Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data. (December 2022)

• Record Type: Journal Article
• Authors: Krishnan, Anjali; Yu, Ju-Chi; Miles, Rona; Beaton, Derek; Rabin, Laura A.; Abdi, Hervé
• Is Part Of: Methods in psychology. Volume 7(2022)
• Publication Date: 2022-12
• Subjects: Discriminant correspondence analysis -- DICA -- MUDICA -- Mental health literacy; ANOVA Analysis of Variance -- BADA Barycentric Discriminant Analysis -- CA Correspondence Analysis -- DICA Discriminant Correspondence Analysis -- MANOVA Multivariate Analysis of Variance -- MCA Multiple Correspondence Analysis -- MUDICA Multiblock Discriminant Correspondence Analysis -- PCA Principal Component Analysis; Psychology -- Periodicals; 150.5
• DOI: 10.1016/j.metip.2022.100100
• Languages: English
• ISSNs: 2590-2601

Abstract

Abstract: Psychological research often involves complex datasets that cannot easily be analyzed using traditional statistical methods. Multiblock Discriminant Correspondence Analysis (multiblock dica, also called mudica ) examines group differences in large, structured categorical datasets and identifies blocks of variables that contribute to these differences. Data for this illustration were obtained from a study on mental health literacy ( N = 648) that included 33 questions that were arranged into four blocks: etiology, symptoms, treatment, and general knowledge of psychological disorders. With non-parametric inference tests and results displayed as intuitive maps, mudica revealed differences in performance across groups not readily detectable using standard methods.