Dataset Information

Measuring transnational social fields through binational link-tracing sampling.

ABSTRACT: We advance bi-national link-tracing sampling design, an innovative data collection methodology for sampling from so-called "transnational social fields", i.e. transnational networks embedding migrants, returned migrants and non-migrants. This paper describes our contributions to this methodology and its empirical implementation, and evaluates the features of the resulting networks (sample), with the aim to guide future research. We performed 303 face-to-face structured interviews on sociodemographic variables, migration trajectories and personal networks of people living in a Romanian migration sending community (Dâmbovița) and in a migration receiving Spanish town (Castellón). Inter-connecting the personal networks, we built a multi-layered complex network structure embedding 4,855 nominated people, 5,477 directed ties (nominations) and 2,540 edges. Results indicate that the link-tracing nomination patterns are affected by sex and residence homophily. Our research contributes to the emerging efforts of applying social network analysis to the study of international migration.

SUBMITTER: Hancean MG

PROVIDER: S-EPMC8202912 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Measuring transnational social fields through binational link-tracing sampling.

Hâncean Marian-Gabriel MG Lubbers Miranda Jessica MJ Molina José Luis JL

PloS one 20210614 6

We advance bi-national link-tracing sampling design, an innovative data collection methodology for sampling from so-called "transnational social fields", i.e. transnational networks embedding migrants, returned migrants and non-migrants. This paper describes our contributions to this methodology and its empirical implementation, and evaluates the features of the resulting networks (sample), with the aim to guide future research. We performed 303 face-to-face structured interviews on sociodemogra ...[more]

PMID: 34125853

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Project description:Social interactions are essential for well-being. Therefore, researchers increasingly attempt to capture an individual's social context to predict well-being, including mood. Different tools are used to measure various aspects of the social context. Digital phenotyping is a commonly used technology to assess a person's social behavior objectively. The experience sampling method (ESM) can capture the subjective perception of specific interactions. Lastly, egocentric networks are often used to measure specific relationship characteristics. These different methods capture different aspects of the social context over different time scales that are related to well-being, and combining them may be necessary to improve the prediction of well-being. Yet, they have rarely been combined in previous research. To address this gap, our study investigates the predictive accuracy of mood based on the social context. We collected intensive within-person data from multiple passive and self-report sources over a 28-day period in a student sample (Participants: N = 11, ESM measures: N = 1313). We trained individualized random forest machine learning models, using different predictors included in each model summarized over different time scales. Our findings revealed that even when combining social interactions data using different methods, predictive accuracy of mood remained low. The average coefficient of determination over all participants was 0.06 for positive and negative affect and ranged from - 0.08 to 0.3, indicating a large amount of variance across people. Furthermore, the optimal set of predictors varied across participants; however, predicting mood using all predictors generally yielded the best predictions. While combining different predictors improved predictive accuracy of mood for most participants, our study highlights the need for further work using larger and more diverse samples to enhance the clinical utility of these predictive modeling approaches.

Dataset Information

Measuring transnational social fields through binational link-tracing sampling.

Publications

Measuring transnational social fields through binational link-tracing sampling.

Similar Datasets

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