Albert Gifi’s scientific contributions

... MCA seeks to find an optimal multidimensional structure, also referred to as 'solution', where cases are positioned close to the characteristics they have, and, conversely, characteristics are positioned close to the cases that have these characteristics. MCA should not be regarded as a 'hard' classification technique, because characteristics can be shared by different subgroups and classification is therefore generally fuzzy (Gifi 1990). ...

Reference:

Mapping the Problem Constellations of Older Adults under Guardianship: A Multiple Correspondence Analysis of Adult Guardianship Court Files in the Netherlands

... The MCA technique aims to show the group changes to rows and columns of categorical data arranged in the form of a contingency table in graphical form in a less-dimensional space [20]. This technique is very advantageous because it is easier to apply than such other alternatives as Chi-square analysis, G-test, Z-test, Fisher Exact test or Log-linear models, because it provides more detailed information to the researcher, and it can present the results in visual form [18,21,22]. Each of the trait levels reviewed through MCA is represented with a dot in a multi-dimensional space. ...

Reference:

Characterization of Qualitative Morphological Traits in Indigenous Goats in Ethiopia Using Multivariate Analysis