Solving the Problem of Unattributed Political Violence

High rates of missing perpetrator information in political violence data pose a serious challenge for studies into militant group behavior and the microdynamics of conflict more generally. In this article we introduce multiple imputation (Ml) as the best available method for minimizing the impact of...

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Bibliographic Details
Published in:The Journal of conflict resolution Vol. 61; no. 7; pp. 1537 - 1564
Main Authors: Bauer, Vincent, Ruby, Keven, Pape, Robert
Format: Journal Article
Language:English
Published: Los Angeles, CA SAGE Publishing 01.08.2017
SAGE Publications
SAGE PUBLICATIONS, INC
Subjects:
Attribution
Bias
Conflict
Conflict resolution
Group dynamics
Militancy
Multiple imputation
Political violence
Politics
Quantitative analysis
Strikes
Terrorism
Unmanned aerial vehicles
Variables
Violence
Pakistan
multiple imputation
terrorism
events data
civil wars
conflict
ISSN:0022-0027, 1552-8766
Online Access:Get full text
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Summary:High rates of missing perpetrator information in political violence data pose a serious challenge for studies into militant group behavior and the microdynamics of conflict more generally. In this article we introduce multiple imputation (Ml) as the best available method for minimizing the impact of missing perpetrator information on quantitative analyses of political violence, a method that can easily be incorporated into most quantitative research designs. Ml will produce unbiased attributions when the reasons for missingness are known and can be controlled for using observed variables, rendering responsibility for unclaimed attacks, "missing at random" (MAR) – which we show is a reasonable assumption in the case of political violence based on current theory of militant group claiming. We lay out the logics and steps of Ml, identify variables and data sources, and demonstrate that Ml produced better results in the case of the Pakistani Taliban's response to drone strikes.
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ISSN:0022-0027
1552-8766
DOI:10.1177/0022002715612575