Abstract
The aim of this work is to present new classification maps in health informatics and to show that they are useful in data analysis. A statistical method, correspondence analysis, has been applied for obtaining these maps. This approach has been applied to studies on expectations and worries related to the retirement threshold. For this purpose two questionnaires formulated by ourselves have been constructed. Groups of individuals and their answers to particular questions are represented by points in the classification maps. The distribution of these points reflects psychological attitudes of the considered population. In particular, we compared structures of the maps searching for factors such as gender, marital status, kind of work, economic situation, and intellectual activity related to the attendance the University of the Third Age, which are essential at the retirement threshold. Generally, in Polish society, retirement is evaluated as a positive experience and the majority of retirees do not want to return to their professional work. This result is independent of the kind of work and of the gender.
Highlights
Classification studies are a valuable source of information in various areas of science
We study the influence of factors, such as gender, kind of work, marital status, intellectual activity related to the attendance the University of the Third Age, economic situation, on the expectations and on the worries related to the retirement threshold from the Polish perspective
We have shown that Correspondence Analysis (CA) classification maps are a convenient tool for the studies on the role of different factors in changing the quality of life after the retirement threshold, such as gender [42] and marital status [43] in four domains, job position in Physical Health and Psychological domains [44], or in Social
Summary
Classification studies are a valuable source of information in various areas of science. The problem of classification is related to the problem of similarity of objects. One-dimensional sets may be classified in a unique way according to one, properly chosen, aspect of similarity. The problem becomes more complicated if we consider multidimensional sets, i.e., objects characterized by several different aspects. The degree of similarity depends on the selected aspects, on the number of aspects considered and on the mathematical measure establishing the relations between different properties. One of class of objects considered by us is biological sequences. Both graphical and numerical classification of these objects is possible using methods based on Graphical Representations [1,2]
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