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

February 2006

SEX OFFENDER SENTENCING IN WASHINGTON STATE:
PREDICTING RECIDIVISM BASED ON THE LSI-R
The 2004 Legislature directed the Washington State
Institute for Public Policy (Institute) to conduct a
comprehensive analysis and evaluation of the impact
and effectiveness of current sex offender sentencing
policies.1 Because this is an extensive topic, we are
publishing a series of reports.
Two previous reports in this sex offender sentencing
series addressed the prediction of felony sex
recidivism.2 Thus far, we have found that the
prediction tool used by the the End of Sentence
Review Committee has little to no predictive
accuracy.3 In addition, we determined a “static” risk
tool being developed by the Institute for the
Department of Corrections (DOC) predicts felony and
violent felony recidivim moderately well but does not
accurately predict felony sex recidivism.4
One additional risk tool used by DOC warrants review:
the Level of Service Inventory-Revised (LSI-R). A
2003 Institute study found that this instrument is not a
strong predictor of felony and violent felony recidivism
for Washington State offenders.5
This report analyzes the relative accuracy of
the LSI-R in predicting felony sex recidivism
for Washington State sex offenders.

SUMMARY
In 1999, the Washington State Department of Corrections
began using a risk for reoffense tool, the Level of Service
Inventory-Revised (LSI-R), as part of the offender risk
classification system. A 2003 Institute study found that
this instrument is not a strong predictor of felony and
violent felony recidivism for Washington State offenders.
This report analyzes the relative accuracy of the LSI-R in
predicting felony sex recidivism for Washington State sex
offenders.
Findings
• For sex offenders, the LSI-R score predicts felony
sex recidivism with weak accuracy.
• Five items on the LSI-R can be combined to predict
felony sex recidivism with moderate accuracy.
• Based on these five items, 4 percent of the study
sample can be placed in a high risk group with an
11 percent chance of recidivating with a felony sex
offense.
These results are encouraging, since they indicate that
moderate predictive accuracy for felony sex recidivism is
possible. The question remains for Washington State:
Can a more accurate prediction tool be created?
Answering this question requires the following:
1. A rigorous review of existing sex offender risk
assessment research;
2. Involvement of staff who will use the tool; and

1

ESHB 2400, Chapter 176, Laws of 2004.
2
R. Barnoski, 2006, Sex Offender Sentencing in Washington
State: Predicting Recidivism Based on Demographics and
Criminal History, Olympia: Washington State Institute for Public
Policy (Document No. 06-01-1207); and R. Barnoski, 2006, Sex
Offender Sentencing in Washington State: Sex Offender Risk
Level Classification Tool and Recidivism, Olympia: Washington
State Institute for Public Policy (Document No. 06-01-1204).
3
Sex Offender Risk Level Classification Tool.
4
Predicting Recidivism Based on Demographics and Criminal
History.
5
R. Barnoski, 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.

3. Statistical analyses of key items to create a tool with
the highest predictive accuracy.

This report focuses on predicting felony sex
recidivism.6 Measuring sex offense recidivism
requires that the offender have a five-year time
period in the community and one additional year for
processing in the courts.7 Because DOC began
using the LSI-R in 1999, recidivism rates can be
calculated for offenders placed in the community
during that year. That is, 1999 is the only year LSIR and felony sex recidivism data are both available,
due to the recidivism measurement requirements.
Exhibit 1 displays the felony sex recidivism rates for
sex offenders with and without an LSI-R. During
1999, 1,102 sex offenders were placed in the
community following confinement in prison or jail or
were sentenced to community supervision. An LSIR was administered by DOC staff within 90 days of
community placement to 602 (55 percent) of these
offenders.

Exhibit 2

5-Year Felony Sex Recidivism Rates
By LSI-R Score
15%

11.5%
10%

5.6%
4.2%
5%

2.0%
0.0%
0%

0–9 (6%)

10–19 (33%) 20–29 (35%) 30–39 (21%)

40–54 (4%)

LSI-R Score

Sex offenders with an LSI-R have higher felony
sex recidivism rates (3.8 percent) than those
without an LSI-R (2.4 percent); this is statistically
significant at the 0.18 probability level. That is,
sex offenders with LSI-R scores have a higher
chance of reoffending.
Exhibit 1

5-Year Felony Sex Recidivism Rates of
Sex Offenders With and Without an LSI-R

With LSI-R
Without LSI-R
Total

Number
of
Offenders
602
500
1,102

Percent
of
Offenders
55%
45%
100%

5-Year
Felony Sex
Recidivism*
3.8%
2.4%
3.2%

The best measure of predictive accuracy between
recidivism and the risk-level categories is the Area
Under the Receiver Operating Characteristic
(AUC).8 An AUC can vary between .500 and
1.00. AUCs in the .500s indicate little to no
predictive accuracy, .600s weak, .700s moderate,
and those above .800 have strong predictive
accuracy.9
The AUC for Exhibit 1 is 0.650, indicating that the
LSI-R score has weak predictive accuracy for
felony sex recidivism. However, some of the
individual items on the LSI-R may have stronger
predictive accuracy.

* Statistically significant at the 0.18 probability level.

Exhibit 2 shows the relationship between felony
sex recidivism rates and LSI-R scores. The number
in parentheses is the percentage of sex offenders in
the study sample with that range of scores. For
example, 6 percent of sex offenders had an LSI-R
score between 0 and 9, and these offenders had a
felony sex recidivism rate of 0 percent. In
comparison, 4 percent of the sex offenders with an
LSI-R score of 40 to 54 had an 11.5 percent
recidivism rate.
8

6

Felony sex recidivism is defined as a conviction for a felony
sex offense in a Washington State court.
7
R. Barnoski, 2005, Sex Offender Sentencing in Washington
State: Measuring Recidivism, Olympia: Washington State
Institute for Public Policy, Document No. 05-08-1202.

M.E. Rice & G.T. Harris, 2005, Comparing Effect Sizes in
Follow-Up Studies: ROC Area, Cohen’s d, and r, Law and
Human Behavior 29(5): 615-620. V.L. Quinsey, G.T. Harris,
M.E. Rice, & C.A. Cormier, 2005, Violent Offenders:
Appraising and Managing Risk, Second Edition,
Washington, DC: American Psychological Association.
9
T.G. Tape, 2003, Interpreting Diagnostic Tests, The Area
Under the ROC Curve, Omaha: University of Nebraska
Medical Center, see: http://gim.unmc.edu/dxtests/roc3.htm.

Exhibit 3 shows the five items included in the
resulting felony sex recidivism prediction equation.
The most influential item in the equation measures
whether the offender was “ever punished for
institutional misconduct.” The item measuring
“financial problems” has an odds ration of less than
1.0, indicating that having financial problems was
associated with a lower felony sex recidivism rate—
the opposite of what one might expect. The AUC for
predicting felony sex recidivism from these items is
0.778, indicating moderate predictive accuracy.

total sample is 3.8 percent; the low risk group’s
rate is 1.5 percent, and the high risk group’s rate
is 11.4 percent. Seventy-seven percent of the
sample is in the low risk group, and 23 percent is
in the high risk group.
Exhibit 4

Recidivism Rates Based on Multivariate Analysis
for LSI-R’s Low- and High-Risk Groups
Five-Year Recidivism Rate

Technical Appendix A shows the AUCs for each
item on the LSI-R.10 Twelve items have AUCs in the
0.600s indicating weak accuracy in predicting felony
sex recidivism; the remaining items have little to no
predictive accuracy. Multivariate statistical
analyses, stepwise logistic regression, were used to
determine if these individual LSI-R items can be
combined to form a better predictor of felony sex
recidivism. Five items were retained in the
prediction equation.11

11.4%

3.8%
1.5%

Total (100%)

Low (77%)

High (23%)

LSI-R Risk Level

Exhibit 3

Combination of LSI-R Items Best Predicting
5-Year Felony Sex Recidivism
AUC = 0.778

LSI-R Item
8. Ever punished for
institutional misconduct
23. Dissatisfaction with marital
or equivalent situation
21. Financial problems score
53. Poor attitude toward
sentence
26. Criminal family/spouse

Odds
Ratio

Prob.
Level

Std.
Est.

3.7

0.02

0.36

1.7
0.4

0.03
0.05

0.26
-0.24

2.2
2.1

0.10
0.11

0.22
0.21

Prob. Level = probability level
Std. Est. = Standardized parameter estimate

Discussion. The results of the multivariate
analysis of the individual LSI-R items are
encouraging, since the AUC indicates moderate
predictive accuracy for felony sex recidivism.
That is, items from the LSI-R may contribute to a
better predictor of felony sex recidivism.
However, this question still remains for
Washington State: Can a more accurate
prediction tool be created?
Answering this question requires the following:
1. A rigorous review of existing sex offender

risk assessment research;
2. Involvement of staff who will use the tool; and

Exhibit 4 displays the felony sex recidivism rates for
offenders classified as either low or high risk for
sexual reoffending based on the prediction equation
in Exhibit 3. It was not possible to form a moderate
risk group. The felony sex recidivism rate for the

10

Most LSI-R items have a yes or no response with a yes
counted as one risk point and a no counted as zero points. The
items with a four-point response are ordered so that higher
scores coincide with less satisfactory or higher risk responses.
In addition, the LSI-R scoring manual converts all of these fourpoint responses to yes/no responses when computing the LSI-R
total score. These are labeled item scores in this report.
11
Only items with a probability level below 0.15 are retained in
the stepwise regression.

3. Statistical analyses of key items to create a

tool with the highest predictive accuracy.

Technical Appendix A

Predictive Accuracy of Individual LSI-R Items
For Washington State Sex Offenders
LSI-R Item
8. Ever Punished for Miss Conduct
53. Poor Attitude Toward Sentence
23. Dissatisfaction Family
24. Non-Rewarding Parents
25. Non-Rewarding Relatives Score
29. Live in High Crime Area
51. Supportive of Crime
51. Supportive of Crime Score
9. Violation/Charge on Supervision
24. Non-Rewarding Parents Score
31. Better Use of Time
26. Criminal Family/Spouse
23. Dissatisfaction Family Score
31. Better Use of Time Score
35. Absence of Non-criminal Acquaintances
33. Some Criminal Acquaintances
52. Unfavorable Attitude Toward Convention
34.Some Criminal Friends
5. Arrested Under Age 16
38. Drug Problem Ever
19. Peer Interactions Score
20. Authority Interactions Score
43. School/Work Problems
27. Unsatisfactory Accommodation
13. Never Employed a Full Year
17. Suspended or Expelled
45. Other Drug Alcohol Indicators
54. Poor Attitude Toward Supervision
1. At Least One Prior Adult Conviction
14. Ever Been Fired
2. Two or More Prior Adult Convictions
36. Absence of Non-criminal Friends
49. Mental Health Current
42. Martial/Family Problems

AUC
0.660
0.628
0.627
0.627
0.627
0.614
0.612
0.611
0.607
0.603
0.602
0.601
0.592
0.591
0.589
0.587
0.578
0.577
0.576
0.576
0.576
0.576
0.575
0.573
0.571
0.571
0.570
0.569
0.568
0.563
0.558
0.557
0.556
0.554

LSI-R Item
16. Education Less Than Grade 12
19. Peer Interactions
20. Authority Interactions
37. Alcohol Problem Ever
28. Moved Three or More Times in a Year
40. Current Drug Problem
41. Law Violations Problem
27. Unsatisfactory Accommodation Score
39. Current Alcohol Problem
11. Currently Unemployed
52. Unfavorable Attitude to Convention Score
50. Psychological Indicators
6. Ever Incarcerated
46. Emotional/Personal Moderate Inference
12. Frequently Unemployed
4. Three or More Present Offenses
40. Current Drug Problem Score
22. Reliance on Social Assistance
21. Financial Problems Score
10. Record of Assault/Violence
30. Lack of Leisure/Recreation
47. Active Psychosis
39. Current Alcohol Problem Score
18. Participation or Performance Score
3. Three or More Prior Adult Convictions
25. Non Rewarding Relatives
32. Social Isolate
18. Participation or Performance
7. Escape History
44. Medical Problems
48. Mental Health Past Treatment
15. Education Less Than Grade 10
21. Financial Problems

AUC
0.553
0.550
0.550
0.547
0.546
0.546
0.546
0.545
0.543
0.542
0.539
0.537
0.536
0.535
0.531
0.528
0.527
0.526
0.525
0.524
0.522
0.522
0.518
0.515
0.514
0.514
0.513
0.510
0.508
0.507
0.502
0.501
0.500

AUC = Area Under the Receiver Operating Characteristic
Most LSI-R items have a yes or no response with a yes counted as one risk point and a no counted as zero points. The items
with a four-point response are ordered so that higher scores coincide with less satisfactory or higher risk responses. In addition,
the LSI-R scoring manual converts all of these four-point responses to yes/no responses when computing the LSI-R total score.
These are labeled item scores in this report (e.g. item 25 “Non-Rewarding Relative Score” is a yes/no version of item 25 “NonRewarding Relative”).

For further information, contact Robert Barnoski at
(360) 586-2744 or barney@wsipp.wa.gov

Document No. 06-02-1201

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 development of all activities. The Institute’s mission is to carry out practical
research, at legislative direction, on issues of importance to Washington State.