Integrating Conflict Event Data

The growing multitude of sophisticated event-level data collection enables novel analyses of conflict. Even when multiple event data sets are available, researchers tend to rely on only one. We instead advocate integrating information from multiple event data sets. The advantages include facilitatin...

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Bibliographic Details
Published inThe Journal of conflict resolution Vol. 63; no. 5; pp. 1337 - 1364
Main Authors Donnay, Karsten, Dunford, Eric T., McGrath, Erin C., Backer, David, Cunningham, David E.
Format Journal Article
LanguageEnglish
Published Los Angeles, CA Sage Publications, Inc 01.05.2019
SAGE Publications
SAGE PUBLICATIONS, INC
Subjects
Advocacy
Applied Methods Feature
Conflict resolution
Data analysis
Data collection
Data quality
Datasets
Estimation
Flowcharts
Measurement
Network management systems
Social conflict
Software reviews
Terrorism
protests
event data
data integration
conflict
civil wars
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Summary:The growing multitude of sophisticated event-level data collection enables novel analyses of conflict. Even when multiple event data sets are available, researchers tend to rely on only one. We instead advocate integrating information from multiple event data sets. The advantages include facilitating analysis of relationships between different types of conflict, providing more comprehensive empirical measurement, and evaluating the relative coverage and quality of data sets. Existing integration efforts have been performed manually, with significant limitations. Therefore, we introduce Matching Event Data by Location, Time and Type (MELTT)—an automated, transparent, reproducible methodology for integrating event data sets. For the cases of Nigeria 2011, South Sudan 2015, and Libya 2014, we show that using MELTT to integrate data from four leading conflict event data sets (Uppsala Conflict Data Project–Georeferenced Event Data, Armed Conflict Location and Event Data, Social Conflict Analysis Database, and Global Terrorism Database) provides a more complete picture of conflict. We also apply multiple systems estimation to show that each of these data sets has substantial missingness in coverage.
ISSN:0022-0027
1552-8766
DOI:10.1177/0022002718777050