Journal of Interpersonal Violence28(7) 1537 –1558
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468250 JIV28710.1177/0886260512468250Journal of Interpersonal ViolenceMessing and Thaller2012
1Arizona State University, Phoenix AZ, USA
Corresponding Author:Jill Theresa Messing, MSW, PhD, Assistant Professor, School of Social Work, Arizona State University, 411 N Central Ave., Suite 800, Phoenix AZ 85004, USA Email: [email protected]
The Average Predictive Validity of Intimate Partner Violence Risk Assessment Instruments
Jill Theresa Messing, MSW, PhD1 and Jonel Thaller, MSW1
Abstract
The field of intimate partner violence (IPV) risk assessment (predicting recidivism, lethality) is fast growing, and the majority of research exam-ining the predictive validity of IPV risk assessment instruments has been conducted in the past decade. This study examines the average predictive validity weighted by sample size of five stand alone IPV risk assessment instruments that have been validated in multiple research studies using the Receiver Operating Characteristic Area Under the Curve (AUC). The On-tario Domestic Assault Risk Assessment (ODARA) has the highest average weighted AUC (=.666, k=5) followed, in order of most to least predictive, by the Spousal Assault Risk Assessment (SARA; AUC=.628, k=6), the Dan-ger Assessment (DA; AUC=.618, k=4), the Domestic Violence Screening Inventory (DVSI; AUC=.582, k=3), and the Kingston Screening Instrument for Domestic Violence (K-SID; AUC=.537, k=2). The effect size for the average AUCs for IPV risk assessment instruments is small, with the excep-tion of a medium effect size for the ODARA. Of the 20 measures of predic-tive validity included in this analysis, the risk assessment was administered correctly in nine (45%). IPV risk assessment is relatively new, and the use of proxy instruments and utilization of risk assessment instruments in settings for which they were not created is widespread. While waiting for a more
Article
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1538 Journal of Interpersonal Violence 28(7)
rigorous body of research, factors in addition to predictive validity must be taken into consideration (e.g., setting, outcome, skills of the assessor, access to information) when choosing which risk assessment instrument is appropriate for use in a particular practice setting.
Keywords
intimate partner violence, domestic violence, homicide, risk assessment, predictive validity, receiver operating characteristic
Lifetime prevalence of intimate partner violence (IPV) in the United States is estimated to be between 25% and 35% (Black et al., 2011; Breiding, Black, & Ryan, 2005; Tjaden & Thoennes, 2000). IPV has significant con-sequences for victims, including poor health and mental health outcomes (Campbell, 2002). Between 30% and 70% of femicide (Campbell & Runyon, 1998; Russell, 1992, 2001) victims are killed by an intimate (Arbuckle et al., 1996; Bailey et al., 1997; Campbell, Glass, Sharps, Laughon, & Bloom, 2007; Catalano, Smith, Snyder, & Rand, 2009; Kellermann & Heron, 1999; Puzone, Saltzman, Kresnow, Thompson, & Mercy, 2000; Rennison & Welchans, 2000). Physical IPV is reported to have preceded an intimate partner homicide in 65% to 80% of cases, making IPV the single largest risk factor for intimate partner femicide (Campbell et al., 2003, 2007; Moracco, Runyon, & Butts, 1998; Pataki, 1997; Sharps, Campbell, Campbell, Gary, & Webster, 2003).
Any effort to manage perpetrators of IPV implies a calculation of risk (Hilton, 2010; Kropp, 2004), and victims of IPV should be educated about their risk and potential risk factors (Campbell, 2004). IPV risk assessments have been suggested for use in counseling abused women (e.g., Campbell, 2004; Kress, 2008), determining who is appropriate for batterers’ treatment (e.g., Jones & Gondolf, 2001; Maiuro & Eberle, 2008; Morgan & Gilchrist, 2010), and identifying who is likely to be killed by intimate partner (e.g., Campbell, Webster, & Glass, 2009; Hilton, Grant, & Rice, 2001) as well as informing police, prosecutorial, and judicial responses to domestic violence (e.g., Bennett, Goodman, & Dutton, 2000; Hilton, Harris, Rice, Lang, Cormier & Lines, 2004; Roehl & Guertin, 2000). Risk assessment instruments also have the ability to provide a consistent language and measurement of risk factors across disparate intervention systems and actors (Kropp, 2004; Shepherd, Falk, & Elliot, 2002).
The use of IPV risk assessment within the field is growing, as is schol-arly literature devoted to the subject (for reviews, see Dutton & Kropp,
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Messing and Thaller 1539
2000; Kropp 2008, 2004). Within overburdened service systems, the need to determine and treat the most serious cases of IPV has brought about a proliferation of statistical assessments and standardized decision-making tools. With reliability and validity data published for only three risk assess-ment instruments by 2000 (Dutton & Kropp, 2000), the majority of validation studies have been conducted within the past decade. To begin to understand the aggregate measured efficacy of these tools, this research calculates the average predictive validity weighted by sample size of five validated IPV risk assessment instruments—the Danger Assessment (DA), the Domestic Violence Screening Inventory (DVSI), the Kingston Screening Instrument for Domestic Violence (K-SID), the Spousal Assault Risk Assessment (SARA), and the Ontario Domestic Assault Risk Assessment (ODARA).
Predictive ValidityPredictive validity, the correct prediction of future events, is the most impor-tant measurement of the efficacy of a risk assessment instrument. The receiver operating characteristic (ROC) is currently the most common means to assess predictive validity of risk assessment instruments (Douglas, Blanchard, Guy, Reeves, & Weir, 2000) and has been suggested as a standard measure of predictive validity across disciplines (Rice & Harris, 2005). The ROC is a curve shown on a graph that plots sensitivity as a function of the false-positive rate, and the area under the curve (AUC) is the proportion of the graph that lies under the plotted ROC curve (Douglas et al., 2000; Rice & Harris, 1995). The AUC can range from 0 to 1.0, with .50 indicating pre-diction no better than chance and 1.0 indicating perfect positive prediction (Douglas et al., 2000). The AUC can be interpreted as the probability that any randomly selected recidivist would have a higher score on the risk assessment instrument than any randomly selected nonrecidivist (Rice & Harris, 1995, 2005). For example, an AUC of .65 indicates a 65% chance that a randomly selected recidivist would have a higher score on the risk assess-ment instrument than a randomly selected nonrecidivist (Douglas et al., 2000; Rice & Harris, 1995).
MethodAverage AUCs, weighted by sample size, were calculated for each stand alone IPV risk assessment instrument where more than one AUC was avail-able as well as for victim prediction of risk. An average AUC is considered significantly different than chance when the 95% confidence interval (CI) for the average does not include .50; average AUCs are considered significantly
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1540 Journal of Interpersonal Violence 28(7)
different from one another when 95% CIs do not overlap (Hilton, Harris, Popham, & Lang, 2010).
Study and sample characteristics may affect predictive validity (Yang, Wong, & Coid, 2010), and validation studies often report several AUCs based on the same or similar samples within a single study. Therefore, choices were made about which AUCs were included in the average with the aim of achieving as much consistency as possible in regard to study features. Where available, full samples (rather than subsamples) and samples used to test the instrument (as opposed to samples used in the creation of the instru-ment) were used. Predictive validity may also vary depending on the out-come measure (Yang et al., 2010). Thus, when multiple outcomes were reported, a measure of any IPV reassault was utilized rather than a measure of severe reassault or violent reassault against victims other than an intimate partner. Hilton and Harris (2009) make an excellent argument for removing ambiguous recidivists (i.e., where it is unknown whether the victim was an intimate partner) from the follow-up sample. However, to be consistent with other research studies, we utilized AUCs that included ambiguous recidivists as perpetrators who did not reassault their intimate partners. Similarly, Campbell, O’Sullivan, Roehl, and Webster (2005) controlled for victim protective actions in some analyses to manage actions that might interfere with the prediction of future violence, but only analyses that did not control for victim protective actions were included in the average AUC to be consistent across research studies. When multiple follow-up time periods were included (ranging from 6 months to 5.1 years), the longest follow-up period was chosen, and time periods during which the offender was incarcerated or otherwise had no contact with the victim were excluded. AUCs based on continuous scoring were utilized wherever possible. Only data collected prospectively—that is, information obtained prior to knowl-edge of recidivism—were included. Studies were excluded when the out-come was predetermined, such as when a sample of approximately half recidivists and nonrecidivists was selected.
ResultsTwenty-five articles and reports have examined the predictive validity of one or more stand alone IPV risk assessment instruments or the ability of an IPV survivor to predict her own risk. Of these, three examined a risk assessment instrument that has only been validated once (Bourgon & Bonta, 2004; Murphy, Morrel, Elliott, & Neavins, 2003; Williams & Grant, 2006), one examined an adaptation of an existing risk assessment instrument (Messing,
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Amanor-Boadu, Cavanaugh, Glass & Campbell, in press), and another nine used a statistic other than the ROC to examine predictive validity (Andrés-Pueyo, López, & Álvarez, 2008; Bell, Cattaneo, Goodman, & Dutton, 2008; Cairns, 2004; Cattaneo, Bell, Goodman, Dutton, 2007; Cattaneo & Goodman, 2003; Goodman, Dutton, & Bennett, 2000; Hisashima, 2008; Weisz, Tolman, & Saunders, 2000; Wong & Hisashima, 2008). Two others were excluded because they were not prospective (Campbell et al., 2009) or had a predeter-mined outcome (Kropp & Hart, 2000). Therefore, ten separate studies met the inclusion criteria and were utilized in the analysis (Belfrage et al., 2012; Campbell et al., 2005; Gran & Wedin, 2002; Heckert & Gondolf, 2004; Hilton et al., 2004, 2010; Hilton & Harris, 2009; Hilton, Harris, Rice, Houghton & Eke, 2008; Rettenberger & Eher, 2012; Williams & Houghton, 2004). These studies represent 20 validations of IPV risk assessment instru-ments and two validations of victim prediction of risk.
To date, multiple research studies have examined the predictive abil-ity of five stand alone IPV risk assessment instruments. The DA, admin-istered and scored via victim interview, is intended to predict lethality and was developed for use by health care and social service providers to assist with safety planning, victim empowerment, and education (Campbell, 1986; Campbell et al., 2003). The DVSI, scored through crim-inal justice case file information, is intended to assist criminal justice decision makers in determining the best pretrial option for arrestees (Williams & Houghton, 2004). The K-SID, scored based on criminal jus-tice case files, is intended to inform decision making around criminal sentencing, probation, and release (Gelles & Straus, 1990 as cited in Campbell et al., 2005). The SARA, scored by a skilled evaluator with access to case files, clinical information, and/or interviews with the vic-tim and offender was created as a violence prevention tool for use in the criminal justice system (Kropp & Hart, 2000). Finally, the ODARA, scored using information from criminal case files and developed for use by police officers, is intended to predict the frequency and severity of future violence (Hilton et al., 2004).
Reassault—which occurred in 21% (Belfrage et al., 2012) to 49% (Hilton et al., 2008) of cases—was measured through criminal justice data (e.g., arrest, conviction) (Belfrage et al., 2012; Grann & Wedin, 2002; Hilton et al., 2004, 2008, 2010; Hilton & Harris, 2009; Rettenberger & Eher, 2012; Williams & Houghton, 2004) or victim report (Campbell et al., 2005; Heckert & Gondolf, 2004). Sample size ranged from 56 (Grann & Wedin, 2002) to 1,465 (Williams & Houghton, 2004). An intervention occurred in all studies; data were collected after perpetrators were in police
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1542 Journal of Interpersonal Violence 28(7)
custody (Hilton & Harris, 2009; Hilton et al., 2004, 2010; Grann & Wedin, 2002; Rettenberger & Eher, 2012), placed on probation (Grann & Wedin, 2002; Hilton & Harris, 2009), confronted or arrested by police (Belfrage et al., 2012; Grann & Wedin, 2002; Hilton et al., 2004; Hilton & Harris, 2009; Rettenberger & Eher, 2012), enrolled in batterer’s treatment pro-grams (Grann & Wedin, 2002; Heckert & Gondolf, 2004), or after victims had sought help from various agencies (Campbell et al., 2005).
As demonstrated in Table 1, the ODARA (.666) has the highest average weighted AUC followed, in order of most to least predictive, by the SARA (.628), DA (.618), DVSI (.582), and K-SID (.537). None of the 95% confi-dence intervals (CI) for the average AUCs of risk assessment instruments cross .50 (i.e., they all predict better than chance), and none of these 95% CIs cross one another (i.e., they all predict reassault significantly better or worse than one another). The average AUC for victim risk prediction is .615, and the 95% CI crosses the 95% CI of the DA. The effect size for the average AUCs of IPV risk assessments is small, with the exception of a medium effect size for the ODARA (Rice & Harris, 2005).
DiscussionAs demonstrated, it is possible to create average AUCs for the most widely used and tested risk assessments and to compare the predictive validity of these risk assessments across settings and samples using the ROC curve. However, there is a notable lack of standardization across studies. When creating average AUCs, 20 analyses across five risk assessment instruments were included; of these 20 analyses, only 9 (45%) administered the risk
Table 1. Calculated Average AUC of IPV Risk Assessments
InstrumentAverage
AUC 95% CI AUC Range Effect Size k n
ODARA .666 .665-.668 .638-.72 d = .608 5 1,053SARA .628 .627, 629 .59-.65 d = .453-.467 6 2,656DA .618 .616, 620 .56-.70 d = .424 4 2,519DVSI .582 .581, 584 .508-.61 d = .283-.297 3 2,896K-SID .537 .536, 538 .516-.57 d = .127-.141 2 1,281Victim assessment .615 .614, 616 .599-.64 d = .410-.424 2 1,281
Note: ODARA = Ontario Domestic Assault Risk Assessment; SARA = Spousal Assault Risk Assessment; DA = Danger Assessment; DVSI = Domestic Violence Screening Inventory; K-SID = Kingston Screening Instrument for Domestic Violence.
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Messing and Thaller 1543
assessment correctly. Of these nine, five of the correctly administered risk assessments were used in tests of the ODARA. The use of proxy instru-ments (e.g., using fewer items or different questions than intended) and the utilization of risk assessment instruments in settings for which they were not created (e.g., gathering case file information as opposed to interview data and vice versa) is widespread. Outcomes (e.g., reassault, repeat reas-sault, severe violence, lethality) and time to outcome (6-61.2 months) range widely. To gain a more nuanced understanding of the predictive ability of these risk assessment instruments and the utility of the average AUC analy-sis resulting from this analysis, it is important to attend to the specific meth-odological features used in validation studies of these risk assessment instruments (see Appendix).
The DA was intended to predict lethality, and its predictive power increases when examining severe reassault or when predicting attempted femicide. Yet, to maintain uniformity of studies in the average AUC analy-sis, only research that examined reassault as an outcome was included. The DA had an AUC of .916 when predicting attempted femicides (Campbell et al., 2009), an AUC of .674 when predicting severe reassault not controlling for protective actions taken by the victim, and an AUC of .687 for severe reassault when controlling for victim protective actions (Campbell et al., 2005). The DA was updated from 15 to 20 items in 2003 (Campbell et al., 2003), yet three of the four studies included in the average AUC used the previous 15-item DA (Heckert & Gondolf, 2004; Hilton et al., 2004, 2008). It is important also to note the use of proxy items (Heckert & Gondolf, 2004) and the coding of the DA from criminal justice case files (Hilton et al., 2004, 2008) though this instrument is intended to be used in an inter-view with the victim.
The SARA is typically tested for predictive validity utilizing a score cre-ated by summing the items though the original intent was for clinicians to review the items then apply structured professional judgment (Kropp & Hart, 2000); when using summary risk ratings, the AUC for the SARA ranges from .57 (Belfrage et al., 2012) to .70 (Kropp & Hart, 2000) though neither of these AUCs were included in the average. Three of the six research studies included in the average coded the 20-item SARA based on correctional system case files only (Grann & Wedin, 2002; Hilton et al., 2004, 2008) rather than combining case file information with interview data as intended by the instrument’s creators. Similar to the DA, it is impor-tant to note the use of proxy items and the exclusion of certain items in vali-dation studies included in the average AUC (Heckert & Gondolf, 2004).
Fewer analyses are available for the K-SID and the DVSI and, similar to the DA and SARA, these instruments were not administered correctly in the majority of studies. While the average AUC of the DVSI and the K-SID
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1544 Journal of Interpersonal Violence 28(7)
were both significantly different than .50, both of these instruments have been found in previous studies to have AUCs that do not differ from chance. AUCs for the DVSI differed significantly from chance when examining severe reassault, severe reassault controlling for victim protective actions (Campbell et al., 2005), severe threatening behaviors, and severe physical assault (Williams & Houghton, 2004), but did not significantly differ from chance in the same studies when examining reassault (Campbell et al., 2005), controlling behaviors, less threatening behaviors, or less severe vio-lence (Williams & Houghton, 2004). As such, the DVSI may be more adept at predicting severe reassault. The K-SID appeared to perform better when controlling for victim protective actions, regardless of the outcome assessed (Campbell et al., 2005).
When compared with the validation of other instruments, research studies examining the ODARA are the most consistent in terms of methodology, and the same primary researchers conducted four of five validation studies. A notable difference among studies conducted by Hilton and colleagues is their definition of an intimate partner relationship, with only the most recent research including dating partners who had not cohabited (Hilton et al., 2010).
The predictive validity of victims’ prediction of risk is not standardized, thus there is no criterion for correct administration. However, consistency in measurement is important to ensure that studies are measuring the same con-struct. Both studies measuring victims’ prediction of risk asked women how likely it was that their partner would be physically abusive or violent in the future. Heckert and Gondolf (2004) collapsed their 5-point scale (very likely to very unlikely) into very likely/likely and all else, while Campbell and col-leagues (2005) assessed perception of risk on a 10-point scale (from no chance to sure it will happen). In the Campbell and colleagues study, women appeared to be slightly better at predicting future violence when controlling for protective actions.
While predictive validity is the most important test of the efficacy of a risk assessment instrument, additional factors must be taken into consideration when choosing which risk assessment instrument is appropriate for use in a particular setting (Yang et al., 2010). The SARA, DVSI, and K-SID were cre-ated for use within the criminal justice system, the ODARA was created for use by police officers and the DA was created for use by health and social service professionals. Risk assessments were created to be used in safety planning with victims (DA), for the prevention of future violence (SARA), to assist with criminal justice decision making (ODARA, DVSI, K-SID), or for a combination of these functions (SARA). The setting and function of the risk assessment will also determine the assessor’s access to various types of infor-mation. The DA is intended to be administered through an interview with the victim; the ODARA, K-SID, and DVSI are all intended to be completed
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Messing and Thaller 1545
using criminal justice case file information; and the SARA may require access to case files, clinical information, and interviews with the victim and offender. The SARA is somewhat unique in that the assessor is intended to have advanced training. Finally, evaluators of risk may also want to consider the outcome they are interested in predicting. While most risk assessments are intended to predict reassault or reoffense, the DA is intended to predict homicide.
This study has several limitations. First, conservative inclusion criteria enhance the rigor of the analysis but exclude studies that may provide valu-able information about predictive validity. Conversely, the criteria could have been even more stringent. In particular, as previously described, risk assess-ment instruments were incorrectly administered in many of the studies included in the analysis. Correct instrument administration would be an ideal inclusion criterion, but would also severely limit the data available for analy-sis and, therefore, was not practical. The use of overlapping CIs to determine whether the average AUCs are different from one another is also a conserva-tive strategy, and the translational meaning of this distinction is questionable. In a criminal justice, health, or social service setting, there may be little prac-tical difference between a 62% and 63% chance that a randomly selected recidivist would score higher on a particular instrument than a randomly selected nonrecidivist. Finally, this review is limited in that it only examines research studies that utilized the ROC statistic. Regardless, the strengths and novelty of this analysis outweigh its limitations.
IPV risk assessment is relatively new and, as such, rigorous study of predictive validity using the AUC as a standard measure is necessary (Douglas et al., 2000; Rice & Harris, 2005), as is reporting the standard errors of AUCs to facilitate meta-analysis (Walter, 2002). Ideally, valida-tion studies for IPV risk assessment instruments would correctly administer instruments and evaluate them across a range of diverse populations for the outcome and in the setting for which the instrument was intended. Future research should also consider utilizing more nuanced outcomes (e.g., sever-ity, frequency of reassault; Heckert & Gondolf, 2005), removing ambigu-ous recidivists from analyses (Hilton & Harris, 2009) and controlling for victim protective actions (Campbell et al., 2005). The management of risk is an important aspect of evidence-based intervention with survivors and perpetrators of IPV. The need for research that attends to the unique con-textual factors of practice with these clients (Gondolf, 2001) is necessary to create interventions that incorporate research evidence, clinical exper-tise, and client self-determination (Gambrill, 2012). While waiting for a more rigorous body of research on IPV risk assessment, users must con-sider the intended setting, outcome, and skills of the assessor as well as access to the victim, offender, and case files when choosing a risk assess-ment instrument.
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1546
App
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1547
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1548
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incl
uded
in
terv
iew
su
mm
arie
s
Can
ada
Rea
ssau
lt; a
ppro
x.
equa
l num
ber
of
reci
divi
sts
and
nonr
ecid
ivis
ts
chos
en
37.6
to
46.6
51%
Perp
etra
tors
at
tend
ed g
roup
tr
eatm
ent
W
illia
ms
and
Hou
ghto
n,
2004
Yesa
Yes
.65
434
Cri
min
al ju
stic
e ca
se fi
les,
vict
im
inte
rvie
w w
ith a
su
bsam
ple
Uni
ted
Stat
esR
eass
ault
or a
re
stra
inin
g or
der
viol
atio
n
18?
Full
sam
ple
(n =
14
65)
reci
divi
sm
rate
= 2
9%;
sum
med
item
s fo
r to
tal s
core
a Cor
rect
ly a
dmin
iste
red,
but
AU
C is
bas
ed o
n a
sum
med
tot
al s
core
, not
the
sum
mar
y ri
sk r
atin
g.
App
endi
x (c
ont
inue
d)
at ARIZONA STATE UNIV on May 12, 2013jiv.sagepub.comDownloaded from
1549
Inst
rum
ent
Art
icle
C
itatio
nC
orre
ctly
A
dmin
iste
red?
Incl
uded
in
Ave
rage
AU
CA
UC
Sam
ple
size
Dat
a So
urce
Cou
ntry
Out
com
e M
easu
reM
onth
s to
O
utco
me
% o
f re
cidi
vist
sO
ther
C
onsi
dera
tions
OD
AR
AH
ilton
et
al.,
2010
Yes
Yes
.638
150
Cri
min
al
just
ice
case
fil
es
Can
ada
Rea
ssau
lt60
27%
All
perp
etra
tors
in
carc
erat
ed fo
r IP
V
H
ilton
and
H
arri
s, 20
09
Yes
(1) Y
es(1
) .6
7(1
) 39
1C
rim
inal
ju
stic
e ca
se
files
Can
ada
(1)
Rea
ssau
lt60
(1)
26.8
%(1
-3)
Mar
ital/
coha
bitin
g re
latio
nshi
p
(2
) N
o(2
) .7
4(2
) 30
9(2
) R
eass
ault;
re
mov
ed
ambi
guou
s no
nrec
idiv
ists
(n
= 8
2)
(2)
33%
(2)
105
reci
divi
sts
and
204
unam
bigu
ous
nonr
ecid
ivis
ts
(3
) N
o(3
) .7
1-.8
0 (m
ean
=
.74)
(3)
200
(3)
Rea
ssau
lt; r
ando
m
sam
ples
, mat
ched
nu
mbe
r of
re
cidi
vist
s an
d un
ambi
guou
s no
nrec
idiv
ists
(3)
50%
(3)
10 r
ando
m
sam
ples
cho
sen
from
tot
al
N =
210
: eac
h sa
mpl
e n
= 1
00
reci
divi
sts
and
100
unam
bigu
ous
nonr
ecid
ivis
ts
H
ilton
et
al.,
2008
Yes
Yes
.65
346
Cri
min
al
just
ice
case
fil
es
Can
ada
Rea
ssau
lt61
.241
%M
arita
l/coh
abiti
ng
rela
tions
hip
H
ilton
et
al.,
2004
Yes
(1) Y
es(1
) .7
2(1
) 10
0C
rim
inal
ju
stic
e ca
se
files
Can
ada
Rea
ssau
lt51
.6(1
) 26
%(1
-2)
Spou
ses
only
(2
) N
o(2
) .7
7(2
) 58
9(2
) 30
%(2
) C
onst
ruct
ion
sam
ple
Ret
tenb
erge
r &
Ehe
r, 20
10
Yes
Yes
.71
66O
ffend
er
eval
uatio
nA
ustr
iaR
eass
ault
54.9
21.2
%A
ll pe
rpet
rato
rs
inca
rcer
ated
; or
igin
al o
ffens
es
“sex
ually
m
otiv
ated
”
Rese
arch
Exa
min
ing
the
Pred
ictive
Valid
ity o
f the
OD
ARA
Usin
g RO
Cs
at ARIZONA STATE UNIV on May 12, 2013jiv.sagepub.comDownloaded from
1550
Inst
rum
ent
Art
icle
C
itatio
nC
orre
ctly
A
dmin
iste
red?
Incl
uded
in
Ave
rage
AU
CA
UC
Sam
ple
size
Dat
a So
urce
Cou
ntry
Out
com
e M
easu
reM
onth
s to
O
utco
me
% o
f rec
idiv
ists
Oth
er
Con
side
ratio
ns
DV
SIC
ampb
ell
et a
l., 20
05
No
(1) Y
es(1
) .5
0878
2V
ictim
rep
ort
Uni
ted
Stat
es(1
) R
eass
ault
6-12
31.1
%D
ata
not
gath
ered
fr
om c
rim
inal
ju
stic
e ca
se fi
les
(2
) N
o(2
) .5
97(2
) Se
vere
rea
ssau
lt
(3
) N
o(3
) .5
95(3
) R
eass
ault
cont
rolli
ng
for
prot
ectiv
e ac
tions
(4
) N
o(4
) .6
16(4
) Se
vere
rea
ssau
lt co
ntro
lling
fo
r pr
otec
tive
actio
ns
H
ilton
et
al.,
2008
No
Yes
.61
649
Cri
min
al ju
stic
e ca
se fi
les
Can
ada
Rea
ssau
lt61
.241
%11
-item
mod
ifica
tion;
m
arita
l/coh
abiti
ng
rela
tions
hip
W
illia
ms
and
Hou
ghto
n,
2004
Yes
(1) Y
es(1
) .6
1(1
) 14
65(1
-6)
Cri
min
al
just
ice
case
fil
es
Uni
ted
Stat
es(1
) R
eass
ault
or
rest
rain
ing
orde
r vi
olat
ion
(1)
1829
%
(2
) N
o(2
) .5
8(2
) 12
5(2
-6) V
ictim
re
port
on
follo
w-u
p
(2)
Con
trol
ling
beha
vior
s(2
) 6
(2-6
) 35
%-
80%
(2-6
) 35
% o
f of
fend
ers
used
ph
ysic
al fo
rce
on fo
llow
-up,
65
% e
ngag
ed
in c
ontr
ollin
g be
havi
ors,
80%
th
reat
ened
/ver
bally
ab
used
the
ir
part
ner
Rese
arch
Exa
min
ing
the
Pred
ictive
Valid
ity o
f the
DVS
I Usin
g RO
Cs
(con
tinue
d)
at ARIZONA STATE UNIV on May 12, 2013jiv.sagepub.comDownloaded from
1551
(con
tinue
d)
Inst
rum
ent
Art
icle
C
itatio
nC
orre
ctly
A
dmin
iste
red?
Incl
uded
in
Ave
rage
AU
CA
UC
Sam
ple
size
Dat
a So
urce
Cou
ntry
Out
com
e M
easu
reM
onth
s to
O
utco
me
% o
f rec
idiv
ists
Oth
er
Con
side
ratio
ns
(3
) N
o(3
) .5
6(3
) 12
5(3
) Le
ss t
hrea
teni
ng
beha
vior
s(3
) 6
(4
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o(4
) .4
9(4
) 12
5(4
) Le
ss s
ever
e ph
ysic
al v
iole
nce
(4)
6
(5
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o(5
) .6
8(5
) 12
5(5
) Se
vere
th
reat
enin
g be
havi
ors
(5)
6
(6
) N
o(6
) .6
5(6
) 12
5(6
) Se
vere
phy
sica
l vi
olen
ce(6
) 6
App
endi
x (c
ont
inue
d)
at ARIZONA STATE UNIV on May 12, 2013jiv.sagepub.comDownloaded from
1552
Rese
arch
Exa
min
ing
the
Pred
ictive
Valid
ity o
f the
K-S
ID U
sing
ROCs
Inst
rum
ent
Art
icle
C
itatio
nC
orre
ctly
A
dmin
iste
red?
Incl
uded
in
Ave
rage
AU
CA
UC
Sam
ple
size
Dat
a So
urce
Cou
ntry
Out
com
e
Mea
sure
Mon
ths
to
Out
com
e%
of
reci
divi
sts
Oth
er
Con
side
ratio
ns
K-S
IDC
ampb
ell e
t al
., 20
05N
o(1
) Yes
(1)
.516
782
Vic
tim
repo
rtU
nite
d St
ates
(1)
Rea
ssau
lt6-
1231
.1%
Use
s al
l ite
ms;
base
d on
vic
tim r
epor
t, no
t cr
imin
al ju
stic
e fil
es
(2
) N
o(2
) .5
14(2
) Se
vere
rea
ssau
lt
(3
) N
o(3
) .5
95(3
) R
eass
ault
cont
rolli
ng fo
r pr
otec
tive
actio
ns
(4
) N
o(4
) .6
16(4
) Se
vere
rea
ssau
lt co
ntro
lling
for
prot
ectiv
e ac
tions
H
ecke
rt a
nd
Gon
dolf,
200
4N
oYe
s.5
749
9V
ictim
re
port
Uni
ted
Stat
esR
epea
t re
assa
ult
(mor
e th
an 1
tim
e)15
23%
Did
not
use
all
11 it
ems;
Perp
etra
tors
att
ende
d gr
oup
trea
tmen
t
at ARIZONA STATE UNIV on May 12, 2013jiv.sagepub.comDownloaded from
1553
Rese
arch
Exa
min
ing
the
Pred
ictive
Valid
ity o
f Vict
im A
sses
smen
t of R
isk U
sing
ROCs
Inst
rum
ent
Art
icle
C
itatio
nC
orre
ctly
A
dmin
iste
red?
Incl
uded
in
Ave
rage
AU
CA
UC
Sam
ple
size
Dat
a So
urce
Cou
ntry
Out
com
e M
easu
reM
onth
s to
O
utco
me
% o
f re
cidi
vist
sO
ther
C
onsi
dera
tions
Vic
tim
asse
ssm
ent
Cam
pbel
l et
al.,
2005
n/a
(1) Y
es(1
) .5
9978
2V
ictim
re
port
Uni
ted
Stat
es(1
) R
eass
ault
6-12
31.1
%Li
kelih
ood
of
reas
saul
t ra
ted
on a
sca
le o
f 1-1
0
(2
) N
o(2
) .6
10(2
) Se
vere
rea
ssau
lt
(3
) N
o(3
) .6
19(3
) R
eass
ault
cont
rolli
ng
for
prot
ectiv
e ac
tions
(4
) N
o(4
) .6
19(4
) Se
vere
rea
ssau
lt co
ntro
lling
fo
r pr
otec
tive
actio
ns
H
ecke
rt a
nd
Gon
dolf,
20
04
n/a
Yes
.64
499
Vic
tim
repo
rtU
nite
d St
ates
Rep
eat
reas
saul
t (m
ore
than
1
time)
1523
%Be
lief t
hat
viol
ence
is li
kely
; pe
rpet
rato
rs
atte
nded
gro
up
trea
tmen
t
at ARIZONA STATE UNIV on May 12, 2013jiv.sagepub.comDownloaded from
1554 Journal of Interpersonal Violence 28(7)
Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
FundingThe author(s) received no financial support for the research, authorship, and/or pub-lication of this article.
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[*Included in average ROC analysis; +Considered for inclusion in average ROC analy-sis.]
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Bios
Jill Theresa Messing, MSW, PhD is an assistant professor in the School of Social Work at Arizona State University. She earned her MSW and PhD in social welfare at the University of California, Berkeley, and went on to complete a postdoctoral fellow-ship in interdisciplinary violence research at Johns Hopkins University (T32-MH20014) where she studied with Dr. Jacquelyn Campbell, PhD, RN, FAAN. She specializes in intervention research and is the principal investigator on the National Institute of Justice funded Oklahoma Lethality Assessment Study (#2008-WG-BX-0002), the coprincipal investigator on the National Science Foundation funded Legal Mobilization and Intimate Partner Victimization study (#1154098) and a coinvestigator on the National Institute of Mental Health funded study The use of Computerized Safety Decision Aids With Victims of Intimate Partner Violence (#1R01 MH085641-01A1). Her interest areas are intimate partner violence, domestic homicide/femicide, risk assessment, criminal justice-social service collaborations, and evidence-based practice.
Jonel Thaller, MSW, is a doctoral student in social work at Arizona State University, with an interest in the prevention of violence against women, reproductive coercion, and public perceptions of intimate partner violence. She is currently working as a research assistant on a National Institute of Mental Health grant examining the efficacy of an Internet-based safety intervention for women living in abusive relationships.
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