Chapters authored
Semantic Map: Bringing Together Groups and Discourses
This chapter presents a multivariate analysis method which is developed in two steps using a combination of Hierarchical cluster analysis (HCA) and Factorial Correspondence Analysis (AFC). To explain and describe the steps of the method, we use an application example on a survey dataset from young students in Thessaloniki trying to investigate their behavioral profiles in terms of political characteristics and how these may be affected about their attendance to a civic education course offered by the Political Science department in the Aristotle University of Thessaloniki. The method is explained step by step on this example serving as a manual of its application to the researcher. HCA assigns subjects into cluster membership variables and in the next stage, these new variables are jointly analyzed with AFC. Correspondence analysis manages to extract the dimensions of the phenomenon in the study, explaining the inner antithesis between the categories but also giving the opportunity to visualize the information in a two-dimensional space, a semantic map, making interpretation more comprehensive. HCA is then applied again to the AFC’s coordinates of the categories constructing profiles of subjects, assigning them to the categories of the variables.
Part of the book: Data Clustering