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Title: Assessing similarity among groups and global components in a dual STATIS multiple correspondence analysis Authors:  Aida Eslami - Laval University (Canada) [presenting]
Lauren Faye Toogood - University of Exeter (United Kingdom)
Herve Abdi - University of Texas at Dallas (United States)
Abstract: In multivariate analysis, when variables are categorical the standard descriptive exploratory method is multiple correspondence analysis (MCA). MCA assumes that observations are independent and originate from a homogeneous population. However, the observations often comprise several groups known a priori (e.g., sex, ethnicity), a configuration known as multi-group data structure. In multi-group data, individuals from the same group are likely to be more similar to each other than to individuals from other groups. To take into account the group structure in MCA, we recently developed a new method called dual STATIS-MCA. This method lets us create both global and group components and loadings. In addition, to measure the similarity between these global and group components and loadings we used different approaches based on (1) the vector correlation (RV) coefficients, a multivariate generalization of the squared Pearson correlation coefficient, (2) Tuckers congruence coefficient, and (3) a method we previously developed. We illustrate this new procedure with a real case study.