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Location, location, location: Close ties among older continuing care retirement community residents

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  • Liat Ayalon
  • Inbal Yahav

Abstract

This study examines two theoretical explanations for the existence of close ties among continuing care retirement community residents: the attractiveness theory, which suggests that residents who possess certain attributes are more likely to be perceived as appealing to others; and the homophily theory, which argues that individuals are more likely to have close ties with people who share similar attributes. As a variant of the homophily theory, we also examined whether sharing a physical location makes the existence of certain connections more likely. Data from four continuing care retirement communities were used. To test the attractiveness theory, correlations between the number of individuals who named a person as a significant contact (ego’s in-degree) and ego attributes were examined. To test the homophily theory, the median value of existing ties was compared against all possible social ties as though they were randomly formed. Finally, to further test the role of the institutional culture against various motivations that drive social ties—attractiveness and homophily—we used link prediction models with random forests. In support of the homophily theory, beyond the institutional culture, the only consistent predictor of the existence of close ties among residents was sharing a wing in the retirement community (geographic proximity). Therefore, we discuss the role of the physical location in the lives of older adults.

Suggested Citation

  • Liat Ayalon & Inbal Yahav, 2019. "Location, location, location: Close ties among older continuing care retirement community residents," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-17, November.
  • Handle: RePEc:plo:pone00:0225554
    DOI: 10.1371/journal.pone.0225554
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    References listed on IDEAS

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    1. Markus H. Schafer, 2015. "Editor's choice On the Locality of Asymmetric Close Relations: Spatial Proximity and Health Differences in a Senior Community," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 70(1), pages 100-110.
    2. Fragkiskos Papadopoulos & Maksim Kitsak & M. Ángeles Serrano & Marián Boguñá & Dmitri Krioukov, 2012. "Popularity versus similarity in growing networks," Nature, Nature, vol. 489(7417), pages 537-540, September.
    3. Raf Guns & Ronald Rousseau, 2014. "Recommending research collaborations using link prediction and random forest classifiers," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1461-1473, November.
    4. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    5. Markus H. Schafer, 2011. "Health and Network Centrality in a Continuing Care Retirement Community," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 66(6), pages 795-803.
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