Abstract
The need for support becomes stronger in situations of pressure, uncertainty and overload caused by unfavorable economic, demographic or social circumstances. Especially in countries—such as Italy—where an adequate welfare system is lacking, the individual’s social space can represent a resilience (anti-frailty) tool through the activation of a support network. While the literature has mainly analyzed the support that some vulnerable categories (e.g., elderly and youths) receive from their family, we focus on individuals living in Italy in the first stages of their family life, with the aim of describing their support network. We construct the potential support ego-centered (PSE) network—at partner and couple level—of individuals living in couple using data from the survey “Family and Social Subjects” carried out in Italy in 2009 by the Italian National Statistical Institute. Furthermore, we compare the network typologies detected using two alternative clustering techniques with the objective of finding the partners’ and couples’ network types and verifying whether traditional strong support received by the family persists in Italy and/or whether new kinds of support networks are emerging. Several PSE network typologies, ranging from empty to comprehensive networks, were determined with a fair match between the two procedures. Analysis revealed the importance of friends and neighbors, especially in the North of Italy, to the support of partners and couple as a whole.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
This does not introduce a limitation, since the percentage of people who are other members in the data set is low (see, e.g., Gabrielli and Meggiolaro 2015). For example, the 2011 Italian population census showed that the mononuclear households without other members are the 96 % of the total households composed of couples with or without children.
We considered individuals in the same age range because we focused on partners “at par” with respect to potential relational (mainly instrumental) resources they can access to in that phase of life course (mainly family and working conditions). In this case, we treated age as an indicator of comparable conditions among partners in their social space and life context.
The answer categories are: everyday, some times a week, once a week, some times a month but less than 4, some times a year, never.
The answer categories are: in another apartment of the same building, in the same municipality, in another municipality of Italy—less than 16 km, from 16 to 50 km, more than 50 km, abroad.
As in Amati et al. (2015), the available data did not investigate the potential role of co-resident people as source of support.
We defined a binary variable to code the presence (1) and the absence (0) of a certain alter category in the PSE-network. For instance, the alter category “parents” was coded as 1 if a respondent declared to have contact at least once a week with at least one not-cohabiting parent living no farther than 16 km even in a different municipality. Otherwise, the code is set to 0. If this condition was verified for one parent (both), it added a value of one (two) in the resulting PSE-network size.
The age of (cohabiting and not-cohabiting) children among couples with partners aged 18–34 years ranged between 0 and 16 with mean age of 3.4 and standard deviation 3.2. The age of (cohabiting and not-cohabiting) children among couples with partners aged 35–44 years ranged between 0 and 25 with mean age of 8.5 and SD 5. Only 5 % of couples aged 35–44 had cohabiting children older than 18 years old. This percentage decreased to the 1 % if we accounted only for the not-cohabiting children older than 18 years old.
During the past 12 months, less than the 1 % of individuals in couples in both age groups (18–34 and 35–44 years) received “non-health benefits or house assistance benefits from the Municipality or cooperative” or “health benefits at home, from an LHU (Local Health Unit) or cooperative”. Less than 2 % of individuals in couples in the 35–44 age group received economic support from Municipality or charitable institution, but the 4.8 and 3.1 % of individuals in the couples in the 18-34 age group received this type of support, respectively, from Municipality and other public body. Conversely, the 99.5 % of them received economic support from private body (mainly kin).
Ongaro and Mazzucco (2009), for instance, studied intentions and attitudes towards family life of young people aged 18–34 years as individuals at the beginning of their union formation.
The questions related to the presence of relatives to whom a person “can rely on” distinguished between several types of relatives gathered from the viewpoint of the respondent. In particular, there were specific questions referring to parents-in-law, brothers-in-law and sister-in-law. Therefore, parents and siblings were enumerated only once when computing the number of potential people in the couple PSE-network. However, the number of relatives might be overestimated because of the question: “Are there other relatives on whom you can rely on?”. The number of friends may be also overestimated since partners can have mutual friends.
Hereafter, we refer to the ADDATI’s procedure simply by using the name of the software. The classification process is implemented in ADDAWIN package which can be downloaded from the following link: http://circe.iuav.it/~silvio/addawin_site/addawin_en.html.
The analysis based on a TwoStep algorithm is performed with the SPSS software.
The silhouette index (Rousseeuw 1987) varies between −1 and 1. It takes value 1 when the inertia within group is 0, i.e., when the units are well-clustered, and value −1 when the between inertia is close to 0, i.e., the units are misclassified. The value 0 represents an intermediate clustering solution.
References
Adams, J., Faust, K., & Lovasi, G. S. (2012). Capturing context: Integrating spatial and social network analyses. Social Networks, 34, 1–5.
Agneessens, F., Waege, H., & Lievens, J. (2006). Diversity in social support by role relations: A typology. Social Networks, 28, 427–441.
Amati V., Rivellini G., & Zaccarin S. (2015). Potential and effective support networks of young Italian adults. Social Indicators Research, 122(3), 807–831. doi:10.1007/s11205-014-0706-7
Bacher, J., Wenzig, K., & Vogler, M., (2004). SPSS TwoStep cluster—A first evaluation. http://www.statisticalinnovations.com/products/twostep.pdf.
Berkman, L. F., Glass, T., Brissette, I., & Seeman, T. E. (2000). From social integration to health: Durkheim in the new millennium. Social Science and Medicine, 51, 843–857.
Bernardi, L. (2011). A mixed-methods social networks study design for research on transnational families. Journal of Marriage and Family, 73(4), 788–803.
Bernardi, L., Keim, S., & von der Lippe, H. (2007). Social influences on fertility. A comparative mixed methods study in Eastern and Western Germany. Journal of Mixed Methods Research, 1(1), 23–47.
Bost, K. K., Cox, M. J., Burchinal, M. R., & Payne, C. (2002). Structural and supportive changes in couples’ family and friendship networks across the transition to parenthood. Journal of Marriage and Family, 64, 517–531.
Brandes, U., Lerner, J., & Nagel, U. (2011). Network ensemble clustering using latent roles. Advances in Data Analysis and Classification, 5, 81–94.
Breiger, R. L. (2004). The analysis of social network. In M. Hardy & A. Bryman (Eds.), Handbook of data analysis (pp. 505–526). London: Sage Publications.
Bühler, C., & Fratczak, E. (2004). Social capital and fertility intentions: The case of Poland. MPIDR, Working Paper, WP 2004-012.
Chatuvedi, A., Green, P. E., & Caroll, J. D. (2001). K-modes clustering. Journal of Classification, 1, 35–55.
Chiu, T., Fang, D., Chen, J., Wang, Y., & Jeris, C. (2001). A robust and scalable clustering algorithm for mixed type attributes in large database environment. In Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining (pp. 263–268).
Choroszewicz M., & Wolff P. (2010). 51 million young EU adults lived with their parent(s) in 2008, Statistics in Focus 50/2010. Eurostat.
Cohen, S. (2004). Social relationships and health. American Psychologist, 59, 676–684.
Dahlin, E., Kelly, E., & Moen, P. (2008). Is work the new neighborhood? Social ties in the workplace, family and neighborhood. The Sociological Quarterly, 49(4), 719–736.
Dalla Zuanna, G., & Micheli, G. A. (Eds.). (2004). Strong family and low fertility: A paradox? New perspective in interpreting contemporary family and reproductive behaviour. Dordrecht: Kluwer Academic Publishers.
Diday, E. (1971). La méthode des nuées dynamiques. Revue de Statistique appliquées, 19, 19–34.
Diener, E., & Oishi, S. (2005). The nonobvious social psychology of happiness. Psychological Inquiry, 16, 162–167.
Dykstra, P. A., Bühler, C., Fokkema, T., Petrič, G., Platinovšek, R., Kogovšek, T., et al. (2016). Social network indices in the Generations and Gender Survey: An appraisal. Demographic Research, 34, 995–1036.
Eckeronde, J., Gore, S. (1981). Stressful events and social support: the significance of context. In B. Gotlieb (Ed.), Social Networks and Social Support (pp. 43–68). Beverly Hills, CA:Sage.
Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2001). Hierarchical clustering. In Cluster analysis (5th Ed., pp. 71–110). Wiley.
Fiori, K., Antonucci, T. C., & Smith, J. (2007). Social network types among older adults: A multidimensional approach. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 62, 322–330.
Gabrielli, G., & Meggiolaro, S. (2015). Famiglie e nuove famiglie. In A. De Rose & S. Strozza (Eds.), Rapporto sulla Popolazione (pp. 141–163). Bologna: Il Mulino.
Gallagher, E. N., & Vella-Brodrick, D. A. (2008). Social support and emotional intelligence as predictors of subjective well-being. Personality and Individual Differences, 44(7), 1551–1561.
Ganster, D. C., & Victor, B. (2011). The impact of social support on mental and physical health. British Journal of Medical Psychology, 61(1), 17–36.
García-Faroldi, L. (2015). Welfare State and social support: An international comparison. Social Indicators Research, 121(3), 697–722.
Greenacre, M. (2007). Correspondence analysis in practice (2nd ed.). New York: Chapman & Hall\CRC.
Griguolo, S. (2008). ADDATI per Windows. Un pacchetto per l’analisi esplorativa dei dati, Venezia: Università IUAV di Venezia - Dipartimento di Pianificazione.
Härdle, W. K., & Simar, L. (2012). Applied multivariate statistical analysis. Berlin: Springer.
Hasson-Ohayon, I., Goldzweig, G., Braun, M., & Galinsky, D. (2010). Women with advanced breast cancer and their spouses: Diversity of support and psychological distress. Psycho-Oncology, 19(11), 1195–1204.
Hlebec, V., Mrzel, M., & Kogovšek, T. (2009). Social support network and received support at stressful events. Metodološki zvezki, 6(2), 155–171.
Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of classification, 2, 193–218.
Ibarra, H. (1997). Paving an alternative route: Gender differences in managerial networks. Social Psychology Quarterly, 60, 91–102.
Istat. (2006). Parentela e reti di solidarietà. Collana Informazioni (vol. 23). Roma.
Istat. (2009). Famiglia e Soggetti Sociali. Anno 2009. Nota metodologica http://www.istat.it/it/archivio/81546.
Istat. (2011). Come cambiano le forme familiari—Anno 2009, Statistica report, 15.
Istat-CNEL. (2014). Bes 2014. Roma: Il benessere equo e sostenibile.
Istat-CNEL. (2015). Bes 2015. Roma: Il benessere equo e sostenibile.
Kalmijn, M., & Vermunt, J. K. (2007). Homogeneity of social networks by age and marital status: A multilevel analysis of ego-centered networks. Social Networks, 29, 25–43.
Keim, S., Klärner, A., & Bernardi, L. (2009). Qualifying social influence on fertility intentions composition, structure and meaning of fertility-relevant social networks in Western Germany. Current Sociology, 57(6), 888–907.
Knijn, T. C. M., & Liefbroer, A. C. (2006). More than kind: Instrumental support in families. In P. A. Dykstra, M. Kalmijn, T. Knijn, A. Komter, A. Liefbroer, & C. H. Mulder (Eds.), Family solidarity in the Netherlands (pp. 89–106). Amsterdam: Dutch University Press.
Lebart, L., Morineau, A., & Warwick, K. M. (1984). Multivariate descriptive statistic analysis. Correspondence analysis and related techniques for large matrices. New York: Wiley.
Lee, R. P. L., Ruan, D., & Lai, G. (2005). Social structure and support networks in Beijing and Hong Kong. Social Networks, 27(3), 249–274.
Litwin, H. (2001). Social network type and morale in old age. The Gerontologist, 41, 516–524.
Martino, F., & Spoto, A. (2006). Social network analysis: A brief theoretical review and further perspectives in the study of information technology. Psychology Journal, 4(1), 53–86.
McPherson, M., Smith-Lovin, L., & Brashears, M. (2006). Social isolation in America: Changes in core discussion networks over two decades. American Sociological Review, 71(3), 353–375.
Micheli, G. A. (2000). Kinship, family and social network: The anthropological embedment of fertility change in Southern Europe. Demographic Research, 13, 3.
Moore, G. (1990). Structural determinants of men’s and women’s personal networks. American Sociological Review, 55, 726–735.
Mulder, C. H., & van der Meer, M. J. (2009). Geographical distances and support from family members. Population, Space and Place, 15, 381–399.
Ongaro, F., & Mazzuco, S. (2009). Parental separation and family formation:evidence from Italy. Advances in Life Course Research, 14(3), 119–130.
Pattison, P., & Robins, G. (2004). Building models for social space: Neighbourhood-based models for social networks and affiliation structures. Mathematics and Social Sciences, 4, 11–29.
Peterson, J. L., Rintamaki, L. S., Brashers, D. E., Goldsmith, D. J., & Neidig, J. L. (2012). The forms and functions of peer social support for people living with HIV. Journal of the Association of Nurses in AIDS Care, 23(4), 294–305.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics, 20, 53–65.
Sarason, I. G., Levine, H. M., Basham, R. B., & Sarason, B. R. (1983). Assessing social support: The social support questionnaire. Journal of Personality and Social Psychology, 44, 127–139.
Scott, J. (2000). Social network analysis: A handbook (2nd ed.). London: Sage Publications.
Shor, E., Roelfs, D. J., & Yogev, T. (2013). The strength of family ties: A meta-analysis and meta-regression of self-reported social support and mortality. Social Networks, 35, 626–638.
Smith, K. P., & Christakis, N. A. (2008). Social networks and health. Annual Review of Sociology, 34, 405–429.
Song, L., Son, J., & Lin, N. (2011). Social support. In J. Scott & P. J. Carrington (Eds.), The Sage handbook of social network analysis (pp. 116–128). London: Sage Publication.
Taylor, S. E. (2007). Social support. In H. S. Friedman & R. Cohen Silver (Eds.), Foundations of health psychology (pp. 145–171). Oxford: Oxford University Press.
Timm, N. H. (2002). Applied multivariate analysis. New York: Springer.
Vinh, N. X., Epps, J., & Bailey, J. (2010). Information theoretic measures for clustering comparison: Variants, properties, normalization and correction for chance. Journal of Machine Learning Research, 11, 2837–2854.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.
Wellman, B. (1981). The Community Question. American Journal of Sociology, 84, 1201–1231.
Wexler Sherman, C., Webster, N. J., & Antonucci, T. C. (2013). Dementia caregiving in the context of late-life remarriage: Support networks, relationship quality, and well-being. Journal of Marriage and Family, 75(5), 1149–1163.
Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: An efficient data clustering method for very large databases. ACM SIGMOD Record, 25, 103–114.
Zhu, X., Woo, S. E., Porter, C., & Brzezinski, M. (2013). Pathways to happiness: From personality to social networks and perceived support. Social Network, 35(3), 382–393.
Author information
Authors and Affiliations
Corresponding author
Appendices
Appendix 1
The first six rows of each table contain the percentage distribution of the presence of the different alters in each cluster (referred to as cluster profile), whereas the latter the percentage distribution computed on the entire sample (referred to as sample profile). The name of the PSE-network typology is determined comparing the cluster profiles with the sample profile.
For instance, if we focus on men aged 18–34, Cluster 1 is characterized by a profile where all the PSE-networks always include at least one parent, one sibling and one neighbor (100 %), and in very high proportions, at least one other relative (90.8 %) and one friend (92.8 %). These percentages are higher than those in the sample profile, consequently, the PSE-networks belonging to this group almost always include all the alter categories and was labelled as Comprehensive PSE-network. In contrast, Cluster 3 comprises of PSE-networks never including parents (0 %), consisting of at least one sibling for only 2.2 %, at least one other relative for 3.3 %, friends for 49.5 % and neighbors for 35.2 %. These percentages are lower than those in the sample profile and suggest that the PSE-networks belonging to this group rarely include any of the alter categories. As a result, we labeled this group as Limited PSE-network. In a similar way we derived all the other PSE-network typologies. The following explains the labeling of each group of PSE-networks. Table 6 shows the results for the ADDATI procedure, Table 7 shows the results for the Two-step method.
Appendix 2
Rights and permissions
About this article
Cite this article
Amati, V., Meggiolaro, S., Rivellini, G. et al. Relational Resources of Individuals Living in Couple: Evidence from an Italian Survey. Soc Indic Res 134, 547–590 (2017). https://doi.org/10.1007/s11205-016-1443-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11205-016-1443-x