We present the theoretical framework and the results of a pilot survey conducted in Calabria, a region in the south of Italy, to investigate the prevalence of two sensitive characteristics, namely induced abortion among foreign women residing in this region, and irregular immigrant status. Collecting data on these two attributes by means of traditional survey modes typically produces underestimates of the diffusion of the phenomena due to the stigmatizing nature of the investigated topics. In order to overcome this problem, we employ an alternative data collection method known as the Randomized\ud Response Technique. In particular, we focus on the implementation of the crossed model recently proposed by Lee et al. (Stat Probab Lett 83:399–409, 2013) to estimate two sensitive characteristics and some related measures of association
Providing support outside the household can be considered an actual sign of an active social life for the elderly. Adopting an ego–network perspective, we study support Italian elders provide to kin or non–kin. More specifically, using Italian survey data, we build the ego–centered networks of social contacts elders entertain and the ego–networks of support elders provide to other non–cohabitant kin or non–kin. Since ego–network data are inherently multilevel, we use Bayesian multilevel models to analyze variation in support ties, controlling for the characteristics of elders and their contacts. This modeling strategy enables dealing with sparseness and alter–alter overlap in the ego support network data and to disentangle the effects related to the ego (the elder), the dyad ego–alter, the kind of support provided, as well as social contacts and contextual variables. The results suggest that the elderly in Italy who provide support outside their household — compared to all elders in the sample — are younger, healthier, more educated, and embedded in a more diversified ego–network of social contacts. The latter also conveys both the type and the recipient of the support, with the elderly who entertain few relationships with kin being more prone to provide aid to non–kin. Further, a “peer homophily” effect in directing elder support to a non–kin is also found.
Personalized learning models can cut student dropout rates, boost student success, improve the integration of online and on-site students, better support teachers in mixed-teaching modalities, enhance accessibility, and more.
In this paper, a log-linear multidimensional Rasch model is proposed for capture-recapture analysis of registration data. In the model, heterogeneity of capture probabilities is taken into account, and registrations are viewed as dichotomously scored indicators of one or more latent variables that can account for correlations among registrations. It is shown how the probability of a generic capture profile is expressed under the log-linear multidimensional Rasch model and how the parameters of the traditional log-linear model are derived from those of the log-linear multidimensional Rasch model. Finally, an application of the model to neural tube defects data is presented.
The policies for containing the spread of the SARS-CoV2 virus include a number of measures aimed at reducing physical contacts. In this paper, we explore the potential impact of such containment measures on social relations of both young adults and the elderly in Italy. We propose two ego-centered network definitions accounting for physical distance in light of the COVID-19 containment measures: the easy-to-reach network, that represents an accessible source of support that can be activate in case of new lockdown; the accustomed-to-reach network, which includes proximity and habit to meet in person. The approach used for constructing personal (ego-centered) networks on data from the most recent release of Families and Social Subject survey allows us to bring to the foreground people exposed to relational vulnerability. The analysis of the most vulnerable individuals by age, gender, and place of residence reveals that living alone is often associated with a condition of relational vulnerability for both the elderly and for young adults.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.