Measuring social capital and support networks of immigrant youth
This paper addresses the importance of the diagnosis of 'personal communities' as relational systems that may influence the academic pathways of young immigrants. As part of a longitudinal study of the academic persistence of young people in their transition from compulsory to post-compuls...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | España |
| Recursos: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/102132 |
| Acesso em linha: | https://hdl.handle.net/2445/102132 |
| Access Level: | acceso abierto |
| Palavra-chave: | Capital social (Economia) Xarxes socials Immigrants Rendiment acadèmic Capital stock Social networks Academic achievement |
| Resumo: | This paper addresses the importance of the diagnosis of 'personal communities' as relational systems that may influence the academic pathways of young immigrants. As part of a longitudinal study of the academic persistence of young people in their transition from compulsory to post-compulsory education, a 'personal network questionnaire' has been developed. This instrument allows the relational structure of students to be captured and represented, and the impact of this structure on educational outcomes to be analysed. It measures and explores the network of inter-relations with adults (family, educational and recreational professionals, etc.) and peers in different settings. The theoretical elements underpinning its design and implementation are the interweaving of the student social capital and social support system to which they have or may have access to, and the Social Network Analysis (SNA) approach as the methodological framework. This network approach is rendered highly significant and valuable for professionals in educational diagnosis to assess relational vulnerability and design programs of intervention and counseling. With graphic techniques, we can somewhat address this challenge by examining patterns in relational data, experimenting with these data and putting forward hypotheses. |
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