (SEM)
A coefficient alpha has disadvantages because it is sample dependent and influenced by
variance (Nunnally et al., 1994). SEM is the estimate of error associated with the respondents’
observed score compared to the hypothetical true score (DeVellis, 2003). Thresholds for SEM
offer that it should not be higher than 5 percent of the range of possible scale and subscale scores
43
(Springer et al., 2002). The SEM for the TCPIS was 1.8%, illustrating that the scale’s standard
error of measurement was well below the specified threshold of 5%.
Selection Effects
The CFA sample required the assessment of selection effects. The final model derived by
the CFA was summated to form the TCPIS global score for this analysis. The Mann-Whitney
Test revealed a statistic of 888.00 with a p-value of .77, offering that selections effects were not
significant. If present, selection effects could have influenced the assessment of the TCPIS’s
psychometric properties as well compromised the theoretical model. The methods employed by
this study were valuable in deterring selection effects in both samples. Selection effects have
overestimated many outcomes in previous peer influence research. This strengthens the results
from this study and the psychometric properties of the TCPIS.
Demographics
Demographic variables were correlated with the summated TCPIS score using the
Pearson Product-Moment Correlation (Pearson’s r) for correlations between two continuous
variables, the Mann-Whitney Test for comparisons between the TCPIS and the race importance
variable. A Kruskall-Wallis Test was used for comparisons between the TCPIS and the race of
the teen court participant. An ANOVA was used for comparisons between the TCPIS and
gender, grade level, grade average, and household income. The purpose was to determine which
demographic variables had an influence on responses to the TCPIS. This would identify possible
moderators and mediators for future teen court research, and to flag possible control variables for
subsequent posthoc analyses.
The results suggested that the correlation between age and the TCPIS was significant,
producing a Pearson’s r coefficient of -.310 with a p-value of .000. Gender was not significant
with an F-statistic of 1.78 with a p-value of .18 and a Levene’s statistic of .10. The importance of
the race of the volunteers was also not significant with a Mann-Whitney statistic of 888 at a pvalue of .77. The race of the participants was not significant with a Kruskall-Wallis statistic of
6.04 with a p-value of .42. There were no differences across the categories for household income
with an F-statistic of 1.87 with a p-value of .09 and a Leven’s statistic of .57. There were no
differences across grade average (F = .75, p-value = .56, Levene’s statistic =.80), but there were
differences across the grade level of the participants with an F-statistic of 4.58 with a p-value of
.00 (Levene’s statistic = .30). Theses outcomes offer that when considering scores on the TCPIS
44
the age and grade level of the participant may be influential variables to consider in subsequent
research.
Validity
This chapter assessed two types of validity, construct validity (convergent and
discriminant) and concurrent validity, using Pearson’s r correlation. The household income
variable, one of the variables used for the discriminant analyses, is a categorical variable and
requires a different statistical analysis. An ANOVA was used for this comparison.
Convergent validity was established by examining the relationship between the
summated TCPIS score and the Positive Perceptions of Teen Court domain. This association
resulted in a Pearson’s r coefficient of .55 (R2 = .31) with a p-value of .000. This study
hypothesized that the TCPIS would have a strong correlation with this domain, more
importantly, stronger than the discriminant variables.
The variables used to examine discriminant validity, the Prosocial Bond domain of the
YSQ and household income, were assessed for their association with the TCPIS. Results
suggested that the Prosocial Bond domain was significant with a Pearson’s r correlation of .31
(R2 = .09) and a p-value of .000. This study hypothesized that this variable would produce a
weak correlation with the TCPIS, and weaker than the convergent correlation. This correlation
was weak, below .50 (Pedhazur et al., 1991), and lower than the above correlation of .55. This
suggests that the TCPIS can discriminate between a measure of positive peer influence and a
measure of prosocial bonds, a similar but different latent construct. The household income
variable was assessed to determine if there were significant differences in TCPIS responses
across income levels. There were no differences with an F-statistic of 1.87 and a p-value of .09
and a Leven’s statistic of .57. This study hypothesized that the relationship between household
income and the TCPIS would be nonsignificant, supporting discriminant validity because income
should not be related to positive peer influence.
For concurrent validity, the correlation coefficient between the TCPIS and the summated
Youth self-administered questionnaire (YSQ) resulted in a significant correlation of r = .46 (R2 =
.21) with a p-value of .000. This study hypothesized that this correlation would be strong,
demonstrating that the TCPIS was empirically associated with a standardized instrument.
Overall, the TCPIS showed strong construct and concurrent validity, increasing the utility of the
scale.
45
Limitations
Within any study, there are shortcomings that should be mentioned. The sample was not a
probability sample, and participants could not be randomly assigned to court participation. In
some sociobehavioral studies, conditions exist in which a true experimental design is
inappropriate or not practical (Gordon, 1999; LoGalbo et al., 2001; Butts, 2002; Weisz et al.,
2002; Forgays et al., 2005). In the juvenile justice system, judges place adolescents into
treatment conditions based on prior offenses, severity of offense, and the needs of the adolescent.
In this setting, assigning an adolescent to a certain treatment condition is complicated and at
times unethical, as the juvenile justice system is hesitant to allow researchers the ability to
manipulate the court processes at this time.
Another limitation is that the sample was obtained from Florida and all responses were
self-report. Subsequent research should attempt to include a wide array of geographic regions to
increase the generalizability of results. Additional measures should be included so that results are
not solely based on self-report like program completion and recidivism. An interesting outcome
worth noting that is not necessarily a limitation is that the majority of items that were
problematic in the TCPIS and the YSQ analyses were reversed scored items, suggesting that this
technique may be inappropriate for this population or age group.
Implications and Discussion
The purpose of this research is to develop a measure of positive peer influence so that (1)
a theoretical evaluation of teen courts can be conducted, (2) positive peer influence mechanisms
may be identified, and (3) subsequent research may utilize this instrument in an attempt to assess
the impact of positive peer influence on youth outcomes. It is difficult for a program to evolve if
there is no theoretical evaluation of its processes. A program cannot determine which program
components are more effective.
The results from the final 14-item model suggest that positive peer influence is
operationalized by three correlated factors that focus on formulating a positive identity, positive
cognitions, and modeling positive behavior. For instance, factor one includes items that focus on
the participant’s positive cognitive definitions: “I will be able to make better choices in my life
because of the peer volunteers.” The second factor deals with reformulating the participants’
identity to be more positive like “If I started acting like a peer volunteer, my life would be more
positive.” Lastly, the final domain addresses the observations of the positive peers in teen court:
46
“Peer volunteers are positive role models.” “I can learn a lot from watching the peer volunteers
in teen court.”
This information fills a large gap conceptually because previous research was unable to
identify if positive peer influence even existed and was not capable of determining which theory
was present. Previous literature conceptually supports the use of social learning theory (SLT) in
teen courts (Dick et al., 2004). However, the results from this study refute this conceptual claim
with the outcomes from this study offering a different structure and conceptual focus. As
mentioned, SLT has very broad concepts (Warr, 2002). This new theoretical model derived by
the CFA offered a more parsimonious configuration. As discussed in chapter two, the SLT
variables lack specificity, and the TCPIS domains provide more clarity as to how individuals can
transition from negative attitudes to positive. The TCPIS items focus more on making changes in
the youth’s life whereas SLT items provide more of a general assessment of influence. In
addition, absent from this new structure is differential association (interactions) and
reinforcement. Instead of these domains, a new construct emerged focusing on changing ones’
identity to become more positive. In order to break away from the negative behavior pattern,
youth would need to begin to view themselves as positive. This information is crucial for teen
courts to begin to make programmatic changes to improve upon existing mechanisms, and it
could only have been discovered in this kind of analysis of positive peer influence.
Programmatic Change
Now that a scale has been developed that has shown promising psychometrics, teen
courts can begin to identify specific mechanisms within these courts that affect youth outcomes.
First, teen courts need to determine what processes focus more on positive identity development,
positive attitudes, and modeling. If positive peer influence mechanisms are limited, teen courts
can begin to develop these mechanisms and enhance the exposure to these constructs.
Once a connection is made between youth outcomes (recidivism/ program completion)
and these domains, courts can begin to develop additional opportunities for youth to observe
behaviors exhibited by the positive peer volunteers. Moreover, individuals who score low at
pretest/intake may benefit, pending future research, from early preventative strategies or more
exposure to positive peers than those that score high on the TCPIS.
47
Future Directions
Research should assess the relationship between the various teen court models and their
impact on perceptions of positive peer influence. For example, the use of a peer judge or jury
may be more influential than the use of an adult judge. Moreover, the connection between youth
outcomes and scores on the TCPIS has not been confirmed and this should be the next step.
Overall, the TCPIS has shown to be a useful tool in evaluating positive peer influence and teen
courts will benefit from the implementation of this instrument in their evaluations.
The impact of positive peer influence should be broadened to include individuals who are
not a part of teen court. For instance, Future research should consider revising the scale for
application to high school students, habitual offenders, and first-time status of offenders so that
the utility of the scale may be expanded. In doing so, school-based programs may begin to
develop strategies that focus on positive mentoring and the use of positive peer influence as
preventive measure. In turn, positive peer influence may reduce the number of school and
misdemeanor infractions thus reducing the number of individuals who enter into the juvenile
justice system.
Future research intended to develop measures within a population of youth should
reconsider the use of reverse coded items. An outcome from this study is that items that were
deleted from both the reliability analysis, from the EFA results, and from the CFA were for the
most part reverse coded items. This may indicate that the items were either poor items or that the
reverse coding strategy may not be appropriate for this age group or population.
YSQ
Another interesting finding is that the exploratory factor analysis conducted on the YSQ
within this study, suggested that the original structure reported in Butts (2002) was not accurate,
with modifications made to the entire scale. The scale retained its original five-factor structure;
however, some items were dropped and new items emerged in different factors. This is useful
information for the developers and future teen court researchers. This outcome provides evidence
that studies should regularly evaluate the psychometric properties of scales, conducting this kind
of evaluation before comparisons or conclusions are made.
Researchers should not assume that just because the scale was used and tested that it
retains its reliability and validity from sample to sample. This includes the findings from this
study. A scale only becomes more reliable and valid when the results from one study are
48
replicated in future research. For instance, the reliability analysis reported that the TCPIS had a
reliability coefficient of .89, subsequent assessments of the scale’s reliability, from a different
sample, should be as close to .89 as possible.
Conclusions
The preliminary results from this study suggest that the TCPIS has strong reliability (α =
.89), was able to differentiate between theoretically related and unrelated constructs (construct
validity), and established concurrent validity. The scale’s utility increases with only fourteen
items to complete, lowering the burden for teen court participants and staff. Overall, these
preliminary results suggest that the TCPIS is a useful tool that can be used to measure the effects
of positive peer influence in teen courts. It will also assist in identifying effective peer influence
strategies, resulting in the improvement of the teen court program that way this program can
remain a driving force in diverting youth from the juvenile justice system.
Items from the TCPIS solely address the perceptions of peer influence within teen courts.
Nonetheless, general non-teen-court-dimensions of peer influence may also exist. Future
research should adapt the items so that a vast array of youth can respond expanding the
evaluation of types of positive peer influence that would be generalizable to a larger population.
The results from this study offer that there may be positive peer influence present within teen
courts. These outcomes do not clarify whether responses address a youth’s general tendency to
be prosocial or if the perceptions are due to the teen court context. In short, what exactly some of
the questions measure is not necessarily clear and future research should address these
ambiguities.
49
CHAPTER FOUR
ASSESSING THE PREDICTIVE VALIDITY OF THE TCPIS
The epidemiology of adolescent delinquency is well documented, and scholars have
identified specific youth traits and social influences that affect the prevalence of delinquent
behavior (Hirschi, 2005; Warr, 1993a; 1993b; 1996). Negative peers have proven repeatedly to
be among the strongest predictors of delinquency (Warr, 2002), but positive peers have received
little attention (Vorath et al., 1985). Teen courts are a diversion program that focuses on
deterring first-time status offenders from reoffending (Butts, 2000). Although teen court
advocates describe positive peer influence as a major feature of their intervention, teen courts
have progressed without any empirical evidence as to whether positive peer influence even exists
within these programs or if it affects youth outcomes. The Teen Court Peer Influence Scale
(TCPIS) was developed to measure positive peer influence in an attempt to fill these conceptual
and empirical gaps.
Another purpose of the TCPIS, that has not been established, is the ability to identify
individuals who are more susceptible of reoffending. This can only be achieved if the TCPIS has
strong predictive validity. This chapter investigates the predictive validity of the TCPIS by
assessing the scale’s relationship with the participant’s perceptions of teen court experience,
prosocial attitudes, and perceived future acts of delinquency.
TCPIS
The original twenty-seven items for the TCPIS were developed using qualitative
interviews, observations, and focus groups. These items were assessed by experts for content and
face validity. The scale was then disseminated to six teen court sites with 404 youth participating
in the study. The sample was split into two groups; the first sample was assessed using a series of
exploratory factor analyses (EFA) to determine the factor structure of the scale, and the second
sample was used for a confirmatory factor analysis (CFA) to assess the structure and conceptual
cohesion of the scale. These analyses suggested that positive peer influence was operationalized
through three domains that focus on positive identity development (α = .81), positive cognitions
(α = .81), and modeling (α = .70). These domains are reciprocal, meaning that they are
correlated. The revised TCPIS included fourteen items that produced a global reliability
coefficient of .88, and demonstrated strong construct and concurrent validity. The primary focus
50
of this chapter is to add to the psychometric analysis of this measure by examining predictive or
criterion-related validity and illustrating the measure’s utility.
Predictive Validity
Predictive validity refers to scores on a test, in this case the TCPIS, predicting some
criterion variable that the researcher wishes to explain (McDonald, 1999). If the TCPIS is
associated with criteria measures such as perceived delinquency, it may assist with estimating the
likelihood of an adolescents’ continued progress through the program and performance after
program completion. This would allow parents, teachers, teen courts, and social workers the
opportunity to implement appropriate interventions to youth who are at-risk. The first step in
determining the TCPIS’s predictive validity is identifying criteria measures.
Dependent Variables and Hypotheses
This study began by identifying single-item dependent variables for the predictive
validity analysis from the Youth Self-administered Questionnaire (YSQ), developed by Butts
(2002) from a sample of 532 teen court participants. This scale was the best measure to include
in the validity analysis because the YSQ was the only scale available that had been disseminated
to teen court participants and provided potential variables for the predictive validity analysis. The
original YSQ contained five domains, which were confirmed with an exploratory factor analysis
on the current study sample. It produced a reliability coefficient of .72, and items had a four
response option format ranging from 1= strongly disagree to 4= strongly agree. In all analyses,
the summated TCPIS score was used as the independent variable in the regression analysis for
the predictive validity assessment. It contains three domains, produced a reliability coefficient of
.88, and has a seven response option format ranging from 1= strongly disagree to 7 = strongly
agree. The following highlight the included measures and their associated hypothesis.
Teen Court Variables
Items 4, 5, 9, and 29 in the YSQ address the participant’s perceptions about teen court.
Item 4 (“Teen court was more interesting than I expected”) provides information about the
youth’s experience and item 5 (“Teen court people care about my rights”) provides information
about perceptions of how the participant was treated in teen court. The hypotheses for item 4 and
5 are as scores on the TCPIS increase, the scores for these items will also increase because high
scores on the TCPIS should translate to positive experiences in teen court. Item 9 (“Coming to
teen court was a waste of time “) focuses on the motivation of the teen court participant and as
51
scores on the TCPIS increase, scores for this item will decrease. Item 29 (“You can learn a lot
about the law in teen court”) addresses the increase of knowledge about the justice system due to
the youth’s experience in teen court. The scores for this item should increase as items for the
TCPIS increase.
Perceived Delinquency
An adolescent’s perceptions have been shown to be significantly associated with future
acts of delinquency (Simourd & Van De Ven, 1999). For instance, if a youth perceives that he or
she will reoffend in the future or has low personal self-control, research purports that these youth
are more likely to be related to property damage and substance abuse (Hay, 1999). Item 35 (“I
will probably be arrested again someday”) of the YSQ addresses whether the participant believes
he/she will reoffend in the future. This study hypothesizes that as the scores on the TCPIS
increase, the response to item 35 will decrease, representing that individuals who score high on
the TCPIS maybe less likely to reoffend.
Prosocial Attitudes
Conversely, adolescents whom exhibit prosocial attitudes, value their future, and wish to
become a better person are less likely to reoffend (Edwards, 1996; Aas, Klepp, Laberg, & Aaro,
1995; Chung & Elias, 1996). If a person believes they are valuable, they are less likely to be
involved in delinquent behavior (Ludwig & Pittman, 1999). Youth that display higher levels of
empathy, sympathy, and prosocial behaviors have lower levels of aggression (Miller &
Elsenberg, 1988; Tremblay, Phil, Vitaro, & Dobkin, 1994).
Items 24 and 28 of the YSQ address the participants’ self-efficacy or prosocial attitudes.
Item 24 states that “Teen court makes me think about my future,” suggesting that individuals
who are beginning to think about their future are less likely to reoffend. Similar, item 28 offers,
“Being in teen court makes you a better person.” These items address the prosocial attitudes of
the participant and this study hypothesizes that as scores on the TCPIS increase so will scores for
these items.
Sample and Data Collection
Data were gathered from 404 participants within six teen courts sites in the state of
Florida. Although split samples were utilized for the exploratory and confirmatory analyses, this
analysis utilized the complete sample of 404 participants to ensure that this study had enough
statistical power (.80) to detect a small effect size of .20. The effect size refers to the magnitude
52
of the treatment effect or in this case, positive peer influence. In order to detect a small treatment
effect the analyses must have strong power. A researcher wants to detect as much explained
variance or effect as possible.
The power analysis was conducted a priori to determine the appropriate sample size. This
analysis determined that when conducting a regression analysis using one independent variable,
this study would require a sample size of 141 or greater to detect a small effect size of .15
(Pedhazur et al., 1991; Kline, 2005; Cohen, 1962; Cohen, 2003; Shadish et al, 2002). A sample
of 404 participants exceeded this threshold.
The TCPIS was administered to a nonprobability sample of teen court participants from
September 2006 to February 2007. Consent from the guardian and assent from the youth were
obtained at intake interview and the scale was provided to youth who participated at exit
interview. Respondents were given credit for two community service hours for participating in
the study. This study received approval from the Human Subjects Committee of the Internal
Review Board at Florida State University. Consent was also obtained from each participating
court site before the TCPIS was administered.
Data Analysis
This study conducted separate regression analyses, one for each dependent variable (7),
to determine the TCPIS’s predictive validity. A Pearson r correlation was not used because it
only provides a general interpretation. For instance, if there was a significant correlation between
the TCPIS and item 35, interpretations of this relationship could only suggest that they had a
strong positive relationship. It does not provide specific statements about the expected
performance on the criteria variables, given scores on the TCPIS (Pedhazur et al., 1991).
The data were screened using SPSS 14.0 software. Missing values were handled using
the list-wise deletion method due to the small number of missing data (< 2%) and the large
sample size. The TCPIS and dependent variables were assessed for outliers using descriptive
statistics. The skewness and kurtosis of the data were inspected using a threshold of 2.5 (Kline,
2005). All items met this threshold except item 35 from the YSQ. This item produced
standardized residuals of 2.16 (skewness) and 4.4 (kurtosis). Outliers can be problematic when
fitting a regression line to the data (Pedhazur et al., 1991). A transformation was used to correct
this problem. Log transformations are useful when the standard deviation and mean of the item
53
are proportional and the item is positively skewed (Howell, 2002). This was the case with item
35. This transformation corrected the skewness (1.55) and kurtosis (1.2) of the item.
This study must be careful when interpreting results based on transformed data because
the transformation can alter the nature of the data (Pedhazur et al., 1991). Conclusions about the
relationship between the TCPIS and item 35 can become problematic if the transformation is not
considered. This study assessed whether there were dramatic differences in the outcomes for the
original item and the transformed item. There were no dramatic differences found. Research also
suggests that if the nature of the data is not altered the interpretations are not problematic (Kline,
2005). Prior to placing the summated score into a regression equation, the assumptions of this
parametric test were evaluated.
Assumptions
A regression analysis contains five parametric assumptions: correct fit, independence,
normality, constant variance, and exact IV (Pedhazur et al., 1991). Correct fit refers to the
functional form of the relationship, meaning that a linear line should be fit to a linear distribution
(Tate, 1998). Problems occur when a study attempts to fit a linear line to a curvilinear model.
This may be assessed via residual scatterplots. Each measure was assessed to determine if there
was a violation to this assumption, and there were no violations.
Independence can be violated through repeated measures, nonrandom selection of
subjects, and contextual effects (Tate, 1998). A person has repeated measures when the TCPIS is
completed at intake and at exit interview. This is not a component of this research design.
Possible threats to this assumption could occur because this study did not use randomization due
to systematic constraints employed by the juvenile justice system, preventing the randomization
of participants. This is a limitation of this study.
Another possible threat to independence is contextual effects. Contextual effects or
nested effects refer to the possibility of group effects (court sites) influencing individual
responses on the TCPIS (Pedhazur et al., 1991). This threat was assessed through conducting an
ANOVA to determine if there were any statistical differences in scores on the TCPIS across
court sites. Assumptions for the ANOVA include normality, a continuous outcome variable, and
homogeneity of variance (Levene’s test > .05). The ANOVA produced an F-statistic of 2.84, pvalue .02, and a Levene’s statistic of .15. This result was significant, suggesting that there were
differences between court sites supporting the possibility of contextual effects. Post hoc analyses
54
revealed that there were significant differences between Leon county teen court (N= 32) and
Escambia county (N=84) with a difference in means scores of -9.61 (p-value = .03). Leon
County scored higher on the TCPIS than Escambia County.
These statistics may be due to the differences in the demographics of the sample or the
actual sample size. An ANOVA was conducted to determine statistical differences on
demographics between these two court sites that would account for the differences in scores on
the TCPIS. The age of the participant and grade level were significantly correlated with the
TCPIS in chapter three. These demographic variables were not significantly different across
court sites within this analysis. The differences in scores on the TCPIS across court sites may be
due to the discrepancies in sample size, which affect the mean scores of the court sites.
Regardless, the presence of contextual effects is a limitation of this study and future research
should address this problem using hierarchical linear modeling (HLM).
Normality refers to the distribution of scores. The Normal Probability Plot of the
standardized residuals was inspected to determine normality. Histograms and scatter plots
offered additional information about the distribution of the data. This study concluded that there
were no violations to normality. Constant variance refers to the variability of the residuals within
the data. A residual plot with residuals that increased with an increasing predictor variable would
represent a violation of this assumption, which was not present within this data (Tate, 1998).
Lastly, the exact IV assumption refers to the amount of measurement error within the
independent variable. The amount of error can be estimated by 1- α. If the reliability of the
TCPIS exceeds the specified threshold of .7 (Pedhazur et al., 1991), the possibility of violating
this assumption is minute. The reliability for the TCPIS was .88 exceeding this threshold.
Findings
Sample
Teen court participants on average were 15 years of age, 59.1% White, 25.3% African
American, 11.7% Hispanic, 1.0% Asian, and 2.9 % other. Respondents were 55.6 % male, in the
ninth grade, and reported a ‘B’ in school performance. The household income per year averaged
$35,001-$45,000 and 94% of youth stated that the race of the peer volunteers was not important.
These demographics are representative of first-time status offenders entering the Florida juvenile
justice system (N= 27,315): 54% male, 58% White, 26% African American, 14% Hispanic, 2%
other, and on average fourteen years of age (FDJJ, 2006).
55
Predictive Validity
The results from the regression analyses are found in Table 4.1. Each row represents a
separate regression analysis with only one independent variable (TCPIS) in each equation. The
regression equation that included item 4 produced an r = .49 (R2= .24) with a p-value of .000.
The TCPIS also predicted scores on item 5 (r = .44; R2 = .20; p-value <.000). Theses results
confirmed this study’s hypotheses with scores for these items increasing for every unit increase
on the TCPIS. The perceptions about teen court increased as perceptions about positive peer
influence increased supporting that high scores on the TCPIS predict positive perceptions of teen
court.
The results for item 9 were significant (r =- .46; R2 = .21; p-value <.000) offering that as
scores on the TCPIS increased the scores for item 9 decreased. The TCPIS explained 21% of the
variance in scores for item 9. Individuals who scored high on the TCPIS had positive perceptions
about the teen court experience. The last teen court variable, item 29, suggested that the TCPIS
explained 26% of the variance in scores for this item. The hypotheses for both item 9 and 29
were supported.
The results for item 28 suggested that the TCPIS explained 38% of the variance for
scores on item 28. The hypothesis for this item was met with scores on item 28 increasing .378
for every one-unit increase on the TCPIS. This relationship was also statistically significant with
a p-value of .000. The TCPIS also explained 26% of the variance for scores on item 24 with
scores increasing .258 for every one-unit increase on the TCPIS. This result was significant (pvalue = .000) and consistent with this study’s hypothesis for this item. These results suggest that
individuals who score high on the TCPIS are more likely to think about their future and want to
become a better person.
For item 35, the TCPIS predicted 11% of the score for this item with scores decreasing
.324 for every unit increase on the TCPIS. This relationship was significant and consistent with
the hypothesis for this item. This suggested that as individuals increase their perceptions about
positive peer influence their perceptions of themselves committing another delinquent act
decreased. Although the explained variance (11%) for scores on item 35 was weak, this result
was significant and offered preliminary information that the TCPIS’s predictive validity is
encouraging.
56
Table 4.1: OLS Regression Analyses Results (N= 404)
Unstandardized
Coefficient
Dependent variable
B
Standardized
coefficient
Std. error
B
T-value
Significance
R
R2
Teen court was more interesting than I
expected (Item 4)
.03
.002
.49
11.07
.000**
.49
.24
Teen court people care about my rights
(Item 5)
.02
.002
.44
9.87
.000**
.44
.20
Coming to teen court was a waste of time
(Item 9)
-.02
.002
-.46
-10.35
.000**
-.46
.21
You can learn a lot about the law in teen court
(Item 29)
.02
.002
.51
11.96
.000**
.51
.26
Being in teen court makes you a better person
(Item 28)
.03
.002
.62
15.57
.000**
.62
.38
Teen court makes me think about my future
.03
.002
.51
11.75
.000**
.51
.26
-.004
.001
-.34
-5.01
.000**
-.34
.11
(Item 24)
I will probably be arrested again someday
(Item 35)
Note: ** significant at p-value < .001
The TCPIS is in its developmental stages. The results from this chapter offer that the
scale is capable of predicting scores on criteria measures. The hypothesized relationships
between the predictor variable (TCPIS) and these measures were supported. Some relationships
were stronger, with the TCPIS explaining more variance for the prosocial attitude items than for
perceived delinquency. This information is foundational and subsequent research should
consider the limitations of this study.
Limitations
Self-report and nonrandomization of participants are limitations of this study. Other
problem areas are that this study only contained one wave of data collection. Research designs
addressing predictive validity ideally contain multiple observations so a growth analysis can be
conducted (Pedhazur et al., 1991). This study did not have multiple observation points due to
57
limited resources and minimal teen court staff to conduct multiple administrations of the scale.
Future research should assess the TCPIS’s predictive validity over various time points so that
results may become more convincing. The TCPIS should be administered at intake, exit
interview, 3-months, and 6-months post completion of teen court. As mentioned, contextual
effects may have influenced the results of the regression analysis. A study that utilizes an HLM
growth analysis could address a design that contained both contextual effects and repeated
measures.
The information obtained about the TCPIS’s predictive validity is preliminary and should
be considered as such. The scale requires further predictive assessments using longitudinal
measures of recidivism at 3 months, 6 months, and 1-year follow-up so that scores on the TCPIS
can be linked to recidivism. The nonprobability of the sample limits the generalizability of the
study and the external validity of the findings. Various samples, teen court models, and
geographic regions should be used in future research to strengthen the psychometrics of the
TCPIS. Given these limitations, subsequent research should evaluate the psychometric properties
of the TCPIS to confirm or adapt the results found within this study.
Future Directions
Ensuing research should determine whether the TCPIS could distinguish between youth
who are at-risk of reoffending from youth who are not at-risk of reoffending. The summated
score for the TCPIS, through additional analyses, could be segmented into levels of positive peer
influence. Outcome measures like longitudinal measures of recidivism could be assessed to
determine if there are statistical differences across levels on the TCPIS. This would suggest
whether the TCPIS could differentiate between individuals who score low on the TCPIS from
those that score high on the scale in relation to whether they reoffended or not. Low scores on
the TCPIS should be associated with the youth reoffending. This is useful when identifying
youth who may be at-risk based on their scores on the TCPIS. If there are no differences between
someone who scores a 35 on the TCPIS from someone who scores a 70, than the TCPIS is not an
accurate measure of whether youth are at-risk of reoffending.
This study does not include longitudinal outcomes. Stronger measures of delinquency
are required to make further claims that the TCPIS can be used to identify at-risk youth. This
revision would strengthen the utility of the scale with teen courts being able to administer, with
confidence, the TCPIS to youth at intake. These intake scores could identify youth who would be
58
more likely to reoffend and additional interventions could be employed. Obviously, the
additional interventions used for this population of at-risk teen court participants should be
evaluated for their effectiveness. There is no point is subjecting at-risk participants to ineffective
interventions. The purpose is to give teen courts the ability to identify youth who may recidivate
after the program so that this behavior could be prevented reducing the burden placed on
families, courts, and society.
Social Work Practice
The social work profession espouses social justice values in their mission for those who
are oppressed or vulnerable; including the promotion of social justice for at-risk youth (National
Association of Social Workers, 2006). This statement assumes that social workers have the tools
to differentiate between youth who are vulnerable from those that are not at-risk. If this is our
mission, social work researchers should take on the responsibility of developing measures that
evaluate the risk and protective factors of vulnerable youth. This would allow social workers the
opportunity to intervene and provide additional resources to these youth.
Another embedded responsibility within our mission is the need to evaluate programs that
serve vulnerable populations. Social justice includes vulnerable populations receiving evidencebased services. More importantly, teen courts require greater attention as to how they are
developed and implemented theoretically. Are there gaps between how teen courts should run
theoretically and how they are currently administered? Social workers should be on the frontlines
of evaluating programs that are provided to at-risk youth because the outcomes of these youth
are of interest to the profession. Poor outcomes by youth have proven to lead to broken families,
financial constraints to the system and the family, and larger costs to society (NIMH, 2000).
From the results of this study, the TCPIS can provide social workers with a scale that can
be used in subsequent research to determine if scores on the TCPIS identify youth who may be
at-risk of not completing the teen court program or reoffending. This would allow further
services for these youth, preventing the youth from reentering into the juvenile justice system.
Once the TCPIS is broadened to include questions that youth from various populations, not teen
court, could respond. Social workers could disseminate the scale at juvenile intake to determine
if youth are appropriate candidates for teen court. Not all prevention or intervention strategies are
appropriate for all offenders. The ability to link individuals to services that are best suited for
59
their progression could limit the amount of time a youth spends in the juvenile justice system
thus decreasing the amount of money spent.
Conclusions
This chapter, and the preceding chapters, focused on assessing the TCPIS’s psychometric
properties. An EFA and CFA concluded that the TCPIS contains fourteen observed variables,
with three domains that have reciprocal relationships. The reliability analysis suggested that the
TCPIS has strong reliability with a coefficient of .88. The validity analyses, construct and
concurrent validity, offered that the TCPIS’s demonstrated validity. Finally, this chapter
evaluated the predictive validity of the scale with outcomes suggesting that the TCPIS’s
predictive validity was promising. The combination of these outcomes report that the TCPIS
proved to be a valuable measure. However, reliability and validity of a scale is not achieved after
just one study and outcomes from this study are not authoritative. Results are sample-dependent
and these methods should be replicated in the future to ensure the stability of the TCPIS.
The results suggest that the TCPIS can be utilized in future teen court research to
determine whether specific strategies used by these courts are effective. It can also be used to
identify youth who are at-risk of reoffending based on their scores on the TCPIS. The
administration of the TCPIS in subsequent research will provide teen courts with outcomes that
will assist in providing evidence-based practice to youth.
60
CHAPTER FIVE
FUTURE RESEARCH EFFORTS
The results from this study must be disseminated to expand teen court and peer influence
literature. Publishable articles from this dissertation include a conceptual article that identifies
gaps in teen court research. Research solely focuses on recidivism data with an absence of a
theoretical evaluation of teen courts, more specifically, positive peer influence. The article will
make suggestions for filling these gaps through the development of a scale that captures the
foundational concept of these courts. A study that addresses the theoretical premise of teen
courts could have important policy implications for the program. Teen courts can begin to assess
how they are designed theoretically, how they are implemented, and identify if there is a divide
between theory and practice. Guidelines developed by Godwin (1996) for the implementation
and operation of teen courts do not address positive peer influence mechanisms because no
empirical evidence existed from teen court research prior to this study (Godwin, 1996). If certain
types of influence are established and there is a connection between these influences and positive
youth outcomes, teen courts can begin to develop procedures that enhance these mechanisms.
The targeted journals for this publication are Juvenile and Family Court Journal or Crime and
Delinquency.
Using the content from chapter two, a second article will be submitted that addresses the
development of the Teen Court Peer Influence Scale (TCPIS) and the results from the
exploratory factor analysis (EFA). The discussion will cover the four-factor structure identified
by the EFA, conceptual makeup of the factors, and the implications of these findings to peer
influence and teen court research. These results fill gaps in teen court theory and more broadly in
criminological theory. Journals that will be targeted for article submission are Research on
Social Work Practice, Criminal Justice and Behavior, Journal of Criminal Justice, Criminal
Justice Policy Review, Journal of Crime and Justice, and Justice Research Policy.
A third article will be submitted that covers the results in chapter three, the confirmatory
factor analysis (CFA), and the theoretical, programmatic, and policy implications from these
results. The fourth chapter also has potential for a publication(s) with results suggesting that the
TCPIS has strong predictive validity. An additional analysis that should be included within this
article is an evaluation of whether results would differ given control variables. In addition, the
predictive analysis should be broadened to include subscales and single item predictors within
61
the TCPIS and their relationship with youth outcomes. This would add whether different positive
peer influence variables influence youth outcomes in different ways. This article will also expand
the utility of the scale, and elaborate on how teen courts can begin to utilize the TCPIS. Finally,
the evaluation of the YSQ conducted within this study needs to be discussed because the
structure of the scale altered from its original form. Future teen court researchers should be
aware of the discrepancies between the Butts (2002) study and the results from this research.
These articles will be submitted to the following journals: Research on Social Work Practice,
Criminal Justice and Behavior, and Journal of Criminal Justice.
Subsequent Teen Court Research
Absent from this study and the data set are longitudinal outcomes like program
completion, recidivism, family cohesion, and prosocial attitudes. An ensuing study that will
advance the outcomes from this dissertation is one that includes a quasi-experimental design,
utilizes various court models, uses an appropriate comparison group, and addresses nested effects
using a HLM growth analysis. This study will require external funding from sources such as the
following: National Institute of Justice, National Association of Teen Courts, Office of Juvenile
Justice and Delinquency Prevention, Bureau of Justice Assistance (BJA), Bureau of Justice
Statistics (BJS), Community Capacity Development Office (CCDO), Office for Victims of
Crime (OVC), Office of National Drug Control Policy (ONDCP), and Office of Community
Oriented Policing Services (COPS).
If the TCPIS can predict scores over time points, this would strengthen the psychometric
properties of the scale. Additionally, if participants show change from intake to exit interview,
and at follow-up, this would expand the evidence regarding the effectiveness of teen courts.
These efforts could be broken into four separate articles. The first article would address a
comparison between the TCPIS’s psychometric properties from this study with the results from
another sample of teen court participants (national sample). The second article will focus on the
connection between the TCPIS and longitudinal outcomes. The third will cover the change in
scores from intake to exit interview, and a fourth article will emphasize the differences in various
teen court models. Teen courts are not aware of which court model is more effective and given
that this study has identified types of positive peer influence, different models may provide
variations in positive peer influence exposure. Each of these articles will be submitted to one of
the following journals: Research on Social Work Practice, Criminal Justice and Behavior,
62
Journal of Criminal Justice, Criminal Justice Policy Review, Journal of Crime and Justice, and
Justice Research Policy.
Expanding the TCPIS
The TCPIS is confined to a population of teen court participants, and it would benefit
from an adaptation of the items. The items would be revised to address positive peer influence
more generally so that a vast array of populations could respond. For example, consider the
following items from the TCPIS: “Peer volunteers can change my beliefs about criminal
behavior”; “My friends should be more like the peer volunteers.” These items can be expanded
so that they read “Positive youth can change my beliefs about criminal behavior” and “If my
friends were more positive I would not get into trouble.” The purpose is to develop items that
capture the affect of positive peer influence that are not just applicable to teen court participants
and to identify ways in which individuals can begin to become more positive. A systematic
research synthesis on measures of peer influence would assist in developing the item pool. These
items would be disseminated to high school students along with other outcome measures (school
performance, prosocial attitudes, and prosocial bonds). This study would also require an external
funding source.
The results from this study would address perceptions of positive peer influence from
various individuals, youth who are positive, negative, and first-time offenders. This would have
policy implications for school systems, teen courts, various fields of study, and the juvenile
justice system. Types of positive peer influence could be identified so that schools could
implement programs that could utilize these influences such as mentor programs (i.e. Big
Brothers and Sisters). The juvenile justice system could utilize this scale to identify youth that
would be more appropriate for teen courts. The system could also develop programs that utilize
the identified types of influence. The results from this study would be generalizable to a larger
population of youth rather than just teen court participants. Results could point to more macro
implications with types of positive peer influence being associated with health outcomes, risky
behaviors, graduation rates, and attending college.
Another study using this revised instrument would include administering it to various
samples, high school students, first-time status offenders, and more severe or violent offenders to
determine if there are differences in the samples. These differences may point to a progression
from nondelinquent to delinquent and to serious offender. Results could suggest that positive
63
peers are a protective factor when considering the prevalence of delinquency, substance use,
smoking, risky sexual behaviors, and family outcomes. This would inform all aspects of social
work practice and research.
Social Work’s Responsibility
Social work employs a strength-based approach to service delivery, practice, policy, and
education (NASW, 2006). To focus on the negative aspects of youth would be not only repetitive
but it would not serve youth or adhere to the mission of social work. Social work should take the
lead in evaluating the protective factors of youth so that intervention strategies can be employed
to identify at-risk youth and provide evidence-based treatment to this population. To advance the
body of knowledge about positive peers would improve scholarship and public policy. If positive
peers play a role in deterring not only delinquency but also health issues, the impact of research
in this area would be vast. It is social work’s responsibility to provide evidence-based practice to
youth including the advancement of research on positive peers.
.
64
APPENDIX A
Consent letter
County _________________
Dear Parent:
I am a Doctoral student under the direction of Dr. Darcy Siebert, in the
Department/Division/College of Social Work at Florida State University. I am conducting a
research study to examine the impact of peer influence on teen court participants.
Your child's participation will involve the completion of a twenty-five minute survey at the end
of their court hearing. Your participation, as well as that of your child, in this study is voluntary.
If you or your child chooses not to participate or to withdraw from the study at any time, there
will be no penalty, and it will not affect your child's treatment within teen court. All survey
materials are confidential and will be kept secure. The results of the research study may be
published, but your child's name will not be used. Results of this study may also be obtained
through contacting the researchers.
Although there may be no direct benefit to your child, the possible benefit of your child's
participation is improving the effectiveness of teen courts as well as understanding the effects of
positive peer pressure.
If you have any questions concerning this research study or your child's participation in the
study, please call me Kenneth Scott Smith, (850) 228-8240 or by e-mail at
[email protected], Dr. Siebert may be reached at (850) 644-4751 or by e-mail at
[email protected].
Sincerely,
Kenneth Scott Smith, MSW
*******
I give consent for my child _
________ to participate in the above study.
Parent's Name: ________________________________
Parent's Signature _________________________________ (Date) ________________
If you have any questions about your rights as a subject/participant in this research, or if you feel
you have been placed at risk, you can contact the Chair of the Human Subjects Committee,
Institutional Review Board, through the Vice President for the Office of Research at (850) 6448633.
65
APPENDIX B
Assent Form
Hello, I would like your help in a study that I am conducting. Your parent(s) have given
permission for you to participate in this concerning your attitudes about peer influence. Your
participation in this project is voluntary and you may stop participation in this study at any time.
If you choose not to participate, it will not affect your treatment, care, teen court outcome in any
way. If you decide to participate in the study all information is confidential meaning that your
parents, the judge, or other peers involved with teen court will not have access to your answers.
Signing here means that you have read this form or have had it read to you and that you are
willing to be in this study.
Name: ________________________________________________________
66
APPENDIX C
67
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BIOGRAPHICAL SKETCH
Education
PhD, 2007
Social Work
Florida State University
M.S.W., May 2003
Social Work
University of Texas at Austin
B.A., August 2000
Psychology
Texas State University
Research training & skills
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Statistics certificate (MINOR)
Data management
Statistical programs: SPSS, LISREL, AMOS, M+, SAS
Psychometrics: classical measurement theory, item response theory
Managed multi-site research design and methodology
Regression, ANOVA, MANOVA, ANCOVA, Hierarchical linear modeling (HLM), Growth
modeling
! Instrument construction and analyses
! Factorial analyses: exploratory and confirmatory
! Built collaborative research relationships with five Florida judges, ten courts, and the juvenile
justice system
Research Interests
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Measurement & psychometrics
Instrument construction
Evaluation of juvenile justice programs and specialized courts
Evaluation of faith-based organizations
Adolescent delinquency
Substance abuse
Family involvement in treatment components
Measuring adolescent and parental attitudes about the juvenile justice system
Effects of positive peer influence on treatment outcomes.
Increasing the engagement of parents within the juvenile justice system
Policy analysis
Teaching Experience (BSW)
Statistics
Chemical Dependency
Interview & Recording
Intro to Social Work
Practice 1
Publications
Smith, S., & Teasley, M. (In Press). An assessment of the prevalence of social work research assessing
faith-based programs: A movement towards evidence-based practice. In progress for submission
to Journal of Religion & Spirituality in Social Work.
79
Smith, S., McLaughlin, A., & Chonody, J. Development and validation of the Civil Rights for Gays and
Lesbians index (CRGLI). In progress for submission to Research on Social Work Practice.
Smith, S. An evaluation of teen courts: A call for a theoretical analysis. Potential journals: Crime &
Delinquency; Youth & Society; Criminology; Criminal Justice & Behaviors. In Progress.
Smith, S., Siebert, D, & Mears, D. The development and validation of the Teen Court Peer Influence
Scale (TCPIS). Potential journals: Crime & Delinquency; Youth & Society; Criminology;
Criminal Justice & Behaviors. In Progress.
Smith, S., Siebert, D, & Mears, D. Peer-driven justice: The impact of positive peer influence within an atrisk population. Potential journals: Crime & Delinquency; Youth & Society; Criminology;
Criminal Justice & Behaviors. In Progress.
Professional Membership & Association
National Association of Social Workers
Council on Social Work Education
Society for Social Work Research
Professional Experience
Family Therapist: September 2004- May 2007
Intensive Crisis Counseling
# Conduct psychosocial assessments and implement treatment plans for individuals and families
# Provide counseling services - crisis intervention, grief and loss, substance abuse, trauma, domestic
violence, physical and sexual abuse, and treatment of other various psychological disorders
# Provide referrals to community resources
Social Worker: January 2004 – August 2004
St. David’s Medical Center
# Acute care unit and emergency room
# Assisted the patient/family to achieve an effective transition from hospital to post-hospital care
through psychosocial evaluation, counseling, information and referral, patient education, and
assistance with discharge planning.
# Responsible for policies and procedures, record keeping and reporting, performance improvement,
and interfacing with other hospital units, community service agencies, chemical dependency, and
psychiatric treatment facilities.
# Provided counseling services - crisis intervention, grief and loss, substance abuse, trauma, domestic
violence, and physical and sexual abuse.
Prevention Specialist/ Intern: August 2002- August 2003
#
#
#
#
#
Phoenix Academy
Facilitated therapeutic group for fifteen adolescent males
Substance Abuse and dual diagnosis clients
Facilitated various group, individual, family, and public educational presentations
Promoted and utilized Life Skills and other curriculum for youth
Created and developed after school programs for at-risk youth
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Case Manager: August 2000- August 2002
#
#
#
#
Phoenix Academy
Accountable for overseeing forty- eight adolescents
Conducted meetings with clients and family members (treatment plans)
Responsible for marketing
Conducting presentations in the region seven area (Texas), outreach
Mental Health Professional: 1995- August 2000
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#
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Brown School Health Care Center
Maintained therapeutic atmosphere
Provided life skills training
Assured safety among entire behavioral unit
Coordinated activities and schedules
Experience with clients with various mental health disorders
81