Comparer des méthodes

	Qualitative Comparative Analysis	Wordfish Scaling
Domaine	Political Science	Political Science
Famille	Process / pipeline	Latent structure
Année d'origine	1987	2008
Auteur d'origine	Charles C. Ragin	Jonathan Slapin and Sven-Oliver Proksch
Type	Set-theoretic, configurational comparative method	Unsupervised latent-position model for word-count data
Source fondatrice	Ragin, C. C. (1987). The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies. Berkeley: University of California Press. ISBN: 9780520058347	Slapin, J. B., & Proksch, S.-O. (2008). A Scaling Model for Estimating Time-Series Party Positions from Texts. American Journal of Political Science, 52(3), 705–722.
Alias	QCA, csQCA, fsQCA, Configurational comparative method	Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation
Apparentées	3	4
Résumé	Qualitative Comparative Analysis (QCA) is a set-theoretic, configurational method that identifies which combinations of conditions are necessary or sufficient for an outcome across a set of cases. Developed by Charles Ragin, it treats each case as a configuration of set memberships, builds a truth table of all logically possible combinations, and uses Boolean algebra to minimize them into the simplest expressions that account for the outcome. It bridges qualitative case knowledge and cross-case generalization, embracing causal complexity through conjunctural causation, equifinality, and asymmetry.	Wordfish scaling is an unsupervised text-as-data method that estimates a single latent position for each political document — a party manifesto, a legislative speech, a press release — directly from its word frequencies, without any reference texts or hand coding. Introduced by Slapin and Proksch in 2008, it models word counts as draws from a Poisson distribution whose rate depends on a document position and word-specific parameters, recovering, for example, a left–right ordering of parties purely from how often each word appears in each text.