Using dynamic risk factors to predict criminal recidivism ...Factor Component of the Offender Intake...
Transcript of Using dynamic risk factors to predict criminal recidivism ...Factor Component of the Offender Intake...
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Using dynamic risk factors to predict
criminal recidivism in a sample of male
and female offenders
Shelley Brown, Ph.D., Carleton University & Larry Motiuk, Ph.D.
Correctional Service of Canada
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Caveats
• Brown, S.L. & Motiuk, L.L. (2005). The Dynamic
Factor Component of the Offender Intake and
Assessment Process: A psychometric, meta-
analytic and field review (R-164). Research
Branch, Correctional Service of Canada,
Ottawa: Ontario
• The points of view expressed are those of the authors
and do not necessarily reflect those of Correctional
Service of Canada
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Introduction
• Gender neutrality
– Implicitly assumes or explicitly states that males and females are similar, “The Gender-Similarities Hypothesis” (Hyde, 2005)
– Same theories, same risk factors, same barriers to treatment, same risk/treatment approaches
• Gender specificity = female specificity
– Explicitly states that females are different, “The Gender Difference Hypothesis” (Hyde, 2005)
– Different theories, different risk factors, different pathways in and out of crime, different barriers to treatment, different risk/treatment approaches
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Research Questions
• Do gender-specific risk factors exist?
– Factors that only predict in one gender
• Do gender-salient risk factors exist?
– Factors that predict in both genders but the strength of the
magnitude is stronger for one particular gender
• Do gender-neutral risk factors exist?
– Factors that predict to the same degree in both genders
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Method
• N = 1, 530 federally sentenced offenders
– Released between 1994 and 2000
– 765 women
– 765 men (random stratified sample)
– Original sampling frame = 15, 479: release cohort
• Recidivism
– 3 year fixed follow-up
– Return to federal custody with conviction
• Dynamic Factor Identification Analysis (DFIA)
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Analytic Approach
• Analysis conducted separately for each gender
• Odds ratios
• The odds ratio is a ratio of odds: it is the odds of
recidivism in one group (e.g., women with employment
problems) compared to another group (women without
employment problems)
• Evidence for an effect = 95% CI does not contain 1
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Analytic Approach
• Evidence for gender-specificity
– No effect for one gender (i.e., OR confidence interval contained 1 and effect judged absent) and at least a small, moderate or large effect for the other gender
– Small OR (1.4 – 2.2) (reciprocal: 0.71 – 0.42)
– Moderate OR (2.3 -3.6) (reciprocal: .43 - .28)
– Large OR (3.7+) (reciprocal: .27 – 0)
• Evidence for gender-saliency
– An effect (small, moderate, or large) is present in both genders but the size of the effect is stronger by at least one level for one gender (confidence intervals could overlap).
• Evidence for gender-neutrality
– An effect of the same magnitude is present in both genders
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Gender Specific Predictors: Women
Employment
• Less than grade 8s
• Less than grade 10m
• Memory problemss
• Concentration problemss
• Numeracy problemss
• Dissatisfied with tradem
• Participated prior
employment programm-
• Completed prior
employment programl-
Marital/Family
• Poor relations with fatherm
• Witnessed spousal abuses
• Victim of spousal violencem
• Parenting problemsm
Associates
• None!
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Gender Specific Predictors: Women
Substance Abuse
• Combines alcohol/drugsm
• Drug use/stressm
• Drug use interferes with
employments
• Law violations/drug usem
• Drug use interferes with
physicalm
Community
• No credits
Personal/Emotional
• Poor problem solvings
Criminal attitudes
• Marital/family holds no
valuem
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Gender Salient Predictors: Women
Employment
• No skill/trade/professionm
• Unemployed at time of
arrestl
• Unstable job historym
Marital/Family
• None
Associates
• Has many criminal
acquaintances
• Has many criminal friendsm
• Criminogenic
neighbourhoodm
Substance Abuse
• Abused drugsm
• Drug use interfers with
marital/familym
Community Functioning
• none
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Gender Salient Predictors: Women
Personal/Emotional
• Aggressivem
• Poor stress managementm
• Manipulativem
Criminal attitudes
• Negative towards lawm
• Lacks directionm
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Gender Neutral Predictors
Employment
• Unemployed 90% of timem
• Unemployed 50% of timem
Marital/Family
• Unattached to family of
originals
• Negative maternal
relationss
• Dysfunctional parentss
• Criminal family of origins
Associates
• Associates with substance
abusersm
• Unattached to community
groupss
• Difficulty communicating
with otherss
Substance Abuse
• Assesseds, participateds,
treated for past substance
abusem
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Gender Neutral Predictors
Community Functioning
• No bank account
Personal/Emotional
• Poor time managements
Criminal attitudes
• Negative towards
correctionss
• Disrespects commercial
propertys
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Gender Specific Predictors: Men
Employment
• None
Associates
• None
Marital/Family
• Spousal abuse perpetrators
Substance Abuse
• Early age drinkingl
• Drinks regularlys
• Early age drug usem
• Drug use spreess
• Social drug usem
Community Functioning
• Has no hobbiess
• Has used social assistances
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Gender Specific Predictors: Men
Personal/Emotional
• Unrealistic goalss
• Impulsives
• Thrill-seekers
• Not conscientiousm
• Poor conflict resolutions
Criminal Attitudes
• Negative toward polices
• Negative toward courtss
• Values substance abuses
• Non-conformings
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Gender Salient Predictors: Men
Employment
• No employment historym
• Difficulty meeting job
requirementss
Associates
• None
Marital/Family
• None
Substance Abuse
• None
Community Functioning
• Unstable accommodationsm
Personal/Emotional
• none
Criminal attitudes
• none
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Gender specific, salient and neutral
predictors of recidivism
28.9
18.824.6
4.3
23.2
female specific
female salient
male specific
male salient
gender neutral
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Conclusions
• Evidence for gender neutrality, specificity and salience
• Some support for feminist pathway and economic
marginalization theory
• Gender neutral measures have the potential to become
gender informed
• Need more primary research
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Limitations
• The ‘single-wave’ study design precludes a ‘true test’ of
dynamic factors
• Pitfalls associated with secondary data
• Recidivism measure – return to federal custody only
• Looking for gender differences in a measure that was not
originally built from the ground up for women
• Moderated regressions not conducted
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The beginning…..
• “…even the female criminal is monotonous and uniform compared with her male companion, just as the general woman is inferior to man…due to her being Atavistically nearer to her origin than the male”
• Lombroso, 1895
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The present..…
• “ …After all, in talents, men are on average more mathematical, more technically minded, women more verbal; in tastes, men are more interested in things, women in people; in temperaments, men are more competitive, risk-taking, single-minded, status-conscious, women far less so (Helena Cronin, evolutionary theorist, London School of Economics as cited in the Globe and Mail, January 5th, 2008)
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The newspaper headline was…
• Gender matters!
• Men really do outperform women!
• Men really are from Mars and women from Venus!
• Gender differences prevail!
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23 Thank You
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Contact Information
Shelley Brown, Ph.D.
Department of Psychology
Carleton University
Ottawa, Ontario
K1S 5B6
Tel: 613-520-2600, ext. 1505
Fax: 613-520-3667
Email: [email protected]