An extension of multiple correspondence analysis for identifying heterogeneous subgroups of respondents

DOI10.1007/s11336-004-1173-xzbMath1306.62435OpenAlexW2152416281MaRDI QIDQ2260961

Heungsun Hwang, Hec Montréal, William R. Dillon, Yoshio Takane

Publication date: 6 March 2015

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-004-1173-x

zbMATH Keywords

alternating least squares\(k\)-meansmultiple correspondence analysiscluster-level respondent heterogeneity

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to psychology (62P15)

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Cites Work

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• Generalized constrained multiple correspondence analysis
• Multiclus: a new method for simultaneously performing multidimensional scaling and cluster analysis
• Additive structure in qualitative data: An alternating least squares method with optimal scaling features
• Numerical taxonomy with fuzzy sets
• Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
• On Using Principal Components Before Separating a Mixture of Two Multivariate Normal Distributions
• Estimating Smooth Monotone Functions
• Least squares quantization in PCM
• Factorial \(k\)-means analysis for two-way data.

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