A clustering procedure for mixed-type data to explore ego network typologies: an application to elderly people living alone in Italy

• Published in: Statistical methods & applications Vol. 30; no. 5; pp. 1507 - 1533
• Main Authors: Pelle, Elvira, Pappadà, Roberta
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2021; Springer Nature B.V
• Subjects: Chemistry and Earth Sciences; Cluster analysis; Clustering; Computer Science; Economics; Empirical analysis; Finance; Health Sciences; Humanities; Insurance; Law; Management; Mathematics and Statistics; Medicine; Network analysis; Older people; Original Paper; Physics; Statistical Theory and Methods; Statistics; Statistics for Business; Statistics for Engineering; Statistics for Life Sciences; Statistics for Social Sciences; Ego network; Mixed-type data; Hierarchical clustering; Prototype
• ISSN: 1618-2510; 1613-981X; 1613-981X
• DOI: 10.1007/s10260-021-00591-5

The analysis of ego networks has attracted a great attention recently and found application in many areas of the social sciences. In particular, the identification of network typologies has become a crucial task and a powerful tool to capture aspects of the social space or personal community in which people are embedded. In this work, we propose a distance-based clustering procedure to identify homogeneous groups of ego networks that are only described by a small number of compositional variables. The proposed approach is motivated by the empirical study of ego networks of contacts extracted from the “Family and Social Subjects” (FSS) Survey conducted by the Italian National Statistical Institute in 2016, which is not specifically oriented to network analysis. We focus on elderly respondents living alone, which can be regarded as a vulnerable category, with the aim to describe their network of contacts. First, mining relational information in FSS data, we derive the ego networks of respondents. Then, we develop a methodology for coping with the presence of heterogeneous data and small amount of information from a network perspective. To this aim, we introduce a dissimilarity measure for mixed-type data, and exploit hierarchical clustering for grouping ego networks according to their composition. In doing so, we intend to make our approach applicable to various surveys.