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Solving the Problem of Unattributed Political Violence

Author

Listed:
  • Vincent Bauer
  • Keven Ruby
  • Robert Pape

Abstract

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 (MI) 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. MI 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 MI, identify variables and data sources, and demonstrate that MI produced better results in the case of the Pakistani Taliban’s response to drone strikes.

Suggested Citation

  • Vincent Bauer & Keven Ruby & Robert Pape, 2017. "Solving the Problem of Unattributed Political Violence," Journal of Conflict Resolution, Peace Science Society (International), vol. 61(7), pages 1537-1564, August.
  • Handle: RePEc:sae:jocore:v:61:y:2017:i:7:p:1537-1564
    DOI: 10.1177/0022002715612575
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    References listed on IDEAS

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    1. Shellman, Stephen M., 2004. "Time Series Intervals and Statistical Inference: The Effects of Temporal Aggregation on Event Data Analysis," Political Analysis, Cambridge University Press, vol. 12(1), pages 97-104, January.
    2. David A Jaeger & Zahra Siddique, 2018. "Are Drone Strikes Effective in Afghanistan and Pakistan? On the Dynamics of Violence between the United States and the Taliban," CESifo Economic Studies, CESifo, vol. 64(4), pages 667-697.
    3. White, Ian R. & Daniel, Rhian & Royston, Patrick, 2010. "Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2267-2275, October.
    4. King, Gary & Honaker, James & Joseph, Anne & Scheve, Kenneth, 2001. "Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation," American Political Science Review, Cambridge University Press, vol. 95(1), pages 49-69, March.
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