the average predictive © the author(s) 2013 validity of ... · pdf file1538 journal of...

Journal of Interpersonal Violence28(7) 1537 –1558

© The Author(s) 2013Reprints and permission:

sagepub.com/journalsPermissions.navDOI: 10.1177/0886260512468250

http://jiv.sagepub.com

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

at ARIZONA STATE UNIV on May 12, 2013jiv.sagepub.comDownloaded from

http://jiv.sagepub.com/

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,



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




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,




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




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.




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




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




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.



1546

App

endi

xRe

sear

ch E

xam

inin

g th

e Pr

edict

ive Va

lidity

of t

he D

ange

r Ass

essm

ent U

sing

ROCs

Inst

rum

ent

Art

icle

C

itatio

n

Inst

rum

ent

corr

ectly

ad

min

iste

red

Incl

uded

in

aver

age

AU

CA

UC

Sam

ple

size

Dat

a

Sour

ceC

ount

ryO

utco

me

Mea

sure

Mon

ths

to

outc

ome

% o

f re

cidi

vist

sO

ther

Con

side

ratio

ns

Dan

ger

A

sses

smen

tC

ampb

ell

et a

l., 20

05

Yes

(1) Y

es(1

) .6

3578

2V

ictim

re

port

Uni

ted

Stat

es(1

) R

eass

ault

6-12

31.1

%20

-item

inst

rum

ent

(2

) N

o(2

) .6

70(2

) Se

vere

rea

ssau

lt

(3

) N

o(3

) .6

74(3

) R

eass

ault

cont

rolli

ng

for

prot

ectiv

e ac

tions

(3

) N

o(4

) .6

87(4

) Se

vere

rea

ssau

lt co

ntro

lling

fo

r pr

otec

tive

actio

ns

C

ampb

ell

et a

l. 20

09Ye

s(1

) N

o(1

) .9

16(1

) 62

1V

ictim

re

port

Uni

ted

Stat

esA

ttem

pted

fem

icid

en/

a(1

) 31

.2%

(1 &

2)

Cas

e co

ntro

l; re

tros

pect

ive;

(2

) he

lp s

eeki

ng o

nly

(2

) N

o(2

) .8

62(2

) 19

6(2

) 53

.1%

H

ecke

rt

and

Gon

dolf,

20

04

No

Yes

.749

9V

ictim

re

port

Uni

ted

Stat

esR

epea

t re

assa

ult

(mor

e th

an 1

tim

e)

1523

%15

-item

inst

rum

ent;

Perp

etra

tors

at

tend

ed g

roup

tr

eatm

ent

H

ilton

et

al.,

2008

No

Yes

.59

589

Cri

min

al

just

ice

case

fil

es

Can

ada

Rea

ssau

lt61

.249

%15

-item

inst

rum

ent

H

ilton

et

al.,

2004

No

Yes

.56

649

Cri

min

al

just

ice

case

fil

es

Can

ada

Rea

ssau

lt51

.630

%15

-item

inst

rum

ent



1547

Rese

arch

Exa

min

ing

the

Pred

ictive

Valid

ity o

f the

SAR

A U

sing

ROCs

Inst

rum

ent

Art

icle

C

itatio

n

Inst

rum

ent

corr

ectly

ad

min

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

SAR

ABe

lfrag

e et

al.,

2012

Yesa

Yes

(1)

.63

429

Cri

min

al ju

stic

e in

vest

igat

ion

Swed

enR

eass

ault

1821

%(1

&2)

Adm

inis

tere

d by

fron

tline

of

ficer

s; (1

) Su

mm

ed it

ems

for

tota

l sco

re

Ye

sN

o(2

) .5

7(2

) U

sed

sum

mar

y ri

sk r

atin

g

G

rann

and

W

edin

, 200

2N

oYe

s.6

556

Cri

min

al Ju

stic

e C

ase

File

sSw

eden

Rea

ssau

lt60

28%

All

offe

nder

s w

ith p

erso

nalit

y di

sord

er d

iagn

osis

; su

mm

ed it

ems

for

tota

l sco

re

H

ecke

rt a

nd

Gon

dolf,

20

04

No

Yes

.64

499

Vic

tim r

epor

tU

nite

d St

ates

Rep

eat

reas

saul

t (m

ore

than

1

time)

1523

%D

id n

ot u

se

all 2

0 ite

ms;

Sum

med

item

s fo

r to

tal s

core

; pe

rpet

rato

rs

atte

nded

gro

up

trea

tmen

t

H

ilton

et

al.,

2008

No

Yes

.59

589

Cri

min

al ju

stic

e ca

se fi

les

Can

ada

Rea

ssau

lt61

.249

%Su

mm

ed it

ems

for

tota

l sco

re (con

tinue

d)



1548

Inst

rum

ent

Art

icle

C

itatio

n

Inst

rum

ent

corr

ectly

ad

min

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

H

ilton

et

al.,

2004

No

Yes

.64

649

Cri

min

al ju

stic

e ca

se fi

les

Can

ada

Rea

ssau

lt51

.630

%Su

mm

ed it

ems

for

tota

l sco

re

K

ropp

and

H

art,

2000

Yes

No

.70

102

Cri

min

al ju

stic

e ca

se fi

les,

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)



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



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)



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

) N

o(4

) .4

9(4

) 12

5(4

) Le

ss s

ever

e ph

ysic

al v

iole

nce

(4)

6

(5

) N

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)



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



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




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.

References

[*Included in average ROC analysis; +Considered for inclusion in average ROC analy-sis.]

+Andrés-Pueyo, A., López, S., & Álvarez, E. (2008). Assessment of the risk of inti-mate partner violence and the SARA. Papeles del Psicólogo, 29(1), 107-122.

Arbuckle, J., Olson, L., Howard, M., Brillman, J., Anctil, C., & Sklar, D. (1996). Safe at home? Domestic violence and other homicides among women in New Mexico. Annals of Emergency Medicine, 27, 210-215.

Bailey, J. E., Kellerman, A. L., Somes, G. W., Banton, J. G., Rivara, F. P., & Rushford, N. P. (1997). Risk factors for violent death of women in the home. Archives of Internal Medicine, 157, 777-782.

*Belfrage, H., Strand, S., Storey, J. E., Gibas, A. L., Kropp, P. R., & Hart, S. D. (2012). Assessment and management of risk for intimate partner violence by police offi-cers using the Spousal Assault Risk Assessment Guide. Law & Human Behavior, 36(1), 60-67.

+Bell, M. E., Cattaneo, L. B., Goodman, L. A., & Dutton, M. A. (2008). Assessing the risk of future psychological abuse: Predicting the accuracy of battered women’s predictions. Journal of Family Violence, 23(2), 69-80.

+Bennett, L. E., Goodman, L. A., & Dutton, M. A. (2000). Risk assessment among batterers. arrested for domestic assault: The salience of psychological abuse. Vio-lence against Women, 6, 1190-1203.

Black, M. C., Basile, K. C., Breiding, M. J., Smith, S. G., Walters, M. L., Merrick, M. T., & Stevens, M. R. (2011). The National Intimate Partner and Sexual Violence Survey: 2010 summary report. Atlanta GA: Centers for Disease Control and Prevention.

+Bourgon, G., & Bonta, J. (2004). Risk assessment for general assault and partner abusers 2004-04. Ottawa, Ontario, Canada: Public Safety and Emergency Pre-paredness. Available at www.psepc.gc.ca.

Breiding, M., Black, M. C., & Ryan, G. W. (2005). Prevalence and risk factors of intimate partner violence in eighteen U.S. States/Territories. American Journal of Preventive Medicine, 34(2), 112-118.




+Cairns, K. V. (2004). Alberta Mental Health Board domestic violence treatment program evaluation: Report to the partnering ministries Committee. Edmonton, Alberta, Canada: Alberta Mental Health Board.

Campbell, J. C. (1986). Assessment of risk for homicide for battered women. Advances in Nursing Science, 8, 36-51.

Campbell, J. C. (2002). Health consequences of intimate partner violence. Lancet, 359, 1336.

Campbell, J. C. (2004). Helping women understand their risk in situation of intimate partner violence. Journal of Interpersonal Violence, 19, 1464-1477.

Campbell, J. C., Glass, N., Sharps, P. W., Laughon, K., & Bloom, T. (2007). Intimate partner homicide: Review and implications for research and policy. Trauma, Vio-lence & Abuse, 8, 246-269.

Campbell, J. C., & Runyon, C. W. (1998). Femicide: Guest Editors’ Introduction. Homicide Studies, 2, 347-352.

*Campbell, J. C., O’Sullivan, C., Roehl, J., & Webster, D. W. (2005). Intimate part-ner violence risk assessment validation study: The RAVE study (Final report to the National Institute of Justice; NCJ 209731-209732). Washington, DC: U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Statistics.

+Campbell, J. C., Webster, D. W., & Glass, N. (2009). The danger assessment: Valida-tion of a lethality risk assessment instrument for intimate partner femicide. Jour-nal of Interpersonal Violence, 24, 653-674.

Campbell, J. C., Webster, D., Koziol-McLain, J., Block, C. R., Campbell, D. W., Curry, M. A., & Laughon, C. (2003). Risk factors for femicide in abusive rela-tionships: Results from a multisite case control study. American Journal of Public Health, 93, 1089-1097.

Catalano, S., Smith, E., Snyder, H., & Rand, M. (2009). Female victims of violence (Bureau of Statistics Selected Findings. NCJ 228356). Washington, DC: U.S. Department of Justice, Office of Justice Programs.

+Cattaneo, L. B., Bell, M. E., Goodman, L. A., & Dutton, M. A. (2007). Intimate partner violence victims’ accuracy in assessing their risk of re-abuse. Journal of Family Violence, 22, 429-440.

+Cattaneo, L. B., & Goodman, L. A. (2003). Victim-reported risk factors for continued abusive behavior: Assessing the dangerousness of arrested batterers. Journal of Community Psychology, 31, 349-369.

Douglas, K. S., Blanchard, A. J. E., Guy, L. S., Reeves, K. A., & Weir, J. (2000). HCR-20 violence risk assessment scheme: Overview and annotated bibliog-raphy. Retrieved from http://escholarship.umassmed.edu/cgi/viewcontent .cgi?article=1362&context=psych_cmhsr

Dutton, D. G., & Kropp, P. R. (2000). A review of domestic violence risk instruments. Trauma,Violence & Abuse, 1, 171-181.




Gambrill, E. (2012). Critical thinking in clinical practice (3rd ed., pp. 275-296). Hoboken, NJ: John Wiley.

Gondolf, E. W. (2001). Limitations of experimental evaluation of batterer programs. Trauma, Violence & Abuse, 2(1), 79-88.

+Goodman, L. A., Dutton, M. A., & Bennett, L. (2000). Predicting repeat abuse among arrested batterers: Use of the danger assessment scale in the criminal justice sys-tem. Journal of Interpersonal Violence, 15(1), 63-74.

*Grann, M., & Wedin, I. (2002). Risk factors for recidivism among spousal assault and spousal homicide offenders. Psychology, Crime, and Law, 8, 5-23.

*Heckert, D. A., & Gondolf, E. W. (2004). Battered women’s perceptions of risk ver-sus risk factors and instruments in predicting repeat reassault. Journal of Interper-sonal Violence, 19, 778-800.

Heckert, D. A., & Gondolf, E. W. (2005). Do multiple outcomes and conditional fac-tors improve prediction of batterer reassault? Violence and Victims, 20(1), 3-24.

Hilton, N. Z. (2010). Risk assessment for criminal justice, offender intervention, and victim services. Washington, DC: American Psychological Association.

Hilton, N. Z., Grant, T. H., & Rice, M. E. (2001). Predicting violence by serious wife assaulters. Journal of Interpersonal Violence, 16, 408-423.

*Hilton, N.Z., Grant, T. H., Rice, M. E., Houghton, R. E., & Eke, A. W. (2008). An in depth actuarial assessment for wife assault recidivism: The Domestic Violence Risk Appraisal Guide. Law and Human Behavior, 32, 150-163.

*Hilton, N.Z., & Harris, G.T. (2009). How non-recidivism affects predictive accuracy: Evidence from a cross-validation of the Ontario Domestic Assault Risk Assess-ment (ODARA). Journal of Interpersonal Violence, 24, 326-337.

*Hilton, N.Z., Harris, G.T., Popham, S., & Lang, C. (2010). Risk assessment among incarcerated male domestic violence offenders. Criminal Justice and Behavior, 37, 815-832.

*Hilton, N.Z., Harris, G.T., Rice, M.E., Lang, C., Cormier, C.A., & Lines, K.J. (2004). A brief actuarial assessment for the prediction of wife assault recidivism: The Ontario Domestic Assault Risk Assessment. Psychological Assessment, 16, 267-275.

+Hisashima, J. (2008). Validation study of the Domestic Violence Screening Instru-ment (DVSI). Honolulu: Hawaii State Department of Health.

Jones, A. S., & Gondolf, E. W. (2001). Time-varying risk factors for reassault among batterer program participants. Journal of Family Violence, 16, 345-359.

Kellerman, S., & Heron, A. (1999). Firearms and family violence. Emergency Medi-cine Statistics Reports, 47, 28-37.

Kress, V. M. (2008). Counseling clients involved with violent intimate partners: The mental health counselor’s role in promoting client safety. Journal of Mental Health Counseling, 30, 200-210.




Kropp, P. R. (2004). Some questions regarding spousal assault risk assessment. Vio-lence Against Women, 10, 676-697.

Kropp, P. R (2008). Intimate partner violence risk assessment and management. Vio-lence and Victims, 23(2), 202-220.

+Kropp, P. R., & Hart, S. D. (2000). The Spousal Assault Risk Assessment (SARA) Guide: Reliability and validity in adult male offenders. Law and Human Behavior, 24, 101-118.

Maiuro, R. D., & Eberle, J. A. (2008). State standards for domestic violence perpetra-tor treatment: Current status, trends, and recommendations. Violence and Victims, 23, 133-155.

+Messing, J. T., Amanor-Boadu, Y., Cavanaugh, C. E., Glass, N., & Campbell, J. C. (in press). Culturally competent intimate partner violence risk assessment: Adapting the danger assessment for immigrant women. Social Work Research.

Moracco, K. E., Runyan, C. W., & Butts, J. (1998). Femicide in North Carolina. Homicide Studies, 2, 422-446.

Morgan, W., & Gilchrist, E. (2010). Risk assessment with intimate partner sex offend-ers. Journal of Sexual Aggression, 16, 361-372.

+Murphy, C. M., Morrel, T. M., Elliott, J. D., & Neavins, T. M. (2003). A prognostic indicator scale for the treatment of partner abuse perpetrators. Journal of Interper-sonal Violence, 18, 1087-1105.

Pataki, G. (1997). Intimate partner homicides in New York State. Albany: State of New York.

Puzone, C. A., Saltzman, L. E., Kresnow, M., Thompson, M. P., & Mercy, J. A. (2000). National trends in intimate partner homicide: United States, 1976-1995. Violence Against Women, 6, 409-426.

Rennison, C. M., & Welchans, S. (2000). Intimate partner violence (Bureau of Justice Statistics Special Report, 1-11). Washington, DC: U.S. Department of Justice.

*Rettenberger, M. & Eher, R. (2012). Actuarial risk assessment in sexually motivated intimate partner violence. Law and Human Behavior. Advance online publication

Rice, M. E., & Harris, G. T. (1995). Violent recidivism: Assessing predictive validity. Journal of Consulting and Clinical Psychology, 63, 737-748.

Rice, M. E., & Harris, G. T. (2005). Comparing effect sizes in follow-up studies: ROC area, Cohen’s d, and r. Law & Human Behavior, 29, 615-620.

Roehl, J., & Guertin, K. (2000). Intimate partner violence: The current use of risk assessments in sentencing orders. Justice System Journal, 21, 171-198.

Russell, D. (1992). Introduction. In J. Radford & D. E. H. Russell (Eds.), Femicide: The politics of woman killing. New York, NY: Twayne.

Russell, D. (2001). Introduction: The politics of femicide. In D. Russell & R. A. Harmes (Eds.), Femicide in global perspective (pp. 3-11). New York, NY: Teachers Col-lege Press.

Sharps, P., Campbell, J. C., Campbell, D., Gary, F., & Webster, D. (2003). Risky mix: Drinking, drug use and homicide. NIJ Journal, 250, 9-13.




Shepherd, M. F., Falk, D. R., & Elliott, B. A. (2002). Enhancing coordinate com-munity responses to reduce recidivism in cases of domestic violence. Journal of Interpersonal Violence, 17, 551-569.

Tjaden, P., & Thoennes, N. (2000). Prevalence and consequences of male-to-female and female-to-male intimate partner violence as measured by the National Vio-lence Against Women Survey. Violence Against Women, 6, 142-161.

Walter, S. D. (2002). Properties of the summary receiver operating characteristic (SROC) for diagnostic test data. Statistics in Medicine, 21, 1237-1256.

+Weisz, A. N., Tolman, R. M., & Saunders, D. G. (2000). Assessing the risk of severe domestic violence. Journal of Interpersonal Violence, 15, 75-90.

+Williams, K. R., & Grant, S. R. (2006). Empirically examining the risk of intimate partner violence: The revised Domestic Violence Screening Instrument (DVSI-R). Public Health Reports, 131, 400-408.

*Williams, K. R., & Houghton, A. B. (2004). Assessing the risk of domestic violence reoffending: A validation study. Law and Human Behavior, 28, 437-455.

+Wong, T., & Hisashima, J. (2008). Domestic violence exploratory study on the DVSI and SARA. Honolulu: Hawaii State Department of Health.

Yang, M., Wong, S. C. P., & Coid, J. (2010). The efficacy of violence prediction: A meta-analytic comparison of nine risk assessment tools. Psychological Bulletin, 136, 740-767.

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.



the average predictive © the author(s) 2013 validity of ... · pdf file1538 journal of...

Documents