Abstract

This capstone project examines and analyzes data for the Community Engagement to Reduce Victimization (CERV) program in Rochester, New York. By developing a logic model, gap analysis, and utilizing statistical models, the project not only better understands the program design, but also assesses gaps and their patterns in the data to provide structured recommendations to strengthen the data infrastructure, improving data completeness and reliability. These improvements will strengthen the program overall, helping to reduce community violence throughout Rochester. Across three working papers, the project begins by examining the current data and analyses on the program and develops a detailed logic model to highlight current and missing outputs, indicating there are gaps within the data. The project then shifts to understanding where exactly these gaps exist in the data through a comprehensive gap analysis. Once this analysis is developed, the project shifts to focusing on patterns within the gaps, determining whether the missing variables are random and systematic to provide a detailed roadmap offering recommendations to reduce and remove these gaps by improving the overall data infrastructure for the program. Together, these sections aim to not only examine the program as it currently stands, but also analyze data gaps to develop structured recommendations to improve the overall quality of the program.

Publication Date

5-1-2026

Document Type

Master's Project

Student Type

Graduate

Degree Name

Criminal Justice (MS)

Department, Program, or Center

Criminal Justice, Department of

College

College of Liberal Arts

Advisor

Irshad Altheimer

Campus

RIT – Main Campus

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