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Volume 71 Number 3
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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
Method
Results
Discussion
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.
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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).
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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.
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Method
Sample
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.
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Measures
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.
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Analysis
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.
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Results
Demographics
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.
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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.
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Discussion
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
recidivism.
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).
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References
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 www.uscourts.gov
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 %
Sex
Male 892 77.9 675 74.8 217 89.3
Female 253 22.1 227 25.2 26 10.7
Race
White 970 84.7 780 86.5 190 78.2
Nonwhite 175 15.3 122 13.5 53 21.8
Marital*
Married 252 22.0 186 20.6 66 27.5
Single 814 71.1 640 71.0 174 72.5
Recidivism
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
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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
level.
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
England
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
