Lost in Translation: How Aggregation Masks Key Insights in Event Data

Event-level data, typically derived from reports of social conflict, often masks crucial variations by aggregating multiple sources. This analysis argues that standard aggregation methods limit researchers' ability to evaluate report-specific measurement issues and discard valuable information about these differences. We advocate for using report-level data instead.

The article demonstrates how aggregated event data obscures important details when used as predictors or outcomes. Using both simulated experiments and the Mass Mobilization in Autocracies Database (MMAD), we show that incorporating report-level variation improves analytical precision significantly. This approach better addresses measurement error inherent in traditional event datasets, providing richer insights for political scientists studying conflict dynamics.