Families,social life,and well-being at older ages

As people age, many aspects of their lives tend to change, including the constellation of people with whom they are connected, their social context, their families, and their health—changes that are often interrelated. Wave I of the National Social Life, Health, and Aging Project (NSHAP) has yielded rich information on intimate ties, especially dyads and families, and on social connections generally. Combined with extensive biological and other health measures, NSHAP enables researchers to address key questions on health and aging. We begin with recent findings on intimate dyads, then move to social participation, and finally to elder mistreatment. Among dyads, we find that whereas sexual activity drops sharply with age for both women and men, gender differences in partner loss as well as psychosocial and normative pressures constrain women’s sex more than men’s. However, surviving partnerships tend to be emotionally and physically satisfying and are marked by relatively frequent sex. In contrast to sex, nonsexual intimacy is highly prevalent at older ages, especially among women. Older adults are also socially resilient—adapting to the loss of social ties by increasing involvement with community and kin networks. Despite these social assets, older adults remain vulnerable to mistreatment. Overall, these findings yield a mixed picture of gender-differentiated vulnerabilities balanced by proactive adaptation and maintenance of social and dyadic assets.     

An application of nonparametric regression to missing data in large market surveys

Non-response (or missing data) is often encountered in large-scale surveys. To enable the behavioural analysis of these data sets, statistical treatments are commonly applied to complete or remove these data. However, the correctness of such procedures critically depends on the nature of the underlying missingness generation process. Clearly, the efficacy of applying either case deletion or imputation procedures rests on the unknown missingness generation mechanism. The contribution of this paper is twofold. The study is the first to propose a simple sequential method to attempt to identify the form of missingness. Second, the effectiveness of the tests is assessed by generating (experimentally) nine missing data sets by imposed MCAR, MAR and NMAR processes, with data removed.