From the Rust Belt to the Sun Belt: The Predictive Accuracy of the Arizona Youth Assessment System (AZYAS-Reentry) for Juveniles on Parole Supervision

Abstract

This study had two objectives: (1) to evaluate the predictive accuracy of the AZYAS-Reentry tool and (2) to test whether predictive accuracy levels were consistent across sex and race/ethnicity. Data from 249 Arizona adolescents released from secure care on parole supervision between June 2020 and July 2023 were examined. The mean AZYAS-Reentry score classifies much of the sample as moderate risk. Bivariate analyses show a medium effect size for AZYAS-Reentry on parole revocation for the full sample (r = .344). Predictive accuracy is the highest for African American youth (r = .534), followed by Hispanic (r = .398) and white youth (r = .191). Higher predictive accuracy for African Americans is driven by specific domains (e.g., values, beliefs, and attitudes). The one sex-based difference is the stronger effect of juvenile justice history for females. Findings support using AZYAS-Reentry and highlight the utility of targeting domains to plan interventions across sex and race/ethnicity.

Keywords

justice-involved youth parole recidivism risk assessment risk-need-responsivity

Get full access to this article

View all access options for this article.

References

Andrews

Bonta

Hoge

(1990). Classification for effective rehabilitation: Rediscovering psychology. Criminal Justice and Behavior, 17(1), 19–52. https://doi.org/10.1177/0093854890017001004

Arizona Department of Juvenile Corrections. (2024, June). Just the facts. https://adjc.az.gov/publications/reports-and-data

Baglivio

Jackowski

(2013). Examining the validity of a juvenile offending risk assessment instrument across gender and race/ethnicity. Youth Violence and Juvenile Justice, 11(1), 26–43. https://doi.org/10.1177/1541204012440107

Baird

(2009). A question of evidence: A critique of risk assessment models used in the justice system. National Council on Crime and Delinquency.

Bonta

Andrews

(2016). The psychology of criminal conduct (6th ed.). Routledge.

Cabaniss

Frabutt

Kendrick

Arbuckle

(2007). Reducing disproportionate minority contact in the juvenile justice system: Promising practices. Aggression and Violent Behavior, 12(4), 393–401. https://doi.org/10.1016/j.avb.2006.09.004

Campbell

Papp

Barnes

Onifade

Anderson

(2018). Risk assessment and juvenile justice: An interaction between risk, race, and gender. Criminology & Public Policy, 17(3), 525–545. https://doi.org/10.1111/1745-9133.12377

Chapman

Desai

Falzer

Borum

(2006). Violence risk and race in a sample of youth in juvenile detention: The potential to reduce disproportionate minority confinement. Youth Violence and Juvenile Justice, 4(2), 170–184. https://doi.org/10.1177/1541204006286316

Chesney-Lind

Shelden

(2014). Girls, delinquency, and juvenile justice (4th ed.). Wiley.

10.

Childs

Peck

Brady

(2024). Predictive bias in juvenile risk assessment: Considering race/ethnicity and sex. Crime and Delinquency, 70(3), 1034–1059. https://doi.org/10.1177/00111287221143936

11.

DataOhio. (2024, November 9). Youth services current parole population. https://data.ohio.gov/wps/portal/gov/data/view/youth-services-current-parole-population

12.

Flores

Bechtel

Lowenkamp

(2016). False positives, false negatives, and false analyses: A rejoinder to “Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks.” Federal Probation, 80(2), 38–46.

13.

Fraenkel

Wallen

Hyun

(2012). How to design and evaluate research in education (8th ed.). McGraw-Hill.

14.

Grove

(2005). Clinical versus statistical prediction: The contribution of Paul E. Meehl. Journal of Clinical Psychology, 61(10), 1233–1243. https://doi.org/10.1002/jclp.20179

15.

Hamilton

(2019). The sexist algorithm. Behavioral Sciences & the Law, 37(2), 145–157. https://doi.org/10.1002/bsl.2406

16.

Hamilton

Kowalski

Campagna

Kobie

Kigerl

(2024). Comparing meters to yards: A nationally representative evaluation of gender bias in risk assessment. Justice Quarterly, 41(6), 845–869. https://doi.org/10.1080/07418825.2023.2286238

17.

Hamilton

Kowalski

Kigerl

Routh

(2019). Optimizing youth risk assessment performance: Development of the modified positive achievement change tool in Washington State. Criminal Justice and Behavior, 46(8), 1106–1127. https://doi.org/10.1177/0093854819857108

18.

Hannah-Moffat

(2016). Purpose and context matters: Creating a space for meaningful dialogues about risk and need. In Taxman

(Ed.), Handbook on risk and need assessment (pp. 447–462). Routledge.

19.

Hanson

Bourgon

Helmus

Hodgson

(2009). The principles of effective correctional treatment also apply to sexual offenders: A meta-analysis. Criminal Justice and Behavior, 36(9), 865–891. https://doi.org/10.1177/0093854809338545

20.

Harris

Lockwood

Mengers

Stoodley

(2011). Measuring recidivism in juvenile corrections. Journal of Juvenile Justice, 1(1), 1–16.

21.

Hilton

Harris

Rice

(2006). Sixty-six years of research on clinical versus actuarial prediction of violence. The Counseling Psychologist, 34(3), 400–409. https://doi.org/10.1177/0011000005285877

22.

Hockenberry

(2022). Juveniles in residential placement, 2019. Office of Juvenile Justice and Delinquency Prevention.

23.

Hubbard

Matthews

(2008). Reconciling the differences between the “gender-responsive” and the “what works” literatures to improve services for girls. Crime and Delinquency, 54(2), 225–258. https://doi.org/10.1177/0011128706296733

24.

Kanestrøm

Stallvik

Lydersen

Skokauskas

Hoftun

Nilsen

Kaasbøll

(2024). Criminogenic risk assessment beyond juvenile justice: Exploring the predictive ability of the Youth Level of Service/Case Management Inventory (YLS/CMI) in Norway. Criminal Justice and Behavior, 51(7), 975–993. https://doi.org/10.1177/00938548241237181

25.

Kim

(2015). Statistical notes for clinical researchers: Post-hoc multiple comparisons. Restorative Dentistry & Endodontics, 40(2), 172–176. https://doi.org/10.5395/rde.2015.40.2.172

26.

Latessa

Lovins

Ostrowski

(2009). The Ohio Youth Assessment System. University of Cincinnati, Center for Criminal Justice Research.

27.

Lenhard

(2014). Hypothesis tests for comparing correlations. Psychometrica. https://www.psychometrica.de/correlation.html

28.

Lovins

Latessa

(2013). Creation and validation of the Ohio Youth Assessment System (OYAS) and strategies for successful implementation. Justice Research & Policy, 15(1), 67–93. https://doi.org/10.3818/JRP.15.1.2013.67

29.

Lowder

Morrison

Kroner

Desmarais

(2019). Racial bias and LSI-R assessments in probation sentencing and outcomes. Criminal Justice and Behavior, 46(2), 210–233. https://doi.org/10.1177/0093854818789977

30.

Lowenkamp

Latessa

Holsinger

(2006). The risk principle in action: What have we learned from 13,676 offenders and 97 correctional programs? Crime and Delinquency, 52(1), 77–93. https://doi.org/10.1177/0011128705281747

31.

McCafferty

(2018). Unjust disparities? The impact of race on juvenile risk assessment outcomes. Criminal Justice Policy Review, 29(5), 423–442. https://doi.org/10.1177/0887403416634163

32.

McCafferty

Newsome

Sullivan

(2017). Report: Study of AZYAS-residential. University of Cincinnati, The Corrections Institute.

33.

Menard

(2002). Applied logistic regression analysis. Sage.

34.

Miller

Toman

Pederson

(2024). Examining the utility of the Texas Risk Assessment System to predict community supervision compliance and recidivism. Criminal Justice and Behavior, 51(8), 1257–1274. https://doi.org/10.1177/00938548241240991

35.

Muir

Viljoen

Jonnson

Cochrane

Rogers

(2020). Predictive validity of the Structured Assessment of Violence Risk in Youth (SAVRY) with Indigenous and Caucasian female and male adolescents on probation. Psychological Assessment, 32(6), 594–607. https://doi.org/10.1037/pas0000816

36.

Nahm

(2022). Receiver operating characteristic curve: Overview and practical use for clinicians. Korean Journal of Anesthesiology, 75(1), 25–36. https://doi.org/10.4097/kja.21209

37.

National Institute of Justice. (2024). 2023 review and revalidation of the First Step Act risk assessment tool. U.S. Department of Justice, Office of Justice Programs.

38.

Obuchowski

Lieber

Wians

(2004). ROC curves in clinical chemistry: Uses, misuses, and possible solutions. Clinical Chemistry, 50(7), 1118–1125. https://doi.org/10.1373/clinchem.2004.031823

39.

Odgers

Moretti

(2002). Aggressive and antisocial girls: Research update and future challenges. International Journal of Forensic Mental Health, 1(2), 103–119. https://doi.org/1080/14999013.2002.10471166

40.

Olver

Stockdale

Helmus

Woods

Termeer

Prince

(2024). Too risky to use, or too risky not to? Lessons learned from over 30 years of research on forensic risk assessment with Indigenous persons. Psychological Bulletin, 150(5), 487–553. https://doi.org/10.1037/bul0000414

41.

Peduzzi

Concato

Kemper

Holford

Feinstein

(1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 49(12), 1373–1379. https://doi.org/10.1016/S0895-4356(96)00236-3

42.

Perrault

Vincent

Guy

(2017). Are risk assessments racially biased? Field study of the SAVRy and yLS/CMI in probation. Psychological Assessment, 29(6), 664–678. https://doi.org/10.1037/pas0000445

43.

Prendergast

Pearson

Podus

Hamilton

Greenwell

(2013). The Andrews’ principles of risk, needs, and responsivity as applied in drug treatment programs: Meta-analysis of crime and drug use outcomes. Journal of Experimental Criminology, 9(3), 275–300. https://doi.org/10.1007/s11292-013-9178-z

44.

Pusch

Holtfreter

(2018). Gender and risk assessment in juvenile offenders: A meta-analysis. Criminal Justice and Behavior, 45(1), 56–81. https://doi.org/10.1177/0093854817721720

45.

Quenneville

(2024, October 7). Getting to know youth justice in Ohio. ACLU of Ohio. https://www.acluohio.org/en/news/getting-know-youth-justice-ohio

46.

Reisig

Holtfreter

Morash

(2006). Assessing recidivism risk across female pathways to crime. Justice Quarterly, 23(3), 384–405. https://doi.org/10.1080/07418820600869152

47.

Rice

Harris

(2005). Comparing effect sizes in follow-up studies: ROC area, Cohen’s d, and r. Law and Human Behavior, 29(5), 615–620. https://doi.org/10.1007/s10979-005-6832-7

48.

Schwalbe

(2007). Risk assessment for juvenile justice: A meta-analysis. Law and Human Behavior, 31(5), 449–462. https://doi.org/10.1007/s10979-006-9071-7

49.

Schwalbe

Fraser

Day

Arnold

(2004). North Carolina Assessment of Risk (NCAR): Reliability and predictive validity with juvenile offenders. Journal of Offender Rehabilitation, 40(1-2), 1–22. https://doi.org/10.1300/J076v40n01_01

50.

Scott

Brown

(2018). Risks, strengths, gender, and recidivism among justice-involved youth: A meta-analysis. Journal of Consulting and Clinical Psychology, 86(11), 931–945. https://doi.org/10.1037/ccp0000343

51.

Scott

Brown

Skilling

(2019). Predictive and convergent validity of the youth assessment and screening instrument in a sample of male and female justice-involved youth. Criminal Justice and Behavior, 46(6), 811–831. https://doi.org/10.1177/0093854819842585

52.

Shields

(2018, March 12). Manufacturing a high wage Ohio: Deindustrialization and recovery. Policy Matters Ohio. https://www.policymattersohio.org/research-policy/fair-economy/work-wages/state-of-working-ohio/manufacturing-a-high-wage-ohio-deindustrialization-and-recovery

53.

Skeem

Lowenkamp

(2016). Risk, race, and recidivism: Predictive bias and disparate impact. Criminology, 54(4), 680–712. https://doi.org/10.1111/1745-9125.12123

54.

Smith

Cullen

Latessa

(2009). Can 14,737 women be wrong? A meta-analysis of the LSI-R and recidivism for female offenders. Criminology & Public Policy, 8(1), 183–208. https://doi.org/10.1111/j.1745-9133.2009.00551.x

55.

Sullivan

McCafferty

Newsome

Mandalari

(2022). Predictive validity and measurement invariance in juvenile risk assessment: Implications for racial and ethnic disparities in juvenile justice. Journal of Crime and Justice, 45(4), 409–429. https://doi.org/10.1080/0735648X.2021.1997788

56.

Taxman

Smith

(2021). Risk-need-responsivity (RNR) classification models: Still evolving. Aggression and Violent Behavior, 59, 101459. https://doi.org/10.1016/j.avb.2020.101459

57.

Vincent

Viljoen

(2020). Racist algorithms or systemic problems? Risk assessments and racial disparities. Criminal Justice and Behavior, 47(12), 1576–1584. https://doi.org/10.1177/0093854820954501

58.

Wachter

(2015). Statewide risk assessment in juvenile probation. National Center for Juvenile Justice.