Washington State Criminal Records Audit: Meeting 1- Review of Research Design Washington State Institute for Public Policy September 13, 2006 Robert (Barney) Barnoski Elizabeth Drake Laura Harmon (360)

2 of 25 Washington State Institute for Public Policy   Created in 1983 by the state Legislature   Mission: Carry out non-partisan research on projects assigned either by the legislature or the Institute’s Board of Directors – –8 legislators – –4 higher education provosts or presidents – –4 state agency directors

3 of 25 Authority  OFM contracted with the Institute to conduct the audit of the Washington State criminal history record systems as part of the National Criminal History Improvement Program.  This project was approved by the Institute Board.

4 of 25 Objective of the Criminal Records Audit  Compare Washington State’s criminal history databases for adult felons to determine completeness and accuracy among the databases over time.  Databases to be studied: –Washington State Patrol –Department of Corrections –Administrative Office of the Courts –Sentencing Guidelines Commission.  Time Period: 1992 through 2005.

5 of 25 Determine the Completeness and Accuracy Among the Databases Over Time DOC AOCSGC WSP

6 of 25 Our Experience in Criminal Records Databases  We have developed a recidivism database at the Institute using AOC and DOC data.  Recidivism database used for dozens of juvenile and adult recidivism studies.  Barnoski, Robert (2005). Sex Offender Sentencing in Washington State: Comparing Arrests to Court Filings. Olympia, Washington State Institute for Public Policy, Document No

7 of 25 WSU Criminal Records Audits  WSU has done three criminal records audits: 1994, 1997, and  In 2002, WSU did an assessment of the accuracy of criminal history records in the Washington State Identification System (WASIS).  Approach to the analysis: –Sampling of 1,200 cases from SGC from five counties.  Accuracy rates have improved over time.  Institute will contact WSU about methodology.

8 of 25 Differences With Institute Study  Entire databases will be used. –All counties are included.  All automated matching. –No hand coding.  Rely on the database structure.

9 of 25 What We Will Talk About Today  The Criminal Records Oversight Committee  Project Timeline  Research Design –Data to be analyzed –Analyses to be performed

10 of 25 Criminal Records Oversight Committee Members  Administrative Office of the Courts  Department of Corrections  Washington State Patrol  Sentencing Guidelines Commission  Office of Financial Management  Department of Information Services  Washington Association of County Clerks  Washington Association of Sheriffs and Police Chiefs  Washington Association of Prosecuting Attorneys

11 of 25 Oversight Committee’s Responsibilities  To provide the Institute with technical guidance on data and business practices.  To ensure analyses accurately reflect the status of the criminal history databases and records.

12 of 25 Project Milestones √ Oversight Committee Review  Research Design –√ –√ Today  Data Integration Programming – –One-on-one work with AOC, DOC, SGC, and WSP.  Data Analyses Results –√ –√ February 2006  Draft Report –√ –√ March 2007  Final Report – – April 30, 2007

13 of 25 Phases of Research Design  Phase 1: Official Person Identifier Analysis –Analysis of persons across agencies using official person identifiers.  Phase 2: Unofficial Person Identifier Analysis –Analysis of persons across agencies using name, DOB, and gender.  Phase 3: SCOMIS Case Number Analysis –Analysis of SCOMIS Cases and charges across agencies.

14 of 25 Phases of Research Design Continued  Phase 4: Process Control Number Analysis (PCN) –Analysis of PCNs across agencies.  Phase 5: Composite View –Analysis of consistency across person identifiers, SCOMIS Case Numbers, and PCN’s.  Phase 6: Records Quality Index –Analysis of performance quality.

15 of 25 Phase 1: Official Person Identifier Analysis  State Patrol Identification Number (SID)  DOC Person Identification Number  AOC Person Identification Number  SGC – SID

16 of 25 Phase 1: Official Person Identifier Analysis DOC to WSP 1) Percentage of DOC Person Numbers with an associated SID. 2) Percentage of SID’s in DOC’s database that are associated with multiple DOC Person Numbers. 3) Percentage of SID’s in DOC’s database found in WSP’s database. 4) Percentage of DOC and WSP matches with the same demographics (name, gender, DOB).

17 of 25 Phase 1: Official Person Identifier Analysis  Repeat analysis for: –AOC to DOC: by DOC Person Number –AOC to WSP: by SID Person Number –SGC to WSP: by SID Person Number –We cannot do DOC to AOC because there is no AOC person identifier in DOC’s database.  Summarize completeness and accuracy of official person identifiers over time.

18 of 25 Phase 2: Unofficial Person Identifier Analysis 1) Analyze the completeness of demographic data in each database (DOB, name, aliases, gender, ethnicity). 2) Analyze the reliability of identifying a person from one database to another using demographics.

19 of 25 Phase 3: SCOMIS Case Number Analysis DOC to AOC 1) Percentage of DOC commitments with an associated AOC SCOMIS Case Number. 2) Percentage of DOC SCOMIS Case Numbers found in AOC’s database. 3) Percentage of matching SCOMIS Case Numbers with matching official person identifiers and demographics. 4) Percentage of matching SCOMIS Case Numbers with matching offenses.

20 of 25 Phase 3: SCOMIS Case Number Analysis  Repeat analysis by SCOMIS Case Number for: –AOC to DOC –AOC to WSP –AOC to SGC –SGC to AOC –WSP to AOC  Summarize completeness and accuracy of SCOMIS Case Numbers over time.

21 of 25 Phase 4: Process Control Number Analysis WSP to DOC 1) Percentage of WSP arrests with a PCN. 2) Percentage of PCNs in DOC’s database found in WSP’s database. 3) Percentage of matching PCNs with matching official person identifiers and demographics.

22 of 25 Phase 4: Process Control Number Analysis  Repeat analysis for: –WSP to AOC  Summarize completeness and accuracy of PCNs over time.

23 of 25 Phase 5: Composite View  Describe what is meant by consistency across person identifiers, SCOMIS Case Numbers, and PCNs.  Attempt to statistically summarize this consistency.

24 of 25 Phase 6: Records Quality Index (RQI)  A measure developed for the National Criminal History Improvement Program (NCHIP) for the Bureau of Justice Statistics.  The RQI is a measure used to gauge performance. –Assess the status of records quality. –Identify critical records improvement activities by pinpointing deficiencies. –Help BJS target specific deficiencies for future funding cycles.

25 of 25 The End of Meeting 1: Research Design  Questions?  Meeting 2: Data Analyses Results in February 2007.