ArticlePDF Available

The predictive validity of the LSI-R on a sample of offenders drawn from the records of the iowa department of corrections data management system



Content may be subject to copyright.
Volume 71 Number 3
The Predictive Validity of the LSI-R on a Sample of
Offenders Drawn from the Records of the Iowa
Department of Corrections Data Management System
Christopher T. Lowenkamp, Ph.D.
Kristin Bechtel, M.S.
University of Cincinnati
Overview of risk assessment development and predictive validity
OFFENDER ASSESSMENT and classification have become common practice throughout
correctional programming over the past two decades. In particular, one risk/ needs assessment
tool, the Level of Service Inventory-Revised (LSI-R), has gained widespread popularity in
correctional settings. While multiple studies have demonstrated the predictive validity of the LSI-
R in various correctional settings and populations, research continues to stress the importance of
examining the predictive validity of the LSI-R (Andrews, 1982; Andrews & Bonta, 1995; Bonta
& Andrews, 1993). As posited by Gottfredson and Moriarty (2006), examining the predictive
validity of a risk/needs assessment is paramount since the samples used in the development of a
risk tool should be representative of the population that the instrument is intended to be used
upon. Further, they suggest that findings generated from similar samples should be cautiously
interpreted due to the possibility of overestimating the tool’s validity in predicting recidivism.
Given this warning, agencies should consider examining the predictive validity of their targeted
populations in order to determine the instrument’s reliability and validity with their offender
base. The current study attempts to demonstrate the utility of such a practice by examining the
predictive validity of the LSI-R on a sample of probationers and parolees in Iowa.
back to top
Overview of risk assessment development and predictive validity
History of risk assessment
Previously, risk assessment involved a professional, or clinical, judgment concerning an
individual’s risk to recidivate. Typically, this was measured by the intuition or “gut feelings” of
the practitioner from an offender’s self-report or through a file review of official records (Bonta,
1996; Gottfredson, Moriarty, 2006; Latessa, 2003-2004). Unlike actuarial risk assessments, these
early predictions of risk were potentially based on subjective biases, rather than on standardized
objective risk measures, and were ultimately difficult to replicate (Bonta, 1996, 2000). Given
these associated problems, this form of assessment has failed to demonstrate its ability to
effectively measure an individual’s likelihood for future offending. Hence, there is little research
support for the predictive validity and reliability of clinical judgments (Bonta, 2000;
Lowenkamp, Holsinger, & Latessa, 2001).
Predictive measures found in first generation instruments, such as the Burgess Scale, are
primarily static risk factors. While these factors are at least based on objective and easy-to-
replicate measures that do demonstrate some reliability, there are noted disadvantages to such
tools (Latessa, 2003- 2004). First, they do not incorporate dynamic risk factors, which could be
targeted for change. Second, reassessment would be futile, and could only register increases of
risk levels (Latessa, 2003-2004).
Second generation instruments, such as the Salient Factor Score or the Wisconsin Client
Management Classification System, are empirically supported, but the risk measures are not
necessarily grounded in theory and, similar to the Burgess scale, most of the measures are static
(Andrews, Bonta & Wormith, 2006). Third generation instruments, such as the LSI-R, are not
only empirically supported, but also include dynamic risk factors that are theoretically derived
(Andrews et al., 2006). These dynamic risk factors, also known as criminogenic needs, comprise
the areas to target for change in the offender.
Developers of the third-generation risk assessments noted the importance of testing the reliability
and validity of their instruments. In order to do this, samples of offending populations were
needed to examine the predictive validity of these tools. Baseline measures for recidivism were
generated to determine the targeted population’s recidivism rate. With these data, the predictive
validity of the instrument was then evaluated for the specified offending group. As previously
stated, tests of predictive validity should be repeated, even for similar offending groups, as slight
variations from one sample to the next can potentially overestimate the tool’s reliability
(Gottfredson & Moriarty, 2006).
back to top
Predictive validity of the LSI-R
The LSI-R is a standardized actuarial instrument that contains 54 items and produces a summary
risk score that can be categorized into five risk levels. Based on the Multi Health Systems
(MHS) cutoff scores, ranges have been designated that indicate an individual’s risk category.
Specifically, the risk categories are: 1) Low, which ranges from a 0 to 13 overall risk score; 2)
Low/Moderate, which ranges from 14 to 23 overall risk score; 3) Moderate, which ranges from
24 to 33 overall risk score; 4) Moderate/High, which ranges from 34-40 overall risk score; and 5)
High, which ranges from 41 to 54. Higher risk levels reflect an increase in the propensity to
commit future criminal acts. These 54 static and dynamic items are divided into 10 domains. The
10 criminogenic domains include criminal history, education/employment, financial, familial
relationships, accommodations, leisure and recreation, companions, alcohol and drug use,
emotional health and attitudes, and orientations (Andrews & Bonta, 1995). Information to score
the LSI-R is primarily gathered through offender self-report during a structured interview and
available collateral information, such as official court records.
Based on the principles of effective correctional intervention, and specifically the risk principle,
offenders should be separated by their risk level (Andrews, Bonta & Hoge, 1990). Further,
multiple studies and meta-analyses have repeatedly shown that the intensity and dosage of
programming, supervision and services should be related to the offender’s risk level (Andrews et
al., 1990; Andrews & Dowden, 1999, 2006; Dowden & Andrews, 1999a, 1999b, 2000; Lipsey &
Wilson, 1998; Lowenkamp, Latessa, & Holsinger, 2006). Simply put, offenders that demonstrate
a higher risk should receive the majority of services. Likewise, lower-risk offenders should be
diverted from programming that includes a higher-risk population. Several studies have revealed
that the lower-risk group’s recidivism rate is likely to increase under these circumstances
(Andrews, Zinger, Hoge, Bonta, Gendreau & Cullen, 1990; Andrews & Dowden, 1999; Dowden
& Andrews, 1999a, 1999b; Lipsey & Wilson, 1998; Lowenkamp & Latessa, 2005). Specifically,
programming designed for the high-risk offending populations, such as residential treatment,
should not be provided to the low-risk offender. These programs have been found to increase the
associated recidivism rates of their lowerrisk clients (Lowenkamp & Latessa, 2005).
Numerous studies have established support for the validity of the LSI-R in various correctional
settings and populations. Specifically, in a review of validation studies, Andrews and Bonta
(1995) reported that the instrument was a valid predictor of offending outcomes for incarcerated
populations as well as offenders in community and residential settings. In one recent study
examining an incarcerated population, Simourd (2004) evaluated the predictive ability of the
LSI-R on a sample of Canadian offenders to assess whether the instrument was valid for inmates
serving lengthier incarcerations. He found that the LSI-R was an effective tool for predicting
both general (r= .44) and violent recidivism (r= .26). Similarly, in a study examining the
predictive validity of the LSI-R on jail inmates, Holsinger, Lowenkamp and Latessa (2004) found
a positive correlation between the total risk score and recidivism (r= .40). Finally, meta-analytic
reviews have also suggested that the LSI-R is a valid predictor of future recidivism for offending
populations (Gendreau, Little & Goggin, 1996; Gendreau, Goggin & Smith, 2002).
Support for the predictive validity of the LSI-R has also been noted for samples comparing
ethnicities, sex, and age (Andrews & Bonta, 1995). 1 In one empirical test examining the
predictive validity of the LSI-R on the sex of the offender, Lowenkamp et al. (2001) found it
was a valid assessment for males (r= .22) and performed equally as well, and in some instances,
better for females (r= .37).
As indicated, multiple evaluations have demonstrated the predictive ability of the LSI-R.
However, one study sought to address a gap in research concerning practitioner training and
adherence to the guidelines concerning the administration of the LSI-R (Whiteacre, 2004). Flores
and colleagues (2006) explored the predictive validity of the LSI-R, focusing on the
“implementation integrity” and how this may impact the tool’s ability to produce valid results on
their targeted population. Specifically, they found a significant positive correlation (r= .21)
between the total LSI-R score and future recidivism for practitioners trained on the LSI-R. This
correlation increased to r= .25 when the instrument had been in use at that agency for at least
three years. In contrast, the correlation between the LSI-R score and recidivism for untrained
practitioners revealed an insignificant correlation (r=.08).
The current research explores the relevance of examining the predictive validity of the LSI-R on
a sample of probationers and parolees from Iowa. This process is more commonly referred to as
norming the tool on the targeted population in order to demonstrate that the LSI-R has predictive
validity for this sample. In particular, this study will profile the offenders who have been
assessed on the LSI-R to determine if this risk and needs instrument was able to predict
recidivism for these two distinct groups. It should be noted that individuals on parole may have a
higher criminogenic risk to recidivate than those who have been placed on community
supervision. As such, the analyses for these groups will be conducted separately and the reported
findings will be presented with the total sample and then both samples individually.
back to top
This sample of offenders is comprised of probationers and parolees from the State of Iowa.
Initial LSI-R assessments were completed between the dates of May 12, 2003 and November 21,
2003, leaving a total sample size of 1,145 cases. Specifically, the total sample included 902
initial probation assessments and 243 parole assessments.
back to top
Six primary independent variables were used to predict recidivism within the multivariate
analyses for the total sample, as well as both the probation and parole samples separately.
Demographic variables that were included in the current study were sex, race, age, and marital
status. With the exception of age, which remained as a metric level of measurement, sex, race
and marital status were coded dichotomously. Specifically, for the variable labeled sex, 0 = male
and 1 = female. Race was coded as 0 = white and 1= nonwhite, and for the offender’s marital
status, 0 = married and 1 = single. Two additional independent continuous variables included
time at risk and the total risk score of the offender based on the initial assessment of the LSI-R.
Time at risk was measured as the number of days from the start of supervision until the end of
the follow-up period. Finally, the offender’s total LSI-R was measured as a limited metric
ranging from 0 to 54. In order to provide further descriptive information regarding the offenders,
these data included two continuous variables that examined the time to failure, which was
measured in the number of days before a violation, and the days to recidivism, which was
measured as the days until re-arrest for a felony or indictable misdemeanor. While each of these
variables applies to the total sample as well as to the separate probation and supervision samples,
an additional independent variable labeled as supervision status was included in the multivariate
analysis for the total sample. Specifically, this dichotomous measure was coded as 0 = probation
and 1 = parole.
Only one dependent variable, recidivism, was considered in the current study. This measure
examined whether or not an offender was re-arrested based on a felony charge or an indictable
misdemeanor. In particular, this variable was coded as 0 = no felony charge or indictable
misdemeanor and 1 = yes, the offender experienced a felony charge or indictable misdemeanor.
back to top
As described above, each analysis will report the findings for the entire sample as well as
treating the probation and parole groups separately. This is done in an effort to assess the
predictive validity of the LSI-R on both groups, which rather expectedly may have varying risk
factors and scores attributed to receiving different sentencing or supervision status types. There
are three components for this analysis. The first section will describe the offenders based on
demographic data as well as profiling the offenders from their LSI-R scores. Within the first
section of the analysis, the descriptive statistics for each of the independent demographic
variables as well as the dependent variable are presented. The second section of this report will
focus on the validation of the LSI-R tool in predicting future criminal behavior among the
probation and parole samples. Both bivariate and multivariate analyses were conducted to
determine how well the LSI-R performed in predicting future criminal behavior of probationers
and parolees. In addition, a receiver operating characteristic analysis was conducted in order to
address any potential bias that may have impacted the strength of the correlation coefficients
from the bivariate analysis.
back to top
Table 1 describes the demographic characteristics for the total sample, and both supervision
status types. While the majority of offenders were single, white males, with an average moderate
LSI-R risk score of 25, it is interesting to note some of the differences between the probation
and parole groups. 2 At the start of supervision, probationers averaged 30 years of age, while
the parolees were slightly over 33 years of age. The range for probationers’ ages was 16 to 66
years, while the parolees’ ages ranged from 19 to 60 years. In addition, the modal value for the
probationers’ ages was 19, yet the parolees were most likely to be 23 years of age. This may not
be surprising, since these ages may reflect that the more youthful group would initially receive
probationary sentences, while those who have received prison sentences already are much more
likely to be older and perhaps of higher risk due to establishing a prior offense history. 3
Out of the total sample (N= 1,145), 428 (33.2 percent) offenders were found to have recidivated
during the follow-up period. Based on supervision status, nearly 31 percent of the probationers
recidivated, in comparison to almost 43 percent of the parolees. A comparison of these findings
with the days-to-recidivism measure found that the probationers do fail faster than the parolees,
but a higher percentage of parolees recidivated in comparison to the probationers.
back to top
Validation of the LSI-R
Three separate analyses contributed to examining the predictive validity of the LSI-R on this
sample. First, for the purposes of setting up the multivariate models, bivariate correlations were
calculated for both the probation and parole groups examining recidivism by total LSI-R score.
Second, a receiver operating characteristic (ROC) analysis was also conducted, since this statistic
is not biased by a sample’s selection ratio or base rates (Mossman, 1994). The correlation
coefficient, as produced by the bivariate correlation analysis, can be impacted from two sources:
1) the selection ratio, which is the percentage of recidivists as determined by the risk/ needs
assessment and 2) the base rate, which reflects the actual recidivists in the sample (Flores,
Lowenkamp, Smith & Latessa, 2006, p. 47). Finally, the multivariate models were constructed to
identify if the LSI-R overall score was a significant predictor of recidivism while controlling for
other variables.
Table 2 depicts the results of the bivariate correlations, which indicate that the LSI-R is a
significant predictor of recidivism. With the exception of female parolees and non-white
parolees, the total LSI-R score was significantly correlated with recidivism, for both the total
sample (r= .245, p< .01) as well as probation (r= .233, p< .01) and parole (r= .254, p< .01). 4
The ROC analysis produced a curve for this sample of Iowa offenders that represents the ratio of
true positives (those who actually recidivated) to false positives (those who did not recidivate).
For the probation sample, the ROC analysis found a ratio of 276:626 offenders. This resulted in
an area under the curve of .644 (p< .01). Regarding the parolees, the ROC analysis ratio
produced was 104:139. This resulted in an area under the curve of .652 (p< .01). As described
by Rice and Harris (1995), these values under the curve can be treated as percentages, since the
value is based on a ratio. Therefore, for the probationers, there was a 64.4 percent chance that a
randomly selected recidivist earned a higher LSI-R score than a randomly selected nonrecidivist.
Similarly, this value would be 65.2 percent for the parolees.
Both the bivariate and ROC analyses have revealed that the LSI-R is a valid predictor of
recidivism for a sample of probationers and parolees in Iowa. This final analysis will examine
the predictive ability of the LSI-R while considering the effect of sex, race, age, marital status,
supervision status, and time at risk. Table 3 illustrates the results from the multivariate logistical
regression models. Regarding the entire sample, results from Table 3 reveal that sex, race, age,
supervision status type, and total LSI-R score are significant predictors of recidivism. Parolees
were more likely to experience recidivism than probationers. Finally, for the total LSI-R score,
the higher the total risk score, the more likely that a case would result in recidivism. Similar
significant results were noted when examining the model for probationers. However, the findings
were not consistently demonstrated for the parole group model. Both sex and age became
insignificant predictors of recidivism for the parole sample. Yet, race and total LSI-R score
remained statistically significant predictors of recidivism and the coefficients remained in the
expected direction.
back to top
Overall, the sample is comprised of offenders who are of moderate risk based on the MHS
cutoffs. Regarding the validation of the LSI-R for these two groups, all of the analyses, both
bivariate and multivariate, as well as the ROC analysis, suggest that the total LSI-R score is
significantly related to predicting future criminal activity. As such, this risk/needs assessment is a
valid and valuable tool for both supervision status types.
There are several practical purposes for programs and facilities to norm and validate the
instrument on their specific population. First, agencies can use the instrument to designate
supervision levels for their clients. Second, upon an initial assessment, case managers can create
individualized case plans for the offender that targets their specific criminogenic needs or risk
factors. Third, and specific to the current study, agencies can develop appropriate cutoff scores
with which to manage their offending population and provide appropriate and beneficial service
delivery. Fourth, offenders can be reassessed during their supervision as well as at the conclusion
to determine if the assigned treatment and services reduced a client’s risk factors associated with
Ultimately, programs are more likely to demonstrate their efficacy when utilizing a standardized
and objective risk measure, such as the LSI-R, which identifies an individual’s risk level as well
as his or her criminogenic needs. Actuarial assessment practices are clearly relevant and needed
within the current correctional climate. With proper implementation and interpretation,
correctional programs will have the ability to appropriately allocate funding and resources that
may increase appropriate placement and treatment effectiveness and potentially enhance public
safety (Flores et al., 2006; Latessa & Lowenkamp, 2005).
back to top
The articles and reviews that appear in Federal Probation express the points of view of the persons who wrote them and
not necessarily the points of view of the agencies and organizations with which these persons are affiliated. Moreover,
Federal Probation's publication of the articles and reviews is not to be taken as an endorsement of the material by the
editors, the Administrative Office of the U.S. Courts, or the Federal Probation and Pretrial Services System. Published by
the Administrative Office of the United States Courts
Publishing Information
Table 1: Descriptives for the Sample (N= 1,145)
Continuous Variables Total Sample Mean Probation Mean Parole Mean
Age 30.8 30.1 33.4
Time at Risk 761.29 760.67 763.60
Time to Failure 619.55 624.27 602.02
Days to Recidivism 334.55 315.60 384.85
Total LSI-R Score 24.96 24.42 26.97
Categorical Variables N % N % N %
Male 892 77.9 675 74.8 217 89.3
Female 253 22.1 227 25.2 26 10.7
White 970 84.7 780 86.5 190 78.2
Nonwhite 175 15.3 122 13.5 53 21.8
Married 252 22.0 186 20.6 66 27.5
Single 814 71.1 640 71.0 174 72.5
No 765 66.8 276 30.6 104 42.8
Yes 380 33.2 626 69.4 139 57.2
*N’s may be slightly smaller than total N due to missing data.
Table 2: Bivariate correlations predicting
recidivism by total LSI-R score (N= 1,145)
95% Confidence Intervals
Group N r Lower Upper
Total Sample 1145 .245* .190 .298
Males 892 .247* .185 .307
Females 253 .205* .084 .320
Whites 970 .219* .159 .278
Non-whites 175 .242* .098 .376
Total Probation 902 .233* .171 .293
Males 675 .231* .159 .301
Females 227 .219* .092 .339
Whites 780 .199* .131 .265
Non-whites 122 .292* .121 .446
Total parole 243 .254* .133 .368
Males 217 .271* .143 .390
Females 26 .05w9 -.336 .436
Whites 190 .275* .139 .401
Non-whites 53 .100 -.175 .360
* p< .01=
Table 3: Logistic regression predicting recidivism for
the Sample (N=1145)
Variables Total Samplea Probationers Parolees
Group B SE B SE B SE
Sex -.408 ** .180 -.381 ** .194 -.575 .475
Race .789 * .181 .757 * .216 .852 ** .334
Marital -.210 .175 -.226 .212 -.192 .315
Age -.034 * .008 -.037 * .009 -.024 .016
Supervision Status .458 * .164 N/A N/A N/A N/A
Time at Risk .001 .003 .003 .003 -.001 .005
Total Score .059 * .008 .058 * .009 .066 * .019
Constant -2.369 1.958 -3.038 2.282 -.297 3.865
a Chi-Square: 121.022, p < .001, -2 Log Likelihood: 1231.358, Cox and Snell: .107, Nagelkerke: .149
b Chi-Square: 82.577, p < .001, -2 Log Likelihood: 928.596, Cox and Snell: .095, Nagelkerke: .135
c Chi-Square: 26.450, p < .001, -2 Log Likelihood: 301.428, Cox and Snell: .104, Nagelkerke: .140
* p < .001
** p < .05
Butterfield, F. (October 22, 2003). “Study finds hundreds of thousands of inmates mentally ill.”
The New York Times, Late Edition, Section A, Page 14, Column 1.
Downs v. United States, 522 F. 2nd 990 (1975). Human Rights Watch. (2003). “Ill equipped:
U.S. prisons and offenders with mental illness.”
McMains, M.J. & Mullins, W.C. (2006). Crisis negotiations: Managing critical incidents and
hostage situations in law enforcement and corrections. Anderson Publishing, a member of the
Lexis Nexis Group.
Meichenbaum, D. (1976). “A stress instructional approach to stress management: A proposal for
stress inoculation training.” In I. Sarason & C.D. Spielberger (eds), Stress and Anxiety in Modern
Life. New York: Wiley.
Nicholls, T.L.; Roesch, R.; Olley, M.C.; Ogloff, J.R.P. & Hemphill, J.F. (200x). Jail screening
assessment tool: Guidelines for mental health screening in jails. Mental Health, Law & Policy
Institute: Simon Fraser University.
Parascandola, R. (Wednesday, November 24, 2004).“Actors play a role in police work.” New
York Newsday.
Sharfstein, S.S. (September 21, 2000). The impact of the mentally ill offender on the criminal
justice system. Testimony presented at the U.S. House Subcommittee on Crime. Retrieved 2/2/04
from policy/leg_res/ apa_testimony/testimony
Stonybrook News (December 12, 2006). “New clinical skills center at SBUMC brings frontier of
modern medical education to students, practitioners.”
The Commission on Safety and Abuse in American Prisons. (2006). “Confronting confinement.”
The Correctional Association of New York. (June 2004). “Mental health in the house of
corrections—a study of mental health care in New York State prisons.”
The Sentencing Project (January 2002). Mentally ill offenders in the criminal justice system: An
analysis and prescription.
The Washington Post (2005). “Frontline: The new asylums.”
Vecchi, G.M. (2002). “Hostage/Barricade management: A hidden conflict within law
enforcement.” FBI Law Enforcement Bulletin.
Weir, K. (March 13, 2002). “Patient actors teach future doctors humanity to go with the
Science.” NY: Columbia News Service. cns/2002-03-
The Predictive Validity of the LSI-R on a Sample of Offenders Drawn
from the Records of the Iowa Department of Corrections Data
Management System
Andrews, D.A. (1982). The Level of Supervision Inventory (LSI): The first follow-up. Toronto,
Ontario, Canada: Ontario Ministry of Correctional Services.
Andrews, D.A. & Bonta, J. (1995). LSI-R: The Level of Service Inventory- Revised. Toronto,
Ontario, Canada: Multi-Health Systems, Inc.
Andrews, D.A. Bonta, J. & Hoge, R.D. (1990). Classification for effective rehabilitation:
Rediscovering psychology. Criminal Justice and Behavior, 17, 19-52.
Andrews, D.A. & Dowden, C. (1999). A meta-analytic investigation into effective correctional
intervention for female offenders. Forum on Corrections Research, 11, 18-21.
Andrews, D.A. & Dowden, C. (2006). Risk principle of case classification in correctional
treatment. International Journal of Offender Therapy and Comparative Criminology, 50, 88-100.
Andrews, D.A. Zinger, I. Hoge, R.D., Bonta, J., Gendreau, P., & Cullen, F.T. (1990). Does
correctional treatment work? A clinically relevant and psychologically informed meta-analysis.
Criminology, 8, 369-404.
Bonta, J. (1996). Risk-needs assessment and treatment. In A.T. Harland (Ed.), Choosing
correctional options that work: Defining the demand and evaluating the supply (pp. 18-32).
Thousand Oaks, CA: Sage.
Bonta, J. (2000, August). Offender assessment: General issues and considerations. Forum on
Corrections Research, 14-18.
Bonta, J. & Andrews, D.A. (1993). The Level of Supervision Inventory: An overview. IARCA
Journal, 5(4), 6-8.
Dowden, C. & Andrews, D.A. (1999a). What works for female offenders: A meta-analytic
review. Crime and Delinquency, 45, 438-452.
Dowden, C. & Andrews, D.A. (1999b). What works in young offender treatment: A
Forum on Corrections Research, 11, 21-24.
Flores, A.W., Lowenkamp, C.T., Holsinger, A.M., Latessa, E.J. (2006). Predicting outcome with
the Level of Service Inventory-Revised: The importance of implementation integrity. Journal of
Criminal Justice, 34, 523-529.
Gottfredson, S.D. & Moriarty, L.J. (2006). Statistical risk assessment: Old problems and new
applications, Crime & Delinquency, 52, 178-200.
Holsinger, A.M., Lowenkamp, C.T. & Latessa, E.J. (2006). Exploring the validity of the Level of
Service Inventory-Revised with Native American offenders. Journal of Criminal Justice, 34, 331-
Latessa, E.J. (2003-2004). Best practices of classification and assessment. Journal of Community
Corrections, Winter, 4-6, 27-30.
Lipsey, M.W. & Wilson, D.B. (1998). Effective intervention for serious juvenile offenders: A
synthesis of research. In R. Loeber & D. Farrington (Eds.), Serious and Violent Juvenile
Offenders: Risk Factors and Successful Interventions. Thousand Oaks, CA: Sage.
Lowenkamp, C.T. & Latessa, E.J. (2005). Increasing the effectiveness of correctional
programming through the risk principle: Identifying offenders for residential placement.
Criminology & Public Policy, 4, 263-290.
Lowenkamp, C.T. & Latessa, E.J. (2005, 4th Quarter). The role of offender risk assessment tools
and how to select them. For the Record, Ohio Judicial Conference, 18-20.
Lowenkamp, C.T., Holsinger, A.M. & Latessa, E.J. (2001). Risk/need assessment, offender
classification, and the role of childhood abuse. Criminal Justice and Behavior, 28, 543-563.
Lowenkamp, C.T., Latessa, E.J., & Holsinger, A. (2006). The risk principle in action: What have
we learned from 13,676 offenders and 97 correctional programs? Crime and Delinquency, 52,
Mossman, D. (1994). Assessing predictions of violence: Being accurate about accuracy. Journal
of Consulting and Clinical Psychology, 62, 783-792.
Rice, M.E. & Harris, G.T. (1995). Violent recidivism: Assessing predictive accuracy. Journal of
Consulting and Clinical Psychology, 63, 737-748.
Schlager, M.D. & Simourd, D.J. (2007). Validity of the Level of Service Inventory-Revised (LSI-
R) among African American and Hispanic male offenders. Criminal Justice and Behavior, 34,
Whiteacre, K. (2004). Case manager experiences with the LSI-R at a federal community
corrections center. Corrections Compendium, 29, 1-5, 32-35.
Probation and Parole Officers Speak Out—Caseload and Workload
Andrews, D., Zinger, I., Hoge, R., Bonta, J., Gendreau, P., & Cullen, F. (1990). Does
correctional treatment work? A clinically relevant and psychologically informed meta-
analysis.Criminology, 28 (3): 369-404.
Beckett, K. (1997). Making crime pay. New York: Oxford University Press.
Bonta, J., Wallace-Capretta, S., and Rooney, J. (2000). A quasi-experimental evaluation of
intensive rehabilitation supervision probation. Criminal Justice and Behavior, 27(3): 312-329.
Bouley, E., & Wells, T. (2001). Attitudes of citizens in a southern rural county toward juvenile
crime and justice issues. Journal of Contemporary Criminal Justice, 17(1), 60-70.
Bureau of Justice Statistics. (2004a). Probation and parole in the United States, 2003.
Washington, DC: Bureau of Justice Statistics, U.S. Department of Justice.
Bureau of Justice Statistics. (2004b). Justice expenditures and employment in the United States,
2001. Washington, DC: Bureau of Justice Statistics, U.S. Department of Justice.
Cullen, F., Fisher, B., & Applegate, B. (2000). Public opinion about punishment and corrections.
In M. Tonry (Ed.), Crime and justice: A review of research, volume 27 (pp. 1-79). Chicago, IL:
University of Chicago Press.
Dedel-Johnson, K., Austin, J., and Davies, G. (2002). Banking low-risk offenders: Is it a good
investment? The Institute on Crime, Justice, and Corrections at the George Washington
DeMichele, M., Paparozzi, M., & Payne, B. (unpublished manuscript). Probation and Parole’s
burgeoning caseloads and workload management: Practitioner perspectives and policy
implications. Submitted to the Journal of Criminal Justice (March, 2007).
Gist, N. (2000). Creating a new criminal justice system for the 21st century: Findings and results
from state and local program evaluations. Washington, DC: Bureau of Justice Assistance.
Gottfredson, M. and Hirschi, T. (1990). A general theory of crime. Stanford, CA: Stanford
University Press.
Lane, J., Turner, S., and Flores, C. (2004). Researcher-practitioner collaboration in community
corrections: Overcoming hurdles for successful partnerships. Criminal Justice Review, 29(1): 97-
Langan, P. (1994). “Between prison and probation: Intermediate sanctions,” Science, 264: 791-
Lucken, K. (1997). The dynamics of penal reform. Crime, Law, & Social Change, 26: 367-384.
uery=SB932&currpage=1&showstatus=on&press1 =docs (last visited April 27, 2007).
57 Howard Zehr, 2002, 12, supra.
The Predictive Validity of the LSI-R on a Sample of Offenders Drawn from the Records of
the Iowa Department of Corrections Data Management System
1 The predictive validity of the LSI-R by race and ethnicity has been mixed and is still requiring
additional research. However, studies have reported modest predictive validity by ethnicity
(Holsinger, Lowenkamp & Latessa, 2006) and low predictive validity by race (Schlager &
Simourd, 2007).
2 A t-test, comparing the difference in means, or average LSI-R total scores, found that there was
a significant difference in the actual total scores between probation and parole. However, based
on the MHS cutoffs, both supervision status types would still be categorized as a moderate risk
3 The average score on the Criminal History domain for the Parole Group was 6.70 and the
average score on the Criminal History domain for the Probation Group was 3.94. A t-test
indicated that there was a significant difference between these two risk scores (p<.001).
4 The smaller sample size of female parolees (N=26) and non-white parolees (N=53), may
account for the lack of significance with these correlations.
Probation and Parole Officers Speak Out—Caseload and Workload Allocation
1 This lack of certainty of punishment is contrary to traditional conceptions of deterrence
theories, which are predicated on the notion of offenders perceiving that criminal behaviors and
technical violations will be met with punishment. Many jurisdictions are finding it difficult to
respond adequately to noncompliant probationer behaviors due to overcrowding and funding
issues, with some courts actually informally requesting that only the most serious probation
violators be brought back to court.
2 See Warchol (2000) and Bonta, Wallace- Capretta, and Rooney (2000) for a more complete
historical development of ISPs. On the effects of caseload size, see Worrall, Schram, Hays, and
Newman (2004)
Thacher, Augustus, and Hill—The Path to Statutory Probation in the United States and
1 The author is grateful to Professors Andrew Karmen and John Kleinig of John Jay College of
Criminal Justice for their helpful comments
Looking at the Law—Probation Officers’ Authority to Require Drug Testing
1 In 1984, Congress replaced the Federal Probation Act with provisions in the SRA that repealed
the chapter in Title 18 that contained the Federal Probation Act (except for ‘3656, which was
renumbered ‘3672), effective November 1, 1987. While new ’3603 applied only to offenses
committed after November 1, 1987, the following language in ’3655, construed by courts to
authorize officers to require drug tests, was carried over to new ’3603: ’3655. Duties of probation
officers. The probation officer shall furnish to each probationer under his supervision a written
statement of the conditions of probation and shall instruct him regarding the same. He shall keep
informed concerning the conduct and condition of each probationer under his supervision and
shall report thereon to the court placing such person on probation. He shall use all suitable
methods, not inconsistent with the conditions imposed by the court, to aid probationers and to
bring about improvements in their conduct and condition. 18 U.S.C. ’3655 (1984) (repealed).
2 See United States v. Stephens, 424 F. 3d 876, 885 (9th Cir. 2005) (Clifton, J., concurring in
... Analyses of other pretrial tools including the PSA (LJAF, 2013) and Virginia Pretrial Risk Assessment (VPRAI) (Danner, VanNostrand, Spruance, 2016) found that these instruments predict recidivism outcomes equally well for white and minority defendants. In addition to these studies, there is a growing but still relatively limited body of research investigating the issue of racial bias and predictive parity for a variety of risk instruments at the post-conviction stage including the Post-Conviction Risk Assessment, COMPAS, and Level of Service Inventory -Revised (Brennan, Dieterich, & Ehret, 2009;Lowenkamp, Holsinger, & Cohen, 2015;Lowenkamp & Bechtel, 2007;. These studies showed white and minority offenders being successfully differentiated by the above-mentioned risk tools according to their likelihood of reoffending with the level of predictive accuracy being essentially the same for whites and minorities. ...
... It is also worth noting that in the fully specified models race was not a significant predictor in terms of predicting any re-arrests though it was a significant predictor, with blacks more likely to be re-arrested than non-Hispanic whites, in the prediction of violent re-arrests. These findings are mostly consistent with research on the post-conviction risk assessment in use in the US Probation system (see and research being generated with other risk assessments (see Brennan et al., 2009;Dieterich et al., 2016;Flores et al., 2016;Lowenkamp & Bechtel, 2007). ...
Full-text available
The Pretrial Risk Assessment (PTRA) instrument was developed for use in the U.S. federal pretrial system. Specifically, this instrument was constructed to help federal officers assess the likelihood that defendants will commit pretrial violations including being re-arrested for any or violent crimes, failing to make court appearances, or having a revocation while on pretrial release. While previous studies have demonstrated the PTRA's predictive validity, these efforts primarily used development and validation samples and did not investigate the PTRA for predictive bias by defendant demographic characteristics. The current research evaluates the PTRA's capacity to predict various forms of pretrial violations on 85,339 defendants with officer completed PTRA assessments. Bivariate and multivariate models were estimated by race, ethnicity, and sex. Results show that the PTRA performs well at predicting pretrial violations as the area under the receiver operating characteristic curve ranged from 0.65 to 0.73 depending upon the subsamples and outcomes being predicted. Moreover, the PTRA manifested strong predictive capacities irrespective of defendant race, ethnicity, or sex.
... Anchoring the daunting discretionary responsibilities of predicting recidivism and planning supervision are general rehabilitation practices and risk assessments. Many developments in the application of theoretical constructs have arisen in the way of risk/needs assessments (e.g., Brennan, Dieterich, & Ehret, 2009;Lowenkamp & Bechtel, 2007). Such developments of actuarial tools have been shown to be quite helpful in emphasizing the targeting of offender needs and matching those needs with available treatment for responsivity (Cohen & Whetzel, 2014;Hamilton, 2011;Taxman, Byrne, & Young, 2002). ...
... Secondly, with exception to a few (Grommon et al., 2013;Grattet & Lin, 2016), the research that does exist almost exclusively focuses on the use of swift-and-certain sanctions on probationers rather than parolees. This is a problem because parolees typically possess a higher risk (including a lengthier criminal history), have convictions for more serious offenses, and have different (often more severe) criminogenic needs (Lowenkamp & Bechtel, 2007). Third, little investigative attention has been given to the ability of swift-and-certain sanctioning to actually address criminogenic needs-a possible reason for the mixed findings. ...
While many great strides have been made in supervision generally toward more evidence-based practices, the primary tenets of conditional release have remained unchanged, untested, and assumption based. This essay examines the fundamental tenets of conditional release and how they have been widely overlooked in spite of the evidence-based movement. By laying out the problems in practice, recording, and definition, as well as gaps in the literature, I display several areas where future research can progress both knowledge and policy. I argue that the crux of issues surrounding conditional release is the notion that it is a test of readiness and should be regarded as such. By viewing the practice from this perspective, the inadequacies of state systems to address criminogenic needs become glaringly apparent. Following this explication, it is consequently clear as to why the released person may not be ready and how successful reentry may have less to do with individual accountability and more to do with a rehabilitative ideal.
... Only three studies permitted comparisons of predictive accuracy by offender race-and indicated that levels of predictive utility were identical (area under the ROC curve or AUCs = .69 on the "COMPAS"; Brennan, Dieterich, and Ehret, 2009) or highly similar (odds ratio or OR = 1.03 [Black] and 1.04 [White] on the Levels of Services Inventory-Revised or LSI-R; Kim, 2010;Lowenkamp and Bechtel, 2007) across groups. Formal tests of predictive bias were not reported, nor were mean score differences. ...
... The degree and form of association between PCRA total scores and arrest were similar for Black and White offenders. These findings are consistent with past studies indicating that the degree of association between other "risk-needs" tools and recidivism are similar for Black and White offenders (Brennan, Dieterich, and Ehret, 2009;Kim, 2010;Lowenkamp and Bechtel, 2007). But we went beyond past research to test whether the form of the relationship between risk and recidivism is similar across races. ...
One way to unwind mass incarceration without compromising public safety is to use risk assessment instruments in sentencing and corrections. Although these instruments figure prominently in current reforms, critics argue that benefits in crime control will be offset by an adverse effect on racial minorities. Based on a sample of 34,794 federal offenders, we examine the relationships among race, risk assessment (the Post Conviction Risk Assessment [PCRA]), and future arrest. First, application of well-established principles of psychological science revealed little evidence of test bias for the PCRA — the instrument strongly predicts arrest for both Black and White offenders and a given score has essentially the same meaning — i.e., same probability of recidivism — across groups. Second, Black offenders obtain higher average PCRA scores than White offenders (d= 0.34; 13.5% non-overlap in groups’ scores), so some applications could create disparate impact. Third, most (66%) of the racial difference in PCRA scores is attributable to criminal history — which is already embedded in sentencing guidelines. Finally, criminal history is not a proxy for race, but instead mediates the relationship between race and future arrest . Data are more helpful than rhetoric, if the goal is to improve practice at this opportune moment in history.
... where more negative values are indicative of larger reductions in dynamic risk during placement. This composite measure provides an estimate of treatment progress, establishing whether the services provided are affecting the levels of dynamic risk, in keeping with suggested best practice standards ( Lowenkamp & Bechtel, 2007; see also Schlager & Pacheco, 2011 ). ...
Recently there has been growing concern regarding the staffing challenges that plague the U.S. correctional system. This study examines whether staffing challenges within residential facilities are associated with changes in dynamic risk and the likelihood of reoffending among a sample of serious juvenile offenders returning to the community from residential placement. Using administrative data on 2,022 youth who completed a court-imposed placement, in combination with information drawn from a provider’s human resources database, we employ several analytical techniques to untangle the effects of staffing difficulties on youth outcomes. Results indicate that the rate of unscheduled absences was associated with changes in dynamic risk and the duration of placement. Absences were also related to recidivism prior to accounting for changes in dynamic risk and length of stay, suggesting a more complex interrelationship between facility staffing challenges and youth outcomes. Implications for policy and future research are discussed.
... Although static risk factors were shown to predict long-term recidivism (e.g., Harris & Rice, 2003), the assessment of change in offender risk level, however, requires the consideration of dynamic (changeable) risk factors. Since they are sensitive to changes and responsive to interventions, focusing the treatment on criminogenic needs (dynamic factors) is considered a fundamental component of the RNR-model (Lowenkamp & Bechtel, 2007;Simourd, 2004). Meta-evaluations of the efficacy of correctional treatment suggested that taking criminogenic needs in a community setting into consideration helps reducing recidivism up to 10% (Andrews & Bonta, 2010;Lipsey, 1995;Lösel, 1995). ...
Many studies reveal that unchangeable static variables, such as prior offending history and membership in high-risk demographic subgroups, consistently predict recidivism. Recent risk assessment research focuses on dynamic attributes — attitudes, values, and interpersonal skills that are modified by new experiences and, thus, may change during the residential stay. The current work is dedicated to examining one specific dynamic factor – the hostile attributions bias (HAB) – as well as its dynamic change over time. We supposed that the complex nature of cognitive biases can be defined in various ways, e.g. as immanent personality disposition, as social-cognitive interpretation bias, and even as perceptual bias. Therefore, the present dissertational study integrated mixed quantitative and qualitative research, acknowledging that combined approaches are best suitable for assessing complex phenomena in social science research since they can provide real-life contextual understandings and multi-level perspectives on diverse research questions. We used a multi-method approach to assess HAB as well as multiple statistical approaches to determine which method is most sensitive to changes in the treatment of distorted cognitions. We evaluated the sensitivity to change of three tools (structured questionnaires, semiprojective tool and computer-based perception task) using three statistical methods for identifying aggregate (group ES, Cohen’s d and SRM) and individual changes over time (RCI, individual ES and SEM). The semiprojective method was shown to be sensitive to identifying the largest proportion of change at both aggregate and individual level. At the individual level all three assessment methods showed sensitivity to change. The use of multi-method research is highly relevant for determining intervention changes in corrective settings. Implications for clinical practice, recommendations for future research, and study limitations are discussed. Multi-method assessment of the hostile attribution bias in juvenile violent offenders: determining the sensitivity to change of three different assessment methods
... One important probationer feature was the predicted risk for recidivism that the agency used to classify and structure supervision. Prior research has demonstrated that risk assessment instruments are valid predictors of rearrest (Barnoski & Aos, 2003;Gendreau et al., 1996;Girard & Wormith, 2004;Lowenkamp, 2004;Lowenkamp & Bechtel, 2007;Lowenkamp & Latessa, 2002). The agency in the current study used a risk assessment instrument to classify probationers into low-, moderate-, or high-risk supervision units. ...
Probation supervision is marked by the dual roles of surveillance and casework. A key feature of supervision that aligns with the goals of community safety through surveillance is the use of officer–probationer contacts. The current study explores the relationship between missed probation contacts and rearrest while on supervision in a surveillance-driven context. Logistic regression analyses modeled the effects of missed contacts on rearrests using probation data from a large supervision agency (n = 3,809). Analyses included the overall percentage of missed contacts and missed contacts above/below the median and mean percentage of missed contacts to subsequent rearrests while on supervision. Overall, the percentage of missed contacts increased the likelihood of rearrest while on probation. Furthermore, the percentage of missed probation contacts that significantly predicted rearrest was lower than expected (4.17%). The results suggest that missing contacts while on probation has a negative impact on probation success. Implications of these findings are discussed.
... It is also worth noting that in the fully specified models race was not a significant predictor in terms of predicting any rearrests, although it was a significant predictor, with Blacks more likely to be rearrested than non-Hispanic whites, in the prediction of violent rearrests. These findings are mostly consistent with research on the postconviction risk assessment in use in the U.S. probation system (see and research being generated with other risk assessments (see Brennan, Dieterich, & Ehret, 2009;Dieterich et al., 2016;Flores et al., 2016;Lowenkamp & Bechtel, 2007). ...
The pretrial risk assessment instrument (PTRA) was developed for use in the U.S. federal pretrial system. Specifically, this instrument was constructed to help officers assess the likelihood that defendants will commit pretrial violations including being rearrested for new crimes, missing court appearances, or being revoked while on pretrial release. This research evaluates the PTRA’s capacity to predict pretrial violations on 85,369 defendants with officer-completed PTRA assessments. Bivariate and multivariate models were estimated by race, ethnicity, and sex. Results show that the PTRA performs well at predicting pretrial violations as the area under the receiver operating characteristic curve ranged from 0.65 to 0.73 depending upon the subsamples and outcomes being predicted. Moreover, the PTRA predicted new criminal arrest activity equally well for non-Hispanic Whites and Blacks, while for Hispanics and females, findings show the instrument validly predicting rearrest activity, with some evidence of overprediction depending upon the outcome being examined. © 2018 International Association for Correctional and Forensic Psychology.
... Yet, even without such differences, the items found to predict recidivism for the development sample may no longer be predictive when applied to another population. It is imperative, therefore, to ensure tools are effective in populations outside of those in which they were developed (Lowenkamp & Bechtel, 2007). As Austin (2006, p. 59) argued, "Generally, if a risk assessment instrument has not been tested on multiple populations under varying conditions, it will not work well on populations it has not been tested on." ...
When sex offenders in Minnesota are assigned risk levels prior to their release from prison, correctional staff frequently exercise professional judgment by overriding the presumptive risk level per an offender’s score on the Minnesota Sex Offender Screening Tool−3 (MnSOST-3), a sexual recidivism risk-assessment instrument. These overrides enabled us to evaluate whether the use of professional judgment resulted in better predictive performance than did reliance on “actuarial” judgment (MnSOST-3). Using multiple metrics, we also compared the performance of a home-grown instrument (the MnSOST-3) with a global assessment (the revised version of the Static-99 [Static-99R]) in predicting sexual recidivism for 650 sex offenders released from Minnesota prisons in 2012. The results showed that use of professional judgment led to a significant degradation in predictive performance. Likewise, the MnSOST-3 outperformed the Static-99R for both sexual recidivism measures (rearrest and reconviction) across most of the performance metrics we used. These results imply that actuarial tools and home-grown tools are preferred relative to those that include professional judgment and those developed on different populations.
... OR ϭ 1.04, Cramer's V ϭ 0.13; Black: OR ϭ 1.03, Cramer's V ϭ 0.09; Hispanic: OR ϭ 1.03, Cramer's V ϭ 0.10; non-White: r pb ϭ .24; Lowenkamp & Bechtel, 2007;Kim, 2010). ...
With the population of adults under correctional supervision in the United States at an all-time high, psychologists and other professionals working in U.S. correctional agencies face mounting pressures to identify offenders at greater risk of recidivism and to guide treatment and supervision recommendations. Risk assessment instruments are increasingly being used to assist with these tasks; however, relatively little is known regarding the performance of these tools in U.S. correctional settings. In this review, we synthesize the findings of studies examining the predictive validity of assessments completed using instruments designed to predict general recidivism risk, including committing a new crime and violating conditions of probation or parole, among adult offenders in the United States. We searched for studies conducted in the United States and published between January 1970 and December 2012 in peer-reviewed journals, government reports, Master's theses, and doctoral dissertations using PsycINFO, the U.S. National Criminal Justice Reference Service Abstracts, and Google. We identified 53 studies (72 samples) conducted in U.S. correctional settings examining the predictive validity of 19 risk assessment instruments. The instruments varied widely in the number, type, and content of their items. For most instruments, predictive validity had been examined in one or two studies conducted in the United States that were published during the reference period. Only two studies reported on inter-rater reliability. No instrument emerged as producing the "most" reliable and valid risk assessments. Findings suggest the need for continued evaluation of the performance of instruments used to predict recidivism risk in U.S. correctional agencies.
Most people released from incarceration in the criminal justice system return to prison within 3 years. To improve community reentry, national initiatives have promoted new and revitalized programming, including peer mentorship, though this approach remains largely unstudied. Fifty-five men participated within a pilot randomized controlled trial investigating the effect of peer mentorship upon recidivism. Hierarchical binary logistic regression including recidivism risk, as well as group assignment to either a standard services for community reentry condition or standard services plus peer mentorship condition, showed that those receiving mentorship had significantly lower recidivism. It appears that peer mentorship with a model focus upon early intervention, relationship quality, criminal desistance, social navigation, and gainful citizenship may promote the complex task of early community reentry. Given this pilot’s small sample, future research should confirm this association on a larger scale, enabling longitudinal and treatment component analyses examining the relative contributions of mentorship model factors.
Full-text available
Previous research has established that the LSI-R is predictive of correctional program performance and post-release recidivism. Other studies have established the instruments validity and reliability. Very little research, however, has focused on how the LSI-R is used by correctional staff, which is important to understanding how effective the instrument is in practice. In the current study, open-ended, structured interviews were conducted with case managers at a Midwestern Federal corrections center to determine how staff used the LSI-R to classify residents. Results indicated a degree of discontent among staff with the LSI-R; administrators thought staff needed more training with the LSI-R, while many staff members considered the instrument a waste of time and not helpful in determining programming. When used during offender interviews, many staff regarded the LSI-R only as a guide for discussion, although some staff did go through the instrument item by item. The LSI-R was considered most useful for assessing offender risk and needs and for planning supervision. The most frequent problems identified with the LSI-R by staff were concerns about its accuracy in predicting future behavior, concerns about bias, concerns about the replication of information, and concerns about unclear items. Overall, despite the general negativity among case staff regarding the LSI-R, most still reported using it in some way. However, the LSI-R was largely not used in the decisionmaking process concerning offender programming. Recommendations emerging from the findings include managerial clarification of the role of the LSI-R within the offender classification system and improved staff training with the instrument. The findings also underscore the need for more research projects focused on how assessment instruments are utilized in the field.
Full-text available
Careful reading of the literature on the psychology of criminal conduct and of prior reviews of studies of treatment effects suggests that neither criminal sanctioning without provision of rehabilitative service nor servicing without reference to clinical principles of rehabilitation will succeed in reducing recidivism. What works, in our view, is the delivery of appropriate correctional service, and appropriate service reflects three psychological principles: (1) delivery of service to higher risk cases, (2) targeting of criminogenic needs, and (3) use of styles and modes of treatment (e.g., cognitive and behavioral) that are matched with client need and learning styles. These principles were applied to studies of juvenile and adult correctional treatment, which yielded 154 phi coefficients that summarized the magnitude and direction of the impact of treatment on recidivism. The effect of appropriate correctional service (mean phi = .30) was significantly (p
[Correction Notice: An erratum for this article was reported in Vol 73(4) of Journal of Consulting and Clinical Psychology (see record 2007-16787-001). In this article, several errors are present on pp. 738 and 746. The corrections are listed in the erratum.] Until very recently, there has been little evidence of the ability of either clinicians or actuarial instruments to predict violent behavior. Moreover, a confusing variety of measures have been proposed for the evaluation of the accuracy of predictions. This report demonstrates that receiver operating characteristics (ROCs) have advantages over other measures inasmuch as they are simultaneously independent of the base rate for violence in the populations studied and of the particular cutoff score chosen to classify cases as likely to be violent. In an illustration of the value of this approach, the base rates of violence were altered with the use of data from 3.5-, 6-, and 10-year follow-ups of 799 previously violent men. Base rates for the 10-year follow-up were also altered by changing the definition of violent recidivism and by examining a high-risk subgroup. The report also shows how ROC methods can be used to compare the performance of different instruments for the prediction of violence. The report illustrates how ROCs facilitate decisions about whether, at a particular base rate, the use of a prediction instrument is warranted. Finally, some of the limitations of ROCs are outlined, and some cautionary remarks are made with regard to their use.
"Get tough" control policies in the United States are often portrayed as the reflection of the public's will: Americans are punitive and want offenders locked up. Research from the past decade both reinforces and challenges this assessment. The public clearly accepts, if not prefers, a range of punitive policies (e. g., capital punishment, three-strikes-and-you're-out laws, imprisonment). But support for get-tough policies is "mushy." Thus citizens may be willing to substitute a sentence of life imprisonment without parole for the death penalty. Especially when nonviolent offenders are involved, there is substantial support for intermediate sanctions and for restorative justice. Despite three decades of criticism, rehabilitation-particularly for the young-remains an integral part of Americans' correctional philosophy. There is also widespread support for early intervention programs. In the end, the public shows a tendency to be punitive and progressive, wishing the correctional system to achieve the diverse missions of doing justice, protecting public safety, and reforming the wayward.
Several meta-analytic reviews strongly support the Sclinically relevant and psychologically informed principles of human service, risk, need and general responsivity. More recently, meta-analyses have demonstrated that these principles are applicable to female offenders 2 and are effective in reducing both general 3 and violent 4 recidivism. The current investigation provides an in-depth examination of the principles of human service, risk, need and general responsivity for young offenders (younger than 18 years). Further analyses are conducted on the "more promising" and "less promising" treatment targets outlined by Andrews and Bonta. 5 The results demonstrate that the mean effect size under conditions of adherence to each of the principles is significantly higher than for conditions of non-adherence. These results have important implications for both correctional administrators and front-line staff involved in delivering correctional treatment programs to young offenders.
Statistically based risk assessment devices are widely used in criminal justice settings. Their promise remains largely unfulfilled, however, because assumptions and premises requisite to their development and application are routinely ignored and/or violated. This article provides a brief review of the most salient of these assumptions and premises, addressing the base rate and selection ratios, methods of combining predictor variables and the nature of criterion variables chosen, cross-validation, replicability, and generalizability. The article also discusses decision makers’ choices to add or delete items from the instruments and suggests recommendations for policy makers to consider when adopting risk assessments. Suggestions for improved practice, practical and methodological, are made.
Funding agencies now generally require that projects include an evaluation component as part of their grant proposals, but there are no clear guidelines to help researchers and practitioners work together once funding is awarded. The authors of this article have experience working together as the evaluators and program manager on a recently completed four-year demonstration project designed to provide multiagency services to youth on probation in South Oxnard, California. We believe that our experiences can help other researchers and practitioners to understand practical and philosophical components of collaboration. We first discuss our differing perspectives on key issues and resulting tensions that arose. Next, we discuss strategies that we used to create a good working relationship, as well as the benefits that we gained from this partnership.