Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data

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.