Skip navigation
CLN bookstore

Wsipp Woaa Doc Static Risk Tnstrement 2007

Download original document:
Brief thumbnail
This text is machine-read, and may contain errors. Check the original document to verify accuracy.
Washington State
Institute for
Public Policy
110 Fifth Avenue Southeast, Suite 214 • PO Box 40999 • Olympia, WA 98504-0999 • (360) 586-2677 • FAX (360) 586-2793 • www.wsipp.wa.gov

March 2007

WASHINGTON’S OFFENDER ACCOUNTABILITY ACT:
DEPARTMENT OF CORRECTIONS’ STATIC RISK INSTRUMENT‡
BACKGROUND
SUMMARY
The Offender Accountability Act (OAA) was enacted by
the Washington State Legislature in 1999. The OAA
affects how the Department of Corrections (DOC)
supervises convicted felony offenders after their
release. One purpose of the OAA is “to reduce the risk
of reoffending by offenders in the community.”1 DOC is
required to classify and supervise felony offenders
according to their risk for future offending.
As part of the 1999 law, the Washington State Institute
for Public Policy (Institute) was directed to study the
impact of the OAA on recidivism. In our 2003 report,
the Institute analyzed the validity of DOC’s risk for
reoffense instrument, the Level of Service Inventory—
Revised (LSI-R).2 The LSI-R is a 54-question survey
which includes “static” and “dynamic” risk factors (see
sidebar on page 2 for definitions). In the analysis of
the LSI-R, the Institute also determined how the
predictive accuracy of the LSI-R could be strengthened
by including more static risk information about an
offender’s prior record of convictions.3
Subsequently, DOC asked the Institute to develop a
new static risk instrument based on offender
demographics and criminal history. DOC made this
decision because the new static risk instrument,
compared with assessments that include both static
and dynamic items, has the following advantages:
• Increased predictive accuracy;
• Prediction of three types of high risk offenders:
drug, property, and violent;
• Increased objectivity;
• Decreased time to complete the assessment; and
• Accurate recording of criminal history for use in
other DOC reporting requirements.

The 1999 Offender Accountability Act (OAA) affects how
the Department of Corrections (DOC) supervises convicted
felony offenders in the community. The Washington State
Institute for Public Policy (Institute) was directed by the
Legislature to evaluate the OAA.
The OAA requires DOC to supervise felony offenders
according to their risk for future offending. Risk for future
offending is estimated using instruments that classify
offenders into groups with similar characteristics. Criminal
behavior is difficult to predict; even the most accurate
instruments, like this one, cannot predict with absolute
certainty who will subsequently reoffend.
In our 2003 report, the Institute evaluated the validity of
DOC’s risk assessment tool and found that the tool could
be strengthened by including more information about an
offender’s prior record of convictions. Subsequently, DOC
asked the Institute to develop a new “static risk” instrument
based on offender demographics and criminal history
because of the following advantages:
•

Increased predictive accuracy;

•

Prediction of three types of high risk offenders: drug,
property, and violent;

•

Increased objectivity;

•

Decreased time to complete the assessment; and

•

Accurate recording of criminal history for use in other
DOC reporting requirements.

This report describes our evaluation of the validity of the
static risk instrument developed for DOC.

Finding
Analyses indicate that the static risk instrument has
moderate predictive accuracy for Washington State felony
offenders, exceeding the accuracy of DOC’s previous risk
assessment instrument. In addition, the risk classification
scheme can be generalized to future cohorts of offenders
with little loss in accuracy.

This report describes our evaluation of the validity
of the static risk instrument developed for the
Washington State Department of Corrections.
1

RCW 9.94A.010
R. Barnoski & S. Aos. (2003). Washington’s offender
accountability act: An analysis of the Department of
Corrections’ risk assessment. Olympia: Washington State
Institute for Public Policy, Document No. 03-12-1202.
2

‡Suggested citation: Robert Barnoski and Elizabeth K. Drake.
(2007). Washington’s Offender Accountability Act: Department of
Corrections’ Static Risk Assessment. Olympia: Washington State
Institute for Public Policy.

Exhibit 1 lists the risk factors within each of the six
categories on the static risk instrument.

METHODOLOGY
In 2006, the Institute developed a static risk
instrument for the Department of Corrections (see
sidebar below for a definition of static risk). The
static risk instrument is displayed in Appendix A of
this report. Two steps are taken to design prediction
instruments, such as DOC’s static risk instrument.

Exhibit 1

Offender Risk Factors in Prediction Equations
Demographics
Age at time of current sentence
Gender
Juvenile Record

In the first step, the static risk instrument was
developed based on the recidivism patterns of a
“construction sample.” The construction sample
included all offenders released from prison/jail or
placed on community supervision from 1986 to
March 2000 (308,423 observations).

Felony convictions
Non-sex violent felony convictions
Felony sex convictions
Commitments to state juvenile institution
Commitment to the Department of Corrections
Current commitment to the Department of Corrections
Adult Felony Record

The second step, called cross validation, measures
how well the instrument works for a different
“validation sample.” Cross validation demonstrates
how well the results from the construction sample
can be generalized to other cohorts of offenders.
The statistical model derived from the construction
sample is applied to all offenders released from
prison/jail or placed on community supervision from
2001 through September 2002 (51,648
observations).

Commitments to Department of Corrections
Felony homicide
Felony sex
Felony violent property
Felony assault offense—not domestic violence
Felony domestic violence assault or protection order violation
Felony weapon
Felony property
Felony drug
Felony escape
Adult Misdemeanor Record

This study follows the state’s definition of recidivism
recommended by the Institute.4 Recidivism is
defined as a subsequent conviction in a Washington
State Superior Court for a felony offense committed
within three years of placement in the community. In
addition, one year is allowed for the offense to be
adjudicated in court.

Misdemeanor assault—not domestic violence
Misdemeanor domestic violence assault or violation of a
protection order
Misdemeanor sex
Misdemeanor other domestic violence
Misdemeanor weapon
Misdemeanor property
Misdemeanor drug
Misdemeanor escapes
Misdemeanor alcohol
Adult Sentence Violations

Three types of recidivism are predicted using a
separate prediction equation for each:
• Any felony recidivism,
• Property or violent felony recidivism, and
• Violent felony recidivism.

Sentence/supervision violations

When developing the instrument for the construction
sample, the factors most strongly associated with
recidivism were organized into the following six
categories: demographics, juvenile record,
commitments to DOC, adult felony record, adult
misdemeanor record, and adult sentence violations.
The criminal record counts are based on sentences
in a Washington State court. Each sentence is
classified by the most serious offense involved and
is counted once.

Recidivism rates were used to determine the values
for each factor. Appendix B shows the percentage
distribution of the validation sample for each value of
the risk factor. For example, 39 percent of the sample
was age 20 to 29. Appendix B also shows the
recidivism rates for each value of the risk factor. For
example, the felony recidivism rate for offenders age
20 to 29 was 35.7 percent.
What Is “Static” Risk and “Dynamic” Risk?
Risk factors that cannot decrease, such as criminal history, are
static. Once a criminal record is obtained, it will always be a
part of an offender’s history. Dynamic risk factors, such as drug
dependency, can decrease through treatment or intervention.

4

R. Barnoski. (1997). Standards for improving research
effectiveness in adult and juvenile justice. Olympia:
Washington State Institute for Public Policy, Document No.
97-12-1201, pg. 2.

a

2

D.A. Andrews & J. Bonta. (1998). The psychology of criminal
conduct. Cincinnati, Ohio: Anderson Publishing Co.

When developing the instrument for the construction
sample, multivariate regression was used to
determine equations that weight and combine the
risk factors to best predict the three types of
recidivism.5 The instrument produces three scores:
felony, property/violent, and violent scores.
(Appendix C displays the weights used for each risk
factor.) These scores are calculated by multiplying
the value of the static risk factor by the weight for the
factor. For example, if an offender is between ages
30 and 39 at the time of the offender’s current
sentence, 3 points (see Appendix A) are multiplied
by 5 (see Appendix C) to get the weighted age for
the felony score. The weighted values are summed
to produce the total felony score. The process is
repeated for the property/violent and violent scores.

CROSS-VALIDATION RESULTS
The best measure for determining how accurately
a score predicts an event like recidivism is a
statistic called the area under the receiver
operating characteristic (AUC).6 The AUC ranges
from .500 to 1.000. This statistic is .500 when
there is no association and 1.000 when there is
perfect association. AUCs in the .500s indicate
little to no predictive accuracy, .600s weak, .700s
moderate, and above .800 strong predictive
accuracy.
Exhibit 3 presents the AUCs for recidivism and the
three equations in both the construction and
validation samples. For example, the AUC is
0.756 when predicting any felony recidivism in the
construction sample compared with a 0.742 AUC
for the validation sample.7 Two conclusions are
drawn from Exhibit 3:

Risk scores of the construction sample were then
analyzed to ascertain the threshold or cutoff scores
used to classify offenders into risk levels. Typically,
offenders are classified into low, moderate, and high
risk for reoffense. Having the three types of risk
scores allows us to break the high risk level into
more specific levels: high risk for drug, property, or
violent recidivism, resulting in the following five risk
levels:
•

High violent risk

•

High property risk

•

High drug risk

•

Moderate risk

•

Low risk

• All of the AUCs are in the mid .700s,
indicating moderate predictive accuracy for all
three equations in both the construction and
validation samples.
• The AUCs in the validation sample are only
slightly smaller than those in the construction
sample AUCs. This means the prediction
models are robust and the risk equations can
be generalized to other cohorts of offenders
with little loss in accuracy.
Exhibit 3

Exhibit 2 shows the rules developed to classify
offenders into the five risk levels.

AUCs of Prediction Equations

Exhibit 2
Recidivism by
Predicted Felony

Classification Rules for Risk Levels
Classification Rules

Risk Level

AUCs
Construction
Validation
Sample
Sample
(N=308,423)

(N=51,648)

Any Felony

0.756

0.742

Violent Score is greater than or equal to 38

High Violent

Property/Violent Felony

0.757

0.733

Not High Violent Risk and Property/Violent
Score is greater than or equal to 50

High Property

Violent Felony

0.745

0.732

Not High Violent Risk and not High
Property Risk and Felony Score is greater
than or equal to 64

High Drug

Not High Risk and Property/Violent Felony
Score is greater than or equal to 38

Moderate

Not High Risk and not Moderate Risk and
Felony Score is less than 64

Low
6

V. Quinsey, G. Harris, M. Rice, & C. Cormier. (1998).
Violent offenders: Appraising and managing risk.
Washington D.C.: American Psychological Association;
P. Jones. (1996). Risk prediction in criminal justice. In
A. Harland (Ed.), Choosing correctional options that work.
Thousand Oaks, CA: Sage, pp. 33–68.
7
The AUCs for the LSI-R were in the .640 to .660 range.
Barnoski & Aos (2003).

5

Logistic regression is used to identify the significant
variables, and ordinary least squares regression is used to
obtain the variable weighting. These weights are transformed
to whole numbers to minimize shrinkage, tailoring the weights
to the construction sample.

3

Exhibit 4 displays the recidivism rates for each of
the risk levels for the validation sample. The bottom
axis of Exhibit 4 shows the percentage of offenders
in each risk level. For example, 32 percent of the
offenders in the validation sample are classified as
low risk. In addition, the bars in the chart show the
recidivism rates for each risk level. Therefore, for
low risk offenders, 16 percent recidivated with a
felony offense, 7 percent with felony drug, 4 percent
with a felony property, and 3 percent with a violent
felony.8

Between 47 and 57 percent of offenders in the
three high risk levels recidivated with a felony.
• For high drug risk offenders, 25 percent
recidivated with a felony drug offense.
• For high property risk offenders, 28 percent
recidivated with a felony property offense.
• For high violent risk offenders, 23 percent
recidivated with a violent felony offense.

Exhibit 4

Recidivism Rates for Each Risk Level of the Validation Sample

Felony Recidivism
Felony Drug Recidivism
Felony Property Recidivism
47%
Violent Felony Recidivism

57%
53%

28%
25%

24%

23%
19%

16%
13%
10%

7%
4% 3%

Low (32%)

WSIPP, 2007

6%

11%

13%

8%

7%

Moderate
(24%)

13%

High Drug
(9%)

Risk Level

8

The drug, property and violent felony rates do not sum to
the felony rate because a small percentage of felony
offenders recidivate with other miscellaneous felony offenses.

4

High Property
(19%)

High Violent
(16%)

In order to evaluate the effectiveness of the static
risk instrument, recidivism rates of various
subgroups were also analyzed. These subgroups
are based on the type of sentence the offender
received, gender, ethnicity, and most serious offense
in the offender’s conviction history. These results,
presented in Appendix D of this report, indicate the
following:

CONCLUSIONS
In this study, a sample of offenders was used to
determine if the static risk instrument developed by
the Institute for the Department of Corrections can
be generalized to future cohorts of offenders.
Results of the study indicate that the prediction
models used to develop the static risk instrument
have moderate predictive accuracy for all three
types of recidivism. Furthermore, the results can
be generalized to future cohorts of offenders with
little loss in predictive accuracy.

Felony recidivism:
•

Eleven of the 13 subgroups have moderate
predictive accuracy with AUCs in the .700s for
felony recidivism.

•

Weak predictive accuracy was obtained for
offenders whose most serious offense was a
felony drug conviction.

•

Strong predictive accuracy is associated with
sex offenders.

Property/violent recidivism:
•

Ten of the 13 subgroups have moderate
predictive accuracy for violent property
recidivism.

•

Weak predictive accuracy was found for three
subgroups: African Americans, Asian
Americans, and offenders whose most serious
offense was a felony drug conviction.

Violent felony recidivism:
•

Twelve of the 13 subgroups have moderate
predictive accuracy for violent felony
recidivism.

•

Weak predictive accuracy was found for
offenders whose most serious offense was a
violent non-sex crime.

5

Appendix A

Department of Corrections’ Static Risk Instrument
Offender Risk Factors
I. Demographics
1. Age at time of current sentence

O
O
O
O

60 or older
50 to 59
40 to 49
30 to 39

(0)
(1)
(2)
(3)

O 20 to 29
O 18 to 19
O 13 to 17

(4)
(5)
(6)

2. Gender

O

Female

(0)

O Male

(1)

II. Juvenile Record
(All prior and current times the offender was sentenced. Each sentence is defined by a unique or different date of sentence.)
3. Prior juvenile felony convictions

O
O
O

None
One
Two

(0)
(1)
(2)

O Three
O Four
O Five or more

(3)
(4)
(5)

4. Prior juvenile non-sex violent felony convictions for: homicide,
robbery, kidnapping, assault, extortion, unlawful imprisonment,
custodial interference, domestic violence, or weapon

O
O

None
One

(0)
(1)

O Two or more

(2)

5. Prior juvenile felony sex convictions

O

None

(0)

O One or more

(1)

6. Prior commitments to a juvenile institution

O
O

None
One

(0)
(1)

O Two or more

(2)

(1)
(2)
(3)

O Fourth
O Fifth or more

(4)
(5)

III. Commitment to the Department of Corrections
7. Current commitment to the Department of Corrections

O
O
O

First
Second
Third

IV. Total Adult Felony Record
(All prior and current times the offender was sentenced. Each sentence is defined by a unique or different date of sentence.)
8. Felony homicide offense: murder/manslaughter

O

None

(0)

O One or more

(1)

9. Felony sex offense

O
O

None
One

(0)
(1)

O Two or more

(2)

10. Felony violent property conviction for a felony robbery/
kidnapping/extortion/unlawful imprisonment/custodial
interference offense/harassment/burglary 1/arson 1

O
O

None
One

(0)
(1)

O Two or more

(2)

11. Felony assault offense—not domestic violence related

O
O

None
One

(0)
(1)

O Two
O Three or more

(2)
(3)

12. Felony domestic violence assault or violation of a domestic
O
violence related protection order, restraining order, or no-contact O
order/harassment/malicious mischief

None
One

(0)
(1)

O Two or more

(2)

13. Felony weapon offense

O
O

None
One

(0)
(1)

O Two or more

(2)

14. Felony property offense

O
O
O

None
One
Two

(0)
(1)
(2)

O Three
O Four
O Five or more

(3)
(4)
(5)

15. Felony drug offense

O
O

None
One

(0)
(1)

O Two
O Three or more

(2)
(3)

16. Felony escape

O

None

(0)

O One or more

(1)

6

V. Total Adult Misdemeanor Record
Total number of sentences, past and current, involving a misdemeanor conviction for:
17. Misdemeanor assault offense—not domestic violence
related

O
O
O

None
One
Two

(0)
(1)
(2)

O Three
O Four
O Five or more

(3)
(4)
(5)

18. Misdemeanor domestic violence assault or violation of a O
domestic violence related protection order, restraining
O
order, or no-contact order

None
One

(0)
(1)

O Two or more

(2)

19. Misdemeanor sex offense

O
O

None
One

(0)
(1)

O Two or more

(2)

20. Misdemeanor other domestic violence: any non-violent
misdemeanor convictions such as trespass, property
destruction, malicious mischief, theft, etc., that are
connected to domestic violence

O

None

(0)

O One or more

(1)

21. Misdemeanor weapon offense

O

None

(0)

O One or more

(1)

22. Misdemeanor property offense

O
O

None
One

(0)
(1)

O Two
O Three or more

(2)
(3)

23. Misdemeanor drug offense

O
O

None
One

(0)
(1)

O Two or more

(2)

24. Misdemeanor escapes

O

None

(0)

O One or more

(1)

25. Misdemeanor alcohol offense

O

None

(0)

O One or more

(1)

O Three
O Four
O Five or more

(3)
(4)
(5)

VI. Total Sentence/Supervision Violations
26. Total sentence/supervision violations

O
O
O

7

None
One
Two

(0)
(1)
(2)

Appendix B

Validation Sample:
Percentage Distribution of Demographics and Recidivism Rates for Static Risk Factors
Type of Recidivism
Percentage
Distribution of
Population

Value
Felony
Demographics
1. Age at time of current sentence
60 or older
0
1%
8.4%
50 to 59
1
4%
18.2%
40 to 49
2
17%
28.7%
30 to 39
3
30%
36.7%
20 to 29
4
39%
35.7%
18 to 19
5
9%
39.1%
13 to 17
6
1%
42.5%
2. Gender
Female
0
21%
28.5%
Male
1
79%
35.9%
Juvenile Record
3. Prior juvenile felony convictions
None
0
81%
30.4%
One
1
8%
45.2%
Two
2
4%
50.3%
Three
3
3%
58.0%
Four
4
2%
63.3%
Five or more
5
2%
64.5%
4. Prior juvenile non-sex violent felony convictions
None
0
95%
33.2%
One
1
4%
54.0%
Two or more
2
1%
61.8%
5. Prior juvenile felony sex convictions
None
0
98%
34.2%
One or more
1
2%
44.7%
6. Prior commitments to a juvenile institution
None
0
93%
32.5%
One
1
4%
54.7%
Two or more
2
3%
64.3%
Commitment to the Department of Corrections
7. Current commitment to the Department of Corrections
First
1
46%
21.3%
Second
2
21%
34.7%
Third
3
12%
44.5%
Fourth
4
7%
50.0%
Fifth or more
5
14%
60.0%
Adult Felony Record
8. Felony homicide offense
None
0
99%
34.4%
One or more
1
1%
24.7%
9. Felony sex offense
None
0
94%
35.2%
One or more
1
5%
21.2%
Two or more
2
0%
20.8%
10. Felony violent property conviction
None
0
92%
33.5%
One or more
1
7%
43.3%
Two or more
2
1%
49.4%
11. Felony assault - not domestic violence
None
0
85%
34.1%
One or more
1
14%
34.3%
Two or more
2
1%
46.2%
Three or more
3
0%
46.0%
12. Felony domestic violence assault
None
0
94%
34.0%
One or more
1
5%
37.4%
Two or more
2
1%
55.9%
13. Felony weapon offense
None
0
94%
33.7%
One or more
1
5%
44.4%
Two or more
2
0%
54.3%

Felony
Drug

Type of Recidivism
Percentage
Distribution of
Value
Population
Felony
Adult Felony Record (continued)
14. Felony property offense
None
0
52%
26.3%
One or more
1
28%
34.5%
Two or more
2
10%
49.7%
Three or more
3
5%
58.3%
Four or more
4
2%
56.6%
Five or more
5
3%
63.0%
15. Felony drug offense
None
0
55%
28.5%
One or more
1
27%
35.2%
Two or more
2
10%
46.4%
Three or more
3
8%
57.5%
16. Felony escape
None
0
96%
33.4%
One or more
1
4%
54.8%
Adult Misdemeanor Record
17. Misdemeanor assault offense - not domestice violence
None
0
81%
32.0%
One or more
1
14%
41.0%
Two or more
2
3%
51.6%
Three or more
3
1%
54.0%
Four or more
4
0%
58.5%
Five or more
5
0%
67.4%
18. Misdemeanor domestice violence assault
None
0
82%
31.7%
One
1
10%
43.0%
Two or more
2
8%
50.4%
19. Misdemeanor sex offense
None
0
97%
34.1%
One
1
1%
42.2%
Two or more
2
1%
50.2%
20. Misdemeanor other domestic violence
None
0
98%
34.1%
One
1
2%
48.0%
21. Misdemeanor weapon offense
None
0
95%
33.4%
One
1
5%
53.1%
22. Misdemeanor property offense
None
0
64%
27.1%
One
1
18%
40.3%
Two
2
8%
49.2%
Three
3
10%
57.4%
23. Misdemeanor drug offense
None
0
81%
31.0%
One
1
13%
46.4%
Two
2
5%
55.5%
24. Misdemeanor escapes
None
0
99%
34.1%
One
1
1%
57.9%
25. Misdemeanor alcohol offense
None
0
76%
32.8%
One
1
24%
39.1%
Adult Sentence Violations
26. Total sentence/supervision violations
None
0
69%
26.9%
One
1
10%
42.3%
Two
2
7%
49.0%
Three
3
4%
52.7%
Four
4
3%
55.5%
Five or more
5
7%
62.8%

Felony Violent
Property Felony

3.4%
7.5%
12.7%
12.9%
9.3%
7.1%
5.2%

2.2%
5.0%
9.6%
14.3%
14.0%
16.8%
14.5%

2.5%
4.8%
5.7%
8.4%
11.1%
13.8%
20.3%

11.1%
10.5%

13.7%
13.1%

3.0%
11.0%

10.3%
11.5%
11.7%
12.5%
12.5%
12.8%

11.6%
17.8%
20.3%
23.1%
25.6%
22.2%

7.5%
14.1%
16.6%
20.4%
22.1%
26.7%

10.5%
12.2%
14.0%

12.9%
18.6%
15.2%

8.6%
21.5%
30.4%

10.6%
9.1%

13.1%
18.7%

9.3%
12.0%

10.5%
12.6%
12.7%

12.6%
19.1%
24.6%

8.4%
20.1%
24.5%

6.1%
10.4%
13.6%
15.5%
20.8%

8.2%
12.7%
16.2%
20.5%
24.0%

6.4%
10.1%
12.9%
12.6%
13.3%

10.6%
7.5%

13.3%
6.1%

9.4%
10.1%

10.9%
5.2%
6.5%

13.7%
5.6%
4.9%

9.5%
7.5%
8.6%

10.5%
11.0%
13.7%

12.9%
16.0%
20.1%

8.9%
14.6%
14.4%

10.9%
8.9%
8.9%
8.8%

13.7%
10.2%
12.0%
13.3%

8.4%
13.7%
23.3%
21.2%

10.8%
7.8%
9.0%

13.4%
9.2%
9.5%

8.6%
19.0%
36.2%

10.4%
13.1%
16.1%

13.1%
14.3%
12.1%

9.0%
15.3%
24.7%

8

Felony
Drug

Felony
Property

Violent
Felony

10.5%
10.0%
11.5%
13.1%
12.0%
10.9%

6.3%
14.1%
24.6%
31.7%
33.8%
39.8%

8.6%
9.3%
11.9%
11.8%
9.5%
10.9%

4.6%
12.7%
21.3%
32.2%

12.8%
12.8%
15.4%
14.9%

9.9%
8.6%
8.5%
8.9%

10.3%
17.8%

12.8%
22.2%

9.2%
11.9%

10.1%
11.9%
14.2%
16.7%
15.0%
15.9%

12.7%
14.2%
19.0%
15.5%
12.6%
15.2%

8.0%
13.5%
16.5%
19.8%
30.4%
34.8%

10.2%
12.8%
12.0%

12.8%
14.7%
15.1%

7.6%
13.9%
21.6%

10.3%
18.0%
27.3%

13.2%
14.0%
13.3%

9.4%
8.6%
7.2%

10.6%
12.3%

13.1%
16.3%

9.2%
18.6%

10.3%
16.8%

12.9%
18.3%

9.0%
16.4%

9.0%
12.2%
13.4%
15.6%

9.1%
15.7%
21.3%
27.9%

7.8%
11.2%
12.9%
12.8%

9.1%
15.5%
21.1%

12.0%
17.6%
20.7%

8.8%
11.8%
12.4%

10.6%
15.6%

13.1%
23.6%

9.3%
15.2%

10.4%
11.1%

12.8%
14.4%

8.5%
12.1%

8.1%
12.4%
14.9%
15.8%
18.7%
22.5%

10.0%
16.5%
18.0%
21.9%
23.6%
25.6%

7.9%
11.8%
14.5%
13.1%
11.6%
12.2%

Appendix C

Static Risk Factor Weighting
Static Risk Factor Weighting

Felony
Score
+5
+5
+4
+2
-3
+4

Property
& Violent
Score
+4
+4
+4
+2
-2
3

Violent
Score
+2
+4
+2
+5
-1
+2

+2

+1

+1

Current Commitment to the Department Of Corrections

-5
-4

-3
-2

+1
+2

+6
+1

+5
+2

+5
+4

+3
+3
+4
+6
+5

+6
+2
+5
-2
+3

+10
+5
0
0
+1

Felony Homicide Offense
Felony Sex Offense
Felony Violent Property Conviction for a Felony Robbery/
Kidnapping/Extortion/Unlawful Imprisonment/Custodial Interference Offense
Felony Assault Offense—Not Domestic Violence Related
Felony Domestic Violence Assault or Violation of a Domestic Violence Related
Protection Order, Restraining Order, or No-Contact Order
Felony Weapon Offense
Felony Property Offense
Felony Drug Offense
Felony Escape

+2

+2

+3

+2
+3
-3
+6
+4
+3
+4
-1

+3
-1
-1
+4
+4
+1
+3
-1

+3
0
+1
+4
+1
0
+2
+1

Misdemeanor Assault Offense – Not Domestic Violence Related
Misdemeanor Domestic Violence Assault or Violation of a Domestic Violence Related
Protection Order, Restraining Order, or No-Contact Order
Misdemeanor Sex Offense
Misdemeanor Other Domestic Violence
Misdemeanor Weapon Offense
Misdemeanor Property Offense
Misdemeanor Drug Offense
Misdemeanor Escapes
Misdemeanor Alcohol Offense

+5

+3

+1*

Total Sentence/Supervision Violations (*three or more scored as 3 for violent score)

Static Risk Factor
Age at Time of Sentence for Current Offense
Gender
Prior Juvenile Felony Convictions
Prior Juvenile Non-Sex Violent Felony Convictions
Prior Juvenile Felony Sex Convictions
Prior Commitments to a Juvenile Institution

9

Appendix D

Validity of Offender Subgroups
APPENDIX D SUMMARY OF FINDINGS

In order to evaluate the effectiveness of the static
risk assessment, we analyze the recidivism rates
of subgroups of the validation sample. These
subgroups include gender and ethnicity as well as
sentence type and most serious offense.

Felony recidivism. Of the 13 subgroups, 11 have
moderate predictive accuracy for felony recidivism.
Weak predictive accuracy was obtained for
offenders whose most serious offense was a
felony drug conviction. The AUC for sex offenders,
however, shows strong predictive accuracy for
felony recidivism.

For each subgroup, the analysis:
• compares the percentage distribution of
offenders,
• displays the AUCs for the prediction risk
scores and recidivism, and

Property/violent recidivism. Ten of the 13
subgroups have moderate predictive accuracy for
property/violent recidivism. Weak predictive
accuracy was found for African Americans, Asian
Americans, and offenders whose most serious
offense was a felony drug conviction.

• displays the recidivism rates by each risk
level.
The results of these analyses follow on pages 11
through 14.

Violent felony recidivism. Findings indicate
moderate predictive accuracy for violent felony
recidivism for 12 of the 13 subgroups. Weak
predictive accuracy was found for offenders whose
most serious offense was a violent non-sex crime.

How to read the recidivism by risk category
charts. Lower recidivism rates are expected for
offenders classified as low and moderate risk. In
general, recidivism rates should become
increasingly higher reading left to right. For
example, felony recidivism rates in Exhibit 7
increase as the risk level increases. However,
when looking at a particular type of recidivism,
such as felony drug, offenders classified as high
drug risk are expected to have higher recidivism
rates relative to the other risk categories.

10

Sentence Type
Exhibit 7

Exhibit 5 compares the percentage distribution of
offenders sentenced with community supervision
and offenders sentenced to prison by risk level.
Thirty-five percent of community offenders are low
risk to reoffend compared to 22 percent of prison
offenders. Twenty-nine percent of the offenders
sentenced to prison are at high risk to reoffend
with a violent offense compared with 12 percent on
community supervision.

Recidivism Rates by Risk Category for
Community Supervision and Prison Sentences
80%

Felony Recidivism
48%
43%

59%
53%55% 55%

40%

24%23%

Exhibit 5

16%14%

Percentage Distribution by Risk Level
60%

0%

Community Supervision
Prison
40%

Low

Moderate

High Drug

High
Property

High Violent

50%

35%

Felony Drug Recidivism
27%

29%

25%

22%
20%

28%
24%

17%
13%
9%

12%

11%

25%

16%
15%
12%
12%

0%
Low Risk
WSIPP, 2007

Moderate
Risk

High Drug

High
Property

High Violent

7% 6%

6% 6%

Low

Moderate

0%

Exhibit 6 displays the AUCs for the three risk
scores and recidivism. The AUCs for the total
validation sample are displayed for reference.
The AUCs show there is moderate predictive
strength for both sentence types for all types of
recidivism. The sentence subgroup and total
sample AUCs are similar.

High
Property

High Violent

50%

Felony Property Recidivism
28%29%
25%

20%
17%

15%

Exhibit 6
10%

AUCs for Risk Scores Predicting Type of
Recidivism by Sentence Type

Sentence Type
Community
Prison
Total Sample

High Drug

5%

8%

8%

Moderate

High Drug

3%

0%

Type of Recidivism
Property/
Violent
Felony
Violent
0.734
0.726
0.736
0.741
0.744
0.717
0.742
0.733
0.732

Low

High
Property

High Violent

50%

Violent Felony Recidivism

24%
22%

25%

Exhibit 7 displays the recidivism rates for
offenders sentenced to community supervision
compared with offenders sentenced to prison by
each of the risk levels. There are no differences in
recidivism rates for the different risk levels, which
again indicates that the static risk assessment
predicts equally well for both prison and
community supervision offenders.

3% 4%

7% 7%

8% 7%

Moderate

High Drug

11%10%

0%
Low

Community Supervision

11

WSIPP, 2007

High
Property

High Violent

Prison

Gender
Exhibit 10

Exhibit 8 shows the percentage distribution of
males and females by risk level. Fifty-one percent
of female offenders are low risk to reoffend
compared with 27 percent for males. Two percent
of females are at high risk to reoffend with a violent
offense, compared with 20 percent of males.

Recidivism Rates by Risk Category for Gender
80%

Felony Recidivism
46%47%

Exhibit 8

Percentage Distribution by Risk Level

55%57%
52%54%

40%

28%
23%

60%

51%

16%16%
Females

40%

0%

Males
27%

Low

25%
20%

20%
16%

20%

Moderate

High Drug

High
Property

High Violent

20%
50%

11% 9%

Felony Drug Recidivism
2%

0%
Low Risk
WSIPP, 2007

Moderate
Risk

High Drug

High
Property

High Violent

25%25%
25%

14%13%

Exhibit 9 displays the AUCs for the three risk
scores and recidivism. The AUCs show there is
moderate predictive strength for both genders on
all types of recidivism.

8% 7%

5%

0%
Low

Exhibit 9

Moderate

High Drug

High
Property

High Violent

50%

AUCs for Risk Scores Predicting Type of
Recidivism by Gender

Gender
Male
Female
Total Sample

8%

16%
13%

Felony Property Recidivism
31%
27%

Type of Recidivism
Property/
Violent
Felony
Violent
0.743
0.731
0.701
0.720
0.717
0.722
0.742
0.733
0.732

25%

16%
6%

20%
18%

16%
12%
8%

4%

0%
Low

Exhibit 10 displays the recidivism rates for male
and female offenders by each risk level. There is
little difference in male and female recidivism rates
for felony and felony drug recidivism. Females
have higher property recidivism rates than males
at each level of risk. However, males have higher
violent felony recidivism rates than females. That
is, the risk classification scheme discriminates risk
for reoffense equally well within each gender, but
underestimates property recidivism and
overestimates violent felony recidivism for females.

Moderate

High Drug

High
Property

High Violent

50%

Violent Felony Recidivism

23%

25%

16%
8%
2%

4%

12%

10%

3%

4%

Moderate

High Drug

5%

0%
Low

Females
WSIPP, 2007

12

High
Property

Males

High Violent

Ethnicity
Exhibit 11 shows the percentage distribution of ethnicity
by risk level. Thirty-four percent of Asian Americans and
39 percent of Hispanics are low risk offenders. Twentyseven percent of African Americans and 28 percent of
Native Americans are at a high risk to reoffend with a
violent offense.

Exhibit 13

Recidivism Rates by Risk Category for Ethnicity
80%

Felony Recidivism
60%

Exhibit 11
40%

Percentage Distribution by Risk Level
60%

20%

European American
African American
Native American
Asian American
Hispanic

50%
40%

0%
Low

Moderate

High Drug

High
Property

High Violent

High
Property

High Violent

High
Property

High Violent

High
Property

High Violent

30%
50%

Felony Drug Recidivism

20%
40%
10%
30%

0%

Low Risk
WSIPP, 2007

Moderate
Risk

High Drug

High
Property

High
Violent

20%
10%

Exhibit 12 displays the AUCs for the prediction risk scores
and recidivism. The AUCs show there is moderate
predictive strength by ethnicity except for violent property
felony recidivism for African and Asian Americans, which
show rates just below moderate predictive strength.

Low

Moderate

High Drug

50%

Felony Property Recidivism

Exhibit 12

40%

AUCs for Risk Scores Predicting Type of
Recidivism by Ethnicity

Ethnicity
European
African
Native
Asian
Hispanic
Total Sample

0%

30%

Type of Recidivism
Property/
Violent
Felony
Violent
0.736
0.740
0.730
0.723
0.691
0.700
0.716
0.733
0.716
0.748
0.678
0.710
0.742
0.774
0.729
0.742
0.733
0.732

20%
10%
0%
Low

Moderate

High Drug

50%

Violent Felony Recidivism

Exhibit 13 displays the recidivism rates of offenders by
ethnicity for each of the risk levels. For felony property
recidivism, Asian Americans classified as high drug have a
recidivism rate similar to Asian Americans classified as
high property. Ideally, these high drug offenders would be
classified as high property. This appears to be a
difference in ethnicity that is not fully captured by the static
risk instrument.

40%
30%
20%
10%
0%
Low

For felony drug recidivism, African Americans classified as
high property and high violent risk have higher recidivism
rates than other ethnicities in these risk categories;
however, they are captured in a higher risk category.

High Drug

European American
Asian American
WSIPP, 2007

13

Moderate

African American

Native American

Hispanic

Most Serious Offense
Exhibit 14 shows the percentage distribution of
offenses by risk level. Over 60 percent of all drug
offenders are classified as low risk. In addition, 54
percent of all sex offenders are classified as low risk.

Exhibit 16
Recidivism Rates by Risk Category for
Most Serious Offense Type
80%

Felony Recidivism

Exhibit 14

Percentage Distribution by Risk Level

60%

80%

Drug
Property
Sex
Violent Not Sex

60%

40%

20%
40%
0%
Low

20%

Moderate

High Drug

High
Property

High Violent

High
Property

High Violent

High
Property

High Violent

High
Property

High Violent

50%

0%
Low Risk
WSIPP, 2007

Moderate
Risk

High Drug

High
Property

Felony Drug Recidivism

High Violent
40%
30%

Exhibit 15 displays the AUCs for the prediction risk
scores and recidivism. The AUCs show there is
weak to strong prediction depending on the most
serious offense type and the type of recidivism. For
drug offenders, there is weak prediction for felony
and violent property recidivism, but moderate
prediction for violent recidivism. There is also weak
prediction for violent non-sex offenders with violent
recidivism. For sex offenders, prediction of felony
recidivism is strong.

20%
10%
0%
Low

50%

High Drug

Felony Property Recidivism

40%

Exhibit 15

AUCs for Risk Scores Predicting Type of
Recidivism by Offense Type

Offense Type
Drug
Property
Sex
Violent non-sex
Total Sample

Moderate

30%
20%

Type of Recidivism
Property/
Felony
Violent
Violent
0.683
0.674
0.709
0.743
0.723
0.714
0.802
0.764
0.740
0.740
0.714
0.687
0.742
0.733
0.732

10%
0%
Low

Moderate

High Drug

50%

Violent Felony Recidivism
40%

Exhibit 16 displays the recidivism rates by most
serious offense type for each of the risk levels. There
are differences in property and drug recidivism rates
by offense type. Property offenders classified as high
property and high violent have the highest felony
property recidivism rates. In addition, drug offenders
classified as high property and high violent have the
highest felony drug recidivism rates. This indicates
these types of offenders have a very diverse criminal
record. Regardless, on the seriousness scale, they
are already considered high risk and are supervised
at a higher level.

30%
20%
10%
0%
Low

Drug
WSIPP, 2007

14

Moderate

Property

High Drug

Sex

Violent Not Sex

For further information, contact: Robert Barnoski at (360) 586-2744 or barney@wsipp.wa.gov;
or Elizabeth K. Drake at (360) 586-2767 or ekdrake@wsipp.wa.gov.
Document No. 07-03-1201
Washington State
Institute for
Public Policy
The Washington State Legislature created the Washington State Institute for Public Policy in 1983. A Board of Directors—representing the legislature,
the governor, and public universities—governs the Institute and guides the
16development of all activities. The Institute’s mission is to carry out practical
research, at legislative direction, on issues of importance to Washington State.

 

 

Federal Prison Handbook - Side
Advertise Here 4th Ad
Federal Prison Handbook - Side