Mapping discourses through Hierarchical Clustering on Principal Components

This article proposes a methodological framework that combines a Q-concourse questionnaire with Multiple Factor Analysis and Hierarchical Clustering on Principal Components (MFA/HCPC) to derive discourse typologies from large-N survey data. The framework offers four advantages. First, the Q-concourse translates naturally occurring statements into survey items, importing the contextual richness of qualitative discourse analysis into a quantitative design. Second, MFA reduces dimensionality while weighting thematically defined item blocks equally, preventing any correlated subset of variables from dominating the solution and preserving within-block covariance patterns. Third, HCPC scales efficiently to large samples, exploring up to 2ᵏ multidimensional configurations and thus overcoming the combinatorial limits of traditional Q-methodology. Fourth, by locating each respondent within the clustered factor space, HCPC links individual viewpoints to coherent narrative patterns, enabling precise reconstruction and extrapolation of discourse structures. The framework’s utility is illustrated with a national survey on human-genome editing in Australia.