Comparer des méthodes

	Analyse quantitative de contenu bayésienne	Recherche confirmatrice bayésienne
Domaine	Conception de la recherche	Conception de la recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s (convergence of content analysis and Bayesian statistics)	1961 (Jeffreys); 2009–2018 (contemporary confirmatory formulation)
Auteur d'origine	Integration of Krippendorff's content analysis framework with Bayesian statistical inference (Gelman et al.)	Harold Jeffreys (theoretical foundation); Jeffrey Rouder, Eric-Jan Wagenmakers (applied confirmatory framework)
Type	Quantitative research design	Quantitative hypothesis-testing framework
Source fondatrice	Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage. ISBN: 978-1506395661	Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237.
Alias	Bayesian content analysis, Bayesian text analysis, probabilistic content analysis, BQCA	Bayesian hypothesis testing, confirmatory Bayesian analysis, Bayes factor hypothesis testing, BCR
Apparentées	5	1
Résumé	Bayesian quantitative content analysis systematically codes and counts features in textual or media content, then quantifies patterns and tests hypotheses using Bayesian statistical inference. Unlike classical frequency-based content analysis, it incorporates prior knowledge or domain expectations into the estimation process, producing posterior probability distributions over content parameters rather than single point estimates with p-values. The approach is particularly valuable when prior research, expert knowledge, or pilot data exist and when uncertainty quantification around content proportions and category frequencies is important.	Bayesian confirmatory research is a quantitative framework that tests pre-specified hypotheses by computing the Bayes factor — a ratio expressing how much more likely the observed data are under one hypothesis than another. Unlike classical null-hypothesis significance testing (NHST), it provides direct evidence for both the alternative and the null hypothesis, supports optional stopping rules under certain conditions, and updates prior beliefs with observed data through Bayes' theorem.