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

Author

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  • Elvira Pelle

    (University of Modena and Reggio Emilia)

  • Roberta Pappadà

    (University of Trieste)

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.

Suggested Citation

  • Elvira Pelle & Roberta Pappadà, 2021. "A clustering procedure for mixed-type data to explore ego network typologies: an application to elderly people living alone in Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1507-1533, December.
  • Handle: RePEc:spr:stmapp:v:30:y:2021:i:5:d:10.1007_s10260-021-00591-5
    DOI: 10.1007/s10260-021-00591-5
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    References listed on IDEAS

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    1. Viviana Amati & Silvia Meggiolaro & Giulia Rivellini & Susanna Zaccarin, 2017. "Relational Resources of Individuals Living in Couple: Evidence from an Italian Survey," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 134(2), pages 547-590, November.
    2. Ulrik Brandes & Jürgen Lerner & Uwe Nagel, 2011. "Network ensemble clustering using latent roles," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(2), pages 81-94, July.
    3. Bien, Jacob & Tibshirani, Robert, 2011. "Hierarchical Clustering With Prototypes via Minimax Linkage," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1075-1084.
    4. Maja Djundeva & Pearl A Dykstra & Tineke Fokkema & Deborah Carr, 2019. "Is Living Alone “Aging Alone†? Solitary Living, Network Types, and Well-Being," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 74(8), pages 1406-1415.
    5. Vacca, Raffaele, 2020. "Structure in personal networks: Constructing and comparing typologies," Network Science, Cambridge University Press, vol. 8(2), pages 142-167, June.
    6. Viviana Amati & Giulia Rivellini & Susanna Zaccarin, 2015. "Potential and Effective Support Networks of Young Italian Adults," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 122(3), pages 807-831, July.
    7. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    8. Christian Hennig & Tim F. Liao, 2013. "How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(3), pages 309-369, May.
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    Cited by:

    1. González-Casado, Miguel A. & Molina, Jose Luis & Sánchez, Angel, 2023. "Towards a General Typology of Personal Network Structures," SocArXiv 23efd, Center for Open Science.

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