Although the empirical and analytical study of terrorism has grown dramatically in the past decade and a half to incorporate more sophisticated statistical and econometric methods, data validity is still an open, first-order question. Specifically, methods for treating missing data often rely on strong, untestable, and often implicit assumptions about the nature of the missing values. The current study drew on Manski's concept of no-assumption bounds to demonstrate the vulnerability of empirical results to different tactics for treating missing cases. This report concludes by advocating for researchers to be transparent when building analytical models about the assumptions they are making about the nature of the data and their implications for the analysis and its interpretation. This article concludes by advocating for researchers, when building their models, to be transparent about the assumptions they are making about the nature of the data and their implications for the analysis and its interpretation. (Publisher abstract modified)
Studying Terrorism Empirically: What We Know About What We Don't Know
NCJ Number
252394
Journal
Journal of Contemporary Criminal Justice Volume: 33 Issue: 3 Dated: 2017 Pages: 273-291
Date Published
2017
Length
19 pages
Annotation
Using a recently available open-source database on political extremists who were radicalized in the United States, this study shows how point estimates of basic conditional probabilities can vary dramatically depending on the amount of missing data in certain variables and the methods used to address this issue.
Abstract
Date Published: January 1, 2017