Is this project an undergraduate, graduate, or faculty project?
Graduate
group
What campus are you from?
Daytona Beach
Authors' Class Standing
Claire Lebakken, Graduate Student Christina Curenton, Graduate Student Caroline Howard, Graduate Student
Lead Presenter's Name
Claire Lebakken
Faculty Mentor Name
Dr. Preshant Shekhar
Abstract
This project aims to identify which event-level and structural covariates are most predictive of terrorism outcomes. Using the Global Terrorism Database (1970–2020), we examine whether fatalities, injuries, attack type, target type, and actor type, combined with national-level conditions, can reliably predict outcomes such as lone actor versus group involvement, attack method, target selection, and property damage. Event-level data are merged with World Bank Development Indicators and Freedom House scores to incorporate economic and governance contexts. After cleaning the data, creating dummy variables, and log-transforming skewed measures (e.g., GDP per capita), we apply logistic and multinomial logistic regression models to test the predictive power of individual variables and combinations of covariates. The core objective is not to confirm a single theory, but to evaluate which micro-level incident characteristics and macro-level structural factors provide meaningful predictive insight. By comparing the relative influence of severity measures and country-level conditions, this study contributes to understanding what features most strongly shape terrorism outcomes and informs future data-driven approaches in risk assessment and policy development.
Did this research project receive funding support from the Office of Undergraduate Research.
No
Included in
Terrorism by the Numbers: Event and Structural Determinants of Attack Outcomes
This project aims to identify which event-level and structural covariates are most predictive of terrorism outcomes. Using the Global Terrorism Database (1970–2020), we examine whether fatalities, injuries, attack type, target type, and actor type, combined with national-level conditions, can reliably predict outcomes such as lone actor versus group involvement, attack method, target selection, and property damage. Event-level data are merged with World Bank Development Indicators and Freedom House scores to incorporate economic and governance contexts. After cleaning the data, creating dummy variables, and log-transforming skewed measures (e.g., GDP per capita), we apply logistic and multinomial logistic regression models to test the predictive power of individual variables and combinations of covariates. The core objective is not to confirm a single theory, but to evaluate which micro-level incident characteristics and macro-level structural factors provide meaningful predictive insight. By comparing the relative influence of severity measures and country-level conditions, this study contributes to understanding what features most strongly shape terrorism outcomes and informs future data-driven approaches in risk assessment and policy development.