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. We draw on Manski’s idea of no-assumption bounds to demonstrate the vulnerability of empirical results to different tactics for treating missing cases. Using a recently available open-source database on political extremists who radicalized in the United States, we show 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. We conclude 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.
All Science Journal Classification (ASJC) codes