The Ohio Collaborative Community-Police Advisory Board has required all of its member law enforcement agencies to collect data on all self-initiated traffic contacts by 2020, and release to the public a documented annual administrative review of agency practices regarding biased-based policing. The data required to be collected include, at a minimum, the race and gender of the driver of the vehicle stopped.

While this is a positive move towards strengthening police-community relations and increasing agency transparency, if collected and analyzed incorrectly it could result in a public relations disaster that could needlessly upset the community and destroy the morale of your personnel.

This course is designed for the personnel in your agency who will be responsible for gathering and tabulating these traffic stop data, and writing the resulting report for the public. This course will expose these individuals to the current state of the art methods for collecting and analyzing traffic stop data in a fair and valid manner and reveal the pitfalls of using improper analysis techniques. The goal of the course is to teach the personnel responsible for this data collection effort how to conduct their analysis in a manner that is honest and fair to both the community and your officers.

Course Objectives:

• • The Issue of Biased-Based Policing

• • • Public perceptions

• • • Repercussions for police legitimacy

• • • Legal repercussions for police agencies

• • Types of Bias-Based Policing Studies and Reports

• • • Types of mandates for reporting

• • • Types of information to report

• • • External versus internal evaluations

• • The Best Way to Organize Your Report

• • • Overall agency response to ethics and fairness

• • • Overall agency efforts toward diversity and inclusion

• • • Stop data is only a part of the report

• • Understanding the Disproportionality Index

• • • What is it?

• • • How is it calculated?

• • • What does it mean?

• • Comparing Apples to Apples

• • • Reporting data by district rather than agency-wide

• • • Reporting special unit data separately

• • • Separating proactive and reactive activities

• • Finding the Proper Valid Benchmark

• • • The numerous problems with using Census statistics

• • • Understanding how the wrong benchmark hurts the entire study

• • • Ensuring the benchmark matches the activity targeted police activity

• • The Importance of Data Validity

• • • Importance of officer buy-in and training

• • • Importance of data collection instruments

• • • The potential sources of invalid data

• • Internal Benchmarking

• • • How to truly detect biased policing among the ranks

• • • Comparing officers against their peers

• • • Ruling out legitimate reasons for disparities

• • How to Get Help

• • • Sources of technical assistance

• • • Screening outside researchers

• • Developing in-house expertise