, Volume 28, Issue 1, pp 163-189

Cross-Classified Multilevel Models: An Application to the Criminal Case Processing of Indicted Terrorists

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Abstract

This study provides an application of cross-classified multilevel models to the study of early case processing outcomes for suspected terrorists in U.S. federal district courts. Because suspected terrorists are simultaneously nested within terrorist organizations and criminal court environments, they are characterized by overlapping data hierarchies that involve cross-nested ecological contexts. Cross-classified models provide a useful but underutilized approach for analyzing such data. Using the American Terrorism Study (ATS), this research examines case dismissals, trial adjudications and criminal convictions for a sample of 574 terrorist suspects. Findings indicate that diverse factors affect case processing outcomes, including legal factors such as the number of counts, number of co-defendants, and statute of indictment, extralegal factors such as the ethnicity of the offender, and incident characteristics such as the type of terrorism target. Case processing outcomes also vary significantly across both terrorist groups and criminal courts and are partially explained by select group and court characteristics including the type of terrorist organization and the terrorism trial rate of the court. Results are discussed vis-à-vis contemporary research on terrorism punishments and future directions are suggested for additional applications of cross-classified models in criminological research.

This material is based upon work supported by the Science and Technology Directorate of the U.S. Department of Homeland Security under Grant Award Number 2008-ST-061-ST0004, made to the National Consortium for the Study of Terrorism and Responses to Terrorism (START, www.start.umd.edu). The views and conclusions contained in this document are those of the author and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security or START.