Building and Using a Dataset to Produce Varied Recidivism

and Failure Measures

ICRN/NCRP Meeting - May 11, 2016Fred Klunk, Director of Research

Thomas Powers, Research Analyst

We are…

• The Statistical Reporting and Evidence-based Program Evaluation Office (we call ourselves “Research” or “Stats”)

• Largely divided into two parts, with a good deal of overlap in responsibilities

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PENN STATE!

Before We Get Started

• Outcome = first event

• Hot urine = positive urinalysis

• CPV vs. TPV

• Changes from Act 122

• LSI-R Assessment

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“If we want to change what CEOs care about, we should change what we measure.”

- Dan Ariely, author of Predictably Irrational: The

Hidden Forces That Shape Our Decisions

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How Do We Typically Measure Success?

• Many outcome studies consider recidivism as the primary outcome measure

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Why Consider Recidivism?

• Data is available

• It’s easy

• Recidivism is costly

• Often, it is the best outcome measure

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Why Consider Other Outcome Measures?

• Some outcome types may be more program-relevant

• It allows you to identify covariates based upon different outcome measures

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How Do We Build It?

• Similar to a standard recidivism dataset

• Identify the at-risk date and the end of supervision date

• Mix in any relevant demographics or descriptive characteristics

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The “Magic”

• Why just consider one outcome?

• What are the common elements between different outcome types?

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Identifying Permanent Outcomes

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What Does Supervision Look Like?

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Sample Data

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OffenderID EventDate EventID EventDescription Permanent

123AB 1/1/2012 23 Released to Supervision No

123AB 6/26/2012 15 Low-level Violation No

123AB 11/3/2012 15 Low-level Violation No

123AB 2/6/2013 18 Absconder Status No

123AB 7/10/2013 13 High-level Violation No

123AB 7/10/2013 17 Detention Status No

123AB 7/20/2013 11 Parole Violator Center Placement No

123AB 12/30/2013 13 High-level Violation No

123AB 1/27/2014 10 TPV Arrest No

123AB 1/27/2014 17 Detention Status No

123AB 2/21/2014 6 TPV Recommit Yes

Calculating Outcomes

• A single “event record” per release is calculated

• Three values are calculated for each event type:

An “event occurred” bit flag (0/1)

The number of days to the event

The number of months to the event

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First Violation Time Calculation

Violation occurred = 1Days to first violation = 93Months to first violation = 3

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First TPV Center Placement Time Calculation

TPV placement occurred = 1Days to first TPV placement = 707Months to first TPV placement = 23

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Censor Time Calculation

Absconding occurred = 0Days to first absconding event = 1,354Months to first absconding event = 44

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Sample Outcomes Dataset

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Identifier 797AB 329AB 333AB 976AB 326AB 404AB 187AB 212AB 632AB

Release Date 9/24/12 3/24/12 10/14/12 9/4/12 2/14/12 1/3/12 10/19/12 12/25/12 8/17/12

Censor Days 1,094 716 1,175 742 511 1,038 1,171 1,112 823

Recommit Censor 0 1 0 1 1 0 0 0 1

Recommit Days 1,094 716 1,175 742 511 1,038 1,171 1,112 823

Recommit Months 36 23 38 24 16 34 38 36 27

Arrest Censor 0 1 0 1 1 0 0 1 1

Arrest Days 1,094 632 1,175 396 469 1,038 1,171 48 785

Arrest Months 36 20 38 13 15 34 38 1 25

Positive Urine Censor 0 0 0 0 1 0 1 1 1

Positive Urine Days 1,094 716 1,175 1,137 182 1,038 119 34 23

Positive Urine Months 36 23 38 37 6 34 3 1 0

… … … … … … … … … …

What Do Results Look Like?

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What Do Results Look Like?

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What Do Results Look Like?

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What Do Results Look Like?

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Benefits of this Method

• New definitions of failure or success

• Easy to explore curiosities

• Very “linkable” to other data sets

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Negatives of this Method

• Large up-front time commitment

• Data is very structured; can be less flexible

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Future Directions

• Building out data on treatment programming / control group creation

• Including data that precedes Parole Board data

• Identifying “positive” outcomes

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Questions/Comments/Suggestions

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