The author(s) shown below used Federal funding provided by the U.S. 

Department of Justice to prepare the following resource: 

Document Title: Final Technical Report: An Evaluation of 

Advocacy-based Mentoring as a Treatment 

Intervention for Chronic Delinquency 

Author(s): Michael J. Karcher, Ed.D., Ph.D., David A. 

Johnson, Ph.D. 

Document Number: 250454 

Date Received: December 2016 

Award Number: 2011-JU-FX-0001 

This resource has not been published by the U.S. Department of 

Justice. This resource is being made publically available through the 

Office of Justice Programs’ National Criminal Justice Reference 

Service. 

Opinions or points of view expressed are those of the author(s) and 

do not necessarily reflect the official position or policies of the U.S. 

Department of Justice. 
Final Technical Report : 2011-JU-FX-0001 p. 1

Final Technical Report 

AN EVALUATION OF ADVOCACY-BASED MENTORING AS A TREATMENT 

INTERVENTION FOR CHRONIC DELINQUENCY 

The U.S. Department of Justice, Office of Justice Programs 

Office of Juvenile Justice and Delinquency Prevention
 

OJJDP FY 2011 Research on Best Practices for Mentoring 

Michael J. Karcher, Ed.D., Ph.D. 

Professor and Principal Investigator 

Department of Educational Psychology 

University of Texas, San Antonio 

210-458-2302 

with assistance from 

David A. Johnson, Ph.D., Assistant Professor 

Communication Disorders and Counseling, School, and Educational Psychology 

Bayh College of Education 

Indiana State University 

Text for the first study, section one, was initially drafted by Dr. Mitch Miller.
 

Disclaimer: This project was supported by Grant No. (2011-JU-FX-0001) awarded by 

the Office of Juvenile Justice and Delinquency Prevention, Office of Justice Programs, 

U.S. Department of Justice. Points of view or opinions in this document are those of the 

authors and do not necessarily represent the official position or polices of the U.S. 

Department of Justice. 

Acknowledgements: Drs. Karcher and Johnson would like to acknowledge the great 

assistance provided by Martin J. D'Urso, Esq, Alan Kassirer, and Dr. Michael Marks at 

Youth Advocate Programs, Inc. and the Directors, Staff and Advocates at the Camden, 

NJ, Mobile, AL, Las Vegas, NV, Lebanon, PA, and Toledo, OH Youth Advocate 

Programs for their assistance in working with youth, parents and referring authorities to 

secure access to necessary data. Finally, we would like to thank Jennifer Tyson, Social 

Science Analyst, Innovation and Research Division at the Office of Juvenile Justice and 

Delinquency Prevention for her support and direction throughout the project. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 2

Study Overview and Conclusions 

The primary goal of the proposed research project was to provide estimates of the 

effectiveness of youth advocacy in general, and more specifically as delivered through 

the Youth Advocate Programs, Inc. (YAP). YAP is a national nonprofit organization 

active in twenty states that provides a treatment intervention for reducing serious and 

chronic delinquency for court-referred youth. This study examined processes and 

outcomes in the YAP program in four cities to inform juvenile justice policy and practice 

regarding the possible benefits of advocacy-based interventions for this population. 

This grant focused on evaluating four Youth Advocate Programs, Inc. (YAP) in 

separate regions of the country in order to increase variability in model delivery and 

youth participants. The research design was based on information collected about YAP 

services from prior research and focused both on identifying key mentoring and advocacy 

processes that may interrupt chronic delinquency and measuring proximal and distal 

outcomes related to crime and prosocial behavior of participation in youth mentoring 

with paid mentors who prioritized advocacy as one element of mentoring. 

Project Goals to achieve these objectives were two-fold: (1) Estimate the degree 

to which intended program objectives were realized (i.e., program impact and 

effectiveness) through attempts to quantify the association between participation in the 

YAP program and changes in youth delinquency and on related outcomes using a 

rigorous quasi-experimental research; and (2) identify ways in which advocacy and 

specific types of mentoring interactions contribute to youth outcomes through program 

participation. The overall goal of this study was, therefore, to better understand the 

viability of advocacy as an intervention for youth at high risk for future criminal activity, 

to identify critical practices that may be relevant to YAP and other programs using 

individualized treatment approaches to reduce delinquency and recidivism through 

advocacy efforts, and to learn more about which interpersonal interactions and participant 

characteristics are most influential in successful advocacy efforts. 

Two adaptations to the originally proposed methods and design of the project 

were necessitated by factors and events beyond the control of the report authors. A 

formative evaluation of each program’s fidelity of implementation was omitted from the 

final report because the data on which initial findings were to be based were not available 

once unforeseeable changes in project staffing occurred. Second, initially the quasiexperimental method for detecting effects associated with program participation was 

propensity score analyses. Data collected by a consultant on the project, however, were 

unsuitable for propensity score analyses. Therefore, this report does not include results 

related to implementation fidelity or propensity score analyses of causal effects. In short, 

the data was insufficient to cross check findings or reach reliable conclusions. However, 

another quasi-experimental design was used to estimate program causal effects, thereby 

allowing Study 1 to address the question about program impact. Study 2 uses program 

activities and participant characteristics to try to explain the changes reported in Study 1. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

																																																							

 

 

Final Technical Report: 2011-JU-FX-0001 p. 3

Study 1: Outcome Evaluation 

For the quasi-experimental outcome analyses, the recurrent institutional cycle 

(RIC) quasi-experimental design was employed (Campbell & Stanley, 1963)1

 instead of 

the planned propensity score analyses because we found we lacked sufficient data to 

properly match youth on key variables, those most characteristic of YAP participants. As 

one example, the state-level data collected to match youth in the court system with youth 

participating in the YAP program in Las Vegas did not include information on youth’s 

involvement in sex trafficking. Yet this youth characteristic is disproportionately higher 

among court referrals to YAP in Las Vegas. With very limited demographic background 

information such as this reliable matching could not be achieved, because it would mask 

problematically large unobserved differences between YAP’s court-referred youth and 

the comparison group to whom their outcomes would be compared. Lacking confidence 

that any attributions of program-related outcomes could be generated from this data, an 

alternative design, one Campbell and Stanley identified as a highly rigorous quasiexperimental design, was selected for addressing the most problematic internal validity 

threat in quasi-experimental causal effect analyses—selection. 

Statistical analysis of program effects on recidivism, deinstitutionalization, and 

pro-social activity (e.g., school and employment status) were estimated using the 

recurrent institutional cycle (RIC) design as described first by Campbell and Stanley 

(1963). The RIC design minimizes selection threats by comparing program graduates 

(i.e., the treatment group) to a counterfactual untreated group (i.e., comparison group) 

reflecting the pre-treatment data of the youth collected prior to their participation in YAP. 

This method can provide the closest approximation of the counterfactual state of what a 

YAP participant at a given age would look like had that person actually not participated 

in YAP. Campbell and Stanley argue that in some situations the counterfactual group in 

the RIC design is better than the counterfactual group derived from random assignment. 

What this design does not allow, however, is the analysis of differences between 

the treatment and the untreated comparison group on variables not collected prior to 

entering the program. This is because, where no pre-intervention assessments of 

phenomenon are available, the intervention group has no pre-data for this outcome 

variable. For example, recidivism was not a viable outcome variable because the data we 

received from the states did not include dates of adjudication needed to the estimate 

recidivism relative to a specific determination, such as a referral to YAP or other 

alternative program. 

However, there was considerable other data available assessing a variety of crime 

and recidivism-related phenomena to undertake the recurrent institutional cycle (RIC) 

analyses. Unique to the RIC design, we relied on the data regularly collected by YAP to 

1 Campbell, D. T. & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. 

Dallas, TX: Houghton Mifflin Company. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 4

assess the graduates’ involvement in crime subsequent to graduation, as this variable was 

collected prior to treatment for all youth, then at discharge, and finally at 12 months post 

treatment for program completers. 

Data collection for the 163 participating youth in the study ended June 15, 2014. 

Of these, 163 youth completed pre-surveys, 133 youth and Advocate pairs completed 2-

month surveys, and 103 youth and Advocate pairs completed 4-month surveys. To 

address the issue of attrition, we used multiple imputation to estimate missing data on 

participant outcomes at the 2-month and 4-month time point as well as for absent follow 

up data for those who could not be located a year post discharge. 

Conclusions about program related changes. Findings from Study 1 suggest 

that participation in the YAP program was related to several self-reported outcomes, 

including improvements in academic connectedness and declines in self-reported 

misconduct. Equally large and consistent were improvements on disposition (crime 

engagement), educational engagement, and pursuit of employment were present at 

discharge. Furthermore, several benefits of program participation found at discharge were 

maintained 12 months post-discharge. 

The RIC research design used for Study 1 requires a wide range of tests to rule 

out rival hypotheses, and wading through these analyses can be tedious. Ultimately it is 

the comparisons between treated and untreated youth that are of greatest interest to most 

readers. The results of these analyses revealed several differences favoring those who had 

participated in the YAP program in addition to those reported in the prior paragraph. 

Cross-cohort as well as pooled sample tests of within and between-group differences 

were computed. Subsequently, separate whole-sample hierarchical analyses (Mplus twolevel analysis) were conducted in order to pool all youth and make treated-untreated 

group comparisons, controlling for the nestedness of the data within persons and by 

accounting for missing data using FIML, of change related to program participation. 

The main effect results suggested that participation in the YAP program for the 

youth in this sample was associated with increases on several measures of prosocial 

behavior. Pooling the sample provided just barely sufficient statistical power to detect the 

expectably small effect sizes typical of mentoring (d = -.20), so a p-value of .10 was 

considered indicative of a non-chance finding and .20 as a trend, just as was done in the 

original impact study conducted on the Big Brothers Big Sisters community based 

mentoring program in the 1990s. Given these cut off scores, we found that the treated 

youth had significantly higher connectedness to teachers and a trend toward higher 

connectedness to school and a self-in-the-future. 

The most compelling evidence of change associated with participation in YAP 

was the reduction in misconduct (self-reported) after program completion. Whole sample 

tests were corroborated by smaller-sample comparisons between treated and untreated 

youth in the between cohort comparisons. For cross-cohort comparisons the typical effect 

size for mentoring (d = -.20) was used instead of statistical probabilities as benchmark for 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

																																																							

 

 

Final Technical Report: 2011-JU-FX-0001 p. 5

assessing the presence and absence of meaningful change. In these comparisons, four of 

the five cohorts showed small (d = -.20) to moderate (d = -.45) declines in misconduct. 

Further corroborating evidence of program-related change is that the average pre-post 

change from entry to discharge on misconduct (d = -.25) and was comparable in size to 

the average the between-groups/cross-cohort differences (d = .20). Furthermore, these 

effect sizes reflect changes associated with program participation that are comparable to 

or larger than the effects of most mentoring programs.2 

Relatively large, positive changes also were observed both at discharge and 12-

months post-discharge on criminality, educational engagement, and employment status. 

From entry to 12-months post-discharge, the severity of YAP youths’ disposition (status 

offense, misdemeanor, felony) was lower, their school participation (e.g., attendance) was 

higher, and they reported more effort to secure employment than untreated youth of the 

same age who had not yet begun YAP. 

Ib. Study 2: Program Practices and Advocate Characteristics 

Using structural equation modeling, data from three time points (entry, two and 

four months, which is the average time of discharge) were included in path analyses to 

test predictions about the role of specific Advocate and mentoring interactions derived 

from the TEAM framework (Karcher & Nakkula, 2010). These were primarily that play 

and serious problem-focused conversations would explain rates of misconduct reported at 

discharge. 

Structural models were utilized so that the different types of interactions engaged 

in between the start of the match through the second month and then between two and 

four months could serve as separate, unique predictors and moderators of outcomes, 

adjusting for the contribution to each made by other variables in the model, specifically 

the potentially confounding background characteristics of youth. Additional variables 

were subsequently included in these models to factor in the direct and indirect 

contributions made by both youth and Advocate characteristics in explaining changes in 

misconduct. 

The first part of Study 2 looked at changes that occurred among youth during their 

time in YAP on self-reported misconduct as misconduct, which was the variable on 

which the most compelling evidence of program-related changes was found in Study 1. 

Of course to make inferences regarding program practices from these analyses, the 

reports of misconduct by youth, both at entry and at discharge, must be sufficiently 

accurate, reliable and valid indicators of true misconduct. Fortunately, the corroboration 

of findings in Study 1 using the self-reported misconduct measure with findings using 

2 DuBois, D. L., Holloway, B. E., Valentine, J. C., & Cooper, H. (2002). Effectiveness of 

mentoring programs for youth: A meta-analytic review. American Journal of 

Community Psychology, 30, 157-197. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 6

other indicators of misconduct, combined with the stability of findings over time on the 

other indicators (between discharge and 12 month follow-up reports), provide convergent 

and predictive validity evidence to support the utility of the self-reported misconduct 

scale as the primary outcome measure in Study 2. 

The first analysis simply assessed whether or not change in self-reported 

misconduct was evident across entry, the midpoint (two months), and discharge. This 

structural model provided evidence of change across each time point that corroborated 

the findings from Study 1. Following these analyses we then conducted a series of 

structural models to test TEAM framework-based hypotheses about the way in which 

activities might explain these changes. 

Structural path model analyses focused primarily on the relative contributions of 

playful/recreational activities, casual conversations, and problem-oriented discussions 

engaged in by Advocates with their mentees during the first and second half of their time 

together. The baseline model focused on rates of activities during the first and second 

half of the relationship separately. This allowed us to estimate how differences in what 

happened in the first and second half of the relationship may have uniquely contributed to 

variability in rates of misconduct among participating youth at discharge beyond what 

would have been predicted from youths’ starting levels of misconduct. Next, youth 

demographic characteristics were factored into these models. 

We then incorporated into these baseline path models two Advocate 

characteristics to see if the role of activities on program outcomes was influenced by 

Advocates’ background characteristics and to see if these specific Advocate 

characteristics contributed directly to program-related outcomes. These path models also 

included control variables to account for variability associated with youths’ age and sex. 

Using two final path models we compared the main and indirect effects of mentor 

education and prior teaching experience on program-related changes in youths’ 

misconduct. 

Two sets of path models were conducted. A first was run using observed (mean) 

score assessments of activity frequency. We felt these would make the translation or use 

of the findings by practitioners who may subsequently choose to use the same activity 

logs in their work easier. A second set of analyses was conducted using confirmatory 

factor analysis derived activity frequency scores. Using factor scores helped account for 

error within observed score assessments and allowed for a clearer view of what specific 

interactions each activity factor score reflected most. These models used problemfocused discussion frequencies while the observed score models used frequencies of 

casual conversations as the complementary activity to play in our hypothesized model 

tests. This was done for both theoretical and statistical reasons explained in the results 

section. 

Overview of Findings. The path model analyses presented in Study 2, 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 7

particularly the final observed score model that incorporated relevant youth 

characteristics (considered potential confounds) revealed that the benefits of program 

participation varied as a consequence of the frequency of activities at specific time points 

in the relationship and as a function of at least these two Advocate characteristics (prior 

teaching experience and educational attainment), both of which contributed to program 

outcomes directly and indirectly. 

First, the models revealed that higher rates of play and lower rates of both casual 

conversations and problem-focused discussions later in the match all predicted less 

misconduct at discharge. However, high levels of play at the start of the relationship was 

associated with more misconduct at discharge. These findings suggest the timing of 

activities is as important as the type of activities the matches engage in. 

Second, there were direct, positive effects of being matched with Advocates who 

had been teachers and with more educated Advocates. Advocates with prior teaching 

spent less time learning about the youth through casual conversation and engaged in less 

problem-focused discussion later in their relationship. This appears to be one way that 

Advocates with teaching experience indirectly affected lower rates of misconduct at 

discharge. 

Conversely, despite the positive, direct effect of having a more educated 

Advocate, there also was an indirect, negative effect of being matched with Advocates 

with more education. The negative effect on youth matched with more educated 

Advocates appears to have resulted from their lower frequency of play later in their 

relationship. While it is not clear what accounts for the direct, positive effects of these 

two Advocate characteristics, the findings suggest that hiring ex-teachers and more 

educated adults to be Advocates may be a useful staffing strategy. 

Role of activities and Advocate characteristics. Contrary to our expectations 

and first hypothesis, activities engaged in initially were not related to the level of 

misconduct the youth reported at entry. Youths’ self-reported misconduct (at entry) did 

not seem to influence what type of activity occurred first in the match. Rather, initial 

misconduct rates were better predictor of activities in the second half of the relationship. 

Thus it does not seem that Advocates with youth reporting more misconduct at entry 

chose at the outset to engage in more problem-focused (less playful) activities with their 

mentees. 

Problem focused interactions. The results of factor-based assessments of activities 

revealed the potentially damaging consequences of Advocates’ focusing more on their 

youth’s problems later in the relationship. Such Advocate behaviors include directing the 

youth’s attention to what the Advocate thinks the youth is doing wrong or needs to do 

differently to be successful in the future. This approach is consistent with Morrow and 

Styles’ (1995) prescriptive mentoring style and appears to have contributed to higher 

rates of misconduct reported at discharge. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 8

Problem-discussion frequency in the first half of the match was unrelated to 

misconduct at discharge, but higher rates of problem-focused discussions later in the 

match predicted higher misconduct at discharge. This suggests that it may be unwise for a 

mentor to impose a problem-focus discussion focus after getting to know the youth. 

Delaying problem-focused discussion until after the mentor has gotten to know the youth 

may affirm for the youth that he or she is a problem to be dealt with (as confirmed by the 

mentor’s focus) rather than someone adults want to spend time playing with, learning 

more about (talking with), or helping somehow through engagement in the youth’s 

community (doing with), which are the other activity types in the TEAM framework. 

Therefore, attending to when a problem-focused approach is taken in a match may 

be very helpful in maximizing the benefits of mentoring this type of youth. These 

findings do not support the use of what Morrow and Styles’ called the developmental 

style. It does not appear best, when mentoring court-referred youth, to emphasize 

cultivating a friendship first and allow problems to arise naturally over time. The findings 

regarding the role of play early in the match concur with this view. 

Playful interactions. The models using both observed scores and factor scores of 

the frequency of play revealed that matches with a higher frequency of play in the second 

part of their relationship tended to include the youth reporting the lowest rates of 

misconduct at discharge, holding constant the influence of youth and Advocate 

characteristics. This finding held both for observed score analyses, in which all four play 

indicators made equal contributions to the mean score for play, and the factor score 

analyses, in which the play assessment was primarily an indicator of the frequency of 

time spent playing sports. 

Playing early in the match did not appear to be a successful approach. Although 

the same findings was not observed in the path model using factor scores for play, the 

path models using observed (mean) scores for play frequency found that higher rates of 

play at the start of the match were associated with higher misconduct at discharge. This 

finding comports with the interpretation above that focusing on play early and problems 

later may be less effective than dealing with the problems facing the youth from the start 

and allowing a friendship to develop through playful interactions later. 

An alternative explanation could be that it was youth characteristics (age, sex, or 

starting misconduct) that explain why early play contributes to higher rates of misconduct 

over time, except that youth characteristics were only minimally useful in explaining 

what types of activities matches engaged in during the match. Problem-focused 

interactions occurred more frequently at both time points among girls than boys, and play 

occurred less for girls than boys in the first half of the relationship. But neither age or sex 

of the youth were directly related to rates of misconduct at discharge. 

Youth characteristics were, however, predictors of with whom they were 

matched. Girls’ Advocates were more educated than boys’ Advocates, and boys’ 

Advocates were more likely to have had teaching experience than girls’ Advocates. Older 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

Final Technical Report: 2011-JU-FX-0001 p. 9

youth tended to have both more educated Advocates and Advocates with teaching 

experience. Through these associations, the youths’ age and sex had indirect effects on 

outcomes through influencing with whom they were matched. 

Advocate contributions. The level of Advocates’ education and prior teaching 

experience were two characteristics that partly explained what happened in the match 

over time. We chose these two characteristics because they linked directly to the TEAM 

framework guiding our analyses of playing, casual conversations, and problem-oriented 

discussions. The TEAM Framework, described more fully in the full write-up of Study 2, 

presents the common adult-youth roles of counselor, teacher, supervisor, “vice principal” 

and coach. It proposes what the youth mentoring literature suggests mentoring happens 

when these conventional roles shift from standard, hierarchical relationships to more 

reciprocal relationships that balance structure and relationship development. Those 

propositions were used to inform the hypotheses that were tested in Study 2. 

These Advocate characteristics also represent dissimilarity between the youth and 

the Advocates, which, according to the YAP logic model, should lead to less, not more 

improvement from program participation. The YAP logic model proposes that youthAdvocate similarity is a key factor in what makes YAP participation most effective. We 

assumed these two Advocate characteristics—prior experience in the common adultyouth role of teacher and the Advocate’s education level—would allow inferences about 

the role or consequences of youth-perceived dissimilarity between their Advocates and 

themselves, assuming that the more educated the Advocates were the more dissimilar 

their youth would perceive them. 

Using factor score assessments of activity frequency, we found that those 

Advocates who had prior teaching experience were less likely to engage in more 

problem-focused interactions in the second half of the relationship, thus having an 

indirect positive effect on outcomes through their tendency to engage in fewer 

prescriptive, problem-focused conversations later in the match. Having an Advocate with 

teaching experience also had a direct, positive effect on youth outcomes. 

The second Advocate characteristic that contributed to the benefits of youths’ 

participation in YAP was the Advocate’s level of education. YAP views similarity 

between youth and their Advocates as a primary way in which YAP Advocates are able 

to reach youth and help them. Assuming most of the youth in YAP are from families with 

lower than average educational attainment, which may be incorrect, Advocates with 

college degrees may be expected to be less effective by virtue of their educational 

dissimilarity from their mentees and their mentees’ family members. 

We found a direct positive effect of Advocates’ levels of educational attainment 

on youth outcomes. Those youth mentored by more educated Advocates engaged in less 

misconduct at the conclusion of their time in YAP. Although this association, after 

accounting for the youths’ age and sex. 

There was, however, a potentially negative, indirect effect of Advocates’ 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

Final Technical Report: 2011-JU-FX-0001 p. 10

educational attainment in that matches with more educated Advocates were less likely to 

spend time playing later in the relationship. Because the amount of time Advocates 

played with their youth later in the relationship was a strong predictor of lower reports of 

misconduct at discharge, this may be one way Advocates’ education was not helpful. 

The sex of the youth played an important moderating role. Lower levels of play 

were reported in matches with more educated Advocates, and older girls were most likely 

to be matched with the more educated Advocates, so it could be that older girls are 

simply less likely to engage in sports-type play with their Advocates because it is less 

developmentally “appropriate” or gender-role incongruent. Yet the absence of a direct 

relationship between age and rates of playful activities does not support this view. 

These findings point to ways in which Advocates could be recruited, trained and 

supervised differently. Recruiting more educated Advocates and those with teaching 

experience may provide one way to boost program impact, even though it conflicts with 

YAP’s logical model which places a premium on hiring Advocates who are most like 

those they serve. But, regardless of educational background of the Advocates hired, 

training could be developed to help increase the Advocates’ appreciation for the role of 

play and positive experiences in the match and for helping them to be wary of taking on 

an overly prescriptive, problem-focused approach later in the relationship. Once 

Advocates are trained, staff could provide ongoing supervision and encouragement that 

helps those least likely to play (such as the more educated Advocates) consider its 

benefits and the reasons for engaging in more play over time in their matches. Finally, 

trying to determine why having prior teaching experience may make those Advocates 

more effective could inform both the training and supervision of Advocates regardless of 

their backgrounds as well. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 11

II. The Four Agencies 

Youth Advocate Programs, Inc. 

Youth Advocate Programs, Inc. (YAP) is a national leader in community-based 

services for young people involved in the juvenile justice, child welfare, and mental 

health systems. Based in Harrisburg, PA, YAP currently operates programs in twenty 

states, serving over 2,000 young people and their families at any time. The 

organizational mission is to provide individuals who are, have been, or may become 

system involved or subject to compulsory care with the opportunity to develop, 

contribute, and be valued as assets so that communities have safe, effective, and efficient 

alternatives to institutional placement. 

YAP is distinctive in multiple respects, most notably for its focus on nonresidential community-based programming targeting youth at immediate risk of 

institutionalization due to violent or repeat property offenses. This targeting of youth 

who committed delinquent acts means that YAP engages a uniquely challenging youth 

population otherwise not served by mentoring programs. This form of “reverse cherrypicking” is an intended consequence of YAP’s “no eject – no reject” policy. By this 

policy to accept all referrals from juvenile justice and child welfare agencies, the sample 

included in this study reflects both a difficult population but also one perhaps more 

representative of the true population of youth who are adjudicated and for whom 

effective interventions are needed. 

A consequence of YAP serving a broader and more representative population of 

adjudicated youth who had committed delinquent acts makes it difficult to identify an 

accurate comparison group from non-residential settings, and even to compare the 

magnitude of outcome or change estimates with those of other programs that may restrict 

their program to a less challenging population. For this reason, it was critical that the 

methodology used to assess relative effectiveness or change over time be robust to threats 

to the validity posed by selection threats. 

Beginning at referral, YAP Advocates focus on developing a trusting relationship 

in order to ensure that the youth satisfy community service and court mandates within the 

framework of restorative practices. Such concerns are rarely present in delinquency 

prevention and early onset disruption-focused initiatives. In addition, YAP Advocates 

aim to strengthen family and community relations in order to create or improve resources 

that will continue to deter harmful conduct after the period of YAP engagement is over. 

YAP Advocates are recruited from the same zip-code as youth and families to 

ensure that service providers have a grassroots understanding of community assets, 

challenges, and opportunities. To the extent possible, YAP Advocates are then matched 

with youth based on their similarity and shared interests. Unlike many adult mentors 

who volunteer for programs such as Big Brothers/Big Sisters, YAP Advocate mentors are 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 12

paid, which is a potentially important variable regarding intensity of service delivery and 

youth outcomes (Friends of the Children, 2011). 

In contrast with most advocacy and mentoring programs, YAP’s length of 

services (i.e., dosage) is an average 4-6 months. This shorter term of duration contrasts 

with typically prescribed one-year mentoring relationships, but the abbreviated service 

period theoretically is offset by a greater intensity of mentoring contact between 

Advocates and youth. In fact, when dosage is considered the number of contact hours, 

versus the number of days or months of being matched, it can be said that YAP reflects a 

high-dosage, time-constrained approach to youth mentoring. 

Research Sites 

Study group data was derived from court referred youth in four YAP programs in 

separate and diverse regions of the country (Camden, NJ, Las Vegas, NV, Lebanon, PA, 

and Toledo, OH). Sites were selected according to five main criteria (program size, 

location, data availability, delinquency severity, and generalizability). While YAP 

programs vary in the number of youth referred and served, a review of program 

characteristics indicated that programs serving approximately 50 youth at a time are most 

representative of YAP programs. Programs in separate geographic regions were selected 

for generalizability purposes. Selecting programs with legal referring authorities that 

promised access to data records for outcome analysis was also a key selection factor 

regarding necessary data availability, as were programs focusing on serving youth 

representative of the “type” of youth who had repeatedly committed delinquent acts who 

are most common in YAP programs. In short, site selection sought to include programs 

both representative of a national scope and reflective of YAP programming. This section 

of the report provides a descriptive overview of each of the four sites. 

Camden, NJ 

Referral process. Participation and placement in the Camden YAP reflects a 

combination of court orders and community-based organizations (e.g., Youth Christian 

Ministries) and self-referrals. YAP maintains a relationship with the juvenile court 

system through Community Reintegration into the System (CRIS), a defined initiative for 

at-risk youth. About 20% of youth clients are technically court ordered into the CRIS 

program, which is executed through the delivery of YAP services. Much of the balance 

of the program’s population is a function of referrals come from juvenile probation and 

parole with YAP participation a formal condition. 

Youth population characteristics. The local juvenile judge refers lower level, as 

well as chronic, offenders to YAP – a modality divergence justified by the site Director 

who is committed to treating all youth as high risk for deterrence purposes. The 

demographics of program youth were a racial distribution of 85% African-American, 

10% Latino, 4% White, and 1% Asian and a gender balance of 75% male and 25 % 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 13

female. The majority of clients were 16-19 years of age and from broken homes. 

Delinquent behavior reflects heavy involvement in property crime and drug 

activity. Curiously, most male drug use was limited to marijuana but female youth 

reported harder drug use. Related, two females interviewed admitted prostitution for 

drug money. The largest contextualizing factor for the Camden site, however, was gang 

presence. Program youth are at-risk of gang involvement (Bloods, Crips, MS13) 

although not many were actually members in them yet. 

The Camden YAP youth were the most pronounced of all the sub-samples of 

youth across study sites in terms of serious delinquency. Most were from Camden 

proper, many from the Whitman Park area known for violence and open-air drug dealing. 

The majority of male youth interviewed had witnessed shootings, including a murder in 

broad daylight, years of gang turf warfare, and drug dealing as normative, and expressed 

a clear internalization of criminal subculture (e.g., endorsement of the stop snitching 

movement). The majority had been placed in Camelot and Brook Academy, an 

alternative school. 

Service capacity. The Camden program is large relative to other YAP sites, 

having served more than 100 youth each year of the study period with 86 active youth at 

the date of the last site visit and another 80 on a wait list. The program has a contract 

with the State of New Jersey to serve a minimum of 40 juvenile clients for approximately 

400 hours a week. Though desirable, major issues bar expansion including difficulty 

recruiting additional Advocates and limited resources. Although the Camden facility is 

housed in a large office building setting with a fair amount of space, planning is ongoing 

regarding acquiring a stand-alone building (so as to qualify for a tax credit from the City 

of Camden) to centralize general positive youth development as well as house treatment, 

advocacy, administration services. 

The majority of Advocates identify with the youth they served, having grown up 

in the same neighborhoods and been exposed to numerous risk factors. Some Advocates 

were recently certified as substance abuse counselors, and the site had received external 

funding for substance abuse treatment. All Advocates had completed uniform YAP 

training, and had either completed or had scheduled additional training – all online. 

Generally, the Advocates were well versed in YAP procedures and policies, and 

demonstrated considerable enthusiasm in a spirit of giving back and community service. 

Activities. Advocacy with the family thematically characterized the general 

orientation to services in Camden. Advocates experienced various reactions from the 

families (typically single, female-headed households), ranging from supportive, to 

indifferent (i.e., thought to view YAP as free babysitting), and even hostile (per the 

position that YAP was superseding parental authority). The Camden site also involved 

parents in YAP activities such as cookouts and wraparound services (e.g., anger 

management and drug treatment). 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

Final Technical Report: 2011-JU-FX-0001 p. 14

The primary activity between Advocates and youth, not just in Camden but also in 

Toledo, was basketball as confirmed by all Advocates and most youth interviewed. 

Seemingly innocuous, repeated narratives regarding the value of basketball (e.g., building 

teamwork and interpersonal skills) highlighted a pronounced distinction between the 

salience of individualized versus group-based services delivery. Additional activities 

included a book club where youth were able to meet some of the authors of a book they 

had read and, when required by probation, community service. YAP youth court ordered 

to perform community service did so through participation in Youth Build. Advocates 

had also taken youth to sporting events, museums, movies, and public parks during their 

weekly meetings with specific activities determined according to an individual youth’s 

interests. 

Program fidelity. The Camden program demonstrated program fidelity in terms 

of exposure/dosage, stakeholder engagement, and (lack of) program differentiation. The 

office projects professionalism and all records were in order. The program as delivered, 

however, does not strictly adhere in all respects to the prescribed YAP modality. Whereas 

the YAP modality prescribes that match activities should often transpire in a one-to-one 

context, budgetary constraints resulted in a predominance of group-based time. 

Las Vegas, NV 

Referral process. YAP collaborates with the Las Vegas Metropolitan Police 

Department Vice Unit, juvenile court, and juvenile probation to identify potential youth 

for referral into the program. The program is court ordered and participation is 

mandatory, typically as a requirement of juvenile probation. Judge William Voy presides 

over the juvenile court docket for Clark County and devotes one day per week to dealing 

with youth referred for prostitution-related charges. In particular, the Wednesday 

morning court calendar deals exclusively with these types of youthful offenders. Judge 

Voy is a frequent source of referrals to YAP, as are the juvenile probation and juvenile 

justice systems in Clark County. At this particular site, the program has the full 

cooperation of referring authorities, leading to high numbers of enrollment. 

Youth population characteristics. The Las Vegas YAP services both male and 

female youth between the ages of 11 and 18 years. In terms of racial/ethnic background, 

the Las Vegas YAP was diverse, with White, Black, Hispanic, and Asian/Pacific Islander 

youth represented. Most of the program’s male participants hailed from low 

socioeconomic backgrounds while there was more class diversity for females. More 

specifically, female participants were from a broader range of SES backgrounds, 

including those from the middle classes. Unlike the other YAP sites, the Las Vegas 

program is unique in that the majority of youth serviced are females involved in 

commercial sexual exploitation. Conversely, common offenses leading to referrals for 

males include property crime, graffiti, vandalism, and breaking and entering. In fact, Las 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 15

Vegas has one of the highest rates for juvenile breaking and entering nationwide, a reality 

reflected in the study’s sample. 

The overwhelming majority of females (90%) served by the Las Vegas program 

have a history of commercial sexual exploitation (CSE). Evidence suggests that about 

half of these youth are from Las Vegas while the other half are brought from outside of 

Nevada. Often these are from neighboring Western states such as California or Oregon, 

but there have also been instances of youth brought from across the U.S. as well as 

internationally. The director of the program relayed that more of these referrals appeared 

to be “homegrown” in recent years. The YAP program in Las Vegas is one of very few 

that serves this particularly vulnerable class of juvenile offenders. 

Service capacity. Las Vegas YAP has multiple contracts with the Clark County 

Department of Juvenile Justice System to service various types of youth. For the 

traditional YAP advocacy model, the site has a contract to serve up to 23 youth annually. 

The Las Vegas site also has a grant award from the Department of Juvenile Justice to 

assist 10 youth with employment support training and services. The Las Vegas site is 

among the largest YAP programs evaluated and has an average of 10 youth on the 

waiting list at any time. 

Per the YAP modality, each youth receives approximately 30 hours per month 

which translates to 7.5 hours per week. Advocates and youth are free to design their own 

schedules and split up their hours as they see fit. Most schedules depend on both 

Advocates and youth commitments each week. 

Training for Advocates at the Las Vegas site involves both the typical YAPspecific training (basic and continuing), but also requires additional training for their 

Advocates because of the CSE element. This site also was more successful in terms of 

demographic matching between Advocates and youth. For example, 50% of the staff is 

bilingual (Spanish speakers) and zip code recruitment is common for identifying potential 

mentors for youth. Advocate retention was high at this site relative to the others and the 

Director used her position as an adjunct professor in the Department of Criminal Justice 

at UNLV to maintain a steady pool of potential applicants. Each Advocate is part-time at 

this site (20-25 hours per week) which is intentional on the part of the Director. 

Advocate burn out appears to be less of an issue for part-time employees. Matches 

between Advocates and youth typically last until youth are released from probation 

(about 3-6 months per match). 

Activities. As noted above, each youth receives 30 hours per month of advocacy 

services. Half of these hours are individual one-on-one time between youth and 

Advocate based upon the youth’s Individual Treatment Plan (ITP). This plan is done at 

the start of the mentoring relationships, and includes a lists objectives and goals for each 

area in which the youth requires assistance. The ITP also includes a timeframe for each 

of the goals and objectives, and the plan is updated throughout the program according to 

these benchmarks. The remaining half of hours are completed in a group-based format 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 16

designed to involve all youth regardless of background or specific needs, such as 

assisting at homeless shelters. 

Most activities at the Las Vegas YAP are gender-segregated. At both site visits, 

males and females were not observed participating in activities together, and there is little 

overall interaction between the genders. The Director estimated that for every need for 

which males require address, there are three needs for which females require services. 

For example, at the first year’s site visit, 70% in ‘Girls Circle’ group reported being raped 

during an observed session, a reality not experienced by the male participants. Males 

participate in ‘Boys Council’, which serves to address their developmental and 

criminogenic needs. 

Because Las Vegas has one of the worst school dropout rates in country, this 

program emphasizes school-related activities and assistance (tutoring, acting as a liaison 

between youth and school, etc.). Other activities include the aforementioned “Girls 

Circle” and “Boys Council”, anger management classes, field trips to cultivate individual 

interests (e.g., trips to museums), parenting classes (for those youth who are pregnant or 

who already have children), and visits to the mall, restaurants, or movies. 

Program fidelity. The Las Vegas YAP program adhered closely to the design of 

the modality, as indicated by Advocate training, program components, prescribed 

caseload, intake timeliness, and the development of individualized treatment plans. A 

greater breadth of programming and activities were offered by this site and all Advocates 

were highly qualified, with all possessing at least a Bachelor’s degree. This site more 

than others adhered to demographic matching between youth and Advocates, even going 

so far as to actually execute zip code matching – a reality not common at the other sites 

due to logistical issues. 

This site also did well in terms of meeting dosage requirements for youth. While 

few other sites utilize true one-on-one time between Advocates and youth, Las Vegas 

YAP was so well staffed and organized that at least half of all interaction between mentor 

Advocates and their youth mentees were individualized. Moreover, the activities 

engaged in group format (i.e., Girls Circle, Boys Council) were more robust and 

treatment oriented that group activities at other sites (i.e., playing basketball). Participant 

engagement was also high at this site relative to others, although this was the case more 

so for females than males. Universally across the sites females appeared to be more 

engaged than their male counterparts. This modality may work better with female 

offenders since they are usually more amenable to the social bond and appeared to enjoy 

the camaraderie of the group activities much more so than has been observed with the 

males. 

Finally, this site did well with respect to program differentiation, as indicated by 

fluctuations in program size (the program size was stable), program budget (this site was 

able to obtain outside funding for additional activities), Advocate caseload (the Director 

made sure not overload Advocates), and continuity of staffing (both the Director and 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 17

most of the Advocates were the same across both site visits). 

Lebanon, PA 

Referral process. The Lebanon YAP site is populated from two primary referral 

sources – the County’s Department of Children and Youth Services and the County’s 

Juvenile Probation Office – with about 55% and 45% of program participants coming 

from each source, respectively. Court referred youth typically participated as a condition 

of probation. Juvenile probation facilitates YAP placement with the understanding that 

YAP is a resource for addressing chronic delinquency. 

Youth population characteristics. Many of the youth participating in the 

Lebanon YAP had engaged in delinquent acts less frequently and had engaged in less 

severe delinquent behavior than at the other YAP sites. The only rural site setting in the 

study, common delinquent behaviors included truancy, drugs, vandalism, and shoplifting. 

Though less frequent, more serious criminal behavior among Lebanon YAP youth 

included property crime, sex offenses (including sexual assaults), arson, school violence, 

and weapons at school (mainly knives). Drug and property offenses accounted for 

approximately 30% and 25% of off charges, respectively. The second year site visit to 

Lebanon revealed more serious behaviors in the histories of youth referred to YAP. 

These histories included gun possession and robberies. Truancy also became a more 

common reason for referral to YAP at least in part because the definition of chronic 

truancy – one type of dependency – had been reduced to 10 unexcused absences (the 

majority of YAP youth had about 25 such absences. 

As rural Pennsylvania became more diverse, YAP received more referrals of 

young people who were gang involved. These population changes were reflected in the 

fact that 50% of YAP Lebanon participants were Hispanic, followed by 45% White, with 

a balance of African-American, Native American, and mixed race youth ranging between 

13 and 17 years of age. 

Service capacity. The Lebanon YAP site serviced an average of 100 youth a year 

during the evaluation period with approximately 60 probation office referrals and 40 

referrals from Children &Youth Services each assessed year (loosely served in two 

roughly equal cohorts in six month durations). The site employs ten Advocates (five 

males and five females), four of which are fulltime and, like all other YAP sites 

examined, makes same gender and same zip-code matches. Though the site has four 

bilingual Advocates, the swell of Hispanic clients and the likelihood of more in the future 

pronounced the need for additional Spanish speaking mentors. Part-time Advocates work 

with three or four youth at a time while fulltime Advocates work with seven to ten youth 

on average. Advocate recruitment was not found to be a problem as with the Camden 

site due to successful recruitment efforts at nearby Lebanon Valley College, through 

Career Link, and word of mouth. All Advocates were initially trained per YAP’ uniform 

national training and continued training was delivered by Rutgers University School of 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 18

Social Work. The site had experienced little trouble with Advocate turnover, thereby 

minimizing program differentiation concerns. 

Activities. The Lebanon site delivered the traditional YAP juvenile justice 

modality in a highly structured and professional manner. Activities reported by 

Advocates and youth included visits to a local YMCA, fossil hunting, community service 

(particularly volunteerism at a local foodbank), movies, and help with homework. Due to 

the aforementioned tightening of the definition of truancy and related to homework, 

many YAP participants had been placed in cyber-school – an unsuccessful alternative 

according to the Director, Advocates, and the youth (some of whom did not have access 

to computers and/or the Internet in their homes or elsewhere outside of YAP).

 While the Lebanon site, along with the Las Vegas site, demonstrated adherence to 

the modality theory, generally and, especially in regard to the delivery of individualized 

treatment, Advocates also involved youth in organizational group activities ranging from 

Christmas parties, a community garden planted and maintained by YAP, athletic events, 

and cookouts with the youths’ families. 

Program fidelity. The Lebanon site demonstrated a high level of program 

fidelity, second only to the Las Vegas site. The program featured fairly strict adherence 

to with the YAP modality design, met or exceeded dosage/exposure expectations, and 

demonstrated quality delivery of services. The participants were highly engaged in 

programmatic activities and there was very little program differentiation. Unfortunately 

and despite a letter of understanding promising the availability of outcome data, no one at 

the site or the referring agency was capable of synthesizing and transmitting performance 

measures data – especially disappointing in that the research team provided each site a 

spreadsheet with variable names and operationalization for recording and transmittal. 

Toledo, OH 

Referral process. The Toledo YAP program receives referrals from Lucas 

County Juvenile Court pursuant to a contract between the County and YAP. The 

agreement was to serve up to 20 youth at a time, a number that is maintained throughout 

the year. Youth are court-ordered to participate, typically as a condition of their 

probation. Intake and assessment is done within 48 hours of the referral and matches are 

made shortly thereafter. 

Youth population characteristics. Youth at this site were overwhelmingly 

male (>90%) and African American (>90%). Females only make up about 20% of courtinvolved youth in Ohio and tend to be referred to services other than YAP by the juvenile 

courts. Of the youth served at this site, some were charged with misdemeanors but many 

had either been charged with felonies, including weapons and drug offenses, or scored 

moderate to high risk on the Ohio Youth Assessment Scale. The most common types of 

offenses committed by YAP youth include breaking and entering (the most common 

offense), drug use (usually in the context of probation revocation) or possession, and 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 19

assault. Notably, this site serves youth involved in gangs although these groups tend to 

be more localized (i.e., “cliques” from housing projects) than national (e.g., Blood, 

Crips). The Director reported that almost all youth referred to YAP are gang-involved. 

This site serves youth between the ages of 10 and 18, although most are between the ages 

of 14 and 17. 

Service capacity. The Toledo site was operated under the direction of the 

Director who had a multi-year career in mental health treatment delivery services and had 

previously worked with case management for children and families. The Director herself 

had been an Advocate at one time which contributed to operation of the site. This site 

employed 14 Advocates for a reasonable 1:4 Advocate to youth ratio, although the 

caseload does vary depending on the number of youth enrolled at any time. Generally, 

this program adheres to gender matching although there are times when female 

Advocates are matched with male youth due to the overwhelming number of males 

referred relative to females. When female Advocates are paired with male youth, a 

strength-based matching approach is used. 

Per the YAP modality and the contract with the county, each youth receives 

approximately 10 hours per week (more than the typical 7.5 hours per week established 

by the modality). Youth may receive up to 10 hours per week, typically split into six 

hours of individual interaction and four additional hours of group activity. Advocates 

and youth are free to design their own schedules and split up their hours as they see fit. 

Like other sites, most schedules depend on both Advocates and youth commitments each 

week. Much of the effort made on the part of the Advocates involves going into the 

youth’s community, picking up them up, and transporting them to where they need to be 

(usually--change to some of which is probation-related requirements). 

Training for Advocates at the Toledo site involves the typical YAP-specific 

training (basic and continuing). Recruitment of Advocates includes job fairs, local 

colleges, and referrals from current Advocates. The Toledo site also strives to adhere to 

the zip code matching policy of YAP and is fairly successful in their efforts. The 

Director described Toledo as a relatively small community where people know one 

another; consequently, it is easier to find suitable Advocates in a setting such as this. 

Activities. Activities at the Toledo site were similar to those observed and 

identified at other sites. These include trips to the library, social activities with other 

YAP youth, and visits to Toledo’s minor league baseball team’s games. This site was 

one to specifically mention providing assistance to families as well as youth. This site 

also engages in job preparation and training as unemployment among both the adult and 

youth population in Toledo remains a pressing issue for the community. Activities 

observed at the program’s location were geared mainly toward this goal of finding and 

maintaining gainful employment.

 Other activities include recreational activities, educational field trips, and 

transporting youth to and from organized sports practices. The Advocates also assumed a 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

Final Technical Report: 2011-JU-FX-0001 p. 20

role as liaison between youth and both probation officers and teachers. Advocates 

relayed that individualized time was typically devoted to working on youth’s ISPs. 

Finally, many Advocates reported assisting their mentees with school-related activities 

such as tutoring and homework assistance. 

Program fidelity. The Toledo YAP appeared to possess relatively high levels 

of program fidelity. This program adhered fairly closely to the design of the modality, as 

indicated by Advocate training, program components, prescribed caseload, intake 

timeliness, and the development of individualized treatment plans. This site also did a 

satisfactory job at adhering to demographic matching between youth and Advocates. 

Like the Las Vegas site, Toledo made considerable effort to identify Advocates from the 

same communities as the youth they served. In terms of meeting dosage requirements for 

youth, this site actually exceeded the 7.5 hours per week required by the advocacy 

modality (i.e., 10 hours per week). Intake and assessment is done within 48 hours of the 

referral and matches are made shortly thereafter. Participant engagement was also fairly 

high at this site relative to others, and the research team observed quality interactions 

between youth and Advocates while at the study site. Overall, youth appeared engaged in 

activities and expressed confidence in their assigned Advocate. Finally, this site did well 

with respect to program differentiation, as indicated by fluctuations in program size (the 

program size was stable), program budget, Advocate caseload (typically not exceeding 

4:1 youth to Advocate ratio), and continuity of staffing. 

Concluding Comments 

This study was intended to provide a detailed review of the degree to which each 

site implemented the national YAP model with fidelity along several dimensions. In the 

end, only brief commentary or high-level assessments of program model fidelity in 

program implementation are provided at the end of each section. 

Because the original co-PI on the project, Dr. Mitch Miller, left the project before 

the final year of the study period, staff and agency administrators in each site never had, 

to my knowledge, an opportunity to review and discuss his findings regarding degrees, 

types of fidelity, strengths and weaknesses of model implementation in that site. 

Furthermore, because I was not given access to the data upon which initial fidelity 

comments and reviews were based, I have no way to assess the veracity or rigor of the 

data analyses. For both of these reasons, I do not feel I can present a full picture of what 

was learned to answer the questions under investigation in this part of the study. As a 

result, I have not included in this report all of the information I was given. 

However, the information in Table 2.1 provides information on what happened at 

each location and for how many hours. This should allow useful comparisons about what 

happened in each setting. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 21

Table 2.1 

Descriptive information about treatment dosage in days and hours of contact 

n Mean Std. Dev. Min Max 

Number of hours Toledo 29 9.33 2.03 1 10 

Advocates reported Camden 14 15.57 4.89 10 22 

meeting with youth each Lebanon 29 7.78 3.13 3 22 

week at 2 months Las Vegas 55 7.15 .55 6 10 

Total 127 8.72 3.50 1 22 

Number of days Toledo 25 2.86 .64 1 4 

Advocates reported Camden 12 3.58 1.38 2 7 

meeting with youth each Lebanon 24 2.90 1.10 1 7 

week at 2 months Las Vegas 53 2.81 .52 2 5 

Total 114 2.92 .84 1 7 

Number of hours youth Toledo 20 8.90 3.6 2 20 

reported meeting with Camden 10 16.00 4.53 5 20 

their Advocates each weekLebanon 15 6.35 2.34 1.75 10 

at 2 months Las Vegas 45 7.86 3.58 1.50 30 

Total 90 8.74 4.40 1.50 30 

Number of days youth Toledo 22 2.91 .55 2 4 

reported meeting with Camden 12 3.17 .58 2 4 

their Advocates each weekLebanon 16 3.22 1.70 1 7 

at 2 months Las Vegas 44 2.85 .439 1.5 4 

Total 94 2.97 .83 1 7 

Number of hours Toledo 20 9.90 .45 8 10 

Advocates reported Camden 13 16.15 3.83 9 22 

meeting with youth each Lebanon 18 7.66 1.28 5 10 

week at 4 months Las Vegas 47 7.03 .92 4 10 

Total 98 8.94 3.43 4 22 

Number of days Toledo 16 2.72 .68 2 4 

Advocates reported Camden 13 3.15 .80 2 4 

meeting with youth each Lebanon 17 2.74 .66 1 4 

week at 4 months Las Vegas 45 2.73 .58 2 5 

Total 91 2.79 .65 1 5 

Number of hours youth Toledo 17 9.65 1.06 6 10 

reported meeting with Camden 12 15.00 4.83 3 20 

their Advocates each weekLebanon 14 7.11 2.14 3 10 

at 4 months Las Vegas 45 7.32 1.12 3 10 

Total 88 8.78 3.39 3 20 

Number of days youth Toledo 16 3.16 1.20 2 7 

reported meeting with Camden 13 2.77 .81 1 4 

their Advocates each weekLebanon 12 3.17 1.03 2 5 

at 4 months Las Vegas 41 2.81 .44 2 4 

Total 82 2.93 .80 1 7 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 22

Though it does not completely describe the range of implementation factors and 

mentoring processes at each site, the information provided in this first section should give 

readers of this report a sufficiently detailed picture of what happens at each site to 

support their understanding of the analyses, results, and interpretations found in the 

following two sections. 

All of the information provided in the first section of the report above was 

reviewed by our project liaison at YAP for accuracy and appropriateness. No request to 

change the content of these descriptions was made by YAP so we feel this section 

provides a relatively accurate description of these sites, program practices, and the 

approach to mentoring and advocacy processes at each site during the course of this 

study. 

Descriptive Program Characteristics by Agency 

Table 2.1 above provides descriptive information about treatment dosage in terms 

of number of days and hours of contact between Advocates and youth from the 

perspective of each. Time spent together was reported after two and four months, and is 

presented below for each agency. 

Advocates typically met with their youth on average three days a week and almost 

9 hours per week. Dosage across cities was fairly consistent, except for those at Camden 

who reported twice that many hours together. Cumulatively, meeting 8 hours a week (or 

24 hours a month) puts the number of mentoring hours spent together in just the first four 

months at 100 face-to-face meeting hours (and twice as many in Camden), which far 

surpasses the number of hours most programs have their mentors meet with their mentees 

in a full year of mentoring (or the typical evaluation period in most published studies). 

So, while the YAP model is a short-term program in terms of the number of weeks, it is 

highly intensive during this time. Furthermore, the youth and Advocate reports are fairly 

consistent, providing corroborating support that these numbers are fairly accurate 

estimates of the dosage of the program. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

Final Technical Report: 2011-JU-FX-0001 p. 23

III. Study 1: Outcome Evaluation—Methods, Measures, Design 

For these analyses, the recurrent institutional cycle quasi-experimental (RIC) 

design was employed to address the question about program-related changes on key 

outcome measures. The RIC design was chosen after it became clear that the propensity 

score matching approach was unsuccessful because of insufficient data to properly match 

youth on variables uniquely characteristic of YAP participants. For example, the court 

referred to YAP youth a disproportionately high percentage of girls involved in sex 

trafficking in Las Vegas. Without having this variable in the dataset provided by the state 

from which to identify matches for the YAP youth made creating a comparable matched 

sample impossible. After this unsuccessful effort to estimate program-related changes on 

outcomes using propensity score matching, we turned to the RIC approach. 

Statistical analysis of program effects on recidivism, deinstitutionalization, and 

pro-social activity (e.g., school and employment status) using the recurrent institutional 

cycle quasi-experimental (RIC) entailed estimating change by comparing program 

graduates (i.e., treatment group) to counterfactual comparison groups of similarly aged 

youth who had yet to participate in the YAP program, thereby providing the closest 

approximation of the counterfactual state of what a YAP participant at a given age would 

look like had that person actually not participated in YAP. 

What this design does not allow, however, is the analysis of differences between 

the treatment and the untreated comparison group on variables not collected prior to 

entering the program. This is because where no pre-intervention assessments of 

phenomenon are available, the intervention group cannot provide a counterfactual 

estimate of the presence of this phenomenon or outcome for the kind of youth who enter 

a given program at a given age. For example, recidivism was not a viable outcome 

variable because the data provided by the state did not allow us the identification of dates 

of recidivism post initial court involvement. 

However, there was considerable other data available assessing a variety of crime 

and recidivism related phenomena to undertake the RIC analyses. Specifically we relied 

on the data from YAP to assess the graduates’ involvement in crime subsequent to 

graduation, which was collected prior to treatment for all youth, at discharge, and then 12 

months post treatment for program completers to be useful for this purpose and for 

estimating process-outcome links. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

																																																							 

 

Final Technical Report: 2011-JU-FX-0001 p. 24

Introduction 

In the first part of this section of the report the rationale is provided for using the 

recurrent institutional cycle3 quasi-experimental (RIC) design to investigate questions 

about the effectiveness of the YAP program. An explanation of its strengths, data 

requirements, and necessary assumptions and rival explanation tests (to address specific 

validity threats) is provided. This section presents a number of preliminary checks of the 

data and design that serve as background to the main tests of effectiveness. 

The first set of assumptions to be checked through preliminary data analyses 

regard determining what of the available data could provide useful estimates of the 

counterfactual condition. Namely, which of the assessments, observations, and other data 

are appropriate for use, first conceptually, then for this particular population of youth, and 

finally statistically. 

What happens in the program should be related to program goals, and both should 

be related to the data used for the treatment effectiveness study. Because information on 

what occurred in each site is described in section one of this report, very little additional 

information is provided in this section. 

To identify the best measures for studying this particular program, each of the 

measures was examined for random (non-program) variance due to developmental 

processes (e.g., change due to normative maturational processes) and both site and gender 

variability. Other analyses of the existing data were undertaken to explore how useful 

different scales would be for tapping into importance constructs, processes, mental states, 

or behaviors among the population at hand—the youth in the actual sample. Finally, 

statistical issues related to normality of data as well as robustness to key validity threats 

are examined. 

The Recurrent Institutional Cycle Design 

One problem of using experimental designs in program evaluation is the cost of 

conducting randomized controlled trials of program effects, which are considered by 

many to be the gold standard of evidence and the only viable means of identifying causal 

processes. Others, in particular individuals of the very philanthropic foundations that 

Advocate for the necessity of evidence of program impacts, have initiated explorations of 

the viability of other, quasi-experimental approaches. But the approach used in this study 

has been given very little use despite its potential, particularly for widespread use in the 

field of program evaluation. 

Those who only support a true experiment, in which program participants are 

randomly assigned either to receive or to be denied program services, present agencies 

like YAP with ethical and moral dilemmas to address. To deny youth access to program 

services, which they are viewed as needing, in order to undertake a scientific evaluation 

3 Campbell, D. T. & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. 

Dallas, TX: Houghton Mifflin Company. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

																																																							

 

 

Final Technical Report: 2011-JU-FX-0001 p. 25

of outcomes, is neither something that a university research ethics committees will allow 

nor something that youth’s parents readily agree to without some stipulation of promised 

delayed services. For example, in one novel study of a youth intervention, a school 

district would not allow a no-treatment, randomly assigned experimental control group. 

The school district demanded that the researcher provide an alternative service, which 

was then called a “Lunch Buddy,” to students not receiving the intervention under 

investigation4

. Much to the researchers’ surprise, the comparison condition actually 

outperformed the focal intervention in specific school settings. 

To assess the effect of mentoring program participation for all participants and 

across age levels, we implement the “recurrent institutional cycle design.” This is 

typically a version of an age-cohort matched design approach; but it goes considerably 

further in addressing program change through a variety of complementary analyses that 

rule out rival alternative hypotheses the limit the use of each type of test when conducted 

singly. In addition, it capitalizes on the advantages offered by recently developed 

statistical advances that allow statistical dependence to be accounted for and that allow 

evaluators to exploit the nature of the types of data already collected by many agencies 

repeatedly each year or across intervention cycles. 

The beauty of the design is the careful use of pre- and post-data from youth who 

participate in the program to provide evidence of post-treatment outcomes and as a highly 

approximate counterfactual comparison group. Without the use of external comparison 

groups, the approach provides a uniquely feasible and yet potentially very rigorous quasiexperimental design. 

Intervention-duration based cohort creation. Considering the RIC as a variant 

of the age-cohort quasi-experimental design is a useful way to begin to understand why 

and how it uses participants’ pre and post scores to estimate change associated with 

program participation. Imagine YAP participants are classified into narrow age cohorts 

based on the age when they entered the program and completed their pre-test on outcome 

variables. These youth data reflect their reports on outcome variables at a time before 

initiating participation in the program. For example, the youth who was 13 years and 1 

month at entry (at pre-test) provides one observation of how non-treated youth look on 

those variables at that age—this pre-test data provides one observation in the sample of 

the untreated comparison group. This pre-intervention survey is one “picture of” a YAPtype kid at age 13 and 1 month (13:1) who has not received YAP services. 

If this youth then participated in YAP for 4 months, he would be 13 years and 5 

months (13:5) old when he completed his post-assessment. The post-assessments from 

4 Cavell, T. A., & Hughes, J. N. (2000). Secondary prevention as context for assessing change processes in

aggressive children. Journal of School Psychology, 38(3), 199–235. 

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Final Technical Report: 2011-JU-FX-0001 p. 26

this youth, then, represent what the kind of youth at this age who participates in YAP 

looks like on these outcomes after participating in the program. 

This is how a given individual provides both information for the treated group and 

the untreated (comparison) group by being assigned to two statistically distinct age cohort 

groups--both as a 13 year, 1 month old untreated comparison group member and a 13 

year 5 month old treatment group member. Of course, there are many problems posed by 

this design, but these can be systematically addressed with adequate data. 

Design for estimating change associated with program participation. This 

approach was used to construct a counterfactual comparison group against which to 

compare participating youths’ outcome scores after 4 months of participation in YAP. 

The pre-test data for entering program participants was chosen to represent the 

counterfactual state for youth who entered the program at that age. 

The second element of this design is that it requires at least three cohorts of 

participants and the separation of individuals’ pre and post-tests data into separate cohorts 

within each intervention cycle. By making comparisons across cohorts, multiple tests of 

program-related change can be replicated. By creating within-cohort cycle-length groups 

addresses fundamental limitations of between-group tests with statistically dependent 

data. The dependency would otherwise result from the separation of pre and post-data 

from the individual youth and assignment of each to separate non-independent groups— 

treated and the counterfactual. 

The issue statistical dependency dealt with in two ways. First, it is addressed 

through the use of within-cohort tests across smaller statistically independent agecohorts. Figure 3.1 illustrates this quasi-experimental design whereby the pre-test data 

from youth entering one intervention cycle are assigned to the “untreated” cohort group 

for their intervention cycle. In that illustration, Lou’s pre-test data for intervention cycle 

group 2 is used as the untreated comparison group that is compared to the “treated” data 

from Tom’s intervention cycle number 1. In this way, the post-test data from one cohort 

is compared to the pre-test data from the subsequent intervention cycle cohort. This 

secures statistical independence (Tom’s pre- and post-data are not compared). 

This does, however, create a sometimes untenable number of small treatment 

effect comparison groups and lead to many highly underpowered between group tests that 

simultaneously inflate Type 1 error rates. 

When the statistical analyses described in the next section are beyond the skills of 

evaluators and there is an external demand for just a few adequately powered statistical 

tests of the likelihood of the observed between group differences, these smaller agespecific (or intervention cycle duration differentiation) groups can be pooled. Using one 

or two tests (e.g., the second including analyses of covariance) affords greater statistical 

power and simultaneously provides a basic way to statistically accounts for data 

dependency, whole-sample tests (including all youths’ pre and post-tests data) can be 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 27

made. But these tests require some method of maturational processes to be ruled out since 

the “untreated” group will, on the whole, be one age or intervention cycle-length younger 

than the “treated” group. Using age as a covariate is one way to address this, while the 

other is to analyze the amount of change typically observed across the pre-test scores of 

different ages. 

A final method of assignment is to pool all youth in the study into one test of 

differences between treated and untreated youth that explicitly accounts for data 

dependency through the nesting of difference-test groups within each person. This can be 

particularly helpful in countering the fact that analyses of covariance cannot address or 

sensibly adjust group-specific scores on covariates like sex, race, and prior misbehavior 

rates because, in the end these are the same for both the treated and untreated groups (by 

the fact that it is the same people in each group), so there is no mean difference by which 

to adjust the groups. Therefore, based on the inclusion of the same youth as treated and 

non-treated group participants, treatment (mentored) and untreated (comparison) youth 

will create groups that will not differ on any of these demographic or other risk factor 

variables, such that analyses of covariance are unnecessary to equate groups, except on 

age. Covariates are simply included to remove from outcome variance that can be 

explained by known characteristics and which would otherwise be considered random 

error in the model, making estimation of differences due to program participation less 

easy to discriminate effectively. Using a second set of difference analyses to address data 

dependency, covariates are not needed. 

It is this second type of whole-sample probability of between untreated and 

treated group difference analyses (in which these two data points are nested within person 

using hierarchical modeling) is most valuable. It requires greater skills in statistical 

analyses that many agency-specific evaluators may have (or have the resources to seek 

consultation) and so are not essential for sufficient use of the RIC analyses but which, 

when used, render the RIC one of the strongest quasi-experimental designs. 

The corroboration of estimates of change. What is critical for the full 

implementation of a RIC design, and what makes the RIC different more rigorous that 

running either the weaker post-test only between-group difference tests or the pre- postchange tests alone, is the use of pre-post and between-group tests in combination and 

comparing these estimates to corroborate evidence of change generated by each separate 

approach. Furthermore, and also required for proper use of the RIC is the comparison of 

program-related change estimates across multiple cohorts. This provides further evidence 

of the corroboration of program-related differences. 

Figure 3.1 below provides an example of how the use of multiple (minimally 

three) cohorts to conduct multiple within-person and between-group difference tests can 

be done. Shown in the figure are two individuals, Tom and Lou’s’, who enter the 

program in different cohorts. This figure does not show the third cohort because the third 

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Final Technical Report: 2011-JU-FX-0001 p. 28

cohort simply allows a replication of what is shown in the figure for two cohorts. For 

simplicity and clarity, the figure illustrates how tests of the significance or the magnitude 

of individual’s pre- and post-post assessments are made. Within individual change for 

Tom compares his pre-treatment (at age 12 years, nine months) to his post-treatment 

score (at 13 years, 0 months). Pre-post change for Lou’s represents the difference 

between his pre-treatment score (at age 13 years, one month) and his and post-treatment 

score (at age 13 years, 5 months). Notice, there will be both a degree (or magnitude) of 

difference and a direction of change (increases or decreases of the post-score to the prescore). These pre-post change estimates reveal whether there was any change related to 

program participation. In some cases, such as when the outcome is a phenomenon known 

to increase over the length of time represented by the age or intervention cycle length 

used to create cohorts, no change can reflect a treatment effect. Otherwise, no change on 

variables not likely to change as a result of maturation would suggest no program-related 

change and therefore no program-related treatment effect. 

The second difference estimate of change is the comparison of mean scores for 

the untreated youth and the treated youth. Between-group (across intervention cohorts) 

change provides the second critical test of program-related change. In this test, Tom’s 

comparison/untreated cohort’s pre-test average is compared to Lou’s intervention/treated 

cohort’s mean post-test score to generate a treated vs. untreated comparison of estimated 

change associated with program participation. 

Not illustrated in Figure 3.1 is that with one more intervention cohort, the tests of 

both within-youth (pre-post) and between untreated and treated groups differences can be 

examined for corroboration across cohorts. Comparing the comparability of these two 

types of program-related change can either corroborate (or contradict) the estimates of 

change observed in analyses run with earlier cohorts. 

The inclusion of tests across multiple cohorts and comparisons of the size and 

direction of differences, both pre-post (within) and treatment/untreated (between) group 

comparisons, provides a third method of corroboration of evidence. The effect size of this 

treated vs. untreated difference can be compared to the difference between treated and 

untreated groups in subsequent (and prior) intervention cohorts, such as Lou’s group’s 

post-test mean score being compared to the average pre-score of the intervention cohort 

that follows his. These comparisons can be conducted for all youth in each cohort, or be 

stratified by age (or other moderators) when maturational processes are at play, or when 

there is significant variability across cohorts (or across moderating characteristics of the 

participants who do and don’t tend to complete the program: attriters). 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
.

Final Technical Report: 2011-JU-FX-0001 p. #*

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Figure 3.1. Representation of the Age Cohort, Recurrent Institutional Cycle Design 

Missing data and attrition. An important issue related to the RIC design’s 

comparison of treated vs. untreated individuals is the problem of attrition and missing 

data. The comparison of pre- and post-test scores that does not account for attrition is 

always problematic. Making comparisons across intervention cohort groups without 

addressing missing data, particularly when non-completion of the program is associated 

with specific youth characteristics, observed or not observed, will make the 

counterfactual comparison group much less useful, especially if characteristics related to 

missing data are also associated with program outcomes. In programs like YAP, the pool 

of those who complete the program (the treatment group) will often not include the youth 

who entered the program at greatest risk for misconduct and quit prematurely, such that 

the comparison group means will likely be less favorable (higher in misconduct) because 

the highest risk youth are in the comparison group but no longer in the treated group. So 

detection of missing data and its treatment can be vital to effectively using the RIC 

design. In this study, FIML was used in the structural modeling of hierarchical (nested 

dyadic) data and multiple-imputation was used to construct a dataset that estimated scores 

for individuals missing data at discharge or twelve months post-discharge. 

The reliance on standardized effect size estimates. Another way of 

corroborating evidence is to compare the results of estimated differences between groups 

(effect size coefficients) to significance tests testing the hypothesis of no difference. 

Significance tests like those associated with t-tests and F-tests address Type I error 

(claiming differences that are really due to chance) by relying on the probability of a 

given difference occurring in a sample of a specific size were this effect not really one 

found in the population (i.e., by running the same tests with many different samples). 

Significance tests are most appropriate with larger samples and when fewer (not more) 

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necessarily reflect the official position or policies of the U.S. Department of Justice 

Final Technical Report : 2011-JU-FX-0001 p. 31

instilled. Yet evidence of consistent differences across measures found for between just 

two cohorts remains subject to several validity threats, such as history effects for only 

one cohort’s experience that might not be revealed in another cohort. So the use of 

multiple cross-cohort estimates allows corroboration to determine the validity of this 

particular threat. Additionally, when comparison across cohorts reveals similarly sized 

change or difference effect sizes, then the hypothesis that change is due to program 

participation is even more credible. 

It should be noted that there are issues of statistical significance that must be 

made on a conceptual or philosophical level before proceeding, and that for most of the 

comparisons made effect size measures of magnitude of an effect will be more reliable 

that tests of significance. Because there are sub-samples being compared, most tests will 

be inadequately powered (thus yield a high Type II error rate). But there also are many, 

many tests being conducted, so reliance on significance tests also could yield high Type I 

error rates. Although one correction for high Type I error rates is to adjust (by making 

more stringent) the significance criterion use for test statistics, this is unwise when 

sample sizes are small as making such adjustments to significance levels is not yet 

considered an accommodation for small samples sizes by many in the social sciences. 

Therefore, only the final tests of between-group differences, those with the whole sample 

(those sufficiently powered by including multiple imputed data sets or the whole sample 

with assessments nested within youth), consider the need to adjust probability criterion to 

keep Type I error rates low. 

The RIC design depends on effect size estimate consistency rather than 

significance test probabilities. Of course, if an extremely large sample provides data that 

allows the smaller cohort sample sizes to be sufficiently powered to be used for the 

preliminarily validity-check tests described below, then it may be possible to use them 

and necessary to both adjust the significance level for the number of comparisons (e.g., 

Bonferroni or Benjamini-Hochberg adjustment) or the size of the sample. 

Measurements & Design 

Prosocial behaviors and attitudes. This funding initiative was not only 

interested in recidivism rates subsequent to program participation but also about prosocial 

behaviors and attitudes. As one phenomenon reflecting prosocial attitudes and behaviors, 

ecological connectedness captures these elements and has been found to predict 

criminality and educational success in prior research. These were one set of outcomes 

assessed as measures of program change. 

Hemingway: Measure of Adolescent Connectedness (5.5 version; Karcher, 2003, 

see Karcher & Sass, 2010). The Hemingway instrument consists of nine scales that assess 

adolescents’ caring for and involvement in specific relationships and activities. The 

Connectedness to School scale focuses on the importance youth place on school and how 

active they are in being successful in school. Connectedness to Teachers assesses effort 

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Final Technical Report: 2011-JU-FX-0001 p. 32

to get along with teachers and concerns about earning teachers' respect and trust. Self-inthe-Future asks about the behaviors and qualities of youth that they perceive will help 

them have a positive future. The scales have demonstrated good three-month test-retest 

reliability, a distinct factor structure, evidence of convergent and discriminant validity, 

and invariance across sex and race.5 For the present study, factor scores were computed 

and fit indices and other analysis output is available by request. Factor scores were used 

because coefficient alpha reliability estimates for the observed scores for the 

connectedness subscales when used was School (α = .84), Teachers (α = .65), Self-in-theFuture (α = .82), Family (α = .74), and Friends (α = .70) were lower than ideal. Factor 

loadings and fit indices are available from the Author and can be found in the article by 

Karcher and Sass (2010). The items for each subscale are located in Appendix H. 

Items adapted for the Advocate reports of two connectedness scales are presented here: 

Connectedness to School 

4. This youth works hard at school. 

9. This youth enjoys being at school. 

14. This youth gets bored at school. 

20. This youth currently does well in school. 

25. This youth feels good about him/herself at school. 

Connectedness to Teachers 

6. This youth cares what his/her teachers think of him/her. 

11. This youth does not get along with some of his/her teachers. 

16. This youth wants to be respected by his/her teachers. 

22. This youth tries to get along with his/her teachers. 

27. This youth always tries hard to earn his/her teachers’ trust. 

30. This youth usually likes his/her teachers. 

Expects to finish high school, start college, finish college and find a job. From the 

Big Brothers Big Sisters School Based Mentoring study (Herrera et al. (2007). 

“These sentences are about your plans for high school and college. Circle one number to show how sure 

you are about each question. Check 1 if you’re NOT AT ALL SURE or 4 if you are VERY SURE” 

Table 3.1 (Circle one) 

Expectations for School and Work 

“How sure are you that you will” 

Not At All 

Sure 

Not Really 

Sure Mostly Sure Very Sure 

...finish high school? 1 2 3 4 

... go to college? 1 2 3 4 

.... finish college? 1 2 3 4 

...find a good job? 1 2 3 4 

5 Karcher, M. J. & Sass, D. (2010). A multicultural assessment of adolescent connectedness: Testing 

measurement invariance across gender and ethnicity. Journal of Counseling Psychology, 57(3) 274-289 

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Final Technical Report: 2011-JU-FX-0001 p. 33

YAP assessments of educational and vocational engagement. These data are 

collected regularly as part of YAP’s program evaluation 

Current living situation. These data are collected regularly as part of YAP’s 

program evaluation. 

Table 3.2 

Living Situation at Entry 

Independently Foster family In-patient substance 

abuse 

Homeless 

With Parent(s) Supervised independent 

living 

Non-secure detention AWOL 

Adult Relative Group home or group 

residence 

Secure detention 

Adult family 

friend 

Residential facility Incarcerated 

Adoptive 

family 

Mental health facility Living in a shelter 

Educational (situation) engagement. Only those in bold were used in analyses 

Table 3.3 

Current Educational Situation 

(If school is currently closed, check the box for when school was last in session.) 

Working on or received a Graduate Degree 

Working on or received an Undergraduate Degree 

Has taken non-degreed Post High School courses 

Graduated – received regular high school diploma 

Graduated – received GED 

Enrolled – attending 4-5 days/week (Home schooled applies) 

Enrolled – attending 1-3 days/week (Home schooled applies) 

Enrolled but not attending school 

Tardy or leaving early at least once a week 

Awaiting enrollment in a new school or re-enrollment 

Temporarily suspended from school 

Permanently expelled from school 

Legally withdrawn from school – working on GED 

Did not graduate and not working on any type of Diploma 

Too young for school / pre-kindergarten 

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Final Technical Report: 2011-JU-FX-0001 p. 34

Employment status. The employment status variable ranges from 0 (too 

young/disability; cannot work), to 1 (unemployed), 2 (actively seeking employment), 3 

(working part time), and 4 (working full time). 

Self-reported deviancy/misconduct 

Misconduct. Below is the format in which the items which were used to create the 

misconduct measure were collected. These items were originally developed by Bradford 

Brown et al.6 

Table 3.4 

Self-reported Misconduct 

How often have you done the following 

in the past year? 

I have 

never 

done this 

Once or 

twice 

I did this 

more than 

two times 

1. Run away from home 

2. Gotten into a gang fight. 

3. Hit a parent 

4. Lied to your parents about something 

important. 

5. Stole something (worth less than $5) 

6. Attacked, physically hurt, or “jumped” 

someone you did not know well 

7. Took something from a store without 

paying for it. 

8. Strong-armed or bullied another kid or 

student 

9. Panhandled (asked for money from 

strangers) 

10. Strong-armed a teacher (or employer) 

Most serious disposition. 

Table 3.5 

Most Serious Disposition at Entry 

None 

Non-Criminal and/or Status Offense 

Misdemeanor/Citation 

Felony 

6 Brown, B. B., Clasen, D. R., & Eicher, S. A. (1986). Perceptions of peer pressure, peer conformity dispositions, 

and self-reported behavior among adolescents. Developmental Psychology, 22, 521–530. 

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Final Technical Report: 2011-JU-FX-0001 p. 35

Sample Information 

At entry. The sample of the study is described below. At intake, the average age 

of youth enrolled in YAP and who participated in this study was fifteen and a half years, 

with a range from eleven to eighteen. More youth were Black (n = 63), but White (n = 

44) and Hispanic (n = 39) youth were also well represented in the sample. The large 

majority of participants were male, with the exception of in Las Vegas, wherein the ratio 

of boys to girls was 4:3. The number of youth in the Las Vegas site was twice that of 

youth in Toledo and Lebanon, while the number of youth from the Camden site was a 

quarter (n = 17) of those from Las Vegas (n = 67). 

Data collection for the 164 participating youth in the study ended June 15, 2014. 

Of these, 164 youth completed pre-surveys, 133 youth and Advocate pairs completed 2-

month surveys, and 103 youth and Advocate pairs completed 4-month surveys. To 

address the issue of attrition, we used multiple imputation to estimate missing data on 

participant outcomes at the 2-month and 4-month time point for absent follow up data for 

those who terminated treatment prematurely. 

Age. The age at entry did not differ across agencies. 

Table 3.6 

Age at Enrollment of Participants in Each Site 

N Mean Youngest Oldest 

Age at entry 

(in days) 

Toledo 

Camden 

Lebanon 

Las Vegas 

Total 

43 

17 

37 

67 

164 

15.71 

15.96 

15.40 

15.84 

15.72 

12.00 

14.35 

10.14 

11.46 

10.14 

18.14 

18.13 

17.99 

20.37 

20.37 

Sex. The descriptive statistics below are for the whole sample at entry. The 

proportion of girls to boys was much higher in the Las Vegas site. 

Table 3.7 

Sex of Participants in Each Site 

City 

Toledo Camden Lebanon Las Vegas Total 

Sex 

Total 

Male 

Female 

37 

6 

43 

16 

1 

17 

32 

5 

37 

38 

29 

67 

123 

41 

164 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 36

Race. There were significantly more Caucasian/White and Hispanic/Latino youth 

in Lebanon and Las Vegas than in the other two sites. Las Vegas also had a greater 

proportion of black/African American youth than Lebanon, but fewer than in Toledo and 

Camden. 

Table 3.8 

Race of Participants in Each Site 

City 

Toledo Camden Lebanon Las Vegas Total 

Race 

Total 

Caucasian 

Af. American 

Hispanic 

Asian 

Pacific Islander 

Bi racial 

Other 

8 

30 

3 

0 

0 

2 

0 

43 

2 

12 

0 

0 

0 

1 

2 

17 

16 

1 

17 

0 

0 

3 

0 

37 

18 

20 

19 

1 

1 

8 

0 

67 

44 

63 

39 

1 

1 

14 

2 

164 

Table 3.9 

Presence of Important Adult at Entry 

YAP City 

Toledo Camden Lebanon Las Vegas Total 

Youth report at 

entry having an 

important adult in 

his or her life 

Reported no 

important adult 

Reported having an 

important adult 

Total 

14 

23 

37 

6 

10 

16 

14 

18 

32 

33 

33 

66 

67 

84 

151 

Combined sample characteristics. Below are the characteristics of the sample at 

entry, discharge, and post-discharge. Although some of these descriptive numbers also 

serve as outcome variables (and thus typically belong in the results section) they are 

presented here to make clear the differences between the population of youth from whom 

complete data were provided and for whom missing information at discharge and postdischarge were imputed or addressed with other missing data techniques (in Mplus). 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 37

Race/ethnicity. 

3.10 

Race/ethnicity at Entry 

Group Name Number Races/Ethnicities Included % 

African American 1 African American 45.9% 

Bi-Racial 2 Bi-Racial 4.9% 

Caucasian 3 Caucasian 29.4% 

Hispanic 4 Hispanic 17.5% 

Other 5 American Indian; Asian; Other 2.4% 

Primary referral reason. 

Table 3.11 

Referral Reason 

Group Name Group 

Number Referral Reasons Included % 

Legal Issues 1 Involvement w/ the Legal System; Diversion 46.7% 

Child Welfare Issues 2 

Abuse; Neglect 

Parent and Child Conflict 

Parental Support; Domestic Violence 

Eviction/Homeless; Reunification 

24.3% 

Mental Health 3 Mental Health 12.9% 

School Issues 4 Truancy; Other School Issues 10.8% 

Other 5 Substance 5.3% 

Living situation. This category reflects responses to the questions youth were 

asked about their living in the community or whether they had been placed out of home 

within the prior 30 days. YAP measures living situation at entry or within 30 days prior 

to entry, depending on whether the youths’ referral to YAP was a condition of release 

from out-of-home placement. The proportions of both groups (exclusions are presented 

below, with categories ranging from least to most restrictive). 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 38

Table 3.12 

Living Situation at Entry 

Group Name Group 

Number 

Living Situation at Entry or Within 30 Days 

Prior to Entry 

% 

Community Living 1 

With parent(s); Adoptive family 

Adult family friend 

Independently; Adult relative 

78.1% 

Foster Care 2 Foster Family 6.9% 

Moderate Supervision 3 Group Home; Non-Secure Detention 1.8% 

Residential Facility 4 

In-patient substance abuse 

Mental health facility 

Residential facility 

3.5% 

Secure Facility 5 Secure detention; Incarcerated 7.9% 

Excluded 6 Supervised independent living 

Homeless, AWOL, Living in a shelter 

1.9% 

It was unclear whether the 1.9% of youth who were homeless, living in a shelter 

or listed as AWOL at entry should be included in analyses because YAP typically doesn’t 

have full information on these youth at entry or discharge making it hard to accurately 

include them in analyses using missing data to address attrition. So this small group of 

youth was not included in the outcome analyses. 

School Attendance. 

Table 3.13 

School Attendance at Entry 

Group Name Group 

Number School Attendance % 

Graduated 

High School 1 

Graduated – received regular high school diploma 

Has taken non-degreed Post High School courses 

Working on or received an Graduate Degree 

Working on or received an Undergraduate Degree 

5.0% 

Earned GED 2 Graduated – received GED 3.3% 

Attending 

Regularly 3 Enrolled – attending 4-5 days/week Legally withdrawn from school – working on GED 70.4% 

Attending Part 

Time 4 Enrolled – attending 1-3 days/week Tardy or leaving early at least once a week 8.4% 

Not Attending 5 

Enrolled but not attending school 

Permanently expelled from school 

Temporarily suspended from school 

Did not Graduate and not working on any type of 

Diploma 

Awaiting enrollment in a new school or re-enrollment 

10.2% 

Other 6 Other 2.5% 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 39

Employment. 

Table 3.14 

Employment at Entry 

Group Name Group 

Number Employment Situation % 

Employed Full 

Time 1 Working Full-Time (at least 35 hours a week) .8% 

Employed Part 

Time 2 

Working 20 hours a week or more, but less than 

full-time 

Working less than 20 hours a week 

3.5% 

Actively Seeking 

Employment 3 Unemployed - Actively seeking employment 17.9% 

Unemployed 4 

Unemployed - Interested but not looking for 

work 

Unemployed - Not interested in working 

43.2% 

Excluded 5

Too young for most work assignments – under 

14 

Unable to work due to mental or physical 

disability 

34.7% 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 40

At discharge. 

Living situation. 

Table 3.15 

Living Situation at Discharge 

Group Name 

Group 

Number Living Situation % 

Community Living 1 

Independently; Adoptive family 

Adult family friend; Adult relative 

With parent(s) 

84.0% 

Foster Care 2 Foster Family 5.3% 

Moderate Supervision 3 Group home or group residence 

Non-secure detention 1.6% 

Residential Facility 4 

In-patient substance abuse 

Mental health facility 

Residential facility 

1.9% 

Secure Facility 5 Secure detention; Incarcerated 2.4% 

Excluded 6 

Supervised independent living 

N/A*; Deceased*; Homeless 

AWOL*; Living in a shelter 

4.8% 

School attendance. 

Table 3.16 

School Attendance at Discharge 

Group 

Name 

Group 

Number School Attendance 

% 

Graduated 

High 

School 

1 

Graduated – received regular high school diploma 

Has taken non-degreed Post High School courses 

Working on or received a Graduate Degree 

Working on or received an Undergraduate Degree 

5.3% 

Earned 

GED 2 Graduated – received GED 3.0% 

Attending 

Regularly 3 Enrolled – attending 4-5 days/week Legally withdrawn from school – working on GED 73.7% 

Attending 

Part Time 4 Enrolled – attending 1-3 days/week Tardy or leaving early at least once a week 5.4% 

Not 

Attending 5 

Enrolled but not attending school 

Permanently expelled from school 

Temporarily suspended from school 

Did not Graduate and not working on any type of 

Diploma 

Awaiting enrollment in a new school or reenrollment 

7.8% 

Too Young 

for School 6 Too Young for School 1.5% 

N/A N/A N/A* 3.3% 

* Includes all clients listed as Excluded in Living Situation at Discharge 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 41

Employment Situation. 

Table 3.17 

Employment at Discharge 

Group Name Group 

Number Employment Situation % 

Employed Full 

Time 1 Working Full-Time (at least 35 hours a week) 1.8% 

Employed Part 

Time 2 

Working 20 hours a week or more, but less 

than full-time 

Working less than 20 hours a week 

7.6% 

Actively Seeking 

Employment 3 Unemployed - Actively seeking employment 18.6% 

Unemployed 4 

Unemployed - Interested but not looking for 

work 

Unemployed - Not interested in working 

40.6% 

Excluded 5 

Too young for most work assignments – under 

14 or Unable to work due to mental or physical 

disability 

28.3% 

N/A N/A N/A* 3.3% 

At 12 months post discharge 

Living situation. 

Table 3.18 

Living Situation 12 months Post Discharge 

Group Name Group 

Number Living Situation % 

Community Living 1 

Independently; Adoptive family 

Adult family friend; Adult relative 

With parent(s) 

88.3% 

Foster Care 2 Foster Family 1.8% 

Moderate 

Supervision 3 Group home or group residence Non-secure detention 0.4% 

Residential Facility 4 In-patient substance abuse 

Mental health facility; Residential facility 4.6% 

Secure Facility 5 Secure detention; Incarcerated 4.4% 

Excluded 6 

Supervised independent living 

N/A*; Deceased*; Homeless 

AWOL*; Living in a shelter 

0.4% 

* Note that the remaining questions in post discharge survey may be answered as N/A. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 42

School attendance. 

Table 3.19 

School Attendance 12 months Post Discharge 

Group Name Group 

Number School Attendance % 

Graduated High 

School 1 

Graduated – received regular high school 

diploma 

Has taken non-degreed Post High School 

courses 

Working on or received a Graduate Degree 

Working on or received an Undergraduate 

Degree 

7.2% 

Earned GED 2 Graduated – received GED 2.9% 

Attending 

Regularly 3 

Enrolled – attending 4-5 days/week 

Legally withdrawn from school – working on 

GED 

79.6% 

Attending Part 

Time 4 Enrolled – attending 1-3 days/week Tardy or leaving early at least once a week 1.1% 

Not Attending 5 

Enrolled but not attending school 

Permanently expelled from school 

Temporarily suspended from school 

Did not Graduate and not working on any type 

of Diploma 

Awaiting enrollment in a new school or reenrollment 

8.0% 

Too Young for 

School 6 Too Young for School 1.2% 

N/A N/A N/A* 0.0% 

* Includes all clients listed as Excluded in Living Situation at Discharge 

Employment Situation. 

Table 3.20 

Employment 12 months Post Discharge 

Group Name Group 

Number Employment Situation % * 

Employed Full 

Time 1 Working Full-Time (at least 35 hours a week) 3.9% 

Employed Part 

Time 2 

Working 20 hours a week or more, but less 

than full-time 

Working less than 20 hours a week 

7.0% 

Working at an 

Internship 6 Volunteer/Internship 0.9% 

Actively Seeking 

Employment 3 Unemployed - Actively seeking employment 7.9% 

Unemployed 4 

Unemployed - Interested but not looking for 

work 

Unemployed - Not interested in working 

61.9% 

Excluded 5 18.4% 

N/A N/A N/A* 0.0% 

* Includes all clients listed as Excluded in Living Situation at Discharge 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 43

Cohort Creation 

Typical age-based cohorts did not turn out to be useful and outcome analyses. 

Nevertheless, a first step was to create cross-cohort treated vs. untreated comparison 

groups. Subsequently, an intervention cycle cohort grouping was created in which 

participants were not matched by age by treatment duration as the way to separate 

individuals across treatment group classifications. A 4-month cohort grouping created a 

window of time comparable to the average duration of program participation so that the 

pre-test for one cohort occurs at roughly the same time as the post for the youth in the 

prior cohort. So, youth who started services within the first four months were placed in 

cohort 1 (June 1, 2012 – September 30, 2012), the second cohort include youth who 

started services during the second four-month period (October 2012-January 2012), et 

cetera. 

This yielded five cohorts, which ranged in size from 25 to 40 participants. For 

cohort five, two youth started services in February of 2014, which would have been the 

first month of what would have been cohort 6. These were the last two youth to be 

enrolled in the program, as we discontinued enrollment in February of 2014. In order to 

keep these two youth in the analyses, we included them in cohort five. The 4-month 

cohort would allow for five pre-post changes comparisons, and four treated vs. untreated 

tests of difference that compare pre-post scores from the same time period (between 

cohorts) in which the pre-test is from the subsequent cohort. 

The benefit of this cohort grouping is that the length of the cohort inclusion 

matches the length of treatment we are assessing (4 months) resulting in clean divisions 

with groupings and testing. Of course, a drawback about this grouping is that the length 

of time used to group the cohorts’ means data are susceptible to history effects. For 

example, the youth in cohort 1 include those who started receiving YAP services in June 

through September, and may have variance in their scores associated with the start of 

school/end of summer or fluctuations in adult supervision associated with being out of 

school, etc. 

This sequence is illustrated below in Figure 3.3. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

	 	 	 	 	 	 	 	 	

	 	

	 	 	 	 	 	 	

	 	

	 	 	 	 	 	

	 	

	 	 	 	 	 	

	 	

	 	 	 	 	 	

	 	

 

 

Final Technical Report: 2011-JU-FX-0001 p. 44

The Five
 

4-Month
 

Cohort
 

Group
 

Cohort 1

C1 n	= 37

Cohort 2

C2 n	= 40

Cohort 3

C3 n	= 34

Cohort 4

C4 n	= 26

Cohort 5

C5 n	= 25
 

Period of time, month and year (e.g., October 2012 is 10-12) in which the Post (Treated Group) and Pre (Untreated 

Comparison) Assessments were completed for those whose participation lasted longer than four months. 

6-

12

7-

12

8-

12

9-

12

10-

12

11-

12

12-

12

1-

13

2-

13

3-

13

4-

13

5-

13

6-

13

7-

13

8-

13

9-

13

10-

13

11-

13

12-

13

1-

14

2-

14

3-

14

4-

14

5-

14

6-

14

Obs 1 (C1 Pre) Obs 2 (C2 Post)

9 9 8 11 9 9 8 11

Obs 3 (C2 Pre) Obs 4 (C2 Post)

14 14 4 8 14 14 4 8

Obs 5 (C3 Pre) Obs 6 (C3 Post)

4 12 9 9 4 12 9 9

Obs 7 (C4 Pre) Obs 8 (C4 Post)

4 4 14 4 4 4 14 4

Obs 9 (C5 Pre) Obs 10 (C5 Post)

12 2 4 7 12 2 4 7

+2

C1 Cross-cohort C2 Cross-cohort C3 Cross-cohort C4 Cross-cohort 

treatment effect treatment effect treatment effect treatment effect 

*Observations (Obs1, Obs2...) are listed according to original RIC design to help visually identify what data is collected from which cohort when 

Figure 3.3 Intervention Cohorts used in Final Analyses 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 45

Cohort characteristics. To understand in what ways these cohorts differed at 

pre-test on key demographic variable as well on the outcome variables a series of 

comparisons were made. Differences between the cohorts in terms of scores on age, 

gender, race, city, and on those outcome variables chosen for the RIC pre-post changes 

and treated vs. untreated comparison t-tests were examined using ANOVA and chisquare tests. An ANOVA was conducted across cohorts as the fixed factor, and age as 

the dependent variable. For the other three categorical covariates (gender, race, city) chisquare tests were used. For the four main YAP outcome variables (misconduct, serious 

disposition, education situation, employment status), ANOVA was used. 

Age. Results of the ANOVA indicated nearly statistically significant differences 

between the cohorts in regards to age (F = 2.41, p = .052). Post-hoc tests revealed the 

difference in age was between Cohort 2 (15.40, n = 40) and Cohort 3 (16.13, n = 34). 

City. Results of the chi-square test indicated that there was a statistically 

significant difference between Cohorts 4 and both Cohorts 3 and 5 in terms of the number 

of youth from each City. The difference was largely that Camden had nine youth in 

Cohort 3, zero youth in Cohort 4, and three youth in Cohort 5. 

Table 3.21 

Differences in Number of Youth in Cohort 3 & 4 by City 

Cohort Grouped by 4 Mos. 

3.00 4.00 Total 

City Toledo Count 

Expected Count 

9 

11.3 

11 

8.7 

20 

20.0 

Camden Count 

Expected Count 

9 

5.1 

0 

3.9 

9 

9.0 

Lebanon Count 

Expected Count 

5 

5.1 

4 

3.9 

9 

9.0 

Las Vegas Count 

Expected Count 

11 

12.5 

11 

9.5 

22 

22.0 

Total Count 

Expected Count 

34 

34.0 

26 

26.0 

60 

60.0 

Table 3.22 

Chi-Square Tests of Proportional Differences Cohorts 3 and 4 by City 

Value Df p (2-tailed) 

Original data Pearson Chi-Square 

Likelihood Ratio 

Linear-by-Linear Assoc. 

N of Valid Cases 

8.39a 

11.71 

.02 

60 

3 

3 

1 

.039 

.008 

.888 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 46

Table 3.23 

Differences in Number of Youth in Cohorts 4 and 5 Across Cities 

Cohort Grouped by 4 Mo 

4.00 5.00 Total 

City Toledo Count 

Expected Count 

11 

7.8 

5 

8.2 

16 

16.0 

Camden Count 

Expected Count 

0 

1.5 

3 

1.5 

3 

3.0 

Lebanon Count 

Expected Count 

4 

7.4 

11 

7.6 

15 

15.0 

Las Vegas Count 

Expected Count 

11 

9.3 

8 

9.7 

19 

19.0 

Total Count 

Expected Count 

26 

26.0 

27 

27.0 

53 

53.0 

Table 3.24 

Chi-Square Tests of Proportional Differences Cohorts 4 and 5 by City 

Value Df p (2-tailed) 

Original data Pearson Chi-Square 

Likelihood Ratio 

Linear-by-Linear 

Association 

N of Valid Cases 

8.98a 

10.32 

.48 

53 

3 

3 

1 

.030 

.016 

.488 

a. 2 cells (25.0%) have expected count less than 5. The minimum expected count 

is 1.47. 

Sex and Race. There were no differences in the proportion of either sex or race 

across the cohorts. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 47

Pre-existing Differences on Variables at Across Cohorts at Pre-Test 

Serious disposition. Results of the ANOVA indicated a statistically significant Ftest, indicating some differences between the cohorts on serious dispositions at entry. 

Post-hoc tests revealed that Cohort 5 was significantly different from all the other cohorts 

in terms of serious disposition. There were no differences between Cohorts 1 through 4. 

Cohort 5’s pre-test data is used as the untreated comparison group in the Cohort 4 (C4) 

treatment effect test. Because Cohort 5 also was older than the other cohorts, and 

disposition increases with age, generally, this C4 between group difference test should be 

given less merit than the other comparisons. 

Education situation. Results of the ANOVA indicated there were no statistically 

significant differences between any of the cohorts on educational status at pre-test. There 

were differences were between Cohort 2 (2.45, n = 38) and Cohort 3 (2.81, n = 32; p = 

.008), and between Cohort 4 (2.29, n = 21) and Cohort 5 (2.74, n = 27), p = .09). But the 

Levine test suggested equality of variance could not be assumed, and therefore the 

Dunnett T3 post-hoc test was chosen for these analyses. The results of the Dunnett T3 

indicated that there were no statistically significant differences between any of the 

groups, including Cohorts 2 and 3, and Cohorts 4 and 5 once bias related to non-equal 

variance was accounted for. 

Employment status. Results of the ANOVA indicated no statistically significant 

differences between any of the cohorts in terms of employment status at entry. Initial 

tests for sequential differences between the cohorts revealed a statistically significant 

difference between Cohort 3 (1.22, p = 32) and Cohort 4 (1.43, n = 21; p = .011). 

However, as equality of variances could not be assumed, and the Dunnett T3 post-hoc 

analyses indicated that there were no statistically significant differences between any of 

the groups, including Cohorts 3 and 4. 

Maturation Tests 

Assessing maturation as a threat to program effect interpretations of change is 

important in the RIC design. To assess the possibility of maturation as an explanation of 

treatment effects, we examined the degree to which, how consistently, and for what age 

groups change (observed cross-sectionally) occurs normatively over time on these 

outcome variables at pre-test. We used the program-prescribed (or average) treatment 

duration (4 months) as the period across which no observed change on a variable would 

allow differences observed between pre- and post-test (within person) scores to be 

considered a function of program participation and not due to normative change over 

time. 

These analyses compare the pre-data for whole sample on mean scores to identify 

any normative differences on each outcome across age groups differing by 4 months, and 

where present to assess magnitude (and direction) of normative (cross-sectional) 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 48

variability. To make the tests of maturation more liberal (decrease Type II error rates) we 

choose a timeframe 6 months (or 1.5 times that of the expected treatment duration) for 

evidence of maturation. Doing this we felt would ensure maturation was not a viable rival 

explanation of differences found among youth over time: “The observed between-group 

differences cannot be attributed to maturational processes because change of this 

magnitude is not normative even over a time period 1.5 times longer than the pre- post 

assessment interval.” 

For these maturation tests the grouping of the participants ranged in ages from 14 

years 0 months to 17 years 0 months. Too few youth were older or younger than this 

timeframe to adequately conduct difference tests. These 118 youth, spanning 3 years, 

were divided into groups based on six-month windows, which yielded seven groups, 

approximately 1.5 times the typical treatment duration for youth in this study. 

The table below lists the number of youth in each group. 

Table 3.25 

Number of Youth in Each Age Group for Maturation Tests 

Age group 14 14.5 15 15.5 16 16.5 17 

N 17 13 23 23 14 28 17 

A series of ANOVAS was conducted using these age groups as the fixed factor, 

and the outcome variables of interest (Employment Status, Education Situation, etc.) as 

the dependent variables (comparing each age group to the subsequent age group: 14 to 

14.5, 14.5 to 15, and 15 to 15.5, etc.). We used multiple imputation to estimate the 

missing data. The average p-value across the five imputation samples pooled was used to 

infer statistically significant differences across the age groups. 

Hemmingway scales. There were no statistically significant differences between 

the sequential age groups on any of the connectedness scales (friend, family, school, 

teacher, or future). 

Misconduct. There were no statistically significant differences between the age 

groups in regards to self-reported misconduct in sequential comparisons. 

Education expectations: Finish high school. For Education Expectations: 

Finish High School, the difference between age groups 14 and 14.5 years approached a 

statistically significance (p = .058). We considered that because this comparison was 

between youth who had just started or were about to start high school (14 year olds, many 

in middle or junior school) and those who had already started (14.5), it made sense that 

there would be a difference in how youth expectation for finishing high school before and 

after starting high school. This effect poses more of a history threat to internal validity 

such that any differences attributed to treatment for youth in these age groups may more 

accurately reflect this historical event that all of them undergo during this time. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 49

Education expectations: Start college. For Education Expectations: Start 

College, age groups 14 and 14.5 also had a statistically significant difference (p = .03). 

The explanation above regarding a difference in expectations to finish high school likely 

applies to this variable as well. 

Employment status. For employment status, there were no statistically 

significant differences between the age groups, except for the groups representing 15.5 

(mean = 1.13) and 16 year olds (mean = 1.54), with pooled p-value of .014. For 

reference, the employment status variable ranges from 0 (too young/disability; cannot 

work), to 1 (unemployed), 2 (actively seeking employment), 3 (working part time), and 4 

(working full time). We felt this difference between the 15.5 and 16 age groups, but not 

between later age groups, made sense given that the 16 age is typically the first age when 

youth can legally be employed. 

Education situation. For Education Situation, there were no statistically 

significant differences between the age groups. 

Living situation. For Living Situation, there were no statistically significant 

differences between the age groups. 

Serious disposition. For Serious Disposition, there were no statistically 

significant differences between the age groups; however, the difference between 16 year 

olds and 16.5 year olds approached statistical significance (p = .07). 

In conclusion, there does not appear that for any of these outcome variables the 

rival threat of maturation is a concern. Very few developmental differences appeared 

among the many comparisons conducted. On only three variables did differences between 

age groups suggest the presence of maturational processes. For all these, a difference was 

only observed between two age groups, and there are very plausible explanations for why 

two of these differences may occurred suggesting these differences are historical and not 

maturational. Only for the variable Serious Disposition was the reason for the single 

between-age group difference not apparent. But for this variable as well, developmental 

differences do not appear to be common or particularly strong and only appeared across 

on age group difference. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

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Final Technical Report : 2011-JU-FX-0001 p. 50

IV. Study 1: Outcome Evaluation—Results 

In this section, analyses and findings are reported from most basic analyses first 

and to most sophisticated or complex analyses last. Some readers (the “statistical 

purists”) may feel that the simplest comparisons are the truest tests of changes associated 

with program participation. This may be true, particularly for traditional tests of 

differences across groups when youth are randomly assigned; but for many quasiexperimental group comparisons the more sophisticated and complex analyses often 

provide better tests of reliable differences, because these analyses are typically run to 

address more of the possible selection threats and other rival interpretations of findings. 

For those findings, readers may wish to start with the final section which reports 

on tests of group differences that address statistical dependency by nesting data within 

youth (to create independent statistical tests) and that reflect the greatest statistical power 

(by being able to pool all youth into one difference test). These comparisons, at the end of 

this results section, provide direct interpretations, whereas the tests in the first couple of 

sections require inferring overall change by comparing differences in effect sizes across 

multiple comparisons. 

Then there are the two types of change statistics and two change estimates. The 

change estimates are the between group differences and the within-person change. Then 

there are the two types of change-estimating statistics. The first, the effect size, which 

reflects the standardized mean difference of within and between group changes. The 

second change statistics is the likelihood or probability test associated with this effect 

size. The bulk of the analyses in this report are of effect sizes, which are examined for 

patterns of consistency. This is because we believe that analyzing the consistency of 

effect sizes and their direction provides an equally persuasive assessment of change. 

Doing so captures the trustworthiness of estimates of change in terms of their replication. 

The tables in this section provide both, effect sizes in columns and indicate statistical 

significance by bolding the effect sizes. 

In the final summary section, convergence in the findings across these different 

analyses is attempted to help the reader integrate the vast number of comparisons made 

and the several separate tests, each designed to address a specific validity threat. 

Preliminary Age-Cohort Matched Comparisons on Outcomes 

Recall that understanding how treated and untreated comparison youth of the 

same age compare on key outcomes is usually a good first step in utilizing the RIC 

design, even though it alone is not sufficient to make strong inferences. What makes the 

RIC approach attractive is that it provides a counterfactual estimate of what the average 

YAP program graduate (treated youth) would look like at that same age had he or she not 

participated in the program. In the RIC design, the untreated youth are very much like the 

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Final Technical Report : 2011-JU-FX-0001 p. 51

treated youth in terms of the typical characteristics of youth who enter YAP, because they 

are, in fact, the youth who enter YAP. So these cohorts were created in order to run agematched comparisons. 

When within cohort sample sizes are sufficiently large to adequately power the 

analyses, this is a very useful method that can address chance findings, corroborate 

through replication, and reveal developmental patterns in program effectiveness. 

We attempted to create “treated” and “untreated” groups of youth sharing 

common 3-month wide age cohorts (at the time of their pre and post-test assessments, 

respectively). These age cohorts were created to differ by 3 month intervals, which is 30 

days less than the typical duration of participation in YAP for the majority of the sample 

in this study. This would ensure that a youth’s pre-test and post-test were included in 

separate cohorts (3 months apart) so that no youth would be included in the same cohort 

as both a treated and untreated. That way these comparisons are statistically independent. 

For example, the pre-score for a “comparison”/untreated youth at age 15 years and 2 

months was included in Cohort 8 which included only youth between 15 years 0 months 

and 15 years 2 months of age. The treatment group youth for this age cohort were 

program graduates who completed their post-test within this same age window. 

Our analyses using these created age-differentiated cohorts generated 

uninformative results, which had cohorts so small in number that comparisons could not 

be trusted. For the purpose of running statistically independent tests, cohorts must be 

created to ensure no individual’s pre and post scores are included in the same cohort. 

These would be very informative had the sample size been larger and in the case that the 

duration of treatment was longer as well. Because a 3-month age window created 21 

cohorts, and because the range in size across cohorts was from 4 to 26, statistical power 

was simply inadequate for separate tests for each cohort. Therefore these findings are 

considered most tentative and not very informative. The series of tests that were run 

comparing untreated and treated youth, just those youth falling within the same cohort at 

the time of their assessment, on each outcome variable are presented in the appendix, 

along with a brief overview of the findings is given. Ultimately, these analyses are not 

very reliable. The results, located in the appendix, are provided mostly to reveal what age 

groups tended to differ on what outcomes and in what direction for later reference by 

readers when considering patterns of change over time that may reflect maturational 

processes. 

Changes from Entry to Discharge: The First Reliable Change Estimations 

This section reports findings from the tests of within- and between-group 

differences across cohorts on key outcome variables. First change from entry to 

discharge are presented and then changes from entry to one year post-discharge are 

presented. As described above, the tables that present the data provide both pre-post 

differences within youth and between treated/untreated comparisons across cohorts of 

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Final Technical Report: 2011-JU-FX-0001 p. 52

youth. The estimates of change are listed as effect sizes, and the significance of the 

whole-sample t and F-tests are denoted by bolding. 

The tables below share the same format and characteristics and therefore deserve 

an introduction. First, the outcome variables are listed at the top of all columns but the 

first, which identifies the specific cohorts (top five rows) or the specific statistical test 

employed (bottom 3 rows). 

Under each variable are two columns. The column on the left includes the effect 

sizes (d) for pre-post (PP) comparisons of individuals own scores within each cohort. 

There are five rows of pre-post comparisons conducted for the youth in each intervention 

cycle for cohorts one through five. At the bottom of each PP column is the average prepost change effect size for the whole sample. Negative effect sizes suggest declines from 

pre-test to the later assessment (either at post-test/discharge or 12 months post-discharge). 

Under the right column (labeled TXCT for Treatment/Control) are four estimates 

of cross-cohort differences, also reported in effect sizes. The effect size in each row 

reflects that cohort’s post-test scores compared to the subsequent cohort’s pre-test scores. 

As there is no subsequent cohort for cohort 5, there is no Treated vs. Untreated 

comparison effect size and that cell is blank. Below that empty cell are the betweengroup (untreated v. treated) differences generated when computed using ANOVA and 

ANCOVA (controlling for sex, age, race). The bottom row provides the average effect 

size of the four between-group (cross-cohort) difference tests (from top four rows). 

Primary measures of prosocial behavior. The first table provides estimates of 

change on the measures of connectedness, which served as the primary as indicators of 

prosocial behavior. Table 2.24 provides the effect sizes for the pre (entry) to post 

(discharge) assessment points. Descriptive statistics for these tests are in table 2.30 and 

2.31. Each of the subtitled sections below summarize these pre-post and treated vs. nontreated comparisons, and reveal the degree to which the separate tests—pre-post (PP), 

cross-cohort comparisons (TXCT), ANOVA and ANCOVA corroborate each other. 

Connectedness to Friends. There was some evidence of program-related changes 

revealed in the PP and TXCT effect sizes, two of five cohorts demonstrating positive prepost change and half of the cohorts having higher scores on connectedness to friends 

among those treated in YAP. The pooled between group difference was meaningful (d = 

.24). The ANOVA similarly indicated small to moderate increases in connectedness to 

friends for YAP participants (d = .34); but this relationship was no longer present after 

controlling for age, sex, race, and city in the ANCOVA (d = .14). 

Connectedness to Family. There was little evidence of program effects in the prepost tests (positive change in only one of five cohorts) and contradictory differences 

across cohorts (two positive effects, one negative, and one absent any difference). In the 

ANOVA there were no treated/untreated group differences, but there was evidence of a 

small positive, statistically significant effect of program participation when controlling 

for age, sex, race, and city in the ANCOVA (d = .17). 

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Final Technical Report : 2011-JU-FX-0001 p. 53

Connectedness to School: The within-person changes from pre- to post changes 

provided inconsistent evidence, varying across cohorts in both size and direction). 

ANOVA and ANCOVA also suggested no change in school connectedness. 

Connectedness to Teachers: Some evidence of treated vs. untreated group 

differences was found with the average effect size for cross-cohort tests of change (d = 

.20) being very similar to the ANOVA tests (d = .22). However, evidence of within 

person, pre-post change was negligible (only seen in one cohort) and was similar in size 

to between-group difference found when controlling for age, sex, race, and city in the 

ANCOVA. 

Connectedness to a Self-in-the-Future: Little and inconsistent evidence for 

change in connectedness to Self-in-the-Future was found in either pre-post or the treated 

vs. non-treatment tests. Similarly, ANOVA or ANCOVA test showed no evidence of 

effects. 

To summarize, evidence of change between entry and discharge was found on 

several of the attitudinal and behavioral self-reported prosocial outcomes. The magnitude 

of the change estimates were typically corroborated across two or more different change 

estimates. Most strong and consistent was the evidence of improvements in 

connectedness to teachers. Although pre-post change was observed in just one cohort and 

between group differences among only two of four cohorts, the average effect size of the 

four cohort specific tests of treatment related differences was .20 and was consistent with 

the ANOVA. The multilevel analyses corroborated these findings. 

Estimates of pre to post change on the other connectedness scales, was less 

consistent, with effect sizes either differing in size across cohorts or in direction 

unexplainably. Consistent pre-post and between group differences were found for Cohort 

Three across all but Connectedness to the Future. Program graduates in Cohort 4 reported 

greater connectedness to friends, family, and teachers than youth in the untreated 

comparison group in the subsequent cohorts. Cohorts 1 and 2 demonstrated almost no 

change and Cohort 5 had mixed change estimates. Descriptive statistics and test statistics 

are presented in Table 4.2 for ANOVA and 4.3 for ANCOVA. 

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Final Technical Report: 2011-JU-FX-0001 p. 54

Table 4.1 

Cohen’s d effect Sizes for Pre Post and Treated v. Untreated Difference T-Tests 

Hem Friend Hem Family Hem School Hem Teacher Hem Future 

Cohort 

Number PP TX CT PP TX CT PP TX CT PP TX CT PP TXC T 

C1 n = 37 .09 -.02 -.19 -.27 -.08 .03 -.14 -.10 -.05 -.03 

C2 n = 40 .10 .15 -.02 -.01 .13 .07 .09 .02 -.14 .05 

C3 n = 34 .43 .41 .34 .23 .34 .45 .39 .47 .19 -.04 

C4 n = 26 .05 .45 -.06 .27 .27 .11 .18 .40 -.08 .19 

C5 n = 27 .26 .14 -.24 .05 -.27 

ANOVA d .34 -.04 .10 .22 -.11 

ANCOVA .14 .17 .10 .11 .13 

Pooled 

PP/TC d .15 .24 .04 .06 .08 .17 .11 .20 -.07 .04 

Notes: PP denotes the effect size for a pre-post t-test comparing the average difference across youths’ preto post scores within a single cohort. TXCT denotes effect sizes that reflect the magnitude of the difference 

between 4-month assessments of the treatment group and pre-treatment assessment scores for the 

subsequent cohort of youth (untreated comparison group). Positive scores indicate increases or higher 

scores for the treated youth. Below these are pooled sample untreated to treated group differences 

estimated using ANOVA and using ANCOVA. The bottom row provides the average pre-post difference 

across all individuals and under TXCT is the average effect size across the four treatment comparisons. All 

reflect Cohen’s d as the effect size, for which d = .20 is “small,” and a d= .40 to .50, is “moderate” in size. 

For the top five rows, bolded scores signify non-negligible (small or d > .20) differences. In the bottom 

three rows, bold reflects statistical significance of p < .05. 

Hierarchical analyses that pool all youth into one between-group test. The 

above analyses are not subject to bias from data dependency because all youth’s pre and 

post scores were separated by or across cohorts (except for the pre- post tests). But they 

do not yield a single significance, only effect sizes and (for the ANOVA and ANCOVA) 

evidence of whether any between-group difference varies across cohorts. 

Hierarchical analyses that nest the individuals’ pre and post scores can remove or 

account for the statistical dependency resulting from pooled data and thereby offer a 

sample size more appropriate for adequately powered significance tests. Using 

hierarchical analyses (Mplus two level analysis) the Hemingway connectedness scales 

were compared across treated and untreated comparison groups. The sample size was not 

sufficiently large to use a probability cutoff score of p < .05 to detect the expectably 

small effect sizes (typical of mentoring, d = .20, see DuBois, et al., 2002), so a p-value of 

.10 was considered indicative of a non-chance finding and p< .15 as a trend. 

Given the probability criterion of .10, the treated youth had significantly higher 

connectedness to school (β = .523, p = .001) and connectedness to teachers (β = .356; p = 

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Final Technical Report: 2011-JU-FX-0001 p. 55

.059). And there was a trend toward higher connectedness to the future (β = .183, p = 

.107) than the untreated controls. The finding of connectedness to teachers in these 

analyses is consistent with the separate cohort analyses in Table 4.1. Improvements in 

connectedness to school and to the future are not. And, unlike in Table 4.1, there were no 

effects of program participation on connectedness to friends or parents. 

Table 4.2 

ANOVA’s for Effect size using Pooled data from MI 

UTSA Variables: ANOVA 

Entry to Discharge 

PostYAP 

Mean 

Post 

SD 

PreYAP 

Mean 

Pre SD F p d 

Connectedness to Friends 20.92 5.18 20.14 5.10 2.09 0.15 0.34 

Connectedness to Family 22.00 5.10 22.08 4.68 0.08 0.77 -0.04 

Connectedness to School 20.65 5.12 20.42 5.02 0.31 0.85 0.10 

Connectedness to Teachers 21.24 5.22 20.74 4.79 1.40 0.24 0.22 

Connectedness to Future 22.44 4.38 22.67 3.83 0.48 0.49 -0.11 

Misconduct 4.65 3.94 5.59 4.17 4.72 0.03 -0.47 

Ed Expect Finish HS 1.19 0.29 1.22 0.28 0.47 0.50 -0.04 

Ed Expect Start college 2.90 0.95 2.86 1.05 0.26 0.61 0.04 

Ed Expect Finish College 1.67 0.33 1.65 0.35 0.53 0.47 0.04 

Work Plan Find a Job 3.39 0.76 3.41 0.74 0.23 0.63 -0.03 

Expect Success at Work 3.46 0.66 3.56 0.65 1.94 0.16 -0.12 

Table 4.3 

ANCOVA for UTSA Variables (Controlling for Age, City, Sex, Race) 

UTSA Variable (pooled 

imputation samples) from 

Entry to Discharge 

PostYAP 

Mean 

Post 

SD 

PreYAP 

Mean 

Pre SD F p d 

Connectedness to Friends 20.92 5.18 20.14 5.10 1.56 0.13 .14 

Connectedness to Family 22.00 5.10 22.08 4.68 2.38 0.01 .17 

Connectedness to School 20.65 5.12 20.42 5.02 0.86 0.58 .10 

Connectedness to Teachers 21.24 5.22 20.74 4.79 0.98 0.46 .11 

Connectedness to Future 22.44 4.38 22.67 3.83 1.30 0.24 .13 

Misconduct 4.65 3.94 5.59 4.17 1.77 0.09 .15 

Ed Expect Finish HS 1.19 0.29 1.22 0.28 0.79 0.64 .10 

Ed Expect Start college 2.90 0.95 2.86 1.05 1.69 0.07 .14 

Ed Expect Finish College 1.67 0.33 1.65 0.35 1.03 0.43 .11 

Plan Find a Job 3.39 0.76 3.41 0.74 0.97 0.48 .11 

Plan Success at Work 3.46 0.66 3.56 0.65 1.24 0.30 .12 

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Final Technical Report : 2011-JU-FX-0001 p. 56

Overall, the strongest evidence of program-related improvements was in 

connectedness to teachers. There was no reliable evidence of negative effects of program 

participation. And although there was evidence of benefits of program participation on 

the other four outcomes, it was not consistent across methods. Evidence from the 

hierarchical analyses suggests participation in YAP was associated with increases in 

connectedness to the future and to school, but not in the separate cohort comparisons. The 

hierarchical analyses also provided no corroborating evidence of the change in 

connectedness to friends and family revealed in the separate cohort comparisons. 

Results for expectations and misconduct. Reported below are cross-cohort and 

pooled cohort change estimates on four measures of conventional activity and one 

measure of unconventional, deviancy-related outcome. Table 4.4 provides the effect sizes 

for each of these pre (entry) to post (discharge) estimates. Descriptive statistics for these 

tests are in Table 4.2 and 4.3. As before, the top five rows provide change estimates for 

the five cohort groups separately. Pre-post (within-person; PP) change and cross-cohort 

treatment effects (TXCT) are presented as standardized mean difference effect sizes (d). 

The bottom three rows list effect sizes for pooled cohort pre-post comparisons PP t-tests 

and ANOVA/ANCOVA in which missing data were imputed 

Expects to finish high school. The pre-post t-tests and cross-cohort effect sizes 

show evidence for change, but in inconsistent directions across cohorts. The ANOVA 

and ANCOVA do not show change. 

Expects to start college. The individual pre-post t-tests effect sizes show 

evidence for change within cohorts, but the within-youth (pre-post) differences are in 

inconsistent directions and so the pooled effect size is negligible. Although the ANOVA 

and ANCOVA do not show change, the average cross-cohort estimate of between group 

differences is positive (d = .17). 

Expects to finish college. The individual pre-post t-tests effect sizes show 

evidence for change within cohorts, but the within-person (pre-post) differences are in 

inconsistent directions and so the pooled effect size is negligible. Although the ANOVA 

and ANCOVA do not show change, the average cross-cohort estimate of change is 

positive (d = .24), as it was for expecting to start college. 

Expects to find a job. The pre-post and cross-cohort effect sizes reveal evidence of 

change but in inconsistent directions. ANOVA and ANCOVA show no change. 

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Final Technical Report: 2011-JU-FX-0001 p. 57

Table 4.4 

Cohen’s d effect sizes for Pre-Post Change T-Tests and Untreated (Pre-) v. Treated 

(Post-scores of Prior Cohort) Difference ANOVAs, ANCOVAs, across Cohorts 

Finish High 

School** 

Start 

College 

Finish 

College Find a Job Misconduct* 

Cohort 

Number PP TX CT PP TX CT PP TX CT PP TX CT PP TX CT 

C1 n = 37 .07 .06 -.28 .03 -.31 .21 -.38 -.06 -.04 -.40 

C2 n = 40 -.07 -.22 .26 .52 .42 .61 -.07 -.04 -.53 -.23 

C3 n = 34 -.55 -.30 .57 -.08 .72 .01 .28 .33 -.51 -.43 

C4 n = 26 .20 .16 -.18 .22 -.19 .11 .00 -.05 -.02 .25 

C5 n = 27 .28 -.07 -.19 .15 -.14 

ANOVA d -.04 .04 .04 -.03 -.47 

ANCOVA d .10 .14 .11 .11 -.15 

Pooled 

PP/TC d -.02 -.08 .06 .17 .09 .24 .00 .04 -.25 -.20 

Notes: PP denotes the effect size for a pre-post t-test comparing the average difference across youths’ 

pre- to post scores within a single cohort. TXCT denotes effect sizes that reflect the magnitude of the 

difference between 4-month assessments of the treatment group and pre-treatment assessment scores for 

the subsequent cohort of youth (untreated comparison group). Positive scores indicate increases or higher 

scores for the treated youth. Below these are pooled sample untreated to treated group differences 

estimated using ANOVA and using ANCOVA. The bottom row provides the average pre-post difference 

across all individuals and under TXCT is the average effect size across the four treatment comparisons. 

All reflect Cohen’s d as the effect size, for which d = .20 is “small,” and a d= .40 to .50, is “moderate” in 

size. For the top five rows, bolded scores signify non-negligible (small or d > .20) differences. In the 

bottom three rows, bold reflects statistical significance of p < .05. ** Values are square root inversion of 

original scores created to approximate a normal distribution. Negative scores indicate an increase in 

expectations of finishing high school among treated youth. 

Misconduct. All indicators suggest declines in self-reported misconduct are 

associated with program participation. Only three of five pre- post comparisons met the d 

= .20 criterion, but this is a phenomenon that generally increases over time. Four of five 

cohorts showed small (d = -.14) to moderate (d = .53) cross-cohort treatment effect 

differences on misconduct. The average change from entry to discharge on misconduct 

also was meaningful (d = -.25), as was the averaged between-groups/cohort difference (d 

= -.20). The ANOVA for between group differences (d = -.47) was statistically 

significant, and at the p < .10 criterion the ANCOVA also suggest small decreases (d = -

.45) in misconduct were associated with program participation once the contributions of 

sex, age, city and race are accounted for (d = .15). However because the effect size for 

the ANCOVA controlling for age was a third that of the ANOVA, we felt tests to rule out 

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Final Technical Report: 2011-JU-FX-0001 p. 58

maturation as a viable validity threat were necessary. These are conducted in the 

following section. Overall, however, the evidence for change on this outcome is strong. 

Hierarchical analyses. The hierarchical models, which nested, treated and 

comparison youth data pooling all youth into one test did not yield significant main 

effects of treatment for any of these five variables. Only on Misconduct was there even a 

trend. For self-reported misconduct the change coefficient was -.126, suggesting lower 

misconduct among treated youth at discharge, but the significance level only reached a p 

= .199 trend. 

In summary, four-month changes in expectations regarding academic and 

vocational success were generally in consistent and unconvincing. Only expectations of 

going to college and graduating from college differing in two cohorts each, but had the 

mean effect size averaged across cohorts being .17 and .24 respectively, with scores 

being higher for treated youth. However, the analyses of program-related change on 

misconduct were compellingly consistent, with only the ANCOVA and multilevel 

analyses suggesting smaller effects than other estimates, which may reflect the role of 

starting risk may be a rival interpretation of the findings. Note, age effects on these 

changes were not apparent in earlier maturation tests, so this validity threat does not seem 

to be at play. Given the role of initial rates of misconduct on declines we are associating 

with program participation, the possibility of regression to the mean needed to be 

addressed. 

Rival internal validity check for regression to the mean on misconduct. One 

of the main critiques of a RIC design, particularly when used to assess a program’s 

impact on an outcome variable (like “misconduct”), which is also the primary criterion 

for admission to the program is regression to the mean effects. People who enter a 

program reporting extremely high (or low) levels of a problematic behavior or state 

(particularly one targeted for reduction by program engagement), will over time naturally 

move toward a less extreme position. (This is well evidenced in the psychotherapy 

literature, though the therapy literature typically is based on the change observed among 

those who voluntarily seek therapy not simply score at the extremes on some 

phenomenon like depression or anxiety.) Furthermore, individuals whose scores are most 

extreme may reflect inaccurately high scores that also will regress to their true score, 

which is closer to the group mean. If this is the case, then you would expect those 

individuals who are so heavily engaged in misconduct as to be sent to YAP would 

naturally decline in misconduct—how much higher could they go? This could appear like 

a treatment effect. 

Therefore, the positive misconduct results reported above are subject to the 

alternative explanation of regression to the mean. To address this problem, we first 

looked at the starting scores on that variable for individuals entering the program. This 

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Final Technical Report: 2011-JU-FX-0001 p. #'

revealed whose reports were most extreme and subject to regression effects. We 

separated the youth who were in the middle, those one SD above and those one SD below 

the mean into three groups reflecting extremely high, average, and extremely low scores. 

Then we created a variable of change over time on misconduct by subtracting the post 

score (discharge) from the pre score (entry). (This required us to impute the missing data 

for those who did not complete the program, many of whom could have been initially 

extreme responders. We then used the 5 imputed datasets to conduct our comparisons.) 

Then we examined their change over time both visually and statistically using ANOVA 

to compare scores across groups. Table 2.26 provides means for teach group across the 

five datasets. 

Table 4.5 

Mean Misconduct at Pre-test for High, Average, and Low Scoring Groups 

As expected, those who entered the program with the highest scores on 

misconduct showed the greatest declines (see Figure 4.1 below). Both the highest and 

lowest scoring groups regressed to the mean, with the lowest scoring group reporting an 

increase. This suggests that the statistical issue of regression to the mean (due to extreme 

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Final Technical Report : 2011-JU-FX-0001 p. 60

responding at pre) does seem to have influenced both extremely high and low scoring 

youth to regress toward the mean. However, the slope or magnitude of change for those 

reporting the most misconduct at pre is twice that of the low misconduct group. Most 

important, evidence that observed change is not a statistical artifact of regression to the 

mean is seen in the reductions in misconduct for the group whose scores were average at 

entry. This suggests that at least some of the declines in misconduct among those 

initially reporting the most can be due to program participation. 

Figure 4.1. Within-person (Pre- to Post-test) Change for Youth Starting at High, 

Average, and Low Levels of Misconduct at Pre-test (Entry). 

To effectively rule out regression to the mean processes, analyses looking at just 

those youth in the middle group were done (n = 174; 87 per treatment group when 

estimates for those in the average-at-entry across imputation datasets were pooled) to 

estimate the changes between treated and untreated groups of youth with similar, average 

entering misconduct scores using a comparably sized sample. Using the five multiply 

imputed datasets, and analyzing data separately by cohort to remove dependency from 

the main effect tests of treatment, a one-way ANOVA was conducted. 

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Table 4.6 

Means for Treated and Untreated Youth on Misconduct 

The pooled mean misconduct scores across all five cohorts and all five imputed 

data sets yielded means of 4.98 for the untreated comparison group and 4.41 for the 

treated group. The pooled effect size across these comparisons was an eta-squared of 

.023 before separating groups into cohorts, and .054 when effect sizes were estimated 

within each group (independent data) and then averaged. These translate to small to 

moderate size effects of program participation in decreasing misconduct. This effect size 

reflects change illustrated by the green line in Figure 4.1 above. The magnitude of this 

effect matches that observed in the earlier estimates of pre-post and across-cohort 

comparisons (see table 4.4). 

Controlling for the influence of sex and age on the averaged differences within 

cohorts between treatment and controls yielded estimated means of 5.02 for the untreated 

comparison group and 4.36 for the treated group, which is larger than the difference 

without these covariates. This is not consistent with the ANCOVA analyses for the 

sample as a whole reported above using the d-statistic for effect size (d = -.14), but those 

analyses did not impute missing values. Arguably, these are more reliable because those 

covariates were balanced when the cohorts were pooled (because the same kids in pre and 

post, so only age differed but it did so the same for all youth). More importantly these 

analyses do not include the low and high scoring individuals, whose opposing change 

coefficients simply add error to the model reported earlier. 

These analyses provide good support for the argument that program effects should 

not be attributed to regression to the mean processes. 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 62

Changes in crime, education and job activity measures. These tests represent 

change from entry to discharge. Effect sizes are presented in the following table and 

summarized briefly below. 

Living situation. The t-tests revealed inconsistent effect sizes across cohorts, 

negligible change, and in different directions, particularly for the treatment-untreated 

differences. The average effect sizes for both t-tests and the ANOVA suggest no change 

over time nor between treated and untreated youth. 

Education situation. Results of the t-test and ANOVA suggest positive changes 

in education situation, indicating that participating in YAP increased the youth’s standing 

in school participation from not enrolled towards full time attendance, graduation, or 

college enrollment. 

Employment status. Results of the t-test and ANOVA suggest positive changes in 

employment status as a function of participating in YAP. These scores would be 

consistent with YAP participation increasing the youth’s interest and attempts to find 

employment. 

Table 4.7 

Cohen’s d effect sizes for Pre Post and Treated vs. Untreated Difference T-Tests for 

RIC Design, with YAP-Collected Data: At Entry (Untreated) vs. Discharge 

(Treatment) 

Living 

Situation 

Education 

Situation 

Employment 

Status 

Serious 

Disposition 

PP TX 

CT PP TX CT PP TX CT PP TX CT 

C1 n = 37 .09 .49 .61 .51 .86 .60 -3.04 -3.58 

C2 n = 40 -.02 -.12 .47 -.03 .31 .09 -3.09 -3.57 

C3 n = 34 .09 .07 .31 .62 .71 .37 -2.17 -2.29 

C4 n = 26 -.27 -.69 1.01 .65 .34 .97 -1.86 -1.07 

C5 n = 27 -.42 .45 1.00 -1.46 

ANOVA -.001 .51 .59 -2.45 

Pooled 

PP/TC d -.11 -.06 .57 .44 .65 .51 -2.32 -2.63 

Notes: PP denotes the effect size for a pre-post t-test comparing the average difference across youths’ 

pre- to post scores within a single cohort. TXCT denotes effect sizes that reflect the magnitude of the 

difference between 4-month assessments of the treatment group and pre-treatment assessment scores 

for the subsequent cohort of youth (untreated comparison group). Positive scores indicate increases or 

higher scores for the treated youth. Below these are pooled sample untreated to treated group 

differences estimated using ANOVA and using ANCOVA. The bottom row provides the average prepost difference across all individuals and under TXCT is the average effect size across the four 

treatment comparisons. All reflect Cohen’s d as the effect size, for which d = .20 is “small,” and a d= 

.40 to .50, is “moderate” in size. For the top five rows, bolded scores signify non-negligible (small or d 

> .20) differences. In the bottom three rows, bold reflects statistical significance of p < .05. 

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Final Technical Report: 2011-JU-FX-0001 p. 63

Serious disposition. Results for both within person change across treatment (PP) 

and treated-untreated differences were large and consistently in the same direction. These 

findings suggest that participating in YAP was associated with a statistically significant 

and large decrease in the most serious disposition (status offense, misdemeanor, felony) 

on the youth’s record relative to the untreated comparison youth. 

Table 4.8 

ANOVA for YAP Variables Entry to Discharge 

YAP Variables Entry 

to Discharge (DC) 

DC DC 

SD 

Pre-

(Entry) 

Pre 

SD 

F p d 

Living Situation 4.51 1.10 4.51 1.18 .05 .86 -.001 

Serious Disposition .32 0.75 2.23 0.80 491.70 .00 -2.45 

Education Situation 2.92 0.65 2.55 0.78 21.27 .00 .51 

Employment Status 1.57 0.83 1.15 0.58 28.72 .00 .59 

Changes from Entry to 12 Months Post Discharge in Education, Employment and 

Job Activity 

The analyses in the table below represent change during the period from entry 

into YAP to 12 months post-discharge. Only data collected by YAP is available for the 

one-year follow up analyses. These tests do not control for length of treatment. 

Living situation. Regarding their living situation, the results are inconsistent in 

regards to change, suggesting youth were not changing in their living situations between 

entry to post-discharge periods. However, there was constrained variance in their living 

situations data. 

Education situation. Results of the t-test and ANOVA suggest positive changes 

in education situation from entry to post-discharge were related to participation in YAP. 

Program participation predicted increases in school participation from not enrolled (low 

scores) towards full time attendance, graduation, or college enrollment. 

Employment Status. Results of the t-test and ANOVA suggest positive changes 

in employment status from entry to post-discharge were observed and were associated 

with participation in YAP. These scores suggest YAP graduates reported greater interest 

in and more attempts to find employment after treatment than youth who had not 

completed YAP. 

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Final Technical Report: 2011-JU-FX-0001 p. 64

Table 4.9 

Cohen’s d effect sizes for Pre Post and Treated vs. Untreated Difference T-Tests for RIC 

Design, with YAP-Collected Data: At Entry (Untreated) vs. 12 Months Post-Discharge 

(Treatment) 

Living 

Situation 

Education 

Situation 

Employment 

Status 

Serious 

Disposition 

PP TX 

CT PP TX CT PP TX CT PP TX CT 

C1 n = 37 .01 .40 .60 .74 1.65 1.64 -1.83 -2.07 

C2 n = 40 -.13 -.15 .45 .32 .88 .99 -1.64 -1.97 

C3 n = 34 .17 .19 .19 .62 .80 1.02 -2.34 -1.99 

C4 n = 26 -.15 -.53 .69 .61 .93 1.71 -1.29 -.66 

C5 n = 27 -.55 .05 1.33 -1.00 

ANOVA d .02 .56 1.36 -1.64 

Pooled PP/TC d -.13 -.11 .53 .57 1.12 1.34 -1.62 -1.67 

Note: PP notes the effect size for a pre-post t-test comparing either a single cohort’s pre-survey scores to 

the same cohort’s 4-month survey scores, or (on bottom row) for the average pre- to post-assessment effect 

size across the five cohorts. TXCT effect sizes reflect the magnitude of the difference between 4-month 

assessments of the treatment group and pre-treatment assessment scores for the subsequent cohort of youth 

(untreated comparison group). These use Cohen’s d as the effect size, for which d = .20 is “small,” a d= .40 

to .50, is “moderate” in size. 

Serious disposition. These findings suggest that from entry to 12 months postdischarge, the severity of a youth’s disposition (status offense, misdemeanor, felony) 

decreased as a function of participating in YAP. 

Table 4.10 

ANOVA for YAP Variables Entry to 12-Months Post-Discharge 

YAP Variable Entry 

to Post-DC 

PostDC Post 

SD 

PreYAP Pre 

SD 

F p d 

Living Situation 4.53 0.91 4.51 1.18 1.10 .48 .02 

Serious Disposition 0.77 0.97 2.23 0.80 222.55 .00 -1.64 

Education Situation 3.07 1.07 2.55 0.78 026.37 .00 .56 

Employment Status 2.30 1.04 1.15 0.58 155.43 .00 1.36 

Hierarchical models to address dependencies in the data. Dyadic data analyses 

were used to address the issue of statistical dependency in the data across the treatment 

and untreated comparison group. These analyses were conducted on two 12 month 

follow-up variables, (1) serious crime and disposition and (2) educational engagement. 

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Final Technical Report : 2011-JU-FX-0001 p. 65

For each variable, multilevel analyses allowed shared variance across treatment 

and untreated comparison groups to be apportioned into random error, thereby freeing 

tests of treatment effects from statistical dependency. In addition, by controlling for age 

of assessment a further separation of the effects of age on observed differences from the 

estimates of differences between the treatment and untreated comparison group was 

achieved. Separate analyses excluded individuals whose ages fell outside the range of the 

complementary treatment group’s age (e.g., those treated youth four months older than 

the oldest comparison youth). 

Serious disposition. First a simple two-level model predicting variability in rates 

of serious disposition for treatment and untreated comparison group members was run 

without any controls for age. The raw data included 247 individuals from 155 clusters. 

The degree of dependency in the data was relatively small. The intraclass 

correlation (or amount of variability in serious disposition that was due to the clustering 

of treatment and comparison observations within individuals, random effects) was .012. 

These treatment-related effects are estimations after the bias in standard errors due to data 

dependency was removed through the modeling of the dependencies in the data. The 

model fit indices were positive suggesting this basic model was a good fit with the data. 

The CFI of .99 and significant Chi-square test coefficient (55.79) both suggested a good 

model fit. 

The unstandardized slope for treatment, which reflects the unadjusted, 

unstandardized mean difference between comparison and treatment youth indicated that 

one year post-treatment YAP graduates were -1.762 (out of a range of 4 points) lower in 

their scores on most serious disposition than the untreated youth. The amount of 

variability in degree of serious disposition among youth that was associated with program 

participation was 26%, which is a statistically significant and large effect. 

The age of assessment was included in a subsequent run of the basic model 

described above. The results were similar and the declines in most serious disposition 

associated with participation in YAP were slightly greater than in the more restricted 

model above. The between group difference was -1.94 with a non-significant contribution 

of age in the prediction of most serious disposition. Age was significantly associated with 

treatment condition (β = .49). Therefore the fixed effect analyses (with missing data 

imputed) in the prior section are consistent with these multilevel analyses. Both of which 

suggest that age the association between age and disposition is negative (r = -.72) for the 

sample as a whole; this, however, is a function of the association between treatment 

groups and age (with treatment group members being on average a year and four months 

older than untreated youth in the untreated comparison group). But given that rates of 

engagement in criminal activity and court involvement increase normatively, the 

treatment effect appears to have suppressed this normative increase. 

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Final Technical Report : 2011-JU-FX-0001 p. 66

Educational engagement. Multilevel analyses of the association between 

engagement in education in terms of attendance also were conducted in which treatment 

group conditions were nested within individuals. Analyses were run on for those with 

scores only the first three categories—no attendance, part-time attendance, and regular 

attendance in school—which are obviously ordinal were used. But the category, “Earned 

GED,” is not. It cannot be effectively argued that earning a GED reflects greater 

educational engagement than attending school regularly. Similarly, having graduated 

from high school—the fifth category—is not an option for younger youth from whom 

this entry would be censored. Therefore, in this model, educational engagement is 

actually a measure of attendance. 

One other issue that arises when comparing the treatment conditions on 

educational engagement, and in particular across age-cohorts, is that just as there were 

more treatment individuals who were old enough to have graduated, there were not 

enough of the treated individuals (whose educational engagement was assessed one year 

post-discharge) below the age of 14 and a half to make appropriate comparisons with the 

untreated comparison group whose lower age range was 13. Therefore, the analyses on 

educational attainment were run only for two of the six age cohorts of research 

participants. The cohort of fourteen to fifteen and a half year old participants and the 

cohort of fifteen and a half year olds to seventeen year olds were included in these 

analyses. 

A simple two-level model predicting variability in rates of educational 

engagement (namely attendance) for treatment and untreated comparison group members 

was run that included the youths’ ages at the time of assessment of educational 

engagement. The raw data included 164 individuals from 124 youth-Advocate clusters or 

pairs. The degree of dependency in the data was larger for educational engagement than 

it was for serious disposition. The intraclass correlation or amount of variability in 

educational engagement that was due to the clustering of treatment and comparison 

observations within individuals (random effects) was .056. This means it was indeed 

important to capture this dependency through the multilevel analyses described below. 

Most model fit indices were positive suggesting this treatment effect model was a 

good fit with the data. These indices include a CFI of 1, TLI of .99, RMSEA = .03, and 

significant Chi-square test coefficient (437). 

The unstandardized slope for treatment, which reflects the unadjusted mean 

difference between comparison and treatment youth, controlling for variability in age, 

indicated that one year post-treatment YAP graduates were 1.08 (out of a range of 3 

points) higher in their scores on educational engagement than those untreated youth 

which. This is a large effect. 

In summary, changes in engagement in actual behaviors related to school and 

employment associated with program participation were consistently large and positive 

across cohort and assessment methods. Pre-post, between group and multilevel analyses 

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Final Technical Report : 2011-JU-FX-0001 p. 67

revealed strong evidence of improvements in educational engagement (attendance) and 

job seeking (or job securing) following participation in the YAP program. The long-term 

changes observed sixteen months post-entry on educational and vocational engagement 

replicated those at four months with change observed consistently across all methods of 

analysis suggest reliable program-related benefits on engagement in school and 

employment. 

Conclusions from Study I 

These RIC analyses suggest positive effects of youths’ participation in YAP on 

several outcome variables, including the prosocial behaviors/attitudes (connectedness), 

self-reported misconduct, and educational, work, and crime involvement after four 

months of program participation. Comparisons between treated and untreated youth on 

employment and educational engagement as well as most serious disposition also were 

found at 12-months post-discharge. 

Prosocial behavior/connectedness to teachers. Regarding short-term changes on 

prosocial behavior (connectedness to friends, family, school, teachers, and the future) 

associated with program participation, statistically significant changes or small or larger 

between-group effect size estimates of differences were observed for the whole sample 

on four of these five measures using analyses of covariance (between-group differences) 

or t-tests of effect sizes (for pre-post change) (see Table 2.24). These results reveal 

between-group differences corroborated pre-post increases in connectedness to friends 

and connectedness to teachers that were small to moderate in size. Group differences in 

connectedness to family were revealed in an analysis of variance, but this difference 

between treated and untreated groups was no longer statistically significant once age, 

gender, and starting levels of misconduct were accounted for. 

Of these differences, however, only increases in connectedness to teachers was 

corroborated by between-group differences estimates using hierarchical analyses that 

accounted for the statistical dependency caused by including youth’s pre and post in the 

same analyses of between-group differences (by nesting assessments within individuals). 

Hierarchical analyses revealed that program participation predicted significantly higher 

reports of school connectedness and connectedness to the future among program 

completers than untreated youth, but these findings were not evidence in the simpler prepost and between group (cross-cohort) comparisons. Conversely, no effects of program 

participation on connectedness to family or friends were revealed in the multilevel 

analyses despite being found in the simpler pre-post and between group analyzes. These 

mixed results render evidence of change equivocal. 

Educational, vocational, and court involvement. Using the data that is regularly 

collected by YAP and that is more objective in nature, we found more consistent 

evidence of change across analysis methods. Looking at youth’s living situation, 

educational situation/engagement (attendance), employment status (interest in/actively 

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Final Technical Report : 2011-JU-FX-0001 p. 68

looking for), and serious disposition with tests of both within person (pre-post) change 

and of differences between treatment/untreated comparison groups. 

On all of these, except for living situation, we observed improvements while in 

YAP (individual-level pre-post change) and evidence of between group differences that 

were related to program participation, both favoring those who participated in YAP. In 

addition, the benefits of program participation on these three outcomes was both large 

and nearly identical across the two approaches to estimating change. 

Finally we conducted 12-month follow-up tests of change associated with 

program participation on the same, regularly collected YAP outcomes. These tests 

provided confirmation of the maintenance of several positive outcomes associated with 

program participation. Benefits of YAP participation on YAP graduates’ educational 

situation at discharge held constant at one year post-discharge. However the benefits on 

educational status associated with program participation suggest YAP graduates 

improved considerably during the one year after completing the program. 

Although the benefits of program participation on YAP graduate’s self-reported 

status offenses, misdemeanors, and felonies (“serious dispositions”) one year-post 

discharge were not as large as those at the end of program participation (at discharge), 

these long-term benefits of program participation are still quite large. Consistent with 

estimates of individual changes from program entry to discharge, the results revealed a 

larger decrease in self-reported status offenses, misdemeanors, and felonies (“serious 

dispositions”) from entry to twelve months post-discharge. Analyses also revealed 

moderate increases in both the youth’s educational situation and in their interest in (or 

effort toward) finding employment one-year post program completion. 

Therefore, the long-term, 16 months post entry, gains in educational engagement 

and relative reductions in criminal activity corroborated effects found at four months. 

Large benefits of program participation on YAP graduates’ self-reported criminal 

disposition (rates of status offenses, misdemeanors, and felonies) were observed both at 

program completion and one year-post discharge. 

Misconduct. The short term, entry-to-discharge changes associated with 

participation in YAP on misconduct represent statistically significant differences between 

the untreated comparison group (pre-treatment) and the treatment group (age-matched 

program completers) at discharge that suggest positive effects of program participation 

on declines in self-reported misconduct (see Table 4.4). Corroboration of change 

estimates was revealed in statistically comparable effects for differences between the 

treated and untreated comparison groups and for pre-post change within individuals. That 

is, on self-reported misconduct, the size of the average person’s decline from entry to 

discharge on misconduct was similar to (or corroborated by) the magnitude of the 

differences between program participants and the counterfactual untreated comparison 

group. 

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Final Technical Report: 2011-JU-FX-0001 p. 69

It should be noted that these relationships between program participation and 

declines in misconduct are of a magnitude that is as large or larger than the effects of 

participating in comparable recidivism reduction mentoring programs (i.e., d = .20, or 

“small,” to d= .40 to .50, or “moderate” effect sizes”), which tend to be more moderate in 

size,7

 and for school-based mentoring programs.8

 Other than the program-related differences in academic and job-related behaviors 

from entry to discharge, the most convincing of the pre-post estimates of program-related 

change was in declines in self-reported misconduct. There was corroboration in the 

direction and magnitude of effect sizes (consistency) across pre-post t-tests, between 

group difference effect sizes for cohorts separately, between group average difference 

effect sizes two-way analyses of variance, and a complementary statistical trend found in 

the hierarchical comparisons. In addition, tests of maturation and regression to the mean 

could be inferred as ruling out these rival hypotheses of these changes associated with 

program participation. 

Overall, the evidence of positive program effects, using the variables we included 

and the design we employed, are strongly positive and consistent. Some studies measure 

changes over time, as did this study. Other designs compare those treated to those nontreated, and we did this too. In combination, and with a number of rival hypotheses like 

maturation and regression to the mean ruled out, the use of the recurrent institutional 

cycle design yielded consistent evidence of positive effects of participation in YAP on 

the most relevant outcomes and across multiple statistical tests. 

7 Tolan P, Henry D, Schoeny M, Bass A (2008). Mentoring interventions to affect juvenile
 

delinquency and associated problems. Campbell Systematic Reviews, 16. DOI:
 

10.4073/csr.2008.16

8 Herrera, C., Grossman, J. B., Kauh, T. J., Feldman, A. F., McMaken, J., & Jucovy, L. Z. (2007).
 

Big Brothers Big Sisters school-based mentoring impact study. Philadelphia: Public/ Private
 

Ventures.
 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 70

V. Study 2: Theory-based Program Practices Process Analyses 

The YAP organizational mission is to provide court-involved youth with 

opportunities to develop personally, contribute to their communities, and have safe, 

effective, and efficient alternatives to institutional placement. Beginning at referral, 

Advocates focus on developing a relationship, strengthening family and community 

relations, and ensuring that youth address community service and court mandates. Adult 

Advocates are selected in terms of their similarity to the youth they serve (e.g., are 

recruited from the same zip-code as youth) to help ensure that service providers have a 

grassroots understanding of community assets, challenges, and opportunities. 

The primary goal of the second study was to assess whether what the youth and 

their mentors do together explains program-related changes and whether pre-match 

characteristics of youth or their mentors predict the kinds of activities they did together. 

A series of analyses are presented that provide insights into who does what and why 

some kinds of activities, at specific points in time during the match, may be most useful. 

Having found in Study 1 reliable associations between program participation on 

declines in misconduct, this variable was selected as the primary criterion variable for 

examining the role of participant characteristics and activity choices in program 

outcomes. Structural equation modeling analyses were conducted for the three separate 

sets of analyses in Study 2 in order to assess whether the relationships between activities 

and changes in misconduct were moderated by youth or Advocate characteristics, 

particularly youth’s starting risk level and Advocates’ prior experience as educators and 

level of their educational attainment. 

These tests are guided by a theoretical model that presents several activity types, 

reflecting three dimensions of mentoring, that have been described in the mentoring 

literature as most useful or important in influencing mentoring relationship quality and 

mentoring program outcomes. This theory, called the TEAM framework, is described in 

the next section. Based on the nature of advocacy, the YAP program model, and the 

youth clients served by YAP, the analyses in this chapter focus on three main types of 

activities—play, problem-focused discussions, and casual conversations with youth. 

Following the presentation of the TEAM framework and a description of what the 

framework says about the role of when specific activities happen during the relationship, 

three hypotheses are presented. These test the value of play and problem-focused 

interactions, the importance of shifts in activities across the life of a match, and the role 

of youth and adult characteristics. These are tested using simple structural equation 

model analyses which are conducted twice, once using observed scores and once using 

factor scores. The series of analyses with each of these two types of activity variables 

starts with just activities predicting misconduct, then adds in youth characteristics, and 

then includes Advocate characteristics to test for direct and indirect effects of Advocates’ 

level education and prior teaching experience. 

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Final Technical Report: 2011-JU-FX-0001 p. 71

The two sets of structural path models are preceded by a simple test of growth 

over time in misconduct by examining the slopes and intercepts of misconduct at entry, 

midway through the match, and at discharge. Then, correlational analyses are presented 

that convey the associations between participant characteristics and starting levels of 

misconduct. These analyses include only youth and Advocate sex and age and both 

Advocate education and teaching experience. Youth sex and both Advocate educational 

characteristics were correlated and subsequently found to be valuable exogenous 

predictors in the models explaining variability in rates of misconduct at discharge. 

There are two parallel sets of path models run, both of which test the same 

sequence of main effects of activities, the contribution of youth characteristics, and then 

the indirect and direct effects of Advocate education variables. The difference between 

the two, parallel analyses is that the first set of analyses conducted used observed activity 

frequency scores that averaged the frequency of all indicators of each activity type to get 

a mean score. 

The second set of analyses were conducted to corroborate the observed score 

findings using factor scores to measure activities. Using factor scores allowed each 

activity indicator (see Table 5.2) to make a unique contribution to that activity factor 

score, rather than given each one the same weight (even though matches likely rarely did 

all of them). These factors score were estimated separately for Advocates and youth and 

separately at the two time points. This provided useful information about how youth 

differed in their views of what happened at the two points in time. 

The mentoring literature has long suggested that mentoring effectiveness varies as 

a function of specific shifts in the focus of conversations and activities across the first and 

second half of the relationship.9 Therefore, the next step was to try to better understand 

whether changes in types of interactions across the treatment period could explain how 

misconduct may be influenced by the mentoring and advocacy relationship interactions. 

The framework that summarizes prior research on the value of changes in the 

rates of different types of activities across the life of a match in effecting program 

outcomes is presented next. This framework, which was created to account for the 

dimensions of the activity focus, purpose, and collaboration in mentoring programs’ 

success, was used to generate three hypotheses that guided the analyses in Study 2. 

9 Karcher, M. J. & Hansen, K. (2014). Mentoring activities and interactions. In D. L. DuBois, & M. J. 

Karcher (Eds.), Handbook of youth mentoring (2nd Edition; pp. 63-72). Thousand Oaks, CA: Sage 

Publications 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

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Final Technical Report: 2011-JU-FX-0001 p. 72

Measuring Activities Using the TEAM Framework 

The TEAM Framework10 was used to conceptually organize the different kinds of 

interactions that take place between Advocates and youth in the YAP program. There are 

three main characteristics the TEAM Framework uses to describe interactions. Activity 

Focus is a term used to reflect how relational vs. directive the mentoring interactions are 

(the focus of activities, discussions)(see Table 5.1). For example, some interactions are 

more about talking and learning about one another while other interactions are about 

doing something together, or trying to solve a problem faced by the youth. 

A second characteristic is about the nature of decision-making in the relationship. 

The Activity Choice or Authorship dimension reflects how collaboratively the 

conversation or activity decisions are made? Who “authors” or drives their story in terms 

of influencing what they do together (regardless of the focus of what they do)? 

Mentoring literature suggests that collaboration is critical to relationship development 

and ownership in a relational process. Collaboration happens when both make unique 

contributions to deciding what to do and the two create a new goal, rather than either one 

dictating what they do. 

Last, the Activity Purpose characterizes the intended outcomes of a conversation 

or activity. In terms of the literature on delinquency, specifically that of Jessor’s Problem 

Behavior Theory (Jessor & Jessor, 1971), two main factors contributing to delinquency: 

conventionality and unconventionality. Conventional activities, values, and goals reflect 

the conventions or values of adult society. Conventional interactions and relationships, 

usually with adults, serve serious, future-oriented, adult-held goals. In contrast, 

unconventional activities, values, goals and interpersonal relationships reflect or are 

organized around the priorities of youth, which tend to reflect peer interactions, presentfocused activities, and the pursuit of fun. 

When there is imbalanced investment by youth in conventional and 

unconventional activities, values, and goals, such that when a youth puts greater personal 

investment in unconventional activities, values, and goals, he or she is at much greater 

risk for criminal activity. Jessor and Jessor (1971) found that youth with few or minimal 

conventional connections or daily activities often harbor disdain toward adults, dismiss 

the consequences of their actions on their future liberties and successes, and challenge or 

subvert rules and laws. 

The efforts of adults to understand the youth’s investment and to give it validity 

through joining in the youth’s world often can trigger the youth’s movement back toward 

greater balance of conventional and unconventional investments in return. Therefore it 

was hypothesized that the inclusion of playful activities in the mentoring relationship 

would lead to stronger youth-Advocate relationships 

10 Karcher, M.J. & Nakkula, M.J (2010). Youth mentoring with a balanced focus, a shared purpose, and 

collaborative interactions. In New Directions for Youth Development (126) [special issue “Play, talk, learn: 

Promising Practices in Youth Mentoring,”] Jossey-Bass. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 73

Table 5.1 

Theoretically Evolving Activities in Mentoring (TEAM) Framework (Karcher & Nakkula, 2010) 

Purpose 

Serves 

conventional 

(adult) purpose 

Adult-led 

spontaneous 

(non-relational) 

Adult-oriented 

preventive and 

developmental 

activities or 

discussions 

(relational focus) 

Conventional 

Unilateral 

Authorship: 

Mentor driven 

(Adult-centric) 

Collaborative Authorship: 

We focus (collaboration) 

Focus: Minimally goaldirected/structured and/or 

highly relational 

Unilateral 

Authorship: 

Mentee driven 

(Youth-centric) 

Purpose 

Serves playful 

(or youthful) 

purpose 

Youth-led 

spontaneous 

(non-relational) 

Youth-oriented 

preventive and 

developmental 

activities or 

discussions 

(relational focus) 

Playful Skill 

1. Preacher 2. Peer 

(classmate) 

(is like Keller & Pryce's 

Acquaintance) 

3. Joker 

4. Counselor 5. Developmental Mentor 

(Morrow & Styles' term 

Developmental Mentor) 

is like Keller & Pryce's 

“Friend” 

6. Playmate 

7. Teacher 8. Instrumental Mentor 9. Teammate 

Skill (Hamilton & Hamilton's Development 

Development (Keller & term Instrumental Mentor is Purpose relevant 

Purpose relevant Pryce's like to the youths’ 

to adult/societal “Tutor”) Keller & Pryce's, “Sage” goals, interests, or 

goals, interests, or emphasizes 

beliefs about what outcomes now 

the mentee needs (Primarily goal-

(Primarily goal- oriented focus) 

oriented focus) 

Remedial/ 10. Colonel 11. Journeyman 12. Coach Remedial/ 

Intervention- (Boss, Vice Interventionoriented: Serves 

adults’ goals 

(goal-oriented) 

Serves 

conventional 

(adult) purpose 

Purpose 

Principal) ("Master Craftsperson" and 

Apprentice) 

oriented: Serves 

youths’ goals 

(goal-oriented) 

Serves playful 

(or youthful) 

purpose 

Purpose 

(Adult-centric) 

Focus: Highly structured 

and goal-directed (and/or 

minimally relational) (Youth-centric) 

Unilateral Collaborative Authorship: Unilateral 

Authorship: We decide together what Authorship: 

“Mentor to focus our time on “Mentee 

focused” focused 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 74

Especially for teens, for whom reciprocal adult-youth relationships signal a 

degree of respect and acknowledgement of the youth’s value to society, engaging in 

activities like play can serve as a sign of respect and interest in the youth that can result in 

increased interest by the youth in adult-valued, conventional activities, and, conversely 

predict lower engagement in anti-adult, criminal misconduct. Play and problem-focused 

discussions, therefore, serve a different purpose but are related in that a balance between 

them is critical just as it is between conventional and unconventional investments. 

Using the TEAM Framework to Predict Change from Mentoring Activities 

A mentoring activity log was used to track what happened in relationship over 

time. Using that log, mentors and mentees reported the frequency of play, talking, 

problem-solving, and advocacy activities at two and four months after matches began. 

Responses ranged from Never (1) to Very Often 5. This log is presented in Table 5.2. 

One mentoring activity that has received empirical examination for its value in 

effecting change, especially with younger youth in community based mentoring 

programs, is play. In this study play was measured using the following activities: Playing 

sports, Athletic activity, or Outdoor game; Creative activities (do art, read for fun, write a 

story or song); Playing cards, board games, computer games; Going to a park, museum, 

movie, community/cultural event, or college. These are shown in the log in Table 5.2. 

The developmental and prescriptive mentoring approaches. The first 

hypothesis tested was that the more misconduct a youth reported when entering the YAP 

program the more frequently the youth and mentor would focus on serious and futureoriented (conventional) interactions, particularly early in the relationship. Mentors might 

immediately focus on the youth’s problems because they think their mentoring 

relationship should be about helping the youth change how they think, act, and plan in 

order to be more successful in society in the future. 

Conversely, youth entering the program reporting having engaged in less 

misconduct might have Advocates who, at the start of the relationship, feel comfortable 

to engage in friendship building activities, such as by playing games and doing sports 

together, to build a solid friendship that could later incorporate more effective problemsolving. Mentors who engaged in this “first-play” and “then-work” approach would be 

taking the approach Morrow and Styles found most effective in their study of the “Big 

Brothers Big Sisters” program, which they called the developmental mentoring style. 

Therefore, knowing whether youths’ risk-taking level was the primary determinant of 

what happened in the match was a critical first step in understanding the link between 

activities and program outcomes. 

The approach Morrow and Styles found least effective with children in the Big 

Brothers Big Sisters program, and which is commonly taken by mentors, is to start off the 

relationship with a goal-directed or problem-focused focus, paying most attention to 

things that reflect adult-relevant problems and attempt to effect success in the future. This 

approach, however, usually starts by assessing the youths’ deficits to identify what 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 75

problematic behaviors, attitudes or knowledge deficiencies need to be corrected. Taking 

this deficit view and imposing a problem-solving approach immediately can be offputting to youth, and is what Morrow and Styles (1995) called a prescriptive approach. 

The prescriptive approach may be even more commonly taken when mentoring 

youth involved with the courts. Those mentors feel compelled to explain to youth the 

problems that can result from their misbehavior and underachievement. This correcting 

the deficits approach may be invited by a youth who is seeking out this information and 

assistance from the mentor. But more often the decision to start off with this approach is 

not collaboratively made. But a conventional focus can be useful when it reflects a 

collaborative goal-directed focus like that of a master-and-apprentice type relationship, 

which is shown in the center column, bottom row of Table 5.1. This is reflected in what 

the Steve and Mary Agnes Hamilton described as the successful instrumental style of 

mentoring. 

The instrumental style. Based on the literature from which the TEAM 

framework is derived, one might expect that the more playing a match did during the first 

half of their relationship the greater the likelihood of positive outcomes (viz. would 

correlate negatively with changes in misconduct across four months). This is consistent 

with the developmental mentoring style described by Morrow and Styles (1995). 

However, two findings counter the expectation that this model would fit best for 

mentoring youth in the YAP program. First, in Morrow and Styles’ study, the mentors 

and mentees did not come together as a result the youth’s misbehavior (their sample was 

not court-referred youth) or some other agreed upon reason or obvious purpose. Second, 

their sample was largely children not adolescents. 

An alternative candidate for the most effective mentoring style in YAP, and the 

one on which our second hypothesis was derived, assumes the youth who enter YAP 

would prefer to engage in the Hamiltons’ instrumental style of mentoring relationship. In 

this style, the mentors and mentees move from an initially goal- and skills-development 

focused approach early in the match toward developing a friendship later in their time 

together. This style moves the master-and-apprentice (“protégé”) relationship toward an 

instrumental relationship by incorporating a reciprocal friendship once work on an agreed 

upon task (or problem) has been chose as their focus. Extended to work with youth in the 

YAP program, we expected that playful interactions later in the relationship would 

indicate that efforts to foster connection and friendship were underway. 

Specifically, play would be more important in the second two-month period 

because by then the Advocate and youth could really have gotten to know each other, 

may have done some purposeful problem solving through advocacy efforts, and may now 

be ready to shift toward friendship development. This pattern of shifting from a focus on 

the problem or goal, which served as the basis for the formation of their relationship, to a 

focus on fun activities parallels Hamilton and Hamilton’s (2005) instrumental style by 

illustrating a balance over time in focus and purpose. Therefore, using the language of the 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 76

TEAM framework, the common adult roles of teacher or coach can expand to a teacher+ 

or coach+ when there is a shift in this balance that introduces a personal relationship and 

sometimes a friendship that consolidates, forming a mentoring relationship. 

This movement from a traditional adult-youth relationship into a coach+, teacher+ 

or Advocate+ relationship is illustrated in the shift in focus inward to the center column 

in Table 5.1, which represents an approach that balances the future purpose (left column) 

and fun purpose (right column). It also tends to happen as a result of an upward shift in 

focus from a more goal-directed to relational focus, or vice versa, during the relationship. 

Another reason to make this our primary hypothesis for this study was that the 

Hamiltons’ research was, like this one, with adolescents (not children), for whom a future 

focus (requiring overcoming problems with successful engagement in society) is more 

developmentally salient. 

In order to test which of these two styles seemed best in explaining when the YAP 

mentoring is most effective in lessening misconduct through program participation, we 

looked at when play occurred. If higher rates of play at the start of the relationship 

predicted lower misconduct at discharge, the first element of the developmental style 

would be evidenced. Whereas, if play later in the match was a better predictor, then 

support for the instrumental style was evidenced. 

Both conversations about family, school, friends, and problem-focused 

discussions were considered to both reflect a more serious, conventional approach to 

mentoring. Were higher rates of either of these to occur later in the relationship, it could 

be argued this provided support for the developmental style as most effective. Whereas, if 

this focus was more predictive of lower misconduct when it happened at the start of the 

relationship, this would better match the instrumental style of collaborating on a mutually 

agreed upon, somewhat more future-oriented, problem-resolving, or skill-building focus. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

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Final Technical Report: 2011-JU-FX-0001 p. 77

Table 5.2 

Data Collection for the TEAM Framework Used Survey Capturing these Activities. 

Conventional, (C), 

Unconventional 

(U), or Varies (V) 

“With your youth/Advocate, 

how often do you do TALK 

about each of the following?” Never 

Hardly 

Ever 

Sometimes Often 

Very 

Often 

U Casual Conversation (Discuss sports, what 

either of you did on the weekend, holiday 

plans, or other events in town, etc.) 

q q q q q

V Conversation on Social Issues (religion, race, 

poverty, etc.) q q q q q

V Conversation About Relationships: (can check 

multiple) 

q Family (C) 

q Teachers or Employers (C) 

q Friends, Peers, Peers (U) 

q Romantic Friend (U) 

qF 

qT 

qY 

qR 

qF 

qT 

qY 

qR 

qF 

qT 

qY 

qR 

qF 

qT 

qY 

qR 

qF 

qT 

qY 

qR 

V Listening & Sharing Info about Self (goals, 

interests, feelings...) q q q q q

C Academics (Discuss grades, school, testing...) q q q q q

C Behavior (Discuss youth’s misbehavior 

related to problems with peers, teachers, 

adults, or the courts) 

q q q q q

C Attendance (Discuss importance of showing 

up school/work) q q q q q

C Future Talk (Discuss college, jobs, goals, 

dreams, etc.; Use computer to research 

opportunities that would benefit youth.) 

q q q q q

U Play Sports, Athletic Activity, Outdoor Game q q q q q

U Creative Activities (do art, read for fun, write 

a story or song) q q q q q

U Played cards, board games, computer games q q q q q

U Go to a park, museum, movie, 

community/cultural event, or college. q q q q q

C Get taken somewhere by my Advocate to 

address a court mandate (e.g., meet with 

probation officer, meet some curfew) 

q q q q q

C Deal with a crisis or emergency q q q q q

C Work on a mandated community service or 

restitution activity q q q q q

C Work on a volunteer community service or 

“give-back” project q q q q q

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 78

Method of Analyses of Change Over Time 

Sample. For the analyses in Study 2, data was available for a total of 161 the 

matches included in Study 1. The youth were 71% black, 19% Latino, and 12% white, 

and 68% male. Mentor/Advocate’s were 68% black, same sex as their mentees, with 

education levels ranging from having completed some high school to having earned a 

college degree. 

Data Overview. We used the data we collected a baseline (pre-survey) to get a 

picture of youths’ and Advocates’ individual characteristics and starting levels on 

misconduct at discharge. Then at four months we re-assessed the outcome of misconduct 

(for youth who had not left YAP by this time point, of course). 

At two and four months into the match, which are effectively the middle and endpoints for most matches, we asked youth and Advocates to report the frequency at which 

each of the four kinds of activities (play, casual conversations, problem-related 

discussions, and “doing” [outside/advocacy efforts]) had happened up to that point in 

time. Bivariate correlations were examined to estimate the strength of the zero-order 

relationships between individual characteristics, activity types, and outcomes, specifically 

misconduct. These provided the foundation for the series of analyses reported below. 

Preliminary Tests for Presence of Evidence of Changes in Misconduct over Time 

The first step in our series of analyses explaining program-related changes in 

misconduct was to ensure that pre-post change in misconduct, observed in the t-tests and 

ANOVAs presented in the prior section, was observed across the three points in time. We 

wanted to be sure there was linear change or growth over time. Recall that Study 1 

reported reductions in the rates of misconduct from pre (entry) to post (discharge) for 

youth who completed the YAP program but did not assess changes over time within the 

mentoring relationship. These preliminary analyses were to estimate whether or not there 

was variability in between these three points in time. 

As revealed in 5.1 below, results from a structural model revealed sizeable and 

statistically significant changes in misconduct between entry and two months as well as 

between two months and discharge. This change means that the difference (change/slope) 

and average score (intercept) on misconduct at time four was not explained by change 

that had already manifest by two months and remained static through discharge. The 

model reveals there were differences in rates of misconduct reported between entry and 

two months, as well as between two and four months. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

Final Technical Report: 2011-JU-FX-0001 p. %'

Figure 5.1. Change in Self-Reported Misconduct Across Two Time Intervals 

Youth and Advocate Demographic Characteristics 

In Table 5.3 below that present correlations between youth and Advocate 

characteristics, we see that Advocate background experiences, and initial rates of 

misconduct, were associated with youth characteristics. Starting with levels of initial 

misconduct, we see that youth sex is the only statistically significant demographic 

characteristic (of youth or Advocates) that is associated with initial rates of misconduct. It 

suggests that girls (coded 1) had higher rates of misconduct than boys (coded 0). This 

variable, therefore was included in all but the first of the models. That way sex-related 

variability in misconduct was accounted for. The uncommonly higher rates of misconduct 

among girls than boys is due largely to the fact that the majority of girls came from 

Vegas, many of whom had been involved in sex trafficking. 

Advocate characteristics. Just as there were only weak associations between 

initial misconduct and types of activities matches engaged in, there were generally weak 

associations between youth misconduct at entry and their Advocate’s background 

experiences (having been a teacher and highest educational level attained). Youth 

reporting more misconduct at entry were not more likely to receive an older Advocate. 

Nor were youth reporting more misconduct more likely to have an Advocate with 

teaching experience. They were, however, slightly more likely to have a more educated 

Advocate (statistical trend, p = .065). 

But sex and age of the youths did predict the type of Advocates with whom they 

were matched. Older youth and girls tended to have more educated mentors. Boys were 

more likely to have Advocates who were older and who had prior teaching experience. 

Older youth also tended to have Advocates with teaching experience (statistical trend, p 

= .06). And Advocates with prior teaching experience tended to be older males. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 80

Table 5.3 

Correlations Between Demographics, Background Experiences and Initial Misconduct 

Y Age Y Sex A Age A Sex 

AP 

teacher 

AP Highest 

education Misc Pre 

Age r 

p 

N 

1 

161 

.095 

.229 

161 

.161* 

.043 

159 

.077 

.329 

161 

.152 

.061 

154 

.172* .100 

.211 

157 

.033 

154 

Sex r 

p 

N 

.095 

.229 

161 

1 

161 

-.313** 

.000 

159 

.896** 

.000 

161 

-.164* .238** .212** 

.042 .003 .008 

154 154 157 

A2 Age r 

p 

N 

.161* 

.043 

159 

-.313** 

.000 

159 

1 

159 

-.311** 

.000 

159 

.208** -.091 

.260 

154 

-.106 

.188 

155 

.010 

154 

AP teacher r 

p 

N 

.152 

.061 

154 

-.164* 

.042 

154 

.208** 

.010 

154 

-.175* 

.030 

154 

1 

154 

-.056 

.489 

154 

-.123 

.134 

150 

AP Highest 

education 

r 

p 

N 

.172* 

.033 

154 

.238** 

.003 

154 

-.091 

.260 

154 

.221** 

.006 

154 

-.056 

.489 

154 

1 

154 

.151 

.065 

150 

Misconduct 

at Entry 

r 

p 

N 

.100 

.211 

157 

.212** 

.008 

157 

-.106 

.188 

155 

.241** 

.002 

157 

-.123 

.134 

150 

.151 

.065 

150 

1 

157 

*= Correlation is significant at the 0.05 level (2-tailed); **= is significant at the 0.01 level. 

Sex differences. There were sex differences found in most analyses, starting with 

the two or three to one ratio of boys to girls. In addition, rates of activities differed at both 

time points on most types of activities. Table 5.4 below reveals that girls reported a 

greater frequency of doing things together, engaging in casual talk, and engaging in 

problem-focused talk than boys in both the first and second half of their relationships. 

Only on rates of engaging in play did boys and girls not differ. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

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Final Technical Report: 2011-JU-FX-0001 p. 81

Table 5.4. 

Observed score differences in rates of program activities between boys and girls 

N Mean SD Std. Error 

ANOVA 

F p 

Doing 

at 2 mos. 

Male 

Female 

Total 

92 

35 

127 

3.15 

3.56 

3.26 

.92558 

.74982 

.89742 

.09650 

.12674 

.07963 5.667 .019 

Talk 

at 2 mos. 

Male 

Female 

Total 

92 

35 

127 

3.55 

3.91 

3.65 

.80412 

.67412 

.78454 

.08384 

.11395 

.06962 5.487 .021 

Play 

at 2 mos. 

Male 

Female 

Total 

92 

35 

127 

3.35 

3.06 

3.27 

.87453 

.98769 

.91190 

.09118 

.16695 

.08092 2.448 .120 

Problem 

Talk 

at 2 mos. 

Male 

Female 

Total 

92 

35 

127 

3.97 

4.34 

4.07 

.85903 

.66682 

.82476 

.08956 

.11271 

.07319 5.226 .024 

Doing 

at 4 mos. 

Male 

Female 

Total 

71 

32 

103 

3.3056 

3.7500 

3.4437 

.99687 

.99461 

1.01259 

.11831 

.17582 

.09977 4.389 .039 

Talk 

at 4 mos. 

Male 

Female 

Total 

71 

32 

103 

3.70 

4.17 

3.85 

.82279 

.55948 

.77958 

.09765 

.09890 

.07681 8.665 .004 

Play 

at 4 mos. 

Male 

Female 

Total 

71 

32 

103 

3.59 

3.53 

3.57 

.78386 

.96250 

.83904 

.09303 

.17015 

.08267 .109 .742 

Problem 

Talk 

at 4 mos. 

Male 

Female 

Total 

71 

32 

103 

3.98 

4.48 

4.14 

.81649 

.74151 

.82306 

.09690 

.13108 

.08110 8.539 .004 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 82

Study 2—Analyses Part 1: Observed Activity Scores Predicting Misconduct 

The first series of models used observed scores for activities to explain rates of 

misconduct at discharge from the activity frequency reports, holding constant the role of 

gender and starting levels of misconduct. As expected based on the sex differences 

presented in Table 5.4, sex of the youth was a significant predictor of what happened at 

the start of the match as well as starting levels of misconduct. Girls had higher reported 

misconduct upon entry than boys, which was somewhat surprising, but reflects the 

disproportionate number of girls in the Las Vegas YAP, many of whom had been 

involved in sex crimes. Table 5.5 and Figure 5.2 also suggest the Boys were more likely 

to begin the matches engaging in playful, recreational activities, while girls were more 

likely engage first in casual conversations. 

There were opposing relationships between the frequency of playing in the first 

versus second half of the match and reported misconduct at discharge. Although rates of 

playing were fairly constant across the life of the matches (e.g., β = .50), higher rates of 

play early in the match predicted higher levels of misconduct at discharge, even after 

controlling for the contributions of sex and starting levels of misconduct (Table 5.5). 

Conversely, higher rates of playing later in the match predicted lower rates of misconduct 

at discharge. 

That youth in matches that were more playful later in the match tended to report 

less misconduct after program completion is consistent with the instrumental approach 

described by the Hamiltons. This assumes, however, that the first half of their 

relationships were focused on some shared goal-focused activities, like effecting youthdesired changes through advocacy efforts, or spending time learning about each other and 

identifying shared interests to pursue. 

And, indeed in Figure 2 there was a negative association between engaging in the 

kind of conversations that help each learn about the other (Talk) early in the match and 

rates of misconduct at discharge. But this path was not statistically significant (β = .14, p 

= .27). This would have supported the explanation that the more time that was spent 

getting to know each other at the start of the relationship the lower the rates of 

misconduct at the end of the relationship. But, again, this association was not statistically 

different from zero. Perhaps their interaction would have been, but we did not test this. 

Therefore, using the casual conversation variable, does not provide additional 

evidence of an instrumental approach at play, unless the conversation was sufficiently 

casual to seem to the youth to have no relevance for their future (i.e., conventional, goaldirected focus). Neither do the data support the value of the developmental style, in 

which play initially serves as the basis for later conversations. Both higher rates of play 

initially and higher rates of casual conversation (statistical trend at p = .10) later were 

associated with higher rates of misconduct at discharge. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001	 p. &+

Figure 5.2. Observed score Baseline Model 1: Assessments of talking and playing at 

two timepoints predicting misconduct at discharge, controlling for youth sex. 

Table 5.5 

Observed Score Model 1 Fit Indices 

Number of Free Parameters 24 

Loglikelihood

H0 Value -509.731 

H1 Value -502.351 

Information Criteria 

Akaike (AIC) 1067.461 

Bayesian (BIC) 1141.265 

Sample-Size Adjusted BIC 1065.290 

(n* = (n + 2) / 24) 

Chi-Square Test of Model Fit

Value 14.759 

Degrees of Freedom 9 

P-Value 0.0978 

RMSEA (Root Mean Square Error Of Approximation)

Estimate 0.063 

90 Percent C.I. 0.000 0.119 

Probability RMSEA <= .05 0.308 

CFI/TLI	 CFI 0.961 

TLI 0.910 

Chi-Square Test of Model Fit for the Baseline Model

Value 169.944 

Degrees of Freedom 21 

P-Value 0.0000 

SRMR (Standardized Root Mean Square Residual) 0.064 

STANDARDIZED MODEL RESULTS 

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Final Technical Report: 2011-JU-FX-0001 p. 84

Two-Tailed 

YPMCS ON

Estimate S.E. Est./S.E. P-Value 

YPSEX 0.203 0.076 2.675 0.007 

Y2PLAY ON

 YPSEX -0.251 0.082 -3.065 0.002 

Y2TALK ON

 YPSEX 0.266 0.081 3.280 0.001 

Y4PLAY ON

 Y2PLAY 0.500 0.069 7.217 0.000 

Y4TALK ON

 Y2TALK 0.599 0.063 9.486 0.000 

Y4MCS ON

 YPMCS 0.418 0.083 5.047 0.000

 Y2PLAY 0.232 0.106 2.185 0.029

 Y4PLAY -0.289 0.108 -2.685 0.007

 Y2TALK -0.137 0.121 -1.125 0.261 

Y4TALK 0.202 0.122 1.656 0.098 

Y4PLAY WITH

 Y4TALK 0.372 0.086 4.309 0.000 

Y2PLAY WITH

 Y2TALK 0.393 0.075 5.244 0.000 

The second model, shown in Figure 5.3 below, incorporates the Advocate 

characteristics of education level and prior teaching experience into the baseline model 

presented in Figure 5.3. The first contribution made by Advocate characteristics is 

through the effect of mentor education on amount of time spent playing later in the 

relationship (β = -.20). More educated Advocates tended to play less in the second half 

of the relationship than did less educated Advocates. And because playing later in the 

relationship predicted lower misconduct at discharge, this relationship reveals a negative 

indirect effect of Advocates’ level of education. 

However, there were two positive direct effects of these two mentor 

characteristics. Mentor education had a direct and positive effect on rates of misconduct 

at discharge (β = -.22). This model (see 5.6) also revealed a positive effect of having prior 

teaching experience. ). In this way, the youth assigned to the more educated 

mentors/Advocates and those with more teaching experience seemed to benefit the most 

from participation in YAP, holding the contribution made by activities constant. 

As found in Model 1 without Advocate characteristics, rates of misconduct at 

discharge vary as a function of when play occurs. In this model, play at both times—early 

and later in the relationship—make statistically significant contributions to rates of 

misconduct at discharge. Playing early seems to contribute to more misconduct (β =.26) 

while playing later contributes to lower rates of misconduct. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. &#

Figure 5.3. Observed Score Model 2: Full Model of Youth, Advocate, and Activity Predictors of 

Rates of Misconduct at Discharge 

Table 5.6 

Observed Score Model 2: Fit indices 

Number of Free Parameters 37 

Loglikelihood 

H0 Value -790.889 

H1 Value -782.711 

Information Criteria 

Akaike (AIC) 1655.778 

Bayesian (BIC) 1769.790 

Sample-Size Adjusted BIC 1652.659 

(n* = (n + 2) / 24) 

Chi-Square Test of Model Fit 

Value 16.356 

Degrees of Freedom 15 

P-Value 0.3588 

RMSEA (Root Mean Square Error Of Approximation) 

Estimate 

90 Percent C.I. 

Probability RMSEA <= .05 

0.024 

0.000 

0.716 

0.080 

CFI/TLI 

CFI 0.992 

TLI 0.981 

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Final Technical Report: 2011-JU-FX-0001 p. 86

Chi-Square Test of Model Fit for the Baseline Model

 Value 211.102

 Degrees of Freedom 36 

P-Value 0.0000 

SRMR (Standardized Root Mean Square Residual) 0.057 

STANDARDIZED MODEL RESULTS 

Two-Tailed 

Estimate S.E. Est./S.E. P-Value 

YPMCS ON

 YPSEX 0.201 0.076 2.651 0.008 

MENTORED ON

 YPSEX 0.192 0.078 2.452 0.014

 YPMCS 0.152 0.080 1.900 0.057 

TAUGHT ON

 YPSEX -0.160 0.079 -2.023 0.043 

Y2PLAY ON

 YPSEX -0.250 0.082 -3.045 0.002 

Y4PLAY ON

 YPSEX 0.044 0.089 0.496 0.620

 Y2PLAY 0.470 0.076 6.169 0.000

 MENTORED -0.195 0.084 -2.315 0.021 

Y2TALK ON

 YPSEX 0.262 0.081 3.219 0.001 

Y4TALK ON

 YPSEX 0.182 0.076 2.382 0.017

 Y2TALK 0.544 0.069 7.917 0.000

 TAUGHT -0.073 0.067 -1.099 0.272 

Y4MCS ON 

YPMCS 0.467 0.077 6.036 0.000

 MENTORED -0.224 0.086 -2.624 0.009

 TAUGHT -0.183 0.076 -2.425 0.015

 Y2PLAY 0.261 0.100 2.610 0.009

 Y4PLAY -0.334 0.099 -3.360 0.001

 Y2TALK -0.153 0.114 -1.346 0.178

 Y4TALK 0.154 0.115 1.347 0.178 

Y2TALK WITH

 Y2PLAY 0.395 0.075 5.292 0.000 

Y4TALK WITH

 Y4PLAY 0.364 0.088 4.163 0.000 

While the same contributions made by play in the prior model held, the 

contributions made by casual conversation were no longer evident. Once the effect of 

having Advocate with teaching experience was included in the model, the relationships 

between rates of talking (casual conversations) and misconduct at discharge is no longer 

statistically significant. It appears having an Advocate with teaching experience may be 

associated with less talking later in the relationship partly because girls were less likely to 

have Advocates with teaching experience but engaged in higher rates of casual 

conversation at both time points. Therefore, the effects of casual conversation may be 

moderated by sex, and conditional, varying as a function of initial rates of misconduct. 

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Final Technical Report : 2011-JU-FX-0001 p. 87

Study 2—Part II: Analyses of Outcomes Related to Factor Score Based Rates of 

Time Spent Playing and Having Problem-Focused Conversations 

This section provides information about how the activities youth and Advocates 

engage in may contribute to changes in misconduct over the course of treatment using 

estimates of activities based on factor analyses conducted separately at two and four 

months. As before, using the TEAM Framework, we attempted a second test of null 

hypothesis that playful/recreational activities early and problem-focused interactions 

later, which Morrow and Styles (1995) found worked well early in the match for youth in 

traditional community based mentoring in the Big Brothers Big Sisters program, also is 

helpful when working with adjudicated youth. This null was rejected based on the 

findings about when playful activities were most helpful. 

This time, however, we examined specifically whether a match’s focus on 

problem resolution and effecting conventional behavioral changes, which Morrow and 

Styles found problematic when they persisted across the relationship or when enacted 

early in the relationship. Both reflect what they labeled the prescriptive style. The present 

analyses examine whether this is similarly problematic when working with youth at much 

greater risk for criminal activity. Understanding the role of problem-correcting activities 

in mentoring court-involved youth may help prepared mentors working with other kinds 

of youth who also are at heightened risk for academic, social, and vocational failure. 

It is not uncommon for mentors of high risk youth, like the YAP Advocates, to 

put a greater emphasis on activities that they believe will serve to eliminate or correct 

youths’ problematic behaviors, attitudes, or knowledge deficiencies; because these 

problems appear to them, as they do to adults generally, to be what causes these failures. 

While that view of how adults help youth is antithetical to the assumptions of 

many about how mentoring works (Rhodes, 2002; DuBois, Neville, et al., 2002), it is 

nevertheless one that is pervasive and commonly manifests in adult-youth relationships 

across the various roles adults play in the lives of youth, from coaches to principals and 

even parents. Taking an approach based on effecting corrections seems most likely to 

occur among mentors working with court-involved youth. And, within programs serving 

court-involved youth, it is possible that the likelihood mentors draw on this prescriptive 

approach may be even greater for mentees who appear most at risk because of their high 

rates of misconduct upon entry to the program. 

There are two other reasons for the shift to using problem-focused conversations 

rather than casual conversations in the models using factor scores. First, to confirm that 

what was happening in the match could be described as more goal directed than 

friendship-building conversations, we wanted to draw a more clear contrast between 

talking and problem-solving. 

Second, functionally, the CFA model fit indices (see Appendix D) were 

considerably better for the problem-focused discussions than casual conversations. This 

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Final Technical Report : 2011-JU-FX-0001 p. 88

appeared to introduce additional error into estimates, which was what the factor score 

analyses were intended to correct for (as it is a limitation of observed, mean score 

variables). Fortunately, running the same model with observed scores for casual 

conversation and then factor score rates of problem-focused discussion, yielded the same 

pattern of reveal statistically significant paths. 

How perceptions of youth risk may influence the mentors’ activity focus. The 

first hypothesis tested in this section was that youths’ starting risk levels, specifically 

youths’ self-reported misconduct at entry into the YAP program, would influence or 

predict the activities they and their mentors engaged in, especially in early in the 

mentoring match when mentors may only have this information about youth on which to 

base their decisions about what their mentees “needs.” Given initial risk status was highly 

related to youth sex, in the tables that follow, factor scores for the four types of activity 

focuses described in the TEAM framework are presented separately for boys and girls. 

The purpose of these tables is to present the general patterns and frequencies of 

engagement in each activity in the first and second half of the mentoring relationship for 

each sex with factor scores specifically. For girls, we see that, between the first and 

second half of the relationship, there is a very small increase in play and similarly a small 

decrease in problem focused interactions and engagement in the community (Doing). 

These differences, based on effect sizes, indicate only negligible differences (less than d 

= .05). For boys, only the frequency of problem-focused conversations (d = .03) differed 

over time. But this increase in problem-focused conversations over time did not even 

approach what is considered a “small” effect (d = .20) or difference. 

Table 5.7 

Activity Frequency Factor Score Descriptive Statistics for Boys 

Minimum Maximum Mean Std. Dev. 

Playing & Recreation 

at 2 Months (“Play2”) -2.13 1.49 .101 .794 

Playing & Recreation 

from 2 to 4 Months (“Play4”) -2.40 1.20 .127 .832 

Problem-focus Talk 

at 2 Months (“Probs2”) -2.87 .89 -.088 .820 

Problem-focus Talk from 

2 to 4 Months(“Probs4”) -2.90 .77 -.133 .730 

Casual Conversation 

at 2 Months (“Talk2”) -1.44 .72 -.067 .455 

Casual Conversation 

from 2 to 4 Months (“Talk4”) -1.31 .49 -.076 .388 

Activities in Community 

at 2 Months (“Do2”) -2.12 1.67 -.115 .888 

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Final Technical Report : 2011-JU-FX-0001 p. 89

Activities in Community 

from 2 to 4 Months (“Do4”) -2.07 1.43 -.137 .895 

Table 5.8 

Activity Frequency Factor Score Descriptive Statistics for Girls 

Minimum Maximum Mean Std. Dev 

Playing & Recreation 

at 2 Months (“Play2”) -2.13 1.49 -.266 .903 

Playing & Recreation 

from 2 to 4 Months (“Play4”) -2.22 1.20 -.281 1.036 

Problem-focus talk 

at 2 Months (“Probs2”) -1.52 .89 .232 .646 

Problem-focus talk from 

2 to 4 Months (“Probs4”) -2.35 .77 .296 .695 

Casual Conversation 

at 2 Months (“Talk2”) -.58 .72 .178 .374 

Casual Conversation 

from 2 to 4 Months (“Talk4”) -.45 .47 .168 .252 

Active in Community 

at 2 Months (“Do2”) -1.55 1.39 .304 .718 

Active in Community 

from 2 to 4 Months (“Do4”) -1.52 1.43 .305 .872 

Thus, as was the case for the observed scores, the mean levels on factor scores 

measuring engagement in different kinds of activities did differ by sex, but for youth of 

both sexes, the average difference between time spent in each type of activity over time 

was negligible. This continuity in the frequency of each type of activity support the 

likelihood that differential associations between rates of activities and self-reported 

misconduct at discharge were more likely due to Advocate characteristics and the nature 

of the relationship that develops early in the match than just youth characteristic, both age 

and sex, as well as starting levels of misconduct. 

Factor score path models. The factor score path model in Figure 5.4 is the same 

model tested first using observed scores (Figure 5.2). The fit indices (Table 5.9) suggest 

this model fit the data well. It reveals that, for the youth as a whole, the level of youthreported misconduct at entry was not strongly associated with whether the match started 

with more or less recreation/play (β = .05) or problem-focused activities (β =.01). 

Therefore, what was happening at two months in the match was not a function of whether 

the youth was more or less seen as a (self-reported) trouble maker (i.e., high in 

misconduct at entry). 

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Final Technical Report: 2011-JU-FX-0001 p. '(

Figure 5.4. Factor Score Model 1: Predicting Misconduct at Discharge from 

Activities at Two Time Points (Standardized Path Coefficients). 

Table 5.9 

Fit Indices for Factor Score Model 1: Effects of Activities on Outcomes 

Number of Free Parameters 19 

Information Criteria 

Akaike (AIC) 1923.776 

Bayesian (BIC) 1991.502 

Sample-Size Adjusted BIC 1931.264 

(n* = (n + 2) / 24) 

Chi-Square Test of Model Fit 

Value 8.074 

Degrees of Freedom 6 

P-Value 0.2327 

RMSEA (Root Mean Square Error Of Approximation) 

Estimate 0.036 

90 Percent C.I. 0.000 0.094 

Probability RMSEA <= .05 0.580 

CFI/TLI 

CFI 0.990 

TLI 0.975 

Chi-Square Test of Model Fit for the Baseline Model 

Value 222.257 

Degrees of Freedom 15 

P-Value 0.0000 

SRMR (Standardized Root Mean Square Residual)= 0.034 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 91

The frequency of play and of problem-focused conversations in the second half of 

the match were, as with the observed scores, the best predictors of misconduct at 

discharge. Activity frequencies at two months explained little variability in rates of 

misconduct at discharge. Rates of misconduct at discharge were lower when more time 

was spent playing later in the relationship. Here, it should be noted, “playing” still refers 

to the four types of recreational activities from the activity log in Table 5.2. But the 

indicator (of the four specific types of activities tallied in the Activity log) that made the 

greatest contribution to the formation of that factor score later in the match, as can be 

seen in the factor models presented in Appendix D, was playing sports. 

The loadings on the play score varied across assessment periods. For play, the 

factor for two-month reports of play reflected balanced contributions (factor loadings) of 

the four play activities although the strongest contributor was sports. At the four-month 

assessment, however, the factor loading of the sports activity was much larger than the 

other types of play. The factor loading of play had increased while the other indicators of 

play decreased. The factor loading for sports was .86 while the factor loadings for the 

other three types of play ranged from .43 to .51. This suggests that factor scores for rates 

of play at four months most reflected time spent playing sports. 

The contributions to rates of misconduct at discharge made by play and problemfocused conversations were opposite. The findings presented in Figure 5.4 suggest that a 

higher frequency of play in the second half of the match predicted lower levels of 

misconduct at discharge (β =-.13, shown as a negative relationship). But, the frequency of 

time spent focusing on problems in the second half of the relationship (reported at 4 

months) predicted higher rates of misconduct at discharge (β =.14, seen as a positive 

relationship). The effects (or prediction coefficients) for these two activity factors were 

roughly similar in size and both relatively small. Activity choices early in the relationship 

were unrelated to outcomes. 

The main interpretation of this is that time spent focusing on problems later in the 

relationship may have resulted in increases in self-reported misconduct. These findings 

are consistent with the instrumental style (described by the Hamiltons) and support 

hypothesis number two that once a relationship is formed around shared activities, 

friendship-enhancing activities become more important in influencing outcomes because 

they consolidate the relationship, shifting it from a coach or instructor adult-youth 

relationship to a true mentoring relationship that is more comprehensive and balanced in 

its functions for the youth. 

Summary. The types of activities engaged in at two months did not strongly 

predict the rates of misconduct at discharge, nor were activities at two months related to 

initial rates of misconduct. This does not support the hypothesis that what happens early 

in the match is a function of the youths’ initial risk level. Nor did initial reports of 

misconduct at entry predict what occurred later in the second half of the relationship. 

Higher risk youth did not appear to have relationships that were more problem-focused at 

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3

4

5

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Final Technical Report: 2011-JU-FX-0001 p. 92

any point in the match. 

The second hypothesis, regarding how program outcomes were related to types of 

activities, also was largely supported by these results. The findings suggest a main, 

unmediated effect of playful, recreational activities on mentoring outcomes, and negative 

consequences of focusing on problems rates of misconduct at discharge. This is 

consistent with the instrumental approach described by the Hamiltons, which is that for 

teens engaging in activities that offer more shared positive experiences later in the 

relationship makes a positive contribution to reductions in misconduct, even though rates 

of play early in the match did not predict rates of misconduct at discharge. 

The Role of Advocate Characteristics 

In this section we add to the path model above (Figure 5.4) two Advocate 

characteristics as predictors of activities and of changes in activity frequency over time. 

Considering the role of Advocate characteristics is useful, in part, because the path model 

in Figures 5.1-5.4 reveal that once a given type of activity is started, it tends to continue. 

Beta coefficients in Model 4 predicting the same types of activities happening between 

two and four months were large for playing (β = .48) and for problem-focused 

discussions (β = .58). This suggests that at least a fourth of the variability in what they 

were doing later (at four months) is explained by (or could be predicted from) what they 

were doing earlier (at two months). Advocates education level also was clearly related to 

activity levels, specifically play. The number of Advocates in each category of 

educational attainment are listed in Table 5.11 and the average rates of playful activities 

in the first and second half of the mentoring relationship across these education levels are 

presented in Figure 5.5, in which each time period is reflected by a separate line. 

Table 5.10 

Number of Advocates’ in Each Category at Each Level of Education Completed 

Level of Education Completed N 

Number of 

Advocates’ at Each 

Level of Education 

Completed 

1. GED 

2. HS graduate 

3. Tech school/ apprenticeship 

4. Some college 

5. Associates degree 

6. College graduate 

7. Some grad school 

8. Grad degree 

3 

9 

4 

33 

13 

40 

7 

3 

At both assessment points, Figure 5.5 below reveals that youth’s reports of time 

spent playing was higher in matches with Advocates having less education. There is a 

clear trend toward less play at higher levels of Advocate educational attainment at two (r 

= -.07, p = .059) and at four months (r = -.27, p < .00). 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report: 2011-JU-FX-0001 p. '+

Figure 5.5. Play Frequency at Two and Four Months in Matches Across Advocate’s 

Levels of Education Completed. 

The Advocate’s level of educational attainment and prior experience as a teacher 

were included Model 2. Consistent with prior analyses, figure 5.6 illustrates that 

Advocates with more education tended to report less play in the second half of the 

relationship. Those Advocates with teaching experience tended to focus less on problemrelated conversations later in the relationship. 

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Final Technical Report: 2011-JU-FX-0001	 p. 94

Figure 5.6. Factor Score Model 2: Misconduct at Discharge Predicted From 

Activities with Indirect Effects of Advocate Backgrounds (with Youth Sex and Age) 

What can be observed in Figure 5.6 above is that Advocates with more years of 

education were less likely to play in the second half of the relationship. Advocates with 

more teaching experience were less likely to engage in problem-focused activities during 

the second half of the match. Both of these findings are consistent with the instrumental 

style of mentoring in which a relationship starts off more goal, task, or problem focused 

but shifts toward the development of a friendship in the later period of the relationship. 

However, these indirect effects do not include the main or direct effects of these 

Advocate characteristics observed in the earlier analyses using observed (not factor) 

scores. 

Table 5.11 

Fit Indices for Factor Score Model 2: Indirect Effects of Advocate Characteristics 

Information Criteria

 Akaike (AIC) 1846.488

 Bayesian (BIC) 1913.471

 Sample-Size Adjusted BIC 1853.239

 (n* = (n + 2) / 24) 

Chi-Square Test of Model Fit

 Value 28.289

 Degrees of Freedom 16 

P-Value 0.0292 

RMSEA (Root Mean Square Error Of Approximation)

 Estimate 0.055

 90 Percent C.I. 0.018 0.088

 Probability RMSEA <= .05 0.360 

CFI/TLI CFI 0.939

 TLI 0.932 

Chi-Square Test of Model Fit for the Baseline Model 

Value 220.797

 Degrees of Freedom 18 

P-Value 0.0000 

SRMR (Standardized Root Mean Square Residual)=0.053 

R-SQUARE

 Observed Two-Tailed

 Variable 

Y4MCS 

Estimate 

0.228 

S.E. 

0.052 

Est./S.E. 

4.360 

P-Value

0.000

 Y4PLAY 0.239 0.050 4.815 0.000

 Y4PROBLEMS 0.364 0.056 6.517 0.000 

However, the fit indices for factor score Model 2 in Table 5.11, specifically the 

CFI/TLI and Chi-Square test are not as good as those in factor score Model 1 without 

Advocate characteristics (Table 5.9). A reason for this may be that while this model 

provides additional information about how often different types of Advocates engage in 

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Final Technical Report: 2011-JU-FX-0001 p. '#

specific types of activities, there may be other variables not accounted for in this model 

or main effects of Advocate characteristics not present in the model but are in the data. 

Therefore, so far we have examined how Advocate characteristics, specifically 

their background experiences as teachers and levels of education influence the types of 

activities that youth engage in with their Advocates. But these indirect tests do not 

account for whether youth characteristics are systematically associated with the types of 

Advocates they are assigned or if they make direct or indirect contributions to outcomes. 

Two models are presented, discussed, and briefly contrasted below. Each includes 

the same variables representing these same two Advocate characteristics, but also include 

the youth demographics of age and sex earlier to be associated with the Advocates’ 

background experiences. 

These two models differ in one way. Both are used to explain changes in 

misconduct as a function of Advocate background characteristics, but the first model 

considers only the indirect paths (shown in Factor Score Model 3, Figure 5.7, Table 

5.12), wherein Advocate background characteristics effect change through their influence 

on mentoring activities. The second and final model presented below, factor score Model 

4 (Figure 5.8, estimates the influence of Advocates’ background characteristics both 

directly and indirectly. The question is, which model fits the data best. 

The first model below is consistent with Factor Score Model 2, suggesting that 

Advocate characteristics, namely prior teaching experience and educational attainment, 

play a role in the shifts that occur in the types of activities youth and Advocates engage in 

between earlier and later in their relationship. However, it includes the contributions 

made by youth age and sex, both of which are associated with Advocate’s background 

characteristics. 

Figure 5.7. Factor Score Model 3: Changes in Misconduct From Activities with 

Indirect Effects of Advocate Backgrounds and Youth Characteristics 

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Final Technical Report: 2011-JU-FX-0001 p. 96

Table 5.12 

Fit Indices for Factor Score Model 3: Indirect Effects of Advocate and Youth 

Characteristics 

Information Criteria

 Akaike (AIC) 2956.452

 Bayesian (BIC) 3066.833

 Sample-Size Adjusted BIC 2968.551

 (n* = (n + 2) / 24) 

Chi-Square Test of Model Fit

Value 44.043

 Degrees of Freedom 29 

P-Value 0.0364 

RMSEA (Root Mean Square Error Of Approximation)

 Estimate 0.045

 90 Percent C.I. 0.012 0.070

 Probability RMSEA <= .05 0.604 

CFI/TLI CFI 0.944

 TLI 0.919 

Chi-Square Test of Model Fit for the Baseline Model

Value 309.386

 Degrees of Freedom 42 

P-Value 0.0000 

SRMR (Standardized Root Mean Square Residual)=0.057 

Factor score Model 3 (Figure 5.7) illustrates that the youth’s sex matters 

considerably. There are indirect contributions made by youth age and sex on changes in 

misconduct as a result of how youth sex and age influence the assignment of Advocates. 

Yet there are no direct associations between youth characteristics (age and sex) and 

changes in misconduct or interaction frequency later in the relationship. Therefore, the 

influence of youth age and sex is indirect, influencing how they get matched with specific 

types of Advocates, whose background influences the selection of a mentoring approach 

that is more or less playful or problem-oriented, and consequentially more or less 

effective, is appears. 

Factor score Model 4 (Figure 5.8) tested both direct and indirect effects of 

Advocate background characteristics on rates of misconduct. In this final Factor Model 4, 

we see Advocate educational background and prior teaching experience made statistically 

significant indirect as well as direct contributions to misconduct rates at discharge, when 

holding constant the other variables in the model (including youth age and sex, starting 

levels of misconduct, and the frequency of activities early in the match). 

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Final Technical Report: 2011-JU-FX-0001 p. '%

Figure 5.8. Factor Score Model 4: Changes in Misconduct From Activities Including 

Indirect and Direct Effects of Advocate Background and Youth Characteristics 

Advocate education level had a statistically significant positive direct effect on 

rates of misconduct but a negative indirect effect on the level of misconduct at discharge 

by contributing to lower levels of play in the second half of the relationship. The direct 

effect of Advocate education on misconduct is that, after controlling for the influence of 

several different activities and participant characteristics, having a more educated 

Advocate predicted lower rates of misconduct, for reasons that are not clear. Yet because 

more educated mentors played less later in the match, it may be that training them about 

the benefits of play later in the match may further increase the benefits of having a more 

educated Advocates serving youth. 

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Final Technical Report: 2011-JU-FX-0001 p. 98

Table 5.13 

Fit Indices for Factor Score Model 4: Direct and Indirect Effects of Advocate 

Characteristics 

MODEL FIT INFORMATION 

Number of Free Parameters 41 

Loglikelihood

H0 Value -1433.584

 H1 Value -1425.204 

Information Criteria

 Akaike (AIC) 2949.168

 Bayesian (BIC) 3095.156

 Sample-Size Adjusted BIC 2965.170

 (n* = (n + 2) / 24) 

Chi-Square Test of Model Fit

Value 16.759

 Degrees of Freedom 19 

P-Value 0.6062 

RMSEA (Root Mean Square Error Of Approximation)

 Estimate 0.000

 90 Percent C.I. 0.000 0.047

 Probability RMSEA <= .05 0.962 

CFI/TLI CFI 1.000

 TLI 1.019 

Chi-Square Test of Model Fit for the Baseline Model

Value 312.718

 Degrees of Freedom 44 

P-Value 0.0000 

SRMR (Standardized Root Mean Square Residual) 0.034

 Two-Tailed 

Estimate S.E. Est./S.E. P-Value 

YPMCS ON

 YPSEX 0.188 0.060 3.133 0.002 

APHIEDU ON

 Y2AGE 0.195 0.058 3.341 0.001

 YPSEX 0.235 0.059 3.994 0.000

 YPMCS 0.118 0.062 1.903 0.057 

APTCHR ON

 Y2AGE 0.125 0.061 2.054 0.040

 YPSEX -0.174 0.062 -2.811 0.005

 YPMCS -0.102 0.067 -1.532 0.126 

Y2PROBLEMS ON

 YPSEX 0.207 0.062 3.345 0.001

 APTCHR 0.102 0.061 1.670 0.095 

Y2PLAY ON

 YPSEX -0.118 0.063 -1.878 0.060 

Y4PLAY ON

 Y2PLAY 0.453 0.054 8.396 0.000

 APHIEDU -0.155 0.059 -2.618 0.009

 YPMCS 0.093 0.067 1.383 0.167 

Y4PROBLEMS ON

 Y2PROBLEMS 0.554 0.049 11.345 0.000

 APTCHR -0.148 0.052 -2.839 0.005

 YPMCS 0.107 0.061 1.771 0.077 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 99

Table 5.13 (continued) 

Y4MCS ON

 YPMCS 0.419 0.061 6.844 0.000

 Y4PLAY -0.211 0.076 -2.783 0.005

 Y4PROBLEMS 0.166 0.081 2.041 0.041

 Y2PLAY 0.077 0.075 1.016 0.310

 Y2PROBLEMS -0.118 0.083 -1.419 0.156 

APHIEDU -0.124 0.064 -1.940 0.052

 APTCHR -0.146 0.060 -2.412 0.016 

Y4PROBLEMS WITH

 Y4PLAY 0.336 0.064 5.265 0.000 

Y2PROBLEMS WITH

 Y2PLAY 0.317 0.058 5.486 0.000 

As in prior models, there was the direct positive effect of having an Advocate 

with teaching experience. Advocates with teaching experience also had a statistically 

significant positive indirect effect through their tendency to spent less time having casual 

conversations later in the match. 

Together, these two Advocate background characteristics accounted for three 

percent of the variance in rates of misconduct reported by youth, holding constant starting 

levels of misconduct and the other participant background variables in the model. 

Combined with the frequency of play and casual conversation later in the match, which 

predicted an additional three percent of variability in changes in misconduct, we see that 

activities and Advocate characteristics explained over ten percent of the variability in 

misconduct at discharge. 

Conclusions from Study 2 

Given the emphasis YAP places on the benefit of similarity between Advocate’s 

and their youth mentees, such as living in the same community or being similar in race or 

other cultural background characteristics, we examined whether an Advocate’s 

effectiveness varied as a function of his or her level of education or prior teaching 

experience. These are two cultural background characteristics on which Advocates and 

their youths’ families may differ, and which YAP can control through hiring practices. 

These variables are also logically related to some of the program related changes on 

connectedness to teachers, connectedness to school and to educational expectations for 

success among youth found in Study 1. They also correspond with traditional roles of 

adults described in the TEAM framework, and having data on the dimensions of a playful 

focus and mentor-driven goal-directed focus, this study allowed a test of whether these 

standard roles can be enhanced through the strategic employment of specific activities 

and specific times in the match. 

Advocate education-related characteristics. The findings from this second 

study do not necessarily comport with the YAP program model assumption that it is those 

Advocates who are most like their youth—who are most similar to their youth on key 

background characteristics such as race and familiarity with the youth’s community— 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 100

who will have the biggest impact. The findings from this study may suggests that there 

are ways in which differences between youth and Advocate can enhance program 

effectiveness. 

Consistently across models and methods of assessing activity frequency, and most 

clearly in factor score Model 4, there were direct and indirect benefits of hiring exteachers to be Advocates as well as of recruiting more highly educated Advocates. More 

educated Advocates also may be able to effect more promising outcomes if they pay 

attention to what they are doing, consider the benefits of incorporating play into their 

match once it is established (which, again, can be introduced perhaps both through 

training and supervision), to increase the likelihood that they engage in the kinds of 

activities that may most benefit the youth. 

Activity focus and timing. These analyses suggest that there may be better and 

worse types of interactions, and that the better mentoring style for YAP participants is an 

instrumental one. This is partly because the benefits (and iatrogenic effects) of program 

activities seem dependent on their timing. Playing is something many mentoring 

programs downplay, and that it seems many adults feel is not a wise use of time with 

court-involved youth; yet it was a robust albeit modest contributor to program outcomes. 

Conversely, it does not appear that increasing the frequency at which the match spends 

time exploring the general aspects of the youths’ lives through casual conversation and 

engaging problem-focused early in the match may maximize the usefulness of this 

interaction focus. 

Two limitations of these analyses and things that are either not known or can’t be 

inferred from these findings should be noted. The first is about causality. These findings, 

based on correlational not experimental relationships among variables, do not provide 

definitive evidence that either problem-focused activities or play (independently) caused 

changes in misconduct. What the findings here present are associations between the 

frequency of specific types of activities and subsequent rates of misconduct. Now, the 

temporal sequence—namely that reports of play frequency are made before rates of 

misconduct are reported—does support the interpretation of this direction of influence. 

This is because, obviously, rates of misconduct can’t cause the frequency of playing in 

the past. But, reports of misconduct are of how much has occurred in the recent past. So, 

it is possible that shifts in misconduct happened before the second half of the match and 

subsequently influenced activities. But that is not the view we are taking of the findings. 

Second, although the models suggests that the frequency of activities from mid to 

end of the relationship are better predictors of rates of misconduct at discharge than the 

frequency of these activities earlier in the match, this may not be so. It is possible that 

associations between activities activity frequency reported at four months and changes in 

misconduct are larger because both were assessed at the same time. Their coadministration may have introduced a method bias that the covariance paths in the 

models between activities at the different assessment periods did not counter. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 101

Caution also should be used in interpreting these findings, because other 

important program outcomes, such as 12-month post-discharge educational engagement 

or most serious disposition, may have quite different associations with program activities 

and Advocate backgrounds. Similar analyses of the activity-outcome links have not yet 

been conducted with those long-term outcomes. So we feel it is not advised to move too 

quickly to effect program policy or hiring changes based on these findings. But this 

research should prompt those individuals whose approach to mentoring court-referred 

youth is to employ advocacy as an element of mentoring to consider the value of 

emphasizing play later in those matches and to try to better understand the reasons for the 

benefits of play. 

A final note about play. What might be reason for the benefits associated with 

play? On this we can only speculate. A perspective we have given thought to and tried to 

determine how to test is that play may contribute to declines in misconduct through the 

way in which fun, playful interactions elicit smiles and positive emotions from mentors. 

Smiles, which we assume are a logical observable consequence of engaging in play, 

could convey to the youth a sense of being valued by the mentor. These smiles and 

positive engagement of the mentors may feel validating, rewarding, and encouraging to 

the youth; who may subsequently take the relationship more seriously and want to work 

even harder to prepare for reentry feeling the mentor has become a friend who “has their 

back.” 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 102

APPENDIX A 

The creation of narrow age groupings to match treated and untreated youth by age to 

reduce dependency assumption violations by restricting participants pre-scores (serving as 

counterfactual untreated comparison group for that age) and treated (post-intervention scores) to 

only one cohort created the following groupings. This table provides the n for each “group” of 

treated youth (“how many youth were age ___ after four months of working with an Advocate?”) 

and comparison youth (“how many youth were age ___ before they started working with their 

Advocate?” ). The larger samples ranges in the youngest and oldest ages reflects and effort to 

address the issue that, by being assessed later, there were more treated responders in the older age 

cohorts and fewer in the youngest cohorts. So to create somewhat more balanced groups, a wider 

age range was used. These also restricted participants’ responses to only one age cohort, which 

resulted in the exclusion of some participant data to allow statistically independent comparisons 

in these groups. 

Group Treated 

Group 

Untreated 

Group 

1 (11-12 years) 5 6 

2 (13yr 0mo – 13yr 5mo) 2 5 

3 (13yr 6mo – 13yr 11mo) 5 8 

4 (14yr 0mo – 2mo) 3 3 

5 (14yr 3mo – 5mo) 6 8 

6 (14yr 6mo – 8mo) 8 7 

7 (14yr 9mo – 11mo) 6 4 

8 (15yr 0mo – 2mo) 5 7 

9 (15yr 3mo – 5mo) 3 11 

10 (15yr 6mo – 8mo) 12 11 

11 (15yr 9mo – 11mo) 13 12 

12 (16yr 0mo – 2mo) 10 8 

13 (16yr 3mo – 5mo) 10 10 

14 (16yr 6mo – 8mo) 8 17 

15 (16yr 9mo – 11mo) 17 10 

16 (17yr 0mo – 2mo) 12 13 

17 (17yr 3mo – 5mo) 13 8 

18 (17yr 6mo – 8mo) 5 7 

19 (17yr 9mo – 11mo) 8 3 

20 (18yr 0mo – 5mo) 11 6 

21 (18yr 6mo – 11mo) 2 0 

These comparisons are of limited use because small sample size of each cohort that 

undoubtedly generated unstable, inaccurate mean scores with huge variability. Below are select 

results from the series of t-tests conducted to compare outcome variables across the treatment and 

untreated comparison groups within each narrow (3 mos.) age cohort grouping category to 

estimate treatment effects across 4 mos.. Those variables that had significant and near-significant 

changes are listed below for each outcome variable by age-cohort group. These are purely 

informational and should not be interpreted as reliable estimates (none meet needed power 

requirements to conduct these significance tests and the small sample sizes tended to yield nonnormal distributions), but purely to convey some information about in what age cohorts changes 

related to program participation seem more likely and for others. 

Running only t-tests (with a p-value of .10 used as evidence of non-negligible 

differences), the analyses below suggest that future expectations more often differed between 

untreated and treated youth than did rates of self-reported misconduct and connectedness. But the 

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Final Technical Report: 2011-JU-FX-0001 p. 103

patterns of change were equivocal, except for expectations to finish high school and self-reported 

misconduct which favored the program completers. The group with the higher score is in bold. 

Outcome variables on which the 

treated and untreated differed 

n for 

treated & 

untreated 

Treated 

mean 

Untreated 

mean 

diff T-test p 

Expect to Finish High School 

Group 2 (13 yr 0 - 5mo) 2, 5 4.67 3.20 1.47 2.34 .067 

Group 8 (15yr 0 – 2mo) 5, 7 3.97 3.23 .733 1.89 .064 

Group 14 (16yr 6 – 8mo) 8, 17 3.91 3.59 .32 1.17 .102 

Group 15 (16yr – 11mo) 17, 10 3.66 3.00 .66 2.28 .031 

Group 16 (17yr 0 – 2mo) 12, 13 3.22 3.64 -.421 -1.87 .075 

Expect to Start College 

Group 3 (13yr 6 - 11mo) 5, 8 2.90 3.73 -.83 -2.68 .022 

Group 5 (14yr 3 – 5mo) 6, 8 3.70 2.47 1.23 2.98 .012 

Group 10 (15yr 6 – 8mo) 12, 11 3.33 2.55 .78 1.81 .091 

Group 15 (16yr – 11mo) 17, 10 3.12 2.00 1.18 3.01 .006 

Expect to Finish College 

Group 3 (13yr 6 - 11mo) 5, 8 2.88 3.61 -.73 -1.85 .092 

Group 5 (14yr 3 – 5mo) 6, 8 3.99 2.67 1.33 3.36 .010 

Group 6 (14yr 6 – 8mo) 8, 7 3.10 3.14 .86 2.12 .079 

Group 10 (15yr 6 – 8mo) 12, 11 3.46 2.55 .92 2.06 .056 

Group 15 (16yr – 11mo) 17, 10 3.06 2.10 .955 2.14 .043 

Expect to Find a Job 

Group 10 (15yr 6 – 8mo) 12, 11 3.51 2.93 .57 1.96 .064 

Group 16 (17yr 0 – 2mo) 12, 13 3.16 3.62 -.45 -2.09 .048 

Group 20 (18yr 0 – 5mo) 11, 6 3.84 3.05 .79 3.38 .004 

Expect to Succeed at Job 

Group 10 (15yr 6 – 8mo) 12, 11 3.53 2.91 .62 2.19 .047 

Group 16 (17yr 0 – 2mo) 12, 13 3.28 3.85 -.57 -3.30 .003 

Group 19 (17yr 9 – 11mo) 8, 3 3.54 4.00 -.46 -2.30 .041 

Group 20 (18yr 0 – 5mo) 11, 6 3.76 3.12 .65 3.10 .007 

Expect to stay out of court syst. 

Group 7 (14yr 9 – 11mo) 8, 7 3.02 4.00 -9.80 -2.45 .058 

Group 9 (15yr 3 – 5mo) 3, 11 4.00 3.62 .38 1.87 .091 

Group 11 (15yr 9 – 11mo) 13, 12 3.16 3.75 -.59 -2.04 .053 

Connectedness to Family 

Group 9 (15yr 3 – 5mo) 3, 11 -.42 .26 -.68 -1.81 .096 

Group 10 (15yr 6 – 8mo) 12, 11 .44 -.12 .56 1.87 .076 

Connectedness to School 

Group 11 (15yr 9 – 11mo) 13, 12 -.43 .18 -.60 -1.90 .072 

Group 13 (16yr 3 – 5mo) 10, 10 .21 -.32 .52 1.76 .096 

Group 15 (16yr – 11mo) 17, 10 .11 -.37 .48 2.47 .021 

Connectedness to Teacher 

Group 11 (15yr 9 – 11mo) 13, 12 -.39 .26 -.65 -1.79 .087 

Connectedness to Self-in-Future 

Group 11 (15yr 9 – 11mo) 13, 12 -.35 .10 -.45 -1.98 .060 

Group 19 (17yr 9 – 11mo) 8, 3 .03 .44 -.41 -2.13 .062 

Misconduct (lower mean bolded) 

Group 3 (13yr 6 - 11mo) 5, 8 .12 -.39 .52 2.56 . 027 

Group 11 (15yr 9 – 11mo) 13, 12 .22 -.20 .41 2.26 .034 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 104

APPENDIX B. 

Piloting or Model Fitting Items in Five Advocacy Measures
 

Five measures of advocacy were created, replicated or tested in this study and 

exploratory associations with Advocate characteristics are reported in this Appendix. 

Without sufficient validity evidence—relying only on content and factorial validity 

evidence provided later in the Appendix—these analyses should be considered 

exploratory. First, we described the nature and initial development of each of these 

measures. 

DuBois Advocacy, Instruction, and Reflecting (D-AIR) scale 

A set of items developed by DuBois to assess advocacy and other mentoring 

activities were factor analyzed to create one measure of advocacy as a part of an 

approach to mentoring youth. These items capture youths’ perceptions of things the 

Advocate does for the youth, mostly when the Advocate is with the youth. These 

activities balance problem solving, role modeling, interpersonal sharing, helping with 

feelings and trying to understand and respond to the needs of the youth. 

In DuBois et al. (2013) meta-analysis, they reported that programs emphasizing 

advocacy and/or teaching had larger effects that those that did not. This measure, 

similarly, collapses advocacy and teaching but also includes indicators of role modeling 

and self-reflection coaching. This view of advocacy is akin to an instrumental mentor 

whose initial focus is on problem-focused discussions, doing, and mastering to help the 

youth in becoming something or someone. 

The three main activities represented in what we are calling the D-AIR measure 

appear to be advocacy (items 8, 9 and 10), instruction (items 1, 2, 3, 4), and reflection 

(items 5, 6, and 7). These items held together as one factor (see Appendix) such that each 

contributed somewhat similarly to the one overall approach to mentoring. We assigned 

the acronym D-AIR to reflect the source of the items (DuBois) and the three functions it 

captures: advocacy (A), instruction (I), and reflection (R). 

1. says or does something to help me with my feelings 

2. gives me advice or ideas about how to do something 

3. shows me how to do something 

4. helps me practice something 

5. shows or tells me things about his/her own life 

6. helps me think about myself or the world in a different way 

7. helps me figure out what is really important to me 

8. helps me by talking with other people who are important in my life 

9. makes sure I have the things I need to be successful 

10. makes sure I have a chance to participate in activities that are good for me 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 	 p. 105

The Blue Ribbon Advocacy Scale 

The Blue Ribbon Scale was developed based on a definition of advocacy made by 

Graig Meyers, then Executive Director of the Blue Ribbon Advocacy Program. His 

definition generated six items. Through a series of exploratory and confirmatory factor 

analyses, a scale was created and the scale ended up including four of these items. The 

two that were dropped either double-loaded on two factors two factors. To create distinct 

measures, items that loaded on the Blue Ribbon factor and the D-AIR factor were 

eliminated. For this reason, the D-AIR scale also had to drop the last two items (11 and 

12) that were redundant or cross-loaded with items in the Blue Ribbon scale. 

The four items on this scale are all about trying to effect change in the youth’s world 

and trying to bridge the worlds of work, school, family, and mental health staff. This is 

advocacy as outreach and fostering collaborations, as compared to D-AIR focus which is 

more interpersonal, psychosocial, skill-building advocacy assessment. 

1. goes to parent-teacher conferences or meetings (originally item #2) 

2. talks with my teacher/teacher aide (originally item #3) 

3. talks with my therapist/counselor (originally item #4) 

4. communicates with people to help youth get a job, get into school, a training 

program (originally item #5) 

YAP Advocacy Scales from the PPV study of YAP lead by Herrera 

An earlier study of the YAP program led by Carla Herrera generated a set of 

items that were direct representations of what Advocates tended to do with youth. We 

conducted additional analyses and confirmatory factor analyses to estimate several types 

of Advocacy common in YAP (see Appendix). These are helping youth develop selfmanagement skills, advance vocationally, develop connections in the community, and 

assist with family issues. We focused on the first three of these in some preliminary 

analyses. 

Individual Self Management Adv. Focus (Relational)(alpha = .74) 

1.	 Worked with individually on life skills such, as healthy decision-making, dealing 

with stress and avoiding risky behaviors? 

2.	 Worked with individually to help him/her manage anger more appropriately? 

3.	 Worked with individually to help him/her avoid using drugs or alcohol? 

Individual Vocational Advocacy Focus (Goal directed) )(alpha = .80) 

1.	 Helped with job searches, job applications, resumes or interviews? 

2.	 Helped find a job (either a supported work job or a regular paying job) 

3.	 Talked to about appropriate work place dress and behavior? 

4.	 Worked with individually on independent living skills such as budgeting, time 

management or using public transportation? 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001	 p. 106

Individual Community Advocacy Focus (Relational)(alpha = .77) 

1.	 Introduced to an adult in the community who could be a mentor, role model or 

friend to the youth? 

2.	 Introduced to another youth in the community who could be a role model or 

friend to the youth? 

3.	 Helped enroll the youth in an arts, sports or recreation club, program or center in 

the community? 

Advocate Personality Characteristics and Advocacy Approaches 

To better understand what types of Advocates more often engage in interpersonal 

mentoring or ecologically based advocacy in the community, we examined the 

associations between three personality characteristics and these advocacy scales. Using 

Exploratory factor analyses on the Advocate pre-match (before meeting the youth) data, 

with a rotated component matrix, the factor loadings and reliabilities for the Extraversion, 

Neuroticism, and Agreeableness personality scales are listed below. Item loadings on 

other scales are included below (but not on computed subscale scores) to illustrate 

overlap. 

Factor 

Stem: I see myself as someone who... 1 2 3 

Extraversion 

...is talkative. 

...is outgoing, sociable. 

...tends to be quiet. (R) 

...is enthusiastic. 

...is full of energy. 

...has an assertive personality. 

...is sometimes shy. (R) 

Neuroticism 

.832 

.820 

.749 

.718 

.717 

.596 

.531 -.388 

-.234 

.298 

-.265 

...can be tense. 

...gets nervous easily. 

...worries a lot. 

...is easily upset. 

...can be moody. 

...is relaxed, handles stress well. (R) 

...remains calm in tense situations. (R) 

Agreeableness 

...is kind and considerate to almost everyone. 

...is helpful and unselfish with others. 

...likes to cooperate with others. 

...has a forgiving nature. 

...is sometimes rude to others. (R) 

...is generally trusting. 

...starts quarrels with others. (R) 

Coefficient a 

-.312 

-.221 

.401 

-.303 

.219 

.85 

.734 

.704 

.694 

.656 

.638 

.616 

.580 

-.300 

.80 

-.224 

-.262 

.794 

.738 

.627 

.606 

.517 

.508 

.424 

.74 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 107

Items for loadings less than .2 were been omitted. Bolded items within the same 

column are included in the same scale. Items denoted by (R) were reverse scored. The 

metric for these scales was from 1 (strongly disagree) to 5 (strongly agree) with 3 (neither 

agree nor disagree) as the midpoint. 

Variances: Factor 1: “Extraversion” = 22.472 

Factor 2: “Neuroticism” = 15.264 

Factor 3: “Agreeableness” = 13.181 

Big 5 personality characteristics. We were also curious about how Advocate 

personality characteristics might predict the types of activities Advocates engaged in with 

youth. The three personality characteristics we examined were extraversion, neuroticism, 

and agreeableness. (The items included in the three personality scales used to generate 

factor scores are provided in the Appendix.) 

Extraversion. Extraverted Advocates described themselves as more talkative, 

outgoing/sociable, enthusiastic, full of energy, and as having an assertive personality, 

tending not to be quiet, but only sometimes shy. 

Neuroticism. Advocates high on the Neuroticism scale said about themselves that 

they can be tense, get nervous easily, worry a lot, are easily upset, can be moody and are 

not relaxed, don’t handle stress well, and do not remain calm in tense situations. 

Agreeableness. Advocates high on the Agreeableness scale tended to more often 

report seeing themselves as kind and considerate to almost everyone, helpful and 

unselfish with others, cooperative with others, forgiving in nature, not often rude to 

others, generally trusting, and so rarely quarrel with others.

 In the correlation table that follows, the top row lists the scales, and the two in grey 

are the two Advocacy scales developed for use in this study. In addition to the three 

personality characteristics of extraversion, neuroticism, and agreeableness, the three other 

advocacy approaches that specifically reflect what Advocates in YAP do (generated in a 

prior PPV study) also are included (The items and factor analyses conducted to determine 

which items best reflected each construct are provided in a later Appendix.) 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 108

Personality Characteristics as Predictors of Self-Reported Advocacy Activities 

Blue Ribbon 

(Advocate 

report at two 

months, A2) 

Advocacy 

D-AIR 

Advocacy, 

Instruction, 

& Relating 

Scale (A2) 

Advocate 

Extraversion 

Advocate 

Neuroticism 

Advocate 

Agreeableness 

Advocate D-AIR at 2 months .20* 

AP Extraversion .105 .23** 

AP Neuroticism -.103 -.22* -.50** 

AP Agreeableness .073 .048 .43** -.45** 

A2 YAP Individual Self 

management Adv. 

.204* .578** .113 -.159✚ .113 

A2 YAP Individual 

Vocational Advocacy 

.247** .319** .140 -.252** .189* 

A2 YAP Individual 

Community Advocacy 

.424** .359** .068 -.158 .028 

When we consider the DuBois Advocacy scale in contrast to the Blue Ribbon 

Scale, the correlations suggest those who engaged in higher levels of the advocacy, 

instruction, and reflection (D-AIR) were often those reporting high levels of extraversion 

and low levels of neuroticism. This is consistent with the assumption that someone taking 

an interpersonal, reflective, teaching, personally disclosing approach would be more 

extraverted and have a thicker skin, particularly with kids who are guarded emotionally. 

When correlated with the three YAP Advocacy scales, we found Advocates taking the DAIR, inclusive approach to advocacy-as-mentoring, also tended to focus on selfdevelopment advocacy, helping youth make better decisions, address substance use and 

anger issues, and cope with problems. Slightly less strong were associations between the 

D-AIR approach and the YAP career and community advocacy scales. But, when 

comparing the D-AIR and the Blue Ribbon scale, the greatest distinction is the cooccurrence of the D-AIR approach and the YAP advocacy as assisting in selfmanagement approach. 

The Blue Ribbon Advocacy Scale correlated only moderately (r = .20) with the DAIR and was most strongly related to the YAP community advocacy scale, which makes 

sense given both are about activities done in order to strengthen the ties across the 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 109

youths’ social ecologies (school, work, clubs). Those who frequently engaged in the Blue 

Ribbon scale advocacy activities of direct outreach to others to explicitly Advocate for 

the youth or help navigate the youths’ efforts to be successful in school, work, family or 

social settings also tended to engage in interpersonal discussions of risk-taking (drugs), 

decision-making, and feelings (self-management), but the association between these was 

weaker. Those activities require more one-on-one interactions with the youth, which are 

more similar to the approach assessed by the D-AIR. 

Both DuBois’ and the Blue Ribbon Advocacy Scale were associated with the 

YAP Vocational Advocacy scale. DuBois’ scale taps into the part of vocational advocacy 

that is about helping the youth learn about the world, about jobs, about how to prepare to 

look for a job and helping youth think about what type of work they might like to do. The 

Blue Ribbon scale (similar to items on the YAP Vocational Advocacy scale) reflects 

actually interfacing with people in the world who can help the youth secure a job. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 110

APPENDIX C 

Factor Models and Fit Indices for the TEAM constructs: Play, Talk, Learn, and Do 

Item Listing and Associated Confirmatory Factor Analyses 

Item Indicator Shown 

in Factor Models 

“With your youth/Advocate, how often do you do TALK about 

each of the following?” 

D1 Talk: Casual Conversation (Discuss sports, what either of you did on the 

weekend, holiday plans, or other events in town, etc.) 

D2 Talk: Conversation on Social Issues (religion, race, poverty, etc.) 

D3 Talk: Conversation About Relationships: (can check multiple) 

q Family (f) 

q Teachers or Employers (te) 

q Friends, Peers, Peers (y) 

q Romantic Friend (r) 

D4 Talk: Listening & Sharing Info about Self (goals, interests, feelings...) 

D5 Learn: Academics (Discuss grades, school, testing...) 

D6 Learn: Behavior (Discuss youth’s misbehavior related to problems with 

peers, teachers, adults, or the courts) 

D7 Learn: Attendance (Discuss importance of showing up school/work) 

D8 Learn: Future Talk (Discuss college, Jobs, goals, dreams, etc.; Use 

computer to research opportunities that would benefit youth.) 

C3 Play: Play Sports, Athletic Activity, Outdoor Game 

C4 Play: Creative Activities (do art, read for fun, write a story or song) 

C5 Play: Played cards, board games, computer games 

C6 Play: Go to a park, museum, movie, community/cultural event, or college. 

E1 Do: Participate in a group class (e.g., life skills, anger management) 

E2 Do: Homework/Tutoring (reading, academics, computer or library work) 

E7 Do: Get taken somewhere by my Advocate to address a court mandate (e.g., 

meet with probation officer, meet some curfew) 

E8 Do: Deal with a crisis or emergency 

E9 Do: Work on a mandated community service or restitution activity 

E10 Do: Work on a volunteer community service or “give-back” project 

In the factor models that follow, TEAM refers to the four activity construction 

(Playing, Talking, Learning/Problem Correcting and Doing), Y is for youth report, A is 

for Advocate report, 2 is for assessment at 2 months, and 4 is for assessment at 4 months. 

“Mod. indices” refers to modification indices proposed by Mplus (the statistical program) 

to increase model fit. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report: 2011-JU-FX-0001 p. )))

APPENDIX D: Confirmatory Factor Models for Activity Frequency Rates 

Y2 Play 

Chi square 2.722, df = 2, p = .256, chi square/df = 1.361 

RMSEA .053, CI .000-.192, p < .05 = .361 

CFI .992 SRMR = .027 

All fit indices suggest a good model fit. No mod. indices suggested or employed. 

Y2 Talk: Casual Conversations 

Chi square = 42.914, df 14, p = .0001, chi square/df = 3.065 

RMSEA = .128, CI .085-.172, p < .05 = .002 

CFI = .918 SRMR = .056 

SRMR is only adequate fit index. No mod. indices suggested or employed. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 112

Y2 Problem-focused Discussions 

EFA suggested items loaded onto one factor. Alpha was .862 and did not improve by 

removing items. 

Chi square = 10.737, df 2, p = .0047, chi square/df = 5.369 

RMSEA = .185, CI .088-.301, p < .05 = .015 

CFI = .964, SRMR = .030 

CFI and SRMR suggest good model fit. No mod. indices suggested or employed. 

Y2 Do 

EFA suggested one factor. Observed score alpha was .830 and did not improve by 

removing any items. The CFA yielded a poor model fit, but mod indices suggested 9 with 

10 (items about comm. service), which improved the model. 

Chi square = 9.854, df = 8, p = .275, chi square/df = 1.232 

RMSEA = .043, CI = .000-.118, p < .05 = .491 

CFI .993 SRMR = .030 

All fit indices suggest a good model fit. No mod. indices suggested or employed. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 113

Advocate reports 

A2 Play 

Chi square = 2.667, df 1, p = .1024, chi square/df = 2.667 

RMSEA = .111, CI .000-.282, p < .05 = .160 

CFI = .987 SRMR = .023 

Chi square, CFI and SRMR suggest a good model fit. No mod. indices suggested or 

employed. 

The item E3 (played sports, athletic activities, or outdoor games) does not really 

contribute to the factor after accounting for the variance it shares with item E5 (Played 

card, board games, computer games). This pattern is the opposite pattern observed for 

youth reports, especially at four months. 

A2 Talk 

Chi square 22.233, df 14, p = .0739 

RMSEA = .066, CI = .000-.115, p < .05 = .274 

CFI = .951 SRMR = .046 No mod. indices suggested or employed. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 114

A2 Problem-focused Discussions 

Chi square = .972, df 2, p = .615, chi square/df = .486 

CFI = 1 SRMR = .010 

All fit indices suggest a very good model fit. No mod. indices suggested or employed.
 

RMSEA = .000, CI = .00-.138, p < .05 = .704 

A2 Do 

Chi square = 7.888, df 8, p = .4444, chi square/df = .986 

RMSEA = .000, CI .000-1.00, p < .050 .668 

CFI 1.0 SRMR .034 

Note that “dealing with crisis or emergency” (E8) is largest contributor to latent variable. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report: 2011-JU-FX-0001 p. ))#

Youth Report at 4 Months 

Y4 Play 

Initial fit was poor, but improved with MIs suggested Y4e5 WITH Y4c4. 

Chi square = 12.70, df 2, p = .6886, chi square/df .161 

RMSEA = .000, CI .000-.193, p < .05 = .724 

CFI = 1.0 SRMR .006 

Fit indices suggest a good model fit. No further mod. indices suggested or employed. 

Y4 Talk: Casual Conversation 

In the EFA, Casual Conversation items loaded onto two factors. Suggested not all topics 

of conversation occurred or co-occurred. The same issue appeared in the CFA models. 

Rotated Component Matrix 

Component 

1 2 

A4 TEAM Talk 1 

A4 TEAM Talk 2 

A4 TEAM Talk 3 Family 

A4 TEAM Talk 3 Teach/Employer 

A4 TEAM Talk 3 Yth 

A4 TEAM Talk 3 Romantic 

A4 TEAM Talk 4 

.182 

-.050 

.537 

.826 

.800 

.814 

.315 

.790 

.685 

.657 

.327 

.300 

-.073 

.639 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 116

Y4 Talk: Casual Conversation 

Chi square = 22.911, df 13, p .0428, 

Chi square/df = 1.762 

RMSEA .086, CI = .015-.143, p < .05 = .146 

CFI = .971 SRMR .036 

Y4 Problem-focused Discussions 

Chi square = 5.195, df 2, p = .0745, chi square/df = 2.5975 

RMSEA = .125, CI .000-.261, p <.05 = .126 

CFI = .987 SRMR = .019 No mod. indices suggested or employed. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 117

Y4 Doing in the Community 

EFA items loaded on factor. Alpha = .886 for observed score assessment. 

Fit indices suggested a poor model fit. Mod indices suggested adding covariances 

between Y4E10 WITH Y4E9 

Revised model: 

Chi square = 22.251, df 8, p = .0045, Chi square/df = 2.781 

RMSEA = 132, CI .068-.198, p < .05 = .021 

CFI = .956 SRMR = .040 

CFI and SRMR suggest a good model fit. Chi-square and RMSEA do not. No mod. 

indices suggested or employed. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

	 	 	 	

Final Technical Report: 2011-JU-FX-0001 p. 118

Advocate Reports at 4 Months 

A4 Play 

MI suggested A4E5 WITH A4E3 (same MI for the A4 Play model) 

Chi square = .275, df 1, p = .5997, Chi square/df .275 

RMSEA .000, CI .000-.209, p < .05 = .644 

CFI = 1.00 SRMR = .009 

Fit indices indicate a good model fit. No mod indices suggested or employed. 

A4	Doing	in	the	Community	

MI	suggested	A4E9	WITH A4E10	

20141103	A4Do	9w10	

Chi	square	=	5.173, df	8, p =	.7389, chi	square/df	=	.647

RMSEA	=	.000,	CI	.000-.083,	p		<	.05	=	.853

CFI =	1.00 SRMR	=	.024	

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 119

A4 Talk: Casual Conversation 

Initially poor fit indices. Modification Indices suggested covarying A4D3F WITH A4D1 

Revised: 

Chi square = 26.32, df 13, p = .0154, 

chi square/df = 2.025 

RMSEA = .099, CI .042-.154, p < .05 = .071 

CFI = .946 SRMR = .067 

Chi square/df, CFI and SRMR suggest adequate model fit. No further mod. indices 

suggested or employed. 

A4 Problem-focused Discussions 

Chi square 6.687, df 2, p = .0353, chi square/df = 3.3435 

RMSEA = .150, CI .034-.283, p < .05 = .069 

CFI .985 SRMR = .017 

CFI and SRMR suggest a good model fit. No mod. indices suggested or employed. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 120

APPENDIX D 

Items in Scales Created or Used for Exploratory Advocacy Analyses 

Scale Name and Items in Youth and Advocate Surveys Response Prompt 

DuBois Advocacy 

Scale 

"How often does 

your Advocate do 

the following things 

when you are 

together?” 

F 1 ... says or does something to help me with my feelings 

F 2 ...gives me advice or ideas about how to do 

something 

F 3 Shows me how to do something 

F 4 Helps me practice something 

F 5 Shows or tells me things about his or her own life 

F 6 Helps me think about myself or the world in a different way 

F 7 Helps me figure out what is really important in my life 

F 8 Helps me by talking with other people who are 

important in my life 

F 9 Makes sure I have the things I need to be successful 

F 10 Makes sure I have the chance to participate in 

activities that are good for me 

F 11 Accompanies me to visit his/her contacts or friends who might 

help me get a job, get into school, enroll in a training program, learn a 

new skill, etc. 

F 12 Help me sign up for an opportunity or program 

G 1 Talks with my family about how I am doing in school. 

Blue Ribbon 

Advocacy Scale 

“How often does 

your Advocate do 

any of the following 

things when you are 

not together?” 

G 2 ( youth only) Goes to parent-teacher conferences or meetings 

G 3 Talks with my teacher/teacher aide 

G 4 Talks with my therapist/counselor. 

G 5 Communicates with people to help youth get a job, 

get into school, a training program 

G 6 Tells other people good things about me. 

G 6 (Advocate only) Tell other people (like teachers, boss) when you 

think the youth needs extra help from them 

Adapted from 

Zand’s (2009) 

Mentor-youth 

Alliance Scale 

H 1 My Advocate cares about me 

H 2 My Advocate is happy when good things happen to me 

H 3 I would feel sad if something bad happened to my Advocate 

“How true is each 

statement about how 

H 4 I try to follow my Advocate’s advice 

H 5 I feel comfortable with my Advocate 

you feel about your 

Advocate?” 

H 6 I enjoy talking with my Advocate 

H 7 I look forward to the time I spend with my Advocate 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 121

H 8 I trust my Advocate 

H 9 My relationship with my Advocate is important to me 

H 10 I feel my Advocate cares about me, even when I do things s/he 

does not approve of 

YAP Factor 1— 

Self Management 

Focus (Relational, 

individual) "How 

often do you and your 

Advocate 

individually..." 

J 1 ...work on life skills such, as healthy decision-making, 

dealing with stress and avoiding risky behaviors? 

J 5 ...work to help me manage anger more appropriately? 

J 8 ...try to help me avoid using drugs or alcohol? 

YAP Factor 2— J 2 ... work on job searchers, job applications, resumes or interviews? 

Vocational 

Development focus 

(Goal, individual) 

"How often do you 

and your Advocate 

J 4 .... Work on independent living skills (budgeting, time management 

or use of public transportation 

J 6 ... work to help me find a job (either a supported work job or 

regular paying job 

individually..." J 9 ... discuss appropriate work place dress and behavior. 

YAP Factor 3— 

Community 

Advocacy focus 

(relational, 

individual) "How 

often do you and your 

Advocate 

individually..." 

J 3 ... go meet an adult in the community who could be a mentor, role 

model or friend 

J 7 ... meet with another youth in the community who could be a role 

model or friend 

J 10 ... help me enrolling an arts, sports or recreation club, program or 

center in the community 

Get help for a medical problem? 

Get help for a drug or alcohol problem? 

YAP Factor 4— 

Focus with Family 

on Family Problem 

Get help for a mental health problem? 

Connect with a social organization (e.g., church, woman’s cooperative, 

a social club) to address social isolation needs? 

Enroll in an educational or training program? 

Get emergency food or clothing or shelter? 

Find a job? 

The youth’s behavior at home? 

The youth’s progress in YAP? 

The youth’s legal situation. 

The youth’s behavior, attendance or grades at school? 

The youth’s behavior at home? 

Factor 5—Focus 

with Family on 

Youth Problem 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 122

APPENDIX E. 

YAP Advocacy Factors Correlates with Public Private Ventures Study Dataset 

Correlations between YAP Advocacy Factors and Advocate Characteristics From 

Dataset Data Originally Collected for Public Private Ventures 

Self- Vocational Family Focus 

Managemen Developme Community Member Family on 

t (Rel) nt (Goal) Advocacy Problem Youth 

Focus Focus (Rel) Focus (Goal) Focus Problem 

What is your age? r 

p 

N 

.059 

.358 

246 

.064 

.313 

248 

.075 

.241 

246 

.112 

.079 

247 

.109 

.088 

246 

Are you female (1) 

or male (0) 

r 

p 

N 

-.035 

.588 

246 

.032 

.612 

248 

.020 

.752 

246 

.091 

.154 

247 

.015 

.810 

246 

What is your race: 

Latino, Hispanic 

r 

p 

N 

-.073 

.250 

249 

-.031 

.630 

251 

-.054 

.394 

249 

-.052 

.416 

250 

.058 

.364 

249 

What is your race 

or ethnicity: 

African American, 

Black 

r 

p 

N 

-.022 

.734 

249 

-.013 

.834 

251 

-.002 

.975 

249 

-.054 

.398 

250 

-.019 

.760 

249 

What is your race 

or ethnicity: 

Caucasian, White 

r 

p 

N 

.044 

.488 

249 

-.026 

.679 

251 

-.037 

.563 

249 

-.035 

.579 

250 

-.034 

.596 

249 

Can you speak any 

languages other 

r -.003 -.040 -.037 -.122 -.027 

than English? (Yes p .958 .527 .561 .055 .671 

= 1) N 247 249 247 248 247 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 123

Factor Analyses, Item Loadings, and Intercorrelations of Items 

Factor 

1: 2: Help 3: Help foster 4: Help 

Problem develop as a community Promote 

focus worker connections self development 

The youth’s behavior, attendance or grades at 

school? 

.872 .303 .417 .470 

The youth’s behavior at home? .838 .317 .438 .474 

The youth’s progress in YAP? .690 .371 .297 .490 

The youth’s legal situation. .580 .308 .389 .435 

Helped with job searches, job applications, 

resumes or interviews? 

.297 .957 .435 .432 

Helped find a job .320 .668 .382 .367 

Talked to about appropriate work place dress 

and behavior? 

Introduced to another youth in the 

.365 .639 .412 .536 

community who could be a role model or 

friend to the youth? 

Introduced to an adult in the community who 

.405 .403 .814 .461 

could be a mentor, role model or friend to the 

youth? 

.320 .350 .738 .397 

Helped enroll in an arts, sports or recreation 

club, program or center in the community? 

Talked with the youth’s schoolteacher or 

.344 .414 .609 .380 

counselor about his/her academic progress or 

behavior? 

.348 .297 .448 .403 

Worked with individually to help him/her 

manage anger more appropriately? 

.432 .334 .379 .767 

Worked with individually to help him/her 

avoid using drugs or alcohol? 

Worked with individually on independent 

.446 .390 .412 .714 

living skills such as budgeting, time 

management or using public transportation? 

Worked with individually on life skills such, 

.435 .577 .476 .615 

as healthy decision-making, dealing with 

stress and avoiding risky behaviors? 

.380 .413 .459 .606 

Assisted with homework assignments or 

schoolwork? 

.347 .361 .426 .442 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report: 2011-JU-FX-0001 p. )*"

APPENDIX F 

Correlations Between YAP Advocacy Factors and Goals in the Advocates ISP’s. 

Some of the Advocate IEP goals were related to the YAP advocacy approaches. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 125

APPENDIX G 

Construction of and Factorial Validity Evidence for the two New Advocacy Scales 

The DuBois’ Derived D-AIR Scale 

In response to the finding, in his latest meta-analytic review of the effectiveness 

of youth mentoring (DuBois et al., 2011), that one moderator of effectiveness was 

whether or not programs that emphasized advocacy and teaching in their mentoring 

model. Measured collectively, those programs with an advocacy and teaching focus were 

more effective. This not only prompted the research funding initiative that supported this 

study, but also led DuBois himself to start to explore advocacy. 

One way he did so was to create a list of items, some of which directly assessed 

advocacy (items 8-12 below). As there was no prior scale construction or validation work 

done on this set of items (below), we collected self-reports on these items and then 

undertook a series of factor analyses to, we assumed, separate the different functions of 

mentors according to which were advocacy, teaching, or other approach. 

However, our initial exploratory analyses did not reveal the items to reflect 

distinct phenomena but rather one general factor. All 12 of the items DuBois developed 

also loaded onto one factor when subjected to confirmatory factor analyses in separate 

analyses of both two and four month (discharge data). It therefore seemed what DuBois 

had created was a measure of advocacy, instruction, and reflection—a more 

comprehensive assessment of advocacy, teaching, and encouragement. Therefore, with 

permission (but not approval, per se) from David DuBois, we decided to include this 

scale as a multi-dimensional measure of advocacy within the context of instruction and 

personal reflection. Although this does not provide “pure assessment” of interpersonal 

advocacy—note, by contrast, the Blue Ribbon Advocacy Scale is about advocacy outside 

the youth-adult relationship—the measure developed from DuBois items is not unlike the 

moderator in his meta-analysis which included both teaching and advocacy in the context 

of mentoring, which we feel this scale nicely captures. 

1. says or does something to help me with my feelings 

2. gives me advice or ideas about how to do something 

3. shows me how to do something 

4. helps me practice something 

5. shows or tells me things about his/her own life 

6. helps me think about myself or the world in a different way 

7. helps me figure out what is really important to me 

8. helps me by talking with other people who are important in my life 

9. makes sure I have the things I need to be successful 

10. makes sure I have a chance to participate in activities that are good for me 

11. accompanies me to visit his/her contacts or friends who might help me get a job, get 

into school, enroll in a training program, learn a new skill, etc.. 

12. help me sing up for an opportunity or program 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

Final Technical Report: 2011-JU-FX-0001 p. 126

(In the analyses that follow, it is revealed that items 11 and 12 had high correlations with 

the Blue Ribbon scale factor and were subsequently omitted to create orthogonal 

measures.) 

The Graig Meyers Derived Blue Ribbon Advocacy Scale 

Then we shifted our focus on testing a set of items that a leader in the mentoring 

field on the use of advocacy in youth mentoring, Graig Meyers who directs the Blue 

Ribbon Youth Mentoring Program proposed. The items he proposed as examples or key 

indicators of advocacy were not meant to reflect all of the dimensions of advocacy in 

youth mentoring, nor did he intend for these to become prompts or items in a survey. He 

had simply responded on DuBois mentoring listserv to a members request for information 

about what “advocacy” was. The question was referring to the DuBois et al. (2013) metaanalysis finding that programs emphasizing advocacy and teaching had larger effects. 

With his permission, using his examples (see email on next page), we made minor 

changes to his prompts in the listserv posting, and subjected them to exploratory and 

confirmatory factor analysis with data collected for this study. 

These six items, based on the items in his original posting which can be found in 

the appendix, were found to load on one statistical factor in exploratory analyses that 

provided good model fit indices when then subjected to confirmatory factor analyses. 

1. talks with my family about how I am doing in school 

2. goes to parent-teacher conferences or meetings 

3. talks with my teacher/teacher aide 

4. talks with my therapist/counselor 

5. communicates with people to help youth get a job, get into school, a training program 

or other program 

6. tells other people good things about me 

Constructing Orthogonal Scales for the Present Study Purposes 

The final step we took before any analyses that would compare these blended and 

pure assessments of advocacy was to subject them to both exploratory and confirmatory 

factor analyses to ensure that they actually reflected distinct approaches and that items 

did not want to load on both, in which case it would be hard to make interpretations about 

what each factor or subscale was measuring. When we did these analyses, we decided 

that two variables on each of the original scales should be removed, at least for the 

present study and namely for the purpose of making independent comparisons of the 

relative utility of each approach (or scale) in predicting changes observed following 

program participation. 

When these two sets of items were subjected to these exploratory and 

confirmatory factor analyses, we learned that items 1 and 6 had problematically large 

correlations with the DuBois advocacy scale factor—they were redundant with what was 

being measured by DuBois items. Therefore, those two items were subsequently omitted. 

Subjected again to confirmatory factor analysis, the resulting 4-item Blue Ribbon 

Advocacy (BRA) scale had equally good fit indices, suggesting it too represented one 

construct (presumably advocacy) and which would be more orthogonal to (independent 

of) DuBois’ AIR scale. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

Final Technical Report: 2011-JU-FX-0001 p. 127

Still, there remained two of the AIR items that wanted to load on the Blue Ribbon 

scale. The last two items (11 and 12) were two of five of the DuBois items that DuBois 

had viewed as specifically about advocacy. These seemed to represent the youth’s 

perceptions of things the Advocate does—how the Advocate Advocates for the youth— 

when the Advocate is with the youth to make connections across the youth’s social 

ecologies of school, work and other programs. Conversely, the Blue Ribbon scale is more 

about when the Advocate does this sort of connecting and communicating when not with 

the youth. Nevertheless, to create orthogonal scales, items 11 and 12 were removed from 

the DuBois AIR scale, making it more blended with only three advocacy items (9 and 

10), 4 about instruction and guidance (2, 3, and 4), and 3 items encouraging youth selfreflection. This teaching might better be called guidance in that it includes attention to the 

unique needs and circumstances of the youth—more like the Teacher+ of the TEAM 

framework. One of the self-reflection items identifies the Advocate modeling selfreflection by personally sharing their own experience (item 5) perhaps partly as a way of 

teaching but that is personal and directly related to the youth, such that it also reflects the 

collaboration indicative of a Counselor+ or Friend+ in the TEAM Framework. 

Similarly, the Blue Ribbon Scale had just 4 (of 6 original) items that did not 

double-load (or duplicate items in the DuBois scale) and were not redundant with the 

AIR scale. Items 1 and 6 were more interpersonal, and item 6 could have happened in the 

presence of the youth (or else how would the youth know about it). Both were unlike the 

other four in that they did not clearly represent the conventional, future oriented, goal 

directed purpose in the TEAM framework that aimed to help improve the youth’s success 

(academic, vocational) and future. The four remaining items on this scale are all about 

trying to effect change in the youth’s world and trying to bridge the worlds of work, 

school, family, and mental health staff. 

With the two modified scale capturing sufficiently distinct elements of advocacy, 

the analyses reported in the following section were undertaken. In their interpretation, we 

viewed the Blue Ribbon Advocacy scale as specifically about advocacy as outreach and 

fostering collaborations across contexts outside the youth-adult relationship, and the 

DuBois’ Interpersonal AIR scale representing the interpersonal, psychosocial, skillbuilding, and “got your back” or “by your side” advocacy effort that may be viewed as a 

way to support the youth’s confidence, skill levels, and self understanding in order to 

improve the youth’s capacity to Advocate for him or herself presently and in the future 

independently. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 128

Background: Source of the Blue Ribbon Advocacy scale. This scale was created using 

the following information from email summary from Graig Meyer (with permission) 

“Most students who enter our program are unfamiliar with the idea of

advocacy. We describe [advocacy] to them as

"when someone helps you even when they're not with you at the time."

It can take a little bit of work for the kids to get their heads around that, but here are some

common examples we'd share with them: 

-Your mentor will talk with your family about how you're doing in school.

-Your mentor will go with your family to parent-teacher conferences.

-Your mentor may talk with your teachers about how you're doing.

-Your mentor may research opportunities that would benefit you.

-Your mentor may help you sign up for an opportunity.

-Your mentor may tell other people good things about you.

-Your mentor may tell other people (like teachers) when they think you need extra help from them 

Practitioners may want to consider how to train mentors to do those things and other advocacy

activities. I hope this helps!

Graig Meyer, Blue Ribbon Mentor-Advocate, Chapel Hill-Carrboro City Schools

www.BlueRibbonMentors.org 

In order to better differentiate the two Advocacy scales, items were subjected to 

an exploratory factor analysis. This revealed a possible third factor, but one with few 

items and that which required cross-loadings for decent model fit. 

Rotated Component Matrix 

Component 

1 2 3 

Y2 DuBois Adv 1 .804 .136 

Y2 DuBois Adv 2 .832 .226 

Y2 DuBois Adv 3 .841 

Y2 DuBois Adv 4 .779 

Y2 DuBois Adv 5 .641 .288 -.201 

Y2 DuBois Adv 6 .829 .187 

Y2 DuBois Adv 7 .803 .190 

Y2 DuBois Adv 8 .786 .174 .189 

Y2 DuBois Adv 9 .772 .323 

Y2 DuBois Adv 10 .711 .285 

Y2 DuBois Adv 11 .558 .393 

Y2 DuBois Adv 12 .633 .357 

Y2 Blue Ribbon Adv 1 .227 .300 .676 

Y2 Blue Ribbon Adv 2 .867 .122 

Y2 Blue Ribbon Adv 3 .879 

Y2 Blue Ribbon Adv 4 .128 .780 .101 

Y2 Blue Ribbon Adv 5 .208 .446 .391 

Y2 Blue Ribbon Adv 6 .142 .877 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 129

Once items with significant cross-over (needing covariances across scale factors 

to achieve good model fit—viz. DuBois 11 and 12, Blue Ribbon 1) and those pulling to 

create a third factor (Blue Ribbon 1 and 6), two factors emerged, one for each of the 

original set of items. Subsequently we fit a confirmatory factor analyses (CFA) of this 

two-factor model of advocacy. 

Rotated Component Matrix 

Component 

1 2 

Y2 DuBois Adv 1 .819 

Y2 DuBois Adv 2 .861 

Y2 DuBois Adv 3 .847 

Y2 DuBois Adv 4 .769 

Y2 DuBois Adv 5 .589 .249 

Y2 DuBois Adv 6 .827 .205 

Y2 DuBois Adv 7 .829 .107 

Y2 DuBois Adv 8 .804 .216 

Y2 DuBois Adv 9 .820 .129 

Y2 DuBois Adv 10 .748 

Y2 Blue Ribbon Adv 2 .882 

Y2 Blue Ribbon Adv 3 .867 

Y2 Blue Ribbon Adv 4 .121 .803 

Y2 Blue Ribbon Adv 5 .264 .553 

Pictured at right, this model, 

for the data collected at two months 

from the youth, fit the data 

reasonable well, with the SRMR and 

the Chi-Square to degrees of 

freedom ratio indices most 

suggestive of a good model fit. 

Chi square = 156.527, df = 73, p = 

.000, chi square/df = 2.144 

RMSEA = .095, CI .074-.115, p < 

.05 = .000 CFI = .929 SRMR = .08 

Note: This DuBois factor model 

allowed within-factor measure crossloadings for items 3 with 4, 5 with 6, 

and 6 with 7. 

Identifying the source of this 

covariance in subsequent work may 

reveal items which might be 

excluded from the scale. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 130

Differentiating the two Advocacy scales for Use in the Study 

Separate scale factor Alternative Model Fit Indices and Analyses 

DuBois Advocacy Youth-Reported Full Scale CFA Model Fit at 2 Months 

Chi-square 109.298, df 51, p = 0000, chi square/df = 2.143. RMSEA = .095, CI .070-

.119, p < .05 = .002; CFI = .947, SRMR = .040. Chi square/df, CFI and SRMR suggest 

adequate model fit using the original items. 

The Blue Ribbon Scale Full Scale CFA Model Fit at 2 Months 

The Blue Ribbon Scale Fit Indices: Original 6-item and Final 4-item Model Used in 

Chi-square = 19.448, df 

= 8, p = .0126, chi 

square/df = 2.431. 

RMSEA = .103, CI .45-

.162, p < .05 = .064. 

CFI = .897. SRMR = 

.065. 

The SRMR is the only fit 

index that suggests a 

good model fit. 

Analyses 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 131

Chi square = .433, df 2, p = 

.8055 

RMSEA = .000, CI .000-.105, p 

< .05 = .857. CFI = 1.0 

SRMR = .012 

All of the fit indices suggest this 

is a good fitting model. Fit 

indices for a two-factor CFA of 

the two measures of advocacy, 

each with two cross-loading 

items removed, suggesting 

separate constructs are captured 

Chi-square = 156.527, df = 73, p = .000, chi square/df = 2.144. RMSEA = .095, CI .074-

.115, p < .05 = .000, CFI = .929

 SRMR = .08 

Two CFA Factor Models of the 

Youth-Reported DuBois 

Advocacy with data at four 

months 

Full scale CFA with data at 

four months: 

Chi square = 99.631, df 48, p = 

.000, chi square/df = 2.0756 

RMSEA = .120, CI .074-.131, 

p < .05 = .002. CFI = .955. 

SRMR = .040 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 132

Scale CFA with data at four 

months, omitting the two items 

that cross-load onto the Blue 

Ribbon Advocacy Scale: 

Chi square = 51.255, df = 27, p = 

.0032, Chi square/df = 1.8983 

RMSEA = .093, CI .053-.132, p < 

.05 = .040 CFI = .97 SRMR = 

.032 

Model fit is good. At the same 

time, there are the three 

covariance relationships between 

the three pairs of items identified 

in the two month data that must 

be allowed in the model to 

achieve this model fit. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 133

Factor Models of the Youth-Reported Blue Ribbon Advocacy Scale at four Months 

Full scale CFA with data at four months for youth-reported Blue Ribbon 

Advocacy: 

Chi square = 18.660, 

df 6, p =.004. Chi 

square/df = 3.11 

RMSEA = .145, CI 

.073-.221, p < .05 = 

.019 CFI = .953 

SRMR = .058 

Chi square/df and 

RMSEA suggest poor 

model fit, while the 

CFI and SRMR 

suggest good model 

fit. 

The required cross-loadings, however, are problematic. 

CFA with no cross-loading items with data at four months for Blue Ribbon Advocacy: 

Chi square = 5.385, df 2, p = 

.0677, chi square/df = 2.6925 

RMSEA = .129, CI .000-.267, 

p < .05 = .116 CFI = .981 

SRMR = .036 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 134

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final Technical Report: 2011-JU-FX-0001 p. 135

APPENDIX H 

Hemingway Measure of Adolescent Connectedness Items (see Karcher & Sass, 2010)
 

Items 1-30 assess connectedness to friends, family, school, teachers, and the future. Each scale 

has 6 items. Connectedness to friends, for example, is the average of items 1, 6, 11, 16, 21 and 

27. One reverse worded item is in each subscale. 

These questions ask how you feel about things like school, other kids your age, and yourself. For 

each sentence, decide how true the sentence is for you. Then circle one number that fits best. 

“How TRUE about you is each sentence? 

Not at all (1); not really (2); sort of (3); true (4); very true (5) 

(1) Spending time with friends is not so important to me. 1 2 3 4 5 

(2) My family has fun together. 1 2 3 4 5 

(3) I work hard at school (or other educational program like GED). 1 2 3 4 5 

(4) I care what my teachers think of me. 1 2 3 4 5 

(5) I will have a good future. 1 2 3 4 5 

(6) I have friends I'm really close to and trust completely. 1 2 3 4 5 

(7) It is important that my parents trust me. 1 2 3 4 5 

(8) I enjoy being at school (or the educational program I attend). 1 2 3 4 5 

(9) I do not get along with some of my teachers. 1 2 3 4 5 

(10) Doing well in school will help me in the future. 1 2 3 4 5 

(11) Spending time with my friends is a big part of my life. 1 2 3 4 5 

(12) I enjoy spending time with my parents. 1 2 3 4 5 

(13) I get bored in school a lot. 1 2 3 4 5 

(14) I want to be respected by my teachers. 1 2 3 4 5 

(15) I do things outside of school to prepare for my future. 1 2 3 4 5 

(16) My friends and I talk openly with each other about personal things.1 2 3 4 5 

(17) My parents and I disagree about many things. 1 2 3 4 5 

(18) I do well in school (or the educational program I attend now). 1 2 3 4 5 

(19) I try to get along with my teachers. 1 2 3 4 5 

(20) I do lots of things in school to prepare for my future. 1 2 3 4 5 

(21) I spend as much time as I can with my friends. 1 2 3 4 5 

(22) My parents and I get along well. 1 2 3 4 5 

(23) I feel good about myself when I am at school. 1 2 3 4 5 

(24) I always try hard to earn my teachers’ trust. 1 2 3 4 5 

(25) I think about my future often. 1 2 3 4 5 

(26) I usually like my teachers. 1 2 3 4 5 

(27) My friends and I spend a lot of time talking about things. 1 2 3 4 5 

(28) I care about my parents very much. 1 2 3 4 5 

(29) What I do now will not affect my future. 1 2 3 4 5 

(30) Doing well in school is important to me. 1 2 3 4 5 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
Final Technical Report : 2011-JU-FX-0001 p. 136

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice