Lost in Aggregation: Improving Event Analysis with Report-Level Data

Most measures of social conflict processes are derived from primary and secondary source reports. In many caseSy reports are used to create event-level data sets by aggregating information from multiple, and often conflicting, reports to single event observations. We argue that this pre-aggregation...

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Bibliographic Details
Published in:American journal of political science 2019-01, Vol.63 (1), p.250-264
Main Authors: Cook, Scott J., Weidmann, Nills B.
Format: Article
Language:English
Subjects:
Aggregate data
AJPS WORKSHOP
Archives & records
Autocracy
Costing
Data
Data collection
Datasets
Demonstrations & protests
Economic models
Estimates
Experiments
Inference
Measurement
Measurement errors
Mobilization
Monte Carlo simulation
Researchers
Social conflict
Variables
Violence
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Summary:Most measures of social conflict processes are derived from primary and secondary source reports. In many caseSy reports are used to create event-level data sets by aggregating information from multiple, and often conflicting, reports to single event observations. We argue that this pre-aggregation is less innocuous than it seems, costing applied researchers opportunities for improved inference. First, researchers cannot evaluate the consequences of different methods of report aggregation. Second, aggregation discards report-level information (i.e., variation across reports) that is useful in addressing measurement error inherent in event data. Therefore, we advocate that data should be supplied and analyzed at the report level. We demonstrate the consequences of using aggregated event data as a predictor or outcome variable, and how analysis can be improved using report-level information directly. These gains are demonstrated with simulated-data experiments and in the analysis of real-world data, using the newly available Mass Mobilization in Autocracies Database (MMAD).
ISSN:0092-5853
1540-5907
DOI:10.1111/ajps.12398