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How Events Enter (or Not) Data Sets: The Pitfalls and Guidelines of Using Newspapers in the Study of Conflict

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  • Leila Demarest
  • Arnim Langer

Abstract

While conflict event data sets are increasingly used in contemporary conflict research, important concerns persist regarding the quality of the collected data. Such concerns are not necessarily new. Yet, because the methodological debate and evidence on potential errors remains scattered across different subdisciplines of social sciences, there is little consensus concerning proper reporting practices in codebooks, how best to deal with the different types of errors, and which types of errors should be prioritised. In this article, we introduce a new analytical framework—that is, the Total Event Error (TEE) framework—which aims to elucidate the methodological challenges and errors that may affect whether and how events are entered into conflict event data sets, drawing on different fields of study. Potential errors are diverse and may range from errors arising from the rationale of the media source (e.g., selection of certain types of events into the news) to errors occurring during the data collection process or the analysis phase. Based on the TEE framework, we propose a set of strategies to mitigate errors associated with the construction and use of conflict event data sets. We also identify a number of important avenues for future research concerning the methodology of creating conflict event data sets.

Suggested Citation

  • Leila Demarest & Arnim Langer, 2022. "How Events Enter (or Not) Data Sets: The Pitfalls and Guidelines of Using Newspapers in the Study of Conflict," Sociological Methods & Research, , vol. 51(2), pages 632-666, May.
  • Handle: RePEc:sae:somere:v:51:y:2022:i:2:p:632-666
    DOI: 10.1177/0049124119882453
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    References listed on IDEAS

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    1. Cullen S. Hendrix & Idean Salehyan, 2015. "No News Is Good News: Mark and Recapture for Event Data When Reporting Probabilities Are Less Than One," International Interactions, Taylor & Francis Journals, vol. 41(2), pages 392-406, March.
    2. King, Gary & Lowe, Will, 2003. "An Automated Information Extraction Tool for International Conflict Data with Performance as Good as Human Coders: A Rare Events Evaluation Design," International Organization, Cambridge University Press, vol. 57(3), pages 617-642, July.
    3. Nils B. Weidmann, 2016. "A Closer Look at Reporting Bias in Conflict Event Data," American Journal of Political Science, John Wiley & Sons, vol. 60(1), pages 206-218, January.
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    1. Daniel Karell & Andrew Linke & Edward Holland & Edward Hendrickson, 2023. "“Born for a Storm†: Hard-Right Social Media and Civil Unrest," American Sociological Review, , vol. 88(2), pages 322-349, April.

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