Publication Date

6-15-2021

Document Type

Thesis

Degree Name

Master of Science in Criminal Justice (MS)

Committee Member

San Miguel, Claudia

Committee Member

Zawisza, Thomas

Committee Member

Ynalvez, Marcus A.

Abstract

Homicide is the most serious form of crime and unfortunately, if you are murdered in America, there is a one in three chance that the police will not identify your killer (Kaste, 2015). Unsolved homicide cases are often referred to as “cold cases.” Very little is understood about cold cases and the circumstances under which a case may go “cold”. To investigate the characteristics of cold cases, data from one of the largest cities in Texas (Houston) will be used to compare similarities and differences from Houston’s cleared homicide cases and cold cases. The factors that are the focus of my research are: victim characteristics of race, sex and, age, crime characteristics of time of day, day of week, seasonality, location of crime, method of death, and motive of death of the victim. The ultimate goal of this research was to build a model of cold cases that can help us to understand what make a case “cold,” why they go unsolved, and identify risk factors of a case that can eventually go cold. After running a binary logistic regression analysis on the data sets provided for this research, some predictors were found to be stronger than others.

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