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

2

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

3

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

4

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

8

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

11

Gender Salient Predictors: Women

Personal/Emotional

• Aggressivem

• Poor stress managementm

• Manipulativem

Criminal attitudes

• Negative towards lawm

• Lacks directionm

12

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

13

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

16

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

17

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

18

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

19

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

20

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

21

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)

22

The newspaper headline was…

• Gender matters!

• Men really do outperform women!

• Men really are from Mars and women from Venus!

• Gender differences prevail!

23 Thank You

24

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: shelley_brown@carleton.ca