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Batterer Program Performance and Recidivism Outcomes for Misdemeanor domestic violence offenders in Dallas

County Court 10

Texas Council on Family ViolenceBIPP Conference April 26, 2013

Denise Paquette Boots, Ph.D.University of Texas at Dallas

Associate Professor, Program in Criminology 

Judge Roberto Canas, J. D.County Court 10, Dallas COunty

Jennifer Wareham, Ph.D.Wayne State University

Associate Professor, Department of Criminal Justice

©2013 This presentation and findings may not be released to third parties or cited without the express consent of Dr. Denise Paquette Boots

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Research Questions“Does treatment have a positive effect in reducing

recidivism in DV offenders over time?”

H1: Treatment will reduce all recidivism arrests in the 12-month period following the last date of involvement in the criminal justice system for the original DV-related arrest.

H2: Treatment will reduce DV recidivism arrests in the 12-month period following the last date of involvement in the criminal justice system for the original DV-related arrest.

H3: Offenders who received conditional dismissals will have less recidivism arrest(s) than offenders who plead guilty to DV and go through BIPP treatment.

Treatment Groups

Sample Stratum

Original Groups Group Name N %

1 1 1 BIPP conditional dismissals 240 10.01 2 2 Other conditional dismissals 180 7.52 3 3 Regular dismissals 344 14.42 4 4 5-year dismissals 53 2.23 5 5 Plea, completed BIPP 392 16.43 5 6 Plea, did not complete BIPP 242 10.13 5 7 Plea, uncertain BIPP outcome 86 3.64 6 8 Plea, deferred adjudication 103 4.34 7 9 Plea, fine only 95 4.05 8 10 Plea, jail 632 26.4-- all 11 Trial, all sentences 16 0.7-- -- 12 Other 9 0.4 Total unique offenders 2,392 100.0

Table 1 SAMPLING GROUPINGSStratifi cation and Treatment Groupings for Disposed Cases in Dal las County Court 10 for 2007 (N = 2,392)

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Table 2: Descriptive Stats Sample (n = 405), and Weighted Sample (n = 2,371).

Variable Unweighted Sample Weighted Sample

n % n %

Gender: Female 63 15.6 318 13.4

Male 342 84.4 2,062 86.6

Total 405 100.0 2,371 100.0

Race: Non-White 207 51.1 1,333 56.2

White 198 48.9 1,039 43.8

Total 405 100.0 2,371 100.0

Victim’s Gender: Female 347 85.7 2,122 89.5

Male 58 14.3 249 10.5

Total 405 100.0 2,371 100.0

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Table 2: Descriptive Stats (Continued)Sample (n = 405), and Weighted Sample (n = 2,371).

Unweighted Sample Weighted Sample

n % n %

Substance Use: No 359 88.6 2,080 87.7

Yes 46 11.4 291 12.3

Total 405 100.0 2,371 100.0

Treatment: Jail 54 13.3 637 26.9

Deferred Adjud 100 24.7 198 8.3

Reg Dismissal 99 24.4 393 16.6

Cond Dismissal 100 24.7 420 17.7

BIPP 52 12.8 723 30.5

Total 405 99.9 2,371 100.0

Cohabits with victim: No 103 25.4 513 21.7

Yes 302 74.6 1,858 78.3

Total 405 100.0 2,371 100.0

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Table 2: Descriptive Stats (Continued)Sample (n = 405), and Weighted Sample (n = 2,371).

Unweighted Sample Weighted Sample

n % n %

Recidivism Arrests: No 356 87.9 1,982 83.6

Yes 49 12.1 389 16.4

Total 405 100.0 2,371 100.0

Recidivism DV Arrest:

No 386 95.3 2,207 93.1

Yes 19 4.7 164 6.9

Total 405 99.9 2,371 100.0

M SD M SD

Age 32.63 9.50 32.30 9.79

Age of First Arrest 25.21 8.77 24.46 8.47

Prior Arrests 0.74 0.82 0.87 0.86

Disadvantage factor .06 .84 .10 .90

Mobility factor .09 .96 .12 .95

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Multivariate Regression Analyses

Logistic regression analyses were then conducted to examine relationships between family violence court disposition and 12-month recidivism, both general arrest and domestic violence charges, while controlling for certain sociodemographic, offense and neighborhood characteristics (at a significance level of p <.05)

These type of analyses basically allow us to examine the unique contribution of each variable of interest in explaining the outcome we are examining while controlling for the other variables in the model.

First, models were estimated with sociodemographic, offense, and treatment group measures.

Next, neighborhood characteristics were added to models to see their predictive ability.

These results are presented in Table 3 that follows

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BIPP Plea Deferred Regular Dismissal Conditional Dismissal

Jail0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40Arrests, Females DV Arrests, Females Arrests, Males DV Arrests, Males

Treatment Groups

Pre

dic

ted

Pro

ba

bili

tie

s

Figure 6: Predicted probabilities for 12-month recidivism total arrests and DV arrests by gender and treatment group

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Summary of Findings Overall, offenders who participated in BIPP treatment programs have more favorable outcomes (arrests, charges and FV incidents) 12 months after the date of last involvement in the DV court, compared to jail and dismissal regular, but not plea deferred or dismissal conditional cases. ◦ Suggests perhaps a labeling issue?

Strongest predictors of future arrest for both general and DV arrest were:◦Having a female victim◦Being perceived as under the influence of substance when the IPV offense occurred

◦Sentenced to jail for the offense

Being a female offender was also strong predictor, especially for DV arrests

Effect of race on general arrest likelihood appears spurious- did not hold across models

◦Non-whites were significantly more likely to be rearrested for any arrest, but when controls for neighborhood were included, effect disappeared

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Summary of Findings (cont) DV offense characteristics, specifically gender of victim and NON-cohabitation were also predictors of recidivism

◦Related to cohabitation, question of whether this was meticulously recorded by LEA? Future research needs to further explore this relationship since it appears here to point toward efficacy efforts for BIPP

When controlling for the effect of other measures, prior arrests only mattered for predicting future DV charges, and this was in the opposite direction than the literature would suggest– WHY?

◦Are these offenders tired of being in the CJS?

Neighborhood characteristics relayed surprising results– concentrated disadvantage did not significantly affect recidivism, controlling for all else

◦Residential mobility, however, increases the risk of future arrest generally, but not for DV-related offenses specifically

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Conclusion & Discussion Evidence suggests that BIPP treatment reduces recidivism arrests in Dallas County Court 10◦ Based on preliminary analyses, some BIPP programs “do a better job”

at reducing recidivism than others (see Jackson et al., 2003)

Based on literature, the DV patterns seen in Dallas County are representative of other large U.S. cities (Dugan et al., 2003)

Geocoded information on concentration of arrests across county suggests policy- or system-based differences that warrant further attention (Miles-Doan & Kelly, 1997; Pearlman et al., 2003)

◦ White/high SES census tracts and zip codes negligible # arrests◦ Females significantly more likely to be rearrested– why? Systematic

bias, data issue, program issues for special needs, specific enforcement in Dallas County?

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Strengths & Future Research Future research to seek local funding to replicate the study beyond 2007 to parse out effects of judges’ policies and gauge outcomes across years

◦ Might include other DV court in Dallas County- should be random assignment◦ Possibly extend to other DV court in same jurisdiction or statewide data to examine

similar factors and compare to these findings

Publications focusing on these multivariate findings is in process of being submitted for peer-review in professional scholarly journal

Policy suggestions to improve gaps in information in court

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Moving Forward- Judge Cañas Many system issues or weaknesses are driven by lack of communicationoLack of uniformity of police reports – Training for police officers to report

factors such as race, age, substance abuse, presence of children and other aggravating factors that may be critical in affecting family violence outcomes

oBIPP providers need to be responsive to information that the referring agencies need – date program started; classes missed; pre-dismissal notice; discharge and reasons for discharge; whether progress is being made; offenders behavior in class

oProbation officers should fully document revocation motions and pay particular attention to violations concerning BIPP – prosecutors and judges need to know

oProsecutors should ensure that essential information about the facts of the case and of the defendant and the victim is forwarded to probation and to BIPP providers

o Judges should facilitate the movement of information by using their leadership positions to create a coordinated response from players in the criminal justice system

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CLOSING THOUGHTS- Judge Cañas NOTES ABOUT BIPP:

◦ BIPP is best – Anger management and couples counseling is not appropriate◦ Program success is not guaranteed – no false hope for victims◦ Greater communication in writing detailed reports (police, attorneys, probation)◦ Compounding problems: alcohol/drug abuse, mental disorders, poverty

BEST PRACTICES FOR WORKING WITH BIPP:◦ BIPPs that are integrated into the criminal justice process produce better results◦ BIPP is most effective when coupled with some type of compliance mechanism◦ Monthly reporting of BIPP programs to Judge with details on progress◦ Information the court gets should also be given to BIPPs◦ Some offenders will be more responsive to how their abuse impacts their

children

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Special thanks to Judge Canas, his staff, the Dallas County Clerk’s office, probation, and the BIPP programs for their assistance on this project

Thanks to Dr. Bob Taylor, Dr. Ryan Getty, Dr. Aaron Bartula, and the Caruth Police Institute at Dallas Police Department for support

CONTACT INFO:

Dr. Denise Paquette Boots

deniseboots@utdallas.edu