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

Elvira Pelle () and Roberta Pappadà ()
Additional contact information
Elvira Pelle: University of Modena and Reggio Emilia
Roberta Pappadà: University of Trieste

Statistical Methods & Applications, 2021, vol. 30, issue 5, No 11, 1507-1533

Abstract: Abstract 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.

Keywords: Ego network; Hierarchical clustering; Prototype; Mixed-type data (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10260-021-00591-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:30:y:2021:i:5:d:10.1007_s10260-021-00591-5

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-021-00591-5

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().