Comparer des méthodes

	Analyse quantitative multivariée de contenu	Analyse de contenu quantitative comparative
Domaine	Conception de la recherche	Conception de la recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1969–2000s	1952 (Berelson); comparative extensions prominent from 1980s onward
Auteur d'origine	Rooted in Holsti (1969) and Neuendorf (2002); multivariate extensions developed in communication and political science research from the 1970s onward	Bernard Berelson (quantitative content analysis); Kimberly Neuendorf (codebook systematization); Hallin & Mancini (comparative media application)
Type	Quantitative research design	Quantitative observational research design
Source fondatrice	Neuendorf, K. A. (2002). The Content Analysis Guidebook. Sage Publications. ISBN: 978-0761919773	Berelson, B. (1952). Content Analysis in Communication Research. Free Press.
Alias	multivariate QCA, multivariate content analysis, MQCA, multivariate text analysis	CQCA, cross-national content analysis, comparative media content analysis, systematic comparative content analysis
Apparentées	6	5
Résumé	Multivariate quantitative content analysis (MQCA) is a systematic, replicable approach to measuring multiple attributes of communication content simultaneously and examining how those attributes relate to each other or to external variables. It extends standard content analysis by applying multivariate statistical techniques — such as factor analysis, cluster analysis, regression, or MANOVA — to coded content data, enabling researchers to uncover complex patterns across many variables at once.	Comparative quantitative content analysis is a systematic, replicable method for counting and categorizing features of communication content — such as news coverage, social media posts, or policy documents — across two or more groups, time periods, outlets, or countries. By applying a standardized codebook to each comparison context, it reveals patterns of similarity and difference in how topics, frames, actors, or sentiments are represented, and allows statistical testing of those differences.