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COMPREHENSIVE
PSYCHOLOGY
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3. Treatment of Violence-prone Youth / R. J. Zagar, et al.
tion, prevention, and cost reduction: (a) using empirically
developed regression equations to identify those prone to
violence; (b) building programs comprising treatments
shown to be eff ective by meta-analyses of intervention
research; and (c) applying and assessing cost-benefi t
analyses of the interventions used to divert those at risk
for violent behavior. The goal of this paper is to review
these three approaches, and then describe a partial appli-cation
in an urban high school system with post hoc analy-sis
of cost-benefi t to show whether the approaches, used
together, can lower violence and reduce costs.
From a developmental/sociological theory of homi-cide
and violence, high school is the fi nal key environ-ment
in the development of violent tendencies among
youth ( Zagar, Busch, Grove, & Hughes, 2009a ; Zagar,
Zagar, Bartikowski, & Busch, 2009 ; Zagar, Zagar, Bar-tikowski,
Busch, & Stark, 2009) . Intercepting violence
in the high schools is expected to lower homicide rates
eff ectively, save lives, and reduce social and govern-mental
costs ( Zagar, Isbell, Busch, & Hughes, 2009 ;
Zagar, Zagar, Bartikowski, & Busch, 2009) .
Organization of This Report
The organization of this report begins with a descrip-tion
of the identifi cation of violence-prone persons with
actuarial measures, then the development of an empiri-cally
derived, predictive regression equation. The second
part includes a review of the meta-analyses of interven-tions
shown to divert youth from criminal behavior, par-ticularly
violence. The third section comprises cost-benefi t
analyses to assess the return on investment ( ROI ) of these
interventions. Finally, a real-world application is described
in which these three approaches could be used to address
wider problems in prisons and communities.
Identifi cation of Violence-prone Persons
Since the pioneering work of Burgess (1928) , investiga-tors
have studied actuarial and/or statistical prediction of
delinquent and later criminal behavior. The criterion was
“return to court” (a subsequent off ense) or “dangerous-ness.”
Eighty-two risks were found consistently. These
risks were categorized as adult court contact; commu-nity,
family, and home risks; individual medical status;
employment; juvenile court contact; peer relationships;
and academics. These demographic characteristics have
been valuable in saving the costs avoided by detecting,
apprehending, convicting, imprisoning, and not paroling
dangerous off enders. More recently the assessments for
violence have been used in clinics, workplaces, and uni-versities
to assess the risk of criminal off ending.
There are two ways to fi nd persons with tendencies
toward violence: (a) using a predictive regression equa-tion;
or (b) employing a case study approach with statis-tical
(actuarial) testing. In the fi rst method, one can use
information such as demographics, life history, academic
records, and standardized test scores to create a valid pre-dictive
regression equation. This method will be discussed.
The second method, the case study approach with statisti-cal
(actuarial) testing, has been discussed in Zagar, Kovach,
Basile, Hughes, Grove, Busch, et al., 2013 , (in review) and
will not be further mentioned here. The regression equa-tion
to identify risk-prone teens has a proven track record
of predicting which youths may become violent, compared
with those less likely to commit off enses against persons.
Since early in the 20 th century, recidivism or “return
to court” has been predicted by tracking criminals over
time and comparing those who “returned to court” ver-sus
those who did not. Male and female adolescent and
adult off enders on 3 continents, (5 countries; 17 states or
provinces) were tracked for up to 10 years in these stud-ies.
Using meta-analyses to compare the eff ect sizes of pre-dictors
or risks, Lipsey and Derzon (1998 ) examined the
predictors of violent behavior. These predictors varied as
a function of age of the sample. For very young off enders,
Farrington (1997 ) reported that a serious fi rst off ense was
the best predictor. For the younger off ender, past behavior
was the best predictor of future violence ( Lipsey & Der-zon,
1998 ). For the older off ender, lack of social ties, hav-ing
antisocial peers, or gang membership were the best
predictors of violence (Lipsey & Derzon 1998 ). Bonta,
Hanson, and Law (1998 ) found that the best predictor of
adult violent behavior was past criminal off enses. Other
risks included early antisocial behavior, convicted parents
and family, poverty, school failure, unemployment, and
drug use. In predicting homicide (Busch, Zagar, Hughes,
Arbit, & Bussell, 1990; Zagar, Arbit, Sylvies, Busch, &
Hughes, 1990 ), criminally violent family and relatives,
physical abuse, gang membership, and alcohol and sub-stance
abuse were the statistically signifi cant risks. Zagar,
Isbell, Busch, and Hughes (2009 ) showed that prior court
contact, lower executive function (poorer decision mak-ing),
infant illnesses, frequent home and school moves,
and alcohol and substance abuse were the best predictors
of homicide from infancy to adulthood.
Zagar and Grove (2010 ) improved the accuracy of
predicting homicide from records of up to 12 years of
early childhood and youth risks in a sample of 1,127
youths to an Area under the Curve ( AUC) = .91 ( Fig.
1 ). Using a similar procedure, among 1,595 adults, with
Shao's bootstrapped logistic regression procedure ( Shao,
1996 ), the AUC was .99. The predictive equations in
a combined adult and youth sample of 2,722 had an
AUC of .96. The accuracy of these results is notewor-thy
because most literature attempting to predict recidi-vism
or violence has AUCs in the region from .69 to .76
( Moosman, 2013 ). For example, Quinsey, Harris, Rice,
and Cormier (1998, 2006 ) tested non-randomly selected
violent off enders without a control group, resulting
in a Receiver Operating Characteristic 3 (ROC) of .76.
3 The Receiver Operating Characteristic is roughly equal to the Area
under the Curve. The Area under the Curve has a range of zero to 1.0,
with .5 being 50% accurate and 1.0 being 100% sensitive and specifi c.
Comprehensive Psychology 2 2013, Volume 2, Article 6
4. Treatment of Violence-prone Youth / R. J. Zagar, et al.
Fig. 1. Regression-weighted scores yield sensitive and specifi c discrimination of 1,127 violent (■) from matched non-violent
youth (Infants …; Children and Teens ■). AUC = .91.S.
Among 200 non-randomly selected sex off enders with-out
a control group, these same researchers obtained
ROC = .70. Thus, the bootstrapped logistic regression
represents a considerable improvement in predictive
accuracy ( Zagar, Busch, Grove, & Hughes, 2009b ; Zagar
& Grove, 2010 ), providing a sound empirical basis for
risks that consistently and reliably predict future vio-lent
criminal activity. These risks include poor execu-tive
functioning (decision making and related abilities),
lower social maturity, weapons possession conviction,
violent family, gang membership or participation, male
gender, academic underachievement, serious illnesses,
prior court contact or arrest, low socioeconomic sta-tus,
substance abuse, previous neurological disorder,
alcohol abuse, head injury, and truancy/suspension
or expulsion ( Table 1 ) ( Busch, et al., 1990 ; Zagar, Arbit,
Sylvies, Busch, & Hughes, 1990 ; Zagar, Arbit, Busch,
Hughes, Schiliro, Sylvies, et al., 1992 ; Zagar, Arbit,
Busch, Hughes, & Sylvies, 1998 ; Zagar, Busch, Grove, &
Hughes, 2009b ; Zagar, Busch, Grove, Hughes, & Arbit,
2009 ; Zagar & Grove, 2010 ). When Chandler, Levitt,
and List (2011) examined 12,989 higher-risk students
among whom there were 500 perpetrators who com-mitted
shootings, the signifi cant personal characteris-tics
predicting shooting were consistent with this prior
research: male gender, academic underachievement,
prior court contact or arrest, low in socioeconomic sta-tus
(SES), truancy, suspension, and expulsion.
Effectiveness and Cost-benefi t of Interventions
In his meta-analyses, Lipsey evaluated the eff ects of
empirical treatments in diverting youth from court.
Lipsey (1992 , 1995 , 1999 , 2006a , 2006b , 2009 ) reviewed
500 treatment studies from 1950–1995. As seen in Fig.
2 , over 50 years, in several meta-analyses the most
eff ective treatments have been identifi ed. The best
interventions for youth were: (1) jobs, resulting in a
37% rate of diversion from court; (2) behavioral ther-apy
or anger management training, with a 25% diver-sion
rate; and (3) multi-modal therapy or personal
mentoring, with a 25% diversion rate ( Lipsey, 1999 ).
Personal characteristics of off enders, treatment and
program variables, and outcome eff ect sizes in terms
of behavioral changes for serious off enders have been
clearly delineated. Lipsey's (1999 ) meta-analysis was
consistent with ad hoc treatments selected by the thera-pist
being less eff ective.
In Fig. 2 , Wilson and Lipsey (2007) demonstrated the
relative treatment eff ect size needed to improve behav-ior,
i.e., how successive increases in treatment dose
were associated with decreases in re-off ending. Mul-timodal,
carefully monitored, and closely supervised
therapies were the best interventions. The most eff ec-tive
programs focused on social skills, activity levels,
and problem behaviors. Treating aggression was con-siderably
less eff ective. However, it could be that this
approach misses the most crucial element of treatment:
targeted, timely application of treatments over the age-specifi
c course of the most risky behaviors.
In the third column of Table 3 is the Washington
State Policy Institute's (2006) list of evidence-based
treatments. Application of these treatments may reduce
future prison construction, criminal justice costs, and
crime rates. For prevention programs, the best Return
on Investment (ROI) was pre-kindergarten interven-tions
for low-income 3- to 4-year-olds, with $20.57 in
savings expected for every dollar spent. For juvenile
off enders, the best ROI was inter-agency coordination
programs, with $25.03 in savings for every dollar spent.
For adult off enders, the best ROI was cognitive behav-ior
therapy, with $98.09 in savings expected for every
dollar spent. Knowledge of the various interventions
and their relative ROIs are important to decision mak-ers.
With a quick glance at Table 2 , one can see that adult
Comprehensive Psychology 3 2013, Volume 2, Article 6
5. Treatment of Violence-prone Youth / R. J. Zagar, et al.
TABLE 1
Eff ect Sizes For Regressions of Four Groups of Homicidal Youth With Controls and One School Shooting Sample
Eff ect Size
(n = 30)
Zagar, et al .,
Eff ect Size
(n = 26)
Zagar, et al. ,
2009b
Eff ect Size
( n = 127)
Zagar, et al .,
2009b
Regression Coeffi cients
( n = 500)
Chandler, Levitt, & List,
2011
1 Poor executive function 1.53 1.71 1.63 1.59
2 Lower social maturity 1.33 1.23 0.91 1.21
3 Weapon possession conviction 1.07 0.99 1.21 1.10
4 Violent family 0.76 0.56 1.08 0.83
5 Gang membership /
participation 0.70 0.79 0.54 0.70
6 Male gender 0.54 0.74 0.89 0.63 *
7 Underachievement 0.45 0.59 0.67 0.57 *
8 Illness 0.42 0.68 1.03 0.57
9 Prior court contact 0.54 0.62 1.10 0.54 *
10 Alcohol and substance abuse 0.40 0.55 0.71 0.52
11 Low socioeconomic status 0.34 0.72 0.92 0.50 *
12 Substance abuse 0.38 0.69 0.46 0.44
13 Neurological disorder 0.42 0.71 0.24 0.43
14 Alcohol abuse 0.29 0.35 0.67 0.41
15 Head injury 0.39 0.52 0.15 0.37
16 Truancy, suspension, expulsion 0.10 0.24 1.03 0.33 *
17 Epilepsy 0.32 0.39 0.30 0.31
18 Jaundice 0.38 0.52 0.29 0.37
19 Sensory and speech disorder 0.49 0.58 1.03 0.33
20 Physically abused 0.47 0.22 0.67 0.41
Note Since only two group regression coeffi cients were provided by Chandler, et al . (2011 ), no eff ect sizes could be computed;
*indicates a risk factor was a predictor of violence at p < .05. Eff ect size is the diff erence between means divided by the pooled
standard deviation (Hedges & Olkin, 1986). An eff ect size of 0.2 is considered small, 0.5 is medium, 0.8 is large, and 1.7 is very
large ( J. Cohen, 1988 ).
cognitive behavior therapy, inter-agency coordination,
job, juvenile diversion program, pre-kindergarten edu-cation,
adult community drug treatment, nurse fam-ily
partnership for children training, anger/aggression
replacement training, juvenile functional family ther-apy,
vocational education, multidimensional treatment,
high school graduation equivalency, teen courts, restor-ative
justice, and multi-systematic therapy all have
ROIs above $10 for every $1 dollar spent.
The Culture of Calm: an Intervention
to Prevent Violence
In 2009, the U.S. Department of Justice (U.S. DOJ)
funded a trial intervention called the “Culture of Calm”
in a Midwestern, urban high school setting within
crime-ridden areas ( Saulny, 2009 ; Shelton & Banchero,
2009 ). The grant provided funding for: (a) develop-ment
of an empirical, predictive, regression equation
to identify at-risk youth; (b) evidence-based interven-tions
( Lipsey, 1999 ) to reduce court contact, compris-
Eff ect Size
(n = 71)
Busch, et al .,
1990
1990
Fig. 2. School-based program eff ects on behaviors: weight-ed
mean eff ects. Data for Social skills through Performance
adapted from Wilson and Lipsey (2007) ; multimodal therapy
(mentor), behavior change (anger management) and jobs
comprised treatment in the U.S. DOJ funded Culture of Calm ,
and were adapted from Lipsey (1999) .
Comprehensive Psychology 4 2013, Volume 2, Article 6
6. Treatment of Violence-prone Youth / R. J. Zagar, et al.
TABLE 2
Estimate of Injury, Lives, and Costs Saved by the Culture of Calm Program (ROI) = 6.42 1
2008 2009 2010 2011 2012 Total Saved
Chicago murders (a) 510 458 449 440 205 2,062
Youth murders (b) 100 90 88 86 40 404
CULTURE OF CALM
Students 0 250 1,700 1,700 1,200 4,850
High schools 0 6 38 38 32 38
Decrease in murders 0 29 28 28 13 104
Murder costs saved a 0 $135 $131 $131 $61 $457
Assaults 12,343 12,478 13,479 12,978 6,787 58,065
Decrease in assaults with injury 0 192 1,309 1,309 924 2,734
Assault costs saved a 0 $5.70 $39 $39 $28 $111
Murder + assault costs saved a 0 $140 $169 $169 $88 $568
U.S. Justice funds spent a 0 $20 $20 $20 $17 $77
Return on investment (ROI) 0 6.05 7.48 7.48 2.65 6.42
Note a Values are in millions of dollars. 1 2012 U.S. dollar costs based on U.S. Department of Agriculture (1994) , Cohen
and Miller (1994 ), Cohen, Miller, and Rossman (1994 ), Cohen (1995, 1998), Cohen and Piquero (2007 ), Lino (2007), Zagar,
Zagar, Bartikowski, and Busch (2009 ), and the Consumer Price Index (2012) in the U.S. Bureau of Labor Statistics (2013).
The national rate of youth homicide is 9.8% (Federal Bureau of Investigation, 2012) but the Chicago youth homicide rate
is 19.6% ( Harris, 2009 ; Ahmed-Ullah, 2011 ; Ritram, 2011 ; Vevea, 2011 ; Dardick, 2012 ; Byrne & Dardick, 2013 ) .
ing anger management training, jobs, and mentors; and
(c) analysis of the cost savings achieved applying these
interventions to lower violence. An age-specifi c model
that focused on particular risks for homicide was pre-sented
to the decision makers (Mayor and staff ) with
specifi c predictive factors for infants, children, teens,
and adults. The model was based on evidence that the
statistical predictions were accurate in identifying indi-viduals
who were at-risk for violent behavior from 1989
to 2009 in Chicago and Cook County ( Zagar, Busch,
Grove, & Hughes, 2009b ). Four distinct, cross-validated
samples of 127 homicidal teens and two larger samples
of youth ( N = 1,127) and adults ( N = 1,595) were used
to model risk factors and interventions. Anger man-agement
training, jobs, and mentoring were empha-sized
as the best interventions for potentially homicidal
youth. The suggestion was to apply one or more of the
above-mentioned, empirically supported interventions
among the at-risk in urban high schools, due to the high
number of homicides and shootings. The grant's devel-opment
of a predictive regression equation using com-munity
data imitated the technique already successful
in identifying at-risk youth.
The schools' CEO and staff used police data from
500 shooting incidents to identify the demographic
characteristics of these most at-risk youths. The logistic
regression fi ndings from these community data ( Harris,
2009 ; Saulny, 2009 , Shelton & Banchero, 2009 ; Ahmed,
2010 ; Chicago Board of Education, 2010; Rossi, 2010 ;
Chandler, et al., 2011 ) were consistent with previous
reports ( Busch, et al., 1990 ; Zagar, Arbit, Sylvies, Busch,
& Hughes, 1990 ; Zagar, Busch, Grove, Hughes, & Arbit,
2009 ; Zagar & Grove, 2010 ). The reliable predictors used
to target the interventions were: male gender; the num-ber
of serious misconduct behaviors per day; below
average academic performance or failing a grade; per-cent
days suspended; percent days absent; juvenile jail;
adult jail; Illinois State Achievement Test (ISAT) read-ing
score; ISAT mathematics score; free lunch status;
and times shot previously.
The intervention began in six high schools in crime-ridden
areas of Chicago. These locations were identi-fi
ed by police as having high violence rates, using crime
statistics. Based on the above-mentioned regression
developed from 500 perpetrators of shooting incidents
among 12,989 higher-risk students, the 250 most at-risk
students across the initial six schools were chosen
for receipt of jobs ( Schochet, Burghardt, & Glazerman,
2001 ), behavior therapy or anger management train-ing
( Alexander, Sexton, & Robbins, 2000 ; Larson, 2005 ),
and multimodal therapy or adult mentoring ( Heng-geler,
Schoenwald, Borduin, Rowland, & Cunningham,
1998 ). These interventions were applied in the six high
schools during 2009 to 250 students; in 38 schools dur-ing
2010 to 1,700 students; in 38 high schools during
2011 to 1,700 students; and in 32 schools during 2012 to
1,200 students.
The Culture of Calm program resulted collectively
in an estimated savings of 104 lives and $491 million
in resources. Specifi cally, there were 32% fewer homi-cides,
46% fewer shootings, and 77% fewer assaults
( Harris, 2009 ; Ahmed Ullah, 2011 ; Ritram, 2011 ; Vevea,
Comprehensive Psychology 5 2013, Volume 2, Article 6
7. Treatment of Violence-prone Youth / R. J. Zagar, et al.
2011 ). The total Return on Investment (ROI ) was 6.42
( Ahmed, 2010 ; Rossi, 2010 ) as indicated in Table 2 . Data
included (a) the youth homicide rate (Federal Bureau
of Investigation, 2012; U.S. Bureau of Justice Statistics,
2006); (b) the 19.6% Chicago rate of youth homicides
(like Chicago, many large U.S. cities' homicide rates are
far above the national rate); (c) the youth homicide rate
during the Culture of Calm; (d) the total students treated
in the Culture of Calm; (e) the number of high schools in
the program; (f) the youth murder reduction during the
Culture of Calm (the number of students enrolled times
the 32% fewer shootings converted to homicides with
an equation developed by O'Flaherty & Sethi, 2010 ).
These data were used to estimate the lifetime cost saved
by reduced youth murders: $4,660,986 per homicide
(from Zagar, Zagar, Bartikowski, & Busch, 2009 , with
the regional Consumer Price Index in 2012 added, times
a 32% decline in killings based on the 46% decrease in
shootings); with these estimates, the total savings over
the four years of the Culture of Calm were $456,776,628.
In addition to the savings from homicides prevented,
it was possible to estimate costs saved from reductions
in assaults. Using the rate of youth high school assaults
with injury, the reduction of youth high school assaults
in Culture of Calm (77%), and the cost of youth assault
with injury of $29,872 per incident ( Zagar, Zagar, Bar-tikowski,
& Busch, 2009 ; the 2012 regional Consumer
Price Index was added times the number of assaults in
participating schools); the savings over four years was
$111,571,920. The outcome in terms of ROI was calcu-lated
from the homicide rate plus the reduced assault
cost under the Culture of Calm, and the U.S. DOJ funds
spent over four academic years on the Culture of Calm
($76,600,000).
Yet another way of looking at the outcome is to con-sider
that, if the cost of program was $76,600,000 and
104 lives were saved, the cost of saving each life was
$730,769. Based on the 2012 dollar value of the loss
of a life at $4,660,986, the ROI for this program was
$4,660,986/$730,769 = 6.38. A summary of this complex
outcome might be, “ Help at-risk youth be productively
busy so they don't have time to murder.” In short, violence
was reduced and resources saved.
Extending Cost-effective Targeting
Statistical prediction supported with eff ective interven-tions
form a strategy that may be diffi cult to improve.
The fi rst increase in eff ectiveness is gained by identify-ing
those in the at-risk population most likely to com-mit
a homicide. Data from shootings in the local police
fi les are easily available. The next step was applying
one or more empirically supported treatments. The
treatment with the greatest eff ect on violence rates was
jobs. In the Chicago Culture of Calm U.S. DOJ grant,
anger management training, jobs, and mentoring were
applied together. Examining Fig. 2 and Tables 2–3 , it is
clear the Chicago Culture of Calm program could be
extended to at-risk infants and children by providing
pre-kindergarten education to the very young, nurses to
train mothers in child-rearing, encouraging interagency
cooperation, and applying cognitive behavior therapy
and functional family therapy for children in middle
school, along with the jobs, mentors, and anger man-agement
for the most at-risk teens in the high schools. It
has been projected that such an age-specifi c targeting of
cost-benefi cial, cost-eff ective interventions could lower
homicide rates by 89% in a city like Chicago (see Table 3
of Zagar, Busch, & Hughes, 2009 ).
There are other ways to extend the use of targeting
and treatment of at-risk and violent youth and adults.
Benefi ts, costs, and effi ciency drive all such decisions.
Approximate lifetime costs, including mental disorders
or prison, associated with various at-risk behaviors are
shown in Fig. 3 . Obviously there is ample economic
reason to address such problems for at-risk individu-als.
Given that approximately 1% of the population is
psychotic and another 1% has depressive or mood dis-orders,
means that in the U.S. there are 6 million men-tally
ill. One percent are pedophiles or sex off enders
and 1% are addicted/alcoholic. Among these 12 mil-lion
at-risk individuals, some could potentially be hom-icidal
or violent, so employing scientifi c approaches to
assist those most at-risk to lead a higher quality of life
will also safeguard the community. Using either regres-sion
equations or actuarial (statistical) tests and then
targeting treatments to these at-risk youth and adults
is important to lower the lifetime costs and the chance
that a small portion of these non-off ender, at-risk indi-viduals
will perpetrate a homicide or become a chronic
delinquent or criminal.
Prisoners with mental disorders (mainly psycho-ses)
could be more eff ectively treated in hospitals; these
comprise some 15% of imprisoned adults and youth. In
Cook County, annual expenses are $225,000 per delin-quent
compared to medical and the psychiatric hospi-tals
at $150,000 per year, which was why since 1955 most
state psychiatric facilities were closed, moving those
with psychiatric disorders into the community and
thence many into prisons ( Primeau, Bowers, Harrison,
& Xu, 2013 ). Using either regression or actuarial (sta-tistical)
tests to fi nd the at-risk, which is more sensitive
and specifi c than clinical judgment, is important from a
cost-benefi cial point of view but also for the safety of the
community, given that some off enders are homicidal.
The relative annual cost estimates of several possible
interventions based on computerized testing, medica-tion,
and case management are presented in Fig. 4 .
Many severely mentally ill in the community are not
monitored and choose not to take medication; a minor-ity
who become violent cause terrible loss of life, includ-
Comprehensive Psychology 6 2013, Volume 2, Article 6
8. Treatment of Violence-prone Youth / R. J. Zagar, et al.
TABLE 3
Checklist of Risks and Treatments to Reduce Violence
Risk Effect Size Treatments With Known Empirical Effectiveness ROIs
1 Poor executive function 1.59 Cognitive behavior therapy in community or jail $98.09
2 Low social maturity 1.21 Juvenile interagency coordination program for high risk
offenders
3 Weapon possession conviction 1.10 Adult job in community or jail $22.64
4 Violent family 0.83 Juvenile diversion program with anger management (25%)
or mentor (25%)
5 Gang membership / participation 0.70 Pre-kindergarten education for low income 3- to 4-year-olds
$20.57
6 Male gender 0.63 Adult community drug treatment for high risk or
offenders
7 Underachievement 0.57 Nurse family partnership for children—training of high
risk mothers
8 Illness 0.57 Juvenile anger or aggression replacement training $16.34
9 Prior court contact 0.54 Juvenile functional family therapy for high risk,
probationer, or jailed
10 Alcohol and substance abuse 0.52 Adult vocational education for high risk, paroled, or jailed $11.62
11 Low socioeconomic status 0.50 Juvenile foster care multidimensional treatment vs. regular
group care
12 Substance abuse 0.44 Adult basic high school general education (Graduate
Equivalency Diploma) for high risk, paroled, or jailed
13 Neurological disorder 0.43 Adult community job and training $10.90
14 Alcohol abuse 0.41 Juvenile teen courts $9.83
15 Head injury 0.37 Juvenile restorative justice for high risk students or low
risk offenders
16 Truancy, suspension, expulsion 0.33 Juvenile multi-systematic therapy $4.27
Note Eff ect sizes are from regressions predicting violence in Zagar and colleagues' fi ve groups. ROI = return on investment. Eff ect
size is the diff erence between means divided by the pooled standard deviation ( Hedges & Olkin, 1985 ); 0.2 is considered small, 0.5 is
medium, 0.8 is large, and 1.7 is very large ( J. Cohen, 1988 ). ROIs are from Washington State Policy Institute (1999).
ing mass murders like that at Sandy Hook, Connecticut,
in 2012 (Federal Bureau of Investigation, 2012; National
Institute of Corrections, 2012). Clearly, despite the high
costs of hospitals, the most dangerous of these individu-als
should be identifi ed for treatment to protect the com-munity,
saving the extreme costs of large-scale violence.
It is clear that any identifi cation system cannot depend
on subjective assessment by psychiatrists or psycholo-gists:
in meta-analyses, Grove, Zald, Lebow, Snitz, and
Nelson (2000 ) reported that in 130 of 136 studies, the sta-tistical
(actuarial) predictions were at least 10% better
than a professional's subjective judgment.
Among off enders, the lifetime costs are higher for
the homicidal and the career off ender, as seen in Fig. 3 .
Specifi cally, adult prison costs average approximately
$30,000 per inmate and juvenile jail $70,000 per inmate.
Electronic monitoring costs approximately $2,500 per
year to supervise parolees, probationers, and prisoners
in their homes, as an inexpensive alternative to prison
for the non-violent. Given the costs of homicide, it is
well identifying those at risk for violence. Computer
testing (about $350 U.S.) via the internet is the least
$25.03
$21.24
$17.92
$17.49
$13.69
$11.20
$11.09
$8.03
expensive option and represents a savings of about 90%
less than full neuropsychiatric evaluation. A brief paper
test costs about $700, a long test $1,000, a psycho-educa-tional
assessment $1,200, a full case study in the schools
$1,600, and a neuropsychological evaluation $2,000. With
price dictating utility, a clear option is for agencies to use
the 24/7 internet availability of a computer test battery
based on an accurate predictive regression equation
to identify the at-risk, and then to employ prevention
strategies or lowest-cost treatments. The other issue
that professors who teach statistical (actuarial) test-ing
acknowledge is that clinicians do not employ tests
in dealing with mentally ill patients, despite the data
showing that judgment is fallible. From both a lifetime
cost and community safety perspective, accurate regres-sion
or statistical (actuarial) testing and targeting treat-ment
makes economic and practical sense.
Limitations
Researchers debate the accuracy of actuarial decision-making
tests for risk of violence. Typically they cite issues
of false negatives and positives and their associated costs.
Comprehensive Psychology 7 2013, Volume 2, Article 6
9. Treatment of Violence-prone Youth / R. J. Zagar, et al.
Fig. 3. At-risk students' lifetime costs in millions of 2012 dol-lars.
Based on the 1990–1992 National Crime Victim Survey,
updated to 2012 U.S. Dollars with the regional Consumer
Price Index (2012) and adapted from Cohen and Miller (1994)
and Cohen (1995) .
Due to the stakes of over- and under-identifi cation, preci-sion
is central to any debate of risk appraisal. Given the
improved accuracy from regression modeling, concern
about over- and under-identifi cation is reduced. The valid-ity
of the set of unique risk factors identifi ed using boot-strapped
regression techniques in fi ve samples challenges
objections to risk appraisal. The prediction of violence and
homicide is practical, reliable, valid, and generalizable.
Analyses comparing the fi ve samples with the demo-graphics
of the E.U. and U.S. populations suggest that the
cross-validated and replicated risks for homicide are gen-eralizable
to those broad areas.
There were, of course, limitations and threats to
validity. There may be issues with the experiment of
nearly 5,000 high school students in that there was not
true random sampling; in any such study, there are
validity threats due to history, selection, and expec-tancy
bias. Offi cial records may not accurately represent
the amount of abuse, delinquency, crime, or other risks.
There may be some bias in refusal or enlistment into the
Culture of Calm. The samples were the largest on homi-cidally
violent youth available, yet still small. Perhaps
other risks may be observed in larger samples, although
the literature review of 80 years of risk patterns was
consistent with the risks identifi ed and cross-validated
in the fi ve samples. There was heterogeneity of vari-ance
on some measures or risks, although for most risks
the assumptions of normality and homogeneity of vari-ance
were met. With over 60 years of successful empiri-cal
treatments, some with 27% to 35% diversion rates, it
seems reasonable to accept any threats to validity and
reliability of actuarial testing, since demonstrably lives
and costs were saved.
It is not acceptable to identify homicide-prone behav-ior
proactively and intervene in a manner that limits indi-viduals'
constitutional freedoms. But one can predict and
prevent violence by applying empirical treatments and
interventions to youths through the public schools or
through employee assistance and other health interven-tions
Fig. 4. Low (■), middle (■) and high (■) estimated annual costs
of treatment and electronic monitoring, jail, and hospitaliza-tion
in U.S. 2012 dollars, based on U.S. Department of Agri-culture
(1994 ), Cohen (1995 ), Zagar, Zagar, Bartikowski, and
Busch (2009 ), National Institute of Corrections (2012 ), and the
Consumer Price Index (2012) in the U.S. Bureau of Labor Sta-tistics
(2013 ).
for adults. Confi dence in the legality and practical
utility of this approach should be high now that actu-arial
identifi cation combined with empirical treatments
has been applied with federal funding in a large urban
center ( Saulny, 2009 ; Shelton & Banchero, 2009 ). Provid-ing
decision makers with actuarial data about individu-als
at entry to high school, university, or workplace and
applying age-appropriate empirical treatments can not
only divert individuals from a career of delinquency and
crime, but also lower violence. Information sharing and
the universal use of mathematical modeling, testing, and
targeting treatments does save lives and costs and could
save much more if widely applied.
Conclusions
The approach of targeting limited resources to violence-preventive
interventions is demonstrably the most cost-eff
ective way to reduce murder and assault. Keeping
youths out of courts predictably occurs in 25% to 37%
of targeted and treated individuals, but the rate could
be higher if treatments were combined. Without actuar-ial
testing, there will be over- or under-identifi cation of
those prone to violent crime, resulting in fatalities, vio-lations
of civil rights, and other negative outcomes. The
use of treatments with less than 25% diversion rates and
“threat assessment” without empirical evidence should
be avoided.
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