ConPar: a method for identifying groups of concordant subject proximity matrices for subsequent multidimensional scaling analyses

DOI10.1016/j.jmp.2004.11.004zbMath1110.91021OpenAlexW1979851869MaRDI QIDQ2483823

Michael J. Brusco, J. Dennis Cradit

Publication date: 1 August 2005

Published in: Journal of Mathematical Psychology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmp.2004.11.004

zbMATH Keywords

Pooling across subjects

Mathematics Subject Classification ID

Clustering in the social and behavioral sciences (91C20) One- and multidimensional scaling in the social and behavioral sciences (91C15)

Related Items (7)

KSC-N: clustering of hierarchical time profile data ⋮ A model for clustering data from heterogeneous dissimilarities ⋮ Representation of individual differences in rectangular proximity data through anti-Q matrix decomposition ⋮ Modeling differences in the dimensionality of multiblock data by means of clusterwise simultaneous component analysis ⋮ The heterogeneous \(p\)-median problem for categorization based clustering ⋮ Combinatorial individual differences scaling within the city-block metric ⋮ A latent class multidimensional scaling model for two-way one-mode continuous rating dissimilarity data

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