Understanding Bias and Item Missing Data in NIBRS

1. Understanding Bias and Item Missing Data in NIBRS American Society of Criminology 2017 Annual Meeting Overcoming Measurement Challenges November 17, 2017 Philadelphia, PA Eman Abdu, Doug Salane and Peter Shenkin Center for Cybercrime Studies Mathematics & Computer Science Dept. John Jay College of Criminal Justice City University of New York

2. Acknowledgements Many students have contributed: Boris Bonderenko, Raul Cabrera and Henry Gallo Inter-university Consortium for Political and Social Research(ICPSR) and National Archive of Criminal Justice Data (NACJD) FBI, Criminal Justice Information Services Division, UCR/NIBRS Groups NSF, NASA and NIJ

3. Goals Provide back ground on FBI’s National Incident-Based Reporting System (NIBRS) Demonstrate utility of having NIBRS data in a relational data base (Oracle 12c) Examine NIBRS data issues: nonresponse bias and extent of item missing data Briefly discuss ongoing work

4. NIBRS Data Structure • Group A offenses (53 crimes) • data on arrest, offense, offender, victim, property • data on incident (administrative) • 56 data elements in 6 main segments • Group B offenses (11 crimes) – social crimes (victimless) • e.g., bad checks, disorderly conduct, driving under influence • only recorded if there is an arrest • new codes 2015: Identity theft (26F), Computer hacking (26G)

5. NIBRS Data Structure • NIBRS Group A offenses – data in 6 major files or segments • An incident can have multiple segments: victims, offenders, offenses, arrestees, property records • Tied together by Agency Identifier (ORI) and incident number • 13 Segment files 6 group A, 1 group B, 3 Windows files, 3 Batch Files

6. NIBRS Relational Database • 59 Tables – 13 Segments + Codebook • Enforces referential integrity – important when uploading new data • Provides SQL query capability and processing capabilities (indices, partitioning, etc.) • Extract required data and relationships • Viewing and reporting tools

15. Code Tables in NIBRS (Type Criminal Activity) CODE DESCRIPTION • B Buying/Receiving • C Cultivating/Manufacturing/Publishing • D Distributing/Selling • E Exploiting Children • J Juvenile Gang Involvement • G Other Gang • N None/Unknown Gang Involvement • O Operating/Promoting/Assisting • P Possessing/Concealing • T Transporting/Transmitting/Importing • U Using/Consuming • I Intentional Abuse and Torture

16. Code Tables in NIBRS (Victim Offender Relationship )

17. Code Tables in NIBRS (Bias Motivation) • 11 Anti-White • 12 Anti-Black or African American • 13 Anti-American Indian or Alaska Native • 14 Anti-Asian • 15 Multi-Racial Group • 21 Anti-Jewish • 22 Anti-Catholic • 23 Anti-Protestant • 24 Anti-Islamic (Moslem) • 25 Other Religion • 26 Multi-Religious Group • 27 Atheism/Agnosticism • 31 Anti-Arab • 32 Anti-Hispanic or Latino • 33 Anti-Not Hispanic or Latino • 41 Anti-Male Homosexual (Gay) • 42 Anti-Female Homosexual (Lesbian) 43 Anti-Lesbian, Gay, Bisexual, or Transgender, Mixed Group (LGBT) • 43 Anti-Lesbian, Gay, Bisexual, or Transgender, Mixed Group (LGBT) • 44 Anti-Heterosexual • 45 Anti-Bisexual • 51 Anti-Physical Disability • 52 Anti-Mental Disability • 88 None • 99 Unknown • 28 Anti-Mormon • 82 Anti-Other Christian • 84 Anti-Hindu • 85 Anti-Sikh • 61 Anti-Male • 62 Anti-Female • 71 Anti-Transgender • 72 Anti-Gender Non-Conforming • 16 Anti-Native Hawaiian or Other Pacific Islander

18. Entity Relationship(6 main segments)

25. Study of selected offenses where offender used a computer • Illustrates use of spreadsheet pivot tables to select desired data • Requires data from the offender and offense segments • Provides age and gender breakdown of the offenders • Examine selected offenses where offender used a computer

28. Spreadsheet Pivot Tables

29. Spreadsheet Pivot Tables

30. BIAS due to Non Response • Compare UCR and NIBRS reporting • Examine Breakdown of Violent and Property Crimes in NIBRS and UCR • Examine Larceny in NIBRS and UCR

31. NIBRS and UCR NIBRS UCR 16,643 LEAs submitted data to UCR (18,439 total ) Includes major municipalities, 83 LEAs covering Group I cities Mainly summary data but with some incident data • 33 states certified, 38% report all crime in NIBRS • Covers 30% of US population (96 million ) • 29% of all crime, 18 LEAs cover Group I cities • 6648 LEAs participated in 2015, over 7000 in 2016

34. NIBRS Breakdown of Violent Crime (1995 – 2015) 1995(1) – 2015(21)

38. Breakdown of Property Crime NIBRS (1995-2015) 1995(1) – 2015(21)

41. Item Missing Data • NIBRS has 53 data elements most of which are mandatory • Data elements such as demographics of victim and offenders, relationships victim/offender and others are of interest to researchers and policy makers • Compare rates of missing data in NIBRS and other sources such as SHR • Examine item missing data in murders

44. Ongoing Work • Time series studies to examine NIBRS missing data, victim-offender relationships, circumstances, location and weapon used • Extract data for specific studies and make it available in Excel Pivot Tables or Data Cubes • Examine effects of police reporting practices on the data, e.g., inaccurate incident times • Prepare for additional NIBRS reporting. DOJ, OJP, BJS and FBI program to create a nationally representative crime sample and NIBRS compliant operational systems increasing NIBRS reporting. (Mainly an IT effort) • Make the relational database publicly available through use of the Oracle Data Pump utility

45. Thank You Eman Abdu Doug Salane and Peter Shenkin dsalane@jjay.cuny.edu 212 237-8836 Center for Cybercrime Studies Math & CS Dept. John Jay College of Criminal Justice