Two professors of psychiatry and behavioral sciences from the George Washington University (GW) School of Medicine and Health Sciences, Philip Candilis, MD, and Allen Dyer, MD, PhD, along with Sean D. Cleary, PhD, MPH, associate professor of epidemiology at the Milken Institute School of Public Health at GW, and two outside researchers recently published a study on latent class analysis of sociopolitical and psychological data as a way of classifying terrorism.
Definitions of terrorist typologies tend to rely on theory, secondary data, or the distinction between lone and group actors. The influence of sociopolitical and psychological factors does not always derive from primary data. Moreover, classification based on the influence of family, ideology, and personality factors on terrorist behavior can be contentious.
“For this study, Dr. Cleary convinced us to use latent class analysis, or LCA, a statistical model that allows you to identify subgroups or categories of individuals,” Candilis said. “With LCA, we developed a typology for terrorism straight from primary data, including common social, family, childhood, ideology, and personality factors.”
Candilis and the team included 21 established variables that represented participants’ attitudes, perceptions, character traits, and behaviors identified in the literature. The team’s results showed that a three-class model, versus a two- or four-class model, was the best approach. The largest class was defined as “non-religious nationalists,” the second largest “oppressed instrumentalists,” and the third “aggrieved antisocials.”
“Our typology needs additional study with larger samples and different settings,” Dyer said, “but our new categorization could offer opportunities to identify those at risk for terrorist behavior and the option to intervene.”
Read the study, “Classifying Terrorism: A Latent Class Analysis of Primary Source Socio-Political and Psychological Data,” in Behavioral Sciences of Terrorism and Political Aggression.