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Exploring the Link between Mentoring
Program Structure & Success Rates: Results
from a National Survey
J. Mitchell Miller & J. C. Barnes &
Holly Ventura Miller & Layla McKinnon
Received: 4 October 2012 / Accepted: 4 October 2012 /
Published online: 19 October 2012
#
Southern Criminal Justice Association 2012
Abstract Though mentoring has emerged as a promising and low-cost intervention
for at-risk youth in recent years, the scientific knowledge base on the topic remains
under-developed. The current study augments the knowledge base on youth mentor-
ing by analyzing programmatic elements of mentoring programs situated in or
adjacent to the juvenile justice system that are predictive of participant success.
Poisson regression was utilized to analyze data collected through a national mentoring
community saturation survey. Findings indicated that mentoring programs that require
more frequent interaction and sustain relationships for longer timeframes realize
higher success rates. Similarly, the use of formal mentor training was also observed
as indicative of the use of evidence based practices and higher success rates, though
likely beyond the logistical and fiscal reach of some local mentoring initiatives. The
implications for further research and the mentoring community are discussed.
Keywords At-risk youth
.
Delinquency reduction
.
Mentoring program
Mentoring entails a relationship between an older and more experienced adult and an
unrelated younger mentee wherein on-going guidance, instruction, and support from
the adult seeks to enhance the character and life skills of the mentee (Rhodes &
DuBois, 2008). The appeal and rise of mentoring is understandable as it is a low-cost
Am J Crim Just (2013) 38:439–456
DOI 10.1007/s12103-012-9188-9
This project was supported by Grant #2010-JU-FX-0118 awarded by the Office of Juvenile Justice and
Delinquency Prevention, Office of Justice Programs, U.S. Department of Justice. The opinions, findings,
and conclusions or recommendations expressed in this publication are those of the author(s) and do not
necessarily reflect those of the Department of Justice. The au thors wish to than k the grant partners
(MENTOR, The National Mentoring Partnership; Global Youth Justice; and the National Partnership for
Juvenile Services) for input and assistance on the development of data collection instruments. We would
also like to thank Barbara Tatem Kelley of the Office of Juvenile Justice and Delinquency Prevention for
project guidance and direction.
J. M. Miller (*)
:
J. C. Barnes
:
H. V. Miller
:
L. McKinnon
University of Texas, San Antonio, San Antonio, TX, USA
e-mail: jm.miller@utsa.edu
delinquency prevention and intervention option that capitalizes on the resources of
local communities and caring individuals. Ment oring services can be customized
to a wide range of needs and situations suitable for delivery in multiple forms,
ranging from individual, group, peer-to-peer, cross-age, and e-mentoring orienta-
tions. Mentoring relationships have dramatically increased in recent years for
youth development, generally, and particularly for at-risk youth as an unprece-
dented amount of federal funding for mentoring initiatives has enabled wide scale
implementation of new mentorin g programs and initiatives (Office of Justice
Programs, 2011).
There has been an ongoing commitment by the U.S. Department of Justice
(USDOJ) to augment the empirical knowledge bas e on youth mentoring toward
bolstering evidence based practices as a major form of delinquency prevention and
reduction. The majority of attention to mentoring has focused on important issues
such as preferred processes for successfully matching adult mentors and youth
mentees, substantive modality elements, generally, and across mentoring forms, and
consideration of which combination of factors and mentoring activities lead to
successful outcomes. Academic disciplines such as counseling and education have
been examining mentoring for over a decade and generally conclude that it is
facilitative of positive youth development, but far less work has based in criminology
and the criminal justice scien ces. This neglect is curious given the increasing focus of
the mentoring community on at-risk youth. The current study focuses on the impor-
tance of certain programmatic elements of mentoring activity within and adjacent to
the juvenile justice system predictive of program success.
The History and Evolution of Youth Mentoring
The proliferation of youth mentoring programs in recent years has been the subject of
considerable research and discussion (DuBois, Portillo, Rhodes, Silverthorn, &
Valentine, 2011). Estimates put the current number of programs and youth population
served at more than 5,000 and approximately 3 million, respectively (MENTOR/
National Mentoring Partnership, 2006). Despite the widespread proliferation of these
programs, there is no officially recognized definition of what constitutes a mentoring
relationship. Mentoring, however, is generally characterized as a relationship wherein
the growth and development of a younger protégé is fostered through instruction and
support provided by an older, more experienced individual (DuBois & Karc her,
2005). Relationships can be formal and a rranged thro ugh an organiz ation th at
matches youth and adults or informal, naturally occurring connections such as those
that develop between teacher and student. The former classification represents an
estimated 30 % of all mentoring relationships (Wood & Mayo-Wilson, 2012) and is a
focus of this review.
While the dimensions and attributes of m entoring relationships can vary
across programs and settings, the common focus or purpose is to provide
positive or pro-social influence on youth development in areas where it may
be lacking. This theme of youth development is evident in the developmental
stages of mentoring identified by Baker and McGuire (2005) that illustrate its
growth and evolution in the United States. The noticeable increase and prevalence of
440 Am J Crim Just (2013) 38:439–456
delinquent behavior that accompanied the industrialization and urbanization boom of
the early 20th Century prompted creation of juvenile courts and demand for preven-
tion and intervention efforts.
Part of mentoring’s attractiveness lies in the fact that it provides a seemingly
simplistic and inexpensive remedy to the problem of diverting socially and econom-
ically disadvantaged youth away from risky or delinquent behaviors (Cavell & Smith,
2005; DuBois & Karcher, 2005; Keller, 2005; McCluskey, Noller, Lamoureux, &
McCluskey, 2004; Smith & Stormont, 2011). Matching of disaf fected children and
adolescents with caring adults who can offer emotional and social support that may be
lacking at home or school is expected to counterbalance negative influences, help youth to
overcome hardships, and avoid criminal involvement. Such assumptions and expectations
are grounded more in faith than theory and do not consider the potential for participant
characteristics, program structure and delivery, and fidelity to affect intermediate and long-
term outcomes (Bogat & Liang, 2005; Newburn & Shiner, 2006; Rhodes, 2005; Rhodes
& DuBois, 2 008). Furthermore, the significance of adequate training, quality relation-
ships, specified goals, and linking program processes and activities with desired outcomes
can be overlook ed or ignored (Bouffard & Bergseth, 2008; Keller, 2005; Nakkula &
Harris, 2005; Pryce & Keller, 2011; Spencer, 2006; Spencer, 2007; Thompson &
Zand, 2010; Tolan, Henry, Schoeny, & Bass, 2008 ). It is this absence of theoretical
foundation and inattention to processes and practices that likely explains the mixed
findings and positive, but limited, degree of impact documented in the evaluation
literature (Coyne, Duffy, & Wandersman, 2005; DuBois & Silverthorn, 2005; DuBois
et al., 2011; Grossman, Chan, Schwartz, & Rhodes, 2011; Newburn & Shiner, 2006;
Rhodes & DuBois, 2008; Tolan et al., 2008; Wood & Mayo-Wilson, 2012).
Mentoring as a Delinquency Prevention/Reduction Tool
Although no formal mentorin g typology ex ists and some variation and overlap occur,
interventions can be classified as site-based or community-based according to where
services are delivered (Sipe, 2005). Site-based programs generally operate out of
schools, faith-based organizations, and local service clubs (Dappen & Isernhagen,
2006; DuBois & Karcher, 2005) and use either paid or volunteer mentors. Activities
are highly structured, may be group oriented, involve little or no interaction outside
program functions, and relationships are often short-lived (Portwood & Ayers, 2005;
Pryce & Keller, 2011; Smith & Stormont, 2011). Community-based national organ-
izations such as Big Brothers Big Sisters, Boys and Girls Clubs, and United Way
represent a slight majority of programs, are characterized by one-on-one mentor-
protégé matches, and involve less structured off-site activities (DuBois & Karcher,
2005). Participants have voice in scheduling and the selection of activities in these
relationships that tend to be longer as a minimum one-year commitment is usually
recommended (DuBois et al., 2011; Portwood & Ayers, 2005 ).
Since gaining wide acceptance as an intervention for socially and emotionally
vulnerable youth, mentoring has also been enthusiastically embraced as a remedy for
misconduct and delinquency among at-risk youth. At-risk is a broad classification but
typically encompasses youth who, due to personal or environmental disadvantages,
are more susceptible to negative life outcomes (Bouffard & Bergseth, 2008) but have
Am J Crim Just (2013) 38:439–456 441
not yet been labeled as delinquent or as an offender. Mentoring for this group is
expected to function as a primary prevention or early intervention strategy to
divert participants from the justice system. The term ‘system-involved’ refers to
youthful offenders who may be incarcerated or under community supervision.
In these setti ngs, m entoring i s ut ilized as a reentry or aftercare approach to
reduce or prevent recidivism (Bazron, Brock, Read, & Segal, 2006; Bouffard &
Bergseth, 2008; Blechman & Bopp, 2005; Enriquez, 2011). While mentoring inter-
ventions have been the focus of numerous studies and positive findings have fueled
youth service, research interest, and funding of corresponding juvenile offender
programs, very little research has focused on mentoring for system involved youth,
specifically (DuBois et al., 2011).
Mirroring outcomes with other populations, mentoring for at-risk and system-
involved youth has generally positive but mixed e ffects (Bouffard & Bergseth,
2008; Dallos & Comley-Ross, 2005; Dappen & Isernhag en, 2006; DuBois et al.,
2011; Enriquez, 2011; Keating, Tomishima, Foster, & Alessandri, 2002; Laakso &
Nygaard, 2007; Langhout, Rhodes, & Osborne, 2004; LoSc iuto, Rajala, Townsend,
& Taylor, 1996; Newburn & Shiner, 2006; Thomas, Lorenzetti, & Spragins, 2011;
Tolan et al., 2008; Wood & Mayo-Wilson, 2012), a fairly consistent observation even
in quasi-experimental designs. Participants report overall positive experiences and
benefits (Dallos & Comley-Ross, 2005; Laakso & Nygaard, 2007; Thompson &
Zand, 2010) and findings indicate improved behavior and attitudes are associated
with mentoring interventions (Dappen & Isernhagen, 2006; DuBois et al., 2011;
Keating et al., 2002; LoSciuto et al., 1996; Thomas et al., 2011; Tolan et al., 2008;
Wood & Mayo-Wilson, 2012. Single studies and meta-analyses, however, reveal a
consistently muted effect size and that outcomes related to delinquency prevention
and reduction vary or are rarely evaluated (DuBois, Holloway, Valentine & Cooper,
2002; DuBois et al., 2011; Enriquez Jr, 2011; Keating et al., 2002; Newburn &
Shiner, 2006; Thomas et al., 2011; Tolan et al., 2008; Wood & Mayo-Wilson, 2012).
Furthermore, program practices and relationships – the change agents of men-
toring
– receive far less scrutiny and assessment than outcomes in determining
effectiveness (Dallos & Comley-Ross, 2005; Keller, 2005; Rhodes & DuBois,
2008; Spencer, 2006).
Mentoring is linked with modest reductions in initiation to alcohol and illicit
substances and additional use, violence, and delinquency in general (Bouffard &
Bergseth, 2008; DuBois et al., 2002; DuBois et al., 2011; LoSciuto et al., 1996;
Thomas et al., 2011; To lan et al., 2008). Infrequent review of yo uth offending
outcomes and low baseline substance use among younge r adolescents, however,
make accurate assessment of effectiveness difficult (DuBois et al., 2011; Thomas et
al., 2011). Improved school attendance, academic performance and achievement, and
the development of vocational skills provide additional examples of behavi oral out-
comes reflecting mentoring effectiveness (Dappen & Isernhagen, 2006; DuBois et al.,
2011; Laakso & Nygaard, 2007; Langhout et al., 2004; Newburn & Shiner, 2006 ;
Wood & Mayo-Wilson, 2012). Positive attitudinal, social, and emotional change
are also associated with delinquency-focused mentoring (Bazron et al., 2006;
DuBois et al., 2011; Laakso & Nygaard, 2007). Increased levels of confidence,
positive outlook, and self-image have been consistently observed across multiple
studies (DuBois et al., 2011; Laakso & Nygaard, 2007; Keating et al., 2002; LoSciuto
442 Am J Crim Just (2013) 38:439–456
et al., 1996; Wood & Mayo-Wilson, 2012) and qualitative findings suggest partic-
ipants’ interpersonal skills and relations with family and peers are also enhanced
through mentoring (Dallos & Comley-Ross, 2005; Langhout et al., 2004; Thompson
& Zand, 2010).
These findings demonstrate the ability of mentoring to generate positive
results across multiple dimensions, including “hard” (behavioral) and “soft”
(developmental) outcomes (DuBois et al., 2011). Yet, reviews indicate improve-
ments and benefits may not be sustained long term (Rhodes & DuBois, 2008),
particularly if the duration of mentoring relationships are brief (Du Bois et al., 2011;
Wood & Mayo-Wilson, 2012). Additionally, effect sizes tend to be small across all
outcomes (DuBois et al., 2002; DuBois et al., 2011) and fail to reach significance in
some cases (Wood & Mayo-Wilson, 2012). While results are largely positive regard-
ing mentoring as a delinquency prevention strategy, some studies conclude there may
have limited potential for success with certain individuals with special needs and/or
co-occurring conditions, particular groups, and some contexts (DuBois et al., 2011;
Enriquez, 2011; Jones-Brown & Henriquez, 1997; Keating et al., 2002 ; Langhout et
al., 2004; Pryce & Keller, 2011; Spencer, 2007).
Findings from a program targeting juvenile probationers, for example, suggest
mentoring may not produce desired effects with chronic offenders, as re-arrest was
three times higher for participants compared to a control group (Enriquez, 2011).
Results here also signal that one-on-one mentoring may not offer any advantage
over group mentoring because recidivism likelihood appears to be the same
regardless of metho d used, an observation with strong fiscal and program
design implications. Also worth noting is the fact that this study reinforces
earlier findings that mentoring alone is not as successful as when supplemented
with other treatments ( Bouffard & Bergseth, 2008). When considered with other
study outcomes highlighting lengthier multidimensional programs and more precise
targeting of participants (Bouffard & Bergseth, 2008; Keating et al., 2002; LoSciuto
et al., 1996), mentoring appears to produce more positive results when used as a
delinquency prevention or part of a comprehensive approach rather than as a reduc-
tion strategy. Although, Jones-Brown and Henriquez (1997) and Blechman and Bopp
(2005) make the observation that at-risk youth fare better with mentoring than their
counterparts subjected to more punitive responses such as boot camp, or waiver to
adult court, and probation.
Similar to outcomes with at-risk groups, mentoring results for reentry and aftercare
participants have also been inconsistent (Blechman & Bo pp, 2005; Bouffard &
Bergseth, 2008; Enriquez Jr, 2011) and the lack of rigorous testing has slowed
evidence based practice specificiation, especially across system settings and partici-
pant populations. In a recent meta-analysis by DuBois et al. (2011), programs aimed
at reducing juvenile offending were omitted due to underrepresentation and the
potential for unreliable findings in this area. Mentoring also shows promise
when used as a therapeutic approach in lieu of harsher treatment (Jones-Brown
&Henriquez,1997) as findings suggest positive effects are stronger and more likely
as part of a comprehensive reentry program (Dubberley, 2006), but less effective for
chronic offenders (Enriquez Jr, 2011). It remains unclear whether and exactly how
much system-involved and high-risk youth can benefit from mentorin g (Bouffard &
Bergseth, 2008; Enriquez, 2011). Understanding the influence of ment oring setting,
Am J Crim Just (2013) 38:439–456 443
site location (school, community, justice system), population cha racteristics (risk
level, needs), and the referral process (social, legal) can help contextualize findings
and provide direction for future research.
Current Study
The overall impact of mentoring on youth development has been positive with
regards to outcomes such as improved attitudes, self-perception (LoSciuto et al.,
1996), interpersonal relations, reduced truancy, dropout rates (Dondero, 1997; Jones-
Brown & Henriquez, 1997), and reduced levels of substance abuse (Bouffard &
Bergseth, 2008; LoSciuto et al., 1996; Thomas et al., 2011; Tolan et al., 2008). These
and other benefits, unfortunately, have been quite small in scale and have been shown
to vary with program structure and relationship duration (DuBois et al., 2011;
Enriquez, 2011; Keller, 2005; LoSciuto et al., 1996; Wood & Mayo-Wilson, 2012)
as longer and better designed programs enhance positive effects of mentoring while
shorter or prematurely terminated matches can have adverse consequences (Rhodes
& DuBois, 2008).
Given the lack of robustness and variability of mentoring effects, several concerns
and limitations have emerged in the research literature. Outcomes and effectiveness
differ for certain populations (DuBois et al., 2011; Enriquez, 2011; Keating et al., 2002;
Smith & Stormont, 2011; Spencer, 2007; Tolan et al., 2008) and across different
locations and settings (Bouffard & Bergseth, 2008; Dallos & Comley-Ross, 2005;
Dappen & Isernhagen, 2006;Langhoutetal.,2004; Portwood & Ayers, 2005).
Additionally, research notes several problem areas practitioners and evaluators have
neglected: modeling or structuring of programs (DuBois et al., 2011), delivery and
implementation (Rhodes & DuBois, 2008), mentoring relationship preferred substan-
tive activity and quality (Keller, 2005), and targeting of at-risk populations (Smith &
Stormont, 2011). The following analysis of data drawn from a national survey of
mentoring programs examines if and how between-program differences impact
success rates for mentored youth referred from the juvenile justice system.
Methods
Sampling Strategy
Mentoring is a relatively new juvenile justice intervention strategy and, as such,
posed several unique challenges to conducting a national survey. In order to carry out
a probability sample such as a simple random or stratified random sample, it is
necessary to have access to a sample frame - a list of known eligible respondents/
participants (Groves et al., 2009). In the absence of a meaningful sample frame, only
cluster sampling remains as a viable probability sample option. A cluster sample
entails several general guidelines, the most basic being that the researcher starts with
a higher level of aggregation than realized from final survey participation. Most
researchers conducting a cluster sample will begin with a list of the 50 United States
and work “down” from there (i.e., randomly choosing counties within those states,
444 Am J Crim Just (2013) 38:439–456
then choosing cities within the chosen counties, and then selecting participants from
those locales).
Although cluster sampling addresses the ostensible problem of having no sample
frame, it does rest on several assumptions that may or may not hold when the survey
is for the mentoring community. Most importan tly, the cluster sample assumes that all
states (if that is the beginning level of aggregation) have mentoring programs at
equivalent (or, at least, proportional) rates. While this assumption may hold, there is
no known data source that can be referenced for confirmation. Put differently, a
cluster sample poses a risk that states, counties, and cities may be selected that
actually have no mentoring programs available to be studied which would lead to
an increase in sampling error.
In light of these issues, a targeted saturation sampling approach guided the current
study. While the sampling strategy used is a non-probability sample, there were
several features of the chosen design that made it the most attractive option. Primar-
ily, the targeted saturation design ensured that mentoring programs would be con-
tacted, that eligible participants would have the opportunity to respond, and that wide
coverage of mentoring programs would be achieved. The targeted saturation sam-
pling strategy utilized the networking resources of four agencies: Glob al Youth
Justice (GYJ), The National Partnership for Juvenile Services (NPJS), MENTOR,
and The Office of Juvenile Justice and Delinquency Prevention (OJJDP). GYJ used
its organizational membership database to reach juvenile justice professionals within
several primary settings, including Teen/Youth Court Diversion Programs, Delin-
quency and Dependency Courts, and Juvenile Probation Departments. The databa se
included contact information for approximately 3,100 individuals in those settings.
NPJS used its organizational membership database to reach individuals and facilities
that fall within the juvenile detention, juvenile corrections, or juvenile probation
settings. The database included contact information for approximately 1,000
individuals in those settings. MENTOR’s distribution list covers a broad list
of programs and mentoring practitioners. There are close to 12,000 contacts in
the list but the survey completion instructions regarding eligible respondents
effected considerable attrition. Finally, OJJDP posted a call for participants to
its national JuvJust listserv wi th thousands of practitioners representing a large
number of potential eligible respondents.
The final sample included 1,197 respondents. It is important to note, however, that
the analytic sample sizes varied from question to question due to built in skip patterns
in the survey. These analytic sample sizes varied prim arily as a function of the type of
program the respondent represented and as a function of mis sing data (i.e., item non-
response). Because mentoring programs are not specific to one location, one region of
the U.S., or one culture, a primary aim of the current study was to draw information
from mentoring programs located across the U.S. and in different cultural settings.
Table 1 presents statistics on the “spread” of the final sample across the 50 States. As
can be seen, all 50 States were represented, as was Washington D.C. As expected,
more populated states expectedly provided mo re respondents compared to less
populated states (e.g., California provided 68 respondents while Rhode Island only
provided 3). As reflected in Table 1, no region of the country was overlooked which
minimizes concern that the results from any qu antitative analysi s will be biased
toward specific regions of the country.
Am J Crim Just (2013) 38:439–456 445
Another, perhaps more important, indicator of sample coverage gauges the types
of communities from which the respondents hailed. The majority of the respondents
indicated that their program was located in an urban (54.26 %) or a suburban
(18.72 %) setting. Little more than 1 % of the sampled programs were located in a
Tribal setting and 25.64 % of the sampled programs were located in a rural setting.
Based on these results and in light of generalizability concerns, it may be important to
control for a program’s community setting when conducting multivariate analysis.
Response Rate
Due to the sampling strategy outlined above, typical response rates would not
provide an appropriate overview of the sample’s coverage or an indication of
sampling efficacy. As there is no national register of mentoring programs to constitute
a sampling frame from which a probability sam ple could be drawn, the most
Table 1 Sample Coverage
Although it is possible that these
respondents were from Guam
and Puerto Rico, it is more likely
that the respondent from Guam
was intending to select Georgia
and the two respondents from
Puerto Rico intended to select
Pennsylvania
State Number of
respondents
State Number of
respondents
Alabama 15 Nevada 9
Alaska 10 New Hampshire 9
Arizona 13 New Jersey 20
Arkansas 4 New Mexico 9
California 68 New York 58
Colorado 28 North Carolina 31
Connecticut 15 North Dakota 8
Delaware 4 Ohio 28
Florida 67 Oklahoma 8
Georgia 30 Oregon 17
Hawaii 9 Pennsylvania 26
Idaho 9 Rhode Island 3
Illinois 38 South Carolina 7
Iowa 27 South Dakota 9
Indiana 26 Tennessee 18
Kansas 13 Texas 39
Kentucky 10 Utah 4
Louisiana 12 Virginia 21
Maine 2 Vermont 6
Massachusetts 23 Washington 13
Maryland 16 Wisconsin 22
Michigan 42 West Virginia 5
Minnesota 20 Wyoming 4
Mississippi 7 Wash. D.C. 8
Missouri 13 Guam
a
1
Montana 5 Puerto Rico
a
2
Nebraska 7
446 Am J Crim Just (2013) 38:439–456
appropriate available sampling option was to realize wide coverage of the United
States mentoring community. While these limiting features preclude calculation of a
conventional response rate, the completion rate is observable. The completion rate, an
indicator of the success of the survey implementation strategy, is calculated by
carrying out the following basic formula:
Completion Rate ¼
Number of Respondents Who Completed Survey
Number of Respondents Who Started Survey
100
The completion rate for this survey was 64.22 %. Given that a little more than one
third of respondents did not complete started surveys, it is likely that the instrument
may have been too lengthy or overly complex.
Dependent Variable
Mentees Meeting/Exceeding Goals Mentoring program respondents were asked
the following question: “On average, what percentage of the m entees referred to
your program from juvenile justice settings meet or exceed the goals set for
them?” Responses were given on a five-point scale w here 00 fewer than 10 %,
1011 - 25 %,20 26 - 50 %,30 51 - 75 %, and 40 76 - 100 %.
Key Covariates
Meeting Frequency The frequency with which mentors and mentees meet was
gauged by one question asked to the respondent: “On average, how frequently do
mentors and juvenile justice involved mentees meet?” Responses were coded so that
10 1–2 times a month,20 3–4 times a month, and 30 more than 4 times a month.
Meeting Length All respondents from mentoring programs were asked to report on
the length of the typical mentor-mentee meeting. Responses were coded such that
00 less than one hour,10 one hour to less than two hours,20 two hours to less than
three hours, and 30 three hours or more.
Mentor Training Respondents were asked whether their mentoring program provided
special training or guidance to mentors working with youth from juvenile
justice settings. Answers were coded so that 00 never,10 rarely,20 sometimes,
and 30 always.
Control Variables
Background Che cks Respondents were asked about the frequency with which their
program performs background checks on mentors. Answers were given on a four-
point scale where 00 never,10 rarely,2
0 sometimes, and 30 always.
Indiv idualiz ed Mentoring Res pondents from mentoring pr ograms were given the
following question: “What is the typical approach to mentoring for juvenile justice
involved youth in your program? (click all that apply)” Participants were then given
Am J Crim Just (2013) 38:439–456 447
the choice between “individually based mentoring (i.e., one-to-one)”, “group-based
mentoring (one mentor/multiple youth)”, “team-based mentoring (multiple mentors/
multiple youth)”, “e-mentoring (i.e., over the internet or via email)”,or“other”.
Responses to this question were dichotomized where 00 not individually based
mentoring and 10 individually based mentoring.
Years in Operation Respondents were asked to report on the number of years their
program had been in operation. Responses were coded in whole numbers as years
ranging from 1 (1 year or less)to21(more than 20).
Percent of Youth Who are Male Participants reported the percentage of youth referred
to the mentoring program from a juvenile justice setting that were male. Responses
ranged from 1 (0–15 %)to6(76 – 100 %).
Percent o f You th Who are African-American Respondents also reported on the
percentage of youth referred to mentoring from a juvenile justice setting that were
black. Answers were coded on a scale ranging from 10 0–15 % to 60 76 – 100 %.
Community Type As noted above, respondents reported on the community setting of
their mentoring program. The majority of all respondents’ programs ope rat e d in
an urban setting. Thus, all responses were dichotomized so that 00 non-urban
and 10 urban.
Mentoring Facility Respondents were asked the following: “Where does mentoring
typically take place?” This variable was dichotomized so that 00 somewhere other
than a designated “mentoring” facility and 10 in a designated “mentoring” facility.
Analysis Plan
Quantitative analysis involved two steps. The first step to the analysis utilized
various descriptive statistical techniques in order to provide an overview of the
sample and of certain features of mentoring that are frequently encountered. The
second step to the analysis utilized two inferential statistical techniques to unpack
the correlation between certain programmatic elements of ment oring programs
and mentee success rates.
As can be seen in Fig. 1
, t he majority o f respondents (~60 %) represe nted
mentoring programs. This result was expected due to the sampling strategy utilized
(i.e., contact list from MENTOR was one of the primary sampling frames) and
is reassuring that the sampling strategy netted information from eligible and
appropriate respondents. The remainder of the respondents (~40 % ) represented
a juvenile justice setting (i.e., juvenile probation, juvenile detention, juvenile
corrections, delinquency court, youth court/teen court diversion program, and
dependency court). Because of the structure of the survey and the types of
questions asked to the different respondents, the current analysis was restricted
to respondents from mentoring programs (N ranged between 491 and 591 for the
multivariat e models). There were no differences in the representativeness of the
448 Am J Crim Just (2013) 38:439–456
sample when restricted to mentoring respondents (i.e., all 50 states and Washington
D.C. were represented and the community context of the respondents mirrored that of
the full sample).
Findings
Presented in Table 2 are descriptive statistics and a series of bivariate zero-order
correlation coefficients between the key variables used in the analysis. All correla-
tions statistically significant at the p<.05 (two-tailed tests) are highlighted with an
asterisk (*). Several findings are worthy of discussion. First, mentor-mentee meeting
frequency appeared to be positively associated with the likelihood that mentees will
meet any goals set for them (r0 .23). Similarly, programs that utilized longer meetings
between mentors and mentees tended to report more mentee succes s in meeting their
goals (r0 .18). Third, mentees wer e more likely to meet their goals when they were
served by mentoring programs that were more likely to utilize training programs for
their mentors (r0 .27).
A series of Poisson regression models were analyzed in Table 3. Model 1 explored
the relationship between mentor-mentee meeting frequency (coded 10 “1–2 times a
month”,20 “3–4 times a month”,30 “more than 4 times a month”) and the rate at
630
328
0
100
200
300
400
500
600
700
Juvenile Justice SettingMentoring Program
Number of Respondents
Program Type
Fig. 1 R espondents who com-
pleted the survey, by program
type
Table 2 Zero-order correlations—mentoring programs Only
Mean
(S.D.)
Min.-
Max.
Mentees meeting
goals
Meeting
frequency
Meeting
length
Mentor
training
Mentees meeting goals 2.53 (1.13) 0–4 –
Meeting frequency 1.93 (.66) 1–3 .23* –
Meeting length 1.36 (.86) 0–3 .18* .12* –
Mentor training 2.25 (.99) 0–3 .27* .12* .12* –
*p<.05, two-tailed tests
Am J Crim Just (2013) 38:439–456 449
which mentees meet/exceed the goals set for them. As can be seen, meeting frequen-
cy was positively related to the rate of mentees expected to achieve their goals . This
relationship is plotted graphically in Fig. 2. A sim ilar relationship was uncovered in
model 2 where the average mentor-mentee meeting length and the rate at which
mentees meet/exceed their goals was explored. The bivariate relationship between
these two variables is plotted in Fig. 3.
Model 3 explored the relationship between mentor training (coded 00 “never”,
10 “rarely”,20 “sometimes”,30 “always”) and the rate at which mentees meet/
exceed their goals. Once again, a positive relationship was found and the predicted
Table 3 Poisson regression of percentage level of mentees meeting/exceeding goals on key independent
variables and covariates—mentoring programs only
Model 1 Model 2 Model 3 Model 4
b SE b SE b SE b SE
Key independent variables
Meeting frequency .15* .04 .10* .04
Meeting length .09* .03 .07* .03
Mentor training .15* .03 .13* .04
Covariates
Background checks .002 .06
Individualized mentoring (no0 0, yes0 1) .09 .08
Years in operation .01
a
.004
Percent of youth are male .004 .02
Percent of youth are African-American −.02 .02
Community type (Other0 0, Urban0 1) −.07 .06
Mentoring facility (no0 0, yes0 1) −.03 .08
*p<.05,
a
p<.10, two-tailed tests
Dependent variable was coded 00 fewer than 10 %,1011 %–25 %,20 26 % –50 %,30 51 %–75 %,40
76 %–100 %
2.18
0
0.5
1
1.5
2
2.5
3
3.5
1-2 Times a Month
Predicted Level of Mentees Meeting/Exceeding Goals
2.55
3-4 Times a Month More than 4 a Month
Mentor-Mentee Meeting Frequency
2.97
Fig. 2 Predicted level of ment-
ees meeting/exceeding goals as a
function of mentor-mentee
meeting frequency—mentoring
programs only. Note: Dependent
variable was coded 00 fewer
than 10 %,1011 %–25 %,
20 26 %–50 %,30 51 %–75 %,
40 76 %–100 %
450 Am J Crim Just (2013) 38:439–456
rate at which mentees achieve their goals as a function of mentor training frequency
was plotted in Fig. 4.
The final Poisson regression model is presented in model 4. This model included
all of the key independent variables analyzed in models 1 through 3 along with the
covariates. As shown, each of the key independent variables maintained their sign
and level of statistical significance when entered into the model simultaneously.
These findings indicate that each of these variables is a robust predictor of the rate
at which mentees will meet /achieve their goals. Presented in Fig. 5 are the predicted
rates at which mentees achieve their goals as a function of the three key independent
variables as set at graded levels. The first bar (labeled “Minimum”) displays the
predicted rate of mentees achieving their goals when the key independent variables
are set at their minimum values (i.e., when meeting frequency is low [between 1 and 2
times a month], when meeting lengths are short [less than 1 h], and mentor training is
never used). The second bar (labeled “Maximum”) displays the predicted rate of
mentees achieving their goals when the key independent variables are set at
their m aximum values (i.e., when meet ing frequency is high [more than 4 times
2.24
2.44
2.67
2.92
0
0.5
1
1.5
2
2.5
3
3.5
Less Than One Hour One Hour to Less Than
Two Hours
Two Hours to Less Than
Three Hours
Three Hours or More
Predicted Level of Mentees Meeting/Exceeding Goals
T
yp
ical Mentor-Mentee Meetin
g
Len
g
th
Fig. 3 Predicted level of mentees
meeting/exceeding goals as a
function of length of meeting
between mentor and mentee—
mentoring programs only. Note:
Dependent variable was coded
00 fewer than 10 %,1011 %–
25 %,20 26 %–50 %,30 51 %–
75 %,40 76 %–100 %
1.75
2.04
2.38
2.77
0
0.5
1
1.5
2
2.5
3
Never Rarely Sometimes Always
Predicted Level of Mentees Meeting/Exceeding Goals
Fre
q
uenc
y
of Mentor Trainin
g
Fig. 4 Predicted level of ment-
ees meeting/exceeding goals as a
function of the frequency of
mentor training—mentoring
programs only. Note: Dependent
variable was coded 00 fewer
than 10 %,1011 %–25 %,
20 26 %–50 %,30 51 %–75 %,
40 76 %–100 %
Am J Crim Just (2013) 38:439–456 451
a month], when meeting lengths are long [3 h o r more], and mentor training is
always used). A large difference in the predicted rates emerged—around a 2
point difference which could translate into more than a 700 fold increase in
goal achievement likelihood).
1
Discussion
Cavell, DuBois, Karcher, Keller, and Rhodes, (2009; p.2) observed that, while
extrapolations from the mentoring database suggest that programs offer substantial
fiscal and social returns on investment, empirical confirmation of impact is needed.
This study affirmed three primary findings regarding mentoring program elements
predictive of goal attainment for at risk and system involved youth. First, the
frequency of meeting between matched pairs of mentees and mentors was positively
correlated with positive outcomes. Second, the duration of ment oring relationships
was also positively correlated with youth success. Last, the formal training (and
continued training) of mentors was observed a s indicative of program performance
and goal realization.
These observations, particularly the first two, are neither novel nor altogether
previously unstudied. Ostensibly, training bolsters both the frequency of inter-
action within and the durat ion of m entori ng relati onships. Consideratio n of the
distribution of mentoring programs throughout and adjacent to juvenile justice
system settings, however, reveals both opportunities and challenges. Additional
1.52
3.44
0
0.5
1
1.5
2
2.5
3
3.5
4
MaximumMinimum
Predicted Level of Mentees Meeting/Exceeding Goals
Ke
y
Inde
p
endent Variable Settin
g
Fig. 5 Two predictions of level
of mentees meeting/exceeding
goals based on different condi-
tions—mentoring programs only.
Note: Minimum0 key indepen-
dent variables were set to their
“minimum” category;
Maximum0 key independent
variables were set to their
“maximum” category; all other
covariates were set to their
means; Dependent variable was
coded 00 fewer than 10 %,
1011 %–25 %,20 26 %–50 %,
30 51 %–75 %,40 76 %–100 %
1
It is worth noting that a similar pattern of findings emerged when the sample was restricted to mentoring
programs that serve juvenile-justice-involved youth. Specifically, mentoring respondents were asked
whether at least 10 % of the youth served by their program are referred from the juvenile justice system.
When the sample was restricted to respondents who said “yes” to this question, the pattern of findings for
models 1, 2, and 3 were substantively identical. Findings from model 4 changed slightly: the effect of
meeting frequency was .13 (p<.05), the effect of meeting length was .07 (p>.05), and the effect of mentor
training was .16 (p<.05).
452 Am J Crim Just (2013) 38:439–456
formal training, both initial and on-going, shou l d pro v e fr ui tf u l in te rm s of
enhancing the quality of mentoring services delivery and program expansion.
Through training, programs have the opportunity to embrace and increase the
use of evidence b ased practices and perhaps better retain quality mentors.
The associated challenge, regrettably, concerns services availability relative to
need. Remember that a rough majority (60 %) of juvenile justice setting
respondents reported using mentoring services, with only 40 % of mentoring
programs serving justice system involved youth. Juvenile justice settings
aligned with national youth service organizations are more likely to develop,
afford, and utilize training than programs relying on experiential and organic
modality development. Accordingly, it is predictable that established organiza-
tions and agencies are more apt to bolster professionalism t hrough researcher-
practitioner partnerships and evidence based practice implementation that, ulti-
mately, should manifest in improved youth outcomes. If accurate, this trend
may dichotomize the mentoring communit y.
As the infusion of ex ploratory and evaluative knowledge enhances the
quality of programs over time, demonstrated success will facilitate additional
resource acquisition, program sustainability, and expansion. The total number of
youth served i s almost certain to increase, but not categorically across contexts
nor, much more importantly, relative to need. Targeting youth most likely to
benefit from mentoring is an established and well-known best p ractice axiom
that may be more problematic and pronounced in delinquency reduction as
opposed to youth development environments. Delinquency reduction program-
ming, both by national level service and public agency providers, have long
engaged cherry-picking (Barnes, Miller, Miller, & Gibson, 2008; Barnes, Miller,
& Miller, 2009), often failing to reach chronic delinquents that are likely to become
further involved in the justice juggernaut. In that alignment with a national organi-
zation is almost a scope condition for mentoring program delivery for the majority of
the mentoring community, especially for services delivered within the system, need-
ful youth in rural and unpopulated settings stand to lose out to economies of scale
where it is not financially feasible for organizations to operate.
At-risk youth in remote settings may not have the opportunity to be matched with
an avail able, much less a formally trained, mentor as local mentoring initiatives
typically have minimal infrastructures and resources. Where resources are scare,
there will be a natural inclination to do more with less, which may mean increased
reliance on group rather than the individualized treatment that characterizes most
mentoring strategies (Day, 2006). Accordingly, it will be vital to monitor and evaluate
the impact of the mentoring movement toward realizing optimal reach as well as
program development and expansion.
References
Baker, D. B., & McGuire, C. P. (2005). Mentoring in historical perspective. In D. L. DuBois & M. J.
Karcher (Eds.), Handbook of youth mentoring (pp. 14–29).
Barnes, J. C., Miller, J. M., Miller, H. V., & Gibson, C. L. (2008). Juvenile drug court program admission,
demeanor and cherry-picking: a research note. American Journal of Criminal Justice, 33(2).
Am J Crim Just (2013) 38:439–456 453
Barnes, J. C., Miller, H. V., & Miller, J. M. (2009). Identifying leading characteristics associated with
juvenile drug court admission and success: A research note. Youth Violence and Juvenile Justice, 7(4),
350–360.
Bazron, B. J., Brock, L., Read, N., & Segal , A. (2006). The mentoring tool-kit: Resources for
developing programs for incarcerated youth (Unabridged version). Washington, D.C.: National
Evaluation and Technical Assistance Center for the Edu cation of Children and Youth who are
Neglected, Delinquent and At-Risk, U.S. Department of Education.
Blechman, E. A., & Bopp, J. M. (2005). Juvenile offenders. In D. L. DuBois & M. J. Karcher (Eds.),
Handbook of youth mentoring (pp. 454–466).
Bogat, G. A., & Liang, B. (2005). Gender in mentoring relationships. In D. L. DuBois & M. J. Karcher
(Eds.), Handbook of youth mentoring (pp. 205–217).
Bouffard, J. A., & Bergseth, K. J. (2008). The impact of reentry services on juvenile offenders’ recidivism.
Youth Violence and Juvenile Justice, 6(3), 295–318.
Cavell, T. A. & Smith, A. (2005). Mentoring children. In D. L. DuBois & M. J. Karcher (Eds.), Handbook
of youth mentoring (pp. 160–176).
Cavell T., DuBois D., Karcher M., Keller T., & Rhodes J. (2009). Strengthening mentoring opportunities
for at-risk youth [policy brief]. http://educationnorthwest.org/webfm_send/237. February.
Coyne, S. M., Duffy, J. L., & Wandersman, A. (2005). Mentoring for results: Accountability at the
individual, program, community, and policy levels. In D. L. DuBois & M. J. Karcher (Eds.), Handbook
of youth mentoring (pp. 546–560).
Dallos, R., & Comley-Ross, P. (2 005). Young people’s experience of mentoring: building trust and
attachments. Clinical Child Psychology and Psychiatry, 10(3), 369–383.
Dappen, L., & Isernhagen, J. C. (2006). Urban and nonurban schools: examination of a statewide student
mentoring program. Urban Education, 41(2), 151–168.
Day, A. (2006). The power of social support: mentoring and resilience. Reclaiming Children and Youth, 14
(4), 196–198.
Dondero, G. M. (1997). Mentors: beacons of hope. Adolescence, 32(128), 881–886.
Dubberley, S. (2006). Mentoring for young people leaving care. Probation Journal, 53(2), 172–173.
DuBois, D. L., Holloway, B. E., Valentine, J. C., & Cooper, H. (2002). Effectiveness of mentoring
programs: a meta-analytical re view. American Journal of Community Psychology, 30,157–197.
doi:10.1023/A:1014628810714.
DuBois, D. L., & Karcher, M. J. (2005). Youth mentoring: Theory, research and practice. In D. L. DuBois
& M. J. Karcher (Eds.), Handbook of youth mentoring (pp. 2–11).
DuBois, D. L., Portillo, N., Rhodes, J. E., Silverthorn, N., & Valentine, J. C. (2011). How effective are
mentoring programs for youth? A systematic assessment of the evidence. Psychological Science in the
Public Interest, 12(2), 57–91.
DuBois, D. L., & Silverthorn, N. (2005). Research methodology. In D. L. DuBois & M. J. Karcher (Eds.),
Handbook of youth mentoring (pp. 44–64).
Enriquez, A. E. Jr. (2011). Juvenile system mentoring programs in Bexar county: Referrals and recidivism.
(Unpublished master’s thesis). University of Texas at San Antonio.
Grossman, J. B., Chan, C. S., Schwartz, S. E., & Rhodes, J. E. (2011). The test of time in school-based
mentoring: the role of relationship duration and re-matching on academic outcomes. American Journal
of Community Psychology. doi: 10.1007/s10464-011-9435-0.
Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey
methodology (2nd ed.). Hoboken: Wiley.
Jones-Brown, D. D., & Henriquez, Z. W. (1997). Promises and pitfalls of mentoring as a juvenile justice
strategy. Social Justice, 24(4), 212–233.
Keating, L. M., Tomishima, M. A., Foster, S., & Alessandri, M. (2002). The effects of a mentoring program
on at-risk youth. Adolescence, 37(148), 717–734.
Keller, T. E. (2005). The stages and development of mentoring relationships. In D. L. DuBois & M. J.
Karcher (Eds.), Handbook of youth mentoring (pp. 82–99).
Laakso, J., & Nygaard, J. (2007). Children of incarcerated parents project: How a mentoring program can
make a difference. Unpublished final report prepared for Washington State Department of Corrections
and Big Brothers Big Sisters of King/Pierce County.
Langhout, R. D., Rhodes, J. E., & Osborne, L. E. (2004). An exploratory study of youth mentoring in an
urban context: adolescents’ perceptions of relationship styles. Journal of Youth and Adolescence, 33
(4), 293–306.
454 Am J Crim Just (2013) 38:439–456
LoSciuto, L., Rajala, A. K., Townsend, T. N., & Taylor, A. S. (1996). An outcome evaluation of across ages: an
intergenerational approach to drug prevention. Journal of Adolescent Research, 11(1), 116–129.
McCluskey, K. M., Noller, R. B., Lamoureux, K., & McCluskey, A. L. A. (2004). Unlocking hidden
potential through mentoring. Reclaiming Children and Youth, 13(2), 85–93.
MENTOR/National Mentoring Partnership. (2006). Mentoring in America 2005: A snapshot of the
current state of mentoring. Retrieved February 20, 2012, from http://www.mentoring.org/downloads/
mentoring_523.pdf
Nakkula, M. J., & Harris, J. T. (2005). Assessment of mentoring relationships. In D. L. DuBois & M. J.
Karcher (Eds.), Handbook of youth mentoring (pp. 100–117).
Newburn, T., & Shiner, M. (2006). Young people, mentoring and social inclusion. Youth Justice, 6(1), 23–41.
Office of Justice Programs. (2011). Fact sheet: Mentoring. Washington, D.C.: U.S. Department of Justice.
Retrieved February 20, 2012, from http://www.ojp.usdoj.gov/newsroom/factsheets/ojpfs_
mentoring.html
Portwood, S. G., & Ayers, P. M. (2005). Schools. In D. L. DuBois & M. J. Karcher (Eds.), Handbook of
youth mentoring (pp. 336–347).
Pryce, J. M., & Keller, T. E. (2011). Interpersonal tone within school-based youth mentoring relationships.
Youth & Society. doi:10.1177/0044118X11409068. Advance online publication.
Rhodes, J. E. (2005). A model of youth mentoring. In D. L. DuBois & M. J. Karcher (Eds.), Handbook of
youth mentoring (pp. 30–43).
Rhodes, J. E., & DuBois, D. L. (2008). Mentoring relationships and programs for youth. Current Directions
in Psychological Science, 17(4), 254–258.
Sipe, C. L. (2005). Toward a typology of mentoring. In D. L. DuBois & M. J. Karcher (Eds.), Handbook of
youth mentoring (pp. 65–80).
Smith, C. A., & Stormont, M. A. (2011). Building an effective school-based mentoring program. Inter-
vention in School and Clinic, 47(1), 14–21.
Spencer, R. (2006). Understanding the mentoring process between adolescents and adults. Youth & Society,
37(3), 287–315.
Spencer, R. (2007). “It ’s not what I expected”: a qualitative study of youth mentoring relationship failures.
Journal of Adolescent Research, 22(4), 331–354.
Thomas R. E., Lorenzetti, D., & Spragins, W. (2011). Mentoring adolescents to prevent drug and
alcohol use. Cochrane Database of Systematic Reviews 2011, 11, CD007381. doi:10.1002/
14651858.CD007381.pub2
Thompson, N. R., & Zand, D. H. (2010). Mentees’ Perceptions of their interpersonal relationships: the role
of the mentor-youth bond. Youth & Society, 41(3), 434–445.
Tolan, P., Henry, D., Schoeny, M., & B ass, A., (2008). Mentoring inte rventions to affect
juvenile delinquency and associated problems.Campbell Systematic Reviews, 16. doi:
10.4073 /
csr.2008.16.
Wood, S., & Mayo-Wilson, E. (2012). School-based mentoring for adolescents: a systematic review and
meta-analysis. Research on Social Work Practice. doi:10.1177/1049731511430836. Advance online
publication.
J. Mitchell Miller is a Professor in the Department of Criminal Justice at the University of Texas at San
Antonio where he teaches and researches in the areas of justice system program evaluation, delinquency
reduction, and the drugs-crime nexus. Dr. Mil ler is the author of several journal articles and books,
including Criminological Theory: A Brief Introduction, 3rd ed. (Pearson, 2011) and Crime & Criminals
(Oxford, 2009). Currently, he is evaluating the role of social support in the mentoring of chronic youth for
the Office of Juvenile Justice & Delinquency Prevention and editing The Handbook of Qualitative
Criminology (Routledge, 2014) with Heith Copes.
J. C. Barnes is an assistant professor in the Department of Criminology at The University of Texas at
Dallas. His research seeks to understand how genetic and environmental factor s combine to im pact
criminological phenomena. Recent works have attempted to reconcile behavioral genetic findings with
theoretical developments in criminology. His work can be found in journals such as Aggressive Behavior,
Criminology, Death Studies, Intelligence, Journal of Marriage and Family, Justice Quarterly, and Phys-
iology & Behavior.
Am J Crim Just (2013) 38:439–456 455
Holly Ventura Miller is an Associate Professor in the Department of Criminal Justice at the University of
Texas at San Antonio and a National Institute of Justice W.E.B. DuBois Fellow. Her research interests
include immigration and crime, program evaluation, and criminological theory testing. Her recent work has
appeared in Justice Quarterly, Crime and Delinquency, and Journal of Criminal Justice.
Layla McKinnon is employed by the Texas Department of Public Safety in the Law Enforcement
Education, Training & Research division. She has previous work experience in law enforcement support
and as a graduate research assistant at the University of Texas, San Antonio. Her research interests include
criminal justice policy evaluation, policing, police training and environmental criminology. She earned a
BS in criminology and criminal justice from Portland State University in 2009 and is currently pursuing a
MS in justice policy at the University of Texas, San Antonio.
456 Am J Crim Just (2013) 38:439–456
