Appendix A
Description of the MnSOSTR Scales
The Minnesota Sex Offender Screening Tool – Revised (MnSOSTR) is an
example of what are called actuarial scales. Actuarial scales are
most commonly associated with insurance companies as statistical devices
for assessing risks of a particular insured. Statistical models and
computer programs sort through a myriad of data on, for example,
automobile drivers (e.g., age, sex, occupation, type of car, locale,
income level, etc.) to determine what factors relate to or predict auto
accidents and with what strength. Thus, insurance companies know that
being male and under the age of 25 are high predictors of auto accidents,
while other factors are less useful in predicting accidents (such as
grades in school).
Psychologists have similarly sought models that
predict human behavior and have, for over 40 years, known that statistical
models match or, more frequently, exceed, clinical judgment in accuracy
about human behavior.^{1} Not
surprisingly, with states seeking to identify which sex offenders are at
greatest risk to reoffend, some psychologists undertook to analyze
statistically viable predictors of sex offending behavior. The
MnSOSTR is the most comprehensive and reliable of these attempts.
The researchers who developed the MnSOSTR (Douglas L. Epperson, Ph.D. of
Iowa State University, Stephen J. Huot, M.Ed. and James D. Kaul, Ph.D.
both of the Minnesota Department of Corrections) collected criminal and
psychological records of 256 released sex offenders.^{2}
From the data obtained, Epperson, et al generated 30 factors on which each
offender was rated. Next, computers statistically analyzed how well
each factor discriminated between the 90 released sex offenders who
subsequently were rearrested for a new sex offense within 6 years, and the
166 who committed no new offenses.
Based on several computer runs through the data, 16 items were
eventually included in the scale as most discriminatory. The
analysis also permitted the researchers to assign relative weights to some
factors which were shown to have higher predictive validity than others.
Notwithstanding the extensive numbercrunching used
to develop the scale, the computer models were unable to generate a
configuration of factors that accounted for any more than 20% of the
factors that contribute to recidivism. Thus, 80% of what contributes
to reoffending has yet to be identified. In the end, the MnSOSTR,
while the most accurate actuarial scale available for assessing reoffense
risk, yields an r^{3} value of .45.
While this r value is three times better than another commonly used scale,
the RRASOR (which, on the same sample of 256 offenders, yielded an r of
.13 — a rate barely better than chance), and more than 4 times higher than
the r of .10 for clinical judgment found by Hanson and Bussière (1998), it
nevertheless is considered to fall in the moderate range of predictive
accuracy.
The primary risk associated with actuarial scales of less than high
predictive validity is the problem of false positives. A false
positive occurs when one classifies an individual who would not have
committed a new sex offense as likely to commit a sex offense. The
risk of false positives is quite high, even with the MnSOSTR. The
difficulty in using the MnSOSTR for predictions is complicated by the
fact that its base rate of 35% recidivism, as compared to a base rate in
the range of 15% as found by Hanson and Bussière (1998) and others,
greatly underestimates the risk of false positives. Assuming a 15%
recidivism rate and a population of 1000 released sex offenders, and using
the cut score of 8 on the MnSOSTR that NJ currently employs, will result
in classifying appropriately 100 of the 150 actual recidivists as SVPs,
but will false identify as SVPs, 120 of the 850 who would not reoffend.
This is a false positive rate in excess of the true positive rate (see
Figure 1 below).

Actual
Recidivism
1000 Offenders — 15% reoffense rate 

MnSOSTR
Prediction
(Prediction based on obtaining a
score of 8 or higher) 

Reoffenders 
NonReoffenders 

Predicted Reoffenders 
True Positive
97

False Positive
123

220^{4} 
Predicted NonReoffenders 
False Negative
53

True Negative
727

780 

150 
850 



Figure 1
MnSOSTR Predictions
Based on Cut Score of 8
NOTES ON FIGURE 1: Assuming a 15% Recidivism
Rate, Epperson, et al predicts that 44% of those evaluated
on the MnSOSTR who obtain a score of 8 or higher will reoffend.
Approximately 22% of the MnSOSTR norm sample obtained a score of
8 or above. Using a hypothetical sample of 1000
soontobereleased sex offenders, one can then determine that 220
of them (22% of 1000) will obtain a score of 8 or higher.
Thus, 44% of that group of 220 offenders will reoffend, or 97
individuals. That leaves 123 individuals (or 56%) who have
been identified by the MnSOSTR, but who will not reoffend: the
False Positive Group. Since we know that 150 offenders will
actually reoffend (based on the 15% recidivism rate), we can
subtract the True Positive Group of 97 from 150 to determine that
the MnSOSTR will fail to identify 53 individuals who will
reoffend. Finally, knowing that 850 offenders will not
reoffend, we can subtract the False Positive rate of 123 from 850
to determine that 727 individuals were correctly identified by the
MnSOSTR as not likely to reoffend. The resulting comparison
of True to False Positives shows that for every 3 individuals the
state correctly commits, it will erroneously commit 4 individuals. 

Thus, the risk of false positives is unacceptably high (56% of those
identified). In fact, inasmuch as the 15% recidivism rate is more in
line with actual recidivism research than the 35% used on the MnSOSTR,
Figure 1 more accurately reflects the risk of error with the MnSOSTR as
it is used in New Jersey.
A cut score of 13 applied to the same population, again assuming a 15%
recidivism rate, while decreasing the false positive rate, still only
brings it back to the false positive rate achieved using the cut score of
8 and a 35% recidivism rate. As Figure 2 illustrates, the MnSOSTR
will still incorrectly classify 30% of the offenders as recidivists,
albeit better than the 56% error rate at the cut score of 8, but still
unacceptably high.

Actual
Recidivism
1000 Offenders — 15% reoffense rate 

MnSOSTR
Prediction
(Prediction based on obtaining a
score of 13 or higher) 

Reoffenders 
NonReoffenders 

Predicted Reoffenders 
True Positive
42

False Positive
18

60^{5} 
Predicted NonReoffenders 
False Negative
108

True Negative
832

9480 

150 
850 



Figure 2
MnSOSTR Predictions
Based on Cut Score of 13
NOTES ON FIGURE 2: Assuming a 15% Recidivism
Rate, Epperson, et al predicts that 70% of those evaluated
on the MnSOSTR who obtain a score of 13 or higher will reoffend.
Approximately 6% of the MnSOSTR norm sample obtained a score of
13 or above. Using a hypothetical sample of 1000
soontobereleased sex offenders, one can then determine that 60
of them (6% of 1000) will obtain a score of 13 or higher.
Thus, 70% of that group of 60 offenders will reoffend, or 42
individuals. That leaves 18 individuals (or 30%) who have
been identified by the MnSOSTR, but who will not reoffend: the
False Positive Group. Since we know that 150 offenders will
actually reoffend (based on the 15% recidivism rate), we can
subtract the True Positive Group of 42 from 150 to determine that
the MnSOSTR will fail to identify 108 individuals who will
reoffend. Finally, knowing that 850 offenders will not
reoffend, we can subtract the False Positive rate of 18 from 850
to determine that 832 individuals were correctly identified by the
MnSOSTR as not likely to reoffend. The resulting comparison
of True to False Positives shows that for every 7 individuals the
state correctly commits, it will erroneously commit 3 individuals. 

The MnSOSTR represents a major advance in the ability to quantify the
factors that predict sex reoffending. Notwithstanding its improved
reliability over clinical judgments, uninformed and uneducated acceptance
of the scores it yields can easily lead to overconfidence in the decisions
derived from it. Recalling that the MnSOSTR has only identified 20%
of the factors that go into predicting sexual reoffending behavior, the
legal system must recognize the limitations of the scale when relying upon
it to make life altering decisions.
This holds even more true when less reliable scales are utilized such
as the Static 99 or the RRASOR. Especially in consideration of the
more reasonable recidivism rate of 15%, using the cut score of 8, as
preferred by the State of New Jersey, will result in more individuals who
would not reoffend being committed, than actual high risk offenders.
Use of appropriate cut scores and consideration of appropriate base rates
can go a long way to reducing the risk of erroneously committing
individuals who are ready to return to society.
Footnotes
^{1} Grove, W. M. & Meehl, P.E. (1996).
Comparative efficiency of formal (mechanical, algorithmic) and informal
(subjective, impressionistic) prediction procedures: The
clinical/statistical controversy. Psychology, Public Policy, and Law,
2, 293323. [Back]
^{2} The sample was disproportionately loaded
with a higher percentage of highrisk offenders and offenders who were
known to have reoffended since release, to ensure that enough reoffenders
were present in the sample to meet the needs of the statistical analysis.
[Back]
^{3} r is a statistical coefficient of
reliability that ranges from –1 to 1 where either –1 or 1 equals perfect
prediction and 0 indicates chance or random prediction. The closer
to 0, the less reliable the predictions.
[Back]
^{4} Based on MnSOSTR prediction that 22% of
a screened population will obtain a score of 8 or higher. The
MnSOSTR predicts that 44% of this group will actually reoffend, assuming
a 15% recidivism rate. [Back]
^{5} Based on MnSOSTR prediction that 6% of
a screened population will obtain a score of 8 or higher. The
MnSOSTR predicts that 70% of this group will actually reoffend, assuming
a 15% recidivism rate. [Back] 