The importance of place and time in analysing social support and the influence of personal networks and geographical areas on adolescents'life chances

对家庭和社会支持的研究更广泛地认识到空间和场所的重要性。邻里关系研究显示，生活在贫困和偏远地区的青年更容易受到社会孤立。然而，大多数研究都研究了空间背景（社区，学校）的作用，并在很大程度上忽略了个人网络环境，可能导致错误归因错误。社会网络的研究，反过来，往往局限于物理距离的分析，并普遍未能考虑到网络和空间环境影响社会支持的机会。这个博士项目的目标是弥合这一鸿沟，并研究如何从个人关系的支持不同的网络和住宅区应用交叉分类的多层次建模方法与个人（或以自我为中心）的网络数据。该方法被用来分析一个大型的调查样本的18-20岁的年轻人生活在瑞士（n= 40 000），包括整个国家队列的年轻男子的这个年龄，其中的结果变量的利益是关系（分析单位）是否从一个替代的年轻人（自我）是支持的信息提供，建议，情感上的帮助或榜样。支持度不仅取决于个体特征，还取决于个体网络的性质（如密度）和自我和分身所在的区域（如剥夺），三层模型可以同时研究自我/自我网络（第二层）和区域（第三层）的依赖性。交叉分类多层次模型（CCMM）非常适合处理没有严格分层的数据，因为一个自我的所有改变不一定生活在同一个区域内。该项目利用网络数据多层次建模方面的最新进展，还旨在确定建立青年健康和社会行为及结果模型的最佳方式。

Research on family and social support more widely recognises the importance of space and place. It is well known from neighbourhood studies that youth living in disadvantaged and remote areas are more likely to experience social isolation. However, most studies have examined the role of spatial contexts (neighbourhoods, schools) and largely ignored the personal network context, possibly leading to misattribution errors. Social network research is, in turn, often limited to the analysis of physical distance and has generally failed to consider that both networks and spatial environments affect opportunities for social support. The objective of this PhD project is to bridge this divide and examine how support from personal ties varies with both networks and residential areas applying a cross-classified multilevel modelling approach with personal (or egocentric) network data. The approach is used to analyse a large survey sample of young adults aged 18-20 living in Switzerland (n=40,000), including the full national cohort of young men of this age, where the outcome variable of interest is whether the tie (unit of analysis) from an alter to the young adult (ego) is supportive in terms of information provision, advice, emotional help or role model. The central premise is that support not only hinges on individual or tie characteristics, but also on the properties of personal networks (e.g. density) and areas (e.g. deprivation) where egos and alters live.A three-level modelling strategy can simultaneously investigate ego/ego-network (level2) and area (level 3) dependences. Cross-classified multilevel models (CCMM) are well suited for handling data that are not strictly hierarchical, since all alters of an ego do not necessarily live within the same area. Drawing on recent advances in multilevel modelling with network data, the project also aims to determine the best ways of modelling young adults' health and social behaviours and outcomes.