Hierarchical Quantitative Content Analysis
Hierarchical quantitative content analysis is a systematic method for coding and counting text or media content using nested, tree-structured category schemes. Rather than a flat list of mutually exclusive codes, categories are organized into parent-child levels — broad themes subdivide into specific sub-themes — enabling researchers to aggregate or disaggregate frequencies at any level of the hierarchy and to produce richly structured numerical summaries of large corpora.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage. · ISBN 978-1506395678
- Neuendorf, K. A. (2016). The Content Analysis Guidebook (2nd ed.). Sage. · ISBN 978-1412979474
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.