ScholarGate
Assistant

Compare methods

Review your selected methods side by side; rows that differ are highlighted.

Content Analysis×Event Data Analysis of Conflict×
FieldQualitativeInternational Relations
FamilyProcess / pipelineProcess / pipeline
Year of originSystematised through Krippendorff's methodology work; 4th edition 20181994
OriginatorKlaus Krippendorff (systematic formulation); roots in early 20th-century communications researchPhilip Schrodt (KEDS/TABARI); ICEWS team (Boschee et al.)
TypeQualitative / mixed-method research techniqueAutomated extraction of structured political events from news text
Seminal sourceKrippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage. ISBN: 978-1506395661Schrodt, P. A., Davis, S. G., & Weddle, J. L. (1994). Political science: KEDS — A program for the machine coding of event data. Social Science Computer Review, 12(4), 561–588. See also Gerner, Schrodt et al. (1994), Machine coding of event data using regional and international sources, International Studies Quarterly, 38(1), 91–119. DOI ↗
Aliasesİçerik Analizi, systematic content coding, quantitative content analysisPolitical Event Data, Machine-Coded Conflict Event Data, Conflict Event Extraction, Who-Did-What-to-Whom Event Coding
Related54
SummaryContent analysis is a systematic research technique for reducing text, visual, or media material into coded categories so that patterns can be counted, compared, and interpreted. Formalised by Klaus Krippendorff in his widely cited methodology textbook (latest edition 2018), the method sits at the boundary of qualitative and quantitative inquiry: it imposes structured, replicable coding on inherently meaning-laden material.Event data analysis is the automated extraction of structured records of political interactions — who did what to whom, when, and where — from large volumes of news text, for the quantitative study of conflict and cooperation. Pioneered for machine coding by Philip Schrodt with the KEDS and TABARI systems and scaled in projects such as ICEWS and GDELT, it turns unstructured reporting into dated actor-action-target triples coded to an ontology like CAMEO, which can then be aggregated into time series of interstate or intrastate hostility.
ScholarGateDataset
  1. v1
  2. 1 Sources
  3. PUBLISHED
  1. v1
  2. 2 Sources
  3. PUBLISHED

Go to search Download slides

ScholarGateCompare methods: Content Analysis · Event Data Analysis of Conflict. Retrieved 2026-06-25 from https://scholargate.app/en/compare