1. Using Data to Transform Community Correction Interventions
Mike Randle
Vice President of Summit County Programs
Derek Ault
Lead Research Specialist
Oriana House, Inc.

2. Objectives Using state or other secondary data
Recidivism and commitment data
Using Assessment Data
ORAS, CTS
Collecting your own data
Offender Input
CQI and QA processes to improve fidelity
EPICS, CPP, and ORAS
Establishing research partnerships
Focus group with offenders or staff

3. Using State or other secondary data
Problem 1

4. Secondary Data Sources
ODRC collects recidivism, commitment and release data for us
Reports posted on website
Can be used to identify trends and compare to state averages

5. County Recidivism Three year recidivism rates by county
Recidivism defined as return to prison within timeframe
Can breakdown rates to examine recidivism from technical violations and new crimes
Additional state reports breaks down rates by follow-up period, sex, and release types

6. 2011 DRC Exits – 3 Year Recidivism Rate
Recidivism Reports – County*
County of Commitment
2011 DRC Exits – 3 Year Recidivism Rate
Total
2011 Total
2010 Total
Percentage
No Recidivism
Technical Violation
New Crime
Recidivism Rate
Points Change N %
Summit
856
69.4 80
6.5
298
24.1
1234
30.6
29.8
0.8
State Total
16280
72.5
1205
5.4
4970
22.1
22455
27.5
27.1
0.4
*ODRC. (2015). County Three Year Recidivism Rates.

7. CBCF, HH, ISP-407 Recidivism
Includes all programs that receive funding from the ODRC
CY 2010 Cohort (1, 2, and 3 year recidivism rates)
CY 2011 Cohort (1 and 2 year recidivism rates)
CY 2012 Cohort (1 year recidivism rate)
Recidivism defined as, “failed supervision or community placement resulting in placement in Ohio’s prisons”

8. Recidivism Reports – CBCF, HH, ISP-407*
Program Type
1st Year
2nd Year
3rd Year %
Completing Program N
CBCF
Successful
15.3
26.6
29.9
4693
80.6
Unsuccessful
55.3
62.9
65.7
1127
19.4
Total
23.0
33.6
36.8
5820
100.0 HH
10.3
18.7
22.2
4223
67.9
45.8
52.9
1994
32.1
21.7
29.7
32.8
6217
ISP - 407
4.6
8.7
10.8
4678
54.4
46.9
53.0
54.6
3923
45.6
23.9
28.9
30.8
8601 .
*ODRC. (2014). CBCF, Halfway House, and ISP-407 Recidivism Report

9. County Level Commitments*
FY and CY annual commitment reports
County and offense level data
Breakdown by sex
Committing County
1st Deg
2nd Deg
3rd Deg
4th Deg
5th Deg
Total N %
Summit 95
8.7
195
17.9
384
35.2
194
17.8
222
20.4
1090
1790
9.0
3154
15.9
5794
29.2
4007
20.2
5078
25.6
19823
*ODRC. (2016). FY 2016 Commitment Report.

10. Application of Secondary Data

11. Using Client/Offender Assessment Data
Problem 2

12. Assessment Data
Routinely collected as part of offender intake or eligibility requirements
Captures information on various offender characteristics
While useful at the client/offender level, in the aggregate:
Identify trends in client/offender population
Ensure adherence to evidence base practices
Measure change in client/offender behavior

13. ORAS Identifying client risk over years
Trends looking at overall and domain scorea
Intake Risk Level
Q1 2016
Q1 2017 N %
Low 37
17.8 16
10.9
Moderate 62
29.8 72
49.0
High 95
45.7 53
36.1
Very High 14
6.7 6
4.1
Total
208
100.0
147

14. Family and Social Support Neighborhood Problems
ORAS
CST Error Rates Q
CST Domains
Criminal History
Edu./
Emp./ Finances
Family and Social Support
Neighborhood Problems
Substance Use
Total # of errors 34 20 11 14
Average # of errors
.23
.13
.07
.09
Error Rate(%)
3.80
2.24
1.48
4.70
4.56
N = 149 Assessments

15. TCU Criminal Thinking Scales
Developed from the work of Glen Walters and the Bureau of Prisons in 1996  Psychological Inventory of Criminal Thinking Styles (PICTS)
Provides an overall score and six subscale scores
TCU CTS Scales
Definition
Personal Irresponsibility
Blaming others/external factors for criminal behavior.
Entitlement
Feeling of privilege
Power Orientation
Need for power/ control over others
Justification
Minimalization of seriousness of antisocial acts
Cold Heartedness
Callousness
Criminal Rationalization
Negative attitude toward law and authority figures

16. TCU Criminal Thinking Scales
36 items rated on a 5-point Likert scale
(1 = strongly disagree, 2 = disagree, 3 = uncertain, 4 = agree, 5 = strongly agree)
Scales contain an average of 6 items each
Subscale scores are obtain by summing and dividing by the number of items included and multiplying by ten (10 – 50)

17. TCU Criminal Thinking Scales
Knight, K., Garner, R. G., Simpson, D. D., Morey, J. T., & Flynn, P. M. (2006). An Assessment of Criminal Thinking. Crime Delinquency, 52(1).

18. TCU Criminal Thinking Scales
Specialized Cognitive Offender Programming and Education (SCOPE) – 2012

19. Application of Assessment Data

20. Collecting our own data
Problem 3

21. Survey Data Easy method to collect client/offender satisfaction
Identify population of focus
Establishing timelines
What to ask

22. Designing Surveys Residential Exit Evaluation Reports Demographics
Intake
Overall Programming
Drug and Alcohol Treatment
Cog Skills, Employment, and Education Programming
My Caseworker
Staff at the Post and on the Floor
Post Release
Facility Safety & Drug Use

23. Designing Surveys Be specific in your survey questions
My Caseworker/PO…
…helped me create realistic goals.
…helped me identify triggers/targets.
…helped create a plan to address triggers/targets.
…acknowledged my concerns, opinions and feelings.
…treated me respectfully.
I liked my Caseworker/PO.

24. Designing Surveys Avoid double barreled or complex questions
Role playing helped me practice what I learned in class.
Role playing helped me practice what I learned in class and to identify my high risk thoughts/thinking errors.
Role playing helped me identify my high risk thoughts/thinking errors.

25. Designing Surveys
Keep language in mind (e.g. double negatives, jargon, complex sentences, bias, and reading level)
The staff at the post and on the floor acknowledged my behavior and explained why it was positive or negative.
The staff at the post and on the floor effectively used CCP skills.

26. Collecting Survey Data
Data software – MS Excel, Access, SPSS, Remark
Coding System
Strongly Disagree Disagree Agree Strongly Agree

27. Analysis
Frequency – The number of how often a value occurs within a particular category. Often represented by ‘n’

28. Analysis
Mean – (AKA Average) Sum of all responses divided by the total number of responses.

29. Analysis
Percentage – An amount of something, often expressed as a number out of 100.

30. Survey Response Rates

31. Application of Satisfaction Data

32. QA / CQI processes to ensure fidelity
Problem 4

33. Case Management
Effective Practices in Correctional Settings II (EPICS-II)
Audio tape submissions based on overall proficiency

34. Case Management

35. Case Management

36. Dual Coding

37. Additional QA and CQI Processes
Core Correctional Practices (CCP)
Frontline staff
ORAS administration
Caseworkers & Intake Specialists
Group observation
Cog Skill Specialists
Education and Employment Specialists

38. Application of QA/CQI data

39. Establishing research partnerships
Problem 5

40. Using Outside Researchers
Some projects may need an outside evaluator/researcher to address:
Limited resources
Conflicts of interest
Desire to maintaining anonymity or confidentiality
Identifying research partnerships
Local Universities
State or county level organizations

41. Recent Research Collaborations
PTSD and Trauma Interventions
Ongoing training and evaluation
Employee Focus Groups
Maintain anonymity
Client Utilization of Services
Geospatial analysis and software

42. Steps to identifying local research partnerships
Discussion
Steps to identifying local research partnerships

43. Questions?