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      The Changing Relationship Between Partnership Status and Loneliness: Effects Related to Aging and Historical Time

      , 1
      The Journals of Gerontology: Series B
      Oxford University Press (OUP)

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          Abstract

          Objectives

          Partnership status is a central predictor of loneliness. The strength of this predictive relationship, however, may decrease in the course of aging and over historical time. Moreover, there may be aging-related and historical changes in the satisfaction with partnership and singlehood.

          Method

          Longitudinal data from the German Ageing Survey (DEAS) assessed in 2008 and 2014 (N = 6,188, age range: 40–85 years) was analyzed with multigroup structural equation models to disentangle aging-related and historical changes in the relationship of partnership status and loneliness and in the average level of satisfaction with partnership and singlehood.

          Results

          With advancing age, partnership status became less predictive of loneliness and the satisfaction with being single increased. Among later-born cohorts, the association between partnership status and loneliness was less strong than among earlier-born cohorts. Later-born single people were more satisfied with being single than their earlier-born counterparts. There was no indication for an aging-related or historical decrease in partnership satisfaction.

          Discussion

          The relevance of partnership status as a predictor of social well-being is neither universal nor stable but appears to change in the course of aging and across historical time.

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          Most cited references50

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          Taking time seriously: A theory of socioemotional selectivity.

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            A 6-Item Scale for Overall, Emotional, and Social Loneliness: Confirmatory Tests on Survey Data

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              Latent variable modeling of differences and changes with longitudinal data.

              This review considers a common question in data analysis: What is the most useful way to analyze longitudinal repeated measures data? We discuss some contemporary forms of structural equation models (SEMs) based on the inclusion of latent variables. The specific goals of this review are to clarify basic SEM definitions, consider relations to classical models, focus on testable features of the new models, and provide recent references to more complete presentations. A broader goal is to illustrate why so many researchers are enthusiastic about the SEM approach to data analysis. We first outline some classic problems in longitudinal data analysis, consider definitions of differences and changes, and raise issues about measurement errors. We then present several classic SEMs based on the inclusion of invariant common factors and explain why these are so important. This leads to newer SEMs based on latent change scores, and we explain why these are useful.
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                Author and article information

                Journal
                The Journals of Gerontology: Series B
                Oxford University Press (OUP)
                1079-5014
                1758-5368
                September 2020
                August 13 2020
                December 22 2018
                September 2020
                August 13 2020
                December 22 2018
                : 75
                : 7
                : 1423-1432
                Affiliations
                [1 ]German Centre of Gerontology, Berlin, Germany
                Article
                10.1093/geronb/gby153
                30590817
                d3f49d5e-55f3-4bce-b544-5b906de42eb5
                © 2018

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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