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Two Wrongs Make a Right: Addressing Underreporting in Binary Data from Multiple Sources

  • Scott J. Cook (a1), Betsabe Blas (a2), Raymond J. Carroll (a3) (a4) and Samiran Sinha (a3)

Media-based event data—i.e., data comprised from reporting by media outlets—are widely used in political science research. However, events of interest (e.g., strikes, protests, conflict) are often underreported by these primary and secondary sources, producing incomplete data that risks inconsistency and bias in subsequent analysis. While general strategies exist to help ameliorate this bias, these methods do not make full use of the information often available to researchers. Specifically, much of the event data used in the social sciences is drawn from multiple, overlapping news sources (e.g., Agence France-Presse, Reuters). Therefore, we propose a novel maximum likelihood estimator that corrects for misclassification in data arising from multiple sources. In the most general formulation of our estimator, researchers can specify separate sets of predictors for the true-event model and each of the misclassification models characterizing whether a source fails to report on an event. As such, researchers are able to accurately test theories on both the causes of and reporting on an event of interest. Simulations evidence that our technique regularly outperforms current strategies that either neglect misclassification, the unique features of the data-generating process, or both. We also illustrate the utility of this method with a model of repression using the Social Conflict in Africa Database.

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Authors’ note: For their helpful comments and suggestions, thanks to Kenneth Benoit, Graeme Blair, Chad Hazlett, Florian Hollenbach, Idean Salyehan, Nils Weidmann, the reviewers, and the editor(s). Replication materials are available online at Cook et al. (2016). All inquiries should be sent to the corresponding author at

Contributing Editor: R. Michael Alvarez

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Political Analysis
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